diff --git "a/1080.jsonl" "b/1080.jsonl" new file mode 100644--- /dev/null +++ "b/1080.jsonl" @@ -0,0 +1,417 @@ +{"seq_id": "466178440", "text": "import requests\nimport json\nfrom googleapiclient import discovery\nfrom googleapiclient.http import BatchHttpRequest\nfrom google.oauth2 import service_account\nfrom oauth2client.client import GoogleCredentials\n\ncredentials = GoogleCredentials.get_application_default()\nservice = discovery.build('logging', 'v2', credentials=credentials)\n\n\nclass Instance:\n\tdef __init__(self, name, ip):\n\t\tself.name = name\n\t\tself.ip = ip\n\nclass Firewall:\n\tdef __init__(self, project, name, sourceRanges, allowed, targetTags, direction, affected_instances):\n\t\tself.project = project\n\t\tself.name = name\n\t\tself.sourceRanges = sourceRanges\n\t\tself.allowed = allowed\n\t\tself.targetTags = targetTags\n\t\tself.direction = direction\n\t\tself.affected_instances = affected_instances\n\nclass Protocol:\n\tdef __init__(self, protocol, ports):\n\t\tself.protocol = protocol\n\t\tself.ports = ports\n\ndef identify(service_account_name, project_name):\n\ttry:\n\t\tbody = {\"resourceNames\": [f\"projects/{project_name}\"]}\n\t\trequest = service.entries().list(body=body)\n\t\twhile request is not None:\n\t\t\tresponse = request.execute()\n\t\t\ttry:\n\t\t\t\tfor entry in response.get('items'):\n\t\t\t\t\tprint(entry)\n\t\t\texcept:\n\t\t\t\tpass\n\t\t\trequest = service.entries().list_next(previous_request=request, previous_response=response)\n\t\t\n\t\tsourceRanges = response.get('sourceRanges')\n\t\tallowed = response.get('allowed')\n\t\tif allowed is None:\n\t\t\traise Exception('422, Egress Direction')\n\n\t\ttargetTags = response.get('targetTags')\n\t\tdirection = response.get('direction')\n\n\t\t# Parse the allowed ports\n\t\tallowed = parse_protocol(allowed)\n\n\t\t# If targetTags is empty, the firewall rule is applied to all instances\n\t\tif targetTags is None:\n\t\t\taffected_instances = 'All Instances'\n\t\telse:\n\t\t\taffected_instances = get_affected_instances(project_name, targetTags)\n\t\t\n\t\toutput = Firewall(project_name, firewall_name, sourceRanges, allowed, targetTags, direction, affected_instances)\n\t\treturn output\n\texcept Exception as e:\n\t\treturn(e)\n\ndef parse_protocol(allowed):\n\toutput = []\n\tfor item in allowed:\n\t\tprotocol = item['IPProtocol']\n\t\tports = item.get('ports')\n\t\tif ports is None:\n\t\t\tports = ''\n\t\toutput.append(Protocol(protocol, ports)) \n\treturn output\n\ndef list_zones(project_name):\n\trequest = service.zones().list(project=project_name)\n\tresponse = request.execute()\n\tzones = [zone['name'] for zone in response['items']]\n\treturn zones\t\n\ndef get_affected_instances(project_name, targetTags):\n\taffected_instances = []\n\tinstances_list = []\n\tzones = list_zones(project_name)\n\n\tbatch = service.new_batch_http_request()\n\n\tfor zone in zones:\n\t\tbatch.add(service.instances().list(project=project_name, zone=zone))\n\n\tbatch.execute()\n\n\tfor i in batch._responses:\n\t\tif json.loads(batch._responses[i][1]).get('items') is not None:\n\t\t\tfor instance in json.loads(batch._responses[i][1]).get('items'):\n\t\t\t\tinstances_list.append(instance)\n\n\tfor tag in targetTags:\n\t\tfor instance in instances_list:\n\t\t\tif (instance.get('tags').get('items') is not None) and (tag in instance.get('tags').get('items')):\n\t\t\t\tinstance_name = instance['name']\n\t\t\t\ttry:\n\t\t\t\t\tinstance_ip = \"`\" + str(instance.get('networkInterfaces')[0].get('accessConfigs')[0].get('natIP')) + \"`\"\n\t\t\t\t\tif instance.get('networkInterfaces')[0].get('accessConfigs')[0].get('natIP') is None:\n\t\t\t\t\t\tinstance_ip = ''\n\t\t\t\texcept:\n\t\t\t\t\tinstance_ip = ''\n\t\t\t\taffected_instances.append(Instance(instance_name, instance_ip))\n\n\treturn affected_instances\n\ndef identify_reply_message(url, token, channel, thread_ts, output):\t\n\tdata = {\n\t\t\t'token' : token,\n\t\t\t'channel' : channel, \n\t\t\t'thread_ts' : thread_ts\n\t}\n\t\t\t\t\n\tif str(type(output)) != \"\":\n\t\tdata.update({\"text\":str(output)})\n\t\tr = requests.post(url=url, data=data)\n\t\tquit()\n\n\n\tparsed_affected_instances = \"\"\n\tif output.affected_instances == 'All Instances':\n\t\tparsed_affected_instances = '`All Instances`'\n\telse:\n\t\tfor instance in output.affected_instances:\n\t\t\tparsed_affected_instances += \"• \" + instance.name + \" \" + instance.ip + \" \\n\"\n\n\tparsed_protocol = \"\"\n\tfor protocol in output.allowed:\n\t\tif protocol.ports != '':\n\t\t\tparsed_protocol += \"`\" + protocol.protocol + \"`\\n\"\n\t\t\tif len(protocol.ports) > 1:\n\t\t\t\tfor port in protocol.ports:\n\t\t\t\t\tparsed_protocol += \"• \" + str(port) + \" \\n\"\n\t\t\telse:\n\t\t\t\tparsed_protocol += \"• \" + str(protocol.ports[0]) + \" \\n\"\n\t\telse:\n\t\t\tparsed_protocol += \"`\" + protocol.protocol + \"` \\n\"\n\n\tattachments = [\n\t\t{\n\t\t\t\"mrkdwn_in\": [\"text\",\"value\"],\n\t\t\t\"color\": \"#36a64f\",\n\t\t\t\"fallback\": \"Query for \" + output.name,\n\t\t\t\"fields\": [\n\t\t\t\t{\n\t\t\t\t\t\"title\": \"Project\",\n\t\t\t\t\t\"value\": output.project,\n\t\t\t\t\t\"short\": True\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"title\": \"Name\",\n\t\t\t\t\t\"value\": output.name,\n\t\t\t\t\t\"short\": True\n\t\t\t\t}, \n\t\t\t\t{\n\t\t\t\t\t\"title\": \"Direction\",\n\t\t\t\t\t\"value\": output.direction,\n\t\t\t\t\t\"short\": True\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"title\": \"Protocol\",\n\t\t\t\t\t\"value\": parsed_protocol,\n\t\t\t\t\t\"short\": True\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"title\": \"Source\",\n\t\t\t\t\t\"value\": str(output.sourceRanges),\n\t\t\t\t\t\"short\": True\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"title\": \"Affected Instances\",\n\t\t\t\t\t\"value\": parsed_affected_instances\n\t\t\t\t}\n\t\t\t],\n\t\t\t\"footer\": \"dollhouse\",\n\t\t\t\"footer_icon\": \"https://platform.slack-edge.com/img/default_application_icon.png\"\n\t\t}\n\t]\n\n\n\tdata.update({\"attachments\": json.dumps(attachments)})\n\tr = requests.post(url=url, data=data)\n", "sub_path": "bot/commands/idenfity_helper.py", "file_name": "idenfity_helper.py", "file_ext": "py", "file_size_in_byte": 5272, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "oauth2client.client.GoogleCredentials.get_application_default", "line_number": 8, "usage_type": "call"}, {"api_name": "oauth2client.client.GoogleCredentials", "line_number": 8, "usage_type": "name"}, {"api_name": "googleapiclient.discovery.build", "line_number": 9, "usage_type": "call"}, {"api_name": "googleapiclient.discovery", "line_number": 9, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 96, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 97, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 123, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 188, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 189, "usage_type": "call"}]} +{"seq_id": "577154377", "text": "from sklearn.neighbors import KNeighborsClassifier\n\"\"\"\n使用python程序模拟KNN算法\n\n\n\"\"\"\nimport numpy as np\nimport collections as cs\n\ndata = np.array([\n [203, 1], [126, 1], [89, 1], [70, 1], [196, 2], [211, 2], [221, 2], [311, 3], [271, 3]\n])\nfeature = data[:, 0] # 特征\nprint(feature)\n\nlabel = data[:, -1] # 结果分类\nprint(label)\n\npredictPoint = 200 # 预测数据\nprint(\"预测输入特征为：\" + str(predictPoint))\n\ndistance = list(map(lambda x: abs(predictPoint - x), feature)) # 各点到预测点的距离\nprint(distance)\n\nsortIndex = np.argsort(distance) # 排序，返回排序后各数据的原始下标\nprint(sortIndex)\n\nsortLabel = label[sortIndex] # 根据下标重新进行排序\nprint(sortLabel)\n\n# k = 3 # 设置k值大小为3\n\nfor k in range(1, label.size + 1):\n result = cs.Counter(sortLabel[0:k]).most_common(1)[0][0] # 根据k值计算前k个数据中出现次数最多的分类，即为预测的分类\n print(\"当k=\" + str(k) + \"时预测分类为：\" + str(result))\n", "sub_path": "experiment7/num1.py", "file_name": "num1.py", "file_ext": "py", "file_size_in_byte": 1017, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 25, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "463863631", "text": "import pygame\nfrom resource_handler import *\nfrom pygame.locals import *\n\nclass Bullet(pygame.sprite.Sprite):\n def __init__(self, pos, speed, enemy):\n pygame.sprite.Sprite.__init__(self)\n self.image, self.rect = load_image('bullet.png', -1)\n screen = pygame.display.get_surface()\n self.area = screen.get_rect()\n self.rect.center = pos\n self.onscreen = True\n self.speed = speed\n self.firerate = 60\n if enemy:\n self.image = pygame.transform.flip(self.image, 1, 0)\n\n def update(self):\n self.move()\n\n def move(self):\n newpos = self.rect.move(-(self.speed), 0)\n self.rect = newpos\n if not self.area.collidepoint(self.rect.midright):\n self.onscreen = False\n", "sub_path": "Bullet.py", "file_name": "Bullet.py", "file_ext": "py", "file_size_in_byte": 773, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pygame.sprite", "line_number": 5, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pygame.display.get_surface", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.transform.flip", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 16, "usage_type": "attribute"}]} +{"seq_id": "421132875", "text": "import MySQLdb\r\n\r\ndb = MySQLdb.connect(\"localhost\",\"root\",\"0000\",\"mydb\" )\r\n\r\ncursor = db.cursor()\r\n\r\nname = raw_input(\"Enter the First name of the Employee:\")\r\ntemp = raw_input(\"Enter the update you want to do: \")\r\nvalue = raw_input(\"It's new value: \")\r\n\r\ntry:\r\n cursor.execute(\"UPDATE EMPLOYEE SET %s = %s WHERE FIRST_NAME = %s\",(temp,value,name))\r\n\r\n db.commit()\r\nexcept:\r\n\r\n db.rollback()\r\n\r\ndb.close()\r\n", "sub_path": "mysql/update.py", "file_name": "update.py", "file_ext": "py", "file_size_in_byte": 414, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "MySQLdb.connect", "line_number": 3, "usage_type": "call"}]} +{"seq_id": "114448614", "text": "import curses\nimport time\n\n\ndef percentage():\n win = curses.newwin(0, 0, 0, 0)\n # win.border(0)\n loading = 0\n while loading < 100:\n loading += 1\n time.sleep(0.03)\n update_progress(win, loading)\n\n\ndef update_progress(win, progress):\n rangex = (30 / float(100)) * progress\n pos = int(rangex)\n display = '#'\n if pos != 0:\n win.addstr(0, pos, f'{display}')\n win.refresh()\n\n\ncurses.initscr()\npercentage()\n# curses.endwin()\n", "sub_path": "05.application/01.mnistnet/test.library/curses/ex01.py", "file_name": "ex01.py", "file_ext": "py", "file_size_in_byte": 477, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "curses.newwin", "line_number": 6, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 11, "usage_type": "call"}, {"api_name": "curses.initscr", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "449970427", "text": "import tushare as ts\nimport pandas as pd\nimport time\nimport os\n\ninputdatadir = 'D:/Works/python/report/input_data'\npd.set_option('expand_frame_repr', False)\n\n# 从tushare获取指定日期的数据\ndef get_today_all_ts(date):\n date_now = date\n pro = ts.pro_api('e239683c699765e4e49b43dff2cf7ed7fc232cc49f7992dab1ab7624')\n df_daily = pro.daily(trade_date=date_now)\n df_daily_basic = pro.daily_basic(trade_date=date_now)\n df_basics = pro.stock_basic()\n df_all = pd.merge(left=df_daily, right=df_daily_basic, on='ts_code', how='outer')\n df_all = pd.merge(left=df_all, right=df_basics, on='ts_code', how='outer')\n df_all['ts_code'] = df_all['ts_code'].astype(str) + ' '\n\n # 保存数据\n df_all.to_csv(inputdatadir+'/'+ str(date_now) + '_ts.csv', index=False, encoding='utf_8_sig')\n print('%sis downloaded.' % (str(date_now)))\n print(df_all)\n return df_all\n\nif __name__ == '__main__':\n print('start...')\n print('get daily data')\n get_today_all_ts(date='20220327')\n print('end')\n", "sub_path": "Rabbit/getdailydata.py", "file_name": "getdailydata.py", "file_ext": "py", "file_size_in_byte": 1025, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pandas.set_option", "line_number": 7, "usage_type": "call"}, {"api_name": "tushare.pro_api", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "69406013", "text": "#!/usr/bin/env python\n# Unit Name: currency_server.db\n# Created By: Virgil Dupras\n# Created On: 2008-04-20\n# Copyright 2010 Hardcoded Software (http://www.hardcoded.net)\n# \n# This software is licensed under the \"BSD\" License as described in the \"LICENSE\" file, \n# which should be included with this package. The terms are also available at \n# http://www.hardcoded.net/licenses/bsd_license\n\nfrom datetime import date, datetime, timedelta\nimport xml.etree.cElementTree as ET\nimport xml.parsers.expat\nimport re\n\nfrom .hscommon.currency import Currency, RatesDB as RatesDBBase\nfrom .hscommon import sqlite\n\n\nDB_PATH = '/var/sqlite/currency.db'\n# DB_PATH = '/Users/hsoft/Desktop/currency.db'\nRE_ENDS_WITH_PARENS = re.compile(r\"\\([^(]+\\)$\")\n\nclass RatesDB(RatesDBBase):\n \"\"\"The RatesDB on the server side automatically updates itself using Bank of Canada's rates\n \n Bank of Canada uses n/a values for week-ends, holidays and future dates. We want to ignore those\n values when importing.\n \"\"\"\n def __init__(self, dbpath=DB_PATH):\n RatesDBBase.__init__(self, sqlite.ThreadedConn(dbpath, False))\n \n def get_CAD_values(self, start, end, currency_code):\n \"\"\"Returns [(date, value)] for each CAD value the DB has for 'currency'.\n \n The values are in date order.\n \"\"\"\n str_start = '%d%02d%02d' % (start.year, start.month, start.day)\n str_end = '%d%02d%02d' % (end.year, end.month, end.day)\n sql = \"select date, rate from rates where date >= ? and date <= ? and currency = ?\"\n cur = self.con.execute(sql, [str_start, str_end, currency_code])\n return [(datetime.strptime(date, '%Y%m%d').date(), rate) for (date, rate) in cur]\n \n def import_bank_of_canada_rates(self, source):\n \"\"\"Import rates from a Bank of Canada lookup xml file\"\"\"\n root = ET.fromstring(source.read().strip())\n for observation in root.getiterator('Observation'):\n currency_element = observation.find('Currency_name')\n name = currency_element.text.strip()\n # Some currency names have (), some not, but if we can't find it, try without the ()\n if name not in Currency.by_name:\n name = RE_ENDS_WITH_PARENS.sub('', name).strip() # remove the parens at the end of the name\n currency_code = Currency(name=name).code\n date_element = currency_element.find('Observation_date')\n rate_element = currency_element.find('Observation_data')\n try:\n rate = float(rate_element.text.strip())\n except (ValueError, AttributeError): # probably n/a\n continue\n year, month, day = date_element.text.strip().split('-')\n self.set_CAD_value(date(int(year), int(month), int(day)), currency_code, rate)\n", "sub_path": "db.py", "file_name": "db.py", "file_ext": "py", "file_size_in_byte": 2814, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "re.compile", "line_number": 22, "usage_type": "call"}, {"api_name": "hscommon.currency.RatesDB", "line_number": 24, "usage_type": "name"}, {"api_name": "hscommon.currency.RatesDB.__init__", "line_number": 31, "usage_type": "call"}, {"api_name": "hscommon.currency.RatesDB", "line_number": 31, "usage_type": "name"}, {"api_name": "hscommon.sqlite.ThreadedConn", "line_number": 31, "usage_type": "call"}, {"api_name": "hscommon.sqlite", "line_number": 31, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 42, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 42, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.fromstring", "line_number": 46, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 46, "usage_type": "name"}, {"api_name": "hscommon.currency.Currency.by_name", "line_number": 51, "usage_type": "attribute"}, {"api_name": "hscommon.currency.Currency", "line_number": 51, "usage_type": "name"}, {"api_name": "hscommon.currency.Currency", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "31551417", "text": "from django.db.models import ProtectedError\nfrom django.test import TestCase\n\nfrom .models import Group, Student\n\n\nclass ModelsTestCase(TestCase):\n def setUp(self):\n group = Group.objects.create(name=\"Group 1\")\n\n student = Student()\n student.FirstName = \"Joe\"\n student.LastName = \"Doe\"\n student.IndexNo = \"111\"\n student.Group = group\n\n def group_with_students_removal(self):\n with self.assertRaises(ProtectedError):\n Group.objects.all().delete()\n", "sub_path": "StudentsDjango/students/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 512, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.test.TestCase", "line_number": 7, "usage_type": "name"}, {"api_name": "models.Group.objects.create", "line_number": 9, "usage_type": "call"}, {"api_name": "models.Group.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 9, "usage_type": "name"}, {"api_name": "models.Student", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models.ProtectedError", "line_number": 18, "usage_type": "argument"}, {"api_name": "models.Group.objects.all", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Group.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 19, "usage_type": "name"}]} +{"seq_id": "467502234", "text": "import os\nimport requests\nimport json\n\n\npath = os.getenv('LOCALAPPDATA') + '/CCP/EVE/c_eve_sharedcache_tq_tranquility/settings_Default/'\n\ndirectory = os.listdir(path)\n\nmasterChar = input('Master Character name? ')\n\nmasterAccount = (requests.get(\"https://esi.evetech.net/latest/search/\", params={\"categories\":[\"character\"], \"datasource\": \"tranquility\", \"language\": \"en-us\", \"search\": masterChar, \"strict\": \"true\"}).json())['character']\n\nif (len(masterAccount)) > 1:\n raise Exception(\"Multiple characters returned, please contact Nosha Izia ingame\")\nelse:\n masterAccount = str(masterAccount[0])\n\nnonMasterFiles = []\n\nsyncedAccounts = []\n\nfor item in directory:\n item = item.split('.')\n if len(item) == 2:\n if item[1] == 'dat':\n item = item[0].split('_')\n if len(item) == 3:\n if item[2] != masterAccount:\n nonMasterFiles.append('core_char_' + item[2] + '.dat')\n\nwith open(path + 'core_char_' + masterAccount + '.dat', 'rb') as f:\n masterDetails = f.read()\n\nfor item in nonMasterFiles:\n with open(path + item, 'wb') as f:\n f.truncate(0)\n f.write(masterDetails)\n\nprint(\"Done!\")\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1168, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "os.getenv", "line_number": 6, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 8, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "632153393", "text": "import sqlite3\nfrom contextlib import closing\n\nwith sqlite3.connect(\"agenda.db\") as conexao:\n with closing(conexao.cursor()) as cursor:\n cursor.execute('delete from agenda where nome = \"Maria\"')\n\n print(\"Registros alterados: \", cursor.rowcount)\n if cursor.rowcount == 1:\n conexao.commit()\n else:\n conexao.rollback()\n print(\"Alteracoes abortadas\")\n\n for registro in conexao.execute('select * from agenda'):\n print(f\"Nome: {registro[0]}\\nTelefone: {registro[1]}\")", "sub_path": "11_Banco_Dados/11_delete_agenda.py", "file_name": "11_delete_agenda.py", "file_ext": "py", "file_size_in_byte": 543, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sqlite3.connect", "line_number": 4, "usage_type": "call"}, {"api_name": "contextlib.closing", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "241564454", "text": "import numpy as np\nimport pylab as plt\nfrom matplotlib.colors import ListedColormap\n\ncmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\ncmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n\n\ndef plot_surface(X, y, clf):\n h = 0.2\n x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n np.arange(y_min, y_max, h))\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n\n Z = Z.reshape(xx.shape)\n plt.figure(figsize=(8, 8))\n plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n\n # Plot also the training points\n plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)\n plt.xlim(xx.min(), xx.max())\n plt.ylim(yy.min(), yy.max())\n", "sub_path": "logistic_regression/dmia/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 787, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "matplotlib.colors.ListedColormap", "line_number": 5, "usage_type": "call"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pylab.figure", "line_number": 18, "usage_type": "call"}, {"api_name": "pylab.pcolormesh", "line_number": 19, "usage_type": "call"}, {"api_name": "pylab.scatter", "line_number": 22, "usage_type": "call"}, {"api_name": "pylab.xlim", "line_number": 23, "usage_type": "call"}, {"api_name": "pylab.ylim", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "579144319", "text": "# -*- coding:utf-8 -*-\nimport numpy as np\nimport tensorflow as tf\n\nimport vgg19\nimport utils\n# install tensorflow0.8.0,scikit-image\n# sudo apt-get install python-pip python-dev\n# sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0rc0-cp27-none-linux_x86_64.whl\n# 可使用sudo pip install --upgrade tensorflow更新到最新版本\n\n# sudo pip install scikit-image\n#\nimg1 = utils.load_image(\"./test_data/tiger.jpeg\")\nimg2 = utils.load_image(\"./test_data/puzzle.jpeg\")\n\nbatch1 = img1.reshape((1, 224, 224, 3))\nbatch2 = img2.reshape((1, 224, 224, 3))\n\n# numpy.concatenate((a1, a2, ...), axis=0, out=None) Join a sequence of arrays along an existing axis.\nbatch = np.concatenate((batch1, batch2), 0)\n\n# with tf.Session(config=tf.ConfigProto(gpu_options=(tf.GPUOptions(per_process_gpu_memory_fraction=0.7)))) as sess:\nwith tf.device('/cpu:0'):\n with tf.Session() as sess:\n\n images = tf.placeholder(\"float\", [2, 224, 224, 3])\n feed_dict = {images: batch}\n\n vgg = vgg19.Vgg19()\n # tf.variable_scope可以让变量有相同的命名，包括tf.get_variable得到的变量，还有tf.Variable的变量\n # tf.name_scope可以让变量有相同的命名，只是限于tf.Variable的变量\n with tf.name_scope(\"content_vgg\"):\n vgg.build(images)\n\n prob = sess.run(vgg.prob, feed_dict=feed_dict)\n print(prob)\n utils.print_prob(prob[0], './synset.txt')\n utils.print_prob(prob[1], './synset.txt')\n", "sub_path": "test_vgg19.py", "file_name": "test_vgg19.py", "file_ext": "py", "file_size_in_byte": 1510, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "utils.load_image", "line_number": 14, "usage_type": "call"}, {"api_name": "utils.load_image", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.device", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 27, "usage_type": "call"}, {"api_name": "vgg19.Vgg19", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 33, "usage_type": "call"}, {"api_name": "utils.print_prob", "line_number": 38, "usage_type": "call"}, {"api_name": "utils.print_prob", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "7404533", "text": "import pygame,math,random\nimport numpy as np\n\nblack=(0,0,0)\nred=(255,0,0)\ngreen=(0,255,0)\nblue=(0,0,255)\nwhite=(255,255,255)\ngray=(120,120,120)\n\nspace=0\nbody=1\nhead=2\napple=3\n\nclass game:\n\n def __init__(self,x,y):\n self.running=False\n self.x=x\n self.y=y\n self.bw=60\n self.w=x*self.bw\n self.h=y*self.bw\n self.center=(int(x/2),int(y/2))\n self.highscore=1\n self.steps=0\n self.matrix=[[space for _ in range(x)] for _ in range(y)]\n\n def start(self):\n pygame.init()\n self.clock=pygame.time.Clock()\n self.display=pygame.display.set_mode((self.w,self.h))\n pygame.display.set_caption('AI Snake')\n self.font=pygame.font.Font('freesansbold.ttf',24)\n py=snake(self.center)\n self.player=py\n self.matrix[self.player.y][self.player.x]=head\n self.genApple()\n self.running=True\n \n def genApple(self):\n sp=[]\n for y in range(self.y):\n for x in range(self.x):\n if self.matrix[y][x]==space:\n sp.append((x,y))\n self.apple=random.choice(sp)\n self.matrix[self.apple[1]][self.apple[0]]=apple\n\n def update(self):\n op=(self.player.x,self.player.y)\n self.player.move()\n pos=(self.player.x,self.player.y)\n self.matrix[op[1]][op[0]]=space\n self.matrix[pos[1]][pos[0]]=head\n if pos==self.apple:\n self.player.length+=1\n self.body.append(op)\n elif pos in self.player.body or pos[0]<0 or pos[0]==self.x or pos[1]<0 or pos[1]==self.y:\n self.running=False\n\n def draw(self):\n self.display.fill(black)\n for y in range(len(self.matrix)):\n for x in range(len(self.matrix[y])):\n if self.matrix[y][x]==head:\n color=blue\n elif self.matrix[y][x]==body:\n color=green\n elif self.matrix[y][x]==apple:\n color=red\n else:\n color=black\n pygame.draw.rect(self.display,color,(x*self.bw,y*self.bw,self.bw,self.bw))\n for x in range(self.x+1):\n pygame.draw.rect(self.display,gray,(x*self.bw-2,0,4,self.w))\n for y in range(self.y+1):\n pygame.draw.rect(self.display,gray,(0,y*self.bw-2,self.h,4))\n\n render=self.font.render('Score: '+str(self.player.length),True,white)\n self.display.blit(render,(10,10))\n\n render=self.font.render('Highscore: '+str(self.highscore),True,white)\n self.display.blit(render,(10,40))\n\n for e in pygame.event.get():\n if e.type==pygame.QUIT:\n pygame.quit()\n raise SystemExit\n\n pygame.display.update() \n\nclass snake:\n\n def __init__(self,pos):\n self.x,self.y=pos\n self.body=[]\n self.length=1\n self.dir=0\n\n def move(self):\n if self.body:\n for b in range(len(self.body)-1,0,-1):\n self.body[b]=self.body[b-1]\n self.body[0]=(self.x,self.y)\n if self.dir=='up':\n self.y-=1\n elif self.dir=='down':\n self.y+=1\n elif self.dir=='left':\n self.x-=1\n elif self.dir=='right':\n self.x+=1\n\ndef main():\n g=game(9,9)\n g.start()\n while g.running:\n g.draw()\n g.clock.tick(5)\n g.update()\n\nif __name__=='__main__':\n main()", "sub_path": "snake.py", "file_name": "snake.py", "file_ext": "py", "file_size_in_byte": 3460, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pygame.init", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 34, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 35, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 48, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 75, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 75, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 77, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 79, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 87, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 87, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 88, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 89, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 92, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 92, "usage_type": "attribute"}]} +{"seq_id": "275851575", "text": "# -*- encoding: UTF-8 -*-\nimport sqlalchemy.orm\n\nfrom imomo import errors\nfrom imomo.models import Site\n\n\nclass Manager(object):\n \"\"\"Manager base class that provides shared utility methods between all\n manager classes.\n\n The manager classes are in charge of providing and/or executing complex\n database queries to the handlers, the managers also provide validation\n of business logic and transform database errors into application errors,\n i.e. IMomoError.\n \"\"\"\n\n @classmethod\n def get_site(cls, session, site_code):\n \"\"\"Retrieves a site instance from the database given its local site\n code.\n\n Args:\n session\n site_code\n Raises:\n SiteDoesNotExistError\n Returns:\n The loaded Site instance.\n \"\"\"\n site = session.query(Site).filter(Site.site_code == site_code).first()\n\n if site is None:\n raise errors.SiteDoesNotExistError()\n return site\n\n @classmethod\n def verify_site(cls, session, site_id, source_id):\n \"\"\"Utility method that ensures that the given site id exists and the\n site belongs to the source with the given source_id.\n\n Args:\n session: The database session to use.\n site_id: The site id to verify.\n source_id: The source that is requesting the verification.\n\n Raises:\n SiteNotInSourceError: If the site exists but is in a different\n source than the given one.\n SiteDoesNotExistError: If the given site id is not valid.\n \"\"\"\n site_source_id = session.query(Site.source_id).filter(\n Site.id == site_id).scalar()\n if site_source_id is None:\n raise errors.SiteDoesNotExistError()\n if site_source_id != source_id:\n raise errors.SiteNotInSourceError()\n\n @staticmethod\n def unique_value_query(query):\n \"\"\"Executes the query and asserts that the result\n is strictly one record.\n\n Note that the error is raised as a failed assert, this method\n should be used only when the error is expected to come from a\n programming error otherwise it would be hidden in production.\n \"\"\"\n try:\n return query.one()\n except sqlalchemy.orm.exc.NoResultFound:\n assert False, 'No records stored in the database.'\n except sqlalchemy.orm.exc.MultipleResultsFound:\n assert False, 'Multiple records stored in the database.'\n\n @staticmethod\n def unique_or_no_value_query(query,):\n \"\"\"Executes the query and asserts that the result is zero or one\n record.\n\n Note that the error is raised as a failed assert, this method\n should be used only when the error is expected to come from a\n programming error otherwise it would be hidden in production.\n \"\"\"\n try:\n return query.one()\n except sqlalchemy.orm.exc.NoResultFound:\n return None\n except sqlalchemy.orm.exc.MultipleResultsFound:\n assert False, 'Multiple records stored in the database'\n", "sub_path": "imomo/managers/manager.py", "file_name": "manager.py", "file_ext": "py", "file_size_in_byte": 3128, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "imomo.models.Site", "line_number": 31, "usage_type": "argument"}, {"api_name": "imomo.models.Site.site_code", "line_number": 31, "usage_type": "attribute"}, {"api_name": "imomo.errors.SiteDoesNotExistError", "line_number": 34, "usage_type": "call"}, {"api_name": "imomo.errors", "line_number": 34, "usage_type": "name"}, {"api_name": "imomo.models.Site.source_id", "line_number": 52, "usage_type": "attribute"}, {"api_name": "imomo.models.Site", "line_number": 52, "usage_type": "name"}, {"api_name": "imomo.models.Site.id", "line_number": 53, "usage_type": "attribute"}, {"api_name": "imomo.models.Site", "line_number": 53, "usage_type": "name"}, {"api_name": "imomo.errors.SiteDoesNotExistError", "line_number": 55, "usage_type": "call"}, {"api_name": "imomo.errors", "line_number": 55, "usage_type": "name"}, {"api_name": "imomo.errors.SiteNotInSourceError", "line_number": 57, "usage_type": "call"}, {"api_name": "imomo.errors", "line_number": 57, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.orm", "line_number": 70, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm", "line_number": 70, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.orm", "line_number": 72, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm", "line_number": 72, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.orm", "line_number": 86, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm", "line_number": 86, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.orm", "line_number": 88, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm", "line_number": 88, "usage_type": "name"}]} +{"seq_id": "468745297", "text": "# -*- coding: utf-8 -*-\n\nimport json\nimport csv\nimport re\n\n\ndef appendJsonToFile(_dict, _path_file):\n with open(_path_file, 'ab') as f:\n # ファイルの末尾（2）に移動（オフセット0）\n f.seek(0, 2)\n\n # ファイルが空かチェック\n if (f.tell() == 0):\n f.write('['.encode())\n f.write('\\n'.encode())\n\n else:\n # ファイルの末尾（2）から -2 文字移動\n f.seek(-2, 2)\n # 最後の文字（]）を削除\n f.truncate()\n # ファイルの末尾（2）から -2 文字移動\n f.seek(-2, 2)\n # 最後の文字（}）を削除\n f.truncate()\n # 配列のセパレーターを書き込む\n f.write('},'.encode())\n f.write('\\n'.encode())\n\n with open(_path_file, 'a') as f:\n json.dump(_dict, f, ensure_ascii=False, indent=2)\n\n with open(_path_file, 'ab') as f:\n # JSON 配列を閉じる\n f.write('\\n'.encode())\n f.write(']'.encode())\n f.write('\\n'.encode())\n\n\n#\n#\n#\n\n\n# def convertToList(_isinstance):\n# if (isinstance(_isinstance, dict)):\n# _isinstance = [_isinstance]\n\n# return _isinstance\n\n\n# with open('../data/detailedTimetables.json', 'r') as f:\n# detailedTimetables = json.load(f)\n\n# lineCodes = []\n\n# for detailedTimetable in detailedTimetables:\n# detailedTimetableTimeTable = detailedTimetable['TimeTable']\n# HourTables = detailedTimetableTimeTable['HourTable']\n# HourTables = convertToList(HourTables)\n\n# codePairs = []\n\n# for HourTable in HourTables:\n# if ('MinuteTable' not in HourTable):\n# continue\n\n# MinuteTables = HourTable['MinuteTable']\n# MinuteTables = convertToList(MinuteTables)\n\n# for MinuteTable in MinuteTables:\n# Stop = MinuteTable['Stop']\n\n# codePair = {\n# \"kindCode\": Stop['kindCode'],\n# \"nameCode\": Stop['nameCode']\n# }\n\n# if (codePair in codePairs):\n# continue\n\n# codePairs.append(codePair)\n\n# if (Stop['lineCode'] not in lineCodes):\n# lineCodes.append(Stop['lineCode'])\n\n# with open('lineCodes.csv', 'w') as f:\n# writer = csv.writer(f, lineterminator='\\n')\n# writer.writerow(lineCodes)\n\n\n#\n# ↑ lineCodes\n#\n\n\n# with open('../data/trainTimetables.json', 'r') as f:\n# trainTimetables = json.load(f)\n\n# trainTimetablesSorted = {}\n\n# for trainTimetable in trainTimetables:\n# lineCode = trainTimetable['lineCode']\n# trainTimetable.pop('lineCode')\n# trainTimetablesSorted[lineCode] = trainTimetable\n\n# with open('../data/trainTimetablesSorted.json', 'w') as f:\n# json.dump(trainTimetablesSorted, f,\n# ensure_ascii=False, separators=(',', ':'))\n\n\n#\n# ↑ trainTimetablesSorted\n#\n\n\n# with open('../data/trainTimetables.json', 'r') as f:\n# trainTimetables = json.load(f)\n\n# counter = 0\n\n# for trainTimetable in trainTimetables:\n# counter += 1\n\n# if (counter > 3):\n# break\n\n# appendJsonToFile(trainTimetable, 'trainTimetablesSample.json')\n\n\n#\n# ↑ trainTimetablesSample\n#\n\n\n# with open('../data/trainTimetablesSorted.json', 'r') as f:\n# trainTimetablesSorted = json.load(f)\n\n# appendJsonToFile(trainTimetablesSorted['34254'], 'trainTimetablesSample34254.json')\n\n\n#\n# ↑ trainTimetable を lineCode で探す\n#\n", "sub_path": "timetable/train/p.py", "file_name": "p.py", "file_ext": "py", "file_size_in_byte": 3464, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "json.dump", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "224494218", "text": "import numpy as np\n\n# 在位加减乘除\na = np.ones(3)*1\nb = np.ones(3)*2\nnp.add(a,b,out=b) # 在位加法，赋值到b\nnp.divide(a,2,out=a) # 在位除法，赋值到a\nnp.negative(a,out=a)\nnp.multiply(a,b, out=a)\n# print(a)\n# print(b)\n\n# 取整数\nc = np.random.uniform(0, 10, 10)\n# print(c)\n# print(c - c%1)\n# print(np.floor(c))\n# print(np.ceil(c) - 1)\n# print(np.trunc(c))\n\n# 创建一个矩阵，数值从0到4\nd = np.zeros((5,5))\nd += np.arange(5)\n# print(d)\n\n# 从迭代器中生成一个数组\ndef generate():\n for i in range(10):\n yield i\ne = np.fromiter(generate(), dtype=float, count=-1) # 从迭代器中拿多少个元素，-1指全部\n# print(e)\n\n# 创建一个长度10到等宽向量，0,11之间，不含0,11\nf = np.linspace(0, 11, 11, endpoint=False)[1:]\n# print(f)\n\n# 向量排序\ng = np.random.randint(0,10,10)\ng.sort()\n# print(g)\n\n# 除了np.sum 求和\n# print(np.add.reduce(g))\n# print(g)\n\n# 检查数组是否相等\nh1 = np.random.randint(0,2,2)\nh2 = np.random.randint(0,2,2)\nequal1 = np.allclose(h1,h2)\nequal2 = np.array_equal(h1,h2)\n# print(equal1)\n# print(equal2)\n\n# 创建只读数组\ni = np.zeros(10)\ni.flags.writeable = False\n# i[0] = 1\n\n# 笛卡尔坐标转极坐标\nj = np.random.random((10, 2))\nj1, j2 = j[:,0], j[:,1]\nR = np.sqrt(j1**2+j2**2)\nT = np.arctan(j2, j1)\n# print(R,T)\n\n# 最大值替换\nk = np.random.random(10)\n# print(k)\nk[k.argmax()] = 0 # argmax最大值的索引\n# print(k)\n\n# 网格点坐标\nl = np.zeros((5,5), [('x', float), ('y',float)])\nl['x'], l['y'] = np.meshgrid(np.linspace(0,1,5), np.linspace(0,1,5))\n# print(l)\n\n# cauchy矩阵(Cij = 1 / (xi - yj))\nm1 = np.arange(8)\nm2 = m1 + 0.5\nC = 1.0 / np.subtract.outer(m1, m2)\n# print(np.linalg.det(C))\n\n# numpy标量最大值，最小值\n# for dtype in [np.int8, np.int32, np.int64]:\n# pass\n# # print(np.iinfo(dtype).min)\n# # print(np.iinfo(dtype).max)\n#\n# for dtype in [np.float32, np.float64]:\n# pass\n# # print(np.finfo(dtype).min)\n# # print(np.finfo(dtype).max)\n# # print(np.finfo(dtype).eps)\n\n# 打印数组中所有的数值\n# np.set_printoptions(threshold=np.nan)\n# n = np.zeros((16,16))\n# print(n)\n\n# 找到与目标最接近的值\no = np.arange(100)\ntarget = 44.8\nindex = (np.abs(o - target)).argmin()\n# print(o[index])\n\n# 创建一个表示位置(x,y)与颜色(r,g,b)的结构化数组\n\n# Z = np.zeros(10, [ ('position', [ ('x', float, 1),\n# ('y', float, 1)]),\n# ('color', [ ('r', float, 1),\n# ('g', float, 1),\n# ('b', float, 1)])])\n# print (Z)\n\n# 随机向量间，点与点的距离\np = np.random.random((10,2))\n# print(p)\n# 方法1\nX,Y = np.atleast_2d(p[:,0],p[:,1])\n# D = np.sqrt((X - X.T) ** 2 - (Y - Y.T) ** 2)\n# print(D)\n# 方法2\nimport scipy.spatial\nD = scipy.spatial.distance.cdist(p, p)\n# print(D)\n\n# astype\nq = np.arange(10,dtype=np.int32)\nq = q.astype(np.float, copy=False)\n# print(q)\n\n# enumerate 等价操作\nr = np.arange(20).reshape(4,5)\nfor i, v in np.ndenumerate(r):\n # print(i,v)\n pass\nfor i, v in np.ndindex(r.shape):\n # print(i, r[i])\n pass\n\n# Gaussian-like数组\ns1, s2 = np.meshgrid(np.linspace(-1,1,10), np.linspace(-1,1,10))\nt = np.sqrt(s1 * s1 + s2 * s2)\nsigma, mu = 1.0, 0.0\nG = np.exp(-((t - mu) ** 2 / (2.0 * sigma ** 2)))\n# print(G)\n\n# 随机在数组中放置P个元素\nn = 10\np = 30\nz = np.zeros(((n, n, n)))\nnp.put(z, np.random.choice(range(n*n*n), p, replace=False), 1) # replace=False 无放回\n# print(z)\n\n# 减去一个矩阵中每一行的平均值\nu = np.random.randint(5, 10, (5, 10))\nv = u - u.mean(axis=1, keepdims=True) # keepdims 保持维度不变\n# print(u)\n# print(v)\n\n# 通过第n列数组进行排序\nw = np.random.randint(0, 10, (3, 3))\n# print(w)\n# print(w[w[:, 1].argsort()]) # argsort()返回排序后的索引\n\n# 检查一个二维数组是否有空列\ny = np.random.randint(0,3,(3,10))\n# print(y)\n# print((~y.any(axis=0)).any())\n\n# 近似值\nz = np.random.randint(0,10,10)\ntarget = 3.4\nm = z.flat[np.abs(z - target).argmin()]\n# print(m)\n\n# 用迭代器计算不同形状的数组\na = np.arange(3).reshape(3,1)\nb = np.arange(3).reshape(1,3)\nit = np.nditer([a,b,None])\nfor x, y, z in it:\n z[...] = x + y\n# print(it.operands[2])\n\n# 创建一个有name属性的数组类\nclass NameArray(np.ndarray):\n def __new__(cls, array, name='no name'):\n obj = np.asarray(array).view(cls)\n obj.name = name\n return obj\n def __array_finalize__(self, obj):\n if obj is None: return\n self.info = getattr(obj, 'name', 'no name')\n\na = NameArray(np.arange(10), 'range_10')\n# print(a.name)\n\n# 考虑一个给定的向量，如何对由第二个向��索引的每个元素加1(小心重复的索引)?\na = np.ones(10)\nb = np.random.randint(0, len(a), 20)\nc = np.bincount(b, minlength=len(a))\na += c\nprint(a)\n", "sub_path": "Numpy_execrise/Q100_normal.py", "file_name": "Q100_normal.py", "file_ext": "py", "file_size_in_byte": 4862, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.ones", "line_number": 4, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.negative", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.fromiter", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.allclose", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 55, "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.sqrt", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.arctan", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 67, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.subtract.outer", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 116, "usage_type": "attribute"}, {"api_name": "numpy.atleast_2d", "line_number": 119, "usage_type": "call"}, {"api_name": "scipy.spatial.spatial.distance.cdist", "line_number": 124, "usage_type": "call"}, {"api_name": "scipy.spatial.spatial", "line_number": 124, "usage_type": "attribute"}, {"api_name": "scipy.spatial", "line_number": 124, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 128, "usage_type": "attribute"}, {"api_name": "numpy.float", "line_number": 129, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.ndenumerate", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.ndindex", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.put", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 152, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 156, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 162, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 167, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 172, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.nditer", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 186, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 200, "usage_type": "attribute"}, {"api_name": "numpy.bincount", "line_number": 201, "usage_type": "call"}]} +{"seq_id": "243390340", "text": "\n# %%\nimport datetime as dt\n#%%\n\nimport glob\n\nimport matplotlib as plt\nimport numpy as np\nimport pandas as pd\nimport plotly.express as px\nimport seaborn as sns\nfrom datetime import timezone\n\n\n# Standard quick checks\ndef dfChkBasics(dframe, valCnt = False): \n cnt = 1\n print('\\ndataframe Basic Check function -')\n \n try:\n print(f'\\n{cnt}: info(): ')\n cnt+=1\n print(dframe.info())\n except: pass\n\n print(f'\\n{cnt}: describe(): ')\n cnt+=1\n print(dframe.describe())\n\n print(f'\\n{cnt}: dtypes: ')\n cnt+=1\n print(dframe.dtypes)\n\n try:\n print(f'\\n{cnt}: columns: ')\n cnt+=1\n print(dframe.columns)\n except: pass\n\n print(f'\\n{cnt}: head() -- ')\n cnt+=1\n print(dframe.head())\n\n print(f'\\n{cnt}: shape: ')\n cnt+=1\n print(dframe.shape)\n\n if (valCnt):\n print('\\nValue Counts for each feature -')\n for colname in dframe.columns :\n print(f'\\n{cnt}: {colname} value_counts(): ')\n print(dframe[colname].value_counts())\n cnt +=1\n\n# examples:\n#dfChkBasics(df)\n#%%\n## DC data clean up and shaping \n#read in data sets for DC\n#%%\n\n\nmar19= pd.read_csv(\"DC_Mar19.csv\")\nprint(\"mar\")\nprint(mar19.columns)\napril19= pd.read_csv(\"DC_Apr19.csv\")\nprint(\"apr\")\nprint(april19.columns)\nmay19= pd.read_csv(\"DC_May19.csv\")\nprint(\"may\")\nprint(may19.columns)\njune19= pd.read_csv(\"DC_Jun19.csv\")\nprint(\"june\")\nprint(june19.columns)\nmarch20= pd.read_csv(\"DC_Mar20.csv\")\nprint(\"mar\")\nprint(march20.columns)\napril20= pd.read_csv(\"DC_Apr20.csv\")\nprint(\"april\")\nprint(april20.columns)\nmay20=pd.read_csv(\"DC_May20.csv\")\nprint(\"May\")\nprint(may20.columns)\njun20= pd.read_csv(\"DC_jun20.csv\")\nend_stat=pd.read_csv(\"end.csv\")\nstart_stat=pd.read_csv(\"start.csv\")\n\n#%%\nprint(len(may20))\nprint(len(may19))\nprint(len(april19))\nprint(len(april20))\nprint(len(mar19))\nprint(len(march20))\n#%%\n\n#print data sets to take a look # seems that april has different columns names\n\n\nprint(\"_____break_______\")\n#Rename columns \napril20.rename(columns={'started_at': 'Start date', 'ended_at': 'End date','start_station_name': 'Start station','start_station_id': 'Start station number','end_station_name': 'End station','end_station_id': 'End station number','member_casual': 'Member type', }, inplace=True)\nmay20.rename(columns={'started_at': 'Start date', 'ended_at': 'End date','start_station_name': 'Start station','start_station_id': 'Start station number','end_station_name': 'End station','end_station_id': 'End station number','member_casual': 'Member type', }, inplace=True)\njun20.rename(columns={'started_at': 'Start date', 'ended_at': 'End date','start_station_name': 'Start station','start_station_id': 'Start station number','end_station_name': 'End station','end_station_id': 'End station number','member_casual': 'Member type', }, inplace=True)\n\n#%%\n#check for column renaming \napril20_head= april20.head()\nprint(april20_head)\n\n\n\n#%%\nprint(april20.head())\n#%%\n\n#%%\n#make the df's ready for concatination \nframes=[mar19, april19, may19, june19, march20, april20, may20, jun20]\n\n#concat all the dataframes and drop column that won't be used \ndc_data = pd.concat(frames)\n \n\n#%%\n#dc_data=dc_data.merge(april20, on=\"Start station\", how=\"inner\")\n\ndc_data=dc_data.merge(start_stat, on=\"Start station\", how=\"left\")\n\ndc_data=dc_data.merge(end_stat, on=\"End station\", how=\"left\")\nprint(\"complete - ready to continue\") \n\n\n\n#null_data=dc_data.isnull.sum()\n\n#print(\"ready\")\n#print(null_data)\n\n\n\n#%% # Function to get percents of missing values \ndef missing_values_table(df):\n # Utility function, identify missing data and show percentages.\n mis_val = df.isnull().sum()\n mis_val_percent = 100 * df.isnull().sum() / len(df)\n mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n mis_val_table_ren_columns = mis_val_table.rename(\n columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n mis_val_table_ren_columns = mis_val_table_ren_columns[mis_val_table_ren_columns.iloc[:,1] != 0].sort_values('% of Total Values', ascending=False).round(1)\n print(\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\nThere are \" + str(mis_val_table_ren_columns.shape[0]) + \" columns that have missing values.\")\n return mis_val_table_ren_columns\n\n\n\n\n#%%\n#function to determine if data is missing \nmissing_values_table(dc_data)\n\n\n\n# %%\nprint(len(dc_data))\n\n# %%\n#dc_data=dc_data.drop(columns=[\"end_lat\", \"end_lng\", \"start_lat\", \"start_lng\"])\t \n# drop small na's\ndc_data=dc_data.dropna(subset=[\"End station number\"])\ndc_data=dc_data.dropna(subset=[\"Start date\"])\ndc_data=dc_data.dropna(subset=[\"End date\"])\ndc_data=dc_data.dropna(subset=[\"Member type\"])\ndc_data=dc_data.dropna(subset=[\"end_lat_y\"])\ndc_data=dc_data.dropna(subset=[\"end_lng_y\"])\ndc_data=dc_data.dropna(subset=[\"start_lat_y\"])\ndc_data=dc_data.dropna(subset=[\"start_lng_y\"])\ndc_data=dc_data.dropna(subset=[\"Start station number\"])\ndc_data=dc_data.dropna(subset=[\"Start station\"])\n\n# %%\nmissing_values_table(dc_data)\n\n\n\n\n#%%\ndfChkBasics(dc_data)\nprint(len(dc_data))\n# %%\n# standardize categorical columns/ and date time columns create new categorical columns\ndc_data.rename(columns={\"Start date\": \"start_date\", \"End date\": \"end_date\",}, inplace=True)\ndc_data[\"Member type\"].replace({\"member\": \"Member\", \"casual\": \"Casual\"}, inplace=True)\n\n\n#%%\ndef create_dto(row, colname):\n# for index, row in dc_data.iterrows():\n if type(row[colname]) is not str:\n return \"Unknown\"\n else:\n # Try the various known time formats.\n dtFormat = [\n '%Y-%m-%d %H:%M',\n '%Y-%m-%d %H:%M:%S',\n '%m/%d/%y %H:%M',\n ]\n # save cell data to local variable\n cell_contents = row[colname]\n # Drop decimal timestamp precision, if it exists.\n cell_contents = cell_contents.split('.')[0]\n for i in dtFormat:\n try:\n dto = dt.datetime.strptime(cell_contents,i)\n return (\n dto,\n dto.strftime(\"%Y-%m-%d\"),\n dto.strftime(\"%H:%M\")\n )\n except ValueError:\n pass\n else:\n print(\"Failed to parse: {:s}\".format(cell_contents))\ndc_data['start_dto'], dc_data['start_date_formatted'], dc_data['start_time_formatted'] \\\n = zip(*dc_data.apply(lambda row: create_dto(row,\"start_date\"), axis=1))\ndc_data['end_dto'], dc_data['end_date_formatted'], dc_data['end_time_formatted'] \\\n = zip(*dc_data.apply(lambda row: create_dto(row,\"end_date\"), axis=1))\n \nprint(dc_data.tail())\n\ndc_data['weekday'] = dc_data.apply(lambda row: row[\"start_dto\"].weekday() < 5, axis=1)\n\n#%%\n\nprint(len(dc_data))\n# %%\ndef determine_pandemic(row):\n# for index, row in dc_data.iterrows():\n dto = row[\"start_dto\"]\n if dto.year == 2020:\n return True\n return False\ndc_data['pandemic'] = dc_data.apply(lambda row: determine_pandemic(row), axis=1)\n#%%\ndef determine_commuter(row):\n# for index, row in dc_data.iterrows():\n dto = row[\"start_dto\"]\n weekday=row[\"weekday\"]\n if (dto.hour in range(6, 10) or dto.hour in range(16, 19)) and weekday:\n return True\n return False\ndc_data['commuter'] = dc_data.apply(lambda row: determine_commuter(row), axis=1)\n\n#%%\nprint(dc_data.head())\n#%%\ndc_data['Duration'] = dc_data.apply(lambda row: (row[\"end_dto\"] - row[\"start_dto\"]).total_seconds(), axis=1)\n#%%\n# drop excessive duration \ndc_data=dc_data[dc_data[\"Duration\"]<18000]\ndc_data=dc_data[dc_data[\"Duration\"]>60]\n\nprint(len(dc_data))\n\n\n\n#%%\nmissing_values_table(dc_data)\n\n\n#%%\n# add year/month column \ndc_data['Month_Year'] = dc_data['end_dto'].dt.strftime('%Y-%m')\n\ndc_data['day_of_week'] = dc_data['end_dto'].dt.day_name()\nprint(\"complete - ready to continue\") \n#%%\nmissing_values_table(dc_data)\n#%%\nprint(dc_data.columns)\n\n\n#%%\n# function to make commuter/pandemic column \ndef make_pandemic_commuter(row):\n if row['pandemic'] and row['commuter']:\n return \"Pandemic Commuter\"\n elif row['pandemic'] and not row['commuter']:\n return \"Pandemic Noncommuter\"\n elif not row['pandemic'] and row['commuter']:\n return \"Nonpandemic Commuter\"\n elif not row['pandemic'] and not row['commuter']:\n return \"Nonpandemic Noncommuter\"\ndc_data['pandemic-commuter'] = dc_data.apply(lambda row: make_pandemic_commuter(row), axis=1)\n\n\n#%%\n# function to make weekend column\ndef make_pandemic_weekend(row):\n if row['pandemic'] and row['weekday']:\n return \"Pandemic Weekday\"\n elif row['pandemic'] and not row['weekday']:\n return \"Pandemic Nonweekday\"\n elif not row['pandemic'] and row['weekday']:\n return \"Nonpandemic Weekday\"\n elif not row['pandemic'] and not row['weekday']:\n return \"Nonpandemic Nonweekday\"\ndc_data['pandemic-weekday'] = dc_data.apply(lambda row: make_pandemic_weekend(row), axis=1)\n\n\n#%%\n\ndc_data.to_csv(\"dc_data.csv\")\n#%%\ncolum=dc_data[dc_data[\"Start station\"]== \"Yuma St & Tenley Circle NW\"]\n#%%\n\n\n#%%\nlen(colum)\n\n\n\n\n\n# %%\n", "sub_path": "Bike_preprocessing.py", "file_name": "Bike_preprocessing.py", "file_ext": "py", "file_size_in_byte": 8895, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pandas.read_csv", "line_number": 64, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 67, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 70, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 73, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 76, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 79, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 82, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 85, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 86, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 87, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 123, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 148, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 214, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 214, "usage_type": "attribute"}]} +{"seq_id": "186500393", "text": "from functional_tests.test_utils.helper import UITestCase\nfrom integrator.models import Integration\nimport json\nimport mock\nimport logging\n\nlogging.disable(logging.CRITICAL)\n\nclass IndexPageTest(UITestCase):\n def test_create_integration_button_rendering(self):\n self.browser.get(self.live_server_url + '/')\n self.wait_till_element_is_clickable('add_integration')\n self.browser.find_element_by_id('add_integration')\n\n def test_display_of_existing_integrations(self):\n integration_data = json.loads(open(self.test_data_directory + 'helpscout_integration_form.json').read())\n integration = Integration(**integration_data)\n with mock.patch('integrator.views.ui.integration.views.Integration') as integration_mock:\n integration_mock.objects.all = mock.Mock()\n conf = {'return_value': [integration]}\n integration_mock.objects.all.configure_mock(**conf)\n self.browser.get(self.live_server_url + '/')\n integration_list_element = self.browser.find_element_by_id('helpscout')\n self.assertIn('integration_detail', integration_list_element.get_attribute('class'))\n\n def test_navigation_index_to_create_integration_page(self):\n self.browser.get(self.live_server_url + '/')\n self.wait_till_element_is_clickable('add_integration')\n\n add_integration = self.browser.find_element_by_id('add_integration')\n add_integration.click()\n\n self.browser.back()\n add_integration = self.browser.find_element_by_id('add_integration')\n\n", "sub_path": "functional_tests/ui_tests/test_index_page.py", "file_name": "test_index_page.py", "file_ext": "py", "file_size_in_byte": 1556, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.disable", "line_number": 7, "usage_type": "call"}, {"api_name": "logging.CRITICAL", "line_number": 7, "usage_type": "attribute"}, {"api_name": "functional_tests.test_utils.helper.UITestCase", "line_number": 9, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 16, "usage_type": "call"}, {"api_name": "integrator.models.Integration", "line_number": 17, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 18, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "447054361", "text": "import cv2\nimport numpy as np\n\nfrom model import FacialExpressionModel\n\n\nmodel = FacialExpressionModel(\"model.json\", \"model_weights.h5\")\n\nfacec = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n\n\nclass VideoCamera(object):\n\n def __init__(self):\n self.video = cv2.VideoCapture(0)\n\n def __del__(self):\n self.video.release()\n\n def get_frame(self):\n\n\n \n _, frame = self.video.read()\n\n gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n scaleFactor = 1.3\n minNeighbors = 5\n faces = facec.detectMultiScale(gray_frame, scaleFactor, minNeighbors)\n\n for (x, y, w, h) in faces:\n\n\n roi = gray_frame[y:y+h, x:x+w]\n roi = cv2.resize(roi, (48, 48))\n prediction = model.predict_emotion(\n roi[np.newaxis, :, :, np.newaxis])\n Symbols = {\"Happy\": \":)\", \"Sad\": \":}\", \"Surprise\": \"!!\",\n \"Angry\": \"?\", \"Disgust\": \"#\", \"Neutral\": \".\", \"Fear\": \"~\"}\n Text = str(prediction) + Symbols[str(prediction)]\n Text_Color = (180, 105, 255)\n\n Thickness = 4\n Font_Scale = 2\n Font_Type = cv2.FONT_HERSHEY_DUPLEX\n\n cv2.putText(frame, Text, (x, y), Font_Type,\n Font_Scale, Text_Color, Thickness)\n xc = int((x + x+w)/2)\n yc = int((y + y+h)/2)\n radius = int(w/2)\n\n cv2.circle(frame, (xc, yc), radius, (0, 255, 0), Thickness)\n _, jpeg = cv2.imencode('.jpg', frame)\n\n return jpeg.tobytes()\n", "sub_path": "camera.py", "file_name": "camera.py", "file_ext": "py", "file_size_in_byte": 1564, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "model.FacialExpressionModel", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 35, "usage_type": "call"}, {"api_name": "model.predict_emotion", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 37, "usage_type": "attribute"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 45, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.imencode", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "114973835", "text": "from setuptools import setup, find_packages\n\nimport os\n\nmodular_blocks = __import__('modular_blocks')\n\n\nsetup(\n name='django-modular-blocks',\n packages=find_packages(),\n author='Gabriel Pichot',\n author_email='gabriel.pichot@gmail.com',\n url='https://github.com/gpichot/django-modular-blocks',\n description=(\n 'Django Modular Blocks ease the integration of third'\n 'parties application as blocks in a page.'\n ),\n include_package_data=True,\n classifiers=[\n 'Framework :: Django',\n 'Programming Language :: Python',\n ],\n keywords=['modular', 'modules', ],\n install_requires=[\n 'Django >= 1.5',\n ],\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 671, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "setuptools.setup", "line_number": 8, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "605952695", "text": "'''\nSupport for RFC 2136 dynamic DNS updates.\nRequires dnspython module.\n'''\n# Import python libs\nimport logging\n\n\nlog = logging.getLogger(__name__)\n\n\ntry:\n import dns.query\n import dns.update\n dns_support = True\nexcept ImportError as e:\n dns_support = False\n\ndef __virtual__():\n '''\n Confirm dnspython is available.\n '''\n if dns_support:\n return 'ddns'\n return False\n\n\ndef update(zone, name, ttl, rdtype, data, nameserver='127.0.0.1', replace=False):\n '''\n Add, replace, or update a DNS record.\n nameserver must be an IP address and the minion running this module\n must have update privileges on that server.\n If replace is true, first deletes all records for this name and type.\n\n CLI Example::\n\n salt ns1 ddns.update example.com host1 60 A 10.0.0.1\n '''\n fqdn = '{}.{}'.format(name, zone)\n request = dns.message.make_query(fqdn, rdtype)\n answer = dns.query.udp(request, nameserver)\n\n rdtype = dns.rdatatype.from_text(rdtype)\n rdata = dns.rdata.from_text(dns.rdataclass.IN, rdtype, data)\n \n is_update = False\n for rrset in answer.answer:\n if rdata in rrset.items:\n rr = rrset.items\n if ttl == rrset.ttl:\n if replace and (len(answer.answer) > 1\n or len(rrset.items) > 1):\n is_update = True\n break\n return None\n is_update = True\n break\n\n dns_update = dns.update.Update(zone)\n if is_update:\n dns_update.replace(name, ttl, rdata)\n else:\n dns_update.add(name, ttl, rdata)\n answer = dns.query.udp(dns_update, nameserver)\n if answer.rcode() > 0:\n return False\n return True\n\n\ndef delete(zone, name, rdtype=None, data=None, nameserver='127.0.0.1'):\n '''\n Delete a DNS record.\n\n CLI Example::\n\n salt ns1 ddns.delete example.com host1 A\n '''\n fqdn = '{}.{}'.format(name, zone)\n request = dns.message.make_query(fqdn, (rdtype or 'ANY'))\n\n answer = dns.query.udp(request, nameserver)\n if not answer.answer:\n return None\n\n dns_update = dns.update.Update(zone)\n\n if rdtype:\n rdtype = dns.rdatatype.from_text(rdtype)\n if data:\n rdata = dns.rdata.from_text(dns.rdataclass.IN, rdtype, data)\n dns_update.delete(name, rdata)\n else:\n dns_update.delete(name, rdtype)\n else:\n dns_update.delete(name)\n\n answer = dns.query.udp(dns_update, nameserver)\n if answer.rcode() > 0:\n return False\n return True\n", "sub_path": "salt/modules/ddns.py", "file_name": "ddns.py", "file_ext": "py", "file_size_in_byte": 2570, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "dns.query.message.make_query", "line_number": 40, "usage_type": "call"}, {"api_name": "dns.query.message", "line_number": 40, "usage_type": "attribute"}, {"api_name": "dns.query", "line_number": 40, "usage_type": "name"}, {"api_name": "dns.query.query.udp", "line_number": 41, "usage_type": "call"}, {"api_name": "dns.query.query", "line_number": 41, "usage_type": "attribute"}, {"api_name": "dns.query", "line_number": 41, "usage_type": "name"}, {"api_name": "dns.query.rdatatype.from_text", "line_number": 43, "usage_type": "call"}, {"api_name": "dns.query.rdatatype", "line_number": 43, "usage_type": "attribute"}, {"api_name": "dns.query", "line_number": 43, "usage_type": "name"}, {"api_name": "dns.query.rdata.from_text", "line_number": 44, "usage_type": "call"}, {"api_name": "dns.query.rdata", "line_number": 44, "usage_type": "attribute"}, {"api_name": "dns.query", "line_number": 44, "usage_type": "name"}, {"api_name": "dns.query.rdataclass", "line_number": 44, "usage_type": "attribute"}, {"api_name": "dns.query.update.Update", "line_number": 59, "usage_type": "call"}, {"api_name": "dns.query.update", "line_number": 59, "usage_type": "attribute"}, {"api_name": "dns.query", "line_number": 59, "usage_type": "name"}, {"api_name": "dns.query.query.udp", "line_number": 64, "usage_type": "call"}, {"api_name": "dns.query.query", "line_number": 64, "usage_type": "attribute"}, {"api_name": "dns.query", "line_number": 64, "usage_type": "name"}, {"api_name": "dns.query.message.make_query", "line_number": 79, "usage_type": "call"}, {"api_name": "dns.query.message", "line_number": 79, "usage_type": "attribute"}, {"api_name": "dns.query", "line_number": 79, "usage_type": "name"}, {"api_name": "dns.query.query.udp", "line_number": 81, "usage_type": "call"}, {"api_name": "dns.query.query", "line_number": 81, "usage_type": "attribute"}, {"api_name": "dns.query", "line_number": 81, "usage_type": "name"}, {"api_name": "dns.query.update.Update", "line_number": 85, "usage_type": "call"}, {"api_name": "dns.query.update", "line_number": 85, "usage_type": "attribute"}, {"api_name": "dns.query", "line_number": 85, "usage_type": "name"}, {"api_name": "dns.query.rdatatype.from_text", "line_number": 88, "usage_type": "call"}, {"api_name": "dns.query.rdatatype", "line_number": 88, "usage_type": "attribute"}, {"api_name": "dns.query", "line_number": 88, "usage_type": "name"}, {"api_name": "dns.query.rdata.from_text", "line_number": 90, "usage_type": "call"}, {"api_name": "dns.query.rdata", "line_number": 90, "usage_type": "attribute"}, {"api_name": "dns.query", "line_number": 90, "usage_type": "name"}, {"api_name": "dns.query.rdataclass", "line_number": 90, "usage_type": "attribute"}, {"api_name": "dns.query.query.udp", "line_number": 97, "usage_type": "call"}, {"api_name": "dns.query.query", "line_number": 97, "usage_type": "attribute"}, {"api_name": "dns.query", "line_number": 97, "usage_type": "name"}]} +{"seq_id": "108240887", "text": "import sys\nimport sqlite3\nfrom PyQt5.QtWidgets import *\nfrom PyQt5 import QtWidgets\nfrom work_ui import Ui_Form\nfrom work_edit_dialog import Ui_Dialog\n\n\nclass ReadOnlyDelegate(QtWidgets.QStyledItemDelegate):\n def createEditor(self, parent, option, index): # Создан для запрета на редактирование таблицы\n return\n\n\nclass Work(QWidget, Ui_Form):\n def __init__(self):\n super().__init__()\n self.setupUi(self)\n self.con = sqlite3.connect(\"database/production.db\")\n self.cur = self.con.cursor()\n self.initUI()\n\n def initUI(self):\n # Подключаем сигнал зависящий от изменения текста lineEdit\n self.lineEdit.textChanged.connect(self.load_table)\n # Подключаем событие для кнопки pb_edit (pb от сокращения PushButon), pb_add соответственно\n self.pb_edit.clicked.connect(self.edit_elem)\n self.pb_add.clicked.connect(self.add_elem)\n self.load_table()\n\n def load_table(self):\n # Создаём запрос для сортировки tools (бд), начало Названия товара должно начинаться с self.lineEdit.text()\n result = self.cur.execute(\"\"\"SELECT * FROM works WHERE Название like ?\"\"\",\n (self.lineEdit.text() + \"%\", )).fetchall()\n # Получаем список заголовков таблицы\n title_list = [i[1] for i in self.cur.execute(\"pragma table_info(works)\").fetchall()]\n # Заполняем tableWidget\n header = self.tableWidget.horizontalHeader()\n self.tableWidget.setColumnCount(len(title_list))\n self.tableWidget.setHorizontalHeaderLabels(title_list)\n self.tableWidget.setRowCount(0)\n delegate = ReadOnlyDelegate(self.tableWidget)\n for i, elem in enumerate(result):\n self.tableWidget.setRowCount(i + 1)\n # Используем класс delegate (10) для запрета на редактирования столбца i\n self.tableWidget.setItemDelegateForRow(i, delegate)\n for j, elem1 in enumerate(elem):\n self.tableWidget.setItem(i, j, QTableWidgetItem(str(elem1)))\n for i in range(4): # Задаём свойства расширения для каждого столбца каждой таблицы\n header.setSectionResizeMode(i, QtWidgets.QHeaderView.Stretch) # Stretch - максимально расшириться\n header.setSectionResizeMode(0, QtWidgets.QHeaderView.ResizeToContents) # ResizeToContents - минимально\n\n def add_elem(self):\n # Класс вызывает диалоговое окно и передаёт нужные параметры для работы.\n dialog = Editdialog(\"add\", self.con, self.cur)\n dialog.show()\n # Отключаем основное окно до окончания работы диалогового окна\n self.setEnabled(False)\n dialog.exec()\n self.setEnabled(True)\n # После изменений обновляем таблицу\n self.load_table()\n\n def edit_elem(self):\n rows = list(set([i.row() for i in self.tableWidget.selectedItems()]))\n # Получаем список выделенных строк\n if len(rows) != 1: # Строка обязательно должна быть одна\n return 0\n # Создаём и заполняем список с данными о выделенной строке\n select_row = []\n for i in range(4):\n select_row.append(self.tableWidget.item(rows[0], i).text())\n # Класс вызывает диалоговое окно и передаёт нужные параметры для работы.\n dialog = Editdialog(\"edit\", self.con, self.cur, select_row)\n dialog.show()\n # Отключаем основное окно до окончания работы диалогового окна\n self.setEnabled(False)\n dialog.exec()\n self.setEnabled(True)\n # После изменений обновляем таблицу\n self.load_table()\n\n\nclass Editdialog(QDialog, Ui_Dialog): # Диалог используемый для добавления и редактирования элементов склада\n def __init__(self, type_dialog, *args):\n super().__init__()\n self.setupUi(self)\n self.type = type_dialog\n self.con = args[0]\n self.cur = args[1]\n self.select_row = args[-1]\n self.initUI()\n\n def initUI(self):\n self.buttonBox.accepted.connect(self.acept_data)\n self.buttonBox.rejected.connect(self.reject_data)\n if self.type == \"edit\": # Если диалог направлен на редактирование данных - вбиваем данные в форму\n self.le_name.setText(self.select_row[1])\n self.le_ei.setText(self.select_row[2])\n self.dsb_price.setValue(float(self.select_row[3].replace(\",\", \".\")))\n\n def acept_data(self):\n try:\n # Получаем введенные пользователем данные\n name = self.le_name.text()\n price = float(self.dsb_price.text().replace(\",\", \".\"))\n ei = self.le_ei.text() # Единица измерения\n if name and ei and price: # В случае правильно введённых данных\n if self.type == \"add\":\n self.cur.execute(\"INSERT INTO works(Название, 'Ед. изм', 'Стоимость р')\"\n \"VALUES(?, ?, ?)\", (name, ei, price))\n else:\n self.cur.execute(\"UPDATE works SET 'Название' = ?, 'Ед. изм' = ?,\"\n \" 'Стоимость р' = ? WHERE id = ?\", (name, ei, price, self.select_row[0]))\n self.con.commit()\n self.close()\n else:\n self.lineEdit_error.setText(\"Некоторые поля не заполнены\")\n except ValueError:\n self.lineEdit_error.setText(\"Некорректные значения полей\")\n except sqlite3.IntegrityError:\n self.lineEdit_error.setText(\"Название занято\")\n\n def reject_data(self):\n self.close()\n\n\ndef except_hook(cls, exception, traceback):\n sys.__excepthook__(cls, exception, traceback)\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n ex = Work()\n ex.show()\n sys.excepthook = except_hook\n sys.exit(app.exec())\n", "sub_path": "код проекта/Work.py", "file_name": "Work.py", "file_ext": "py", "file_size_in_byte": 6883, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "PyQt5.QtWidgets.QStyledItemDelegate", "line_number": 9, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 9, "usage_type": "name"}, {"api_name": "work_ui.Ui_Form", "line_number": 14, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 18, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHeaderView", "line_number": 49, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 49, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHeaderView", "line_number": 50, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 50, "usage_type": "name"}, {"api_name": "work_edit_dialog.Ui_Dialog", "line_number": 83, "usage_type": "name"}, {"api_name": "sqlite3.IntegrityError", "line_number": 120, "usage_type": "attribute"}, {"api_name": "sys.__excepthook__", "line_number": 128, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 132, "usage_type": "attribute"}, {"api_name": "sys.excepthook", "line_number": 135, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 136, "usage_type": "call"}]} +{"seq_id": "651754683", "text": "#!/usr/bin/python\n\n\n\"\"\"\n Starter code for the regression mini-project.\n \n Loads up/formats a modified version of the dataset\n (why modified? we've removed some trouble points\n that you'll find yourself in the outliers mini-project).\n\n Draws a little scatterplot of the training/testing data\n\n You fill in the regression code where indicated:\n\"\"\"\n\nimport os\nimport sys\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\n\n\nsys.path.append(os.getcwd())\nsys.path.insert(0, \"./tools/\")\n\nfrom tools.feature_format import featureFormat, targetFeatureSplit\n\ndef bonus_regression(features_list):\n dictionary = pickle.load(\n open(\"./final_project/final_project_dataset_modified_unix.pkl\", \"rb\"))\n # list the features you want to look at--first item in the\n # list will be the \"target\" feature\n data = featureFormat(dictionary, features_list, remove_any_zeroes=True, sort_keys = \"./tools/python2_lesson06_keys_unix.pkl\")\n target, features = targetFeatureSplit(data)\n\n # training-testing split needed in regression, just like classification\n\n feature_train, feature_test, target_train, target_test = train_test_split(\n features, target, test_size=0.5, random_state=42)\n train_color = \"b\"\n test_color = \"r\"\n\n # Your regression goes here!\n # Please name it reg, so that the plotting code below picks it up and\n # plots it correctly. Don't forget to change the test_color above from \"b\" to\n # \"r\" to differentiate training points from test points.\n reg_salary = LinearRegression().fit(feature_train, target_train)\n\n # printing slop and intercept\n slope = reg_salary.coef_[0]\n intercept = reg_salary.intercept_\n score1 = reg_salary.score(feature_train, target_train)\n score2 = reg_salary.score(feature_test, target_test)\n print(\"slope: {}, \\n intercept: {}, \\n score_trainingdata: {}, \\n score_testdata: {}\".format(slope, intercept, score1, score2))\n\n # draw the scatterplot, with color-coded training and testing points\n for feature, target in zip(feature_test, target_test):\n plt.scatter(feature, target, color=test_color)\n for feature, target in zip(feature_train, target_train):\n plt.scatter(feature, target, color=train_color)\n # labels for the legend\n plt.scatter(feature_test[0], target_test[0], color=test_color, label=\"test\")\n plt.scatter(feature_test[0], target_test[0], color=train_color, label=\"train\")\n # draw the regression line, once it's coded\n try:\n plt.plot(feature_test, reg_salary.predict(feature_test))\n except NameError:\n pass\n\n reg_outlier = LinearRegression().fit(feature_test, target_test)\n plt.plot(feature_train, reg_outlier.predict(feature_train), color=\"g\")\n\n print(reg_outlier.coef_)\n\n plt.xlabel(features_list[1])\n plt.ylabel(features_list[0])\n plt.legend()\n plt.show()\n\n# features_list = [\"bonus\", \"salary\"]\nbonus_regression([\"bonus\", \"salary\"])\n\nbonus_regression([\"bonus\", \"long_term_incentive\"])\n", "sub_path": "regression/finance_regression.py", "file_name": "finance_regression.py", "file_ext": "py", "file_size_in_byte": 3123, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sys.path.append", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.path.insert", "line_number": 27, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 32, "usage_type": "call"}, {"api_name": "tools.feature_format.featureFormat", "line_number": 36, "usage_type": "call"}, {"api_name": "tools.feature_format.targetFeatureSplit", "line_number": 37, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 41, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 73, "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.xlabel", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}]} +{"seq_id": "431090153", "text": "import SocketServer \nimport threading\nimport argparse\nimport jedi\nimport json\nimport sys\nimport os\n\nimport transports\nimport constants\nfrom utils import echo\nfrom handlers import preinit, initialize, didOpen, hover, definition, references\n\n\ndef serve(args):\n\tif args.stdio:\n\t\tmode = \"stdio\"\n\telse:\n\t\tmode = \"sockets\";\n\n\tif mode == \"stdio\":\n\t\techo(\"Python language server using stdio transport...\")\n\t\tserver = transports.StdioTransport()\n\t\twhile True:\n\t\t\tserver.handle(sys.stdin, sys.stdout)\n\n\telif mode == \"sockets\":\n\t\techo(\"Python language server listening on {}:{}\".format(args.host, args.port))\n\t\thost = args.host\n\t\tport = int(args.port)\n\n\t\ttry:\n\t\t\tserver = SocketServer.TCPServer((host, port), transports.SocketTransport)\n\t\t\tserver.serve_forever()\n\t\t\t\n\t\texcept KeyboardInterrupt:\n\t\t\tserver.shutdown()\n\t\t\tsys.exit()\n\n\telse:\n\t\techo(\"Invalid mode '{}'\".format(mode))\n\n\ndef query(args):\n\tif args.path == \"\":\n\t\techo(\"ls-python: path is empty\")\n\t\tsys.exit(2)\n\n\telif args.line < 1:\n\t\techo(\"ls-python: line is not valid\")\n\t\tsys.exit(2)\n\n\telif args.column < 0:\n\t\techo(\"ls-python: column is not valid\")\n\t\tsys.exit(2)\n\n\tif args.subcmd == \"hover\":\n\t\thover(args)\n\n\telif args.subcmd == \"definition\":\n\t\tdefinition(args)\n\n\telif args.subcmd == \"references\":\n\t\treferences(args)\n\n\telse:\n\t\techo(\"Sorry, I don't understand..\")\n\n\ndef addSourceArgs(parser):\n\t# TODO: Look into a cleaner way of doings this, i.e. groups or something\n\tparser.add_argument('line', help='The line to perform actions on (starting with 1).', nargs='?', default=1, type=int)\n\tparser.add_argument('column', help='The column of the cursor (starting with 0).', nargs='?', default=0, type=int)\n\tparser.add_argument('path', help='The path of the file in the file system.', nargs='?', default=constants.default_path)\n\n\ndef main():\n\tparser = argparse.ArgumentParser(description=\"Python Jedi\")\n\n\t# Allow preinitialization (importation of costly packages) to be controlled\n\tsubparsers = parser.add_subparsers(help=\"commands\")\n\tserver_parser = subparsers.add_parser('serve', help=\"Run as a server\")\n\n\t# Server mode args\n\tserver_parser.add_argument(\"--pre\", help='', default=\"none\")\n\tserver_parser.add_argument(\"--stdio\", action='store_true', help='Runs the server over stdio', default=False)\n\tserver_parser.add_argument(\"--host\", help='The port to host the language server on', nargs='?', default=constants.default_host)\n\tserver_parser.add_argument(\"--port\", help='The hostname to listen on', nargs='?', default=constants.default_port)\n\tserver_parser.set_defaults(func=serve)\n\n\t# 'hover' args\n\thover_parser = subparsers.add_parser('hover', help=\"Hover mode\")\n\taddSourceArgs(hover_parser)\n\n\thover_parser.set_defaults(func=hover)\n\n\targs = parser.parse_args()\n\n\tif args.pre is not \"none\":\n\t\tpreinit(args)\n\n\targs.func(args)\n\n\nif __name__ == \"__main__\":\n\tmain()\n", "sub_path": "src/python/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 2804, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "utils.echo", "line_number": 22, "usage_type": "call"}, {"api_name": "transports.StdioTransport", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 25, "usage_type": "attribute"}, {"api_name": "utils.echo", "line_number": 28, "usage_type": "call"}, {"api_name": "SocketServer.TCPServer", "line_number": 33, "usage_type": "call"}, {"api_name": "transports.SocketTransport", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 38, "usage_type": "call"}, {"api_name": "utils.echo", "line_number": 41, "usage_type": "call"}, {"api_name": "utils.echo", "line_number": 46, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 47, "usage_type": "call"}, {"api_name": "utils.echo", "line_number": 50, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 51, "usage_type": "call"}, {"api_name": "utils.echo", "line_number": 54, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 55, "usage_type": "call"}, {"api_name": "handlers.hover", "line_number": 58, "usage_type": "call"}, {"api_name": "handlers.definition", "line_number": 61, "usage_type": "call"}, {"api_name": "handlers.references", "line_number": 64, "usage_type": "call"}, {"api_name": "utils.echo", "line_number": 67, "usage_type": "call"}, {"api_name": "constants.default_path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 78, "usage_type": "call"}, {"api_name": "constants.default_host", "line_number": 87, "usage_type": "attribute"}, {"api_name": "constants.default_port", "line_number": 88, "usage_type": "attribute"}, {"api_name": "handlers.hover", "line_number": 95, "usage_type": "name"}, {"api_name": "handlers.preinit", "line_number": 100, "usage_type": "call"}]} +{"seq_id": "333904769", "text": "#!/usr/bin/env python\n\nimport sys, tempfile, os, shutil\n\ndef replace_license(filename, newlicense, oldlicenseid):\n sourcef = open(filename)\n (tempf, tempn) = tempfile.mkstemp()\n\n oldlicense_found = False\n license_written = False\n endcpr_found = False\n\n for line in sourcef:\n if not oldlicense_found and not license_written:\n oldlicense_found = line.startswith(oldlicenseid)\n if not oldlicense_found:\n os.write(tempf, line)\n elif oldlicense_found and not license_written:\n license_written = True\n os.write(tempf, newlicense)\n os.write(tempf, \"*/\\n\")\n elif license_written and not endcpr_found:\n endcpr_found = line.startswith(\"*/\")\n else:\n os.write(tempf, line)\n\n os.close(tempf)\n sourcef.close()\n shutil.move(tempn, filename)\n\nif __name__ == \"__main__\":\n licensef = open(sys.argv[1])\n oldlicenseid = sys.argv[2]\n newlicense = licensef.read()\n licensef.close()\n for fn in sys.argv[3:]:\n replace_license(fn, newlicense, oldlicenseid)\n", "sub_path": "scripts/change_license.py", "file_name": "change_license.py", "file_ext": "py", "file_size_in_byte": 1003, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "tempfile.mkstemp", "line_number": 7, "usage_type": "call"}, {"api_name": "os.write", "line_number": 17, "usage_type": "call"}, {"api_name": "os.write", "line_number": 20, "usage_type": "call"}, {"api_name": "os.write", "line_number": 21, "usage_type": "call"}, {"api_name": "os.write", "line_number": 25, "usage_type": "call"}, {"api_name": "os.close", "line_number": 27, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 32, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 36, "usage_type": "attribute"}]} +{"seq_id": "129231142", "text": "import urllib\nimport ssl\nimport urllib.request\nimport base64\nimport json\n\nALI_APPCODE = \"70533dd2ebcc472e91d817d125ee643a\"\nALI_HOST = 'https://ocrapi-invoice.taobao.com'\nALI_PATH = '/ocrservice/invoice'\nALI_METHOD = 'POST'\n\n\ndef get_file_content_base64(filePath):\n \"\"\" 读取图片 \"\"\"\n with open(filePath, 'rb') as fp:\n return base64.b64encode(fp.read())\n\n\ndef scan_vat_invoice(filepath):\n appcode = ALI_APPCODE\n url = ALI_HOST + ALI_PATH\n bodys = {}\n b64_data = get_file_content_base64(filepath)\n bodys['img'] = b64_data.decode('utf8')\n post_data = json.dumps(bodys)\n post_data_bytes = post_data.encode('utf8')\n request = urllib.request.Request(url, post_data_bytes)\n request.add_header('Authorization', 'APPCODE ' + appcode)\n # 根据API的要求，定义相对应的Content - Type\n request.add_header('Content-Type', 'application/json; charset=UTF-8')\n ctx = ssl.create_default_context()\n ctx.check_hostname = False\n ctx.verify_mode = ssl.CERT_NONE\n try:\n response = urllib.request.urlopen(request, context=ctx)\n content = response.read()\n if (content):\n print(content.decode('utf-8'))\n except Exception as e:\n print(e)\n\n\nscan_vat_invoice('jt.jpg')\n", "sub_path": "invoice_validate/ali.py", "file_name": "ali.py", "file_ext": "py", "file_size_in_byte": 1253, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "base64.b64encode", "line_number": 16, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 25, "usage_type": "call"}, {"api_name": "urllib.request.Request", "line_number": 27, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 27, "usage_type": "attribute"}, {"api_name": "ssl.create_default_context", "line_number": 31, "usage_type": "call"}, {"api_name": "ssl.CERT_NONE", "line_number": 33, "usage_type": "attribute"}, {"api_name": "urllib.request.urlopen", "line_number": 35, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 35, "usage_type": "attribute"}]} +{"seq_id": "499944538", "text": "from collections import Counter\n\n\ndef main():\n \"\"\"\n A python program to find the wordcount in a file for each line and then\n print the output.\n Finally store the output back to the file.\n\n Input:a file includes two line\n Python Course\n Deep Learning Course\n\n Output:\n Python: 1\n Course: 2\n Deep: 1\n Learning: 1\n \"\"\"\n file = open(\"input_file.txt\", \"r\", encoding=\"utf-8\")\n wordcount = Counter(file.read().split())\n f = open(\"output_file.txt\", \"w\")\n for item in wordcount.items():\n f.write(\"{}: {}\\n\".format(*item))\n\n\nif __name__ == \"__main__\":\n main()", "sub_path": "ICP2/3_word_count.py", "file_name": "3_word_count.py", "file_ext": "py", "file_size_in_byte": 610, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "collections.Counter", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "556424089", "text": "#%% [markdown]\n# # 202 Tensor\n#\n# View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n# My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n#\n# Dependencies:\n# * torch: 0.4.1\n#\n# tensor in torch is to build a computational graph,\n# but this graph is dynamic compared with a static graph in Tensorflow or Theano.\n# So torch does not have placeholder, torch can just pass tensor to the computational graph.\n#\n\n#%%\nimport torch\n# from torch.autograd import Variable\n\n#%%\ntensor0 = torch.FloatTensor([[1, 2], [3, 4]]) # build a tensor\nprint(tensor0) # [torch.FloatTensor of size 2x2]\ntensor0.requires_grad_(True) # by requires_grad flag for compute gradients\nprint(tensor0) # [torch.FloatTensor of size 2x2]\n\nvariable = torch.tensor(\n [[1, 2], [3, 4]], dtype=torch.float,\n requires_grad=True) # build a tensor, usually for compute gradients\nprint(variable) # [torch.FloatTensor of size 2x2]\n#\n\n#%%\ntensor0.requires_grad_(False) # by requires_grad flag for close gradients\nt_out = torch.mean(tensor0 * tensor0) # x^2\nv_out = torch.mean(variable * variable) # x^2\nprint(t_out)\nprint(v_out)\n\n#%%\nv_out.backward() # backpropagation from v_out\n\n#%% [markdown]\n# $$ v_{out} = {{1} \\over {4}} sum(variable^2) $$\n#\n# the gradients w.r.t the variable,\n#\n# $$ {d(v_{out}) \\over d(variable)} = {{1} \\over {4}} 2 variable = {variable \\over 2}$$\n#\n# let's check the result pytorch calculated for us below:\n\n#%%\nvariable.grad\n\n#%%\nvariable # this is data in variable format\n\n#%%\nvariable.detach() # this is data in tensor format\n\n#%%\nvariable.detach().numpy() # numpy format\n\n#%% [markdown]\n# Note that we did `.backward()` on `v_out` but `variable` has been assigned new values on it's `grad`.\n#\n# As this line\n# ```\n# v_out = torch.mean(variable*variable)\n# ```\n# will make a new variable `v_out` and connect it with `variable` in computation graph.\n\n#%%\ntype(v_out)\n\n#%%\ntype(v_out.data)\n", "sub_path": "tutorial-contents-vscode-0.4.1/202_tensor.py", "file_name": "202_tensor.py", "file_ext": "py", "file_size_in_byte": 1923, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "torch.FloatTensor", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 26, "usage_type": "attribute"}, {"api_name": "torch.mean", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "316364778", "text": "from django.shortcuts import render, HttpResponse\nfrom project.models import Project\nfrom .utils import get_top_projects_for_3_months\nimport locale\nimport sys\nfrom .utils import review_email_notification\nfrom project.models import Review\n\ndef index(request):\n # Last 3 projects\n last_three_projects = Project.objects.filter(is_posted=True).order_by('-id')[:4]\n last_three_projects_asc = reversed(last_three_projects)\n\n # Most popular projects (3 months)\n top_projects_ids = get_top_projects_for_3_months()\n top_projects = Project.objects.filter(id__in=top_projects_ids)\n\n # Paid query\n return render(request, 'index.html', locals())\n\n\ndef about(request):\n top_projects_ids = get_top_projects_for_3_months()\n top_projects = Project.objects.filter(id__in=top_projects_ids)\n return render(request, 'about.html', locals())\n\n\ndef custom404(request):\n return render(request, '404.html', status=404)\n\n\ndef custom500(request):\n # exception_type, exception_value, exception_traceback = sys.exc_info()\n return render(request, '500.html', status=500)\n\n\ndef view_locale(request):\n loc_info = \"getlocale: \" + str(locale.getlocale()) + \\\n \"
getdefaultlocale(): \" + str(locale.getdefaultlocale()) + \\\n \"
fs_encoding: \" + str(sys.getfilesystemencoding()) + \\\n \"
sys default encoding: \" + str(sys.getdefaultencoding())\n \"
sys default encoding: \" + str(sys.getdefaultencoding())\n\n return HttpResponse(loc_info)\n\n\ndef test_email(request):\n review = Review.objects.get(pk=7)\n review_email_notification(review.project_id, review)\n return HttpResponse()", "sub_path": "upgrademystartup/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1649, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "project.models.Project.objects.filter", "line_number": 11, "usage_type": "call"}, {"api_name": "project.models.Project.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "project.models.Project", "line_number": 11, "usage_type": "name"}, {"api_name": "utils.get_top_projects_for_3_months", "line_number": 15, "usage_type": "call"}, {"api_name": "project.models.Project.objects.filter", "line_number": 16, "usage_type": "call"}, {"api_name": "project.models.Project.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "project.models.Project", "line_number": 16, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 19, "usage_type": "call"}, {"api_name": "utils.get_top_projects_for_3_months", "line_number": 23, "usage_type": "call"}, {"api_name": "project.models.Project.objects.filter", "line_number": 24, "usage_type": "call"}, {"api_name": "project.models.Project.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "project.models.Project", "line_number": 24, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 25, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 29, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 34, "usage_type": "call"}, {"api_name": "locale.getlocale", "line_number": 38, "usage_type": "call"}, {"api_name": "locale.getdefaultlocale", "line_number": 39, "usage_type": "call"}, {"api_name": "sys.getfilesystemencoding", "line_number": 40, "usage_type": "call"}, {"api_name": "sys.getdefaultencoding", "line_number": 41, "usage_type": "call"}, {"api_name": "sys.getdefaultencoding", "line_number": 42, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 44, "usage_type": "call"}, {"api_name": "project.models.Review.objects.get", "line_number": 48, "usage_type": "call"}, {"api_name": "project.models.Review.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "project.models.Review", "line_number": 48, "usage_type": "name"}, {"api_name": "utils.review_email_notification", "line_number": 49, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "502705865", "text": "import json\nimport openpyxl\n\njsondata = open(\"路外停車資訊.json\", 'r', encoding='UTF-8')\n\ndata = jsondata.read()\ndata = json.loads(data)\n\n#宣告一個試算表\nworkbook = openpyxl.Workbook()\nworkbook.remove_sheet(workbook.get_sheet_by_name('Sheet'))\nworkbook.create_sheet('桃園市停車場資訊')\n\n#操作一個工作表\nsheet = workbook.get_sheet_by_name('桃園市停車場資訊')\n\n#print (len(data['parkingLots']))\n\n#寫入值\nsheet['A1'] = 'areaId'\nsheet['B1'] = 'areaName'\nsheet['C1'] = 'parkName'\nsheet['D1'] = 'totalSpace'\nsheet['E1'] = 'surplusSpace'\nsheet['F1'] = 'payGuide'\nsheet['G1'] = 'introduction'\nsheet['H1'] = 'address'\nsheet['I1'] = 'wgsX'\nsheet['J1'] = 'wgsY'\nsheet['K1'] = 'parkId'\nx = 0\nwhile x < len(data['parkingLots']):\n sheet.cell(row = x+2, column = 1).value = data['parkingLots'][x]['areaId']\n sheet.cell(row = x+2, column = 2).value = data['parkingLots'][x]['areaName']\n sheet.cell(row = x+2, column = 3).value = data['parkingLots'][x]['parkName']\n sheet.cell(row = x+2, column = 4).value = data['parkingLots'][x]['totalSpace']\n sheet.cell(row = x+2, column = 5).value = data['parkingLots'][x]['surplusSpace']\n sheet.cell(row = x+2, column = 6).value = data['parkingLots'][x]['payGuide']\n sheet.cell(row = x+2, column = 7).value = data['parkingLots'][x]['introduction']\n sheet.cell(row = x+2, column = 8).value = data['parkingLots'][x]['address']\n sheet.cell(row = x+2, column = 9).value = data['parkingLots'][x]['wgsX']\n sheet.cell(row = x+2, column = 10).value = data['parkingLots'][x]['wgsY']\n sheet.cell(row = x+2, column = 11).value = data['parkingLots'][x]['parkId']\n x+=1\n\n#記得存檔歐\nworkbook.save('test1.xlsx')\n\n\n \n\n", "sub_path": "d1_homework.py", "file_name": "d1_homework.py", "file_ext": "py", "file_size_in_byte": 1708, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "json.loads", "line_number": 7, "usage_type": "call"}, {"api_name": "openpyxl.Workbook", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "68222296", "text": "import numpy as np\nimport cv2\n\ncapture = cv2.VideoCapture(0)\nRED = (0, 0, 255)\nGREEN = (0, 255, 0)\nYELLOW = (0, 255, 255)\nBLUE = (255, 0, 0)\nCYAN = (255, 255, 0)\nWHITE = (255, 255, 255)\n\n# Capture first frame\nret, frame = capture.read()\n\n# Define the codec and create VideoWriter object\nfourcc = cv2.VideoWriter_fourcc('X','V','I','D')\nwriter = cv2.VideoWriter('output.avi', fourcc, 20, (640, 480))\n\n# recorder toggle\nrecorder = False\n\nwhile(ret):\n\twindow_frame = frame.copy()\n\n\tcv2.putText(window_frame, \"Press [s] to start/stop recording.\", (0, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.45, GREEN, 1)\n\tcv2.putText(window_frame, \"Press [esc] to quit.\", (0, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.45, GREEN, 1)\n\n\tif recorder:\n\t\tcv2.putText(window_frame, \"[RECORDING]\", (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.45, RED, 2)\n\t\t\n\t\twriter.write(frame)\n\n\t# Display the resulting frame\n\tcv2.imshow('frame', window_frame)\n\n\t# press ESC to quit\n\tif cv2.waitKey(33) == 27:\n\t\tbreak\n\n\t# press 's' to toggle recording\n\tif cv2.waitKey(33) & 0xFF == ord('s'):\n\t\tif recorder == True:\n\t\t\trecorder = False\n\t\telse:\n\t\t\trecorder = True\n\n\t# Capture next frame\n\tret, frame = capture.read()\n\n\n# When everything done, release the capture\ncapture.release()\nwriter.release()\ncv2.destroyAllWindows()", "sub_path": "examples/python/utilities/record_webcam.py", "file_name": "record_webcam.py", "file_ext": "py", "file_size_in_byte": 1253, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "cv2.VideoCapture", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 25, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 29, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "566318185", "text": "from pages.cdn_overview_page import cdnOverviewPage\nimport allure\nimport pytest\n\n@allure.title(\"点击证书总数，跳转正常\")\n@allure.title(\"#\")\ndef test_cert_total(login):\n '''用例描述：1.先登录\n 2.点击证书总数'''\n driver = login\n cdnOverviewPage(driver).click_cert_total()\n cdnOverviewPage(driver).check_cert_total()\n\n@allure.title(\"点击即将过期证书数，跳转正常\")\n@allure.title(\"#\")\ndef test_cert_expire(login):\n '''用例描述：1.先登录\n 2.点击证书即将过期数'''\n driver = login\n cdnOverviewPage(driver).click_cert_expire()\n cdnOverviewPage(driver).check_cert_total()\n\n@pytest.mark.skip(\"需调整\")\n@allure.title(\"验证证书总数，数量正常\")\n@allure.title(\"#\")\ndef test_cert_total_num(login,db_cert_total):\n '''用例描述：1.先登录\n 2.对比证书总量与数据库中证书数量'''\n driver = login\n cert_total = db_cert_total[0].get('count(*)')\n cdnOverviewPage(driver).check_cert_total_num(cert_total)\n\n@allure.title(\"验证即将过期证书数，��量正常\")\n@allure.title(\"#\")\n@pytest.mark.skip(\"验证证书即将过期数，实现方法待定\")\ndef test_cert_expire_num(login):\n '''用例描述：1.先登录\n 2.验证证书即将过期数'''\n pass\n\n\n", "sub_path": "ConsoleClient/case/overview/test_cert_statistics.py", "file_name": "test_cert_statistics.py", "file_ext": "py", "file_size_in_byte": 1280, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pages.cdn_overview_page.cdnOverviewPage", "line_number": 11, "usage_type": "call"}, {"api_name": "pages.cdn_overview_page.cdnOverviewPage", "line_number": 12, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 5, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 6, "usage_type": "call"}, {"api_name": "pages.cdn_overview_page.cdnOverviewPage", "line_number": 20, "usage_type": "call"}, {"api_name": "pages.cdn_overview_page.cdnOverviewPage", "line_number": 21, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 14, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 15, "usage_type": "call"}, {"api_name": "pages.cdn_overview_page.cdnOverviewPage", "line_number": 31, "usage_type": "call"}, {"api_name": "pytest.mark.skip", "line_number": 23, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 23, "usage_type": "attribute"}, {"api_name": "allure.title", "line_number": 24, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 25, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 33, "usage_type": "call"}, {"api_name": "allure.title", "line_number": 34, "usage_type": "call"}, {"api_name": "pytest.mark.skip", "line_number": 35, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 35, "usage_type": "attribute"}]} +{"seq_id": "96704334", "text": "from django.shortcuts import render\nfrom .models import Product, ProductImages, Category\nfrom django.core.paginator import Paginator\nfrom django.db.models import Count, Q\nfrom django.shortcuts import get_object_or_404\n# Create your views here.\n\ndef productlist(request, category_slug=None):\n category = None\n productlist = Product.objects.get_queryset().order_by('id')\n category_list = Category.objects.annotate(total_products=Count('product'))\n \n # category filter\n if category_slug :\n category =get_object_or_404(Category, slug=category_slug)\n productlist = productlist.filter(category=category)\n\n # search filter\n search_query = request.GET.get('q')\n if search_query :\n productlist = productlist.filter(\n Q(name__icontains = search_query) |\n Q(description__icontains = search_query) |\n Q(condition__icontains = search_query) |\n Q(brand__brand_name__icontains = search_query)\n )\n\n # pagination\n paginator = Paginator(productlist,1)\n page = request.GET.get('page')\n productlist = paginator.get_page(page)\n\n template = 'Product/product_list.html'\n context = { \n 'product_list' : productlist, \n 'category_list' : category_list,\n 'category' : category}\n return render(request, template, context)\n\ndef productdetail(request,product_slug):\n productdetail =get_object_or_404(Product, slug=product_slug)\n productimages = ProductImages.objects.filter(product=productdetail)\n context = { 'product_detail' : productdetail , 'product_images' : productimages }\n template = 'Product/product_detail.html'\n return render(request, template, context)", "sub_path": "product/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1692, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "models.Product.objects.get_queryset", "line_number": 10, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 10, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 10, "usage_type": "name"}, {"api_name": "models.Category.objects.annotate", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 11, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 15, "usage_type": "call"}, {"api_name": "models.Category", "line_number": 15, "usage_type": "argument"}, {"api_name": "django.db.models.Q", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 25, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 29, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 38, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 41, "usage_type": "call"}, {"api_name": "models.Product", "line_number": 41, "usage_type": "argument"}, {"api_name": "models.ProductImages.objects.filter", "line_number": 42, "usage_type": "call"}, {"api_name": "models.ProductImages.objects", "line_number": 42, "usage_type": "attribute"}, {"api_name": "models.ProductImages", "line_number": 42, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "306139926", "text": "import datetime\nfrom datetime import datetime as dt\nfrom fbprophet import Prophet\nimport pandas as pd\n\ndef days_in_month(month: dt):\n last_month = month.replace(month=month.month-1)\n return (month.date() - last_month.date()).days\n\nclass ProphetModel():\n def __init__(self, df: pd.DataFrame):\n '''\n df - DataFrame with columns named 'y' and 'ds'\n '''\n\n self.prophet = Prophet()\n self.prophet.fit(df)\n self.df = df\n\n def predict(self, month: dt):\n '''\n month - datetime of the first of the month to predict\n return - predicted spending for that month\n '''\n num_days = days_in_month(month)\n latest = pd.to_datetime(self.df.ds.tail(1).item())\n num_future = (latest - month).days + num_days\n\n future = self.prophet.make_future_dataframe(periods=num_future)\n future['cap'] = 8.5\n fcst = self.prophet.predict(future)\n\n return sum(fcst.yhat.tail(num_days))\n\nif __name__ == \"__main__\":\n data = pd.read_csv(\"sample_data.csv\")\\\n .rename({\"Price\":\"y\", \"Date\":\"ds\"}, axis=1)\n data.ds = data.ds.apply(lambda d: dt.strptime(d, \"%Y-%m-%d\"))\n print(data.tail())\n p = ProphetModel(data)\n\n # predicted = p.predict(dt.strptime(\"2030-12-01\", \"%Y-%m-%d\"))\n predicted = p.predict(dt.combine(datetime.date(2022, 12, 1), dt.min.time()))\n print(predicted)\n\n\nimport os\ndef init_Prophet():\n data_path = str(os.path.dirname(os.path.abspath(__file__))) + '/sample_data.csv'\n data = pd.read_csv(data_path).rename({\"Price\":\"y\", \"Date\":\"ds\"}, axis=1)\n data.ds = data.ds.apply(lambda d: dt.strptime(d, \"%Y-%m-%d\"))\n return ProphetModel(data)\n\n", "sub_path": "Server/app/Predict/prophetmodel.py", "file_name": "prophetmodel.py", "file_ext": "py", "file_size_in_byte": 1685, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "datetime.datetime", "line_number": 6, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 11, "usage_type": "attribute"}, {"api_name": "fbprophet.Prophet", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "name"}, {"api_name": "pandas.to_datetime", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "name"}, {"api_name": "datetime.datetime.combine", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 43, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime.min.time", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime.min", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 51, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 51, "usage_type": "name"}]} +{"seq_id": "645078946", "text": "from flask import Flask, request, Response, render_template, json, jsonify, flash\nimport requests\nimport re\nimport itertools\nfrom flask_wtf.csrf import CSRFProtect\nfrom flask_wtf import FlaskForm\nfrom wtforms import StringField, SubmitField\n\n#Key to use on webster api\nkey = '325142d6-6793-4d6f-8f67-8b23d76755e0'\n\n\n# Select field added in the template, validation inside of the function itself, not the form.\nclass WordForm(FlaskForm):\n avail_letters = StringField(\"Letters\")\n submit = SubmitField(\"Go\")\n\ncsrf = CSRFProtect()\napp = Flask(__name__)\napp.config[\"SECRET_KEY\"] = \"row the boat\"\ncsrf.init_app(app)\n\n@app.route('/')\n@app.route('/index')\ndef index():\n form = WordForm()\n return render_template(\"index.html\", form=form)\n\n\n@app.route('/words', methods=['POST','GET'])\ndef letters_2_words():\n\n\n form = WordForm()\n if form.validate_on_submit():\n letters = form.avail_letters.data\n # This is just to show how to get the word length from the request form.\n print(request.form.get('wordlength'))\n word_length = request.form.get('wordlength')\n pattern = request.form.get('pattern')\n print(request.form.get('pattern'))\n if ((letters == '' and pattern == '')):\n return render_template(\"index.html\", form=form)\n elif (word_length != 'default' and len(pattern) != int(word_length) and pattern != ''):\n return render_template(\"index.html\", form=form)\n else:\n return render_template(\"index.html\", form=form)\n\n with open('sowpods.txt') as f:\n good_words = set(x.strip().lower() for x in f.readlines())\n\n #build the set and depending on the parameters passed in via the form, utilize word length or pattern.\n word_set = set()\n if letters != '':\n for l in range(3,len(letters)+1):\n for word in itertools.permutations(letters,l):\n w = \"\".join(word)\n if w in good_words:\n if word_length != 'default':\n if int(word_length) == len(w) and re.fullmatch(pattern,w) != None and pattern != '':\n word_set.add(w)\n elif int(word_length) == len(w) and pattern == '':\n word_set.add(w)\n else:\n if re.fullmatch(pattern,w) != None and pattern != '':\n word_set.add(w)\n elif pattern == '':\n word_set.add(w)\n #Same Pattern, just go through all words if none are specified.\n else:\n for w in good_words:\n if word_length != 'default':\n if int(word_length) == len(w) and re.fullmatch(pattern,w) != None and pattern != '':\n word_set.add(w)\n elif int(word_length) == len(w) and pattern == '':\n word_set.add(w)\n else:\n if re.fullmatch(pattern,w) != None and pattern != '':\n word_set.add(w)\n elif pattern == '':\n word_set.add(w)\n\n\n #sorting the word list first by alphabet and then by length.\n wordlist = list(word_set)\n wordlist = sorted(wordlist)\n wordlist = sorted(wordlist,key=len,reverse=True)\n\n return render_template('wordlist.html',\n wordlist=wordlist,\n name=\"CS4131\")\n\n\n#New Route to make requests from the flask app, this hides the api key from the users who can see the Javascript.\n@app.route('/def/', methods=['GET'])\ndef getDef(word):\n resp = requests.get(\"https://www.dictionaryapi.com/api/v3/references/collegiate/json/\" + word + \"?key=\" + key)\n data = resp.json()\n if type(data[0]) != dict:\n return jsonify('No Def. Found')\n else:\n alt_def = data[0]['shortdef']\n return jsonify(alt_def)\n\n\n@app.route('/proxy')\ndef proxy():\n result = requests.get(request.args['url'])\n resp = Response(result.text)\n resp.headers['Content-Type'] = 'application/json'\n return resp\n\n\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 4008, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask_wtf.FlaskForm", "line_number": 14, "usage_type": "name"}, {"api_name": "wtforms.StringField", "line_number": 15, "usage_type": "call"}, {"api_name": "wtforms.SubmitField", "line_number": 16, "usage_type": "call"}, {"api_name": "flask_wtf.csrf.CSRFProtect", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 47, "usage_type": "call"}, {"api_name": "itertools.permutations", "line_number": 56, "usage_type": "call"}, {"api_name": "re.fullmatch", "line_number": 60, "usage_type": "call"}, {"api_name": "re.fullmatch", "line_number": 65, "usage_type": "call"}, {"api_name": "re.fullmatch", "line_number": 73, "usage_type": "call"}, {"api_name": "re.fullmatch", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 89, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 97, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 103, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 108, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 108, "usage_type": "name"}, {"api_name": "flask.Response", "line_number": 109, "usage_type": "call"}]} +{"seq_id": "305347165", "text": "import logging\n\nfrom django.utils import timezone\nfrom django.db import models\nfrom django.contrib.contenttypes.models import ContentType\n\nfrom . import fields\n\nlogger = logging.getLogger(__name__)\n\n\nclass Schedulable(models.Model):\n \"\"\"\n Abstract model that should be implemented\n by models that need to be scheduled.\n \"\"\"\n\n class Meta:\n abstract = True\n\n def get_scheduled_filter_args(self):\n \"\"\"\n Hook to provide the arguments to identify\n the object being operated on.\n \"\"\"\n\n return {\n 'pk': self.pk\n }\n\n def schedule(self, when=None, action=None, **kwargs):\n \"\"\"\n Schedule an update of this object.\n\n when: The date for the update.\n\n action: if provided it will be looked up\n on the implementing class and called with\n **kwargs. If action is not provided each k/v pair\n in kwargs will be set on self and then self\n is saved.\n\n kwargs: any other arguments you would like passed\n for this change. Saved as a json object so must cleanly\n serialize.\n \"\"\"\n\n # when is empty or passed, just save it now.\n if not when or when <= timezone.now():\n self.do_scheduled_update(action, **kwargs)\n else:\n ctype = ContentType.objects.get_for_model(self.__class__)\n Schedule(\n content_type=ctype,\n object_args=self.get_scheduled_filter_args(),\n when=when,\n action=action,\n json_args=kwargs\n ).save()\n\n def do_scheduled_update(self, action, **kwargs):\n \"\"\"\n Do the actual update.\n\n action: if provided it will be looked up\n on the implementing class and called with\n **kwargs. If action is not provided each k/v pair\n in kwargs will be set on self and then self\n is saved.\n\n kwargs: any other you passed for this update\n passed along to whichever method performs\n the update.\n \"\"\"\n\n action = getattr(self, action, None)\n if callable(action):\n return action(**kwargs)\n else:\n for k, v in kwargs.items():\n setattr(self, k, v)\n self.save()\n\n\nclass Schedule(models.Model):\n \"\"\"\n Model to store scheduled updates.\n \"\"\"\n\n content_type = models.ForeignKey(ContentType)\n object_args = fields.JSONField()\n\n when = models.DateTimeField()\n action = models.CharField(max_length=255, null=True)\n json_args = fields.JSONField()\n\n def do_updates(self):\n # Only run if we are ready\n if self.when <= timezone.now():\n klass = self.content_type.model_class()\n for obj in klass.objects.filter(**self.object_args):\n obj.do_scheduled_update(self.action, **self.json_args)\n self.delete()\n\n class Meta:\n app_label = 'scheduling'\n", "sub_path": "scarlet/scheduling/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2941, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 49, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 49, "usage_type": "name"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects.get_for_model", "line_number": 52, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 52, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 85, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 85, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 90, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 90, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 90, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 93, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 93, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 94, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 94, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 99, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 99, "usage_type": "name"}]} +{"seq_id": "113675128", "text": "from django.shortcuts import render\n\n# Create your views here.\nfrom django.db import connection\nimport sqlite3\nfrom pymongo import MongoClient\n\ndef ddata(request):\n data = request.GET.copy()\n with MongoClient(\"mongodb://172.17.0.3:27017/\") as client:\n result = list(client.ddb.ddetail.find({}))\n data['page_obj'] = result\n return render(request, 'board/ddata.html', context=data)\n\ndef listwithmongo(request):\n data = request.GET.copy()\n with MongoClient('mongodb://172.17.0.2:27017/') as client:\n mydb = client.mydb\n result = list(mydb.economic.find({}))\n data['page_obj'] = result\n return render(request, 'board/listwithmongo.html', context=data)\n\ndef listwithrawquery(request):\n data = request.GET.copy()\n # data = dict()\n # connection.row_factory = sqlite3.Row\n # cursor = connection.cursor()\n with sqlite3.connect(\"db.sqlite3\") as con:\n con.row_factory = sqlite3.Row\n cur = con.cursor();\tcur.execute(\"select * from economic\")\n data['rows'] = cur.fetchall()\n\n for row in data['rows']:\n print(f\"{row['title']}, {row['link']}\")\n\n return render(request, 'board/listwithrawquery.html', context=data)\n\nfrom django.core.paginator import Paginator\ndef listwithrawquerywithpaginator(request):\n data = request.GET.copy()\n # data = dict()\n # connection.row_factory = sqlite3.Row\n # cursor = connection.cursor()\n with sqlite3.connect(\"db.sqlite3\") as con:\n con.row_factory = sqlite3.Row\n cur = con.cursor();\tcur.execute(\"select * from economic\")\n contact_list = cur.fetchall()\n\n paginator = Paginator(contact_list, 5) # Show 15 contacts per page.\n\n page_number = request.GET.get('page')\n page_number = page_number if page_number else 1 \n data['page_obj'] = paginator.get_page(page_number)\n\n page_obj=data['page_obj']\n for row in page_obj:\n print(f\"{row['title']}, {row['link']}\")\n\n return render(request, 'board/listwithrawquerywithpaginator.html', context=data)\n\nfrom pymongo import MongoClient\nfrom board.mongopaginator import MongoPaginator\n\n# def listwithmongo(request):\n# data = request.GET.copy()\n# with MongoClient('mongodb://10.0.0.5:27017/') as client:\n# mydb = client.mydb\n# result = list(mydb.economic.find({}))\t\t\t# get Collection with find()\n \n# result_page = []\n# for info in result:\t\t\t\t\t\t# Cursor\n# # del info(_id)\n# temp = {'title':info['title'], 'link':info['link']}\n# result_page.append(temp)\n# print(type(info), info)\n# data['page_obj'] = result\n \n# return render(request, 'board/listwithmongo.html', context=data)\n\ndef listwithmongowithpaginator(request):\n data = request.GET.copy()\n with MongoClient('mongodb://192.168.0.6:27017/') as client:\n mydb = client.mydb\n contact_list = mydb.economic.find({})\t\t\t# get Collection with find()\n for info in contact_list:\t\t\t\t\t\t# Cursor\n print(info)\n\n paginator = MongoPaginator(contact_list, 5) # Show 15 contacts per page.\n\n page_number = request.GET.get('page', 1)\n data['page_obj'] = paginator.get_page(page_number)\n\n page_obj=data['page_obj']\n for row in page_obj:\n print(f\"{row['title']}, {row['link']}\")\n\n return render(request, 'board/listwithrawquerywithpaginator.html', context=data)\n\n\n# 구름 새컨테이너 생성\n# 이름 learn_django1\n# 지역 서울\n# 공개 private\n# 템플릿 깃허브\n# 소프트웨어 장고\n# 추가모듈 몽고디비설치 선택\n\n# 구름 새터미널에서 mongod 입력\n# 위에 goormide 옆에 window에서 new terminal window 선택\n# mongo 입력\n# show dbs\n# new terminal window 하나 더 만든다\n# ls 하고 cd datas/ 들어가서 ls 확인\n# python3 ./scrapingandinsertmongo.py 입력하면 에러가 날 텐데\n# pip3 install -U pip pymongo\n# pip3 install -U pip bs4\n# python3 ./scrapingandinsertmongo.py\n\n#두번째 터미널에서 \n# show mydb \n# show collections \n# economic.find 하는거 \n\n\n\n# 구름 장고 gui\n# 실행하면 에러가 뜰텐데 migrate && 까지 삭제해준다\n# 실행하고 url 카피해서 board/listwithmongo 붙여줘서 브라우저에서 연다\n\n\n\n", "sub_path": "board/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4229, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pymongo.MongoClient", "line_number": 10, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 13, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 17, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.shortcuts.render", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 45, "usage_type": "attribute"}, {"api_name": "django.core.paginator.Paginator", "line_number": 49, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 59, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 82, "usage_type": "call"}, {"api_name": "board.mongopaginator.MongoPaginator", "line_number": 88, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 97, "usage_type": "call"}]} +{"seq_id": "577517933", "text": "# -*- coding: utf-8 -*-\n'''\n An implementation of sequence to sequence learning\n for performing ensemble morphosyntactic analyses\n'''\nfrom __future__ import print_function\nfrom keras.models import Sequential\nfrom keras import layers\nimport numpy as np\nfrom six.moves import range\nfrom prepare_data import SawarefData\nfrom vis import SawarefVis\nfrom character_table import colors, CharacterTable\n\n\nMYPATH = \"/morpho/output/\"\n# Parameters for the model and dataset.\nTRAINING_SIZE = 50000\nEPOCHS = 3\n# DIGITS = 3\n# REVERSE = True\n# Try replacing GRU, or SimpleRNN.\nRNN = layers.LSTM\nHIDDEN_SIZE = 128\nBATCH_SIZE = 128\nLAYERS = 1\nEMBEDDINGS = 100\nITERATIONS = 10\nREVERSE = False\n\nsawarefData = SawarefData(MYPATH, EMBEDDINGS)\n\nquestions, expected, _, SENTLEN = sawarefData.get2DSentenceJoinedFeatures(\n REVERSE, skipNAs=False)\nquestions = sawarefData.removeAlignment(questions, SENTLEN)\n# questions_padded = pad_sequences(questions)\n# expected_padded = pad_sequences(expected)\n\nctable_x = CharacterTable(\n set(\"-\").union(set([xx for x in questions for xx in x])))\n\nctable_y = CharacterTable(\n set(\"-\").union(set([xx for x in expected for xx in x])))\n\n# Maximum length of input is 'int + int' (e.g., '345+678'). Maximum length of\n# int is DIGITS.\n# MAXLEN = DIGITS + 1 + DIGITS\n\n\nprint('Total ayat questions:', len(questions))\n\nprint('Vectorization...')\nx = np.zeros((len(questions), SENTLEN,\n len(ctable_x.chars)), dtype=np.bool)\n# len(ctable_x.chars) + EMBEDDINGS), dtype=np.bool)\ny = np.zeros((len(expected), SENTLEN,\n len(ctable_y.chars)), dtype=np.bool)\nfor i, sentence in enumerate(questions):\n x[i] = ctable_x.encode(sentence, SENTLEN)\n # x[i] = np.concatenate((ctable_x.encode([sentence], SENTLEN),\n # np.array([embeddings[i]])), 1)\nfor i, sentence in enumerate(expected):\n y[i] = ctable_y.encode(sentence, SENTLEN)\n# Shuffle (x, y) in unison as the later parts of x will almost all be larger\n# digits.\nindices = np.arange(len(y))\nnp.random.shuffle(indices)\nx = x[indices]\ny = y[indices]\n\n# Explicitly set apart 10% for validation data that we never train over.\nsplit_at = len(x) - len(x) // 10\n(x_train, x_val) = x[:split_at], x[split_at:]\n(y_train, y_val) = y[:split_at], y[split_at:]\n\nprint('Training Data:')\nprint(x_train.shape)\nprint(y_train.shape)\n\nprint('Validation Data:')\nprint(x_val.shape)\nprint(y_val.shape)\n\nprint('Build model...')\nmodel = Sequential()\n# \"Encode\" the input sequence using an RNN, producing an output of HIDDEN_SIZE.\n# Note: In a situation where your input sequences have a variable length,\n# use input_shape=(None, num_feature).\nmodel.add(layers.Bidirectional(\n RNN(HIDDEN_SIZE),\n input_shape=(None, len(ctable_x.chars))))\n# input_shape=(None, len(ctable_x.chars) + EMBEDDINGS)))\n# As the decoder RNN's input, repeatedly provide with the last hidden state of\n# RNN for each time step. Repeat 'DIGITS + 1' times as that's the maximum\n# length of output, e.g., when DIGITS=3, max output is 999+999=1998.\nmodel.add(layers.Dropout(0.5))\nmodel.add(layers.RepeatVector(SENTLEN))\n# The decoder RNN could be multiple layers stacked or a single layer.\nfor _ in range(LAYERS):\n # By setting return_sequences to True, return not only the last output but\n # all the outputs so far in the form of (num_samples, timesteps,\n # output_dim). This is necessary as TimeDistributed in the below expects\n # the first dimension to be the timesteps.\n model.add(RNN(HIDDEN_SIZE, return_sequences=True))\n\n# Apply a dense layer to the every temporal slice of an input. For each of step\n# of the output sequence, decide which character should be chosen.\nmodel.add(layers.TimeDistributed(\n layers.Dense(len(ctable_y.chars))))\nmodel.add(layers.Activation('softmax'))\nmodel.compile(loss='categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy', 'sparse_categorical_accuracy'])\nmodel.summary()\n\n\n# Train the model each generation and show predictions against the validation\n# dataset.\nfor iteration in range(1, ITERATIONS + 1):\n print()\n print('-' * 50)\n print('Iteration', iteration)\n history = model.fit(x_train, y_train,\n batch_size=BATCH_SIZE,\n epochs=EPOCHS,\n validation_data=(x_val, y_val))\n # Select 10 samples from the validation set at random so we can visualize\n # errors.\n\n for i in range(10):\n ind = np.random.randint(0, len(x_val))\n rowx, rowy = x_val[np.array([ind])], y_val[np.array([ind])]\n preds = model.predict_classes(rowx, verbose=0)\n q = ctable_x.decode(rowx[0])\n correct = ctable_y.decode(rowy[0])\n guess = ctable_y.decode(preds[0], calc_argmax=False)\n # print('Q', q[::-1] if REVERSE else q, end=' ')\n print('Q', q, end=' ')\n print('T', correct, end=' ')\n if correct == guess:\n print(colors.ok + '☑' + colors.close, end=' ')\n else:\n print(colors.fail + '☒' + colors.close, end=' ')\n print(guess)\n\n\ny_pred = []\ny_actual = []\nfor i in range(len(y_val)):\n y_actual.append(ctable_y.decode(y_val[i]))\n y_pred.append(ctable_y.decode(\n model.predict_classes(x_val[np.array([i])])[0],\n calc_argmax=False))\n # print(y_actual[i], y_pred[i])\n\nSawarefVis(y_actual, y_pred,\n ctable_y.chars)\n", "sub_path": "main-sentence-seq.py", "file_name": "main-sentence-seq.py", "file_ext": "py", "file_size_in_byte": 5349, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "keras.layers.LSTM", "line_number": 23, "usage_type": "attribute"}, {"api_name": "keras.layers", "line_number": 23, "usage_type": "name"}, {"api_name": "prepare_data.SawarefData", "line_number": 31, "usage_type": "call"}, {"api_name": "character_table.CharacterTable", "line_number": 39, "usage_type": "call"}, {"api_name": "character_table.CharacterTable", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 67, "usage_type": "attribute"}, {"api_name": "keras.models.Sequential", "line_number": 85, "usage_type": "call"}, {"api_name": "keras.layers.Bidirectional", "line_number": 89, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 89, "usage_type": "name"}, {"api_name": "keras.layers.Dropout", "line_number": 96, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 96, "usage_type": "name"}, {"api_name": "keras.layers.RepeatVector", "line_number": 97, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 97, "usage_type": "name"}, {"api_name": "six.moves.range", "line_number": 99, "usage_type": "call"}, {"api_name": "keras.layers.TimeDistributed", "line_number": 108, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 108, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 109, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 109, "usage_type": "name"}, {"api_name": "keras.layers.Activation", "line_number": 110, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 110, "usage_type": "name"}, {"api_name": "six.moves.range", "line_number": 119, "usage_type": "call"}, {"api_name": "six.moves.range", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 131, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 132, "usage_type": "call"}, {"api_name": "character_table.colors.ok", "line_number": 141, "usage_type": "attribute"}, {"api_name": "character_table.colors", "line_number": 141, "usage_type": "name"}, {"api_name": "character_table.colors.close", "line_number": 141, "usage_type": "attribute"}, {"api_name": "character_table.colors.fail", "line_number": 143, "usage_type": "attribute"}, {"api_name": "character_table.colors", "line_number": 143, "usage_type": "name"}, {"api_name": "character_table.colors.close", "line_number": 143, "usage_type": "attribute"}, {"api_name": "six.moves.range", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 152, "usage_type": "call"}, {"api_name": "vis.SawarefVis", "line_number": 156, "usage_type": "call"}]} +{"seq_id": "78305930", "text": "#import sys\nimport copy\nfrom pprint import pprint\nimport itertools\n#sys.stdin = open('14502.txt', 'r')\ndef isWall(x, y):\n if x > N-1 or x < 0 :\n return True\n if y > M-1 or y < 0 :\n return True\n return False\ndef comb(k, now, temp):\n global result\n if k == 3:\n result.append(temp)\n else:\n for i in range(now+1, len(stack)+1):\n comb(k+1, i, temp + [i])\ndef DFS(x, y):\n dx = [-1, 0, 1, 0]\n dy = [0, 1, 0, -1]\n #check = []\n stack = [[x, y]]\n while True:\n if stack == []:\n #return check\n return\n x, y = stack.pop()\n for mode in range(4):\n test_x = x + dx[mode]\n test_y = y + dy[mode]\n if isWall(test_x, test_y) == False and clone_map[test_x][test_y] == 0: #and [test_x, test_y] not in check and [test_x, test_y] not in temp:\n stack.append([test_x, test_y])\n #check.append([test_x, test_y])\n clone_map[test_x][test_y] = 2\n\n#TC = int(input())\n#for test_case in range(1, TC+1):\nN, M = map(int, input().split())\ndatamap = [list(map(int, input().split())) for _ in range(N)]\n\nmax = 0\n\n\n\nstack = []\nvirus = []\n#zero_count = 0\nfor i in range(N):\n for j in range(M):\n if datamap[i][j] == 2:\n virus.append([i, j])\n elif datamap[i][j] == 0:\n stack.append([i, j])\n #zero_count +=1\n\nmax = 0\n#print(stack)\n#print(len(stack))\nzz = [i for i in range(len(stack))]\n#print(zz)\nd = list(itertools.combinations(zz, 3))\n\n#print(len(d))\nwhile True:\n #count = zero_count\n if d == []:\n break\n\n clone_map = copy.deepcopy(datamap)\n clone_virus = copy.deepcopy(virus)\n #pprint(clone_map)\n first, second, third = d.pop()\n #print(first, second, third)\n clone_map[stack[first][0]][stack[first][1]] = 1\n clone_map[stack[second][0]][stack[second][1]] = 1\n clone_map[stack[third][0]][stack[third][1]] = 1\n # temp = []\n #pprint(clone_map)\n for _ in range(len(virus)):\n virus_x, virus_y = clone_virus.pop()\n DFS(virus_x, virus_y)\n # virus_count = DFS(virus_x, virus_y)\n # temp += virus_count\n # if len(temp) + 3 == count:\n # break\n # for x, y in virus_count:\n # if clone_map[x][y] == 0:\n # clone_map[x][y] = 2\n #pprint(clone_map)\n # count -= (len(temp) + 3)\n count = 0\n for line in clone_map:\n count +=line.count(0)\n\n if count > max:\n max = count\n\nprint(max)", "sub_path": "BOJ/14502. 연구소(8월버전).py", "file_name": "14502. 연구소(8월버전).py", "file_ext": "py", "file_size_in_byte": 2508, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "itertools.combinations", "line_number": 62, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 70, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "487272617", "text": "#!/usr/bin/env python\n\n# Picking up where `write_model.jl` left off\n\nimport argparse\n\nparser = argparse.ArgumentParser(description=\"Convert model visibilities in HDF5 format to ALMA NPZ save files.\")\nparser.add_argument(\"--fname-model\", default=\"model.hdf5\", help=\"The name of the model visibilities HDF5 file.\")\nparser.add_argument(\"--fname-resid\", default=\"resid.hdf5\", help=\"The name of the model visibilities HDF5 file.\")\nparser.add_argument(\"--descending\", action=\"store_true\", help=\"Should the frequencies be packed in a descending order (e.g., 13CO)?\")\nparser.add_argument(\"--out-model\", default=\"model.vis.npz\", help=\"The output file for the model.\")\nparser.add_argument(\"--out-resid\", default=\"resid.vis.npz\", help=\"The output file for the residuals.\")\nargs = parser.parse_args()\n\n\nfrom astropy.io import fits\nimport h5py\nimport numpy as np\nimport shutil\n\ncc = 2.99792458e10 # [cm s^-1]\n\n# Read all of the data from the HDF5 file\nfid = h5py.File(args.fname_model, \"r\")\n\nlams = fid[\"lams\"][:] * 1e-6 # [m]\nuu = fid[\"uu\"][:,:] # [klam]\nvv = fid[\"vv\"][:,:] # [klam]\nreal = fid[\"real\"][:,:] # [Jy]\nimag = fid[\"imag\"][:,:] # [Jy]\nweight = fid[\"invsig\"][:,:]**2\nfid.close()\n\n# Convert u and v from kilo-lambda back to meters\nu = uu[0,:] * 1e3 * lams[0] # [m]\nv = vv[0,:] * 1e3 * lams[0] # [m]\n\n\n# This means we will have to reverse the order of the real, imaginary, and weights\n\n# This file has categories\n# ['Re', 'Wt', 'u', 'Im', 'v']\n\n# len(data[\"u\"]) => 24555\n# len(data[\"v\"]) => 24555\n# data['Re'].shape => (50, 24555)\n# data['Im'].shape => (50, 24555)\n# data['Wt'].shape => (50, 24555)\n\n# Sean delivered the data set as an NPZ file.\n# I kept everything in increasing *wavelength* order\n# He kept everything in increasing *frequency* order\n\nif args.descending:\n # Therefore, if he gave me a dataset that is with frequency decreasing, don't need to do anything (e.g. 13CO).\n print(\"Keeping the model in frequency descending order.\")\n np.savez(args.out_model, u=u, v=v, Re=real[:, :], Im=imag[:, :], Wt=weight[:, :] )\nelse:\n # But if he gave me a dataset with frequency increasing, then I need to flip the order here (e.g., 12CO).\n print(\"Flipping the model to frequency increasing order.\")\n np.savez(args.out_model, u=u, v=v, Re=real[::-1, :], Im=imag[::-1, :], Wt=weight[::-1, :] )\n\n\n# Now repeat everything for the residuals\n# Read all of the data from the HDF5 file\nfid = h5py.File(args.fname_resid, \"r\")\n\nlams = fid[\"lams\"][:] * 1e-6 # [m]\nuu = fid[\"uu\"][:,:] # [klam]\nvv = fid[\"vv\"][:,:] # [klam]\nreal = fid[\"real\"][:,:] # [Jy]\nimag = fid[\"imag\"][:,:] # [Jy]\nweight = fid[\"invsig\"][:,:]**2\nfid.close()\n\n# Convert u and v from kilo-lambda back to meters\nu = uu[0,:] * 1e3 * lams[0] # [m]\nv = vv[0,:] * 1e3 * lams[0] # [m]\n\nif args.descending:\n # Therefore, if he gave me a dataset that is with frequency decreasing, don't need to do anything (e.g. 13CO).\n print(\"Keeping the residuals in frequency descending order.\")\n np.savez(args.out_resid, u=u, v=v, Re=real[:, :], Im=imag[:, :], Wt=weight[:, :] )\nelse:\n # But if he gave me a dataset with frequency increasing, then I need to flip the order here (e.g., 12CO).\n print(\"Flipping the residuals to frequency increasing order.\")\n np.savez(args.out_resid, u=u, v=v, Re=real[::-1, :], Im=imag[::-1, :], Wt=weight[::-1, :] )\n", "sub_path": "scripts/write_ALMA.py", "file_name": "write_ALMA.py", "file_ext": "py", "file_size_in_byte": 3319, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 61, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 87, "usage_type": "call"}]} +{"seq_id": "185150080", "text": "#!/usr/bin/env python3\n\nimport pymysql.cursors\n\nfrom config import pconfig as config\nfrom db_api import BaseDatabase\n# forcing a modern version of mysql for the features\nfrom exceptions_peri import MysqlVersionError\n\nMYSQL_VERSION_REQUIRED = config.get(\"misc\", \"mysql_version_required\")\n\n\nclass DatabaseCreator(BaseDatabase):\n def __init__(self, target=None):\n super(DatabaseCreator, self).__init__(target)\n # BaseDatabase class creates self.connection\n cursor = self.connection.cursor()\n version = self.mysql_version(cursor)\n\n if not version or not self.mysql_version_okay(version):\n raise MysqlVersionError('MySQL version >= {v} required.'.format(\n v=MYSQL_VERSION_REQUIRED))\n if target and target == \"testing\":\n self.db = config.get('database', 'test_db')\n else:\n self.db = config.get('database', 'db')\n \n self.user = config.get('database', 'user')\n\n self.drop_db(cursor)\n self.create_db(cursor)\n self.use_db(cursor)\n\n self.create_ranges(cursor)\n self.create_slots(cursor)\n\n self.create_trigger_bins_ranges(cursor)\n self.create_trigger_ains_slots(cursor)\n self.create_trigger_aupd_slots(cursor)\n self.create_function_alloc_realloc_slot(cursor)\n self.create_function_dealloc_slot(cursor)\n\n cursor.close()\n\n def drop_db(self, cursor):\n print(\"Dropping database {}\".format(self.db))\n cursor.execute('DROP DATABASE IF EXISTS {}'.format(self.db))\n self.connection.commit()\n\n def create_db(self, cursor):\n print(\"Creating database {}\".format(self.db))\n cursor.execute('CREATE SCHEMA {}'.format(self.db))\n self.connection.commit()\n\n def use_db(self, cursor):\n print(\"Making {} active\".format(self.db))\n cursor.execute('USE {}'.format(self.db))\n self.connection.commit()\n\n def create_ranges(self, cursor):\n from queries import create_ranges\n print(\"Creating range table\")\n cursor.execute(create_ranges)\n self.connection.commit()\n\n def create_slots(self, cursor):\n from queries import create_slots\n print(\"Creating slot table\")\n cursor.execute(create_slots)\n self.connection.commit()\n\n def create_trigger_bins_ranges(self, cursor):\n from queries import create_t_before_ins_ranges as bi_r\n print(\"Creating bins_ranges trigger\")\n cursor.execute(bi_r, self.user)\n self.connection.commit()\n\n def create_trigger_ains_slots(self, cursor):\n from queries import create_t_after_ins_slots as ai_s\n print(\"Creating ains_slots trigger\")\n cursor.execute(ai_s, self.user)\n self.connection.commit()\n\n def create_trigger_aupd_slots(self, cursor):\n from queries import create_t_after_upd_slots as au_s\n print(\"Creating aup_slots trigger\")\n cursor.execute(au_s, self.user)\n self.connection.commit()\n\n def create_function_alloc_realloc_slot(self, cursor):\n from queries import create_f_alloc_realloc_slot as f_al\n print(\"Creating alloc/realloc slot function\")\n cursor.execute(f_al, self.user)\n self.connection.commit()\n\n def create_function_dealloc_slot(self, cursor):\n from queries import create_f_dealloc_slot as f_dl\n print(\"Creating deallocate_slot function\")\n cursor.execute(f_dl, self.user)\n self.connection.commit()\n\n def mysql_version(self, cursor):\n from queries import version_check\n print(\"Checking version\")\n cursor.execute(version_check)\n data = cursor.fetchone()\n if 'innodb_version' in data.values():\n return data['Value']\n return None\n\n def mysql_version_okay(self, the_version):\n # the_version like '5.5.10'\n ver = the_version.split('.')\n need_ver = MYSQL_VERSION_REQUIRED.split('.')\n for i, ver_split in enumerate(ver):\n if int(ver_split) < int(need_ver[i]):\n return False\n print(\"Version okay, continuing\")\n return True\n\n\nif __name__ == \"__main__\":\n print(\"Creating or recreating database...\")\n creator = DatabaseCreator()\n print(\"Done.\\n\")\n", "sub_path": "periastron/create_db.py", "file_name": "create_db.py", "file_ext": "py", "file_size_in_byte": 4251, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "config.pconfig.get", "line_number": 10, "usage_type": "call"}, {"api_name": "config.pconfig", "line_number": 10, "usage_type": "name"}, {"api_name": "db_api.BaseDatabase", "line_number": 13, "usage_type": "name"}, {"api_name": "exceptions_peri.MysqlVersionError", "line_number": 21, "usage_type": "call"}, {"api_name": "config.pconfig.get", "line_number": 24, "usage_type": "call"}, {"api_name": "config.pconfig", "line_number": 24, "usage_type": "name"}, {"api_name": "config.pconfig.get", "line_number": 26, "usage_type": "call"}, {"api_name": "config.pconfig", "line_number": 26, "usage_type": "name"}, {"api_name": "config.pconfig.get", "line_number": 28, "usage_type": "call"}, {"api_name": "config.pconfig", "line_number": 28, "usage_type": "name"}, {"api_name": "queries.create_ranges", "line_number": 63, "usage_type": "name"}, {"api_name": "queries.create_slots", "line_number": 69, "usage_type": "name"}, {"api_name": "queries.create_t_before_ins_ranges", "line_number": 75, "usage_type": "name"}, {"api_name": "queries.create_t_after_ins_slots", "line_number": 81, "usage_type": "name"}, {"api_name": "queries.create_t_after_upd_slots", "line_number": 87, "usage_type": "name"}, {"api_name": "queries.create_f_alloc_realloc_slot", "line_number": 93, "usage_type": "name"}, {"api_name": "queries.create_f_dealloc_slot", "line_number": 99, "usage_type": "name"}, {"api_name": "queries.version_check", "line_number": 105, "usage_type": "name"}, {"api_name": "{'create_ranges': 'queries.create_ranges', 'create_slots': 'queries.create_slots', 'bi_r': 'queries.create_t_before_ins_ranges', 'ai_s': 'queries.create_t_after_ins_slots', 'au_s': 'queries.create_t_after_upd_slots', 'f_al': 'queries.create_f_alloc_realloc_slot', 'f_dl': 'queries.create_f_dealloc_slot', 'version_check': 'queries.version_check'}", "line_number": 124, "usage_type": "call"}]} +{"seq_id": "86107426", "text": "# Copyright 2020 Soil, Inc.\n# Copyright 2011 OpenStack Foundation\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport functools\nimport six\n\nfrom soil.api import extensions\nfrom soil.api import server\nfrom soil.api.server import wsgi\nfrom soil.api import versions\nfrom soil.api.v1.openstack.compute import instances\nfrom soil.api.v1.openstack.network import networks\nfrom soil.api.v1.vmware import vcenter\n\n\ndef _create_controller(main_controller, action_controller_list):\n \"\"\"This is a helper method to create controller with a\n list of action controller.\n \"\"\"\n\n controller = wsgi.Resource(main_controller())\n for ctl in action_controller_list:\n controller.register_actions(ctl())\n return controller\n\n\nversion_controller = functools.partial(_create_controller,\n versions.VersionsController, [])\n\ninstances_controller = functools.partial(_create_controller,\n instances.InstancesController, [])\n\nnetworks_controller = functools.partial(_create_controller,\n networks.NetworksController, [])\n\nvcenter_controller = functools.partial(_create_controller,\n vcenter.vCenterController, [])\n\n\nROUTE_LIST = (\n ('', '/'),\n ('/', {\n 'GET': [version_controller, 'all']\n }),\n ('/versions', {\n 'GET': [version_controller, 'index']\n }),\n ('/osp/instances', {\n 'GET': [instances_controller, 'index'],\n 'POST': [instances_controller, 'create'],\n }),\n ('/osp/networks', {\n 'POST': [networks_controller, 'create'],\n }),\n ('/vmware/vcenter', {\n 'GET': [vcenter_controller, 'index']\n })\n)\n\n\nclass APIRouter(server.APIRouter):\n ExtensionManager = extensions.ExtensionManager\n\n def _setup_routes(self, mapper):\n for path, methods in ROUTE_LIST:\n if isinstance(methods, six.string_types):\n mapper.redirect(path, methods)\n continue\n\n for method, controller_info in methods.items():\n controller = controller_info[0]()\n action = controller_info[1]\n mapper.create_route(path, method, controller, action)\n", "sub_path": "soil/soil/api/v1/router.py", "file_name": "router.py", "file_ext": "py", "file_size_in_byte": 2769, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "soil.api.server.wsgi.Resource", "line_number": 33, "usage_type": "call"}, {"api_name": "soil.api.server.wsgi", "line_number": 33, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 39, "usage_type": "call"}, {"api_name": "soil.api.versions.VersionsController", "line_number": 40, "usage_type": "attribute"}, {"api_name": "soil.api.versions", "line_number": 40, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 42, "usage_type": "call"}, {"api_name": "soil.api.v1.openstack.compute.instances.InstancesController", "line_number": 43, "usage_type": "attribute"}, {"api_name": "soil.api.v1.openstack.compute.instances", "line_number": 43, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 45, "usage_type": "call"}, {"api_name": "soil.api.v1.openstack.network.networks.NetworksController", "line_number": 46, "usage_type": "attribute"}, {"api_name": "soil.api.v1.openstack.network.networks", "line_number": 46, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 48, "usage_type": "call"}, {"api_name": "soil.api.v1.vmware.vcenter.vCenterController", "line_number": 49, "usage_type": "attribute"}, {"api_name": "soil.api.v1.vmware.vcenter", "line_number": 49, "usage_type": "name"}, {"api_name": "soil.api.server.APIRouter", "line_number": 73, "usage_type": "attribute"}, {"api_name": "soil.api.server", "line_number": 73, "usage_type": "name"}, {"api_name": "soil.api.extensions.ExtensionManager", "line_number": 74, "usage_type": "attribute"}, {"api_name": "soil.api.extensions", "line_number": 74, "usage_type": "name"}, {"api_name": "six.string_types", "line_number": 78, "usage_type": "attribute"}]} +{"seq_id": "563411194", "text": "import numpy as np\nfrom hashlib import sha1\nfrom numpy import all, array, uint8\nimport collections\n\nclass hashable(object):\n\n def __init__(self, wrapped, tight=False):\n r'''Creates a new hashable object encapsulating an ndarray.\n\n wrapped\n The wrapped ndarray.\n\n tight\n Optional. If True, a copy of the input ndaray is created.\n Defaults to False.\n '''\n self.__tight = tight\n self.__wrapped = array(wrapped) if tight else wrapped\n self.__hash = int(sha1(wrapped.view(uint8)).hexdigest(), 16)\n\n def __eq__(self, other):\n return all(self.__wrapped == other.__wrapped)\n\n def __hash__(self):\n return self.__hash\n\n def unwrap(self):\n r'''Returns the encapsulated ndarray.\n\n If the wrapper is \"tight\", a copy of the encapsulated ndarray is\n returned. Otherwise, the encapsulated ndarray itself is returned.\n '''\n if self.__tight:\n return array(self.__wrapped)\n\n return self.__wrapped\n\nclass Board:\n\n def __init__(self,rows,cols):\n self.rows = rows\n self.cols = cols\n\n # INTERFACE FOR MCST\n\n def init_representation(self):\n state = np.zeros((1 + (self.rows * self.cols * 2) + self.rows + self.cols), dtype=np.int8)\n state[0] = 1\n return state\n\n def current_player(self, state):\n return state.item(0)\n\n def next_state(self, state, move):\n new_state = np.copy(state)\n currentplayer = state.item(0)\n row,cols,ori = self.translate_to_coord(move)\n new_state[move] = currentplayer\n\n if self.is_box(new_state, row, cols, ori, currentplayer):\n new_state[0] = currentplayer\n else:\n new_state[0] = self.update_player(currentplayer)\n\n return new_state, move\n\n def legal_plays(self, state):\n plays = np.where(state == 0)[0]\n return plays\n\n def is_finished(self, state):\n plays = np.where(state == 0)[0]\n if len(plays) == 0:\n return True\n return False\n\n def register_state(self, state, row, cols, ori, player):\n new_state = np.copy(state)\n\n move = self.translate_to_move(row, cols, ori)\n new_state[move] = player\n\n if self.is_box(new_state, row, cols, ori, player):\n new_state[0] = player\n else:\n new_state[0] = self.update_player(player)\n\n return new_state, move\n\n # Utility Functions\n def winner(self, state):\n board = state[1:]\n win = collections.Counter(board).most_common()[0][0]\n return win\n\n def translate_to_move(self,row,cols,ori):\n move = (row * (self.cols * 2 + 1)) + cols + 1\n if ori == \"v\":\n move += self.cols\n return move\n\n def translate_to_coord(self, move):\n\n move2 = move - 1\n\n rows = move2 // (self.cols * 2 + 1)\n cols = move2 % (self.cols * 2 + 1)\n\n # move = 2 + (row * (self.cols * 2 + 1)) + cols\n # move = 2 + (row * (self.cols * 2 + 1)) + cols + self.cols\n\n if cols < self.cols:\n return rows, cols, \"h\"\n\n else:\n cols -= self.cols\n return rows, cols, \"v\"\n\n def update_player(self, player):\n new_player = (player + 1) % 2\n if new_player == 0:\n new_player = 2\n return new_player\n\n def is_box(self, new_state, row, cols, ori, player):\n if ori == \"h\":\n if row < self.rows:\n box1_a = new_state.item(self.translate_to_move(row, cols, \"v\"))\n box1_b = new_state.item(self.translate_to_move(row, cols + 1, \"v\"))\n box1_c = new_state.item(self.translate_to_move(row + 1, cols, ori))\n if box1_a == box1_b == box1_c == player:\n return True\n if row > 1:\n box2_a = new_state.item(self.translate_to_move(row - 1, cols, \"v\"))\n box2_b = new_state.item(self.translate_to_move(row - 1, cols + 1, \"v\"))\n box2_c = new_state.item(self.translate_to_move(row - 1, cols, ori))\n if box2_a == box2_b == box2_c == player:\n return True\n elif ori == \"v\":\n if cols > 1:\n box1_a = new_state.item(self.translate_to_move(row, cols - 1, \"h\"))\n box1_b = new_state.item(self.translate_to_move(row + 1, cols - 1, \"h\"))\n box1_c = new_state.item(self.translate_to_move(row, cols - 1, ori))\n if box1_a == box1_b == box1_c == player:\n return True\n if cols < self.cols:\n box2_a = new_state.item(self.translate_to_move(row, cols, \"h\"))\n box2_b = new_state.item(self.translate_to_move(row + 1, cols, \"h\"))\n box2_c = new_state.item(self.translate_to_move(row, cols + 1, ori))\n if box2_a == box2_b == box2_c == player:\n return True\n return False\n", "sub_path": "baseline/board.py", "file_name": "board.py", "file_ext": "py", "file_size_in_byte": 5001, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "hashlib.sha1", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 20, "usage_type": "argument"}, {"api_name": "numpy.all", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 79, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 94, "usage_type": "call"}]} +{"seq_id": "46246301", "text": "\"\"\"\nderive_field_objects.py\n\nProduce file field-objects-raw.json given calibration-*camera.json, as well as\ncamera model info from stitching_info.json and field model info.\n\nRequires either \"swapped\" info to be present in the calibration JSON,\nor \"--swapped\" argument to be present, since field-objects-raw.json requires\n\"view\" and \"camera\" keys for each point in key \"pts\".\n\nIf a mapped field object is in neither camera's frame, then we pick the frame\nwhere it's less off-screen and take the closest point.\n\nExample usage:\n python3 derive_field_objects.py \\\n -lc .../calibration/calibration-lcamera.json \\\n -rc .../calibration/calibration-rcamera.json \\\n -f .../calibration/refined_field.json \\\n -s .../stitch/no_stitching_info_file.json \\\n -o .../stitch/field-objects-raw.json \\\n [optionally] --swapped False\n\"\"\"\n\nimport argparse\nimport json\nimport numpy as np\nimport os\n\nfrom src.mappers.pinhole_mapper import PinholeMapper\nfrom src.mappers.undistort_mapper import MLUndistortMapper\nfrom src.util.field_model import FieldModel\n\n\ndef create_pinhole_mapper(calibration_data_path):\n\n assert os.path.exists(calibration_data_path), \\\n \"failed to find calibration data {}\".format(calibration_data_path)\n with open(calibration_data_path, 'r') as jf:\n calibration_data = json.load(jf)\n\n if \"swapped\" in calibration_data:\n swapped = calibration_data[\"swapped\"]\n else:\n swapped = None\n\n K_p = np.array(calibration_data[\"pinhole_model\"][\"K\"])\n R_p = np.array(calibration_data[\"pinhole_model\"][\"R\"])\n T_p = np.array(calibration_data[\"pinhole_model\"][\"T\"])\n width = np.array(calibration_data[\"pinhole_model\"][\"w\"])\n height = np.array(calibration_data[\"pinhole_model\"][\"h\"])\n\n return PinholeMapper(K_p, R_p, T_p, (width, height)), swapped\n\n\ndef check_field_model_consistency(\n field_model_path, lcamera_calibration_path, rcamera_calibration_path):\n field_link = os.path.join(*field_model_path.split(\"/\")[-2:])\n field_resources_links = []\n for r in [lcamera_calibration_path, rcamera_calibration_path]:\n with open(r, 'r') as jf:\n json_data = json.load(jf)\n if \"field_link\" in json_data:\n field_resources_links.append(json_data[\"field_link\"])\n assert json_data[\"field_link\"] == field_link, \\\n \"field mismatch! resource {}: {} != {}\".format(\n r, json_data[\"field_link\"], field_link)\n\n\ndef pt_rect_distance(pt, w, h):\n\n dx = max(abs(pt[0] - .5 * w) - .5 * w, 0)\n dy = max(abs(pt[1] - .5 * h) - .5 * h, 0)\n distance = dx * dx + dy * dy\n\n return distance\n\n\ndef snap_to_frame(pt, w, h):\n\n new_pt = pt.copy()\n new_pt[0] = max(new_pt[0], 0)\n new_pt[0] = min(new_pt[0], w)\n new_pt[1] = max(new_pt[1], 0)\n new_pt[1] = min(new_pt[1], h)\n\n return new_pt\n\n\nif __name__ == \"__main__\":\n\n # Parse arguments:\n ap = argparse.ArgumentParser()\n ap.add_argument(\n \"-lc\", \"--lcamera_calibration_path\", required=True, type=str,\n help=\"Path to calibration-lcamera.json\")\n ap.add_argument(\n \"-rc\", \"--rcamera_calibration_path\", required=True, type=str,\n help=\"Path to calibration-rcamera.json\")\n ap.add_argument(\n \"-f\", \"--field_model_path\", required=True, type=str,\n help=\"Path to field model JSON\")\n ap.add_argument(\n \"-s\", \"--stitching_info_path\", required=True, type=str,\n help=\"Path to stitching info JSON (only need camera model)\")\n ap.add_argument(\n \"-o\", \"--output_path\", required=True, type=str,\n help=\"Path to output JSON\")\n ap.add_argument(\n \"--swapped\", required=False, type=bool,\n help=\"Optional argument to determine if cameras are swapped\"\n )\n args = vars(ap.parse_args())\n\n # Unpack inputs\n lcamera_pinhole_mapper, l_swapped = \\\n create_pinhole_mapper(args[\"lcamera_calibration_path\"])\n rcamera_pinhole_mapper, r_swapped = \\\n create_pinhole_mapper(args[\"rcamera_calibration_path\"])\n field = FieldModel(args[\"field_model_path\"])\n undistorter = MLUndistortMapper(\n data_filename=args[\"stitching_info_path\"],\n file_type=\"stitching_info\")\n with open(args[\"stitching_info_path\"], 'r') as jf:\n stitching_info = json.load(jf)\n w = stitching_info[\"input_size\"][\"width\"]\n h = stitching_info[\"input_size\"][\"height\"]\n\n # Validate inputs\n swapped = None\n if l_swapped is None and r_swapped is None and args[\"swapped\"] is not None:\n raise ValueError(\"Could not determine \\\"swapped\\\" \"\n \"info from calibration JSON\")\n else:\n if args[\"swapped\"] is not None:\n swapped = args[\"swapped\"]\n else:\n assert l_swapped == r_swapped, \"need l_swapped == r_swapped\"\n swapped = l_swapped\n check_field_model_consistency(\n args[\"field_model_path\"],\n args[\"lcamera_calibration_path\"],\n args[\"rcamera_calibration_path\"]\n )\n\n # Construct field objects in field coordinates\n x = .5 * field.touch_line_length\n y = .5 * field.goal_line_length\n field_points = np.array([\n [-x, -y, 0], # near left corner\n [-x, 0, 0], # left goal\n [-x, y, 0], # far left corner\n [0, -y, 0], # near center line\n [0, 0, 0], # center point\n [0, y, 0], # far center line\n [x, -y, 0], # near right corner\n [x, 0, 0], # right goal\n [x, y, 0], # far right corner\n ])\n\n # Map field objects to image coordinates\n lcamera_points_undistorted = lcamera_pinhole_mapper.map_pts(field_points)\n rcamera_points_undistorted = rcamera_pinhole_mapper.map_pts(field_points)\n lcamera_points = undistorter.map_pts_inv(lcamera_points_undistorted)\n rcamera_points = undistorter.map_pts_inv(rcamera_points_undistorted)\n if swapped:\n left_points = rcamera_points\n right_points = lcamera_points\n else:\n left_points = lcamera_points\n right_points = rcamera_points\n\n # Construct output data\n output_data = {}\n pts = []\n pts_index = 0\n while pts_index < 9:\n left_distance = pt_rect_distance(left_points[pts_index], w, h)\n right_distance = pt_rect_distance(right_points[pts_index], w, h)\n if left_distance == 0:\n cur_pt = {\n \"x\": int(round(left_points[pts_index][0])),\n \"y\": int(round(left_points[pts_index][1])),\n \"view\": \"left\",\n \"camera\": \"RCamera\" if swapped else \"LCamera\"\n }\n elif right_distance == 0:\n cur_pt = {\n \"x\": int(round(right_points[pts_index][0])),\n \"y\": int(round(right_points[pts_index][1])),\n \"view\": \"right\",\n \"camera\": \"LCamera\" if swapped else \"RCamera\"\n }\n else:\n if left_distance <= right_distance:\n temp_point = snap_to_frame(left_points[pts_index], w, h)\n cur_pt = {\n \"x\": int(round(temp_point[0])),\n \"y\": int(round(temp_point[1])),\n \"view\": \"left\",\n \"camera\": \"RCamera\" if swapped else \"LCamera\"\n }\n else:\n temp_point = snap_to_frame(right_points[pts_index], w, h)\n cur_pt = {\n \"x\": int(round(temp_point[0])),\n \"y\": int(round(temp_point[1])),\n \"view\": \"left\",\n \"camera\": \"RCamera\" if swapped else \"LCamera\"\n }\n print(\"Warning! snapping point to frame\")\n pts.append(cur_pt)\n\n pts_index += 1\n\n output_data[\"pts\"] = pts\n output_data[\"swapped\"] = int(swapped)\n assert len(pts) == 9, \"need 9 \\\"pts\\\", instead got {}\".format(len(pts))\n\n # Write output JSON\n if not os.path.exists(os.path.dirname(args[\"output_path\"])):\n os.makedirs(os.path.dirname(args[\"output_path\"]))\n with open(args[\"output_path\"], 'w') as jf:\n json.dump(output_data, jf, indent=4, sort_keys=True)\n\n", "sub_path": "derive_field_objects.py", "file_name": "derive_field_objects.py", "file_ext": "py", "file_size_in_byte": 8100, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "os.path.exists", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 50, "usage_type": "call"}, {"api_name": "src.mappers.pinhole_mapper.PinholeMapper", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 61, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 92, "usage_type": "call"}, {"api_name": "src.util.field_model.FieldModel", "line_number": 119, "usage_type": "call"}, {"api_name": "src.mappers.undistort_mapper.MLUndistortMapper", "line_number": 120, "usage_type": "call"}, {"api_name": "json.load", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 220, "usage_type": "call"}, {"api_name": "os.path", "line_number": 220, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 220, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 221, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 221, "usage_type": "call"}, {"api_name": "os.path", "line_number": 221, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 223, "usage_type": "call"}]} +{"seq_id": "222226642", "text": "import cv2\nimport numpy as np\nimport math\n\n\n#img=cv2.imread('hough_simple_1.pgm', cv2.IMREAD_GRAYSCALE)\n\nsobelx = np.array([[1,2,0,-2,-1], [2,3,0,-3,-2],[3,5,0,-5,-3], [2,3,0,-3,-2], [1,2,0,-2,-1]])\nsobely = np.array([[-1,-2,-3,-2,-1], [-2,-3,-5,-3,-2], [0,0,0,0,0], [2,3,5,3,2], [1,2,3,2,1]])\n\n#used sobel 5x5 matrix\n\ndef p5(image_in):\n #gaussian = cv2.getgaussiankernel(image_in, )\n dst1 = cv2.filter2D(image_in, -1, sobelx)\n dst2 = cv2.filter2D(image_in, -1, sobely)\n\n for i in range(image_in.shape[0]):\n for j in range(image_in.shape[1]):\n xmag = dst1[i,j]**2\n ymag = dst2[i,j]**2\n magnitude = math.sqrt(xmag+ymag)\n image_in.itemset((i,j), magnitude)\n \n", "sub_path": "Assignment1/p5.py", "file_name": "p5.py", "file_ext": "py", "file_size_in_byte": 731, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.array", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.filter2D", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.filter2D", "line_number": 16, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "510009295", "text": "import boto3\nfrom botocore.exceptions import ClientError\nfrom inventorycalculator.errors import S3StorageError\nfrom OneTicketLogging import elasticsearch_logger\n\n\n_logger = elasticsearch_logger(__name__)\n\n\nclass S3Storage:\n def __init__(self, bucket_name: str):\n self._bucket_name = bucket_name\n self._client = boto3.client('s3')\n\n def upload(self, key: str, data: str):\n try:\n self._client.put_object(\n Body=data.encode(),\n Key=key,\n Bucket=self._bucket_name\n )\n except ClientError as e:\n _logger.error(e)\n raise S3StorageError('Unable to upload given data')\n\n def get(self, key: str) -> str:\n try:\n return self._client.get_object(\n Key=key,\n Bucket=self._bucket_name\n )['Body'].read().decode('utf-8')\n except ClientError as e:\n _logger.error(e)\n raise S3StorageError(f'Resource not exists by given key:{key}')\n", "sub_path": "inventorycalculator/core/storages/s3_storage.py", "file_name": "s3_storage.py", "file_ext": "py", "file_size_in_byte": 1026, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "OneTicketLogging.elasticsearch_logger", "line_number": 7, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 13, "usage_type": "call"}, {"api_name": "botocore.exceptions.ClientError", "line_number": 22, "usage_type": "name"}, {"api_name": "inventorycalculator.errors.S3StorageError", "line_number": 24, "usage_type": "call"}, {"api_name": "botocore.exceptions.ClientError", "line_number": 32, "usage_type": "name"}, {"api_name": "inventorycalculator.errors.S3StorageError", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "537572179", "text": "import sys\nimport os.path\n\nfrom PyQt5.QtCore import QTimer\nfrom PyQt5.QtQml import QQmlApplicationEngine\nfrom PyQt5.QtGui import QGuiApplication\n\nfrom controllers.main_controller import MainController\n\n\ndef main():\n app = QGuiApplication(sys.argv)\n qml_engine = QQmlApplicationEngine()\n\n main_controller = MainController(app)\n context = qml_engine.rootContext()\n context.setContextProperty(\"main\", main_controller)\n\n this_directory = os.path.dirname(os.path.abspath(__file__))\n qml_path = os.path.join(this_directory, 'qml/main.qml')\n qml_engine.load(qml_path)\n\n main_window = qml_engine.rootObjects()[0]\n main_window.show()\n\n QTimer.singleShot(0, main_controller.startup)\n sys.exit(app.exec_())\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "projectdir/plotapp/__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 772, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "PyQt5.QtGui.QGuiApplication", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "PyQt5.QtQml.QQmlApplicationEngine", "line_number": 13, "usage_type": "call"}, {"api_name": "controllers.main_controller.MainController", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.path.dirname", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 19, "usage_type": "name"}, {"api_name": "os.path.path.abspath", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 20, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QTimer.singleShot", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QTimer", "line_number": 26, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "636065360", "text": "import SCons\nimport atexit\nimport cx_Oracle\n\n_owner = env.USERNAME.upper()\n_table_cache = {}\n_cxn = cx_Oracle.connect(\"/\")\n_cxn.autocommit = 1\natexit.register(_cxn.close)\n\nenv.Default(\".\")\n\nclass _SQLTable(SCons.Node.Node):\n\n NodeInfo = SCons.Node.FS.FileNodeInfo\n BuildInfo = SCons.Node.FS.FileBuildInfo\n\n def __init__(self, name):\n SCons.Node.Node.__init__(self)\n self.name = name\n self.store_info = 1\n self.ninfo = self.new_ninfo()\n self.changed_since_last_build = 1\n self.dir = Dir(\"#.\")\n self.set_nocache()\n\n def __str__(self):\n return self.name\n\n def built(self):\n SCons.Node.Node.built(self)\n SCons.Node.store_info_map[self.store_info](self)\n\n def str_for_display(self):\n return \"'\" + self.__str__() + \"'\"\n\n def is_up_to_date(self):\n if not self.exists():\n return None\n else:\n return not self.changed()\n\n @SCons.Memoize.CountMethodCall\n def exists(self):\n \"\"\"\n The table exists if it has an entry in all_tables.\n \"\"\"\n try:\n return self._memo[\"exists\"]\n except KeyError:\n pass\n cur = _cxn.cursor()\n cur.execute(\"\"\"\n SELECT table_name\n FROM user_tables\n WHERE table_name = '{}'\n \"\"\".format(self.name.upper()))\n exists = cur.fetchone() is not None\n if exists:\n cur.execute(\"\"\"\n SELECT COUNT(*)\n FROM {}\n \"\"\".format(self.name.upper()))\n count = cur.fetchone()\n exists = count is not None and count[0] > 0\n cur.close()\n self._memo[\"exists\"] = exists\n return exists\n\n def get_csig(self):\n ninfo = self.get_ninfo()\n try:\n return ninfo.csig\n except AttributeError:\n pass\n if self.exists():\n csig = SCons.Util.MD5signature(self.get_contents())\n else:\n csig = 0\n ninfo.csig = csig\n return csig\n\n @SCons.Memoize.CountMethodCall\n def get_size(self):\n \"\"\"\n The table's size is the bytes on disk as reported by the segments table.\n \"\"\"\n try:\n return self._memo[\"get_size\"]\n except KeyError:\n pass\n if self.exists():\n cur = _cxn.cursor()\n cur.execute(\"\"\"\n SELECT bytes\n FROM user_segments\n WHERE segment_type = 'TABLE' AND\n segment_name = '{}'\n \"\"\".format(self.name.upper()))\n size = cur.fetchone()[0]\n cur.close()\n else:\n size = 0\n self._memo[\"get_size\"] = size\n return size\n\n @SCons.Memoize.CountMethodCall\n def get_timestamp(self):\n \"\"\"\n The table's timestamp is its last DDL time from all_objects.\n \"\"\"\n try:\n return self._memo[\"get_timestamp\"]\n except KeyError:\n pass\n if self.exists():\n cur = _cxn.cursor()\n cur.execute(\"\"\"\n SELECT FLOOR(last_ddl_time - TO_DATE('19700101', 'YYYYMMDD'))*24*3600 AS mtime\n FROM user_objects\n WHERE object_type = 'TABLE' AND\n object_name = '{}'\n \"\"\".format(self.name.upper()))\n ts = cur.fetchone()[0]\n cur.close()\n else:\n ts = 0\n self._memo[\"get_timestamp\"] = ts\n return ts\n\n @SCons.Memoize.CountMethodCall\n def get_stored_info(self):\n try:\n return self._memo['get_stored_info']\n except KeyError:\n pass\n try:\n sconsign_entry = self.dir.sconsign().get_entry(self.name)\n except (KeyError, EnvironmentError):\n sconsign_entry = SCons.SConsign.SConsignEntry()\n sconsign_entry.binfo = self.get_binfo()\n sconsign_entry.ninfo = self.get_ninfo()\n self._memo['get_stored_info'] = sconsign_entry\n return sconsign_entry\n\n @SCons.Memoize.CountMethodCall\n def get_contents(self):\n try:\n return self._memo['get_contents']\n except KeyError:\n pass\n cur = _cxn.cursor()\n cur.execute(\"\"\"\n SELECT comments\n FROM user_tab_comments\n WHERE table_name = '{}'\n \"\"\".format(self.name.upper()))\n contents = cur.fetchone()[0]\n self._memo['get_contents'] = contents\n return contents\n\n\ndef SQLTable(name):\n name = name.upper()\n try:\n return _table_cache[name]\n except KeyError:\n node = _SQLTable(name)\n _table_cache[name] = node\n env.Default(node)\n return node\n\n\nExport(['SQLTable'])\n", "sub_path": "source/lib/SCons/sql_table_node.py", "file_name": "sql_table_node.py", "file_ext": "py", "file_size_in_byte": 4963, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "cx_Oracle.connect", "line_number": 7, "usage_type": "call"}, {"api_name": "atexit.register", "line_number": 9, "usage_type": "call"}, {"api_name": "SCons.Node", "line_number": 13, "usage_type": "attribute"}, {"api_name": "SCons.Node", "line_number": 15, "usage_type": "attribute"}, {"api_name": "SCons.Node", "line_number": 16, "usage_type": "attribute"}, {"api_name": "SCons.Node.Node.__init__", "line_number": 19, "usage_type": "call"}, {"api_name": "SCons.Node", "line_number": 19, "usage_type": "attribute"}, {"api_name": "SCons.Node.Node.built", "line_number": 31, "usage_type": "call"}, {"api_name": "SCons.Node", "line_number": 31, "usage_type": "attribute"}, {"api_name": "SCons.Node", "line_number": 32, "usage_type": "attribute"}, {"api_name": "SCons.Memoize", "line_number": 43, "usage_type": "attribute"}, {"api_name": "SCons.Util.MD5signature", "line_number": 77, "usage_type": "call"}, {"api_name": "SCons.Util", "line_number": 77, "usage_type": "attribute"}, {"api_name": "SCons.Memoize", "line_number": 83, "usage_type": "attribute"}, {"api_name": "SCons.Memoize", "line_number": 107, "usage_type": "attribute"}, {"api_name": "SCons.SConsign.SConsignEntry", "line_number": 140, "usage_type": "call"}, {"api_name": "SCons.SConsign", "line_number": 140, "usage_type": "attribute"}, {"api_name": "SCons.Memoize", "line_number": 131, "usage_type": "attribute"}, {"api_name": "SCons.Memoize", "line_number": 146, "usage_type": "attribute"}]} +{"seq_id": "303929252", "text": "# -*- coding: utf-8 -*-\n\nimport re\nfrom six import iteritems\n\nfrom sphinx import addnodes\nfrom sphinx.directives import ObjectDescription\nfrom sphinx.domains import Domain, ObjType, Index\nfrom sphinx.locale import _, __\nfrom sphinx.roles import XRefRole\nfrom sphinx.util import logging\nfrom sphinx.util.nodes import make_refnode\n\n\nlogger = logging.getLogger(__name__)\n\npy_sig_re = re.compile(\n r'''^ ([\\w.]*\\.)? # class name(s)\n (\\w+) \\s* # thing name\n (?: \\(\\s*(.*)\\s*\\) # optional: arguments\n (?:\\s* -> \\s* (.*))? # return annotation\n )? $ # and nothing more\n ''', re.VERBOSE)\n\n\ndef _pseudo_parse_arglist(signode, arglist):\n paramlist = addnodes.desc_parameterlist()\n stack = [paramlist]\n try:\n for argument in arglist.split(','):\n argument = argument.strip()\n ends_open = ends_close = 0\n while argument.startswith('['):\n stack.append(addnodes.desc_optional())\n stack[-2] += stack[-1]\n argument = argument[1:].strip()\n while argument.startswith(']'):\n stack.pop()\n argument = argument[1:].strip()\n while argument.endswith(']') and not argument.endswith('[]'):\n ends_close += 1\n argument = argument[:-1].strip()\n while argument.endswith('['):\n ends_open += 1\n argument = argument[:-1].strip()\n if argument:\n stack[-1] += addnodes.desc_parameter(argument, argument)\n while ends_open:\n stack.append(addnodes.desc_optional())\n stack[-2] += stack[-1]\n ends_open -= 1\n while ends_close:\n stack.pop()\n ends_close -= 1\n if len(stack) != 1:\n raise IndexError\n except IndexError:\n signode += addnodes.desc_parameterlist()\n signode[-1] += addnodes.desc_parameter(arglist, arglist)\n else:\n signode += paramlist\n\n\nclass ComObjectBase(ObjectDescription):\n option_spec = dict()\n doc_field_types = []\n allow_nesting = False\n\n def get_signature_prefix(self, sig):\n return ''\n\n def needs_arglist(self):\n return False\n\n def handle_signature(self, sig, signode):\n m = py_sig_re.match(sig)\n if m is None:\n raise ValueError\n name_prefix, name, arglist, retann = m.groups()\n\n classname, fullname = (name_prefix.rstrip('.'), name_prefix + name) if name_prefix else ('', name)\n\n signode['class'] = classname\n signode['fullname'] = fullname\n\n if name_prefix:\n signode += addnodes.desc_addname(name_prefix, name_prefix)\n\n signode += addnodes.desc_name(name, name)\n if not arglist:\n if self.needs_arglist():\n signode += addnodes.desc_parameterlist()\n return fullname, name_prefix\n\n _pseudo_parse_arglist(signode, arglist)\n return fullname, name_prefix\n\n def get_index_text(self, modname, name):\n raise NotImplementedError('must be implemented in subclasses')\n\n def add_target_and_index(self, name_cls, sig, signode):\n fullname = name_cls[0]\n if fullname not in self.state.document.ids:\n signode['names'].append(fullname)\n signode['ids'].append(fullname)\n signode['first'] = (not self.names)\n self.state.document.note_explicit_target(signode)\n objects = self.env.domaindata['com-object']['objects']\n objects[fullname] = (self.env.docname, self.objtype)\n\n indextext = self.get_index_text(None, name_cls)\n if indextext:\n self.indexnode['entries'].append(('single', indextext, fullname, '', None))\n\n def before_content(self):\n prefix = None\n if self.names:\n (fullname, name_prefix) = self.names[-1]\n if name_prefix:\n prefix = name_prefix.strip('.')\n if prefix:\n self.env.ref_context['com-object'] = prefix\n\n def after_content(self):\n self.env.ref_context['com-object'] = None\n\n\nclass ComObjectClassmember(ComObjectBase):\n def needs_arglist(self):\n return self.objtype.endswith('method')\n\n def get_signature_prefix(self, sig):\n return ''\n\n def get_index_text(self, modname, name_cls):\n name, cls = name_cls\n result = ''\n if self.objtype == 'method':\n clsname, methname = name.rsplit('.', 1)\n result = _('{}() ({} method)'.format(methname, clsname))\n\n return result\n\n\nclass ComObjectXRefRole(XRefRole):\n def process_link(self, env, refnode, has_explicit_title, title, target):\n refnode['com-object'] = env.ref_context.get('com-object')\n if not has_explicit_title:\n title = title.lstrip('.')\n\n if target[0] == '.':\n target = target[1:]\n refnode['refspecific'] = True\n return title, target\n\n\nclass ComObjectIndex(Index):\n name = 'comobjectindex'\n localname = _('ComObject Index')\n shortname = _('comobjects')\n\n def generate(self, docnames=None):\n return [], False\n\n\nclass ComObjectDomain(Domain):\n name = 'com-object'\n label = 'ComObject'\n object_types = {\n 'method': ObjType(_('method'), 'meth'),\n }\n\n directives = {\n 'method': ComObjectClassmember\n }\n roles = {\n 'meth': ComObjectXRefRole(fix_parens=True),\n }\n initial_data = {\n 'objects': {}\n }\n indices = [\n ComObjectIndex,\n ]\n\n def clear_doc(self, docname):\n for fullname, (fn, _l) in list(self.data['objects'].items()):\n if fn == docname:\n del self.data['objects'][fullname]\n\n def find_obj(self, name, type, searchmode=0):\n if name[-2:] == '()':\n name = name[:-2]\n\n if not name:\n return []\n\n objects = self.data['objects']\n matches = []\n\n if searchmode == 1:\n objtypes = list(self.object_types) if type is None else self.objtypes_for_role(type)\n if objtypes is not None:\n searchname = '.' + name\n matches = [(oname, objects[oname]) for oname in objects\n if oname.endswith(searchname) and objects[oname][1] in objtypes]\n else:\n if name in objects:\n matches.append((name, objects[name]))\n return matches\n\n def resolve_xref(self, env, fromdocname, builder, type, target, node, contnode):\n searchmode = node.hasattr('refspecific') and 1 or 0\n matches = self.find_obj(target, type, searchmode)\n if not matches:\n return None\n elif len(matches) > 1:\n match_list = ', '.join(match[0] for match in matches)\n warning = 'more than one target found for cross-reference {}: {}'.format(target.repr(), match_list)\n logger.warning(__(warning), type='ref', subtype='com-object', location=node)\n name, obj = matches[0]\n\n return make_refnode(builder, fromdocname, obj[0], name, contnode, name)\n\n def get_objects(self):\n for refname, (docname, type) in iteritems(self.data['objects']):\n yield (refname, refname, type, docname, refname, 1)\n\n def get_full_qualified_name(self, node):\n target = node.get('reftarget')\n return None if target is None else '.'.join(filter(None, [None, target]))\n\n\ndef setup(app):\n app.add_domain(ComObjectDomain)\n\n return {\n 'version': 'builtin',\n 'env_version': 1,\n 'parallel_read_safe': True,\n 'parallel_write_safe': True,\n }\n", "sub_path": "docs/source/_extensions/CComDomain.py", "file_name": "CComDomain.py", "file_ext": "py", "file_size_in_byte": 7721, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sphinx.util.logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "sphinx.util.logging", "line_number": 15, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 17, "usage_type": "call"}, {"api_name": "re.VERBOSE", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sphinx.addnodes.desc_parameterlist", "line_number": 27, "usage_type": "call"}, {"api_name": "sphinx.addnodes", "line_number": 27, "usage_type": "name"}, {"api_name": "sphinx.addnodes.desc_optional", "line_number": 34, "usage_type": "call"}, {"api_name": "sphinx.addnodes", "line_number": 34, "usage_type": "name"}, {"api_name": "sphinx.addnodes.desc_parameter", "line_number": 47, "usage_type": "call"}, {"api_name": "sphinx.addnodes", "line_number": 47, "usage_type": "name"}, {"api_name": "sphinx.addnodes.desc_optional", "line_number": 49, "usage_type": "call"}, {"api_name": "sphinx.addnodes", "line_number": 49, "usage_type": "name"}, {"api_name": "sphinx.addnodes.desc_parameterlist", "line_number": 58, "usage_type": "call"}, {"api_name": "sphinx.addnodes", "line_number": 58, "usage_type": "name"}, {"api_name": "sphinx.addnodes.desc_parameter", "line_number": 59, "usage_type": "call"}, {"api_name": "sphinx.addnodes", "line_number": 59, "usage_type": "name"}, {"api_name": "sphinx.directives.ObjectDescription", "line_number": 64, "usage_type": "name"}, {"api_name": "sphinx.addnodes.desc_addname", "line_number": 87, "usage_type": "call"}, {"api_name": "sphinx.addnodes", "line_number": 87, "usage_type": "name"}, {"api_name": "sphinx.addnodes.desc_name", "line_number": 89, "usage_type": "call"}, {"api_name": "sphinx.addnodes", "line_number": 89, "usage_type": "name"}, {"api_name": "sphinx.addnodes.desc_parameterlist", "line_number": 92, "usage_type": "call"}, {"api_name": "sphinx.addnodes", "line_number": 92, "usage_type": "name"}, {"api_name": "sphinx.locale._", "line_number": 140, "usage_type": "call"}, {"api_name": "sphinx.roles.XRefRole", "line_number": 145, "usage_type": "name"}, {"api_name": "sphinx.domains.Index", "line_number": 157, "usage_type": "name"}, {"api_name": "sphinx.locale._", "line_number": 159, "usage_type": "call"}, {"api_name": "sphinx.locale._", "line_number": 160, "usage_type": "call"}, {"api_name": "sphinx.domains.Domain", "line_number": 166, "usage_type": "name"}, {"api_name": "sphinx.domains.ObjType", "line_number": 170, "usage_type": "call"}, {"api_name": "sphinx.locale._", "line_number": 170, "usage_type": "call"}, {"api_name": "sphinx.locale.__", "line_number": 220, "usage_type": "call"}, {"api_name": "sphinx.util.nodes.make_refnode", "line_number": 223, "usage_type": "call"}, {"api_name": "six.iteritems", "line_number": 226, "usage_type": "call"}]} +{"seq_id": "616403966", "text": "import numpy\r\nimport pandas as pd\r\nfrom sys import argv\r\nfrom argparse import ArgumentParser as Parse\r\nimport datetime\r\nimport Calculations\r\n\r\n# #open file\r\n# zonedf_path = r\"C:\\Users\\nick\\Documents\\GitHub\\NYC-Taxi-Tips\\taxi+_zone_lookup.csv\"\r\n\r\n# greendf_path = r\"C:\\Users\\nick\\Documents\\GitHub\\NYC-Taxi-Tips\\datafiles\\green_tripdata_2018-01.csv\"\r\n\r\n# yellowdf_path = r\"C:\\Users\\nick\\Documents\\GitHub\\NYC-Taxi-Tips\\datafiles\\yellow_tripdata_2018-01.csv\"\r\n\r\n# fhvdf_path = r\"C:\\Users\\nick\\Documents\\GitHub\\NYC-Taxi-Tips\\datafiles\\fhv_tripdata_2018-01.csv\"\r\n\r\n# saves link to mock zone csv\r\nzonedf = pd.read_csv(r\"C:\\Users\\nick\\Documents\\GitHub\\NYC-Taxi-Tips\\mock_zones.csv\")\r\n\r\n# creates dictionary of zone IDs associated with boroughs\r\nzone_id_dict = Calculations.init_zone_dict(zonedf)\r\n\r\n\r\n# -y -g -f include data from given service providers (y for yellow, etc.)\r\nparser = Parse(description='Given start and end borough, displays average cost.')\r\nparser.add_argument('-y', action=\"store_true\", default=False)\r\nparser.add_argument('-g', action=\"store_true\", default=False)\r\nparser.add_argument('-f', action=\"store_true\", default=False)\r\n\r\n# --start 00/00/0000 00:00:00 --end 00/00/0000 00:00:00\r\nparser.add_argument(\"--start\", nargs=2, action=\"store\")\r\nparser.add_argument(\"--end\", nargs=2, action=\"store\")\r\n\r\n# --sborough BoroughName --eborough BoroughName\r\nparser.add_argument(\"--sborough\", nargs=1, action=\"store\")\r\nparser.add_argument(\"--eborough\", nargs=1, action=\"store\")\r\n\r\n# parse arguments\r\nargs = vars(parser.parse_args()) \r\n\r\n# must select at least one data source. else, error\r\nif args[\"y\"] is False and args[\"g\"] is False and args[\"f\"] is False:\r\n print(\"error: must choose at least one list\")\r\n exit()\r\n\r\n# create datetime object for command line start information\r\nif \"start\" in args and args[\"start\"] is not None:\r\n year, month, day = map(int, args[\"start\"][0].split('-'))\r\n start_date = datetime.date(year, month, day)\r\n start_time = datetime.datetime.strptime(args[\"start\"][1],'%H:%M:%S').time()\r\n time_object_start = datetime.datetime.combine(start_date, start_time)\r\n # if end date is not given, make end date into the future\r\n if \"end\" in args and args[\"end\"] is None:\r\n end_date = datetime.datetime(2050, 1, 1)\r\n end_time = datetime.datetime.strptime(\"00:00:00\",'%H:%M:%S').time()\r\n time_object_end = datetime.datetime.combine(end_date, end_time)\r\n# create datetime object for command line end information\r\nif \"end\" in args and args[\"end\"] is not None:\r\n year, month, day = map(int, args[\"end\"][0].split('-'))\r\n end_date = datetime.date(year, month, day)\r\n end_time = datetime.datetime.strptime(args[\"end\"][1],'%H:%M:%S').time()\r\n time_object_end = datetime.datetime.combine(end_date, end_time)\r\n # if start date is not given, include all information from beginning\r\n if \"start\" in args and args[\"start\"] is None:\r\n start_date = datetime.datetime(1900, 1, 1)\r\n start_time = datetime.datetime.strptime(\"00:00:00\",'%H:%M:%S').time()\r\n time_object_start = datetime.datetime.combine(start_date, start_time)\r\n# read in yellow information\r\nif args[\"y\"] is True:\r\n tot_list = []\r\n tot_list.append(pd.read_csv(r\"C:\\Users\\nick\\Documents\\GitHub\\NYC-Taxi-Tips\\mock_yellow_data.csv\",index_col=0, header=0))\r\n df = pd.concat(tot_list, axis = 0, ignore_index = False)\r\n # filter by pick up date/time\r\n if ((\"start\" in args and args[\"start\"] is not None) or (\"end\" in args and args[\"end\"] is not None)):\r\n df['tpep_pickup_datetime'] = pd.to_datetime(df['tpep_pickup_datetime'])\r\n mask = (df['tpep_pickup_datetime'] > time_object_start) & (df['tpep_pickup_datetime'] <= time_object_end)\r\n df.loc[mask]\r\n df = df.loc[mask]\r\n # filter if start/end info is given\r\n df = Calculations.calc_start_end(df, args, zone_id_dict, 'PULocationID', 'DOLocationID') \r\n df.to_csv(\"filtered_yellow_data.csv\")\r\n# read in green information\r\nif args[\"g\"] is True:\r\n tot_list = []\r\n tot_list.append(pd.read_csv(r\"C:\\Users\\nick\\Documents\\GitHub\\NYC-Taxi-Tips\\mock_green_data.csv\",index_col=0, header=0))\r\n df = pd.concat(tot_list, axis = 0, ignore_index = False)\r\n # filter by pick up date/time\r\n if ((\"start\" in args and args[\"start\"] is not None) or (\"end\" in args and args[\"end\"] is not None)):\r\n df['lpep_pickup_datetime'] = pd.to_datetime(df['lpep_pickup_datetime'])\r\n mask = (df['lpep_pickup_datetime'] > time_object_start) & (df['lpep_pickup_datetime'] <= time_object_end)\r\n df.loc[mask]\r\n df = df.loc[mask]\r\n # filter if start/end info is given\r\n df = Calculations.calc_start_end(df, args, zone_id_dict, 'PULocationID', 'DOLocationID') \r\n df.to_csv(\"filtered_green_data.csv\")\r\n# read in for-hire information\r\nif args[\"f\"] is True:\r\n tot_list = []\r\n tot_list.append(pd.read_csv(r\"C:\\Users\\nick\\Documents\\GitHub\\NYC-Taxi-Tips\\mock_fhv_data.csv\",index_col=0, header=0))\r\n df = pd.concat(tot_list, axis = 0, ignore_index = False)\r\n # filter by pick up date/time\r\n if ((\"start\" in args and args[\"start\"] is not None) or (\"end\" in args and args[\"end\"] is not None)):\r\n df['Pickup_DateTime'] = pd.to_datetime(df['Pickup_DateTime'])\r\n mask = (df['Pickup_DateTime'] > time_object_start) & (df['Pickup_DateTime'] <= time_object_end)\r\n df.loc[mask]\r\n df = df.loc[mask]\r\n # filter if start/end info is given\r\n df = Calculations.calc_start_end(df, args, zone_id_dict, 'PUlocationID', 'DOlocationID') \r\n df.to_csv(\"filtered_fhv_data.csv\")\r\n\r\n\r\n\r\n\r\n\r\n", "sub_path": "NYCTaxi.py", "file_name": "NYCTaxi.py", "file_ext": "py", "file_size_in_byte": 5572, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pandas.read_csv", "line_number": 18, "usage_type": "call"}, {"api_name": "Calculations.init_zone_dict", "line_number": 21, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 50, "usage_type": "attribute"}, {"api_name": "datetime.datetime.combine", "line_number": 51, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 51, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 55, "usage_type": "attribute"}, {"api_name": "datetime.datetime.combine", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 56, "usage_type": "attribute"}, {"api_name": "datetime.date", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 61, "usage_type": "attribute"}, {"api_name": "datetime.datetime.combine", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 62, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 66, "usage_type": "attribute"}, {"api_name": "datetime.datetime.combine", "line_number": 67, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 72, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 75, "usage_type": "call"}, {"api_name": "Calculations.calc_start_end", "line_number": 80, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 85, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 86, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 89, "usage_type": "call"}, {"api_name": "Calculations.calc_start_end", "line_number": 94, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 99, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 100, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 103, "usage_type": "call"}, {"api_name": "Calculations.calc_start_end", "line_number": 108, "usage_type": "call"}]} +{"seq_id": "321742832", "text": "#!/usr/bin/env python\n\nfrom __future__ import print_function\n\nimport sys\nimport os\nimport argparse\nfrom lib_mean import create_output_file, calc_monthly_mean, update_file_history\n\n\n\"\"\"\nWhat this script does:\n\nCalculate monthly means over a number of years from CICE output.\n\nHow to run this script:\n\n./cice_monthly_mean.py 01/iceh.nc 02/iceh.nc out.nc --vars ih\n\nWhere 01/iceh.nc 02/iceh.nc contain a number of monthly averaged fields.\nAfterwards out.nc will contain a mean of all Jan, Feb, Mar, etc.\n\nIn order to get a yearly mean using the output of this script one can just do:\n\nncra -v out.nc yearly_mean.nc\n\n\"\"\"\n\ndef main():\n\n parser = argparse.ArgumentParser()\n parser.add_argument('input_files', nargs='+', help=\"\"\"\n The input data files in NetCDF format. These files can\n be given in any order. They MUST appear before any other\n arguments/options.\"\"\")\n parser.add_argument('output_file', default='ice_monthly.nc', help=\"\"\"\n The name of the output file.\"\"\")\n parser.add_argument('--vars', default=None, nargs='+',\n help='A list of the variables to average.')\n parser.add_argument('--copy_vars', default=[], nargs='+',\n help=\"\"\"A list of the variables copy across but not\n included in the averaging.\"\"\")\n\n args = parser.parse_args()\n\n create_output_file(args.input_files[0], args.vars + args.copy_vars,\n args.output_file)\n calc_monthly_mean(args.input_files, args.vars, args.output_file)\n update_file_history(args.output_file, ' '.join(sys.argv))\n\n return 0\n\nif __name__ == '__main__':\n sys.exit(main())\n", "sub_path": "analyse/cice_monthly_mean.py", "file_name": "cice_monthly_mean.py", "file_ext": "py", "file_size_in_byte": 1741, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 31, "usage_type": "call"}, {"api_name": "lib_mean.create_output_file", "line_number": 46, "usage_type": "call"}, {"api_name": "lib_mean.calc_monthly_mean", "line_number": 48, "usage_type": "call"}, {"api_name": "lib_mean.update_file_history", "line_number": 49, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 49, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "244679038", "text": "from selenium import webdriver\nimport requests\n\ndef get_links(url):\n \"\"\"Find all links on page at the given url.\n Return a list of all link addresses, as strings.\n \"\"\"\n links=[]\n browser = webdriver.Firefox()\n browser.get(url)\n elements = browser.find_elements_by_tag_name('a')\n for ele in elements:\n links.append(ele.get_attribute('href'))\n browser.close()\n return links\n\n\n\n\ndef invalid_urls(urllist):\n invalid_links = []\n for url in urllist:\n r = requests.head(url)\n if r.status_code == 404:\n invalid_links.append(url)\n return invalid_links \n\nlinks = get_links(\"https://cpske.github.io/ISP/\")\nfor link in links:\n print(\"Link: \"+link)\ninvalid_links = invalid_urls(links) \nfor link in invalid_links:\n print(\"Invalid: \" + link)\n", "sub_path": "polls/tests/link_invalid_url_test.py", "file_name": "link_invalid_url_test.py", "file_ext": "py", "file_size_in_byte": 810, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "selenium.webdriver.Firefox", "line_number": 9, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 9, "usage_type": "name"}, {"api_name": "requests.head", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "645498380", "text": "from Bio import SeqIO\nfrom Bio import SeqUtils\nfrom os.path import exists\nfrom _collections import defaultdict\nfrom multiprocessing import Pool\n\nimport os\nimport re\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport itertools as it\n\n\nSNP_METH_DIR = \"/home/raian/Bioinformatics_institute/RESEARCH/\" \\\n \"spring2015_bees/arat/data/snp_and_methylation/\"\n \nSNP_FILENAME = sys.argv[1] #SNP_METH_DIR + \"ct_snps.bed\" # sys.argv[0]\nMETH_FILENAME = sys.argv[2] #SNP_METH_DIR + \"497.methratio.txt\" # sys.argv[1] \nAMEL_FILENAME = sys.argv[3] #SNP_METH_DIR + \"amel.fasta\"\nWINDOW_SIZE = 1000\n\ndef occurences_chh(string):\n count = 0\n ind = 0\n while ind < WINDOW_SIZE - 3:\n if string[ind] == 'C' or string[ind] == 'G':\n count += 1\n ind += 1\n return count \n\n\ndef occurences_chg(string):\n count = 0\n ind = 0\n while ind < WINDOW_SIZE - 3:\n if string[ind] == 'C' and string[ind+2] == 'G':\n count += 1\n ind += 1 \t\n\n return count \n\n\ndef occurrences_cg(string):\n count = 0\n ind = 0\n while ind < WINDOW_SIZE - 2:\n if string[ind] == 'C' and string[ind+1] == 'G':\n count += 1\n ind += 2\n else:\n ind += 1 \t\n\n return count \n\n\ndef process_record(seq, name, snpl, methl, pstdout):\n print(\"INFO: Processing %s...\" % name)\n sys.stdout.flush()\n\n snp_cg_meth = []\n snp_chg_meth = []\n snp_chh_meth = []\n\n seq_len = len(seq)\n start = 0\n end = WINDOW_SIZE\n \n while end <= seq_len:\n snp_cnt = 0\n meth_cg_cnt = 0\n meth_chg_cnt = 0\n meth_chh_cnt = 0\n\n seq_window = seq[start:end]\n seqw_gc = SeqUtils.GC(seq_window)\n\n for line_split in snpl:\n try:\n if start <= (int(line_split[1]) - 1) < end:\n snp_cnt += 1\n elif (int(line_split[1]) - 1) >= end:\n break\n except:\n print(line_split) \n \n for line_split in methl:\n if start <= (int(line_split[1]) - 1) < end and float(line_split[4]) > 0:\n if line_split[3] == \"CG\":\n meth_cg_cnt += 1 \n elif line_split[3] == \"CHG\":\n meth_chg_cnt += 1\n else:\n meth_chh_cnt += 1 \n elif (int(line_split[1]) - 1) >= end:\n break \n \n cpg_cnt = occurrences_cg(seq_window)\n chg_cnt = occurences_chg(seq_window)\n chh_cnt = occurences_chh(seq_window) \n\n if cpg_cnt != 0:\n meth_ratio = meth_cg_cnt/(cpg_cnt*1.0)\n if meth_ratio <= 1.0:\n snp_cg_meth.append((meth_ratio, snp_cnt, seqw_gc)) \n else:\n print(\"Meth level > 1.0\") \n else:\n snp_cg_meth.append((0, snp_cnt, seqw_gc)) \n\n if chg_cnt != 0:\n meth_ratio = meth_chg_cnt/(chg_cnt*1.0)\n if meth_ratio <= 1.0:\n snp_chg_meth.append((meth_ratio, snp_cnt, seqw_gc)) \n else:\n print(\"Meth level > 1.0\") \n else:\n snp_chg_meth.append((0, snp_cnt, seqw_gc)) \n \n if chh_cnt != 0:\n meth_ratio = meth_chh_cnt/(chh_cnt*1.0)\n if meth_ratio <= 1.0:\n snp_chh_meth.append((meth_ratio, snp_cnt, seqw_gc)) \n else:\n print(\"Meth level > 1.0\") \n else:\n snp_chh_meth.append((0, snp_cnt, seqw_gc)) \n \n start += WINDOW_SIZE\n end += WINDOW_SIZE \n\n print(\"INFO: %s has been processed...\" % name)\n sys.stdout.flush()\n return (snp_cg_meth, snp_chg_meth, snp_chh_meth) \n\n\ndef build_scatter(snp_meth_level_info, context):\n print(\"Building the scatter plot for %s context...\" % context)\n plt.figure()\n plt.scatter([val[0] for val in snp_meth_level_info],\n [val[1] for val in snp_meth_level_info])\n plt.xlabel(\"Methylation level\")\n plt.ylabel(\"#C->T\")\n plt.title(\"%s context\" % context)\n plt.savefig(\"snp_data/%s.snp.meth.png\" % context)\n\n print(\"Writing regression data...\")\n with open(\"snp_data/%s.regr.txt\" % context, \"w\") as outp:\n for (meth, snp, gc) in snp_meth_level_info:\n outp.write(\"%s\\t%s\\t%s\\n\" % (meth, snp, gc)) \n \n\ndef main(): \n snp_cg_meth = []\n snp_chg_meth = []\n snp_chh_meth = []\n\n print(\"Reading SNP and methylation data...\")\n \n with open(SNP_FILENAME, \"r\") as snpf:\n snp_lines = snpf.readlines()\n with open(METH_FILENAME, \"r\") as methf:\n meth_lines = methf.readlines()[1:] \n\n\n print(\"Splitting SNPs by chr id...\")\n\n snpl = dict()\n \n prev_line = snp_lines[0].split()\n chr_snp = [prev_line]\n cnt = 0\n\n for line in snp_lines[1:]:\n lsplit = line.split()\n if prev_line[0] == lsplit[0]:\n chr_snp.append(lsplit)\n else: \n snpl[chr_snp[-1][0]] = chr_snp[:] \n chr_snp = [lsplit]\n prev_line = lsplit\n\n if len(chr_snp) > 0:\n snpl[chr_snp[-1][0]] = chr_snp \n\n print(\"Splitting methylation by chr id...\") \n\n methl = defaultdict(list) \n for line in meth_lines:\n methl[line.split()[0]].append(line.split()) \n\n p = Pool(processes=8)\n print(\"Applying...\")\n output = [p.apply_async(process_record, args=(record.seq, record.name, \n snpl[record.name], methl[record.name], \n sys.stdout)) \n for record in SeqIO.parse(open(AMEL_FILENAME), \"fasta\")]\n print(\"Getting individual results...\") \n results = [o.get() for o in output]\n\n print(\"Collecting results...\")\n for (cg, chg, chh) in results:\n snp_cg_meth += cg\n snp_chg_meth += chg\n snp_chh_meth += chh \n\n del results\n\n print(\"Results have been collected...\")\n\n print(\"Building scatter plots...\")\n build_scatter(snp_cg_meth, \"CG\")\n build_scatter(snp_chg_meth, \"CHG\")\n build_scatter(snp_chh_meth, \"CHH\") \n \nif __name__ == \"__main__\":\n main()", "sub_path": "scripts/count_snp_and_methylation_level.py", "file_name": "count_snp_and_methylation_level.py", "file_ext": "py", "file_size_in_byte": 6336, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 59, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 59, "usage_type": "attribute"}, {"api_name": "Bio.SeqUtils.GC", "line_number": 76, "usage_type": "call"}, {"api_name": "Bio.SeqUtils", "line_number": 76, "usage_type": "name"}, {"api_name": "sys.stdout.flush", "line_number": 133, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 133, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "_collections.defaultdict", "line_number": 188, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 192, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 196, "usage_type": "attribute"}, {"api_name": "Bio.SeqIO.parse", "line_number": 197, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 197, "usage_type": "name"}]} +{"seq_id": "551876751", "text": "\"\"\"\nThe Python standard library's 'calendar' module allows you to\nrender a calendar to your terminal.\nhttps://docs.python.org/3.6/library/calendar.html\n\nWrite a program that accepts user input of the form\n `14_cal.py month [year]`\nand does the following:\n - If the user doesn't specify any input, your program should\n print the calendar for the current month. The 'datetime'\n module may be helpful for this.\n - If the user specifies one argument, assume they passed in a\n month and render the calendar for that month of the current year.\n - If the user specifies two arguments, assume they passed in\n both the month and the year. Render the calendar for that\n month and year.\n - Otherwise, print a usage statement to the terminal indicating\n the format that your program expects arguments to be given.\n Then exit the program.\n\"\"\"\n\nimport sys\nimport calendar\nfrom datetime import datetime\nimport time\n\n# Still need to include some of the rules at the top regarding if 1 argument assume it is month and print\n# Calendar of that month\n\nvalid_months = [\n 'january', 'february', 'march', \n 'april', 'may', 'june', 'july', \n 'august', 'september', 'october', \n 'november', 'december', \n 'jan', 'feb', 'mar', 'apr', 'jun', \n 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']\n\ntry:\n month, year = input(\"Enter a month and a year(1900 - 2020): \").split()\nexcept: \n print(\"Pleae enter a valid Month and Year(1900 - 2020) \\ne.g.: February 2020\")\n print(\"The current Month and Year:\", datetime.today().strftime(\"%B\"), datetime.today().strftime(\"%Y\"))\nelse: \n if (len(month) > 0) and (1900 <= int(year) <= 2020):\n if month.lower() in valid_months:\n print('You entered', month.capitalize(), year)\n else: \n print('You entered an invalid month')\n else:\n print(\"Pleae enter a valid Month and Year(1900 - 2020) \\ne.g.: February 2020\")\n print(\"The current Month and Year:\", datetime.today().strftime(\"%B\"), datetime.today().strftime(\"%Y\"))\n", "sub_path": "src/14_cal.py", "file_name": "14_cal.py", "file_ext": "py", "file_size_in_byte": 1969, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "datetime.datetime.today", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 42, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 51, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 51, "usage_type": "name"}]} +{"seq_id": "287119871", "text": "import cv2\nimport numpy as np\nimport sys\n\nsys.setrecursionlimit(100000)\n\ndef region_grow (i,j,g_min,g_max,dir): \n if array_img[i,j]>g_min and array_img[i,j]1 and i1:\n region_grow(i-1,j,g_min,g_max,1)\n region_grow(i,j+1,g_min,g_max,3)\n region_grow(i+1,j,g_min,g_max,5)\n region_grow(i,j-1,g_min,g_max,7)\n region_grow(i+1,j+1,g_min,g_max,4)\n region_grow(i-1,j-1,g_min,g_max,8)\n region_grow(i+1,j-1,g_min,g_max,6)\n region_grow(i-1,j+1,g_min,g_max,2)\n \ndef chain (image): \n image [image == 255] = 1\n h_img,w_img=image.shape\n chain_array=np.array([8,1,2,7,0,3,6,5,4])\n chain_dir=np.array ([(-1,-1),(-1,0),(-1,1),(0,-1),(0,0),(0,1),(1,-1),(1,0),(1,1)])\n chain_dir1=np.array([(-1,0),(0,-1),(0,1),(-1,0),(0,0),(-1,0),(0,-1),(0,-1),(1,1)])\n chain_dir2=np.array([(0,-1),(0,1),(-1,0),(1,0),(0,0),(1,0),(0,1),(0,1),(1,0)])\n \n # getting starting point for the chain-code\n for x1 in range (2,w_img-2):\n for y1 in range (2,h_img-1):\n if image[y1,x1] != 1:\n a = image [y1-1,x1] + image [y1+1,x1] + image [y1,x1-1] + image [y1,x1+1] + image [y1-1,x1+1] + image [y1+1,x1+1] + image [y1-1,x1-1] + image [y1+1,x1-1] \n image_chain [y1,x1] = a\n for x1 in range (2,w_img-2):\n for y1 in range (2,h_img-1):\n if image[y1,x1] == 0:\n spx = x1\n spy = y1\n break\n if spx>0 or spy>0:\n break \n \n # getting chain code from labelled (region_grow) and smoothed (CV2.findContours) image\n sumarray_max=np.amax(image_chain[spy-1:spy+2, spx-1:spx+2])\n chain_list=np.array([h_img,w_img,spy,spx,0])\n while np.sum(image_chain) > 0 and sumarray_max > 0:\n image_chain[spy,spx]=0\n b=np.argmax(image_chain[spy-1:spy+2, spx-1:spx+2])\n sumarray_max=np.amax(image_chain[spy-1:spy+2, spx-1:spx+2])\n chain_list=np.append(chain_list,chain_array[b])\n y1,x1=chain_dir[b] \n y2,x2=chain_dir1[b]\n y3,x3=chain_dir2[b] \n image_chain[spy+y2,spx+x2]=0\n image_chain[spy+y3,spx+x3]=0\n spx=spx+x1\n spy=spy+y1\n return chain_list\n\ndef draw_chain (canvas_y, canvas_x, starty, startx, code, line):\n chain_dir=np.array ([(0,0),(-1,0),(-1,1),(0,1),(1,1),(1,0),(1,-1),(0,-1),(-1,-1)])\n canvas=np.zeros(shape=[canvas_y, canvas_x, 3], dtype=np.uint8)\n for i in code[2:]:\n y,x=chain_dir[i]\n y=y+starty\n x=x+startx\n canvas=cv2.line(canvas,(startx,starty),(x,y),(0,255,0),1)\n starty=y\n startx=x \n return canvas\n\ndef draw_roi(event,x,y,flags,param):\n global ix,iy,roi, img_test, img_start,buffer\n if event == cv2.EVENT_LBUTTONDOWN:\n img_test=img_start[y:y+120,x:x+180]\n roi=1 \n elif event == cv2.EVENT_MOUSEMOVE:\n if y+120 < h_img and x+180 < w_img:\n cv2.rectangle(img_start,(x,y),(x+180,y+120), (0,255,0),2)\n cv2.circle(img_start,(x+90,y+60),20,(0,255,255),2)\n cv2.line(img_start,(x+90,y+30),(x+90,y+90),(0,255,0),2)\n cv2.line(img_start,(x+60,y+60),(x+120,y+60),(0,255,0),2)\n cv2.imshow(windowName,img_start)\n img_start=np.copy(img)\n \n# get the \"region of Interest\" (ROI)\" by draw_roi()\n# it is necessary to left-click first, followed by pressing a key on keyboard\n# function has to be rewritten, because of insufficent method of moving the box \n\nwindowName=\"Sonobild\"\nimg = cv2.imread(\"BN-1.bmp\",0)\nimg_start = cv2.imread(\"BN-1.bmp\",0)\n\nh_img, w_img=img_start.shape\n#h_img, w_img=(0,200)\n#array_img=img[h_img:h_img+120,w_img:w_img+180]\n\nroi=0\ncv2.namedWindow(windowName)\ncv2.imshow(windowName, img_start)\ncv2.setMouseCallback(windowName, draw_roi)\ncv2.imshow(windowName, img_start)\nwhile roi==0:\n cv2.waitKey(0)\ncv2.destroyAllWindows()\narray_img=img_test\n\ncv2.namedWindow(\"ROI\")\ncv2.moveWindow(\"ROI\",0,0)\ncv2.imshow (\"ROI\",array_img)\n\n# draw the original image from comparision to segementation steps\ncv2.namedWindow(\"Orginal Image\")\ncv2.moveWindow(\"Orginal Image\",480,0)\ncv2.imshow (\"Orginal Image\",img)\n\n\n# get the mean value and the standard deviation of the central region of the ROI\nhight_img,width_img=array_img.shape\nlabelled=np.zeros (array_img.shape)\nstart_point_y=round(hight_img/2)\nstart_point_x=round(width_img/2)\nmean=np.mean(array_img[start_point_y-20:start_point_y+20,start_point_x-10:start_point_x+10])\nstd=np.std(array_img[start_point_y-10:start_point_y+10,start_point_x-10:start_point_x+10])\nupper_limit=mean+std\nif mean-2*std>0:\n lower_limit=mean-2*std\nelif mean-std<0:\n lower_limit=0\n\n# image segmentation step 1 by region growing using region_grow ()\ncontours=np.copy(array_img)\ncontours.fill(0)\ndirection=[120,180,start_point_y,start_point_x,0]\nwhile np.sum(labelled)==0:\n region_grow (start_point_y,start_point_x,lower_limit,upper_limit,0)\n np.sum(labelled)\n start_point_y=start_point_y-1 \ncv2.namedWindow(\"Region filling\")\ncv2.moveWindow(\"Region filling\",0,210)\ncv2.imshow (\"Region filling\",labelled)\n\n# image segmentation step 2 by \"filling the holes\" resulting in moderatly smoothing the surface \nnpMask=np.array(labelled,dtype=\"i8\")\n_,labelled_holes_filled, hierarchy = cv2.findContours(npMask, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)\ncv2.drawContours(contours, labelled_holes_filled, -1, (255, 0, 0 ),10)\ncontours = ~contours\ncv2.namedWindow(\"Filling the holes\")\ncv2.moveWindow(\"Filling the holes\",0,400)\ncv2.imshow (\"Filling the holes\",contours)\n\nmerged=cv2.add(array_img,contours)\n\ncv2.namedWindow(\"Extracted liver segment\")\ncv2.moveWindow(\"Extracted liver segment\",0,600)\ncv2.imshow (\"Extracted liver segment\",merged)\n\n# get chain-code of surface using chain () and draw chain-code by draw_chain ()\nimage_chain=np.zeros((120,180))\nchain_code=chain (contours)\nsurface_chain=draw_chain (120,180,32,2,chain_code[3:],1)\ncv2.namedWindow(\"chain_code\")\ncv2.moveWindow(\"chain_code\",1100,0)\ncv2.imshow (\"chain_code\",surface_chain)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n# Print chain code of surface from chain () and tissue from region_grow ()\nprint (\"chain code of surface: \",chain_code)\nprint (\"chain code of tissue: \", direction)", "sub_path": "code/chaincode/chain_code_ROI.py", "file_name": "chain_code_ROI.py", "file_ext": "py", "file_size_in_byte": 6422, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sys.setrecursionlimit", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 63, "usage_type": "attribute"}, {"api_name": "cv2.line", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.EVENT_LBUTTONDOWN", "line_number": 75, "usage_type": "attribute"}, {"api_name": "cv2.EVENT_MOUSEMOVE", "line_number": 78, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 92, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 93, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 101, "usage_type": "call"}, {"api_name": "cv2.setMouseCallback", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 103, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 105, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 106, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 109, "usage_type": "call"}, {"api_name": "cv2.moveWindow", "line_number": 110, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 111, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 114, "usage_type": "call"}, {"api_name": "cv2.moveWindow", "line_number": 115, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 138, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 140, "usage_type": "call"}, {"api_name": "cv2.moveWindow", "line_number": 141, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 145, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 146, "usage_type": "call"}, {"api_name": "cv2.RETR_CCOMP", "line_number": 146, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 146, "usage_type": "attribute"}, {"api_name": "cv2.drawContours", "line_number": 147, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 149, "usage_type": "call"}, {"api_name": "cv2.moveWindow", "line_number": 150, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 151, "usage_type": "call"}, {"api_name": "cv2.add", "line_number": 153, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 155, "usage_type": "call"}, {"api_name": "cv2.moveWindow", "line_number": 156, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 160, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 163, "usage_type": "call"}, {"api_name": "cv2.moveWindow", "line_number": 164, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 165, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 166, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 167, "usage_type": "call"}]} +{"seq_id": "525148282", "text": "import logging\n\n\nclass MyLog:\n logging.basicConfig(level=logging.INFO, filename='bank_main.log', filemode='a',\n format='%(name)s - %(levelname)s - %(message)s')\n formatter = logging.Formatter(\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\")\n # Log Levels: Info, Warning, Error, Critical, Debug, etc...\n # Handlers are used to better manage where a logger will log to.\n console_handler = logging.StreamHandler()\n console_handler.setFormatter(formatter)\n logger = logging.getLogger(__name__)\n logger.addHandler(console_handler)\n\n @staticmethod\n def info_log(message=None):\n if message is not None:\n MyLog.logger.info(message)\n else:\n MyLog.logger.info(\"We went to the next step in the program\")\n\n @staticmethod\n def warning_log(message=None):\n if message is not None:\n MyLog.logger.warning(message)\n else:\n MyLog.logger.info(\"Warning! Must have valid values!\")\n\n @staticmethod\n def error_log(message=None):\n if message is not None:\n MyLog.logger.error(message)\n else:\n MyLog.logger.info(\"Warning! An error has occurred!\")\n\n\ndef _test():\n MyLog().info_log(\"The test log was successful!\")\n\n\nif __name__ == '__main__':\n _test()\n", "sub_path": "cust_logging/my_logger.py", "file_name": "my_logger.py", "file_ext": "py", "file_size_in_byte": 1309, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.basicConfig", "line_number": 5, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 5, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 7, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "600620617", "text": "import logging\n\n\n# 输出到文件\nlogging.basicConfig(level=logging.DEBUG,\n format='%(asctime)s %(module)s %(name)s %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s',\n datefmt='%Y-%m-%d %H:%M:%S',\n filename='output.log',\n filemode='w')\n\nlogger = logging.getLogger(__name__)\n\n\nlogger.info('This is a log info')\nlogger.debug('Debugging')\nlogger.warning('Warning exists')\nlogger.info('Finish')\n", "sub_path": "log/basic_file.py", "file_name": "basic_file.py", "file_ext": "py", "file_size_in_byte": 478, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.basicConfig", "line_number": 5, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 5, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "186859379", "text": "import socket\nimport uuid\nimport time\n\nimport proto_orchestrator_classes as poc\nfrom proto_orchestrator_classes import node\nfrom proto_orchestrator_classes import pool\n\n\n# The prototype orchestrator server\n\nnodes_per_pool = 2\nchildren_per_pool = 2\n\n# orchestrator socket\norch_sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\norch_sock.bind((\"127.0.0.1\", 5005))\n\npoc.main_socket = orch_sock\n\nnext_node_port = 5050\n\n# Tree\nroot_pool = pool(None)\n\n\n# Tree Insertion for new nodes:\n# - search lowest level (top down)\n# - Find pool with min number of nodes\n# IF number of nodes in pool < N\n# - Insert into this pool\n# ELSE\n# - create new level with M children for each pool in higher level\n# - insert into pool with min number of entries\n\ndef get_lowest_level():\n # traverse tree to find leaves\n current_pool_list = [root_pool]\n current_children_pools = root_pool.children\n while len(current_children_pools) > 0:\n if len(current_children_pools) > len(current_pool_list):\n current_pool_list = current_children_pools\n else:\n raise Exception(\"Child list shorter than parent... This is not a tree\")\n next_children_pools = []\n for children_pool in current_children_pools:\n next_children_pools += children_pool.children\n current_children_pools = next_children_pools\n return current_pool_list\n\n\ndef get_insert_pool(the_node: node):\n lowest_tree_level = get_lowest_level()\n # get minimum from level\n min_used_pool = min(lowest_tree_level, key=lambda x: len(x.members))\n # check level for space\n if len(min_used_pool.members) < nodes_per_pool:\n # Insert into this pool\n return min_used_pool\n else:\n # Create new level\n for lt_pool in lowest_tree_level:\n new_children = [pool(lt_pool) for _ in range(children_per_pool)]\n # Add children to the parent\n lt_pool.add_children(new_children)\n \n # Make recursive call to insert node\n print(\"Inserting recursive\")\n return get_insert_pool(the_node)\n\nprint(\"Started up, entering main loop\")\n\nwhile True:\n data, adr = orch_sock.recvfrom(1024)\n msg = data.decode('UTF-8')\n \n print(\"Message received\")\n\n node_ip = \"127.0.0.1\"\n node_port = next_node_port\n node_uuid = uuid.uuid4()\n\n orch_sock.sendto(str.encode(\"initial_info[\"+node_uuid.hex+\",\"+node_ip+\",\"+str(node_port)+\"]\"), adr)\n # Replace this by some ACK message \n time.sleep(1)\n\n # make node object\n the_node = node(node_uuid, node_ip, node_port)\n \n insert_pool = get_insert_pool(the_node)\n\n insert_pool.introduce_pool_to_member(the_node)\n insert_pool.introduce_parents_to_member(the_node)\n insert_pool.introduce_children_to_member(the_node)\n\n insert_pool.add_member(the_node)\n\n next_node_port += 1\n\n# a pool is a interconnected list\n# each pool has a fully connected parent pool\n\n\n", "sub_path": "src_prototype/proto_orchestrator.py", "file_name": "proto_orchestrator.py", "file_ext": "py", "file_size_in_byte": 2928, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "socket.socket", "line_number": 16, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 16, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 16, "usage_type": "attribute"}, {"api_name": "proto_orchestrator_classes.main_socket", "line_number": 19, "usage_type": "attribute"}, {"api_name": "proto_orchestrator_classes.pool", "line_number": 24, "usage_type": "call"}, {"api_name": "proto_orchestrator_classes.node", "line_number": 52, "usage_type": "name"}, {"api_name": "proto_orchestrator_classes.pool", "line_number": 63, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 81, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 85, "usage_type": "call"}, {"api_name": "proto_orchestrator_classes.node", "line_number": 88, "usage_type": "call"}]} +{"seq_id": "238331060", "text": "from django.test import TestCase\n\nfrom .models import Location, Dog\n\n# Create your tests here.\n\nclass SearchTestCase(TestCase):\n \n fixtures=['test_data']\n\n def setUp(self):\n #Location.objects.create(city=\"Corvallis\", state=\"OR\", zipcode=97333)\n pass\n\n def test_can_lookup_location(self):\n\n location = Location.objects.get(zipcode=97333)\n \n self.assertEqual(location.city, \"Corvallis\")\n self.assertEqual(location.state, \"OR\")\n\n def test_dog_by_location_lookup(self):\n\n location = Location.objects.get(zipcode=97333)\n self.assertIsNotNone(location)\n\n dogs = Dog.objects.filter(location=location)\n self.assertIsNotNone(dogs)\n\n def test_different_dog_location_lookup(self):\n\n location_1 = Location.objects.get(zipcode=97333)\n self.assertIsNotNone(location_1)\n\n location_2 = Location.objects.get(zipcode=97331)\n self.assertIsNotNone(location_2)\n\n dogs_1 = Dog.objects.filter(location=location_1)\n self.assertIsNotNone(dogs_1)\n\n dogs_2 = Dog.objects.filter(location=location_2)\n self.assertIsNotNone(dogs_2)\n\n self.assertNotEqual(dogs_1, dogs_2)\n", "sub_path": "matchmaking/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 1191, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.test.TestCase", "line_number": 7, "usage_type": "name"}, {"api_name": "models.Location.objects.get", "line_number": 17, "usage_type": "call"}, {"api_name": "models.Location.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "models.Location", "line_number": 17, "usage_type": "name"}, {"api_name": "models.Location.objects.get", "line_number": 24, "usage_type": "call"}, {"api_name": "models.Location.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "models.Location", "line_number": 24, "usage_type": "name"}, {"api_name": "models.Dog.objects.filter", "line_number": 27, "usage_type": "call"}, {"api_name": "models.Dog.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "models.Dog", "line_number": 27, "usage_type": "name"}, {"api_name": "models.Location.objects.get", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Location.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "models.Location", "line_number": 32, "usage_type": "name"}, {"api_name": "models.Location.objects.get", "line_number": 35, "usage_type": "call"}, {"api_name": "models.Location.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "models.Location", "line_number": 35, "usage_type": "name"}, {"api_name": "models.Dog.objects.filter", "line_number": 38, "usage_type": "call"}, {"api_name": "models.Dog.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "models.Dog", "line_number": 38, "usage_type": "name"}, {"api_name": "models.Dog.objects.filter", "line_number": 41, "usage_type": "call"}, {"api_name": "models.Dog.objects", "line_number": 41, "usage_type": "attribute"}, {"api_name": "models.Dog", "line_number": 41, "usage_type": "name"}]} +{"seq_id": "440084647", "text": "import time\n\nfrom selenium import webdriver\nfrom webdriver_manager.chrome import ChromeDriverManager\n\ndriver = webdriver.Chrome(ChromeDriverManager().install())\nURL = \"https://ambitious-sky-0d3acbd03.azurestaticapps.net/k3.html\"\n\ndriver.get(URL)\n\ntext_input = driver.find_element_by_id(\"title\")\nerror_message = driver.find_element_by_xpath(\"/html/body/form/span\")\ntext_error_message_illegal = \"Only a-z and 0-9 characters allewed\"\ntext_error_message_length = \"Title should be at least 8 characters; you entered 4.\"\n\ntest_data = [\"abcd1234\", \"teszt233@\", \"abcd\"]\n\n\ndef fill_and_return_error(text):\n time.sleep(2)\n text_input.clear()\n text_input.send_keys(text)\n return error_message.text\n\n#* Helyes kitöltés esete:\n# * title: abcd1234\n# * Nincs validációs hibazüzenet\ndef test_positive():\n assert fill_and_return_error(test_data[0]) == \"\"\n\n#* Illegális karakterek esete:\n# * title: teszt233@\n# * Only a-z and 0-9 characters allewed.\ndef test_illegal():\n assert fill_and_return_error(test_data[1]) == text_error_message_illegal\n#* Tul rövid bemenet esete:\n# * title: abcd\n# * Title should be at least 8 characters; you entered 4.\n\ndef test_short():\n assert fill_and_return_error(test_data[2]) == text_error_message_length\n", "sub_path": "testproject/k3.py", "file_name": "k3.py", "file_ext": "py", "file_size_in_byte": 1266, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 6, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 6, "usage_type": "name"}, {"api_name": "webdriver_manager.chrome.ChromeDriverManager", "line_number": 6, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "149705412", "text": "import matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.ndimage as ndi\n\n\ndef quick_plot(image, median_smoothig=3):\n \"\"\"Display image with matplotlib.pyplot\n\n Parameters\n ----------\n image : Adorned image or numpy array\n Input image.\n\n Returns\n -------\n fig, ax\n Matplotlib figure and axis objects.\n \"\"\"\n fig, ax = plt.subplots(figsize=(10, 7))\n display_image = image.data\n if median_smoothig is not None:\n display_image = ndi.median_filter(display_image, size=median_smoothig)\n height, width = display_image.shape\n try:\n pixelsize_x = image.metadata.binary_result.pixel_size.x\n pixelsize_y = image.metadata.binary_result.pixel_size.y\n except AttributeError:\n extent_kwargs = [-(width / 2), +(width / 2), -(height / 2), +(height / 2)]\n ax.set_xlabel(\"Distance from origin (pixels)\")\n else:\n extent_kwargs = [\n -(width / 2) * pixelsize_x,\n +(width / 2) * pixelsize_x,\n -(height / 2) * pixelsize_y,\n +(height / 2) * pixelsize_y,\n ]\n ax.set_xlabel(\n \"Distance from origin (meters) \\n\" \"1 pixel = {} meters\".format(pixelsize_x)\n )\n ax.set_xlim(extent_kwargs[0], extent_kwargs[1])\n ax.set_ylim(extent_kwargs[2], extent_kwargs[3])\n ax.imshow(display_image, cmap=\"gray\", extent=extent_kwargs)\n return fig, ax\n\n\ndef select_point(image):\n \"\"\"Return location of interactive user click on image.\n\n Parameters\n ----------\n image : AdornedImage or 2D numpy array.\n\n Returns\n -------\n coords\n Coordinates of last point clicked in the image.\n Coordinates are in x, y format.\n Units are the same as the matplotlib figure axes.\n \"\"\"\n fig, ax = quick_plot(image)\n coords = []\n\n def on_click(event):\n print(event.ydata, event.xdata)\n coords.append(event.ydata)\n coords.append(event.xdata)\n\n fig.canvas.mpl_connect(\"button_press_event\", on_click)\n plt.show()\n return np.flip(coords[-2:], axis=0) # coordintes in x, y format\n\n\ndef _rectangles_overlap(bottomleft_1, topright_1, bottomleft_2, topright_2):\n \"\"\"Compare two rectangles and return True if they are overlapping.\n\n Parameters\n ----------\n bottomleft_1 : listlike, float\n x, y coordinate of bottom left corner of rectangle 1.\n topright_1 : listlike, float\n x, y coordinate of top right corner of rectangle 1.\n bottomleft_2 : listlike, float\n x, y coordinate of bottom left corner of rectangle 2.\n topright_2 : listlike, float\n x, y coordinate of top right corner of rectangle 2.\n\n Returns\n -------\n boolean\n True if rectangles are overlapping, False if they do not overlap.\n \"\"\"\n # check if bottom_left_1 is above top_right_2\n if bottomleft_1[1] > topright_2[1]:\n return False\n # check if bottom_left_2 is above top_right_1\n elif bottomleft_2[1] > topright_1[1]:\n return False\n # check if top_right_1 is to the left of bottom_left_2\n elif topright_1[0] < bottomleft_2[0]:\n return False\n # check if top_right_2 is to the left of bottom_left_1\n elif topright_2[0] < bottomleft_1[0]:\n return False\n # else, rectangles are overlapping\n else:\n return True\n\n\nclass InteractiveRectangle(object):\n def __init__(\n self,\n fig,\n ax,\n roi_size_x=1e-6,\n roi_size_y=1e-6,\n fov_x=None,\n fov_y=None,\n central_lamella_height=None,\n existing_fiducial=None,\n min_distance_from_lamella=0.0,\n ):\n \"\"\"Interactive tool for the user to click and set ROI position.\n\n Parameters\n ----------\n fig : matplotlib figure object\n Figure displaying ion beam image on real space axes.\n ax : matplotlib axes object\n Figure axes must be in real space units.\n roi_size_x : float, optional\n The size in real space of the ROI in x, by default 1e-6\n roi_size_y : float, optional\n The size in real space of the ROI in y, by default 1e-6\n fov_x : listlike, float, optional\n Field of view minimum and maximum in x, by default None\n fov_y : listlike, float, optional\n Field of view minimum and maximum in y, by default None\n central_lamella_height : float, optional\n Height of lamella region, by default None\n existing_fiducial : Matplotlib rectangle patch, optional\n min_distance_from_lamella : float, optional\n Separation between fiducial and lamella milling in real space,\n by default 0.\n \"\"\"\n self.fig = fig\n self.ax = ax\n self.roi_size_x = roi_size_x\n self.roi_size_y = roi_size_y\n self.field_of_view_x = fov_x\n self.field_of_view_y = fov_y\n self.central_lamella_height = central_lamella_height\n self.buffer = min_distance_from_lamella\n self.existing_fiducial = existing_fiducial\n self.coords = []\n\n self.rect = matplotlib.patches.Rectangle((0, 0), 0, 0, fill=False, color=\"y\")\n self.ax.add_artist(self.rect)\n if central_lamella_height:\n self.rect_lamella = matplotlib.patches.Rectangle(\n (0, 0), 0, 0, fill=False, color=\"c\"\n )\n self.ax.add_artist(self.rect_lamella)\n self.ax.set_title(\"Click to set the ROI marker\")\n if existing_fiducial:\n self.ax.add_artist(existing_fiducial)\n self.fig.canvas.mpl_connect(\"button_press_event\", self.on_click)\n\n def on_click(self, event):\n if event.inaxes is None:\n return\n # Ensure we are not too close to the edge\n if self.field_of_view_x:\n if (event.xdata - (self.roi_size_x / 2)) <= self.field_of_view_x[0]:\n print(\"Too close to the edge, please reselect.\")\n return\n elif (event.xdata + (self.roi_size_x / 2)) >= self.field_of_view_x[1]:\n print(\"Too close to the edge, please reselect.\")\n return\n if self.field_of_view_y:\n if (event.ydata - (self.roi_size_y / 2)) <= self.field_of_view_y[0]:\n print(\"Too close to the edge, please reselect.\")\n return\n elif (event.ydata + (self.roi_size_y / 2)) >= self.field_of_view_y[1]:\n print(\"Too close to the edge, please reselect.\")\n return\n print(event.xdata, event.ydata)\n self.coords = [event.xdata, event.ydata]\n self.rect.set_x(event.xdata - (self.roi_size_x / 2))\n self.rect.set_y(event.ydata - (self.roi_size_y / 2))\n self.rect.set_width(self.roi_size_x)\n self.rect.set_height(self.roi_size_y)\n # Also display the lamella itself, if appropriate\n if self.central_lamella_height:\n self.rect_lamella.set_x(event.xdata - (self.roi_size_x / 2))\n self.rect_lamella.set_y(event.ydata - (self.central_lamella_height / 2))\n self.rect_lamella.set_width(self.roi_size_x)\n self.rect_lamella.set_height(self.central_lamella_height)\n # # Ensure there is sufficent separation between the lamella & fiducial\n if self.existing_fiducial is not None:\n bottom_left_1 = np.array(self.existing_fiducial.xy) - self.buffer\n top_right_1 = np.array(self.existing_fiducial.xy) + np.array(\n [\n self.existing_fiducial.get_width() + self.buffer,\n self.existing_fiducial.get_height() + self.buffer,\n ]\n )\n bottom_left_2 = self.rect_lamella.xy\n top_right_2 = np.array(self.rect_lamella.xy) + np.array(\n [self.rect_lamella.get_width(), self.rect_lamella.get_height()]\n )\n if _rectangles_overlap(\n bottom_left_1, top_right_1, bottom_left_2, top_right_2\n ):\n print(\"Lamella too close to the fiducial marker\")\n return\n\n self.fig.canvas.draw()\n\n def show(self):\n plt.show()\n", "sub_path": "autolamella/display.py", "file_name": "display.py", "file_ext": "py", "file_size_in_byte": 8148, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "matplotlib.pyplot.subplots", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "scipy.ndimage.median_filter", "line_number": 23, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "numpy.flip", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 157, "usage_type": "attribute"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 160, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 221, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 221, "usage_type": "name"}]} +{"seq_id": "509630649", "text": "import torch\n\nimport torchvision\nimport tqdm\n\nfrom TOOLS import gan_losses\nfrom TOOLS import sinkhorn_autodiff\n\n\ndef train_func(data_loader, G, D, G_ema, ema, z_train, g_optimizer, d_optimizer, z_sample, train_dict, args, myargs):\n def train(**kwargs):\n for i, (imgs, _) in enumerate(tqdm.tqdm(data_loader)):\n train_dict['batches_done'] += 1\n step = train_dict['batches_done']\n summary = {}\n summary_d_logits_mean = {}\n summary_wd = {}\n\n G.train()\n G_ema.train()\n\n imgs = imgs.cuda()\n bs = imgs.size(0)\n\n z_train.sample_()\n f_imgs = G(z_train[:bs])\n\n # train D\n with torch.no_grad():\n sinkhorn_d = sinkhorn_autodiff.sinkhorn_loss(x=imgs.view(bs, -1), y=f_imgs.view(bs, -1).detach(),\n epsilon=args.sinkhorn_eps, niter=args.sinkhorn_niter,\n cuda=True, pi_detach=args.sinkhorn_pi_detach)\n summary_wd['D_sinkhorn_d'] = sinkhorn_d.item()\n\n r_logit = D(imgs)\n r_logit_mean = r_logit.mean()\n f_logit = D(f_imgs.detach())\n f_logit_mean = f_logit.mean()\n summary_d_logits_mean['D_r_logit_mean'] = r_logit_mean.item()\n summary_d_logits_mean['D_f_logit_mean'] = f_logit_mean.item()\n\n # Wasserstein-1 Distance\n wd = r_logit_mean - f_logit_mean\n gp = gan_losses.wgan_gp_gradient_penalty(imgs.data, f_imgs.data, D)\n d_loss = -wd + gp * 10.0 + torch.relu(wd - sinkhorn_d.item())\n summary_wd['wd'] = wd.item()\n summary['gp'] = gp.item()\n summary['d_loss'] = d_loss.item()\n\n D.zero_grad()\n d_loss.backward()\n d_optimizer.step()\n\n if step % args.n_critic == 0:\n # train G\n z_train.sample_()\n f_imgs = G(z_train)\n\n sinkhorn_d = sinkhorn_autodiff.sinkhorn_loss(x=imgs.view(imgs.size(0), -1), y=f_imgs.view(f_imgs.size(0), -1),\n epsilon=args.sinkhorn_eps, niter=args.sinkhorn_niter,\n cuda=True, pi_detach=args.sinkhorn_pi_detach)\n summary_wd['G_sinkhorn_d'] = sinkhorn_d.item()\n\n f_logit = D(f_imgs)\n f_logit_mean = f_logit.mean()\n g_loss = - f_logit_mean + args.lambda_sinkhorn * sinkhorn_d\n summary_d_logits_mean['G_f_logit_mean'] = f_logit_mean.item()\n summary['g_loss'] = g_loss.item()\n\n D.zero_grad()\n G.zero_grad()\n g_loss.backward()\n g_optimizer.step()\n\n # end iter\n ema.update(train_dict['batches_done'])\n\n if i % args.sample_every == 0:\n # sample images\n G.eval()\n G_ema.eval()\n G_z = G(z_sample)\n merged_img = torchvision.utils.make_grid(G_z, normalize=True, pad_value=1, nrow=16)\n myargs.writer.add_images('G_z', merged_img.view(1, *merged_img.shape), train_dict['batches_done'])\n # G_ema\n G_ema_z = G_ema(z_sample)\n merged_img = torchvision.utils.make_grid(G_ema_z, normalize=True, pad_value=1, nrow=16)\n myargs.writer.add_images('G_ema_z', merged_img.view(1, *merged_img.shape), train_dict['batches_done'])\n # x\n merged_img = torchvision.utils.make_grid(imgs, normalize=True, pad_value=1, nrow=16)\n myargs.writer.add_images('x', merged_img.view(1, *merged_img.shape), train_dict['batches_done'])\n # checkpoint\n myargs.checkpoint.save_checkpoint(checkpoint_dict=myargs.checkpoint_dict, filename='ckpt.tar')\n # summary\n for key in summary:\n myargs.writer.add_scalar('train_vs_batch/%s'%key, summary[key], train_dict['batches_done'])\n myargs.writer.add_scalars('train_vs_batch', summary_d_logits_mean, train_dict['batches_done'])\n myargs.writer.add_scalars('wd', summary_wd, train_dict['batches_done'])\n\n G.train()\n elif train_dict['batches_done'] <= 20000:\n for key in summary:\n myargs.writer.add_scalar('train_vs_batch/%s' % key, summary[key], train_dict['batches_done'])\n myargs.writer.add_scalars('train_vs_batch', summary_d_logits_mean, train_dict['batches_done'])\n myargs.writer.add_scalars('wd', summary_wd, train_dict['batches_done'])\n return train", "sub_path": "BigGAN-PyTorch-1-exp-master/DCGAN/train_func_wgan_gp_sinkhorn.py", "file_name": "train_func_wgan_gp_sinkhorn.py", "file_ext": "py", "file_size_in_byte": 4219, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "tqdm.tqdm", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 29, "usage_type": "call"}, {"api_name": "TOOLS.sinkhorn_autodiff.sinkhorn_loss", "line_number": 30, "usage_type": "call"}, {"api_name": "TOOLS.sinkhorn_autodiff", "line_number": 30, "usage_type": "name"}, {"api_name": "TOOLS.gan_losses.wgan_gp_gradient_penalty", "line_number": 44, "usage_type": "call"}, {"api_name": "TOOLS.gan_losses", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.relu", "line_number": 45, "usage_type": "call"}, {"api_name": "TOOLS.sinkhorn_autodiff.sinkhorn_loss", "line_number": 59, "usage_type": "call"}, {"api_name": "TOOLS.sinkhorn_autodiff", "line_number": 59, "usage_type": "name"}, {"api_name": "torchvision.utils.make_grid", "line_number": 83, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 83, "usage_type": "attribute"}, {"api_name": "torchvision.utils.make_grid", "line_number": 87, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 87, "usage_type": "attribute"}, {"api_name": "torchvision.utils.make_grid", "line_number": 90, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 90, "usage_type": "attribute"}]} +{"seq_id": "376440253", "text": "import sqlite3\nimport json\n\nfrom flask import g, current_app\n\nimport reimu.config\n\n\ndef init_db():\n pass\n\n\nclass RowObject(object):\n \"\"\"Table row object.\"\"\"\n def __init__(self, row, columns):\n for i, column in enumerate(columns):\n setattr(self, column, row[i])\n\n\ndef connect():\n \"\"\"Connect to database from application.\"\"\"\n g.db = sqlite3.connect(current_app.config['DATABASE'])\n\n\ndef disconnect():\n \"\"\"Close application's database connection.\"\"\"\n db = getattr(g, 'db', None)\n if db is not None:\n db.close()\n\n\ndef select(query, arguments=(), single=False, row_type='object'):\n \"\"\"Select one or more rows from database.\"\"\"\n cursor = g.db.cursor()\n cursor.execute(query, arguments)\n rows = cursor.fetchall()\n\n # Convert rows to desired type\n columns = [col[0] for col in cursor.description]\n if row_type == 'object':\n rows = [RowObject(row, columns) for row in rows]\n elif row_type == 'dict':\n rows = [dict(zip(columns, row)) for row in rows]\n\n # Unpack list if needed\n if single:\n return rows[0] if len(rows) else None\n else:\n return rows if len(rows) else []\n\n\ndef count(table):\n \"\"\"Count all rows in given table.\"\"\"\n cursor = g.db.cursor()\n cursor.execute('SELECT COUNT() FROM {};'.format(table))\n result = cursor.fetchone()[0]\n return result\n\n\ndef update(query, arguments=()):\n \"\"\"Update a row\"\"\"\n cursor = g.db.cursor()\n cursor.execute(query, arguments)\n g.db.commit()\n\n\ndef insert(query, arguments=()):\n \"\"\"Insert a row, return it's id\"\"\"\n cursor = g.db.cursor()\n cursor.execute(query, arguments)\n g.db.commit()\n return cursor.lastrowid\n", "sub_path": "reimu/db.py", "file_name": "db.py", "file_ext": "py", "file_size_in_byte": 1693, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask.g.db", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.g", "line_number": 27, "usage_type": "argument"}, {"api_name": "flask.g.db.cursor", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.g.db", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.g.db.cursor", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.g.db", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.g.db.cursor", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.g.db", "line_number": 62, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.g.db.commit", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.g.db", "line_number": 64, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.g.db.cursor", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.g.db", "line_number": 69, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 69, "usage_type": "name"}, {"api_name": "flask.g.db.commit", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.g.db", "line_number": 71, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 71, "usage_type": "name"}]} +{"seq_id": "335077282", "text": "# The MIT License (MIT)\n\n# Copyright (c) 2021-2022 Krux contributors\n\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\nfrom collections import deque\nimport os\nimport time\nimport pygame as pg\nimport cv2\nfrom kruxsim import events\nfrom kruxsim.mocks.board import BOARD_CONFIG\n\nCOMMANDS = [\"press\", \"touch\", \"qrcode\", \"screenshot\", \"wait\", \"include\", \"x\"]\n\n\nclass SequenceExecutor:\n def __init__(self, sequence_filepath):\n self.filepath = sequence_filepath\n self.command = None\n self.command_params = []\n self.command_fn = None\n self.command_timer = 0\n self.key = None\n self.key_checks = 0\n self.touch_pos = None\n self.touch_checks = 0\n self.camera_image = None\n commands = load_commands(self.filepath)\n if commands[0][0] == \"wait\" and BOARD_CONFIG[\"krux\"][\"display\"][\"touch\"]:\n commands = commands[0:1] + [(\"press\", [\"BUTTON_A\"])] + commands[1:]\n commands.append((\"wait\", [\"1\"]))\n self.commands = deque(commands)\n\n def execute(self):\n if self.command_fn:\n if time.time() - self.command_timer > 0.1:\n print(\"Executing (%s, %r)\" % (self.command, self.command_params))\n self.command_timer = 0\n self.command_fn()\n self.command_fn = None\n self.command_params = []\n elif self.commands:\n cmd, params = self.commands.popleft()\n self.command_timer = time.time()\n self.command = cmd\n self.command_params = params\n if cmd == \"press\":\n self.command_fn = self.press_key\n elif cmd == \"touch\":\n self.command_fn = self.touch\n elif cmd == \"qrcode\":\n self.command_fn = self.show_qrcode\n elif cmd == \"screenshot\":\n self.command_fn = self.request_screenshot\n elif cmd == \"wait\":\n self.command_timer += float(params[0])\n self.command_fn = self.wait\n\n def press_key(self):\n key = self.command_params[0]\n self.key = None\n self.key_checks = 0\n if key == \"BUTTON_A\":\n self.key = pg.K_RETURN\n elif key == \"BUTTON_B\":\n self.key = pg.K_DOWN\n elif key == \"BUTTON_C\":\n self.key = pg.K_UP\n\n def touch(self):\n self.touch_pos = (self.command_params[0], self.command_params[1])\n self.touch_checks = 0\n\n def show_qrcode(self):\n filename = self.command_params[0]\n self.camera_image = cv2.imread(\n os.path.join(os.path.dirname(self.filepath), \"qrcodes\", filename),\n cv2.IMREAD_COLOR,\n )\n\n def request_screenshot(self):\n filename = self.command_params[0]\n pg.event.post(pg.event.Event(events.SCREENSHOT_EVENT, {\"filename\": filename}))\n\n def wait(self):\n pass\n\n\ndef load_commands(sequence_filepath):\n commands = []\n\n # If the sequence doesn't exist, it may be board-specific; look for it within a subfolder named for the board\n filepath = sequence_filepath\n if not os.path.exists(filepath):\n filepath = os.path.join(\n os.path.dirname(sequence_filepath),\n BOARD_CONFIG[\"type\"],\n os.path.basename(sequence_filepath),\n )\n\n with open(filepath, \"r\") as sequence_file:\n raw_commands = sequence_file.readlines()\n for raw_command in raw_commands:\n if not any(raw_command.startswith(cmd) for cmd in COMMANDS):\n continue\n num_times = 1\n if raw_command.startswith(\"x\"):\n num_times = int(raw_command[1:].split()[0])\n raw_command = raw_command.split(\" \", 1)[1]\n cmd_parts = raw_command.strip().split()\n cmd = cmd_parts[0]\n params = cmd_parts[1:] if len(cmd_parts) > 1 else []\n for _ in range(num_times):\n if cmd == \"include\":\n commands.extend(\n load_commands(\n os.path.join(os.path.dirname(sequence_filepath), params[0])\n )\n )\n else:\n commands.append((cmd, params))\n return commands\n", "sub_path": "simulator/kruxsim/sequence.py", "file_name": "sequence.py", "file_ext": "py", "file_size_in_byte": 5266, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "kruxsim.mocks.board.BOARD_CONFIG", "line_number": 46, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 49, "usage_type": "call"}, {"api_name": "time.time", "line_number": 53, "usage_type": "call"}, {"api_name": "time.time", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.K_RETURN", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 83, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 85, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 94, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pygame.event.post", "line_number": 100, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pygame.event.Event", "line_number": 100, "usage_type": "call"}, {"api_name": "kruxsim.events.SCREENSHOT_EVENT", "line_number": 100, "usage_type": "attribute"}, {"api_name": "kruxsim.events", "line_number": 100, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "kruxsim.mocks.board.BOARD_CONFIG", "line_number": 114, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 134, "usage_type": "call"}]} +{"seq_id": "44427736", "text": "# -*- coding: utf-8 -*-\nfrom Public.requests import requ\nfrom branch.log import Log\nfrom branch.operate_db import Operate_db\nfrom config.readyaml import Getyaml\n\nreques=requ()\nclass TestApi(object):\n\tdef __init__(self,url,key,connent,fangshi,param_place,assertdata):\n\t\tself.url = url\n\t\tself.key = key\n\t\tself.connent = connent\n\t\tself.fangshi = fangshi\n\t\tself.param_place = param_place\n\t\tself.assertdata = assertdata\n\n\tdef get_param(self):\n\t\tif self.param_place != 'database':\n\t\t\treturn self.connent\n\t\telse:\n\t\t\t#获取数据库名\n\t\t\tself.database = Getyaml(yamlparam=\"interface_db\",interface=self.url).port_db()\n\t\t\tLog().info('当前接口涉及数据库：%s' % self.database)\n\t\t\t#执行数据库操作\n\t\t\tpost_data = Operate_db(self.database,self.url).Perform()\n\t\t\tLog().info('数据格式为：%s' % post_data)\n\t\t\treturn post_data\n\n\tdef testapi(self):\n\t\tif self.fangshi=='POST':\n\t\t\t#self.parem = {'key': self.key, 'info': self.connent}\n\t\t\tself.response=reques.post(self.url, self.get_param(), self.assertdata)\n\t\telif self.fangshi==\"GET\":\n\t\t\tself.parem = {'key': self.key, 'info': self.connent}\n\t\t\tself.response = reques.get(self.url, self.get_param())\n\t\telif self.fangshi == \"PUT\":\n\t\t\t#self.parem = {'key': self.key, 'info': self.connent}\n\t\t\tself.response = reques.putfile(self.url, self.get_param(), self.assertdata)\n\t\telif self.fangshi == \"DELETE\":\n\t\t\tself.parem = {'key': self.key, 'info': self.connent}\n\t\t\tself.response = reques.delfile(self.url, self.get_param())\n\t\treturn self.response\n\t# def getJson(self):\n\t# \tjson_data = self.testapi()\n\t# \treturn json_data", "sub_path": "Public/select_request.py", "file_name": "select_request.py", "file_ext": "py", "file_size_in_byte": 1567, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "Public.requests.requ", "line_number": 7, "usage_type": "call"}, {"api_name": "config.readyaml.Getyaml", "line_number": 22, "usage_type": "call"}, {"api_name": "branch.log.Log", "line_number": 23, "usage_type": "call"}, {"api_name": "branch.operate_db.Operate_db", "line_number": 25, "usage_type": "call"}, {"api_name": "branch.log.Log", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "74546105", "text": "import numpy as np\r\nimport pandas as pd\r\nfrom sklearn.cluster import KMeans\r\nimport matplotlib.pyplot as plt\r\ndata=pd.read_csv('q5.csv',header=0,index_col='person_id')\r\nwant_data=pd.DataFrame(data.loc[:,['ttl_txn','ttl_to','avg_to_per_qty']])\r\n#print(want_data)\r\nwant_data_zs=1.0*(want_data-want_data.mean())/want_data.std()\r\n#print(want_data_zs)\r\n\r\n# elbow rule to select K\r\n'''\r\nfrom scipy.spatial.distance import cdist\r\nK=range(1,10)\r\nmeandistortions=[]\r\nfor k in K:\r\n kmeans=KMeans(n_clusters=k)\r\n kmeans.fit(want_data_zs)\r\n meandistortions.append(sum(np.min(\r\n cdist(want_data_zs,kmeans.cluster_centers_,\r\n 'euclidean'),axis=1))/want_data_zs.shape[0])\r\nplt.plot(K,meandistortions,'bx-')\r\nplt.xlabel('k')\r\nplt.ylabel('average disortion')\r\nplt.show()\r\n'''\r\nk=3\r\niteration=500\r\nmodel=KMeans(n_clusters=k,n_jobs=4,max_iter=iteration)\r\nmodel.fit(want_data_zs)\r\n\r\nr1=pd.Series(model.labels_).value_counts()\r\nr2=pd.DataFrame(model.cluster_centers_)\r\nr=pd.concat([r2,r1],axis=1)\r\nr.columns=list(want_data.columns)+['number of cluster']\r\nprint(r)\r\n\r\nr=pd.concat([want_data,pd.Series(model.labels_,index=want_data.index)],axis=1)\r\nr.columns=list(want_data.columns)+['cluster group']\r\noutputfile='data_type.csv'\r\nr.to_csv(outputfile)\r\n\r\ndef density_plot(data,title):\r\n import matplotlib.pyplot as plt\r\n plt.figure()\r\n for i in range(len(data.iloc[0])):\r\n (data.iloc[:,i]).plot(kind='kde',label=data.columns[i],linewidth=2)\r\n plt.ylabel('density')\r\n plt.xlabel('num of people')\r\n plt.title('density curve of group %s ' %title)\r\n plt.legend()\r\n return plt\r\ndef density_plot(data):\r\n import matplotlib.pyplot as plt\r\n p=data.plot(kind='kde',linewidth=2,subplots=True,sharex=False)\r\n [p[i].set_ylabel('density') for i in range(k)]\r\n plt.legend()\r\n return plt\r\n\r\npic_output='gd_'\r\nfor i in range(k):\r\n density_plot(want_data[r['cluster group']==i]).savefig('%s%s.png' %(pic_output,i))\r\n", "sub_path": "Q5/Q5.py", "file_name": "Q5.py", "file_ext": "py", "file_size_in_byte": 1960, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pandas.read_csv", "line_number": 5, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 6, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 32, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}]} +{"seq_id": "234821729", "text": "\"\"\"Unit tests for inventory Managment\"\"\"\n# pylint: disable=import-error\nfrom unittest import TestCase\nimport io\nimport sys\nfrom unittest.mock import patch\nfrom inventory_management.electric_appliances_class import ElectricAppliances\nfrom inventory_management.furniture_class import Furniture\nfrom inventory_management.inventory_class import Inventory\nfrom inventory_management.main import main_menu, get_price, add_new_item, item_info, exit_program, FULL_INVENTORY\nfrom inventory_management.market_prices import get_latest_price\n\n\nclass TestInventoryManagement(TestCase):\n \"\"\"Class for housing the tests\"\"\"\n def test_inventory(self):\n \"\"\"best the base inventory class\"\"\"\n test = Inventory(0, 'desc', 1, 2)\n self.assertEqual(test.return_as_dictionary(),\n {'product_code': 0,\n 'description': 'desc',\n 'market_price': 1,\n 'rental_price': 2})\n\n def test_electric_appliances(self):\n \"\"\"test electric appliances class\"\"\"\n test = ElectricAppliances(777, 'Look with your special eyes', 999, 888, 'MYBRAND', 2)\n self.assertEqual(test.return_as_dictionary(),\n {'product_code': 777,\n 'description': 'Look with your special eyes',\n 'market_price': 999,\n 'rental_price': 888,\n 'brand': 'MYBRAND',\n 'voltage': 2})\n\n def test_furniture(self):\n \"\"\"test out the furniture class\"\"\"\n test = Furniture(17, 'desc', 354, 144, 'bronze', 'yuge')\n self.assertEqual(test.return_as_dictionary(),\n {'product_code': 17,\n 'description': 'desc',\n 'market_price': 354,\n 'rental_price': 144,\n 'material': 'bronze',\n 'size': 'yuge'})\n\n def test_main_menu(self):\n \"\"\"tests for main menu function\"\"\"\n self.assertTrue(main_menu('1'), 'add_new_item')\n self.assertTrue(main_menu('2'), 'item_info')\n self.assertTrue(main_menu('q'), 'exit_program')\n\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n with patch('builtins.input', side_effect=['popcorn','q']):\n main_menu()\n sys.stdout = sys.__stdout__\n self.assertIn('Please choose from the following options', capturedOutput.getvalue())\n self.assertIn('1. Add a new item to the inventory', capturedOutput.getvalue())\n self.assertIn('2. Get item information', capturedOutput.getvalue())\n self.assertIn('q. Quit', capturedOutput.getvalue())\n\n\n def test_get_price(self):\n \"\"\"test the get price function\"\"\"\n self.assertEqual(None, get_price(134))\n\n\n def test_add_new_item(self):\n inventory = ['0', 'desc', '1', 'n', 'n']\n furniture = [17, 'desc', 354, 'y', 'bronze', 'XL']\n electric = [777, 'desc', 999, 'n', 'y', 'MYBRAND', 2]\n\n with patch('builtins.input', side_effect=inventory):\n add_new_item()\n\n self.assertEqual(FULL_INVENTORY['0'], {'product_code': '0', 'description': 'desc',\n 'market_price': 24, 'rental_price': '1'})\n\n with patch('builtins.input', side_effect=furniture):\n add_new_item()\n\n self.assertEqual(FULL_INVENTORY[17], {'product_code': 17, 'description': 'desc',\n 'market_price': 24, 'rental_price': 354,\n 'material': 'bronze', 'size': 'XL'})\n\n with patch('builtins.input', side_effect=electric):\n add_new_item()\n\n self.assertEqual(FULL_INVENTORY[777], {'product_code': 777, 'description': 'desc',\n 'market_price': 24, 'rental_price': 999,\n 'brand': 'MYBRAND', 'voltage': 2})\n\n def test_item_info(self):\n \"\"\" asert equal \"\"\"\n new_furniture = ['159', 'desc', '52', 'n', 'n']\n with patch('builtins.input', side_effect=new_furniture):\n add_new_item()\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n with patch('builtins.input', side_effect=['159']):\n item_info()\n sys.stdout = sys.__stdout__\n self.assertEqual(capturedOutput.getvalue(),\n 'product_code:159\\ndescription:desc\\nmarket_price:24\\nrental_price:52\\n')\n\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n with patch('builtins.input', side_effect=[56456456498406]):\n item_info()\n sys.stdout = sys.__stdout__\n self.assertEqual(capturedOutput.getvalue(), 'Item not found in inventory\\n')\n\n\n def test_exit_program(self):\n \"\"\" assert raises \"\"\"\n with self.assertRaises(SystemExit):\n exit_program()\n\n\n def test_get_latest_price(self):\n \"\"\"test the get latest price function\"\"\"\n self.assertEqual(24, get_latest_price(134))\n", "sub_path": "students/thomas_sulgrove/lesson01/assignment/test_unit.py", "file_name": "test_unit.py", "file_ext": "py", "file_size_in_byte": 5142, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "unittest.TestCase", "line_number": 14, "usage_type": "name"}, {"api_name": "inventory_management.inventory_class.Inventory", "line_number": 18, "usage_type": "call"}, {"api_name": "inventory_management.electric_appliances_class.ElectricAppliances", "line_number": 27, "usage_type": "call"}, {"api_name": "inventory_management.furniture_class.Furniture", "line_number": 38, "usage_type": "call"}, {"api_name": "inventory_management.main.main_menu", "line_number": 49, "usage_type": "call"}, {"api_name": "inventory_management.main.main_menu", "line_number": 50, "usage_type": "call"}, {"api_name": "inventory_management.main.main_menu", "line_number": 51, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 53, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 54, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch", "line_number": 55, "usage_type": "call"}, {"api_name": "inventory_management.main.main_menu", "line_number": 56, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 57, "usage_type": "attribute"}, {"api_name": "sys.__stdout__", "line_number": 57, "usage_type": "attribute"}, {"api_name": "inventory_management.main.get_price", "line_number": 66, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 74, "usage_type": "call"}, {"api_name": "inventory_management.main.add_new_item", "line_number": 75, "usage_type": "call"}, {"api_name": "inventory_management.main.FULL_INVENTORY", "line_number": 77, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 80, "usage_type": "call"}, {"api_name": "inventory_management.main.add_new_item", "line_number": 81, "usage_type": "call"}, {"api_name": "inventory_management.main.FULL_INVENTORY", "line_number": 83, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 87, "usage_type": "call"}, {"api_name": "inventory_management.main.add_new_item", "line_number": 88, "usage_type": "call"}, {"api_name": "inventory_management.main.FULL_INVENTORY", "line_number": 90, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 97, "usage_type": "call"}, {"api_name": "inventory_management.main.add_new_item", "line_number": 98, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 99, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 100, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch", "line_number": 101, "usage_type": "call"}, {"api_name": "inventory_management.main.item_info", "line_number": 102, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 103, "usage_type": "attribute"}, {"api_name": "sys.__stdout__", "line_number": 103, "usage_type": "attribute"}, {"api_name": "io.StringIO", "line_number": 107, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 108, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch", "line_number": 109, "usage_type": "call"}, {"api_name": "inventory_management.main.item_info", "line_number": 110, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 111, "usage_type": "attribute"}, {"api_name": "sys.__stdout__", "line_number": 111, "usage_type": "attribute"}, {"api_name": "inventory_management.main.exit_program", "line_number": 118, "usage_type": "call"}, {"api_name": "inventory_management.market_prices.get_latest_price", "line_number": 123, "usage_type": "call"}]} +{"seq_id": "149023476", "text": "import sys\n\nfrom PyQt5.QtWidgets import QMainWindow,QApplication,QVBoxLayout,QWidget,QMessageBox\nfrom PyQt5.QtGui import QColor,QPainter,QFont\nfrom PyQt5.QtCore import QPoint,pyqtSignal,QCoreApplication\n\n\nclass Board(QWidget):\n\n ending_signal = pyqtSignal(str)\n\n def __init__(self):\n super().__init__()\n self.init_game()\n\n def init_game(self):\n self.win = None\n self.turn = 'black'\n self.black_piece_pos = []\n self.white_piece_pos = []\n self.ending_signal.connect(self.show_ending_message)\n self.update()\n\n def paintEvent(self, e):\n qp = QPainter()\n qp.begin(self)\n qp.setBrush(QColor(255,215,0))\n qp.drawRect(0,0,self.width()-1,self.height()-1)\n points_list =[x for x in range(32,962,62)]\n for i in points_list:\n qp.drawLine(i,32,i,900)\n qp.drawLine(32,i,900,i)\n qp.setPen(QColor('black'))\n qp.setFont(QFont('SimSun',10))\n string_1 = [str(x) for x in range(1,16)]\n string_2 = 'abcdefghijklmno'\n for i,j in zip(points_list,string_1):\n qp.drawText(i,933,j)\n for i,j in zip(points_list,string_2):\n qp.drawText(930,i,j)\n qp.setBrush(QColor('black'))\n for pos in self.black_piece_pos:\n qp.drawEllipse(pos,30,30)\n qp.setBrush(QColor('white'))\n for pos in self.white_piece_pos:\n qp.drawEllipse(pos,30,30)\n qp.end()\n\n def mousePressEvent(self, e):\n if e.pos().x() < 962 and e.pos().y() < 962:\n pos = self.cal_pos(e.pos())\n if (pos not in self.black_piece_pos) and (pos not in self.white_piece_pos):\n if self.turn == 'black':\n self.black_piece_pos.append(pos)\n self.check_win(pos)\n elif self.turn == 'white':\n self.white_piece_pos.append(pos)\n self.check_win(pos)\n\n self.update()\n\n def change_turn(self):\n if self.turn == 'black':\n self.turn = 'white'\n elif self.turn == 'white':\n self.turn = 'black'\n\n def check_win(self,pos):\n x, y= pos.x(),pos.y()\n condition= {}\n condition[1] = [QPoint(x+62,y),QPoint(x+62*2,y),QPoint(x+62*3,y),QPoint(x+62*4,y)]\n condition[2] = [QPoint(x+62,y),QPoint(x+62*2,y),QPoint(x+62*3,y),QPoint(x-62,y)]\n condition[3] = [QPoint(x + 62, y), QPoint(x + 62 * 2, y), QPoint(x -62, y), QPoint(x - 62 * 2, y)]\n condition[4] = [QPoint(x + 62, y), QPoint(x - 62, y), QPoint(x - 62 * 2, y), QPoint(x - 62 * 3, y)]\n condition[5] = [QPoint(x - 62*4, y), QPoint(x - 62 * 3, y), QPoint(x - 62 * 2, y), QPoint(x - 62 * 1, y)]\n condition[6] = [QPoint(x,y+62),QPoint(x,y+62*2),QPoint(x,y+62*3),QPoint(x,y+62*4)]\n condition[7] = [QPoint(x,y+62),QPoint(x,y+62*2),QPoint(x,y+62*3),QPoint(x,y-62)]\n condition[8] = [QPoint(x , y+ 62), QPoint(x , y+ 62 * 2), QPoint(x , y-62), QPoint(x , y- 62 * 2)]\n condition[9] = [QPoint(x , y+ 62), QPoint(x, y - 62), QPoint(x, y - 62 * 2), QPoint(x , y- 62 * 3)]\n condition[10] = [QPoint(x , y- 62*4), QPoint(x , y- 62 * 3), QPoint(x , y- 62 * 2), QPoint(x , y- 62 * 1)]\n condition[11] = [QPoint(x+62,y+62),QPoint(x+62*2,y+62*2),QPoint(x+62*3,y+62*3),QPoint(x+62*4,y+62*4)]\n condition[12] = [QPoint(x+62,y+62),QPoint(x+62*2,y+62*2),QPoint(x+62*3,y+62*3),QPoint(x-62,y-62)]\n condition[13] = [QPoint(x + 62, y+ 62), QPoint(x + 62 * 2, y + 62 * 2), QPoint(x -62, y-62), QPoint(x - 62 * 2, y- 62 * 2)]\n condition[14] = [QPoint(x + 62, y+ 62), QPoint(x - 62, y- 62), QPoint(x - 62 * 2, y- 62 * 2), QPoint(x - 62 * 3, y- 62 * 3)]\n condition[15] = [QPoint(x - 62*4, y- 62*4), QPoint(x - 62 * 3, y- 62 * 3), QPoint(x - 62 * 2, y- 62 * 2), QPoint(x - 62 * 1, y - 62 * 1)]\n condition[16] = [QPoint(x-62,y+62),QPoint(x-62*2,y+62*2),QPoint(x-62*3,y+62*3),QPoint(x-62*4,y+62*4)]\n condition[17] = [QPoint(x-62,y+62),QPoint(x-62*2,y+62*2),QPoint(x-62*3,y+62*3),QPoint(x+62,y-62)]\n condition[18] = [QPoint(x -62, y+ 62), QPoint(x - 62 * 2, y + 62 * 2), QPoint(x +62, y-62), QPoint(x + 62 * 2, y- 62 * 2)]\n condition[19] = [QPoint(x - 62, y+ 62), QPoint(x + 62, y- 62), QPoint(x + 62 * 2, y- 62 * 2), QPoint(x + 62 * 3, y- 62 * 3)]\n condition[20] = [QPoint(x + 62*4, y- 62*4), QPoint(x + 62 * 3, y- 62 * 3), QPoint(x + 62 * 2, y- 62 * 2), QPoint(x + 62 * 1, y - 62 * 1)]\n for k,i in condition.items():\n count = 0\n if self.turn == 'black':\n for piece in i:\n if piece in self.black_piece_pos:\n count += 1\n if count == 4:\n self.win = 'black'\n elif self.turn == 'white':\n for piece in i:\n if piece in self.white_piece_pos:\n count += 1\n if count == 4:\n self.win = 'white'\n if not self.win:\n self.change_turn()\n elif self.win == 'black':\n self.ending_signal.emit('black win!')\n elif self.win == 'white':\n self.ending_signal.emit('white win!')\n\n\n def show_ending_message(self,ms):\n msg = QMessageBox(QMessageBox.Information,'Game over!',ms,QMessageBox.NoButton,self)\n msg.addButton('&Replay',QMessageBox.AcceptRole)\n msg.addButton('&Exit',QMessageBox.RejectRole)\n if msg.exec() == QMessageBox.AcceptRole:\n self.init_game()\n else:\n QCoreApplication.quit()\n\n @staticmethod\n def cal_pos(pos):\n a,b = pos.x(),pos.y()\n if a % 62-32 <31:\n a = a // 62 *62+32\n else:\n a = (a //62+1)*62+32\n if b % 62-32 <31:\n b = b// 62 *62+32\n else:\n b = (b //62+1)*62+32\n\n return QPoint(a,b)\n\n\n\n\nclass Game(QMainWindow):\n def __init__(self):\n super().__init__()\n self.board = Board()\n\n l = QVBoxLayout()\n l.addWidget(self.board)\n w = QWidget()\n w.setLayout(l)\n w.setFixedHeight(1000)\n w.setFixedWidth(1000)\n self.setCentralWidget(w)\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n game = Game()\n game.show()\n sys.exit(app.exec())", "sub_path": "wzq.py", "file_name": "wzq.py", "file_ext": "py", "file_size_in_byte": 6330, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 8, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 10, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 25, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 33, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 34, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 41, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 44, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 71, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 72, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 73, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 74, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 75, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 76, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 77, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 78, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 79, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 80, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 81, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 82, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 83, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 84, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 85, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 86, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 87, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 88, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 89, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 90, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 114, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Information", "line_number": 114, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.NoButton", "line_number": 114, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.AcceptRole", "line_number": 115, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 115, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.RejectRole", "line_number": 116, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 116, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.AcceptRole", "line_number": 117, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 117, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QCoreApplication.quit", "line_number": 120, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QCoreApplication", "line_number": 120, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 134, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 139, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 144, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 146, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 154, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 154, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 157, "usage_type": "call"}]} +{"seq_id": "553548841", "text": "#予測\nfrom collections import defaultdict\nimport math\n#def predict_all(model_file,input_file):\n #load w from model_file\n #model_fileは未作成：testプログラムにおいて読み込む\n\n\ndef predict_one(w,phi):\n score = 0\n for name,value in phi.items():\n if name in w:\n score += value * w[name]\n if score >= 0:\n return 1\n else:\n return -1\n\ndef create_features(x):\n phi = defaultdict(lambda :0)\n words = x.strip().split()\n for word in words:\n phi['UNI:' + word] += 1\n return phi\n\"\"\"\ndef update_weights(w,phi,y):\n c = 0.0001\n for name,value in w.items():\n if abs(value) < c:\n w[name] = 0\n else:\n w[name] -= sign(value) * c\n for name,value in phi.items():\n w[name] += value * y\n return w\n\"\"\"\ndef sign(x):\n if x >= 0:\n return 1\n else:\n return -1\n\nsigmoid = defaultdict(lambda :0)\n\ndef sigm(x,word):\n if x >= 0:\n sigmoid[word] += (math.exp(x)/(1+math.exp(x))**2)\n return sigmoid[word]\n else:\n sigmoid[word] -= (math.exp(x)/(1+math.exp(x))**2)\n return sigmoid[word]\n\n\nif __name__ == '__main__':\n w = defaultdict(lambda :0)\n l = 20 #iteration? : 試行数？\n margin = 20\n c = 0.0001\n for i in range(l):\n with open('../../data/titles-en-train.labeled','r') as t_f:\n for line in t_f:\n phi = defaultdict(lambda :0)\n y,x = line.strip().split('\\t') #y is int , x is words\n y = float(y)\n for word,value in create_features(x).items():\n phi[word] = value\n val = w[word] * phi[word] * y\n if val <= margin:\n if abs(w[word]) < c:\n w[word] = 0\n else:\n w[word] += sigm(w[word],word) * c\n# w[word] -= sign(w[word]) * c\n w[word] += phi[word] * y\n with open('model_file.txt','w') as m_f:\n# for line in m_f:\n for word,value in w.items():\n m_f.write('{}\\t{}\\n'.format(word,value))\n", "sub_path": "yohta/tutorial06/train_svm.py", "file_name": "train_svm.py", "file_ext": "py", "file_size_in_byte": 2171, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "collections.defaultdict", "line_number": 20, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 43, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 47, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 50, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 55, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "122496930", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Feb 23 11:00:36 2020\r\n\r\n@author: Ismail\r\n\"\"\"\r\n\r\nimport h5py\r\nimport cv2\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.patches as patches\r\nfrom PIL import Image\r\n\r\nfilename = '../data/coco_val2017_vg_detector_features_adaptive.h5'\r\nID = int(input(\"enter image id to visualize\"))\r\nimage = cv2.imread('im'+str(ID)+'.jpg')\r\n\r\nwith h5py.File(filename, 'r') as f:\r\n # List all groups\r\n print(\"Keys: %s\" % f.keys())\r\n a_group_key = list(f.keys())[0]\r\n\r\n # Get the data\r\n data = list(f[a_group_key])\r\n index = 0\r\n # for i in range(4500):\r\n # if (f['image_id'][i]==ID):\r\n # index = i\r\n # break\r\n print (f['image_id'][index])\r\n boxes = np.array(f['boxes'][index])\r\n boxes = boxes.reshape(boxes.size//4,4)\r\n feat = np.array(f['features'][index])\r\n feat = feat.reshape(feat.size//2048,2048)\r\n print(boxes.shape)\r\n print (f['height'][index],f['width'][index],np.amax(boxes,axis=0))\r\n # print(feat.shape, boxes.shape)\r\n raise Exception()\r\n # print(boxes)\r\n fig,ax = plt.subplots(1)\r\n ax.imshow(image)\r\n \r\n cmap = plt.get_cmap('gnuplot')\r\n colors = [cmap(i) for i in np.linspace(0, 1, boxes.shape[0])]\r\n \r\n for i,box in enumerate(boxes):\r\n \t#if f['features'][index][i]>=0.5:\r\n\t # Create a Rectangle patch\r\n\t rect = patches.Rectangle((box[0],box[1]),box[2]-box[0],box[3]-box[1],linewidth=1,edgecolor=colors[i],facecolor='none')\r\n\t # Add the patch to the Axes\r\n\t ax.add_patch(rect)\r\n\t plt.text(box[0],box[1],'weight: '+str(f['features'][index][i]),color='red')\r\nplt.savefig('features_im'+str(ID)+'_t.png')\t\r\nplt.show()\r\n ", "sub_path": "getImages/visFeatures.py", "file_name": "visFeatures.py", "file_ext": "py", "file_size_in_byte": 1724, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "cv2.imread", "line_number": 17, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}]} +{"seq_id": "228312483", "text": "from config import cfg\nfrom glob import glob\nfrom os.path import join, exists\nfrom os import makedirs\nimport pandas as pd\nimport urllib.request as request\n\n\ndef read_data():\n dfs = []\n files = glob(join(cfg.external_data_path, \"*.csv\"))\n for file in files:\n df = pd.read_csv(file)\n df.dropna(inplace=True)\n df.drop_duplicates(subset=\"image-src\", keep=False, inplace=True)\n df[\"height\"] = pd.to_numeric(df[\"height\"], downcast=\"float\")\n df[\"weight\"] = pd.to_numeric(df[\"weight\"], downcast=\"float\")\n df.reset_index(drop=True, inplace=True)\n data = df.drop(\n [column for column in df.columns if column not in cfg.useful_columns], 1)\n dfs.append(data)\n\n frame = pd.concat(dfs, axis=0, ignore_index=True)\n frame.drop_duplicates(subset=\"image-src\", keep=False, inplace=True)\n frame.reset_index(drop=True, inplace=True)\n frame.to_csv(join(cfg.intermediate_data_path,\n \"unclean_combined_annotation.csv\"), index=False)\n _ = frame.info()\n return frame\n\n\ndef check_url(dataframe):\n dataframe['image-src'] = cfg.web+dataframe['image-src']\n index_of_bad_urls = []\n for index, url in enumerate(dataframe['image-src']):\n try:\n request.urlopen(url)\n except:\n print(f\"{url} is not good!\")\n index_of_bad_urls.append(index)\n # print(index_of_bad_urls)\n dataframe.drop(index_of_bad_urls, inplace=True)\n dataframe.reset_index(drop=True, inplace=True)\n dataframe.to_csv(join(cfg.intermediate_data_path,\n \"combined_annotation.csv\"), index=False)\n _ = dataframe.info()\n return dataframe\n\n\ndef crawl_data_from_frame(dataframe=None):\n if not dataframe:\n filename = join(cfg.intermediate_data_path, \"combined_annotation.csv\")\n dataframe = pd.read_csv(filename)\n if not exists(cfg.raw_test_data_path):\n makedirs(cfg.raw_test_data_path)\n bmi = dataframe['weight'] / \\\n ((dataframe['height']/100)*(dataframe['height']/100))\n dataframe['bmi'] = bmi\n height = dataframe['height']/100\n dataframe['height'] = height\n for index, url in enumerate(dataframe['image-src']):\n images_name = str(index).zfill(4) + \".jpg\"\n raw_path_for_file = join(cfg.raw_test_data_path, images_name)\n cropped_path_for_file = join(cfg.cropped_data_path, images_name)\n request.urlretrieve(url, raw_path_for_file)\n dataframe.iloc[index, 2] = cropped_path_for_file\n\n cols = ['image-src', 'height', 'weight', 'bmi']\n dataframe = dataframe[cols]\n dataframe.rename(columns={'image-src': 'Path',\n 'bmi': \"BMI\"}, inplace=True)\n dataframe.to_csv(join(cfg.test_data_path,\n \"annotation.csv\"), index=False)\n _ = dataframe.info()\n\n\ndef crawl_data():\n dataframe = read_data()\n clean_dataframe = check_url(dataframe=dataframe)\n crawl_data_from_frame(dataframe=clean_dataframe)\n\n\nif __name__ == \"__main__\":\n crawl_data()\n", "sub_path": "src/data/scrape.py", "file_name": "scrape.py", "file_ext": "py", "file_size_in_byte": 3033, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "glob.glob", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "config.cfg.external_data_path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 11, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 17, "usage_type": "call"}, {"api_name": "config.cfg.useful_columns", "line_number": 20, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 20, "usage_type": "name"}, {"api_name": "pandas.concat", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "config.cfg.intermediate_data_path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 26, "usage_type": "name"}, {"api_name": "config.cfg.web", "line_number": 33, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 33, "usage_type": "name"}, {"api_name": "urllib.request.urlopen", "line_number": 37, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 37, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "config.cfg.intermediate_data_path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 44, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "config.cfg.intermediate_data_path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 52, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 54, "usage_type": "call"}, {"api_name": "config.cfg.raw_test_data_path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 54, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 55, "usage_type": "call"}, {"api_name": "config.cfg.raw_test_data_path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 55, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "config.cfg.raw_test_data_path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 63, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "config.cfg.cropped_data_path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 64, "usage_type": "name"}, {"api_name": "urllib.request.urlretrieve", "line_number": 65, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 65, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "config.cfg.test_data_path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 72, "usage_type": "name"}]} +{"seq_id": "74680434", "text": "import pygame, pygame.gfxdraw, time\n\nimport things\n\nSHOW_TIME = False\n\nclass PygView(object):\n \n def __init__(self, layout, config):\n \"\"\"Initialize pygame, window, background, font,...\n default arguments \n \"\"\"\n self.radius = config['radius']\n self.aspect = config['aspect']\n self.w = config['width']\n self.h = int(self.w / self.aspect)\n self.fps = float(config['fps'])\n self.RESTITUTION = float(config['restitution'])\n\n pygame.init()\n pygame.display.set_caption(\"Press ESC to quit\")\n self.screen = pygame.display.set_mode((self.w, self.h), pygame.DOUBLEBUF)\n self.background = pygame.Surface(self.screen.get_size()).convert() \n self.background.fill((255, 255, 255)) # fill background white\n \n self.clock = pygame.time.Clock()\n self.playtime = 0\n self.time_font = pygame.font.SysFont('monospace', 17)\n self.layout = layout\n\n self.make_objects()\n\n def make_objects(self):\n self.paddle = None\n self.balls = [things.Ball( (200, 150), (-2.1, 1.74), self.radius, self)]\n self.brickMap = None\n\n def fix_bg(self, contour = None, maxcost = None):\n \"\"\"painting on the surface\"\"\"\n pygame.display.flip()\n self.screen.blit(self.background, (0, 0)) \n\n def stash_balls(self):\n for ball in self.balls:\n ball.update()\n pygame.gfxdraw.filled_circle(self.background, int(ball.pos[0]), int(ball.pos[1]), ball.radius, (145, 120, 170))\n\n def run(self):\n \"\"\"The mainloop\n \"\"\"\n running = True\n while running:\n # handle any events\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False \n elif event.type == pygame.KEYDOWN:\n if event.key == pygame.K_ESCAPE:\n running = False\n # Clean the background\n self.background.fill((255, 255, 255)) # fill background white\n if SHOW_TIME:\n self.playtime += self.clock.tick(self.fps) / 1000.0\n self.draw_text(self.time_font, \"FPS: {:6.3} {:6.3} sec.\".format(self.clock.get_fps(), self.playtime), (15, self.h - 15))\n # get all game-state from cpp-engine\n \n # update all objects' position, color, etc.\n self.stash_balls()\n\n # finally, update screen\n pygame.display.flip()\n self.screen.blit(self.background, (0, 0)) \n pygame.quit()\n\n def draw_text(self, font, txt, pos, color=(0,0,0)):\n t = font.render(txt, True, (0, 0, 0))\n self.screen.blit(t, pos)\n\n def transform(self, coords):\n # coords must lie in [0.0, 1.0] ~ x, y\n return (int(coords[0] * self.w), int(coords[1] * self.h))\n\n# call with width of window and fps\nconfig = {'width' : 400,\n 'height' : 400 * 9 / 16,\n 'friction' : 0,\n 'restitution' : 1,\n 'radius' : 8,\n 'aspect' : 16.0/9,\n 'fps' : 30}\nlayout = \"\"\nmyWin = PygView(layout, config)\nmyWin.fix_bg()\n#time.sleep(2)\n\nmyWin.run()\n#myWin.run()", "sub_path": "cpp/smashit/sim.py", "file_name": "sim.py", "file_ext": "py", "file_size_in_byte": 3238, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pygame.init", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.display.set_caption", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.DOUBLEBUF", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 28, "usage_type": "attribute"}, {"api_name": "things.Ball", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.display.flip", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.gfxdraw.filled_circle", "line_number": 46, "usage_type": "call"}, {"api_name": "pygame.gfxdraw", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pygame.K_ESCAPE", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 71, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "570522738", "text": "\"\"\"Support for ReCollect Waste sensors.\"\"\"\nfrom __future__ import annotations\n\nfrom aiorecollect.client import PickupType\n\nfrom homeassistant.components.sensor import (\n SensorDeviceClass,\n SensorEntity,\n SensorEntityDescription,\n)\nfrom homeassistant.config_entries import ConfigEntry\nfrom homeassistant.const import CONF_FRIENDLY_NAME\nfrom homeassistant.core import HomeAssistant, callback\nfrom homeassistant.helpers.entity_platform import AddEntitiesCallback\nfrom homeassistant.helpers.update_coordinator import (\n CoordinatorEntity,\n DataUpdateCoordinator,\n)\n\nfrom .const import CONF_PLACE_ID, CONF_SERVICE_ID, DOMAIN, LOGGER\n\nATTR_PICKUP_TYPES = \"pickup_types\"\nATTR_AREA_NAME = \"area_name\"\n\nSENSOR_TYPE_CURRENT_PICKUP = \"current_pickup\"\nSENSOR_TYPE_NEXT_PICKUP = \"next_pickup\"\n\nSENSOR_DESCRIPTIONS = (\n SensorEntityDescription(\n key=SENSOR_TYPE_CURRENT_PICKUP,\n name=\"Current pickup\",\n ),\n SensorEntityDescription(\n key=SENSOR_TYPE_NEXT_PICKUP,\n name=\"Next pickup\",\n ),\n)\n\n\n@callback\ndef async_get_pickup_type_names(\n entry: ConfigEntry, pickup_types: list[PickupType]\n) -> list[str]:\n \"\"\"Return proper pickup type names from their associated objects.\"\"\"\n return [\n t.friendly_name\n if entry.options.get(CONF_FRIENDLY_NAME) and t.friendly_name\n else t.name\n for t in pickup_types\n ]\n\n\nasync def async_setup_entry(\n hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback\n) -> None:\n \"\"\"Set up ReCollect Waste sensors based on a config entry.\"\"\"\n coordinator = hass.data[DOMAIN][entry.entry_id]\n\n async_add_entities(\n [\n ReCollectWasteSensor(coordinator, entry, description)\n for description in SENSOR_DESCRIPTIONS\n ]\n )\n\n\nclass ReCollectWasteSensor(CoordinatorEntity, SensorEntity):\n \"\"\"ReCollect Waste Sensor.\"\"\"\n\n _attr_device_class = SensorDeviceClass.DATE\n _attr_has_entity_name = True\n\n def __init__(\n self,\n coordinator: DataUpdateCoordinator,\n entry: ConfigEntry,\n description: SensorEntityDescription,\n ) -> None:\n \"\"\"Initialize the sensor.\"\"\"\n super().__init__(coordinator)\n\n self._attr_extra_state_attributes = {}\n self._attr_unique_id = f\"{entry.data[CONF_PLACE_ID]}_{entry.data[CONF_SERVICE_ID]}_{description.key}\"\n self._entry = entry\n self.entity_description = description\n\n @callback\n def _handle_coordinator_update(self) -> None:\n \"\"\"Respond to a DataUpdateCoordinator update.\"\"\"\n self.update_from_latest_data()\n self.async_write_ha_state()\n\n async def async_added_to_hass(self) -> None:\n \"\"\"Handle entity which will be added.\"\"\"\n await super().async_added_to_hass()\n self.update_from_latest_data()\n\n @callback\n def update_from_latest_data(self) -> None:\n \"\"\"Update the state.\"\"\"\n if self.entity_description.key == SENSOR_TYPE_CURRENT_PICKUP:\n try:\n event = self.coordinator.data[0]\n except IndexError:\n LOGGER.error(\"No current pickup found\")\n return\n else:\n try:\n event = self.coordinator.data[1]\n except IndexError:\n LOGGER.info(\"No next pickup found\")\n return\n\n self._attr_extra_state_attributes.update(\n {\n ATTR_PICKUP_TYPES: async_get_pickup_type_names(\n self._entry, event.pickup_types\n ),\n ATTR_AREA_NAME: event.area_name,\n }\n )\n self._attr_native_value = event.date\n", "sub_path": "homeassistant/components/recollect_waste/sensor.py", "file_name": "sensor.py", "file_ext": "py", "file_size_in_byte": 3670, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "homeassistant.components.sensor.SensorEntityDescription", "line_number": 29, "usage_type": "call"}, {"api_name": "homeassistant.components.sensor.SensorEntityDescription", "line_number": 33, "usage_type": "call"}, {"api_name": "homeassistant.config_entries.ConfigEntry", "line_number": 42, "usage_type": "name"}, {"api_name": "aiorecollect.client.PickupType", "line_number": 42, "usage_type": "name"}, {"api_name": "homeassistant.const.CONF_FRIENDLY_NAME", "line_number": 47, "usage_type": "argument"}, {"api_name": "homeassistant.core.callback", "line_number": 40, "usage_type": "name"}, {"api_name": "homeassistant.core.HomeAssistant", "line_number": 54, "usage_type": "name"}, {"api_name": "homeassistant.config_entries.ConfigEntry", "line_number": 54, "usage_type": "name"}, {"api_name": "homeassistant.helpers.entity_platform.AddEntitiesCallback", "line_number": 54, "usage_type": "name"}, {"api_name": "const.DOMAIN", "line_number": 57, "usage_type": "name"}, {"api_name": "homeassistant.helpers.update_coordinator.CoordinatorEntity", "line_number": 67, "usage_type": "name"}, {"api_name": "homeassistant.components.sensor.SensorEntity", "line_number": 67, "usage_type": "name"}, {"api_name": "homeassistant.components.sensor.SensorDeviceClass.DATE", "line_number": 70, "usage_type": "attribute"}, {"api_name": "homeassistant.components.sensor.SensorDeviceClass", "line_number": 70, "usage_type": "name"}, {"api_name": "homeassistant.helpers.update_coordinator.DataUpdateCoordinator", "line_number": 75, "usage_type": "name"}, {"api_name": "homeassistant.config_entries.ConfigEntry", "line_number": 76, "usage_type": "name"}, {"api_name": "homeassistant.components.sensor.SensorEntityDescription", "line_number": 77, "usage_type": "name"}, {"api_name": "const.CONF_PLACE_ID", "line_number": 83, "usage_type": "name"}, {"api_name": "const.CONF_SERVICE_ID", "line_number": 83, "usage_type": "name"}, {"api_name": "homeassistant.core.callback", "line_number": 87, "usage_type": "name"}, {"api_name": "const.LOGGER.error", "line_number": 105, "usage_type": "call"}, {"api_name": "const.LOGGER", "line_number": 105, "usage_type": "name"}, {"api_name": "const.LOGGER.info", "line_number": 111, "usage_type": "call"}, {"api_name": "const.LOGGER", "line_number": 111, "usage_type": "name"}, {"api_name": "homeassistant.core.callback", "line_number": 98, "usage_type": "name"}]} +{"seq_id": "344237348", "text": "'''\nhttps://qiita.com/seradaihyo/items/006c5f1c86314a3b7a38\npythonでwikipediaをスクレイピングする\n'''\n#\n# import requests,bs4\n#\n# # urlの取得\n#\n#\n# url = 'https://ja.wikipedia.org/wiki/Python'\n# res = requests.get(url)\n#\n# # htmlの取得\n# soup = bs4.BeautifulSoup(res.text, 'html.parser')\n#\n# # 選択した要素を取得\n# index = soup.select('#toc')\n#\n# # 出力する\n# for i in index:\n# print(i.getText())\n\n\n'''\n先にコメントアウトでコードの処理を考えていく方法\n'''\n\n'''\nスクレイピングに必要な流れ\nhtmlのソースコードを取得\nクラス、文字列などから必要なものを取得する\nfor文で回して出力する(必要に応じて)\n'''\n\n# 必要なライブラリの取得\n# import requests\n# import pandas as pd\n# from bs4 import BeautifulSoup\n\n\n# htmlのソースを取得する\n# url = 'https://ja.wikipedia.org/wiki/%E3%83%A1%E3%82%A4%E3%83%B3%E3%83%9A%E3%83%BC%E3%82%B8'\n# html = requests.get(url)\n# soup = BeautifulSoup(html.content, 'html.parser')\n#\n# # テスト\n# # print('htmlのソース')\n# # print('soupのみ')\n# print(soup)\n# print('soup.prettify')\n# print(soup.prettify)\n\n\n# 文字を取り出す 文字以外の要素を削除\n# for script in soup(['script', 'style']):\n# script.decompose()\n\n\n# テキストのみを取得　タグは全部取る\n# text = soup.get_text()\n\n\n# テキストを改行ごとにリストに入れて、リスト内の要素の前後の空白を削除\n# line=[]\n# for line in text.splitlines():\n# lines.append(line.strip)\n#\n#\n# text = '\\n'.join(line for line in lines if line)\n\n\n\n'''\namazonprimevideoでその映画が無料で見られるかを確認するプログラム\n'''\n\n'''\nwikipediaの「今日は何の日」から一日一回取得する\n'''\n\n'''\n※練習\n日経ビジネスから\n新着記事の見出しとURLを取得する\n'''\n\n# import requests\n# from bs4 import BeautifulSoup\n# import re\n#\n# urlName = 'https://business.nikkei.com/'\n# url = requests.get(urlName)\n# soup = BeautifulSoup(url.content, 'html.parser')\n#\n# elems = soup.find_all('span')\n# for elem in elems:\n# try:\n# string = elem.get('class').pop(0) #spanから'class'を取り出す\n# if string in 'category': #in→その文字列が含まれているかを判定してくれる\n# print(elem.string)\n# title = elem.find_next_sibling('h3') #兄弟要素を取得する\n# print(title.text.replace('\\n',''))\n# r = elem.find_previous('a')\n# print(urlName + r.get('href'), '\\n')\n# except:\n# print('エラーです')\n\n\n\n\n# 取得したいクラス名 mainpage-content-text\n\n\n# 日経ビジネス電子版から新着記事の見出しとURLを取得する。https://business.nikkei.com/\n\n# import requests\n# from bs4 import BeautifulSoup\n# import re\n#\n# #urlとhtmlのコンテンツを取得する\n# urlName = 'https://business.nikkei.com/'\n# url = requests.get(urlName)\n# soup = BeautifulSoup(url.content, 'html.parser')\n#\n#\n# # beautifulsoupでhtmlの解析をする\n#\n# elems = soup.find_all('span') #span要素をすべてelemsに格納\n#\n# for elem in elems:\n# try:\n# string = elem.get(\"class\").pop(0) #elemからclassを取り出す\n# if string in 'category': #文字列の中に'カテゴリ'があった場合\n# print(elem.string) #テキスト名を抜き出す\n# title = elem.find_next_sibling('h3') #find_next_sibling()で同じ深さのh3を検索する\n# print(title.text.replace('\\n', '')) #タイトルをプリントする\n# r = elem.find_previous('a') #find_previous()でaタグを探す。\n# print(urlName + r.get('href'), '\\n')\n# except:\n# pass\n\n\n\nimport requests\nfrom bs4 import BeautifulSoup\nimport re\nimport datetime\n\ntoday = datetime.date.today() #今日の日付を出力する\n\nurlName = 'https://ja.wikipedia.org/wiki/%E3%83%A1%E3%82%A4%E3%83%B3%E3%83%9A%E3%83%BC%E3%82%B8'\n\nurl = requests.get(urlName)\nsoup = BeautifulSoup(url.content, 'html.parser')\n\nelems = soup.select('.mainpage-onthisday',)\n\n\nprint(today)\nprint('本日のできごと')\n\n\nfor elem in elems:\n result = []\n print(elem.text)\n result.append(elem.text)\n # /nのところで改行をしてリスト形式にする\n # printをするまえに上記を行えば更にきれいになる？\n\n# 結果を出力したい\n# with open('result.html', 'a', encoding='utf-8,') as f:\n# print(result, file=f)\n", "sub_path": "wikipedia.py", "file_name": "wikipedia.py", "file_ext": "py", "file_size_in_byte": 4503, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "datetime.date.today", "line_number": 150, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 150, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 154, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 155, "usage_type": "call"}]} +{"seq_id": "434322796", "text": "#!python\n# -*- coding: utf-8 -*-#\n###########################################################################\n# Author : Bhishan Poudel; Physics Graduate Student, Ohio University\n# Date : Sep 21, 2017\n# Last update :\n###########################################################################\n\"\"\"\n:Topic: Ridge Regression With Grad descent\n\n:Ref: http://hyperanalytic.net/ridge-regression\n\n:Algorithm::\n\n grad_ols = (h-t).T @ X / N\n grad_ridge = (grad_ols + shrinkage * w ) # shrinkage /N for some cases.\n w = w - l_rate * grad_ridge\n\n\"\"\"\n# Imports\nimport numpy as np\nfrom sklearn import datasets\nfrom scipy import stats\n\ndef ridge_batch_grad_desc(X, t, shrinkage, iters, l_rate):\n \"\"\"Calculate weight vector using Ridge Regression L2 norm.\n\n Args:\n X(matrix): Design matrix with bias term.\n\n t(column vector): Target column vector (shape = 1, samples)\n\n shrikage(float): L2 regularization shrikage hyper parameter.\n\n iters(int): Number of iterations.\n\n l_rate(float): Learning rate for gradient descent algorithm.\n\n \"\"\"\n X=np.array(X)\n t = np.array(t)\n t =t.reshape(len(t),1)\n N = len(t)\n w = np.ones(X.shape[1])\n w = w.reshape(1,len(w))\n\n print(\"x.shape = {}\".format(X.shape))\n print(\"t.shape = {}\".format(t.shape))\n print(\"w.shape = {}\".format(w.shape))\n print(\"shrinkage = {}\".format(shrinkage))\n print(\"iters = {}\".format(iters))\n print(\"l_rate = {}\".format(l_rate))\n for i in range(0, iters):\n h = X @ w.T\n MSE = np.square(h - t).mean()\n print (\"iteration:\", i, \"MSE:\", MSE)\n grad_ols = (h-t).T @ X / N\n grad_ridge = (grad_ols + shrinkage * w ) # shrinkage /N for some cases.\n w = w - l_rate * grad_ridge\n\n # make w row vector\n w = w.reshape(1, X.shape[1]) # shape = 1, feature + 1\n return w\n\ndef main():\n \"\"\"Run main function.\"\"\"\n diabetes = datasets.load_diabetes()\n\n X = diabetes.data\n y = diabetes.target\n intercept = np.ones(len(X))\n X = np.append(intercept, X)\n X = np.reshape(X,(442,11))\n\n Z = stats.zscore(X, axis=0)\n Y = stats.zscore(y)\n\n w = ridge_batch_grad_desc(Z,Y,.1,5000,.1)\n print (w)\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "Machine_Learning_Univ_Course_(2017Fall)/Extra_hw/Extra_hw01/ridge_regression/ridge_BGD.py", "file_name": "ridge_BGD.py", "file_ext": "py", "file_size_in_byte": 2222, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.datasets.load_diabetes", "line_number": 67, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 67, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 73, "usage_type": "call"}, {"api_name": "scipy.stats.zscore", "line_number": 75, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 75, "usage_type": "name"}, {"api_name": "scipy.stats.zscore", "line_number": 76, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 76, "usage_type": "name"}]} +{"seq_id": "5738429", "text": "import asyncio\nimport unittest\n\nfrom unittest import mock\nfrom tests.aio_test_base import asynctest\n\nfrom aiohttp_cors.preflight_handler import _PreflightHandler\n\n\nclass TestPreflightHandler(unittest.TestCase):\n \"\"\"Unit tests for PreflightHandler\"\"\"\n\n def setUp(self):\n self.loop = asyncio.new_event_loop()\n\n def tearDown(self):\n self.loop.close()\n\n @asynctest\n @asyncio.coroutine\n def test_raises_when_handler_not_extend(self):\n request = mock.Mock()\n handler = _PreflightHandler()\n with self.assertRaises(NotImplementedError):\n yield from handler._get_config(request, 'origin', 'GET')\n", "sub_path": "tests/unit/test_preflight_handler.py", "file_name": "test_preflight_handler.py", "file_ext": "py", "file_size_in_byte": 651, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "unittest.TestCase", "line_number": 10, "usage_type": "attribute"}, {"api_name": "asyncio.new_event_loop", "line_number": 14, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 22, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 22, "usage_type": "name"}, {"api_name": "aiohttp_cors.preflight_handler._PreflightHandler", "line_number": 23, "usage_type": "call"}, {"api_name": "tests.aio_test_base.asynctest", "line_number": 19, "usage_type": "name"}, {"api_name": "asyncio.coroutine", "line_number": 20, "usage_type": "attribute"}]} +{"seq_id": "132966502", "text": "#!/usr/bin/env python\n\nimport rospy\nfrom sensor_msgs.msg import Image # Image is the message type\nfrom cv_bridge import CvBridge # Package to convert between ROS and OpenCV Images\n\nimport sys\nimport argparse\nimport numpy as np\nimport cv2\nimport os, glob, time\n\nfrom detect_arrow_webcam import *\n\nROS_TOPIC = \"/realsense/color/image_raw\" #'/mrt/camera/color/image_raw'\n\n\nclass ImageSubscriber:\n\n \"\"\"Subscribes to ROS Topic and calls image_callback\"\"\"\n\n def __init__(self, image_topic):\n \"\"\"\n\n :image_topic: string\n\n \"\"\"\n rospy.init_node(\"image_sub\", anonymous=True)\n self.br = CvBridge()\n self.sub = rospy.Subscriber(image_topic, Image, self.image_callback)\n self.font = cv2.FONT_HERSHEY_SIMPLEX\n # Blue color in BGR\n self.color = (255, 255, 0)\n self.org = (50, 50)\n self.fontScale = 1\n self.thickness = 2\n self.vid_file = cv2.VideoWriter(\n \"arrow.mp4\", cv2.VideoWriter_fourcc(*\"MP4V\"), 10, (640, 480)\n )\n rospy.spin()\n print(\"all done!\")\n self.vid_file.release()\n cv2.destroyAllWindows()\n\n def image_callback(self, data):\n \"\"\"Converts ROS Image, passes to arrow_detect and displays detected\n\n :data: Image\n :returns: None\n\n \"\"\"\n cv_img = self.br.imgmsg_to_cv2(data)\n found, theta, orient, direction, output = arrow_detect(cv_img)\n print(\"shape: \", output.shape)\n\n if direction == 1:\n direction = \"Right\"\n elif direction is None:\n direction = \"not found\"\n else:\n direction = \"Left\"\n\n output = cv2.putText(\n output,\n direction,\n self.org,\n self.font,\n self.fontScale,\n self.color,\n self.thickness,\n cv2.LINE_AA,\n )\n\n self.vid_file.write(output)\n cv2.imshow(\"Arrow\", output)\n cv2.waitKey(20)\n # if cv2.waitKey(1) & 0xFF == ord('q'):\n # break\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"-t\", \"--topic\", help=\"ROS Topic to subscribe to\", default=ROS_TOPIC\n )\n args = parser.parse_args()\n\n subscriber = ImageSubscriber(args.topic)\n # cv2.destroyAllWindows()\n", "sub_path": "src/motion_plan/src/detect_arrow_ros.py", "file_name": "detect_arrow_ros.py", "file_ext": "py", "file_size_in_byte": 2315, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "rospy.init_node", "line_number": 28, "usage_type": "call"}, {"api_name": "cv_bridge.CvBridge", "line_number": 29, "usage_type": "call"}, {"api_name": "rospy.Subscriber", "line_number": 30, "usage_type": "call"}, {"api_name": "sensor_msgs.msg.Image", "line_number": 30, "usage_type": "argument"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 31, "usage_type": "attribute"}, {"api_name": "cv2.VideoWriter", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 38, "usage_type": "call"}, {"api_name": "rospy.spin", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 71, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 75, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 76, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "596849207", "text": "\n\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nfrom params import *\nfrom .Model import *\n\n\n\n\n\n\n# Train the model\n_, nb_img_tain, nb_img_test, testingPartition, trainedclassifier, _, trainConfusionMat = model.train(10)\n\n# Test the model\ntestErrorMat = model.test(testingPartition, trainedclassifier)\n\n\n\ndef plot_trainConfusionMatrix(trainConfusionMat, nb_img_tain):\n \n trainCM = trainConfusionMat/nb_img_tain\n\n fig, ax = plt.subplots(1, figsize=(10,10))\n sns.heatmap(trainCM, annot=True)\n ax.set_xlabel('model predictions', fontsize=10)\n ax.set_ylabel('actual', fontsize=10)\n plt.title(\"Training data confusion matrix\", fontsize=15)\n plt.show()\n\n\n\n\n\ndef plot_testErrorMatrix(testErrorMat, nb_img_test):\n \n testEM = testErrorMat/nb_img_test\n\n fig, ax = plt.subplots(1, figsize=(10,10))\n sns.heatmap(testEM, annot=True)\n ax.set_xlabel('model predictions', fontsize=10)\n ax.set_ylabel('actual', fontsize=10)\n plt.title(\"Test data error matrix\", fontsize=15)\n plt.show()", "sub_path": "src/confusionMatrix.py", "file_name": "confusionMatrix.py", "file_ext": "py", "file_size_in_byte": 1023, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "matplotlib.pyplot.subplots", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}]} +{"seq_id": "108024313", "text": "#! /usr/bin/python\n\n# George-Cristian Muraru, 342C4\n\n### Query privacy \n# Non-adaptive approach - injects files that could break all futures queries\n# The token t is deterministically computed and sent to the server\n# The server sends back the file identifiers of that files that contain that word\n# The server can tell when quaries repeat -> learn the query pattern and the\n# file-access pattern\n\n### The goal of the server is to determine the keywords corresponding to the\n#tokens -> violates query privacy -> and then file privacy\n\nfrom Crypto.Cipher import DES\nimport time\nimport matplotlib.pyplot as plt\n\nfrom math import log\nimport time\nimport random\nimport string\n\nkey = \"bsearcha\"\naes = DES.new(key)\n\n\nserver_files = []\n\ndef random_word(length):\n\tword = ''.join([random.choice(string.lowercase + string.uppercase) for i in range(length)])\n\tword = word.rjust(16)\n\treturn word\n\n\ndef enc_file(f_in):\n\treturn set([aes.encrypt(mess) for mess in f_in])\n\ndef enc(m):\n\treturn aes.encrypt(m)\n\ndef dec(m):\n\treturn aes.decrypt(m)\n\n\ndef populate_vocabulary(voc_size):\n\tK = set()\n\n\twhile len(K) < voc_size:\n\t\tK.add(random_word(random.randint(4,16)))\n\n\tprint (\"Added voc with size {0}\".format(voc_size))\n\tenc_K = [enc(word) for word in K]\n\treturn (list(K), enc_K)\n\ndef send_word_to_client(word):\n\treturn enc(word)\n\ndef byte2bin(bval, length):\n\treturn bin(bval)[2:].zfill(length)\n\ndef inject_files(K):\n\tF = [set() for _ in range(int(log(len(K), 2)))]\n\n\tfor i in range(len(K)):\n\t\tfor i_file, bit in enumerate(byte2bin(i, int(log(len(K), 2)))):\n\t\t\tif bit == '1':\n\t\t\t\tF[i_file].add(K[i])\n\n\tF_enc = [enc_file(f_in) for f_in in F]\n\n\treturn (F, F_enc)\n\ndef recover(F_enc, K, query_word):\n\tindex_word = 0\n\n\tfor i in range(len(F_enc)):\n\t\tif query_word in F_enc[i]:\n\t\t\tindex_word += pow(2, len(F_enc) - i - 1)\n\n\treturn (K[index_word], index_word)\n\n\nif __name__ == \"__main__\":\n\ttimes = []\n\tvoc = [64, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072]\n\n\t# Test\n\tfor vocabulary_size in voc:\n\t\tK, K_enc = populate_vocabulary(vocabulary_size)\n\t\tF, F_enc = inject_files(K)\n\n\t\ttotal = 0\n\t\tfor i in range(10000):\n\t\t\tindex = random.choice(range(len(K)))\n\t\t\tquery_word = K_enc[index]\n\n\t\t\tstart = time.time()\n\t\t\tword, index_word = recover(F_enc, K, query_word)\n\t\t\tend = time.time()\n\t\t\ttotal += (end - start)\n\n\t\t\tassert index_word == index\n\t\t\tassert word == K[index]\n\n\t\ttimes.append(total / 10000.0)\n\t\n\tprint (voc)\n\tprint (times)\n\tplt.ylabel(\"Average time to find the word\")\n\tplt.xlabel(\"Number of words in the vocabulary\")\n\tplt.plot(voc, times, \"-o\")\n\tplt.show()\n\n", "sub_path": "binary_attack.py", "file_name": "binary_attack.py", "file_ext": "py", "file_size_in_byte": 2545, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "Crypto.Cipher.DES.new", "line_number": 25, "usage_type": "call"}, {"api_name": "Crypto.Cipher.DES", "line_number": 25, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 31, "usage_type": "call"}, {"api_name": "string.lowercase", "line_number": 31, "usage_type": "attribute"}, {"api_name": "string.uppercase", "line_number": 31, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 50, "usage_type": "call"}, {"api_name": "math.log", "line_number": 63, "usage_type": "call"}, {"api_name": "math.log", "line_number": 66, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 95, "usage_type": "call"}, {"api_name": "time.time", "line_number": 98, "usage_type": "call"}, {"api_name": "time.time", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}]} +{"seq_id": "156190543", "text": "################################################################\n# GPT2 Language Model\n################################################################\nimport sys\nimport os\n\nroot = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))\nif root not in sys.path:\n sys.path.append(root)\n\n\nimport pandas as pd\nimport numpy as np\nimport torch\nfrom pytorch_transformers import BertTokenizer, BertModel, BertForMaskedLM, WordpieceTokenizer, GPT2Tokenizer, GPT2Model\nfrom . import utils\nfrom .tokenizer import tokenize \n\n\nparameters = {'small':{'LAYER_COUNT':12, 'FEATURE_COUNT':768},\n 'medium':{'LAYER_COUNT':24, 'FEATURE_COUNT':1024}\n }\n\n\n\nclass GPT2(object):\n \"\"\"Container module for GPT2.\"\"\"\n\n def __init__(self, gpt2_model, language, name, loi, cuda=False):\n super(GPT2, self).__init__()\n # Load pre-trained model tokenizer (vocabulary)\n # Crucially, do not do basic tokenization; PTB is tokenized. Just do wordpiece tokenization.\n if gpt2_model not in ['small', 'medium']:\n raise ValueError(\"GPT2 model must be small or medium\")\n self.model = GPT2Model.from_pretrained('gpt2{}'.format('' if gpt2_model=='small' else '-medium'), output_hidden_states=True)\n self.tokenizer = GPT2Tokenizer.from_pretrained('gpt2{}'.format('' if gpt2_model=='small' else '-medium'))\n\n self.language = language\n self.LAYER_COUNT = parameters[gpt2_model]['LAYER_COUNT']\n self.FEATURE_COUNT = parameters[gpt2_model]['FEATURE_COUNT']\n self.name = name\n self.loi = np.array(loi) if loi else np.arange(parameters[gpt2_model]['LAYER_COUNT']) # loi: layers of interest\n self.cuda = cuda\n\n def __name__(self):\n return self.name\n\n\n def generate(self, path, language, textgrid):\n \"\"\" Input text should have one sentence per line, where each word and every \n symbol is separated from the following by a space. No token should be included,\n as they are automatically integrated during tokenization.\n \"\"\"\n activations = []\n self.model.eval()\n iterator = tokenize(path, language, path_like=True, train=False)\n if self.cuda:\n self.model.to('cuda')\n for line in iterator:\n line = line.strip() # Remove trailing characters\n\n tokenized_text = self.tokenizer.tokenize(line)\n indexed_tokens = self.tokenizer.convert_tokens_to_ids(tokenized_text)\n mapping = utils.match_tokenized_to_untokenized(tokenized_text, line)\n\n # Convert inputs to PyTorch tensors\n tokens_tensor = torch.tensor([indexed_tokens]).to('cuda') if self.cuda else torch.tensor([indexed_tokens])\n\n with torch.no_grad():\n encoded_layers = self.model(tokens_tensor) # last_hidden_state, pooled_last_hidden_states, all_hidden_states\n # filtration\n if self.cuda:\n encoded_layers = encoded_layers.to('cpu')\n encoded_layers = np.vstack(encoded_layers[2][1:]) # retrieve all the hidden states (dimension = layer_count * len(tokenized_text) * feature_count)\n encoded_layers = encoded_layers[self.loi, :, :]\n activations += utils.extract_activations_from_tokenized(encoded_layers, mapping)\n \n result = pd.DataFrame(np.vstack(activations), columns=['layer-{}-{}'.format(layer, index) for layer in self.loi for index in range(self.FEATURE_COUNT)])\n return result\n\n", "sub_path": "models/english/GPT2/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 3543, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pytorch_transformers.GPT2Model.from_pretrained", "line_number": 35, "usage_type": "call"}, {"api_name": "pytorch_transformers.GPT2Model", "line_number": 35, "usage_type": "name"}, {"api_name": "pytorch_transformers.GPT2Tokenizer.from_pretrained", "line_number": 36, "usage_type": "call"}, {"api_name": "pytorch_transformers.GPT2Tokenizer", "line_number": 36, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 42, "usage_type": "call"}, {"api_name": "tokenizer.tokenize", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 74, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "499431966", "text": "import json\nimport os\nimport socket\n\n\nclass Consumer:\n def __init__(self, port, download_dir, address='localhost', multicast_group_addr='224.3.29.71',\n multicast_group_port=10000,\n timeout=5):\n self.port = port\n self.address = address\n self.multicast_group_addr = multicast_group_addr\n self.multicast_group_port = multicast_group_port\n self.timeout = timeout\n self.download_dir = download_dir.rstrip(\" /\")\n\n def start(self):\n print('Staring a consumer on (%s, %d)' % ('localhost', self.port))\n with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as receiverSock:\n receiverSock.bind((self.address, self.port))\n receiverSock.listen(1)\n receiverSock.settimeout(self.timeout)\n\n with socket.socket(socket.AF_INET, socket.SOCK_DGRAM, socket.IPPROTO_UDP) as sock:\n sock.setsockopt(socket.IPPROTO_IP, socket.IP_MULTICAST_TTL, 2)\n\n while True:\n fileName = input('Enter the filename to download: ')\n if not fileName:\n print(\"File name must not be empty\")\n continue\n\n print('Asking the producers for a file: %s' % fileName)\n\n request = json.dumps({'fileName': fileName, 'address': self.address, 'port': str(self.port)})\n requestBytes = bytes(request, 'UTF-8')\n sock.sendto(requestBytes, (self.multicast_group_addr, self.multicast_group_port))\n\n try:\n conn, addr = receiverSock.accept()\n except socket.timeout:\n print('No file after %d seconds' % self.timeout)\n continue\n except OSError as why:\n print('Unable to accept a connection: ' + str(why))\n continue\n\n with conn:\n print('Producer from %s seems to have a file %s' % (addr, fileName))\n\n try:\n self.__save_content_to_file(conn, fileName)\n except OSError as why:\n print('Unable to save the file %s: %s' % (fileName, str(why)))\n\n def __save_content_to_file(self, sock, file_name):\n tmp_download_dir = self.download_dir + '/' + 'tempDownloads'\n os.makedirs(tmp_download_dir, exist_ok=True)\n tmp_file = tmp_download_dir + '/' + file_name\n file = self.download_dir + '/' + file_name\n\n if not os.path.exists(file):\n with open(tmp_file, 'wb') as out:\n while True:\n data = sock.recv(1024)\n if not data: break\n out.write(data)\n\n os.rename(tmp_file, file)\n print('%s is saved to %s' % (file_name, file))\n else:\n print('%s is already present' % file)\n", "sub_path": "computer-networks/hw1-producer-consumer/ctd/old.version/consumer.py", "file_name": "consumer.py", "file_ext": "py", "file_size_in_byte": 2989, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "socket.socket", "line_number": 19, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 19, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 19, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 24, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 24, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 24, "usage_type": "attribute"}, {"api_name": "socket.IPPROTO_UDP", "line_number": 24, "usage_type": "attribute"}, {"api_name": "socket.IPPROTO_IP", "line_number": 25, "usage_type": "attribute"}, {"api_name": "socket.IP_MULTICAST_TTL", "line_number": 25, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 35, "usage_type": "call"}, {"api_name": "socket.timeout", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "487062019", "text": "from lxml import etree as ET\n\nwith open('text.txt', \"a\") as mfile: \n parser = ET.XMLParser(recover=True)\n ttree = ET.parse('data.xml',parser=parser)\n root = ttree.getroot()\n for thread in root.findall('Page'):\n for post in thread.findall('Post'):\n if post.text == None:\n continue\n print(post.text)\n mfile.write(str(post.text) + '\\n\\n')\n print('Done')\n\n\n", "sub_path": "apertium-tools/scrapers-misc/haos.ucoz.kz-forum2txt.py", "file_name": "haos.ucoz.kz-forum2txt.py", "file_ext": "py", "file_size_in_byte": 423, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "lxml.etree.XMLParser", "line_number": 4, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 4, "usage_type": "name"}, {"api_name": "lxml.etree.parse", "line_number": 5, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 5, "usage_type": "name"}]} +{"seq_id": "22315624", "text": "from django.db import models\nfrom django.utils import timezone\n\nfrom django.conf import settings\n\nDRAFT = 0\nHIDDEN = 1\nPUBLISHED = 2\n\nUPLOAD_TO = settings.MEDIA_ROOT + 'blog_image_uploads/'\n\n\ndef entries_published(queryset):\n \"\"\"Return only the entries published\"\"\"\n now = timezone.now()\n return queryset.filter(\n models.Q(start_publication__lte=now) | \\\n models.Q(start_publication=None),\n models.Q(end_publication__gt=now) | \\\n models.Q(end_publication=None),\n status=PUBLISHED)\n\n\nclass EntryPublishedManager(models.Manager):\n \"\"\"Manager to retrieve published entries\"\"\"\n\n def get_query_set(self):\n \"\"\"Return published entries\"\"\"\n return entries_published(\n super(EntryPublishedManager, self).get_query_set())\n\n def on_site(self):\n \"\"\"Return entries published on current site\"\"\"\n return super(EntryPublishedManager, self).get_query_set(\n )\n\n def search(self, pattern):\n \"\"\"Top level search method on entries\"\"\"\n try:\n return self.advanced_search(pattern)\n except:\n return self.basic_search(pattern)\n\n def advanced_search(self, pattern):\n \"\"\"Advanced search on entries\"\"\"\n from zinnia.search import advanced_search\n return advanced_search(pattern)\n\n def basic_search(self, pattern):\n \"\"\"Basic search on entries\"\"\"\n lookup = None\n for pattern in pattern.split():\n query_part = models.Q(content__icontains=pattern) | \\\n models.Q(excerpt__icontains=pattern) | \\\n models.Q(title__icontains=pattern)\n if lookup is None:\n lookup = query_part\n else:\n lookup |= query_part\n\n return self.get_query_set().filter(lookup)\n", "sub_path": "blogger/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 1826, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 10, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 15, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.Manager", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "zinnia.search.advanced_search", "line_number": 47, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 53, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 53, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 54, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 54, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 55, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 55, "usage_type": "name"}]} +{"seq_id": "285486504", "text": "\"\"\"Using word frequencies to create a summary.\r\n\"\"\"\r\n\r\nimport argparse\r\nimport json\r\nimport string\r\nimport random\r\nimport pprint\r\n\r\nfrom nltk import pos_tag\r\nfrom nltk.collocations import BigramAssocMeasures\r\nfrom nltk.collocations import BigramCollocationFinder \r\nfrom nltk.corpus import wordnet\r\nfrom nltk.tokenize import word_tokenize\r\nfrom nltk.corpus import stopwords\r\nfrom nltk.corpus import words as nltk_words\r\nfrom nltk.stem import WordNetLemmatizer\r\nfrom nltk.probability import FreqDist\r\n\r\nimport constants\r\n\r\n\r\n###########################\r\n# PART OF SPEECH TAG TRANSLATOR FROM `pos_tag` TAGS to `wordnet` TAGS\r\n###########################\r\n# source for tags: https://pythonprogramming.net/natural-language-toolkit-nltk-part-speech-tagging/\r\n# NB: wordnet has a ADV_SAT tag, but I have no idea what that is\r\nDEFAULT_TAG = wordnet.NOUN\r\n\r\nPOS_TRANSLATOR = {\r\n 'CC': DEFAULT_TAG, # coordinating conjunction\r\n 'CD': DEFAULT_TAG, # cardinal digit\r\n 'DT': DEFAULT_TAG, # determiner\r\n 'EX': DEFAULT_TAG, # existential there (like: \"there is\" ... think of it like \"there exists\")\r\n 'FW': DEFAULT_TAG, # foreign word\r\n 'IN': DEFAULT_TAG, # preposition/subordinating conjunction\r\n 'JJ': wordnet.ADJ, # adjective 'big'\r\n 'JJR': wordnet.ADJ, # adjective, comparative 'bigger'\r\n 'JJS': wordnet.ADJ, # adjective, superlative 'biggest'\r\n 'LS': DEFAULT_TAG, # list marker 1)\r\n 'MD': wordnet.VERB, # modal could, will\r\n 'NN': wordnet.NOUN, # noun, singular 'desk'\r\n 'NNS': wordnet.NOUN, # noun plural 'desks'\r\n 'NNP': wordnet.NOUN, # proper noun, singular 'Harrison'\r\n 'NNPS': wordnet.NOUN, # proper noun, plural 'Americans'\r\n 'PDT': wordnet.ADJ, # predeterminer 'all the kids'\r\n 'POS': DEFAULT_TAG, # possessive ending parent's\r\n 'PRP': DEFAULT_TAG, # personal pronoun I, he, she\r\n 'PRP$': DEFAULT_TAG, # possessive pronoun my, his, hers\r\n 'RB': wordnet.ADV, # adverb very, silently,\r\n 'RBR': wordnet.ADV, # adverb, comparative better\r\n 'RBS': wordnet.ADV, # adverb, superlative best\r\n 'RP': wordnet.ADV, # particle give up\r\n 'TO': DEFAULT_TAG, # to go 'to' the store.\r\n 'UH': DEFAULT_TAG, # interjection errrrrrrrm\r\n 'VB': wordnet.VERB, # verb, base form take\r\n 'VBD': wordnet.VERB, # verb, past tense took\r\n 'VBG': wordnet.VERB, # verb, gerund/present participle taking\r\n 'VBN': wordnet.VERB, # verb, past participle taken\r\n 'VBP': wordnet.VERB, # verb, sing. present, non-3d take\r\n 'VBZ': wordnet.VERB, # verb, 3rd person sing. present takes\r\n 'WDT': DEFAULT_TAG, # wh-determiner which\r\n 'WP': DEFAULT_TAG, # wh-pronoun who, what\r\n 'WP$': DEFAULT_TAG, # possessive wh-pronoun whose\r\n 'WRB': wordnet.ADV # wh-abverb where, when\r\n}\r\n\r\n\r\ndef parse_arguments():\r\n \"\"\"Parses command-line arguments.\r\n\r\n Returns:\r\n - args (argparse.Namespace): The parsed arguments\r\n \"\"\"\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument('-f', '--file', type=str, help='The path to the JSON file containing processed text')\r\n parser.add_argument('-w', '--num_words', type=int, help='The number of frequent words to print out', default=20)\r\n parser.add_argument('-c', '--num_collocations', type=int, help='The number of collocations to print out',\r\n default=10)\r\n parser.add_argument('-cw', '--collocation_window', type=int, help='The window for searching for collocations',\r\n default=5)\r\n return parser.parse_args()\r\n# End of parse_arguments()\r\n\r\n\r\ndef load_records(file, preview_records=False):\r\n \"\"\"Loads the records from the JSON file. Also filters out empty records.\r\n\r\n Params:\r\n - file (str): The path to the JSON file\r\n\r\n Returns:\r\n - records (list): The contents of the JSON file\r\n \"\"\"\r\n with open(file, 'r') as json_file:\r\n records = json_file.readlines()\r\n records = [json.loads(record) for record in records]\r\n records = list(filter(lambda record: record[constants.TEXT] != '', records))\r\n if preview_records:\r\n print(\"=====Random Sample of Records=====\")\r\n pprint.pprint(random.choices(records, k=10))\r\n return records\r\n# End of load_records()\r\n\r\n\r\ndef tokenize_records(records):\r\n \"\"\"Tokenizes the records into word lists. Filters out any stopwords in the list.\r\n\r\n Params:\r\n - records (list): The non-empty records from the JSON file\r\n\r\n Returns:\r\n - tokenized_records (list>): The tokenized text content of the records\r\n \"\"\"\r\n contents = map(lambda record: record[constants.TEXT], records)\r\n tokenized_records = [word_tokenize(record.lower()) for record in contents]\r\n lemmatized_records = lemmatize_words(tokenized_records)\r\n lemmatized_words = list()\r\n for lemmatized_record in lemmatized_records:\r\n lemmatized_words.extend(lemmatized_record)\r\n return lemmatized_words\r\n# End of tokenize_records()\r\n\r\n\r\ndef lemmatize_words(records):\r\n \"\"\"Lemmatizes the words in the tokenized sentences.\r\n\r\n Lemmatization works best when the words are tagged with their corresponding part of speech, so the words are first\r\n tagged using nltk's `pos_tag` function.\r\n\r\n NB: There is a good chance that this tagging isn't 100% accurate. For that matter, lemmatization isn't always 100%\r\n accurate.\r\n\r\n Params:\r\n - records (list>): The word-tokenized records\r\n\r\n Returns:\r\n - lemmatized_records (list)): The lemmatized words from all the records\r\n \"\"\"\r\n print('Length of tagged_records: {:d}'.format(len(records)))\r\n print('Total number of words: {:d}'.format(sum([len(record) for record in records])))\r\n tagged_records = map(lambda record: pos_tag(record), records)\r\n tagged_records = filter_stopwords(tagged_records)\r\n lemmatizer = WordNetLemmatizer()\r\n lemmatized_records = list()\r\n for record in tagged_records:\r\n try:\r\n lemmatized_record = list(map(lambda word: lemmatizer.lemmatize(word[0], POS_TRANSLATOR[word[1]]), record))\r\n except Exception as err:\r\n print(record)\r\n raise err\r\n lemmatized_records.append(lemmatized_record)\r\n print('Total number of words after filtering: {:d}'.format(len(lemmatized_records)))\r\n return lemmatized_records\r\n# End of lemmatize_words()\r\n\r\n\r\ndef filter_stopwords(tagged_records):\r\n \"\"\"Filters stopwords, punctuation, and contractions from the tagged records. This is done after tagging to make\r\n sure that the tagging is as accurate as possible.\r\n\r\n Params:\r\n - tagged_records (list>>): The records, with each word tagged with its part of speech\r\n\r\n Returns:\r\n - filtered_records (list>>): The records, with unimportant words filtered out\r\n \"\"\"\r\n print('Filtering stopwords')\r\n stop_words = list(stopwords.words('english'))\r\n stop_words.extend(string.punctuation)\r\n stop_words.extend(constants.CONTRACTIONS)\r\n stop_words.extend(constants.MYSQL_STOPWORDS)\r\n dictionary_words = set(nltk_words.words())\r\n\r\n def not_dictionary_word(word): \r\n return word[0] not in dictionary_words and word[1] not in ['NNP', 'NNPS']\r\n\r\n filtered_records = [filter(lambda word: word[0] not in stop_words, record) for record in tagged_records]\r\n filtered_records = [filter(lambda word: not_dictionary_word, record) for record in filtered_records]\r\n filtered_records = [filter(lambda word: not word[0].replace('.', '', 1).isdigit(), record)\r\n for record in filtered_records] # see https://stackoverflow.com/a/23639915/5760608\r\n filtered_records = [list(filter(lambda word: word[1] in POS_TRANSLATOR.keys(), record))\r\n for record in filtered_records]\r\n return filtered_records\r\n# End of filter_stopwords()\r\n\r\n\r\ndef extract_frequent_words(records, num_words, no_counts=False):\r\n \"\"\"Stems the words in the given records, and then counts the words using NLTK FreqDist.\r\n\r\n Stemming is done using the English Snowball stemmer as per the recommendation from \r\n http://www.nltk.org/howto/stem.html\r\n\r\n NB: There is also a Lancaster stemmer available, but it is apparently very aggressive and can lead to a loss of\r\n potentially useful words (source: https://stackoverflow.com/a/11210358/5760608)\r\n\r\n Params:\r\n - records (list): The tokenized records from the JSON file\r\n - num_words (int): The number of words to extract\r\n - no_counts (bool): If True, frequent words will not include the word counts\r\n\r\n Returns:\r\n - frequent_words (list or list>): The list of most frequent words\r\n \"\"\"\r\n word_counts = FreqDist(records)\r\n frequent_words = word_counts.most_common(num_words)\r\n if no_counts:\r\n frequent_words = [word[0] for word in frequent_words]\r\n print(\"=====The {:d} Most Frequent Words=====\".format(num_words))\r\n print(frequent_words)\r\n return frequent_words\r\n# End of extract_frequent_words()\r\n\r\n\r\ndef extract_collocations(records, num_collocations, collocation_window, compare_collocations = False):\r\n \"\"\"Extracts the most common collocations present in the records.\r\n\r\n Params:\r\n - records (list>): The tokenized and lemmatized records from the JSON file\r\n - num_collocations (int): The number of collocations to show\r\n - collocation_window (int): The text window within which to search for collocations\r\n\r\n Returns:\r\n - best_collocations (list>): The highest scored collocations present in the records\r\n \"\"\"\r\n bigram_measures = BigramAssocMeasures()\r\n bigram_finder = BigramCollocationFinder.from_words(records, window_size=collocation_window)\r\n bigram_finder.apply_freq_filter(min_freq=3)\r\n best_collocations = bigram_finder.nbest(bigram_measures.raw_freq, num_collocations)\r\n print(\"=====The {:d} Most Frequent Collocations=====\".format(num_collocations))\r\n pprint.pprint(best_collocations)\r\n if compare_collocations:\r\n print(\"=====The {:d} Best Collocations (Pointwise Mutual Information)=====\".format(num_collocations))\r\n pprint.pprint(bigram_finder.nbest(bigram_measures.pmi, num_collocations))\r\n print(\"=====The {:d} Best Collocations (Student's t test)=====\".format(num_collocations))\r\n pprint.pprint(bigram_finder.nbest(bigram_measures.student_t, num_collocations))\r\n print(\"=====The {:d} Best Collocations (Chi-square test)=====\".format(num_collocations))\r\n pprint.pprint(bigram_finder.nbest(bigram_measures.chi_sq, num_collocations))\r\n print(\"=====The {:d} Best Collocations (Mutual Information)=====\".format(num_collocations))\r\n pprint.pprint(bigram_finder.nbest(bigram_measures.mi_like, num_collocations))\r\n print(\"=====The {:d} Best Collocations (Likelihood Ratios)=====\".format(num_collocations))\r\n pprint.pprint(bigram_finder.nbest(bigram_measures.likelihood_ratio, num_collocations))\r\n print(\"=====The {:d} Best Collocations (Poisson Stirling)=====\".format(num_collocations))\r\n pprint.pprint(bigram_finder.nbest(bigram_measures.poisson_stirling, num_collocations))\r\n print(\"=====The {:d} Best Collocations (Jaccard Index)=====\".format(num_collocations))\r\n pprint.pprint(bigram_finder.nbest(bigram_measures.jaccard, num_collocations))\r\n print(\"=====The {:d} Best Collocations (Phi-square test)=====\".format(num_collocations))\r\n pprint.pprint(bigram_finder.nbest(bigram_measures.phi_sq, num_collocations))\r\n print(\"=====The {:d} Best Collocations (Fisher's Exact Test)=====\".format(num_collocations))\r\n pprint.pprint(bigram_finder.nbest(bigram_measures.fisher, num_collocations))\r\n print(\"=====The {:d} Best Collocations (Dice's Coefficient)=====\".format(num_collocations))\r\n pprint.pprint(bigram_finder.nbest(bigram_measures.dice, num_collocations))\r\n return best_collocations\r\n# End of extract_collocations()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n args = parse_arguments()\r\n records = load_records(args.file, False)\r\n tokenized_records = tokenize_records(records)\r\n extract_frequent_words(tokenized_records, args.num_words, True)\r\n extract_collocations(tokenized_records, args.num_collocations, args.collocation_window, False)\r\n", "sub_path": "wordcount.py", "file_name": "wordcount.py", "file_ext": "py", "file_size_in_byte": 12393, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "nltk.corpus.wordnet.NOUN", "line_number": 28, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 28, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.ADJ", "line_number": 37, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 37, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.ADJ", "line_number": 38, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 38, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.ADJ", "line_number": 39, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 39, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.VERB", "line_number": 41, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 41, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.NOUN", "line_number": 42, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 42, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.NOUN", "line_number": 43, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 43, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.NOUN", "line_number": 44, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 44, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.NOUN", "line_number": 45, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 45, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.ADJ", "line_number": 46, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 46, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.ADV", "line_number": 50, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 50, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.ADV", "line_number": 51, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 51, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.ADV", "line_number": 52, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 52, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.ADV", "line_number": 53, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 53, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.VERB", "line_number": 56, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 56, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.VERB", "line_number": 57, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 57, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.VERB", "line_number": 58, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 58, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.VERB", "line_number": 59, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 59, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.VERB", "line_number": 60, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 60, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.VERB", "line_number": 61, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 61, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.ADV", "line_number": 65, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 65, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 75, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 97, "usage_type": "call"}, {"api_name": "constants.TEXT", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pprint.pprint", "line_number": 101, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 101, "usage_type": "call"}, {"api_name": "constants.TEXT", "line_number": 115, "usage_type": "attribute"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 116, "usage_type": "call"}, {"api_name": "nltk.pos_tag", "line_number": 142, "usage_type": "call"}, {"api_name": "nltk.stem.WordNetLemmatizer", "line_number": 144, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 169, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 169, "usage_type": "name"}, {"api_name": "string.punctuation", "line_number": 170, "usage_type": "attribute"}, {"api_name": "constants.CONTRACTIONS", "line_number": 171, "usage_type": "attribute"}, {"api_name": "constants.MYSQL_STOPWORDS", "line_number": 172, "usage_type": "attribute"}, {"api_name": "nltk.corpus.words.words", "line_number": 173, "usage_type": "call"}, {"api_name": "nltk.corpus.words", "line_number": 173, "usage_type": "name"}, {"api_name": "nltk.probability.FreqDist", "line_number": 205, "usage_type": "call"}, {"api_name": "nltk.collocations.BigramAssocMeasures", "line_number": 226, "usage_type": "call"}, {"api_name": "nltk.collocations.BigramCollocationFinder.from_words", "line_number": 227, "usage_type": "call"}, {"api_name": "nltk.collocations.BigramCollocationFinder", "line_number": 227, "usage_type": "name"}, {"api_name": "pprint.pprint", "line_number": 231, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 234, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 236, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 238, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 240, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 242, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 244, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 246, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 248, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 250, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 252, "usage_type": "call"}]} +{"seq_id": "102232211", "text": "\"\"\"\nCheckerPlugin.py\n\nCopyright (c) 2008 by Panopta LLC\njason@panopta.com\n\nBase class for plugin functionality for remote service checks.\n\"\"\"\ncheckers = []\nimport cPickle\nfrom Queue import Queue\nfrom ConfigParser import ConfigParser\nimport DNS\nfrom datetime import datetime, timedelta\n\n\nclass DummySchedule:\n def __init__(self, **entries): \n self.__dict__.update(entries)\n\n\nclass CheckerPlugin(object):\n textkey = None\n\n def __init__(self, config=None, result_queue=None, **kwargs):\n \"\"\" Perform all initialization for the check plugin. \"\"\"\n self.config = config\n self.options = kwargs\n if not result_queue:\n self.__result_queue = Queue()\n self.testing = True\n else: self.__result_queue = result_queue\n\n def check(self, schedule):\n raise NotImplementedError\n \n @classmethod\n def test(cls, schedule, dummy_data={}, **kwargs):\n \"\"\" basic stub for the testing framework \"\"\"\n \n DNS.ParseResolvConf()\n \n config = ConfigParser()\n config.read(\"config.cfg\")\n \n checker = cls(config, **kwargs)\n schedule = DummySchedule(**schedule)\n schedule.metadata = cPickle.dumps(dummy_data)\n \n if hasattr(schedule, \"fqdn\") and not kwargs.get(\"nodns\"):\n # this is usually done in the DNSThread, but since we're not running a\n # full-blown threaded checker, just resolve it ourselves now\n r = DNS.Request(schedule.fqdn, qtype='A').req()\n for a in r.answers:\n if a['typename'] != 'A': continue\n schedule.ip_address = a['data']\n schedule.dns_ttl_expiration = datetime.now() + timedelta(seconds=int(a['ttl']))\n break\n \n checker.check(schedule)\n results = checker.test_results\n \n return bool(results[\"result\"]), results[\"result_time\"], results[\"result_duration\"]\n \n @property\n def test_results(self):\n return self.__result_queue.get()\n\n def reportResults(self, schedule, result_time, result, duration, metadata=\"\"):\n \"\"\" Submit the results to the appropriate handler. \"\"\"\n results = {'schedule': schedule,\n 'result': result,\n 'result_time': result_time,\n 'result_duration': duration,\n 'metadata': metadata,\n 'result_type': 'service',\n }\n\n self.__result_queue.put(results)\n \n reportServiceResults = reportResults\n\n def reportResourceResults(self, schedule, result_time, value, metadata=''):\n \"\"\"Submit the results for a resource check\"\"\"\n results = {'schedule': schedule,\n 'value': value,\n 'result_time': result_time,\n 'metadata': metadata,\n 'result_type': 'resource'}\n self.__result_queue.put(results)\n\n def loadMetadata(self, schedule): \n if not schedule.metadata: return {} \n return cPickle.loads(schedule.metadata)\n\n\n", "sub_path": "MainComponents/build/appliance/build/tmp/src/checker_plugins/CheckerPlugin.py", "file_name": "CheckerPlugin.py", "file_ext": "py", "file_size_in_byte": 3093, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "Queue.Queue", "line_number": 30, "usage_type": "call"}, {"api_name": "DNS.ParseResolvConf", "line_number": 41, "usage_type": "call"}, {"api_name": "ConfigParser.ConfigParser", "line_number": 43, "usage_type": "call"}, {"api_name": "cPickle.dumps", "line_number": 48, "usage_type": "call"}, {"api_name": "DNS.Request", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 57, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 57, "usage_type": "call"}, {"api_name": "cPickle.loads", "line_number": 94, "usage_type": "call"}]} +{"seq_id": "613524595", "text": "import logging\n\nfrom .data_handler import ProteinDataHandler\nfrom .globals import DATASETS\n\nlogging.basicConfig(level=logging.INFO)\nLOGGER = logging.getLogger()\n\n\ndef main():\n for target in DATASETS['eval']:\n dh = ProteinDataHandler(target, structures_version=3)\n\n nas =(dh.target_pdb_cm==-1).sum()\n LOGGER.info(f'Nas: {nas}')\n\n\nif __name__ == '__main__':\n main()", "sub_path": "archive/na_sanity_check.py", "file_name": "na_sanity_check.py", "file_ext": "py", "file_size_in_byte": 390, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.basicConfig", "line_number": 6, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 6, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "globals.DATASETS", "line_number": 11, "usage_type": "name"}, {"api_name": "data_handler.ProteinDataHandler", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "14908751", "text": "from django.shortcuts import render\r\nfrom django.http import JsonResponse\r\nfrom django.http import HttpResponse\r\nfrom .forms import FahrerForm, Lieferform\r\nfrom .models import Fahrer, Fahrzeug, Kunde, Produkte, Lieferungen\r\nfrom .utils import render_to_pdf\r\nfrom django.db.models import Sum\r\nfrom django.db.models import Q\r\n\r\n# Create your views here.\r\n\r\ndef liefer_list(request):\r\n lieferlist = Lieferungen.objects.order_by('-datum_von')\r\n context = {'lieferlist': lieferlist}\r\n return render(request, 'collect/liefer_list.html', context)\r\n\r\ndef import_list(request):\r\n lieferlist = Lieferungen.objects.order_by('-datum_von').filter(id_aufnehmer__nachname='Scheuenstuhl')\r\n summe = Lieferungen.objects.order_by('-datum_von').filter(id_aufnehmer__nachname='Scheuenstuhl').aggregate(Sum('menge'))\r\n context = {'lieferlist': lieferlist,'summe':summe}\r\n return render(request, 'collect/import.html', context)\r\n\r\ndef filter_list(request, pk):\r\n lieferlist = Lieferungen.objects.order_by('-datum_von').filter(Q(id_aufnehmer__id=pk) | Q(id_abgeber__id=pk))\r\n summe = Lieferungen.objects.order_by('-datum_von').filter(id_aufnehmer__id=pk).aggregate(Sum('menge'))\r\n context = {'lieferlist': lieferlist,'summe':summe}\r\n return render(request, 'collect/import.html', context)\r\n\r\ndef test(request):\r\n if request.method == \"POST\":\r\n fahrer_form = FahrerForm(request.POST)\r\n if fahrer_form.is_valid():\r\n Fahrer = fahrer_form.save(commit=False)\r\n Fahrer.save()\r\n\r\n else:\r\n fahrer_form = FahrerForm()\r\n return render(request, 'collect/test.html', {'fahrerform': fahrer_form})\r\n\r\ndef lieferschein(request):\r\n if request.method == \"POST\":\r\n liefer_form = Lieferform(request.POST)\r\n if liefer_form.is_valid():\r\n Lieferungen = liefer_form.save(commit=False)\r\n Lieferungen.save()\r\n\r\n else:\r\n liefer_form = Lieferform()\r\n return render(request, 'collect/lieferschein.html', {'lieferform': liefer_form})\r\n\r\ndef liefer_form_data(request, pk):\r\n\r\n\r\n fahrzeug_form_data = Fahrzeug.objects.filter(pk=pk).values() #getting the liked posts\r\n fahrzeug_form_data = list(fahrzeug_form_data)\r\n return JsonResponse(fahrzeug_form_data, safe=False) # Sending an success response\r\n #return HttpResponse(is_private)\r\n\r\ndef validate_username(request, pk):\r\n username = request.GET.get('username', None)\r\n username = pk\r\n\r\n data = {\r\n 'is_taken': username\r\n }\r\n return JsonResponse(data)\r\n\r\n\r\n\r\ndef generatepdf(request, pk):\r\n pdfdata = Lieferungen.objects.filter(pk=pk).order_by('-datum_von')[0]\r\n npk_gesamt={}\r\n npk_gesamt[\"n\"] = int(pdfdata.menge * pdfdata.id_produkt.n)\r\n npk_gesamt[\"p\"] = int(pdfdata.menge * pdfdata.id_produkt.p)\r\n npk_gesamt[\"k\"] = int(pdfdata.menge * pdfdata.id_produkt.k)\r\n context = {'pdfdata': pdfdata,'npk_gesamt': npk_gesamt}\r\n pdf = render_to_pdf('collect/invoice.html', context)\r\n return HttpResponse(pdf, content_type='application/pdf')", "sub_path": "collect/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3044, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "models.Lieferungen.objects.order_by", "line_number": 13, "usage_type": "call"}, {"api_name": "models.Lieferungen.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "models.Lieferungen", "line_number": 13, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}, {"api_name": "models.Lieferungen.objects.order_by", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Lieferungen.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.Lieferungen", "line_number": 18, "usage_type": "name"}, {"api_name": "models.Lieferungen.objects.order_by", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Lieferungen.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.Lieferungen", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 19, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Lieferungen.objects.order_by", "line_number": 24, "usage_type": "call"}, {"api_name": "models.Lieferungen.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "models.Lieferungen", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 24, "usage_type": "call"}, {"api_name": "models.Lieferungen.objects.order_by", "line_number": 25, "usage_type": "call"}, {"api_name": "models.Lieferungen.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "models.Lieferungen", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 25, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 27, "usage_type": "call"}, {"api_name": "forms.FahrerForm", "line_number": 31, "usage_type": "call"}, {"api_name": "models.Fahrer", "line_number": 33, "usage_type": "name"}, {"api_name": "models.Fahrer.save", "line_number": 34, "usage_type": "call"}, {"api_name": "models.Fahrer", "line_number": 34, "usage_type": "name"}, {"api_name": "forms.FahrerForm", "line_number": 37, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 38, "usage_type": "call"}, {"api_name": "forms.Lieferform", "line_number": 42, "usage_type": "call"}, {"api_name": "models.Lieferungen", "line_number": 44, "usage_type": "name"}, {"api_name": "models.Lieferungen.save", "line_number": 45, "usage_type": "call"}, {"api_name": "models.Lieferungen", "line_number": 45, "usage_type": "name"}, {"api_name": "forms.Lieferform", "line_number": 48, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 49, "usage_type": "call"}, {"api_name": "models.Fahrzeug.objects.filter", "line_number": 54, "usage_type": "call"}, {"api_name": "models.Fahrzeug.objects", "line_number": 54, "usage_type": "attribute"}, {"api_name": "models.Fahrzeug", "line_number": 54, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 56, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 66, "usage_type": "call"}, {"api_name": "models.Lieferungen.objects.filter", "line_number": 71, "usage_type": "call"}, {"api_name": "models.Lieferungen.objects", "line_number": 71, "usage_type": "attribute"}, {"api_name": "models.Lieferungen", "line_number": 71, "usage_type": "name"}, {"api_name": "utils.render_to_pdf", "line_number": 77, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "364195858", "text": "# coding: utf-8\nimport logging\n'''\n日志设定文件\n'''\ndef config_logging(filename=None, format='%(asctime)s, %(levelname)s %(message)s', level=logging.INFO,\n logger=None, category=None, scribe_host='127.0.0.1', scribe_port=1464, console_log_level=None,\n name=None, propagate=True, backend=1, kafka_topic=None, scribe_format=None, multi_process_logger_kwargs={},\n scribe_log_level=logging.WARNING, databus_channel=None, databus_key=None, databus_format=None,\n sentry_dsn=None, sentry_level=logging.ERROR, file_format=None):\n if logger is None:\n logger = logging.getLogger()\n # need a clean state, for some module may have called logging functions already (i.e. logging.info)\n # in that case, a default handler would been appended, causing undesired output to stderr\n for handler in logger.handlers:\n logger.removeHandler(handler)\n formatter = logging.Formatter(format)\n logger.setLevel(level)\n if not propagate:\n logger.propagate = False\n if filename:\n if 'when' not in multi_process_logger_kwargs:\n multi_process_logger_kwargs['when'] = 'midnight'\n handler = MultiProcessRotatingFileHandler(filename=filename, **multi_process_logger_kwargs)\n file_formatter = formatter\n if file_format:\n file_formatter = logging.Formatter(file_format)\n handler.setFormatter(file_formatter)\n logger.addHandler(handler)\n # if category or kafka_topic:\n # scribe = ScribeLogHandler(category=category, backend=backend, \\\n # kafka_topic=kafka_topic, host=scribe_host, port=scribe_port)\n # scribe_formatter = formatter\n # if scribe_format:\n # scribe_formatter = logging.Formatter(scribe_format)\n # scribe.setFormatter(scribe_formatter)\n # scribe.setLevel(scribe_log_level)\n # logger.addHandler(scribe)\n # if databus_channel:\n # from pyutil.databus import DatabusLogHandler\n # databus = DatabusLogHandler(databus_channel, databus_key)\n # databus_formatter = formatter\n # if databus_format:\n # databus_formatter = logging.Formatter(databus_format)\n # databus.setFormatter(databus_formatter)\n # logger.addHandler(databus)\n if console_log_level is not None:\n ch = logging.StreamHandler()\n formatter = logging.Formatter(format)\n ch.setFormatter(logging.Formatter(format))\n ch.setLevel(console_log_level)\n logger.addHandler(ch)\n # if sentry_dsn is not None:\n # import raven\n # from raven.handlers.logging import SentryHandler\n # from raven.transport.registry import TransportRegistry, default_transports\n # raven.Raven = None\n # raven.Client.logger = logging.getLogger('raven')\n # raven.Client._registry = TransportRegistry(transports=default_transports)\n # client = raven.Client(sentry_dsn)\n # handler = SentryHandler(client)\n # handler.setLevel(sentry_level)\n # logger.addHandler(handler)\n\n\nimport time\nfrom logging.handlers import TimedRotatingFileHandler\nimport os\n\nfrom filelock import FileLock\n\n\nMIDNIGHT = 24 * 60 * 60 # 00:00:00\nSECONDS_PER_DAY = 60 * 60 * 24\n\n\nclass MultiProcessRotatingFileHandler(TimedRotatingFileHandler):\n def __init__(self, filename, when='h', interval=1, backupCount=0, encoding=None, utc=False):\n super(MultiProcessRotatingFileHandler, self).__init__(filename, when, interval, backupCount, encoding, False,\n utc)\n d, f = os.path.split(filename)\n self.lock_file_name = os.path.join(d, '.' + f)\n\n def computeRollover(self, currentTime):\n \"\"\"\n Work out the rollover time based on the specified time.\n 都在整数时间点rollover\n \"\"\"\n if self.when == 'MIDNIGHT' or self.when.startswith('W'):\n if self.utc:\n t = time.gmtime(currentTime)\n else:\n t = time.localtime(currentTime)\n currentHour = t[3]\n currentMinute = t[4]\n currentSecond = t[5]\n secondsToMidnight = MIDNIGHT - ((currentHour * 60 + currentMinute) * 60 +\n currentSecond)\n result = currentTime + secondsToMidnight\n if self.when.startswith('W'):\n day = t[6]\n if day != self.dayOfWeek:\n if day < self.dayOfWeek:\n daysToWait = self.dayOfWeek - day\n else:\n daysToWait = self.dayOfWeek - day + 7\n result += SECONDS_PER_DAY * daysToWait\n else:\n result = currentTime + self.interval - currentTime % self.interval\n return result\n\n def doRollover(self):\n \"\"\"\n do a rollover; in this case, a date/time stamp is appended to the filename\n when the rollover happens. However, you want the file to be named for the\n start of the interval, not the current time. If there is a backup count,\n then we have to get a list of matching filenames, sort them and remove\n the one with the oldest suffix.\n rollover时两种情况:\n 1、检查要被rename to的文件是否已经存在，如果已经存在，说明有另外的进程已经rollover了，那本进程\n a）reopen\n b) 更新rollover时间\n 2、如果不存在，说明本进程是最先抢到rollover锁的，那本进程：\n a) rename\n b) 删除旧日志\n c) reopen\n d) 更新rollover时间\n \"\"\"\n if self.stream:\n self.stream.close()\n self.stream = None\n # get the time that this sequence started at and make it a TimeTuple\n currentTime = int(time.time())\n t = self.rolloverAt - self.interval\n if self.utc:\n timeTuple = time.gmtime(t)\n else:\n timeTuple = time.localtime(t)\n dfn = self.baseFilename + \".\" + time.strftime(self.suffix, timeTuple)\n\n with FileLock(self.lock_file_name):\n if not os.path.exists(dfn):\n self._rollover(dfn)\n self.stream = self._open()\n self._updateRolloverAt(currentTime)\n\n def _rollover(self, dfn):\n # Issue 18940: A file may not have been created if delay is True.\n if os.path.exists(self.baseFilename):\n os.rename(self.baseFilename, dfn)\n\n if self.backupCount > 0:\n for s in self.getFilesToDelete():\n os.remove(s)\n\n def _updateRolloverAt(self, currentTime):\n \"\"\"\n 更新下一次rollover的时间点\n :param currentTime:\n :return:\n \"\"\"\n newRolloverAt = self.computeRollover(currentTime)\n while newRolloverAt <= currentTime:\n newRolloverAt = newRolloverAt + self.interval\n self.rolloverAt = newRolloverAt\n\n", "sub_path": "backup/utilog.py", "file_name": "utilog.py", "file_ext": "py", "file_size_in_byte": 7011, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.INFO", "line_number": 6, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 10, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 48, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 49, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.handlers.TimedRotatingFileHandler", "line_number": 77, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "time.gmtime", "line_number": 91, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 93, "usage_type": "call"}, {"api_name": "time.time", "line_number": 133, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 136, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 138, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 139, "usage_type": "call"}, {"api_name": "filelock.FileLock", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path", "line_number": 142, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 150, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 154, "usage_type": "call"}]} +{"seq_id": "347721705", "text": "#!/usr/bin/python\n\nimport multiprocessing\nimport subprocess\nimport os\n\ndef do_args():\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-n\", \"--network\", help=\"specify network range to sweep\", required=\"True\")\n return parser.parse_args()\n\ndef pinger( job_q, results_q ):\n DEVNULL = open(os.devnull,'w')\n while True:\n ip = job_q.get()\n if ip is None: break\n\n try:\n subprocess.check_call(['ping','-c1',ip],\n stdout=DEVNULL)\n results_q.put(ip)\n except:\n pass\n \ndef check_ip():\n import re\n import sys\n wb = {}\n params = re.match(r'(?P(\\d{1,3}\\.){3}).*', args.network)\n try:\n wb = params.groupdict()\n except:\n print('Failed to extract IP range. Please check.')\n sys.exit()\n return wb[\"range\"]\n\n\nif __name__ == '__main__':\n args = do_args()\n network_range = check_ip()\n pool_size = 255\n\n jobs = multiprocessing.Queue()\n results = multiprocessing.Queue()\n\n pool = [ multiprocessing.Process(target=pinger, args=(jobs,results))\n for i in range(pool_size) ]\n\n for p in pool:\n p.start()\n\n for i in range(1,255):\n jobs.put(\"%s{0}\".format(i) % network_range)\n\n for p in pool:\n jobs.put(None)\n\n for p in pool:\n p.join()\n\n while not results.empty():\n ip = results.get()\n print(ip)\n", "sub_path": "pingsweep.py", "file_name": "pingsweep.py", "file_ext": "py", "file_size_in_byte": 1451, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "call"}, {"api_name": "os.devnull", "line_number": 14, "usage_type": "attribute"}, {"api_name": "subprocess.check_call", "line_number": 20, "usage_type": "call"}, {"api_name": "re.match", "line_number": 30, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 35, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 44, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 45, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "106483433", "text": "\"\"\"\nTEST_WR.PY Unit tests for write_results()\n\"\"\"\nimport os\nimport sys\nimport tempfile\n\nimport numpy as np\nimport pytest\n\nfrom rcrbounds import write_results\n\n\n# Basic functionality\ndef test_wr_basic():\n \"\"\"write the specified array to the specified text file\"\"\"\n moment_vector = np.zeros(5)\n with tempfile.TemporaryDirectory() as tmp:\n outfile = os.path.join(tmp, 'pout.txt')\n write_results(moment_vector, outfile)\n\n\n# Exceptions to handle\ndef test_wr_readonly(read_only_file):\n \"\"\"warn and continue if file is read-only\"\"\"\n moment_vector = np.zeros(5)\n with pytest.warns(UserWarning, match=\"Cannot write\"):\n write_results(moment_vector, read_only_file)\n\n\ndef test_wr_badfolder():\n \"\"\"warn and continue if folder does not exist\"\"\"\n moment_vector = np.zeros(5)\n with pytest.warns(UserWarning, match=\"Cannot write\"):\n write_results(moment_vector, \"nonexistent-path-name/pout.txt\")\n\n\n@pytest.mark.skipif(sys.platform != 'win32', reason=\"Windows test\")\ndef test_wr_illegalname():\n \"\"\"warn and continue if file name is illegal\"\"\"\n moment_vector = np.zeros(5)\n with pytest.warns(UserWarning, match=\"Cannot write\"):\n write_results(moment_vector, \"?\")\n", "sub_path": "python/testing/test_wr.py", "file_name": "test_wr.py", "file_ext": "py", "file_size_in_byte": 1216, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.zeros", "line_number": 17, "usage_type": "call"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "rcrbounds.write_results", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 26, "usage_type": "call"}, {"api_name": "pytest.warns", "line_number": 27, "usage_type": "call"}, {"api_name": "rcrbounds.write_results", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 33, "usage_type": "call"}, {"api_name": "pytest.warns", "line_number": 34, "usage_type": "call"}, {"api_name": "rcrbounds.write_results", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 41, "usage_type": "call"}, {"api_name": "pytest.warns", "line_number": 42, "usage_type": "call"}, {"api_name": "rcrbounds.write_results", "line_number": 43, "usage_type": "call"}, {"api_name": "pytest.mark.skipif", "line_number": 38, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 38, "usage_type": "attribute"}]} +{"seq_id": "257764226", "text": "import sqlite3\nimport json\n# import codecs\nimport os\n\ncx = sqlite3.connect(os.path.join(os.path.expanduser('~')+\"/AppData/Local/Netease/CloudMusic/Library/webdb.dat\"))\ncx.row_factory = sqlite3.Row\n\ndef getPlaylist():\n cu = cx.cursor()\n cu.execute(\"select * from web_playlist\")\n playlists = []\n for item in cu.fetchall():\n playlist = (item[\"pid\"],getPlaylistNameFromJson(item[\"playlist\"]))\n playlists.append(playlist)\n playlists = list(map(lambda x: (x[0], x[1].decode('GBK', 'ignore')), playlists))\n\n print('Listing playlists:')\n print('========= START ==========')\n\n for i, _ in enumerate(playlists):\n print(i, _[1])\n if i % 10 == 0 and i > 0:\n input('Press enter to continue')\n\n print('========== END ===========')\n\n try:\n index = int(input('Choose playlist you wanna export:'))\n playlist = playlists[index]\n except (IndexError, ValueError):\n print('Invalid input, exiting...')\n exit(1)\n return playlist\n\n\n\ndef getPlayListMusic(pid):\n cu = cx.cursor()\n cu.execute(\"select * from web_playlist_track where pid=?\",[pid])\n musics = []\n for item in cu.fetchall():\n musics.append(item[\"tid\"]);\n return musics\n\ndef getOfflineMusicDetail(tid):\n cu=cx.cursor()\n cu.execute(\"select * from web_offline_track where track_id=?\",[tid])\n music = cu.fetchone()\n if music is None:\n return None\n detail = (getMusicNameFromJson(music[\"detail\"]), music[\"relative_path\"])\n return detail\n\ndef writePlaylistToFile(pid, playlistName):\n file = open(os.path.join(playlistName + \".m3u\"), \"w\", encoding='utf-8')\n count = 0\n try:\n file.writelines(\"#EXTM3U\")\n musicIds = getPlayListMusic(pid)\n for tid in musicIds:\n if tid is not None:\n detail = getOfflineMusicDetail(tid)\n if detail is not None:\n count = count + 1\n file.writelines(u\"\\n#EXTINF:\" + detail[0] + u\"\\n\" + detail[1])\n except Exception as e:\n raise\n else:\n pass\n finally:\n file.close()\n if count <= 0:\n os.remove(playlistName + \".m3u\")\n\ndef getPlaylistNameFromJson(jsonStr):\n playlistDetail = json.loads(jsonStr)\n return playlistDetail[\"name\"].encode(\"GBK\", 'ignore');\n\ndef getMusicNameFromJson(jsonStr):\n musicDetail = json.loads(jsonStr)\n return musicDetail[\"name\"];\n\ndef main():\n pid, name = getPlaylist()\n writePlaylistToFile(pid, name)\n\nif __name__ == '__main__':\n main()", "sub_path": "create_playlist3.py", "file_name": "create_playlist3.py", "file_ext": "py", "file_size_in_byte": 2538, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sqlite3.connect", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 6, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 74, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 77, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "526473413", "text": "from django.conf.urls import url, include\nfrom django.contrib import admin\nfrom home import views as home_views\nfrom accounts import views as accounts_views\nfrom paypal.standard.ipn import urls as paypal_urls\nfrom paypal_store import views as paypal_views\nfrom products import views as product_views\nfrom magazines import views as magazines_views\nfrom threads import views as forum_views\nfrom polls import api_views\nfrom threads import api_views as thread_api_views\n\nurlpatterns = [\n url(r'^admin/', admin.site.urls),\n url(r'^$', home_views.get_index, name='index'),\n url(r'^register/$', accounts_views.register, name='register'),\n url(r'^profile/$', accounts_views.profile, name='profile'),\n url(r'^login/$', accounts_views.login, name='login'),\n url(r'^logout/$', accounts_views.logout, name='logout'),\n url(r'^cancel_subscription/$', accounts_views.cancel_subscription, name='cancel_subscription'),\n #url(r'^subscriptions_webhook/$', accounts_views.subscriptions_webhook, name='subscriptions_webhook'),\n url(r'^a-very-hard-to-guess-url/', include(paypal_urls)),\n\n url(r'^paypal-return/$', paypal_views.paypal_return),\n url(r'^paypal-cancel/$', paypal_views.paypal_cancel),\n url(r'^products/$', product_views.all_products, name='products'),\n url(r'^magazines/$', magazines_views.all_magazines, name='magazines'),\n url(r'^forum/$', forum_views.forum, name='forum'),\n url(r'^threads/(?P\\d+)/$', forum_views.threads, name='threads'),\n url(r'^new_thread/(?P\\d+)/$', forum_views.new_thread, name='new_thread'),\n url(r'^thread/(?P\\d+)/$', forum_views.thread, name='thread'),\n url(r'^post/new/(?P\\d+)/$', forum_views.new_post, name='new_post'),\n url(r'^post/edit/(?P\\d+)/(?P\\d+)/$',forum_views.edit_post, name='edit_post'),\n url(r'^post/delete/(?P\\d+)/(?P\\d+)/$', forum_views.delete_post, name='delete_post'),\n url(r'^thread/vote/(?P\\d+)/(?P\\d+)/$', forum_views.thread_vote, name='cast_vote'),\n url(r'^threads/polls/$', api_views.PollViewSet.as_view(), name='threads_poll_api1'),\n url(r'^threads/polls/(?P[\\d]+)$', api_views.PollInstanceView.as_view(), name='poll-instance'),\n url(r'^threads/polls/vote/(?P\\d+)/$', api_views.VoteCreateView.as_view(), name='create_vote'),\n url(r'^threads/threads/$', api_views.ThreadViewSet.as_view(), name='threads_threads'),\n url(r'^threads/post/update/(?P[\\d+]+)/$', thread_api_views.PostUpdateView.as_view(),\n name=\"update-poll\"),\n url(r'^threads/post/delete/(?P[\\d]+)/$', thread_api_views.PostDeleteView.as_view(), name='delete-poll'),\n\n]\n", "sub_path": "we_are_social/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2692, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 14, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "home.views.get_index", "line_number": 15, "usage_type": "attribute"}, {"api_name": "home.views", "line_number": 15, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "accounts.views.register", "line_number": 16, "usage_type": "attribute"}, {"api_name": "accounts.views", "line_number": 16, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "accounts.views.profile", "line_number": 17, "usage_type": "attribute"}, {"api_name": "accounts.views", "line_number": 17, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "accounts.views.login", "line_number": 18, "usage_type": "attribute"}, {"api_name": "accounts.views", "line_number": 18, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "accounts.views.logout", "line_number": 19, "usage_type": "attribute"}, {"api_name": "accounts.views", "line_number": 19, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "accounts.views.cancel_subscription", "line_number": 20, "usage_type": "attribute"}, {"api_name": "accounts.views", "line_number": 20, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 22, "usage_type": "call"}, {"api_name": "paypal.standard.ipn.urls", "line_number": 22, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "paypal_store.views.paypal_return", "line_number": 24, "usage_type": "attribute"}, {"api_name": "paypal_store.views", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "paypal_store.views.paypal_cancel", "line_number": 25, "usage_type": "attribute"}, {"api_name": "paypal_store.views", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "products.views.all_products", "line_number": 26, "usage_type": "attribute"}, {"api_name": "products.views", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "magazines.views.all_magazines", "line_number": 27, "usage_type": "attribute"}, {"api_name": "magazines.views", "line_number": 27, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}, {"api_name": "threads.views.forum", "line_number": 28, "usage_type": "attribute"}, {"api_name": "threads.views", "line_number": 28, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 29, "usage_type": "call"}, {"api_name": "threads.views.threads", "line_number": 29, "usage_type": "attribute"}, {"api_name": "threads.views", "line_number": 29, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 30, "usage_type": "call"}, {"api_name": "threads.views.new_thread", "line_number": 30, "usage_type": "attribute"}, {"api_name": "threads.views", "line_number": 30, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 31, "usage_type": "call"}, {"api_name": "threads.views.thread", "line_number": 31, "usage_type": "attribute"}, {"api_name": "threads.views", "line_number": 31, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 32, "usage_type": "call"}, {"api_name": "threads.views.new_post", "line_number": 32, "usage_type": "attribute"}, {"api_name": "threads.views", "line_number": 32, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 33, "usage_type": "call"}, {"api_name": "threads.views.edit_post", "line_number": 33, "usage_type": "attribute"}, {"api_name": "threads.views", "line_number": 33, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 34, "usage_type": "call"}, {"api_name": "threads.views.delete_post", "line_number": 34, "usage_type": "attribute"}, {"api_name": "threads.views", "line_number": 34, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 35, "usage_type": "call"}, {"api_name": "threads.views.thread_vote", "line_number": 35, "usage_type": "attribute"}, {"api_name": "threads.views", "line_number": 35, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 36, "usage_type": "call"}, {"api_name": "polls.api_views.PollViewSet.as_view", "line_number": 36, "usage_type": "call"}, {"api_name": "polls.api_views.PollViewSet", "line_number": 36, "usage_type": "attribute"}, {"api_name": "polls.api_views", "line_number": 36, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 37, "usage_type": "call"}, {"api_name": "polls.api_views.PollInstanceView.as_view", "line_number": 37, "usage_type": "call"}, {"api_name": "polls.api_views.PollInstanceView", "line_number": 37, "usage_type": "attribute"}, {"api_name": "polls.api_views", "line_number": 37, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 38, "usage_type": "call"}, {"api_name": "polls.api_views.VoteCreateView.as_view", "line_number": 38, "usage_type": "call"}, {"api_name": "polls.api_views.VoteCreateView", "line_number": 38, "usage_type": "attribute"}, {"api_name": "polls.api_views", "line_number": 38, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 39, "usage_type": "call"}, {"api_name": "polls.api_views.ThreadViewSet.as_view", "line_number": 39, "usage_type": "call"}, {"api_name": "polls.api_views.ThreadViewSet", "line_number": 39, "usage_type": "attribute"}, {"api_name": "polls.api_views", "line_number": 39, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 40, "usage_type": "call"}, {"api_name": "threads.api_views.PostUpdateView.as_view", "line_number": 40, "usage_type": "call"}, {"api_name": "threads.api_views.PostUpdateView", "line_number": 40, "usage_type": "attribute"}, {"api_name": "threads.api_views", "line_number": 40, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 42, "usage_type": "call"}, {"api_name": "threads.api_views.PostDeleteView.as_view", "line_number": 42, "usage_type": "call"}, {"api_name": "threads.api_views.PostDeleteView", "line_number": 42, "usage_type": "attribute"}, {"api_name": "threads.api_views", "line_number": 42, "usage_type": "name"}]} +{"seq_id": "530420959", "text": "from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\n\nurlpatterns = patterns('',\n # Examples:\n url(r'^$', 'feedback.views.home', name='home'),\n # url(r'^blog/', include('blog.urls')),\n #url(r'^polls/', include('polls.urls', namespace=\"polls\")),\n url(r'^feedback/', include('feedback.urls', namespace=\"feedback\")),\n #url(r'^(?P\\w+)/$','feedback.views.index2', name='index2'),\n #url(r'^my/', 'feedback.views.my_view', name='my'),\n url(r'^login/', 'feedback.views.Login', name='login'),\n url(r'^getmail/', 'feedback.views.getmail', name='getmail'),\n url(r'^logout/', 'feedback.views.Logout', name='logout'),\n url(r'^register/', 'feedback.views.register', name='register'),\n url(r'^success/', 'feedback.views.success', name='success'),\n url(r'^unsuccess/', 'feedback.views.unsuccess', name='unsuccess'),\n #url(r'^analyse/', 'feedback.views.analyse', name='analyse'),\n url(r'^admin/', include(admin.site.urls)),\n)\n", "sub_path": "coursefeedback/coursefeedback/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 991, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 4, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 19, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 19, "usage_type": "name"}]} +{"seq_id": "60309273", "text": "from django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.models import User\nfrom django.core.exceptions import PermissionDenied\nfrom django.db import transaction\nfrom django.shortcuts import render, redirect, HttpResponseRedirect, get_object_or_404, HttpResponse\nfrom django.urls import reverse, reverse_lazy\nfrom django.views.generic import View, TemplateView, DetailView, DeleteView\n\nfrom customers_app.forms import LoginForm, UserCreateForm, CustomerForm\nfrom customers_app.helpers import are_passwords_match, filter_and_sort_customers_by_query_params\nfrom customers_app.models import Customer, Photo\n\nURLS = {\n 'home_url': reverse_lazy('home'),\n 'list_url': reverse_lazy('customers-list'),\n 'voting_url': reverse_lazy('customers-voting'),\n 'auth_url': reverse_lazy('customers-auth'),\n 'logout_url': reverse_lazy('customers-logout'),\n 'create_url': reverse_lazy('customers-create'),\n}\n\n\nclass HomePage(TemplateView):\n \"\"\"\n Render home page with links to pages of project\n \"\"\"\n template_name = 'home.html'\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n context.update({\n 'list_url': URLS['list_url'],\n 'voting_url': URLS['voting_url'],\n 'auth_url': URLS['auth_url'],\n 'create_url': URLS['create_url']\n })\n\n user = self.request.user\n if user.is_authenticated:\n context['detail_url'] = reverse_lazy('customers-detail', kwargs={'pk': user.customer.id})\n\n return context\n\n def get(self, request, *args, **kwargs):\n\n user = request.user\n if user.is_authenticated:\n try:\n user.customer\n except User.customer.RelatedObjectDoesNotExist:\n return HttpResponse('User is not a customer. If you are admin - create Customer model for yourself. '\n 'If you are not admin - say admin to create Customer model for you ')\n\n context = self.get_context_data(**kwargs)\n return self.render_to_response(context)\n\n\ndef logout_view(request):\n logout(request)\n return redirect('customers-auth')\n\n\nclass CustomersVotingView(TemplateView):\n \"\"\"\n Render template with photos and points for each photo.\n Allows to vote for certain photo by click on button near photo's points\n \"\"\"\n template_name = \"customers_voting.html\"\n\n def get_context_data(self, **kwargs):\n if not self.request.user.is_authenticated:\n raise PermissionDenied\n\n context = super().get_context_data(**kwargs)\n context.update({\n 'photos': Photo.objects.filter(customer__isnull=False),\n 'max_points': Photo.max_points\n })\n return context\n\n def get(self, request, *args, **kwargs):\n context = self.get_context_data(**kwargs)\n return self.render_to_response(context)\n\n def post(self, request, *args, **kwargs):\n context = self.get_context_data(**kwargs)\n\n photo = get_object_or_404(Photo, pk=request.POST.get('id_of_photo'))\n photo.add_point()\n\n return self.render_to_response(context)\n\n\nclass CustomersListView(TemplateView):\n \"\"\"\n Render template with information about customers\n Allows filter and sort customer's information by buttons\n \"\"\"\n template_name = \"customers_list.html\"\n\n def get_context_data(self, **kwargs):\n if not self.request.user.is_authenticated:\n raise PermissionDenied\n\n context = super().get_context_data(**kwargs)\n\n customers = Customer.objects.all()\n query_params = self.request.GET\n\n context['customers'] = filter_and_sort_customers_by_query_params(query_params, customers)\n return context\n\n\nclass CustomersDetailView(DetailView):\n \"\"\"\n Render template with information about certain customer\n \"\"\"\n template_name = \"customers_detail.html\"\n model = Customer\n\n def get_object(self, queryset=None):\n if not self.request.user.is_authenticated:\n raise PermissionDenied\n\n obj = super().get_object()\n return obj\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n pk = self.get_object().pk\n\n context.update({\n 'delete_url': reverse_lazy('customers-delete', kwargs={'pk': pk}),\n 'logout_url': URLS['logout_url']\n })\n return context\n\n\nclass CustomersDeleteView(DeleteView):\n \"\"\"\n Delete certain customer\n \"\"\"\n template_name = 'customers_confirm_delete.html'\n model = Customer\n success_url = URLS['home_url']\n\n def get_object(self, queryset=None):\n if not self.request.user.is_authenticated:\n raise PermissionDenied\n\n obj = super().get_object()\n if self.request.user.is_staff:\n return obj\n\n if self.request.user.customer != obj:\n raise PermissionDenied\n\n return obj\n\n\nclass CustomersCreateView(View):\n \"\"\"\n Render registration template\n \"\"\"\n user_form_class = UserCreateForm\n customer_form_class = CustomerForm\n\n template_name = 'customers_create.html'\n\n def get(self, request, *args, **kwargs):\n if not request.user.is_staff:\n raise PermissionDenied\n\n return render(request,\n self.template_name,\n {'user_form': self.user_form_class,\n 'customer_form': self.customer_form_class,\n 'auth_url': URLS['auth_url']\n }\n )\n\n @transaction.atomic()\n def post(self, request, *args, **kwargs):\n if not request.user.is_staff:\n raise PermissionDenied\n\n user_form = UserCreateForm(request.POST)\n\n customer_form = CustomerForm(request.POST, request.FILES or None)\n if user_form.is_valid() and customer_form.is_valid():\n if are_passwords_match(user_form):\n user_cleaned_data = user_form.cleaned_data\n user_cleaned_data.pop('confirm_password')\n\n user = User.objects.create_user(**user_cleaned_data)\n customer = customer_form.save(commit=False)\n\n photo = Photo.objects.create(photo=request.FILES['photo'])\n\n customer.user = user\n customer.photo = photo\n\n customer.save()\n\n return HttpResponseRedirect(reverse('customers-detail', kwargs={'pk': customer.pk}))\n\n return render(request, self.template_name, {'user_form': user_form, 'customer_form': customer_form})\n\n\nclass CustomersAuthView(View):\n \"\"\"\n Render authentication template\n \"\"\"\n template_name = 'customers_auth.html'\n form_class = LoginForm\n\n def get(self, request, *args, **kwargs):\n return render(request,\n self.template_name,\n {'form': self.form_class, 'create_url': URLS['create_url']}\n )\n\n def post(self, request, *args, **kwargs):\n user_form = LoginForm(request.POST)\n if user_form.is_valid():\n if are_passwords_match(user_form):\n user_cleaned_data = user_form.cleaned_data\n user = authenticate(username=user_cleaned_data['username'], password=user_cleaned_data['password'])\n if user:\n login(request, user)\n return redirect('home')\n else:\n msg = 'Invalid login or password'\n user_form.errors['__all__'] = user_form.error_class([msg])\n\n return render(request, self.template_name, {'form': user_form})\n", "sub_path": "customers_app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 7689, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.urls.reverse_lazy", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 19, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 23, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 40, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.customer", "line_number": 50, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 50, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 51, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 59, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 60, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 63, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 72, "usage_type": "name"}, {"api_name": "customers_app.models.Photo.objects.filter", "line_number": 76, "usage_type": "call"}, {"api_name": "customers_app.models.Photo.objects", "line_number": 76, "usage_type": "attribute"}, {"api_name": "customers_app.models.Photo", "line_number": 76, "usage_type": "name"}, {"api_name": "customers_app.models.Photo.max_points", "line_number": 77, "usage_type": "attribute"}, {"api_name": "customers_app.models.Photo", "line_number": 77, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 88, "usage_type": "call"}, {"api_name": "customers_app.models.Photo", "line_number": 88, "usage_type": "argument"}, {"api_name": "django.views.generic.TemplateView", "line_number": 94, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 103, "usage_type": "name"}, {"api_name": "customers_app.models.Customer.objects.all", "line_number": 107, "usage_type": "call"}, {"api_name": "customers_app.models.Customer.objects", "line_number": 107, "usage_type": "attribute"}, {"api_name": "customers_app.models.Customer", "line_number": 107, "usage_type": "name"}, {"api_name": "customers_app.helpers.filter_and_sort_customers_by_query_params", "line_number": 110, "usage_type": "call"}, {"api_name": "django.views.generic.DetailView", "line_number": 114, "usage_type": "name"}, {"api_name": "customers_app.models.Customer", "line_number": 119, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 123, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 133, "usage_type": "call"}, {"api_name": "django.views.generic.DeleteView", "line_number": 139, "usage_type": "name"}, {"api_name": "customers_app.models.Customer", "line_number": 144, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 149, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 156, "usage_type": "name"}, {"api_name": "django.views.generic.View", "line_number": 161, "usage_type": "name"}, {"api_name": "customers_app.forms.UserCreateForm", "line_number": 165, "usage_type": "name"}, {"api_name": "customers_app.forms.CustomerForm", "line_number": 166, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 172, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 174, "usage_type": "call"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 185, "usage_type": "name"}, {"api_name": "customers_app.forms.UserCreateForm", "line_number": 187, "usage_type": "call"}, {"api_name": "customers_app.forms.CustomerForm", "line_number": 189, "usage_type": "call"}, {"api_name": "customers_app.helpers.are_passwords_match", "line_number": 191, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.create_user", "line_number": 195, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 195, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 195, "usage_type": "name"}, {"api_name": "customers_app.models.Photo.objects.create", "line_number": 198, "usage_type": "call"}, {"api_name": "customers_app.models.Photo.objects", "line_number": 198, "usage_type": "attribute"}, {"api_name": "customers_app.models.Photo", "line_number": 198, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 205, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 205, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 207, "usage_type": "call"}, {"api_name": "django.db.transaction.atomic", "line_number": 182, "usage_type": "call"}, {"api_name": "django.db.transaction", "line_number": 182, "usage_type": "name"}, {"api_name": "django.views.generic.View", "line_number": 210, "usage_type": "name"}, {"api_name": "customers_app.forms.LoginForm", "line_number": 215, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 218, "usage_type": "call"}, {"api_name": "customers_app.forms.LoginForm", "line_number": 224, "usage_type": "call"}, {"api_name": "customers_app.helpers.are_passwords_match", "line_number": 226, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 228, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 230, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 231, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 236, "usage_type": "call"}]} +{"seq_id": "142629402", "text": "import keras\nfrom keras.applications.vgg16 import VGG16, preprocess_input\nfrom keras.preprocessing.image import load_img,img_to_array\nfrom keras.models import Model\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nclass vizdnn:\n\n def __init__(self, model_arch, layer_name, image_name):\n \"\"\"\n model_arch: Model architecture \n layer_name: Neural Network layer of interest\n image_name: Test image name\n \"\"\"\n self.model = model_arch\n self.layer_name = layer_name\n self.image_name = image_name\n\n def preprocess_image(self):\n input_width = self.model.input_shape[1]\n input_height = self.model.input_shape[2]\n image = load_img(self.image_name, target_size=(input_width , input_height))\n image = img_to_array(image)\n image = np.expand_dims(image, axis=0)\n image = preprocess_input(image) \n return image \n\n\n def get_layer(self):\n model_layers_dic = { layer.name: layer for layer in self.model.layers}\n trimmed_model = Model(inputs=self.model.inputs, outputs= model_layers_dic[self.layer_name].output)\n feature_maps = trimmed_model.predict(self.preprocess_image())\n return feature_maps\n\n def viz_feature_map(self , feature_map):\n plt.figure(figsize = (25 , 25))\n square = int(np.sqrt(feature_map.shape[-1]))\n index = 1\n for _ in range(square):\n for _ in range(square):\n ax = plt.subplot(square, square, index)\n ax.set_xticks([])\n ax.set_yticks([])\n plt.imshow(feature_map[0, :, :, index-1] , cmap= 'YlOrRd')\n index += 1\n return plt.show()\n", "sub_path": "build/lib/vizdnn/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1571, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "keras.preprocessing.image.load_img", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.img_to_array", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 26, "usage_type": "call"}, {"api_name": "keras.applications.vgg16.preprocess_input", "line_number": 27, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}]} +{"seq_id": "518090362", "text": "\"\"\"OCSS URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.9/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url,include\nfrom django.contrib import admin\nfrom onlinecourse import views\n\nurlpatterns = [\n url(r'^$',views.login),\n url(r'^bbs/',include('bbs.urls')),\n url(r'^register/',views.register),\n url(r'^home/',views.home),\n url(r'^test/',include('onlinecourse.urls')),\n url(r'^test01/',views.test01),\n url(r'^admin/', admin.site.urls),\n url(r'^index/',views.index),\n url(r'^stu_add/',views.stu_add),\n url(r'^tea_add/',views.tea_add),\n url(r'^cou_add/',views.cou_add),\n url(r'^stu_modify/',views.stu_modify),\n url(r'^tea_modify/',views.tea_modify),\n url(r'^cou_modify/',views.cou_modify),\n url(r'^help/',views.help),\n\n # url(r'^test/',views.test),\n url(r'^.*$',views.error),]\n", "sub_path": "OCSS/OCSS/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1387, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "onlinecourse.views.login", "line_number": 21, "usage_type": "attribute"}, {"api_name": "onlinecourse.views", "line_number": 21, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "onlinecourse.views.register", "line_number": 23, "usage_type": "attribute"}, {"api_name": "onlinecourse.views", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "onlinecourse.views.home", "line_number": 24, "usage_type": "attribute"}, {"api_name": "onlinecourse.views", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 25, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "onlinecourse.views.test01", "line_number": 26, "usage_type": "attribute"}, {"api_name": "onlinecourse.views", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 27, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}, {"api_name": "onlinecourse.views.index", "line_number": 28, "usage_type": "attribute"}, {"api_name": "onlinecourse.views", "line_number": 28, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 29, "usage_type": "call"}, {"api_name": "onlinecourse.views.stu_add", "line_number": 29, "usage_type": "attribute"}, {"api_name": "onlinecourse.views", "line_number": 29, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 30, "usage_type": "call"}, {"api_name": "onlinecourse.views.tea_add", "line_number": 30, "usage_type": "attribute"}, {"api_name": "onlinecourse.views", "line_number": 30, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 31, "usage_type": "call"}, {"api_name": "onlinecourse.views.cou_add", "line_number": 31, "usage_type": "attribute"}, {"api_name": "onlinecourse.views", "line_number": 31, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 32, "usage_type": "call"}, {"api_name": "onlinecourse.views.stu_modify", "line_number": 32, "usage_type": "attribute"}, {"api_name": "onlinecourse.views", "line_number": 32, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 33, "usage_type": "call"}, {"api_name": "onlinecourse.views.tea_modify", "line_number": 33, "usage_type": "attribute"}, {"api_name": "onlinecourse.views", "line_number": 33, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 34, "usage_type": "call"}, {"api_name": "onlinecourse.views.cou_modify", "line_number": 34, "usage_type": "attribute"}, {"api_name": "onlinecourse.views", "line_number": 34, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 35, "usage_type": "call"}, {"api_name": "onlinecourse.views.help", "line_number": 35, "usage_type": "attribute"}, {"api_name": "onlinecourse.views", "line_number": 35, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 38, "usage_type": "call"}, {"api_name": "onlinecourse.views.error", "line_number": 38, "usage_type": "attribute"}, {"api_name": "onlinecourse.views", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "191822874", "text": "# uncompyle6 version 3.6.7\n# Python bytecode 2.6 (62161)\n# Decompiled from: Python 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)]\n# Embedded file name: /home/uittenbroek/Projects/buildout-nuffic/src/collective.newrelic/collective/newrelic/patches/catalog_tool.py\n# Compiled at: 2013-12-24 05:41:42\nimport newrelic.agent, newrelic.api\nfrom Products.CMFPlone.CatalogTool import CatalogTool\nfrom collective.newrelic.utils import logger\nCatalogTool.original_cmfplone_catalogtool_searchResults = CatalogTool.searchResults\n\ndef newrelic_searchResults(self, REQUEST=None, **kw):\n trans = newrelic.agent.current_transaction()\n with newrelic.api.database_trace.DatabaseTrace(trans, str(kw), self):\n result = self.original_cmfplone_catalogtool_searchResults(REQUEST, **kw)\n return result\n\n\nCatalogTool.searchResults = newrelic_searchResults\nlogger.info('Patched Products.CMFPlone.CatalogTool:CatalogTool.searchResults with instrumentation')", "sub_path": "pycfiles/collective.newsflash-1.0/catalog_tool.py", "file_name": "catalog_tool.py", "file_ext": "py", "file_size_in_byte": 978, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "Products.CMFPlone.CatalogTool.CatalogTool.original_cmfplone_catalogtool_searchResults", "line_number": 9, "usage_type": "attribute"}, {"api_name": "Products.CMFPlone.CatalogTool.CatalogTool", "line_number": 9, "usage_type": "name"}, {"api_name": "Products.CMFPlone.CatalogTool.CatalogTool.searchResults", "line_number": 9, "usage_type": "attribute"}, {"api_name": "newrelic.agent.agent.current_transaction", "line_number": 12, "usage_type": "call"}, {"api_name": "newrelic.agent.agent", "line_number": 12, "usage_type": "attribute"}, {"api_name": "newrelic.agent", "line_number": 12, "usage_type": "name"}, {"api_name": "newrelic.agent.api.database_trace.DatabaseTrace", "line_number": 13, "usage_type": "call"}, {"api_name": "newrelic.agent.api", "line_number": 13, "usage_type": "attribute"}, {"api_name": "newrelic.agent", "line_number": 13, "usage_type": "name"}, {"api_name": "Products.CMFPlone.CatalogTool.CatalogTool.searchResults", "line_number": 18, "usage_type": "attribute"}, {"api_name": "Products.CMFPlone.CatalogTool.CatalogTool", "line_number": 18, "usage_type": "name"}, {"api_name": "collective.newrelic.utils.logger.info", "line_number": 19, "usage_type": "call"}, {"api_name": "collective.newrelic.utils.logger", "line_number": 19, "usage_type": "name"}]} +{"seq_id": "337206462", "text": "from typing import Dict\n\nfrom meiga import Result, Error, Success\nfrom meiga.assertions import assert_success, assert_failure\nfrom meiga.decorators import meiga\n\nfrom alice import Onboarding, Config\n\n\ndef test_should_return_an_error_when_the_api_key_is_not_configured():\n\n config = Config()\n onboarding = Onboarding.from_config(config)\n\n result = onboarding.create_user()\n\n assert_failure(result)\n\n\ndef test_should_do_complete_onboarding_process(\n given_valid_api_key,\n given_any_selfie_image_media_data,\n given_any_document_front_media_data,\n given_any_document_back_media_data,\n):\n @meiga\n def do_complete_onboarding() -> Result[dict, Error]:\n config = Config(api_key=given_valid_api_key)\n\n onboarding = Onboarding.from_config(config)\n\n user_id = onboarding.create_user().unwrap_or_return()\n onboarding.add_selfie(\n user_id=user_id, media_data=given_any_selfie_image_media_data\n ).unwrap_or_return()\n document_id = onboarding.create_document(\n user_id=user_id, type=\"idcard\", issuing_country=\"ESP\"\n ).unwrap_or_return()\n onboarding.add_document(\n user_id=user_id,\n document_id=document_id,\n media_data=given_any_document_front_media_data,\n side=\"front\",\n manual=True,\n ).unwrap_or_return()\n onboarding.add_document(\n user_id=user_id,\n document_id=document_id,\n media_data=given_any_document_back_media_data,\n side=\"back\",\n manual=True,\n ).handle()\n onboarding.document_properties(\n user_id=user_id, document_id=document_id\n ).unwrap_or_return()\n\n report = onboarding.create_report(user_id=user_id).unwrap_or_return()\n\n certificate_id = onboarding.create_certificate(\n user_id=user_id\n ).unwrap_or_return()\n\n _ = onboarding.retrieve_certificate(\n user_id=user_id, certificate_id=certificate_id\n ).unwrap_or_return()\n\n _ = onboarding.retrieve_certificates(user_id=user_id).unwrap_or_return()\n\n onboarding.delete_user(user_id).unwrap_or_return()\n\n return Success(report)\n\n result = do_complete_onboarding()\n\n assert_success(result, value_is_instance_of=Dict)\n", "sub_path": "tests/test_integration_onboarding.py", "file_name": "test_integration_onboarding.py", "file_ext": "py", "file_size_in_byte": 2306, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "alice.Config", "line_number": 12, "usage_type": "call"}, {"api_name": "alice.Onboarding.from_config", "line_number": 13, "usage_type": "call"}, {"api_name": "alice.Onboarding", "line_number": 13, "usage_type": "name"}, {"api_name": "meiga.assertions.assert_failure", "line_number": 17, "usage_type": "call"}, {"api_name": "alice.Config", "line_number": 28, "usage_type": "call"}, {"api_name": "alice.Onboarding.from_config", "line_number": 30, "usage_type": "call"}, {"api_name": "alice.Onboarding", "line_number": 30, "usage_type": "name"}, {"api_name": "meiga.Success", "line_number": 71, "usage_type": "call"}, {"api_name": "meiga.decorators.meiga", "line_number": 26, "usage_type": "name"}, {"api_name": "meiga.Result", "line_number": 27, "usage_type": "name"}, {"api_name": "meiga.Error", "line_number": 27, "usage_type": "name"}, {"api_name": "meiga.assertions.assert_success", "line_number": 75, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 75, "usage_type": "name"}]} +{"seq_id": "53673828", "text": "from flask import Flask,redirect, url_for, render_template,request, session, Blueprint\nfrom bs4 import BeautifulSoup as soup\nimport string\nimport requests\nimport wikipedia\nimport json\nfrom pprint import pprint\n\nfrom website import func\nimport PIL.Image\n\n\nfrom wikipedia.wikipedia import summary\n\n\n#from transformer into translate\n\nviews = Blueprint('views', __name__)\n#Global variables\n\ninfobox={}\nimages={}\ninfoSummary = {}\n\n\n\n\n#@views.route('/')\n@views.route(\"/\", methods=[\"POST\", \"GET\"])\ndef getInput():\n if request.method == 'POST':\n #Retrieve the seraching target\n aim = request.form['content']\n section = request.form[\"section\"]\n session[\"section\"] = section\n #Store it in session\n session[\"content\"] = aim\n return redirect(url_for(\"views.scrape\"))\n else:\n return render_template(\"search.html\")\n \n\n\n#Home page\n@views.route(\"/home\")\ndef displayHome():\n return render_template(\"home.html\")\n\n#Car service\n@views.route(\"/car\", methods=[\"POST\", \"GET\"])\ndef car():\n if request.method == 'POST':\n #Store car brand in the session\n session[\"car\"] = request.form[\"car\"]\n #Go to scrape_car\n return redirect(url_for(\"views.scrape_car\"))\n else:\n return render_template(\"car.html\")\n\n#Scraping car images\n@views.route(\"/scrape_car\", methods=[\"POST\", \"GET\"])\ndef scrape_car():\n cars = {}\n if request.method == \"POST\":\n pass\n else:\n if \"car\" in session:\n #Retrieve the car brand\n brand = session[\"car\"]\n else:\n return redirect(url_for(\"car\"))\n\n\n #Format the input\n brand = func.formatStr(brand)\n #Retrieve image tag from infobox html \n img = func.access_car_wiki(brand)\n\n brand = func.formatStr(brand)\n wikiURL = \"https://en.wikipedia.org/wiki/\"+brand\n data = requests.get(wikiURL)\n #Returns an array containing all the html code\n contents = soup(data.content, \"html.parser\")\n #Returns an array containing infobox html code\n info = contents(\"td\", {\"class\":\"infobox-image\"})[0]\n #print(info)\n img = info.find_all(\"img\")[0]\n \n\n cars[\"path\"] = \"C:\\OSU\\CS361\\WebScrapper\\car.json\"\n cars[\"img\"] = \"https:\"+img[\"src\"]\n cars[\"brand\"] = brand\n\n json_car = json.dumps(cars, indent=len(cars))\n with open(\"car.json\", \"w\") as f:\n f.write(json_car)\n\n\n cars = func.write_cars_json(brand, img)\n return render_template(\"scrape_car.html\", name=brand, img=cars[\"img\"])\n \n\n\n\n@views.route(\"/scrape\", methods=[\"POST\", \"GET\"])\ndef scrape():\n \n if request.method == 'POST':\n #Retrieve the language from the user's request\n language = request.form['language']\n #Store the language at the session temporarily\n session[\"language\"] = language\n info = session[\"summary\"]\n #Update the summary json file by adding language\n summary = func.update_summary(info, language)\n print(summary)\n return render_template(\"scrape.html\", part = session[\"section\"], summary=summary[\"context\"], content=infobox, language=language)\n\n else:\n content = \"\"\n if \"content\" in session and \"section\" in session:\n content = session[\"content\"]\n section = session[\"section\"]\n else:\n return redirect(url_for(\"getInput\"))\n \n #Get all the content from Wikipedia\n search_result = wikipedia.page(wikipedia.search(content)[0])\n #Retrieve the scraping summary\n infoSummary = func.write_summary_json(search_result.summary)\n #Retrieve the scraping images\n images = func.write_image_json(search_result.images)\n session[\"summary\"] = infoSummary\n\n return render_template(\"scrape.html\", part=section, summary=infoSummary[\"context\"], images=images[\"links\"])\n\n\n\n@views.route(\"/transform\", methods=[\"POST\", \"GET\"])\ndef transform():\n if request.method == \"POST\":\n if \"language\" in session:\n language = session[\"language\"]\n \n #Translate via my partner's service in the backend\n #Support other language\n with open(\"output.txt\", \"r\", encoding=\"utf8\") as f:\n #Retrieve scraping data(dictionary)\n translated_content = f.read()\n #Go to a separate web page to display translated content\n return render_template(\"transform.html\", language=language, content=translated_content)\n else:\n return redirect(url_for(\"views.scrape\"))\n\n\n@views.route(\"/carImage\", methods=[\"POST\", \"GET\"])\ndef showImage():\n if request.method == \"POST\":\n\n imgSrc = \"website/new_img.jpg\"\n carImage = PIL.Image.open(imgSrc)\n carImage.show()\n \n return render_template(\"carImage.html\")\n\n else:\n return redirect(url_for(\"views.scrape_car\"))\n\n\n\n", "sub_path": "website/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4923, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask.Blueprint", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 33, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 52, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 52, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 64, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 67, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 69, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 71, "usage_type": "call"}, {"api_name": "website.func.formatStr", "line_number": 75, "usage_type": "call"}, {"api_name": "website.func", "line_number": 75, "usage_type": "name"}, {"api_name": "website.func.access_car_wiki", "line_number": 77, "usage_type": "call"}, {"api_name": "website.func", "line_number": 77, "usage_type": "name"}, {"api_name": "website.func.formatStr", "line_number": 79, "usage_type": "call"}, {"api_name": "website.func", "line_number": 79, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 81, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 83, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 94, "usage_type": "call"}, {"api_name": "website.func.write_cars_json", "line_number": 99, "usage_type": "call"}, {"api_name": "website.func", "line_number": 99, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 108, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 108, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 110, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 110, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 112, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 113, "usage_type": "name"}, {"api_name": "wikipedia.wikipedia.summary", "line_number": 115, "usage_type": "name"}, {"api_name": "website.func.update_summary", "line_number": 115, "usage_type": "call"}, {"api_name": "website.func", "line_number": 115, "usage_type": "name"}, {"api_name": "wikipedia.wikipedia.summary", "line_number": 116, "usage_type": "argument"}, {"api_name": "flask.render_template", "line_number": 117, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 117, "usage_type": "name"}, {"api_name": "wikipedia.wikipedia.summary", "line_number": 117, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 121, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 122, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 123, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 125, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 125, "usage_type": "call"}, {"api_name": "wikipedia.page", "line_number": 128, "usage_type": "call"}, {"api_name": "wikipedia.search", "line_number": 128, "usage_type": "call"}, {"api_name": "website.func.write_summary_json", "line_number": 130, "usage_type": "call"}, {"api_name": "website.func", "line_number": 130, "usage_type": "name"}, {"api_name": "website.func.write_image_json", "line_number": 132, "usage_type": "call"}, {"api_name": "website.func", "line_number": 132, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 133, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 135, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 141, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 141, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 142, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 143, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 151, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 153, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 153, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 158, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 158, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 161, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 161, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 161, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 164, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 167, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 167, "usage_type": "call"}]} +{"seq_id": "200392313", "text": "import pymongo\nimport datetime\nimport numpy as np\nfrom gensim.models.doc2vec import Doc2Vec\nimport ipdb\n# import MeCab\n\nclass Sentence(dict):\n __client = pymongo.MongoClient()\n db = __client['language_analysis']\n collection_name = ''\n __getattr__ = dict.__getitem__\n __setattr__ = dict.__setitem__\n # DEFAULT_DOC2VEC_MODEL_NAME = \"jawiki_wakati_neo_200-210_17\"\n DEFAULT_DOC2VEC_MODEL_NAME = \"jawiki_wakati_neo_001-300_3\"\n # DEFAULT_DOC2VEC_MODEL_NAME = \"jawiki_line_200-300_5\"\n DEFAULT_COLLECTION_NAME = \"line_talk\"\n\n __structure__ = {\n '_id': int,\n # 'user_name': str,\n # 'user_id': int,\n 'content': str,\n 'wakati_content': str,\n 'chasen_content': str,\n 'docvecs': dict\n }\n\n def __init__(self, initial_dict):\n for key, value_type in self.__structure__.items():\n self[key] = initial_dict.get(key)\n try:\n self[key] = value_type(self[key])\n except:\n pass\n\n # @overrides(dict)\n def update(self):\n return self.db[self.collection_name].update_one(\n {'_id': self._id},\n self\n )\n\n @classmethod\n def find(cls, *args, **kwargs):\n cls.db[cls.collection_name].find()\n talks = cls.db[cls.collection_name].find(*args, **kwargs)\n talk_list = []\n for i, talk in enumerate(talks):\n talk_list.append(cls(talk))\n return talk_list\n\n @classmethod\n def find_one(cls, filter, *args, **kwargs):\n talk = cls.db[cls.collection_name].find_one(filter, *args, **kwargs)\n if talk:\n return cls(talk)\n else:\n return cls({})\n\n @classmethod\n def update_one(cls, filter, update, upsert=False):\n return cls.db[cls.collection_name].update_one(filter, update, upsert)\n\n\n @classmethod\n def get_doc2vec_model(cls, doc2vec_model_name):\n doc2vec_model_name_base = '_'.join(doc2vec_model_name.split('_')[:-1])\n doc2vec_model_filename = 'models/{}/{}.model'.format(\n doc2vec_model_name_base, doc2vec_model_name)\n doc2vec_model = Doc2Vec.load(doc2vec_model_filename)\n return doc2vec_model\n\n @classmethod\n def infer_docvecs(cls, doc2vec_model_name=DEFAULT_DOC2VEC_MODEL_NAME,\n collection_name=DEFAULT_COLLECTION_NAME):\n cls.collection_name = collection_name\n doc2vec_model = cls.get_doc2vec_model(doc2vec_model_name)\n talks = cls.find({})\n for talk in talks:\n # print(talk.wakati_content)\n docvec = doc2vec_model.infer_vector(\n talk.wakati_content\n ).tolist()\n if talk.docvecs is None:\n talk.docvecs = {doc2vec_model_name: docvec}\n else:\n talk.docvecs[doc2vec_model_name] = docvec\n talk.update_one({\n '_id': talk._id,\n }, {'$set': {'docvecs': talk.docvecs}})\n # {doc2vec_model_name: docvec}\n\n @classmethod\n def most_similar_with_id(cls, id, topn=10,\n model_name=DEFAULT_DOC2VEC_MODEL_NAME,\n collection_name=DEFAULT_COLLECTION_NAME):\n cls.collection_name = collection_name\n base_talk = cls.find_one({'_id': id})\n # print(\"id: {}, content: {}\".format(base_talk._id, base_talk.content))\n if base_talk.docvecs:\n try:\n base_talk_docvec = np.array(base_talk.docvecs[model_name])\n except KeyError as e:\n raise FileExistsError(\"{} docvec is not exist. Please run Sentense.infer_docvecs for {}.\")\n cls.most_similar_with_docvec(\n base_talk_docvec, topn=topn,\n model_name=model_name, collection_name=collection_name)\n else:\n return None\n\n @classmethod\n def most_similar_with_doc(cls, document, topn=10,\n model_name=DEFAULT_DOC2VEC_MODEL_NAME,\n collection_name=DEFAULT_COLLECTION_NAME):\n print(document)\n wakati_tagger = MeCab.Tagger(\"-Owakati\")\n wakati_document = wakati_tagger.parse(str(document))\n doc2vec_model = cls.get_doc2vec_model(model_name)\n docvec = doc2vec_model.infer_vector(wakati_document)\n cls.most_similar_with_docvec(\n docvec, topn=topn,\n model_name=model_name, collection_name=collection_name)\n\n @classmethod\n def most_similar_with_docvec(cls, base_talk_docvec, topn=10,\n model_name=DEFAULT_DOC2VEC_MODEL_NAME,\n collection_name=DEFAULT_COLLECTION_NAME):\n cls.collection_name = collection_name\n doc2vec_model = cls.get_doc2vec_model(model_name)\n base_talk_docvec_length = np.linalg.norm(base_talk_docvec)\n talks = cls.find({})\n similarities = []\n ids = []\n for talk in talks:\n try:\n docvec = np.array(talk.docvecs[model_name])\n except KeyError as e:\n docvec = doc2vec_model.infer_vector(talk.wakati_content)\n talk.docvecs[model_name] = docvec\n talk.update()\n docvec_length = np.linalg.norm(docvec)\n similarity = np.dot(base_talk_docvec, docvec) / (\n base_talk_docvec_length * docvec_length)\n if similarity.any():\n similarities.append(similarity)\n ids.append(talk._id)\n most_similar_indexes = np.argsort(similarities)[::-1]\n for index in most_similar_indexes[:topn]:\n document = cls.find_one({'_id': ids[index]})\n reply_document = cls.find_one({'_id': ids[index] + 1})\n print(\"id: {}, similarity: {}, content: {}\".format(\n document._id, similarities[index], document.content,\n # np.linalg.norm(np.array(document.docvecs[model_name]))\n ))\n print(\"\\treply: {}\".format(reply_document['content']))\n\n\nif __name__ == '__main__':\n # Sentence.infer_docvecs()\n sentence = Sentence({})\n sentence.most_similar_with_id(1)\n # Sentence.most_similar_with_doc('あしたきますか')\n import ipdb\n ipdb.set_trace()", "sub_path": "lang_analysis/doc2vec/sentence.py", "file_name": "sentence.py", "file_ext": "py", "file_size_in_byte": 6236, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pymongo.MongoClient", "line_number": 9, "usage_type": "call"}, {"api_name": "gensim.models.doc2vec.Doc2Vec.load", "line_number": 71, "usage_type": "call"}, {"api_name": "gensim.models.doc2vec.Doc2Vec", "line_number": 71, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 131, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 142, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 148, "usage_type": "call"}, {"api_name": "ipdb.set_trace", "line_number": 165, "usage_type": "call"}]} +{"seq_id": "573589229", "text": "import setuptools\nimport os\n\nfrom orchestrator.common import setup as common_setup\n\nrequires = common_setup.parse_requirements()\ndepend_links = common_setup.parse_dependency_links()\nproject = 'orchestrator-facade'\n\n\nsetuptools.setup(\n name=project,\n version=\"1.0.0\",\n description='The facade of M&O projects',\n author='EMC Labs China',\n author_email='Layne.Peng@emc.com',\n url='http://dcade.lss.emc.com/',\n classifiers=[\n 'Environment :: OpenStack',\n 'Intended Audience :: Information Technology',\n 'Intended Audience :: System Administrators',\n 'License :: OSI Approved :: Apache Software License',\n 'Operating System :: POSIX :: Linux',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 2.7',\n ],\n packages=setuptools.find_packages(exclude=['bin', 'smoketests']),\n install_requires=requires,\n dependency_links=depend_links,\n include_package_data=True,\n setup_requires=['setuptools_git>=0.4'],\n scripts=['bin/orchestrator-facade'],\n data_files = [(['/etc/orchestrator-facade'][os.sep == '\\\\'],['etc/orchestrator-facade/orchestrator.conf.sample', 'etc/orchestrator-facade/logging.conf.sample']),\n (['/opt/orchestrator/orchestrator/static'][os.sep == '\\\\'], map(lambda x: 'orchestrator/static/'+x, os.listdir('orchestrator/static')))]\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1415, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "orchestrator.common.setup.parse_requirements", "line_number": 6, "usage_type": "call"}, {"api_name": "orchestrator.common.setup", "line_number": 6, "usage_type": "name"}, {"api_name": "orchestrator.common.setup.parse_dependency_links", "line_number": 7, "usage_type": "call"}, {"api_name": "orchestrator.common.setup", "line_number": 7, "usage_type": "name"}, {"api_name": "setuptools.setup", "line_number": 11, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 28, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "301224524", "text": "# -*- coding: utf-8 -*-\n\n'''\nЗадание 25.2c\n\nСкопировать класс CiscoTelnet из задания 25.2b и изменить метод send_config_commands добавив проверку команд на ошибки.\n\nУ метода send_config_commands должен быть дополнительный параметр strict:\n* strict=True значит, что при обнаружении ошибки, необходимо сгенерировать исключение ValueError\n* strict=False значит, что при обнаружении ошибки, надо только вывести на стандартный поток вывода сообщене об ошибке\n\nМетод дожен возвращать вывод аналогичный методу send_config_set у netmiko (пример вывода ниже).\nТекст исключения и ошибки в примере ниже.\n\nПример создания экземпляра класса:\nIn [1]: from task_25_2c import CiscoTelnet\n\nIn [2]: r1_params = {\n ...: 'ip': '192.168.100.1',\n ...: 'username': 'cisco',\n ...: 'password': 'cisco',\n ...: 'secret': 'cisco'}\n\nIn [3]: r1 = CiscoTelnet(**r1_params)\n\nIn [4]: commands_with_errors = ['logging 0255.255.1', 'logging', 'i']\nIn [5]: correct_commands = ['logging buffered 20010', 'ip http server']\nIn [6]: commands = commands_with_errors+correct_commands\n\nИспользование метода send_config_commands:\n\nIn [7]: print(r1.send_config_commands(commands, strict=False))\nПри выполнении команды \"logging 0255.255.1\" на устройстве 192.168.100.1 возникла ошибка -> Invalid input detected at '^' marker.\nПри выполнении команды \"logging\" на устройстве 192.168.100.1 возникла ошибка -> Incomplete command.\nПри выполнении команды \"i\" на устройстве 192.168.100.1 возникла ошибка -> Ambiguous command: \"i\"\nconf t\nEnter configuration commands, one per line. End with CNTL/Z.\nR1(config)#logging 0255.255.1\n ^\n% Invalid input detected at '^' marker.\n\nR1(config)#logging\n% Incomplete command.\n\nR1(config)#i\n% Ambiguous command: \"i\"\nR1(config)#logging buffered 20010\nR1(config)#ip http server\nR1(config)#end\nR1#\n\nIn [8]: print(r1.send_config_commands(commands, strict=True))\n---------------------------------------------------------------------------\nValueError Traceback (most recent call last)\n in \n----> 1 print(r1.send_config_commands(commands, strict=True))\n\n...\n\nValueError: При выполнении команды \"logging 0255.255.1\" на устройстве 192.168.100.1 возникла ошибка -> Invalid input detected at '^' marker.\n\n'''\n\nimport telnetlib\nimport time\nimport clitable\nimport re\n\n\nclass CiscoTelnet:\n def __init__(self, **kwargs):\n self.t = telnetlib.Telnet(kwargs['ip'])\n self.t.read_until(b'Username:')\n self._write_line(kwargs['username'])\n self.t.read_until(b'Password:')\n self._write_line(kwargs['password'])\n self._write_line('enable')\n self._write_line(kwargs['secret'])\n self._write_line('terminal length 0')\n # Just to clear buffer\n self.t.read_until(b'terminal length 0')\n self.t.read_until(b'#')\n\n def _write_line(self, command):\n self.t.write(command.encode('utf-8') + b'\\n')\n\n def _parse_command_dynamic(self, command_output, attributes_dict, index_file='index', templ_path='templates'):\n cli_table = clitable.CliTable(index_file, templ_path)\n cli_table.ParseCmd(command_output, attributes_dict)\n headers = list(cli_table.header)\n result = [dict(zip(headers, list(row))) for row in cli_table]\n return result\n\n def send_show_command(self, command, templates, parse):\n self._write_line(command)\n time.sleep(3)\n command_output = self.t.read_very_eager().decode('utf-8')\n if parse:\n attributes = {'Command': command, 'Vendor': 'cisco_ios'}\n result = self._parse_command_dynamic(command_output, attributes, templ_path=templates)\n else:\n result = command_output\n return result\n\n def send_config_commands(self, commands, strict=False):\n result = ''\n if type(commands) == str:\n commands = [commands]\n self._write_line('conf t')\n for command in commands:\n self._write_line(command)\n time.sleep(3)\n command_output = self.t.read_very_eager().decode('utf-8')\n error = re.search(r'^% (.+)\\n', command_output, flags=re.M)\n if error:\n error_msg = (f'При выполнении команды \"{command}\" на устройстве {self.t.host}'\n f' возникла ошибка -> {error.group(1)}')\n if strict:\n raise ValueError(error_msg)\n print(error_msg)\n result += command_output\n return result\n\n\ndef main():\n r1_params = {\n 'ip': '192.168.100.1',\n 'username': 'cisco',\n 'password': 'cisco',\n 'secret': 'cisco'}\n\n test = CiscoTelnet(**r1_params)\n # print(test.send_show_command('sh ip int bri', 'templates', True))\n commands_with_errors = ['logging 0255.255.1', 'logging', 'i']\n correct_commands = ['logging buffered 20010', 'ip http server']\n commands = commands_with_errors + correct_commands\n # print(test.send_config_commands(commands, strict=False))\n print(test.send_config_commands(commands, strict=True))\n test.t.close()\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "exercises/25_oop_basics/task_25_2c.py", "file_name": "task_25_2c.py", "file_ext": "py", "file_size_in_byte": 5800, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "telnetlib.Telnet", "line_number": 72, "usage_type": "call"}, {"api_name": "clitable.CliTable", "line_number": 88, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 96, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 112, "usage_type": "call"}, {"api_name": "re.search", "line_number": 114, "usage_type": "call"}, {"api_name": "re.M", "line_number": 114, "usage_type": "attribute"}]} +{"seq_id": "596064161", "text": "from cloud.aws import *\nfrom cloud.response import Response\nimport base64\n\n# Define the input output format of the function.\n# This information is used when creating the *SDK*.\ninfo = {\n 'input_format': {\n 'session_id': 'str',\n 'file_path': 'str',\n 'index': 'int',\n },\n 'output_format': {\n 'base64': 'bin',\n 'index': 'int',\n 'size': 'int',\n 'success': 'bool'\n }\n}\n\n\ndef do(data, boto3):\n body = {}\n params = data['params']\n app_id = data['app_id']\n user = data['user']\n\n user_id = user.get('id', None)\n\n def has_permission(_item):\n read_groups = _item['read_groups']\n if 'owner' in read_groups:\n owner_id = _item['owner']\n if owner_id == user_id:\n return True\n user_group = user['group']\n return user_group in read_groups\n\n file_path = params.get('file_path')\n index = params.get('index')\n\n table_name = 'storage-{}'.format(app_id)\n bucket_name = 'storage-{}'.format(app_id)\n\n s3 = S3(boto3)\n dynamo = DynamoDB(boto3)\n item = dynamo.get_item(table_name, file_path).get('Item')\n if item:\n if has_permission(item):\n if item['type'] == 'split_file':\n file_key = item['file_key']\n file_bin = s3.download_file_bin(bucket_name, file_key)\n file_b64 = base64.b64encode(file_bin).decode('utf-8')\n body['success'] = True\n body['base64'] = file_b64\n body['index'] = item['index']\n body['size'] = item['size']\n return Response(body)\n else:\n body['success'] = False\n body['message'] = 'file_path is not a split_file'\n return Response(body)\n else:\n body['success'] = False\n body['message'] = 'permission denied'\n return Response(body)\n else:\n body['success'] = False\n body['message'] = 'file_path: {} does not exist'.format(file_path)\n return Response(body)\n\n", "sub_path": "aws_interface/cloud/storage/download_split_b64.py", "file_name": "download_split_b64.py", "file_ext": "py", "file_size_in_byte": 2071, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "base64.b64encode", "line_number": 53, "usage_type": "call"}, {"api_name": "cloud.response.Response", "line_number": 58, "usage_type": "call"}, {"api_name": "cloud.response.Response", "line_number": 62, "usage_type": "call"}, {"api_name": "cloud.response.Response", "line_number": 66, "usage_type": "call"}, {"api_name": "cloud.response.Response", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "366342499", "text": "import time\nimport math\nimport numpy as np\nfrom collections import Counter\nimport pprint\n\nfrom sklearn.tree import DecisionTreeClassifier\n\n# TODO: Debug - Pruning, CART Pruning\n# TODO: Try batch prediction and visualization\n# TODO: Support Continuous Data ; Feed sample-weight\n\n\n# Util\n\nclass Cluster:\n def __init__(self, data, labels, base=2):\n self._data = np.array(data).T\n self._counters = Counter(labels)\n self._labels = np.array(labels)\n self._cache = None\n self._base = base\n\n def ent(self, ent=None, eps=1e-12):\n _len = len(self._labels)\n if ent is None:\n ent = [_val for _val in self._counters.values()]\n return max(eps, -sum([_c / _len * math.log(_c / _len, self._base) for _c in ent]))\n\n def gini(self, p=None):\n if p is None:\n p = [_val for _val in self._counters.values()]\n return 1 - sum([(_p / len(self._labels)) ** 2 for _p in p])\n\n def con_chaos(self, idx, criteria=\"ent\"):\n if criteria == \"ent\":\n _method = lambda cluster: cluster.ent()\n elif criteria == \"gini\":\n _method = lambda cluster: cluster.gini()\n else:\n raise NotImplementedError(\"Conditional info criteria '{}' not defined\".format(criteria))\n data = self._data[idx]\n features = list(sorted(set(data)))\n self._cache = tmp_labels = [data == feature for feature in features]\n label_lst = [self._labels[label] for label in tmp_labels]\n rs = 0\n for data_label, tar_label in zip(tmp_labels, label_lst):\n tmp_data = self._data.T[data_label]\n _ent = _method(Cluster(tmp_data, tar_label, base=self._base))\n rs += len(tmp_data) / len(data) * _ent\n return rs\n\n def info_gain(self, idx, criteria=\"ent\", get_con_chaos=False):\n if criteria in (\"ent\", \"ratio\"):\n _con_chaos = self.con_chaos(idx)\n _gain = self.ent() - _con_chaos\n if criteria == \"ratio\":\n _gain = _gain / self.ent([np.sum(_cache) for _cache in self._cache])\n elif criteria == \"gini\":\n _con_chaos = self.con_chaos(idx, criteria=\"gini\")\n _gain = self.gini() - _con_chaos\n else:\n raise NotImplementedError(\"Info_gain criteria '{}' not defined\".format(criteria))\n return (_gain, _con_chaos) if get_con_chaos else _gain\n\n\n# Node\n\nclass CvDNode:\n def __init__(self, tree=None, max_depth=None, base=2, ent=None,\n depth=0, parent=None, is_root=True, prev_feat=\"Root\"):\n self._data = self.labels = None\n self._max_depth = max_depth\n self._base = base\n self._ent = ent\n self.criteria = None\n self.children = {}\n self.category = None\n\n self.tree = tree\n if tree is not None:\n tree.nodes.append(self)\n self.feature_dim = None\n self._depth = depth\n self.parent = parent\n self._is_root = is_root\n self._prev_feat = prev_feat\n self.weight = 0\n self.leafs = {}\n self.pruned = False\n\n def __getitem__(self, item):\n if isinstance(item, str):\n return getattr(self, \"_\" + item)\n return\n\n @property\n def key(self):\n return self._depth, self._prev_feat, id(self)\n\n @property\n def height(self):\n if self.category is not None:\n return 1\n return 1 + max([_child.height for _child in self.children.values()])\n\n @property\n def prev_feat(self):\n return self._prev_feat\n\n def copy(self):\n _new_node = self.__class__(\n None, self._max_depth, self._base, self._ent,\n self._depth, self.parent, self._is_root, self._prev_feat)\n _new_node.tree = self.tree\n _new_node.feature_dim = self.feature_dim\n _new_node.category = self.category\n _new_node.labels = self.labels\n _new_node.pruned = self.pruned\n if self.children:\n for key, node in self.children.items():\n _new_node.children[key] = node.copy()\n else:\n _new_node.category = self.category\n if self.leafs:\n for key, leaf in self.leafs.items():\n _new_node.leafs[key] = leaf.copy()\n return _new_node\n\n def feed_tree(self, tree):\n self.tree = tree\n self.tree.nodes.append(self)\n\n def feed_data(self, data, labels):\n self._data = np.array(data).T\n self.labels = np.array(labels)\n\n def stop(self, eps):\n if (\n self._data.shape[1] == 1 or (self._ent is not None and self._ent <= eps)\n or (self._max_depth is not None and self._depth >= self._max_depth)\n ):\n self._handle_terminate()\n return True\n return False\n\n def early_stop(self, max_gain, eps):\n if max_gain <= eps:\n self._handle_terminate()\n return True\n return False\n\n def crop(self, x=None):\n x = self._data if x is None else x\n _mask = np.ones(len(x), dtype=np.bool)\n _mask[self.feature_dim] = False\n return x[_mask]\n\n def get_class(self):\n _counter = Counter(self.labels)\n return max(_counter, key=(lambda key: _counter[key]))\n\n def get_threshold(self):\n if self.category is None:\n rs = 0\n for leaf in self.leafs.values():\n _cluster = Cluster(None, leaf, self._base)\n rs += len(leaf) * _cluster.ent()\n return Cluster(None, self.labels, self._base).ent() - rs / (self.weight - 1)\n return 0\n\n def _gen_children(self, features, new_data, con_chaos):\n for feat in set(features):\n _feat_mask = features == feat\n _new_node = self.__class__(\n self.tree, self._max_depth, self._base, ent=con_chaos,\n depth=self._depth + 1, parent=self, is_root=False, prev_feat=feat)\n self.children[feat] = _new_node\n _new_node.fit(new_data[:, _feat_mask].T, self.labels[_feat_mask])\n\n def _handle_terminate(self):\n self.tree.depth = max(self._depth, self.tree.depth)\n self.category = self.get_class()\n _parent = self\n while _parent is not None:\n _parent.leafs[self.key] = self.labels\n _parent.weight += 1\n _parent = _parent.parent\n\n def fit(self, data, labels, eps=1e-8):\n if data is not None and labels is not None:\n self.feed_data(data, labels)\n if self.stop(eps):\n return\n _cluster = Cluster(self._data.T, self.labels, self._base)\n _max_gain, _con_chaos = _cluster.info_gain(0, criteria=self.criteria, get_con_chaos=True)\n _max_feature = 0\n for i in range(1, len(self._data)):\n _tmp_gain, _tmp_con_chaos = _cluster.info_gain(i, criteria=self.criteria, get_con_chaos=True)\n if _tmp_gain > _max_gain:\n (_max_gain, _con_chaos), _max_feature = (_tmp_gain, _tmp_con_chaos), i\n if self.early_stop(_max_gain, eps):\n return\n self.feature_dim = _max_feature\n self._gen_children(self._data[_max_feature], self.crop(), _con_chaos)\n if self._is_root:\n self.tree.prune()\n\n def prune(self):\n self.category = self.get_class()\n dw = self.weight - 1\n self.weight = 1\n _pop_lst = [key for key in self.leafs]\n self.mark_pruned()\n _parent = self\n while _parent is not None:\n for _k in _pop_lst:\n _parent.leafs.pop(_k)\n _parent.leafs[self.key] = self.labels\n _parent.weight -= dw\n _parent = _parent.parent\n self.children = {}\n\n def mark_pruned(self):\n self.pruned = True\n if self.children is not None:\n for _child in self.children.values():\n _child.mark_pruned()\n\n def predict_one(self, x):\n if self.category is not None:\n return self.category\n try:\n return self.children[x[self.feature_dim]].predict_one(self.crop(x))\n except KeyError:\n return self.get_class()\n\n def predict(self, x):\n if self.category is not None:\n if self._is_root:\n return [self.category] * len(x)\n return self.category\n x = np.atleast_2d(x)\n return [self.predict_one(xx) for xx in x]\n\n def view(self, indent=4):\n print(\" \" * indent * self._depth, self)\n for _node in sorted(self.children.values()):\n _node.view()\n\n def __lt__(self, other):\n return self.prev_feat < other.prev_feat\n\n def __str__(self):\n if self.children:\n return \"CvDNode ({}) ({} -> {})\".format(\n self._depth, self._prev_feat, self.feature_dim)\n return \"CvDNode ({}) ({} -> class: {})\".format(\n self._depth, self._prev_feat, self.category)\n\n __repr__ = __str__\n\n\nclass ID3Node(CvDNode):\n def __init__(self, *args, **kwargs):\n CvDNode.__init__(self, *args, **kwargs)\n self.criteria = \"ent\"\n\n\nclass C45Node(CvDNode):\n def __init__(self, *args, **kwargs):\n CvDNode.__init__(self, *args, **kwargs)\n self.criteria = \"ratio\"\n\n\n# Tree\n\nclass CvDBase:\n def __init__(self, max_depth=None, node=None):\n self.nodes = []\n self.trees = []\n self._threshold_cache = None\n self._max_depth = max_depth\n if node is None:\n self.root = CvDNode(self, max_depth)\n else:\n self.root = node\n self.root.feed_tree(self)\n self.depth = 1\n\n @staticmethod\n def acc(y, y_pred):\n return np.sum(np.array(y) == np.array(y_pred)) / len(y)\n\n def copy(self):\n _new_tree = self.__class__(self._max_depth, node=self.root.copy())\n _new_tree.nodes = [_node.copy() for _node in self.nodes]\n _new_tree.depth = self.depth\n return _new_tree\n\n def fit(self, data=None, labels=None, eps=1e-8):\n self.root.fit(data, labels, eps)\n _arg = np.argmax([CvDBase.acc(labels, tree.predict(data)) for tree in self.trees])\n _tar_tree = self.trees[_arg]\n self.nodes = _tar_tree.nodes\n self.depth = _tar_tree.depth\n self.root = _tar_tree.root\n\n def prune(self):\n self.trees.append(self.copy())\n if self.depth <= 2:\n return\n _nodes = [_node for _node in self.nodes if _node.category is None]\n if self._threshold_cache is None:\n _thresholds = [_node.get_threshold() for _node in _nodes]\n else:\n _thresholds = self._threshold_cache\n _arg = np.argmin(_thresholds)\n _nodes[_arg].prune()\n _thresholds[_arg] = _nodes[_arg].get_threshold()\n self.depth = self.root.height\n for i in range(len(self.nodes) - 1, -1, -1):\n if self.nodes[i].pruned:\n self.nodes.pop(i)\n for i in range(len(_thresholds) - 1, -1, -1):\n if _nodes[i].pruned:\n _thresholds.pop(i)\n self._threshold_cache = _thresholds\n if self.depth > 2:\n self.prune()\n else:\n self.trees.append(self.copy())\n pass\n\n def predict_one(self, x):\n return self.root.predict_one(x)\n\n def predict(self, x):\n return self.root.predict(x)\n\n def view(self):\n self.root.view()\n\n def __str__(self):\n return \"CvDTree ({})\".format(self.depth)\n\n __repr__ = __str__\n\n\nclass ID3Tree(CvDBase):\n def __init__(self, *args, **kwargs):\n if \"node\" not in kwargs:\n CvDBase.__init__(self, node=ID3Node(), *args, **kwargs)\n else:\n CvDBase.__init__(self, *args, **kwargs)\n\n\nclass C45Tree(CvDBase):\n def __init__(self, *args, **kwargs):\n if \"node\" not in kwargs:\n CvDBase.__init__(self, node=C45Node(), *args, **kwargs)\n else:\n CvDBase.__init__(self, *args, **kwargs)\n\nif __name__ == '__main__':\n _data, _x, _y = [], [], []\n with open(\"data.txt\", \"r\") as file:\n for line in file:\n _data.append(line.split(\",\"))\n np.random.shuffle(_data)\n for line in _data:\n _y.append(line.pop(0))\n _x.append(line)\n _x, _y = np.array(_x).T, np.array(_y)\n for _i, line in enumerate(_x):\n _dic = {_c: i for i, _c in enumerate(set(line))}\n for _j, elem in enumerate(line):\n _x[_i][_j] = _dic[elem]\n _x = _x.T\n train_num = 5000\n x_train = _x[:train_num]\n y_train = _y[:train_num]\n x_test = _x[train_num:]\n y_test = _y[train_num:]\n\n _t = time.time()\n _tree = C45Tree()\n _tree.fit(x_train, y_train)\n _tree.view()\n _y_pred = _tree.predict(x_test)\n print(np.sum(_y_pred == y_test) / len(y_test))\n print(time.time() - _t)\n\n _t = time.time()\n _sk_tree = DecisionTreeClassifier()\n _sk_tree.fit(x_train, y_train)\n _y_pred = _tree.predict(x_test)\n print(np.sum(_y_pred == y_test) / len(y_test))\n print(time.time() - _t)\n", "sub_path": "CvDTree/CvDTree.py", "file_name": "CvDTree.py", "file_ext": "py", "file_size_in_byte": 13004, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "math.log", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 155, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.atleast_2d", "line_number": 243, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 370, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 370, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 374, "usage_type": "call"}, {"api_name": "time.time", "line_number": 386, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 391, "usage_type": "call"}, {"api_name": "time.time", "line_number": 392, "usage_type": "call"}, {"api_name": "time.time", "line_number": 394, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 395, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 398, "usage_type": "call"}, {"api_name": "time.time", "line_number": 399, "usage_type": "call"}]} +{"seq_id": "650615738", "text": "from django.utils.translation import ugettext_lazy as _\nfrom django.core.urlresolvers import reverse\nfrom django import forms\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import Layout, LayoutObject, Div, Submit, HTML, Button, Row, Field, ButtonHolder, Fieldset\n\n\nclass ContactoForm(forms.Form):\n required_css_class = 'required'\n\n nombre = forms.CharField(max_length=255, label=_(\"nombre_completo\"), help_text=\"\", required=True)\n email = forms.CharField(max_length=155, label=_(\"email\"), help_text=\"\", required=True)\n telefono = forms.IntegerField(label=_(\"Numero Telefonico\"), help_text=\"\", required=True, widget=forms.TextInput())\n mensaje = forms.CharField(max_length=255, label=_(\"mensaje\"), help_text=\"\", required=True)\n\n\n def __init__(self, *args, **kwargs):\n super(ContactoForm, self).__init__(*args, **kwargs)\n\n cancel_url = reverse('home')\n\n self.helper = FormHelper()\n self.helper.form_method = 'POST'\n self.helper.form_class = 'form-horizontal'\n self.helper.label_class = 'col-sm-4'\n self.helper.field_class = 'col-sm-6'\n self.helper.attrs = {\"onsubmit\": \"return validarForm();\"}\n\n self.helper.layout = Layout(\n Div(\n\n Fieldset(\n '',\n 'nombre',\n 'email',\n 'telefono',\n 'mensaje'\n ),\n\n ))", "sub_path": "afiansa_django/applications/website/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1449, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "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": 11, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 11, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 11, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 12, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 12, "usage_type": "call"}, {"api_name": "django.forms.IntegerField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 13, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 13, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 13, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 14, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 14, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 20, "usage_type": "call"}, {"api_name": "crispy_forms.helper.FormHelper", "line_number": 22, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Layout", "line_number": 29, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Div", "line_number": 30, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Fieldset", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "456792253", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import division\n\n\"\"\" \nCreates a ResNeXt Model as defined in:\n\nXie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2016). \nAggregated residual transformations for deep neural networks. \narXiv preprint arXiv:1611.05431.\n\n\"\"\"\n\n__author__ = \"Pau Rodríguez López, ISELAB, CVC-UAB\"\n__email__ = \"pau.rodri1@gmail.com\"\n\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn import init\n\n\nclass ResNeXtBottleneck(nn.Module):\n \"\"\"\n RexNeXt bottleneck type C (https://github.com/facebookresearch/ResNeXt/blob/master/models/resnext.lua)\n \"\"\"\n\n def __init__(self, in_channels, out_channels, stride, groups, base_width, widen_factor):\n \"\"\" Constructor\n\n Args:\n in_channels: input channel dimensionality\n out_channels: output channel dimensionality\n stride: conv stride. Replaces pooling layer.\n groups: num of convolution groups.\n base_width: base number of channels in each group.\n widen_factor: factor to reduce the input dimensionality before convolution.\n \"\"\"\n super(ResNeXtBottleneck, self).__init__()\n width_ratio = out_channels / (widen_factor * 64.)\n D = groups * int(base_width * width_ratio)\n self.conv_reduce = nn.Conv2d(in_channels, D, kernel_size=1, stride=1, padding=0, bias=False)\n self.bn_reduce = nn.BatchNorm2d(D)\n self.conv_conv = nn.Conv2d(D, D, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False)\n self.bn = nn.BatchNorm2d(D)\n self.conv_expand = nn.Conv2d(D, out_channels, kernel_size=1, stride=1, padding=0, bias=False)\n self.bn_expand = nn.BatchNorm2d(out_channels)\n\n self.shortcut = nn.Sequential()\n if in_channels != out_channels:\n self.shortcut.add_module('shortcut_conv',\n nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, padding=0,\n bias=False))\n self.shortcut.add_module('shortcut_bn', nn.BatchNorm2d(out_channels))\n\n def forward(self, x):\n bottleneck = self.conv_reduce(x)\n bottleneck = F.relu(self.bn_reduce(bottleneck))\n bottleneck = self.conv_conv(bottleneck)\n bottleneck = F.relu(self.bn(bottleneck))\n bottleneck = self.conv_expand(bottleneck)\n bottleneck = self.bn_expand(bottleneck)\n residual = self.shortcut(x)\n return F.relu(residual + bottleneck)\n\n\nclass CifarResNeXt(nn.Module):\n \"\"\"\n ResNext optimized for the Cifar dataset, as specified in\n https://arxiv.org/pdf/1611.05431.pdf\n \"\"\"\n\n def __init__(self, groups, base_width, depth=29, nlabels=10, widen_factor=4):\n \"\"\" Constructor\n\n Args:\n groups: number of convolution groups.\n depth: number of layers.\n nlabels: number of classes\n base_width: base number of channels in each group.\n widen_factor: factor to adjust the channel dimensionality\n \"\"\"\n super(CifarResNeXt, self).__init__()\n self.groups = groups\n self.depth = depth\n self.block_depth = (self.depth - 2) // 9\n self.base_width = base_width\n self.widen_factor = widen_factor\n self.nlabels = nlabels\n self.output_size = 64\n self.stages = [64, 64 * self.widen_factor, 128 * self.widen_factor, 256 * self.widen_factor]\n\n self.conv_1_3x3 = nn.Conv2d(3, 64, 3, 1, 1, bias=False)\n self.bn_1 = nn.BatchNorm2d(64)\n self.stage_1 = self.block('stage_1', self.stages[0], self.stages[1], 1)\n self.stage_2 = self.block('stage_2', self.stages[1], self.stages[2], 2)\n self.stage_3 = self.block('stage_3', self.stages[2], self.stages[3], 2)\n self.classifier = nn.Linear(self.stages[3], nlabels)\n init.kaiming_normal(self.classifier.weight)\n\n for key in self.state_dict():\n if key.split('.')[-1] == 'weight':\n if 'conv' in key:\n init.kaiming_normal(self.state_dict()[key], mode='fan_out')\n if 'bn' in key:\n self.state_dict()[key][...] = 1\n elif key.split('.')[-1] == 'bias':\n self.state_dict()[key][...] = 0\n\n def block(self, name, in_channels, out_channels, pool_stride=2):\n \"\"\" Stack n bottleneck modules where n is inferred from the depth of the network.\n\n Args:\n name: string name of the current block.\n in_channels: number of input channels\n out_channels: number of output channels\n pool_stride: factor to reduce the spatial dimensionality in the first bottleneck of the block.\n\n Returns: a Module consisting of n sequential bottlenecks.\n\n \"\"\"\n block = nn.Sequential()\n for bottleneck in range(self.block_depth):\n name_ = '%s_bottleneck_%d' % (name, bottleneck)\n if bottleneck == 0:\n block.add_module(name_, ResNeXtBottleneck(in_channels, out_channels, pool_stride, self.groups,\n self.base_width, self.widen_factor))\n else:\n block.add_module(name_,\n ResNeXtBottleneck(out_channels, out_channels, 1, self.groups, self.base_width,\n self.widen_factor))\n return block\n\n def forward(self, input):\n x = input['img']\n x = self.conv_1_3x3(x)\n x = F.relu(self.bn_1(x))\n x = self.stage_1(x)\n x = self.stage_2(x)\n x = self.stage_3(x)\n x = F.avg_pool2d(x, 8, 1)\n x = x.view(-1, self.stages[3])\n return self.classifier(x)\n\ndef CifarResNeXt29(model_TAG):\n model_TAG_list = model_TAG.split('_')\n widen_factor = int(model_TAG_list[3])\n groups = int(model_TAG_list[4])\n base_width = int(model_TAG_list[5])\n\n model = CifarResNeXt(groups=groups, base_width=base_width, widen_factor=widen_factor)\n return model\n\n", "sub_path": "src/models/resnext.py", "file_name": "resnext.py", "file_ext": "py", "file_size_in_byte": 6044, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "torch.nn.Module", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 65, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 96, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 97, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 102, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 120, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 135, "usage_type": "name"}, {"api_name": "torch.nn.functional.avg_pool2d", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 139, "usage_type": "name"}]} +{"seq_id": "615545792", "text": "# -*- coding: utf-8 -*-\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom PyQt5.QtCore import pyqtSignal, QObject\nfrom design.ui_post_controls import Ui_post_controls\n\nclass signals(QObject):\n\tsubscribe = pyqtSignal(bool) # True for subscript, False for unsubscribe\n\treport = pyqtSignal()\n\tmute = pyqtSignal()\n\thide = pyqtSignal()\n\tdelete = pyqtSignal()\n\nclass post_controls(QtWidgets.QWidget):\n\tdef __init__(self, own_post=False, is_subscribed=False, parent=None):\n\t\tQtWidgets.QWidget.__init__(self, parent=parent)\n\t\tself.ui = Ui_post_controls()\n\t\tself.ui.setupUi(self)\n\t\t\n\t\tself.signals = signals()\n\t\t\n\t\tself.is_subscribed = is_subscribed\n\t\tself.subscriped_icon = QtGui.QIcon(QtGui.QPixmap(\":/icons/icons/notify_post_active.svg\"))\n\t\tself.unsubscriped_icon = QtGui.QIcon(QtGui.QPixmap(\":/icons/icons/notify_post_normal.svg\"))\n\t\t\n\t\tif own_post:\n\t\t\tself.ui.report.hide()\t\t\t\t\t\t\t\t\t\t# hide report button\n\t\t\tself.ui.subscribe.hide()\t\t\t\t\t\t\t\t\t# hide subscribe button\n\t\t\tself.ui.mute.hide()\t\t\t\t\t\t\t\t\t\t\t# hide mute button\n\t\t\tself.ui.delete_.clicked.connect( self.delete )\n\t\telse:\n\t\t\tif self.is_subscribed:\n\t\t\t\tself.ui.subscribe.setIcon( self.subscriped_icon )\n\t\t\telse:\n\t\t\t\tself.ui.subscribe.setIcon( self.unsubscriped_icon )\n\t\t\t\n\t\t\tself.ui.delete_.clicked.connect( self.hide_post )\n\t\t\tself.ui.delete_.setToolTip(\"Click here to hide this post.\")\n\t\t\t\n\t\t\tself.ui.report.clicked.connect( self.report )\n\t\t\tself.ui.report.setToolTip(\"Click here to report this post.\")\n\t\t\t\n\t\t\tself.ui.subscribe.clicked.connect( self.subscribe )\t\t\t# toggle\n\t\t\t\n\t\t\tself.ui.mute.clicked.connect( self.mute )\n\t\t\tself.ui.mute.setToolTip(\"Click here to block author of this post.\")\n\n\tdef confirm(self, title, quesion):\n\t\tanswer = QtWidgets.QMessageBox.question(self, title, quesion, QtWidgets.QMessageBox.Yes, QtWidgets.QMessageBox.No)\n\t\tif answer == QtWidgets.QMessageBox.Yes:\n\t\t\treturn True\n\t\treturn False\n\n\tdef hide_post(self):\t\t\t\t\t\t\t\t\t\t\t\t# used if not own_post\n\t\tif self.confirm(\"Question\", \"Are you sure you want to hide this post?\"):\n\t\t\tself.ui.delete_.clicked.disconnect()\n\t\t\tself.signals.hide.emit()\n\n\tdef delete(self):\t\t\t\t\t\t\t\t\t\t\t\t\t# used if own_post\n\t\tif self.confirm(\"Question\", \"Are you sure you want to delete this post?\"):\n\t\t\tself.ui.delete_.clicked.disconnect()\n\t\t\tself.signals.delete.emit()\n\n\tdef mute(self):\n\t\tif self.confirm(\"Question\", \"Are you sure you want block this author?\"):\n\t\t\tself.ui.mute.clicked.disconnect()\n\t\t\tself.signals.mute.emit()\n\n\tdef subscribe(self):\n\t\tif self.is_subscribed:\n\t\t\tself.signals.subscribe.emit(False)\n\t\telse:\n\t\t\tself.signals.subscribe.emit(True)\n\n\tdef report(self):\n\t\tself.ui.report.clicked.disconnect()\n\n\t\"\"\" These functions will be called after remote action succesfull\"\"\"\n\tdef unsubscribed(self):\n\t\tself.is_subscribed = False\n\t\tself.ui.subscribe.setIcon( self.unsubscriped_icon )\n\t\tself.ui.subscribe.setToolTip(\"Click to subscript to this post.\")\n\n\tdef subscribed(self):\n\t\tself.is_subscribed = True\n\t\tself.ui.subscribe.setIcon( self.subscriped_icon )\n\t\tself.ui.subscribe.setToolTip(\"Click to unsubscript to this post.\")\n\n\tdef reported(self):\n\t\tself.ui.report.hide()\n\t\t#TODO turn icon red.\n\n\t\"\"\" guess these functions can be deleted since to post will be\n\tdeleted from stream \"\"\"\n\tdef muted(self):\n\t\tpass\n\n\tdef deleted(self):\n\t\tpass\n\n\tdef hided(self):\n\t\tpass\n", "sub_path": "widgets/post_controls.py", "file_name": "post_controls.py", "file_ext": "py", "file_size_in_byte": 3268, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "PyQt5.QtCore.QObject", "line_number": 6, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 7, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 8, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 9, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 10, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 11, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 13, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 13, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget.__init__", "line_number": 15, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 15, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 15, "usage_type": "name"}, {"api_name": "design.ui_post_controls.Ui_post_controls", "line_number": 16, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 22, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 22, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 22, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 23, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 23, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 23, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.question", "line_number": 48, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 48, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 48, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 49, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 49, "usage_type": "name"}]} +{"seq_id": "168008546", "text": "import numpy as np\nimport torch\nimport gym\nimport argparse\nimport os\nfrom baselines import bench\nimport sys\nimport time\n\nimport utils\nimport TD3\nimport EmbeddedTD3\nimport RandomPolicy\nimport OurDDPG\nimport DDPG\nfrom DummyDecoder import DummyDecoder\nfrom RandomPolicy import RandomPolicy, ConstantPolicy\nfrom RandomEmbeddedPolicy import RandomEmbeddedPolicy\n\nimport sys\n# so it can find the action decoder class and LinearPointMass\n# sys.path.insert(0, '../action-embedding')\nfrom pointmass import point_mass\n\nimport reacher_family\n\ndef render_policy(policy, filename, render_mode='rgb_array', eval_episodes=5):\n frames = []\n avg_reward = 0.\n for episode in range(eval_episodes):\n obs = env.reset()\n policy.reset()\n frames.append(env.render(mode=render_mode))\n done = False\n while not done:\n if any([isinstance(policy, EmbeddedTD3.EmbeddedTD3),\n isinstance(policy, RandomEmbeddedPolicy)]):\n action, _, _ = policy.select_action(np.array(obs))\n else:\n action = policy.select_action(np.array(obs))\n obs, reward, done, _ = env.step(action)\n avg_reward += reward\n frame = env.render(mode=render_mode)\n # frame[:, :, 1] = (frame[:, :, 1].astype(float) + reward * 100).clip(0, 255)\n\n frames.append(frame)\n if render_mode == 'human':\n time.sleep(0.05)\n\n avg_reward /= eval_episodes\n print(\"---------------------------------------\")\n print(\"Evaluation over %d episodes: %f\" % (eval_episodes, avg_reward))\n print(\"---------------------------------------\")\n\n utils.save_gif('{}.mp4'.format(filename),\n [torch.tensor(frame.copy()).float()/255 for frame in frames],\n color_last=True)\n\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--name\", default=None) # Job name\n parser.add_argument(\"--policy_name\", default=\"TD3\") # Policy name\n parser.add_argument(\"--env_name\", default=\"HalfCheetah-v1\") # OpenAI gym environment name\n parser.add_argument(\"--seed\", default=0, type=int) # Sets Gym, PyTorch and Numpy seeds\n\n parser.add_argument(\"--decoder\", default=None, type=str) # Name of saved decoder\n parser.add_argument(\"--dummy_decoder\", action=\"store_true\") # use a dummy decoder that repeats actions\n parser.add_argument('--dummy_traj_len', type=int, default=1) # traj_len of dummy decoder\n parser.add_argument('--human', action=\"store_true\") # render interactively\n args = parser.parse_args()\n\n if args.env_name.startswith('dm'):\n import dm_control2gym\n _, domain, task = args.env_name.split('.')\n env = dm_control2gym.make(domain_name=domain, task_name=task)\n env_max_steps = 1000\n else:\n env = gym.make(args.env_name)\n env_max_steps = env._max_episode_steps\n\n env.seed(args.seed)\n torch.manual_seed(args.seed)\n np.random.seed(args.seed)\n\n state_dim = env.observation_space.shape[0]\n action_dim = env.action_space.shape[0]\n max_action = float(env.action_space.high[0])\n\n if args.policy_name == 'TD3':\n policy = TD3.load('policy', 'results/{}'.format(args.name))\n elif args.policy_name == 'EmbeddedTD3':\n policy = EmbeddedTD3.load('policy', 'results/{}'.format(args.name))\n elif args.policy_name == 'random':\n if args.decoder:\n decoder = load_decoder(args.env_name, args.decoder)\n policy = RandomEmbeddedPolicy(1, decoder, 4)\n elif args.dummy_decoder:\n decoder = DummyDecoder(action_dim, args.dummy_traj_len, env.action_space)\n policy = RandomEmbeddedPolicy(1, decoder, 1)\n else:\n policy = RandomPolicy(env.action_space)\n elif args.policy_name == 'constant':\n policy = ConstantPolicy(env.action_space)\n else:\n assert False\n\n\n render_mode = 'human' if args.human else 'rgb_array'\n render_policy(policy, \"{}_{}\".format(args.env_name, args.name), render_mode)\n", "sub_path": "render_policy.py", "file_name": "render_policy.py", "file_ext": "py", "file_size_in_byte": 4158, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "EmbeddedTD3.EmbeddedTD3", "line_number": 36, "usage_type": "attribute"}, {"api_name": "RandomEmbeddedPolicy.RandomEmbeddedPolicy", "line_number": 37, "usage_type": "argument"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 48, "usage_type": "call"}, {"api_name": "utils.save_gif", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 56, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 62, "usage_type": "call"}, {"api_name": "dm_control2gym.make", "line_number": 77, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 85, "usage_type": "attribute"}, {"api_name": "TD3.load", "line_number": 92, "usage_type": "call"}, {"api_name": "EmbeddedTD3.load", "line_number": 94, "usage_type": "call"}, {"api_name": "RandomEmbeddedPolicy.RandomEmbeddedPolicy", "line_number": 98, "usage_type": "call"}, {"api_name": "DummyDecoder.DummyDecoder", "line_number": 100, "usage_type": "call"}, {"api_name": "RandomEmbeddedPolicy.RandomEmbeddedPolicy", "line_number": 101, "usage_type": "call"}, {"api_name": "RandomPolicy.RandomPolicy", "line_number": 103, "usage_type": "call"}, {"api_name": "RandomPolicy.ConstantPolicy", "line_number": 105, "usage_type": "call"}]} +{"seq_id": "398927701", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\"\"\"\n File Name: proxy_spider.py\n Date: 09/13/2017\n Author: hackrflov\n Email: hackrflov@gmail.com\n Python Version: 2.7\n\"\"\"\n\n\nimport re\nimport json\nimport time\nimport logging\nlog = logging.getLogger('scrapy.spider')\nfrom lxml import html\nfrom datetime import datetime, timedelta\n\nimport scrapy\nimport crawler.settings as st\nfrom pymongo import MongoClient\n\nclass ProxySpider(scrapy.Spider):\n\n name = 'proxy'\n\n def __init__(self, *args, **kwargs):\n super(ProxySpider, self).__init__(*args, **kwargs)\n self.connect()\n\n def connect(self):\n log.info('Connecting to MongoDB...')\n host = st.MONGO_HOST\n db = st.MONGO_DB\n usr = st.MONGO_USERNAME\n pwd = st.MONGO_PASSWORD\n if usr and pwd:\n uri = 'mongodb://{u}:{p}@{h}/{d}'.format(u=usr,p=pwd,h=host,d=db)\n else:\n uri = 'mongodb://{h}/{d}'.format(h=host,d=db)\n client = MongoClient(uri)\n self.clt = client[db][st.MONGO_COLLECTION]\n\n def start_requests(self):\n while True:\n log.info('Start to fetch proxy...')\n meta = {'download_timeout': st.CRAWL_TIMEOUT}\n last_dt = datetime.now()\n\n for url in st.PROXY_SITES_BY_REGX['urls']:\n yield scrapy.Request(url=url, meta=meta, dont_filter=True, callback=self.parse_regx)\n\n for site in st.PROXY_SITES_BY_XPATH:\n meta['ip_xpath'] = site['ip_xpath']\n meta['port_xpath'] = site['port_xpath']\n yield scrapy.Request(url=url, meta=meta, dont_filter=True, callback=self.parse_xpath)\n\n for site in st.PROXY_SITES_BY_TXT:\n meta['ip_key'] = site['ip_key']\n meta['port_key'] = site['port_key']\n yield scrapy.Request(url=url, meta=meta, dont_filter=True, callback=self.parse_txt)\n\n log.info('Fetching is finished, waiting for parsing...')\n time.sleep(10)\n\n log.info('Start to update proxy...')\n while True:\n cur_dt = datetime.now()\n if cur_dt - last_dt >= timedelta(seconds=st.FETCH_INTERVAL): # should restart fecthing now\n last_dt = cur_dt\n break\n else:\n log.info('Before refresh: having {} proxies'.format(self.clt.count()))\n docs = self.clt.find()\n for doc in docs:\n proxy = 'http://{}'.format(doc['ip_port'])\n meta = {'proxy': proxy, 'download_timeout': st.UPDATE_TIMEOUT, 'phase': 'update' }\n yield scrapy.Request(url=st.TEST_URL, meta=meta, dont_filter=True, callback=self.parse_test)\n log.debug('Testing [{}]...'.format(doc['ip_port']))\n\n # Deal with records after each round\n self.clt.update({'ace_times': {'$gt': st.MAX_ACE_TIMES } }, { '$set': { 'ace_times': 1, 'bad_times': 0 } }) # if reach record limit, reset it\n self.clt.delete_many({'$where': \"this.ace_times < this.bad_times\" }) # remove terrible proxies\n\n log.info('All update requests have been send, waiting for parsing...')\n time.sleep(st.UPDATE_INTERVAL)\n\n def parse_regx(self, response):\n proxy_list = re.findall(st.PROXY_REGX, response.body)\n for ip_port in proxy_list:\n meta = {'proxy': 'http://{}'.format(ip_port), 'phase': 'fetch' }\n yield scrapy.Request(url=st.TEST_URL, meta=meta, dont_filter=True, callback=self.parse_test)\n log.debug('Testing [{}]...'.format(ip_port))\n\n def parse_xpath(self, response):\n r = html.fromstring(response.body)\n ip_list = r.xpath(response.meta['ip_xpath'])\n ip_list = [ip for ip in ip_list if re.match(r'\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}',ip)]\n port_list = r.xpath(response.meta['port_xpath'])\n for i in range(len(ip_list)):\n ip_port = ip_list[i] + \":\" + port_list[i]\n meta = {'proxy': 'http://{}'.format(ip_port), 'phase': 'fetch' }\n yield scrapy.Request(url=st.TEST_URL, meta=meta, dont_filter=True, callback=self.parse_test)\n log.debug('Testing [{}]...'.format(ip_port))\n\n def parse_txt(self, response):\n data = response.body.split('\\n')\n for msg in data[:-1]:\n msg = json.loads(msg)\n ip = msg[response.meta['ip_key']]\n port = msg[response.meta['port_key']]\n ip_port = '{ip}:{port}'.format(ip=ip, port=port)\n meta = {'proxy': 'http://{}'.format(ip_port), 'phase': 'fetch' }\n yield scrapy.Request(url=st.TEST_URL, meta=meta, dont_filter=True, callback=self.parse_test)\n log.debug('Testing [{}]...'.format(ip_port))\n\n def parse_test(self, response):\n ip_port = re.sub('http://', '', response.meta['proxy'])\n try:\n if 'exception' in response.meta:\n raise Exception(response.meta['exception'])\n else:\n data = json.loads(response.body)['data']['fid']\n seconds = response.request.meta['download_latency']\n self.clt.update_one({ 'ip_port': ip_port }, { '$min': { 'best': seconds }, '$inc': { 'ace_times' : 1 } }, upsert=True)\n log.info('{action} proxy {p}, used {s} seconds'.format(action=response.meta['phase'].capitalize(), p=ip_port, s=seconds))\n except Exception as e:\n if response.meta['phase'] == 'update':\n log.info('Update bad record: {p} details: {e}'.format(p=ip_port, e=e))\n self.clt.update_one({ 'ip_port': ip_port }, { '$inc': { 'bad_times' : 1 } }, upsert=True)\n else:\n log.info('Bad proxy: {p} details: {e}'.format(p=ip_port, e=e))\n\n", "sub_path": "crawler/proxy_spider.py", "file_name": "proxy_spider.py", "file_ext": "py", "file_size_in_byte": 5861, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "scrapy.Spider", "line_number": 24, "usage_type": "attribute"}, {"api_name": "crawler.settings.MONGO_HOST", "line_number": 34, "usage_type": "attribute"}, {"api_name": "crawler.settings", "line_number": 34, "usage_type": "name"}, {"api_name": "crawler.settings.MONGO_DB", "line_number": 35, "usage_type": "attribute"}, {"api_name": "crawler.settings", "line_number": 35, "usage_type": "name"}, {"api_name": "crawler.settings.MONGO_USERNAME", "line_number": 36, "usage_type": "attribute"}, {"api_name": "crawler.settings", "line_number": 36, "usage_type": "name"}, {"api_name": "crawler.settings.MONGO_PASSWORD", "line_number": 37, "usage_type": "attribute"}, {"api_name": "crawler.settings", "line_number": 37, "usage_type": "name"}, {"api_name": "pymongo.MongoClient", "line_number": 42, "usage_type": "call"}, {"api_name": "crawler.settings.MONGO_COLLECTION", "line_number": 43, "usage_type": "attribute"}, {"api_name": "crawler.settings", "line_number": 43, "usage_type": "name"}, {"api_name": "crawler.settings.CRAWL_TIMEOUT", "line_number": 48, "usage_type": "attribute"}, {"api_name": "crawler.settings", "line_number": 48, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 49, "usage_type": "name"}, {"api_name": "crawler.settings.PROXY_SITES_BY_REGX", "line_number": 51, "usage_type": "attribute"}, {"api_name": "crawler.settings", "line_number": 51, "usage_type": "name"}, {"api_name": "scrapy.Request", "line_number": 52, "usage_type": "call"}, {"api_name": "crawler.settings.PROXY_SITES_BY_XPATH", "line_number": 54, "usage_type": "attribute"}, {"api_name": "crawler.settings", "line_number": 54, "usage_type": "name"}, {"api_name": "scrapy.Request", "line_number": 57, "usage_type": "call"}, {"api_name": "crawler.settings.PROXY_SITES_BY_TXT", "line_number": 59, "usage_type": "attribute"}, {"api_name": "crawler.settings", "line_number": 59, "usage_type": "name"}, {"api_name": "scrapy.Request", "line_number": 62, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 69, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 70, "usage_type": "call"}, {"api_name": "crawler.settings.FETCH_INTERVAL", "line_number": 70, "usage_type": "attribute"}, {"api_name": "crawler.settings", "line_number": 70, "usage_type": "name"}, {"api_name": "crawler.settings.UPDATE_TIMEOUT", "line_number": 78, "usage_type": "attribute"}, {"api_name": "crawler.settings", "line_number": 78, "usage_type": "name"}, {"api_name": "scrapy.Request", "line_number": 79, "usage_type": "call"}, {"api_name": "crawler.settings.TEST_URL", "line_number": 79, "usage_type": "attribute"}, {"api_name": "crawler.settings", "line_number": 79, "usage_type": "name"}, {"api_name": "crawler.settings.MAX_ACE_TIMES", "line_number": 83, "usage_type": "attribute"}, {"api_name": "crawler.settings", "line_number": 83, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 87, "usage_type": "call"}, {"api_name": "crawler.settings.UPDATE_INTERVAL", "line_number": 87, "usage_type": "attribute"}, {"api_name": "crawler.settings", "line_number": 87, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 90, "usage_type": "call"}, {"api_name": "crawler.settings.PROXY_REGX", "line_number": 90, "usage_type": "attribute"}, {"api_name": "crawler.settings", "line_number": 90, "usage_type": "name"}, {"api_name": "scrapy.Request", "line_number": 93, "usage_type": "call"}, {"api_name": "crawler.settings.TEST_URL", "line_number": 93, "usage_type": "attribute"}, {"api_name": "crawler.settings", "line_number": 93, "usage_type": "name"}, {"api_name": "lxml.html.fromstring", "line_number": 97, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 97, "usage_type": "name"}, {"api_name": "re.match", "line_number": 99, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 104, "usage_type": "call"}, {"api_name": "crawler.settings.TEST_URL", "line_number": 104, "usage_type": "attribute"}, {"api_name": "crawler.settings", "line_number": 104, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 110, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 115, "usage_type": "call"}, {"api_name": "crawler.settings.TEST_URL", "line_number": 115, "usage_type": "attribute"}, {"api_name": "crawler.settings", "line_number": 115, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 119, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 124, "usage_type": "call"}]} +{"seq_id": "99434983", "text": "from django.http.response import JsonResponse\nfrom django.views.generic.base import View, TemplateView\nfrom django.views.decorators.csrf import csrf_exempt\n\n\nfrom PIL import Image, ImageFilter, ImageEnhance\nfrom tesserocr import PyTessBaseAPI\nfrom models import SWTScrubber\n\nclass OcrFormView(TemplateView):\n template_name = 'documents/ocr_form.html'\nocr_form_view = OcrFormView.as_view()\n\n\nclass OcrView(View):\n def post(self, request, *args, **kwargs):\n with PyTessBaseAPI() as api:\n with Image.open(request.FILES['image']) as image:\n new_image = image.convert('1')\n enh = ImageEnhance.Contrast(image)\n enh_image = enh.enhance(1.3)\n filtered_image = image.filter(ImageFilter.CONTOUR)\n sharpened_image = image.filter(ImageFilter.SHARPEN)\n api.SetImage(image)\n utf8_text = api.GetUTF8Text()\n new_image.save('new.png', 'PNG')\n return JsonResponse({'utf8_text': utf8_text})\nocr_view = csrf_exempt(OcrView.as_view())\n", "sub_path": "ocr_with_django/documents/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1065, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.views.generic.base.TemplateView", "line_number": 10, "usage_type": "name"}, {"api_name": "django.views.generic.base.View", "line_number": 15, "usage_type": "name"}, {"api_name": "tesserocr.PyTessBaseAPI", "line_number": 17, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 18, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 18, "usage_type": "name"}, {"api_name": "PIL.ImageEnhance.Contrast", "line_number": 20, "usage_type": "call"}, {"api_name": "PIL.ImageEnhance", "line_number": 20, "usage_type": "name"}, {"api_name": "PIL.ImageFilter.CONTOUR", "line_number": 22, "usage_type": "attribute"}, {"api_name": "PIL.ImageFilter", "line_number": 22, "usage_type": "name"}, {"api_name": "PIL.ImageFilter.SHARPEN", "line_number": 23, "usage_type": "attribute"}, {"api_name": "PIL.ImageFilter", "line_number": 23, "usage_type": "name"}, {"api_name": "django.http.response.JsonResponse", "line_number": 27, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "419261223", "text": "from flask import Flask, abort\nimport audio\nfrom video import VideoPlayer\nimport json\nimport time\n\nimport random\n\napp = Flask(__name__)\n\nvplayer = VideoPlayer()\n\nwith open(\"conf.json\") as fo:\n conf = json.load(fo)\n sounds = conf[\"sounds\"]\n playlists = conf[\"playlists\"]\n videos = conf[\"videos\"]\n print(conf)\n\n\n@app.route('/', methods=['GET'])\ndef empty():\n return(\"this is the soundplayer app\")\n\n@app.route('/play/', methods=['POST'])\ndef play_sound(sound_id):\n if (sound_id not in sounds):\n abort(404)\n try:\n audio.play_sound(sounds[sound_id])\n except Exception as e:\n print (e)\n return 'playing ' + sound_id\n\n@app.route('/play/random', methods=['POST'])\ndef play_random():\n audio.play_sound(random.choice(sounds.values()))\n return 'playing random song'\n\n@app.route('/playlist/', methods=['POST'])\ndef playlist(listname):\n if (listname not in playlists):\n abort(404)\n audio.playlist(playlists[listname])\n return 'playing playlist ' + listname\n\n@app.route('/video/', methods=['POST'])\ndef play_video(video_id):\n if (video_id not in videos):\n abort(404)\n vplayer.play(videos[video_id])\n return 'playing video ' + video_id\n\n@app.route('/videoget/', methods=['GET'])\ndef play_video_get(video_id):\n print(video_id, videos)\n if (video_id not in videos):\n abort(404)\n vplayer.play(videos[video_id])\n return(\"Playing video \"+video_id)\n\n\n@app.route('/stop', methods=['POST'])\ndef stop():\n stop_sound()\n stop_video()\n\ndef stop_sound():\n audio.stop()\n\ndef stop_video():\n vplayer.stop()\n\n\n\n\nif __name__ == \"__main__\":\n app.run('0.0.0.0', port=8001)\n", "sub_path": "player/build/lib/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1701, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "video.VideoPlayer", "line_number": 11, "usage_type": "call"}, {"api_name": "json.load", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 28, "usage_type": "call"}, {"api_name": "audio.play_sound", "line_number": 30, "usage_type": "call"}, {"api_name": "audio.play_sound", "line_number": 37, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 43, "usage_type": "call"}, {"api_name": "audio.playlist", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 58, "usage_type": "call"}, {"api_name": "audio.stop", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "223733219", "text": "#!/usr/bin/env python\n\nimport sys # used for receiving the command line arguments\nimport os # used for receiving the path of the directory with this python script\nimport operator # sorting dictionaries\nimport datetime # measuring duration\n\n####################################################################################################################\n#\n# START OF THE SCRIPT\n#\n####################################################################################################################\n\n# guard the existance of an input file from the command list arguments\nif len(sys.argv) < 2:\n print(\"Wrong number of arguments. Please pass a path to a file with encrypted text.\")\n exit()\n\n# receive the path to the input file\nPATH_INPUT_FILE = sys.argv[1]\n\n# receive the current working directory\nCURRENT_WORKING_DIRECTORY = os.getcwd()\n\nif PATH_INPUT_FILE[0] is not '/':\n PATH_INPUT_FILE = os.getcwd() + \"/\" + PATH_INPUT_FILE\n\n# guard the existence of the file at the given path\nif not os.path.isfile(PATH_INPUT_FILE):\n print(\"Error - File does not exist: \" + PATH_INPUT_FILE)\n exit(0)\n\n# store start time for measuring duration\nSTART_TIME = datetime.datetime.now()\n\n# defines the line length when printing a preview of the current decryption state of a text\nLINE_LENGTH = 150\n\n# read the encypted content\nTHE_CIPHERTEXT = open(PATH_INPUT_FILE).read()\nprint(\"preview ciphertext: \" + THE_CIPHERTEXT[:LINE_LENGTH])\n\n####################################################################################################################\n#\n# GLOBAL VARIABLES\n#\n####################################################################################################################\nENGLISH_CHAR_FREQUENCY = {\n \" \": 15.00, \"e\": 12.702, \"t\": 9.056, \"a\": 8.167, \"o\": 7.507, \"i\": 6.966, \"n\": 6.749, \"s\": 6.327, \"h\": 6.094,\n \"r\": 5.987, \"d\": 4.2530, \"l\": 4.025, \"c\": 2.782, \"u\": 2.758, \"m\": 2.406, \"w\": 2.360, \"f\": 2.228, \"g\": 2.015,\n \"y\": 1.974, \"p\": 1.9290, \"b\": 1.492, \"v\": 0.978, \"k\": 0.772, \"j\": 0.153, \"x\": 0.150, \"q\": 0.095, \"z\": 0.074}\n\nENGLISH_BIGRAM_FREQUENCY = {\n \"TH\": 2.71, \"EN\": 1.13, \"NG\": 0.89, \"HE\": 2.33, \"AT\": 1.12, \"AL\": 0.88, \"IN\": 2.03, \"ED\": 1.08,\n \"IT\": 0.88, \"ER\": 1.78, \"ND\": 1.07, \"AS\": 0.87, \"AN\": 1.61, \"TO\": 1.07, \"IS\": 0.86, \"RE\": 1.41,\n \"OR\": 1.06, \"HA\": 0.83, \"ES\": 1.32, \"EA\": 1.00, \"ET\": 0.76, \"ON\": 1.32, \"TI\": 0.99, \"SE\": 0.73,\n \"ST\": 1.25, \"AR\": 0.98, \"OU\": 0.72, \"NT\": 1.17, \"TE\": 0.98, \"OF\": 0.71}\n\nENGLISH_TRIGRAM_FREQUENCY = {\n \"THE\": 1.81, \"ERE\": 0.31, \"HES\": 0.24, \"AND\": 0.73, \"TIO\": 0.31, \"VER\": 0.24, \"ING\": 0.72, \"TER\": 0.30,\n \"HIS\": 0.24, \"ENT\": 0.42, \"EST\": 0.28, \"OFT\": 0.22, \"ION\": 0.42, \"ERS\": 0.28, \"ITH\": 0.21, \"HER\": 0.36,\n \"ATI\": 0.26, \"FTH\": 0.21, \"FOR\": 0.34, \"HAT\": 0.26, \"STH\": 0.21, \"THA\": 0.33, \"ATE\": 0.25, \"OTH\": 0.21,\n \"NTH\": 0.33, \"ALL\": 0.25, \"RES\": 0.21, \"INT\": 0.32, \"ETH\": 0.24, \"ONT\": 0.20}\n\n# variable for a list of letters that are save. save in a manner of that they shouldn't be swapped any more cause it is\n# very likely that they are at the right place in the decryption dictionary.\nSAVED_LETTERS = []\n\n# thresthold fur successfull decryption\nMIN_HIT_RATE = 0.9\n\n\n# it's a way more faster to search in keys of dictionaries than in a list. so this functions just loads every entry\n# of the english wordlist file a key in a dictionary. this dictionary is used for the hit rate later.\ndef read_wordlist_english():\n wordlist_file = open(CURRENT_WORKING_DIRECTORY + \"/Ue_1_A_1_Wordlist_English.txt\")\n wordlist = {}\n for word in wordlist_file.read().split('\\r\\n'):\n wordlist[word.lower()] = None\n wordlist_file.close()\n return wordlist\n\n\n# receive the english wordlist and store it in `ENGLISH_WORDLIST`\nENGLISH_WORDLIST = read_wordlist_english()\n\n\ndef get_words_from_wordlist_by_length(word_length):\n long_words = []\n for word in ENGLISH_WORDLIST.keys():\n if len(word) == word_length:\n long_words.append(word)\n\n return long_words\n\n\n# prints the first 1000 characters of the given string if it is longer than 1000 characters. Prints just the string\n# else.\ndef print_short(input_string, max_length=1000):\n if len(input_string) > max_length:\n print(input_string[:max_length])\n else:\n print(input_string)\n\n\n# auxiliary function to print current hit rate and so far decrypted text, or determines the script when given hit rate\n# is higher the `MIN_HIT_RATE`.\ndef print_stats(cleartext, hit_rate):\n if hit_rate >= MIN_HIT_RATE:\n print(\"\\n\\n\\t\\t--> FOUND KEY <--\\n\\n\")\n print(\"finished with a hit rate of: \" + str(hit_rate))\n print(\"\\ncleartext:\\n\" + cleartext[:400])\n print_duration()\n exit()\n print(str(hit_rate) + \" --> \" + cleartext[:LINE_LENGTH])\n\n\n# auxiliary funtion that prints the duration of the script execution.\ndef print_duration():\n duration = datetime.datetime.now() - START_TIME\n print(\"\\nduration \" + str(duration.total_seconds()) + \" s\\n\")\n\n\n# returns a float value between 0 and 1 describing the percentage of words that could be found in the english wordlist\ndef hit_rate(input_string):\n # split the given string into seperated words\n splitted_text = input_string.split(\" \")\n # stored the number of word found in the wordlist\n hits_count = 0\n # iterate through the word in the given string and check their existance in the wordlist\n for possible_word in splitted_text:\n if possible_word in ENGLISH_WORDLIST:\n hits_count += 1\n\n return float(hits_count * 1.0 / len(splitted_text))\n\n\ndef hit_rate_by_key(a_key):\n return hit_rate(decrypt(THE_CIPHERTEXT, a_key))\n\n\n####################################################################################################################\n#\n# CALCULATE FREQUENCIES\n#\n####################################################################################################################\n\n# returns a dictionary to\ndef char_frequency(input_string):\n some_dictionary = {}\n for character in input_string:\n if character in some_dictionary.keys():\n some_dictionary[character] += 1\n else:\n some_dictionary[character] = 1\n return some_dictionary\n\n\ndef ngram_frequency(input_string, factor):\n some_dictionary = {}\n for an_index in range(len(input_string) - factor):\n trigram = input_string[an_index: an_index + factor]\n if trigram in some_dictionary.keys():\n some_dictionary[trigram] += 1\n else:\n some_dictionary[trigram] = 1\n\n return some_dictionary\n\n\ndef bigram_frequency(input_string):\n return ngram_frequency(input_string, 2)\n\n\ndef trigram_frequency(input_string):\n return ngram_frequency(input_string, 3)\n\n\n####################################################################################################################\n#\n# AUXILIARY FUNCTIONS and SWAP FUNCTIONS\n#\n####################################################################################################################\n\n# takes an dictionary and returns a list containing the values ordered by their value in the dictionary.\ndef dictionary_to_sorted_list_by_value(a_dictionary):\n a_tuples_list = list(reversed(sorted(a_dictionary.items(), key=operator.itemgetter(1))))\n a_sorted_list = [x[0] for x in a_tuples_list]\n return a_sorted_list\n\n\n# swaps the given `value_1` and `value_2` in the given dictionary\ndef swap_dictionary(value_1, value_2, dictionary):\n # avoid invalid swapping of the same character\n if value_1 == value_2:\n return dictionary\n # avoid swapping of already identified characters\n elif value_1 in SAVED_LETTERS or value_2 in SAVED_LETTERS:\n return dictionary\n\n for key, value in dictionary.items():\n if value == value_1:\n position_a = key\n elif value == value_2:\n position_b = key\n\n if position_a and position_b:\n dictionary[position_a] = value_2\n dictionary[position_b] = value_1\n\n return dictionary\n\n\ndef swap_and_check(character_1, character_2, input_key, current_hit_rate):\n\n if character_1 == character_2:\n return (current_hit_rate, input_key)\n\n better_key_candidate = input_key.copy()\n\n swap_dictionary(character_2_to_swap, character_1_to_swap, better_key_candidate)\n\n hit_rate_1 = hit_rate_by_key(input_key)\n hit_rate_2 = hit_rate_by_key(better_key_candidate)\n\n if hit_rate_1 < hit_rate_2:\n input_key = better_key_candidate.copy()\n\n higher_hit_rate = max(hit_rate_1, hit_rate_2)\n\n if current_hit_rate != higher_hit_rate:\n print(\"swapping '\" + character_2_to_swap + \"' with '\" + character_1_to_swap + \"'\")\n print_stats(decrypt(THE_CIPHERTEXT, current_best_key), higher_hit_rate)\n\n return (higher_hit_rate, input_key)\n\n\n############################################################################\n# decrypts the given ciphertext with the given key.\ndef decrypt(ciphertext, key):\n cleartext = \"\"\n for a_character in ciphertext:\n cleartext = cleartext + key[a_character]\n return cleartext\n\n\ndef get_long_words(cleartext, word_length):\n long_words = []\n for word in cleartext.split(' '):\n if len(word) == word_length and word not in long_words:\n long_words.append(word)\n\n return long_words\n\n\ndef get_words_by_length(ciphertext, space_character, word_length):\n candidates = {}\n for word in ciphertext.split(space_character):\n if len(word) == word_length:\n if word in candidates.keys():\n candidates[word] += 1\n else:\n candidates[word] = 1\n return candidates\n\n\ndef get_the_candidates(ciphertext, space_character):\n return get_words_by_length(ciphertext, space_character, 3)\n\n\ndef get_of_candidates(ciphertext, space_character):\n return get_words_by_length(ciphertext, space_character, 2)\n\n\n# weight the bigrams\n\ndef weight_bigrams(mapped_bigrams, input_weight_dictionary):\n if input_weight_dictionary:\n weight_dictionary = input_weight_dictionary\n else:\n weight_dictionary = {}\n\n for bigrams_tuple in mapped_bigrams:\n\n first_word = bigrams_tuple[0]\n second_word = bigrams_tuple[1]\n\n for an_index in range(len(first_word)):\n\n first_character = first_word[an_index].lower()\n second_character = second_word[an_index]\n\n a_tuple = (first_character, second_character)\n\n keys = weight_dictionary.keys()\n\n if a_tuple in keys:\n weight_dictionary[a_tuple] += 1\n else:\n weight_dictionary[a_tuple] = 1\n\n return weight_dictionary\n\n\n# returns a list with all\ndef get_double_letters(input_text):\n a_dictionary = {}\n last_letter = ''\n for a_character in input_text:\n if a_character == last_letter:\n double_letters = a_character + a_character\n if double_letters in a_dictionary.keys():\n a_dictionary[double_letters] += 1\n else:\n a_dictionary[double_letters] = 1\n last_letter = a_character\n\n return a_dictionary\n\n\n############################################################################\n# calculation of frequencies\n\n\n# initially create a first\nDICT_CHAR_FREQUENCY = char_frequency(THE_CIPHERTEXT)\n\n# create a list of characters sorted by their associated values in `dict_char_frequency_ciphertext`\nLIST_CHAR_FREQUENCY = dictionary_to_sorted_list_by_value(DICT_CHAR_FREQUENCY)\n\n# guessing the most frequent letter is a whitespace\nCIPHTER_WHITESPACE = dictionary_to_sorted_list_by_value(DICT_CHAR_FREQUENCY)[0]\nprint(\"\\npossible encrypted whitespace: '\" + CIPHTER_WHITESPACE + \"' occures \"\n + str(DICT_CHAR_FREQUENCY[CIPHTER_WHITESPACE]) + \" times in text\")\n\n# remove whitespaces before calculating ngrams\nCIPHERTEXT_WO_WHITESPACES = THE_CIPHERTEXT[:].replace(CIPHTER_WHITESPACE, \"\")\n\n# assuming the first 4 character from the frequency calculation are right\nSAVED_LETTERS.append(\" \")\n\n# the currently best fitting key dictionary\ncurrent_best_key = dict(zip(LIST_CHAR_FREQUENCY, dictionary_to_sorted_list_by_value(ENGLISH_CHAR_FREQUENCY)))\n\n# reference to the last highest hit rate\nlast_higher_hit_rate = 0\n\n####################################################################################################################\n#\n# INiTIAL STATISTICS BY FREQUENCY\n#\n####################################################################################################################\ninitial_cleartext = decrypt(THE_CIPHERTEXT, current_best_key)\ninitial_hit_rate = hit_rate(initial_cleartext)\nprint(\"\\ninitial hit rate --> \" + str(initial_hit_rate))\nprint(initial_cleartext[:LINE_LENGTH])\n# print(\"initial key: \")\n# pprint(current_best_key)\n\n\n####################################################################################################################\n#\n# SEARCH FOR 'the'\n#\n####################################################################################################################\nprint(\"\\ntrying to identify the word 'the'...\")\ndict_the_candidates = get_the_candidates(initial_cleartext, ' ')\nlist_the_candidates = dictionary_to_sorted_list_by_value(dict_the_candidates)\n\nfor an_index in range(1): # optionally increase range to iterate the first 'the' candidates list entries\n the_candidate = list_the_candidates[an_index]\n print(\"best 'the' candidate: '\" + the_candidate + \"'\")\n if the_candidate != \"the\":\n\n better_key_candidate = current_best_key.copy()\n\n if the_candidate[0] != \"t\":\n swap_dictionary(the_candidate[0], 't', better_key_candidate)\n if the_candidate[1] != \"h\":\n swap_dictionary(the_candidate[1], 'h', better_key_candidate)\n if the_candidate[2] != \"e\":\n swap_dictionary(the_candidate[2], 'e', better_key_candidate)\n\n hit_rate_1 = hit_rate_by_key(better_key_candidate)\n hit_rate_2 = hit_rate_by_key(current_best_key)\n\n if hit_rate_1 > hit_rate_2:\n\n SAVED_LETTERS.append(\"t\")\n SAVED_LETTERS.append(\"h\")\n SAVED_LETTERS.append(\"e\")\n\n current_best_key = better_key_candidate.copy()\n higher_hit_rate = max(hit_rate_1, hit_rate_2)\n if last_higher_hit_rate != higher_hit_rate:\n print_stats(decrypt(THE_CIPHERTEXT, current_best_key), higher_hit_rate)\n last_higher_hit_rate = higher_hit_rate\n break\n\n####################################################################################################################\n#\n# SEARCH FOR 'of'\n#\n####################################################################################################################\nprint(\"\\ntrying to identify the word 'of'...\")\ndict_of_candidates = get_of_candidates(decrypt(THE_CIPHERTEXT, current_best_key), ' ')\nlist_of_candidates = dictionary_to_sorted_list_by_value(dict_of_candidates)\n\nfor an_index in range(1): # optionally increase range to iterate the first 'of' candidates list entries\n of_candidate = list_of_candidates[an_index]\n print(\"best 'of' candidate: '\" + of_candidate + \"'\")\n if of_candidate != \"of\":\n\n better_key_candidate = current_best_key.copy()\n\n if of_candidate[0] != \"o\":\n swap_dictionary(of_candidate[0], 'o', better_key_candidate)\n if of_candidate[1] != \"f\":\n swap_dictionary(of_candidate[1], 'f', better_key_candidate)\n\n hit_rate_1 = hit_rate_by_key(better_key_candidate)\n hit_rate_2 = hit_rate_by_key(current_best_key)\n\n if hit_rate_1 > hit_rate_2:\n\n SAVED_LETTERS.append(\"o\")\n SAVED_LETTERS.append(\"f\")\n\n current_best_key = better_key_candidate.copy()\n higher_hit_rate = max(hit_rate_1, hit_rate_2)\n if last_higher_hit_rate != higher_hit_rate:\n print_stats(decrypt(THE_CIPHERTEXT, current_best_key), higher_hit_rate)\n last_higher_hit_rate = higher_hit_rate\n break\n\n####################################################################################################################\n#\n# CHECK DOUBLE LETTERS\n#\n####################################################################################################################\nprint(\"\\ncheck double letters occurences...\\n\")\n\nDEPTH = 14 # range till 0 - 14 (length of the list double letter alternatives)\n\ndouble_letters_dictionary = get_double_letters(CIPHERTEXT_WO_WHITESPACES)\ndouble_letters_orderes_list = dictionary_to_sorted_list_by_value(double_letters_dictionary)[:DEPTH]\n\nfor double_letter_candidate in double_letters_orderes_list:\n for double_letter_candidate_alternative in [\"l\", \"s\", \"o\", \"t\", \"f\", \"p\", \"r\", \"m\", \"c\", \"n\", \"d\", \"g\", \"i\", \"b\"][\n :DEPTH]:\n character_1_to_swap = double_letter_candidate[0]\n character_2_to_swap = double_letter_candidate_alternative\n\n swap_result = swap_and_check(character_1_to_swap, character_2_to_swap, current_best_key, last_higher_hit_rate)\n last_higher_hit_rate = swap_result[0]\n current_best_key = swap_result[1]\n\n####################################################################################################################\n#\n# HANDLE N-GRAMS\n#\n####################################################################################################################\ncleartext_without_whitespaces = decrypt(THE_CIPHERTEXT, current_best_key).replace(\" \", \"\")\n\nprint(\"\\ncalculating bigrams\")\ndict_bigrams = bigram_frequency(cleartext_without_whitespaces)\nenglish_bigram_list = dictionary_to_sorted_list_by_value(ENGLISH_BIGRAM_FREQUENCY)\ncipher_bigram_list = dictionary_to_sorted_list_by_value(dict_bigrams)\n\nmapped_bigrams = zip(english_bigram_list, cipher_bigram_list)\nweight_bigrams_dictionary = weight_bigrams(mapped_bigrams, None)\n\nprint(\"\\ncalculating trigrams\")\ndict_trigrams = trigram_frequency(cleartext_without_whitespaces)\nenglish_trigram_list = dictionary_to_sorted_list_by_value(ENGLISH_TRIGRAM_FREQUENCY)\ncipher_trigram_list = dictionary_to_sorted_list_by_value(dict_trigrams)\n\nmapped_trigrams = zip(english_trigram_list, cipher_trigram_list)\nweight_trigrams_dictionary = weight_bigrams(mapped_trigrams, weight_bigrams_dictionary)\nweight_trigrams_list = dictionary_to_sorted_list_by_value(weight_trigrams_dictionary)\n\nprint(\"\\niterate ngrams list\")\nfor an_entry in weight_trigrams_list:\n\n character_1 = an_entry[0]\n character_2 = an_entry[1]\n\n if character_1 == character_2:\n continue\n\n if character_1 in SAVED_LETTERS or character_2 in SAVED_LETTERS:\n continue\n\n swap_result = swap_and_check(character_1, character_2, current_best_key, last_higher_hit_rate)\n last_higher_hit_rate = swap_result[0]\n current_best_key = swap_result[1]\n\n####################################################################################################################\n#\n# OPTIONALLY LONG WORDS CHECK\n#\n####################################################################################################################\nif \"-longWords\" in sys.argv:\n\n print(\"\\ndo long word check..\")\n\n LONG_WORD_LENGTH = 14\n\n long_words = get_words_from_wordlist_by_length(LONG_WORD_LENGTH)\n print(\"count words with a length of \" + str(LONG_WORD_LENGTH) + \" from wordlist: \" + str(len(long_words)))\n\n long_words_encrypted = get_long_words(decrypt(THE_CIPHERTEXT, current_best_key), LONG_WORD_LENGTH)\n print(\"count words with a length of \" + str(LONG_WORD_LENGTH) + \" from cleartext: \" + str(\n len(long_words_encrypted)) + \"\\n\")\n\n for long_word in long_words:\n\n for long_word_encrypted in long_words_encrypted:\n\n better_key_candidate = current_best_key.copy()\n\n already_swapped_characters = []\n\n for an_index in range(len(long_word_encrypted)):\n\n character_1_to_swap = long_word[an_index]\n character_2_to_swap = long_word_encrypted[an_index]\n\n if character_1_to_swap not in already_swapped_characters:\n if character_1_to_swap != character_2_to_swap:\n swap_dictionary(character_2_to_swap, character_1_to_swap, better_key_candidate)\n\n already_swapped_characters.append(character_1_to_swap)\n already_swapped_characters.append(character_2_to_swap)\n\n hit_rate_1 = hit_rate_by_key(current_best_key)\n hit_rate_2 = hit_rate_by_key(better_key_candidate)\n\n if hit_rate_1 < hit_rate_2:\n current_best_key = better_key_candidate.copy()\n\n higher_hit_rate = max(hit_rate_1, hit_rate_2)\n\n if last_higher_hit_rate != higher_hit_rate:\n print(\"swapping word '\" + long_word + \"' with '\" + long_word_encrypted + \"'\")\n print_stats(decrypt(THE_CIPHERTEXT, current_best_key), higher_hit_rate)\n\n last_higher_hit_rate = higher_hit_rate\n\n####################################################################################################################\n#\n# SWAP WEAKEST CHAR WITH NEIGHBOURS\n#\n####################################################################################################################\nprint(\"\\ncalculate current char frquency...\")\ncurrent_char_frequency = char_frequency(decrypt(THE_CIPHERTEXT, current_best_key))\n\nreversed_sorted_characters_list = list(reversed(dictionary_to_sorted_list_by_value(current_char_frequency)))\n\nprint(\"current char frequencies reversed: \" + str(reversed_sorted_characters_list) + \"\\n\")\n\nfor index_1 in range(len(reversed_sorted_characters_list) - len(SAVED_LETTERS) - 1):\n\n for index_2 in range(len(reversed_sorted_characters_list) - len(SAVED_LETTERS) - 1):\n\n for an_index in [0, 1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8]:\n\n better_key_candidate = current_best_key.copy()\n\n second_index = index_2 + an_index\n\n # avoid array out of bounds exception\n if not (second_index >= len(reversed_sorted_characters_list) or second_index < 0):\n\n character_1 = reversed_sorted_characters_list[index_2]\n character_2 = reversed_sorted_characters_list[second_index]\n\n swap_dictionary(character_1, character_2, better_key_candidate)\n\n hit_rate_1 = hit_rate_by_key(current_best_key)\n hit_rate_2 = hit_rate_by_key(better_key_candidate)\n\n if hit_rate_1 < hit_rate_2:\n current_best_key = better_key_candidate.copy()\n\n higher_hit_rate = max(hit_rate_1, hit_rate_2)\n\n if last_higher_hit_rate != higher_hit_rate:\n print(\"swapping '\" + character_1 + \"' with '\" + character_2 + \"'\")\n print_stats(decrypt(THE_CIPHERTEXT, current_best_key), higher_hit_rate)\n\n last_higher_hit_rate = higher_hit_rate\n\n####################################################################################################################\n#\n# MANUAL CHARACTERS\n#\n####################################################################################################################\n\nif len(sys.argv) > 2:\n print(\"\\nhandle manuel swapping of characters...\")\n manuel_swap_list = []\n for parameter_kandidate in sys.argv:\n if len(parameter_kandidate) == 1:\n manuel_swap_list.append(parameter_kandidate)\n\n if len(manuel_swap_list) > 0 and len(manuel_swap_list) % 2 == 0:\n print(\"characters to swap: \" + str(manuel_swap_list) + \"\\n\")\n an_index = 0\n while an_index < len(manuel_swap_list) - 1:\n\n character_1 = manuel_swap_list[an_index]\n character_2 = manuel_swap_list[an_index + 1]\n\n better_key_candidate = current_best_key.copy()\n\n swap_dictionary(character_1, character_2, better_key_candidate)\n\n hit_rate_1 = hit_rate_by_key(current_best_key)\n hit_rate_2 = hit_rate_by_key(better_key_candidate)\n\n if hit_rate_1 < hit_rate_2:\n current_best_key = better_key_candidate.copy()\n\n higher_hit_rate = max(hit_rate_1, hit_rate_2)\n\n if last_higher_hit_rate != higher_hit_rate:\n print(\"swapping '\" + character_1 + \"' with '\" + character_2 + \"'\")\n print_stats(decrypt(THE_CIPHERTEXT, current_best_key), higher_hit_rate)\n\n an_index += 2\n\nprint(\"Finished without fullfilling result:\")\nprint(\"\\nCurrent state:\")\nprint_stats(decrypt(THE_CIPHERTEXT, current_best_key), higher_hit_rate)\nprint_duration()\n", "sub_path": "Uebung1/Ue_1_A_1_Decrypt.py", "file_name": "Ue_1_A_1_Decrypt.py", "file_ext": "py", "file_size_in_byte": 24502, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 23, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 120, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 120, "usage_type": "attribute"}, {"api_name": "operator.itemgetter", "line_number": 187, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 503, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 600, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 603, "usage_type": "attribute"}]} +{"seq_id": "42064274", "text": "import numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nN4_train = np.array((0.18, 0.182, 0.201, 0.242, 0.261))\r\nN4_test = np.array((0.095, 0.155, 0.183, 0.214, 0.241))\r\n\r\nResNet_train = np.array((0.901, 0.905, 0.897, 0.859, 0.825))\r\nResNet_test = np.array((0.128, 0.184, 0.266, 0.296, 0.345))\r\n\r\nResNetSlw_train = np.array((0.923, 0.879, 0.873, 0.856, 0.839))\r\nResNetSlw_test = np.array((0.128, 0.188, 0.253, 0.292, 0.331))\r\n\r\nVGG_train = np.array((0.567, 0.585, 0.451, 0.454, 0.286))\r\nVGG_test = np.array((0.133, 0.203, 0.254, 0.279, 0.281))\r\n\r\nindex = np.array((50, 100, 200, 300, 500))\r\n\r\nf1 = plt.figure(1)\r\nplt.ylim(0, 1)\r\nplt.plot(index, N4_train, label='CNN train')\r\nplt.plot(index, ResNet_train, label='ResNet train')\r\nplt.plot(index, ResNetSlw_train, label='ResNetShallow train')\r\nplt.plot(index, VGG_train, label='VGG train')\r\nplt.legend()\r\nf2 = plt.figure(2)\r\nplt.ylim(0, 0.4)\r\nplt.plot(index, N4_test, label='CNN test')\r\nplt.plot(index, ResNet_test, label='ResNet test')\r\nplt.plot(index, ResNetSlw_test, label='ResNetShallow test')\r\nplt.plot(index, VGG_test, label='VGG test')\r\nplt.legend()\r\nf3 = plt.figure(3)\r\nplt.plot(index, N4_train / N4_test, label='CNN train / test')\r\nplt.plot(index, ResNet_train / ResNet_test, label='ResNet train / test')\r\nplt.plot(index, ResNetSlw_train / ResNetSlw_test, label='ResNetShallow train / test')\r\nplt.plot(index, VGG_train / VGG_test, label='VGG train / test')\r\nplt.legend()", "sub_path": "plot_stat.py", "file_name": "plot_stat.py", "file_ext": "py", "file_size_in_byte": 1425, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.array", "line_number": 4, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}]} +{"seq_id": "102545113", "text": "from datetime import datetime\nfrom dateutil import tz\n\ndef utc_to_local(utc_string):\n from_zone = tz.tzutc()\n to_zone = tz.tzlocal()\n\n # utc = datetime.utcnow()\n utc = datetime.strptime(utc_string, '%Y-%m-%d %H:%M:%S')\n\n # Tell the datetime object that it's in UTC time zone since \n # datetime objects are 'naive' by default\n utc = utc.replace(tzinfo=from_zone)\n\n # return local time\n return utc.astimezone(to_zone)\n", "sub_path": "src/converter.py", "file_name": "converter.py", "file_ext": "py", "file_size_in_byte": 425, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "dateutil.tz.tzutc", "line_number": 5, "usage_type": "call"}, {"api_name": "dateutil.tz", "line_number": 5, "usage_type": "name"}, {"api_name": "dateutil.tz.tzlocal", "line_number": 6, "usage_type": "call"}, {"api_name": "dateutil.tz", "line_number": 6, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "650924855", "text": "# coding=utf-8\n# Copyright 2022 The Google Research Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Helper functions/classes for model definition.\"\"\"\n\nimport functools\nfrom typing import Any, Callable\n\nfrom flax import linen as nn\nimport jax\nfrom jax import lax\nfrom jax import random\nimport jax.numpy as jnp\n\n\nclass MLP(nn.Module):\n \"\"\"A simple MLP.\"\"\"\n net_depth: int = 8 # The depth of the first part of MLP.\n net_width: int = 256 # The width of the first part of MLP.\n net_activation: Callable[Ellipsis, Any] = nn.relu # The activation function.\n skip_layer: int = 4 # The layer to add skip layers to.\n num_rgb_channels: int = 3 # The number of RGB channels.\n num_sigma_channels: int = 1 # The number of sigma channels.\n\n @nn.compact\n def __call__(self, x):\n \"\"\"Evaluate the MLP.\n\n Args:\n x: jnp.ndarray(float32), [batch, num_samples, feature], points.\n\n Returns:\n raw_rgb: jnp.ndarray(float32), with a shape of\n [batch, num_samples, num_rgb_channels].\n raw_sigma: jnp.ndarray(float32), with a shape of\n [batch, num_samples, num_sigma_channels].\n \"\"\"\n feature_dim = x.shape[-1]\n num_samples = x.shape[1]\n x = x.reshape([-1, feature_dim])\n dense_layer = functools.partial(\n nn.Dense, kernel_init=jax.nn.initializers.glorot_uniform())\n inputs = x\n for i in range(self.net_depth):\n x = dense_layer(self.net_width)(x)\n x = self.net_activation(x)\n if i % self.skip_layer == 0 and i > 0:\n x = jnp.concatenate([x, inputs], axis=-1)\n raw_sigma = dense_layer(self.num_sigma_channels)(x).reshape(\n [-1, num_samples, self.num_sigma_channels])\n raw_rgb = dense_layer(self.num_rgb_channels)(x).reshape(\n [-1, num_samples, self.num_rgb_channels])\n return raw_rgb, raw_sigma\n\n\ndef cast_rays(z_vals, origins, directions):\n return origins[Ellipsis, None, :] + z_vals[Ellipsis, None] * directions[Ellipsis, None, :]\n\n\ndef sample_along_rays(key, origins, directions, num_samples, near, far,\n randomized, lindisp):\n \"\"\"Stratified sampling along the rays.\n\n Args:\n key: jnp.ndarray, random generator key.\n origins: jnp.ndarray(float32), [batch_size, 3], ray origins.\n directions: jnp.ndarray(float32), [batch_size, 3], ray directions.\n num_samples: int.\n near: float, near clip.\n far: float, far clip.\n randomized: bool, use randomized stratified sampling.\n lindisp: bool, sampling linearly in disparity rather than depth.\n\n Returns:\n z_vals: jnp.ndarray, [batch_size, num_samples], sampled z values.\n points: jnp.ndarray, [batch_size, num_samples, 3], sampled points.\n \"\"\"\n batch_size = origins.shape[0]\n\n t_vals = jnp.linspace(0., 1., num_samples)\n if lindisp:\n z_vals = 1. / (1. / near * (1. - t_vals) + 1. / far * t_vals)\n else:\n z_vals = near * (1. - t_vals) + far * t_vals\n\n if randomized:\n mids = .5 * (z_vals[Ellipsis, 1:] + z_vals[Ellipsis, :-1])\n upper = jnp.concatenate([mids, z_vals[Ellipsis, -1:]], -1)\n lower = jnp.concatenate([z_vals[Ellipsis, :1], mids], -1)\n t_rand = random.uniform(key, [batch_size, num_samples])\n z_vals = lower + (upper - lower) * t_rand\n else:\n # Broadcast z_vals to make the returned shape consistent.\n z_vals = jnp.broadcast_to(z_vals[None, Ellipsis], [batch_size, num_samples])\n\n coords = cast_rays(z_vals, origins, directions)\n return z_vals, coords\n\n\ndef posenc(x, min_deg, max_deg, legacy_posenc_order=False):\n \"\"\"Cat x with a positional encoding of x with scales 2^[min_deg, max_deg-1].\n\n Instead of computing [sin(x), cos(x)], we use the trig identity\n cos(x) = sin(x + pi/2) and do one vectorized call to sin([x, x+pi/2]).\n\n Args:\n x: jnp.ndarray, variables to be encoded. Note that x should be in [-pi, pi].\n min_deg: int, the minimum (inclusive) degree of the encoding.\n max_deg: int, the maximum (exclusive) degree of the encoding.\n legacy_posenc_order: bool, keep the same ordering as the original tf code.\n\n Returns:\n encoded: jnp.ndarray, encoded variables.\n \"\"\"\n if min_deg == max_deg:\n return x\n scales = jnp.array([2**i for i in range(min_deg, max_deg)])\n if legacy_posenc_order:\n xb = x[Ellipsis, None, :] * scales[:, None]\n four_feat = jnp.reshape(\n jnp.sin(jnp.stack([xb, xb + 0.5 * jnp.pi], -2)),\n list(x.shape[:-1]) + [-1])\n else:\n xb = jnp.reshape((x[Ellipsis, None, :] * scales[:, None]),\n list(x.shape[:-1]) + [-1])\n four_feat = jnp.sin(jnp.concatenate([xb, xb + 0.5 * jnp.pi], axis=-1))\n return jnp.concatenate([x] + [four_feat], axis=-1)\n\n\ndef volumetric_rendering(rgb, sigma, z_vals, dirs, white_bkgd):\n \"\"\"Volumetric Rendering Function.\n\n Args:\n rgb: jnp.ndarray(float32), color, [batch_size, num_samples, 3]\n sigma: jnp.ndarray(float32), density, [batch_size, num_samples, 1].\n z_vals: jnp.ndarray(float32), [batch_size, num_samples].\n dirs: jnp.ndarray(float32), [batch_size, 3].\n white_bkgd: bool.\n\n Returns:\n comp_rgb: jnp.ndarray(float32), [batch_size, 3].\n disp: jnp.ndarray(float32), [batch_size].\n acc: jnp.ndarray(float32), [batch_size].\n weights: jnp.ndarray(float32), [batch_size, num_samples]\n \"\"\"\n eps = 1e-10\n dists = jnp.concatenate([\n z_vals[Ellipsis, 1:] - z_vals[Ellipsis, :-1],\n jnp.broadcast_to(1e10, z_vals[Ellipsis, :1].shape)\n ], -1)\n dists = dists * jnp.linalg.norm(dirs[Ellipsis, None, :], axis=-1)\n # Note that we're quietly turning sigma from [..., 0] to [...].\n alpha = 1.0 - jnp.exp(-sigma[Ellipsis, 0] * dists)\n accum_prod = jnp.concatenate([\n jnp.ones_like(alpha[Ellipsis, :1], alpha.dtype),\n jnp.cumprod(1.0 - alpha[Ellipsis, :-1] + eps, axis=-1)\n ],\n axis=-1)\n weights = alpha * accum_prod\n\n comp_rgb = (weights[Ellipsis, None] * rgb).sum(axis=-2)\n depth = (weights * z_vals).sum(axis=-1)\n acc = weights.sum(axis=-1)\n # Equivalent to (but slightly more efficient and stable than):\n # disp = 1 / max(eps, where(acc > eps, depth / acc, 0))\n inv_eps = 1 / eps\n disp = acc / depth\n disp = jnp.where((disp > 0) & (disp < inv_eps) & (acc > eps), disp, inv_eps)\n if white_bkgd:\n comp_rgb = comp_rgb + (1. - acc[Ellipsis, None])\n return comp_rgb, disp, acc, weights\n\n\ndef piecewise_constant_pdf(key, bins, weights, num_samples, randomized):\n \"\"\"Piecewise-Constant PDF sampling.\n\n Args:\n key: jnp.ndarray(float32), [2,], random number generator.\n bins: jnp.ndarray(float32), [batch_size, num_bins + 1].\n weights: jnp.ndarray(float32), [batch_size, num_bins].\n num_samples: int, the number of samples.\n randomized: bool, use randomized samples.\n\n Returns:\n z_samples: jnp.ndarray(float32), [batch_size, num_samples].\n \"\"\"\n # Pad each weight vector (only if necessary) to bring its sum to `eps`. This\n # avoids NaNs when the input is zeros or small, but has no effect otherwise.\n eps = 1e-5\n weight_sum = jnp.sum(weights, axis=-1, keepdims=True)\n padding = jnp.maximum(0, eps - weight_sum)\n weights += padding / weights.shape[-1]\n weight_sum += padding\n\n # Compute the PDF and CDF for each weight vector, while ensuring that the CDF\n # starts with exactly 0 and ends with exactly 1.\n pdf = weights / weight_sum\n cdf = jnp.minimum(1, jnp.cumsum(pdf[Ellipsis, :-1], axis=-1))\n cdf = jnp.concatenate([\n jnp.zeros(list(cdf.shape[:-1]) + [1]), cdf,\n jnp.ones(list(cdf.shape[:-1]) + [1])\n ],\n axis=-1)\n\n # Draw uniform samples.\n if randomized:\n # Note that `u` is in [0, 1) --- it can be zero, but it can never be 1.\n u = random.uniform(key, list(cdf.shape[:-1]) + [num_samples])\n else:\n # Match the behavior of random.uniform() by spanning [0, 1-eps].\n u = jnp.linspace(0., 1. - jnp.finfo('float32').eps, num_samples)\n u = jnp.broadcast_to(u, list(cdf.shape[:-1]) + [num_samples])\n\n # Identify the location in `cdf` that corresponds to a random sample.\n # The final `True` index in `mask` will be the start of the sampled interval.\n mask = u[Ellipsis, None, :] >= cdf[Ellipsis, :, None]\n\n def find_interval(x):\n # Grab the value where `mask` switches from True to False, and vice versa.\n # This approach takes advantage of the fact that `x` is sorted.\n x0 = jnp.max(jnp.where(mask, x[Ellipsis, None], x[Ellipsis, :1, None]), -2)\n x1 = jnp.min(jnp.where(~mask, x[Ellipsis, None], x[Ellipsis, -1:, None]), -2)\n return x0, x1\n\n bins_g0, bins_g1 = find_interval(bins)\n cdf_g0, cdf_g1 = find_interval(cdf)\n\n t = jnp.clip(jnp.nan_to_num((u - cdf_g0) / (cdf_g1 - cdf_g0), 0), 0, 1)\n samples = bins_g0 + t * (bins_g1 - bins_g0)\n\n # Prevent gradient from backprop-ing through `samples`.\n return lax.stop_gradient(samples)\n\n\ndef sample_pdf(key, bins, weights, origins, directions, z_vals, num_samples,\n randomized):\n \"\"\"Hierarchical sampling.\n\n Args:\n key: jnp.ndarray(float32), [2,], random number generator.\n bins: jnp.ndarray(float32), [batch_size, num_bins + 1].\n weights: jnp.ndarray(float32), [batch_size, num_bins].\n origins: jnp.ndarray(float32), [batch_size, 3], ray origins.\n directions: jnp.ndarray(float32), [batch_size, 3], ray directions.\n z_vals: jnp.ndarray(float32), [batch_size, num_coarse_samples].\n num_samples: int, the number of samples.\n randomized: bool, use randomized samples.\n\n Returns:\n z_vals: jnp.ndarray(float32),\n [batch_size, num_coarse_samples + num_fine_samples].\n points: jnp.ndarray(float32),\n [batch_size, num_coarse_samples + num_fine_samples, 3].\n \"\"\"\n z_samples = piecewise_constant_pdf(key, bins, weights, num_samples,\n randomized)\n # Compute united z_vals and sample points\n z_vals = jnp.sort(jnp.concatenate([z_vals, z_samples], axis=-1), axis=-1)\n coords = cast_rays(z_vals, origins, directions)\n return z_vals, coords\n\n\ndef add_gaussian_noise(key, raw, noise_std, randomized):\n \"\"\"Adds gaussian noise to `raw`, which can used to regularize it.\n\n Args:\n key: jnp.ndarray(float32), [2,], random number generator.\n raw: jnp.ndarray(float32), arbitrary shape.\n noise_std: float, The standard deviation of the noise to be added.\n randomized: bool, add noise if randomized is True.\n\n Returns:\n raw + noise: jnp.ndarray(float32), with the same shape as `raw`.\n \"\"\"\n if (noise_std is not None) and randomized:\n return raw + random.normal(key, raw.shape, dtype=raw.dtype) * noise_std\n else:\n return raw\n", "sub_path": "snerg/nerf/model_utils.py", "file_name": "model_utils.py", "file_ext": "py", "file_size_in_byte": 10948, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flax.linen.Module", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flax.linen", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 32, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 32, "usage_type": "name"}, {"api_name": "flax.linen.relu", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flax.linen", "line_number": 32, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 53, "usage_type": "call"}, {"api_name": "flax.linen.Dense", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flax.linen", "line_number": 54, "usage_type": "name"}, {"api_name": "jax.nn.initializers.glorot_uniform", "line_number": 54, "usage_type": "call"}, {"api_name": "jax.nn", "line_number": 54, "usage_type": "attribute"}, {"api_name": "jax.numpy.concatenate", "line_number": 60, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 60, "usage_type": "name"}, {"api_name": "flax.linen.compact", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flax.linen", "line_number": 37, "usage_type": "name"}, {"api_name": "jax.numpy.linspace", "line_number": 92, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 92, "usage_type": "name"}, {"api_name": "jax.numpy.concatenate", "line_number": 100, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 100, "usage_type": "name"}, {"api_name": "jax.numpy.concatenate", "line_number": 101, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 101, "usage_type": "name"}, {"api_name": "jax.random.uniform", "line_number": 102, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 102, "usage_type": "name"}, {"api_name": "jax.numpy.broadcast_to", "line_number": 106, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 106, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 129, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 129, "usage_type": "name"}, {"api_name": "jax.numpy.reshape", "line_number": 132, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 132, "usage_type": "name"}, {"api_name": "jax.numpy.sin", "line_number": 133, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 133, "usage_type": "name"}, {"api_name": "jax.numpy.stack", "line_number": 133, "usage_type": "call"}, {"api_name": "jax.numpy.pi", "line_number": 133, "usage_type": "attribute"}, {"api_name": "jax.numpy.reshape", "line_number": 136, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 136, "usage_type": "name"}, {"api_name": "jax.numpy.sin", "line_number": 138, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 138, "usage_type": "name"}, {"api_name": "jax.numpy.concatenate", "line_number": 138, "usage_type": "call"}, {"api_name": "jax.numpy.pi", "line_number": 138, "usage_type": "attribute"}, {"api_name": "jax.numpy.concatenate", "line_number": 139, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 139, "usage_type": "name"}, {"api_name": "jax.numpy.concatenate", "line_number": 159, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 159, "usage_type": "name"}, {"api_name": "jax.numpy.broadcast_to", "line_number": 161, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 161, "usage_type": "name"}, {"api_name": "jax.numpy.linalg.norm", "line_number": 163, "usage_type": "call"}, {"api_name": "jax.numpy.linalg", "line_number": 163, "usage_type": "attribute"}, {"api_name": "jax.numpy", "line_number": 163, "usage_type": "name"}, {"api_name": "jax.numpy.exp", "line_number": 165, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 165, "usage_type": "name"}, {"api_name": "jax.numpy.concatenate", "line_number": 166, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 166, "usage_type": "name"}, {"api_name": "jax.numpy.ones_like", "line_number": 167, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 167, "usage_type": "name"}, {"api_name": "jax.numpy.cumprod", "line_number": 168, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 168, "usage_type": "name"}, {"api_name": "jax.numpy.where", "line_number": 180, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 180, "usage_type": "name"}, {"api_name": "jax.numpy.sum", "line_number": 202, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 202, "usage_type": "name"}, {"api_name": "jax.numpy.maximum", "line_number": 203, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 203, "usage_type": "name"}, {"api_name": "jax.numpy.minimum", "line_number": 210, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 210, "usage_type": "name"}, {"api_name": "jax.numpy.cumsum", "line_number": 210, "usage_type": "call"}, {"api_name": "jax.numpy.concatenate", "line_number": 211, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 211, "usage_type": "name"}, {"api_name": "jax.numpy.zeros", "line_number": 212, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 212, "usage_type": "name"}, {"api_name": "jax.numpy.ones", "line_number": 213, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 213, "usage_type": "name"}, {"api_name": "jax.random.uniform", "line_number": 220, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 220, "usage_type": "name"}, {"api_name": "jax.numpy.linspace", "line_number": 223, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 223, "usage_type": "name"}, {"api_name": "jax.numpy.finfo", "line_number": 223, "usage_type": "call"}, {"api_name": "jax.numpy.broadcast_to", "line_number": 224, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 224, "usage_type": "name"}, {"api_name": "jax.numpy.max", "line_number": 233, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 233, "usage_type": "name"}, {"api_name": "jax.numpy.where", "line_number": 233, "usage_type": "call"}, {"api_name": "jax.numpy.min", "line_number": 234, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 234, "usage_type": "name"}, {"api_name": "jax.numpy.where", "line_number": 234, "usage_type": "call"}, {"api_name": "jax.numpy.clip", "line_number": 240, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 240, "usage_type": "name"}, {"api_name": "jax.numpy.nan_to_num", "line_number": 240, "usage_type": "call"}, {"api_name": "jax.lax.stop_gradient", "line_number": 244, "usage_type": "call"}, {"api_name": "jax.lax", "line_number": 244, "usage_type": "name"}, {"api_name": "jax.numpy.sort", "line_number": 270, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 270, "usage_type": "name"}, {"api_name": "jax.numpy.concatenate", "line_number": 270, "usage_type": "call"}, {"api_name": "jax.random.normal", "line_number": 288, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 288, "usage_type": "name"}]} +{"seq_id": "345494964", "text": "import os\nimport time\nimport subprocess\nimport torch as t\n\nfrom jukebox.hparams import Hyperparams\nfrom jukebox.utils.torch_utils import empty_cache\nfrom jukebox.utils.audio_utils import save_wav, load_audio\nfrom jukebox.make_models import make_model\nfrom jukebox.align import get_alignment\nfrom jukebox.save_html import save_html\nfrom jukebox.utils.sample_utils import split_batch, get_starts\nfrom jukebox.utils.dist_utils import print_once\nimport fire\n\n\ndef sample_partial_window(zs, labels, sampling_kwargs, level, prior, tokens_to_sample, hps):\n \"\"\"Sample a partial window of length= prior.n_ctx:\n for start in get_starts(total_length, prior.n_ctx, hop_length):\n zs = sample_single_window(zs, labels, sampling_kwargs, level, prior, start, hps)\n else:\n zs = sample_partial_window(zs, labels, sampling_kwargs, level, prior, total_length, hps)\n return zs\n\n\ndef _sample(zs, labels, sampling_kwargs, priors, sample_levels, hps):\n \"\"\"Sample multiple levels\"\"\"\n alignments = None\n for level in reversed(sample_levels):\n prior = priors[level]\n prior.cuda()\n empty_cache()\n\n # Set correct total_length, hop_length, labels and sampling_kwargs for level\n assert hps.sample_length % prior.raw_to_tokens == 0, f\"Expected sample_length {hps.sample_length} to be multiple of {prior.raw_to_tokens}\"\n total_length = hps.sample_length//prior.raw_to_tokens\n hop_length = int(hps.hop_fraction[level]*prior.n_ctx)\n zs = sample_level(zs, labels[level], sampling_kwargs[level], level, prior, total_length, hop_length, hps)\n\n prior.cpu()\n empty_cache()\n\n # Decode sample\n x = priors[-1].decode(zs[level:], start_level=level, bs_chunks=zs[level].shape[0])\n logdir = f\"{hps.job_id}_{hps.name}/level_{level}\"\n if not os.path.exists(logdir):\n os.makedirs(logdir)\n t.save(dict(zs=zs, labels=labels, sampling_kwargs=sampling_kwargs, x=x), f\"{logdir}/data.pth.tar\")\n save_wav(logdir, x, hps.sr)\n if alignments is None and priors[-1].n_tokens > 0:\n alignments = get_alignment(x, zs, labels[-1], priors[-1], sampling_kwargs[-1]['fp16'], hps)\n save_html(logdir, x, zs, labels[-1], alignments, hps)\n return zs\n\n\ndef ancestral_sample(labels, sampling_kwargs, priors, hps):\n \"\"\"Generate ancestral samples given a list of artists and genres\"\"\"\n sample_levels = list(range(len(priors)))\n zs = [t.zeros(hps.n_samples,0,dtype=t.long, device='cuda') for _ in range(len(priors))]\n zs = _sample(zs, labels, sampling_kwargs, priors, sample_levels, hps)\n return zs\n\n\ndef upsample(zs, labels, sampling_kwargs, priors, hps):\n \"\"\"Upsample given already generated upper-level codes\"\"\"\n sample_levels = list(range(len(priors) - 1))\n zs = _sample(zs, labels, sampling_kwargs, priors, sample_levels, hps)\n return zs\n\n\ndef primed_sample(x, labels, sampling_kwargs, priors, hps):\n \"\"\"Prompt the model with raw audio input (dimension: NTC) and generate continuations\"\"\"\n sample_levels = list(range(len(priors)))\n zs = priors[-1].encode(x, start_level=0, end_level=len(priors), bs_chunks=x.shape[0])\n zs = _sample(zs, labels, sampling_kwargs, priors, sample_levels, hps)\n return zs\n\n\ndef load_prompts(audio_files, duration, hps):\n \"\"\"Load `duration` seconds of the given audio files to use as prompts\"\"\"\n xs = []\n for audio_file in audio_files:\n x = load_audio(audio_file, sr=hps.sr, duration=duration, offset=0.0, mono=True)\n x = x.T # CT -> TC\n xs.append(x)\n while len(xs) < hps.n_samples:\n xs.extend(xs)\n xs = xs[:hps.n_samples]\n x = t.stack([t.from_numpy(x) for x in xs])\n x = x.to('cuda', non_blocking=True)\n return x\n\n\ndef save_samples(model, device, hps, sample_hps, metas: list):\n \"\"\"Generate and save samples, alignment, and webpage for visualization.\"\"\"\n print(hps)\n from jukebox.lyricdict import poems, gpt_2_lyrics\n vqvae, priors = make_model(model, device, hps)\n\n assert hps.sample_length//priors[-2].raw_to_tokens >= priors[-2].n_ctx, f\"Upsampling needs atleast one ctx in get_z_conds. Please choose a longer sample length\"\n assert isinstance(metas, list)\n total_length = hps.total_sample_length_in_seconds * hps.sr\n offset = 0\n while len(metas) < hps.n_samples:\n metas.extend(metas)\n metas = metas[:hps.n_samples]\n\n labels = [prior.labeller.get_batch_labels(metas, 'cuda') for prior in priors]\n for label in labels:\n assert label['y'].shape[0] == hps.n_samples\n\n lower_level_chunk_size = 32\n lower_level_max_batch_size = 16\n if model == '1b_lyrics':\n chunk_size = 32\n max_batch_size = 16\n else:\n chunk_size = 16\n max_batch_size = 3\n sampling_kwargs = [dict(temp=0.99, fp16=True, chunk_size=lower_level_chunk_size, max_batch_size=lower_level_max_batch_size),\n dict(temp=0.99, fp16=True, chunk_size=lower_level_chunk_size, max_batch_size=lower_level_max_batch_size),\n dict(temp=0.99, fp16=True, chunk_size=chunk_size, max_batch_size=max_batch_size)]\n\n if sample_hps.mode == 'ancestral':\n ancestral_sample(labels, sampling_kwargs, priors, hps)\n elif sample_hps.mode == 'primed':\n assert sample_hps.audio_file is not None\n audio_files = sample_hps.audio_file.split(',')\n top_raw_to_tokens = priors[-1].raw_to_tokens\n duration = (int(sample_hps.prompt_length_in_seconds * hps.sr) // top_raw_to_tokens) * top_raw_to_tokens\n x = load_prompts(audio_files, duration, hps)\n primed_sample(x, labels, sampling_kwargs, priors, hps)\n else:\n raise ValueError(f'Unknown sample mode {sample_hps.mode}.')\n\n\ndef run(mode='ancestral', audio_file=None, prompt_length_in_seconds=12.0, port=29500):\n from jukebox.utils.dist_utils import setup_dist_from_mpi\n from jukebox.utils import queue\n # setup distributed communications\n rank, local_rank, device = setup_dist_from_mpi(port=port)\n while True:\n # connect to db\n db, cur = queue.connectdb()\n offset = 0\n # get the next job\n job = queue.get_next_job(cur)\n if job:\n print(job)\n job_id = job['job_id']\n kw = dict()\n kw['sr'] = 44100\n kw['n_samples'] = 3\n kw['hop_fraction'] = (0.5, 0.5, 0.25)\n kw['model'] = '5b_lyrics'\n kw['levels'] = 3\n kw['sample_length_in_seconds'] = int(job['params']['length'])\n kw['total_sample_length_in_seconds'] = int(job['params']['length'])\n kw['n_samples'] = 15 if '5b_lyrics' == job['params']['model'] else 16\n kw['job_id'] = job_id\n kw['name'] = job['params']['name']\n hps = Hyperparams(kw)\n # artist, lyrics, genre\n metas = Hyperparams(dict(artist=job['params']['artist'],\n genre=job['params']['genre'],\n lyrics=job['params']['lyrics'],\n total_length=job['params']['length']*kw['sr'], # remove hardcoded sr\n offset=offset))\n print(hps)\n sample_hps = Hyperparams(dict(mode=mode,\n audio_file=audio_file,\n prompt_length_in_seconds=prompt_length_in_seconds))\n # Lock the job\n queue.lock(cur, job_id)\n # Start the job\n queue.update_status(cur, job_id, \"top_started\")\n # Log the URL\n curl = subprocess.Popen(os.path.expanduser('./get_ip.sh'), stdout=subprocess.PIPE)\n ip, _ = curl.communicate() # (ip, error)\n url = \"http://{}/jukebox/{}_{}/\".format(ip.decode().strip(), job_id, job['params']['name'])\n\n queue.log(cur,\n job_id,\n \"URL: http://{}/jukebox/{}_{}/\".format(ip.decode().strip(), job_id, job['params']['name']))\n # close db connection to avoid timeout error after sampling\n queue.closedb(db)\n # Run the full generating script here\n with t.no_grad():\n save_samples(job['params']['model'], device, hps, sample_hps, [metas])\n # FINISH\n # open fresh db connection\n db, cur = queue.connectdb()\n # update status\n queue.update_status(cur, job_id, \"upsampling_done\")\n queue.closedb(db)\n else:\n # pause the program for a minute and check back for new jobs\n print('Zzz...')\n time.sleep(60)\n # break the loop\n # break\n\nif __name__ == '__main__':\n fire.Fire(run)\n", "sub_path": "jukebox/sample.py", "file_name": "sample.py", "file_ext": "py", "file_size_in_byte": 11227, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "jukebox.utils.dist_utils.print_once", "line_number": 48, "usage_type": "call"}, {"api_name": "jukebox.utils.torch_utils.empty_cache", "line_number": 60, "usage_type": "call"}, {"api_name": "jukebox.utils.sample_utils.split_batch", "line_number": 66, "usage_type": "call"}, {"api_name": "jukebox.utils.sample_utils.split_batch", "line_number": 67, "usage_type": "call"}, {"api_name": "jukebox.utils.sample_utils.split_batch", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 79, "usage_type": "call"}, {"api_name": "jukebox.utils.dist_utils.print_once", "line_number": 85, "usage_type": "call"}, {"api_name": "jukebox.utils.sample_utils.get_starts", "line_number": 87, "usage_type": "call"}, {"api_name": "jukebox.utils.torch_utils.empty_cache", "line_number": 100, "usage_type": "call"}, {"api_name": "jukebox.utils.torch_utils.empty_cache", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 116, "usage_type": "call"}, {"api_name": "jukebox.utils.audio_utils.save_wav", "line_number": 117, "usage_type": "call"}, {"api_name": "jukebox.align.get_alignment", "line_number": 119, "usage_type": "call"}, {"api_name": "jukebox.save_html.save_html", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 127, "usage_type": "attribute"}, {"api_name": "jukebox.utils.audio_utils.load_audio", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 157, "usage_type": "call"}, {"api_name": "jukebox.make_models.make_model", "line_number": 166, "usage_type": "call"}, {"api_name": "jukebox.utils.dist_utils.setup_dist_from_mpi", "line_number": 209, "usage_type": "call"}, {"api_name": "jukebox.utils.queue.connectdb", "line_number": 212, "usage_type": "call"}, {"api_name": "jukebox.utils.queue", "line_number": 212, "usage_type": "name"}, {"api_name": "jukebox.utils.queue.get_next_job", "line_number": 215, "usage_type": "call"}, {"api_name": "jukebox.utils.queue", "line_number": 215, "usage_type": "name"}, {"api_name": "jukebox.hparams.Hyperparams", "line_number": 230, "usage_type": "call"}, {"api_name": "jukebox.hparams.Hyperparams", "line_number": 232, "usage_type": "call"}, {"api_name": "jukebox.hparams.Hyperparams", "line_number": 238, "usage_type": "call"}, {"api_name": "jukebox.utils.queue.lock", "line_number": 242, "usage_type": "call"}, {"api_name": "jukebox.utils.queue", "line_number": 242, "usage_type": "name"}, {"api_name": "jukebox.utils.queue.update_status", "line_number": 244, "usage_type": "call"}, {"api_name": "jukebox.utils.queue", "line_number": 244, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 246, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 246, "usage_type": "call"}, {"api_name": "os.path", "line_number": 246, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 246, "usage_type": "attribute"}, {"api_name": "jukebox.utils.queue.log", "line_number": 250, "usage_type": "call"}, {"api_name": "jukebox.utils.queue", "line_number": 250, "usage_type": "name"}, {"api_name": "jukebox.utils.queue.closedb", "line_number": 254, "usage_type": "call"}, {"api_name": "jukebox.utils.queue", "line_number": 254, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 256, "usage_type": "call"}, {"api_name": "jukebox.utils.queue.connectdb", "line_number": 260, "usage_type": "call"}, {"api_name": "jukebox.utils.queue", "line_number": 260, "usage_type": "name"}, {"api_name": "jukebox.utils.queue.update_status", "line_number": 262, "usage_type": "call"}, {"api_name": "jukebox.utils.queue", "line_number": 262, "usage_type": "name"}, {"api_name": "jukebox.utils.queue.closedb", "line_number": 263, "usage_type": "call"}, {"api_name": "jukebox.utils.queue", "line_number": 263, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 267, "usage_type": "call"}, {"api_name": "fire.Fire", "line_number": 272, "usage_type": "call"}]} +{"seq_id": "213679122", "text": "import google.auth\nfrom google.cloud import kms_v1\nfrom google.api_core.client_options import ClientOptions\n\ncredentials, _ = google.auth.default()\n\ncert = b\"\"\"\"\"\"\n\nkey = b\"\"\"\"\"\"\n\nproject = \"sijunliu-dca-test\"\n\ndef my_cert_source():\n return cert, key\n\ndef run_sample(client_cert_source):\n options = ClientOptions(client_cert_source=client_cert_source)\n\n client = kms_v1.KeyManagementServiceClient(client_options=options)\n parent = f\"projects/{project}/locations/global\"\n res = client.list_key_rings(request={\"parent\": parent})\n print(res)\n\n\nrun_sample(my_cert_source)", "sub_path": "sample_raw_key.py", "file_name": "sample_raw_key.py", "file_ext": "py", "file_size_in_byte": 603, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "google.auth.auth.default", "line_number": 5, "usage_type": "call"}, {"api_name": "google.auth.auth", "line_number": 5, "usage_type": "attribute"}, {"api_name": "google.auth", "line_number": 5, "usage_type": "name"}, {"api_name": "google.api_core.client_options.ClientOptions", "line_number": 17, "usage_type": "call"}, {"api_name": "google.cloud.kms_v1.KeyManagementServiceClient", "line_number": 19, "usage_type": "call"}, {"api_name": "google.cloud.kms_v1", "line_number": 19, "usage_type": "name"}]} +{"seq_id": "513668170", "text": "from sqlalchemy import and_, func\n\nfrom ...shared.utils.restApi import RestResource\nfrom ...shared.utils.api_utils import build_req_parser\nfrom ..models.security_results import SecurityResultsDAST\nfrom ..models.security_reports import SecurityReport\n\n\nclass TestStatusUpdater(RestResource):\n _put_rules = (\n dict(name=\"test_status\", type=dict, location=\"json\"),\n )\n\n def __init__(self):\n super().__init__()\n self.__init_req_parsers()\n\n def __init_req_parsers(self):\n self._parser_put = build_req_parser(rules=self._put_rules)\n\n def put(self, project_id: int, test_id: int):\n args = self._parser_put.parse_args(strict=False)\n test_status = args.get(\"test_status\")\n\n if not test_status:\n return {\"message\": \"There's no enough parameters\"}, 400\n\n if isinstance(test_id, int):\n _filter = and_(\n SecurityResultsDAST.project_id == project_id, SecurityResultsDAST.id == test_id\n )\n else:\n _filter = and_(\n SecurityResultsDAST.project_id == project_id, SecurityResultsDAST.test_uid == test_id\n )\n test = SecurityResultsDAST.query.filter(_filter).first()\n test.set_test_status(test_status)\n\n if test_status[\"status\"].lower().startswith(\"finished\"):\n if isinstance(test_id, int):\n _filter = and_(\n SecurityReport.project_id == project_id, SecurityReport.id == test_id\n )\n else:\n _filter = and_(\n SecurityReport.project_id == project_id, SecurityReport.test_uid == test_id\n )\n counted_severity = SecurityReport.query.with_entities(\n SecurityReport.severity,\n func.count(SecurityReport.severity)\n ).filter(_filter).group_by(SecurityReport.severity).all()\n\n counted_statuses = SecurityReport.query.with_entities(\n SecurityReport.status,\n func.count(SecurityReport.status)\n ).filter(_filter).group_by(SecurityReport.status).all()\n\n for severity in counted_severity:\n setattr(test, severity[0].lower(), severity[1])\n\n for status in counted_statuses:\n setattr(test, status[0].lower().replace(\" \", \"_\"), status[1])\n test.commit()\n\n return {\"message\": f\"Status for test_id={test_id} of project_id: {project_id} updated\"}, 200\n", "sub_path": "api/update_test_status.py", "file_name": "update_test_status.py", "file_ext": "py", "file_size_in_byte": 2486, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "shared.utils.restApi.RestResource", "line_number": 9, "usage_type": "name"}, {"api_name": "shared.utils.api_utils.build_req_parser", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.and_", "line_number": 29, "usage_type": "call"}, {"api_name": "models.security_results.SecurityResultsDAST.project_id", "line_number": 30, "usage_type": "attribute"}, {"api_name": "models.security_results.SecurityResultsDAST", "line_number": 30, "usage_type": "name"}, {"api_name": "models.security_results.SecurityResultsDAST.id", "line_number": 30, "usage_type": "attribute"}, {"api_name": "sqlalchemy.and_", "line_number": 33, "usage_type": "call"}, {"api_name": "models.security_results.SecurityResultsDAST.project_id", "line_number": 34, "usage_type": "attribute"}, {"api_name": "models.security_results.SecurityResultsDAST", "line_number": 34, "usage_type": "name"}, {"api_name": "models.security_results.SecurityResultsDAST.test_uid", "line_number": 34, "usage_type": "attribute"}, {"api_name": "models.security_results.SecurityResultsDAST.query.filter", "line_number": 36, "usage_type": "call"}, {"api_name": "models.security_results.SecurityResultsDAST.query", "line_number": 36, "usage_type": "attribute"}, {"api_name": "models.security_results.SecurityResultsDAST", "line_number": 36, "usage_type": "name"}, {"api_name": "sqlalchemy.and_", "line_number": 41, "usage_type": "call"}, {"api_name": "models.security_reports.SecurityReport.project_id", "line_number": 42, "usage_type": "attribute"}, {"api_name": "models.security_reports.SecurityReport", "line_number": 42, "usage_type": "name"}, {"api_name": "models.security_reports.SecurityReport.id", "line_number": 42, "usage_type": "attribute"}, {"api_name": "sqlalchemy.and_", "line_number": 45, "usage_type": "call"}, {"api_name": "models.security_reports.SecurityReport.project_id", "line_number": 46, "usage_type": "attribute"}, {"api_name": "models.security_reports.SecurityReport", "line_number": 46, "usage_type": "name"}, {"api_name": "models.security_reports.SecurityReport.test_uid", "line_number": 46, "usage_type": "attribute"}, {"api_name": "models.security_reports.SecurityReport.query.with_entities", "line_number": 48, "usage_type": "call"}, {"api_name": "models.security_reports.SecurityReport.query", "line_number": 48, "usage_type": "attribute"}, {"api_name": "models.security_reports.SecurityReport", "line_number": 48, "usage_type": "name"}, {"api_name": "models.security_reports.SecurityReport.severity", "line_number": 49, "usage_type": "attribute"}, {"api_name": "models.security_reports.SecurityReport", "line_number": 49, "usage_type": "name"}, {"api_name": "sqlalchemy.func.count", "line_number": 50, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 50, "usage_type": "name"}, {"api_name": "models.security_reports.SecurityReport.severity", "line_number": 50, "usage_type": "attribute"}, {"api_name": "models.security_reports.SecurityReport", "line_number": 50, "usage_type": "name"}, {"api_name": "models.security_reports.SecurityReport.severity", "line_number": 51, "usage_type": "attribute"}, {"api_name": "models.security_reports.SecurityReport", "line_number": 51, "usage_type": "name"}, {"api_name": "models.security_reports.SecurityReport.query.with_entities", "line_number": 53, "usage_type": "call"}, {"api_name": "models.security_reports.SecurityReport.query", "line_number": 53, "usage_type": "attribute"}, {"api_name": "models.security_reports.SecurityReport", "line_number": 53, "usage_type": "name"}, {"api_name": "models.security_reports.SecurityReport.status", "line_number": 54, "usage_type": "attribute"}, {"api_name": "models.security_reports.SecurityReport", "line_number": 54, "usage_type": "name"}, {"api_name": "sqlalchemy.func.count", "line_number": 55, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 55, "usage_type": "name"}, {"api_name": "models.security_reports.SecurityReport.status", "line_number": 55, "usage_type": "attribute"}, {"api_name": "models.security_reports.SecurityReport", "line_number": 55, "usage_type": "name"}, {"api_name": "models.security_reports.SecurityReport.status", "line_number": 56, "usage_type": "attribute"}, {"api_name": "models.security_reports.SecurityReport", "line_number": 56, "usage_type": "name"}]} +{"seq_id": "386959121", "text": "# ../entities/attributes.py\n\n# =============================================================================\n# >> IMPORTS\n# =============================================================================\n# Site-Package Imports\n# ConfigObj\nfrom configobj import ConfigObj\n# Sys\nimport sys\n\n# Source.Python Imports\nfrom core import GAME_NAME\nfrom paths import SP_DATA_PATH\n# Entities\nfrom entities import EntitiesLogger\n\n\n# =============================================================================\n# >> ALL DECLARATION\n# =============================================================================\n__all__ = []\n\n\n# =============================================================================\n# >> GLOBAL VARIABLES\n# =============================================================================\n# Get the sp.entities.attributes logger\nEntitiesAttributesLogger = EntitiesLogger.attributes\n\n\n# =============================================================================\n# >> CLASSES\n# =============================================================================\nclass EntityAttributes(dict):\n '''Base Attribute class used to interact with\n entity's based off of ini data files.'''\n\n '''Each class that inherits from EntityAttributes\n must have the following attributes:\n type - used to know which directory within data to get values\n unrepr - used to know what to have ConfigObj unrepr set to\n instance - used to know which class to use to create the objects\n '''\n\n def __missing__(self, entity):\n '''Called the first time an entity is added to the dictionary'''\n\n # Get all attributes for the given entity\n values = self[entity] = self._retrieve_attributes(entity)\n\n # Return the attributes and their values\n return values\n\n def get_game_attributes(self, args):\n '''Returns all attributes for the given entities'''\n\n # Create an empty dictionary\n values = dict()\n\n # Loop through all given entities\n for arg in args:\n\n # Add the entities to the dictionary\n values.update(self[arg])\n\n # Return all attributes for the given entities\n return values\n\n def _retrieve_attributes(self, entity):\n '''Retrieves all attributes for the given entity'''\n\n # Create an empty dictionary\n game_attributes = dict()\n\n # Get the path to the entity's attributes\n inifile = SP_DATA_PATH.joinpath(self.type, entity, GAME_NAME + '.ini')\n\n # Does the file exist?\n if not inifile.isfile():\n\n # Return the empty dictionary\n return game_attributes\n\n # Get the file's contents\n ini = ConfigObj(inifile, unrepr=self.unrepr)\n\n # Loop through all items in the file\n for key in ini:\n\n # Use try/except in case an error occurs\n try:\n\n # Get the object for the current key\n value = self.instance(ini[key])\n\n # Was an error encountered?\n except:\n\n # Get the exception\n exctype, value, trace_back = sys.exc_info()\n\n # Log the error as a warning\n EntitiesAttributesLogger.log_warning(\n 'Unable to add attribute \"{0}\"'.format(key) +\n 'of type \"{0}\" to entity type '.format(self.type) +\n '\"{0}\" due to the following:'.format(entity) +\n '\\n\\t{0}'.format(value))\n\n # Was no error encountered?\n else:\n\n # Add the item to the dictionary\n game_attributes[key] = value\n\n # Return the dictionary\n return game_attributes\n", "sub_path": "addons/source-python/packages/source-python/entities/attributes.py", "file_name": "attributes.py", "file_ext": "py", "file_size_in_byte": 3750, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "entities.EntitiesLogger.attributes", "line_number": 29, "usage_type": "attribute"}, {"api_name": "entities.EntitiesLogger", "line_number": 29, "usage_type": "name"}, {"api_name": "paths.SP_DATA_PATH.joinpath", "line_number": 77, "usage_type": "call"}, {"api_name": "paths.SP_DATA_PATH", "line_number": 77, "usage_type": "name"}, {"api_name": "core.GAME_NAME", "line_number": 77, "usage_type": "name"}, {"api_name": "configobj.ConfigObj", "line_number": 86, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "366985271", "text": "import os\n\nfrom flask import render_template, request, send_from_directory\nfrom app import app\nfrom form import LoginForm\n\n\n@app.route('/')\n@app.route('/index')\ndef hello_world():\n user = {'name': \"Hank\"}\n files = list_download('./')\n return render_template('index.html', title=\"Home Page\", user=user, files=files)\n\n\n\n@app.route(\"/login\")\ndef login():\n form = LoginForm()\n return render_template('login.html', form=form)\n\n\ndef list_download(path):\n files = []\n for file in os.listdir(path):\n if os.path.isfile(os.path.join(path, file)) is True:\n files.append(file)\n return files\n\n\n", "sub_path": "routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 623, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask.render_template", "line_number": 13, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 8, "usage_type": "call"}, {"api_name": "app.app", "line_number": 8, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 9, "usage_type": "call"}, {"api_name": "app.app", "line_number": 9, "usage_type": "name"}, {"api_name": "form.LoginForm", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 20, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 17, "usage_type": "call"}, {"api_name": "app.app", "line_number": 17, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "373361714", "text": "# -*- coding: utf-8 -*-\nimport logging\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\nfrom operator import itemgetter\nimport time\n\nimport openerp\nfrom openerp import SUPERUSER_ID, api\nfrom openerp import tools\nfrom openerp.osv import fields, osv, expression\nfrom openerp.tools.translate import _\nfrom openerp.tools.float_utils import float_round as round\nfrom openerp.tools.safe_eval import safe_eval as eval\n\nimport openerp.addons.decimal_precision as dp\n\n_logger = logging.getLogger(__name__)\n\n\n\nclass account_move(osv.osv):\n _inherit = \"account.move\"\n \n def button_validate(self, cursor, user, ids, context=None):\n \n \n for move in self.browse(cursor, user, ids, context=context):\n # check that all accounts have the same topmost ancestor\n top_common = None\n for line in move.line_id:\n \n analytic_account_id = line and line.analytic_account_id and line.analytic_account_id.id \n if analytic_account_id:\n analytic_state = line and line.analytic_account_id and line.analytic_account_id.state\n analytic_name = line and line.analytic_account_id and line.analytic_account_id.name\n if analytic_state == 'close':\n raise osv.except_osv(_('Warning!'), _('You Cannot Post an Accounting Entry on A Closed Project/Analtyic Account %s'%analytic_name))\n \n account = line.account_id\n top_account = account\n while top_account.parent_id:\n top_account = top_account.parent_id\n if not top_common:\n top_common = top_account\n elif top_account.id != top_common.id:\n raise osv.except_osv(_('Error!'),\n _('You cannot validate this journal entry because account \"%s\" does not belong to chart of accounts \"%s\".') % (account.name, top_common.name))\n return self.post(cursor, user, ids, context=context)\n \n\n\n\nclass account_account(osv.osv):\n \n _inherit = \"account.account\"\n def _check_moves(self, cr, uid, ids, method, context=None):\n line_obj = self.pool.get('account.move.line')\n account_ids = self.search(cr, uid, [('id', 'child_of', ids)], context=context)\n\n if line_obj.search(cr, uid, [('account_id', 'in', account_ids)], context=context):\n if method == 'write':\n pass\n# raise osv.except_osv(_('Error!'), _('You cannot deactivate an account that contains journal items.'))\n elif method == 'unlink':\n raise osv.except_osv(_('Error!'), _('You cannot remove an account that contains journal items.'))\n #Checking whether the account is set as a property to any Partner or not\n values = ['account.account,%s' % (account_id,) for account_id in ids]\n partner_prop_acc = self.pool.get('ir.property').search(cr, uid, [('value_reference','in', values)], context=context)\n if partner_prop_acc:\n return True\n# raise osv.except_osv(_('Warning!'), _('You cannot remove/deactivate an account which is set on a customer or supplier.'))\n return True\n def _check_allow_code_change(self, cr, uid, ids, context=None):\n line_obj = self.pool.get('account.move.line')\n \n for account in self.browse(cr, uid, ids, context=context):\n if account.note == '#re':\n return True\n account_ids = self.search(cr, uid, [('id', 'child_of', [account.id])], context=context)\n if line_obj.search(cr, uid, [('account_id', 'in', account_ids)], context=context):\n raise osv.except_osv(_('Warning !'), _(\"You cannot change the code of account which contains journal items!\"))\n return True", "sub_path": "beta_invoice/account/account.py", "file_name": "account.py", "file_ext": "py", "file_size_in_byte": 3897, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "openerp.osv.osv.osv", "line_number": 22, "usage_type": "attribute"}, {"api_name": "openerp.osv.osv", "line_number": 22, "usage_type": "name"}, {"api_name": "openerp.osv.osv.except_osv", "line_number": 38, "usage_type": "call"}, {"api_name": "openerp.osv.osv", "line_number": 38, "usage_type": "name"}, {"api_name": "openerp.tools.translate._", "line_number": 38, "usage_type": "call"}, {"api_name": "openerp.osv.osv.except_osv", "line_number": 47, "usage_type": "call"}, {"api_name": "openerp.osv.osv", "line_number": 47, "usage_type": "name"}, {"api_name": "openerp.tools.translate._", "line_number": 47, "usage_type": "call"}, {"api_name": "openerp.tools.translate._", "line_number": 48, "usage_type": "call"}, {"api_name": "openerp.osv.osv.osv", "line_number": 54, "usage_type": "attribute"}, {"api_name": "openerp.osv.osv", "line_number": 54, "usage_type": "name"}, {"api_name": "openerp.osv.osv.except_osv", "line_number": 66, "usage_type": "call"}, {"api_name": "openerp.osv.osv", "line_number": 66, "usage_type": "name"}, {"api_name": "openerp.tools.translate._", "line_number": 66, "usage_type": "call"}, {"api_name": "openerp.osv.osv.except_osv", "line_number": 82, "usage_type": "call"}, {"api_name": "openerp.osv.osv", "line_number": 82, "usage_type": "name"}, {"api_name": "openerp.tools.translate._", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "220940997", "text": "import sqlite3\nfrom GannDAO import GannDAO\nganndao = GannDAO()\nconn = sqlite3.connect(\"stock.db\")\nentry = raw_input(\"Enter C/R/D -----> \")\n\nif entry == \"D\":\n symbol = raw_input(\"Enter the Symbol\")\n ganndao.delGann(conn, symbol)\nelif entry == \"C\":\n stype = raw_input(\"Enter the Type of Symbol\")\n symbol = raw_input(\"Enter the Symbol\")\n ganndao.insertGann(conn, stype, symbol)\nelif entry == \"R\":\n cursor = ganndao.selectGann(conn)\n for row in cursor:\n column = len(row)\n counter = 0\n while counter < column:\n print(row[counter])\n counter = counter + 1", "sub_path": "gann.py", "file_name": "gann.py", "file_ext": "py", "file_size_in_byte": 613, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "GannDAO.GannDAO", "line_number": 3, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 4, "usage_type": "call"}]} +{"seq_id": "378423131", "text": "import numpy as np\nfrom math import exp, log\nimport matplotlib.pyplot as plt\n\nn = 200\nd = 4\nx = 3 * (np.random.rand(n, d) - 0.5)\ny = 2 * x[:, 1] - 1 * x[:, 2] + 0.5\ny = y + 0.5 * np.random.rand(n)\ny = 2 * y - 1\n\n\ndef J(w, x, y):\n sum_val = 0\n for u, v in zip(x, y):\n sum_val += log(1 + exp(-v * np.dot(w, u)))\n return sum_val/n + lam * np.dot(w, w)\n\n\ndef grad_J(w, x, y):\n sum_val = 0\n for u, v in zip(x, y):\n sum_val += (-v * u) / (1 + exp(v * np.dot(w, u)))\n return sum_val/n + 2 * lam * w\n\n\ndef hess_J(w, x, y):\n H = np.eye(w.shape[0])\n for u, v in zip(x, y):\n H += (v ** 2 * exp(-v * np.dot(w, u)) * np.dot(u.reshape(-1, 1), u.reshape(1, -1))) / ((1 + exp(-v * np.dot(w, u)))**2)\n assert H.shape == (d, d), \"hessian shape error\"\n return H/n + 2 * lam * np.eye(w.shape[0])\n\n\nw_s = np.ones(d)*0\nw_n = np.ones(d)*0\nnum_round = 1000\nlam = 0.00001\n# alpha is the upper bound of Lipsitz constant\nalpha, _ = np.linalg.eig(np.dot(np.transpose(x), x)/n + 2 * lam * np.eye(d))\nalpha = max(alpha)\nJ_steep = []\nJ_newton = []\nw_steep = []\nw_newton = []\n\nt = 0\nfor i in range(num_round):\n # steepest gradient method\n J_s_1 = J(w_s, x, y)\n w_steep.append(w_s)\n J_steep.append(J_s_1)\n d_s = -grad_J(w_s, x, y)\n w_s += alpha * d_s\n\n # newton method\n J_n_1 = J(w_n, x, y)\n w_newton.append(w_n)\n J_newton.append(J_n_1)\n H = hess_J(w_n, x, y)\n d_n = -np.dot(np.linalg.pinv(H), grad_J(w_n, x, y))\n w_n += alpha * d_n\n\nfig, ax = plt.subplots()\nax.set_yscale(\"log\")\nax.plot([x for x in range(len(J_steep))], [x - J_steep[-1] for x in J_steep], color=\"b\", label=\"Steepest Gradient Descent\")\nax.plot([x for x in range(len(J_newton))], [x - J_newton[-1] for x in J_newton], color=\"r\", label=\"Newton method\")\nplt.xlabel(\"Iteration\")\nplt.ylabel(\"J_t - J_opt\")\nplt.legend()\nplt.show()\n", "sub_path": "Problem1/binary.py", "file_name": "binary.py", "file_ext": "py", "file_size_in_byte": 1850, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.random.rand", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 7, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 9, "usage_type": "attribute"}, {"api_name": "math.log", "line_number": 16, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 17, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 28, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.linalg.eig", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.linalg.pinv", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 61, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "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": "446297406", "text": "\"\"\"python filter to format json.\n\nborrowed from python module json.tool\"\"\"\nimport argparse\nimport collections\nimport json\nimport sys\nimport io\n\n\ndef main():\n prog = 'python -m json.tool'\n description = ('A simple command line interface for json module ' 'to validate and pretty-print JSON objects.')\n parser = argparse.ArgumentParser(prog=prog, description=description)\n parser.add_argument('infile', nargs='?', type=argparse.FileType(), help='a JSON file to be validated or pretty-printed')\n parser.add_argument('outfile', nargs='?', type=argparse.FileType('w'), help='write the output of infile to outfile')\n parser.add_argument('--sort-keys', action='store_true', default=False, help='sort the output of dictionaries alphabetically by key')\n parser.add_argument('--decode-unicode', action='store_true', default=False, help='decode the encoded unicode data')\n options = parser.parse_args()\n\n infile = options.infile or io.TextIOWrapper(sys.stdin.buffer, encoding='utf-8')\n outfile = options.outfile or sys.stdout\n sort_keys = options.sort_keys\n decode_unicode = not options.decode_unicode\n with infile:\n try:\n if sort_keys:\n obj = json.load(infile)\n else:\n obj = json.load(infile, object_pairs_hook=collections.OrderedDict)\n except ValueError as e:\n raise SystemExit(e)\n with outfile:\n json.dump(obj, outfile, sort_keys=sort_keys, indent=4, ensure_ascii=decode_unicode)\n outfile.write('\\n')\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "python/format_json.py", "file_name": "format_json.py", "file_ext": "py", "file_size_in_byte": 1566, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call"}, {"api_name": "argparse.FileType", "line_number": 15, "usage_type": "call"}, {"api_name": "argparse.FileType", "line_number": 16, "usage_type": "call"}, {"api_name": "io.TextIOWrapper", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 22, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 28, "usage_type": "call"}, {"api_name": "json.load", "line_number": 30, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 30, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "644257352", "text": "\"\"\"Convert Epubs from the Masaha Hurra library to OpenITI mARkdown.\n\nThe converter has two main functions:\n* convert_file: convert a single epub file.\n* convert_files_in_folder: convert all epub files in a given folder\n\nUsage examples:\n >>> folder = \"test/masaha/epub/\"\n >>> meta_fp = \"test/masaha/all_books/meta/all_metadata.json\"\n >>> from epub_converter_masaha import convert_file, convert_files_in_folder\n >>> src_fp = folder+\"000008.epub\"\n >>> convert_file(src_fp, meta_fp, dest_fp=folder+\"converted/Masaha000008\")\n >>> convert_files_in_folder(folder, meta_fp, dest_folder=folder+\"converted\")\n Converting all files in folder test/masaha/epub with extensions ['epub']\n\nBoth functions use the MasahaEpubConverter class to do the heavy lifting.\nThe MasahaEpubConverter is a subclass of the GenericEpubConverter,\nwhich in turn is a subclass of the GenericConverter\nfrom the generic_converter module:\n\nGenericConverter\n \\_ GenericEpubConverter\n \\_ MasahaEpubConverter\n\nMethods of both classes:\n\n(methods of GenericConverter are inherited by GenericEpubConverter;\nmethods of GenericConverter with the same name\nin GenericEpubConverter are overwritten by the latter)\n\n=========================== ========================= =======================\ngeneric_converter epub_converter_generic epub_converter_masaha \n=========================== ========================= =======================\n__init__ __init__ __init__ \nconvert_files_in_folder (inherited) (inherited)\nconvert file (inherited) (inherited)\nmake_dest_fp (inherited - generic!) (inherited - generic!)\nget_metadata (inherited - generic!) get_metadata\nget_data get_data (inherited)\npre_process (inherited) (inherited)\nadd_page_numbers (inherited - generic!) (inherited - generic!)\nadd_structural_annotations (inherited - generic!) (inherited - generic!) \nremove_notes remove_notes (inherited)\nreflow (inherited) (inherited)\nadd_milestones (inherited) (inherited)\npost_process (inherited - generic!) post_process\ncompose (inherited) (inherited)\nsave_file (inherited) (inherited)\n convert_html2md convert_html2md\n inspect_epub (inherited)\n sort_html_files_by_toc sort_html_files_by_toc\n add_unique_tags (inherited)\n=========================== ========================= =======================\n\n\nExamples:\n >>> from epub_converter_masaha import MasahaEpubConverter\n >>> from helper.yml2json import yml2json\n >>> folder = \"test/\"\n >>> fn = \"26362727.epub\"\n >>> hc = MasahaEpubConverter(dest_folder=\"test/converted\")\n >>> hc.VERBOSE = False\n >>> meta_fp = \"masaha/all_books/meta/all_metadata.json\"\n >>> hc.metadata_file = meta_fp\n >>> hc.convert_file(folder+fn)\n\n #>>> hc.convert_files_in_folder(folder)\n\n\"\"\"\n\nimport os\nimport json\nimport shutil\nimport re\n\nif __name__ == '__main__':\n from os import sys, path\n root_folder = path.dirname(path.dirname(path.abspath(__file__)))\n root_folder = path.dirname(path.dirname(root_folder))\n sys.path.append(root_folder)\n\nfrom openiti.new_books.convert.epub_converter_generic import GenericEpubConverter\nfrom openiti.new_books.convert.helper import html2md_masaha\nfrom openiti.new_books.convert.helper.yml2json import yml2json\n\n\ndef convert_file(fp, meta_fp, dest_fp=None, verbose=False, overwrite=False):\n \"\"\"Convert one file to OpenITI format.\n\n Args:\n fp (str): path to the file that must be converted.\n meta_fp (str): path to the yml file containing the Masaha metadata\n dest_fp (str): path to the converted file.\n\n Returns:\n None\n \"\"\"\n conv = MasahaEpubConverter(overwrite=overwrite)\n conv.VERBOSE = verbose\n with open(meta_fp, mode=\"r\", encoding=\"utf-8\") as file:\n d = json.load(file)\n conv.metadata_dic = {int(item[\"book_id\"]): item for item in d}\n conv.metadata_file = meta_fp\n conv.convert_file(fp, dest_fp=dest_fp)\n\n##def convert_multifile_text(folder, meta_fp, dest_folder, verbose=False):\n## for i, fn in enumerate(os.listdir(folder)):\n## if i == 0:\n## dest_fp = os.path.join(dest_folder, os.path.splitext(fn)[0])\n## \n \n\ndef convert_files_in_folder(src_folder, meta_fp, dest_folder=None, verbose=False,\n extensions=[\"epub\"], exclude_extensions=[\"yml\"],\n fn_regex=None, overwrite=False):\n \"\"\"Convert all files in a folder to OpenITI format.\\\n Use the `extensions` and `exclude_extensions` lists to filter\\\n the files to be converted.\n\n Args:\n src_folder (str): path to the folder that contains\n the files that must be converted.\n meta_fp (str): path to the yml file containing the Masaha metadata\n dest_folder (str): path to the folder where converted files\n will be stored.\n extensions (list): list of extensions; if this list is not empty,\n only files with an extension in the list should be converted.\n exclude_extensions (list): list of extensions;\n if this list is not empty,\n only files whose extension is not in the list will be converted.\n fn_regex (str): regular expression defining the filename pattern\n e.g., \"-(ara|per)\\d\". If `fn_regex` is defined,\n only files whose filename matches the pattern will be converted.\n\n Returns:\n None\n \"\"\"\n msg = \"Converting all files in folder {} with extensions {}\"\n print(msg.format(src_folder, extensions))\n conv = MasahaEpubConverter(overwrite=overwrite)\n conv.VERBOSE = verbose\n with open(meta_fp, mode=\"r\", encoding=\"utf-8\") as file:\n d = json.load(file)\n conv.metadata_dic = {int(item[\"book_id\"]): item for item in d}\n conv.metadata_file = meta_fp\n conv.convert_files_in_folder(src_folder, dest_folder=dest_folder,\n extensions=extensions,\n exclude_extensions=exclude_extensions,\n fn_regex=fn_regex)\n\n\n################################################################################\n\n\n\n\nclass MasahaEpubConverter(GenericEpubConverter):\n def __init__(self, dest_folder=None, overwrite=True):\n super().__init__(dest_folder=dest_folder, overwrite=overwrite)\n self.toc_fn = \"content.opf\"\n self.metadata_file = None\n\n\n def convert_files_in_folder(self, source_folder, dest_folder=None,\n extensions=[], exclude_extensions=[],\n fn_regex=None):\n \"\"\"Convert all files in a folder to OpenITI format.\\\n Use the `extensions` and `exclude_extensions` lists to filter\\\n the files to be converted.\n\n Args:\n source_folder (str): path to the folder that contains\n the files that must be converted.\n extensions (list): list of extensions; if this list is not empty,\n only files with an extension in the list should be converted.\n exclude_extensions (list): list of extensions;\n if this list is not empty,\n only files whose extension is not in the list will be converted.\n fn_regex (str): regular expression defining the filename pattern\n e.g., \"-(ara|per)\\d\". If `fn_regex` is defined,\n only files whose filename matches the pattern will be converted.\n\n Returns:\n None\n \"\"\"\n failed = []\n if dest_folder:\n self.dest_folder = dest_folder\n fp_list = self.filter_files_in_folder(source_folder, extensions,\n exclude_extensions, fn_regex)\n for fp in fp_list:\n print(fp)\n try:\n self.convert_file(fp)\n except Exception as e:\n print(\"ERROR:\", e)\n failed.append((fp, e))\n \n\n # deal with multivolume texts that are in separate folders:\n multivol_folders = [f for f in os.listdir(source_folder) if f.startswith(\"multivol\")]\n multivol_folders = [os.path.join(source_folder, f) for f in multivol_folders]\n \n for folder in multivol_folders:\n print(folder)\n try:\n first_fn = sorted(os.listdir(folder))[0]\n except Exception as e:\n print(\"folder does not contain files:\", e)\n failed.append((folder, e))\n continue\n outfn = re.sub(\"\\.epub\", \"Vols.automARkdown\", first_fn)\n outfp = os.path.join(dest_folder, outfn)\n if os.path.exists(outfp):\n print(outfp, \"already exists\")\n continue\n temp_folder = os.path.join(dest_folder, \"temp\")\n if os.path.exists(temp_folder):\n shutil.rmtree(temp_folder)\n os.makedirs(temp_folder)\n self.convert_files_in_folder(folder, dest_folder=temp_folder,\n extensions=extensions,\n exclude_extensions=exclude_extensions,\n fn_regex=fn_regex)\n # combine all volumes into one folder:\n combined = []\n endnotes = []\n for i, fn in enumerate(sorted(os.listdir(temp_folder))):\n fp = os.path.join(temp_folder, fn)\n if i == 0:\n outfn = re.sub(\"\\.\", \"Vols.\", fn) \n outfp = os.path.join(dest_folder, outfn)\n with open(fp, mode=\"r\", encoding=\"utf-8\") as file:\n text = file.read()\n if i != 0:\n text = re.split(\"#META#Header#End#?\", text)[-1]\n page = \"PageV{:02d}P{:03d}\"\n text = re.sub(\"PageV\\d+P(\\d+)\", r\"PageV{:02d}P\\1\".format(i+1), text)\n if re.findall(\"### \\|EDITOR\\|[ \\r\\n]+ENDNOTES:?\", text):\n text, notes = re.split(\"### \\|EDITOR\\|[ \\r\\n]+ENDNOTES:?\", text)\n endnotes.append(notes)\n combined.append(text)\n with open(outfp, mode=\"w\", encoding=\"utf-8\") as file:\n text = \"\\n\\n\".join(combined)\n endnotes = \"\\n\\n\".join(endnotes)\n file.write(text + \"\\n\\n### |EDITOR\\|\\n\\nENDNOTES:\\n\\n\" + endnotes)\n print(\"Converting all files done\")\n if failed:\n print(\"These files failed to convert:\")\n for fp, e in failed:\n print(fp, e)\n \n \n\n def sort_html_files_by_toc(self, zp, toc_fp, html_files):\n \"\"\"Gets the table of contents from the Epub file.\n\n Args:\n zp: zipfile object\n toc_fp (str): filepath to the table of contents of the epub.\n html_files(list): an unordered list of the html files in the epub.\n\n Returns:\n toc (list): a list of filepaths to the html files\n in the epub file, in the order specified by the\n table of contents\n \"\"\"\n html_files_dict = {os.path.split(fp)[-1] : fp for fp in html_files}\n toc_data = zp.read(toc_fp)\n toc_data = codecs.decode(toc_data, \"utf-8\")\n soup = BeautifulSoup(toc_data)\n toc_ol = soup.find(\"spine\")\n toc = []\n for item in toc_ol.find_all(\"itemref\"):\n fn = os.path.split(item.get(\"idref\"))[-1]\n if fn in html_files_dict:\n toc.append(html_files_dict[fn])\n return toc\n\n def convert_html2md(self, html):\n \"\"\"Use custom html to mARKdown function for Masaha epubs.\"\"\"\n text = html2md_masaha.markdownify(html)\n return text\n\n def get_metadata(self, metadata_fp):\n \"\"\"Custom method to get the metadata of the Masaha epub file.\"\"\"\n source_fp = self.source_fp\n bookID = os.path.split(source_fp)[1]\n bookID = int(os.path.splitext(bookID)[0])\n meta_dic = self.metadata_dic[bookID]\n meta = [\"#META# {}: {}\".format(k,v) for k,v in sorted(meta_dic.items())]\n return self.magic_value + \"\\n\".join(meta) + self.header_splitter\n\n def post_process(self, text):\n \"\"\"Custom post-processing for masaha texts\"\"\"\n # put page number at the bottom of the page:\n text = re.sub(\"(PageV\\d+P\\d+)(.+?)(?=Page|\\Z)\", r\"\\2\\n\\n\\1\\n\\n\", text, flags=re.DOTALL)\n processed = super().post_process(text)\n return processed\n \n\n\nif __name__== \"__main__\":\n #import doctest\n #doctest.testmod()\n #input(\"Testing finished. Continue?\")\n\n # identify the location of the yml file containing the metadata:\n meta_fp = r\"test\\masaha\\meta\\all_metadata.json\"\n src_folder = \"test/masaha/epub\"\n convert_files_in_folder(src_folder, meta_fp, dest_folder=\"test/converted\", verbose=False)\n## hc.metadata = yml2json(meta_fp, container={})\n \n## fp = r\"test\\26362727.epub\"\n## hc.convert_file(fp)\n## print(\"converted Masaha epub\", fp)\n##\n## hc.convert_files_in_folder(\"test/masaha\")\n## print(\"converted all epub files in folder\", \"test/masaha\")\n\n", "sub_path": "build/lib/openiti/new_books/convert/epub_converter_masaha.py", "file_name": "epub_converter_masaha.py", "file_ext": "py", "file_size_in_byte": 13599, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "os.path.dirname", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "name"}, {"api_name": "os.sys.path.append", "line_number": 80, "usage_type": "call"}, {"api_name": "os.sys.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.sys", "line_number": 80, "usage_type": "name"}, {"api_name": "json.load", "line_number": 101, "usage_type": "call"}, {"api_name": "json.load", "line_number": 143, "usage_type": "call"}, {"api_name": "openiti.new_books.convert.epub_converter_generic.GenericEpubConverter", "line_number": 157, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 201, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 202, "usage_type": "call"}, {"api_name": "os.path", "line_number": 202, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 207, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 213, "usage_type": "call"}, {"api_name": "os.path", "line_number": 213, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path", "line_number": 214, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 217, "usage_type": "call"}, {"api_name": "os.path", "line_number": 217, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 218, "usage_type": "call"}, {"api_name": "os.path", "line_number": 218, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 219, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 220, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 229, "usage_type": "call"}, {"api_name": "os.path", "line_number": 229, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 232, "usage_type": "call"}, {"api_name": "os.path", "line_number": 232, "usage_type": "attribute"}, {"api_name": "re.split", "line_number": 236, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 238, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 239, "usage_type": "call"}, {"api_name": "re.split", "line_number": 240, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 268, "usage_type": "call"}, {"api_name": "os.path", "line_number": 268, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 275, "usage_type": "call"}, {"api_name": "os.path", "line_number": 275, "usage_type": "attribute"}, {"api_name": "openiti.new_books.convert.helper.html2md_masaha.markdownify", "line_number": 282, "usage_type": "call"}, {"api_name": "openiti.new_books.convert.helper.html2md_masaha", "line_number": 282, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 288, "usage_type": "call"}, {"api_name": "os.path", "line_number": 288, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 289, "usage_type": "call"}, {"api_name": "os.path", "line_number": 289, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 297, "usage_type": "call"}, {"api_name": "re.DOTALL", "line_number": 297, "usage_type": "attribute"}]} +{"seq_id": "651553809", "text": "import os\nimport re\nimport sys\nimport json\nimport time\nimport splunk.rest as sr\nfrom splunk.persistconn.application import PersistentServerConnectionApplication\n\nif sys.version_info.major == 2:\n sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), 'libs_py2'))\n sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), 'libs_py2', 'pura_libs_utils'))\nelif sys.version_info.major == 3:\n sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), 'libs_py3'))\n sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), 'libs_py3', 'pura_libs_utils'))\n\nfrom pura_libs_utils import pura_logger_manager as logger_manager\nfrom pura_libs_utils.pura_consts import *\nfrom pura_libs_utils import pura_utils as utils\nfrom pura_libs_utils import six\nfrom builtins import str\n\nlogging = logger_manager.setup_logging('pura_read_progress')\n\nif sys.platform == \"win32\":\n import msvcrt\n # Binary mode is required for persistent mode on Windows.\n msvcrt.setmode(sys.stdin.fileno(), os.O_BINARY)\n msvcrt.setmode(sys.stdout.fileno(), os.O_BINARY)\n msvcrt.setmode(sys.stderr.fileno(), os.O_BINARY)\n\n\nclass ReadProgressHandler(PersistentServerConnectionApplication):\n \"\"\"\n This is a REST handler base-class that makes implementing a REST handler easier.\n\n This works by resolving a name based on the path in the HTTP request and calls it.\n This class will look for a function that includes the HTTP verb followed by the path.abs\n\n For example, if a GET request is made to the endpoint is executed with the path /read_progress,\n then this class will attempt to run a function named get_read_progress().\n Note that the root path of the REST handler is removed. If a POST request is made to the endpoint\n is executed with the path /read_progress, then this class will attempt to execute post_read_progress().\n \"\"\"\n\n def __init__(self, command_line, command_arg):\n PersistentServerConnectionApplication.__init__(self)\n\n @classmethod\n def get_function_signature(cls, method, path):\n \"\"\"\n Get the function that should be called based on path and request method.\n\n :param cls: class\n :param method: type of call (get/post)\n :param path: the rest endpoint for which method is to be called\n\n :return name of the function to be called\n \"\"\"\n\n if len(path) > 0:\n components = path.split(\"pura\")\n path = components[1]\n return method + re.sub(r'[^a-zA-Z0-9_]', '_', path).lower()\n else:\n return method\n\n def handle(self, in_string):\n \"\"\"\n Handler function to call when REST endpoint is hit and process the call\n\n :param in_string: string of arguments\n\n :return Result of REST call\n \"\"\"\n try:\n\n logging.info(\"Handling a request\")\n\n # Parse the arguments\n args = utils.parse_in_string(in_string)\n\n # Get the user information\n self.session_key = args['session']['authtoken']\n self.user = args['session']['user']\n self.host = args['server']['hostname']\n\n # Get the method\n method = args['method']\n\n # Get the path and the args\n if 'rest_path' in args:\n path = args['rest_path']\n else:\n return utils.render_error_json(MESSAGE_NO_PATH_PROVIDED, 403)\n\n # Get the function signature\n function_name = self.get_function_signature(method, path)\n\n try:\n function_to_call = getattr(self, function_name)\n except AttributeError:\n function_to_call = None\n\n # Try to run the function\n if function_to_call is not None:\n logging.info(\"Executing function, name={}\".format(function_name))\n\n return function_to_call()\n\n else:\n logging.warn(\"A request could not be executed since the associated function is missing, name={}\"\n .format(function_name))\n return utils.render_error_json(MESSAGE_PATH_NOT_FOUND, 404)\n\n except Exception as exception:\n logging.exception(MESSAGE_FAILED_HANDLE_REQUEST)\n return utils.render_error_json(str(exception))\n\n def check_session_is_alive(self, scan_key):\n \"\"\"\n Function to check if session has timed-out\n\n :param scan_key: Scan key to fetch entry from KV store\n\n :return (True/False) Session is alive\n \"\"\"\n\n # Check if local directory exists\n if not os.path.isdir(LOCAL_DIR):\n os.makedirs(LOCAL_DIR)\n\n if not os.path.isdir(SESSION_PATH):\n os.makedirs(SESSION_PATH)\n\n file_path = os.path.join(SESSION_PATH, scan_key)\n if os.path.exists(file_path):\n logging.info(MESSAGE_SESSION_FILE_EXISTS.format(str(file_path)))\n try:\n os.remove(file_path)\n except Exception as e:\n logging.exception(MESSAGE_ERROR_REMOVING_SESSION_FILE.format(str(e)))\n return False\n return True\n\n def get_read_progress(self):\n \"\"\"\n Read progress from KV store.\n\n :return response for read progress REST call\n \"\"\"\n\n scan_report = dict()\n scan_report['status'] = PROGRESS_NEW\n scan_report['results'] = {}\n scan_report['message'] = MESSAGE_NO_SCAN_RESULTS\n scan_report['progress'] = 0\n scan_report['host_name'] = str(self.host)\n\n try:\n response, content = sr.simpleRequest('{}?output_mode=json'.format(kvstore_endpoint),\n sessionKey=self.session_key)\n except Exception:\n logging.exception(MESSAGE_EXCEPTION_READ_KVSTORE.format(self.user, self.host))\n return utils.render_error_json(MESSAGE_EXCEPTION_READ_KVSTORE.format(self.user, self.host))\n if response['status'] not in success_codes:\n logging.error(MESSAGE_ERROR_READING_PROGRESS.format(self.user, self.host))\n return utils.render_error_json(MESSAGE_ERROR_READING_PROGRESS.format(self.user, self.host))\n else:\n for entry in json.loads(content):\n if self.host == entry['host'] and self.user == entry['user'] and not entry['cancelled'] and not entry['returned']:\n scan_key = entry['_key']\n session_alive = self.check_session_is_alive(scan_key)\n if session_alive:\n scan_report.update({\n 'status': entry['status'],\n 'message': entry['message'],\n 'progress': entry['progress']\n })\n\n if scan_report['status'] == PROGRESS_COMPLETE:\n results = self.get_latest_results()\n scan_report.update({\n 'results': results\n })\n\n return utils.render_json(scan_report)\n else:\n key = entry['_key']\n entry['cancelled'] = True\n entry['progress'] = 100\n entry['returned'] = True\n entry['status'] = PROGRESS_COMPLETE\n try:\n response, _ = sr.simpleRequest('{}/{}?output_mode=json'.format(kvstore_endpoint, key),\n sessionKey=self.session_key, jsonargs=json.dumps(entry),\n method='POST', raiseAllErrors=True)\n except Exception:\n logging.exception(MESSAGE_EXCEPTION_DELETE_KVSTORE.format(self.user, self.host))\n return utils.render_error_json(MESSAGE_EXCEPTION_DELETE_KVSTORE.format(self.user,\n self.host))\n\n if response['status'] not in success_codes:\n logging.error(MESSAGE_ERROR_CANCEL_SCAN.format(self.user, self.host))\n return utils.render_error_json(MESSAGE_ERROR_CANCEL_SCAN.format(self.user,\n self.host))\n\n results = self.get_latest_results()\n scan_report.update({\n 'status': PROGRESS_ERROR,\n 'progress': 100,\n 'results': results,\n 'message': MESSAGE_UNAUTHORIZED_SCAN_TERMINATION})\n\n return utils.render_json(scan_report)\n else:\n results = self.get_latest_results()\n scan_report.update({\n 'status': PROGRESS_COMPLETE,\n 'progress': 100,\n 'results': results\n })\n return utils.render_json(scan_report)\n\n return utils.render_error_json(MESSAGE_NO_ENTRY_FOUND, 404)\n\n def get_latest_results(self):\n \"\"\"\n Fetch latest results for given user\n\n :return latest results for given user based on timestamp\n \"\"\"\n\n # Check if local directory exists\n if not os.path.isdir(LOCAL_DIR):\n os.makedirs(LOCAL_DIR)\n\n results = dict()\n if not os.path.isdir(REPORT_PATH):\n os.makedirs(REPORT_PATH)\n list_reports = os.listdir(REPORT_PATH)\n\n user_reports = list()\n persistent_user_report = PERSISTENT_FILE_JSON.format(self.user)\n for report in list_reports:\n if self.user == report[:-16] and report != persistent_user_report:\n user_reports.append(report)\n\n latest_timestamp = 0\n for report in user_reports:\n timestamp = (report[:-5])[-10:]\n if int(timestamp) > latest_timestamp:\n latest_timestamp = int(timestamp)\n\n for report in user_reports:\n if str(latest_timestamp) in report:\n report_file = os.path.join(REPORT_PATH, report)\n with open(report_file, 'r') as file_handler:\n results = json.load(file_handler)\n break\n\n return results\n", "sub_path": "apps/python_upgrade_readiness_app/bin/pura_read_progress.py", "file_name": "pura_read_progress.py", "file_ext": "py", "file_size_in_byte": 10547, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sys.version_info", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.path.insert", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path.insert", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.path.insert", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.path.insert", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 14, "usage_type": "call"}, {"api_name": "pura_libs_utils.pura_logger_manager.setup_logging", "line_number": 22, "usage_type": "call"}, {"api_name": "pura_libs_utils.pura_logger_manager", "line_number": 22, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 24, "usage_type": "attribute"}, {"api_name": "msvcrt.setmode", "line_number": 27, "usage_type": "call"}, {"api_name": "sys.stdin.fileno", "line_number": 27, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.O_BINARY", "line_number": 27, "usage_type": "attribute"}, {"api_name": "msvcrt.setmode", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.stdout.fileno", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.O_BINARY", "line_number": 28, "usage_type": "attribute"}, {"api_name": "msvcrt.setmode", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.stderr.fileno", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.O_BINARY", "line_number": 29, "usage_type": "attribute"}, {"api_name": "splunk.persistconn.application.PersistentServerConnectionApplication", "line_number": 32, "usage_type": "name"}, {"api_name": "splunk.persistconn.application.PersistentServerConnectionApplication.__init__", "line_number": 46, "usage_type": "call"}, {"api_name": "splunk.persistconn.application.PersistentServerConnectionApplication", "line_number": 46, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 63, "usage_type": "call"}, {"api_name": "pura_libs_utils.pura_utils.parse_in_string", "line_number": 80, "usage_type": "call"}, {"api_name": "pura_libs_utils.pura_utils", "line_number": 80, "usage_type": "name"}, {"api_name": "pura_libs_utils.pura_utils.render_error_json", "line_number": 94, "usage_type": "call"}, {"api_name": "pura_libs_utils.pura_utils", "line_number": 94, "usage_type": "name"}, {"api_name": "pura_libs_utils.pura_utils.render_error_json", "line_number": 113, "usage_type": "call"}, {"api_name": "pura_libs_utils.pura_utils", "line_number": 113, "usage_type": "name"}, {"api_name": "pura_libs_utils.pura_utils.render_error_json", "line_number": 117, "usage_type": "call"}, {"api_name": "pura_libs_utils.pura_utils", "line_number": 117, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path", "line_number": 129, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "builtins.str", "line_number": 137, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 139, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 141, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 157, "usage_type": "call"}, {"api_name": "splunk.rest.simpleRequest", "line_number": 160, "usage_type": "call"}, {"api_name": "splunk.rest", "line_number": 160, "usage_type": "name"}, {"api_name": "pura_libs_utils.pura_utils.render_error_json", "line_number": 164, "usage_type": "call"}, {"api_name": "pura_libs_utils.pura_utils", "line_number": 164, "usage_type": "name"}, {"api_name": "pura_libs_utils.pura_utils.render_error_json", "line_number": 167, "usage_type": "call"}, {"api_name": "pura_libs_utils.pura_utils", "line_number": 167, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 169, "usage_type": "call"}, {"api_name": "pura_libs_utils.pura_utils.render_json", "line_number": 186, "usage_type": "call"}, {"api_name": "pura_libs_utils.pura_utils", "line_number": 186, "usage_type": "name"}, {"api_name": "splunk.rest.simpleRequest", "line_number": 194, "usage_type": "call"}, {"api_name": "splunk.rest", "line_number": 194, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 195, "usage_type": "call"}, {"api_name": "pura_libs_utils.pura_utils.render_error_json", "line_number": 199, "usage_type": "call"}, {"api_name": "pura_libs_utils.pura_utils", "line_number": 199, "usage_type": "name"}, {"api_name": "pura_libs_utils.pura_utils.render_error_json", "line_number": 204, "usage_type": "call"}, {"api_name": "pura_libs_utils.pura_utils", "line_number": 204, "usage_type": "name"}, {"api_name": "pura_libs_utils.pura_utils.render_json", "line_number": 214, "usage_type": "call"}, {"api_name": "pura_libs_utils.pura_utils", "line_number": 214, "usage_type": "name"}, {"api_name": "pura_libs_utils.pura_utils.render_json", "line_number": 222, "usage_type": "call"}, {"api_name": "pura_libs_utils.pura_utils", "line_number": 222, "usage_type": "name"}, {"api_name": "pura_libs_utils.pura_utils.render_error_json", "line_number": 224, "usage_type": "call"}, {"api_name": "pura_libs_utils.pura_utils", "line_number": 224, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 234, "usage_type": "call"}, {"api_name": "os.path", "line_number": 234, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 235, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 238, "usage_type": "call"}, {"api_name": "os.path", "line_number": 238, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 239, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 240, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 255, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 256, "usage_type": "call"}, {"api_name": "os.path", "line_number": 256, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 258, "usage_type": "call"}]} +{"seq_id": "447051899", "text": "from django.conf.urls import url\n\nfrom . import views\n\napp_name = 'users'\n\nurlpatterns = [\n\n url(r'^register/$',views.user_register,name=\"user_register\"),\n url(r'^login/$',views.user_login,name=\"user_login\"),\n url(r'^logout/$',views.user_logout,name=\"user_logout\"),\n\n\n url(r'^address_add/$',views.address_add,name=\"address_add\"),\n url(r'^address_list/$',views.address_list,name=\"address_list\"),\n\n\n]", "sub_path": "shoppintest/users/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 413, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "335207707", "text": "#Fade Khalifah Rosyad\n#Python script build for send post and receive get for Genesis IVA\n\n#Pada import digunakan 2 request dimana 1 requests dari python dan satu lagi request dari Flask\n#Hal ini terjadi dikarenakan terdapat perbedaan cara penggunaan yang menyebabkan dibutuhkan requests pada function dan request pada route\nimport requests, json\nfrom flask import Flask, render_template, request, json\nfrom datetime import datetime\n\ndef get_timestamp():\n return datetime.now().strftime((\"%d-%m-%Y %H:%M:%S\"))\n\nKVStoreEndpoint = 'http://127.0.0.1:5000/api/v1/kvstore' #Endpoint URL untuk Genesis IVA bagian KVStore\nServicesEndpoint = 'http://127.0.0.1:5000/api/v1/services' #Endpoint URL untuk Genesis IVA bagian Services\nheaders = {'Content-type': 'application/json'} # Header yang dapat diterima oleh Genesis IVA\n\n#Function untuk menerima data dari Genesis IVA dengan method GET (Services)\ndef gettingdatas():\n terimas = requests.get(ServicesEndpoint, headers=headers)\n return terimas\n\n#Function untuk mengirimkan data ke Genesis IVA dengan method POST (Services)\ndef postingdatas(service):\n data = {'service_name': service}\n kirims = requests.post(ServicesEndpoint, data=json.dumps(data), headers=headers)\n return kirims\n\n##Function untuk menerima data dari Genesis IVA dengan method GET (KeyValue)\n#def gettingdata():\n# terima = requests.get(KVStoreEndpoint, headers=headers)\n# return terima\n\n#Function untuk mengirimkan data ke Genesis IVA dengan method POST (KeyValue)\ndef postingdata(key, value):\n data = {'key': key, 'value': value}\n kirim = requests.post(KVStoreEndpoint, data=json.dumps(data), headers=headers)\n return kirim\n\napp = Flask(__name__)\n\n@app.route('/ivaconfig', methods = ['POST', 'GET'])\ndef ivaconfig():\n if request.method == 'POST': \n #iterasi untuk mengirimkan banyak data service secara bersamaan\n i = 0\n while i < 100:\n service = request.form['service' + str(i)]\n kirimins = postingdatas(service)\n i += 1\n timestamp = get_timestamp()\n return render_template('home.html', service = service, timestamp = timestamp)\n\n return render_template('home.html')\n \n@app.route('/keyvalue', methods = ['POST', 'GET'])\ndef keyvalue():\n #if request.method == 'GET':\n terima = requests.get(KVStoreEndpoint, headers=headers) # menghubungkan ke genesis API dan mengambil datanya\n batas = json.loads(terima.text) #mengeluarkan hasil dari get tersebut berupa json\n j = 0\n panjang = len(batas)#menentukan panjang dari keseluruhan array json yang didapat\n listkey = [] #array untuk menampung key\n listvalue = [] #array untuk menampung value\n while j <= (panjang-1):\n tampil = json.loads(terima.text)[j] #memunculkan data ke i pada array yang didapat \n keygen = tampil['key']\n valuegen = tampil['value']\n listkey.append(keygen)\n listvalue.append(valuegen)\n j+=1\n\n #return render_template('home2.html')\n if request.method == 'POST': \n #iterasi untuk mengirimkan banyak data key dan value secara bersamaan\n i = 0\n while i < 1: #Pembatasan pengiriman hanya untuk 100 key dan value\n key = request.form.get('key'+ str(i))\n value = request.form['value'+ str(i)]\n kirimin = postingdata(key, value)\n i += 1\n #timestamp = get_timestamp()\n return render_template('home2.html', listkey=listkey, listvalue=listvalue)\n\n return render_template('home2.html', listkey=listkey, listvalue=listvalue)\n\nif __name__ == '__main__':\n app.run(debug = True, port= 2000)\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 3671, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "datetime.datetime.now", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 11, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.json.dumps", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 25, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.json.dumps", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.Flask", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 47, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 47, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 53, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.json.loads", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.json.loads", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 73, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 78, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 82, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 84, "usage_type": "call"}]} +{"seq_id": "647273232", "text": "import os\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport gym\nimport time\nfrom spinup.algos.sac_pytorch.core import MLP, FlattenMLP, MLPGaussianPolicy\nfrom spinup.utils.logx import EpochLogger\n\nfrom sklearn.cluster import KMeans\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\nclass ReplayBuffer:\n \"\"\"\n A simple FIFO experience replay buffer for SAC agents.\n \"\"\"\n\n def __init__(self, obs_dim, act_dim, size):\n self.obs1_buf = np.zeros([size, obs_dim], dtype=np.float32)\n self.obs2_buf = np.zeros([size, obs_dim], dtype=np.float32)\n self.acts_buf = np.zeros([size, act_dim], dtype=np.float32)\n self.rews_buf = np.zeros(size, dtype=np.float32)\n self.done_buf = np.zeros(size, dtype=np.float32)\n self.ptr, self.size, self.max_size = 0, 0, size\n\n def store(self, obs, act, rew, next_obs, done):\n self.obs1_buf[self.ptr] = obs\n self.obs2_buf[self.ptr] = next_obs\n self.acts_buf[self.ptr] = act\n self.rews_buf[self.ptr] = rew\n self.done_buf[self.ptr] = done\n self.ptr = (self.ptr+1) % self.max_size\n self.size = min(self.size+1, self.max_size)\n\n def sample_batch(self, batch_size=32):\n idxs = np.random.randint(0, self.size, size=batch_size)\n return dict(obs1=torch.Tensor(self.obs1_buf[idxs]).to(device),\n obs2=torch.Tensor(self.obs2_buf[idxs]).to(device),\n acts=torch.Tensor(self.acts_buf[idxs]).to(device),\n rews=torch.Tensor(self.rews_buf[idxs]).to(device),\n done=torch.Tensor(self.done_buf[idxs]).to(device))\n\ndef elbow(X):\n Nc = range(2, 15)\n kmeans = [KMeans(n_clusters=i) for i in Nc]\n score = np.array([kmeans[i].fit(X).score(X) for i in range(len(kmeans))])\n diff = [score[i] - (score[-1] - score[0]) * i / (len(Nc) - 1) - score[0] for i in range(len(Nc))]\n return Nc[np.argmax(diff)]\n\n\"\"\"\n\nSoft Actor-Critic\n\n(With slight variations that bring it closer to TD3)\n\n\"\"\"\ndef sac(env_fn, ac_kwargs=dict(), seed=0, steps_per_epoch=1000, epochs=200, replay_size=int(1e6), \n gamma=0.99, polyak=0.995, lr=1e-3, alpha=0.2, batch_size=100, start_steps=10000, \n max_ep_len=1000, logger_kwargs=dict(), save_path=dict(), save_freq=1):\n torch.manual_seed(seed)\n np.random.seed(seed)\n\n env, test_env = env_fn(), env_fn()\n obs_dim = env.observation_space.shape[0]\n act_dim = env.action_space.shape[0]\n\n # Action limit for clamping: critically, assumes all dimensions share the same bound!\n act_limit = env.action_space.high[0]\n\n # Share information about action space with policy architecture\n ac_kwargs['action_space'] = env.action_space\n\n # Main outputs from computation graph\n policy = MLPGaussianPolicy(obs_dim, act_dim, **ac_kwargs).to(device)\n qf1 = FlattenMLP(obs_dim + act_dim).to(device)\n qf2 = FlattenMLP(obs_dim + act_dim).to(device)\n vf = MLP(obs_dim).to(device)\n \n # Target value network\n vf_targ = MLP(obs_dim).to(device)\n\n # # Experience buffer\n replay_buffer = ReplayBuffer(obs_dim=obs_dim, act_dim=act_dim, size=replay_size)\n\n # Create optimizers\n pi_optimizer = optim.Adam(policy.parameters(), lr=lr)\n qf1_optimizer = optim.Adam(qf1.parameters(), lr=lr)\n qf2_optimizer = optim.Adam(qf2.parameters(), lr=lr)\n vf_optimizer = optim.Adam(vf.parameters(), lr=lr)\n\n # Initializing targets to match main variables\n vf_targ.load_state_dict(vf.state_dict())\n\n def get_action(o, deterministic=False):\n mu, pi, _ = policy(o)\n a = mu if deterministic else pi\n return a.detach().cpu().numpy()[0]\n \n TestEpRet, TestEpLen = [], []\n \n def test_agent(n=10):\n global mu, pi, q1, q2\n for j in range(n):\n o, r, d, ep_ret, ep_len = test_env.reset(), 0, False, 0, 0\n while not(d or (ep_len == max_ep_len)):\n # Take deterministic actions at test time \n a = get_action(torch.Tensor(o).unsqueeze(0).to(device), True)\n o, r, d, _ = test_env.step(a)\n ep_ret += r\n ep_len += 1\n TestEpRet.append(ep_ret)\n TestEpLen.append(ep_len)\n return TestEpRet, TestEpLen\n\n start_time = time.time()\n o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0\n total_steps = steps_per_epoch * epochs\n EpRet, EpLen, LogPi, LossPi = [], [], [], []\n\n # Main loop: collect experience in env and update/log each epoch\n for t in range(total_steps):\n\n \"\"\"\n Until start_steps have elapsed, randomly sample actions\n from a uniform distribution for better exploration. Afterwards, \n use the learned policy. \n \"\"\"\n if t > start_steps:\n a = get_action(torch.Tensor(o).unsqueeze(0).to(device))\n else:\n a = env.action_space.sample()\n\n # Step the env\n o2, r, d, _ = env.step(a)\n ep_ret += r\n ep_len += 1\n\n # Ignore the \"done\" signal if it comes from hitting the time\n # horizon (that is, when it's an artificial terminal signal\n # that isn't based on the agent's state)\n d = False if ep_len==max_ep_len else d\n\n # Store experience to replay buffer\n replay_buffer.store(o, a, r, o2, d)\n\n # Super critical, easy to overlook step: make sure to update \n # most recent observation!\n o = o2\n\n if d or (ep_len == max_ep_len):\n \"\"\"\n Perform all SAC updates at the end of the trajectory.\n This is a slight difference from the SAC specified in the\n original paper.\n \"\"\"\n for j in range(ep_len):\n batch = replay_buffer.sample_batch(batch_size)\n obs1 = batch['obs1']\n obs2 = batch['obs2']\n acts = batch['acts']\n rews = batch['rews']\n done = batch['done']\n\n # Prediction logp_pi, Q1, Q2, V, V‾ \n _, _, logp_pi = policy(obs1)\n q1 = qf1(obs1, acts).squeeze(1)\n q2 = qf2(obs1, acts).squeeze(1)\n v = vf(obs1).squeeze(1)\n v_targ = vf_targ(obs2).squeeze(1)\n\n # Min Double-Q:\n min_q = torch.min(q1, q2).to(device)\n\n # Targets for Q and V regression\n q_backup = rews + gamma*(1-done)*v_targ\n v_backup = min_q - alpha * logp_pi\n\n # Soft actor-critic losses\n qf1_loss = F.mse_loss(q1, q_backup.detach())\n qf2_loss = F.mse_loss(q2, q_backup.detach())\n vf_loss = F.mse_loss(v, v_backup.detach())\n pi_loss = (alpha * logp_pi - q1).mean()\n \n # Q functions train op\n qf1_optimizer.zero_grad()\n qf1_loss.backward(retain_graph=True)\n qf1_optimizer.step()\n\n qf2_optimizer.zero_grad()\n qf2_loss.backward()\n qf2_optimizer.step()\n \n # V function train op\n vf_optimizer.zero_grad()\n vf_loss.backward()\n vf_optimizer.step()\n\n # Policy train op \n pi_optimizer.zero_grad()\n pi_loss.backward()\n pi_optimizer.step()\n \n # Polyak averaging for target variables\n for targ_param, param in zip(vf_targ.parameters(), vf.parameters()):\n targ_param.data.copy_(polyak*targ_param.data + (1-polyak)*param.data)\n \n LogPi.append(logp_pi.mean())\n LossPi.append(pi_loss)\n\n EpRet.append(ep_ret)\n EpLen.append(ep_len)\n o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0\n\n\n # End of epoch wrap-up\n if t > 0 and t % steps_per_epoch == 0:\n epoch = t // steps_per_epoch\n\n # Save model\n if (epoch % save_freq == 0) or (epoch == epochs-1):\n if not os.path.isdir(args.save_path):\n os.makedirs(args.save_path)\n\n ckpt_path = args.save_path + 'model.pth.tar'\n torch.save(policy.state_dict(), ckpt_path)\n \n # Test the performance of the deterministic version of the agent.\n TestEpRet, TestEpLen = test_agent()\n print(\"TestEpRet\", TestEpRet)\n\n # Log info about epoch\n print('---------------------------------------')\n print('Epoch', epoch)\n print('EpRet', np.mean(EpRet))\n print('EpLen', np.mean(EpLen))\n print('TestEpRet', np.mean(TestEpRet))\n print('TestEpLen', np.mean(TestEpLen))\n print('TotalEnvInteracts', t)\n print('LogPi', torch.Tensor(LogPi).mean())\n print('LossPi', torch.Tensor(LossPi).mean())\n print('Time', time.time()-start_time)\n print('---------------------------------------')\n\nif __name__ == '__main__':\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('--env', type=str, default='HalfCheetah-v2')\n parser.add_argument('--save_path', type=str, default='./save_model/')\n parser.add_argument('--hid', type=int, default=[400, 300])\n parser.add_argument('--gamma', type=int, default=0.99)\n parser.add_argument('--seed', '-s', type=int, default=0)\n parser.add_argument('--epochs', type=int, default=200)\n parser.add_argument('--exp_name', type=str, default='sac')\n args = parser.parse_args()\n\n from spinup.utils.run_utils import setup_logger_kwargs\n logger_kwargs = setup_logger_kwargs(args.exp_name, args.seed)\n\n sac(lambda : gym.make(args.env), gamma=args.gamma, seed=args.seed, \n epochs=args.epochs, logger_kwargs=logger_kwargs, save_path=args.save_path)\n", "sub_path": "spinup/algos/sac_pytorch/sac.py", "file_name": "sac.py", "file_ext": "py", "file_size_in_byte": 9954, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "torch.device", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 38, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 43, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 63, "usage_type": "attribute"}, {"api_name": "spinup.algos.sac_pytorch.core.MLPGaussianPolicy", "line_number": 76, "usage_type": "call"}, {"api_name": "spinup.algos.sac_pytorch.core.FlattenMLP", "line_number": 77, "usage_type": "call"}, {"api_name": "spinup.algos.sac_pytorch.core.FlattenMLP", "line_number": 78, "usage_type": "call"}, {"api_name": "spinup.algos.sac_pytorch.core.MLP", "line_number": 79, "usage_type": "call"}, {"api_name": "spinup.algos.sac_pytorch.core.MLP", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 88, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 89, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 90, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 109, "usage_type": "call"}, {"api_name": "time.time", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.nn.functional.mse_loss", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 181, "usage_type": "name"}, {"api_name": "torch.nn.functional.mse_loss", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 182, "usage_type": "name"}, {"api_name": "torch.nn.functional.mse_loss", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 183, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 223, "usage_type": "call"}, {"api_name": "os.path", "line_number": 223, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 224, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 241, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 242, "usage_type": "call"}, {"api_name": "time.time", "line_number": 243, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 248, "usage_type": "call"}, {"api_name": "spinup.utils.run_utils.setup_logger_kwargs", "line_number": 259, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 261, "usage_type": "call"}]} +{"seq_id": "263278543", "text": "import os\nimport asyncio\nimport numpy as np\nimport time\n\nimport pandas as pd\nfrom matplotlib import pyplot as plt\n\nfrom lsst.ts import salobj\nfrom lsst.ts.idl.enums import MTHexapod\n\n\n\nasync def readingTemperatures(hexa):\n dir(temp)\n [getattr(temp,'temperatureC%02d'%i) for i in range(1,8+1)]\n end = Time(datetime.now(), scale='tai')\n start = end - timedelta(seconds=1000)\n df = await client.select_time_series('lsst.sal.ESS.temperature8Ch', '*', start, end, csc_index)\n fig, ax = plt.subplots(1,1, figsize=(15,4))\n for i in range(1,8+1):\n plt.plot(getattr(df, 'temperatureC%02d'%i))\n plt.grid()\n \nasync def prepareEnvironmentforHexa(hexa):\n #Start the telemetry \n #mount telemetry:\n mount_angle = await mount.tel_elevation.next(flush=False, timeout=10.)\n print(\"mount elevation angle\", mount_angle.actualPosition)\n elev = mount_angle.actualPosition \n \nasync def printHexaPosition(hexa):\n pos = await hexa.tel_application.next(flush=True, timeout=10.)\n print(\"Current Hexapod position\")\n print(\" \".join(f\"{p:10.2f}\" for p in pos.position[:3]), end = ' ') \n print(\" \".join(f\"{p:10.6f}\" for p in pos.position[3:]) )\n \nasync def printHexaUncompensatedAndCompensated(hexa):\n posU = await hexa.evt_uncompensatedPosition.aget(timeout=10.)\n print('Uncompensated position')\n print(\" \".join(f\"{p:10.2f}\" for p in [getattr(posU, i) for i in 'xyz']), end = ' ')\n print(\" \".join(f\"{p:10.6f}\" for p in [getattr(posU, i) for i in 'uvw']),' ',\n pd.to_datetime(posU.private_sndStamp, unit='s')) \n posC = await hexa.evt_compensatedPosition.aget(timeout=10.)\n print('Compensated position = (uncompensated + LUT)')\n print(\" \".join(f\"{p:10.2f}\" for p in [getattr(posC, i) for i in 'xyz']), end = ' ')\n print(\" \".join(f\"{p:10.6f}\" for p in [getattr(posC, i) for i in 'uvw']),' ',\n pd.to_datetime(posC.private_sndStamp, unit='s'))\n \n\n \nasync def printHexaUncompensatedAndCompensated(hexa):\n posU = await hexa.evt_uncompensatedPosition.aget(timeout=10.)\n print('Uncompensated position')\n print(\" \".join(f\"{p:10.2f}\" for p in [getattr(posU, i) for i in 'xyz']), end = ' ')\n print(\" \".join(f\"{p:10.6f}\" for p in [getattr(posU, i) for i in 'uvw']),' ',\n pd.to_datetime(posU.private_sndStamp, unit='s')) \n posC = await hexa.evt_compensatedPosition.aget(timeout=10.)\n print('Compensated position = (uncompensated + LUT)')\n print(\" \".join(f\"{p:10.2f}\" for p in [getattr(posC, i) for i in 'xyz']), end = ' ')\n print(\" \".join(f\"{p:10.6f}\" for p in [getattr(posC, i) for i in 'uvw']),' ',\n pd.to_datetime(posC.private_sndStamp, unit='s'))\n \nasync def moveHexaTo0(hexa, actual_z = 0):\n ### command it to collimated position (based on LUT)\n \n need_to_move = False\n try:\n posU = await hexa.evt_uncompensatedPosition.aget(timeout=10.)\n if abs(max([getattr(posU, i) for i in 'xyzuvw']))<1e-8:\n print('hexapod already at LUT position')\n else:\n need_to_move = True\n except asyncio.exceptions.TimeoutError:\n need_to_move = True\n if need_to_move:\n hexa.evt_inPosition.flush()\n #according to XML, units are micron and degree\n await hexa.cmd_move.set_start(x=0,y=0,z=actual_z, u=0,v=0,w=0,sync=True)\n while True:\n state = await hexa.evt_inPosition.next(flush=False, timeout=10)\n print(\"hexa in position?\",state.inPosition, pd.to_datetime(state.private_sndStamp, unit='s'))\n if state.inPosition:\n break\n await printHexaPosition(hexa)\n \nasync def readyHexaForAOS(hexa):\n settings = await hexa.evt_settingsApplied.aget(timeout = 10.)\n hasSettings = 0\n if hasattr(settings, 'settingsVersion'):\n print('settingsVersion = ', settings.settingsVersion, pd.to_datetime(settings.private_sndStamp, unit='s'))\n hasSettings = 1\n if (not hasSettings) or (not settings.settingsVersion[:12] == 'default.yaml'):\n print('YOU NEED TO SEND THIS HEXAPOD TO STANDBY, THEN LOAD THE PROPER CONFIG')\n else:\n hexaConfig = await hexa.evt_configuration.aget(timeout=10.)\n print(\"pivot at (%.0f, %.0f, %.0f) microns \"%(hexaConfig.pivotX, hexaConfig.pivotY, hexaConfig.pivotZ))\n print(\"maxXY = \", hexaConfig.maxXY, \"microns, maxZ= \", hexaConfig.maxZ, \" microns\")\n print(\"maxUV = \", hexaConfig.maxUV, \"deg, maxW= \", hexaConfig.maxW, \" deg\")\n\n lutMode = await hexa.evt_compensationMode.aget(timeout=10)\n if not lutMode.enabled:\n hexa.evt_compensationMode.flush()\n await hexa.cmd_setCompensationMode.set_start(enable=1, timeout=10)\n lutMode = await hexa.evt_compensationMode.next(flush=False, timeout=10)\n print(\"compsensation mode enabled?\",lutMode.enabled, pd.to_datetime(lutMode.private_sndStamp, unit='s'))\n await moveHexaTo0(hexa, actual_z = 100)\n await moveHexaTo0(hexa)\n await printHexaUncompensatedAndCompensated(hexa)\n print(\"Does the hexapod has enough inputs to do LUT compensation? (If the below times out, we do not.)\")\n #Note: the target events are what the hexa CSC checks; if one is missing, the entire LUT will not be applied\n #it also needs to see an uncompensatedPosition (a move would trigger that) in order to move to the compensatedPosition\n a = await hexa.evt_compensationOffset.aget(timeout=10.)\n print('mount elevation = ', a.elevation)\n print('mount azimth = ', a.azimuth)\n print('rotator angle = ', a.rotation)\n print('? temperature = ', a.temperature)\n print('x,y,z,u,v,w = ', a.x, a.y, a.z, a.u, a.v, a.w, pd.to_datetime(a.private_sndStamp, unit='s'))", "sub_path": "procedures/hexaTools.py", "file_name": "hexaTools.py", "file_ext": "py", "file_size_in_byte": 5743, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "matplotlib.pyplot.subplots", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "pandas.to_datetime", "line_number": 43, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 48, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 57, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 62, "usage_type": "call"}, {"api_name": "asyncio.exceptions", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 82, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 91, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 106, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 118, "usage_type": "call"}]} +{"seq_id": "182686339", "text": "# encoding: utf-8\n\"\"\" memcache\n\nhttps://pymemcache.readthedocs.io/en/latest/getting_started.html\n\n\"\"\"\n\nfrom pymemcache.client.base import Client\n\nclient = Client(('localhost', 11211))\nclient.set('some_key', 'some_value')\nresult = client.get('some_key')\n\n\n", "sub_path": "memcache/01_quick_start.py", "file_name": "01_quick_start.py", "file_ext": "py", "file_size_in_byte": 255, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pymemcache.client.base.Client", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "407879052", "text": "from flask import request\nfrom flask_restplus import Resource\nfrom app.main.util.decorator import auth_token_required\nfrom app.main.services.auth_service import AuthService\nfrom app.main.util.dto import AuthDto\n\napi = AuthDto.api\nprovide_auth_token_dto = AuthDto.provide_auth_token\n\n@api.route(\"/login\")\nclass Login(Resource):\n @api.doc(\"log in a user\")\n @api.expect(provide_auth_token_dto, validate=True)\n def post(self):\n post_data = request.json\n\n if post_data:\n return AuthService.provide_auth_token(post_data)\n\n@api.route(\"/logout\")\nclass Logout(Resource):\n @auth_token_required\n @api.doc(\"log out a user\")\n def post(self):\n authorization_header = request.headers.get(\"Authorization\")\n\n return AuthService.dispose_auth_token(authorization_header.split(\" \")[1])\n\n", "sub_path": "app/main/controller/auth_controller.py", "file_name": "auth_controller.py", "file_ext": "py", "file_size_in_byte": 824, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "app.main.util.dto.AuthDto.api", "line_number": 7, "usage_type": "attribute"}, {"api_name": "app.main.util.dto.AuthDto", "line_number": 7, "usage_type": "name"}, {"api_name": "app.main.util.dto.AuthDto.provide_auth_token", "line_number": 8, "usage_type": "attribute"}, {"api_name": "app.main.util.dto.AuthDto", "line_number": 8, "usage_type": "name"}, {"api_name": "flask_restplus.Resource", "line_number": 11, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 15, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 15, "usage_type": "name"}, {"api_name": "app.main.services.auth_service.AuthService.provide_auth_token", "line_number": 18, "usage_type": "call"}, {"api_name": "app.main.services.auth_service.AuthService", "line_number": 18, "usage_type": "name"}, {"api_name": "flask_restplus.Resource", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.request.headers.get", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "app.main.services.auth_service.AuthService.dispose_auth_token", "line_number": 27, "usage_type": "call"}, {"api_name": "app.main.services.auth_service.AuthService", "line_number": 27, "usage_type": "name"}, {"api_name": "app.main.util.decorator.auth_token_required", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "224276894", "text": "#!/usr/bin/env python3\r\n\r\n# import some necessary modules\r\nimport pandas as pd \r\nimport numpy as np \r\nimport os\r\nimport copy\r\nimport datetime\r\nfrom sklearn import linear_model\r\nfrom sklearn.metrics import mean_squared_error, r2_score\r\nimport time\r\n\r\n\r\n\"\"\"\r\n=========================================================\r\nMr Models\r\n=========================================================\r\n[0]- Seed Model: Mr = k1 * (theta / pa) ^ k2\r\n[1]- Uzan & Witczak Model: Mr = k1 * (theta / pa) ^ k2 * (tau / pa) ^ k3\r\n[2]- NCHRP Model: Mr = k1 * (theta / pa) ^ k2 * (tau / pa + 1) ^ k3\r\n=========================================================\r\nFunction\r\n=========================================================\r\nRegression with last 5 seqs\r\n\"\"\"\r\n\r\n# define a class\r\nclass DoRegression:\r\n def __init__(self, root_path, file_names, ):\r\n # air pressure\r\n self.pa = 1\r\n # root path\r\n self.root_path = root_path\r\n # file names list\r\n self.file_names = file_names\r\n # folder number\r\n self.folder_no = len(self.file_names)\r\n # rows to be dropped\r\n self.rows2drop = []\r\n # rows list for train\r\n self.rows_for_train = []\r\n for i in range(0, 95):\r\n self.rows_for_train += list(range(i, 1500+i, 100))\r\n # rows list for predict\r\n self.rows_for_predict = []\r\n for j in range(95, 100):\r\n self.rows_for_predict += list(range(j, 1500+j, 100))\r\n # input data columns\r\n # Sequence, Acq Cycle, Cyclic Axial Load, Cyclic Axial Stress, Axial Permanent Deform, \\\r\n # Axial Permanent Strain, Axial Resilient Deform, Axial Resilient Strain, Axial Resilient Modulus\r\n self.parameters = {\r\n \"Axial Resilient Modulus\": \"\", \"sigma3\": \"\", \"sigmaD\": \"\",\r\n \"theta\": \"\", \"tau\": \"\", \"theta / pa\": \"\",\r\n \"tau / pa\": \"\", \"tau / pa + 1\": \"\",\r\n }\r\n # information about models\r\n self.model_xs = {\r\n # model: [[parameters], [coefficients], [intercepts], ]\r\n \"Seed_Model\": [\"theta / pa\"], \r\n \"Uzan&Witczak_Model\": [\"theta / pa\", \"tau / pa\", ], \r\n \"NCHRP_Model\": [\"theta / pa\", \"tau / pa + 1\", ], \r\n }\r\n # regression result\r\n self.regr_result = {\r\n \"coef_\": [],\r\n \"intercept_\": [], \r\n \"MSE\": [],\r\n \"R2\": [],\r\n }\r\n # result sum \r\n self.result_sum = {\r\n \"Folder\": [],\r\n \"Model\": [],\r\n \"coef_\": [],\r\n \"intercept_\": [], \r\n \"MSE\": [],\r\n \"R2\": [],\r\n }\r\n # start time\r\n self.start_time = datetime.datetime.now()\r\n # log dictionary\r\n self.log = {\r\n \"Folder\": [],\r\n \"Regression\": [],\r\n }\r\n\r\n def set_time0(self, ):\r\n # initiate start_time\r\n self.start_time = datetime.datetime.now()\r\n\r\n def duration(self, time0, ):\r\n # determine duration\r\n return datetime.datetime.now() - time0\r\n\r\n def readcsv(self, ori, sep=\",\", ):\r\n # read csv and return it as dataframe \r\n return pd.read_csv(ori, sep=sep, ) \r\n\r\n def writecsv(self, content, target, index=False, ):\r\n # write csv with dataframe\r\n csv_name = target.split(\".\")\r\n content.to_csv(\"{}.{}\".format(csv_name[0], csv_name[-1]), index=index, ) \r\n\r\n def calculation(self, mr, pressure, axial_cyclic_stress, ):\r\n # calculate some basic parameters\r\n paras = copy.deepcopy(self.parameters)\r\n paras[\"Axial Resilient Modulus\"] = mr\r\n paras[\"sigma3\"] = pressure\r\n paras[\"sigmaD\"] = axial_cyclic_stress\r\n theta = axial_cyclic_stress + 3 * pressure\r\n paras[\"theta\"] = theta\r\n tau = np.sqrt(2) / 3 * axial_cyclic_stress\r\n paras[\"tau\"] = tau\r\n paras[\"theta / pa\"] = theta / self.pa\r\n paras[\"tau / pa\"] = tau / self.pa\r\n paras[\"tau / pa + 1\"] = tau / self.pa + 1\r\n return paras\r\n\r\n def drop_row(self, x_, aim_rows, ):\r\n # drop row list\r\n row_list = []\r\n for seq_i in range(15):\r\n # aim range\r\n aim_range = range((seq_i+1)*100-aim_rows, (seq_i+1)*100)\r\n # aim average value\r\n x_ave = x_.iloc[aim_range].mean()\r\n for cycle_i in range(100):\r\n row_i = seq_i*100 + cycle_i\r\n # add row to drop list when the value is \r\n # less than half of the aim\r\n # or more than twice of the aim\r\n if not 1/4*x_ave <= x_.iloc[row_i] <= 2*x_ave:\r\n row_list.append(row_i)\r\n return row_list\r\n\r\n def regression(self, train_x, train_y, check_x, check_y):\r\n regr = linear_model.LinearRegression()\r\n # regression\r\n regr.fit(train_x, train_y)\r\n self.regr_result[\"coef_\"] = regr.coef_\r\n self.regr_result[\"intercept_\"] = regr.intercept_\r\n # determine and append k1\r\n # k1 = exp(intercept)/Pa\r\n k_temp.append(np.exp(regr.intercept_)/self.pa)\r\n # append k2 (k2 = coef_[0]) if it exists\r\n # append k3 (k3 = coef_[1])\r\n for k_2_3_i in regr.coef_:\r\n k_temp.append(k_2_3_i)\r\n # prediction\r\n predict_y = regr.predict(check_x)\r\n self.regr_result[\"MSE\"] = mean_squared_error(check_y, predict_y)\r\n self.regr_result[\"R2\"] = r2_score(check_y, predict_y)\r\n\r\n\r\n def main(self, ):\r\n # print hint\r\n print(\"Regressing...\")\r\n count_i = 1\r\n for folder_i in self.file_names:\r\n # initiate start time\r\n self.set_time0()\r\n # log\r\n self.log[\"Folder\"].append(folder_i)\r\n # generate mr file path and read file\r\n csv_mr_path = os.path.join(self.root_path, \"{}_mr.csv\".format(folder_i))\r\n temp1 = self.readcsv(csv_mr_path, ).copy()\r\n # get rows to be droppped\r\n self.rows2drop = self.drop_row(\r\n temp1[\"Axial Resilient Modulus\"], 5, \r\n )\r\n # calculating some necessary parameters\r\n try:\r\n # initiate input for calculation\r\n input_ = [\r\n temp1[\"Axial Resilient Modulus\"], \r\n temp1[\"Pressure\"], temp1[\"Cyclic Axial Stress\"],\r\n ]\r\n # temp2 = [theta, tau, theta / pa, sigmaD / pa, tau / pa + 1]\r\n temp2 = self.calculation(*input_)\r\n for model_i in self.model_xs:\r\n # print hint\r\n print(\"Processing[{}/{}]>> {} {}\".format(count_i, self.folder_no, folder_i, model_i))\r\n # generate reg file path\r\n csv_reg_path = os.path.join(\r\n self.root_path, \"{}_{}_reg.csv\".format(folder_i, model_i, ),\r\n )\r\n xs = self.model_xs[model_i]\r\n # the number of x\r\n x_no = len(xs)\r\n # y train and predict dataframe\r\n temp2[\"y\"] = np.log10(temp2[\"Axial Resilient Modulus\"]*1000)\r\n y_train = temp2[\"y\"].iloc[self.rows_for_train]\r\n y_predict = temp2[\"y\"].iloc[self.rows_for_predict]\r\n # x train and predict empty dataframes\r\n x_train = pd.DataFrame([])\r\n x_predict = pd.DataFrame([])\r\n # get x dataframes\r\n for x_i in range(x_no):\r\n temp2[\"x{}\".format(x_i)] = np.log10(temp2[xs[x_i]])\r\n x_train = pd.concat(\r\n [x_train, temp2[\"x{}\".format(x_i)].iloc[self.rows_for_train]], \r\n ignore_index=True, axis=1, \r\n )\r\n x_predict = pd.concat(\r\n [x_predict, temp2[\"x{}\".format(x_i)].iloc[self.rows_for_predict]], \r\n ignore_index=True, axis=1, \r\n )\r\n # drop rows\r\n x_train = x_train.drop(self.rows2drop).to_numpy()\r\n y_train = y_train.drop(self.rows2drop).to_numpy()\r\n # do regression\r\n self.regression(x_train, y_train, x_predict, y_predict, )\r\n # append result\r\n self.result_sum[\"Folder\"].append(folder_i)\r\n self.result_sum[\"Model\"].append(model_i)\r\n for item_i in self.regr_result:\r\n self.result_sum[item_i].append(self.regr_result[item_i])\r\n # output data used for regression \r\n self.writecsv(\r\n pd.DataFrame(temp2), \r\n os.path.join(self.root_path, csv_reg_path, )\r\n )\r\n except:\r\n # print hint\r\n print(\"!!! Regressing: {}\".format(folder_i, ))\r\n # log duration\r\n self.log[\"Regression\"].append(self.duration(self.start_time))\r\n count_i += 1\r\n self.writecsv(\r\n pd.DataFrame(self.result_sum), \r\n os.path.join(self.root_path, \"RLTT_reg_result.csv\", )\r\n )\r\n # write log\r\n self.writecsv(\r\n pd.DataFrame(self.log), \r\n os.path.join(self.root_path, \"last_5_seq_regr_log.csv\", )\r\n )\r\n\r\n\r\n# generate file names\r\n# file path\r\n# Linux path = \"/mnt/c/Users/Chuanjun Lau/Documents/RLTT_Data/Mr\"\r\n# Windows path = r\"D:\\TestData\\RLTT\\Mr\"\r\nroot_path = r\"/mnt/c/Users/Chuanjun Lau/Documents/RLTT_Data/Mr\"\r\n# get all files\r\nfor i, j, k in os.walk(root_path):\r\n if len(j) != 0:\r\n temp0 = k\r\n# get all \"*mr.csv\" files\r\nfile_names = []\r\nfor file_i in temp0:\r\n temp = file_i.split(\"_\")\r\n if temp[-1].split(\".\")[0] == \"mr\":\r\n file_names.append(file_i.split(\"_mr\")[0])\r\n\r\n# Set particular folder name to analysis\r\n# file_names = [\"RLTT_C16_95_1_3_mr.csv\"]\r\n\r\ndemo = DoRegression(root_path, file_names, )\r\ndemo.main()\r\n", "sub_path": "Mr/RLTT_Regression_last_5_cycles.py", "file_name": "RLTT_Regression_last_5_cycles.py", "file_ext": "py", "file_size_in_byte": 10066, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "datetime.datetime.now", "line_number": 80, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 80, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 89, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 89, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 93, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 97, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 112, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 137, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 137, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 144, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 151, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 184, "usage_type": "call"}, {"api_name": "os.path", "line_number": 184, "usage_type": "attribute"}, {"api_name": "numpy.log10", "line_number": 191, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 195, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 199, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 200, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 204, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 220, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 221, "usage_type": "call"}, {"api_name": "os.path", "line_number": 221, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 230, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path", "line_number": 231, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 235, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 236, "usage_type": "call"}, {"api_name": "os.path", "line_number": 236, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 246, "usage_type": "call"}]} +{"seq_id": "633460444", "text": "from django.urls import path, include\nfrom rest_framework.routers import DefaultRouter\n\nfrom profileApi import views\n\nrouter = DefaultRouter()\n# router.register('hello-viewset', views.HelloViewSets, basename='hello-viewset')\nrouter.register('profile', views.UserProfileViewSet)\nrouter.register('feed', views.UserProfileFeedViewSet)\n\nurlpatterns = [\n # path('hello-view/', views.HelloApiView.as_view(), name=\"hello-view\"),\n # path('hello-view/', HelloApiView.as_view(), name=\"hello-view\"),\n # path('', include(router.urls), name=\"hello-viewset-uri\"),\n path('', include(router.urls)),\n path('login/', views.UserLoginApiViews.as_view(), )\n\n]\n", "sub_path": "profileApi/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 654, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "rest_framework.routers.DefaultRouter", "line_number": 6, "usage_type": "call"}, {"api_name": "profileApi.views.UserProfileViewSet", "line_number": 8, "usage_type": "attribute"}, {"api_name": "profileApi.views", "line_number": 8, "usage_type": "name"}, {"api_name": "profileApi.views.UserProfileFeedViewSet", "line_number": 9, "usage_type": "attribute"}, {"api_name": "profileApi.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "profileApi.views.UserLoginApiViews.as_view", "line_number": 16, "usage_type": "call"}, {"api_name": "profileApi.views.UserLoginApiViews", "line_number": 16, "usage_type": "attribute"}, {"api_name": "profileApi.views", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "642344219", "text": "import time\nimport json\nimport requests\nfrom six import moves\n\n\nclass Singularity:\n def __init__(self, config):\n self.config = config\n\n def create_request_body(self):\n request_body = {\n \"id\": self.config['container_name'],\n \"owners\": [self.config.get('singularity_email', '')],\n \"rackSensitive\": False,\n \"loadBalanced\": False,\n \"skipHealthchecks\": True,\n \"requestType\": \"SERVICE\",\n \"requiredSlaveAttributes\": self.config.get('host_attributes', {})\n }\n\n if self.config['slave_placement']:\n request_body['slavePlacement'] = self.config.get('slave_placement', '')\n\n if self.config[\"cron_schedule\"]:\n request_body['schedule'] = self.config.get('cron_schedule', '')\n request_body['scheduleType'] = 'CRON'\n request_body['requestType'] = 'SCHEDULED'\n\n return request_body\n\n def create_deploy_body(self):\n deploy_body = {\n \"requestId\": self.config['container_name'],\n \"unpauseOnSuccessfulDeploy\": True,\n \"message\": \"Initiated by {}\".format(self.config.get('singularity_email', '')),\n \"deploy\": {\n \"requestId\": self.config['container_name'],\n \"id\": \"\",\n \"command\": self.config.get('entrypoint', None), # set command equal to entrypoint\n \"arguments\": self.config.get(\"arguments\", []),\n \"containerInfo\": {\n \"type\": \"DOCKER\",\n \"volumes\": self.config.get('volumes', []),\n \"docker\": {\n \"forcePullImage\": self.config.get(\"force_pull_image\", False),\n \"privileged\": self.config.get(\"privileged\", False),\n \"network\": self.config[\"network_mode\"],\n \"portMappings\": self.config[\"port_mappings\"],\n \"image\": self.config[\"image\"],\n \"parameters\": self.config.get(\"docker_params\", {})\n }\n },\n \"hostname\": self.config[\"container_name\"],\n \"env\": self.config.get('environment', {}),\n \"resources\": {\n \"cpus\": self.config.get('cpus', ''),\n \"memoryMb\": self.config.get('memory', ''),\n \"diskMb\": self.config.get('disk', ''),\n \"numPorts\": self.config.get('num_ports', '') or 1\n },\n \"skipHealthchecksOnDeploy\": True\n }\n }\n return deploy_body\n\n def deploy(self):\n endpoint = self.config.get('singularity_endpoint', '')\n container_name = self.config[\"container_name\"]\n deploy_id = str(int(time.time()))\n\n yn = moves.input(\"Are you sure, you want to deploy '{}' Singularity (y/n)? \".format(container_name))\n yn = yn.lower()\n if yn not in ['yes', 'y']:\n return False\n\n print(\"Creating deploy request for '{}'\".format(container_name))\n request_body = self.create_request_body()\n print(json.dumps(request_body, indent=4))\n\n resp = requests.post(endpoint + '/requests', data=json.dumps(request_body),\n headers={'Content-Type': 'application/json'})\n\n if resp and resp.status_code == 200:\n status_code = 400\n print(\"Deploying '{}'..\".format(deploy_id))\n deploy_body = self.create_deploy_body()\n deploy_body['deploy']['id'] = deploy_id\n print(json.dumps(deploy_body, indent=4))\n while status_code != 200:\n time.sleep(2)\n resp = requests.post(endpoint + '/deploys', data=json.dumps(deploy_body),\n headers={'Content-Type': 'application/json'})\n status_code = resp.status_code\n\n print(\"Deployed '{}' successfully.\".format(deploy_id))\n print(json.dumps(resp.json(), indent=4))\n", "sub_path": "compose_paas/platform/singularity.py", "file_name": "singularity.py", "file_ext": "py", "file_size_in_byte": 4021, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "time.time", "line_number": 70, "usage_type": "call"}, {"api_name": "six.moves.input", "line_number": 72, "usage_type": "call"}, {"api_name": "six.moves", "line_number": 72, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 79, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 81, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 81, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 89, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 91, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 92, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 92, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 97, "usage_type": "call"}]} +{"seq_id": "453234056", "text": "# Name : Gobang game \n# Author: simon \n# e-mail: 2441873702@qq.com\n# Date : 2020.05.27 19:02\n# version: v2\n# To-do : 实现胜负功能判断\n# bug 1 : 当鼠标点击到画布棋盘外仍可显示棋子\n# bug 2 : 棋子会覆盖之前已经绘制的位置 ———— fixed\n\n\nimport pygame,sys\nimport pygame.freetype\n\npygame.init()\n\nfps = 300 # fps setting\nfclock = pygame.time.Clock()\n\n# default str value\nsize = width, height = 800, 600\nborder = 50 \nwlc_str = \"Welcom to gobang game!\"\n\n# default color\nbg_color = (128,138,135)#pygame.Color(\"white\")\nline_color = pygame.Color(\"black\")\n# chess color\nWHITE = 255,255,255\nBLACK = 0,0,0\nfont_color = 0,0,0\n\n# pygame Surface\nscreen = pygame.display.set_mode(size, pygame.RESIZABLE)\nbackground = pygame.Surface(screen.get_size())\n\ncaption = \"Gobang Game\"\npygame.display.set_caption(caption)\n\ndef draw_font(background, string='Hello pygame!',font_size=20, positon=(0,0)):\n\t# font_type = pygame.freetype.Font('C://Windows//Fonts//msyh.ttc', 1)\n\tfont_type = pygame.freetype.Font('./consola.ttf', 1)\t\n\tfont_rect = font_type.render_to(background, positon, string, fgcolor=font_color, size=font_size)\n\tscreen.blit(background, (0, 0))\n\n\ndef draw_chessboard_rect(background, rect_point, border):\n\tx_num = int((width - 1.5 * border) / border)\n\ty_num = int((height - 1.5 * border) / border)\n\tfor num_w in range(x_num):\n\t\tfor num_h in range(y_num):\n\t\t\trect_point.append([num_w*border + 50, num_h*border + 50])\n\tfor item in rect_point:\n\t\ts_rect = item[0], item[1], border, border\n\t\tpygame.draw.rect(background, line_color, s_rect, 1)\n\treturn rect_point\n\n\ndef success(positon):\n\tfor item in positon:\n\t\t# 行\n\t\tif [item[0]+1,item[1]] in positon:\n\t\t\tif [item[0]+2,item[1]] in positon:\n\t\t\t\tif [item[0]+3,item[1]] in positon:\n\t\t\t\t\tif ([item[0]+4,item[1]] in positon):\n\t\t\t\t\t\t# print(\"success!\")\n\t\t\t\t\t\treturn True\n\t\t# 列\n\t\telif [item[0],item[1]+1] in positon:\n\t\t\tif [item[0],item[1]+2] in positon:\n\t\t\t\tif [item[0],item[1]+3] in positon:\n\t\t\t\t\tif [item[0],item[1]+4] in positon:\n\t\t\t\t\t\t# print(\"success!\")\n\t\t\t\t\t\treturn True\n\t\t# 对角\n\t\telif [item[0]+1,item[1]+1] in positon:\n\t\t\tif [item[0]+2,item[1]+2] in positon:\n\t\t\t\tif [item[0]+3,item[1]+3] in positon:\n\t\t\t\t\tif [item[0]+4,item[1]+4] in positon:\n\t\t\t\t\t\t# print(\"success!\")\n\t\t\t\t\t\treturn True\n\n\ndef success_judge(chess_dict):\n\tblack_pos = []\n\twhite_pos = []\n\t# print(chess_dict)\n\t# {'10,4': 2, '10,5': 1, '6,4': 2, '6,5': 1, '8,7': 2, '5,7': 1, '6,9': 2, '9,4': 1, '9,6': 2, '10,8': 1}\n\tfor item in chess_dict:\n\t\tx = item.split(\",\", 1)\n\t\tif chess_dict[item] == 1:\n\t\t\twhite_pos.append([int(x[0]),int(x[1])])\n\t\telse:\n\t\t\tblack_pos.append([int(x[0]),int(x[1])])\n\n\tif success(white_pos):\n\t\tprint(\"white success!\")\n\telif success(black_pos):\n\t\tprint(\"black success!\")\n\n\ndef game_over(delay_time):\n\timport time\n\ttime.sleep(delay_time)\n\tprint(\"game over!\")\n\n# put chess down \ndef chess_down(background, position, color):\n\tpygame.draw.circle(background, color, position, 20, 0)\n\n\n\n\nmouse_pos = []\nblack_position = []\nwhite_position = []\nwhile True:\n\t# event manage\n\tfor event in pygame.event.get():\n\t\t# quit\n\t\tif event.type == pygame.QUIT:\n\t\t\tgame_over(0.1)\n\t\t\tsys.exit()\n\t\t# window resize\n\t\telif event.type == pygame.VIDEORESIZE:\n\t\t\tsize = width, height = event.size[0], event.size[1]\n\t\t\tscreen = pygame.display.set_mode(size, pygame.RESIZABLE)\n\t\t\tbackground = pygame.Surface(screen.get_size())\n\t\telif event.type == pygame.MOUSEBUTTONDOWN:\n\t\t\tmouse_pos.append([event.pos[0],event.pos[1]])\t# .pos --> tuple = (x_pos,y_pos)\n\n\n\trect_point = []\n\tbackground.fill(bg_color)\n\tdraw_chessboard_rect(background, rect_point, border)\n\tdraw_font(background, string=wlc_str)\n\n\tchess_dict = {}\n\tcount, black_num, white_num = 0, 0, 0\n\tfor position in mouse_pos:\n\t\t# position calculate:\n\t\tposition[0] = round(position[0] / 50) * 50\n\t\tposition[1] = round(position[1] / 50) * 50\n\n\t\tkey = str(position[0]//50)+\",\"+str(position[1]//50)\n\t\t# flags \n\t\t# 0 -- no\n\t\t# 1 -- white\n\t\t# 2 -- black\n\n\t\tif key in chess_dict:\n\t\t\t# cannot put down the chess\n\t\t\tprint(\"can't put chess here!\")\n\t\telse:\n\t\t\t# flags = 0\n\t\t\tif count % 2 == 0:\n\t\t\t\tchess_color = BLACK\n\t\t\t\tflags = 2\n\t\t\t\t# black_position.append([position[0]//50, position[1]//50])\n\t\t\t\t# print(len(black_position))\n\t\t\t\t# print(black_position)\n\n\t\t\telse:\n\t\t\t\tchess_color = WHITE\n\t\t\t\tflags = 1\n\t\t\t\t# pygame.draw.circle(background, WHITE, position, 20, 0)\n\t\t\t\t# white_position.append([position[0]//50, position[1]//50])\n\n\t\t\tcount = count + 1\n\t\t\t# 归一化\n\t\t\tnew_dict = {key : flags}\n\t\t\tchess_dict.update(new_dict)\n\t\t\tchess_down(background, position, chess_color)\n\n\tsuccess_judge(chess_dict)\n\t\"\"\"\n\t# judge success or not \n\tif success_judge(black_position):\n\t\tprint(\"black wins!\")\n\telif success_judge(white_position):\n\t\tprint(\"white wins!\")\n\t\"\"\"\n\n\tscreen.blit(background, (0, 0))\n\tfclock.tick(fps)\t# fps each second\n\tpygame.display.update()\n", "sub_path": "gobang/Gobang_v1.2.py", "file_name": "Gobang_v1.2.py", "file_ext": "py", "file_size_in_byte": 4829, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pygame.init", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.Color", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.RESIZABLE", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 34, "usage_type": "call"}, {"api_name": "pygame.display.set_caption", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pygame.freetype.Font", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.freetype", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 54, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 103, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 108, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 108, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 118, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 118, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 120, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 122, "usage_type": "call"}, {"api_name": "pygame.VIDEORESIZE", "line_number": 124, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 126, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pygame.RESIZABLE", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 127, "usage_type": "call"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 185, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 185, "usage_type": "attribute"}]} +{"seq_id": "567228550", "text": "import cProfile\n\nfrom files import *\nfrom objects import *\nfrom video import *\n\ndef showImage(image, windowName):\n\tcv2.namedWindow(windowName)\n\tcv2.imshow(windowName, image)\n\ndef test():\n\tdataset_root_folder = '../aff-wild2'\n\tfiles = getFiles(dataset_root_folder)\n\n\taff_wild_videos = Objects(files, ['mp4', 'avi'])\n\taff_wild_videos_iter = iter(aff_wild_videos)\n\n\tpairs = []\n\tobjects = []\n\tfor object in aff_wild_videos_iter:\n\t\tannotations = getAnnotations(object, files)\n\t\tpairs.append({\n\t\t\t'object': object,\n\t\t\t'annotation': annotation\n\t\t})\n\t\tif len(pairs) % 10000 == 0:\n\t\t\tprint(len(pairs))\n\t\t# objects.append(object)\n\t\t# data = readObject(object)\n\t\t# showImage(data, 'data')\n\t\t# cv2.waitKey(0)\n\tprint(len(objects))\n\n# test()\n# cProfile.run('test()')\n\nimport cv2\nimport numpy as np\nfrom scipy.spatial import distance\n\ndef resize(image, scale = 0.5):\n\twidth = int(image.shape[1] * scale)\n\theight = int(image.shape[0] * scale)\n\treturn cv2.resize(image, (width, height))\n\nimport math\n\ndef getHog(image):\n\tcells_by_side = 16\n\t# print('image size:', image.shape)\n\tcell_size = (math.floor(image.shape[0] / cells_by_side), math.floor(image.shape[1] / cells_by_side)) # h x w in pixels\n\tblock_size = (1, 1) # h x w in cells\n\tnbins = 9 # number of orientation bins\n\n\t# winSize is the size of the image cropped to an multiple of the cell size\n\t# cell_size is the size of the cells of the img patch over which to calculate the histograms\n\t# block_size is the number of cells which fit in the patch\n\t_winSize=(cells_by_side * cell_size[1], cells_by_side * cell_size[0])\n\timage = image[0:_winSize[1], 0:_winSize[0]]\n\t# print('new image size:', image.shape, _winSize[0], _winSize[1])\n\t# print('_winSize:', _winSize, _winSize[0] / cell_size[1], _winSize[1] / cell_size[0])\n\t_blockSize=(block_size[1] * cell_size[1],\n\t\t\t\tblock_size[0] * cell_size[0])\n\t_blockStride=(cell_size[1], cell_size[0])\n\t_cellSize=(cell_size[1], cell_size[0])\n\t# print('_cell_size:', _cellSize)\n\t_nbins=nbins\n\thog = cv2.HOGDescriptor(_winSize=_winSize,\n\t _blockSize=_blockSize,\n\t _blockStride=_blockStride,\n\t _cellSize=_cellSize,\n\t _nbins=_nbins)\n\tresult = hog.compute(image).T[0]\n\t# print('hog size:', result.shape)\n\treturn result\n\ndef getHogMatrix(images):\n\treturn np.array([getHog(image) for image in images])\n\nvideos = map(getVideo, ['../aff-wild2/expr/videos/validation_set/118-30-640x480.mp4'])\nframes = map(getCurrentFrame, videos)\nhogMatrix = getHogMatrix(frames)\nfor v in hogMatrix:\n\tprint(v.shape)\n\tprint(v[:18])\nexit()\n\nimport cupy as cp\nimport time\n\ndef timeit(method):\n def timed(*args, **kw):\n ts = time.time()\n result = method(*args, **kw)\n te = time.time()\n if 'log_time' in kw:\n name = kw.get('log_name', method.__name__.upper())\n kw['log_time'][name] = int((te - ts) * 1000)\n else:\n print('%r %2.2f ms' % (method.__name__, (te - ts) * 1000))\n return result\n return timed\n\nfrom numba import jit\n\ndef distance_cosine(a, b, lib):\n\tnumerator = lib.dot(a, b)\n\ta_norm = lib.sqrt(lib.sum(a ** 2))\n\tb_norm = lib.sqrt(lib.sum(b ** 2))\n\tdenominator = a_norm * b_norm\n\tresult = 1 - numerator / denominator\n\treturn result\n\n@jit(nopython=True)\ndef cosine_similarity_numba(u:np.ndarray, v:np.ndarray):\n assert(u.shape[0] == v.shape[0])\n uv = 0\n uu = 0\n vv = 0\n for i in range(u.shape[0]):\n uv += u[i]*v[i]\n uu += u[i]*u[i]\n vv += v[i]*v[i]\n cos_theta = 1\n if uu!=0 and vv!=0:\n cos_theta = uv/np.sqrt(uu*vv)\n return cos_theta\n\ndef distance_cosine_np(a, b):\n\tnumerator = np.dot(a, b)\n\ta_norm = np.sqrt(np.sum(a ** 2))\n\tb_norm = np.sqrt(np.sum(b ** 2))\n\tdenominator = a_norm * b_norm\n\tresult = 1 - numerator / denominator\n\treturn result\n\n@timeit\ndef distance_cosine_for(A, B, f, lib):\n\tfor i in range(A.shape[0]):\n\t\ta = A[i]\n\t\tb = B[i]\n\t\tf(a, b)\n\ndef generate(lib, size):\n\treturn lib.array([lib.random.uniform(size = 2048) for i in range(size)]), lib.array([lib.random.uniform(size = 2048) for i in range(size)])\n\nwith cp.cuda.Device(0):\n\tfor lib in ['np', 'cp']:\n\t\tprint(lib)\n\t\tprint('generating...')\n\t\tA, B = generate(lib, 512)\n\t\tprint('calculating...')\n\t\tdistance_cosine_for(A, B, distance_cosine, lib)\n# A, B = generate(np, 512)\n# distance_cosine_for(A, B, distance_cosine_np, 'np')\nexit()\n\n# optimized, complexity is n * (n-1) / 2\ndef getSimilaritiesMatrix(matrix):\n\tnumberOfVectors = matrix.shape[0]\n\tresult = np.zeros([numberOfVectors, numberOfVectors])\n\tfor i in range(numberOfVectors):\n\t\tfor j in range(i + 1, numberOfVectors):\n\t\t\tvector1 = matrix[i]\n\t\t\tvector2 = matrix[j]\n\t\t\t# print(vector1.shape, vector1)\n\t\t\t# print(vector2.shape, vector2)\n\t\t\t# exit()\n\t\t\td = distance.cosine(vector1, vector2)\n\t\t\tresult[i][j] = d\n\t\t\tresult[j][i] = d\n\treturn 1 - result\n\ndef getMetrics(similarities_matrix):\n\treturn np.array([np.mean(similarities_matrix)])\n\ndataset_root_folder = '../aff-wild2'\nfiles = getFiles(dataset_root_folder)\nprint('loaded files')\n\naff_wild_videos = Objects(files, ['mp4', 'avi'])\naff_wild_videos_iter = iter(aff_wild_videos)\nall_objects = [object for object in aff_wild_videos_iter]\nprint('loaded objects')\n\ndef getHogSimilaritiesMatrix(images):\n\treturn getSimilaritiesMatrix(getHogMatrix(images))\n\ndef getMetricsDeltas(images1, images2):\n\tmetrics1 = getMetrics(getHogSimilaritiesMatrix(images1))\n\tmetrics2 = getMetrics(getHogSimilaritiesMatrix(images2))\n\treturn metrics2 / metrics1\n\nimport random\n\ndef experiment(attempts = 1):\n\tresults = np.empty([attempts])\n\tfor i in range(attempts):\n\t\tobjects = random.sample(all_objects, 100)\n\t\t# print(objects)\n\t\timages = list(map(readObject, objects))\n\t\tprint('images:', [t for t in map(type, images)])\n\t\tresized_images = list(map(resize, images))\n\t\tresults[i] = getMetricsDeltas(images, resized_images)\n\treturn results\n\nimport os\n\ndef experiment1():\n\tprocessed = 0\n\tpaths = {}\n\tshapes = {}\n\tfor object in all_objects:\n\t\tpath = os.path.normcase(object['dir'] + '/' + object['name'] + '.' + object['extension'])\n\t\tprocessed += 1\n\t\tif processed % 100000 == 0:\n\t\t\tprint('processed', processed, 'images')\n\t\tif path in paths:\n\t\t\tcontinue\n\t\tpaths[path] = True\n\t\timage = readObject(object)\n\t\tshape = str(image.shape)\n\t\tif shape in shapes:\n\t\t\tcontinue\n\t\tshapes[shape] = True\n\t\thog = getHog(image)\n\t\tif hog.shape[0] != 2304:\n\t\t\tprint(hog.shape, object)\n\ncProfile.run('experiment()')", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6424, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "cv2.resize", "line_number": 44, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.HOGDescriptor", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 78, "usage_type": "call"}, {"api_name": "time.time", "line_number": 93, "usage_type": "call"}, {"api_name": "time.time", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 126, "usage_type": "call"}, {"api_name": "numba.jit", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 132, "usage_type": "call"}, {"api_name": "cupy.cuda.Device", "line_number": 147, "usage_type": "call"}, {"api_name": "cupy.cuda", "line_number": 147, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 161, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.cosine", "line_number": 169, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 169, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 197, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 199, "usage_type": "call"}, {"api_name": "os.path.normcase", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path", "line_number": 214, "usage_type": "attribute"}, {"api_name": "cProfile.run", "line_number": 230, "usage_type": "call"}]} +{"seq_id": "236487118", "text": "\"\"\"Rm nms with multi coords\"\"\"\nimport argparse, pandas, csv\n\ndef get_singles(bed_in):\n df = pandas.read_csv(args.bed_in, sep='\\t', usecols=['nm', 'bin'])\n c = {0:'size'}\n g = df.groupby(['nm', 'bin']).size().reset_index().rename(index=str, columns=c)\n singles = set([x + ':' + str(y) for x,y in g[g['size']==1][['nm', 'bin']].values])\n return singles\n\ndef main(args):\n singles = get_singles(args.bed_in)\n with open(args.bed_in) as f, open(args.bed_out, 'w') as fout:\n reader = csv.DictReader(f, delimiter='\\t')\n fields = reader.fieldnames\n print('\\t'.join(fields), file=fout)\n for row in reader:\n key = row['nm'] + ':' + row['bin']\n if key in singles:\n print('\\t'.join([row[x] for x in fields]), file=fout)\n \nif __name__ == \"__main__\":\n desc = 'Rms with mulit entries'\n parser = argparse.ArgumentParser(description=desc)\n argLs = ('bed_in', 'bed_out', )\n for param in argLs:\n parser.add_argument(param)\n args = parser.parse_args()\n main(args)\n", "sub_path": "code/scripts/fix_fly_gene_regions.py", "file_name": "fix_fly_gene_regions.py", "file_ext": "py", "file_size_in_byte": 1057, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pandas.read_csv", "line_number": 5, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 14, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "469755939", "text": "# -*- coding: utf-8 -*-\nimport pymongo\nimport logging\nfrom pymongo import errors\nfrom tools.configs import configs\nfrom tools.middlewares import typeassert\n\n\nclass MongodbUtils(object):\n \"\"\"\n Mongodb 工具类\n \"\"\"\n\n conf = configs\n\n @classmethod\n def conn(cls):\n \"\"\"\n connect to mongodb\n :return:\n \"\"\"\n\n db = None\n try:\n db = pymongo.MongoClient(cls.conf[\"ORIGINAL_DATA_URI\"], appname='european')[cls.conf[\"ORIGINAL_DATA_DB\"]]\n # logging.warning(cls.conf[\"CR_DATA_URI\"])\n except pymongo.errors.OperationFailure as ex:\n logging.error('database connect refuse. reason: {0}.'.format(ex))\n finally:\n return db\n\n @classmethod\n @typeassert(table=str, records=list, primary=tuple, depulicate=bool)\n def insert_records(cls, table, records, primary=(), depulicate= False):\n \"\"\"\n 数据存入Mongodb\n :param table:\n :param records:\n :param primary: primary keys\n :return:\n \"\"\"\n\n db = cls.conn()\n if not db:\n return\n\n try:\n if depulicate :\n for index, record in enumerate(records):\n find_record = cls.find_record(table, record, primary)\n if find_record:\n cls.update_record(table, record, {'_id': find_record['_id']})\n else:\n db[table].insert(record)\n else:\n for index, record in enumerate(records):\n db[table].insert(record)\n except errors as ex:\n logging.error(\"The table {0} insert&update is error: {1}.\".format(table, ex))\n\n @classmethod\n @typeassert(table=str, record=dict, primary=tuple)\n def find_record(cls, table, record, primary=()):\n \"\"\"\n find single record in mongodb\n :param table:\n :param record:\n :param primary:\n :return:\n \"\"\"\n\n db = cls.conn()\n if not db:\n return\n\n condition = {}\n for key in primary:\n condition[key] = record[key]\n find_record = db[table].find(condition)\n\n result = None\n if find_record.count():\n result = find_record[0]\n return result\n\n @classmethod\n @typeassert(table=str, record=dict, condition=dict)\n def update_record(cls, table, record, condition={'_id': None}):\n \"\"\"\n update single record in mongodb\n :param table:\n :param record:\n :param condition:\n :return:\n \"\"\"\n\n db = cls.conn()\n if not db:\n return\n\n condition_value = {}\n for key, value in record.iteritems():\n condition_value[key] = value\n try:\n db[table].update(condition, {\"$set\": condition_value}, False, True)\n except errors as ex:\n logging.error(\"The table {0} update is error: {1}.\".format(table, ex))\n\n @classmethod\n @typeassert(table=str, record=dict)\n def delete_record(cls, table, record):\n \"\"\"\n delete single record in mongodb\n :param table:\n :param record:\n :return:\n \"\"\"\n\n db = cls.conn()\n if not db:\n return\n try:\n db[table].remove({\"_id\": record[\"_id\"]}, multi=False)\n except errors as ex:\n logging.error(\"The table {0} update is error: {1}.\".format(table, ex))\n\n @classmethod\n @typeassert(table=str, condition=dict)\n def get_records(cls, table, condition):\n \"\"\"\n find records in table\n :param table:\n :param condition:\n :return:\n \"\"\"\n\n db = cls.conn()\n if not db:\n return\n\n results_list = list()\n try:\n results = db[table].find(condition)\n if results.count():\n for index, row in enumerate(results):\n results_list.append(row)\n else:\n logging.warning('{0}, no data found.'.format(table))\n except errors as ex:\n msg = \"The table {0} find is error: {1}.\".format(table, ex)\n logging.error(msg)\n return results_list\n\n @classmethod\n @typeassert(table=str, condition=dict, sorted_by=str)\n def get_records_order(cls, table, condition, sorted_by):\n \"\"\"\n find records order by sorted_by in table\n :param table:\n :param condition:\n :param sorted_by:\n :return:\n \"\"\"\n\n db = cls.conn()\n if not db:\n return\n\n results_list = list()\n try:\n results = db[table].find(condition).sort(sorted_by, pymongo.ASCENDING)\n if results.count():\n for index, row in enumerate(results):\n results_list.append(row)\n else:\n logging.warning('{0}, no data found.'.format(table))\n except errors as ex:\n logging.error(\"The table {0} find is error: {1}.\".format(table, ex))\n return results_list\n\n @classmethod\n @typeassert(table=str, key=str)\n def get_distinct_key(cls, table, key):\n \"\"\"\n get distinct key in mongodb\n :param table:\n :param key:\n :return:\n \"\"\"\n\n db = cls.conn()\n if not db:\n return\n\n results_list = list()\n try:\n results = db[table].distinct(key)\n if results:\n for index, row in enumerate(results):\n results_list.append(row)\n else:\n logging.warning('{0}, key: {1}, no data found.'.format(table, key))\n except errors as ex:\n logging.error(\"The table {0} find is error: {1}.\".format(table, ex))\n return results_list\n", "sub_path": "python2/tools/mongodbutils.py", "file_name": "mongodbutils.py", "file_ext": "py", "file_size_in_byte": 5803, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "tools.configs.configs", "line_number": 14, "usage_type": "name"}, {"api_name": "pymongo.MongoClient", "line_number": 25, "usage_type": "call"}, {"api_name": "pymongo.errors", "line_number": 27, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 28, "usage_type": "call"}, {"api_name": "pymongo.errors", "line_number": 58, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 59, "usage_type": "call"}, {"api_name": "tools.middlewares.typeassert", "line_number": 33, "usage_type": "call"}, {"api_name": "tools.middlewares.typeassert", "line_number": 62, "usage_type": "call"}, {"api_name": "pymongo.errors", "line_number": 106, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 107, "usage_type": "call"}, {"api_name": "tools.middlewares.typeassert", "line_number": 87, "usage_type": "call"}, {"api_name": "pymongo.errors", "line_number": 124, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 125, "usage_type": "call"}, {"api_name": "tools.middlewares.typeassert", "line_number": 110, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 148, "usage_type": "call"}, {"api_name": "pymongo.errors", "line_number": 149, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 151, "usage_type": "call"}, {"api_name": "tools.middlewares.typeassert", "line_number": 128, "usage_type": "call"}, {"api_name": "pymongo.ASCENDING", "line_number": 171, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 176, "usage_type": "call"}, {"api_name": "pymongo.errors", "line_number": 177, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 178, "usage_type": "call"}, {"api_name": "tools.middlewares.typeassert", "line_number": 155, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 202, "usage_type": "call"}, {"api_name": "pymongo.errors", "line_number": 203, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 204, "usage_type": "call"}, {"api_name": "tools.middlewares.typeassert", "line_number": 182, "usage_type": "call"}]} +{"seq_id": "207768751", "text": "# Extract data pieces from one web page.\r\nfrom bs4 import BeautifulSoup\r\nimport requests\r\n\r\nurl = \"https://boston.craigslist.org/search/sof\"\r\n\r\nresponse = requests.get(url)\r\n\r\ndata = response.text\r\n\r\nsoup = BeautifulSoup(data,'html.parser')\r\n\r\njobs = soup.find_all('div',{'class':'result-info'})\r\n\r\n\r\nfor job in jobs:\r\n title = job.find('a',{'class':'result-title'}).text\r\n location_tag = job.find('span',{'class':'result-hood'})\r\n # added [2:-1] to remove the brackets, some job positions do not have location -> added loop\r\n location = location_tag.text[2:-1] if location_tag else \"N/A\"\r\n dates = job.find('time',{'class':'result-date'}).text\r\n link = job.find('a',{'class':'result-title'}).get('href')\r\n print(\"Job Title:\", title, \"\\nLocation:\", location, \"\\nDates:\", dates, \"\\nLink:\", link, \"\\n--\")", "sub_path": "craiglist/job_details_wrapper.py", "file_name": "job_details_wrapper.py", "file_ext": "py", "file_size_in_byte": 823, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "requests.get", "line_number": 7, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "589094488", "text": "\"\"\"\nModule to run tests on PypeItPar classes\n\"\"\"\nimport os\nimport numpy\n\nimport pytest\n\nfrom astropy.io import fits\n\nfrom pypeit.images import buildimage\nfrom pypeit import edgetrace, slittrace, specobjs\nfrom pypeit.spectrographs.keck_deimos import KeckDEIMOSSpectrograph\nfrom pypeit.spectrographs.util import load_spectrograph\nfrom pypeit.tests.tstutils import dev_suite_required, cooked_required\n\n\ndef data_path(filename):\n data_dir = os.path.join(os.path.dirname(__file__), 'files')\n return os.path.join(data_dir, filename)\n\ndef deimos_flat_files():\n return [os.path.join(os.getenv('PYPEIT_DEV'), 'RAW_DATA', 'keck_deimos', '830G_M_8500', ifile)\n for ifile in ['DE.20100913.57161.fits.gz', 'DE.20100913.57006.fits.gz']]\n\n\n@cooked_required\ndef test_assign_maskinfo():\n\n # Spectrograph\n keck_deimos = KeckDEIMOSSpectrograph()\n par = keck_deimos.default_pypeit_par()\n # working only on detector 3\n det = 3\n\n # Built trace image\n traceImage = buildimage.buildimage_fromlist(keck_deimos, det, par['calibrations']['traceframe'],\n deimos_flat_files())\n msbpm = keck_deimos.bpm(traceImage.files[0], det)\n\n # load specific config parameters\n par = keck_deimos.config_specific_par(traceImage.files[0])\n trace_par = par['calibrations']['slitedges']\n\n # Run edge trace\n edges = edgetrace.EdgeTraceSet(traceImage, keck_deimos, trace_par, bpm=msbpm, auto=True,\n debug=False, show_stages=False,qa_path=None)\n\n slits = edges.get_slits()\n\n # Test that the maskfile is saved properly\n hdul = fits.open(slits.maskfile)\n det_par = keck_deimos.get_detector_par(hdul, det=det)\n\n specobjs_file = os.path.join(os.getenv('PYPEIT_DEV'), 'Cooked', 'Science',\n 'spec1d_DE.20100913.22358-CFHQS1_DEIMOS_2010Sep13T061231.334.fits')\n # specobjs_file = os.path.join(os.getenv('PYPEIT_DEV'), 'REDUX_OUT', 'keck_deimos',\n # '830G_M_8500', 'Science',\n # 'spec1d_DE.20100913.22358-CFHQS1_DEIMOS_2010Sep13T061231.334.fits')\n sobjs = specobjs.SpecObjs.from_fitsfile(specobjs_file)\n # Init at null\n for sobj in sobjs:\n sobj.MASKDEF_OBJNAME = None\n sobj.RA = None\n sobj.DEC = None\n\n # Run me\n slits.assign_maskinfo(sobjs, det_par['platescale'])\n\n # Test\n assert sobjs[sobjs.SLITID == 496].MASKDEF_OBJNAME == 'ero89', 'Wrong MASKDEF_OBJNAME'\n assert sobjs[sobjs.SLITID == 496].RA == 352.27471667, 'Wrong object RA'\n assert sobjs[sobjs.SLITID == 496].DEC == -3.09223056, 'Wrong object DEC'\n\n # Write sobjs\n sobjs.write_to_fits({}, data_path('tst_sobjs.fits'))\n os.remove(data_path('tst_sobjs.fits'))\n\n\n@dev_suite_required\ndef test_deimosslitmask():\n f = os.path.join(os.environ['PYPEIT_DEV'], 'RAW_DATA', 'keck_deimos', '830G_M_8500',\n 'DE.20100913.22358.fits.gz')\n spec = KeckDEIMOSSpectrograph()\n spec.get_slitmask(f)\n assert spec.slitmask.nslits == 106, 'Incorrect number of slits read!'\n\n", "sub_path": "pypeit/tests/test_slitmask.py", "file_name": "test_slitmask.py", "file_ext": "py", "file_size_in_byte": 3098, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 23, "usage_type": "call"}, {"api_name": "pypeit.spectrographs.keck_deimos.KeckDEIMOSSpectrograph", "line_number": 31, "usage_type": "call"}, {"api_name": "pypeit.images.buildimage.buildimage_fromlist", "line_number": 37, "usage_type": "call"}, {"api_name": "pypeit.images.buildimage", "line_number": 37, "usage_type": "name"}, {"api_name": "pypeit.edgetrace.EdgeTraceSet", "line_number": 46, "usage_type": "call"}, {"api_name": "pypeit.edgetrace", "line_number": 46, "usage_type": "name"}, {"api_name": "astropy.io.fits.open", "line_number": 52, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 52, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 55, "usage_type": "call"}, {"api_name": "pypeit.specobjs.SpecObjs.from_fitsfile", "line_number": 60, "usage_type": "call"}, {"api_name": "pypeit.specobjs.SpecObjs", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pypeit.specobjs", "line_number": 60, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 77, "usage_type": "call"}, {"api_name": "pypeit.tests.tstutils.cooked_required", "line_number": 27, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pypeit.spectrographs.keck_deimos.KeckDEIMOSSpectrograph", "line_number": 84, "usage_type": "call"}, {"api_name": "pypeit.tests.tstutils.dev_suite_required", "line_number": 80, "usage_type": "name"}]} +{"seq_id": "491503428", "text": "import cv2\nimport numpy as np\nimport serial\nimport time\n\ndef find_rect_of_target_color(image):\n hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV_FULL)\n h = hsv[:, :, 0]\n s = hsv[:, :, 1]\n mask = np.zeros(h.shape, dtype=np.uint8)\n #mask[((h < 150) & (h > 90)) & (s > 128)] = 180\n mask[((h < 150) & (h > 90)) & (s > 128)] = 180\n contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n rects = []\n for contour in contours:\n approx = cv2.convexHull(contour)\n rect = cv2.boundingRect(approx)\n rects.append(np.array(rect))\n return rects\n\nif __name__ == \"__main__\":\n tm = cv2.TickMeter()\n tm.start()\n count = 0\n max_count = 10\n fps = 0\n ser = serial.Serial('COM6', 115200, timeout=0.1)\n time.sleep(1)\n \n capture = cv2.VideoCapture(0)\n #capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1280) # カメラ画像の横幅を1280に設定\n #capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 720) # カメラ画像の縦幅を720に設定\n\n while 1:\n _, frame = capture.read()\n if count == max_count:\n tm.stop()\n fps = max_count / tm.getTimeSec()\n tm.reset()\n tm.start()\n count = 0\n cv2.putText(frame, 'FPS: {:.2f}'.format(fps),\n (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0), thickness=2)\n rects = find_rect_of_target_color(frame)\n if len(rects) > 0:\n rect = max(rects, key=(lambda x: x[2] * x[3]))\n print(rect[2], rect[3])\n cv2.rectangle(frame, tuple(rect[0:2]), tuple(rect[0:2] + rect[2:4]), (255, 0, 0), thickness=2)\n if((rect[2]>=12) and (rect[3]>=11)):\n print(\"detected\")\n ser.write(b'a')\n count += 1\n cv2.imshow('LED detection', frame)\n k = cv2.waitKey(1)\n if k == ord('q'):\n break\n capture.release()\n cv2.destroyAllWindows()\n ser.close()\n", "sub_path": "opencv/detect.py", "file_name": "detect.py", "file_ext": "py", "file_size_in_byte": 1953, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "cv2.cvtColor", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV_FULL", "line_number": 7, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 10, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.convexHull", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.TickMeter", "line_number": 22, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 27, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 43, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 58, "usage_type": "call"}]} +{"seq_id": "289378187", "text": "import sqlite3\n\nconnector = sqlite3.connect(\"sqlite_test.db\")\n\n# sql = \"insert into test_table values('1', 'python')\"\n# connector.execute(sql)\n# sql = \"insert into test_table values('2', 'パイソン')\"\n# connector.execute(sql)\n# sql = \"insert into test_table values('3', 'ぱいそん')\"\n# connector.execute(sql)\ncursor = connector.cursor()\ncursor.execute(\"select * from test_table order by id\")\n\nresult = cursor.fetchall()\nfor row in result:\n print(\"code -- \" + str(row[0]))\n print(\"name -- \" + str(row[1]))\n\nconnector.commit()\nconnector.close()\n", "sub_path": "LibraryTest/test3.py", "file_name": "test3.py", "file_ext": "py", "file_size_in_byte": 555, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sqlite3.connect", "line_number": 3, "usage_type": "call"}]} +{"seq_id": "504530461", "text": "import cv2\nimport judger_hand\nimport numpy as np\nimport skimage.io\n\n\ndef nice_contour(contour, height=140):\n min_y = min([c[0][1] for c in contour])\n new_contour = [c for c in contour if c[0][1] < min_y + height]\n return np.array(new_contour)\n\n\ndef find_hand_by_color(img, thre_area=2000, color_range=[[0, 87, 134], [255, 137, 150]], height=140):\n img = np.array(img)\n # Constants for finding range of skin color in YCrCb\n min_YCrCb = np.array(color_range[0], np.uint8)\n max_YCrCb = np.array(color_range[1], np.uint8)\n # Find region with skin tone in YCrCb image\n imageYCrCb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB)\n skinRegion = cv2.inRange(imageYCrCb, min_YCrCb, max_YCrCb)\n\n # Do contour detection on skin region\n _, contours, _ = cv2.findContours(\n skinRegion, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n candidate_box = []\n for i, c in enumerate(contours):\n area = cv2.contourArea(c)\n if area > thre_area:\n contours[i] = nice_contour(contours[i], height)\n x, y, w, h = cv2.boundingRect(contours[i])\n cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)\n cv2.drawContours(img, contours, i, (0, 0, 255), 2)\n candidate_box.append((w * h, [x, y, w, h]))\n\n candidate_box = sorted(candidate_box, key=lambda x: -x[0])[:2]\n bbox = {'bbox': {}}\n for _, box in candidate_box:\n coor = [box[0], box[1], box[0] + box[2], box[1] + box[3]]\n if box[0] + box[2] > 0.6 * img.shape[1]:\n bbox['bbox']['R'] = coor\n else:\n bbox['bbox']['L'] = coor\n print(img.shape)\n print(candidate_box)\n print(bbox)\n return bbox, img\n\n\ntest_files = judger_hand.get_file_names()\noutput_f = judger_hand.get_output_file_object()\nbbox_ = {'bbox': {}}\nfor fname in test_files:\n img = skimage.io.imread(fname)\n bbox, img = find_hand_by_color(img)\n if not bbox['bbox']:\n bbox = bbox_\n for hand, box in bbox['bbox'].items():\n hand = 0 if hand == 'L' else 1\n out = '%s %d %d %d %d %d 1.0 \\n' % (\n fname, box[0], box[1], box[2], box[3], hand)\n print(out)\n output_f.write(out.encode())\n bbox_ = bbox\njudger_hand.judge()\n\n\"\"\" For demo use\n# Camera\ncamera = cv2.VideoCapture(0)\n\nwhile(1):\n # Capture frame from camera\n ret, frame = camera.read()\n frame = cv2.bilateralFilter(frame,5,50,100)\n bbox, frame = find_hand_by_color(frame, color_range=[[0, 140, 80], [255,180,128]], height=300)\n \n cv2.imshow('Hand Detection',frame)\n interrupt=cv2.waitKey(10)\n\"\"\"\n", "sub_path": "hand_skin_detector/hand_skin_detector.py", "file_name": "hand_skin_detector.py", "file_ext": "py", "file_size_in_byte": 2583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 17, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2YCR_CB", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cv2.inRange", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cv2.contourArea", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 32, "usage_type": "call"}, {"api_name": "judger_hand.get_file_names", "line_number": 49, "usage_type": "call"}, {"api_name": "judger_hand.get_output_file_object", "line_number": 50, "usage_type": "call"}, {"api_name": "skimage.io.io.imread", "line_number": 53, "usage_type": "call"}, {"api_name": "skimage.io.io", "line_number": 53, "usage_type": "attribute"}, {"api_name": "skimage.io", "line_number": 53, "usage_type": "name"}, {"api_name": "judger_hand.judge", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "321478208", "text": "# coding:utf-8\n\"\"\"\ncreate on Nov 5, 2019 By Wayne Yu\nFun: 全球各国骨干运营商相对价值评估模型数据爬取程序，本程序主要实现信源信息的获取、整理和计算。\n\n1）从alexa网站中爬取，每个国家的前30个信源网站地址；\n2）拿着这30个信源的网站地址，去HE网站查询该信源所接入的AS号信息;\n3）针对每个AS号，利用CAIDA的全球AS号BGP互联数据，去找他们各自的Peer关系的AS号；\n4）最后再去计算获取每个国家的骨干运营商AS网内的信源数量。\n\n需要查询的国家有美国（US）、日本（JP）、印度（IN）、法国（FR）、新加坡（SG）、澳大利亚（AU）、中国香港（HK）、中国（CN）\n\n\"\"\"\nfrom selenium import webdriver\nimport time\nfrom bs4 import BeautifulSoup\nimport csv\nimport re\n\n\ndef write_to_csv(res_list, des_path):\n \"\"\"\n 把给定的List，写到指定路径的csv文件中\n :param res_list:\n :param des_path:\n :return None:\n \"\"\"\n print(\"write file <%s>...\" % des_path)\n csv_file = open(des_path, 'w', newline='', encoding='utf-8')\n try:\n writer = csv.writer(csv_file)\n for i in res_list:\n writer.writerow(i)\n except Exception as e:\n print(e)\n finally:\n csv_file.close()\n print('write finish!')\n\n\ndef obtain_top_sites_by_country(page_url):\n \"\"\"\n 根据每个国家的Alexa TOP Sites页面，获取每个国家top 30的信源\n :param page_url:\n :return top_sites:\n \"\"\"\n top_sites = []\n # 获取页面信息\n driver.get(page_url)\n time.sleep(1) # 延迟加载，等待页面内容加载完毕\n # 获取页面的html信息\n page_html = driver.page_source\n bs_obj = BeautifulSoup(page_html, \"html.parser\")\n # print(bs_obj)\n tr_list = bs_obj.findAll(\"div\", {\"class\": \"tr site-listing\"})\n for tr_item in tr_list:\n # print(tr_item.find(\"a\").get_text().lower())\n url_item = tr_item.find(\"a\").get_text().lower()\n top_sites.append(url_item)\n return top_sites[0:50]\n\n\ndef obtain_asn_by_site(page_url):\n \"\"\"\n 根据每个网站的域名，去HE网站获取其接入运营商的AS号\n :param page_url:\n :return access_asn:\n \"\"\"\n access_asn = []\n # 获取页面信息\n driver.get(page_url)\n time.sleep(3) # 延迟加载，等待页面内容加载完毕\n # 获取页面html信息\n page_html = driver.page_source\n bs_obj = BeautifulSoup(page_html, \"html.parser\")\n # print(bs_obj)\n ip_info = bs_obj.find(\"div\", {\"id\": \"ipinfo\"})\n ip_info_string = str(ip_info)\n pattern = re.compile(r'>AS\\d+<') # 使用正则表达式，查找页面ip_info中的AS号\n re_return = pattern.findall(ip_info_string)\n for item in re_return:\n item = item[1:-1]\n if item not in access_asn:\n access_asn.append(item)\n return access_asn\n\n\nif __name__ == \"__main__\":\n # countries = [\"US\", \"JP\", \"IN\", \"FR\", \"SG\", \"AU\", \"HK\", \"CN\"]\n countries = [\"FR\"]\n # web_url = \"https://www.alexa.com/topsites/countries/US\" # 爬虫的入口程序\n time_start = time.time()\n # 启动浏览器\n driver = webdriver.Firefox()\n try:\n countries_top_sites_with_as = []\n for countries_item in countries:\n web_url = \"https://www.alexa.com/topsites/countries/\" + countries_item\n countries_top_sites = obtain_top_sites_by_country(web_url)\n print(\"countries top sites:\", countries_top_sites)\n print(\"sleep 10 seconds......\")\n time.sleep(10)\n temp_list = []\n cnt_rank = 1\n for sites_url in countries_top_sites:\n temp_list.append(cnt_rank)\n temp_list.append(sites_url)\n request_url = \"https://bgp.he.net/dns/\" + sites_url + \"#_ipinfo\"\n print(\"request_url:\", request_url)\n site_access_asn = obtain_asn_by_site(request_url)\n print(\"site access asn:\", site_access_asn)\n for site_access_asn_item in site_access_asn:\n temp_list.append(site_access_asn_item)\n countries_top_sites_with_as.append(temp_list)\n temp_list = []\n cnt_rank += 1\n print(countries_top_sites_with_as)\n country_string = web_url.split(\"/\")[-1]\n save_path = \"./data/top_sites_with_as_(\" + country_string + \").csv\"\n write_to_csv(countries_top_sites_with_as, save_path)\n countries_top_sites_with_as = []\n except Exception as e:\n print(e)\n # 关闭浏览器\n driver.quit()\n time_end = time.time()\n print(\"=>Scripts Finish, Time Consuming:\", (time_end - time_start), \"S\")\n", "sub_path": "017RelativeValueOfISP/crawler_info_for_ISP_NoCut.py", "file_name": "crawler_info_for_ISP_NoCut.py", "file_ext": "py", "file_size_in_byte": 4715, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "csv.writer", "line_number": 31, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 50, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 53, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 72, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 75, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 79, "usage_type": "call"}, {"api_name": "time.time", "line_number": 92, "usage_type": "call"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 94, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 94, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 102, "usage_type": "call"}, {"api_name": "time.time", "line_number": 126, "usage_type": "call"}]} +{"seq_id": "422078883", "text": "import torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\nfrom torchvision import transforms\nimport argparse\nimport dataset\nfrom models import tj\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--batch_size\", type = int, default = 4)\nparser.add_argument(\"--n_epochs\", type = int, default = 100)\nparser.add_argument(\"--lr\", type = float, default = 2e-3)\nparser.add_argument(\"--init_weights\", type = bool, default = False)\nparser.add_argument(\"--GPU\", type = bool, default = False)\nargs = parser.parse_args()\nprint(args)\n\n#for reproducibility\ntorch.manual_seed(0)\n\n#load dataset\nprint('loading dataset...')\ntransform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=(0.5,0.5,0.5), std=(0.5,0.5,0.5))])\ntrain_data = dataset.tj(transform = transform, mode = 'train')\nprint('number of data points:', len(train_data))\ntrainloader = DataLoader(dataset = train_data, batch_size = args.batch_size, shuffle = True)\nprint('dataset laoding finished')\nprint('data length: ', len(trainloader))\n\n#create model\ntj = tj()\nif args.init_weights:\n tj.init_weights()\n\noptimizer = torch.optim.Adam(tj.parameters(), lr = args.lr, betas = [0.5, 0.999])\ncurrent_epoch = 1\n\nif args.GPU and torch.cuda.is_available():\n print('using GPU...')\n tj = tj.cuda()\n\n#loss function settings\ncriterionL1 = nn.L1Loss()\ncriterionBCE = nn.BCELoss()\n\nif current_epoch >= args.n_epochs:\n raise Exception('training already finished!')\nelse:\n print('start training!')\n\n#start training\nfor epoch in range(current_epoch, args.n_epochs+1):\n print('current_epoch:', epoch)\n for i, (img, obj, hw, cp) in enumerate(trainloader, 1):\n '''print('obj before:', obj)\n print('obj after:', obj > 0)'''\n obj = (obj > 0).float()\n mask = obj.unsqueeze(2).expand_as(hw)\n if args.GPU and torch.cuda.is_available():\n img = img.cuda()\n obj = obj.cuda()\n hw = hw.cuda()\n cp = cp.cuda()\n mask = mask.cuda()\n\n #train tjCNN\n pred_cp, pred_hw, pred_obj = tj(img)\n #print('pred_obj:', pred_obj)\n cp_loss = criterionL1(pred_cp * mask, cp * mask)\n hw_loss = criterionL1(pred_hw * mask, hw * mask)\n obj_loss = criterionBCE(pred_obj, obj)\n loss = (0.1*cp_loss + 0.1*hw_loss + obj_loss)\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n if i%10 == 0:\n print('loss:', loss.mean())\n\n\n torch.save(tj.state_dict(), './checkpoints/tj_{}.pth'.format(epoch))\n print('model at {}th epoch is saved'.format(epoch))\n\n\n", "sub_path": "traffic_jam_detection/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 2596, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 20, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 24, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 24, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 24, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 24, "usage_type": "call"}, {"api_name": "dataset.tj", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 27, "usage_type": "call"}, {"api_name": "models.tj", "line_number": 32, "usage_type": "name"}, {"api_name": "models.tj.init_weights", "line_number": 34, "usage_type": "call"}, {"api_name": "models.tj", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 36, "usage_type": "attribute"}, {"api_name": "models.tj.parameters", "line_number": 36, "usage_type": "call"}, {"api_name": "models.tj", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 39, "usage_type": "attribute"}, {"api_name": "models.tj", "line_number": 41, "usage_type": "name"}, {"api_name": "models.tj.cuda", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.L1Loss", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.nn.BCELoss", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 60, "usage_type": "attribute"}, {"api_name": "models.tj", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 82, "usage_type": "call"}, {"api_name": "models.tj.state_dict", "line_number": 82, "usage_type": "call"}, {"api_name": "models.tj", "line_number": 82, "usage_type": "name"}]} +{"seq_id": "129174154", "text": "# -*- coding: utf-8 -*-\nimport re\nfrom io import BytesIO\nimport base64\nimport time\nfrom dateutil.relativedelta import relativedelta\nfrom datetime import datetime, timedelta, date\nimport xlwt\nfrom xlwt import *\nfrom odoo import fields, api, models\nfrom odoo.tools.translate import _\nfrom odoo.tools.misc import formatLang\nfrom odoo.exceptions import UserError\n\nclass account_report_general_ledger(models.TransientModel):\n\n _inherit=\"account.report.general.ledger\"\n\n xls_theme_id = fields.Many2one('color.xls.theme','XLS Theme')\n\n @api.multi\n def print_ledgerreport_xls(self):\n current_obj = self\n\n data = {}\n init_balance = self.initial_balance\n sortby = self.sortby\n display_account = self.display_account\n target_move = self.target_move\n\n codes = []\n\n if self.journal_ids:\n codes = [journal.code for journal in self.env['account.journal'].search([('id', 'in', self.journal_ids.ids)])]\n\n if self.account_ids:\n accounts = self.env['account.account'].browse(self.account_ids.ids)\n else:\n accounts = self.env['account.account'].search([])\n\n data['form'] = self.read(['date_from', 'date_to', 'journal_ids','target_move'])[0]\n if self.initial_balance and not self.date_from:\n raise UserError(_(\"You must define a Start Date\"))\n used_context = self._build_contexts(data)\n data['form']['used_context'] = dict(used_context, lang=self.env.context.get('lang', 'en_US'))\n accounts_res = self.env['report.account.report_generalledger'].with_context(data['form'].get('used_context',{}))._get_account_move_entry(accounts, init_balance, sortby, display_account)\n target_move = dict(self.env['account.report.general.ledger'].fields_get(allfields=['target_move'])['target_move']['selection'])[current_obj.target_move]\n sortby = dict(self.env['account.report.general.ledger'].fields_get(allfields=['sortby'])['sortby']['selection'])[current_obj.sortby]\n display_account = dict(self.env['account.report.general.ledger'].fields_get(allfields=['display_account'])['display_account']['selection'])[current_obj.display_account]\n\n fp = BytesIO()\n wb = xlwt.Workbook(encoding='utf-8')\n\n header_style = xlwt.XFStyle()\n font = xlwt.Font()\n pattern = xlwt.Pattern()\n pattern.pattern = xlwt.Pattern.SOLID_PATTERN\n bg_color = current_obj.xls_theme_id.bg_color or 'black'\n pattern.pattern_fore_colour = xlwt.Style.colour_map[bg_color]\n font.height = int(current_obj.xls_theme_id.font_size)\n font.bold = current_obj.xls_theme_id.font_bold\n font.italic = current_obj.xls_theme_id.font_italic\n font_color = current_obj.xls_theme_id.font_color or 'white'\n font.colour_index = xlwt.Style.colour_map[font_color]\n header_style.pattern = pattern\n header_style.font = font\n al3 = Alignment()\n al3.horz = current_obj.xls_theme_id.header_alignment or 0x02\n header_style.alignment = al3\n\n\n column_header_style = xlwt.XFStyle()\n font = xlwt.Font()\n pattern = xlwt.Pattern()\n pattern.pattern = xlwt.Pattern.SOLID_PATTERN\n bg_color = current_obj.xls_theme_id.column_bg_color or 'red'\n pattern.pattern_fore_colour = xlwt.Style.colour_map[bg_color]\n font.height = int(current_obj.xls_theme_id.column_font_size)\n font.bold = current_obj.xls_theme_id.column_font_bold\n font.italic = current_obj.xls_theme_id.column_font_italic\n font_color = current_obj.xls_theme_id.column_font_color or 'white'\n font.colour_index = xlwt.Style.colour_map[font_color]\n column_header_style.pattern = pattern\n column_header_style.font = font\n al3 = Alignment()\n al3.horz = current_obj.xls_theme_id.column_header_alignment\n column_header_style.alignment = al3\n\n\n body_header_style = xlwt.XFStyle()\n font = xlwt.Font()\n pattern = xlwt.Pattern()\n pattern.pattern = xlwt.Pattern.SOLID_PATTERN\n bg_color = current_obj.xls_theme_id.body_bg_color or 'gold'\n pattern.pattern_fore_colour = xlwt.Style.colour_map[bg_color]\n font.height = int(current_obj.xls_theme_id.body_font_size)\n font.bold = current_obj.xls_theme_id.body_font_bold\n font.italic = current_obj.xls_theme_id.body_font_italic\n font_color = current_obj.xls_theme_id.body_font_color or 'white'\n font.colour_index = xlwt.Style.colour_map[font_color]\n body_header_style.pattern = pattern\n body_header_style.font = font\n al3 = Alignment()\n al3.horz = current_obj.xls_theme_id.body_header_alignment\n body_header_style.alignment = al3\n\n\n final_arr_data = {}\n filename = 'General-Ledger-Report.xls'\n ledger_obj = self.pool.get(\"account.report.general.ledger\")\n worksheet = wb.add_sheet(\"GENERAL-LEDGER\" + \".xls\")\n header = current_obj.company_id.name+':'+'General Ledger'\n worksheet.write_merge(0, 0, 0, 8, header, header_style)\n journal_names = []\n journal_string = ''\n for journal_name in self.env['account.journal'].browse(data['form']['journal_ids']):\n journal_names.append(journal_name.code)\n journal_string += journal_name.code + ','\n\n\n header_header_list = [\"Journals:\", \"Display Account:\", \"Sorted By:\", \"Target Moves:\"]\n header_data_list = [journal_string, display_account, sortby, target_move]\n\n header_data = dict(zip(header_header_list, header_data_list))\n row = col = 1\n for key in header_header_list:\n worksheet.write(row, col, key, column_header_style)\n row+=1\n worksheet.write(row, col, header_data[key], body_header_style)\n #if key == 'Filter By:' and header_data[key] in ['Filtered by date', 'Filtered by period']:\n # per_row = row+1\n # for per in period:\n # worksheet.write(per_row, col, per, body_header_style)\n # per_row+=1\n row-=1\n col+=1\n # sending row cursor after 3 new rows\n row +=6\n col = 1\n\n body_header_list = [\"DATE\", \"JRNL\", \"Partner\", \"Ref\", \"Move\", \"Entry Label\", \"Debit\", \"Credit\", \"Balance\"]\n for header in body_header_list:\n worksheet.write(row, col, header, column_header_style)\n col+=1\n\n row+=1\n col=1\n\n tot_currency = 0.0\n company_name = self.company_id.name\n\n for i in range(1,15):\n column = worksheet.col(i)\n column.width = 225 * 30\n body_body_list = ['ldate', 'lcode', 'partner_name', 'lref', 'move_name', 'lname', 'debit', 'credit', 'balance']\n\n for account in accounts_res:\n\n col = 1\n row+=1\n worksheet.write(row, col, account['code'], body_header_style)\n col+=1\n worksheet.write(row, col, account['name'], body_header_style)\n col+=5\n worksheet.write(row, col, formatLang(self.env, account['debit'], currency_obj=current_obj.company_id.currency_id), body_header_style)\n col+=1\n worksheet.write(row, col, formatLang(self.env, account['credit'], currency_obj=current_obj.company_id.currency_id), body_header_style)\n col+=1\n worksheet.write(row, col, formatLang(self.env, account['balance'], currency_obj=current_obj.company_id.currency_id), body_header_style)\n\n for line in account['move_lines']:\n col =1\n row+=1\n for item in body_body_list:\n if item == 'debit':\n line[item] = formatLang(self.env, line[item], currency_obj=current_obj.company_id.currency_id)\n elif item == 'credit':\n line[item] = formatLang(self.env, line[item], currency_obj=current_obj.company_id.currency_id)\n elif item == 'balance':\n line[item] = formatLang(self.env, line[item], currency_obj=current_obj.company_id.currency_id)\n\n worksheet.write(row, col, line[item], body_header_style)\n\n col += 1\n wb.save(fp)\n out = base64.encodestring(fp.getvalue())\n final_arr_data = {}\n final_arr_data['file_stream'] = out\n final_arr_data['name'] = filename\n\n create_id = self.env['account.report.view'].create(final_arr_data)\n return {\n 'nodestroy': True,\n 'res_id': create_id.id,\n 'name': filename,\n 'view_type': 'form',\n 'view_mode': 'form',\n 'res_model': 'account.report.view',\n 'view_id': False,\n 'type': 'ir.actions.act_window',\n }\n", "sub_path": "accounting_xls_reports/report/account_report_general_ledgerxls.py", "file_name": "account_report_general_ledgerxls.py", "file_ext": "py", "file_size_in_byte": 8904, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "odoo.models.TransientModel", "line_number": 15, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 15, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 19, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 19, "usage_type": "name"}, {"api_name": "odoo.exceptions.UserError", "line_number": 43, "usage_type": "call"}, {"api_name": "odoo.tools.translate._", "line_number": 43, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 51, "usage_type": "call"}, {"api_name": "xlwt.Workbook", "line_number": 52, "usage_type": "call"}, {"api_name": "xlwt.XFStyle", "line_number": 54, "usage_type": "call"}, {"api_name": "xlwt.Font", "line_number": 55, "usage_type": "call"}, {"api_name": "xlwt.Pattern", "line_number": 56, "usage_type": "call"}, {"api_name": "xlwt.Pattern", "line_number": 57, "usage_type": "attribute"}, {"api_name": "xlwt.Style", "line_number": 59, "usage_type": "attribute"}, {"api_name": "xlwt.Style", "line_number": 64, "usage_type": "attribute"}, {"api_name": "xlwt.XFStyle", "line_number": 72, "usage_type": "call"}, {"api_name": "xlwt.Font", "line_number": 73, "usage_type": "call"}, {"api_name": "xlwt.Pattern", "line_number": 74, "usage_type": "call"}, {"api_name": "xlwt.Pattern", "line_number": 75, "usage_type": "attribute"}, {"api_name": "xlwt.Style", "line_number": 77, "usage_type": "attribute"}, {"api_name": "xlwt.Style", "line_number": 82, "usage_type": "attribute"}, {"api_name": "xlwt.XFStyle", "line_number": 90, "usage_type": "call"}, {"api_name": "xlwt.Font", "line_number": 91, "usage_type": "call"}, {"api_name": "xlwt.Pattern", "line_number": 92, "usage_type": "call"}, {"api_name": "xlwt.Pattern", "line_number": 93, "usage_type": "attribute"}, {"api_name": "xlwt.Style", "line_number": 95, "usage_type": "attribute"}, {"api_name": "xlwt.Style", "line_number": 100, "usage_type": "attribute"}, {"api_name": "odoo.tools.misc.formatLang", "line_number": 165, "usage_type": "call"}, {"api_name": "odoo.tools.misc.formatLang", "line_number": 167, "usage_type": "call"}, {"api_name": "odoo.tools.misc.formatLang", "line_number": 169, "usage_type": "call"}, {"api_name": "odoo.tools.misc.formatLang", "line_number": 176, "usage_type": "call"}, {"api_name": "odoo.tools.misc.formatLang", "line_number": 178, "usage_type": "call"}, {"api_name": "odoo.tools.misc.formatLang", "line_number": 180, "usage_type": "call"}, {"api_name": "base64.encodestring", "line_number": 186, "usage_type": "call"}, {"api_name": "odoo.api.multi", "line_number": 21, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "221489148", "text": "import cv2 as cv\n\ncascadeLoc = 'cascades/data/haarcascade_frontalface_default.xml'\nface_cascade = cv.CascadeClassifier(cascadeLoc)\n\n\ndef detect_faces(color_img, only_largest):\n \"\"\"\n takes in a color image read in using cv2 and returns a list of cropped\n color images of the detected faces. If only_largest is true, then only\n the largest detected face will be returned.\n \"\"\"\n\n gray = cv.cvtColor(color_img, cv.COLOR_BGR2GRAY)\n faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=2)\n cropped_list = []\n\n i = -1\n largest_index = -1\n largest_size = -1\n\n for (x, y, w, h) in faces:\n i += 1\n size = w * h\n if size > largest_size:\n largest_index = i\n largest_size = size\n\n cropped_color = color_img[y:y+h, x:x+w]\n cropped_list.append(cropped_color)\n\n if only_largest and largest_size > -1:\n cropped_list = [cropped_list[largest_index]]\n\n return cropped_list\n", "sub_path": "detect.py", "file_name": "detect.py", "file_ext": "py", "file_size_in_byte": 980, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "cv2.CascadeClassifier", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 14, "usage_type": "attribute"}]} +{"seq_id": "197227902", "text": "# Original version made by @xdavidhu (github.com/xdavidhu, https://xdavidhu.me/)\n# Modified to work on windows by L0laapk3 (github.com/L0laapk3)\n\nimport serial\nimport io\nimport os\nimport subprocess\nimport signal\nimport time\nimport win32pipe, win32file\n\nimport sys\n\nimport ctypes\n\n\ndef is_admin():\n try:\n return ctypes.windll.shell32.IsUserAnAdmin()\n except:\n return False\n\nif not is_admin():\n ctypes.windll.shell32.ShellExecuteW(None, u\"runas\", unicode(sys.executable), unicode(__file__), None, 1)\n exit(0)\n\ntry:\n serialportInput = raw_input(\"[?] Select a serial port (default COM5'): \")\n \n if serialportInput == \"\":\n serialport = \"COM5\"\n else:\n serialport = serialportInput\nexcept KeyboardInterrupt:\n print(\"\\n[+] Exiting...\")\n exit()\n\ntry:\n canBreak = False\n while not canBreak:\n boardRateInput = raw_input(\"[?] Select a baudrate (default '921600'): \")\n if boardRateInput == \"\":\n boardRate = 921600\n canBreak = True\n else:\n try:\n boardRate = int(boardRateInput)\n except KeyboardInterrupt:\n print(\"\\n[+] Exiting...\")\n exit()\n except Exception as e:\n print(\"[!] Please enter a number!\")\n continue\n canBreak = True\nexcept KeyboardInterrupt:\n print(\"\\n[+] Exiting...\")\n exit()\n \ntry:\n wiresharkCmdInput = raw_input(\"[?] Select wireshark location (default 'D:\\Program Files\\Wireshark\\Wireshark.exe'): \")\n if wiresharkCmdInput == \"\":\n wiresharkCmd = ['D:\\Program Files\\Wireshark\\Wireshark.exe', r'-i\\\\.\\pipe\\wireshark','-k']\n else:\n wiresharkCmd = [wiresharkCmdInput, r'-i\\\\.\\pipe\\wireshark','-k']\nexcept KeyboardInterrupt:\n print(\"\\n[+] Exiting...\")\n exit()\n\ncanBreak = False\nwhile not canBreak:\n try:\n ser = serial.Serial(serialport, boardRate)\n canBreak = True\n except KeyboardInterrupt:\n print(\"\\n[+] Exiting...\")\n exit()\n except:\n print(\"[!] Serial connection failed... Retrying...\")\n time.sleep(2)\n continue\n\nprint(\"[+] Serial connected. Name: \" + ser.name)\n\nprint(\"[?] Waiting for ESP boot..\")\n\n\ncounter = 0\n\ncheck = 0\nwhile check == 0:\n line = ser.readline()\n if b\"<>\" in line:\n check = 1\n print(\"[+] Stream started...\")\n #else: print '\"'+line+'\"'\n\nprint(\"[+] Starting up wireshark...\")\nproc=subprocess.Popen(wiresharkCmd)\n\npipe = win32pipe.CreateNamedPipe(\n r'\\\\.\\pipe\\wireshark',\n win32pipe.PIPE_ACCESS_OUTBOUND,\n win32pipe.PIPE_TYPE_MESSAGE | win32pipe.PIPE_WAIT,\n 1, 65536, 65536,\n 300,\n None)\nwin32pipe.ConnectNamedPipe(pipe, None)\n\n\n\ntry:\n while True:\n \n data = ser.read()\n win32file.WriteFile(pipe, data)\nexcept KeyboardInterrupt:\n print(\"[+] Stopping...\")\n subprocess.call(['taskkill', '/F', '/T', '/PID', str(proc.pid)])\nexcept:\n print(\"[+] Stopping...\")\n\n\nser.close()\nprint(\"[+] Done.\")\n", "sub_path": "extras/SerialSharkWindows.py", "file_name": "SerialSharkWindows.py", "file_ext": "py", "file_size_in_byte": 3004, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "ctypes.windll.shell32.IsUserAnAdmin", "line_number": 19, "usage_type": "call"}, {"api_name": "ctypes.windll", "line_number": 19, "usage_type": "attribute"}, {"api_name": "ctypes.windll.shell32.ShellExecuteW", "line_number": 24, "usage_type": "call"}, {"api_name": "ctypes.windll", "line_number": 24, "usage_type": "attribute"}, {"api_name": "sys.executable", "line_number": 24, "usage_type": "attribute"}, {"api_name": "serial.Serial", "line_number": 72, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 79, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 98, "usage_type": "call"}, {"api_name": "win32pipe.CreateNamedPipe", "line_number": 100, "usage_type": "call"}, {"api_name": "win32pipe.PIPE_ACCESS_OUTBOUND", "line_number": 102, "usage_type": "attribute"}, {"api_name": "win32pipe.PIPE_TYPE_MESSAGE", "line_number": 103, "usage_type": "attribute"}, {"api_name": "win32pipe.PIPE_WAIT", "line_number": 103, "usage_type": "attribute"}, {"api_name": "win32pipe.ConnectNamedPipe", "line_number": 107, "usage_type": "call"}, {"api_name": "win32file.WriteFile", "line_number": 115, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 118, "usage_type": "call"}]} +{"seq_id": "408090316", "text": "#!/usr/bin/env python2\n\n\"\"\" \nReducer\n\"\"\"\n\nimport sys\nimport zipimport\n\n\ntry:\n importer = zipimport.zipimporter('nltk.mod')\n nltk = importer.load_module(\"nltk\")\n nltk.data.path += [\"./nltkData/\"]\nexcept zipimport.ZipImportError:\n import nltk\n\nsys.path.append(\"..\")\nimport TextUtils as tu\n\nfrom nltk.tokenize.punkt import PunktSentenceTokenizer, PunktParameters\nfrom nltk.tokenize import sent_tokenize\n\nfrom cPickle import load\n\n# punkt sentence tokenizer setup\npunkt_param = PunktParameters()\npunkt_param.abbrev_types = set(['dr', 'vs', 'mr', 'mrs', 'prof', 'inc'])\nsentence_splitter = PunktSentenceTokenizer(punkt_param)\n\n# load trained POS tagger from disk\ninput = open(\"t2.pkl\", \"rb\")\ntagger = load(input)\ninput.close()\n\ncurrent_word = None\ncurrent_count = 0\nlength = None\n\nwords = dict()\n\nimport re\ndef normalize_pos(complex):\n # nouns start with A, N, DT, F, P, WP\n #if re.match(\"^([ANFP]|WP|DT)\", complex):\n # return \"NOUN\"\n \n\n return complex\n # verbs: B, D\n\n# treat each line as a filename\nfor line in sys.stdin:\n # read file and split into sentences\n num, filename = line.split('\\t')\n file = open(filename.strip())\n raw = file.read()\n file.close()\n raw = raw.decode('utf8').lower()\n sents = sent_tokenize(raw)\n\n # pos-tag each word in the sentence\n for sent in sents:\n text = nltk.word_tokenize(sent)\n text = tu.filter_non_alpha_words(text)\n tagged_sent = tagger.tag(text)\n\n # then add it to our table of words and counts\n for tag in tagged_sent:\n pos = normalize_pos(tag[1])\n key = \"{0}\\{1}\".format(tag[0].encode('utf8'), pos)\n\n #words[key] = words.setdefault(key, default=0) + 1\n if key in words:\n words[key] += 1\n else:\n words[key] = 1\n\nfrom operator import itemgetter\nsorted_words = sorted(words.items(), key=itemgetter(1), reverse=True)\nfor w in sorted_words:\n print(\"{0}\\t{1}\".format(*w))\n", "sub_path": "U3/reducer.py", "file_name": "reducer.py", "file_ext": "py", "file_size_in_byte": 1986, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "zipimport.zipimporter", "line_number": 12, "usage_type": "call"}, {"api_name": "zipimport.ZipImportError", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "nltk.tokenize.punkt.PunktParameters", "line_number": 27, "usage_type": "call"}, {"api_name": "nltk.tokenize.punkt.PunktSentenceTokenizer", "line_number": 29, "usage_type": "call"}, {"api_name": "cPickle.load", "line_number": 33, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 53, "usage_type": "attribute"}, {"api_name": "nltk.tokenize.sent_tokenize", "line_number": 60, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 64, "usage_type": "call"}, {"api_name": "TextUtils.filter_non_alpha_words", "line_number": 65, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 80, "usage_type": "call"}]} +{"seq_id": "230441201", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\ndef rewrite_client_str_to_client_model(apps, schema_editor):\n Client = apps.get_model(\"entries\", \"Client\")\n Project = apps.get_model(\"entries\", \"Project\")\n \n # For each project, get the text version of the client name, and\n # see if there's already a corresponding Client. If not, create it.\n # Then set the client property to be the new or existing Client.\n for project in Project.objects.all():\n c, created = Client.objects.get_or_create(name=project.client_as_str)\n project.client = c\n project.save()\n\ndef rewrite_client_model_to_client_str(apps, schema_editor):\n Client = apps.get_model(\"entries\", \"Client\")\n Project = apps.get_model(\"entries\", \"Project\")\n \n # For each project, set the text version of the client name to\n # the name from the Client instance.\n for project in Project.objects.all():\n project.client_as_str = project.client.name\n # Must have this or delete of clients cascades to products\n project.client = None\n project.save()\n\n # Now delete all clients. \n Client.objects.all().delete()\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('entries', '0002_auto_20150723_0819'),\n ]\n\n # Switch from a charfield-based client, to a model-based one.\n operations = [\n migrations.AlterField(\n model_name='project',\n name='client',\n field=models.CharField(max_length=200, null=True),\n ),\n # Move the existing client field to the side\n migrations.RenameField(\n model_name='project',\n old_name='client',\n new_name='client_as_str',\n ),\n # Create the client model\n migrations.CreateModel(\n name='Client',\n fields=[\n ('id', models.AutoField(serialize=False, primary_key=True, auto_created=True, verbose_name='ID')),\n ('name', models.CharField(max_length=200)),\n ],\n ),\n # Create a client field in the project. Note it allows null, as\n # a way of avoiding to specify what the default value is.\n migrations.AddField(\n model_name='project',\n name='client',\n field=models.ForeignKey(null=True, to='entries.Client'),\n ),\n # Do the conversion from text to model instance.\n migrations.RunPython(\n rewrite_client_str_to_client_model,\n rewrite_client_model_to_client_str,\n ),\n # Now we can disallow null.\n migrations.AlterField(\n model_name='project',\n name='client',\n field=models.ForeignKey(to='entries.Client'),\n ),\n # And remove the text version of the client\n migrations.RemoveField(\n model_name='project',\n name='client_as_str',\n ),\n ]\n", "sub_path": "timetracker/entries/migrations/0003_move_project_client_to_model.py", "file_name": "0003_move_project_client_to_model.py", "file_ext": "py", "file_size_in_byte": 2963, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 33, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 41, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 44, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 44, "usage_type": "name"}, {"api_name": "django.db.migrations.RenameField", "line_number": 47, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 47, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 53, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 53, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 56, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 56, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 57, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 57, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 62, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 62, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 65, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 65, "usage_type": "name"}, {"api_name": "django.db.migrations.RunPython", "line_number": 68, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 68, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 73, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 73, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 76, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 76, "usage_type": "name"}, {"api_name": "django.db.migrations.RemoveField", "line_number": 79, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 79, "usage_type": "name"}]} +{"seq_id": "342121606", "text": "import raop.helper as helper\nimport raop.pipeline as pipeline\nfrom sklearn.naive_bayes import GaussianNB\n\nkeysInFileName = \"resources/train.json\"\nkeysOutFileName = \"output.json\"\ninFileName = \"output.json\"\noutFileName = \"output2.json\"\n\n\npipeline.removeNonNeededKeys(keysInFileName,keysOutFileName)\npipeline.addPreprocessedKeyVals(inFileName,outFileName)\n\nactualX, actualY = pipeline.getFeatures(outFileName)\n\ngnb = GaussianNB()\ny_pred = gnb.fit(actualX, actualY).predict(actualX)\n", "sub_path": "sandbox/sklearnExperiment.py", "file_name": "sklearnExperiment.py", "file_ext": "py", "file_size_in_byte": 479, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "raop.pipeline.removeNonNeededKeys", "line_number": 11, "usage_type": "call"}, {"api_name": "raop.pipeline", "line_number": 11, "usage_type": "name"}, {"api_name": "raop.pipeline.addPreprocessedKeyVals", "line_number": 12, "usage_type": "call"}, {"api_name": "raop.pipeline", "line_number": 12, "usage_type": "name"}, {"api_name": "raop.pipeline.getFeatures", "line_number": 14, "usage_type": "call"}, {"api_name": "raop.pipeline", "line_number": 14, "usage_type": "name"}, {"api_name": "sklearn.naive_bayes.GaussianNB", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "506844676", "text": "from django.db import models\nfrom django.utils.translation import gettext_lazy as _\n\nfrom apps.core.models.mixins import TimestampMixin\n\n\nclass Video(TimestampMixin, models.Model):\n title = models.CharField(\n max_length=255,\n verbose_name=_('VN__TITLE'),\n help_text=_('HT__TITLE'),\n )\n source_id = models.CharField(\n max_length=255,\n verbose_name=_('VN__SOURCE_ID'),\n help_text=_('HT__SOURCE_ID'),\n )\n url = models.URLField(\n verbose_name=_('VN__URL'),\n help_text=_('HT__URL'),\n )\n application = models.ForeignKey(\n 'application.Application',\n models.CASCADE,\n related_name='videos',\n )\n word = models.ForeignKey(\n 'accumulator.Word',\n models.CASCADE,\n related_name='videos',\n )\n\n class Meta:\n verbose_name = _('VN__VIDEO')\n verbose_name_plural = _('VN__VIDEOS')\n\n def __str__(self):\n return self.title\n", "sub_path": "server/apps/accumulator/models/video.py", "file_name": "video.py", "file_ext": "py", "file_size_in_byte": 958, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "apps.core.models.mixins.TimestampMixin", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 10, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 15, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models.URLField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 19, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 34, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "89907456", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport matplotlib.pyplot as plt \nfrom torch.utils.data import sampler\nimport torch.utils.data as data_utils\nfrom torch.utils.data.sampler import SubsetRandomSampler\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data import TensorDataset\nfrom progress.bar import Bar\nfrom torchvision import datasets, transforms\nfrom torch.autograd import Variable\nfrom synthetic_data_generation import initialize_synthetic_sampler, sample_data_from_sampler\nfrom sup_functions import test_model, test_model_on_gen, weights_init\nfrom models import Net, autoencoder\n#from models import autoencoder2 as autoencoder\n\nimport os\n\nroot = '~/workspace/Projects/Journal_paper/'\ndataset = 'LSUN'\nprint('Loading data')\nopts = {\n 'batch_size': 1000,\n 'mode': 'multi-class',\n 'dataset': 'LSUN',\n 'test_every': 1,\n 'learning_rate': 0.001,\n 'number_of_epochs': 100,\n 'dim': 2048,\n 'nb_classes': 30,\n 'code_size': 32,\n 'betta': 0.2,\n 'add_noise': True,\n 'cuda_device': 0,\n }\n \ntorch.cuda.set_device(opts['cuda_device'])\ncode_size = opts['code_size']\nnb_classes = opts['nb_classes']\ntrainset, testset = {}, {}\n\ntrainset_ = torch.load(root + 'datasets/' + dataset + '_features/trainset.pth')\ntestset_ = torch.load(root + 'datasets/' + dataset + '_features/testset.pth')\ntrainset = data_utils.TensorDataset(trainset_[0], trainset_[1])\ntestset = data_utils.TensorDataset(testset_[0], testset_[1])\n\ntrain_loader = data_utils.DataLoader(trainset, batch_size=opts['batch_size'], shuffle = True)\ntest_loader = data_utils.DataLoader(testset, batch_size=opts['batch_size'], shuffle = False)\n\nautoencoder_model = autoencoder(code_size).cuda()\n#autoencoder_model.apply(weights_init)\nclassifier_model = torch.load(root+'batch_training/results/LSUN/models/LSUN_classifier_original.pth')\n\ncriterion_AE = nn.MSELoss().cuda()\ncriterion_classif = nn.MSELoss().cuda()\n#optimizer = torch.optim.SGD(autoencoder_model.parameters(), lr=opts['learning_rate'], momentum=0.99)\noptimizer_main = torch.optim.Adam(autoencoder_model.parameters(), lr=opts['learning_rate'], betas=(0.9, 0.999),\n weight_decay=1e-5)\n\naccuracies = []\nbest_acc = 0\nacc = test_model(classifier_model, test_loader)\nprint('Accuracy of pretrained model on the original testset: ' + str(acc))\nfor epoch in range(opts['number_of_epochs']):\n bar = Bar('Training: ', max=int(opts['nb_classes']*100000/opts['batch_size']))\n for idx, (train_X, train_Y) in enumerate(train_loader):\n bar.next()\n inputs = train_X.cuda()\n labels = train_Y.cuda()\n optimizer_main.zero_grad()\n #optimizer_class.zero_grad()\n #\n# img = Variable(inputs).cuda()\n # ===================forward=====================\n outputs = autoencoder_model(inputs)\n \n orig_classes = classifier_model(inputs)\n classification_reconstructed = classifier_model(outputs)\n \n loss_classif = criterion_classif(classification_reconstructed, orig_classes)\n loss_AE = criterion_AE(outputs, inputs)\n #\n #loss_classif.backward(retain_graph=True)\n #\n loss = opts['betta']*loss_classif + loss_AE\n # ===================backward====================\n loss.backward()\n #optimizer_class.step()\n optimizer_main.step()\n \n if idx%100==0:\n #plt.plot(range(2048), inputs[0].cpu().detach().numpy(), label='in')\n #plt.plot(range(2048), outputs[0].cpu().detach().numpy(), label='out')\n #plt.legend()\n #plt.savefig('imgs/epoch_'+str(epoch)+'_idx_'+str(idx)+'.png')\n #plt.close()\n print('epoch [{}/{}], loss:{:.4f}'\n .format(epoch+1, opts['number_of_epochs'], loss.item()))\n # ===================log========================\n bar.finish()\n print('epoch [{}/{}], loss:{:.4f}'\n .format(epoch+1, opts['number_of_epochs'], loss.item()))\n if epoch % opts['test_every'] == 0:\n autoencoder_model.eval()\n acc = test_model_on_gen(classifier_model, autoencoder_model, test_loader)\n accuracies.append(acc)\n torch.save(accuracies, 'results/representivity_LSUN_' + str(opts['code_size']) + '_code_size_' + str(opts['nb_classes']) +'_classes.pth')\n if acc>best_acc:\n best_acc=acc\n torch.save(autoencoder_model.state_dict(), 'models/LSUN_' +str(opts['code_size']) + '_code_size_' + str(opts['nb_classes']) +'_classes.pth')\n autoencoder_model.train()\n print('Accuracy on reconstructed testset: ' + str(acc))\n\n#torch.save(model.state_dict(), './conv_autoencoder_LSUN.pth')\n", "sub_path": "gen_model_training/LSUN/autoencoders_LSUN.py", "file_name": "autoencoders_LSUN.py", "file_ext": "py", "file_size_in_byte": 4499, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "torch.cuda.set_device", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 46, "usage_type": "name"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 50, "usage_type": "name"}, {"api_name": "models.autoencoder", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 59, "usage_type": "attribute"}, {"api_name": "sup_functions.test_model", "line_number": 64, "usage_type": "call"}, {"api_name": "progress.bar.Bar", "line_number": 67, "usage_type": "call"}, {"api_name": "sup_functions.test_model_on_gen", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 112, "usage_type": "call"}]} +{"seq_id": "249528386", "text": "import io\nfrom difflib import unified_diff\n\nfrom .exceptions import InvalidTargetFile\nfrom ..logging import get_logger\n\nfrom typing import TYPE_CHECKING\nfrom ..config import Configuration\nfrom ..versioning import Version\n\n\nlogger = get_logger()\n\n\nclass FileUpdater:\n def __init__(self, config: Configuration, current_version: Version, new_version: Version):\n self.config = config\n self.paths = config.files()\n self.current_version = current_version\n self.new_version = new_version\n self.context = {\n \"current_version\": current_version.serialize(),\n \"new_version\": new_version.serialize(),\n }\n\n def _validate(self):\n \"\"\"\n Checks that all files listed in the config have matching text to replace\n \"\"\"\n for path in self.paths:\n options = self.config.get_file_section(path)\n serialized_version = options[\"search\"].format(**self.context)\n if not self._contains(path):\n raise InvalidTargetFile(\n f\"Did not find '{self.current_version}' or '{serialized_version}' in file {path}\"\n )\n return True\n\n def _contains(self, path):\n try:\n with io.open(path, 'rb') as f:\n options = self.config.get_file_section(path)\n serialized_version = options[\"search\"].format(**self.context)\n search_lines = serialized_version.splitlines()\n lookbehind = []\n\n for lineno, line in enumerate(f.readlines()):\n lookbehind.append(line.decode('utf-8').rstrip(\"\\n\"))\n\n if len(lookbehind) > len(search_lines):\n lookbehind = lookbehind[1:]\n\n if (search_lines[0] in lookbehind[0] and\n search_lines[-1] in lookbehind[-1] and\n search_lines[1:-1] == lookbehind[1:-1]):\n logger.info(\"Found '{}' in {} at line {}: {}\".format(\n serialized_version, path, lineno - (len(lookbehind) - 1), line.decode('utf-8').rstrip()))\n return True\n return False\n except FileNotFoundError:\n raise InvalidTargetFile(f\"file listed in config not found: '{path}'\")\n\n def _replace(self, path, dry_run=False):\n with io.open(path, 'rb') as f:\n file_content_before = f.read().decode('utf-8')\n\n options = self.config.get_file_section(path)\n search_for = options[\"search\"].format(**self.context)\n replace_with = options[\"replace\"].format(**self.context)\n\n file_content_after = file_content_before.replace(search_for, replace_with)\n\n if file_content_before == file_content_after:\n # TODO expose this to be configurable\n file_content_after = file_content_before.replace(\n self.current_version.original,\n replace_with,\n )\n\n if file_content_before != file_content_after:\n logger.info(\"{} file {}:\".format(\n \"Would change\" if dry_run else \"Changing\",\n path,\n ))\n logger.info(\"\\n\".join(list(unified_diff(\n file_content_before.splitlines(),\n file_content_after.splitlines(),\n lineterm=\"\",\n fromfile=\"a/\"+path,\n tofile=\"b/\"+path\n ))))\n else:\n logger.info(\"{} file {}\".format(\n \"Would not change\" if dry_run else \"Not changing\",\n path,\n ))\n if not dry_run:\n with io.open(path, 'wb') as f:\n f.write(file_content_after.encode('utf-8'))\n\n def replace(self, dry_run=False):\n if self._validate():\n for path in self.paths:\n self._replace(path, dry_run)\n\n def __str__(self):\n return self.paths\n\n def __repr__(self):\n return ''.format(self.paths)\n", "sub_path": "bumpv/client/files/updater.py", "file_name": "updater.py", "file_ext": "py", "file_size_in_byte": 4018, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.get_logger", "line_number": 12, "usage_type": "call"}, {"api_name": "config.Configuration", "line_number": 16, "usage_type": "name"}, {"api_name": "versioning.Version", "line_number": 16, "usage_type": "name"}, {"api_name": "config.files", "line_number": 18, "usage_type": "call"}, {"api_name": "exceptions.InvalidTargetFile", "line_number": 34, "usage_type": "call"}, {"api_name": "io.open", "line_number": 41, "usage_type": "call"}, {"api_name": "exceptions.InvalidTargetFile", "line_number": 61, "usage_type": "call"}, {"api_name": "io.open", "line_number": 64, "usage_type": "call"}, {"api_name": "difflib.unified_diff", "line_number": 85, "usage_type": "call"}, {"api_name": "io.open", "line_number": 98, "usage_type": "call"}]} +{"seq_id": "359733985", "text": "# Author J'yrens Christenvie , Please acknowledge the author if you are using his code for your game\r\n# People acknowledged : Eric Matthes \r\n\r\n\r\n# 15 / 05 / 2020\r\n# This class controls the Alien that will be placed on the screen\r\n\r\n\r\nimport pygame\r\n\r\nfrom pygame.sprite import Sprite\r\n\r\nclass Alien(Sprite):\r\n \"\"\"A class to represent a single Alien in a fleet \"\"\"\r\n\r\n def __init__(self,ai_game):\r\n \"\"\"Initialize the alien and sets its starting position \"\"\"\r\n super().__init__()\r\n self.screen = ai_game.screen\r\n self.settings = ai_game.settings\r\n\r\n # Load the alien image and set its rect attribute \r\n self.image = pygame.image.load('images/first_alien.bmp')\r\n self.rect = self.image.get_rect()\r\n\r\n # start each new alien near the top left of the screen.\r\n self.rect.x = self.rect.width\r\n self.rect.y = self.rect.height\r\n\r\n # Store the Alien exact horizontal position\r\n self.x = float(self.rect.x)\r\n \r\n def check_edges(self):\r\n \"\"\"Return true if an alien is at the edge of the screen. \"\"\"\r\n screen_rect = self.screen.get_rect()\r\n\r\n if self.rect.right >= screen_rect.right or self.rect.left <= 0:\r\n return True\r\n\r\n def update(self):\r\n \"\"\"Move an alien to the right or left \"\"\"\r\n self.x += (self.settings.alien_speed * \r\n self.settings.fleet_direction)\r\n self.rect.x = self.x \r\n\r\n\r\n \r\n\r\n", "sub_path": "alien.py", "file_name": "alien.py", "file_ext": "py", "file_size_in_byte": 1466, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pygame.sprite.Sprite", "line_number": 13, "usage_type": "name"}, {"api_name": "pygame.image.load", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 23, "usage_type": "attribute"}]} +{"seq_id": "431088258", "text": "#!/usr/bin/env python\n\n\"\"\"\nThis script employs a VERY basic heuristic ('porn' in webpage.lower()) to check\nif we are not 'age_limit' tagging some porn site\n\"\"\"\n\n# Allow direct execution\nimport os\nimport sys\nsys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\n\nfrom test.helper import get_testcases\nfrom youtube_dl.utils import compat_urllib_request\n\nfor test in get_testcases():\n try:\n webpage = compat_urllib_request.urlopen(test['url'], timeout=10).read()\n except:\n print('\\nFail: {0}'.format(test['name']))\n continue\n\n webpage = webpage.decode('utf8', 'replace')\n\n if 'porn' in webpage.lower() and ('info_dict' not in test\n or 'age_limit' not in test['info_dict']\n or test['info_dict']['age_limit'] != 18):\n print('\\nPotential missing age_limit check: {0}'.format(test['name']))\n\n elif 'porn' not in webpage.lower() and ('info_dict' in test and\n 'age_limit' in test['info_dict'] and\n test['info_dict']['age_limit'] == 18):\n print('\\nPotential false negative: {0}'.format(test['name']))\n\n else:\n sys.stdout.write('.')\n sys.stdout.flush()\n\nprint()\n", "sub_path": "devscripts/check-porn.py", "file_name": "check-porn.py", "file_ext": "py", "file_size_in_byte": 1300, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sys.path.insert", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 11, "usage_type": "call"}, {"api_name": "test.helper", "line_number": 16, "usage_type": "name"}, {"api_name": "test.helper.get_testcases", "line_number": 16, "usage_type": "call"}, {"api_name": "youtube_dl.utils.compat_urllib_request.urlopen", "line_number": 18, "usage_type": "call"}, {"api_name": "youtube_dl.utils.compat_urllib_request", "line_number": 18, "usage_type": "name"}, {"api_name": "test.helper", "line_number": 18, "usage_type": "name"}, {"api_name": "test.helper", "line_number": 20, "usage_type": "name"}, {"api_name": "test.helper", "line_number": 25, "usage_type": "name"}, {"api_name": "test.helper", "line_number": 26, "usage_type": "name"}, {"api_name": "test.helper", "line_number": 27, "usage_type": "name"}, {"api_name": "test.helper", "line_number": 28, "usage_type": "name"}, {"api_name": "test.helper", "line_number": 30, "usage_type": "name"}, {"api_name": "test.helper", "line_number": 31, "usage_type": "name"}, {"api_name": "test.helper", "line_number": 32, "usage_type": "name"}, {"api_name": "test.helper", "line_number": 33, "usage_type": "name"}, {"api_name": "sys.stdout.write", "line_number": 36, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 36, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 37, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 37, "usage_type": "attribute"}]} +{"seq_id": "348898576", "text": "#5.3\n#In homework 5.3, we have to create a graph \"same\" as the one in the given pdf file\n#There are some problems by creating the exactly same graph, therefore line chart is allowed\n#the blue line in 5.3 is also allowed to show in line chart instead of smooth curve\n\nimport matplotlib.pyplot as plt\n\nCPU_burst = [6,6,4,6,4,13,13,13]\nguess_time = [10,8,6,6,5,9,11,12]\n\n#output figure of 5.3\n#from scipy.interpolate import spline\nx = range(len(guess_time))\ny = guess_time\nplt.figure(figsize=(6,8))\nplt.xlim(1,max(x)+1)\nplt.ylim(0,max(CPU_burst)+1)\nplt.plot(x,guess_time)\nplt.plot(x,CPU_burst)\nplt.legend(['guess (Taui)','CPU burst (ti)'], loc='upper left')\nplt.title('Prediction of the Length of the Next CPU Burst')\nplt.xlabel('time ->')\nplt.axes().set_aspect(0.4)\nplt.suptitle('wolfe', style='italic', y=-0.3, fontsize=12)\nplt.grid(True)\n#plt.xticks(0,8,1)\n#cell_text = ['CPU burst (ti)']\nTable = plt.table(cellText=[CPU_burst+[\"...\"],guess_time+[\"...\"]],rowLabels=['CPU burst (ti)','\"guess\"(Taui)'],loc=\"bottom\", bbox=[0.3, -0.5, 0.7, 0.3])\n\nplt.show()\n", "sub_path": "HW3_5.3.py", "file_name": "HW3_5.3.py", "file_ext": "py", "file_size_in_byte": 1054, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axes", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.table", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}]} +{"seq_id": "150839731", "text": "#!/usr/bin/python3\n\n'''\n If you're using this script from a certain directory for the very first time,\n make sure you choose 1 in main menu and create local database, holding City info.\n After that you may use any options as you intend.\n If you've already installed it, by executing install.py, then you're good to go.\n'''\n\ntry:\n from colorama import Fore, init as color_init\n from colorama.initialise import reset_all\n from os import environ\n from os.path import join\n from install import __is_init_setup_done__\n import re\n from sys import platform\n from subprocess import run\n from city_info import fetch as fetch_city\n from records import fetch_city_name_id, store_city_name_id\n from weather import fetch as fetch_weather\nexcept ImportError as e:\n print('[!Module Unavailable : {}'.format(str(e)))\n exit(1)\n\n\ndef __is_os_supported__():\n regex = re.compile(r'^(linux)$', flags=re.I)\n if(regex.match(platform)):\n return True\n return False\n\n\ndef __fetch_a_certain_city__(db_name):\n tmp = input('[?]Search by\\n\\t1. CityName ( finds all possible matches )\\n\\t2. CityID\\n>> ')\n resp = {}\n try:\n tmp = int(tmp)\n except ValueError as e:\n resp = {'error': str(e)}\n return resp\n if(tmp not in range(1, 3)):\n resp = {'error': 'bad input'}\n return resp\n if(tmp == 1):\n city_name = input('[?]Get me CityName >> ')\n if(not city_name):\n resp = {'error': 'bad input'}\n return resp\n resp = fetch_city_name_id(city_name=city_name, db_name=db_name)\n if(not resp or resp.get('status')):\n resp = {'error': 'found no record'}\n else:\n city_id = input('[?]Get me CityID >> ')\n if(not city_id):\n resp = {'error': 'bad input'}\n return resp\n resp = fetch_city_name_id(city_id=city_id, db_name=db_name)\n if(not resp or resp.get('status')):\n resp = {'error': 'found no record'}\n return resp\n\n\ndef __get_menu__():\n ch = input('[+]Main Menu:\\n\\t1. Fetch City Names\\n\\t2. Fetch a certain City\\n\\t3. Fetch Weather data of a City\\n[?]Choose one >> ')\n try:\n ch = int(ch)\n except ValueError as e:\n print('[!]Error : {}'.format(str(e)))\n ch = -1\n return ch\n if(ch not in range(1, 4)):\n print('[!]Bad input')\n ch = -1\n return ch\n\n\ndef app(db_name='imd_city_db'):\n run('clear')\n print('[+]City Weather ::\\n\\n***Choose 1 from below list for first time use***\\n')\n ch = __get_menu__()\n if(ch == -1):\n return\n if(ch == 1):\n resp = fetch_city()\n if(not resp.get('error')):\n print('[+]Status after storing record : {}'.format(store_city_name_id(resp, db_name=db_name)))\n print('\\n')\n for i, j in resp.items():\n print('\\t{}\\n'.format(i))\n for k in j:\n for l, m in k.items(): \n print('\\t\\t\\'{}\\' | {}'.format(l, m))\n print('\\n')\n else:\n print('[!]{} -> {}\\n'.format('Error', resp.get('error', ':/')))\n resp = fetch_city_name_id(db_name=db_name)\n for i, j in resp.items():\n print('\\t\\t{}\\t---\\t{}'.format(i, j))\n elif(ch == 2):\n resp = __fetch_a_certain_city__(db_name)\n print('\\n')\n for i, j in resp.items():\n print('\\t{}\\t---\\t{}'.format(i, j))\n print('\\n')\n else:\n resp = __fetch_a_certain_city__(db_name)\n print('\\n')\n if(resp.get('error')):\n print('{}\\n'.format(resp))\n else:\n if(len(resp.keys()) > 1):\n print('[+]Possible Matches found ...\\n')\n for i, j in enumerate(resp.keys()):\n print('\\t{} -> {}'.format(i+1, resp.get(j)))\n tmp = input('\\n[?]Choose one from above >> ')\n try:\n tmp = int(tmp)\n except ValueError as e:\n print('[!]Error : {}'.format(str(e)))\n return\n if(tmp not in range(1, len(resp.keys())+1)):\n print('[!]Bad input')\n return\n resp = {list(resp.keys())[tmp-1]: resp.get(list(resp.keys())[tmp-1])}\n else:\n print('[+]Match found :\\n\\t{}\\n'.format(resp))\n print('[+]Fetching data ...\\n')\n city_id = list(resp.keys())[0]\n weather = fetch_weather(city_id, db_name=db_name)\n if(weather.get('error')):\n print('{}\\n'.format(weather))\n return\n print('[+]Weather Data :\\n')\n pref_it = 'http://city.imd.gov.in/citywx/'\n color_init()\n for i, j in weather.get(city_id).items():\n if(i == 'past_24_hours_weather'):\n print('\\t{}{}{} :\\n'.format(Fore.GREEN, ' '.join([x.capitalize() for x in i.split('_')]), Fore.RESET))\n for k, l in j.items():\n if(k.startswith('Departure from Normal(oC)')):\n k = 'Departure from Normal(oC)'\n print('\\t\\t{:<90} --- {}{}{}'.format(k, Fore.RED, l, Fore.RESET))\n print('\\n')\n elif(i == '7_days_forecast'):\n print('\\t{}{}{} :\\n'.format(Fore.GREEN, ' '.join([x.capitalize() for x in i.split('_')]), Fore.RESET))\n for k in j:\n k[3] = Fore.MAGENTA+pref_it+k[3]+Fore.RESET\n print('\\t\\t{} | {} | {} | {}'.format(*k))\n print('\\n')\n else:\n print('\\t{}{}{}\\t---\\t{}\\n'.format(Fore.GREEN, ' '.join([x.capitalize() for x in i.split('_')]), Fore.RESET, Fore.MAGENTA+pref_it+j+Fore.RESET))\n reset_all()\n print('[+]End\\n')\n return\n\n\nif __name__ == '__main__':\n try:\n if(not __is_os_supported__()):\n print('[!]You need to be on Linux to run this program :)\\n')\n exit(0)\n if(__is_init_setup_done__()):\n app(db_name=join(environ.get('HOME'), '.imd_weather', 'imd_city_db'))\n else:\n app()\n except KeyboardInterrupt:\n print('\\n[!]Terminated')\n finally:\n exit(0)\n", "sub_path": "imd_weather_app.py", "file_name": "imd_weather_app.py", "file_ext": "py", "file_size_in_byte": 6327, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "re.compile", "line_number": 28, "usage_type": "call"}, {"api_name": "re.I", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 29, "usage_type": "argument"}, {"api_name": "records.fetch_city_name_id", "line_number": 50, "usage_type": "call"}, {"api_name": "records.fetch_city_name_id", "line_number": 58, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 79, "usage_type": "call"}, {"api_name": "city_info.fetch", "line_number": 85, "usage_type": "call"}, {"api_name": "records.store_city_name_id", "line_number": 87, "usage_type": "call"}, {"api_name": "records.fetch_city_name_id", "line_number": 97, "usage_type": "call"}, {"api_name": "weather.fetch", "line_number": 130, "usage_type": "call"}, {"api_name": "weather.get", "line_number": 131, "usage_type": "call"}, {"api_name": "colorama.init", "line_number": 136, "usage_type": "call"}, {"api_name": "weather.get", "line_number": 137, "usage_type": "call"}, {"api_name": "colorama.Fore.GREEN", "line_number": 139, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 139, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 139, "usage_type": "attribute"}, {"api_name": "colorama.Fore.RED", "line_number": 143, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 143, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 143, "usage_type": "attribute"}, {"api_name": "colorama.Fore.GREEN", "line_number": 146, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 146, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 146, "usage_type": "attribute"}, {"api_name": "colorama.Fore.MAGENTA", "line_number": 148, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 148, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 148, "usage_type": "attribute"}, {"api_name": "colorama.Fore.GREEN", "line_number": 152, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 152, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 152, "usage_type": "attribute"}, {"api_name": "colorama.Fore.MAGENTA", "line_number": 152, "usage_type": "attribute"}, {"api_name": "colorama.initialise.reset_all", "line_number": 153, "usage_type": "call"}, {"api_name": "install.__is_init_setup_done__", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 164, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 164, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 164, "usage_type": "name"}]} +{"seq_id": "538280846", "text": "#!/usr/bin/env python\n\nimport json\nimport logging\nimport re\nfrom numpy import asarray, rollaxis\n\nfrom pyclowder.utils import CheckMessage\nfrom pyclowder.datasets import download_metadata, get_info, upload_metadata\nfrom terrautils.extractors import TerrarefExtractor, is_latest_file, load_json_file, \\\n build_metadata, build_dataset_hierarchy\nfrom terrautils.betydb import add_arguments, get_sites, get_sites_by_latlon, submit_traits, \\\n get_site_boundaries\nfrom terrautils.geostreams import create_datapoint_with_dependencies\nfrom terrautils.gdal import clip_raster, centroid_from_geojson\nfrom terrautils.metadata import get_extractor_metadata, get_terraref_metadata\n\nimport canopyCover as ccCore\n\n\nlogging.basicConfig(format='%(asctime)s %(message)s')\n\ndef add_local_arguments(parser):\n # add any additional arguments to parser\n add_arguments(parser)\n\nclass CanopyCoverHeight(TerrarefExtractor):\n def __init__(self):\n super(CanopyCoverHeight, self).__init__()\n\n add_local_arguments(self.parser)\n\n # parse command line and load default logging configuration\n self.setup(sensor='stereoTop_canopyCover')\n\n # assign other argumentse\n self.bety_url = self.args.bety_url\n self.bety_key = self.args.bety_key\n\n def check_message(self, connector, host, secret_key, resource, parameters):\n if resource['name'].find('fullfield') > -1 and re.match(\"^.*\\d+_rgb_.*thumb.tif\", resource['name']):\n # Check metadata to verify we have what we need\n md = download_metadata(connector, host, secret_key, resource['parent']['id'])\n if get_extractor_metadata(md, self.extractor_info['name']) and not self.overwrite:\n logging.info(\"skipping dataset %s; metadata indicates it was already processed\" % resource['id'])\n return CheckMessage.ignore\n return CheckMessage.download\n\n return CheckMessage.ignore\n\n def process_message(self, connector, host, secret_key, resource, parameters):\n self.start_message()\n\n tmp_csv = \"canopycovertraits.csv\"\n csv_file = open(tmp_csv, 'w')\n (fields, traits) = ccCore.get_traits_table()\n csv_file.write(','.join(map(str, fields)) + '\\n')\n\n # Get full list of experiment plots using date as filter\n logging.info(connector)\n logging.info(host)\n logging.info(secret_key)\n logging.info(resource)\n ds_info = get_info(connector, host, secret_key, resource['parent']['id'])\n timestamp = ds_info['name'].split(\" - \")[1]\n all_plots = get_site_boundaries(timestamp, city='Maricopa')\n\n successful_plots = 0\n for plotname in all_plots:\n bounds = all_plots[plotname]\n\n # Use GeoJSON string to clip full field to this plot\n try:\n (pxarray, geotrans) = clip_raster(resource['local_paths'][0], bounds)\n if len(pxarray.shape) < 3:\n logging.error(\"unexpected array shape for %s (%s)\" % (plotname, pxarray.shape))\n continue\n ccVal = ccCore.gen_cc_for_img(rollaxis(pxarray,0,3), 5)\n ccVal *= 100.0 # Make 0-100 instead of 0-1\n successful_plots += 1\n if successful_plots % 10 == 0:\n logging.info(\"processed %s/%s plots successfully\" % (successful_plots, len(all_plots)))\n except:\n logging.error(\"error generating cc for %s\" % plotname)\n continue\n\n traits['canopy_cover'] = str(ccVal)\n traits['site'] = plotname\n traits['local_datetime'] = timestamp+\"T12:00:00\"\n trait_list = ccCore.generate_traits_list(traits)\n\n csv_file.write(','.join(map(str, trait_list)) + '\\n')\n\n # Prepare and submit datapoint\n centroid_lonlat = json.loads(centroid_from_geojson(bounds))[\"coordinates\"]\n time_fmt = timestamp+\"T12:00:00-07:00\"\n dpmetadata = {\n \"source\": host + (\"\" if host.endswith(\"/\") else \"/\") + \"files/\" + resource['id'],\n \"canopy_cover\": ccVal\n }\n create_datapoint_with_dependencies(connector, host, secret_key, \"Canopy Cover\",\n (centroid_lonlat[1], centroid_lonlat[0]), time_fmt, time_fmt,\n dpmetadata, timestamp)\n\n # submit CSV to BETY\n csv_file.close()\n submit_traits(tmp_csv, betykey=self.bety_key)\n\n # Add metadata to original dataset indicating this was run\n ext_meta = build_metadata(host, self.extractor_info, resource['parent']['id'], {\n \"plots_processed\": successful_plots,\n \"plots_skipped\": len(all_plots)-successful_plots,\n \"betydb_link\": \"https://terraref.ncsa.illinois.edu/bety/api/beta/variables?name=canopy_cover\"\n }, 'dataset')\n upload_metadata(connector, host, secret_key, resource['parent']['id'], ext_meta)\n\n self.end_message()\n\nif __name__ == \"__main__\":\n extractor = CanopyCoverHeight()\n extractor.start()\n", "sub_path": "canopycover/terra_canopycover.py", "file_name": "terra_canopycover.py", "file_ext": "py", "file_size_in_byte": 5131, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.basicConfig", "line_number": 21, "usage_type": "call"}, {"api_name": "terrautils.betydb.add_arguments", "line_number": 25, "usage_type": "call"}, {"api_name": "terrautils.extractors.TerrarefExtractor", "line_number": 27, "usage_type": "name"}, {"api_name": "re.match", "line_number": 41, "usage_type": "call"}, {"api_name": "pyclowder.datasets.download_metadata", "line_number": 43, "usage_type": "call"}, {"api_name": "terrautils.metadata.get_extractor_metadata", "line_number": 44, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 45, "usage_type": "call"}, {"api_name": "pyclowder.utils.CheckMessage.ignore", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pyclowder.utils.CheckMessage", "line_number": 46, "usage_type": "name"}, {"api_name": "pyclowder.utils.CheckMessage.download", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pyclowder.utils.CheckMessage", "line_number": 47, "usage_type": "name"}, {"api_name": "pyclowder.utils.CheckMessage.ignore", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pyclowder.utils.CheckMessage", "line_number": 49, "usage_type": "name"}, {"api_name": "canopyCover.get_traits_table", "line_number": 56, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 60, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 61, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 62, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 63, "usage_type": "call"}, {"api_name": "pyclowder.datasets.get_info", "line_number": 64, "usage_type": "call"}, {"api_name": "terrautils.betydb.get_site_boundaries", "line_number": 66, "usage_type": "call"}, {"api_name": "terrautils.gdal.clip_raster", "line_number": 74, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 76, "usage_type": "call"}, {"api_name": "canopyCover.gen_cc_for_img", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.rollaxis", "line_number": 78, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 82, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 84, "usage_type": "call"}, {"api_name": "canopyCover.generate_traits_list", "line_number": 90, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 95, "usage_type": "call"}, {"api_name": "terrautils.gdal.centroid_from_geojson", "line_number": 95, "usage_type": "call"}, {"api_name": "terrautils.geostreams.create_datapoint_with_dependencies", "line_number": 101, "usage_type": "call"}, {"api_name": "terrautils.betydb.submit_traits", "line_number": 107, "usage_type": "call"}, {"api_name": "terrautils.extractors.build_metadata", "line_number": 110, "usage_type": "call"}, {"api_name": "pyclowder.datasets.upload_metadata", "line_number": 115, "usage_type": "call"}]} +{"seq_id": "455031091", "text": "import torch\nfrom codes import mvtecad\nfrom functools import reduce\nfrom torch.utils.data import DataLoader\nfrom codes.datasets import *\nfrom codes.networks import *\nfrom codes.networks import multi_center_res_backbone_test\nfrom codes.inspection import eval_encoder_NN_multiK\nfrom codes.utils import *\nfrom datasets import Multi_Center_Dataset\nimport numpy as np\nimport cv2\nimport my_resnet\nfrom my_resnet import Bottleneck\nimport glob\ndef cnn_output_size(H, K, S=1, P=0) -> int:\n \"\"\"\n\n :param int H: input_size\n :param int K: filter_size\n :param int S: stride\n :param int P: padding\n :return:\n \"\"\"\n return 1 + (H - K + 2 * P) // S\n\ndef crop_CHW(image, i, j, K, S=1):\n if S == 1:\n h, w = i, j\n else:\n h = S * i\n w = S * j\n return image[:, h: h + K, w: w + K]\n\n\n\n\nenc = my_resnet.ResNet(Bottleneck,[3, 4, 6, 3],1000).cuda()\n\nckpt = torch.load(\"./model/enc.pth\")\n\nenc.load_state_dict(ckpt)\n\n\n\n\ndef img_infer(img_name):\n a = np.zeros(20)\n img = cv2.imread(img_name)\n mean = np.mean(img)\n img = (img - mean) / 255\n img = cv2.resize(img,(1024,1024))\n # cv2.imshow(\"11\",img)\n # cv2.waitKey(0)\n row = cnn_output_size(1024,256,48)\n col = cnn_output_size(1024,256,48)\n img = np.transpose(img, [2, 0, 1])\n\n for i in range(row):\n for j in range(col):\n\n img_p = crop_CHW(img, i, j, 256, 48)\n aa = np.transpose(img_p, [1, 2, 0])\n in_tensor = torch.from_numpy(img_p.astype(np.float32)).contiguous()\n in_tensor = in_tensor.unsqueeze(0).cuda()\n out = enc(in_tensor)\n out, dis = multi_cls(out)\n\n print(out, \" \", dis)\n # out_f = torch.nn.functional.softmax(out_f, dim=1)\n if a[out[0]] < dis:\n a[out[0]] = dis\n print(a)\n # cv2.namedWindow(\"p\",0)\n # cv2.imshow(\"p\", aa)\n #\n #\n # cv2.waitKey(0)\n if dis >0.6 and out[0]==8:\n aa =cv2.resize(aa,(256,256))\n cv2.imshow(\"p\",aa)\n cv2.waitKey(0)\n # print(torch.argmax(out_f,dim=1))\n return a\n\n\n\nif __name__ == \"__main__\":\n D = 1000\n multi_cls = multi_center_res_backbone_test(D, 20).cuda()\n multi_cls.load_state_dict(torch.load(\"./model/mul.pth\"))\n enc.eval()\n multi_cls.eval()\n\n a = np.zeros(20)\n\n\n a = img_infer(\"./20210311175003974.jpg\")\n print(a)\n\n\n # [0.41824254 0. 0.38242683 1.041839 0. 1.16508436\n # 0.81420916 0.96830255 0. 0.45293742 1.25321531 0.90334588\n # 0. 0.03888164 0.73888123 0. 0. 0.\n # 0. 0.]\n\n #ok center_ dist", "sub_path": "res_backbone_test.py", "file_name": "res_backbone_test.py", "file_ext": "py", "file_size_in_byte": 2703, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "my_resnet.ResNet", "line_number": 38, "usage_type": "call"}, {"api_name": "my_resnet.Bottleneck", "line_number": 38, "usage_type": "argument"}, {"api_name": "torch.load", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 64, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 82, "usage_type": "call"}, {"api_name": "codes.networks.multi_center_res_backbone_test", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 95, "usage_type": "call"}]} +{"seq_id": "237905702", "text": "from keras.models import Sequential\nfrom keras.layers.convolutional import Conv2D, MaxPooling2D\nfrom keras.layers.core import Activation, Dense, Flatten\n\nclass Modle_phase_one:\n @staticmethod\n def build_model(width, height, depth, classes):\n #Initialize he model\n model = Sequential()\n input_shape = (height,width, depth)\n activ = 'relu'\n kernel_size = (5,5)\n\n #First Convolutional layer:\n model.add(Conv2D(30, kernel_size = kernel_size, padding='same', input_shape=input_shape))\n model.add(Activation(activation=activ))\n model.add(MaxPooling2D(pool_size=(2,2), strides= (2,2)))\n\n #Second Convolutional layer:\n model.add(Conv2D(50, kernel_size= kernel_size, padding='same'))\n model.add(Activation(activation=activ))\n model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n\n #Second Convolutional layer:\n # model.add(Conv2D(50, kernel_size= kernel_size, padding='same'))\n # model.add(Activation(activation=activ))\n # model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n\n #Flatten layer:\n model.add(Flatten())\n model.add(Dense(500))\n model.add(Activation(activation=activ))\n\n #Output layer:\n model.add(Dense(classes))\n model.add(Activation(activation='softmax'))\n\n return model\n\n#Modle_phase_one.build_model(96,96,1,2)", "sub_path": "Phase_one_model.py", "file_name": "Phase_one_model.py", "file_ext": "py", "file_size_in_byte": 1402, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "keras.models.Sequential", "line_number": 9, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 15, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 16, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling2D", "line_number": 17, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 20, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 21, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling2D", "line_number": 22, "usage_type": "call"}, {"api_name": "keras.layers.core.Flatten", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 32, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 35, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "365468646", "text": "#!/usr/bin/env python\n \nimport os\ntry:\n from setuptools import setup, find_packages\nexcept ImportError:\n from distutils.core import setup\n \n\nreadme = open('README.rst').read()\ndoclink = \"\"\"\nDocumentation\n-------------\nTO-DO. Meanwhile please rely on the docstrings and jupyter tutorial. \n\"\"\"\n \nPACKAGE_PATH = os.path.abspath(os.path.join(__file__, os.pardir))\n\nwith open(os.path.join(PACKAGE_PATH, 'VERSION')) as version_file:\n __version__ = version_file.read().strip()\n \nsetup(\n name='vip',\n version=__version__,\n description='Package for astronomical high-contrast image processing and exoplanet detection.',\n long_description=readme + '\\n\\n' + doclink + '\\n\\n',\n author='Carlos Gomez',\n author_email='cgomez@ulg.ac.be',\n url='https://github.com/vortex-exoplanet/VIP', \n packages=find_packages(),\n include_package_data=True,\n install_requires=['cython',\n 'numpy >= 1.8',\n 'scipy >= 0.17',\n 'ipython >= 3.2',\n 'jupyter',\n 'astropy >= 1.0.2',\n 'emcee >= 2.1',\n 'corner >= 1.0.2',\n 'pandas >= 0.18',\n 'matplotlib >= 1.4.3',\n 'scikit-learn >= 0.17',\n 'scikit-image >= 0.11',\n 'psutil',\n 'pytest',\n 'photutils >= 0.1',\n 'image_registration',\n 'FITS_tools',\n 'pywavelets',\n 'pyprind'],\n zip_safe=False,\n classifiers=['Development Status :: 4 - Beta',\n 'Intended Audience :: Science/Research',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: MIT License',\n 'Operating System :: MacOS :: MacOS X',\n 'Operating System :: POSIX :: Linux',\n 'Natural Language :: English',\n 'Programming Language :: Python :: 2.7',\n 'Topic :: Scientific/Engineering :: Astronomy'\n ] \n)", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 2161, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "os.path.abspath", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.pardir", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "distutils.core.setup", "line_number": 22, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "456654754", "text": "import subprocess\nimport os\n\nimport ujson\n\nfrom flask import Flask, render_template, request\n\nfrom sqlalchemy import not_\n\nfrom Database import Session, Photo\n\n\napp = Flask(__name__)\n\napp.debug = True\n\n\n@app.route('/')\ndef index():\n session = Session()\n s = '

'+'
'.join(str(_) for _ in session.query(Photo))+'

'\n session.close()\n return s\n\n\n@app.route('/test')\ndef test_gallery():\n session = Session()\n photos = [p for p in session.query(Photo).order_by(Photo.time.desc())]\n output = render_template('GalleryTemplate.html', photos=[p.filename for p in photos], indices=[p.id for p in photos])\n session.close()\n return output\n\n\n@app.route('/get_updates')\ndef fetch(*args, **kwargs):\n known_indices = ujson.loads(request.query_string)\n session = Session()\n new_photos = session.query(Photo).filter(not_(Photo.id.in_(known_indices))).order_by(Photo.time).all()\n return ujson.dumps({'ids': [p.id for p in new_photos],\n 'filenames': [p.filename for p in new_photos],\n })\n\n@app.route('/get_comments')\ndef get_comments():\n session = Session()\n image_name = request.query_string.decode('UTF-8')\n print(image_name)\n image = session.query(Photo).filter_by(filename=image_name).first()\n comments = image.comments\n session.close()\n return comments\n\n\n@app.route('/update_comments', methods=['PUT'])\ndef update_comments():\n image_name = os.path.basename(request.form.get('imagename'))\n comments = request.form.get('comments')\n print(image_name, comments)\n session = Session()\n image = session.query(Photo).filter_by(filename=image_name).first()\n image.comments = comments\n session.commit()\n session.close()\n return \"Updated\"\n\n\n@app.route('/print')\ndef print_command(*args, **kwargs):\n filename = os.path.basename(request.query_string[3:-3]).decode('UTF-8')\n print(\"called print_command with {}\".format(filename))\n if filename in os.listdir('/Users/jonathan/Pictures/PhotoBooth'):\n print_photo('/Users/jonathan/Pictures/PhotoBooth/{}'.format(filename))\n return \"Good!\"\n\ndef print_photo(image_name):\n subprocess.call(['lpr', '-P', 'EPSON_PictureMate_PM_225', '{}'.format(image_name)])\n\nif __name__ == '__main__':\n app.run()\n\n", "sub_path": "BaseServer.py", "file_name": "BaseServer.py", "file_ext": "py", "file_size_in_byte": 2280, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask.Flask", "line_number": 13, "usage_type": "call"}, {"api_name": "Database.Session", "line_number": 20, "usage_type": "call"}, {"api_name": "Database.Photo", "line_number": 21, "usage_type": "argument"}, {"api_name": "Database.Session", "line_number": 28, "usage_type": "call"}, {"api_name": "Database.Photo", "line_number": 29, "usage_type": "argument"}, {"api_name": "Database.Photo.time.desc", "line_number": 29, "usage_type": "call"}, {"api_name": "Database.Photo.time", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 30, "usage_type": "call"}, {"api_name": "ujson.loads", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.request.query_string", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 37, "usage_type": "name"}, {"api_name": "Database.Session", "line_number": 38, "usage_type": "call"}, {"api_name": "Database.Photo", "line_number": 39, "usage_type": "argument"}, {"api_name": "sqlalchemy.not_", "line_number": 39, "usage_type": "call"}, {"api_name": "Database.Photo.id.in_", "line_number": 39, "usage_type": "call"}, {"api_name": "Database.Photo.id", "line_number": 39, "usage_type": "attribute"}, {"api_name": "Database.Photo.time", "line_number": 39, "usage_type": "attribute"}, {"api_name": "ujson.dumps", "line_number": 40, "usage_type": "call"}, {"api_name": "Database.Session", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.request.query_string.decode", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.request.query_string", "line_number": 47, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 47, "usage_type": "name"}, {"api_name": "Database.Photo", "line_number": 49, "usage_type": "argument"}, {"api_name": "os.path.basename", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "flask.request.form.get", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 57, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 58, "usage_type": "name"}, {"api_name": "Database.Session", "line_number": 60, "usage_type": "call"}, {"api_name": "Database.Photo", "line_number": 61, "usage_type": "argument"}, {"api_name": "os.path.basename", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "flask.request.query_string", "line_number": 70, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 70, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 72, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "471411372", "text": "#!/usr/bin/env python3\nimport subprocess\nimport sys\nimport signal\nimport sys\nimport os, shutil\nimport xml.etree.ElementTree as ET\n\nimport time\nimport requests\nfrom xml.etree.ElementTree import Comment\n\nfrom requests import ConnectionError\nfrom selenium.webdriver.common.by import By\n\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\n\nfrom smoketest.mylib.utils import Utils\n\n\ndef ensure_path_exists(path):\n import errno\n try:\n os.makedirs(path)\n except OSError as exception:\n if exception.errno != errno.EEXIST:\n raise\n\n\nlogs_dir = os.path.join(Utils.log_dir(), 'logs')\nsub_path = requests.get('http://localhost:3000/next').content.decode('utf-8');\npath_to_dir = os.path.join(os.getcwd(), 'logs', *sub_path.split('/'))\nensure_path_exists(path_to_dir)\n# except ConnectionError:\n# print \"Need to start webserver. Run ./startup.sh from smoketest dir\", os.getcwd()\n\n\ndef signal_handler(sig, frame):\n print('You pressed Ctrl+C!')\n [p.kill() for p in opens]\n sys.exit(0)\n\n\nsignal.signal(signal.SIGINT, signal_handler)\n\nopens = []\nroot = ET.Element(\"ipAddresses\")\nroot.append(Comment('Auto Generated in multi-run.py'))\n\nrun_dates_path = os.path.join(Utils.log_dir(), 'logs', \"runInfo.txt\")\n\n\ndef run_some(browser, start, end):\n from sys import platform as platform\n\n if platform == \"win32\":\n path_to_python = \"c:\\\\Python27\\\\python.exe\"\n elif platform == \"linux\":\n path_to_python = sys.executable\n else:\n path_to_python = \"/cygdrive/c/cygwin64/bin/python\"\n\n for i in range(start, end):\n time.sleep(2)\n opens.append(subprocess.Popen([path_to_python, \"./runAll.py\", sys.argv[i], browser, path_to_dir]))\n\n [p.wait() for p in opens]\n\nif not os.path.exists(run_dates_path):\n\n run_dates_file = open(run_dates_path, \"a+\")\n\n for date in Utils.get_dirs(logs_dir):\n for run in Utils.get_dirs(os.path.join(Utils.log_dir(), 'logs', date)):\n print('date', date, 'list', os.listdir(os.path.join(Utils.log_dir(), 'logs', date)))\n run_dates_file.write(date + '/' + run + '\\n')\n\nelse:\n run_dates_file = open(run_dates_path, \"a+\")\n run_dates_file.write(sub_path + '\\n')\n\nrun_dates_file.close()\n\nfor i in range(3, len(sys.argv)):\n # path = os.path.join(path_to_dir, Utils.format_ip_address(sys.argv[i]))\n path = os.path.normcase(os.path.join(\"logs\", sub_path, Utils.format_ip_address(sys.argv[i])))\n field = ET.SubElement(root, \"ipAddress\", location=path).text = sys.argv[i]\n\ntree = ET.ElementTree(root)\ntree.write(os.path.join(path_to_dir, 'ip-addresses.xml'))\n\nargStart = 1;\nbrowser = 'chrome'\n\nfor i in range (1, len(sys.argv)):\n if (sys.argv[i] == '-browser'):\n argStart = i + 2\n browser = sys.argv[i+1]\n\nstep = 3\nfor i in range(argStart, len(sys.argv), step):\n run_some(browser, i, min(i + step, len(sys.argv)))\n\nUtils.print_tree(path_to_dir)\n\nres = {}\n\n\ndef get_test_run_info(date):\n\n total_error_count = 0\n total_test_count = 0\n\n for run in Utils.get_dirs(os.path.join(logs_dir, date)):\n for ip_in_runs in Utils.get_dirs(os.path.join(logs_dir, date, run)):\n for xml in os.listdir(os.path.join(logs_dir, date, run, ip_in_runs)):\n if xml.startswith('testresult'):\n fname = os.path.join(logs_dir, date, run, ip_in_runs, xml)\n tree = ET.parse(fname)\n\n test_count = tree.find('totalTestCount').get('totalTestCount')\n error_count = tree.find('errorCount').get('errorCount')\n\n total_test_count += int(test_count)\n total_error_count += int(error_count)\n\n return {'date': date, 'total_test_count': total_test_count, 'total_error_count': total_error_count}\n\n\ndef make_test_summary_xml(date):\n\n summary_data = get_test_run_info(date)\n\n root_ele = ET.Element('results')\n root_ele.append(Comment('Auto Generated by make_test_summary_xml() multi-run.py'))\n\n summary_ele = ET.SubElement(root_ele, 'summary')\n summary_ele.set('date', summary_data['date'])\n summary_ele.set('totalTestCount', str(summary_data['total_test_count']))\n summary_ele.set('totalErrorCount', str(summary_data['total_error_count']))\n\n tree = ET.ElementTree(root_ele)\n\n path = os.path.join(logs_dir, date, 'testsummary.xml')\n tree.write(path)\n\n\nfor log_date in Utils.get_dirs(logs_dir):\n make_test_summary_xml(log_date)\n", "sub_path": "smoketest/multi-run.py", "file_name": "multi-run.py", "file_ext": "py", "file_size_in_byte": 4447, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sys.path.append", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 16, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 24, "usage_type": "call"}, {"api_name": "errno.EEXIST", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "smoketest.mylib.utils.Utils.log_dir", "line_number": 30, "usage_type": "call"}, {"api_name": "smoketest.mylib.utils.Utils", "line_number": 30, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 32, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 41, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 44, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 44, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 47, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 47, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Comment", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "smoketest.mylib.utils.Utils.log_dir", "line_number": 50, "usage_type": "call"}, {"api_name": "smoketest.mylib.utils.Utils", "line_number": 50, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 56, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 58, "usage_type": "name"}, {"api_name": "sys.executable", "line_number": 59, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 64, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 65, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "smoketest.mylib.utils.Utils.get_dirs", "line_number": 73, "usage_type": "call"}, {"api_name": "smoketest.mylib.utils.Utils", "line_number": 73, "usage_type": "name"}, {"api_name": "smoketest.mylib.utils.Utils.get_dirs", "line_number": 74, "usage_type": "call"}, {"api_name": "smoketest.mylib.utils.Utils", "line_number": 74, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "smoketest.mylib.utils.Utils.log_dir", "line_number": 74, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "smoketest.mylib.utils.Utils.log_dir", "line_number": 75, "usage_type": "call"}, {"api_name": "smoketest.mylib.utils.Utils", "line_number": 75, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.normcase", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "smoketest.mylib.utils.Utils.format_ip_address", "line_number": 86, "usage_type": "call"}, {"api_name": "smoketest.mylib.utils.Utils", "line_number": 86, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 86, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 87, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 87, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 87, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.ElementTree", "line_number": 89, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 89, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 95, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 96, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 98, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 101, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 102, "usage_type": "attribute"}, {"api_name": "smoketest.mylib.utils.Utils.print_tree", "line_number": 104, "usage_type": "call"}, {"api_name": "smoketest.mylib.utils.Utils", "line_number": 104, "usage_type": "name"}, {"api_name": "smoketest.mylib.utils.Utils.get_dirs", "line_number": 114, "usage_type": "call"}, {"api_name": "smoketest.mylib.utils.Utils", "line_number": 114, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "smoketest.mylib.utils.Utils.get_dirs", "line_number": 115, "usage_type": "call"}, {"api_name": "smoketest.mylib.utils.Utils", "line_number": 115, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 116, "usage_type": "name"}, {"api_name": "os.listdir", "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": "xml.etree.ElementTree.startswith", "line_number": 117, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 117, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 118, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 118, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 119, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 119, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 134, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 134, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Comment", "line_number": 135, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 137, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 137, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.ElementTree", "line_number": 142, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 142, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "smoketest.mylib.utils.Utils.get_dirs", "line_number": 148, "usage_type": "call"}, {"api_name": "smoketest.mylib.utils.Utils", "line_number": 148, "usage_type": "name"}]} +{"seq_id": "570601160", "text": "import time\n\nfrom selenium import webdriver\nimport pytest\n\n\nclass AmazonWebsiteTest:\n\n @pytest.fixture\n def setUp(self):\n self.driver = webdriver.Chrome(\"/Users/gaurnitai/Desktop/PySeBootcamp/drivers/chromedriver\")\n self.driver.get(\"https://www.amazon.in\")\n self.driver.maximize_window()\n time.sleep(4)\n yield\n time.sleep(3)\n self.driver.close()\n self.driver.quit()\n\n def test_website_title(self, setUp):\n # driver = webdriver.Chrome(\"/Users/gaurnitai/Desktop/PySeBootcamp/drivers/chromedriver\")\n # driver.get(\"https://www.amazon.in\")\n # driver.maximize_window()\n # time.sleep(4)\n self.driver.save_screenshot(\"/Users/gaurnitai/Desktop/dummyrepo/py-se-bootcamp/screenshots\" + \"homepage.png\")\n pageTitle = self.driver.title\n assert pageTitle == \"Online Shopping site in India: Shop Online for Mobiles, Books, Watches, Shoes and More - Amazon.in\"\n\n def test_search_functionality(self, setUp):\n # driver = webdriver.Chrome(\"/Users/gaurnitai/Desktop/PySeBootcamp/drivers/chromedriver\")\n # driver.get(\"https://www.amazon.in\")\n # driver.maximize_window()\n # time.sleep(4)\n self.driver.save_screenshot(\"/Users/gaurnitai/Desktop/dummyrepo/py-se-bootcamp/screenshots\" + \"homepage.png\")\n search_box = self.driver.find_element_by_id(\"twotabsearchtextbox\")\n search_box.send_keys(\"Macbook pro\")\n search_icon = self.driver.find_element_by_xpath(\"//input[@type='submit']\")\n search_icon.click()\n time.sleep(5)\n self.driver.save_screenshot(\n \"/Users/gaurnitai/Desktop/dummyrepo/py-se-bootcamp/screenshots\" + \"productlisting.png\")\n searched_product = self.driver.find_element_by_xpath(\"(//a[@class='a-link-normal a-text-normal'])[1]\")\n assert searched_product.is_displayed() == True\n", "sub_path": "py_se_day15/test_amazon_website.py", "file_name": "test_amazon_website.py", "file_ext": "py", "file_size_in_byte": 1883, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 11, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 11, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 14, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 16, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 9, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "233707922", "text": "\nimport numpy as np\nimport logging\n\nimport MDAnalysis\n\nfrom boxutils import center_mol, rotate_mol\n\nfrom constants import SEL_SPEC_HEAVIES_NOWALL\n\nfrom mdtools import ParallelTool\n\nfrom MDAnalysis.analysis.rms import rmsd\n\nimport sys\n\nfrom IPython import embed\n\nclass Trajconv(ParallelTool):\n prog='trajconv'\n description = '''\\\nCenter and align a structure (PDB or GRO) or trajectory (XTC or TRR) to a reference\nstructure (Requires a GRO and a TPR file)\n\nAutomatically treats PBC for selected molecule group, assuring molecule to be centered\n and aligned is whole in each frame. \n\n NOTE: **This tool assumes the reference structure is whole** and will not work correctly\n otherwise (it will throw an error if it finds the reference structure is broken)\n The reference structure will automaticall be centered according to its COM before any\n fitting or alignment\n\n\n-----------------------------------------------------------------------------\nCommand-line options\n-----------------------------------------------------------------------------\n'''\n \n def __init__(self):\n super(Trajconv,self).__init__()\n \n # Parallel processing by default (this is not actually necessary, but it is\n # informative!)\n self.wm_env.default_work_manager = self.wm_env.default_parallel_work_manager\n\n self.ref_univ = None\n self.other_univ = None\n self.outputfilename = None\n\n # are we processing a trajectory?\n self.do_traj = False\n\n self.start_frame = None\n self.end_frame = None\n\n self.sel_spec = None\n self.rmsd_spec = None\n\n self.sel_spec_other = None\n self.rmsd_spec_other = None\n\n self.rmsd_out = None\n\n self.center_only = None\n\n # Shape: (n_frames, n_rms_specs+1)\n self.rmsd_arr = None\n # rms per-atom (for each frame) for rms on the rmsdspec (empty if none supplied)\n self.rms_per_atom = None\n\n @property\n def n_frames(self):\n return self.last_frame - self.start_frame\n\n \n def add_args(self, parser):\n \n sgroup = parser.add_argument_group('Trajconv options')\n sgroup.add_argument('-s1', '--tprfile1', metavar='TPR', type=str, required=True,\n help='Input topology file (tpr) for ref structure')\n sgroup.add_argument('-s2', '--tprfile2', metavar='TPR', type=str, required=True,\n help='Input topology file (tpr) for structure/trajectory to fit')\n sgroup.add_argument('-c', '--grofile', metavar='GRO', type=str, required=True,\n help='Input reference structure file')\n sgroup.add_argument('-f', '--fitfile', metavar='XTC', type=str, required=True,\n help='Input file to fit to reference. can be GRO or XTC')\n sgroup.add_argument('-b', '--start', type=int, default=0,\n help='First timepoint (in ps)')\n sgroup.add_argument('-e', '--end', type=int, \n help='Last timepoint (in ps) - default is last available')\n sgroup.add_argument('--fitspec', type=str, default=SEL_SPEC_HEAVIES_NOWALL,\n help='MDAnalysis selection string for fitting. Default selects all protein heavy atoms')\n sgroup.add_argument('--fitspec-other', type=str,\n help='Fit spec for selecting the other structure to fit (default: same as fitspec)')\n sgroup.add_argument('--center-only', action='store_true', \n help='If true, only center molecule (no fitting)')\n sgroup.add_argument('--rmsdspec', type=str, \n help='MDAnalysis selection string for rmsd (after fitting on fitspec). Optional.')\n sgroup.add_argument('--rmsdspec-other', type=str,\n help='Sel spec for other structure rmsd (default: same as rmsdspec)')\n agroup = parser.add_argument_group('other options')\n agroup.add_argument('-o', '--outfile', type=str, default='fit.gro',\n help='Output file to write fitted trajectory or structure. File type determined by input')\n agroup.add_argument('-orms', '--outrmsd', type=str, default='rmsd_fit.dat',\n help='Output rmsd values for each frame after fitting')\n\n def process_args(self, args):\n\n #try:\n self.ref_univ = MDAnalysis.Universe(args.tprfile1, args.grofile)\n\n ext = args.fitfile.split('.')[-1]\n if ext in ['trr', 'xtc']:\n self.do_traj = True\n self.other_univ = other_univ = MDAnalysis.Universe(args.tprfile2, args.fitfile)\n elif ext == 'gro' or ext == 'pdb':\n self.other_univ = other_univ = MDAnalysis.Universe(args.tprfile2, args.fitfile)\n else:\n print(\"unknown or missing extension\")\n sys.exit()\n #except:\n # print(\"Error processing input files: {} and {}\".format(args.grofile, args.fitfile))\n # sys.exit()\n\n\n if (args.start > (other_univ.trajectory.n_frames * other_univ.trajectory.dt)):\n raise ValueError(\"Error: provided start time ({} ps) is greater than total time ({} ps)\"\n .format(args.start, (other_univ.trajectory.n_frames * other_univ.trajectory.dt)))\n\n self.start_frame = int(args.start / other_univ.trajectory.dt)\n if args.end is not None:\n self.last_frame = args.end\n else:\n self.last_frame = other_univ.trajectory.n_frames\n\n self.sel_spec = args.fitspec\n self.rmsd_spec = args.rmsdspec\n\n self.sel_spec_other = args.fitspec_other or args.fitspec\n self.rmsd_spec_other = args.rmsdspec_other or args.rmsdspec\n\n self.outfile = args.outfile.split('.')[0]\n self.rmsd_out = args.outrmsd\n\n self.center_only = args.center_only\n\n\n\n def go(self):\n\n header_str = \"fitspec: {}; rmsdspec: {}; fitspec_other: {}; rmsd_spec_other: {}\".format(self.sel_spec, self.rmsd_spec, self.sel_spec_other, self.rmsd_spec_other)\n\n n_frames = self.last_frame - self.start_frame\n\n ndim = 2 if self.rmsd_spec is None else 3\n self.rmsd_arr = np.zeros((self.n_frames, ndim))\n\n self.ref_univ.atoms.write('fit_ref.gro')\n \n if self.rmsd_spec is not None:\n ref_struct = self.ref_univ.select_atoms(self.rmsd_spec)\n other_struct = self.other_univ.select_atoms(self.rmsd_spec_other)\n \n assert ref_struct.n_atoms == other_struct.n_atoms\n \n self.rms_per_atom = np.zeros((self.n_frames, ref_struct.n_atoms))\n\n if self.do_traj:\n with MDAnalysis.Writer(self.outfile + \".xtc\", self.other_univ.atoms.n_atoms) as W:\n for i_frame in range(self.start_frame, self.last_frame):\n if i_frame % 100 == 0:\n print(\"doing frame {} of {}\".format(i_frame, self.last_frame))\n sys.stdout.flush()\n curr_ts = self.other_univ.trajectory[i_frame]\n\n center_mol(self.other_univ, do_pbc=False)\n if not self.center_only:\n rms = rotate_mol(self.ref_univ, self.other_univ, ref_spec=self.sel_spec, other_spec=self.sel_spec_other)\n self.rmsd_arr[i_frame-self.start_frame, 0] = curr_ts.time\n self.rmsd_arr[i_frame-self.start_frame, 1] = rms \n\n if i_frame == self.start_frame:\n self.other_univ.atoms.write('first_frame_fit.gro')\n W.write(self.other_univ.atoms)\n\n\n if self.rmsd_spec is not None and not self.center_only:\n rms_other = rmsd(ref_struct.atoms.positions, other_struct.atoms.positions)\n self.rmsd_arr[i_frame-self.start_frame, 2] = rms_other\n\n self.rms_per_atom[i_frame-self.start_frame, :] = np.sqrt( np.sum((ref_struct.atoms.positions - other_struct.atoms.positions)**2, axis=1) )\n\n\n else:\n center_mol(self.other_univ, do_pbc=False, check_broken=False)\n rms = rotate_mol(self.ref_univ, self.other_univ, ref_spec=self.sel_spec, other_spec=self.sel_spec_other)\n self.other_univ.atoms.write(self.outfile + \".gro\")\n\n self.rmsd_arr[0,0] = 0.0\n self.rmsd_arr[0,1] = rms\n if self.rmsd_spec is not None:\n rms_other = rmsd(ref_struct.atoms.positions, other_struct.atoms.positions)\n self.rmsd_arr[0,2] = rms_other\n\n self.rms_per_atom[0,:] = np.sqrt( np.sum((ref_struct.atoms.positions - other_struct.atoms.positions)**2, axis=1) )\n\n if self.rmsd_spec is not None:\n avg_rms_per_atom = self.rms_per_atom.mean(axis=0)\n self.other_univ.add_TopologyAttr('tempfactors')\n other_struct.tempfactors = avg_rms_per_atom\n other_struct.write('fit_per_atom_rmsd.pdb', bonds=None)\n\n self.ref_univ.add_TopologyAttr('tempfactors')\n ref_struct.tempfactors = avg_rms_per_atom\n ref_struct.write('fit_ref_per_atom_rmsd.pdb', bonds=None)\n\n # Save output\n np.savetxt(self.rmsd_out, self.rmsd_arr, header=header_str)\n np.savez_compressed('rms_per_atom.dat', header=self.rmsd_spec, rms_per_atom=self.rms_per_atom)\n\n\nif __name__=='__main__':\n Trajconv().main()\n", "sub_path": "trajconv.py", "file_name": "trajconv.py", "file_ext": "py", "file_size_in_byte": 9471, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "mdtools.ParallelTool", "line_number": 19, "usage_type": "name"}, {"api_name": "constants.SEL_SPEC_HEAVIES_NOWALL", "line_number": 91, "usage_type": "name"}, {"api_name": "MDAnalysis.Universe", "line_number": 110, "usage_type": "call"}, {"api_name": "MDAnalysis.Universe", "line_number": 115, "usage_type": "call"}, {"api_name": "MDAnalysis.Universe", "line_number": 117, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 166, "usage_type": "call"}, {"api_name": "MDAnalysis.Writer", "line_number": 169, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 173, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 173, "usage_type": "attribute"}, {"api_name": "boxutils.center_mol", "line_number": 176, "usage_type": "call"}, {"api_name": "boxutils.rotate_mol", "line_number": 178, "usage_type": "call"}, {"api_name": "MDAnalysis.analysis.rms.rmsd", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 191, "usage_type": "call"}, {"api_name": "boxutils.center_mol", "line_number": 195, "usage_type": "call"}, {"api_name": "boxutils.rotate_mol", "line_number": 196, "usage_type": "call"}, {"api_name": "MDAnalysis.analysis.rms.rmsd", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.savez_compressed", "line_number": 219, "usage_type": "call"}]} +{"seq_id": "299219456", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nx = [40,42,50,55,60,65,70,75,80,85,90,95]\ny = [6.8,7,8.6,9.1,9.5,10.0,10.3,10.6,12,12.2,13.2,13.3]\n\nfig, ax = plt.subplots()\nax.scatter(x,y)\nstart, end = ax.get_xlim()\nax.xaxis.set_ticks(np.arange(start, end,3.0))\nax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))\n\nstarty, endy = ax.get_ylim()\nax.yaxis.set_ticks(np.arange(starty, endy,0.5))\nax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))\n\nplt.xlabel('Temperature in Degree')\nplt.ylabel('Resistance in Ohm')\nplt.title('Temperature VS Resistance')\n\nplt.show()", "sub_path": "plots.py", "file_name": "plots.py", "file_ext": "py", "file_size_in_byte": 627, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "matplotlib.pyplot.subplots", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.ticker.FormatStrFormatter", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.ticker", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.ticker.FormatStrFormatter", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.ticker", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "567875551", "text": "#!/usr/bin/env python\nimport rospy\nfrom std_msgs.msg import Int32\nfrom geometry_msgs.msg import PoseStamped, Pose\nfrom styx_msgs.msg import TrafficLightArray, TrafficLight\nfrom styx_msgs.msg import Lane\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge\nfrom light_classification.tl_classifier import TLClassifier\nimport tf\nimport cv2\nimport yaml\nimport os\nimport time\n\nfrom scipy.spatial import KDTree\n\nSTATE_COUNT_THRESHOLD = 3\n\nclass TLDetector(object):\n def __init__(self):\n rospy.init_node('tl_detector')\n\n self.pose = None\n self.waypoints = None\n self.camera_image = None\n self.waypoints_2d = None\n self.waypoint_tree = None\n self.lights = []\n self.state = TrafficLight.UNKNOWN\n self.last_state = TrafficLight.UNKNOWN\n self.last_wp = -1\n self.state_count = 0\n self.pred_count = 0\n\n '''\n /vehicle/traffic_lights provides you with the location of the traffic light in 3D map space and\n helps you acquire an accurate ground truth data source for the traffic light\n classifier by sending the current color state of all traffic lights in the\n simulator. When testing on the vehicle, the color state will not be available. You'll need to\n rely on the position of the light and the camera image to predict it.\n '''\n \n sub1 = rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n sub2 = rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n sub3 = rospy.Subscriber('/vehicle/traffic_lights', TrafficLightArray, self.traffic_cb)\n sub6 = rospy.Subscriber('/image_color', Image, self.image_cb)\n\n config_string = rospy.get_param(\"/traffic_light_config\")\n self.config = yaml.load(config_string)\n\n self.upcoming_red_light_pub = rospy.Publisher('/traffic_waypoint', Int32, queue_size=1)\n\n self.bridge = CvBridge()\n self.light_classifier = TLClassifier()\n self.listener = tf.TransformListener()\n \n self.out_images_debug_path = '/home/workspace/out_imgs'\n if os.path.exists(self.out_images_debug_path):\n os.removedirs(self.out_images_debug_path)\n os.makedirs(self.out_images_debug_path)\n\n rospy.spin()\n\n def pose_cb(self, msg):\n self.pose = msg\n\n def waypoints_cb(self, waypoints):\n self.waypoints = waypoints\n if not self.waypoints_2d:\n self.waypoints_2d = [[waypoint.pose.pose.position.x, waypoint.pose.pose.position.y] for waypoint in waypoints.waypoints]\n self.waypoint_tree = KDTree(self.waypoints_2d)\n\n def traffic_cb(self, msg):\n self.lights = msg.lights\n\n def image_cb(self, msg):\n \"\"\"Identifies red lights in the incoming camera image and publishes the index\n of the waypoint closest to the red light's stop line to /traffic_waypoint\n Args:\n msg (Image): image from car-mounted camera\n \"\"\"\n self.has_image = True\n self.camera_image = msg\n# rospy.loginfo(\"Processing traffic lights\")\n light_wp, state = self.process_traffic_lights()\n\n '''\n Publish upcoming red lights at camera frequency.\n Each predicted state has to occur `STATE_COUNT_THRESHOLD` number\n of times till we start using it. Otherwise the previous stable state is\n used.\n '''\n if self.state != state:\n self.state_count = 0\n self.state = state\n elif self.state_count >= STATE_COUNT_THRESHOLD:\n self.last_state = self.state\n light_wp = light_wp if ((state == TrafficLight.RED) or (state == TrafficLight.YELLOW)) else -1\n self.last_wp = light_wp\n self.upcoming_red_light_pub.publish(Int32(light_wp))\n else:\n self.upcoming_red_light_pub.publish(Int32(self.last_wp))\n self.state_count += 1\n\n def get_closest_waypoint(self, x ,y):\n \"\"\"Identifies the closest path waypoint to the given position\n https://en.wikipedia.org/wiki/Closest_pair_of_points_problem\n Args:\n pose (Pose): position to match a waypoint to\n Returns:\n int: index of the closest waypoint in self.waypoints\n \"\"\"\n \n if all(i is not None for i in [self.pose and self.waypoint_tree]):\n closest_idx = self.waypoint_tree.query([x, y],1)[1]\n return closest_idx\n\n def get_light_state(self, light):\n \"\"\"Determines the current color of the traffic light\n Args:\n light (TrafficLight): light to classify\n Returns:\n int: ID of traffic light color (specified in styx_msgs/TrafficLight)\n \"\"\"\n \n if(not self.has_image):\n self.prev_light_loc = None\n return TrafficLight.UNKNOWN\n \n cv_image = self.bridge.imgmsg_to_cv2(self.camera_image, \"bgr8\")\n cv_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB)\n cv_image = cv2.resize(cv_image, (224, 224))\n cv_image = cv_image/255.\n\n #Get classification\n t0 = time.time()\n light_id = self.light_classifier.get_classification(cv_image)\n # print(\"Pred time: \", time.time() - t0)\n \n def get_light_text(id):\n if id == TrafficLight.GREEN:\n return \"GREEN\"\n elif id == TrafficLight.RED:\n return \"RED\"\n elif id == TrafficLight.UNKNOWN:\n return \"UNKNOWN\"\n elif id == TrafficLight.YELLOW:\n return \"YELLOW\"\n\n print('Predicted State: \\t\\t{}'.format(get_light_text(light_id)))\n print('Actual State: \\t\\t{}'.format(get_light_text(light.state)))\n\n# # Saving the image\n# self.pred_count += 1\n# file_name = os.path.join(self.out_images_debug_path, 'img_{}_{}.png'.format(self.pred_count, light_id))\n# cv2.imwrite(file_name, cv_image)\n \n return light_id\n\n def process_traffic_lights(self):\n \"\"\"Finds closest visible traffic light, if one exists, and determines its\n location and color\n Returns:\n int: index of waypoint closes to the upcoming stop line for a traffic light (-1 if none exists)\n int: ID of traffic light color (specified in styx_msgs/TrafficLight)\n \"\"\"\n closest_light = None \n line_wp_idx = None \n\n # List of positions that correspond to the line to stop in front of for a given intersection\n stop_line_positions = self.config['stop_line_positions']\n if(self.pose):\n car_wp_idx = self.get_closest_waypoint(self.pose.pose.position.x, self.pose.pose.position.y)\n diff = 100 # TODO: Replace hardcoding with param\n for i, light in enumerate(self.lights):\n # Get stop line waypoint index\n line = stop_line_positions[i]\n temp_wp_idx = self.get_closest_waypoint(line[0], line[1])\n d = temp_wp_idx - car_wp_idx\n if d >= 0 and d < diff:\n print(\"Diff, d:\", diff, d)\n diff = d \n closest_light = light \n line_wp_idx = temp_wp_idx\n\n if closest_light:\n# rospy.loginfo(\"Closest light found\")\n state = self.get_light_state(closest_light)\n # print('State: ',state)\n return line_wp_idx, state\n \n return -1, TrafficLight.UNKNOWN\n\nif __name__ == '__main__':\n try:\n TLDetector()\n except rospy.ROSInterruptException:\n rospy.logerr('Could not start traffic node.')\n", "sub_path": "ros/src/tl_detector/tl_detector.py", "file_name": "tl_detector.py", "file_ext": "py", "file_size_in_byte": 7643, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "rospy.init_node", "line_number": 22, "usage_type": "call"}, {"api_name": "styx_msgs.msg.TrafficLight.UNKNOWN", "line_number": 30, "usage_type": "attribute"}, {"api_name": "styx_msgs.msg.TrafficLight", "line_number": 30, "usage_type": "name"}, {"api_name": "styx_msgs.msg.TrafficLight.UNKNOWN", "line_number": 31, "usage_type": "attribute"}, {"api_name": "styx_msgs.msg.TrafficLight", "line_number": 31, "usage_type": "name"}, {"api_name": "rospy.Subscriber", "line_number": 44, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.PoseStamped", "line_number": 44, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 45, "usage_type": "call"}, {"api_name": "styx_msgs.msg.Lane", "line_number": 45, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 46, "usage_type": "call"}, {"api_name": "styx_msgs.msg.TrafficLightArray", "line_number": 46, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 47, "usage_type": "call"}, {"api_name": "sensor_msgs.msg.Image", "line_number": 47, "usage_type": "argument"}, {"api_name": "rospy.get_param", "line_number": 49, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 50, "usage_type": "call"}, {"api_name": "rospy.Publisher", "line_number": 52, "usage_type": "call"}, {"api_name": "std_msgs.msg.Int32", "line_number": 52, "usage_type": "argument"}, {"api_name": "cv_bridge.CvBridge", "line_number": 54, "usage_type": "call"}, {"api_name": "light_classification.tl_classifier.TLClassifier", "line_number": 55, "usage_type": "call"}, {"api_name": "tf.TransformListener", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.removedirs", "line_number": 60, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 61, "usage_type": "call"}, {"api_name": "rospy.spin", "line_number": 63, "usage_type": "call"}, {"api_name": "scipy.spatial.KDTree", "line_number": 72, "usage_type": "call"}, {"api_name": "styx_msgs.msg.TrafficLight.RED", "line_number": 99, "usage_type": "attribute"}, {"api_name": "styx_msgs.msg.TrafficLight", "line_number": 99, "usage_type": "name"}, {"api_name": "styx_msgs.msg.TrafficLight.YELLOW", "line_number": 99, "usage_type": "attribute"}, {"api_name": "std_msgs.msg.Int32", "line_number": 101, "usage_type": "call"}, {"api_name": "std_msgs.msg.Int32", "line_number": 103, "usage_type": "call"}, {"api_name": "styx_msgs.msg.TrafficLight.UNKNOWN", "line_number": 129, "usage_type": "attribute"}, {"api_name": "styx_msgs.msg.TrafficLight", "line_number": 129, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 132, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 132, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 133, "usage_type": "call"}, {"api_name": "time.time", "line_number": 137, "usage_type": "call"}, {"api_name": "styx_msgs.msg.TrafficLight.GREEN", "line_number": 142, "usage_type": "attribute"}, {"api_name": "styx_msgs.msg.TrafficLight", "line_number": 142, "usage_type": "name"}, {"api_name": "styx_msgs.msg.TrafficLight.RED", "line_number": 144, "usage_type": "attribute"}, {"api_name": "styx_msgs.msg.TrafficLight", "line_number": 144, "usage_type": "name"}, {"api_name": "styx_msgs.msg.TrafficLight.UNKNOWN", "line_number": 146, "usage_type": "attribute"}, {"api_name": "styx_msgs.msg.TrafficLight", "line_number": 146, "usage_type": "name"}, {"api_name": "styx_msgs.msg.TrafficLight.YELLOW", "line_number": 148, "usage_type": "attribute"}, {"api_name": "styx_msgs.msg.TrafficLight", "line_number": 148, "usage_type": "name"}, {"api_name": "styx_msgs.msg.TrafficLight.UNKNOWN", "line_number": 193, "usage_type": "attribute"}, {"api_name": "styx_msgs.msg.TrafficLight", "line_number": 193, "usage_type": "name"}, {"api_name": "rospy.ROSInterruptException", "line_number": 198, "usage_type": "attribute"}, {"api_name": "rospy.logerr", "line_number": 199, "usage_type": "call"}]} +{"seq_id": "606812130", "text": "# _*_ coding:utf-8 _*_\r\nfrom keras.applications.resnet50 import ResNet50\r\n\r\nfrom keras import layers\r\nfrom keras.layers import Dense\r\nfrom keras import utils\r\nfrom keras.models import Model\r\nfrom keras import backend as K\r\nfrom attention_module import attach_attention_module\r\n\r\n# from . import get_submodules_from_kwargs\r\n# from . import imagenet_utils\r\n# from .imagenet_utils import decode_predictions\r\n# from .imagenet_utils import _obtain_input_shape\r\n\r\nWEIGHTS_PATH = ('https://github.com/fchollet/deep-learning-models/'\r\n 'releases/download/v0.2/'\r\n 'resnet50_weights_tf_dim_ordering_tf_kernels.h5')\r\nWEIGHTS_PATH_NO_TOP = ('https://github.com/fchollet/deep-learning-models/'\r\n 'releases/download/v0.2/'\r\n 'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5')\r\n\r\n\r\n\r\ndef identity_block(input_tensor, kernel_size, filters, stage, block, bn_axis=3):\r\n filters1, filters2, filters3 = filters\r\n conv_name_base = 'res' + str(stage) + block + '_branch'\r\n bn_name_base = 'bn' + str(stage) + block + '_branch'\r\n\r\n x = layers.Conv2D(filters1, (1, 1),\r\n kernel_initializer='he_normal',\r\n name=conv_name_base + '2a')(input_tensor)\r\n x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)\r\n x = layers.Activation('relu')(x)\r\n\r\n x = layers.Conv2D(filters2, kernel_size,\r\n padding='same',\r\n kernel_initializer='he_normal',\r\n name=conv_name_base + '2b')(x)\r\n x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)\r\n x = layers.Activation('relu')(x)\r\n\r\n x = layers.Conv2D(filters3, (1, 1),\r\n kernel_initializer='he_normal',\r\n name=conv_name_base + '2c')(x)\r\n x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)\r\n\r\n x = layers.add([x, input_tensor])\r\n x = layers.Activation('relu')(x)\r\n return x\r\n\r\n\r\ndef conv_block(input_tensor,\r\n kernel_size,\r\n filters,\r\n stage,\r\n block,\r\n strides=(2, 2),\r\n bn_axis=3):\r\n filters1, filters2, filters3 = filters\r\n conv_name_base = 'res' + str(stage) + block + '_branch'\r\n bn_name_base = 'bn' + str(stage) + block + '_branch'\r\n\r\n x = layers.Conv2D(filters1, (1, 1), strides=strides,\r\n kernel_initializer='he_normal',\r\n name=conv_name_base + '2a')(input_tensor)\r\n x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)\r\n x = layers.Activation('relu')(x)\r\n\r\n x = layers.Conv2D(filters2, kernel_size, padding='same',\r\n kernel_initializer='he_normal',\r\n name=conv_name_base + '2b')(x)\r\n x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)\r\n x = layers.Activation('relu')(x)\r\n\r\n x = layers.Conv2D(filters3, (1, 1),\r\n kernel_initializer='he_normal',\r\n name=conv_name_base + '2c')(x)\r\n x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)\r\n\r\n shortcut = layers.Conv2D(filters3, (1, 1), strides=strides,\r\n kernel_initializer='he_normal',\r\n name=conv_name_base + '1')(input_tensor)\r\n shortcut = layers.BatchNormalization(\r\n axis=bn_axis, name=bn_name_base + '1')(shortcut)\r\n\r\n x = layers.add([x, shortcut])\r\n x = layers.Activation('relu')(x)\r\n return x\r\n\r\ndef finite_difference(input_feature):\r\n channel = input_feature._keras_shape[-1]\r\n finite_feature=layers.concatenate(\r\n [K.expand_dims(K.abs(layers.subtract([input_feature[...,i+1],\r\n input_feature[...,i]])),axis=-1) for i in range(channel-1)],axis=-1)\r\n return finite_feature\r\n\r\ndef res_Net50(input,classes=51,attention_module=None):\r\n #global backend, layers, models, keras_utils\r\n #backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs)\r\n #x = layers.Lambda(finite_difference)(input)\r\n #print(x.get_shape())\r\n #exit()\r\n if attention_module is not None:\r\n x=attach_attention_module(input,'fcbam_block')\r\n x = layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(input)\r\n x = layers.Conv2D(128, (7, 7),\r\n strides=(2, 2),\r\n padding='valid',\r\n kernel_initializer='he_normal',\r\n name='conv1_he_normal')(x)\r\n x = layers.BatchNormalization( name='bn_conv1_he_normal')(x)\r\n x = layers.Activation('relu')(x)\r\n x = layers.ZeroPadding2D(padding=(1, 1), name='pool1_pad_he_normal')(x)\r\n x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)\r\n\r\n\r\n if attention_module is not None:\r\n x=attach_attention_module(x,attention_module)\r\n\r\n x = conv_block(x, 3, [64, 64, 256], stage=2, block='a_he_normal', strides=(1, 1))\r\n x = identity_block(x, 3, [64, 64, 256], stage=2, block='b_he_normal')\r\n x = identity_block(x, 3, [64, 64, 256], stage=2, block='c_he_normal')\r\n\r\n\r\n\r\n if attention_module is not None:\r\n x=attach_attention_module(x,attention_module)\r\n\r\n x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')\r\n x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')\r\n x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')\r\n x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')\r\n\r\n\r\n if attention_module is not None:\r\n x=attach_attention_module(x,attention_module)\r\n\r\n x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')\r\n x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')\r\n x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')\r\n x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')\r\n x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')\r\n x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')\r\n\r\n\r\n if attention_module is not None:\r\n x=attach_attention_module(x,attention_module)\r\n\r\n x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')\r\n x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')\r\n x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')\r\n x = layers.GlobalAveragePooling2D(name='avg_pool')(x)\r\n\r\n # linear = layers.Dense(units=512,activation='sigmoid',name='dense_layer_1')(x)\r\n # linear = layers.Dropout(rate=0.75)(linear)\r\n\r\n linear = layers.Dense(units=classes, activation='softmax',name='dense_layer')(x)\r\n\r\n model = Model(inputs=input, outputs=linear)\r\n\r\n weights_path = utils.get_file(\r\n 'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',\r\n WEIGHTS_PATH_NO_TOP,\r\n cache_subdir='models',\r\n md5_hash='a268eb855778b3df3c7506639542a6af')\r\n model.load_weights(weights_path,by_name=True)\r\n return model\r\n\r\n\r\n #x = layers.Dense(self.classes, activation='softmax', name='fc10')(x)\r\n #model=Model(self.input,x,name='resnet50')\r\n #return model", "sub_path": "models/resnet50.py", "file_name": "resnet50.py", "file_ext": "py", "file_size_in_byte": 7044, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "keras.layers.Conv2D", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 30, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 33, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 33, "usage_type": "name"}, {"api_name": "keras.layers.Activation", "line_number": 34, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 34, "usage_type": "name"}, {"api_name": "keras.layers.Conv2D", "line_number": 36, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 36, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 40, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 40, "usage_type": "name"}, {"api_name": "keras.layers.Activation", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 41, "usage_type": "name"}, {"api_name": "keras.layers.Conv2D", "line_number": 43, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 43, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 46, "usage_type": "name"}, {"api_name": "keras.layers.add", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 48, "usage_type": "name"}, {"api_name": "keras.layers.Activation", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 49, "usage_type": "name"}, {"api_name": "keras.layers.Conv2D", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 64, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 67, "usage_type": "name"}, {"api_name": "keras.layers.Activation", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 68, "usage_type": "name"}, {"api_name": "keras.layers.Conv2D", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 70, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 73, "usage_type": "name"}, {"api_name": "keras.layers.Activation", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 74, "usage_type": "name"}, {"api_name": "keras.layers.Conv2D", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 76, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 79, "usage_type": "name"}, {"api_name": "keras.layers.Conv2D", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 81, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 84, "usage_type": "name"}, {"api_name": "keras.layers.add", "line_number": 87, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 87, "usage_type": "name"}, {"api_name": "keras.layers.Activation", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 88, "usage_type": "name"}, {"api_name": "keras.layers.concatenate", "line_number": 93, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 93, "usage_type": "name"}, {"api_name": "keras.backend.expand_dims", "line_number": 94, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 94, "usage_type": "name"}, {"api_name": "keras.backend.abs", "line_number": 94, "usage_type": "call"}, {"api_name": "keras.layers.subtract", "line_number": 94, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 94, "usage_type": "name"}, {"api_name": "attention_module.attach_attention_module", "line_number": 105, "usage_type": "call"}, {"api_name": "keras.layers.ZeroPadding2D", "line_number": 106, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 106, "usage_type": "name"}, {"api_name": "keras.layers.Conv2D", "line_number": 107, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 107, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 112, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 112, "usage_type": "name"}, {"api_name": "keras.layers.Activation", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 113, "usage_type": "name"}, {"api_name": "keras.layers.ZeroPadding2D", "line_number": 114, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 114, "usage_type": "name"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 115, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 115, "usage_type": "name"}, {"api_name": "attention_module.attach_attention_module", "line_number": 119, "usage_type": "call"}, {"api_name": "attention_module.attach_attention_module", "line_number": 128, "usage_type": "call"}, {"api_name": "attention_module.attach_attention_module", "line_number": 137, "usage_type": "call"}, {"api_name": "attention_module.attach_attention_module", "line_number": 148, "usage_type": "call"}, {"api_name": "keras.layers.GlobalAveragePooling2D", "line_number": 153, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 153, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 158, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 158, "usage_type": "name"}, {"api_name": "keras.models.Model", "line_number": 160, "usage_type": "call"}, {"api_name": "keras.utils.get_file", "line_number": 162, "usage_type": "call"}, {"api_name": "keras.utils", "line_number": 162, "usage_type": "name"}]} +{"seq_id": "487958256", "text": "from django.core.management.base import BaseCommand, CommandError\nfrom django.contrib.auth.models import Group\n\nimport requests\n\nfrom mailadmin.models import OrgUnit\n\n\nclass Command(BaseCommand):\n help = 'Adds groups and OrgUnits from inside.studentersamfundet.no.'\n\n def handle(self, *args, **options):\n r = requests.get('https://inside.studentersamfundet.no/api/groups.php')\n res = r.json()\n if 'results' not in res:\n self.stderr.write('Error: No groups, bailing...')\n\n groups = res['results']\n for g in groups:\n if g['posix_group'] != \"\":\n obj, created = Group.objects.get_or_create(name=g['posix_group'])\n ou_obj, ou_created = OrgUnit.objects.get_or_create(name=g['division_name'], inside_id=g['division_id'])\n if 'styret' in g['posix_group']:\n ou_obj.admin_groups.add(obj)\n else:\n ou_obj.member_groups.add(obj)\n\n self.stdout.write('Done.')\n", "sub_path": "mailadmin/management/commands/import_groups.py", "file_name": "import_groups.py", "file_ext": "py", "file_size_in_byte": 1013, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.core.management.base.BaseCommand", "line_number": 9, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Group.objects.get_or_create", "line_number": 21, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Group.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.Group", "line_number": 21, "usage_type": "name"}, {"api_name": "mailadmin.models.OrgUnit.objects.get_or_create", "line_number": 22, "usage_type": "call"}, {"api_name": "mailadmin.models.OrgUnit.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "mailadmin.models.OrgUnit", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "535310026", "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 ('homepage', '0002_notice_notice_content'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='News',\n fields=[\n ('news_id', models.IntegerField(serialize=False, auto_created=True, primary_key=True)),\n ('news_title', models.CharField(max_length=100)),\n ('news_author', models.CharField(max_length=30)),\n ('news_content', models.TextField()),\n ('news_created', models.DateField(auto_now_add=True)),\n ],\n ),\n migrations.CreateModel(\n name='Qna',\n fields=[\n ('qna_id', models.IntegerField(serialize=False, auto_created=True, primary_key=True)),\n ('qna_title', models.CharField(max_length=100)),\n ('qna_author', models.CharField(max_length=30)),\n ('qna_password', models.CharField(max_length=30)),\n ('qna_content', models.TextField()),\n ('qna_created', models.DateField(auto_now_add=True)),\n ],\n ),\n migrations.AlterField(\n model_name='notice',\n name='notice_id',\n field=models.IntegerField(serialize=False, auto_created=True, primary_key=True),\n ),\n ]\n", "sub_path": "homepage/migrations/0003_auto_20151112_1802.py", "file_name": "0003_auto_20151112_1802.py", "file_ext": "py", "file_size_in_byte": 1442, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "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.CreateModel", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "28848415", "text": "from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('', views.speakers_home, name='speakers_home'),\n path('/speakers.html', views.nominate_others),\n path('/form2.html', views.nominate_yourself),\n path('/blog.html', views.blogs),\n path('/about_us.html', views.about_us),\n path('/contact.html', views.contact),\n\n \n path('/speakers.html/form2.html', views.nominate_yourself),\n path('/speakers.html/speakers.html', views.nominate_others),\n path('/speakers.html/blog.html', views.blogs),\n path('/speakers.html/about_us.html', views.about_us),\n path('/contact.html', views.contact),\n\n \n path('/Alana_Golmei', views.speakerDesc1, name='speaker_description1'),\n path('/Anamika_Barua', views.speakerDesc2, name='speaker_description2'),\n path('/Arup_Kumar_Dutta', views.speakerDesc3, name='speaker_description3'),\n path('/Binita_Jain', views.speakerDesc4, name='speaker_description4'),\n path('/Milin_Dutta', views.speakerDesc5, name='speaker_description5'),\n path('/Zoma_Sailo', views.speakerDesc6, name='speaker_description6'),\n path('/Pragnya_Ramjee', views.speakerDesc7, name='speaker_description7'),\n\n path('/Seema_Biswas', views.speakerDesc8, name='speaker_description8'),\n path('/Uddhab_Bharali', views.speakerDesc9, name='speaker_description9'),\n path('/Sankara_Subramaniam', views.speakerDesc10, name='speaker_description10'),\n path('/Hasina_Kharbhih', views.speakerDesc11, name='speaker_description11'),\n path('/Sonjoy_Hazarika', views.speakerDesc12, name='speaker_description12'),\n path('/Ravindranath_Ravi', views.speakerDesc13, name='speaker_description13'),\n\n path('/Aashish_Chandratreya', views.speakerDesc14, name='speaker_description14'),\n path('/Aditya_Gupta', views.speakerDesc15, name='speaker_description15'),\n path('/Bhagvan_Kommadi', views.speakerDesc16, name='speaker_description16'),\n path('/Bidisha_Som', views.speakerDesc17, name='speaker_description17'),\n path('/Nisha_Bora', views.speakerDesc18, name='speaker_description18'),\n path('/Prabhagaran', views.speakerDesc19, name='speaker_description19'),\n path('/Rudy_Wallang', views.speakerDesc20, name='speaker_description20'),\n path('/Seema_Gupta', views.speakerDesc21, name='speaker_description21'),\n path('/Shiva_Sah', views.speakerDesc22, name='speaker_description22'),\n \n path('/speakers.html/Alana_Golmei', views.speakerDesc1, name='speaker_description1'),\n path('/speakers.html/Anamika_Barua', views.speakerDesc2, name='speaker_description2'),\n path('/speakers.html/Arup_Kumar_Dutta', views.speakerDesc3, name='speaker_description3'),\n path('/speakers.html/Binita_Jain', views.speakerDesc4, name='speaker_description4'),\n path('/speakers.html/Milin_Dutta', views.speakerDesc5, name='speaker_description5'),\n path('/speakers.html/Zoma_Sailo', views.speakerDesc6, name='speaker_description6'),\n path('/speakers.html/Pragnya_Ramjee', views.speakerDesc7, name='speaker_description7'),\n\n path('/speakers.html/Seema_Biswas', views.speakerDesc8, name='speaker_description8'),\n path('/speakers.html/Uddhab_Bharali', views.speakerDesc9, name='speaker_description9'),\n path('/speakers.html/Sankara_Subramaniam', views.speakerDesc10, name='speaker_description10'),\n path('/speakers.html/Hasina_Kharbhih', views.speakerDesc11, name='speaker_description11'),\n path('/speakers.html/Sonjoy_Hazarika', views.speakerDesc12, name='speaker_description12'),\n path('/speakers.html/Ravindranath_Ravi', views.speakerDesc13, name='speaker_description13'),\n\n path('/speakers.html/Aashish_Chandratreya', views.speakerDesc14, name='speaker_description14'),\n path('/speakers.html/Aditya_Gupta', views.speakerDesc15, name='speaker_description15'),\n path('/speakers.html/Bhagvan_Kommadi', views.speakerDesc16, name='speaker_description16'),\n path('/speakers.html/Bidisha_Som', views.speakerDesc17, name='speaker_description17'),\n path('/speakers.html/Nisha_Bora', views.speakerDesc18, name='speaker_description18'),\n path('/speakers.html/Prabhagaran', views.speakerDesc19, name='speaker_description19'),\n path('/speakers.html/Rudy_Wallang', views.speakerDesc20, name='speaker_description20'),\n path('/speakers.html/Seema_Gupta', views.speakerDesc21, name='speaker_description21'),\n path('/speakers.html/Shiva_Sah', views.speakerDesc22, name='speaker_description22'),\n\n path('/about_us.html/Jaikishan_Mansukhani', views.memberDesc1, name='member_description1'),\n path('/about_us.html/Anvita_Kodru', views.memberDesc2, name='member_description2'),\n path('/about_us.html/Sreesiddesh_Bhavanasi', views.memberDesc3, name='member_description3'),\n path('/about_us.html/Shivangi_Kumar', views.memberDesc4, name='member_description4'),\n path('/about_us.html/Vishwaprasanna_Hariharan', views.memberDesc5, name='member_description5'),\n path('/about_us.html/Samarth_Saraswat', views.memberDesc6, name='member_description6'),\n path('/about_us.html/Aarya_Shrivastava', views.memberDesc7, name='member_description7'),\n path('/about_us.html/Amey_Varhade', views.memberDesc8, name='member_description8'),\n path('/about_us.html/Anindya_Rajan', views.memberDesc9, name='member_description9'),\n path('/about_us.html/Anisha_Khati', views.memberDesc10, name='member_description10'),\n path('/about_us.html/Ankit_Raj', views.memberDesc11, name='member_description11'),\n path('/about_us.html/Anushka_Anand', views.memberDesc12, name='member_description12'),\n path('/about_us.html/Anushka_Srivastava', views.memberDesc13, name='member_description13'),\n path('/about_us.html/Arpita_Mohapatra', views.memberDesc14, name='member_description14'),\n path('/about_us.html/Ayush_Srivastava', views.memberDesc15, name='member_description15'),\n path('/about_us.html/Digisha_Verma', views.memberDesc16, name='member_description16'),\n path('/about_us.html/Emily_Huiling', views.memberDesc17, name='member_description17'),\n path('/about_us.html/Gourav_Kumar', views.memberDesc18, name='member_description18'),\n path('/about_us.html/Govind_Singh', views.memberDesc19, name='member_description19'),\n path('/about_us.html/Jaideep_Buksagarmath', views.memberDesc20, name='member_description20'),\n path('/about_us.html/Janhavi_Lande', views.memberDesc21, name='member_description21'),\n path('/about_us.html/Lalika_Laya_K', views.memberDesc22, name='member_description22'),\n path('/about_us.html/Miloni_Patel', views.memberDesc23, name='member_description23'),\n path('/about_us.html/Monalisha_Majumder', views.memberDesc24, name='member_description24'),\n path('/about_us.html/Niladri_Sarkar', views.memberDesc25, name='member_description25'),\n path('/about_us.html/Nishant', views.memberDesc26, name='member_description26'),\n path('/about_us.html/Nishtha_Sharma', views.memberDesc27, name='member_description27'),\n path('/about_us.html/Pragnya_Ramjee', views.memberDesc28, name='member_description28'),\n path('/about_us.html/Pratyanshu_Raj_Singh', views.memberDesc29, name='member_description29'),\n path('/about_us.html/Ritik_Singh', views.memberDesc30, name='member_description30'),\n path('/about_us.html/Sai_Sreyas_Ray', views.memberDesc31, name='member_description31'),\n path('/about_us.html/Shatakshi_Kaushal', views.memberDesc32, name='member_description32'),\n path('/about_us.html/Shiva_Sah', views.memberDesc33, name='member_description33'),\n path('/about_us.html/Sudarshan_Birla', views.memberDesc34, name='member_description34'),\n path('/about_us.html/Titiksha', views.memberDesc35, name='member_description35'),\n\n\n # path('add_speaker', views.addSpeaker),\n]\n", "sub_path": "Tedwebsite/speakers/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 7664, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 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": 35, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "django.urls.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": 40, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 41, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 42, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 43, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 45, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 46, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 47, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 48, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 49, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 50, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 51, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 53, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 54, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 55, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 56, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 57, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 58, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 60, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 61, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 62, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 63, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 64, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 65, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 66, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 67, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 68, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 70, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 71, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 72, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 73, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 74, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 75, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 76, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 77, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 78, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 79, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 80, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 81, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 82, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 83, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 84, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 85, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 86, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 87, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 88, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 89, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 90, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 91, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 92, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 93, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 94, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 95, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 96, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 97, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 98, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 99, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 100, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 101, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 102, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 103, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 104, "usage_type": "call"}]} +{"seq_id": "19190122", "text": "from __future__ import absolute_import\nfrom collections import defaultdict\n\n\nclass Metadata(object):\n CAMERA_ITEMS = [\n 'EXIF.FocalLength',\n 'EXIF.FocalLengthIn35mmFormat',\n 'Composite.ScaleFactor35efl',\n 'EXIF.DigitalZoomRatio',\n ('Composite.ShutterSpeed', 'EXIF.ShutterSpeedValue'),\n 'EXIF.ApertureValue',\n 'EXIF.ISO',\n 'EXIF.Flash',\n 'EXIF.WhiteBalance',\n 'EXIF.MeteringMode',\n 'EXIF.ExposureMode',\n 'EXIF.ExposureProgram',\n 'EXIF.ExposureTime',\n 'EXIF.FNumber',\n 'Composite.CircleOfConfusion',\n 'Composite.FOV',\n 'Composite.HyperfocalDistance',\n 'EXIF.BrightnessValue',\n 'Composite.LightValue']\n\n IMAGE_ITEMS = [\n ('PNG.ImageWidth', 'EXIF.ExifImageWidth', 'File.ImageWidth'),\n ('PNG.ImageHeight', 'EXIF.ExifImageHeight', 'File.ImageHeight'),\n 'EXIF.Orientation',\n ('EXIF.XResolution', 'JFIF.XResolution'),\n ('EXIF.YResolution', 'JFIF.YResolution'),\n ('EXIF.ResolutionUnit', 'JFIF.ResolutionUnit'),\n 'MakerNotes.HDRImageType',\n 'EXIF.Gamma',\n 'EXIF.Sharpness',\n 'EXIF.SensingMethod',\n 'EXIF.SceneType',\n 'EXIF.SceneCaptureType',\n 'EXIF.SubjectArea',\n 'EXIF.SubjectDistanceRange',\n 'PNG.Filter',\n 'PNG.Interlace',\n ('PNG.BitDepth', 'File.BitsPerSample'),\n ('PNG.ColorType', 'EXIF.ColorSpace'),\n 'File.ColorComponents',\n 'EXIF.ComponentsConfiguration',\n 'EXIF.YCbCrPositioning',\n 'File.YCbCrSubSampling',\n 'File.FileType',\n 'File.FileSize',\n ('PNG.Compression', 'EXIF.Compression'),\n 'File.EncodingProcess',\n 'File.ExifByteOrder']\n\n ENVIRONMENT_ITEMS = [\n 'EXIF.Software',\n 'EXIF.ExifVersion',\n 'EXIF.FlashpixVersion',\n 'JFIF.JFIFVersion',\n 'XMP.XMPToolkit',\n 'EXIF.InteropVersion',\n 'EXIF.InteropIndex',\n 'Composite.RunTimeSincePowerUp']\n\n HARDWARE_ITEMS = [\n 'EXIF.Make',\n 'EXIF.Model',\n 'EXIF.LensMake',\n 'EXIF.LensModel',\n 'EXIF.LensInfo']\n\n GPS_ITEMS = [\n 'Composite.GPSAltitude',\n 'Composite.GPSLatitude',\n 'Composite.GPSLongitude',\n 'Composite.GPSPosition',\n 'EXIF.GPSDOP',\n 'EXIF.GPSImgDirection',\n 'EXIF.GPSImgDirectionRef']\n\n def __init__(self, attributes):\n self.items = defaultdict(MetadataItem)\n\n for key, data in attributes.iteritems():\n item_key, kind = key.rsplit('.', 1)\n self.items[item_key].add_data(kind, data)\n\n for key, item in self.items.iteritems():\n item.key = key\n\n def __getitem__(self, item_key):\n if self.items[item_key].is_built():\n return self.items[item_key]\n else:\n return None\n\n def get_categories(self):\n return {\n 'camera': self._collect_items(self.CAMERA_ITEMS),\n 'image': self._collect_items(self.IMAGE_ITEMS),\n 'environment': self._collect_items(self.ENVIRONMENT_ITEMS),\n 'hardware': self._collect_items(self.HARDWARE_ITEMS),\n 'gps': self._collect_items(self.GPS_ITEMS)}\n\n def to_dict(self):\n result = {}\n for category, items in self.get_categories().iteritems():\n result[category] = [i.to_dict() for i in items]\n\n return result\n\n def _collect_items(self, category):\n items = [self._lookup_item(key) for key in category]\n\n return [itm for itm in items if itm is not None]\n\n def _lookup_item(self, key):\n if not isinstance(key, tuple):\n key = (key, )\n\n for candidate in key:\n item = self[candidate]\n if item:\n return item\n\n return None\n\n\nclass MetadataItem(object):\n def __init__(self):\n self.key = None\n self.description = None\n self.raw_value = None\n self.value = None\n\n def __repr__(self):\n return '<{} key={}>'.format(self.__class__.__name__, self.key)\n\n def add_data(self, kind, data):\n if kind == 'desc':\n self.description = data\n elif kind == 'num':\n self.raw_value = data\n elif kind == 'val':\n self.value = data\n else:\n raise AttributeError('Unknown kind {}'.format(kind))\n\n def is_built(self):\n if not (self.description or self.key):\n return False\n elif not (self.value or self.raw_value):\n return False\n else:\n return True\n\n def to_dict(self):\n return {\n 'key': self.key,\n 'description': self.description,\n 'raw_value': self.raw_value,\n 'value': self.value}\n\n @property\n def display_name(self):\n return self.description or ''.format(self.key)\n\n @property\n def display_value(self):\n return self.value or ''.format(self.raw_value)\n", "sub_path": "windowbox/models/metadata.py", "file_name": "metadata.py", "file_ext": "py", "file_size_in_byte": 5015, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "collections.defaultdict", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "317169415", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# quiz.py\n#\n\nfrom flask import g\nfrom modele import *\nfrom views import *\n\n\napp.config.update(dict(\n SECRET_KEY='7m0lbl6znb8j92lasdqj4jnhlajsd'\n))\n\n\n@app.before_request\ndef before_request():\n g.db = baza\n g.db.connect()\n\n\n@app.after_request\ndef after_request(response):\n g.db.close()\n return response\n\n\n@app.route(\"/klasa\")\ndef klasa():\n return render_template('klasa/klasa.html')\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n", "sub_path": "Webmaster/quiz/quiz.py", "file_name": "quiz.py", "file_ext": "py", "file_size_in_byte": 501, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask.g.db", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.g.db.connect", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.g.db", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.g.db.close", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.g.db", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "614104986", "text": "import numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom mpl_toolkits.mplot3d import Axes3D\r\n\r\nfig = plt.figure(figsize =(8, 3))\r\nax1 = plt.subplot(231, projection=\"3d\")\r\nax2 = plt.subplot(232, projection=\"3d\")\r\nax3 = plt.subplot(233, projection=\"3d\")\r\nax4 = plt.subplot(234, projection=\"3d\")\r\nax5 = plt.subplot(235, projection=\"3d\")\r\nax6 = plt.subplot(236, projection=\"3d\")\r\n\r\n_x = np.arange(4)\r\n_y = np.arange(5)\r\n_xx, _yy = np.meshgrid(_x, _y)\r\nx, y = _xx.ravel(), _yy.ravel()\r\ntop = x + y\r\nbottom = np.zeros_like(top)\r\nwidth = depth = 1\r\nax1.bar3d(x, y, bottom, width, depth, top, shade=True)\r\nax2.bar3d(x, y, bottom, width, depth, top, shade=True, color=\"m\")\r\nax3.bar3d(x, y, bottom, width, depth, top, shade=True, color=\"c\")\r\nax4.bar3d(x, y, bottom, width, depth, top, shade=True, color=\"r\")\r\nax5.bar3d(x, y, bottom, width, depth, top, shade=True, color=\"g\")\r\nax6.bar3d(x, y, bottom, width, depth, top, shade=True, color=\"b\")\r\nplt.show()\r\n", "sub_path": "cw11/cw11_z4.py", "file_name": "cw11_z4.py", "file_ext": "py", "file_size_in_byte": 946, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 5, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 5, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 6, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "473208755", "text": "from django.urls import path\n\nfrom .import views\nurlpatterns = [\n path('',views.home,name='home'), \n path(r'ds',views.ds,name='ds'),\n path(r'wd',views.wd,name='wd'),\n path(r'tags',views.tags,name='tags'),\n path('open',views.open,name='open'),\n path(r'answer',views.answer,name='answer'),\n path('camp_qs',views.camp_qs,name='camp_qs'), \n path('ask_submit',views.ask_submit,name='ask_submit'),\n\n \n \n\n\n\n \n\n\n]", "sub_path": "hello/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 442, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "547744331", "text": "import numpy as np\nimport pandas as pd\nimport os\nfrom sklearn.model_selection import train_test_split\n\ndef get_data():\n # Load bottleneck features\n data_folder = os.environ[\"AZUREML_DATAREFERENCE_workspaceblobstore\"]\n file_name = os.path.join(data_folder, 'used_cars', 'UsedCars_Affordability.csv')\n \n print(\"Data folder:\", data_folder)\n print(\"Dataset:\", file_name)\n print(\"Data folder content:\", os.listdir(data_folder))\n \n df_affordability = pd.read_csv(file_name, delimiter=',')\n\n features = df_affordability[[\"Age\", \"KM\"]]\n labels = df_affordability[[\"Affordable\"]]\n\n \n # Split the data into training and validation partitions \n train_X, test_X, train_Y, test_Y = train_test_split(features, labels,\n test_size=0.2,\n shuffle=True)\n # Flatten labels\n train_Y = np.ravel(train_Y)\n test_Y = np.ravel(test_Y)\n \n # Convert to float\n train_X = train_X.astype(float)\n test_X = test_X.astype(float)\n \n\n return {'X': train_X, 'y': train_Y, 'X_valid': test_X, 'y_valid': test_Y}\n", "sub_path": "03-AutomatedML/get_data.py", "file_name": "get_data.py", "file_ext": "py", "file_size_in_byte": 1181, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "os.environ", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "646877736", "text": "# encoding: utf-8\n\"\"\"\n대출거래약정서 p2\n\"\"\"\n\nimport datetime\nimport logging\n\nfrom PyPDF2 import PdfFileWriter, PdfFileReader\n\nimport register_loan_contracts as app_root\n\nlogger = logging.getLogger('loan_contracts_www.custom')\n\nDOC_NAME = 'loan_confirm'\n\n\ndef create_pdf_loan_confirm02(self, **kwargs):\n\n original_pdf_file = PdfFileReader(open(app_root.PDF_BASE_DIR + '/' + DOC_NAME + '.pdf', 'rb'))\n output = PdfFileWriter()\n page = original_pdf_file.getPage(1)\n output.addPage(page)\n ts = datetime.datetime.now().isoformat()\n result_file_full_path = app_root.RESULT_FILE_BASE_DIR + '/' + (self.RESULT_FILE_NAME + DOC_NAME) % ts\n output_stream = open(result_file_full_path, 'wb')\n output.write(output_stream)\n output_stream.close()\n\n return result_file_full_path\n", "sub_path": "loan_contracts/register_loan_contracts/createpdfs/loan_confirm02.py", "file_name": "loan_confirm02.py", "file_ext": "py", "file_size_in_byte": 800, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "PyPDF2.PdfFileReader", "line_number": 20, "usage_type": "call"}, {"api_name": "register_loan_contracts.PDF_BASE_DIR", "line_number": 20, "usage_type": "attribute"}, {"api_name": "PyPDF2.PdfFileWriter", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "attribute"}, {"api_name": "register_loan_contracts.RESULT_FILE_BASE_DIR", "line_number": 25, "usage_type": "attribute"}]} +{"seq_id": "574512928", "text": "\nimport os\nfrom notion.client import NotionClient\nfrom flask import Flask\nfrom flask import request\n\n\napp = Flask(__name__)\n\ndef trackWeather(token, URL, weather):\n # notion\n client = NotionClient(token_v2=\"a46654d8d6459ab8b82ea9e60f831870e86152e752ef47099ef51893b5e3b59ccad5241ad02a9dc6ad4d897ba24ce1616d06d9c8174d84d657a5d8c4347460ed66c6cf71914adbdada3f4497ca62\")\n block = client.get_block(\"https://www.notion.so/fluco/Test-Heroku-d78610c1a5aa4a129cb509f5eecec7f7#472993c88905453c9f8ca81ac3c08f0e\")\n block.title = weather\n\ndef createTweet(token, collectionURL, tweet, author, followers):\n # notion\n client = NotionClient(token)\n cv = client.get_collection_view(collectionURL)\n row = cv.collection.add_row()\n row.tweet = tweet\n row.author = author\n row.followers = followers\n\n\ndef createTask(token, collectionURL, content):\n # notion\n client = NotionClient(token)\n cv = client.get_collection_view(collectionURL)\n row = cv.collection.add_row()\n row.task = content\n\n\ndef createReceipt(token, collectionURL, product, content, url, date):\n # notion\n client = NotionClient(token)\n cv = client.get_collection_view(collectionURL)\n row = cv.collection.add_row()\n row.product = product\n row.content = content\n row.url = url\n row.date = date\n\n\ndef createEmail(token, collectionURL, sender, subject, message_url):\n # notion\n client = NotionClient(token)\n cv = client.get_collection_view(collectionURL)\n row = cv.collection.add_row()\n row.sender = sender\n row.subject = subject\n row.message_url = message_url\n\n\n@app.route('/twitter', methods=['GET'])\ndef twitter():\n tweet = request.args.get('tweet')\n author = request.args.get('author')\n followers = request.args.get('followers')\n token_v2 = os.environ.get(\"TOKEN\")\n url = os.environ.get(\"URL\")\n createTweet(token_v2, url, tweet, author, followers)\n return f'added {tweet} to Notion'\n\n\n@app.route('/tasks', methods=['GET'])\ndef tasks():\n todo = request.args.get('task')\n token_v2 = os.environ.get(\"TOKEN\")\n url = os.environ.get(\"URL\")\n createTask(token_v2, url, todo)\n return f'added {todo} to Notion'\n\n\n@app.route('/gmailreceipts', methods=['GET'])\ndef gmailReceipt():\n product = request.args.get('product')\n content = request.args.get('content')\n message_url = request.args.get('url')\n date = request.args.get('date')\n token_v2 = os.environ.get(\"TOKEN\")\n url = os.environ.get(\"URL\")\n createReceipt(token_v2, url, product, content, message_url, date)\n return f'added {product} receipt to Notion'\n\n\n@app.route('/createemail', methods=['GET'])\ndef gmailUrgentEmail():\n sender = request.args.get('sender')\n subject = request.args.get('subject')\n message_url = request.args.get('url')\n token_v2 = os.environ.get(\"TOKEN\")\n url = os.environ.get(\"URL\")\n createEmail(token_v2, url, sender, subject, message_url)\n return f'added email from {sender} to Notion'\n\n@app.route('/getweather', methods=['GET'])\ndef getWeather():\n weather = str(request.args.get('seconds'))\n token_v2 = os.environ.get(\"TOKEN\")\n url = os.environ.get(\"URL\")\n trackWeather(token_v2, url, weather)\n return f'added {weather} to Notion'\n\n\nif __name__ == '__main__':\n app.debug = True\n port = int(os.environ.get(\"PORT\", 5000))\n app.run(host='0.0.0.0', port=port)\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 3361, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "notion.client.NotionClient", "line_number": 12, "usage_type": "call"}, {"api_name": "notion.client.NotionClient", "line_number": 18, "usage_type": "call"}, {"api_name": "notion.client.NotionClient", "line_number": 28, "usage_type": "call"}, {"api_name": "notion.client.NotionClient", "line_number": 36, "usage_type": "call"}, {"api_name": "notion.client.NotionClient", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 57, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 59, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 60, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 61, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 61, "usage_type": "attribute"}, {"api_name": "flask.request.args.get", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 68, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 68, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 69, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 70, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 70, "usage_type": "attribute"}, {"api_name": "flask.request.args.get", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 78, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 79, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 80, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 80, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 81, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 82, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 82, "usage_type": "attribute"}, {"api_name": "flask.request.args.get", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 89, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 89, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 90, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 90, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 90, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 91, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 91, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 92, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 93, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 93, "usage_type": "attribute"}, {"api_name": "flask.request.args.get", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 99, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 99, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 100, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 100, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 101, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 108, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 108, "usage_type": "attribute"}]} +{"seq_id": "477962607", "text": "import requests\nimport pytz \nfrom datetime import datetime, time\n\n\ndef local_time(tstamp, tzone):\n return pytz.utc.localize(datetime.utcfromtimestamp(tstamp)).astimezone(pytz.timezone(tzone)).time()\n\n\ndef check_owl(night_start, night_end, time):\n return night_start < time and night_end > time\n\n\ndef get_night_owls_list(night_start, night_end):\n night_owls_list = set()\n page_number = 1\n number_of_pages = 1\n url = 'https://devman.org/api/challenges/solution_attempts/'\n while page_number <= number_of_pages:\n params = {'page': page_number}\n data = requests.get(url, params=params).json()\n number_of_pages = data['number_of_pages']\n for item in data['records']:\n if item['timestamp'] and check_owl(night_start, night_end, local_time(item['timestamp'], item['timezone'])):\n night_owls_list.add(item['username'])\n page_number += 1\n return night_owls_list\n\n\nif __name__ == '__main__':\n night_start = time(0, 0)\n night_end = time(7, 0)\n print('Midnight developers are:')\n for night_owl in get_night_owls_list(night_start, night_end):\n print(night_owl)", "sub_path": "seek_dev_nighters.py", "file_name": "seek_dev_nighters.py", "file_ext": "py", "file_size_in_byte": 1159, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pytz.utc.localize", "line_number": 7, "usage_type": "call"}, {"api_name": "pytz.utc", "line_number": 7, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 7, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.time", "line_number": 11, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.time", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.time", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "44235807", "text": "import pandas as pd\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn import metrics\nimport seaborn as sns\nimport numpy as np\nfrom sklearn.model_selection import RandomizedSearchCV\nfrom scipy.stats import loguniform\nfrom sklearn.model_selection import RepeatedStratifiedKFold\nfrom sklearn import preprocessing\n\n\nclass DiabetesClassifier:\n def __init__(self) -> None:\n col_names = ['pregnant', 'glucose', 'bp', 'skin',\n 'insulin', 'bmi', 'pedigree', 'age', 'label']\n self.pima = pd.read_csv('diabetes.csv', header=0,\n names=col_names, usecols=col_names)\n print(self.pima.corr().T)\n self.X_test = None\n self.y_test = None\n\n def define_feature(self):\n # ------Solution 1---------\n # feature_cols = ['pregnant', 'glucose', 'bmi', 'age']\n # --------Solution 2 & Solution 3---------\n # feature_cols = ['pregnant', 'glucose', 'pedigree', 'bmi', 'age']\n feature_cols = ['pregnant', 'insulin', 'bmi', 'age']\n X = self.pima[feature_cols]\n y = self.pima.label\n\n return X, y\n\n def train(self):\n # split X and y into training and testing sets\n X, y = self.define_feature()\n X_train, self.X_test, y_train, self.y_test = train_test_split(\n X, y, random_state=0)\n # train a logistic regression model on the training set\n logreg = LogisticRegression(tol=10)\n # -----------_Solution 3------------\n #logreg = LogisticRegression(tol=10)\n # --------Solution 2 ---------\n # train a logistic regression model on the training set\n # logreg = LogisticRegression(\n # C=0.30971587230022724, penalty='l2', solver='saga', max_iter=5000)\n # grid_values = {'penalty': ['l1', 'l2'], 'C': [\n # 0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n # cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n # space = dict()\n # space['solver'] = ['newton-cg', 'lbfgs', 'liblinear']\n # space['penalty'] = ['none', 'l1', 'l2', 'elasticnet']\n # space['C'] = loguniform(1e-5, 100)\n # search = RandomizedSearchCV(\n # logreg, space, n_iter=500, scoring='accuracy', n_jobs=-1, cv=cv, random_state=1)\n # search.fit(X_train, y_train)\n # print(clf.best_score_)\n # model_lr = GridSearchCV(logreg, param_grid=grid_values)\n # model_lr.fit(X_train, y_train)\n # print(search.best_params_)\n # --------Solution 2 & Solution 3 end---------\n\n logreg.fit(X_train, y_train)\n return logreg\n\n def predict(self):\n model = self.train()\n y_pred_class = model.predict(self.X_test)\n return y_pred_class\n\n def calculate_accuracy(self, result):\n return metrics.accuracy_score(self.y_test, result)\n\n def examine(self):\n dist = self.y_test.value_counts()\n print(dist)\n percent_of_ones = self.y_test.mean()\n percent_of_zeros = 1 - self.y_test.mean()\n return self.y_test.mean()\n\n def confusion_matrix(self, result):\n return metrics.confusion_matrix(self.y_test, result)\n\n\nif __name__ == \"__main__\":\n classifer = DiabetesClassifier()\n result = classifer.predict()\n print(f\"Predicition={result}\")\n score = classifer.calculate_accuracy(result)\n print(f\"score={score}\")\n con_matrix = classifer.confusion_matrix(result)\n print(f\"confusion_matrix=${con_matrix}\")\n", "sub_path": "lab3/lab3.py", "file_name": "lab3.py", "file_ext": "py", "file_size_in_byte": 3625, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pandas.read_csv", "line_number": 20, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 40, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 43, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 75, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 75, "usage_type": "name"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 85, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 85, "usage_type": "name"}]} +{"seq_id": "306640536", "text": "import urllib.request\nfrom bs4 import BeautifulSoup\nimport re\nimport pandas as pd\nimport numpy as np\n\nnew_index = []\nwiki = \"https://en.wikipedia.org/wiki/2019_in_spaceflight#Orbital_launches\"\n\n\ndef get_date_series():\n dates = pd.date_range(start='1-1-2019', periods=365, tz='UTC')\n df = pd.DataFrame(np.zeros((365,), dtype=int), index=dates, columns=['value'])\n return df\n\n\ndef check_qualification(new_index, table_rows):\n num = len(new_index)\n start, end = new_index[num - 2], new_index[num - 1]\n # print(start)\n for k in range(start, end):\n # print(str(table_rows[i]))\n if orbit_launch(str(table_rows[k])):\n return True\n\n return False\n\n\ndef orbit_launch(text):\n target_info = ['Successful', 'Operational', 'En Route']\n for target in target_info:\n if re.search(target, text):\n return True\n\n return False\n\n\ndef month_string_to_number(string):\n m = {\n 'jan': 1,\n 'feb': 2,\n 'mar': 3,\n 'apr': 4,\n 'may': 5,\n 'jun': 6,\n 'jul': 7,\n 'aug': 8,\n 'sep': 9,\n 'oct': 10,\n 'nov': 11,\n 'dec': 12\n }\n s = string.strip()[:3].lower()\n\n try:\n out = m[s]\n return out\n except:\n raise ValueError('Not a month')\n\n\ndef gen_index(text):\n pattern = re.compile(r'\\d+\\s\\w+')\n match = pattern.match(text)\n month_raw = match.group().split(' ')[1][:-2]\n month = month_string_to_number(month_raw)\n day = match.group().split(' ')[0]\n year = '2019'\n iso = ' 00:00:00+00:00'\n return year + '-' + str(month) + '-' + day + ' ' + iso\n\n\ndef get_table(url):\n page = urllib.request.urlopen(url)\n soup = BeautifulSoup(page)\n right_table = soup.find('table', class_='wikitable collapsible')\n table_rows = right_table.find_all('tr')\n return table_rows\n\n\ndef spider():\n table_rows = get_table(wiki)\n df = get_date_series()\n for i in range(int(len(table_rows))):\n date_text = table_rows[i].find('td')\n if date_text:\n # find all the distinct launches!\n if re.search(\"\\d+\\s\\w+\\d+:\\d|\\d+\\s\\w+\\[\", date_text.text):\n # find the date of launch\n pattern = re.compile(r'\\d+\\s\\w+')\n match = pattern.match(date_text.text)\n new_index.append(i)\n if len(new_index) >= 2:\n pre_date_text = table_rows[new_index[len(new_index) - 2]].find('td')\n if check_qualification(new_index, table_rows):\n print('This launch is qualified!')\n index = gen_index(pre_date_text.text)\n df['value'][index] += 1\n else:\n print(\"This launch is unqualified!\")\n new_index.remove(new_index[len(new_index) - 2])\n\n else:\n print(\"Tag td is not found!\")\n new_index.append(len(table_rows) - 1)\n pre_date_text = table_rows[new_index[len(new_index) - 2]].find('td')\n index = gen_index(pre_date_text.text)\n df['value'][index] += 1\n df.to_csv(r'export_dataframe.csv', index=True, index_label='Date')\n\n\nif __name__ == '__main__':\n spider()\n", "sub_path": "scrapy.py", "file_name": "scrapy.py", "file_ext": "py", "file_size_in_byte": 3210, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pandas.date_range", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 13, "usage_type": "call"}, {"api_name": "re.search", "line_number": 32, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 63, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 74, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 74, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 74, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 75, "usage_type": "call"}, {"api_name": "re.search", "line_number": 88, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 90, "usage_type": "call"}]} +{"seq_id": "78926429", "text": "\"\"\"\nThis script is intended to be run on an Amazon ECS container, so information for\nthe job either needs to be provided in environment variables (e.g., the\nREACH version and path) or loaded from S3 (e.g., the list of PMIDs).\n\"\"\"\nfrom __future__ import absolute_import, print_function, unicode_literals\nfrom builtins import dict, str\n\nif __name__ == '__main__':\n from indra.tools.reading import run_reach_on_pmids as rr\n import boto3\n import botocore\n import os\n import sys\n import pickle\n import logging\n\n logger = logging.getLogger('run_reach_on_pmids_aws')\n\n client = boto3.client('s3')\n bucket_name = 'bigmech'\n basename = sys.argv[1]\n pmid_list_key = 'reading_results/%s/pmids' % sys.argv[1]\n tmp_dir = sys.argv[2]\n num_cores = int(sys.argv[3])\n start_index = int(sys.argv[4])\n end_index = int(sys.argv[5])\n path_to_reach = os.environ.get('REACH_JAR_PATH')\n reach_version = os.environ.get('REACH_VERSION')\n if path_to_reach is None or reach_version is None:\n print('REACH_JAR_PATH and/or REACH_VERSION not defined, exiting.')\n sys.exit(1)\n\n try:\n pmid_list_obj = client.get_object(Bucket=bucket_name, Key=pmid_list_key)\n # Handle a missing object gracefully\n except botocore.exceptions.ClientError as e:\n if e.response['Error']['Code'] =='NoSuchKey':\n logger.info('Could not find PMID list file at %s, exiting' %\n pmid_list_key)\n sys.exit(1)\n # If there was some other kind of problem, re-raise the exception\n else:\n raise e\n # Get the content from the object\n pmid_list_str = pmid_list_obj['Body'].read().decode('utf8').strip()\n pmid_list = [line.strip() for line in pmid_list_str.split('\\n')]\n\n # Run the REACH reading pipeline\n (stmts, content_types) = rr.run(pmid_list, tmp_dir, num_cores, start_index,\n end_index, False, False, path_to_reach,\n reach_version, cleanup=False, verbose=True)\n # Pickle the content types to S3\n ct_key_name = 'reading_results/%s/content_types/%d_%d.pkl' % \\\n (basename, start_index, end_index)\n logger.info(\"Saving content types for %d papers to %s\" %\n (len(stmts), ct_key_name))\n ct_bytes = pickle.dumps(content_types)\n client.put_object(Key=ct_key_name, Body=ct_bytes, Bucket=bucket_name)\n # Pickle the statements to a bytestring\n pickle_key_name = 'reading_results/%s/stmts/%d_%d.pkl' % \\\n (basename, start_index, end_index)\n logger.info(\"Saving stmts for %d papers to %s\" %\n (len(stmts), pickle_key_name))\n stmts_bytes = pickle.dumps(stmts)\n client.put_object(Key=pickle_key_name, Body=stmts_bytes,\n Bucket=bucket_name)\n\n\n", "sub_path": "indra/tools/reading/run_reach_on_pmids_aws.py", "file_name": "run_reach_on_pmids_aws.py", "file_ext": "py", "file_size_in_byte": 2832, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 24, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 26, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 28, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 29, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 32, "usage_type": "call"}, {"api_name": "botocore.exceptions", "line_number": 37, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 41, "usage_type": "call"}, {"api_name": "indra.tools.reading.run_reach_on_pmids.run", "line_number": 50, "usage_type": "call"}, {"api_name": "indra.tools.reading.run_reach_on_pmids", "line_number": 50, "usage_type": "name"}, {"api_name": "pickle.dumps", "line_number": 58, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 65, "usage_type": "call"}]} +{"seq_id": "508652968", "text": "'''\n@Author: ruoru\n@Date: 2019-12-02 10:43:49\n@LastEditors : ruoru\n@LastEditTime : 2019-12-19 18:55:28\n@Description: https://leetcode-cn.com/explore/interview/card/top-interview-questions-easy/23/dynamic-programming/56/\n'''\n\nfrom typing import List\n\n# daily-exercise/leetcode_53_maximum_subarray.py\n\n\n# 网上参考动态规划的解法\n# dp 存下当前位置能获取到的伪最大和，最后取sum\n# 在当前位置可取得的伪最大和为：max(前一个位置的最大和加上当前位置, 当前位置的数字)\n# 不一定是最大的，因为可能不加上当前位置更大，但是不加上当前位置，就等于是前一个位置的伪最大和\n# 因此最后取 sum 就好了\nclass Solution2:\n def maxSubArray(self, nums: List[int]) -> int:\n m = len(nums)\n if m == 1:\n return nums[0]\n\n dp = [0 for _ in range(m)]\n dp[0] = nums[0]\n\n for i in range(1, m):\n dp[i] = max(dp[i-1]+nums[i], nums[i])\n\n return max(dp)\n\n\n# 在参考答案的基础上，如果考虑的是当前位置的最大和呢？\n# 不能用前一个位置的最大和与当前位置的去比\n# 即 dp[i] 位置不能存的是当前位置的最大和，因为这个最大和可能和下一个位置不是连接的位置\n# 这个答案不正确\nclass Solution2:\n def maxSubArray(self, nums: List[int]) -> int:\n m = len(nums)\n if m == 1:\n return nums[0]\n\n dp = [0 for _ in range(m)]\n dp[0] = nums[0]\n\n for i in range(1, m):\n dp[i] = max([dp[i-1], dp[i-1]+nums[i], nums[i]])\n\n print(dp)\n return dp[m-1]\n\n# 只能说把最后max 的运算提前\n\n\nclass Solution:\n def maxSubArray(self, nums: List[int]) -> int:\n m = len(nums)\n if m == 1:\n return nums[0]\n\n dp = [0 for _ in range(m)]\n dp[0] = nums[0]\n max_sum = dp[0]\n for i in range(1, m):\n dp[i] = max(dp[i-1]+nums[i], nums[i])\n if dp[i] > max_sum:\n max_sum = dp[i]\n\n return max_sum\n\n\n# 这个参考答案也很巧妙\nclass Solution:\n def maxSubArray(self, nums: List[int]) -> int:\n for i in range(1, len(nums)):\n if nums[i - 1] > 0:\n nums[i] += nums[i - 1]\n return max(nums)\n\n\n\nif __name__ == \"__main__\":\n s = Solution()\n print(s.maxSubArray([-2, 1, -3, 4, -1, 2, 1, -5, 4]))\n", "sub_path": "explore_cn/top_interview_questions_easy/f_dynamic_programming/06_dynamic_programming_56.py", "file_name": "06_dynamic_programming_56.py", "file_ext": "py", "file_size_in_byte": 2399, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "typing.List", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 75, "usage_type": "name"}]} +{"seq_id": "522879132", "text": "#!/usr/bin/env python\n\n# Copyright Jacob Bennett 4/8/19\n\nfrom flask import render_template, request, session, flash, send_file, abort, redirect, url_for, jsonify, json\nfrom config import app, db, pepper, bsalt, mailgunkey\nfrom Models.models import User, Resetkey\nfrom utils import codegen, escapeit\nfrom datetime import datetime\nfrom hashlib import md5\nimport bcrypt, requests\n\n@app.route('/i/resetkey', methods=['GET', 'POST'])\ndef resetkey():\n title = 'Reset Key'\n if request.args.get('resetcode'):\n if request.method == 'GET':\n return render_template('resetkey.html', title=title)\n else:\n resetcode = request.args.get('resetcode')\n usercheck = Resetkey.query.filter_by(code=resetcode).first()\n if usercheck:\n user = User.query.filter_by(id=usercheck.userid).first()\n key = request.form['key']\n if len(key) < 6: # Key can't be less than 6\n flash('Key needs to be at least 6 characters!')\n return render_template('resetkey.html', title=title)\n else:\n formkey = key.encode('utf-8')\n theemail = user.email.encode('utf-8')\n pepperkey = pepper.encode('utf-8')\n key = md5(theemail + formkey + pepperkey).hexdigest()\n key = bcrypt.hashpw(key.encode('utf-8'), bsalt).decode('utf-8')\n user.key = key\n db.session.delete(usercheck)\n db.session.commit()\n flash('Key Reset!')\n return redirect(url_for('signin'))\n else:\n abort(404)\n else:\n if request.method == 'GET':\n return render_template('resetkeyreq.html', title=title)\n else:\n email = request.form['email']\n user = User.query.filter_by(email=email).first()\n if user:\n resetcheck = Resetkey.query.filter_by(userid=user.id).first()\n if resetcheck:\n page = {\n 'title': 'Reset link sent',\n 'header': 'Check your email!',\n 'content': 'It appears a request to reset your key as already been received. If the \"Reset Key\" email does not appear in your inbox, check your spam folder.',\n 'time': None\n }\n return render_template('document.html', page=page)\n else:\n resetcode = codegen(size=21)\n resetreq = Resetkey(resetcode, user.id)\n db.session.add(resetreq)\n db.session.commit()\n requests.post(\n \"https://api.mailgun.net/v3/linknob.com/messages\",\n auth=(\"api\", mailgunkey),\n data={\"from\": \"Linknob \",\n \"to\": [email],\n \"subject\": \"Reset Key\",\n \"html\": '

We have received a request to reset your key.

To reset your key, click the button below:

Reset'})\n page = {\n 'title': 'Reset link sent',\n 'header': 'Check your email!',\n 'content': 'An email has been sent to you with a link to reset your key.',\n 'time': None\n }\n return render_template('document.html', page=page)\n else:\n flash('No user is registered with that email!')\n return render_template('resetkeyreq.html', title=title)\n\n# Basic favicon URL\n@app.route('/favicon.ico')\ndef faviconico():\n return send_file('static/images/favicon.ico', mimetype='image/png')\n\n\n@app.route('/api/classify')\n@app.route('/api/classify/')\ndef redirectToClassify(page=None):\n if page:\n return redirect('https://datauplift.com/api/classify/'+page)\n else:\n return redirect('https://datauplift.com/api/classify')\n\n\n\n@app.route('/api/contact', methods=['POST', 'OPTIONS'])\ndef contactme():\n if (request.method == 'POST'):\n reqData = json.loads(request.data)\n name = escapeit(reqData['name'])\n email = escapeit(reqData['email'])\n msg = escapeit(reqData['msg'])\n\n requests.post(\n \"https://api.mailgun.net/v3/linknob.com/messages\",\n auth=(\"api\", mailgunkey),\n data={\"from\": \"Contact Form (Linknob API) \",\n \"to\": [\"jacobwbennett@gmail.com\"],\n \"subject\": name + \" | Contact Form\",\n \"html\": ''+ msg + '
------------------------
From: ' + email + ''})\n\n return jsonify()\n else:\n return jsonify({'status': 'wrong method'})", "sub_path": "Routes/misc.py", "file_name": "misc.py", "file_ext": "py", "file_size_in_byte": 5381, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask.request.args.get", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "Models.models.Resetkey.query.filter_by", "line_number": 21, "usage_type": "call"}, {"api_name": "Models.models.Resetkey.query", "line_number": 21, "usage_type": "attribute"}, {"api_name": "Models.models.Resetkey", "line_number": 21, "usage_type": "name"}, {"api_name": "Models.models.User.query.filter_by", "line_number": 23, "usage_type": "call"}, {"api_name": "Models.models.User.query", "line_number": 23, "usage_type": "attribute"}, {"api_name": "Models.models.User", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 27, "usage_type": "call"}, {"api_name": "config.pepper.encode", "line_number": 31, "usage_type": "call"}, {"api_name": "config.pepper", "line_number": 31, "usage_type": "name"}, {"api_name": "hashlib.md5", "line_number": 32, "usage_type": "call"}, {"api_name": "bcrypt.hashpw", "line_number": 33, "usage_type": "call"}, {"api_name": "config.bsalt", "line_number": 33, "usage_type": "argument"}, {"api_name": "config.db.session.delete", "line_number": 35, "usage_type": "call"}, {"api_name": "config.db.session", "line_number": 35, "usage_type": "attribute"}, {"api_name": "config.db", "line_number": 35, "usage_type": "name"}, {"api_name": "config.db.session.commit", "line_number": 36, "usage_type": "call"}, {"api_name": "config.db.session", "line_number": 36, "usage_type": "attribute"}, {"api_name": "config.db", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 45, "usage_type": "name"}, {"api_name": "Models.models.User.query.filter_by", "line_number": 46, "usage_type": "call"}, {"api_name": "Models.models.User.query", "line_number": 46, "usage_type": "attribute"}, {"api_name": "Models.models.User", "line_number": 46, "usage_type": "name"}, {"api_name": "Models.models.Resetkey.query.filter_by", "line_number": 48, "usage_type": "call"}, {"api_name": "Models.models.Resetkey.query", "line_number": 48, "usage_type": "attribute"}, {"api_name": "Models.models.Resetkey", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 56, "usage_type": "call"}, {"api_name": "utils.codegen", "line_number": 58, "usage_type": "call"}, {"api_name": "Models.models.Resetkey", "line_number": 59, "usage_type": "call"}, {"api_name": "config.db.session.add", "line_number": 60, "usage_type": "call"}, {"api_name": "config.db.session", "line_number": 60, "usage_type": "attribute"}, {"api_name": "config.db", "line_number": 60, "usage_type": "name"}, {"api_name": "config.db.session.commit", "line_number": 61, "usage_type": "call"}, {"api_name": "config.db.session", "line_number": 61, "usage_type": "attribute"}, {"api_name": "config.db", "line_number": 61, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 62, "usage_type": "call"}, {"api_name": "config.mailgunkey", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 75, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 78, "usage_type": "call"}, {"api_name": "config.app.route", "line_number": 13, "usage_type": "call"}, {"api_name": "config.app", "line_number": 13, "usage_type": "name"}, {"api_name": "flask.send_file", "line_number": 83, "usage_type": "call"}, {"api_name": "config.app.route", "line_number": 81, "usage_type": "call"}, {"api_name": "config.app", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 90, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 92, "usage_type": "call"}, {"api_name": "config.app.route", "line_number": 86, "usage_type": "call"}, {"api_name": "config.app", "line_number": 86, "usage_type": "name"}, {"api_name": "config.app.route", "line_number": 87, "usage_type": "call"}, {"api_name": "config.app", "line_number": 87, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.json.loads", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 99, "usage_type": "name"}, {"api_name": "flask.request.data", "line_number": 99, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 99, "usage_type": "name"}, {"api_name": "utils.escapeit", "line_number": 100, "usage_type": "call"}, {"api_name": "utils.escapeit", "line_number": 101, "usage_type": "call"}, {"api_name": "utils.escapeit", "line_number": 102, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 104, "usage_type": "call"}, {"api_name": "config.mailgunkey", "line_number": 106, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 112, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 114, "usage_type": "call"}, {"api_name": "config.app.route", "line_number": 96, "usage_type": "call"}, {"api_name": "config.app", "line_number": 96, "usage_type": "name"}]} +{"seq_id": "154064966", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Sep 12 13:33:47 2016\n\n@author: Jonas\n\"\"\"\n\nfrom __future__ import division\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# one step RK4 adapted to system\ndef rk45(f, t, x, h):\n k1 = f(t, x)\n \n k2 = f(t + (float(1)/4)*h, x + (float(1)/4)*k1*h)\n \n k3 = f(t + (float(3)/8)*h, x + (float(3)/32)*k1*h+(float(9)/32)*k2*h)\n \n k4 = f(t + (float(12)/13)*h, x + (float(1932)/2197)*k1*h-(7200./2197)*k2*h+(7297./2197)*k3*h)\n \n k5 = f(t + h, x+ (439./216)*k1*h-8*k2*h+(3680./513)*k3*h-(845./4104)*k4*h)\n \n k6 = f(t + (1./2)*h, x - (8./27)*k1*h+2*k2*h-(3544./2565)*k3*h+(1859./4104)*k4*h-(11./40*k5*h))\n \n xp = x + (16./135)*k1*h+(1408./2565)*k3*h+(2197./4101)*k4*h-(1./5)*k5*h\n \n xp1 = x + (16./135)*k1*h+(6656./12825)*k3*h+(28561./56430)*k4*h-(9./50)*k5*h+(2./55)*k6*h\n return xp1, t + h, xp\n# time parameters\nt_start = 0.0\nt_stop = 100\nN = 100\nt_step = (t_stop - t_start)/float(N)\n\n# equation parameters\ng = 9.82\nl = 0.1\nx10 = 3\nx20 = 0.5\n\ndef fun(t, x):\n return np.array([(x[1]),\n (-g/l)*math.sin(x[0])-x[1]])\n\n\n# Time variable\ndef clearLists(List1, List2, List3, List4, IVP1, IVP2, List5):\n del List1, List2, List3, List4, List5\n List1 = [0 for x in range(1)]\n List2 = [0 for x in range(1)]\n List3 = [0 for x in range(1)]\n List4 = [0 for x in range(1)]\n List5 = [0 for x in range(1)]\n List1[-1] = IVP1\n List2[-1] = IVP2\n List3[-1] = IVP1\n List4[-1] = IVP2 \n return List1, List2, List3, List4, List5\nT_counter = 0 \ns = 1\ne = 1\ntol= 1e-7\nt = 0\nX1 = [0 for x in range(1)]\nX2 = [0 for x in range(1)]\nY1 = [0 for x in range(1)]\nY2 = [0 for x in range(1)]\n\nwhile e > tol:\n t = 0\n if e > tol:\n X1, X2, Y1, Y2, T_counter = clearLists(X1, X2, Y1, Y2, x10, x20, T_counter)\n while t < t_stop:\n Xp1, t, xp = rk45(fun, t, np.array([X1[-1], X2[-1]]), t_step)\n X1.append(Xp1[0])\n X2.append(Xp1[1])\n Y1.append(xp[0])\n Y2.append(xp[1])\n t = t+t_step\n T_counter.append(t)\n s = 0.84*((float(tol)*t_step)/abs(X1[-1]-Y1[-1]))**(1./4)\n t_step = t_step*s\n e = abs(X1[-1]-Y1[-1])\nprint(\"RK4(5)\") \nplt.plot(X1, X2, 'r-', [x10], [x20], 'k.')\nplt.xlabel('$y_1$')\nplt.ylabel('$y_2$')\nplt.show()\nprint(\"RK4\")\nplt.plot(Y1, Y2, 'b-', [x10], [x20], 'k.')\nplt.xlabel('$y_1$')\nplt.ylabel('$y_2$')\nplt.show()\nplt.plot(T_counter,X1)\nplt.xlabel('t')\nplt.ylabel('y')\nplt.show()\nplt.plot(T_counter,X2)\nplt.xlabel('t')\nplt.ylabel(\"y'\")\nplt.show()\nprint(\"Fejlen er %s, og Antallet af delintervaller er %d\" %(e,len(T_counter)))", "sub_path": "Software/Pendulum-RK4(5).py", "file_name": "Pendulum-RK4(5).py", "file_ext": "py", "file_size_in_byte": 2622, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}]} +{"seq_id": "475993748", "text": "#!/usr/bin/env python\n#\n# tournament.py -- implementation of a Swiss-system tournament\n#\n\nimport psycopg2\n\n\ndef connect():\n \"\"\"Connect to the PostgreSQL database. Returns a database connection.\"\"\"\n return psycopg2.connect(\"dbname=tournament\")\n\n\ndef query(sql):\n \"\"\"Executes a query on the database. Doesn't return any result.\"\"\"\n conn = connect()\n c = conn.cursor()\n try:\n c.execute(sql)\n conn.commit()\n finally:\n # Always close the connection\n conn.close()\n\n\ndef getQueryResult(sql, vars=None):\n conn = connect()\n c = conn.cursor()\n try:\n if vars:\n c.execute(sql, vars)\n else:\n c.execute(sql)\n result = c.fetchall()\n return result\n finally:\n # Always close the connection\n conn.close()\n\n\ndef deleteMatches():\n \"\"\"Remove all the match records from the database.\"\"\"\n query(\"DELETE FROM matches;\")\n\n\ndef deletePlayers():\n \"\"\"Remove all the player records from the database.\"\"\"\n query(\"DELETE FROM players;\")\n\n\ndef deleteTournaments():\n \"\"\"Remove all the tournaments from the database.\"\"\"\n query(\"DELETE FROM tournaments;\")\n\n\ndef countPlayers():\n \"\"\"Returns the number of players currently registered.\"\"\"\n result = getQueryResult(\"SELECT count(*) FROM players;\")\n # The count value is the first (and only) column of the\n # first (and only) row\n return result[0][0]\n\n\ndef registerTournament(name):\n \"\"\"Adds a tournament to the database.\n\n Each tournament has its own set of players and matches.\n\n Args:\n name: Name of the tournament.\n \"\"\"\n conn = connect()\n c = conn.cursor()\n try:\n # Using query parameters to prevent SQL injection\n c.execute((\"INSERT INTO tournaments (name) \"\n \"values (%s) RETURNING id\"), (name,))\n conn.commit()\n return c.fetchone()[0]\n finally:\n # Always close the connection\n conn.close()\n\n\ndef registerPlayer(id_tournament, name):\n \"\"\"Adds a player to the tournament database.\n\n The database assigns a unique serial id number for the player. (This\n should be handled by your SQL database schema, not in your Python code.)\n\n Args:\n id_tournament: id of the current tournament.\n name: the player's full name (need not be unique).\n \"\"\"\n conn = connect()\n c = conn.cursor()\n try:\n # Using query parameters to prevent SQL injection\n c.execute(\"INSERT INTO players (id_tournament, name) values(%s, %s)\", (\n id_tournament, name\n ))\n conn.commit()\n finally:\n # Always close the connection\n conn.close()\n\n\ndef playerStandings(id_tournament):\n \"\"\"Returns a list of the players and their win records, sorted by wins.\n\n The first entry in the list should be the player in first place, or a\n player tied for first place if there is currently a tie.\n\n Args:\n id_tournament: Id of the tournament which you want to get the\n standings.\n\n Returns:\n A list of tuples, each of which contains (id, name, wins, matches):\n id: the player's unique id (assigned by the database)\n name: the player's full name (as registered)\n wins: the number of matches the player has won\n matches: the number of matches the player has played\n \"\"\"\n result = getQueryResult(\n (\"SELECT id, name, won_matches, played_matches \"\n \"FROM standings WHERE id_tournament = %s;\"),\n (id_tournament,)\n )\n return result\n\n\ndef reportMatch(winner, loser):\n \"\"\"Records the outcome of a single match between two players.\n\n Args:\n winner: the id number of the player who won\n loser: the id number of the player who lost\n \"\"\"\n conn = connect()\n c = conn.cursor()\n try:\n c.execute(\"INSERT INTO matches VALUES (%s, %s, %s)\", (\n winner, loser, winner\n ))\n conn.commit()\n finally:\n conn.close()\n\n\ndef swissPairings(id_tournament):\n \"\"\"Returns a list of pairs of players for the next round of a match.\n\n Assuming that there are an even number of players registered, each player\n appears exactly once in the pairings. Each player is paired with another\n player with an equal or nearly-equal win record, that is, a player adjacent\n to him or her in the standings.\n\n Args:\n id_tournament: The id of the current tournament.\n\n Returns:\n A list of tuples, each of which contains (id1, name1, id2, name2)\n id1: the first player's unique id\n name1: the first player's name\n id2: the second player's unique id\n name2: the second player's name\n \"\"\"\n standings = playerStandings(id_tournament)\n\n i = 0\n pairing = []\n while i < len(standings):\n pairing.append((standings[i][0], standings[i][1],\n standings[i + 1][0], standings[i + 1][1]))\n i = i + 2\n\n return pairing\n", "sub_path": "vagrant/tournament/tournament.py", "file_name": "tournament.py", "file_ext": "py", "file_size_in_byte": 4939, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "psycopg2.connect", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "594302195", "text": "import cv2\nimport numpy as np\nimport ImageProcessor\nimport GPS\n\nclass Car():\n\n def __init__(self):\n\n self.imageProcessor = ImageProcessor.ImageProcessor()\n self.gps = GPS.GPS()\n self.current_angle = 90\n self.processedImage = \"\"\n self.turnCommand = tuple()\n\n\n\n def Drive(self, frame):\n\n # Processa a imagem\n lane_lines, self.processedImage = self.imageProcessor.ProcessImage(frame)\n\n # Calcula o Angulo\n self.current_angle, self.processedImage = self.gps.ProcessCommands(self.processedImage, lane_lines, self.current_angle)\n\n # Calcula o quanto tem q virar\n self.Turn()\n\n # Manda o comando\n self.SendCommand()\n\n def SendCommand(self):\n print(\"mandando:\", self.turnCommand)\n\n\n def Turn(self):\n\n anguloReto = 90\n maxVelocidade = 50\n\n angulo = self.current_angle\n diferencaAngulos = abs(anguloReto - angulo) #diferença entre os angulos, o quanto tem que mover abs=módulo\n if angulo == anguloReto:\n diferencaAngulos = angulo #se for o mesmo angulo, conserva\n\n if angulo < anguloReto:\n lado = \"e\" #vê de que lado tem que diminuir\n else:\n lado = \"d\"\n\n #regra de tres pra achar a porção que tem que diminuir, diminui da velocidade max, round pra arredondar\n novaVelocidade = round(maxVelocidade - (diferencaAngulos * maxVelocidade) / anguloReto)\n\n #retorna a nova velocidade de acordo com o lado que vira\n if lado == \"e\":\n self.turnCommand = (novaVelocidade, maxVelocidade)\n else:\n self.turnCommand = (maxVelocidade, novaVelocidade)\n\n def ShowCamera(self, index):\n\n if index == 1:\n cv2.imshow(\"Road with Lane line\", self.processedImage)\n elif index == 2:\n cv2.imshow(\"Road with Lane line\", self.imageProcessor.mask)\n elif index == 3:\n cv2.imshow(\"Road with Lane line\", self.imageProcessor.edges)\n else:\n cv2.imshow(\"Road with Lane line\", self.imageProcessor.cropped_edges)\n", "sub_path": "Visão/Car.py", "file_name": "Car.py", "file_ext": "py", "file_size_in_byte": 2096, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "ImageProcessor.ImageProcessor", "line_number": 10, "usage_type": "call"}, {"api_name": "GPS.GPS", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "310663646", "text": "# Installing on windows 64? See http://www.lfd.uci.edu/~gohlke/pythonlibs/\nimport os\nimport sys\nimport time\nimport logging\nimport numpy as np\nfrom gantry import Gantry\nfrom sensor import Sensor, DataStorage, Pos\nfrom transmitter import siggen\nfrom matplotlib import pyplot as plt\nfrom matplotlib import animation\nimport logging\n\nlogger = logging.getLogger(\"Echidna Device\")\nhdlr = logging.StreamHandler(sys.stdout)\nhdlr.setLevel(logging.DEBUG)\nlogger.addHandler(hdlr)\nlogger.setLevel(logging.DEBUG)\n\nclass Echidna(object):\n \n def __init__(self, sensor_server=\"172.17.5.180\", \n sensor_port=5555,\n sensor_sample_rate=100000,\n sensor_tx_freq=1000,\n gantry_server=\"172.17.5.100\", \n gantry_port=5007,\n store_filename=\"data.log\"):\n \n self.robot = Gantry(host=gantry_server,port=gantry_port)\n self.sensor = Sensor(server=sensor_server,\n port=sensor_port,\n sample_rate=sensor_sample_rate,\n tx_freq=sensor_tx_freq)\n \n self.transmitter = siggen\n self.transmitter.setFrequency(sensor_tx_freq)\n self.transmitter.setVoltage(-2,2)\n self.transmitter.turnOn()\n self._tx_freq = sensor_tx_freq\n \n self.store = DataStorage(store_filename)\n self.pos = Pos(0,0)\n \n def setStore(self,fname):\n if self.store.db.isopen:\n self.store.close()\n self.store = DataStorage(fname)\n \n def setTxFreq(self,freq):\n self._tx_freq = freq\n self.transmitter.setFrequency(freq)\n self.sensor.tx_freq = freq\n \n def stepOverTank(self,fname=\"tank\"):\n self.pos = self.discretizeTank()\n x_size = self.pos.shape[0]\n y_size = self.pos.shape[1]\n for x in range(x_size): \n for y in range(y_size):\n p = self.pos[x,y] \n logger.info(\"Moving to (%f, %f)\" % (p[0],p[1]))\n self.robot.moveAndWait(p[0],p[1], 60)\n d = self.sensor.getSample(100)\n fig, samp = self.plotRadar(5)\n fig.savefig(fname + \"-x%3.2f-y%3.2f(x%d,y%d).tiff\" % (p[0],p[1],x,y))\n plt.close(fig)\n d.setPos(p, (x,y))\n self.store.save(d) \n \n def stepOverPos(self,pos,fname=\"target\"):\n self.count = 0\n self.pos = pos\n x_size = self.pos.shape[0]\n y_size = self.pos.shape[1]\n for x in range(x_size): \n for y in range(y_size):\n p = self.pos[x,y] \n logger.info(\"Moving to (%f, %f)\" % (p[0],p[1]))\n self.robot.move(p[0],p[1], 70)\n #d = self.sensor.getSample(25)\n fig, d = self.plotRadar()\n d.setPos(p)\n self.store.save(d) \n fig.savefig(\"%d-\" % self.count + fname + \"-x%3.2f-y%3.2f.tiff\" % (p[0],p[1]))\n plt.close(fig) \n while(self.robot.busy()):\n time.sleep(0.5) \n self.count = self.count + 1\n \n def freqSweep(self,start,stop,step=1, nsamps=100):\n self.count = 0 \n range = np.arange(start,stop,step) \n samples = []\n for freq in range:\n logger.info(\"%d | Sampling frequency: %f\" % (self.count,freq))\n self.setTxFreq(freq)\n d = self.sensor.getSample(nsamps) \n d.setPos(self.pos.astuple())\n self.store.save(d) \n self.count = self.count + 1\n samples.append(d)\n \n return samples \n \n def discretizeTank(self, width=23.5, length=23.5, steps=50.0):\n x_steps = np.round(np.arange(0,width,width/steps),2)\n y_steps = np.round(np.arange(0,length,length/steps),2)\n pos = np.zeros((x_steps.size,y_steps.size, 2))\n for i in range(x_steps.size):\n for j in range(y_steps.size):\n pos[i][j][0] = x_steps[i]\n pos[i][j][1] = y_steps[j]\n \n return pos\n \n\n def learn_baseline(self, samples=250):\n self.sample_baseline = self.sensor.getSample(samples)\n \n def getDiffSignal(self, samples=10, baseline_samples=250):\n if not hasattr(self,\"sample_baseline\"):\n self.learn_baseline(baseline_samples)\n sample = self.sensor.getSample(samples)\n data = sample.getMeanPower()\n return (data - self.sample_baseline.getMeanPower()), sample\n \n def plotRadar(self, samples=15, baseline_samples=250):\n \n fig = plt.figure()\n ax = fig.add_axes([0.1,0.1,0.8,0.8], polar=True)\n lines = ax.get_lines()\n ax.set_rmax(0.1)\n y, samp = self.getDiffSignal(samples, baseline_samples)\n for val in y: \n sys.stdout.write(\"%3.6f,\"% val)\n sys.stdout.write(\"\\n\")\n \n x = np.arange(y.size)/np.float(y.size)*np.pi*2.0\n data = zip(x,y)\n lines = ax.get_lines()\n for vec in data:\n ax.set_rmax(0.15)\n ax.plot([vec[0],vec[0]],[0,vec[1]],linewidth=3.0) \n ax.set_rmax(0.1)\n plt.show()\n return fig, samp\n\n \n def __del__(self):\n self.store.close()\n \nif __name__=='__main__':\n ech = Echidna()\n \n ", "sub_path": "PYTHON/echidna/echidna.py", "file_name": "echidna.py", "file_ext": "py", "file_size_in_byte": 5672, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 15, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 15, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 16, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 18, "usage_type": "attribute"}, {"api_name": "gantry.Gantry", "line_number": 30, "usage_type": "call"}, {"api_name": "sensor.Sensor", "line_number": 31, "usage_type": "call"}, {"api_name": "transmitter.siggen", "line_number": 36, "usage_type": "name"}, {"api_name": "sensor.DataStorage", "line_number": 42, "usage_type": "call"}, {"api_name": "sensor.Pos", "line_number": 43, "usage_type": "call"}, {"api_name": "sensor.DataStorage", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "sys.stdout.write", "line_number": 136, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 136, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 137, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 137, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 139, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}]} +{"seq_id": "136623327", "text": "from sklearn.datasets import load_iris\nfrom hyperactive import RandomSearchOptimizer\n\niris_data = load_iris()\nX = iris_data.data\ny = iris_data.target\n\n# this defines the model and hyperparameter search space\nsearch_config = {\n \"sklearn.ensemble.RandomForestClassifier\": {\n \"n_estimators\": range(10, 200, 10),\n \"max_depth\": [3, 4, 5, 6],\n \"criterion\": [\"gini\", \"entropy\"],\n }\n}\n\n\nOptimizer = RandomSearchOptimizer(\n search_config, n_iter=1000, n_jobs=1, memory=True, cv=3\n)\n\n# search best hyperparameter for given data\nOptimizer.fit(X, y)\n", "sub_path": "examples/example_memory.py", "file_name": "example_memory.py", "file_ext": "py", "file_size_in_byte": 568, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sklearn.datasets.load_iris", "line_number": 4, "usage_type": "call"}, {"api_name": "hyperactive.RandomSearchOptimizer", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "610013319", "text": "import numpy as np\nfrom skimage.transform import radon\nimport math\nimport cv2\n\n\nclass DetectRotationAngle:\n \"\"\"\n Please refer https://gist.github.com/endolith/334196bac1cac45a4893. The algorithm automatically detect rotation and line spacing of an image of\n text using Radon transform. If image is rotated by the inverse of the output, the lines will be horizontal (though they may be upside-down\n depending on the original image). It doesn't work with black borders\n\n It was found that the code are very accurate/effecient to detect the rotation angle, especially for alternative text lines and\n white spaces in text documents. After minor modification, this angle detection algorithm was integrated into the full text angel\n detection.\n \"\"\"\n\n @classmethod\n def parabolic(cls, f, x):\n \"\"\"\n Please refer https://gist.github.com/endolith/255291#file-parabolic-py\n Quadratic interpolation for estimating the true position of an\n inter-sample maximum when nearby samples are known.\n f is a vector and x is an index of maximum for that vector.\n Returns (vx, vy), the coordinates of the vertex of a parabola that goes\n through point x and its two neighbors.\n Example:\n Defining a vector f with a local maximum at index 3 (= 6), find local\n maximum if points 2, 3, and 4 actually defined a parabola.\n In [3]: f = [2, 3, 1, 6, 4, 2, 3, 1]\n In [4]: parabolic(f, argmax(f))\n Out[4]: (3.2142857142857144, 6.1607142857142856)\n \"\"\"\n xv = 1 / 2. * (f[x - 1] - f[x + 1]) / (f[x - 1] - 2 * f[x] + f[x + 1]) + x\n yv = f[x] - 1 / 4. * (f[x - 1] - f[x + 1]) * (xv - x)\n return xv, yv\n\n @classmethod\n def argmax(cls, x):\n \"\"\"\n Find the true position of an inter-sample maximum.\n\n Parameters\n ----------\n x : an array-like vector\n\n Returns\n -------\n an index, the location of the maximum in x\n \"\"\"\n return cls.parabolic(x, np.argmax(x))[0]\n\n @classmethod\n def rms(cls, x):\n \"\"\"\n Calculate the mean root square\n\n Parameters\n ----------\n x : an array-like vector\n\n Returns\n -------\n a float, mean root square\n \"\"\"\n n = len(x)\n square = sum([i ** 2 for i in x])\n return math.sqrt(square / n + 0.000001)\n\n @classmethod\n def get_rotation_angle(cls, gray_img, min_size=(300, 300)):\n \"\"\"\n Detect the rotation angle using radon transformation. The larger image size is, slower in radon transformation. It was found that image\n size around (400,400) might be the best\n\n Parameters\n ----------\n gray_img : NumPy array, an gray-scale image\n min_size : The minimum image dimention used for the best performance of rotation angle detection.\n Returns\n -------\n two numeric values, rotation angel and line spacing\n \"\"\"\n (rows, cols) = np.where(gray_img == 0) # fill empty areas caused by the original rotation\n gray_img[rows, cols] = 255\n\n if min(gray_img.shape) > min(min_size):\n scale = min(min_size) / min(gray_img.shape)\n else:\n scale = min(gray_img.shape) / min(min_size)\n\n gray_img = cv2.resize(gray_img, (0, 0), fx=scale, fy=scale)\n\n # not sure if it is beneficial\n gauss_x = cv2.Sobel(gray_img, cv2.CV_64F, 1, 0)\n gauss_y = cv2.Sobel(gray_img, cv2.CV_64F, 0, 1)\n gray_img = gauss_x ** 2 + gauss_y ** 2\n\n arr = np.asarray(gray_img)\n arr = arr - np.mean(arr)\n\n # Do the radon transform and display the result\n sinogram = radon(arr)\n\n # Find the RMS value of each row and find \"busiest\" rotation, perfectly with the alternating dark text and white lines\n r = np.array([cls.rms(line) for line in sinogram.transpose()])\n rotation = int(round(cls.argmax(r), 0))\n\n # Take spectrum of busy row and find line spacing\n row = sinogram[:, rotation]\n num_row = len(row)\n window = np.blackman(num_row)\n spectrum = np.fft.rfft(row * window)\n frequency = cls.argmax(abs(spectrum))\n line_spacing = int(num_row / frequency)\n\n return 90 - rotation, line_spacing\n", "sub_path": "src/detect_rotation_angle.py", "file_name": "detect_rotation_angle.py", "file_ext": "py", "file_size_in_byte": 4305, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.argmax", "line_number": 51, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 84, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 92, "usage_type": "call"}, {"api_name": "cv2.Sobel", "line_number": 95, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 95, "usage_type": "attribute"}, {"api_name": "cv2.Sobel", "line_number": 96, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 100, "usage_type": "call"}, {"api_name": "skimage.transform.radon", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.blackman", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.fft.rfft", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 113, "usage_type": "attribute"}]} +{"seq_id": "281554204", "text": "# fmt: off\nimport logging\nfrom pathlib import Path\n\nfrom farm.data_handler.data_silo import DataSilo\nfrom farm.data_handler.processor import TextClassificationProcessor\nfrom farm.modeling.optimization import initialize_optimizer\nfrom farm.infer import Inferencer\nfrom farm.modeling.adaptive_model import AdaptiveModel\nfrom farm.modeling.language_model import LanguageModel\nfrom farm.modeling.prediction_head import MultiLabelTextClassificationHead\nfrom farm.modeling.tokenization import Tokenizer\nfrom farm.train import Trainer\nfrom farm.utils import set_all_seeds, MLFlowLogger, initialize_device_settings\n\ndef doc_classification_multilabel():\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO)\n\n ml_logger = MLFlowLogger(tracking_uri=\"https://public-mlflow.deepset.ai/\")\n ml_logger.init_experiment(experiment_name=\"Public_FARM\", run_name=\"Run_doc_classification\")\n\n ##########################\n ########## Settings\n ##########################\n set_all_seeds(seed=42)\n device, n_gpu = initialize_device_settings(use_cuda=True)\n n_epochs = 1\n batch_size = 32\n\n evaluate_every = 500\n lang_model = \"bert-base-uncased\"\n do_lower_case = True\n\n # 1.Create a tokenizer\n tokenizer = Tokenizer.load(\n pretrained_model_name_or_path=lang_model,\n do_lower_case=do_lower_case)\n\n # 2. Create a DataProcessor that handles all the conversion from raw text into a pytorch Dataset\n # Here we load Toxic Comments Data automaticaly if it is not available.\n\n label_list = [\"toxic\",\"severe_toxic\",\"obscene\",\"threat\",\"insult\",\"identity_hate\"]\n metric = \"acc\"\n\n processor = TextClassificationProcessor(tokenizer=tokenizer,\n max_seq_len=128,\n data_dir=Path(\"../data/toxic-comments\"),\n label_list=label_list,\n label_column_name=\"label\",\n metric=metric,\n quote_char='\"',\n multilabel=True,\n train_filename=\"train.tsv\",\n dev_filename=\"val.tsv\",\n test_filename=None,\n dev_split=0,\n )\n\n # 3. Create a DataSilo that loads several datasets (train/dev/test), provides DataLoaders for them and calculates a few descriptive statistics of our datasets\n data_silo = DataSilo(\n processor=processor,\n batch_size=batch_size)\n\n # 4. Create an AdaptiveModel\n # a) which consists of a pretrained language model as a basis\n language_model = LanguageModel.load(lang_model)\n # b) and a prediction head on top that is suited for our task => Text classification\n prediction_head = MultiLabelTextClassificationHead(num_labels=len(label_list))\n\n model = AdaptiveModel(\n language_model=language_model,\n prediction_heads=[prediction_head],\n embeds_dropout_prob=0.1,\n lm_output_types=[\"per_sequence\"],\n device=device)\n\n # 5. Create an optimizer\n model, optimizer, lr_schedule = initialize_optimizer(\n model=model,\n learning_rate=3e-5,\n device=device,\n n_batches=len(data_silo.loaders[\"train\"]),\n n_epochs=n_epochs)\n\n # 6. Feed everything to the Trainer, which keeps care of growing our model into powerful plant and evaluates it from time to time\n trainer = Trainer(\n model=model,\n optimizer=optimizer,\n data_silo=data_silo,\n epochs=n_epochs,\n n_gpu=n_gpu,\n lr_schedule=lr_schedule,\n evaluate_every=evaluate_every,\n device=device)\n\n # 7. Let it grow\n trainer.train()\n\n # 8. Hooray! You have a model. Store it:\n save_dir = Path(\"../saved_models/bert-german-multi-doc-tutorial\")\n model.save(save_dir)\n processor.save(save_dir)\n\n # 9. Load it & harvest your fruits (Inference)\n basic_texts = [\n {\"text\": \"You fucking bastards\"},\n {\"text\": \"What a lovely world\"},\n ]\n model = Inferencer.load(save_dir)\n result = model.inference_from_dicts(dicts=basic_texts)\n print(result)\n model.close_multiprocessing_pool()\n\n\nif __name__ == \"__main__\":\n doc_classification_multilabel()\n\n# fmt: on\n", "sub_path": "examples/doc_classification_multilabel.py", "file_name": "doc_classification_multilabel.py", "file_ext": "py", "file_size_in_byte": 4555, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.basicConfig", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 20, "usage_type": "attribute"}, {"api_name": "farm.utils.MLFlowLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "farm.utils.set_all_seeds", "line_number": 28, "usage_type": "call"}, {"api_name": "farm.utils.initialize_device_settings", "line_number": 29, "usage_type": "call"}, {"api_name": "farm.modeling.tokenization.Tokenizer.load", "line_number": 38, "usage_type": "call"}, {"api_name": "farm.modeling.tokenization.Tokenizer", "line_number": 38, "usage_type": "name"}, {"api_name": "farm.data_handler.processor.TextClassificationProcessor", "line_number": 48, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 50, "usage_type": "call"}, {"api_name": "farm.data_handler.data_silo.DataSilo", "line_number": 63, "usage_type": "call"}, {"api_name": "farm.modeling.language_model.LanguageModel.load", "line_number": 69, "usage_type": "call"}, {"api_name": "farm.modeling.language_model.LanguageModel", "line_number": 69, "usage_type": "name"}, {"api_name": "farm.modeling.prediction_head.MultiLabelTextClassificationHead", "line_number": 71, "usage_type": "call"}, {"api_name": "farm.modeling.adaptive_model.AdaptiveModel", "line_number": 73, "usage_type": "call"}, {"api_name": "farm.modeling.optimization.initialize_optimizer", "line_number": 81, "usage_type": "call"}, {"api_name": "farm.train.Trainer", "line_number": 89, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 103, "usage_type": "call"}, {"api_name": "farm.infer.Inferencer.load", "line_number": 112, "usage_type": "call"}, {"api_name": "farm.infer.Inferencer", "line_number": 112, "usage_type": "name"}]} +{"seq_id": "47506466", "text": "from sklearn.svm import SVC\nfrom mnist import MNIST\n\n\n'''Example of format\n\n\nmodel = SVC(C=1.0, kernel=’rbf’, degree=3, gamma=’auto_deprecated’, coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200,\n class_weight=None, verbose=False, max_iter=-1, decision_function_shape=’ovr’, random_state=None)\nmodel.fit(X,Y)\ny_hat = model.predict(X_test)\n'''\n\n\nmndataSet = '.\\Dataset'\nmndata = MNIST()\n#mndata.gz =True\nmndata = MNIST(mndataSet)\n#%%importing data into lists and labels\nimages,labels = mndata.load_training()\nimages_test, labels_test = mndata.load_testing()\n#%%\nc = [.25,.5,1,2,4]\nmodel = SVC(C = 1)\nmodel.fit(images, labels)\ny_hat = model.predict(images)\n\nmisClass = 0\nfor x in range(0,45):#len(y_hat)):\n if x%15 == 0:\n print(y_hat[x])\n print(labels[x])\n if y_hat[x] == labels[x]:\n print('ok')\n else:\n misClass = misClass+1\n\ntrain_error = misClass/45", "sub_path": "MNIST_SVC.py", "file_name": "MNIST_SVC.py", "file_ext": "py", "file_size_in_byte": 921, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "mnist.MNIST", "line_number": 16, "usage_type": "call"}, {"api_name": "mnist.MNIST", "line_number": 18, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "460367095", "text": "# -*- coding: utf-8 -*-\n\n'''\nFunctional test for checking that external configuration modules can be\ninjected by the user and their values are correctly used\n'''\nimport unittest\nimport pyowm\n\n\nclass ConfigurationInjectionTestsWebAPI25(unittest.TestCase):\n\n _config_module_name = 'tests.functional.webapi25.external_configuration'\n _non_existent_config_module_name = 'this_will_never_be_a_config_module'\n\n def test(self):\n pyowm.OWM('�b02f5370d�76021a0', '2.5', self._config_module_name)\n\n def test_library_is_instantiated_with_wrong_API_version(self):\n self.assertRaises(ValueError, pyowm.OWM, 'abcd', '0.0')\n\n def test_library_is_instantiated_with_external_config(self):\n \"\"\"\n Test that library is smoothly instantiated also when injecting external\n configuration\n \"\"\"\n try:\n pyowm.OWM('�b02f5370d�76021a0', '2.5', self._config_module_name)\n except Exception:\n self.fail(\"Error raised during library instantiation\")\n\n def test_error_raised_when_providing_non_existent_external_config(self):\n \"\"\"\n Test that library instantiation raises an error when trying to inject\n a non-existent external configuration module\n \"\"\"\n self.assertRaises(Exception, pyowm.OWM, '�b02f5370d�76021a0', '2.5',\n self._non_existent_config_module_name)\n\n def test_library_performs_API_calls_with_external_config(self):\n \"\"\"\n Test that API works correctly with external config values. For testing\n purposes, we do that by specifying None values for JSON parsers, which\n leads to errors raising\n \"\"\"\n try:\n instance = \\\n pyowm.OWM('�b02f5370d��76021a0', '2.5',\n self._config_module_name)\n except:\n self.fail(\"Error raised during library instantiation\")\n self.assertRaises(Exception, instance.weather_at_place, 'London,uk')\n", "sub_path": "tests/functional/webapi25/test_configuration_injection_webapi25.py", "file_name": "test_configuration_injection_webapi25.py", "file_ext": "py", "file_size_in_byte": 1989, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "unittest.TestCase", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pyowm.OWM", "line_number": 17, "usage_type": "call"}, {"api_name": "pyowm.OWM", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pyowm.OWM", "line_number": 28, "usage_type": "call"}, {"api_name": "pyowm.OWM", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pyowm.OWM", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "451475163", "text": "#!/usr/bin/env python3\nfrom fanshim import FanShim\nfrom threading import Lock\nimport paho.mqtt.client as mqtt\nimport colorsys\nimport psutil\nimport argparse\nimport signal\nimport sys\nfrom time import time, sleep, localtime, strftime\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--threshold', type=float, default=-1, help='Temperature threshold in degrees C to enable fan')\nparser.add_argument('--hysteresis', type=float, default=-1, help='Distance from threshold before fan is disabled')\n\nparser.add_argument('--off-threshold', type=float, default=55.0, help='Temperature threshold in degrees C to enable fan')\nparser.add_argument('--on-threshold', type=float, default=65.0, help='Temperature threshold in degrees C to disable fan')\nparser.add_argument('--delay', type=float, default=2.0, help='Delay, in seconds, between temperature readings')\nparser.add_argument('--preempt', action='store_true', default=False, help='Monitor CPU frequency and activate cooling premptively')\nparser.add_argument('--verbose', action='store_true', default=False, help='Output temp and fan status messages')\nparser.add_argument('--nobutton', action='store_true', default=False, help='Disable button input')\nparser.add_argument('--noled', action='store_true', default=False, help='Disable LED control')\nparser.add_argument('--brightness', type=float, default=255.0, help='LED brightness, from 0 to 255')\nparser.add_argument('--mqttUser', default=None, help='User for authentication for mqtt broker')\nparser.add_argument('--mqttPassword', default=None, help='Password for authentication for mqtt broker')\nparser.add_argument('--mqttHost', default='localhost', help='host for mqtt broker')\nparser.add_argument('--mqttPort', type=int, default=1883, help='port for mqtt broker')\nparser.add_argument('--mqttKeepAlive', type=int, default=60, help='Keep alive for mqtt broker')\n\nargs = parser.parse_args()\n\n\ndef clean_exit(signum, frame):\n set_fan(False)\n if not args.noled:\n fanshim.set_light(0, 0, 0)\n sys.exit(0)\n\n\ndef update_led_temperature(temp):\n led_busy.acquire()\n temp = float(temp)\n temp -= args.off_threshold\n temp /= float(args.on_threshold - args.off_threshold)\n temp = max(0, min(1, temp))\n temp = 1.0 - temp\n temp *= 120.0\n temp /= 360.0\n r, g, b = [int(c * 255.0) for c in colorsys.hsv_to_rgb(temp, 1.0, args.brightness / 255.0)]\n fanshim.set_light(r, g, b)\n led_busy.release()\n\n\ndef get_cpu_temp():\n t = psutil.sensors_temperatures()\n for x in ['cpu-thermal', 'cpu_thermal']:\n if x in t:\n return t[x][0].current\n print(\"Warning: Unable to get CPU temperature!\")\n return 0\n\n\ndef get_cpu_freq():\n freq = psutil.cpu_freq()\n return freq\n\n\ndef set_fan(status):\n global enabled\n changed = False\n if status != enabled:\n changed = True\n fanshim.set_fan(status)\n enabled = status\n return changed\n\n\ndef set_automatic(status):\n global armed, last_change\n armed = status\n last_change = 0\n\n# Eclipse Paho callbacks - http://www.eclipse.org/paho/clients/python/docs/#callbacks\ndef on_connect(client, userdata, flags, rc):\n if rc == 0:\n print('MQTT connection established')\n print()\n else:\n print('Connection error with result code {} - {}'.format(str(rc), mqtt.connack_string(rc)))\n #kill main thread\n sys.exit(1)\n\ndef on_publish(client, userdata, mid):\n # print('Data successfully published.')\n pass\n\ndef publish(topic, value):\n print('Publish data {} - {}'.format(topic, value))\n mqtt_client.publish(topic, value)\n\nif args.threshold > -1 or args.hysteresis > -1:\n print(\"\"\"\nThe --threshold and --hysteresis parameters have been deprecated.\nUse --on-threshold and --off-threshold instead!\n\"\"\")\n sys.exit(1)\n\nmqtt_client = mqtt.Client()\nmqtt_client.on_connect = on_connect\nmqtt_client.on_publish = on_publish\n\nif args.mqttUser:\n mqtt_client.username_pw_set(args.mqttUser, args.mqttPassword)\ntry:\n mqtt_client.connect(args.mqttHost,\n args.mqttPort,\n args.mqttKeepAlive)\nexcept:\n print('MQTT connection error. Please check your settings in the configuration file \"config.ini\"')\n sys.exit(1)\nelse:\n mqtt_client.loop_start()\n sleep(1.0) # some slack to establish the connection\n\nfanshim = FanShim()\nfanshim.set_hold_time(1.0)\nfanshim.set_fan(False)\narmed = True\nenabled = False\nled_busy = Lock()\nenable = False\nis_fast = False\nlast_change = 0\nsignal.signal(signal.SIGTERM, clean_exit)\n\nif args.noled:\n led_busy.acquire()\n fanshim.set_light(0, 0, 0)\n led_busy.release()\n\nt = get_cpu_temp()\nif t >= args.threshold:\n last_change = get_cpu_temp()\n set_fan(True)\n\n\nif not args.nobutton:\n @fanshim.on_release()\n def release_handler(was_held):\n global armed\n if was_held:\n set_automatic(not armed)\n elif not armed:\n set_fan(not enabled)\n\n @fanshim.on_hold()\n def held_handler():\n global led_busy\n if args.noled:\n return\n led_busy.acquire()\n for _ in range(3):\n fanshim.set_light(0, 0, 255)\n time.sleep(0.04)\n fanshim.set_light(0, 0, 0)\n time.sleep(0.04)\n led_busy.release()\n\n\ntry:\n while True:\n t = get_cpu_temp()\n publish('fanshim/temperature', t)\n f = get_cpu_freq()\n was_fast = is_fast\n is_fast = (int(f.current) == int(f.max))\n if args.verbose:\n print(\"Current: {:05.02f} Target: {:05.02f} Freq {: 5.02f} Automatic: {} On: {}\".format(t, args.off_threshold, f.current / 1000.0, armed, enabled))\n\n if args.preempt and is_fast and was_fast:\n enable = True\n elif armed:\n if t >= args.on_threshold:\n enable = True\n elif t <= args.off_threshold:\n enable = False\n\n publish('fanshim/active', enable)\n if set_fan(enable):\n last_change = t\n\n if not args.noled:\n update_led_temperature(t)\n\n sleep(args.delay)\nexcept KeyboardInterrupt:\n pass\n", "sub_path": "examples/automatic.py", "file_name": "automatic.py", "file_ext": "py", "file_size_in_byte": 6102, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 12, "usage_type": "call"}, {"api_name": "fanshim.set_light", "line_number": 36, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 37, "usage_type": "call"}, {"api_name": "colorsys.hsv_to_rgb", "line_number": 49, "usage_type": "call"}, {"api_name": "fanshim.set_light", "line_number": 50, "usage_type": "call"}, {"api_name": "psutil.sensors_temperatures", "line_number": 55, "usage_type": "call"}, {"api_name": "psutil.cpu_freq", "line_number": 64, "usage_type": "call"}, {"api_name": "fanshim.set_fan", "line_number": 73, "usage_type": "call"}, {"api_name": "paho.mqtt.client.connack_string", "line_number": 89, "usage_type": "call"}, {"api_name": "paho.mqtt.client", "line_number": 89, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 91, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 106, "usage_type": "call"}, {"api_name": "paho.mqtt.client.Client", "line_number": 108, "usage_type": "call"}, {"api_name": "paho.mqtt.client", "line_number": 108, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 120, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 123, "usage_type": "call"}, {"api_name": "fanshim.FanShim", "line_number": 125, "usage_type": "call"}, {"api_name": "fanshim.set_hold_time", "line_number": 126, "usage_type": "call"}, {"api_name": "fanshim.set_fan", "line_number": 127, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 130, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 134, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 134, "usage_type": "attribute"}, {"api_name": "fanshim.set_light", "line_number": 138, "usage_type": "call"}, {"api_name": "fanshim.on_release", "line_number": 148, "usage_type": "call"}, {"api_name": "fanshim.set_light", "line_number": 163, "usage_type": "call"}, {"api_name": "time.time.sleep", "line_number": 164, "usage_type": "call"}, {"api_name": "time.time", "line_number": 164, "usage_type": "name"}, {"api_name": "fanshim.set_light", "line_number": 165, "usage_type": "call"}, {"api_name": "time.time.sleep", "line_number": 166, "usage_type": "call"}, {"api_name": "time.time", "line_number": 166, "usage_type": "name"}, {"api_name": "fanshim.on_hold", "line_number": 156, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 195, "usage_type": "call"}]} +{"seq_id": "628056158", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jul 18 10:37:05 2018\n\n@author: Brendan\n\"\"\"\n\nimport numpy as np\nimport scipy.signal\nfrom pylab import *\nfrom sunpy.map import Map\nfrom scipy.interpolate import interp1d\nfrom scipy import signal\nimport scipy.misc\nimport astropy.units as u\n#from scipy import fftpack # not working with this called here???\nfrom timeit import default_timer as timer\n#import accelerate # put inside function\nimport glob\nimport matplotlib.pyplot as plt\nfrom scipy import fftpack\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.widgets import SpanSelector\nfrom scipy import fftpack\n\nimport numpy as np\nimport scipy.signal\nfrom scipy.interpolate import interp1d\nfrom scipy import signal\nimport scipy.misc\nimport astropy.units as u\nfrom scipy import fftpack\nfrom astropy.convolution import convolve, Box1DKernel\nfrom numpy.random import randn\nfrom mpi4py import MPI\nfrom scipy.stats.stats import pearsonr \n\n\n# define Power-Law-fitting function (Model M1)\ndef PowerLaw(f, A, n, C):\n return A*f**-n + C\n \n# define Lorentzian-fitting function\ndef Lorentz(f, P, fp, fw):\n return P*(1./ (1.+((np.log(f)-fp)/fw)**2))\n\n# define combined-fitting function (Model M2)\ndef LorentzPowerBase(f2, A2, n2, C2, P2, fp2, fw2):\n return A2*f2**-n2 + C2 + P2*(1./ (1.+((np.log(f2)-fp2)/fw2)**2))\n\ndirectory = 'S:'\ndate = '20130626'\nwavelength = 171\nn_segments = 6\n\ncube_shape = np.load('%s/DATA/%s/%i/derotated_mmap_shape.npy' % (directory, date, wavelength))\nDATA = np.memmap('%s/DATA/%s/%i/derotated_mmap.npy' % (directory, date, wavelength), dtype='int16', mode='r', shape=(cube_shape[0], cube_shape[1], cube_shape[2]))\n\nTIME = np.load('%s/DATA/%s/%i/time.npy' % (directory, date, wavelength))\nEx = np.load('%s/DATA/%s/%i/exposure.npy' % (directory, date, wavelength))\n\nfont_size = 15\n\n# determine frequency values that FFT will evaluate\nif wavelength in [1600,1700]:\n time_step = 24 # add as argument in function call, or leave in as constant?\nelse:\n time_step = 12\n\nt_interp = np.linspace(0, TIME[len(TIME)-1], int((TIME[len(TIME)-1]/time_step)+1)) # interpolate onto default-cadence time-grid\n\n#x0 = [853, 300, 965, 834]\n#y0 = [316, 520, 865, 1413] \n\nx0 = [864, 977, 847]\ny0 = [318, 860, 1428]\n \nx = x0[0]\ny = y0[0]\n \n#pixmed = DATA[:,164,246] / Ex # extract timeseries + normalize by exposure time\npixmed = DATA[:,y,x] / Ex # extract timeseries + normalize by exposure time\n#pixmed = DATA[10,10,:] # Jacks\n\nv=pixmed # 094/131/335 -- intensities too low to trim off negative values\nt=TIME\nv_interp = np.interp(t_interp,t,v) # interpolate pixel-intensity values onto specified time grid\n\n\nnum_seg = np.array([1,3,6,12]) \n\nplt.figure(figsize=(13,9))\nfont_size = 15\n \n\nfor n in range(len(num_seg)):\n \n n_segments = num_seg[n] # break data into 12 segments of equal length\n r = len(t_interp)\n rem = r % n_segments\n freq_size = (r - rem) // n_segments \n \n sample_freq = fftpack.fftfreq(freq_size, d=time_step)\n pidxs = np.where(sample_freq > 0)\n freqs = sample_freq[pidxs]\n \n \n \n data = v_interp \n data = data[0:len(data)-rem] # trim timeseries to be integer multiple of n_segments\n split = np.split(data, n_segments) # create split array for each segment\n avg_array = np.zeros((len(freqs))) # initialize array to hold fourier powers\n avg_array2 = []\n #temp = np.zeros((len(freqs)))\n\n for i in range(0,n_segments): \n \n ## perform Fast Fourier Transform on each segment \n sig = split[i]\n sig_fft = fftpack.fft(sig)\n #sig_fft = fftpack.rfft(sig) # real-FFT\n #sig_fft = np.fft.rfft(sig) # numpy significantly slower than scipy \n #sig_fft = accelerate.mkl.fftpack.fft(sig) # MKL-accelerated is (2x) faster\n #sig_fft = accelerate.mkl.fftpack.rfft(sig) # this is slightly faster\n powers = np.abs(sig_fft)[pidxs]\n norm = len(sig) # to normalize the power\n powers = ((powers/norm)**2)*(1./(sig.std()**2))*2 \n avg_array += powers\n #temp += np.log10(powers)\n \n avg_array /= n_segments # take the average of the segments \n #temp /= n_segments\n #spec_geo = np.power(10,temp)\n \n \n if n_segments == 1:\n avg_array1 = avg_array\n s = avg_array1\n freqs1 = freqs\n #avg_array1 = spec_geo\n elif n_segments == 3:\n avg_array3 = avg_array/3.\n s = avg_array3\n freqs3 = freqs\n #avg_array3 = avg_array\n #avg_array3 = spec_geo/3.\n elif n_segments == 6:\n avg_array6 = avg_array/6.\n s = avg_array6\n freqs6 = freqs\n #avg_array6 = avg_array\n #avg_array6 = spec_geo/6.\n elif n_segments == 12:\n avg_array12 = avg_array/12.\n s = avg_array12\n freqs12 = freqs\n #avg_array12 = avg_array\n #avg_array12 = spec_geo/12.\n \n params = np.zeros((11))\n \n f = freqs\n \n # assign equal weights to all parts of the curve & use as fitting uncertainties\n df = np.log10(freqs[1:len(freqs)]) - np.log10(freqs[0:len(freqs)-1])\n df2 = np.zeros_like(freqs)\n df2[0:len(df)] = df\n df2[len(df2)-1] = df2[len(df2)-2]\n ds = df2\n \n try:\n # initial guesses for fitting parameters\n M1_low = [-0.002, 0.3, -0.01]\n M1_high = [0.002, 6., 0.01]\n nlfit_l, nlpcov_l = scipy.optimize.curve_fit(PowerLaw, f, s, bounds=(M1_low, M1_high), sigma=ds, method='dogbox')\n \n except RuntimeError:\n #print(\"Error M1 - curve_fit failed - %i, %i\" % (l,m)) # turn off because would print too many to terminal\n pass\n \n except ValueError:\n #print(\"Error M1 - inf/NaN - %i, %i\" % (l,m)) # turn off because would print too many to terminal\n pass\n\n A, n, C = nlfit_l # unpack fitting parameters\n\n\n ## fit data to M2 model\n \n # first fit using 'dogbox' method \n try: \n M2_low = [0., 0.3, -0.01, 0.00001, -6.5, 0.05]\n M2_high = [0.002, 6., 0.01, 0.2, -4.6, 0.8]\n \n nlfit_gp, nlpcov_gp = scipy.optimize.curve_fit(LorentzPowerBase, f, s, bounds=(M2_low, M2_high), sigma=ds, method='dogbox', max_nfev=3000)\n #nlfit_gp, nlpcov_gp = scipy.optimize.curve_fit(LorentzPowerBase, f, s, p0 = [A,n,C,0.1,-5.55,0.425], bounds=(M2_low, M2_high), sigma=ds, method='dogbox', max_nfev=3000)\n \n except RuntimeError:\n #print(\"Error M2 - curve_fit failed - %i, %i\" % (l,m)) # turn off because would print too many to terminal\n pass\n \n except ValueError:\n #print(\"Error M2 - inf/NaN - %i, %i\" % (l,m)) # turn off because would print too many to terminal\n pass\n \n A2, n2, C2, P2, fp2, fw2 = nlfit_gp # unpack fitting parameters\n \n # next fit using default 'trf' method\n try:\n \n nlfit_gp2, nlpcov_gp2 = scipy.optimize.curve_fit(LorentzPowerBase, f, s, p0 = [A2, n2, C2, P2, fp2, fw2], bounds=(M2_low, M2_high), sigma=ds, max_nfev=3000)\n #nlfit_gp2, nlpcov_gp2 = scipy.optimize.curve_fit(LorentzPowerBase, f, s, bounds=(M2_low, M2_high), sigma=ds, max_nfev=3000)\n \n except RuntimeError:\n #print(\"Error M2 - curve_fit failed - %i, %i\" % (l,m)) # turn off because would print too many to terminal\n pass\n \n except ValueError:\n #print(\"Error M2 - inf/NaN - %i, %i\" % (l,m)) # turn off because would print too many to terminal\n pass\n \n A22, n22, C22, P22, fp22, fw22 = nlfit_gp2 # unpack fitting parameters \n \n # create model functions from fitted parameters\n m1_fit = PowerLaw(f, A, n, C) \n m2_fit2 = LorentzPowerBase(f, A22,n22,C22,P22,fp22,fw22) \n \n residsM1 = (s - m1_fit)\n chisqrM1 = ((residsM1/ds)**2).sum()\n redchisqrM1 = chisqrM1 / float(f.size-3) \n \n residsM22 = (s - m2_fit2)\n chisqrM22 = ((residsM22/ds)**2).sum()\n redchisqrM22 = chisqrM22 / float(f.size-6) \n \n f_test2 = ((chisqrM1-chisqrM22)/(6-3))/((chisqrM22)/(f.size-6))\n \n amp_scale2 = P22 / PowerLaw(np.exp(fp22), A22, n22, C22) # to extract the lorentzian-amplitude scaling factor\n \n if chisqrM1 > chisqrM22:\n rval = pearsonr(m2_fit2, s)[0] # calculate r-value correlation coefficient\n rollover = (1. / ((C22 / A22)**(-1. / n22))) / 60.\n \n # populate array with M2 parameters\n params[0] = A22\n params[1] = n22\n params[2] = C22\n params[3] = P22\n params[4] = fp22\n params[5] = fw22\n params[6] = f_test2\n params[7] = amp_scale2\n params[8] = rval\n params[9] = rollover\n params[10] = redchisqrM22\n \n fit = m2_fit2\n \n else:\n rval = pearsonr(m1_fit, s)[0]\n rollover = (1. / ((C / A)**(-1. / n))) / 60.\n \n # populate array with M1 parameters\n params[0] = A\n params[1] = n\n params[2] = C\n params[3] = np.NaN\n params[4] = np.NaN\n params[5] = np.NaN\n params[6] = np.NaN\n params[7] = np.NaN\n params[8] = rval\n params[9] = rollover\n params[10] = redchisqrM1\n \n fit = m1_fit\n \n print('%0.3e, %0.3f, %0.3e, %0.3e, %0.2f, %0.2f' % (params[0], params[1], params[2], params[3], (1./np.exp(params[4]))/60., params[5]))\n fwhm = (1. / (np.exp(params[4]+params[5]) - np.exp(params[4]-params[5])))/60.\n \n if n_segments == 1:\n fit1 = fit\n ax1 = plt.subplot2grid((21,22),(0, 0), colspan=10, rowspan=10)\n ax1.set_title('(1) 12-Hour Segment', fontsize=font_size)\n ax1.set_ylabel('Power', fontsize=font_size-2)\n plt.loglog(freqs, s)\n plt.loglog(freqs, fit)\n plt.xlim(10**-5, 10**-1)\n plt.ylim(10**-5.5, 10**0.5) \n plt.text(10**-2.53,10**-0.5,'n = %0.2f' % params[1], fontsize=font_size)\n plt.text(10**-2.53,10**-0.95,'β = %0.2f [min]' % ((1./np.exp(params[4]))/60.), fontsize=font_size) \n plt.text(10**-3,10**-1.4,r'FWHM = %0.2f [min]' % fwhm, fontsize=font_size)\n #avg_array1 = spec_geo\n elif n_segments == 3:\n fit3 = fit\n ax2 = plt.subplot2grid((21,22),(0, 12), colspan=10, rowspan=10)\n ax2.set_title('(3) 4-Hour Segments', fontsize=font_size)\n plt.loglog(freqs, s)\n plt.loglog(freqs, fit)\n plt.xlim(10**-5, 10**-1)\n plt.ylim(10**-5.5, 10**0.5) \n plt.text(10**-2.53,10**-0.5,'n = %0.2f' % params[1], fontsize=font_size)\n plt.text(10**-2.53,10**-0.95,'β = %0.2f [min]' % ((1./np.exp(params[4]))/60.), fontsize=font_size) \n plt.text(10**-3,10**-1.4,r'FWHM = %0.2f [min]' % fwhm, fontsize=font_size)\n #avg_array3 = avg_array\n #avg_array3 = spec_geo/3.\n elif n_segments == 6:\n fit6 = fit\n ax3 = plt.subplot2grid((21,22),(12, 0), colspan=10, rowspan=10)\n ax3.set_title('(6) 2-Hour Segments', fontsize=font_size)\n ax3.set_ylabel('Power', fontsize=font_size-2)\n ax3.set_xlabel('Frequency [Hz]', fontsize=font_size-2)\n plt.loglog(freqs, s)\n plt.loglog(freqs, fit)\n plt.xlim(10**-5, 10**-1)\n plt.ylim(10**-5.5, 10**0.5) \n plt.text(10**-2.53,10**-0.5,'n = %0.2f' % params[1], fontsize=font_size)\n plt.text(10**-2.53,10**-0.95,'β = %0.2f [min]' % ((1./np.exp(params[4]))/60.), fontsize=font_size) \n plt.text(10**-3,10**-1.4,r'FWHM = %0.2f [min]' % fwhm, fontsize=font_size)\n #avg_array6 = avg_array\n #avg_array6 = spec_geo/6.\n elif n_segments == 12:\n fit12 = fit\n ax4 = plt.subplot2grid((21,22),(12, 12), colspan=10, rowspan=10)\n ax4.set_title('(12) 1-Hour Segments', fontsize=font_size)\n ax4.set_xlabel('Frequency [Hz]', fontsize=font_size-2)\n plt.loglog(freqs, s)\n plt.loglog(freqs, fit)\n plt.xlim(10**-5, 10**-1)\n plt.ylim(10**-5.5, 10**0.5) \n plt.text(10**-2.53,10**-0.5,'n = %0.2f' % params[1], fontsize=font_size)\n plt.text(10**-2.53,10**-0.95,'β = %0.2f [min]' % ((1./np.exp(params[4]))/60.), fontsize=font_size) \n plt.text(10**-3,10**-1.4,r'FWHM = %0.2f [min]' % fwhm, fontsize=font_size)\n #avg_array12 = avg_array\n #avg_array12 = spec_geo/12.\n #plt.savefig('C:/Users/Brendan/Desktop/spectra_by_time_segmenting_point.pdf', format='pdf', bbox_inches='tight')\n\n#spectra_seg[jj-245+(ii-163)*3] = powers # construct 3D array with averaged FFTs from each pixel\n#spectra_std = np.std(spectra_seg, axis=0)\n\n#plt.figure(figsize=(15,15))\n#plt.loglog(freqs,avg_array)\n#plt.ylim(10**-6.5,10**0)\n \nplt.rcParams[\"font.family\"] = \"Times New Roman\"\nfont_size = 27\n \nplt.figure(figsize=(13,9))\nax = plt.gca() \nplt.title('Comparison of Temporal Averaging Methods', y=1.01, fontsize=25)\nplt.loglog(freqs1,avg_array1, 'k', linewidth=1.7, label='(1) 12-Hour Segment')\nplt.loglog(freqs3,avg_array3, 'b', linewidth=1.7, label='(3) 4-Hour Segments')\nplt.loglog(freqs6,avg_array6, 'g', linewidth=1.7, label='(6) 2-Hour Segments')\nplt.loglog(freqs12,avg_array12, 'r', linewidth=1.7, label='(12) 1-Hour Segments')\nplt.loglog(freqs1,fit1, 'k--', linewidth=1.7)\nplt.loglog(freqs3,fit3, 'b-', linewidth=1.7)\nplt.loglog(freqs6,fit6, 'g--', linewidth=1.7)\nplt.loglog(freqs12,fit12, 'r--', linewidth=1.7)\n#plt.ylim(10**-6.5,10**0)\n#plt.xlim(10**-5.,10**-1.3)\nplt.xlim(10**-5, 10**-1)\nplt.ylim(10**-5.5, 10**0.5)\nplt.xticks(fontsize=font_size, fontname=\"Times New Roman\")\nplt.yticks(fontsize=font_size, fontname=\"Times New Roman\")\nax.set_ylabel('Power', fontsize=font_size-2)\nax.set_xlabel('Frequency [Hz]', fontsize=font_size-2)\nplt.tick_params(axis='both', which='major', pad=10)\nlegend = plt.legend(loc='lower left', prop={'size':font_size}, labelspacing=0.35)\nfor label in legend.get_lines():\n label.set_linewidth(3.0) # the legend line width\n#plt.savefig('C:/Users/Brendan/Desktop/spectra_temporal_averaging_methods_point.pdf', format='pdf', bbox_inches='tight')\n\n#np.save('C:/Users/Brendan/Desktop/spec_array1_jack.npy', avg_array1)\n#np.save('C:/Users/Brendan/Desktop/spec_array3_jack.npy', avg_array3)\n#np.save('C:/Users/Brendan/Desktop/spec_array6_jack.npy', avg_array6)\n#np.save('C:/Users/Brendan/Desktop/spec_array12_jack.npy', avg_array12)", "sub_path": "GlobalEUV/compare_num_seg_20130626.py", "file_name": "compare_num_seg_20130626.py", "file_ext": "py", "file_size_in_byte": 14256, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.log", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.memmap", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "scipy.fftpack.fftfreq", "line_number": 104, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 104, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 113, "usage_type": "call"}, {"api_name": "scipy.fftpack.fft", "line_number": 121, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 121, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 167, "usage_type": "call"}, {"api_name": "scipy.signal.optimize.curve_fit", "line_number": 176, "usage_type": "call"}, {"api_name": "scipy.signal.optimize", "line_number": 176, "usage_type": "attribute"}, {"api_name": "scipy.signal", "line_number": 176, "usage_type": "name"}, {"api_name": "scipy.signal.optimize.curve_fit", "line_number": 196, "usage_type": "call"}, {"api_name": "scipy.signal.optimize", "line_number": 196, "usage_type": "attribute"}, {"api_name": "scipy.signal", "line_number": 196, "usage_type": "name"}, {"api_name": "scipy.signal.optimize.curve_fit", "line_number": 212, "usage_type": "call"}, {"api_name": "scipy.signal.optimize", "line_number": 212, "usage_type": "attribute"}, {"api_name": "scipy.signal", "line_number": 212, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 239, "usage_type": "call"}, {"api_name": "scipy.stats.stats.pearsonr", "line_number": 242, "usage_type": "call"}, {"api_name": "scipy.stats.stats.pearsonr", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.NaN", "line_number": 268, "usage_type": "attribute"}, {"api_name": "numpy.NaN", "line_number": 269, "usage_type": "attribute"}, {"api_name": "numpy.NaN", "line_number": 270, "usage_type": "attribute"}, {"api_name": "numpy.NaN", "line_number": 271, "usage_type": "attribute"}, {"api_name": "numpy.NaN", "line_number": 272, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 280, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 284, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 284, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 287, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 287, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 288, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 288, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 289, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 289, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 290, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 290, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 291, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 291, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 292, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 292, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 292, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 293, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 293, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 297, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 297, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 299, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 299, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 300, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 300, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 301, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 301, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 302, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 302, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 303, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 303, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 304, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 304, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 304, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 305, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 305, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 310, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 310, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 314, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 314, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 315, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 315, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 316, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 316, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 317, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 317, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 318, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 318, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 319, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 319, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 319, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 320, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 320, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 325, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 325, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 328, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 328, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 329, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 329, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 330, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 330, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 331, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 331, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 332, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 332, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 333, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 333, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 333, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 334, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 334, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 346, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 346, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 349, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 349, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 350, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 350, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 351, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 351, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 352, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 352, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 353, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 353, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 354, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 354, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 355, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 355, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 356, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 356, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 357, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 357, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 358, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 358, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.loglog", "line_number": 359, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 359, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 362, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 362, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 363, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 363, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 364, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 364, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 365, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 365, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 368, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 368, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 369, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 369, "usage_type": "name"}]} +{"seq_id": "415722247", "text": "from googletrans import Translator\n\nimport discord\nfrom discord.ext import commands\nfrom discord.ext.commands import Bot\n\nclass SourceState:\n def __init__(self, bot):\n self.bot = bot\n self.source_id = \"\"\n self.dest_id = \"en\"\n self.translator = Translator()\n self.active_message = None\n self.is_dest=True\n\nclass Translators: \n def __init__(self, bot):\n self.bot = bot\n self.source_states = {}\n\n async def on_reaction_add(self, reaction, user):\n state = self.get_source_state(reaction.message.server)\n if state.active_message and state.active_message.id == reaction.message.id and not reaction.emoji == \"🎵\":\n emoji = reaction.emoji\n index = \"en\"\n if emoji == \"2⃣\":\n index = \"ja\"\n elif emoji == \"3⃣\":\n index = \"ko\"\n if state.is_dest:\n state.dest_id=index\n else:\n state.source_id=index\n state.active_message = None\n await self.bot.delete_message(reaction.message)\n\n\n def get_source_state(self, server):\n state = self.source_states.get(server.id)\n if state is None:\n state = SourceState(self.bot)\n self.source_states[server.id] = state\n return state\n\n def get_embedded_options(self, to=None):\n embed = discord.Embed(color=0x000088)\n embed.set_author(name=\"options\")\n if to:\n x = \"to\"\n else:\n x = \"from\"\n embed.add_field(name=\"1. en\", value=\"translate \" + x + \" English\", inline=False)\n embed.add_field(name=\"2. ja\", value=\"translate \" + x + \" Japanese\", inline=False)\n embed.add_field(name=\"3. ko\", value=\"translate \" + x + \" Korean\", inline=False)\n return embed\n\n @commands.command(pass_context=True)\n async def translate_to(self, ctx):\n if ctx.message.author.bot:\n return\n state = self.get_source_state(ctx.message.server)\n state.is_dest=True\n msg = await self.bot.say(embed=self.get_embedded_options(True))\n await self.bot.add_reaction(msg, emoji=\"1⃣\")\n await self.bot.add_reaction(msg, emoji=\"2⃣\")\n await self.bot.add_reaction(msg, emoji=\"3⃣\")\n await self.bot.add_reaction(msg, emoji=\"🎵\")\n state.active_message = msg\n\n @commands.command(pass_context=True)\n async def translate_from(self, ctx):\n if ctx.message.author.bot:\n return\n state = self.get_source_state(ctx.message.server)\n state.is_dest=False\n msg = await self.bot.say(embed=self.get_embedded_options(False))\n await self.bot.add_reaction(msg, emoji=\"1⃣\")\n await self.bot.add_reaction(msg, emoji=\"2⃣\")\n await self.bot.add_reaction(msg, emoji=\"3⃣\")\n \n await self.bot.add_reaction(msg, emoji=\"🎵\")\n state.active_message = msg\n\n @commands.command(pass_context=True)\n async def translate(self, ctx, *args):\n if ctx.message.author.bot:\n return\n state = self.get_source_state(ctx.message.server)\n words = \"\"\n for word in args:\n words+=word\n words+=\" \"\n if words.isspace():\n return\n # print(\"Translating: \" + words)\n if len(words) > 1024:\n return\n if state.source_id != \"\":\n result = state.translator.translate(words, dest=state.dest_id, src=state.source_id).text\n else:\n result = state.translator.translate(words, dest=state.dest_id).text\n await self.bot.say(result) \n\ndef setup(bot):\n bot.add_cog(Translators(bot))\n print(\"Translator is loaded\")\n", "sub_path": "Translator.py", "file_name": "Translator.py", "file_ext": "py", "file_size_in_byte": 3702, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "googletrans.Translator", "line_number": 12, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 46, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 57, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 57, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 70, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 70, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 84, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 84, "usage_type": "name"}]} +{"seq_id": "83223026", "text": "import numpy as np\nimport h5py\nfrom scipy.optimize import least_squares\nfrom matplotlib import pyplot as plt\nimport json, re\nfrom itertools import cycle\n\nclass FormFactor:\n def __init__(self, qsqlist, samples, mB, mpole, fitForm, numParams, lb, ub, mV=0.77):\n self.qsqlist = np.array(qsqlist)\n self.samples = np.array(samples)\n self.lb = lb\n self.ub = ub\n self.mB = mB\n self.mV = mV\n self.mpole = mpole\n self.fitForm = fitForm\n self.numParams = numParams\n self.errors = None\n def calculateErrors(self):\n self.errors = np.std(self.samples, axis=0, dtype=np.float64)\n def calculateResidue(self, sampleNumber, parameters):\n if self.errors is None:\n self.calculateErrors()\n result = (self.function(self.qsqlist[self.lb:self.ub], parameters) - self.samples[sampleNumber, self.lb:self.ub])/self.errors[self.lb:self.ub]\n return result\n def z(self, qsq):\n tplus = (self.mB+self.mV)**2\n tminus =(self.mB-self.mV)**2\n t0 = tplus*(1-np.sqrt(1-tminus/tplus))\n return (np.sqrt(tplus-qsq)-np.sqrt(tplus-t0))/(np.sqrt(tplus-qsq) + np.sqrt(tplus-t0))\n def poly(self, parameters, x):\n res = 0\n for n, a in enumerate(parameters):\n res += a*x**n\n return res\n def function(self, qsq, parameters):\n return 1.0/(1.0 - qsq/self.mpole**2) * self.poly(parameters, self.z(qsq) - self.z(0))\n def residue(self, parameters):\n return -self.mpole**2 * self.poly(parameters, self.z(self.mpole**2) - self.z(0))\n\nclass Fitter:\n def __init__(self, jsonFilename):\n def getbounds(qsqlist, l, u):\n for i, it in enumerate(qsqlist):\n if it >= l:\n lb = i\n break\n for i, it in reversed(list(enumerate(qsqlist))):\n if it <= u:\n ub = i+1\n break\n return (lb, ub)\n with open(jsonFilename, \"r\") as f:\n input = json.load(f)\n\n self.constraints = input[\"constraints\"]\n self.formFactors = {}\n for dataFilename in input:\n if dataFilename == \"constraints\":\n continue\n with h5py.File(dataFilename, 'r') as f:\n qsqlist = list(f['qsqlist'][...])\n data = {key: f[key][...] for key in f}\n for key in input[dataFilename]:\n mB = np.float(input[dataFilename][key][\"mB\"])\n mpole = np.float(input[dataFilename][key][\"m_pole\"])\n numParams = np.int(input[dataFilename][key][\"num_pars\"])\n fitForm = input[dataFilename][key][\"fit_form\"]\n lb, ub = getbounds(qsqlist, np.int(input[dataFilename][key][\"lb\"]), np.int(input[dataFilename][key][\"ub\"]))\n self.formFactors[key] = FormFactor(qsqlist, data[key], mB, mpole, fitForm, numParams, lb, ub)\n self.NumberOfSamples = len(self.formFactors[list(self.formFactors.keys())[0]].samples)\n self.sampleNumber = 0\n self.fit = None\n\n def constraintsToSets(self):\n pat = re.compile(\"[a-zA-Z0-9_-]+(?=\\([a-zA-Z0-9_.*-]+\\))\")\n pat2 = re.compile(\"(?<=<)[a-zA-Z0-9_-]+(?=>)\")\n return [ [set(pat.findall(c)),c] for c in self.constraints if pat.findall(c) != []] +\\\n [ [set(pat2.findall(c)),c] for c in self.constraints if pat2.findall(c) != []]\n\n def maximalSets(self):\n setList = self.constraintsToSets()\n result = []\n while setList != []:\n ff, constr = setList.pop()\n constr = [constr,]\n for ff2, c2 in reversed(setList):\n if ff.intersection(ff2) != set():\n ff = ff.union(ff2)\n constr.append(c2)\n setList.remove([ff2,c2])\n result.append([ff, constr])\n return [[list(r[0]), list(r[1])] for r in result]\n \n def splitParameters(self, parameters):\n splits = np.cumsum([self.formFactors[ff].numParams for ff in self.partialfflist], axis=-1)\n if len(self.partialfflist) == 1:\n return {self.partialfflist[0]: parameters}\n parameterSplits = np.split(parameters, splits, axis=-1)\n return {key:item for key,item in zip(self.partialfflist, parameterSplits)}\n\n def evalConstraints(self, paramDict):\n BIGNUMBER = 1e8\n res = []\n for constraint in self.partialconstrs:\n constraint = re.sub(\"([0-9A-Za-z_-]+)\\(([0-9A-Za-z_*.-]+)\\)\", \"self.formFactors[\\\"\\g<1>\\\"].function(\\g<2>, paramDict[\\\"\\g<1>\\\"])\", constraint)\n constraint = re.sub(\"<([0-9A-Za-z_-]+)>\", \"self.formFactors[\\\"\\g<1>\\\"].residue(paramDict[\\\"\\g<1>\\\"])\", constraint)\n res.append(BIGNUMBER*(eval(constraint)))\n return np.array(res)\n\n def calculateResidue(self, parameters):\n paramDict = self.splitParameters(parameters)\n result = np.concatenate([self.formFactors[ff].calculateResidue(self.sampleNumber, paramDict[ff]) for ff in self.partialfflist] +\n [self.evalConstraints(paramDict)])\n return result\n\n def generateFit(self, verbose=True):\n maxSets = self.maximalSets()\n constrainedFFs = np.concatenate([ff for ff,c in maxSets])\n for ff in self.formFactors:\n if (ff not in constrainedFFs):\n maxSets.append([[ff],[]])\n self.fit = {key:[] for key in self.formFactors}\n\n for self.partialfflist, self.partialconstrs in maxSets:\n if(verbose):\n print(\"Now fitting FFs:\\n\",self.partialfflist, \"\\n subject to constraints: \\n\", self.partialconstrs)\n for self.sampleNumber in range(self.NumberOfSamples):\n p0 = np.ones(np.sum([self.formFactors[ff].numParams for ff in self.partialfflist]))\n fittedPars = self.splitParameters(least_squares(self.calculateResidue, p0).x)\n for key in fittedPars:\n self.fit[key].append(fittedPars[key])\n\n def meanFitParameters(self):\n return {key: np.mean(self.fit[key], axis=0) for key in self.fit}\n\n def covarianceMatrix(self):\n def cov(samples1, samples2):\n sampleAverage1 = np.mean(samples1, axis=0)\n sampleAverage2 = np.mean(samples2, axis=0)\n return np.mean([np.outer(x, y) for x, y in zip(samples1, samples2)], axis=0) - np.outer(sampleAverage1, sampleAverage2)\n return {name1: {name2: cov(self.fit[name1], self.fit[name2]) for name2 in self.fit} for name1 in self.fit}\n\n def getResidues(self):\n residues = {name:[self.formFactors[name].residue(sample) for sample in self.fit[name]] for name in self.formFactors}\n return {name: (np.mean(residues[name]), np.std(residues[name])) for name in residues}\n\n def plot(self, fflist):\n colorCycler = cycle(\"rbgcmyk\")\n for i, name in enumerate(fflist):\n color = colorCycler.__next__()\n\n cv = np.mean(self.formFactors[name].samples, axis=0)\n err = np.std(self.formFactors[name].samples, axis=0)\n plt.xlabel('$q^2$')\n\n ff = self.formFactors[name]\n qsqlist = np.array(self.formFactors[name].qsqlist)\n lb = self.formFactors[name].lb\n ub = self.formFactors[name].ub\n\n plt.errorbar(qsqlist, cv, yerr=err, fmt='.', label=name, color=color)\n xv = np.arange(qsqlist[lb], qsqlist[ub-1]+0.1, 0.1)\n xv_below = np.arange(min(qsqlist),qsqlist[lb] + 0.1, 0.1) \n xv_above = np.arange(qsqlist[ub-1] + 0.1, max(qsqlist) + 0.1, 0.1)\n\n #alphadict = {n:params for n, params in zip(self.formFactors, self.splitParameters(self.fit))}\n yvfit = ff.function(xv, np.mean(self.fit[name], axis=0))\n yvfit_below = ff.function(xv_below, np.mean(self.fit[name], axis=0))\n yvfit_above = ff.function(xv_above, np.mean(self.fit[name], axis=0))\n\n yverr = np.std([ff.function(xv, sample) for sample in self.fit[name]], axis=0)\n yverr_below = np.std([ff.function(xv_below, sample) for sample in self.fit[name]], axis=0)\n yverr_above = np.std([ff.function(xv_above, sample) for sample in self.fit[name]], axis=0)\n\n plt.plot(xv, yvfit, color=color)\n plt.plot(xv_below, yvfit_below, '--', color=color)\n plt.plot(xv_above, yvfit_above, '--', color=color)\n plt.fill_between(xv, yvfit-yverr, yvfit+yverr, color=color, alpha=0.6)\n plt.fill_between(xv_below, yvfit_below-yverr_below, yvfit_below+yverr_below, color=color, alpha=0.3)\n plt.fill_between(xv_above, yvfit_above-yverr_above, yvfit_above+yverr_above, color=color, alpha=0.3)\n plt.legend()\n plt.show()\n", "sub_path": "functions.py", "file_name": "functions.py", "file_ext": "py", "file_size_in_byte": 8785, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 31, "usage_type": "call"}, {"api_name": "json.load", "line_number": 55, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 70, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 77, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 100, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 107, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 130, "usage_type": "call"}, {"api_name": "scipy.optimize.least_squares", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.outer", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 147, "usage_type": "call"}, {"api_name": "itertools.cycle", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}]} +{"seq_id": "512087841", "text": "from flask import abort, flash, url_for\nfrom flask_login import current_user\n\nfrom .. import db\nfrom ..models.blogs import Item\nfrom ..models.links import Link\n\n\ndef get_item(slug):\n item = Item.query.filter_by(slug=slug).first()\n if not item or not item.author:\n abort(404)\n if current_user != item.author:\n abort(403)\n return item\n\n\ndef create_item(form, author, now):\n \"\"\"\n item.created is None by default, so are item.edited and item.published, when\n we create the item by this function, we pass the parameter now, which is\n the value of datetime.utcnow(). So, if we create item by this function, its\n attributes 'created', 'edited' and 'published' (if the item is public) will\n have the same value.\n :param form:\n :param author:\n :param now:\n :return:\n \"\"\"\n item = Item(abstract=form.abstract.data)\n item.author = author\n item.body = form.body.data\n item.public = form.public.data\n if item.public:\n item.published = now\n item.created = now\n item.edited = now\n db.session.add(item)\n link = Link(url=url_for('blogs.show_item', slug=item.slug, page=1))\n link.item = item\n db.session.add(link)\n db.session.commit()\n if item.public:\n flash('Публичный топик журнала успешно создан.')\n else:\n flash('Приватный топик журнала успешно создан.')\n return item\n\n\ndef change_item(form, item, now):\n \"\"\"\n If we change the item by this function, its attributes 'edited' and\n 'published' (if the item is public) will have the same value, which is\n the value of datetime.utcnow() in the target view functions.\n :param form:\n :param item:\n :param now:\n :return:\n \"\"\"\n item.abstract = form.abstract.data\n item.body = form.body.data\n item.edited = now\n if item.public:\n item.published = now\n item.link.url = url_for('blogs.show_item', slug=item.slug, page=1)\n db.session.add(item)\n db.session.commit()\n return item\n\n\ndef remove_relatives(entity):\n for comment in entity.comments:\n db.session.delete(comment)\n for label in entity.labels:\n entity.labels.remove(label)\n if label.is_empty:\n db.session.delete(label)\n", "sub_path": "journal/blogs/tools.py", "file_name": "tools.py", "file_ext": "py", "file_size_in_byte": 2285, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "models.blogs.Item.query.filter_by", "line_number": 10, "usage_type": "call"}, {"api_name": "models.blogs.Item.query", "line_number": 10, "usage_type": "attribute"}, {"api_name": "models.blogs.Item", "line_number": 10, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 12, "usage_type": "call"}, {"api_name": "flask_login.current_user", "line_number": 13, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 14, "usage_type": "call"}, {"api_name": "models.blogs.Item", "line_number": 30, "usage_type": "call"}, {"api_name": "models.links.Link", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 65, "usage_type": "call"}]} +{"seq_id": "283262695", "text": "from celery.decorators import task\nfrom celery.utils.log import get_task_logger\nfrom django.core.mail import send_mail\nfrom .emails import *\nimport vobject\nfrom .models import *\nfrom datetime import datetime, timedelta\nfrom celery.task import periodic_task\nfrom django.conf import settings\nlogger = get_task_logger(__name__)\nfrom celery.schedules import crontab\nfrom notifications.signals import notify\n\n@task(name=\"send_member_mail\")\ndef send_member_task(email, password,shop,website):\n logger.info(\"in member mail task\")\n return send_member_email(email, password,shop,website)\n\n\n@task(name=\"ws_member_send_businesscard__mail\")\ndef ws_member_send_businesscard__mail(email,member_email,name):\n \n print(\"from task\",email,member_email)\n logger.info(\"in sending business card task\")\n return send_vcf_mail1(email,member_email,name)\n\n@task(name=\"send_vcf_mail\")\ndef send_vcf_email_task(mail,member_mail,name,mem_detail):\n print(\"in send mail\",mail)\n logger.info(\"in send vcf mail task\")\n return send_vcf_mail(mail,member_mail,name,mem_detail)\n\n@task(name=\"send_vcf_mail1\")\ndef send_vcf_email_task1(mail,member_mail,name):\n print(\"in send mail1\",mail)\n print(\"in sebd email memeber\",member_mail)\n logger.info(\"in send vcf mail task\")\n return send_vcf_mail1(mail,member_mail,name)\n\n@periodic_task(\n #run_every=(timedelta(minute=30)),\n run_every=(timedelta(seconds=30)),\n name=\"make_existing_member_businesscard\",\n ignore_result=False\n)\n\n\ndef make_existing_member_businesscard():\n \n member=UserProfile.objects.all()\n for memberemail in member:\n \n user1=User.objects.get(pk=memberemail.user.id)\n email=user1.email\n f=open(\"static/vcards/\"+email+\".vcf\",'w+')\n j = vobject.vCard()\n o = j.add('fn')\n o.value = user1.first_name+\" \"+user1.last_name\n o = j.add('n')\n o.value = vobject.vcard.Name( family=user1.last_name,given=user1.first_name )\n j.add('email')\n j.email.value = email\n j.email.type_param = 'INTERNET'\n j.email.type_param = 'HOME'\n j.add('tel')\n j.tel.type_param=\"cell\"\n\n j.tel.value=str(memberemail.phone_no)\n str1=memberemail.shop_address+\",\"+memberemail.city\n\n o = j.add('note')\n o.value =str1+\",Australia\"\n j.add('url')\n j.url.value=memberemail.website\n #j.add('adr')\n #j.adr.value=str1+\",Australia\"\n #j.adr.type_param='ADR-component-locality'\n f.write(j.serialize())\n f.close()\n\n@periodic_task(\n #run_every=(timedelta(seconds=20)),\n run_every=(crontab(minute='0',hour=\"5\")),\n name=\"check_expiration_mechanic_license_date\",\n ignore_result=False\n)\n\n\ndef check_expiration_mechanic_license_date():\n logger.info(\"in check exp date mechanic license task\")\n # print(\"in license task\")\n license_list=UserProfile.objects.all()\n \n today=datetime.today()\n for license in license_list:\n if license.member_mech_license_expiry_date:\n #logger.info(\"vehicle is \"+str(vehicle.pk)+\" and reg expiry date is\"+str(vehicle.reg_expiry_date)+\" lead--\"+str(vehicle.lead.phone))\n date_diff=license.member_mech_license_expiry_date-today.date()\n # print(\"date difference\",date_diff)\n member_user=User.objects.get(pk=license.user.pk)\n if date_diff.days==0:\n logger.info(\"date is equal to 0\")\n notify.send(member_user,recipient=member_user,verb=\"Your mechanic license is expiring today.Please renew your license\",app_name=\"remainder_mech_license_expiry_date\",activity=\"mec_license_due_1months\",object_id=license.pk)\n elif date_diff.days==30:\n logger.info(\"date is equal to 30\")\n notify.send(member_user,recipient=member_user,verb=\"Your mechanic license is going to expire after 30 days on \" +str(license.member_mech_license_expiry_date)+\" \",app_name=\"remainder_mech_license_expiry_date\",activity=\"mec_license_due_1months\",object_id=license.pk)\n elif date_diff.days==15:\n logger.info(\"date is equal to 15\")\n notify.send(member_user,recipient=member_user,verb=\"Your mechanic license is going to expire after 15 days on \" +str(license.member_mech_license_expiry_date)+\" \",app_name=\"remainder_mech_license_expiry_date\",activity=\"mec_license_due_1months\",object_id=license.pk)\n \n else:\n logger.info(\"date is not equal to 30\")\n else:\n logger.info(\"no date available\") \n \n \n@periodic_task(\n run_every=(timedelta(seconds=30)),\n #run_every=(crontab(minute='12',hour=\"5\")),\n name=\"check_expiration_arc_license_date\",\n ignore_result=False\n)\n\n\ndef check_expiration_arc_license_date():\n logger.info(\"in check exp date mechanic license task\")\n # print(\"in license task\")\n license_list=UserProfile.objects.all()\n \n today=datetime.today()\n for license in license_list:\n if license.member_ARC_license_expiry_date:\n #logger.info(\"vehicle is \"+str(vehicle.pk)+\" and reg expiry date is\"+str(vehicle.reg_expiry_date)+\" lead--\"+str(vehicle.lead.phone))\n date_diff=license.member_ARC_license_expiry_date-today.date()\n # print(\"date difference\",date_diff)\n member_user=User.objects.get(pk=license.user.pk)\n if date_diff.days==0:\n logger.info(\"date is equal to 0\")\n notify.send(member_user,recipient=member_user,verb=\"Your ARC license is expiring today.Please renew your license\",app_name=\"remainder_mech_license_expiry_date\",activity=\"arc_license_due_1months\",object_id=license.pk)\n elif date_diff.days==30:\n logger.info(\"date is equal to 30\")\n notify.send(member_user,recipient=member_user,verb=\"Your ARC license is going to expire after 30 days on \" +str(license.member_ARC_license_expiry_date)+\" \",app_name=\"remainder_arc_license_expiry_date\",activity=\"arc_license_due_1months\",object_id=license.pk)\n elif date_diff.days==15:\n logger.info(\"date is equal to 15\")\n notify.send(member_user,recipient=member_user,verb=\"Your ARC license is going to expire after 15 days on \" +str(license.member_ARC_license_expiry_date)+\" \",app_name=\"remainder_arc_license_expiry_date\",activity=\"arc_license_due_1months\",object_id=license.pk)\n \n else:\n logger.info(\"date is not equal to 30\")\n else:\n logger.info(\"no date available\") ", "sub_path": "Registration/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 6582, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "celery.utils.log.get_task_logger", "line_number": 10, "usage_type": "call"}, {"api_name": "celery.decorators.task", "line_number": 14, "usage_type": "call"}, {"api_name": "celery.decorators.task", "line_number": 20, "usage_type": "call"}, {"api_name": "celery.decorators.task", "line_number": 27, "usage_type": "call"}, {"api_name": "celery.decorators.task", "line_number": 33, "usage_type": "call"}, {"api_name": "vobject.vCard", "line_number": 56, "usage_type": "call"}, {"api_name": "vobject.vcard.Name", "line_number": 60, "usage_type": "call"}, {"api_name": "vobject.vcard", "line_number": 60, "usage_type": "attribute"}, {"api_name": "celery.task.periodic_task", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 94, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 94, "usage_type": "name"}, {"api_name": "notifications.signals.notify.send", "line_number": 103, "usage_type": "call"}, {"api_name": "notifications.signals.notify", "line_number": 103, "usage_type": "name"}, {"api_name": "notifications.signals.notify.send", "line_number": 106, "usage_type": "call"}, {"api_name": "notifications.signals.notify", "line_number": 106, "usage_type": "name"}, {"api_name": "notifications.signals.notify.send", "line_number": 109, "usage_type": "call"}, {"api_name": "notifications.signals.notify", "line_number": 109, "usage_type": "name"}, {"api_name": "celery.task.periodic_task", "line_number": 81, "usage_type": "call"}, {"api_name": "celery.schedules.crontab", "line_number": 83, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 130, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 130, "usage_type": "name"}, {"api_name": "notifications.signals.notify.send", "line_number": 139, "usage_type": "call"}, {"api_name": "notifications.signals.notify", "line_number": 139, "usage_type": "name"}, {"api_name": "notifications.signals.notify.send", "line_number": 142, "usage_type": "call"}, {"api_name": "notifications.signals.notify", "line_number": 142, "usage_type": "name"}, {"api_name": "notifications.signals.notify.send", "line_number": 145, "usage_type": "call"}, {"api_name": "notifications.signals.notify", "line_number": 145, "usage_type": "name"}, {"api_name": "celery.task.periodic_task", "line_number": 117, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 118, "usage_type": "call"}]} +{"seq_id": "640885437", "text": "#! /usr/bin/env python\n# by caozj\n# Nov 1, 2018\n# 9:16:57 PM\n\nimport os\nimport sys\nimport argparse\nimport json\n\nimport numpy as np\nimport h5py\n\nsys.path.append(\"../..\")\nimport Cell_BLAST.message\nimport Cell_BLAST.utils\nimport Cell_BLAST.data\nimport run_classifier\n\nos.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-m\", \"--model\", dest=\"model\", type=str, required=True)\n parser.add_argument(\"-q\", \"--query\", dest=\"query\", type=str, required=True)\n parser.add_argument(\"-o\", \"--output\", dest=\"output\", type=str, required=True)\n parser.add_argument(\"-d\", \"--device\", dest=\"device\", type=str, choices=[\"\", \"0\", \"1\", \"2\", \"3\"], default=\"\")\n cmd_args = parser.parse_args()\n\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = cmd_args.device\n if not os.path.exists(os.path.dirname(cmd_args.output)):\n os.makedirs(os.path.dirname(cmd_args.output))\n return cmd_args\n\n\ndef main():\n cmd_args = parse_args()\n\n Cell_BLAST.message.info(\"Building model...\")\n with open(os.path.join(cmd_args.model, \"cmd_args.json\"), \"r\") as f:\n model_args = Cell_BLAST.utils.dotdict(json.load(f))\n model = run_classifier.build_model(model_args)\n\n Cell_BLAST.message.info(\"Reading query...\")\n query = Cell_BLAST.data.ExprDataSet.read_dataset(cmd_args.query)\n query = query.normalize()\n query.exprs = np.log1p(query.exprs)\n query = query[:, Cell_BLAST.data.read_hybrid_path(\"%s//uns/%s\" % (\n model_args.input, model_args.genes\n ))]\n query_latent = model.inference(query.exprs)\n\n query_pred = model.classify(query.exprs)\n query_class = query_pred.argmax(axis=1)\n query_confidence = query_pred.max(axis=1)\n query_confidence = -np.log(1 + 1e-8 - query_confidence)\n\n with h5py.File(cmd_args.output, \"w\") as f:\n f.create_dataset(\"latent\", data=query_latent)\n f.create_dataset(\"class\", data=query_class)\n f.create_dataset(\"confidence\", data=query_confidence)\n\n\nif __name__ == \"__main__\":\n main()\n Cell_BLAST.message.info(\"Done!\")\n", "sub_path": "Utilities/run_classifier_as_dimension_reduction.py", "file_name": "run_classifier_as_dimension_reduction.py", "file_ext": "py", "file_size_in_byte": 2070, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sys.path.append", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 20, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 24, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 32, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "Cell_BLAST.message.message.info", "line_number": 40, "usage_type": "call"}, {"api_name": "Cell_BLAST.message.message", "line_number": 40, "usage_type": "attribute"}, {"api_name": "Cell_BLAST.message", "line_number": 40, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "Cell_BLAST.message.utils.dotdict", "line_number": 42, "usage_type": "call"}, {"api_name": "Cell_BLAST.message.utils", "line_number": 42, "usage_type": "attribute"}, {"api_name": "Cell_BLAST.message", "line_number": 42, "usage_type": "name"}, {"api_name": "json.load", "line_number": 42, "usage_type": "call"}, {"api_name": "run_classifier.build_model", "line_number": 43, "usage_type": "call"}, {"api_name": "Cell_BLAST.message.message.info", "line_number": 45, "usage_type": "call"}, {"api_name": "Cell_BLAST.message.message", "line_number": 45, "usage_type": "attribute"}, {"api_name": "Cell_BLAST.message", "line_number": 45, "usage_type": "name"}, {"api_name": "Cell_BLAST.message.data.ExprDataSet.read_dataset", "line_number": 46, "usage_type": "call"}, {"api_name": "Cell_BLAST.message.data", "line_number": 46, "usage_type": "attribute"}, {"api_name": "Cell_BLAST.message", "line_number": 46, "usage_type": "name"}, {"api_name": "numpy.log1p", "line_number": 48, "usage_type": "call"}, {"api_name": "Cell_BLAST.message.data.read_hybrid_path", "line_number": 49, "usage_type": "call"}, {"api_name": "Cell_BLAST.message.data", "line_number": 49, "usage_type": "attribute"}, {"api_name": "Cell_BLAST.message", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 57, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 59, "usage_type": "call"}, {"api_name": "Cell_BLAST.message.message.info", "line_number": 67, "usage_type": "call"}, {"api_name": "Cell_BLAST.message.message", "line_number": 67, "usage_type": "attribute"}, {"api_name": "Cell_BLAST.message", "line_number": 67, "usage_type": "name"}]} +{"seq_id": "302181070", "text": "from sqlalchemy import create_engine, MetaData, and_, func, Table\nfrom sqlalchemy.orm import sessionmaker\nfrom voucher_value import voucher_info\nfrom database_setup import Engine, VoucherHead, VoucherBody, SubSys, MatchTable, Currency\nfrom configuation import DB_DI, DB_TJ, DI_CODE, TJ_CODE\nfrom datetime import datetime\nfrom tkinter import messagebox\nimport pymssql\nimport _mssql\nfrom gui import ShowMessage\nimport logging\nlogging.basicConfig(level=logging.DEBUG, filename=\"log.txt\")\n# 数据导入时，会先将所有的数据导入中间表，然后从中间表中将数据分别导入不同的账套，所以中间表作为数据源是固定的，这里将连接字符串固定下来\nDB_Session = sessionmaker(bind=Engine)\nInter_Session = DB_Session()\n\n\ndef insert_interface():\n \"\"\"\n 将Excel的源数据插入中间表，源数据来自于另一个Python脚本voucher_value\n :return:\n \"\"\"\n voucher_head, voucher_body = voucher_info()\n if not voucher_head:\n return 0\n # 查询系统中当前的会计期间，只处理当前会计期间的凭证，之前和之后期间的凭证不处理\n open_year, open_period = Inter_Session.query(SubSys.Fyear, SubSys.Fperiod).filter(SubSys.Fcheckout == 0).first()\n fp = open(\"log.txt\", \"a\")\n fp.write(\"\\n\" + \"*\"*40 + \"\\n\")\n fp.write(\"\\n\" + str(datetime.now()) + \"\\n\")\n message = \"开始向接口表插入数据\\n\"\n message += \"接口会计期间为\" + str(open_year) + \"年\" + str(open_period) + '月\\n'\n # fp.write(\"开始向接口表插入数据\\n\")\n fp.write(message)\n show_message = ShowMessage(message)\n show_message.start()\n # 删除当前会计期间中，c_VoucherHead和c_VoucherBody中的所有数据，然后导入新数据\n Inter_Session.query(VoucherBody).filter(\n and_(VoucherBody.year == open_year, VoucherBody.period == open_period)).delete(\n synchronize_session=False)\n Inter_Session.query(VoucherHead).filter(\n and_(VoucherHead.year == open_year, VoucherHead.period == open_period)).delete(\n synchronize_session=False)\n Inter_Session.commit()\n message += \"接口表清除完毕\\n\"\n fp.write(\"接口表清除完毕\\n\")\n for each in voucher_head:\n head_data = VoucherHead(*each)\n Inter_Session.add(head_data)\n Inter_Session.commit()\n for each in voucher_body:\n body_data = VoucherBody(*each)\n Inter_Session.add(body_data)\n Inter_Session.commit()\n message += \"接口表导入完成, 共导入数据\" + str(len(voucher_body)) + \"行\\n\"\n fp.write(message)\n show_message = ShowMessage(message)\n show_message.start()\n fp.close()\n return 1\n\n\ndef interface_to_kingdee(database, account_book):\n \"\"\"\n 从接口表中取出数据，插入金蝶的凭证表t_Voucher和t_VoucherEntry\n :param database: 要插入的数据库名称，金蝶中不同的账套对应不同的数据库\n :param account_book: 中间表中账套代码，也是JDE种���套的代码， account_book和上一个参数database是一一对应关系\n :return:\n \"\"\"\n # 建立engine， metadata 和 session\n engine = create_engine(\"mssql+pymssql://appadmin:N0v1terp@srvshasql01/%s?charset=utf8\" % database)\n metadata = MetaData(bind=engine)\n db_session = sessionmaker(bind=engine)\n session = db_session()\n # 通过金蝶系统的表t_Subsys查询系统中当前的会计期间，只处理当前会计期间的凭证，之前和之后期间的凭证不处理\n sub_sys = Table('t_SubSys', metadata, autoload=True, autoload_with=engine)\n open_year, open_period = session.query(sub_sys.c.Fyear, sub_sys.c.Fperiod).filter(sub_sys.c.Fcheckout == 0).first()\n # 清除当前会计期间中凭证头和凭证行的数据,已过账的数据不作处理\n fp = open(\"log.txt\", \"a\")\n fp.write(\"-\"*20 + \"\\n\")\n fp.write(\"开始对账套\" + database + \"导入凭证\\n\")\n fp.write(\"账套\" + database + \"的当前会计期间为\" + str(open_year) + \"年\" + str(open_period) + \"月\\n\")\n conn = engine.connect()\n # 映射金蝶系统的凭证表t_Voucher和t_VoucherEntry, 后续会在这两张表中做清除和插入\n voucher = Table('t_Voucher', metadata, autoload=True, autoload_with=engine)\n voucher_entry = Table('t_VoucherEntry', metadata, autoload=True, autoload_with=engine)\n # 找到当前会计期间的FVoucherID，然后在凭证表t_Voucher和t_VoucherEntry中删除对应的凭证\n delete_id = session.query(voucher.c.FVoucherID).filter(voucher.c.FYear == open_year,\n voucher.c.FPeriod == open_period,\n voucher.c.FPosted == 0)\n stmt = voucher.delete().where(voucher.c.FVoucherID.in_(delete_id))\n conn.execute(stmt)\n stmt = voucher_entry.delete().where(voucher_entry.c.FVoucherID.in_(delete_id))\n conn.execute(stmt)\n fp.write(\"凭证删除完毕\\n\")\n show_message = ShowMessage(\"凭证删除完毕\\n\")\n show_message.start()\n # 从接口表VouchHead中取出头信息，注意：因为针对VoucherHead和VoucherBody建立了Relation关系，\n # 所以可以通过VoucherHead的voucher_bodies属性返回Voucher的Body信息\n # 取出接口表中行合计大于1的凭证\n voucher_head = Inter_Session.query(VoucherHead).filter(\n and_(VoucherHead.year == open_year, VoucherHead.period == open_period, VoucherHead.line_count > 1,\n VoucherHead.account_book == account_book))\n # voucher_number指凭证号，每插入一张凭证，凭证号加1\n voucher_number = 1\n # 遍历VoucherHead复合条件的数据，逐行插入金蝶的凭证头和凭证行\n for each in voucher_head:\n # serial_number是t_Voucher中的一个字段，为递增字段，不受期间影响，每次加一；所以每次插入凭证前，\n # 先取出当前系统的最大值，然后加1插入t_Voucher中\n serial_number = session.query(func.max(voucher.c.FSerialNum)).first()[0]\n # 如果是空账套，没有serial_number，返回的是None，所以要指定初始值为1\n if not serial_number:\n serial_number = 1\n stmt = voucher.insert().values(FVoucherID=-1, FDate=each.voucher_date, FYear=each.year, FPeriod=each.period,\n FGroupID=1,\n FNumber=voucher_number, FEntryCount=each.line_count,\n FDebitTotal=each.total_amount,\n FCreditTotal=each.total_amount, FPreparerID=16393, FSerialNum=serial_number + 1,\n FTransDate=each.voucher_date, FAttachments=0)\n voucher_number += 1\n conn.execute(stmt)\n # 金蝶凭证头的触发器会在插入后生成凭证头的ID，先取出这个ID，用于凭证行插入\n voucher_id = session.query(voucher.c.FVoucherID).filter(voucher.c.FSerialNum == serial_number + 1).first()[0]\n # 根据Relation关系由VoucherHead直接关联到对应的VoucherBody信息\n voucher_bodies = each.voucher_bodies\n # 针对VoucherHead中的每一行头信息，通过SqlAlchemy的Relation关系，可以直接调用每一行的voucher_bodies属性关联到VoucherBody对象\n for each_line in voucher_bodies:\n # VoucherBody中只有jde_account，此时通过MatchTable转化为account_id\n account_id = session.query(MatchTable.F_101).filter(MatchTable.FNumber == each_line.jde_account).first()[0]\n # 将VoucherBody中的币别代码，如“CNY\"和\"USD\"转化为币别ID，如1， 1001\n currency_id = session.query(Currency.FCurrencyID).filter(Currency.FNumber == each_line.currency).first()[0]\n # fdc是凭证的借贷方，正数在借方，负数在贷方，注意此处规定了借贷方，所以后续金额不再分正负，全部取绝对值\n fdc = 1 if each_line.amount_cny > 0 else 0\n stmt = voucher_entry.insert().values(FVoucherID=voucher_id, FEntryID=each_line.line_number,\n FExplanation=each_line.voucher_description, FAccountID=account_id,\n FCurrencyID=currency_id, FExchangeRate=each_line.exchange_rate,\n FDC=fdc,\n FAmountFor=abs(each_line.amount_for),\n FAmount=abs(each_line.amount_cny),\n FExchangeRateType=1)\n conn.execute(stmt)\n fp.write(\"账套\" + database + \"的凭证导入完成\\n\")\n fp.close()\n\nif __name__ == \"__main__\":\n try:\n result = insert_interface()\n if result:\n interface_to_kingdee(DB_DI, DI_CODE)\n messagebox.showinfo(\"Succeed\", \"Step1 is done successfully\")\n interface_to_kingdee(DB_TJ, TJ_CODE)\n messagebox.showinfo(\"Succeed\", \"The import is done successfully\")\n except:\n messagebox.showerror(\"Error\", \"Please see log for error message\")\n logging.exception(\"Error:\")", "sub_path": "temp.py", "file_name": "temp.py", "file_ext": "py", "file_size_in_byte": 9187, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.basicConfig", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 14, "usage_type": "call"}, {"api_name": "database_setup.Engine", "line_number": 14, "usage_type": "name"}, {"api_name": "voucher_value.voucher_info", "line_number": 23, "usage_type": "call"}, {"api_name": "database_setup.SubSys.Fyear", "line_number": 27, "usage_type": "attribute"}, {"api_name": "database_setup.SubSys", "line_number": 27, "usage_type": "name"}, {"api_name": "database_setup.SubSys.Fperiod", "line_number": 27, "usage_type": "attribute"}, {"api_name": "database_setup.SubSys.Fcheckout", "line_number": 27, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "name"}, {"api_name": "gui.ShowMessage", "line_number": 35, "usage_type": "call"}, {"api_name": "database_setup.VoucherBody", "line_number": 38, "usage_type": "argument"}, {"api_name": "sqlalchemy.and_", "line_number": 39, "usage_type": "call"}, {"api_name": "database_setup.VoucherBody.year", "line_number": 39, "usage_type": "attribute"}, {"api_name": "database_setup.VoucherBody", "line_number": 39, "usage_type": "name"}, {"api_name": "database_setup.VoucherBody.period", "line_number": 39, "usage_type": "attribute"}, {"api_name": "database_setup.VoucherHead", "line_number": 41, "usage_type": "argument"}, {"api_name": "sqlalchemy.and_", "line_number": 42, "usage_type": "call"}, {"api_name": "database_setup.VoucherHead.year", "line_number": 42, "usage_type": "attribute"}, {"api_name": "database_setup.VoucherHead", "line_number": 42, "usage_type": "name"}, {"api_name": "database_setup.VoucherHead.period", "line_number": 42, "usage_type": "attribute"}, {"api_name": "database_setup.VoucherHead", "line_number": 48, "usage_type": "call"}, {"api_name": "database_setup.VoucherBody", "line_number": 52, "usage_type": "call"}, {"api_name": "gui.ShowMessage", "line_number": 57, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 71, "usage_type": "call"}, {"api_name": "sqlalchemy.MetaData", "line_number": 72, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 73, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 76, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 85, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 86, "usage_type": "call"}, {"api_name": "gui.ShowMessage", "line_number": 96, "usage_type": "call"}, {"api_name": "database_setup.VoucherHead", "line_number": 101, "usage_type": "argument"}, {"api_name": "sqlalchemy.and_", "line_number": 102, "usage_type": "call"}, {"api_name": "database_setup.VoucherHead.year", "line_number": 102, "usage_type": "attribute"}, {"api_name": "database_setup.VoucherHead", "line_number": 102, "usage_type": "name"}, {"api_name": "database_setup.VoucherHead.period", "line_number": 102, "usage_type": "attribute"}, {"api_name": "database_setup.VoucherHead.line_count", "line_number": 102, "usage_type": "attribute"}, {"api_name": "database_setup.VoucherHead.account_book", "line_number": 103, "usage_type": "attribute"}, {"api_name": "database_setup.VoucherHead", "line_number": 103, "usage_type": "name"}, {"api_name": "sqlalchemy.func.max", "line_number": 110, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 110, "usage_type": "name"}, {"api_name": "database_setup.MatchTable.F_101", "line_number": 129, "usage_type": "attribute"}, {"api_name": "database_setup.MatchTable", "line_number": 129, "usage_type": "name"}, {"api_name": "database_setup.MatchTable.FNumber", "line_number": 129, "usage_type": "attribute"}, {"api_name": "database_setup.Currency.FCurrencyID", "line_number": 131, "usage_type": "attribute"}, {"api_name": "database_setup.Currency", "line_number": 131, "usage_type": "name"}, {"api_name": "database_setup.Currency.FNumber", "line_number": 131, "usage_type": "attribute"}, {"api_name": "configuation.DB_DI", "line_number": 149, "usage_type": "argument"}, {"api_name": "configuation.DI_CODE", "line_number": 149, "usage_type": "argument"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 150, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 150, "usage_type": "name"}, {"api_name": "configuation.DB_TJ", "line_number": 151, "usage_type": "argument"}, {"api_name": "configuation.TJ_CODE", "line_number": 151, "usage_type": "argument"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 152, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 152, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 154, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 154, "usage_type": "name"}, {"api_name": "logging.exception", "line_number": 155, "usage_type": "call"}]} +{"seq_id": "51181847", "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 ]\n\n operations = [\n migrations.CreateModel(\n name='Project',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('title', models.CharField(unique=True, max_length=200)),\n ('description', models.TextField()),\n ('slug', models.SlugField(unique=True)),\n ('dateStarted', models.DateTimeField(null=True)),\n ('dateDelivered', models.DateTimeField(null=True)),\n ],\n ),\n ]\n", "sub_path": "pcomp/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 741, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "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.CreateModel", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.SlugField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "132244880", "text": "import functools\n\nfrom flask import (\n\tBlueprint, g, request, Response, jsonify, flash, redirect, render_template, session, url_for,\n\tcurrent_app as app\n)\nfrom werkzeug.security import check_password_hash, generate_password_hash\nimport simplejson\n\nfrom ..models import (\n\tAdmin, Agent, AdminAgent\n)\nfrom ..libraries import (\n\tvalidate\n)\n\nbp = Blueprint(\"agent\", __name__, url_prefix=\"/agent\")\n\ndef login_required(view):\n\t@functools.wraps(view)\n\tdef wrapped_view(**kwargs):\n\t\tif g.user is None:\n\t\t\tflash(\"Please login to carry out request\", \"info\")\n\t\t\treturn redirect(url_for(\"agent.login\"))\n\t\telse:\n\t\t\treturn view(**kwargs)\n\n\treturn wrapped_view\n\n\n@bp.before_request\ndef load_logged_in_user():\n\tagent_id = session.get(\"agent_id\")\n\n\tif agent_id is None:\n\t\tg.user = None\n\t\tg.type = None\n\telse:\n\t\tg.user = Agent.find_one(agent_id)\n\t\tg.type = \"agent\"\n\n\n@bp.route(\"/confirm/\", methods=(\"GET\", \"POST\"))\ndef confirm(code):\n\tif g.user is not None:\n\t\treturn redirect(url_for(\"agent.home\"))\n\t\n\tagent = Agent.find_one({\"confirm_code\": code})\n\tif agent is None:\n\t\tflash(\"Invalid confirmation link\", \"danger\")\n\t\treturn redirect(url_for(\"agent.login\"))\n\telif agent.confirmed:\n\t\tflash(\"Account already confirmed\", \"info\")\n\t\treturn redirect(url_for(\"agent.login\"))\n\t\n\tif request.method == \"GET\":\n\t\treturn render_template(\"agent/confirm.html\", code=code)\n\telse:\n\t\terr = validate.validate_confirm_agent(request.form)\n\t\tif err:\n\t\t\tflash(err, \"danger\")\n\t\t\treturn render_template(\"agent/confirm.html\", code=code)\n\t\tagent.password = generate_password_hash(request.form[\"password\"])\n\t\tagent.confirmed = 1\n\t\tagent.save()\n\n\t\tflash(\"Confirmation successful. Please login to continue\", \"success\")\n\t\treturn redirect(url_for(\"agent.login\"))\n\n\n@bp.route(\"/login\", methods=(\"GET\", \"POST\"))\ndef login():\n\tif request.method == \"POST\":\n\t\terr = validate.validate_login_agent(request.form)\n\t\tif err:\n\t\t\tflash(err, \"danger\")\n\t\t\treturn render_template(\"agent/login.html\")\n\t\telse:\n\t\t\tphone = request.form[\"phone\"]\n\t\t\tagent = Agent.find_one({\"phone\": phone})\n\t\t\tsession.clear()\n\t\t\tsession[\"agent_id\"] = agent.id\n\t\t\tflash(\"Login succesful\", \"success\")\n\n\t\t\treturn redirect(url_for(\"agent.home\"))\n\telse:\n\t\treturn render_template(\"agent/login.html\")\n\n\n@bp.route(\"/home\", methods=(\"GET\",))\n@login_required\ndef home():\n\tagent = g.user\n\tadmins = agent.get_admins(True)\n\tinvite_count = agent.count_invites()\n\treturn render_template(\"agent/home.html\", agent=agent, admins=admins, invite_count=invite_count)\n\n\n@bp.route(\"/pending\", methods=(\"GET\",))\n@login_required\ndef pending():\n\tagent = g.user\n\tadmins = agent.get_admins(False)\n\treturn render_template(\"agent/pending.html\", agent=agent, admins=admins)\n\n\n@bp.route(\"/accept/\", methods=(\"GET\",))\n@login_required\ndef accept(invite_id):\n\tad_ag = AdminAgent.find_one(invite_id)\n\tif ad_ag is None or g.user.id != ad_ag.agent_id:\n\t\tflash(\"Error with request\", \"danger\")\n\telse:\n\t\tad_ag.accepted = 1\n\t\tad_ag.save()\n\t\tflash(\"Invitation accepted successfully\", \"success\")\n\treturn redirect(url_for(\".home\"))\n\n\n@bp.route(\"/decline/\", methods=(\"GET\",))\n@login_required\ndef decline(invite_id):\n\tad_ag = AdminAgent.find_one(invite_id)\n\tif ad_ag is None or g.user.id != ad_ag.agent_id:\n\t\tflash(\"Error with request\", \"danger\")\n\telse:\n\t\tad_ag.delete()\n\t\tflash(\"Invitation declined successfully\", \"success\")\n\treturn redirect(url_for(\".home\"))\n\n\n@bp.route(\"/remove_admin\", methods=(\"POST\",))\n@login_required\ndef remove_admin():\n\tadmin_id = request.form.get(\"admin_id\")\n\tif not admin_id or not Admin.find_one(admin_id):\n\t\tmsg = \"Error with your request\"\n\t\tstatus = False\n\t\tflash(msg, \"danger\")\n\telse:\n\t\tad_ag = AdminAgent.find_one({\n\t\t\t\"admin_id\": admin_id,\n\t\t\t\"agent_id\": g.user.id\n\t\t})\n\t\tif ad_ag is not None:\n\t\t\tad_ag.delete()\n\t\tmsg = \"Business removed successfully\"\n\t\tstatus = True\n\t\tflash(msg, \"success\")\n\treturn jsonify({\n\t\t\"status\": status,\n\t\t\"message\": msg\n\t})\n\n", "sub_path": "app/controllers/agent.py", "file_name": "agent.py", "file_ext": "py", "file_size_in_byte": 3879, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask.Blueprint", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 24, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.g.type", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 39, "usage_type": "name"}, {"api_name": "models.Agent.find_one", "line_number": 39, "usage_type": "call"}, {"api_name": "models.Agent", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.g.type", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 45, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 46, "usage_type": "call"}, {"api_name": "models.Agent.find_one", "line_number": 48, "usage_type": "call"}, {"api_name": "models.Agent", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 57, "usage_type": "call"}, {"api_name": "libraries.validate.validate_confirm_agent", "line_number": 59, "usage_type": "call"}, {"api_name": "libraries.validate", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 62, "usage_type": "call"}, {"api_name": "werkzeug.security.generate_password_hash", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 63, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 63, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 73, "usage_type": "name"}, {"api_name": "libraries.validate.validate_login_agent", "line_number": 74, "usage_type": "call"}, {"api_name": "libraries.validate", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 79, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 79, "usage_type": "name"}, {"api_name": "models.Agent.find_one", "line_number": 80, "usage_type": "call"}, {"api_name": "models.Agent", "line_number": 80, "usage_type": "name"}, {"api_name": "flask.session.clear", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 82, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 87, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 93, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 93, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 102, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 102, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 104, "usage_type": "call"}, {"api_name": "models.AdminAgent.find_one", "line_number": 110, "usage_type": "call"}, {"api_name": "models.AdminAgent", "line_number": 110, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 111, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 111, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 112, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 116, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 117, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 117, "usage_type": "call"}, {"api_name": "models.AdminAgent.find_one", "line_number": 123, "usage_type": "call"}, {"api_name": "models.AdminAgent", "line_number": 123, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 124, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 124, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 125, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 128, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 135, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 135, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 135, "usage_type": "name"}, {"api_name": "models.Admin.find_one", "line_number": 136, "usage_type": "call"}, {"api_name": "models.Admin", "line_number": 136, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 139, "usage_type": "call"}, {"api_name": "models.AdminAgent.find_one", "line_number": 141, "usage_type": "call"}, {"api_name": "models.AdminAgent", "line_number": 141, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 143, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 143, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 149, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 150, "usage_type": "call"}]} +{"seq_id": "636222109", "text": "\"\"\"Test suite for Hardware Report.\"\"\"\n\nfrom pyspark.sql import SparkSession\nfrom datetime import datetime\nimport mozetl.hardware_report.summarize_json as summarize_json\n\n\ndef test_run_tests():\n \"\"\"Test if helper functions work as expected.\"\"\"\n # Does |get_OS_arch| work as expected?\n assert (\n summarize_json.get_OS_arch(\"x86\", \"Windows_NT\", False) == \"x86\"\n ), \"get_OS_arch should report an 'x86' for an x86 browser with no is_wow64.\"\n assert (\n summarize_json.get_OS_arch(\"x86\", \"Windows_NT\", True) == \"x86-64\"\n ), \"get_OS_arch should report an 'x86-64' for an x86 browser, on Windows, using Wow64.\"\n assert (\n summarize_json.get_OS_arch(\"x86\", \"Darwin\", True) == \"x86\"\n ), \"get_OS_arch should report an 'x86' for an x86 browser on non Windows platforms.\"\n assert (\n summarize_json.get_OS_arch(\"x86-64\", \"Darwin\", True) == \"x86-64\"\n ), \"get_OS_arch should report an 'x86-64' for an x86-64 browser on non Windows platforms.\"\n assert (\n summarize_json.get_OS_arch(\"x86-64\", \"Windows_NT\", False) == \"x86-64\"\n ), \"get_OS_arch should report an 'x86-64' for an x86-64 browser on Windows platforms.\"\n\n # Does |vendor_name_from_id| behave correctly?\n assert (\n summarize_json.vendor_name_from_id(\"0x1013\") == \"Cirrus Logic\"\n ), \"vendor_name_from_id must report the correct vendor name for a known vendor id.\"\n assert (\n summarize_json.vendor_name_from_id(\"0xfeee\") == \"Other\"\n ), \"vendor_name_from_id must report 'Other' for an unknown vendor id.\"\n\n # Make sure |invert_device_map| works as expected.\n device_data = {\"feee\": {\"family\": {\"chipset\": [\"d1d1\", \"d2d2\"]}}}\n inverted_device_data = summarize_json.invert_device_map(device_data)\n assert (\n \"0xfeee\" in inverted_device_data\n ), \"The vendor id must be prefixed with '0x' and be at the root of the map.\"\n assert (\n len(list(inverted_device_data[\"0xfeee\"].keys())) == 2\n ), \"There must be two devices for the '0xfeee' vendor.\"\n assert all(\n device_id in inverted_device_data[\"0xfeee\"]\n for device_id in (\"0xd1d1\", \"0xd2d2\")\n ), \"The '0xfeee' vendor must contain the expected devices.\"\n assert all(\n d in inverted_device_data[\"0xfeee\"][\"0xd1d1\"] for d in (\"family\", \"chipset\")\n ), \"The family and chipset data must be reported in the device section.\"\n\n # Let's test |get_device_family_chipset|.\n global device_map\n device_map = inverted_device_data\n assert (\n summarize_json.get_device_family_chipset(\"0xfeee\", \"0xd1d1\", device_map)\n == \"family-chipset\"\n ), (\n \"The family and chipset info must be returned as '-' \",\n \"for known devices.\",\n )\n assert (\n summarize_json.get_device_family_chipset(\"0xfeee\", \"0xdeee\", device_map)\n == \"Unknown\"\n ), \"Unknown devices must be reported as 'Unknown'.\"\n assert (\n summarize_json.get_device_family_chipset(\"0xfeeb\", \"0xdeee\", device_map)\n == \"Unknown\"\n ), \"Unknown families must be reported as 'Unknown'.\"\n\n\ndef test_prepare_data():\n \"\"\"Test prepare_data function on sample data.\"\"\"\n data = {\n \"browser_arch\": \"x86\",\n \"os_name\": \"Windows_NT\",\n \"os_version\": \"6.1\",\n \"memory_mb\": 4286,\n \"is_wow64\": False,\n \"gfx0_vendor_id\": \"0xfeee\",\n \"gfx0_device_id\": \"0xd1d1\",\n \"screen_width\": 1280,\n \"screen_height\": 1024,\n \"cpu_cores\": 2,\n \"cpu_vendor\": \"SomeCpuVendor\",\n \"cpu_speed\": 3261,\n \"has_flash\": True,\n }\n\n prepared_data = summarize_json.prepare_data(data, device_map)\n assert (\n prepared_data[\"browser_arch\"] == \"x86\"\n ), \"The browser architecture must be correct.\"\n assert prepared_data[\"cpu_cores\"] == 2, \"The number of CPU cores must be correct.\"\n assert (\n prepared_data[\"cpu_speed\"] == 3.3\n ), \"The CPU speed must be in GHz and correctly rounded to 1 decimal.\"\n assert (\n prepared_data[\"cpu_vendor\"] == \"SomeCpuVendor\"\n ), \"The CPU vendor must be correct.\"\n assert (\n prepared_data[\"cpu_cores_speed\"] == \"2_3.3\"\n ), \"The CPU cores and speed must be correctly merged together.\"\n assert (\n prepared_data[\"gfx0_vendor_name\"] == \"Other\"\n ), \"The GPU vendor name must be correctly converted from the vendor id.\"\n assert (\n prepared_data[\"gfx0_model\"] == \"family-chipset\"\n ), \"The GPU family and chipset must be correctly derived from vendor and device ids.\"\n assert (\n prepared_data[\"resolution\"] == \"1280x1024\"\n ), \"The screen resolution must be correctly concatenated.\"\n assert prepared_data[\"memory_gb\"] == 4, \"The RAM memory must be converted to GB.\"\n assert (\n prepared_data[\"os\"] == \"Windows_NT-6.1\"\n ), \"The OS string must contain the OS name and version.\"\n assert (\n prepared_data[\"os_arch\"] == \"x86\"\n ), \"The OS architecture must be correctly inferred.\"\n assert (\n prepared_data[\"has_flash\"] is True\n ), \"The flash plugin must be correctly reported.\"\n\n\ndef test_aggregate_data():\n \"\"\"Test aggregate_data function on sample data.\"\"\"\n raw_data = [\n {\n \"browser_arch\": \"x86\",\n \"os_name\": \"Windows_NT\",\n \"os_version\": \"6.1\",\n \"memory_mb\": 4286,\n \"is_wow64\": False,\n \"gfx0_vendor_id\": \"0xfeee\",\n \"gfx0_device_id\": \"0xd1d1\",\n \"screen_width\": 1280,\n \"screen_height\": 1024,\n \"cpu_cores\": 2,\n \"cpu_vendor\": \"SomeCpuVendor\",\n \"cpu_speed\": 3261,\n \"has_flash\": True,\n },\n {\n \"browser_arch\": \"x86\",\n \"os_name\": \"Windows_NT\",\n \"os_version\": \"6.1\",\n \"memory_mb\": 4286,\n \"is_wow64\": False,\n \"gfx0_vendor_id\": \"0xfeee\",\n \"gfx0_device_id\": \"0xd1d1\",\n \"screen_width\": 1280,\n \"screen_height\": 1024,\n \"cpu_cores\": 2,\n \"cpu_vendor\": \"SomeCpuVendor\",\n \"cpu_speed\": 3261,\n \"has_flash\": True,\n },\n {\n \"browser_arch\": \"x86-64\",\n \"os_name\": \"Darwin\",\n \"os_version\": \"15.3\",\n \"memory_mb\": 8322,\n \"is_wow64\": False,\n \"gfx0_vendor_id\": \"0xfeee\",\n \"gfx0_device_id\": \"0xd1d2\",\n \"screen_width\": 1320,\n \"screen_height\": 798,\n \"cpu_cores\": 4,\n \"cpu_vendor\": \"SomeCpuVendor\",\n \"cpu_speed\": 4211,\n \"has_flash\": True,\n },\n ]\n\n spark = SparkSession.builder.appName(\"hardware_report_dashboard\").getOrCreate()\n\n # Create an rdd with the pepared data, then aggregate.\n data_rdd = spark.sparkContext.parallelize(\n [summarize_json.prepare_data(d, device_map) for d in raw_data]\n )\n agg_data = summarize_json.aggregate_data(data_rdd)\n\n assert (\n agg_data[(\"os_arch\", \"x86\")] == 2\n ), \"Two 'x86' OS architectures must be reported.\"\n assert (\n agg_data[(\"os_arch\", \"x86-64\")] == 1\n ), \"One 'x86-64' OS architecture must be reported.\"\n assert (\n agg_data[(\"has_flash\", True)] == 3\n ), \"All the entries had the flash plugin, so this must be 3.\"\n\n\ndef test_collapse_buckets():\n \"\"\"Test collapse_buckets function on sample data.\"\"\"\n agg_data = {\n (\"has_flash\", True): 72,\n (\"has_flash\", False): 2,\n (\"resolution\", \"1280x1024\"): 50,\n (\"resolution\", \"2560x1440\"): 8,\n (\"resolution\", \"2563x1440\"): 8,\n (\"resolution\", \"640x480\"): 6,\n (\"resolution\", \"640x472\"): 2,\n (\"resolution\", \"0x0\"): 15, # Invalid data.\n (\"os\", \"Windows_NT-6.11\"): 34,\n (\"os\", \"Windows_NT-5.10\"): 8,\n (\"os\", \"Windows_NT-4\"): 8,\n (\"os\", \"FunkyOS-4\"): 1,\n (\"os\", \"Darwin-11.0\"): 22,\n (\"os\", \"Darwin-1.0\"): 1,\n }\n\n threshold = 10\n collapsed_data = summarize_json.collapse_buckets(agg_data, threshold)\n\n # Test that keys with values above the threshold are kept.\n assert (\n \"os\",\n \"Darwin-11.0\",\n ) in collapsed_data, \"Keys with enough elements must not be collapsed.\"\n assert (\n \"has_flash\",\n True,\n ) in collapsed_data, \"Keys with enough elements must not be collapsed.\"\n assert (\n \"resolution\",\n \"1280x1024\",\n ) in collapsed_data, \"Keys with enough elements must not be collapsed.\"\n assert (\n \"os\",\n \"Windows_NT-6.11\",\n ) in collapsed_data, \"Keys with enough elements must not be collapsed.\"\n\n # Test resolution collapsing.\n assert (\n \"resolution\",\n \"~2600x1400\",\n ) in collapsed_data, (\n \"Collapsed resolutions with enough elements must be reported, prefixed with ~.\"\n )\n assert (\n \"resolution\",\n \"Other\",\n ) in collapsed_data, (\n \"Collapsed resolutions with not enough elements must be reported as 'Other'.\"\n )\n assert (\n collapsed_data[(\"resolution\", \"Other\")] == 23\n ), \"The 640x* and the 0x0 resolution must be reported in the 'Other' bucket.\"\n\n # Test that whitelisted keys are not collapsed.\n assert (\n \"has_flash\",\n False,\n ) in collapsed_data, \"Whitelisted keys must not be collapsed.\"\n\n\ndef test_finalize_data():\n \"\"\"Test finalize_data function on sample data.\"\"\"\n collapsed_data = {\n (\"os\", \"Darwin-11.0\"): 22,\n (\"resolution\", \"1280x1024\"): 50,\n (\"os\", \"Windows_NT-6.11\"): 34,\n (\"resolution\", \"~2600x1400\"): 16,\n (\"os\", \"Other\"): 2,\n (\"has_flash\", True): 72,\n (\"os\", \"Windows_NT-Other\"): 16,\n (\"has_flash\", False): 2,\n (\"resolution\", \"Other\"): 8,\n }\n\n finalized_data = summarize_json.finalize_data(\n collapsed_data, 74, 0.1, 0.2, datetime.strptime(\"20160703\", \"%Y%m%d\")\n )\n\n # Check that the basic fields are in the finalized data.\n assert (\n finalized_data[\"broken\"] == 0.1\n ), \"The ratio of broken data must be correctly reported.\"\n assert (\n finalized_data[\"inactive\"] == 0.2\n ), \"The ratio of inactive clients must be correctly reported.\"\n assert (\n finalized_data[\"date\"] == \"2016-07-03\"\n ), \"The first day of the reporting period must be reported.\"\n\n # Make sure that all the reported numbers are ratios.\n all_ratios = [\n (v >= 0.0 and v <= 1.0) for (k, v) in finalized_data.items() if k != \"date\"\n ]\n assert all(all_ratios), \"All the reported entries must be ratios.\"\n\n\ndef test_validate_finalized_data():\n \"\"\"Test valiate_finalized_data on sample data.\"\"\"\n MISSING_KEYS = {\"browserArch_x86\": 1.0, \"cpuCores_2\": 1.0}\n\n assert (\n summarize_json.validate_finalized_data(MISSING_KEYS) is False\n ), \"The validator must fail when expected keys are missing\"\n\n KEYS_NOT_ADDING_UP = {\n \"browserArch_x86\": 0.5,\n \"browserArch_x64\": 0.4,\n \"cpuCores_1\": 1.0,\n \"cpuCoresSpeed_2_2.2\": 1.0,\n \"cpuVendor_Vendor1\": 1.0,\n \"cpuSpeed_2.2\": 1.0,\n \"gpuVendor_Vendor3\": 1.0,\n \"gpuModel_Model1\": 1.0,\n \"resolution_800x600\": 1.0,\n \"ram_2\": 1.0,\n \"osName_SomeOS\": 1.0,\n \"osArch_x64\": 1.0,\n \"hasFlash_True\": 1.0,\n }\n\n assert (\n summarize_json.validate_finalized_data(KEYS_NOT_ADDING_UP) is False\n ), \"The validator must fail when the reported values don't add up to 1.0\"\n\n WORKING_DATA = {\n \"browserArch_x86\": 0.7,\n \"browserArch_x64\": 0.29,\n \"cpuCores_1\": 0.5,\n \"cpuCores_2\": 0.5,\n \"cpuCoresSpeed_2_2.2\": 0.1,\n \"cpuCoresSpeed_2_2.4\": 0.9,\n \"cpuVendor_Vendor1\": 0.725,\n \"cpuVendor_Vendor2\": 0.275,\n \"cpuSpeed_2.2\": 0.1,\n \"cpuSpeed_2.4\": 0.9,\n \"gpuVendor_Vendor3\": 0.9,\n \"gpuVendor_Vendor4\": 0.1,\n \"gpuModel_Model1\": 0.00001,\n \"gpuModel_Model2\": 0.99999,\n \"resolution_800x600\": 1.0,\n \"ram_2\": 1.0,\n \"osName_SomeOS\": 1.0,\n \"osArch_x64\": 1.0,\n \"hasFlash_True\": 1.0,\n \"broken\": 0.1,\n \"inactive\": 0.1,\n \"date\": \"2017-03-26\",\n }\n\n assert summarize_json.validate_finalized_data(\n WORKING_DATA\n ), \"The validator must not fail when the reported data is correct\"\n\n\ndef test_get_longitudinal_version_weekday():\n date = datetime.strptime(\"20180913\", \"%Y%m%d\")\n assert summarize_json.get_longitudinal_version(date) == \"longitudinal_v20180915\"\n\n\ndef test_get_longitudinal_version_sunday():\n date = datetime.strptime(\"20180909\", \"%Y%m%d\")\n assert summarize_json.get_longitudinal_version(date) == \"longitudinal_v20180915\"\n\n\ndef test_get_longitudinal_version_saturday():\n date = datetime.strptime(\"20180908\", \"%Y%m%d\")\n assert summarize_json.get_longitudinal_version(date) == \"longitudinal_v20180908\"\n\n\ndef test_generate_report():\n \"\"\"Test generate_report function on sample data.\"\"\"\n spark = SparkSession.builder.appName(\"hardware_report_dashboard\").getOrCreate()\n\n df = spark.read.json(\"tests/longitudinal_schema.json\")\n df.createOrReplaceTempView(\"longitudinal_v20160709\")\n\n expected = {\n \"cpuCores_4\": 1.0,\n \"osArch_x86\": 1.0,\n \"gpuModel_SI-PITCAIRN\": 1.0,\n \"gpuVendor_AMD\": 1.0,\n \"ram_8\": 1.0,\n \"browserArch_x86\": 1.0,\n \"cpuVendor_GenuineIntel\": 1.0,\n \"osName_Windows-10.0\": 1.0,\n \"broken\": 0.0,\n \"inactive\": 0.0,\n \"cpuSpeed_4.0\": 1.0,\n \"date\": \"2016-07-03\",\n \"cpuCoresSpeed_4_4.0\": 1.0,\n \"hasFlash_False\": 1.0,\n \"resolution_1920x1200\": 1.0,\n }\n\n assert (\n summarize_json.generate_report(\n datetime.strptime(\"20160703\", \"%Y%m%d\"),\n datetime.strptime(\"20160710\", \"%Y%m%d\"),\n spark,\n )[\"hwsurvey-weekly-20160307-20160907.json\"]\n == expected\n )\n", "sub_path": "tests/hardware_report/test_summarize_json.py", "file_name": "test_summarize_json.py", "file_ext": "py", "file_size_in_byte": 13827, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "mozetl.hardware_report.summarize_json.get_OS_arch", "line_number": 12, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 12, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.get_OS_arch", "line_number": 15, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 15, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.get_OS_arch", "line_number": 18, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 18, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.get_OS_arch", "line_number": 21, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 21, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.get_OS_arch", "line_number": 24, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 24, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.vendor_name_from_id", "line_number": 29, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 29, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.vendor_name_from_id", "line_number": 32, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 32, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.invert_device_map", "line_number": 37, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 37, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.get_device_family_chipset", "line_number": 56, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 56, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.get_device_family_chipset", "line_number": 63, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 63, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.get_device_family_chipset", "line_number": 67, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 67, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.prepare_data", "line_number": 90, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 90, "usage_type": "name"}, {"api_name": "pyspark.sql.SparkSession.builder.appName", "line_number": 175, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 175, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 175, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.prepare_data", "line_number": 179, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 179, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.aggregate_data", "line_number": 181, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 181, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.collapse_buckets", "line_number": 214, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 214, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.finalize_data", "line_number": 272, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 272, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 273, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 273, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.validate_finalized_data", "line_number": 299, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 299, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.validate_finalized_data", "line_number": 319, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 319, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.validate_finalized_data", "line_number": 347, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 347, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 353, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 353, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.get_longitudinal_version", "line_number": 354, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 354, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 358, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 358, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.get_longitudinal_version", "line_number": 359, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 359, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 363, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 363, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.get_longitudinal_version", "line_number": 364, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 364, "usage_type": "name"}, {"api_name": "pyspark.sql.SparkSession.builder.appName", "line_number": 369, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 369, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 369, "usage_type": "name"}, {"api_name": "mozetl.hardware_report.summarize_json.generate_report", "line_number": 393, "usage_type": "call"}, {"api_name": "mozetl.hardware_report.summarize_json", "line_number": 393, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 394, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 394, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 395, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 395, "usage_type": "name"}]} +{"seq_id": "530286087", "text": "import xml.sax\t \r\nfrom collections import Counter\r\nimport matplotlib.pyplot as plot \r\nfrom copy import deepcopy \r\n\r\n \r\ntags=[] #list of xml tags\r\nlabels=[] #list of all labels\r\nlabeldict={} #hash to labels mapping\r\ntitledict={} #hash to title mapping\r\nclass XMLhandler(xml.sax.ContentHandler):\r\n def __init__(self):\r\n self.tags=[] #list of all tags\r\n self.articlenumber=0\r\n self.content=\"\"\r\n self.articlehash=\"\"\r\n self.hash=\"\"\r\n def startElement(self,name,attrs): #runs at start of all tags (name)\r\n if name not in self.tags:\r\n self.tags.append(name)\r\n tags.append(name)\r\n self.content=\"\"\r\n def endElement(self,name):\r\n if name==\"hash\":\r\n self.hash=self.content.strip()\r\n labeldict[self.hash]=[]\r\n if name==\"name\":\r\n labels.append(self.content.strip())\r\n labeldict[self.hash].append(self.content.strip())\r\n if name==\"title\":\r\n titledict[self.hash]=self.content.strip()\r\n self.content= \"\"\r\n \r\n def characters(self,content):\r\n w = content.encode('utf-8').strip()\r\n if w > 0:\r\n self.content+= w + \"\\n\"\r\n \r\n \r\n#XML PARSING \r\nsource = \"tag-data.xml\" \r\nparser = xml.sax.make_parser()\r\nparser.setContentHandler(XMLhandler())\r\nparser.parse(source) \r\n\r\n#NOTE in the xml file \"css\" and \"files\" also come in the ...., so i remove them manually\r\nlabeldict.pop(\"css\")\r\nlabeldict.pop(\"files\")\r\n\r\n#TO TAKE LABELS WITH AT LEAST N OCCURENCES ONLY\r\nN=1132\r\nfreqs = Counter(labels)\r\npairs=sorted(freqs.items(), key=lambda item: item[1], reverse=True)\r\nXY=zip(*pairs[:500])\r\nplot.plot(range(len(XY[1])),XY[1])\r\nindex= zip(*pairs)[1].index(N) - 1\r\n\r\nnewpairs=pairs[:index+1]\r\nnewlabels=zip(*newpairs)[0]\r\nnewlabeldict=deepcopy(labeldict)\r\n\r\n\r\n\r\n#REMOVE EXTRA LABELS FROM LABEL DICTIONARY (HASH-LABEL MAPPING)\r\n#ALSO CHECK IF WE LOST ANY DOCUMENT; IN CASE A DOCUMENT HAS 0 LABELS\r\n\r\nse=set(newlabels)\r\nfor i in labeldict:\r\n newlabeldict[i]=se.intersection(labeldict[i])\r\n if len(newlabeldict[i])==0:\r\n newlabeldict.pop(i)\r\n \r\n#TURNS OUT WE LOST 31 DOCUMENTS\r\n \r\n#only newlabeldict and alllabels is important extract it leave rest\r\n", "sub_path": "other models/tag_extractor.py", "file_name": "tag_extractor.py", "file_ext": "py", "file_size_in_byte": 2379, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "xml.sax.sax", "line_number": 11, "usage_type": "attribute"}, {"api_name": "xml.sax", "line_number": 11, "usage_type": "name"}, {"api_name": "xml.sax.sax.make_parser", "line_number": 42, "usage_type": "call"}, {"api_name": "xml.sax.sax", "line_number": 42, "usage_type": "attribute"}, {"api_name": "xml.sax", "line_number": 42, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "629688950", "text": "from enum import Enum\n\nimport math\n\n\nclass TillageMethod(Enum):\n fall_plaw=\"fall_plaw\"\n spring_plow=\"spring_plow\"\n mulch_tillage=\"mulch_tillage\"\n ridge_tillage=\"ridge_tillage\"\n zone_tillage=\"zone_tillage\"\n no_till=\"no_till\"\n unknown=\"unknown\"\n\nclass AntiErosionPractice(Enum):\n no_practice=\"no_practice\"\n cross_slope=\"cross_slope\"\n contour_farming=\"contour_farming\"\n strip_cropping_cross_slope=\"strip_cropping_cross_slope\"\n strip_cropping_contour=\"strip_cropping_contour\"\n unknown=\"unknown\"\n\n\nclass ErosionModel(object):\n \"\"\"Inputs:\n average_annual_precipitation: mm/year\n slope: ratio\n slope_length: m\n tillage_method: TillageMethod\n anti_erosion_practice: AntiErosionPractice\n soil_erodibility_factor: t h/(MJ*mm)\n crop_factor: number\n mean_elevation_m: m\n nb_wet_days_per_year: day/year\n climate_zone_specific: text\n\n Outputs:\n m_Erosion_eroded_soil: kg soil/(ha*year)\n \"\"\"\n\n _input_variables = [\"average_annual_precipitation\",\n \"slope\",\n \"slope_length\",\n \"tillage_method\",\n \"anti_erosion_practice\",\n \"soil_erodibility_factor\",\n \"crop_factor\",\n \"mean_elevation_m\",\n \"nb_wet_days_per_year\",\n \"climate_zone_specific\"\n ]\n\n _POW_FOR_SLOPE_UNDER_1_PERCENT = 0.2\n _POW_FOR_SLOPE_1_TO_3_5_PERCENT = 0.3\n _POW_FOR_SLOPE_3_5_TO_5_PERCENT = 0.4\n _POW_FOR_SLOPE_OVER_5_PERCENT = 0.5\n\n _TILLAGE_METHOD_FACTOR = {TillageMethod.unknown:1.0,\n TillageMethod.fall_plaw: 1.0,\n TillageMethod.spring_plow: 0.9,\n TillageMethod.mulch_tillage: 0.6,\n TillageMethod.ridge_tillage: 0.35,\n TillageMethod.zone_tillage: 0.25,\n TillageMethod.no_till: 0.25}\n\n _ANTI_EROSION_PRACTICE_FACTOR = {AntiErosionPractice.unknown: 1,\n AntiErosionPractice.no_practice: 1,\n AntiErosionPractice.cross_slope: 0.75,\n AntiErosionPractice.contour_farming: 0.5,\n AntiErosionPractice.strip_cropping_cross_slope: 0.37,\n AntiErosionPractice.strip_cropping_contour: 0.25}\n\n _USUAL_EQUATION = lambda p, e, s: -3172.0 + 7.562 * p\n\n _EROSITIVITY_FACTOR_FORMULAE = {\"polar_frost\": lambda p, e, s: 10 ** (-10.66 + 2.43 * math.log10(p)),\n \"polar_tundra\": lambda p, e, s: 10 ** (-10.66 + 2.43 * math.log10(p)),\n \"snow_winter_continental\": lambda p, e, s: 10 ** (1.882 + 0.819 * math.log10(p)),\n \"snow_humid_continental\": lambda p, e, s: 10 ** (-1.259 + 3.862 * math.log10(s)),\n \"snow_summer_continental\": lambda p, e, s: 10 ** (4.416 + 0.0594 * math.log10(p)),\n \"arid_desert_cold\": lambda p, e, s: 0.809 * (p ** 0.957) + 0.000189 * (s ** 6.285),\n \"warm_humid_hot\": lambda p, e, s:\n 10 ** (0.524 + 0.462 * math.log10(p) + 1.97 * math.log10(s)\n - 0.106 * math.log10(e)),\n \"warm_humid_warm\": lambda p, e, s:\n 10 ** (-7.694 + 4.1407 * math.log10(p) - 2.586 * math.log10(s)),\n \"warm_humid_cold\": lambda p, e, s:\n 10 ** (-7.694 + 4.1407 * math.log10(p) - 2.586 * math.log10(s)),\n \"warm_summer_dry_hot\": lambda p, e, s: -944.0 + 3.08 * p,\n \"warm_summer_dry_warm\": lambda p, e, s: 98.35 + 0.000355 * (p ** 1.987),\n \"warm_summer_dry_cold\": lambda p, e, s: -944.0 + 3.08 * p,\n \"warm_winter_dry_hot\": _USUAL_EQUATION,\n \"warm_winter_dry_cool\": _USUAL_EQUATION,\n \"warm_winter_dry_cold\": _USUAL_EQUATION,\n \"snow_humid_hot\": lambda p, e, s:\n 10 ** (-1.99 + 0.737 * math.log10(p) + 2.033 * math.log10(s)),\n \"snow_humid_warm\": lambda p, e, s:\n 10 ** (-0.5 + 0.266 * math.log10(p) + 3.1 * math.log10(s) - 0.131 * math.log10(e)),\n \"snow_humid_cold\": lambda p, e, s: 10 ** (-1.259 + 3.862 * math.log10(s)),\n \"snow_summer_dry_hot\": lambda p, e, s: 10 ** (1.882 + 0.819 * math.log10(p)),\n \"snow_summer_dry_warm\": lambda p, e, s: 10 ** (2.166 + 0.494 * math.log10(p)),\n \"snow_summer_dry_cold\": lambda p, e, s: 10 ** (4.416 + 0.0594 * math.log10(p)),\n \"snow_winter_dry_hot\": lambda p, e, s: 38.5 + 0.35 * p,\n \"snow_winter_dry_warm\": lambda p, e, s: 38.5 + 0.35 * p,\n \"snow_winter_dry_cold\": lambda p, e, s: 10 ** (1.882 + 0.819 * math.log10(p)),\n \"arid_steppe_cold\": lambda p, e, s:\n 10 ** (0.0793 + 0.887 * math.log10(p) + 1.892 * math.log10(s)\n - 0.429 * math.log10(e)),\n \"equatorial_humid\": _USUAL_EQUATION,\n \"equatorial_monsonnal\": _USUAL_EQUATION,\n \"equatorial_summer_dry\": lambda p, e, s: -669.3 + 7 * p - 2.719 * e,\n \"equatorial_winter_dry\": _USUAL_EQUATION,\n \"arid_steppe_hot\": lambda p, e, s:\n 10 ** (-7.72 + 1.595 * math.log10(p) + 2.068 * math.log10(s)),\n \"arid_desert_hot\": lambda p, e, s: 0.0438 * (p ** 1.61)}\n\n def __init__(self, inputs):\n #TODO: Should we log usage of default value?\n for key in ErosionModel._input_variables:\n setattr(self, key, inputs[key])\n\n def compute(self):\n erosivity_factor = self._compute_erosivity_factor()\n slope_factor = self._compute_slope_factor()\n tillage_factor = self._compute_tillage_factor()\n practice_factor = self._compute_practice_factor()\n eroded_soil = 1000.0 * erosivity_factor * self.soil_erodibility_factor \\\n * slope_factor * self.crop_factor * tillage_factor * practice_factor;\n return {\"m_Erosion_eroded_soil\": eroded_soil}\n\n def _compute_erosivity_factor(self):#MJ mm/(ha*h*yr)\n return self._EROSITIVITY_FACTOR_FORMULAE[self.climate_zone_specific] \\\n (self.average_annual_precipitation,\n self.mean_elevation_m,\n self.average_annual_precipitation / self.nb_wet_days_per_year)\n\n def _compute_slope_factor(self):\n return (self.slope_length * 3.28083 / 72.6) ** self._compute_pow_for_slope_factor() * (\n 65.41 * (math.sin(self.slope)) ** 2 \\\n + 4.56 * (math.sin(self.slope)) + 0.065)\n\n def _compute_pow_for_slope_factor(self):\n if (self.slope < 0.01):\n return self._POW_FOR_SLOPE_UNDER_1_PERCENT\n elif (self.slope < 0.035):\n return self._POW_FOR_SLOPE_1_TO_3_5_PERCENT\n elif (self.slope < 0.05):\n return self._POW_FOR_SLOPE_3_5_TO_5_PERCENT\n else:\n return self._POW_FOR_SLOPE_OVER_5_PERCENT\n\n def _compute_tillage_factor(self):\n return self._TILLAGE_METHOD_FACTOR[self.tillage_method]\n\n def _compute_practice_factor(self):\n return self._ANTI_EROSION_PRACTICE_FACTOR[self.anti_erosion_practice]\n", "sub_path": "src/main/python/models/erosionmodel.py", "file_name": "erosionmodel.py", "file_ext": "py", "file_size_in_byte": 8132, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "enum.Enum", "line_number": 6, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 15, "usage_type": "name"}, {"api_name": "math.log10", "line_number": 75, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 76, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 77, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 78, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 79, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 82, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 83, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 85, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 87, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 95, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 97, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 98, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 99, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 100, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 101, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 104, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 106, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 107, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 113, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 138, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 139, "usage_type": "call"}]} +{"seq_id": "172107304", "text": "from ast import literal_eval\nfrom bs4 import BeautifulSoup\nimport requests\n\ndef macdonald(page):\n '''\n 맥도날드 페이지에서, 메뉴들을 크롤링 해온다.\n '''\n \n #url, header 설정\n url = \"https://www.mcdelivery.co.kr/kr/browse/menu.html?daypartId=2&catId={}\".format(page)\n u_a = \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.82 Safari/537.36\"\n \n #응답 값을 받아오고, bs 객체로 변환한다.\n response = requests.get(url, headers={\"USER-AGENT\":u_a})\n soup = BeautifulSoup(response.text, 'html.parser')\n \n #상품부분에 해당하는 부분을 find, find_all로 필터링 한다.\n products_box_soup = soup.find(\"div\", {\"id\":\"product-cards\"})\n products = products_box_soup.find_all(\"a\", {\"class\": \"btn btn-block action-create btn-yellow\"})\n\n #필터링 된 정보를 재가공 한다.\n informations = []\n for product in products:\n data = product.get('onclick')\n if data.startswith('onProductClick'):\n informations.append(data[len('onProductClick('): -1])\n \n result = []\n for information in informations:\n temp_dict = {}\n menu = literal_eval(information)\n temp_dict['name'] = menu['name']\n temp_dict['price'] = menu['price'][2:]\n temp_dict['category'] = menu['cat']\n result.append(temp_dict)\n \n return result", "sub_path": "src/crawl/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1419, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 16, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "259963347", "text": "from django import forms\nfrom django.db import models\nfrom django.conf import settings\n\nclass RichTextWidget(forms.Textarea):\n def __init__(self, *args, **kwargs):\n defaults = {'attrs': {'class': 'zb_rich'}}\n defaults.update(kwargs)\n\n super(RichTextWidget, self).__init__(*args, **defaults)\n\n\nclass RichTextFormField(forms.CharField):\n def __init__(self, widget=None, *args, **kwargs):\n widget = RichTextWidget()\n if 'widget' in kwargs:\n del kwargs['widget']\n super(RichTextFormField, self).__init__( \\\n widget=widget, *args, **kwargs)\n\n\nclass RichTextField(models.TextField):\n\n def __init__(self, **kwargs):\n # For faking the field for south ORM\n self.add_addl_fields = not kwargs.pop('no_frozen_fields', False)\n super(RichTextField, self).__init__(**kwargs)\n\n def formfield(self, **kwargs):\n defaults = {'form_class': RichTextFormField}\n defaults.update(kwargs)\n return super(RichTextField, self).formfield(**defaults)\n\n def get_prep_value(self, value):\n if settings.ENABLE_RTF_CLEAN and value and len(value.strip()) > 0:\n from lxml.html.clean import clean_html\n html = clean_html(value)\n else:\n html = value\n\n return html\n\n\n", "sub_path": "demo/rich_text.py", "file_name": "rich_text.py", "file_ext": "py", "file_size_in_byte": 1302, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.forms.Textarea", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 5, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.conf.settings.ENABLE_RTF_CLEAN", "line_number": 35, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 35, "usage_type": "name"}, {"api_name": "lxml.html.clean.clean_html", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "23299467", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jan 19 19:49:25 2020\n\n@author: eternal_demon\n\"\"\"\n\nfrom setuptools import setup\n\nwith open(\"README.md\", \"r\") as f:\n long_description = f.read()\n\nsetup(\n name=\"datahandler_bhanu\", # Replace with your own username\n version=\"0.2\",\n author=\"Bhanu/eternal_demon\",\n author_email=\"aggarwal.bhanu02@gmail.com\",\n description=\"Handling Missing values in a given data set and imputing them.\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/eternaldemon/datahandler\",\n download_url=\"https://github.com/eternaldemon/datahandler/archive/0.2.tar.gz\",\n packages=[\"datahandler_bhanu\"],\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n ],\n python_requires='>=3.6',\n)\n", "sub_path": "pypi_install_script/datahandler_bhanu-0.2.tar/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 905, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "setuptools.setup", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "576865051", "text": "import numpy as np\nimport pandas as pd\nimport random\n\nfrom psi.data.sinks.api import TableStore\n\n\ndef test_table_create_append():\n store = TableStore(name='event_log')\n store.prepare()\n rows = [\n {\n 'channel': 'A',\n 'fs': 100e3,\n 'result': 5,\n 'valid': True,\n },\n # Mix up order to make sure that we still get good output.\n {\n 'valid': True,\n 'result': 5,\n 'channel': 'A',\n 'fs': 100e3,\n },\n ]\n for row in rows:\n store.process_table(row, flush=True)\n filename = store.get_filename()\n assert pd.read_csv(filename).equals(pd.DataFrame(rows))\n\n\ndef _random_row():\n return {\n 'channel': ''.join(random.choice('ABCabc123') for i in range(10)),\n 'fs': np.random.uniform(100, 100e3),\n 'result': int(np.random.uniform(-5, 5500)),\n 'valid': bool(np.random.uniform(0, 2)),\n }\n\n\ndef test_table_append_first_row_speed(benchmark):\n store = TableStore(name='event_log')\n store.prepare()\n benchmark(store.process_table, _random_row())\n\n\ndef test_table_append_10k_rows_speed(benchmark):\n store = TableStore(name='event_log')\n store.prepare()\n data = [_random_row() for i in range(10000)]\n store.process_table(data)\n row = _random_row()\n benchmark(store.process_table, row)\n", "sub_path": "tests/test_sinks.py", "file_name": "test_sinks.py", "file_ext": "py", "file_size_in_byte": 1369, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "psi.data.sinks.api.TableStore", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 29, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 37, "usage_type": "attribute"}, {"api_name": "psi.data.sinks.api.TableStore", "line_number": 42, "usage_type": "call"}, {"api_name": "psi.data.sinks.api.TableStore", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "201522874", "text": "\nimport glob\nimport numpy as np\nimport cv2\nimport random\nimport argparse\n'''\npython3 check_batch_img_label_visualize.py \\\n --images=\"../data/dataset1/train/\" \\\n --annotations=\"../data/dataset1/trainannot/\" \\\n --n_classes=2 \n'''\ndef imageSegmentationGenerator( images_path , segs_path , n_classes ):\n\n\tassert images_path[-1] == '/'\n\tassert segs_path[-1] == '/'\n\n\timages = glob.glob( images_path + \"*.jpg\" ) + glob.glob( images_path + \"*.png\" ) + glob.glob( images_path + \"*.jpeg\" )\n\timages.sort()\n\t# print(images)\n\tsegmentations = glob.glob( segs_path + \"*.jpg\" ) + glob.glob( segs_path + \"*.png\" ) + glob.glob( segs_path + \"*.jpeg\" )\n\tsegmentations.sort()\n\n\tcolors = [ ( random.randint(0,255),random.randint(0,255),random.randint(0,255) ) for _ in range(n_classes) ]\n\n\tassert len( images ) == len(segmentations)\n\n\tf = open(\"r.txt\", \"w\")\n\n\tfor im_fn , seg_fn in zip(images,segmentations):\n\t\tprint(f'im_fn = {im_fn}, seg_fn={seg_fn}')\n\t\tprint(' im_fn.split(/)[-1] =', im_fn.split('/')[-1] )\n\t\tprint(' seg_fn.split(/)[-1] =', seg_fn.split('/')[-1] )\n\t\tassert( im_fn.split('/')[-1] == seg_fn.split('/')[-1] )\n\n\t\timg = cv2.imread( im_fn )\n\t\tseg = cv2.imread( seg_fn )\n\n\t\tprint(img.shape)\n\t\tprint(seg.shape)\n\t\t\n\t\tfor i in range(seg.shape[0]):\n\t\t\tfor j in range(seg.shape[1]):\n\t\t\t\tf.write('[%03d,%03d,%03d] ' % (seg[i][j][0],seg[i][j][1],seg[i][j][2]) )\n\t\t\tf.write('\\n')\n\t\tprint(np.unique( seg ))\n\n\t\tseg_img = np.zeros_like( seg )\n\n\t\tfor c in range(n_classes):\n\t\t\tseg_img[:,:,0] += ( (seg[:,:,0] == c )*( colors[c][0] )).astype('uint8')\n\t\t\tseg_img[:,:,1] += ((seg[:,:,0] == c )*( colors[c][1] )).astype('uint8')\n\t\t\tseg_img[:,:,2] += ((seg[:,:,0] == c )*( colors[c][2] )).astype('uint8')\n\n\t\tcv2.imshow(\"img\" , img )\n\t\tcv2.imshow(\"seg_img\" , seg_img )\n\t\tcv2.waitKey()\n\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--images\", type = str )\nparser.add_argument(\"--annotations\", type = str )\nparser.add_argument(\"--n_classes\", type=int )\nargs = parser.parse_args()\n\n\nimageSegmentationGenerator(args.images , args.annotations , args.n_classes ) \n", "sub_path": "gen_semantic_data/check_batch_img_label_visualize.py", "file_name": "check_batch_img_label_visualize.py", "file_ext": "py", "file_size_in_byte": 2070, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "glob.glob", "line_number": 18, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 21, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 57, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "23580579", "text": "#!/usr/bin/python\n# encoding: utf-8\n\nimport sys\nimport argparse\nimport json\n\n\ndef main(opts, args):\n vars_args = dict(zip(opts.names, args)) if opts.names else {}\n alfred = {'alfredworkflow': {'arg': '', 'variables': vars_args}}\n sys.stdout.write(json.dumps(alfred))\n return 0\n\n\ndef parse_opts():\n parser = argparse.ArgumentParser()\n parser.add_argument('--names', nargs='*')\n opts, rest = parser.parse_known_args()\n return (opts, rest)\n\nif __name__ == '__main__':\n opts, rest = parse_opts()\n sys.exit(main(opts, rest))\n", "sub_path": "alfred/alfred/split_args.py", "file_name": "split_args.py", "file_ext": "py", "file_size_in_byte": 550, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sys.stdout.write", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 12, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 12, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "331823287", "text": "from datetime import date, datetime\n\n\ndef birthday_to_age(birthday) -> int:\n today = date.today()\n birthday = datetime.strptime(birthday, '%m/%d/%Y')\n return today.year - birthday.year - \\\n ((today.month, today.day) < (birthday.month, birthday.day))\n\n\nif __name__ == '__main__':\n print(birthday_to_age('03/18/2000'))\n", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 339, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "datetime.date.today", "line_number": 5, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 5, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 6, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 6, "usage_type": "name"}]} +{"seq_id": "538089454", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Jan 19 14:36:41 2019\r\n\r\n@author: Sai Krishna Mendu\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nfilename=\"data.txt\"\r\n\r\n\r\ndata=np.loadtxt( filename,skiprows=1,delimiter=\"\\t\")\r\n\r\nfor i in range(1,11):\r\n for r in range(1,10): \r\n for m in range(0,999):\r\n if i+r<11:\r\n x=data[m,i]\r\n y=data[m,i+r]\r\n z=str(i)\r\n q=str(i+r)\r\n plt.xlabel(\"feature \"+z)\r\n plt.ylabel(\"feature \"+q)\r\n plt.title(\"feature \"+z+\"vs \"+q)\r\n if data[m,0]==2:\r\n plt.scatter(x,y,color=\"blue\") \r\n else:\r\n plt.scatter(x,y,color=\"red\")\r\n else:\r\n break\r\n plt.savefig(\"ft.\"+z+\"vs\"+q+\".png\")\r\n plt.show()\r\n \r\n\"\"\"\r\nOut of all the files i have found that by the graph of feature 1 versus \r\nfeature 2 were perfectly differentiated.I have completed the first three steps\r\nby performing the for loop in python and first created a numpy array.\r\n\"\"\" \r\n\r\n \r\n\r\n \r\n \r\n ", "sub_path": "Sai Krishna Mendu_180102042/code.py", "file_name": "code.py", "file_ext": "py", "file_size_in_byte": 1159, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.loadtxt", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"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.scatter", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "13863688", "text": "import argparse\nimport os\nimport sys\nimport math\nimport cv2\n\nimport numpy as np\nimport multiprocessing\nfrom sklearn.metrics import confusion_matrix\n\nsys.path.append('../..')\n\ncaffe_path = '../../lib/caffe-action'\n\nsys.path.append(os.path.join(caffe_path, 'python'))\nfrom pyActionRecog.action_caffe import CaffeNet\n\ndef build_net():\n net_proto = '/home/gzn/code/repository/temporal-segment-networks/models/bn_inception_kinetics_rgb_pretrained/bn_inception_rgb_deploy.prototxt'\n net_weights = '/home/gzn/code/repository/temporal-segment-networks/models/bn_inception_kinetics_rgb_pretrained/bn_inception_kinetics_rgb_pretrained.caffemodel'\n num_worker = 2\n gpu_list = [0, 1]\n global net\n my_id = multiprocessing.current_process()._identity[0] \\\n if num_worker > 1 else 1\n if gpu_list is None:\n net = CaffeNet(net_proto, net_weights, my_id-1)\n else:\n net = CaffeNet(net_proto, net_weights, gpu_list[my_id - 1])\n\ndef extract_cnn(videoName, fps_sample = 5.0, batch_size = 1, layer_name = 'inception_5b/output', frame_max = None):\n global net\n\n cnn4v = []\n # get frame\n video = cv2.VideoCapture(videoName)\n try:\n fps = video.get(cv2.cv.CV_CAP_PROP_FPS)*1.0\n except:\n fps = 30.0\n vData = []\n step = int(round(fps/fps_sample))\n\n ret, frame = video.read()\n if frame is not None:\n vData.append(frame)\n\n p_frame = 1\n while ret:\n ret, frame = video.read()\n if p_frame%step == 0:\n if frame is not None:\n vData.append(frame)\n if frame_max is not None:\n if p_frame >= frame_max:\n break\n p_frame+=1\n\n num_frame = len(vData)\n\n first = True\n for i in range(0,num_frame,batch_size):\n if i+batch_size <= num_frame:\n inputData = vData[i:i+batch_size]\n featMaps = net.predict_single_frame(inputData, layer_name, frame_size=(298, 224),\n over_sample=True, multicrop=False)\n if first:\n cnn4v = featMaps\n first = False\n else:\n cnn4v = np.concatenate((cnn4v,featMaps))\n\n # last batch\n if num_frame%batch_size:\n inputData = vData[(num_frame//batch_size)*batch_size:num_frame]\n featMaps = net.predict_single_frame(inputData, layer_name, frame_size=(298, 224),\n over_sample=True, multicrop=False)\n if first:\n cnn4v = featMaps\n else:\n cnn4v = np.concatenate((cnn4v,featMaps))\n\n\n return cnn4v\n\n# build_net()\n# import time\n# for batch in [1,8,16,32,64]:\n# start = time.time()\n# cnn4v = extract_cnn('/data/datasets/UCF-101/ApplyEyeMakeup/v_ApplyEyeMakeup_g01_c01.avi',batch_size=int(batch))\n# end = time.time()\n# print 'batch = %d, Running times = %.3f'%(batch, end-start)", "sub_path": "DBS/ExtractCNN/extractCNNbyTSN.py", "file_name": "extractCNNbyTSN.py", "file_ext": "py", "file_size_in_byte": 2883, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sys.path.append", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 15, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "multiprocessing.current_process", "line_number": 24, "usage_type": "call"}, {"api_name": "pyActionRecog.action_caffe.CaffeNet", "line_number": 27, "usage_type": "call"}, {"api_name": "pyActionRecog.action_caffe.CaffeNet", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.cv", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "209709519", "text": "from bs4 import BeautifulSoup\nfrom urllib.request import urlopen\n\n\n# 라이브러리를 임포트 시켜온다.\n\n\nclass MelonMusic(object):\n # class 디스크에 저장 ,가상 ,값을 가질 수 없다.self 메모리에 저장\n uni = ''\n\n # init의 약어 생성자에 값을 넣을때는 init필요'''\n\n def __str__(self): # = to string\n return f'입력하신 URL {self.uni}'\n\n def scrap(self, url):\n pass\n\n @staticmethod\n def get_ranking(soup, texts):\n count = 0\n\n print(f'==========={texts} RANKING ===========')\n\n for i in soup.find_all(name='p', attrs=({\"class\": texts})): #

\n count += 1\n print(f'{str(count)} RANKING')\n print(f'{texts}:{i.find(\"a\").text}')\n\n\n @staticmethod # 클래스 안에 실행파일 느낌.\n def main():\n bugs = MelonMusic() # 클래스를 생성해주고 아래서 값을 넣는다\n\n while 1:\n menu = int(input('0, EXIT 1. INPUT URL 2. GET RANK'))\n if menu == 0:\n print('종료하겟습니다.')\n break\n elif menu == 1:\n bugs.uni = input('URL 입력하세요 ')\n\n elif menu == 2:\n print(f'Input URL is{bugs}')\n soup = BeautifulSoup(urlopen(bugs.uni), 'lxml') # url 오픈\n choice = int(input('0: 가수 1: 제목'))\n if choice == 0:\n texts = 'artist'\n MelonMusic.get_ranking(soup, texts)\n elif choice == 1:\n texts = 'title'\n MelonMusic.get_ranking(soup, texts)\n elif menu == 3:\n pass\n else:\n print('잘못입력하였습니다.')\n continue\n\n\nMelonMusic.main() #태그값이 다르고 html 호환이 되는지 모르겟다.\n", "sub_path": "web_scraping/melonmusic.py", "file_name": "melonmusic.py", "file_ext": "py", "file_size_in_byte": 1896, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 46, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "203210179", "text": "# scrapy parse --spider=institutiones_com -d 3 'http://institutiones.com/download/books.html'\n# scrapy crawl institutiones_com\n\nfrom scrapy.spider import CrawlSpider, Rule\nfrom scrapy.linkextractors import LinkExtractor\nfrom scrapy.selector import Selector\n\n\nclass institutiones_com(CrawlSpider):\n name = 'institutiones_com'\n start_urls = ['http://institutiones.com/download/books.html']\n allowed_domains = ['institutiones.com']\n\n rules = (\n Rule(LinkExtractor(restrict_xpaths='//table[@class=\"blog\"]//tr/td[@align=\"center\"]', allow='start='),\n follow=True),\n Rule(LinkExtractor(restrict_xpaths='//td[@class=\"contentheading\"]/h2', allow='.html'), callback='book')\n )\n\n def book(self, response):\n selector = Selector(response)\n book = dict()\n book['URL'] = response.url\n\n author = selector.xpath('//td[@valign=\"top\"]/p/strong[contains(text(), \"Автор\")]/../text()').extract_first()\n if author:\n author = author.replace(':', '').strip()\n else:\n author = selector.xpath('//td[@valign=\"top\"]/p[contains(text(), \"Автор\")]/text()').extract_first()\n if author:\n author = author.replace('Автор:', '').strip()\n else:\n author = selector.xpath(\n '//td[@valign=\"top\"]/p//span[contains(text(), \"Автор\")]/../../text()').extract_first()\n if author:\n author = author.split()\n else:\n author = selector.xpath(\n '//td[@valign=\"top\"]/p//span[contains(text(), \"Автор\")]/../text()').extract_first()\n if author:\n author.replace(':', '').split()\n book['Автор'] = author\n\n office = selector.xpath(\n '//td[@valign=\"top\"]/p/strong[contains(text(), \"Издательство\")]/../text()').extract_first()\n if office:\n office = office.replace(':', '').strip()\n else:\n office = selector.xpath(\n '//td[@valign=\"top\"]/p[contains(text(), \"Издательство\")]/text()').extract_first()\n if office:\n office = office.replace('Издательство:', '').strip()\n else:\n office = selector.xpath(\n '//td[@valign=\"top\"]/p//span[contains(text(), \"Издательство\")]/../../text()').extract_first()\n if office:\n office = office.split()\n book['Издательство'] = office\n\n name = selector.xpath('//td[@class=\"contentheading\"]/h1/text()').extract_first().strip()\n name = name.split(' - ', 1)[0]\n book['Название'] = name\n\n book['Пиратка 1'] = selector.xpath('//td[@valign=\"top\"]//a[@class=\"doclink\"]/@href').extract_first()\n\n yield book", "sub_path": "institutiones_com.py", "file_name": "institutiones_com.py", "file_ext": "py", "file_size_in_byte": 2900, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "scrapy.spider.CrawlSpider", "line_number": 9, "usage_type": "name"}, {"api_name": "scrapy.spider.Rule", "line_number": 15, "usage_type": "call"}, {"api_name": "scrapy.linkextractors.LinkExtractor", "line_number": 15, "usage_type": "call"}, {"api_name": "scrapy.spider.Rule", "line_number": 17, "usage_type": "call"}, {"api_name": "scrapy.linkextractors.LinkExtractor", "line_number": 17, "usage_type": "call"}, {"api_name": "scrapy.selector.Selector", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "280240861", "text": "\"\"\"Python implementation of Viterbi algorithm for word segmentation\nA clean-up of this: http://norvig.com/ngrams/ch14.pdf\n-\nYou also need 'unigrams.txt' and 'bigrams.txt' to run the segmentation. The ngrams\nused in this implementation is from the 'unigrams.txt' and 'bigrams.txt' provided\nhere: http://norvig.com/ngrams/\n-\nUsage:\n>>> viterbi('thisisasentence')\n(-8.89803279104842, ['this', 'is', 'a', 'sentence'])\n>>> viterbi('idontthinkyoucandealwiththis')\n(-15.311752931970325, ['i', 'dont', 'think', 'you', 'can', 'deal', 'with', 'this'])\n>>>\n\"\"\"\nimport functools\nimport math\nimport time\n\n\nclass ProbDist(dict):\n ### Probability distribution estimated from unigram/bigram data\n def __init__(self, datafile=None, unigram=True, N=1024908267229):\n data = {}\n with open(datafile) as f:\n for line in f:\n k, v = line.rstrip().split('\\t')\n data[k] = int(v)\n\n for k, c in data.items():\n self[k] = self.get(k, 0) + c\n\n if unigram:\n self.unknownprob = lambda k, N: 10 / (N * 10 ** len(k)) # avoid unknown long word\n else:\n self.unknownprob = lambda k, N: 1 / N\n\n self.N = N\n\n def __call__(self, key):\n if key in self:\n return self[key] / self.N\n else:\n return self.unknownprob(key, self.N)\n\n # def __getitem__(self, key):\n # if key in self:\n # return self[key] / self.N\n # else:\n # return self.unknownprob(key, self.N)\n\n\nP_unigram = ProbDist('unigrams.txt')\nP_bigram = ProbDist('bigrams.txt', False)\n\ncount = 0\n\n\ndef conditionalProb(word_curr, word_prev):\n ### Conditional probability of current word given the previous word.\n try:\n return P_bigram[word_prev + ' ' + word_curr] / P_unigram[word_prev]\n except KeyError:\n return P_unigram(word_curr)\n\n\n@functools.lru_cache(maxsize=2 ** 20)\ndef viterbi(text, prev='', maxlen=10):\n if not text:\n return 0.0, []\n\n textlen = min(len(text), maxlen)\n splits = [(text[:i + 1], text[i + 1:]) for i in range(textlen)]\n\n candidates = []\n for first_word, remain_word in splits:\n # pdb.set_trace()\n first_prob = math.log10(conditionalProb(first_word, prev))\n remain_prob, remain_word = viterbi(remain_word, first_word)\n\n candidates.append((first_prob + remain_prob, [first_word] + remain_word))\n\n return max(candidates)\n\n\nstart = time.time()\n# print(viterbi('thisisasentence'))\nDict = P_unigram\n# Dict = {\"经常\": 0.1, \"经\": 0.05, \"有\": 0.1, \"常\": 0.001, \"有意见\": 0.1, \"歧\": 0.001, \"意见\": 0.2, \"分歧\": 0.2, \"见\": 0.05, \"意\": 0.05,\n# \"见分歧\": 0.05, \"分\": 0.1}\nmax_length = 8\nsentence = \"idontthinkyoucandealwiththis\"\n# sentence = \"经常有意见分歧\"\nsentence_length = len(sentence)\n\n\ndef get_dag():\n dag_dict = {}\n for idx in range(sentence_length):\n end_positions = []\n end_pos = idx\n frag = sentence[idx]\n while end_pos < sentence_length and end_pos - idx < max_length:\n if frag in Dict:\n end_positions.append(end_pos)\n end_pos += 1\n frag = sentence[idx:end_pos + 1]\n if not end_positions:\n end_positions.append(idx)\n dag_dict[idx] = end_positions\n return dag_dict\n\n\ndag = get_dag()\nprint(dag)\n\n\ndef get_prob(key):\n prob = -math.log(P_unigram[key] / P_unigram.N)\n # print(f'prob of {key} is {prob}')\n return prob\n\n\nroute = {sentence_length: (0, 0)}\nfor idx in range(sentence_length - 1, -1, -1):\n distance = (((get_prob(sentence[idx:x + 1]) or 0) + route.get(x + 1)[0], x) for x in dag[idx])\n route[idx] = min(distance)\nprint(route)\n\nx = 0\nwords = []\nwhile x < sentence_length:\n y = route[x][1] + 1\n word = sentence[x:y]\n words.append(word)\n x = y\nprint(words)\n\nend = time.time()\nprint((end - start) * 1000)\n", "sub_path": "try/viterbi_segment.py", "file_name": "viterbi_segment.py", "file_ext": "py", "file_size_in_byte": 3888, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "math.log10", "line_number": 77, "usage_type": "call"}, {"api_name": "functools.lru_cache", "line_number": 66, "usage_type": "call"}, {"api_name": "time.time", "line_number": 85, "usage_type": "call"}, {"api_name": "math.log", "line_number": 118, "usage_type": "call"}, {"api_name": "time.time", "line_number": 138, "usage_type": "call"}]} +{"seq_id": "432494345", "text": "import ast\nimport re\nimport pandas as pd\nfrom pyquery import PyQuery as pq\nfrom selenium import webdriver\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom time import sleep\n\n\ndef generate_download_url(url):\n url = re.sub('report-covers','report-pdfs',url)\n url = re.sub('/profile.jpg','.pdf',url)\n result = re.search('(.*?AWSAccessKeyId.*?AKIAJZQ4KYD2D35QKCDA).*?',url,re.S)\n return result.group(1)\n\n\ndef get_token(urls):\n names, links = [], []\n for url in urls:\n try:\n result = re.search('(\\d.*?)\\:\\s(http.*?$)', url, re.S)\n name = result.group(1)\n link = result.group(2)\n browser = webdriver.Chrome()\n browser.set_window_size(50, 100)\n browser.get(link)\n doc = pq(browser.page_source)\n # while True:\n # sleep(0.5)\n # if EC.visibility_of_element_located(By.CSS_SELECTOR, \"body\"):\n # break\n # else:\n # browser.refresh()\n # doc = pq(browser.page_source)\n browser.close()\n # get the token\n text = doc('body > div > div > div.col-md-4 > img').attr('src')\n link = generate_download_url(text)\n file_name = re.search('.*?report-pdfs(.*?pdf.*?)AWSAccessKeyId.*?', link, re.S)\n file_name = file_name.group(1)\n file_name = file_name[6:-1]\n names.append(name + '' + file_name)\n links.append(link)\n except: pass\n return names, links\n\n\n\nif __name__ == \"__main__\":\n df = pd.read_csv('draft.csv')\n df['download'] = [[]]*len(df)\n for i in range(554, len(df)):\n df['Report'][i] = ast.literal_eval(df['Report'][i])\n df['Report'][i], df['download'][i] = get_token(df['Report'][i])\n print(i)\n df.to_csv('download.csv', index=False)\n print('Successfully scraped all file links!')\n", "sub_path": "code/Jane_make_url.py", "file_name": "Jane_make_url.py", "file_ext": "py", "file_size_in_byte": 1945, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "re.sub", "line_number": 11, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 12, "usage_type": "call"}, {"api_name": "re.search", "line_number": 13, "usage_type": "call"}, {"api_name": "re.S", "line_number": 13, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 21, "usage_type": "call"}, {"api_name": "re.S", "line_number": 21, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 24, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 24, "usage_type": "name"}, {"api_name": "pyquery.PyQuery", "line_number": 27, "usage_type": "call"}, {"api_name": "re.search", "line_number": 39, "usage_type": "call"}, {"api_name": "re.S", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 50, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "67013668", "text": "#!/usr/bin/python3\n\nimport sys\nimport os\nimport re\nimport itertools\nimport collections\nimport numpy as np\nimport progressbar #to prevent confusion this needs progressbar2 (pip3 install progressbar2 --user)\nfrom nltk.corpus import stopwords\nfrom sklearn.feature_extraction import stop_words\nfrom sklearn.svm import SVC\nfrom movie_classification import uniques, remove_markup, remove_hearing_impaired, remove_speaker, parse_subtitle, tokenize, to_list\nfrom gensim.models.doc2vec import Doc2Vec, TaggedDocument\n\ndef doc2vec_model(genres):\n '''\n Creates a doc2vec model, and saves it\n '''\n features = []\n all_genres = []\n nltk_stopword_set = set(stopwords.words(\"english\")) #179 words\n scikit_stopword_set = set(stop_words.ENGLISH_STOP_WORDS) #318 words\n union_stopword_set = nltk_stopword_set | scikit_stopword_set # 378 words\n\n for genre in genres:\n filenames = [files for files in os.listdir(\"subtitles/\" + genre)]\n file_counter = 0\n for file in filenames:\n if file_counter == 150: #max amount of files per genre\n break\n file_counter += 1\n data = parse_subtitle(genre, file)\n try:\n len(data[0][1]) #check if file uses correct time format (e.g. 12:12:12)\n except IndexError:\n file_counter -= 1\n continue\n dialogue = [remove_speaker(remove_hearing_impaired(remove_markup(item[3]))) for item in data if item[5] >= 3]\n dialogue_one_list = list(itertools.chain.from_iterable([tokenize(line) for line in dialogue]))\n bag = uniques([tok if not tok.isupper() else tok.lower() for tok in dialogue_one_list], union_stopword_set) \n features.append(bag)\n all_genres.append(genre)\n\n features = [to_list(str(lst)) for lst in features]\n tagged_data = [TaggedDocument(words=bow, tags=[str(idx)]) for idx, bow in enumerate(features)]\n\n max_epochs = 200\n vec_size = 20 \n alpha = 0.025\n\n model = Doc2Vec(vector_size=vec_size,\n alpha=alpha, \n min_alpha=0.025,\n min_count=1,\n dm = 0) #dm = 0 uses bow, dm = 1 preserves word order\n \n model.build_vocab(tagged_data)\n\n print(\"#### TRAINING DOC2VEC MODEL\\n\")\n\n bar = progressbar.ProgressBar(maxval=max_epochs).start()\n for idx, epoch in enumerate(range(max_epochs)):\n model.train(tagged_data,\n total_examples=model.corpus_count,\n epochs=model.iter)\n model.alpha -= 0.0002\n model.min_alpha = model.alpha\n bar.update(idx)\n\n bar.finish()\n\n model.save(\"d2v_150.model\")\n print(\"Model Saved\")\n\ndef main():\n categories = [\"Comedy\", \"Drama\", \"Documentary\", \"Horror\"]\n doc2vec_model(categories)\n\nif __name__ == '__main__':\n main()\n\n\n", "sub_path": "doc2vec.py", "file_name": "doc2vec.py", "file_ext": "py", "file_size_in_byte": 2861, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "nltk.corpus.stopwords.words", "line_number": 22, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 22, "usage_type": "name"}, {"api_name": "sklearn.feature_extraction.stop_words.ENGLISH_STOP_WORDS", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sklearn.feature_extraction.stop_words", "line_number": 23, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 27, "usage_type": "call"}, {"api_name": "movie_classification.parse_subtitle", "line_number": 33, "usage_type": "call"}, {"api_name": "movie_classification.remove_speaker", "line_number": 39, "usage_type": "call"}, {"api_name": "movie_classification.remove_hearing_impaired", "line_number": 39, "usage_type": "call"}, {"api_name": "movie_classification.remove_markup", "line_number": 39, "usage_type": "call"}, {"api_name": "itertools.chain.from_iterable", "line_number": 40, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 40, "usage_type": "attribute"}, {"api_name": "movie_classification.tokenize", "line_number": 40, "usage_type": "call"}, {"api_name": "movie_classification.uniques", "line_number": 41, "usage_type": "call"}, {"api_name": "movie_classification.to_list", "line_number": 45, "usage_type": "call"}, {"api_name": "gensim.models.doc2vec.TaggedDocument", "line_number": 46, "usage_type": "call"}, {"api_name": "gensim.models.doc2vec.Doc2Vec", "line_number": 52, "usage_type": "call"}, {"api_name": "progressbar.ProgressBar", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "173196149", "text": "# -*- coding: utf-8 -*-\r\n#\r\n\r\n'''\r\n显示参考图片的关键点和三维坐标信息\r\n\r\n鼠标滚轮可以垂直移动图片，按下Ctrl键的同时使用滚轮可以水平移动图片\r\n鼠标左键，会在图片左上角打印出对应的三维空间坐标\r\n'''\r\n\r\n# Python 2/3 compatibility\r\nfrom __future__ import print_function\r\n\r\nimport argparse\r\nimport math\r\n\r\nimport numpy as np\r\nimport cv2\r\n\r\nimport sys\r\nsys.path.append('..')\r\nfrom manager import stereo_rectify, triangulate_points\r\nfrom calibration.feature import match_images\r\nfrom calibration.recover import rotation_matrix\r\n\r\nkp_stereo_match_threshold = 100\r\n\r\ndef anorm2(a):\r\n return (a*a).sum(-1)\r\n\r\ndef anorm(a):\r\n return np.sqrt( anorm2(a) )\r\n\r\ndef draw_str(dst, target, s):\r\n x, y = target\r\n # cv2.putText(dst, s, (x+1, y+1), cv2.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 0), thickness = 2, lineType=cv2.LINE_AA)\r\n # cv2.putText(dst, s, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (255, 255, 255), lineType=cv2.LINE_AA)\r\n cv2.putText(dst, s, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 255, 255))\r\n\r\ndef load_keypoint_data(filename):\r\n npzfile = np.load(filename)\r\n keypoints = [cv2.KeyPoint(x, y, size, angle, response, int(octave), int(class_id))\r\n for x, y, size, angle, response, octave, class_id in npzfile['keypoints']]\r\n return keypoints, npzfile['descriptors'], npzfile['points3d']\r\n\r\ndef explore_keypoint(win, img, kps, points):\r\n h, w = img.shape[:2]\r\n w1, h1 = 1200, 600\r\n maxOffset = max(0, w - w1), max(0, h - h1)\r\n offset = [0, 0]\r\n green = (0, 255, 0)\r\n red = (0, 0, 255)\r\n white = (255, 255, 255)\r\n kp_color = (51, 103, 236)\r\n vis0 = cv2.drawKeypoints(img, kps, None, flags=0, color=green)\r\n cv2.imshow(win, vis0)\r\n\r\n pts = []\r\n for kp in kps:\r\n pts.append(np.int32(kp.pt))\r\n\r\n delta = 120 << 16;\r\n speed = 30\r\n cur_vis = np.zeros((h1, w1), np.uint8)\r\n def onmouse(event, x, y, flags, offset):\r\n x0, y0 = offset\r\n redraw = False\r\n if event & cv2.EVENT_MOUSEWHEEL:\r\n dt = -int((flags - (flags & 0xffff)) / delta)\r\n if flags & cv2.EVENT_FLAG_CTRLKEY:\r\n dx = x0 + dt * speed\r\n x0 = max(0, min(maxOffset[0], dx))\r\n else:\r\n dy = y0 + dt * speed\r\n y0 = max(0, min(maxOffset[1], dy))\r\n offset[:] = [x0, y0]\r\n redraw = True\r\n cur_vis = vis0[y0:, x0:].copy()\r\n if flags & cv2.EVENT_FLAG_LBUTTON:\r\n r = 2.6\r\n m = anorm(np.array(pts) - (x + x0, y + y0)) < r\r\n idxs = np.where(m)[0]\r\n tx, ty = 0, 50\r\n for i in idxs:\r\n draw_str(cur_vis, (tx, ty), '%5d: %s' % (i, points[i].ravel()))\r\n ty += 50\r\n redraw = True\r\n if redraw:\r\n cv2.imshow(win, cur_vis)\r\n cv2.setMouseCallback(win, onmouse, offset)\r\n\r\ndef calculate_points3d(K, R, t, pts1, pts2):\r\n '''根据针孔相机模型两张图片对应的像素坐标计算对应的空间三维坐标\r\n >>> t = np.float32([-4, 0, 0])\r\n >>> K = np.float32([[2380, 0, 1223], \\\r\n [0, 2380, 1631], \\\r\n [0, 0, 1]])\r\n >>> pts1 = np.float32([[1226., 1237.], \\\r\n [1097., 375.]])\r\n >>> pts2 = np.float32([[1188.,1234.8], \\\r\n [1058.40002441, 373.20001221]])\r\n >>> pt3d = [[ 0.31874507, -41.58947622, 250.5377471 ], \\\r\n [ -13.05410563, -130.24889215, 246.64372768]]\r\n >>> calculate_points3d(K, None, t, pts1, pts2)\r\n [array([ 0.3214155 , -41.45054333, 249.68944372]), array([ -13.02433003, -129.96615533, 246.09709895])]\r\n '''\r\n fx, fy = K[0][0], K[1][1]\r\n cx, cy = K[0][2], K[1][2]\r\n cp = np.array([K[0][2], K[1][2]])\r\n dx, dy, dz = t.ravel()\r\n\r\n n = pts1.shape[0]\r\n\r\n b1 = np.zeros(2 * n).reshape(-1, 2)\r\n b2 = ( pts2 - cp ) * dz - np.array([fx * dx, fy * dy])\r\n b = np.hstack((b1, b2))\r\n\r\n a0 = np.array([[fx, 0], [0, fy]] * n)\r\n a1 = np.hstack((a0, (-pts1 + np.array([cx, cy])).reshape(-1, 1))).reshape(-1, 2, 3)\r\n a2 = np.hstack((a0, (-pts2 + np.array([cx, cy])).reshape(-1, 1))).reshape(-1, 2, 3)\r\n a = np.stack((a1, a2), axis=1).reshape(-1, 4, 3)\r\n return [np.linalg.lstsq(a[i], b[i])[0] for i in range(n)]\r\n\r\ndef handle_stereo_images(config):\r\n focal = [float(s) for s in config.focal.split(',')]\r\n rotate = config.yaw * math.pi / 180\r\n offset = [float(s) for s in config.offset.split(',')]\r\n filename1, filename2 = config.images\r\n feature, count = config.feature, config.nFeatures\r\n asift = config.asift\r\n homography = config.homography\r\n imgLeft, imgRight = cv2.imread(filename1, 0), cv2.imread(filename2, 0)\r\n h, w = imgLeft.shape[:2]\r\n # R, _jacob = cv2.Rodrigues(rotation_matrix(rotate, axis='y').A)\r\n R = rotation_matrix(rotate, axis='y').A\r\n T = np.float64(offset)\r\n distCoeffs = np.zeros(4)\r\n fx, fy = focal\r\n cx, cy = (w-1)/2, (h-1)/2\r\n K = np.float64([[w*fx, 0, cx],\r\n [0, h*fy, cy],\r\n [0, 0, 1]])\r\n\r\n pts1, pts2, keypoints, descriptors = match_images(imgLeft, imgRight,\r\n feature + '-flann', asift=asift, n=count,\r\n homography=homography)\r\n # if len(keypoints) < kp_stereo_match_threshold:\r\n # raise RuntimeError('双目照片匹配的关键点个数 %d 少于 %d' % (len(keypoints), kp_stereo_match_threshold))\r\n\r\n if config.myself:\r\n points3d = calculate_points3d(K, R, T, pts1, pts2)\r\n else:\r\n if config.correct:\r\n E, mask = cv2.findEssentialMat(pts1, pts2, K)\r\n retval, R, t, mask = cv2.recoverPose(E, pts1, pts2, K)\r\n\r\n nv = np.matrix(R) * np.float64([0, 0, 1]).reshape(3, 1)\r\n yaw = np.arctan2(nv[0], nv[2])\r\n print(\"自动校正得到的相机水平偏转角度为 %8.2f\" % (yaw[0] / math.pi * 180))\r\n\r\n nv = np.matrix(R) * np.float64([0, 1, 0]).reshape(3, 1)\r\n pitch = np.arctan2(nv[2], nv[1])\r\n print(\"自动校正得到的相机仰角为 %8.2f\" % (pitch[0] / math.pi * 180))\r\n\r\n nv = np.matrix(R) * np.float64([1, 0, 0]).reshape(3, 1)\r\n roll = np.arctan2(nv[1], nv[0])\r\n print(\"自动校正得到的相机旋转角度为 %8.2f\" % (roll[0] / math.pi * 180))\r\n\r\n distCoeffs = np.zeros(4)\r\n P1, P2, Q = stereo_rectify(K, distCoeffs, K, distCoeffs, (h, w), R, T)\r\n # print (h, w)\r\n # print ('R:', R)\r\n # print ('T:', T)\r\n # print ('K:', K)\r\n # print ('P1:', P1)\r\n # print ('P2:', P2)\r\n points3d = triangulate_points(P1, P2, pts1.T, pts2.T)\r\n return keypoints, points3d\r\n\r\ndef main(params=None):\r\n parser = argparse.ArgumentParser(description='查看图片关键点三维坐标')\r\n parser.add_argument('images', metavar='FILENAME', nargs=2, help='双目图片文件名称')\r\n parser.add_argument('--show', action='store_true', help='在窗口中显示包含关键点的图片')\r\n parser.add_argument('--save', action='store_true', help='保存包含关键点的图片')\r\n parser.add_argument('--myself', action='store_true', help='使用自己的算法计算')\r\n parser.add_argument('--output', metavar=\"path\", help='输出文件的路径')\r\n parser.add_argument('--mask', metavar=\"x0,y0,x1,y1\", help='选择区域（x0, y0, x1, y1)')\r\n parser.add_argument('--asift', action='store_true', help='使用asift算法')\r\n parser.add_argument('--correct', action='store_true', help='是否自动校正角度偏差')\r\n parser.add_argument('--homography', action='store_true', help='使用 Homography 进行过滤')\r\n parser.add_argument('--tilt', metavar=\"n\", type=int, default=3, help='设置 asift 的 tilt 参数')\r\n parser.add_argument('--feature', choices=['orb', 'sift', 'surf'], default='orb', help='特征名称')\r\n parser.add_argument('--nFeatures', metavar='n', type=int, default=2000, help='特征数目')\r\n parser.add_argument('--offset', metavar=\"dx,dy,dz\", help='两张照片拍摄地点的相对位移（dx,dy,dz)')\r\n parser.add_argument('--yaw', metavar=\"n\", type=int, default=0, help='两张照片的相对偏转角度（度数）')\r\n parser.add_argument('--focal', metavar=\"fx,fy\", help='相机内参（fx,fy)')\r\n args = parser.parse_args(params)\r\n\r\n kps, pt3s = handle_stereo_images(args)\r\n\r\n img = cv2.imread(args.images[0], 0)\r\n win = 'view3d'\r\n # cv2.namedWindow(win, cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)\r\n cv2.namedWindow(win)\r\n cv2.moveWindow(win, 0, 0)\r\n cv2.setWindowTitle(win, 'KeyPoint 3D Viewer')\r\n explore_keypoint(win, img, kps, pt3s)\r\n cv2.waitKey()\r\n cv2.destroyAllWindows()\r\n\r\nif __name__ == '__main__':\r\n # 单元测试\r\n # python -m doctest -v check_point3d.py\r\n\r\n # HUAWEI SLA00 FOCAL: 0.9722,0.7292\r\n # HUAWEI G80 FOCAL: 1.15,0.85\r\n # IPHONE 6S FOCAL: 1.167,0.875\r\n # params = ['--offset', ' -120,0,0', '--yaw', '0', '--focal', '0.9722,0.7292',\r\n # 'stereo/map-left.jpg', 'stereo/map-right-120.jpg']\r\n main()\r\n", "sub_path": "src/verify/check_point3d.py", "file_name": "check_point3d.py", "file_ext": "py", "file_size_in_byte": 9251, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sys.path.append", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.KeyPoint", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.drawKeypoints", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 64, "usage_type": "attribute"}, {"api_name": "cv2.EVENT_MOUSEWHEEL", "line_number": 68, "usage_type": "attribute"}, {"api_name": "cv2.EVENT_FLAG_CTRLKEY", "line_number": 70, "usage_type": "attribute"}, {"api_name": "cv2.EVENT_FLAG_LBUTTON", "line_number": 79, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 89, "usage_type": "call"}, {"api_name": "cv2.setMouseCallback", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.linalg.lstsq", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 122, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 126, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 132, "usage_type": "call"}, {"api_name": "calibration.recover.rotation_matrix", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 140, "usage_type": "call"}, {"api_name": "calibration.feature.match_images", "line_number": 144, "usage_type": "call"}, {"api_name": "cv2.findEssentialMat", "line_number": 154, "usage_type": "call"}, {"api_name": "cv2.recoverPose", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 158, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 159, "usage_type": "attribute"}, {"api_name": "numpy.matrix", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 162, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 163, "usage_type": "attribute"}, {"api_name": "numpy.matrix", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 166, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 167, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 169, "usage_type": "call"}, {"api_name": "manager.stereo_rectify", "line_number": 170, "usage_type": "call"}, {"api_name": "manager.triangulate_points", "line_number": 177, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 181, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 201, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 204, "usage_type": "call"}, {"api_name": "cv2.moveWindow", "line_number": 205, "usage_type": "call"}, {"api_name": "cv2.setWindowTitle", "line_number": 206, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 208, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 209, "usage_type": "call"}]} +{"seq_id": "291771668", "text": "import numpy as np\r\nimport cv2\r\nimport datetime\r\nimport time\r\n\r\ncap = cv2.VideoCapture(0) # my webcam\r\n\r\ntemplate = cv2.imread('RealCounter.png',0)\r\ntemplate2 = cv2.imread('6OClock.png', 0)\r\ntemplate3 = cv2.imread('12OClock.png', 0)\r\ntemplate4 = cv2.imread('3OClock.png', 0)\r\ntemplate5 = cv2.imread('9OClock.png', 0)\r\n\r\nwhile(cap.isOpened()):\r\n ret, frame = cap.read()\r\n img_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\r\n \r\n#Counter\r\n w, h = template.shape[::-1]\r\n\r\n res = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED)\r\n threshold = 0.42\r\n loc = np.where( res >= threshold)\r\n\r\n for pt in zip(*loc[::-1]):\r\n cv2.rectangle(frame, pt, (pt[0] + w, pt[1] + h), (0,255,255), 2)\r\n\r\n#12 O'Clock\r\n w3, h3 = template3.shape[::-1]\r\n\r\n res = cv2.matchTemplate(img_gray,template3,cv2.TM_CCOEFF_NORMED)\r\n threshold = 0.75\r\n loc = np.where( res >= threshold)\r\n\r\n #time.sleep(1)\r\n for pt in zip(*loc[::-1]):\r\n cv2.rectangle(frame, pt, (pt[0] + w3, pt[1] + h3), (0,0,255), 2)\r\n ret,frame = cap.read()\r\n \r\n flow_meter = frame[120:170, 210:500]\r\n cv2.imwrite('test_numbers.png', flow_meter)\r\n #time.sleep(1)\r\n\r\n cv2.imshow('Original', frame)\r\n #cv2.imshow('Grey', img_gray)\r\n \r\n if cv2.waitKey(1) & 0xFF == ord('q'):\r\n break\r\n\r\ncap.release()\r\ncv2.destroyAllWindows()\r\nexecfile('train_and_test.py')\r\n\r\n\r\n", "sub_path": "VideoFeedTest1.py", "file_name": "VideoFeedTest1.py", "file_ext": "py", "file_size_in_byte": 1408, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "cv2.VideoCapture", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.matchTemplate", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.TM_CCOEFF_NORMED", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.matchTemplate", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.TM_CCOEFF_NORMED", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "558020258", "text": "from django.contrib.auth.models import User\nfrom rest_framework import permissions\n\nfrom crossing_point.models import Human\n\n\nclass IsManagerOrReadOnly(permissions.BasePermission):\n def has_object_permission(self, request, view, obj):\n if request.method in permissions.SAFE_METHODS:\n return True\n return obj.user == request.user\n\n\nclass ManagerPermissions(permissions.BasePermission):\n allowed_user_roles = (User.is_superuser, Human.is_manager)\n\n def has_permission(self, request, view):\n if User.is_superuser or Human.is_manager:\n # is_allowed_user = request.user.role in self.allowed_user_roles\n return True\n else:\n return False\n\n\nclass CUDModelPermissions(permissions.DjangoModelPermissions):\n perms_map = {\n 'GET': [],\n 'OPTIONS': [],\n 'HEAD': ['%(crossing_point)s.read_%(model_name)s'],\n 'POST': ['%(app_label)s.add_%(model_name)s'],\n 'PUT': ['%(app_label)s.change_%(model_name)s'],\n 'PATCH': ['%(app_label)s.change_%(model_name)s'],\n 'DELETE': ['%(app_label)s.delete_%(model_name)s'],\n }\n", "sub_path": "crossing_point/permission.py", "file_name": "permission.py", "file_ext": "py", "file_size_in_byte": 1108, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "rest_framework.permissions.BasePermission", "line_number": 7, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 7, "usage_type": "name"}, {"api_name": "rest_framework.permissions.SAFE_METHODS", "line_number": 9, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 9, "usage_type": "name"}, {"api_name": "rest_framework.permissions.BasePermission", "line_number": 14, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 14, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.is_superuser", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 15, "usage_type": "name"}, {"api_name": "crossing_point.models.Human.is_manager", "line_number": 15, "usage_type": "attribute"}, {"api_name": "crossing_point.models.Human", "line_number": 15, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.is_superuser", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 18, "usage_type": "name"}, {"api_name": "crossing_point.models.Human.is_manager", "line_number": 18, "usage_type": "attribute"}, {"api_name": "crossing_point.models.Human", "line_number": 18, "usage_type": "name"}, {"api_name": "rest_framework.permissions.DjangoModelPermissions", "line_number": 25, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "470740863", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.stats\n\nimport meta\nimport meta_session\nfrom plots import significance_bar, significance_text\nfrom tasks import task\nfrom utils import mannwhitneyu\n\n\n@task(\n groups=meta_session.groups,\n savepath={\n key: (\"replays-session\", f\"{key}_replay_prop_byphase.svg\")\n for key in [\n \"exclusive\",\n \"exclusive_ph2\",\n \"difference\",\n \"difference_ph2\",\n ]\n },\n)\ndef plot_group_replay_prop_byphase(infos, group_name, *, replay_prop_byphase, savepath):\n _plot_replay_metric(\n replay_prop_byphase,\n [\"only_u\", \"only_full_shortcut\"],\n ylabel=\"Proportion of SWRs\\nthat are replays\",\n title=f\"{meta.title_labels[group_name]}\"\n if group_name not in [\"all\", \"combined\"]\n else None,\n ylim=0.45 if group_name not in [\"all\", \"combined\"] else None,\n savepath=savepath[\"exclusive\"],\n )\n _plot_replay_metric(\n replay_prop_byphase,\n [\"only_u_ph2\", \"only_full_shortcut_ph2\"],\n ylabel=\"Proportion of SWRs\\nthat are replays\",\n title=\"Phase 2 familiar tuning curves\",\n savepath=savepath[\"exclusive_ph2\"],\n )\n _plot_replay_metric(\n replay_prop_byphase,\n [\"difference\"],\n ylabel=\"Replay proportion\\nfor shortcut - familiar\",\n title=f\"{meta.title_labels[group_name]}\"\n if group_name not in [\"all\", \"combined\"]\n else None,\n ylim=0.45 if group_name not in [\"all\", \"combined\"] else None,\n color_byvalue=True,\n savepath=savepath[\"difference\"],\n )\n _plot_replay_metric(\n replay_prop_byphase,\n [\"difference_ph2\"],\n ylabel=\"Replay proportion\\nfor shortcut - familiar\",\n title=\"Phase 2 familiar tuning curves\",\n color_byvalue=True,\n savepath=savepath[\"difference_ph2\"],\n )\n\n\n@task(\n groups=meta_session.groups,\n savepath={\n f\"exclusive-{trajectory}\": (\n \"replays-session\",\n f\"exclusive_replay_prop_normalized_byphase_{trajectory}.svg\",\n )\n for trajectory in meta.trajectories\n },\n)\ndef plot_replay_prop_normalized_byphase(\n infos,\n group_name,\n *,\n replay_prop_normalized_byphase,\n savepath,\n):\n for trajectory in meta.trajectories:\n _plot_replay_metric(\n replay_prop_normalized_byphase,\n [f\"only_{trajectory}\"],\n ylabel=\"Replay proportion /\\nmean replay proportion\",\n title=f\"{meta.title_labels[group_name]}\"\n if group_name not in [\"all\", \"combined\"]\n else None,\n savepath=savepath[f\"exclusive-{trajectory}\"],\n )\n\n\n@task(\n groups=meta_session.groups,\n savepath={\n key: (\"replays-session\", f\"{key}_replay_prop_byexperience_bytrial.svg\")\n for key in [\"exclusive\", \"difference\"]\n },\n)\ndef plot_group_replay_prop_byexperience(\n infos,\n group_name,\n *,\n replay_prop_byexperience_bytrial,\n savepath,\n):\n _plot_replay_metric(\n replay_prop_byexperience_bytrial,\n [\"only_u\", \"only_full_shortcut\"],\n ylabel=\"Proportion of SWRs\\nthat are replays\",\n title=f\"{meta.title_labels[group_name]}\"\n if group_name not in [\"all\", \"combined\"]\n else None,\n savepath=savepath[\"exclusive\"],\n )\n _plot_replay_metric(\n replay_prop_byexperience_bytrial,\n [\"difference\"],\n ylabel=\"Replay proportion\\nfor shortcut - familiar\",\n color_byvalue=True,\n title=f\"{meta.title_labels[group_name]}\"\n if group_name not in [\"all\", \"combined\"]\n else None,\n savepath=savepath[\"difference\"],\n )\n\n\n@task(\n groups=meta_session.groups,\n savepath={\n key: (\"replays-session\", f\"{key}_replay_prop_byexperience_nofeeder_bytrial.svg\")\n for key in [\"exclusive\", \"difference\"]\n },\n)\ndef plot_group_replay_prop_byexperience_nofeeder_bytrial(\n infos,\n group_name,\n *,\n replay_prop_byexperience_nofeeder_bytrial,\n savepath,\n):\n _plot_replay_metric(\n replay_prop_byexperience_nofeeder_bytrial,\n [\"only_u\", \"only_full_shortcut\"],\n ylabel=\"Proportion of path SWRs\\nthat are replays\",\n title=f\"{meta.title_labels[group_name]}\"\n if group_name not in [\"all\", \"combined\"]\n else None,\n savepath=savepath[\"exclusive\"],\n )\n _plot_replay_metric(\n replay_prop_byexperience_nofeeder_bytrial,\n [\"difference\"],\n ylabel=\"Path replay proportion\\nfor shortcut - familiar\",\n color_byvalue=True,\n title=f\"{meta.title_labels[group_name]}\"\n if group_name not in [\"all\", \"combined\"]\n else None,\n savepath=savepath[\"difference\"],\n )\n\n\n@task(\n groups=meta_session.groups,\n savepath={\n key: (\n \"replays-session\",\n f\"{key}_replay_prop_byexperience_feederonly.svg\",\n )\n for key in [\"exclusive\", \"difference\"]\n },\n)\ndef plot_group_replay_prop_byexperience_feederonly(\n infos,\n group_name,\n *,\n replay_prop_byexperience_feederonly,\n savepath,\n):\n original_xlabels = meta.on_task\n labels = meta.on_task_labels\n\n _plot_replay_metric(\n replay_prop_byexperience_feederonly,\n [\"only_u\", \"only_full_shortcut\"],\n ylabel=\"Proportion of feeder SWRs\\nthat are replays\",\n title=f\"{meta.title_labels[group_name]}\"\n if group_name not in [\"all\", \"combined\"]\n else None,\n original_xlabels=original_xlabels,\n labels=labels,\n savepath=savepath[\"exclusive\"],\n )\n _plot_replay_metric(\n replay_prop_byexperience_feederonly,\n [\"difference\"],\n ylabel=\"Feeder replay proportion\\nfor shortcut - familiar\",\n color_byvalue=True,\n title=f\"{meta.title_labels[group_name]}\"\n if group_name not in [\"all\", \"combined\"]\n else None,\n original_xlabels=original_xlabels,\n labels=labels,\n savepath=savepath[\"difference\"],\n )\n\n\ndef _plot_replay_metric(\n replay_metric,\n trajectories,\n ylabel,\n ylim=None,\n color_byvalue=False,\n title=None,\n original_xlabels=meta.experiences,\n labels=meta.experiences_labels,\n savepath=None,\n):\n assert savepath is not None\n orig_xlabels = list(next(iter(replay_metric.values())))\n if orig_xlabels == meta.task_times:\n xlabels = list(meta.task_times_labels.values())\n else:\n assert orig_xlabels == original_xlabels, orig_xlabels\n xlabels = list(labels.values())\n x = np.arange(len(xlabels))\n width = 0.8 / (len(trajectories))\n fig, ax = plt.subplots(figsize=(8, 6))\n\n # Plot bars\n heights = {}\n for i, trajectory in enumerate(trajectories):\n if color_byvalue:\n color = [\n meta.colors[\"full_shortcut\"] if np.mean(val) > 0.0 else meta.colors[\"u\"]\n for val in list(replay_metric[trajectory].values())\n ]\n else:\n color = meta.colors[trajectory]\n means = np.array([np.mean(val) for val in replay_metric[trajectory].values()])\n sems = np.array(\n [scipy.stats.sem(val) for val in replay_metric[trajectory].values()]\n )\n plt.bar(\n x + i * width,\n means,\n yerr=sems,\n width=width,\n color=color,\n ecolor=\"k\",\n )\n heights[trajectory] = means + sems\n\n if len(trajectories) == 2 and \"full_shortcut\" in trajectories[1]:\n pval = {\n xlabel: mannwhitneyu(\n replay_metric[trajectories[0]][xlabel],\n replay_metric[trajectories[1]][xlabel],\n )\n for xlabel in orig_xlabels\n }\n for i, xlabel in enumerate(orig_xlabels):\n significance_bar(\n start=i - 0.05,\n end=i + width + 0.05,\n height=max(\n heights[trajectories[0]][i],\n heights[trajectories[1]][i],\n 0,\n ),\n pval=pval[xlabel],\n )\n elif len(trajectories) == 1 and trajectories[0].startswith(\"difference\"):\n prefix = \"only_\" if trajectories[0].startswith(\"difference\") else \"\"\n suffix = \"_ph2\" if trajectories[0].endswith(\"_ph2\") else \"\"\n pval = {\n xlabel: mannwhitneyu(\n replay_metric[f\"{prefix}u{suffix}\"][xlabel],\n replay_metric[f\"{prefix}full_shortcut{suffix}\"][xlabel],\n )\n for xlabel in orig_xlabels\n }\n\n for i, xlabel in enumerate(orig_xlabels):\n significance_text(\n x=i,\n height=max(heights[trajectories[0]][i], 0),\n pval=pval[xlabel],\n )\n else:\n # trajectories == [\"u\"] or [\"full_shortcut\"], for normalized plots\n for left, right in zip(\n meta.rest_times[:-1] + meta.run_times[:-1],\n meta.rest_times[1:] + meta.run_times[1:],\n ):\n pval = mannwhitneyu(\n replay_metric[trajectories[0]][left],\n replay_metric[trajectories[0]][right],\n )\n start = meta.task_times.index(left)\n end = meta.task_times.index(right)\n significance_bar(\n start=start,\n end=end,\n height=max(\n list(heights[trajectories[0]])[start : end + 1] + [0],\n ),\n pval=pval,\n )\n\n offset = 0.0\n if len(trajectories) == 2:\n offset = 0.2\n elif len(trajectories) == 3:\n offset = 0.8 / 3\n plt.xticks(x + offset, xlabels, fontsize=meta.fontsize, rotation=meta.xtickrotation)\n plt.ylabel(ylabel, fontsize=meta.fontsize)\n if \"normalized\" in savepath:\n plt.axhline(1.0, c=\"k\", ls=\"--\")\n plt.setp(ax.get_yticklabels(), fontsize=meta.fontsize)\n plt.locator_params(axis=\"y\", nbins=5)\n if ylim is not None:\n plt.ylim(0, ylim)\n if title is not None:\n plt.title(title, fontsize=meta.fontsize)\n\n ax.spines[\"right\"].set_visible(False)\n ax.spines[\"top\"].set_visible(False)\n ax.yaxis.set_ticks_position(\"left\")\n ax.xaxis.set_ticks_position(\"bottom\")\n\n plt.tight_layout(h_pad=0.003)\n\n plt.savefig(savepath, bbox_inches=\"tight\", transparent=True)\n plt.close(fig)\n\n\n@task(\n groups=meta_session.analysis_grouped,\n savepath=(\"replays\", \"replay_participation_rate.svg\"),\n)\ndef plot_replay_participation_rate(\n infos,\n group_name,\n *,\n replay_participation_rate,\n replay_participation_rate_pval,\n savepath,\n):\n fig, ax = plt.subplots(figsize=(16, 6))\n\n width = 0.8 / len(replay_participation_rate)\n x = np.arange(len(replay_participation_rate[\"prerecord\"]))\n\n heights = [[], [], []]\n for i, phase in enumerate(meta.task_times):\n means = np.array(\n [np.mean(val) for val in replay_participation_rate[phase].values()]\n )\n sems = np.array(\n [scipy.stats.sem(val) for val in replay_participation_rate[phase].values()]\n )\n for ix, height in enumerate(means + sems):\n heights[ix].append(height)\n rects = ax.bar(\n x + (i * width),\n means,\n width=width,\n color=f\"{((7 - i) / 7) * 0.6 + 0.1:f}\",\n yerr=sems,\n ecolor=\"k\",\n label=meta.task_times_labels[phase],\n )\n\n for val, rect in zip(replay_participation_rate[phase].values(), rects):\n ax.annotate(\n f\"{len(val)}\",\n xy=(rect.get_x() + rect.get_width() / 2, 0),\n xytext=(0, 3), # 3 points vertical offset\n textcoords=\"offset points\",\n ha=\"center\",\n va=\"bottom\",\n color=\"w\",\n fontsize=meta.fontsize_small,\n )\n\n gap = 0.005\n for i, (left, right) in enumerate(zip(meta.task_times[:-1], meta.task_times[1:])):\n for j, pval in enumerate(\n replay_participation_rate_pval[(left, right)].values()\n ):\n significance_bar(\n start=i * width + j + gap,\n end=((i + 1) * width) + j - gap,\n height=max(heights[j][i : i + 2]),\n pval=pval,\n )\n\n plt.legend(fontsize=meta.fontsize_small)\n plt.xticks(\n x + width * 3,\n [\"Unique\", \"Non-unique\", \"No place field\"],\n fontsize=meta.fontsize,\n )\n plt.ylabel(\"Proportion of replay participation\", fontsize=meta.fontsize)\n plt.setp(ax.get_yticklabels(), fontsize=meta.fontsize)\n\n ax.spines[\"right\"].set_visible(False)\n ax.spines[\"top\"].set_visible(False)\n ax.yaxis.set_ticks_position(\"left\")\n ax.xaxis.set_ticks_position(\"bottom\")\n\n plt.tight_layout(h_pad=0.003)\n\n plt.savefig(savepath, bbox_inches=\"tight\", transparent=True)\n plt.close(fig)\n", "sub_path": "plot_replays.py", "file_name": "plot_replays.py", "file_ext": "py", "file_size_in_byte": 12910, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "meta.title_labels", "line_number": 29, "usage_type": "attribute"}, {"api_name": "meta.title_labels", "line_number": 46, "usage_type": "attribute"}, {"api_name": "tasks.task", "line_number": 12, "usage_type": "call"}, {"api_name": "meta_session.groups", "line_number": 13, "usage_type": "attribute"}, {"api_name": "meta.trajectories", "line_number": 80, "usage_type": "attribute"}, {"api_name": "meta.title_labels", "line_number": 85, "usage_type": "attribute"}, {"api_name": "tasks.task", "line_number": 63, "usage_type": "call"}, {"api_name": "meta_session.groups", "line_number": 64, "usage_type": "attribute"}, {"api_name": "meta.trajectories", "line_number": 70, "usage_type": "attribute"}, {"api_name": "meta.title_labels", "line_number": 110, "usage_type": "attribute"}, {"api_name": "meta.title_labels", "line_number": 120, "usage_type": "attribute"}, {"api_name": "tasks.task", "line_number": 92, "usage_type": "call"}, {"api_name": "meta_session.groups", "line_number": 93, "usage_type": "attribute"}, {"api_name": "meta.title_labels", "line_number": 145, "usage_type": "attribute"}, {"api_name": "meta.title_labels", "line_number": 155, "usage_type": "attribute"}, {"api_name": "tasks.task", "line_number": 127, "usage_type": "call"}, {"api_name": "meta_session.groups", "line_number": 128, "usage_type": "attribute"}, {"api_name": "meta.on_task", "line_number": 179, "usage_type": "attribute"}, {"api_name": "meta.on_task_labels", "line_number": 180, "usage_type": "attribute"}, {"api_name": "meta.title_labels", "line_number": 186, "usage_type": "attribute"}, {"api_name": "meta.title_labels", "line_number": 198, "usage_type": "attribute"}, {"api_name": "tasks.task", "line_number": 162, "usage_type": "call"}, {"api_name": "meta_session.groups", "line_number": 163, "usage_type": "attribute"}, {"api_name": "meta.experiences", "line_number": 214, "usage_type": "attribute"}, {"api_name": "meta.experiences_labels", "line_number": 215, "usage_type": "attribute"}, {"api_name": "meta.task_times", "line_number": 220, "usage_type": "attribute"}, {"api_name": "meta.task_times_labels.values", "line_number": 221, "usage_type": "call"}, {"api_name": "meta.task_times_labels", "line_number": 221, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 234, "usage_type": "call"}, {"api_name": "meta.colors", "line_number": 234, "usage_type": "attribute"}, {"api_name": "meta.colors", "line_number": 238, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 240, "usage_type": "call"}, {"api_name": "scipy.stats.stats.sem", "line_number": 241, "usage_type": "call"}, {"api_name": "scipy.stats.stats", "line_number": 241, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 241, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 243, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 243, "usage_type": "name"}, {"api_name": "utils.mannwhitneyu", "line_number": 255, "usage_type": "call"}, {"api_name": "plots.significance_bar", "line_number": 262, "usage_type": "call"}, {"api_name": "utils.mannwhitneyu", "line_number": 276, "usage_type": "call"}, {"api_name": "plots.significance_text", "line_number": 284, "usage_type": "call"}, {"api_name": "meta.rest_times", "line_number": 292, "usage_type": "attribute"}, {"api_name": "meta.run_times", "line_number": 292, "usage_type": "attribute"}, {"api_name": "meta.rest_times", "line_number": 293, "usage_type": "attribute"}, {"api_name": "meta.run_times", "line_number": 293, "usage_type": "attribute"}, {"api_name": "utils.mannwhitneyu", "line_number": 295, "usage_type": "call"}, {"api_name": "meta.task_times.index", "line_number": 299, "usage_type": "call"}, {"api_name": "meta.task_times", "line_number": 299, "usage_type": "attribute"}, {"api_name": "meta.task_times.index", "line_number": 300, "usage_type": "call"}, {"api_name": "meta.task_times", "line_number": 300, "usage_type": "attribute"}, {"api_name": "plots.significance_bar", "line_number": 301, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 315, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 315, "usage_type": "name"}, {"api_name": "meta.fontsize", "line_number": 315, "usage_type": "attribute"}, {"api_name": "meta.xtickrotation", "line_number": 315, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 316, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 316, "usage_type": "name"}, {"api_name": "meta.fontsize", "line_number": 316, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 318, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 318, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 319, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 319, "usage_type": "name"}, {"api_name": "meta.fontsize", "line_number": 319, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.locator_params", "line_number": 320, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 320, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 322, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 322, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 324, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 324, "usage_type": "name"}, {"api_name": "meta.fontsize", "line_number": 324, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 331, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 331, "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.close", "line_number": 334, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 334, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 349, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 349, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 352, "usage_type": "call"}, {"api_name": "meta.task_times", "line_number": 355, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 357, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 359, "usage_type": "call"}, {"api_name": "scipy.stats.stats.sem", "line_number": 360, "usage_type": "call"}, {"api_name": "scipy.stats.stats", "line_number": 360, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 360, "usage_type": "name"}, {"api_name": "meta.task_times_labels", "line_number": 371, "usage_type": "attribute"}, {"api_name": "meta.fontsize_small", "line_number": 383, "usage_type": "attribute"}, {"api_name": "meta.task_times", "line_number": 387, "usage_type": "attribute"}, {"api_name": "plots.significance_bar", "line_number": 391, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 398, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 398, "usage_type": "name"}, {"api_name": "meta.fontsize_small", "line_number": 398, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 399, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 399, "usage_type": "name"}, {"api_name": "meta.fontsize", "line_number": 402, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 404, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 404, "usage_type": "name"}, {"api_name": "meta.fontsize", "line_number": 404, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 405, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 405, "usage_type": "name"}, {"api_name": "meta.fontsize", "line_number": 405, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 412, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 412, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 414, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 414, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 415, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 415, "usage_type": "name"}, {"api_name": "tasks.task", "line_number": 337, "usage_type": "call"}, {"api_name": "meta_session.analysis_grouped", "line_number": 338, "usage_type": "attribute"}]} +{"seq_id": "429584827", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Nov 13 18:43:56 2018\r\n\r\n@author: phuongnh\r\n\"\"\"\r\nfrom math import gcd\r\nimport numpy as np\r\nimport dataloader as dtl\r\nimport numpy as np\r\nfrom sklearn import datasets\r\nfrom sklearn import svm\r\nfrom sklearn.neural_network import MLPClassifier\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.datasets import make_blobs\r\nfrom sklearn.naive_bayes import GaussianNB\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn.dummy import DummyClassifier\r\nfrom sklearn import preprocessing\r\nfrom sklearn.datasets import load_iris\r\nfrom sklearn.model_selection import StratifiedKFold\r\nfrom sklearn.metrics import f1_score\r\nfrom sklearn.linear_model import SGDClassifier\r\nfrom sklearn.metrics import precision_score, recall_score, accuracy_score\r\nfrom timeit import default_timer as timer\r\n\r\n# Library for statistical significant test\r\nfrom scipy import stats\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.datasets import load_digits\r\n\r\n# Load data from sklearn - classification - digits\r\n#digits = load_digits()\r\n#bigdata = [digits.data, digits.target]\r\n\r\n# Load data from csv file\r\n#trainset = dtl.load_cardiac_trainset(190)\r\n#testset = dtl.load_cardiac_testset(190)\r\n\r\nbigdata = dtl.load_cardiac_trainset(380)\r\n\r\n# Over the test collection\r\nAccuracy_NNN = []\r\nRecall_NNN = []\r\nprecision_NNN = []\r\n\r\nAccuracy_LLL = []\r\nRecall_LLL = []\r\nprecision_LLL = []\r\n\r\nAccuracy_NBB = []\r\nRecall_NBB = []\r\nprecision_NBB = []\r\n\r\nAccuracy_RDD = []\r\nRecall_RDD = []\r\nprecision_RDD = []\r\n\r\n# Over the training collection \r\nAccuracy_NNN_t = []\r\nRecall_NNN_t = []\r\nprecision_NNN_t = []\r\n\r\nAccuracy_LLL_t = []\r\nRecall_LLL_t = []\r\nprecision_LLL_t = []\r\n\r\nAccuracy_NBB_t = []\r\nRecall_NBB_t = []\r\nprecision_NBB_t = []\r\n\r\nAccuracy_RDD_t = []\r\nRecall_RDD_t = []\r\nprecision_RDD_t = []\r\n# Runtime over test set\r\nRuntime_NNN = []\r\nRuntime_LLL= []\r\nRuntime_NBB = []\r\nRuntime_RDD = []\r\n\r\n# Runtime over training set\r\nRuntime_NNN_t = []\r\nRuntime_LLL_t= []\r\nRuntime_NBB_t = []\r\nRuntime_RDD_t = []\r\n\r\nfor i in range(20):\r\n # shuffle\r\n #idx = np.arange(bigdata[0].shape[0])\r\n idx = list(range(380))\r\n np.random.shuffle(idx)\r\n X = bigdata[0][idx]\r\n y = bigdata[1][idx]\r\n \r\n #trainset = dtl.load_displacement_trainset(190)\r\n #testset = dtl.load_displacement_testset(190)\r\n \r\n Xtrain = preprocessing.scale(X[:190,:])\r\n #Xtrain = X[:190,:]\r\n ytrain = y[:190]\r\n #ytrain = y[:190,:]\r\n #print(Xtrain)\r\n \r\n #Xtrain = Xtrain[:, :1]\r\n #print(ytrain)\r\n \r\n Xtest = preprocessing.scale(X[190:,:])\r\n #Xtest = X[190:,:]\r\n ytest = y[190:]\r\n \r\n \r\n # Convert [1,0,0] to 0. [0,1,0] to 1. [0,0,1] to 2\r\n ytrain_ = np.zeros(ytrain.shape[0])\r\n for i in range(ytrain.shape[0]):\r\n index = np.argmax(ytrain[i])\r\n if index == 0:\r\n ytrain_[i] = int(0)\r\n elif index == 1:\r\n ytrain_[i] = int(1)\r\n else:\r\n ytrain_[i] = int(2)\r\n \r\n ytest_ = np.zeros(ytest.shape[0])\r\n for i in range(ytest.shape[0]):\r\n index = np.argmax(ytest[i])\r\n if index == 0:\r\n ytest_[i] = int(0)\r\n elif index == 1:\r\n ytest_[i] = int(1)\r\n else:\r\n ytest_[i] = int(2)\r\n \r\n # Find number of each class\r\n #numclass1 = (ytest_ == 0).sum()\r\n #print(numclass1)\r\n \r\n \r\n # Neural network with one hidden layer - 16 neurons in hidden layer\r\n clf = MLPClassifier(solver='sgd', alpha=0, activation='logistic',\r\n batch_size=1,max_iter=1000,\r\n learning_rate='constant',learning_rate_init=0.1,\r\n shuffle=True,\r\n hidden_layer_sizes=(12,))\r\n \r\n NN_start_t = timer()\r\n # Training model\r\n clf.fit(Xtrain, ytrain_) \r\n Runtime_NNN_t.append(timer()-NN_start_t) \r\n \r\n # Predict from training model\r\n #print(clf.predict(Xtrain))\r\n \r\n # Find f1_score for neural network\r\n #ypred_Logistic = clf.predict(Xtest)\r\n #f1_score = f1_score(ytest, ypred_Logistic, average='weighted')\r\n #print('f1 score {0}'.format(f1_score))\r\n \r\n print(\"Neural network - The training score: %.3f\" % (clf.score(Xtrain, ytrain_)))\r\n print('Neural network - The test score is {0}'.format(clf.score(Xtest, ytest_)))\r\n \r\n \r\n # Multiclass Logistic regression with stochastic gradient descent\r\n clf_SGD = SGDClassifier(loss=\"log\", max_iter=1000, shuffle = True,\r\n learning_rate = 'constant', eta0 = 0.01, \r\n average = False, l1_ratio =0,\r\n penalty ='none', power_t = 1)\r\n \r\n SGD_start_t = timer()\r\n # Training model\r\n clf_SGD.fit(Xtrain, ytrain_)\r\n Runtime_LLL_t.append(timer()-SGD_start_t)\r\n \r\n # Predict model\r\n #ypred_Logistic = clf.predict(Xtest)\r\n \r\n print(\"Multinomial Logistic Regression - The training score: %.3f (%s)\" % (clf_SGD.score(Xtrain, ytrain_), 'SGD multinomial'))\r\n print(\"Multinomial Logistic Regression - The testing score: %.3f (%s)\" % (clf_SGD.score(Xtest, ytest_), 'SGD multinomial'))\r\n #print(\"Logistic - Number of mislabeled points out of a total %d points : %d\" % (trainset[0].shape[0],(ytest_ != ypred_Logistic).sum()))\r\n \r\n # Gaussian Naive bayes \r\n clf_NB = GaussianNB()\r\n NB_start_t = timer()\r\n clf_NB.fit(Xtrain, ytrain_) \r\n Runtime_NBB_t.append(timer()-NB_start_t)\r\n #print(\"Number of mislabeled points out of a total %d points : %d\" % (Xtrain.shape[0],(ytest_ != y_pred).sum()))\r\n print(\"Naive Bayes - training score: %.3f (%s)\" % (clf_NB.score(Xtrain, ytrain_), 'Naive Bayes'))\r\n print(\"Naive Bayes - testing score: %.3f (%s)\" % (clf_NB.score(Xtest, ytest_), 'Naive Bayes'))\r\n\r\n # Random predictor\r\n clf_random = DummyClassifier(strategy = 'uniform')\r\n RD_start_t = timer()\r\n clf_random.fit(Xtrain, ytrain_)\r\n Runtime_RDD_t.append(timer()-RD_start_t)\r\n print(\"Random Predictor - testing score: %.3f (%s)\" % (clf_random.score(Xtest, ytest_), 'Random predictor'))\r\n \r\n # Prediction over test set\r\n NN_start = timer()\r\n ypred_NN=clf.predict(Xtest)\r\n Runtime_NNN.append(timer()-NN_start)\r\n \r\n SGD_start = timer()\r\n ypred_Logistic=clf_SGD.predict(Xtest)\r\n Runtime_LLL.append(timer()-SGD_start)\r\n \r\n NB_start = timer()\r\n ypred_NB = clf_NB.predict(Xtest)\r\n Runtime_NBB.append(timer()-NB_start)\r\n \r\n RD_start = timer()\r\n ypred_RD = clf_random.predict(Xtest)\r\n Runtime_RDD.append(timer()-RD_start)\r\n \r\n # Prediction over training set\r\n ypred_NN_t=clf.predict(Xtrain)\r\n ypred_Logistic_t=clf_SGD.predict(Xtrain)\r\n ypred_NB_t = clf_NB.predict(Xtrain)\r\n ypred_RD_t = clf_random.predict(Xtrain)\r\n \r\n print(60*'-')\r\n \r\n # Over the test set\r\n accuracy_NN = accuracy_score(ytest_, ypred_NN)\r\n precision_NN=precision_score(ytest_, ypred_NN, average='macro')\r\n recall_NN = recall_score(ytest_, ypred_NN, average='macro')\r\n print(\"Accuracy call for Neural Network: {0}\".format(accuracy_NN))\r\n print(\"Precision call for Neural Network: {0}\".format(precision_NN))\r\n print(\"Recall call for Neural Network: {0}\".format(recall_NN))\r\n \r\n accuracy_Lg = accuracy_score(ytest_, ypred_Logistic)\r\n precision_Lg=precision_score(ytest_, ypred_Logistic, average='macro')\r\n recall_Lg=recall_score(ytest_, ypred_Logistic, average='macro')\r\n print(\"Accuracy call for Logistic: {0}\".format(accuracy_Lg))\r\n print(\"Precision call for Logistic: {0}\".format(precision_Lg))\r\n print(\"Recall call for Logistic regression: {0}\".format(recall_Lg))\r\n \r\n \r\n accuracy_NB = accuracy_score(ytest_, ypred_NB)\r\n precision_NB=precision_score(ytest_, ypred_NB, average='macro')\r\n recall_NB=recall_score(ytest_, ypred_NB, average='macro')\r\n print(\"Accuracy call for Naive Bayes: {0}\".format(accuracy_NB))\r\n print(\"Precision call for Naive Bayes: {0}\".format(precision_NB))\r\n print(\"Recall call for Naive Bayes: {0}\".format(recall_NB))\r\n \r\n accuracy_RD = accuracy_score(ytest_, ypred_RD)\r\n precision_RD=precision_score(ytest_, ypred_RD, average='macro')\r\n recall_RD=recall_score(ytest_, ypred_RD, average='macro')\r\n \r\n print(60*'-')\r\n \r\n # Over the training set\r\n accuracy_NN_t = accuracy_score(ytrain_, ypred_NN_t)\r\n precision_NN_t=precision_score(ytrain_, ypred_NN_t, average='macro')\r\n recall_NN_t = recall_score(ytrain_, ypred_NN_t, average='macro')\r\n \r\n accuracy_Lg_t = accuracy_score(ytrain_, ypred_Logistic_t)\r\n precision_Lg_t=precision_score(ytrain_, ypred_Logistic_t, average='macro')\r\n recall_Lg_t=recall_score(ytrain_, ypred_Logistic_t, average='macro')\r\n \r\n accuracy_NB_t = accuracy_score(ytrain_, ypred_NB_t)\r\n precision_NB_t=precision_score(ytrain_, ypred_NB_t, average='macro')\r\n recall_NB_t=recall_score(ytrain_, ypred_NB_t, average='macro')\r\n \r\n accuracy_RD_t = accuracy_score(ytrain_, ypred_RD_t)\r\n precision_RD_t=precision_score(ytrain_, ypred_RD_t, average='macro')\r\n recall_RD_t=recall_score(ytrain_, ypred_RD_t, average='macro')\r\n \r\n # Update the collection over the test set\r\n Accuracy_NNN.append(accuracy_NN)\r\n Recall_NNN.append(recall_NN)\r\n precision_NNN.append(precision_NN)\r\n \r\n Accuracy_LLL.append(accuracy_Lg)\r\n Recall_LLL.append(recall_Lg)\r\n precision_LLL.append(precision_Lg)\r\n \r\n Accuracy_NBB.append(accuracy_NB)\r\n Recall_NBB.append(recall_NB)\r\n precision_NBB.append(precision_NB)\r\n \r\n Accuracy_RDD.append(accuracy_RD)\r\n Recall_RDD.append(recall_RD)\r\n precision_RDD.append(precision_RD)\r\n \r\n \r\n # Update the collection over the training set\r\n Accuracy_NNN_t.append(accuracy_NN_t)\r\n Recall_NNN_t.append(recall_NN_t)\r\n precision_NNN_t.append(precision_NN_t)\r\n \r\n Accuracy_LLL_t.append(accuracy_Lg_t)\r\n Recall_LLL_t.append(recall_Lg_t)\r\n precision_LLL_t.append(precision_Lg_t)\r\n \r\n Accuracy_NBB_t.append(accuracy_NB_t)\r\n Recall_NBB_t.append(recall_NB_t)\r\n precision_NBB_t.append(precision_NB_t)\r\n \r\n Accuracy_RDD_t.append(accuracy_RD_t)\r\n Recall_RDD_t.append(recall_RD_t)\r\n precision_RDD_t.append(precision_RD_t)\r\n\r\nprint(60*'-')\r\nprint('The results below over test set')\r\n\r\nprint(\"Mean Accuracy call for Neural Network: {0}\".format(np.mean(Accuracy_NNN)))\r\nprint(\"Mean Precision call for Neural Network: {0}\".format(np.mean(precision_NNN)))\r\nprint(\"Mean Recall call for Neural Network: {0}\".format(np.mean(Recall_NNN)))\r\n\r\nprint(\"Mean Accuracy call for Logistic: {0}\".format(np.mean(Accuracy_LLL)))\r\nprint(\"Mean Precision call for Logistic: {0}\".format(np.mean(Recall_LLL)))\r\nprint(\"Mean Recall call for Logistic regression: {0}\".format(np.mean(precision_LLL)))\r\n\r\nprint(\"Mean Accuracy call for Naive Bayes: {0}\".format(np.mean(Accuracy_NBB)))\r\nprint(\"Mean Precision call for Naive Bayes: {0}\".format(np.mean(precision_NBB)))\r\nprint(\"Mean Recall call for Naive Bayes: {0}\".format(np.mean(Recall_NBB)))\r\n\r\nprint(\"Mean Accuracy call for Random Predictor: {0}\".format(np.mean(Accuracy_RDD)))\r\nprint(\"Mean Precision call for Random Predictor: {0}\".format(np.mean(precision_RDD)))\r\nprint(\"Mean Recall call for Random Predictor: {0}\".format(np.mean(Recall_RDD)))\r\n\r\nprint(20*'-')\r\nprint('The results below over trainning set')\r\nprint(\"T - Mean Accuracy call for Neural Network: {0}\".format(np.mean(Accuracy_NNN_t)))\r\nprint(\"T- Mean Precision call for Neural Network: {0}\".format(np.mean(precision_NNN_t)))\r\nprint(\"T- Mean Recall call for Neural Network: {0}\".format(np.mean(Recall_NNN_t)))\r\n\r\nprint(\"T- Mean Accuracy call for Logistic: {0}\".format(np.mean(Accuracy_LLL_t)))\r\nprint(\"T- Mean Precision call for Logistic: {0}\".format(np.mean(Recall_LLL_t)))\r\nprint(\"T- Mean Recall call for Logistic regression: {0}\".format(np.mean(precision_LLL_t)))\r\n\r\nprint(\"T- Mean Accuracy call for Naive Bayes: {0}\".format(np.mean(Accuracy_NBB_t)))\r\nprint(\"T- Mean Precision call for Naive Bayes: {0}\".format(np.mean(precision_NBB_t)))\r\nprint(\"T- Mean Recall call for Naive Bayes: {0}\".format(np.mean(Recall_NBB_t)))\r\n\r\nprint(\"T- Mean Accuracy call for Random Predictor: {0}\".format(np.mean(Accuracy_RDD_t)))\r\nprint(\"T- Mean Precision call for Random Predictor: {0}\".format(np.mean(precision_RDD_t)))\r\nprint(\"T- Mean Recall call for Random Predictor: {0}\".format(np.mean(Recall_RDD_t)))\r\n\r\nprint(\"T- Mean Run time - Training for Neural Network: {0}\".format(np.mean(Runtime_NNN_t)))\r\nprint(\"T- Mean Run time - Training for Logistic Regression: {0}\".format(np.mean(Runtime_LLL_t)))\r\nprint(\"T- Mean Run time - Training for Naive Bayes: {0}\".format(np.mean(Runtime_NBB_t)))\r\nprint(\"T- Mean Run time - Training for Random Predictor: {0}\".format(np.mean(Runtime_RDD_t)))\r\n\r\n\r\nprint(\"Mean Run time - Prediction for Neural Network: {0}\".format(np.mean(Runtime_NNN)))\r\nprint(\"Mean Run time - Prediction for Logistic Regression: {0}\".format(np.mean(Runtime_LLL)))\r\nprint(\"Mean Run time - Prediction for Naive Bayes: {0}\".format(np.mean(Runtime_NBB)))\r\nprint(\"Mean Run time - Prediction for Random Predictor: {0}\".format(np.mean(Runtime_RDD)))\r\n\r\n\r\ndef autolabel(rectangles):\r\n \"\"\"attach some text vi autolabel on rectangles.\"\"\"\r\n for rect in rectangles:\r\n height = rect.get_height()\r\n ax.text(rect.get_x() + rect.get_width() / 2.,\r\n 1.05 * height, '%.4f' % height,\r\n ha='center', va='bottom')\r\n plt.setp(plt.xticks()[1], rotation=30)\r\n\r\n#bar_colors = ['b','g','r','c','m','y']\r\n## Plot figures for runtime over training data set\r\n#plt.figure()\r\n#cls_names = [\"Neural Network\", \"Multinomial Logistic Regression\", \"Naive Bayes\"]\r\n#cls_runtime = []\r\n#cls_runtime.append(np.mean(Runtime_NNN_t))\r\n#cls_runtime.append(np.mean(Runtime_LLL_t))\r\n#cls_runtime.append(np.mean(Runtime_NBB_t))\r\n##cls_runtime.append(2.236502099)\r\n##cls_runtime.append(1.1119242)\r\n##cls_runtime.append(0.008482879)\r\n#ax = plt.subplot()\r\n#rectangles = plt.bar(range(len(cls_names)), cls_runtime, width = 0.5,\r\n# color = bar_colors)\r\n#ax.set_xticks(np.linspace(0, len(cls_names)-1, len(cls_names)))\r\n#ax.set_xticklabels(cls_names, fontsize = 8)\r\n#ymax = max(cls_runtime)*1.3\r\n#ax.set_ylim((0,ymax))\r\n#plt.ylabel('Runtime (s)')\r\n#plt.title('Training Times')\r\n#autolabel(rectangles)\r\n#plt.show()\r\n#\r\n#\r\n## Plot figures for runtime over prediction\r\n#plt.figure()\r\n#cls_names = [\"Neural Network\", \"Multinomial Logistic Regression\", \"Naive Bayes\"]\r\n#cls_runtime = []\r\n#cls_runtime.append(np.mean(Runtime_NNN))\r\n#cls_runtime.append(np.mean(Runtime_LLL))\r\n#cls_runtime.append(np.mean(Runtime_NBB))\r\n##cls_runtime.append(0.00450972)\r\n##cls_runtime.append(0.000755058)\r\n##cls_runtime.append(0.003842247)\r\n#ax = plt.subplot()\r\n#rectangles = plt.bar(range(len(cls_names)), cls_runtime, width = 0.5,\r\n# color = bar_colors)\r\n#ax.set_xticks(np.linspace(0, len(cls_names)-1, len(cls_names)))\r\n#ax.set_xticklabels(cls_names, fontsize = 8)\r\n#ymax = max(cls_runtime)*1.3\r\n#ax.set_ylim((0,ymax))\r\n#plt.ylabel('Runtime (s)')\r\n#plt.title('Prediction Times')\r\n#autolabel(rectangles)\r\n#plt.show()\r\n\r\n\r\nprint(60*'-')\r\n## Paired-t test\r\n# Between Neural network - Logistic regression\r\nttest_ac_1 = stats.ttest_rel(Accuracy_NNN,Accuracy_LLL)\r\nttest_pre_1 = stats.ttest_rel(precision_NNN,precision_LLL)\r\nttest_re_1 = stats.ttest_rel(Recall_NNN,Recall_LLL)\r\nttest_run_1 = stats.ttest_rel(Runtime_NNN,Runtime_LLL)\r\nprint('Accuracy - Pair-t test between Neural and Logistic {0}'.format(ttest_ac_1))\r\nprint('Precision - Pair-t test between Neural and Logistic {0}'.format(ttest_pre_1))\r\nprint('Recall - Pair-t test between Neural and Logistic {0}'.format(ttest_re_1))\r\nprint('Runtime - Pair-t test between Neural and Logistic {0}'.format(ttest_run_1))\r\nprint(60*'-')\r\n\r\n# Between Neural network - Naive Bayes\r\nttest_ac_2 = stats.ttest_rel(Accuracy_NNN,Accuracy_NBB)\r\nttest_pre_2 = stats.ttest_rel(precision_NNN,precision_NBB)\r\nttest_re_2 = stats.ttest_rel(Recall_NNN,Recall_NBB)\r\nttest_run_2 = stats.ttest_rel(Runtime_NNN,Runtime_NBB)\r\nprint('Accuracy - Pair-t test between Neural and Naive {0}'.format(ttest_ac_2))\r\nprint('Precision - Pair-t test between Neural and Naive {0}'.format(ttest_pre_2))\r\nprint('Recall - Pair-t test between Neural and Naive {0}'.format(ttest_re_2))\r\nprint('Runtime - Pair-t test between Neural and Naive {0}'.format(ttest_run_2))\r\nprint(60*'-')\r\n\r\n# Between Logistic regression - Naive Bayes\r\nttest_ac_3 = stats.ttest_rel(Accuracy_LLL,Accuracy_NBB)\r\nttest_pre_3 = stats.ttest_rel(precision_LLL,precision_NBB)\r\nttest_re_3 = stats.ttest_rel(Recall_LLL,Recall_NBB)\r\nttest_run_3 = stats.ttest_rel(Runtime_LLL,Runtime_NBB)\r\nprint('Accuracy - Pair-t test between Logistic and Naive {0}'.format(ttest_ac_3))\r\nprint('Precision - Pair-t test between Logistic and Naive {0}'.format(ttest_pre_3))\r\nprint('Recall - Pair-t test between Logistic and Naive {0}'.format(ttest_re_3))\r\nprint('Runtime - Pair-t test between Logistic and Naive {0}'.format(ttest_run_3))\r\n\r\ndef autolabel(rectangles):\r\n \"\"\"attach some text vi autolabel on rectangles.\"\"\"\r\n for rect in rectangles:\r\n height = rect.get_height()\r\n ax.text(rect.get_x() + rect.get_width() / 2.,\r\n 1.05 * height, '%.4f' % height,\r\n ha='center', va='bottom')\r\n plt.setp(plt.xticks()[1], rotation=30)", "sub_path": "Mini-Project/MultipleRuns.py", "file_name": "MultipleRuns.py", "file_ext": "py", "file_size_in_byte": 17335, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "dataloader.load_cardiac_trainset", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 92, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing.scale", "line_number": 99, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 99, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.scale", "line_number": 108, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 108, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 126, "usage_type": "call"}, {"api_name": "sklearn.neural_network.MLPClassifier", "line_number": 140, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 146, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 149, "usage_type": "call"}, {"api_name": "sklearn.linear_model.SGDClassifier", "line_number": 164, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 169, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 172, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.GaussianNB", "line_number": 182, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 183, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 185, "usage_type": "call"}, {"api_name": "sklearn.dummy.DummyClassifier", "line_number": 191, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 192, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 194, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 198, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 200, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 202, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 204, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 206, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 208, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 210, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 212, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 223, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 224, "usage_type": "call"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 225, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 230, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 231, "usage_type": "call"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 232, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 238, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 239, "usage_type": "call"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 240, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 245, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 246, "usage_type": "call"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 247, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 252, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 253, "usage_type": "call"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 254, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 256, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 257, "usage_type": "call"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 258, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 260, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 261, "usage_type": "call"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 262, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 264, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 265, "usage_type": "call"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 316, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 328, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 329, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 330, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 334, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 336, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 337, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 340, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 343, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 347, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 348, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 349, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 359, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 359, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 359, "usage_type": "call"}, {"api_name": "scipy.stats.ttest_rel", "line_number": 411, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 411, "usage_type": "name"}, {"api_name": "scipy.stats.ttest_rel", "line_number": 412, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 412, "usage_type": "name"}, {"api_name": "scipy.stats.ttest_rel", "line_number": 413, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 413, "usage_type": "name"}, {"api_name": "scipy.stats.ttest_rel", "line_number": 414, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 414, "usage_type": "name"}, {"api_name": "scipy.stats.ttest_rel", "line_number": 422, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 422, "usage_type": "name"}, {"api_name": "scipy.stats.ttest_rel", "line_number": 423, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 423, "usage_type": "name"}, {"api_name": "scipy.stats.ttest_rel", "line_number": 424, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 424, "usage_type": "name"}, {"api_name": "scipy.stats.ttest_rel", "line_number": 425, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 425, "usage_type": "name"}, {"api_name": "scipy.stats.ttest_rel", "line_number": 433, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 433, "usage_type": "name"}, {"api_name": "scipy.stats.ttest_rel", "line_number": 434, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 434, "usage_type": "name"}, {"api_name": "scipy.stats.ttest_rel", "line_number": 435, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 435, "usage_type": "name"}, {"api_name": "scipy.stats.ttest_rel", "line_number": 436, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 436, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 449, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 449, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 449, "usage_type": "call"}]} +{"seq_id": "60702725", "text": "#! /usr/bin/env python\n\"\"\"This Module provides the functions from bitcoin.de Trading API\"\"\"\n\nimport requests\nimport time\nimport json\nimport hmac\nimport hashlib\nimport logging\nimport codecs\n\n# these two lines enable debugging at httplib level\n# (requests->urllib3->httplib)\n# you will see the REQUEST, including HEADERS and DATA, and RESPONSE with\n# HEADERS but without DATA.\n# the only thing missing will be the response.body which is not logged.\nimport http.client\nhttp.client.HTTPConnection.debuglevel = 1\n\n\nlogging.basicConfig()\nlogging.getLogger().setLevel(logging.DEBUG)\nrequests_log = logging.getLogger(\"requests.packages.urllib3\")\nrequests_log.setLevel(logging.DEBUG)\nrequests_log.propagate = True\n\n__version__ = '0.1'\n\n\nclass Connection:\n \"\"\"To provide connection credentials to the trading API\"\"\"\n def __init__(self, api_key, api_secret):\n self.api_key = api_key\n self.api_secret = api_secret\n\n# Bitcoin.de API URI\napihost = 'https://api.bitcoin.de'\napiversion = 'v1'\norderuri = apihost + '/' + apiversion + '/' + 'orders'\ntradeuri = apihost + '/' + apiversion + '/' + 'trades'\naccounturi = apihost + '/' + apiversion + '/' + 'account'\n# set initial nonce\nnonce = int(time.time())\n# disable unsecure SSL warning\nrequests.packages.urllib3.disable_warnings()\n\n\ndef HandleRequestsException(e):\n \"\"\"Handle Exception from request.\"\"\"\n print(e[0][0])\n print(e[0][1])\n\n\ndef HandleAPIErrors(r):\n \"\"\"To handle Errors from BTCDE API.\"\"\"\n if r.status_code != 200 and r.status_code != 201 and r.status_code != 204:\n reader = codecs.getreader(\"utf-8\")\n content = json.load(reader(r.raw))\n errors = content.get('errors')\n print('Code: ' + str(errors[0]['code']))\n print('Message: ' + errors[0]['message'])\n print('With URL: ' + r.url)\n return False\n else:\n return True\n\n\ndef APIConnect(conn, method, params, uri):\n \"\"\"Transform Parameters to URL\"\"\"\n global nonce\n # set header\n header = {'content-type':\n 'application/x-www-form-urlencoded; charset=utf-8'}\n encoded_string = ''\n if params:\n for key, value in sorted(params.items()):\n encoded_string += str(key) + '=' + str(value) + '&'\n encoded_string = encoded_string[:-1]\n url = uri + '?' + encoded_string\n else:\n url = uri\n # raise nonce before using\n nonce += 1\n if method == 'POST':\n md5_encoded_query_string = hashlib.md5(encoded_string.encode()).hexdigest()\n else:\n md5_encoded_query_string = hashlib.md5(b'').hexdigest()\n hmac_data = method + '#' + \\\n url + '#' + conn.api_key + \\\n '#' + str(nonce) + '#' + md5_encoded_query_string\n hmac_signed = hmac.new(bytearray(conn.api_secret.encode()), msg=hmac_data.encode(), digestmod=hashlib.sha256).hexdigest()\n # set values for header\n header.update({'X-API-KEY': conn.api_key,\n 'X-API-NONCE': str(nonce),\n 'X-API-SIGNATURE': hmac_signed})\n try:\n if method == 'GET':\n r = requests.get(url, headers=(header),\n stream=True, verify=False)\n elif method == 'POST':\n r = requests.post(url, headers=(header), data=encoded_string,\n stream=True, verify=False)\n elif method == 'DELETE':\n r = requests.delete(url, headers=(header),\n stream=True, verify=False)\n # Handle API Errors\n if HandleAPIErrors(r):\n # get results\n result = r.json()\n else:\n result = {}\n except requests.exceptions.RequestException as e:\n HandleRequestsException(e)\n return result\n\n\ndef showOrderbook(conn, OrderType, **args):\n \"\"\"Search Orderbook for offers.\"\"\"\n # Build parameters\n if OrderType == 'buy' or OrderType == 'sell':\n params = {'type': OrderType}\n else:\n print('problem')\n params.update(args)\n return APIConnect(conn, 'GET', params, orderuri)\n\n\ndef createOrder(conn, OrderType, max_amount, price, **args):\n \"\"\"Create a new Order.\"\"\"\n # Build parameters\n params = {'type': OrderType, 'max_amount': max_amount, 'price': price}\n params.update(args)\n return APIConnect(conn, 'POST', params, orderuri)\n\n\ndef deleteOrder(conn, order_id):\n \"\"\"Delete an Order.\"\"\"\n newuri = orderuri + \"/\" + order_id\n params = {'order_id': order_id}\n return APIConnect(conn, 'DELETE', params, newuri)\n\n\ndef showMyOrders(conn, **args):\n \"\"\"Query and Filter own Orders.\"\"\"\n newuri = orderuri + '/my_own'\n return APIConnect(conn, 'GET', args, newuri)\n\n\ndef showMyOrderDetails(conn, order_id):\n \"\"\"Details to an own Order.\"\"\"\n newuri = orderuri + '/' + order_id\n params = {'order_id': order_id}\n return APIConnect(conn, 'GET', params, newuri)\n\n\ndef executeTrade(conn, order_id, OrderType, amount):\n \"\"\"Buy/Sell on a specific Order.\"\"\"\n newuri = tradeuri + '/' + order_id\n params = {'order_id': order_id, 'type': OrderType, 'amount': amount}\n return APIConnect(conn, 'POST', params, newuri)\n\n\ndef showMyTrades(conn, **args):\n \"\"\"Query and Filter on past Trades.\"\"\"\n return APIConnect(conn, 'GET', args, tradeuri)\n\n\ndef showMyTradeDetails(conn, trade_id):\n \"\"\"Details to a specific Trade.\"\"\"\n newuri = tradeuri + '/' + trade_id\n params = {'trade_id': trade_id}\n return APIConnect(conn, 'GET', params, newuri)\n\n\ndef showAccountInfo(conn):\n \"\"\"Query on Account Infos.\"\"\"\n params = {}\n return APIConnect(conn, 'GET', params, accounturi)\n\n\ndef showOrderbookCompact(conn):\n \"\"\"Bids and Asks in compact format.\"\"\"\n params = {}\n return APIConnect(conn, 'GET', params, orderuri + '/compact')\n\n\ndef showPublicTradeHistory(conn, since_tid=None):\n \"\"\"All successful trades of the las 7 days.\"\"\"\n if since_tid is not None:\n params = {'since_tid': since_tid}\n else:\n params = {}\n return APIConnect(conn, 'GET', params, tradeuri + '/history')\n\n\ndef showRates(conn):\n \"\"\"Query of the average rate last 3 and 12 hours.\"\"\"\n newuri = apihost + '/' + apiversion + '/rates'\n params = {}\n return APIConnect(conn, 'GET', params, newuri)\n\n\ndef showAccountLedger(conn, **args):\n \"\"\"Query on Account statement.\"\"\"\n return APIConnect(conn, 'GET', args, accounturi + '/ledger')\n", "sub_path": "btcde.py", "file_name": "btcde.py", "file_ext": "py", "file_size_in_byte": 6336, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "http.client.client", "line_number": 18, "usage_type": "attribute"}, {"api_name": "http.client", "line_number": 18, "usage_type": "name"}, {"api_name": "logging.basicConfig", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 22, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 24, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 43, "usage_type": "call"}, {"api_name": "requests.packages.urllib3.disable_warnings", "line_number": 45, "usage_type": "call"}, {"api_name": "requests.packages", "line_number": 45, "usage_type": "attribute"}, {"api_name": "codecs.getreader", "line_number": 57, "usage_type": "call"}, {"api_name": "json.load", "line_number": 58, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 85, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 87, "usage_type": "call"}, {"api_name": "hmac.new", "line_number": 91, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 91, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 98, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 101, "usage_type": "call"}, {"api_name": "requests.delete", "line_number": 104, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 112, "usage_type": "attribute"}]} +{"seq_id": "311043627", "text": "\nfrom __future__ import print_function\n__author__ = 'zoulida'\nimport datetime\nimport numpy as np\nimport pandas as pd\nimport sklearn\n\n#from pandas.io.data import DataReader\n#import pandas_datareader.data as web\nfrom pandas_datareader.data import DataReader\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.lda import LDA\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.qda import QDA\nfrom sklearn.svm import LinearSVC, SVC\n\n\ndef create_lagged_series(symbol, startdate, enddate, lags=5):\n\n # Obtain stock information from Yahoo Finance\n '''ts = DataReader(\n \tsymbol, \"yahoo\",\n \tstartdate-datetime.timedelta(days=365),\n \tenddate\n )'''\n\n import DBStock.dbQueryPools as dbpool\n ts = dbpool.queryMySQL_plot_stock_market(symbol, startdate, enddate)\n ts['Date'] = pd.to_datetime(ts['Date'])\n ts.set_index('Date', inplace=True)\n ts = ts[(True^ts['Close'].isin([0]))]#条件删除去除值为0的行\n #打印不换行\n #pd.set_option('display.height',1000)\n #pd.set_option('display.max_rows',500)\n #pd.set_option('display.max_columns',500)\n pd.set_option('display.width',1000)\n #print(ts)\n\n # Create the new lagged DataFrame\n tslag = pd.DataFrame(index=ts.index)\n tslag[\"Today\"] = ts[\"Close\"]\n tslag[\"Volume\"] = ts[\"Volume\"]\n\n\n # Create the shifted lag series of prior trading period close values\n for i in range(0, lags):\n tslag[\"Lag%s\" % str(i+1)] = ts[\"Close\"].shift(i+1)\n #print(tslag)\n\n # Create the returns DataFrame\n tsret = pd.DataFrame(index=tslag.index)\n #tsret[\"Volume\"] = tslag[\"Volume\"]\n tsret[\"Today\"] = tslag[\"Today\"].pct_change()*100.0\n #tsret[\"Close\"] = ts[\"Close\"]\n tsret = tsret.join(ts)\n #print('tsret')\n #print(tsret)\n\n # If any of the values of percentage returns equal zero, set them to\n # a small number (stops issues with QDA model in scikit-learn)\n for i,x in enumerate(tsret[\"Today\"]):\n if (abs(x) < 0.0001):\n tsret[\"Today\"][i] = 0.0001\n #print(tsret)\n\n # Create the lagged percentage returns columns\n for i in range(0, lags):\n tsret[\"Lag%s\" % str(i+1)] = \\\n tsret[\"Today\"].shift(i+1)\n #tslag[\"Lag%s\" % str(i+1)].pct_change()*100.0\n #if (abs(tsret[\"Lag%s\" % str(i+1)]) < 0.0001):\n # tsret[\"Lag%s\" % str(i+1)] = 0.0001\n #print(tsret)\n\n # Create the \"Direction\" column (+1 or -1) indicating an up/down day\n tsret[\"Direction\"] = np.sign(tsret[\"Today\"].shift(-1))\n tsret = tsret[tsret.index >= startdate]\n\n return tsret\n\ndef addFeature(snpret):\n import FeatureBase.FeatureUtils as FU\n snpret = FU.CCI(snpret)\n snpret = FU.BBANDS(snpret)\n snpret = FU.EVM(snpret)\n snpret = FU.ForceIndex(snpret)\n snpret = FU.SMA(snpret)\n snpret = FU.EWMA(snpret)\n snpret = FU.ROC(snpret)\n snpret = FU.OBV(snpret)\n import talib\n real = talib.AD(snpret.High, snpret.Low, snpret.Close, snpret.Volume)\n AD = pd.Series(real, name = 'AD')\n snpret = snpret.join(AD)\n #snpret = FU.BBANDS(snpret)\n #snpret = FU.BBANDS(snpret)\n return snpret\n\ndef plot_forest_importances(X, y):\n import matplotlib.pyplot as plt\n from sklearn.ensemble import ExtraTreesClassifier\n\n # Build a forest and compute the feature importances\n forest = ExtraTreesClassifier(n_estimators=250,\n random_state=0)\n\n forest.fit(X, y)\n importances = forest.feature_importances_\n std = np.std([tree.feature_importances_ for tree in forest.estimators_],\n axis=0)\n indices = np.argsort(importances)[::-1]\n\n # Print the feature ranking\n print(\"Feature ranking:\")\n\n for f in range(X.shape[1]):\n print(\"%d. feature %d (%f)\" % (f + 1, indices[f], importances[indices[f]]))\n\n # Plot the feature importances of the forest\n plt.figure()\n plt.title(\"Feature importances\")\n plt.bar(range(X.shape[1]), importances[indices],\n color=\"r\", yerr=std[indices], align=\"center\")\n plt.xticks(range(X.shape[1]), indices)\n plt.xlim([-1, X.shape[1]])\n plt.show()\n\n#if __name__ == \"__main__\":\n#def reduceMain(symbol = '600016', startdate = '2014-01-01', enddate = '2018-12-29'):\nimport toolsproject.timeTool as tT\ndef classMain(symbol = '600016', startdate = tT.getDateStrBefore(100), enddate = tT.getDateStrNow()):\n\n # Create a lagged series of the S&P500 US stock market index\n snpret = create_lagged_series(\n \tsymbol, startdate,\n \tenddate, lags=5\n )\n\n #print(snpret)\n\n snpret = addFeature(snpret)\n print(snpret)\n snpret = snpret.dropna(axis=0, how='any') # 删除表中任何含有NaN的行\n\n # Use the prior two days of returns as predictor\n # values, with direction as the response\n X = snpret[[\"CCI\",\"Close\",\"换手率\",\"Volume\",\"Today\",\"Lag1\",\"Lag2\",\"Lag3\",\"Lag4\",\"Lag5\",\"Upper BollingerBand\",\n \"Lower BollingerBand\",\"EVM\",\"ForceIndex\",\"SMA\",\"Rate of Change\",\"OBV\",\"AD\"]]\n y = snpret[\"Direction\"]\n #print(y)\n\n\n # Create the (parametrised) models\n #print(\"Hit Rates/Confusion Matrices:\\n\")\n models = [#(\"LR\", LogisticRegression()),\n #(\"LDA\", LDA()),\n #(\"QDA\", QDA()),\n #(\"LSVC\", LinearSVC()),\n #(\"RSVM\", SVC(\n #\tC=1000000.0, cache_size=200, class_weight=None,\n # coef0=0.0, degree=3, gamma=0.0001, kernel='rbf',\n # max_iter=-1, probability=False, random_state=None,\n # shrinking=True, tol=0.001, verbose=False)\n #),\n\n (\"RF\", RandomForestClassifier(\n \tn_estimators=1000, criterion='gini',\n max_depth=None, min_samples_split=2,\n min_samples_leaf=1, max_features='auto',\n bootstrap=True, oob_score=False, n_jobs=1,\n random_state=None, verbose=0)\n )]\n\n\n\n\n #print(X_train,y_train)\n #print(np.isnan(X_train).any())#判断是否有空值\n #print(np.isnan(y_train).any())\n # Iterate through the models\n for m in models:\n\n X = X.reset_index(drop=True)\n #print(X)\n #y = y.reset_index(drop=True)\n #print(y)\n\n from sklearn.model_selection import KFold\n n_splits = 5\n kf = KFold(n_splits,shuffle=False)\n\n avgScore = 0\n for train_index, test_index in kf.split(X):\n\n X_train, X_test, y_train, y_test = X.loc[train_index], X.loc[test_index], y[train_index], y[test_index] # 这里的X_train，y_train为第iFold个fold的训练集，X_val，y_val为validation set\n #print(X_train, X_test, y_train, y_test)\n print('======================================')\n\n\n # Train each of the models on the training set\n m[1].fit(X_train, y_train)\n\n # Make an array of predictions on the test set\n pred = m[1].predict(X_test)\n\n # Output the hit-rate and the confusion matrix for each model\n print(\"%s:\\n%0.3f\" % (m[0], m[1].score(X_test, y_test)))\n avgScore += m[1].score(X_test, y_test)\n #print(pred, y_test)\n print(\"%s\\n\" % confusion_matrix(y_test, pred, labels=[-1.0, 1.0]))#labels=[\"ant\", \"bird\", \"cat\"]\n avgScore /= n_splits\n print(avgScore)\n #Feature importances with forests of trees\n #plot_forest_importances(X, y)\n return avgScore", "sub_path": "forcastPa/HS300Class_CrossValidationDEF.py", "file_name": "HS300Class_CrossValidationDEF.py", "file_ext": "py", "file_size_in_byte": 7412, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "DBStock.dbQueryPools.queryMySQL_plot_stock_market", "line_number": 30, "usage_type": "call"}, {"api_name": "DBStock.dbQueryPools", "line_number": 30, "usage_type": "name"}, {"api_name": "pandas.to_datetime", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 42, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 78, "usage_type": "call"}, {"api_name": "FeatureBase.FeatureUtils.CCI", "line_number": 85, "usage_type": "call"}, {"api_name": "FeatureBase.FeatureUtils", "line_number": 85, "usage_type": "name"}, {"api_name": "FeatureBase.FeatureUtils.BBANDS", "line_number": 86, "usage_type": "call"}, {"api_name": "FeatureBase.FeatureUtils", "line_number": 86, "usage_type": "name"}, {"api_name": "FeatureBase.FeatureUtils.EVM", "line_number": 87, "usage_type": "call"}, {"api_name": "FeatureBase.FeatureUtils", "line_number": 87, "usage_type": "name"}, {"api_name": "FeatureBase.FeatureUtils.ForceIndex", "line_number": 88, "usage_type": "call"}, {"api_name": "FeatureBase.FeatureUtils", "line_number": 88, "usage_type": "name"}, {"api_name": "FeatureBase.FeatureUtils.SMA", "line_number": 89, "usage_type": "call"}, {"api_name": "FeatureBase.FeatureUtils", "line_number": 89, "usage_type": "name"}, {"api_name": "FeatureBase.FeatureUtils.EWMA", "line_number": 90, "usage_type": "call"}, {"api_name": "FeatureBase.FeatureUtils", "line_number": 90, "usage_type": "name"}, {"api_name": "FeatureBase.FeatureUtils.ROC", "line_number": 91, "usage_type": "call"}, {"api_name": "FeatureBase.FeatureUtils", "line_number": 91, "usage_type": "name"}, {"api_name": "FeatureBase.FeatureUtils.OBV", "line_number": 92, "usage_type": "call"}, {"api_name": "FeatureBase.FeatureUtils", "line_number": 92, "usage_type": "name"}, {"api_name": "talib.AD", "line_number": 94, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 95, "usage_type": "call"}, {"api_name": "sklearn.ensemble.ExtraTreesClassifier", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "toolsproject.timeTool.getDateStrBefore", "line_number": 133, "usage_type": "call"}, {"api_name": "toolsproject.timeTool", "line_number": 133, "usage_type": "name"}, {"api_name": "toolsproject.timeTool.getDateStrNow", "line_number": 133, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 168, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 192, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 212, "usage_type": "call"}]} +{"seq_id": "317230644", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.misc import electrocardiogram\nfrom scipy.signal import find_peaks\nx = electrocardiogram()[2000:4000]\npeaks, _ = find_peaks(x, height=0)\nprint(peaks)\nprint(x)\nprint(peaks[0], x[peaks[0]])\nplt.plot(x)\nfor index in peaks:\n plt.plot(index, x[index], \"x\")\nplt.plot(65, 0.705, \"x\")\nplt.plot(np.zeros_like(x), \"--\", color=\"gray\")\nplt.show()\n", "sub_path": "Klad.py", "file_name": "Klad.py", "file_ext": "py", "file_size_in_byte": 399, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "scipy.misc.electrocardiogram", "line_number": 5, "usage_type": "call"}, {"api_name": "scipy.signal.find_peaks", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "numpy.zeros_like", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "348976024", "text": "# Abstract Path Placeholder Object Class.\n#\n# These objects specify additional attribute to a path:\n# - is the path intended to be used as an input or an output ?\n# - is the path either a file or a directory ?\n# Providing these additional attributes let the underlying file processors\n# decide how to load the file. ie. if file is loaded by a docker image and is\n# an input file, then docker will configure the file's directory as a read only\n# volume, and remap the underlying file path within the container's volume.\n# Additionally, it provides runtime path validation and folder creation when\n# relevant.\n#\n# @note\n# It is not possible to inherith Path due to misimplementation, cf.\n# https://bugs.python.org/issue24132\n# Thus we create our own class instead, in order to be able to rely on\n# polymorphism. This is often prominent for placeholder objects, as these are\n# used for their intrinsic object type, rather than their implementation.\n\nfrom pathlib import Path\n\nclass PathPlaceHolder():\n _path: str\n name: str\n\n def __init__(self, path: str):\n path = Path(path)\n self._path = path\n self.name = path.name\n self.parent = path.parent\n\n def __str__(self) -> str:\n return str(self._path)\n", "sub_path": "src/path/PathPlaceHolder.py", "file_name": "PathPlaceHolder.py", "file_ext": "py", "file_size_in_byte": 1241, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pathlib.Path", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "177322009", "text": "#encoding: utf-8\n\"\"\"AiiDA Parser for a aiida_vasp.VaspCalculation\"\"\"\nimport numpy as np\nfrom aiida.orm import DataFactory\n\nfrom aiida_vasp.io.doscar import DosParser\nfrom aiida_vasp.io.eigenval import EigParser\nfrom aiida_vasp.io.kpoints import KpParser\nfrom aiida_vasp.io.outcar import OutcarParser\nfrom aiida_vasp.io.vasprun import VasprunParser\nfrom aiida_vasp.parsers.base import BaseParser\n\nLINKNAME_DICT = {\n 'parameters': 'output_parameters',\n 'kpoints': 'output_kpoints',\n 'structure': 'output_structure',\n 'array': 'output_array',\n 'trajectory': 'output_trajectory',\n 'bands': 'output_band',\n 'dos': 'output_dos',\n 'chgcar': 'chgcar',\n 'wavecar': 'wavecar',\n 'born_charges': 'born_charges',\n}\n\nDEFAULT_OPTIONS = {\n 'add_bands': False,\n 'add_chgcar': False,\n 'add_dos': False,\n 'add_kpoints': False,\n 'add_parameters': True,\n 'add_structure': True,\n 'add_wavecar': False,\n 'should_parse_DOSCAR': False,\n 'should_parse_EIGENVAL': False,\n 'should_parse_IBZKPT': False,\n 'should_parse_OUTCAR': True,\n 'should_parse_vasprun.xml': True,\n}\n\n# Dictionary holding all the quantities which can be parsed by the vasp parser. Currently those coincide\n# with the output nodes, however this might change in a later version. Also at the moment the aditional\n# information in the values is not used.\nPARSABLE_QUANTITIES = {\n 'parameters': {\n 'parsers': ['OUTCAR', 'vasprun.xml'],\n 'nodeName': 'parameters',\n 'prerequesites': []\n },\n 'structure': {\n 'parsers': ['CONTCAR'],\n 'nodeName': 'structure',\n 'prerequesites': []\n },\n 'bands': {\n 'parsers': ['EIGENVAL', 'vasprun.xml'],\n 'nodeName': 'bands',\n 'prerequesites': ['structure']\n },\n 'kpoints': {\n 'parsers': ['EIGENVAL', 'IBZKPT'],\n 'nodeName': 'kpoints',\n 'prerequesites': []\n },\n 'dos': {\n 'parsers': ['vasprun.xml', 'DOSCAR'],\n 'nodeName': 'dos',\n 'prerequesites': []\n },\n 'chgcar': {\n 'parsers': ['CHGCAR'],\n 'nodeName': 'chgcar',\n 'prerequesites': []\n },\n 'wavecar': {\n 'parsers': ['WAVECAR'],\n 'nodeName': 'wavecar',\n 'prerequesites': []\n },\n}\n\nPARSABLE_FILES = {\n 'DOSCAR': {\n 'parser_class': DosParser,\n 'is_critical': False,\n 'status': 'Unknown'\n },\n 'EIGENVAL': {\n 'parser_class': EigParser,\n 'is_critical': False,\n 'status': 'Unknown'\n },\n 'IBZKPT': {\n 'parser_class': KpParser,\n 'is_critical': False,\n 'status': 'Unknown'\n },\n 'OUTCAR': {\n 'parser_class': OutcarParser,\n 'is_critical': True,\n 'status': 'Unknown'\n },\n 'vasprun.xml': {\n 'parser_class': VasprunParser,\n 'is_critical': False,\n 'status': 'Unknown'\n },\n}\n\n\nclass VaspParser(BaseParser):\n \"\"\"\n Parses all Vasp calculations.\n\n The parser will check which quantities to parse and which nodes to add\n to the calculation based on the 'parser_settings' card in the 'settings' ParameterData of the\n corresponding VaspCalculation.\n Parser Settings usage:\n Parser settings can be passed through the input node `settings` as follows::\n settings = ParameterData(dict={\n 'parser_settings': {\n ...\n }\n })\n Valid keys for `parser_settings` are:\n * `add_`, where quantity is one of:\n 'parameters': Parameterdata node containing various quantities from OUTCAR and vasprun.xml.\n 'bands': Band structure node parsed from EIGENVAL.\n 'dos': ArrayData node containing the DOS parsed from DOSCAR.\n 'kpoints': KpointsData node parsed from IBZKPT.\n 'wavecar': FileData node containing the WAVECAR file.\n 'chgcar': FileData node containing the CHGCAR file.\n \"\"\"\n\n def __init__(self, calc):\n super(VaspParser, self).__init__(calc)\n\n self.out_folder = None\n\n self._settings = DEFAULT_OPTIONS\n calc_settings = self._calc.get_inputs_dict().get('settings')\n if calc_settings:\n self._settings.update(calc_settings.get_dict().get('parser_settings', DEFAULT_OPTIONS))\n\n self._check_and_validate_settings()\n\n self._nodes_to_add = list(PARSABLE_QUANTITIES.keys())\n self._parsable_files = PARSABLE_FILES\n\n self._parsers = {\n 'vasprun.xml': None,\n 'DOSCAR': None,\n 'IBZKPT': None,\n 'OUTCAR': None,\n 'EIGENVAL': None,\n }\n\n self._quantities_to_parse = []\n self._output_nodes = {}\n\n def parse_with_retrieved(self, retrieved):\n\n self.check_state()\n self.out_folder = self.get_folder(retrieved)\n\n if not self.out_folder:\n return self.result(success=False)\n\n # Get all specialised file parsers. Warnings will be issued if a file should be parsed and\n # the corresponding files do not exist.\n success = self._set_file_parsers()\n\n if not success:\n # A critical file i.e. OUTCAR does not exist. Abort parsing.\n return self.result(success=False)\n\n # Get an initial list of quantities which should be parsed.\n self._update_parsing_list()\n\n # Parse all implemented quantities in the nodesToAdd list, if they should be parsed. The list\n # might get dynamically updated during the loop.\n while self._quantities_to_parse:\n quantity = self._quantities_to_parse.pop(0)\n if self._settings['add_' + quantity]:\n if not self._check_prerequesites(quantity):\n continue\n self._output_nodes.update(getattr(self, '_get_' + quantity)())\n\n # Add output nodes if the corresponding data exists.\n for key, value in self._output_nodes.iteritems():\n if value:\n self._set_node(key, value)\n\n return self.result(success=True)\n\n def _check_and_validate_settings(self):\n \"\"\"Check the settings and set which files should be parsed based on the input.\"\"\"\n\n import copy\n new_settings = copy.deepcopy(self._settings)\n\n for key, value in self._settings.iteritems():\n if not key.startswith('add_'):\n # only keys starting with 'add_' will change the behaviour of the parser so get the next one.\n continue\n if not value:\n # The quantity should not be added, so the corresponding files do not have to be parsed.\n continue\n quantity = key[4:]\n if quantity not in PARSABLE_QUANTITIES:\n self.logger.warning('{0} has been requested by setting add_{0}'.format(quantity) +\n ' however it has not been implemented. Please check the docstrings' +\n ' in aiida_vasp.parsers.vasp.py for valid input.')\n continue\n\n for filename in PARSABLE_QUANTITIES[quantity]['parsers']:\n new_settings['should_parse_' + filename] = value\n\n self._settings = new_settings\n\n def _update_parsing_list(self):\n \"\"\"Add all quantities, which should be parsed to the quantitiesToParse list.\"\"\"\n\n for quantity in self._nodes_to_add:\n if quantity in self._quantities_to_parse:\n continue\n if getattr(self, '_should_parse_' + quantity)():\n self._quantities_to_parse.append(quantity)\n\n def _set_file_parsers(self):\n \"\"\"\n Set the specific file parsers for OUTCAR, DOSCAR, EIGENVAL and vasprun.xml.\n\n Return False if a critical file is missing, which will abort the parsing.\n \"\"\"\n\n for key, value in self._parsable_files.iteritems():\n if not self._settings['should_parse_' + key]:\n continue\n if self._parsers[key]:\n continue\n\n # We should parse this file and the parser has not been set yet.\n file_to_parse = self.get_file(key)\n if not file_to_parse:\n self._parsers[key] = None\n if value['is_critical']:\n self.logger.error('{} not found, look at the scheduler output for troubleshooting.'.format(key))\n return False\n\n # The file is not critical\n if self._settings['should_parse_' + key]:\n self.logger.warning('{0} not found, but should be parsed.'.format(key))\n else:\n # The file should be parsed and has been found\n self._parsers[key] = value['parser_class'](file_to_parse)\n\n # All critical files have been found, so we can safely return True.\n return True\n\n def _check_prerequesites(self, quantity):\n \"\"\"\n Check whether the prerequesites of a given quantity have been met.\n\n If not either requeue or prevent this quantity from being parsed.\n \"\"\"\n\n prerequesites = PARSABLE_QUANTITIES[quantity]['prerequesites']\n for preq in prerequesites:\n if preq in self._output_nodes:\n # requirement met, check the next one\n continue\n\n # Requirement not met yet.\n if preq in self._quantities_to_parse:\n # The prerequesite is in the queue, requeue this quantity and return\n self._quantities_to_parse.append(quantity)\n return False\n\n # The prerequesite is not met and also not in the queue. Don't parse this quantity.\n return False\n\n # All requirements have been met\n return True\n\n def _should_parse_dos(self):\n \"\"\"Return True if dos should be parsed.\"\"\"\n\n if not self._parsers['vasprun.xml']:\n return False\n if not self._parsers['DOSCAR']:\n return False\n\n if self._settings['add_dos'] and not self._parsers['vasprun.xml'].is_static:\n self.logger.warning('Adding a DOS node has been requested by setting \"add_dos = True\".' +\n ' However, for calculating a DOS a static calculation is recommended.')\n\n return self._settings['add_dos']\n\n def _get_dos(self):\n \"\"\"Return a doscar array node wrapped in a dictionary.\"\"\"\n\n vrp = self._parsers['vasprun.xml']\n dcp = self._parsers['DOSCAR']\n\n if not vrp or not dcp:\n return {'dos': None}\n\n dosnode = DataFactory('array')()\n # vrp.pdos is a numpy array, and thus not directly bool-convertible\n if vrp.pdos.size > 0:\n pdos = vrp.pdos.copy()\n for i, name in enumerate(vrp.pdos.dtype.names[1:]):\n num_spins = vrp.pdos.shape[1]\n # ~ pdos[name] = dcp[:, :, i+1:i+1+ns].transpose(0,2,1)\n cur = dcp.pdos[:, :, i + 1:i + 1 + num_spins].transpose(0, 2, 1)\n cond = vrp.pdos[name] < 0.1\n pdos[name] = np.where(cond, cur, vrp.pdos[name])\n dosnode.set_array('pdos', pdos)\n num_spins = 1\n if dcp.tdos.shape[1] == 5:\n num_spins = 2\n tdos = vrp.tdos[:num_spins, :].copy()\n for i, name in enumerate(vrp.tdos.dtype.names[1:]):\n cur = dcp.tdos[:, i + 1:i + 1 + num_spins].transpose()\n cond = vrp.tdos[:num_spins, :][name] < 0.1\n tdos[name] = np.where(cond, cur, vrp.tdos[:num_spins, :][name])\n dosnode.set_array('tdos', tdos)\n return {'dos': dosnode}\n\n def _should_parse_bands(self):\n \"\"\"Return True if bands should be parsed.\"\"\"\n\n if not self._parsers['EIGENVAL']:\n return False\n\n if self._settings['add_bands'] and not self._parsers['vasprun.xml'].is_static:\n self.logger.warning('Adding a band_structure node has been requested by setting' +\n ' \"add_bands = True\". However, for calculating a band structure' + ' a static calculation is recommended.')\n\n return self._settings['add_bands']\n\n def _get_bands(self):\n \"\"\"\n Create a bands and a kpoints node from values in eigenvalue.\n\n :returns: bsnode, kpout\n\n * bsnode: BandsData containing eigenvalues from EIGENVAL\n and occupations from vasprun.xml\n * kpout: KpointsData containing kpoints from EIGENVAL,\n both bsnode as well as kpnode come with cell unset\n \"\"\"\n eig = self.get_file('EIGENVAL')\n if not eig:\n return {'bands': None, 'kpoints': None}\n\n _, kpoints, bands = EigParser.parse_eigenval(eig)\n bsnode = DataFactory('array.bands')()\n kpout = DataFactory('array.kpoints')()\n # Take the output structure if available.\n structure = None\n if 'structure' in self._output_nodes:\n structure = self._output_nodes['structure']\n if structure is None:\n structure = self._calc.inp.structure\n bsnode.set_cell(structure.get_ase().get_cell())\n kpout.set_cell(structure.get_ase().get_cell())\n if self._calc.inp.kpoints.get_attrs().get('array|kpoints'):\n bsnode.set_kpointsdata(self._calc.inp.kpoints)\n if self._calc.inp.kpoints.labels:\n bsnode.labels = self._calc.inp.kpoints.labels\n else:\n bsnode.set_kpoints(kpoints[:, :3], weights=kpoints[:, 3], cartesian=False)\n bsnode.set_bands(bands, occupations=self._parsers['vasprun.xml'].occupations)\n kpout.set_kpoints(kpoints[:, :3], weights=kpoints[:, 3], cartesian=False)\n return {'bands': bsnode, 'kpoints': kpout}\n\n def _should_parse_kpoints(self):\n \"\"\"Return True if IBZKPT should be parsed.\"\"\"\n\n if not self._parsers['IBZKPT']:\n return False\n\n return self._settings['add_kpoints']\n\n def _get_kpoints(self):\n \"\"\"Create a DB Node for the IBZKPT file\"\"\"\n\n kpp = self._parsers['IBZKPT']\n\n if kpp is None:\n return {'kpoints': None}\n\n kpout = DataFactory('array.kpoints')()\n kpout.set_kpoints(kpp.kpoints, weights=kpp.weights, cartesian=kpp.cartesian)\n\n return {'kpoints': kpout}\n\n def _should_parse_chgcar(self):\n \"\"\"Return True if CHGCAR should be parsed.\"\"\"\n\n if self._settings['add_chgcar'] and not self._parsers['vasprun.xml'].is_sc:\n self.logger.warning('Adding a CHGCAR node has been requested by setting \"add_chgcar = True\".' +\n ' However, the calculation is not selfconsistent.')\n\n return self._settings['add_chgcar'] and self._parsers['vasprun.xml'].is_sc\n\n def _get_chgcar(self):\n \"\"\"Create a DB Node for the CHGCAR file\"\"\"\n\n chgc = self.get_file('CHGCAR')\n\n if chgc is None:\n return {'chgcar': None}\n\n chgnode = DataFactory('vasp.chargedensity')()\n chgnode.set_file(chgc)\n\n return {'chgcar': chgnode}\n\n def _should_parse_structure(self):\n \"\"\"Return True if Structure should be parsed.\"\"\"\n\n return self._settings['add_structure']\n\n def _get_structure(self):\n \"\"\"Read CONTCAR for output structure.\"\"\"\n\n from ase.io import read\n structure = DataFactory('structure')()\n cont = self.get_file('CONTCAR')\n if not cont:\n self.logger.info('CONTCAR not found!')\n return {'structure': None}\n structure.set_ase(read(cont, format='vasp'))\n return {'structure': structure}\n\n def _should_parse_wavecar(self):\n \"\"\"Return True if WAVECAR should be parsed.\"\"\"\n\n if self._settings['add_wavecar'] and not self._parsers['vasprun.xml'].is_sc:\n self.logger.warning('Adding a WAVECAR node has been requested by setting \"add_wavecar = True\".' +\n ' However, the calculation is not selfconsistent.')\n\n return self._settings['add_chgcar'] and self._parsers['vasprun.xml'].is_sc\n\n def _get_wavecar(self):\n \"\"\"Create a DB Node for the WAVECAR file\"\"\"\n\n wfn = self.get_file('WAVECAR')\n\n if wfn is None:\n return {'wavecar': None}\n\n wfnode = DataFactory('vasp.wavefun')()\n wfnode.set_file(wfn)\n\n return {'wavecar': wfnode}\n\n def _should_parse_parameters(self):\n \"\"\"Return True if Parameters should be parsed.\"\"\"\n\n return self._settings['add_parameters']\n\n def _get_parameters(self):\n \"\"\"Create ParameterData holding output parsed from OUTCAR and vasprun.xml.\"\"\"\n\n output = DataFactory('parameter')()\n if not self._parsers['OUTCAR'] and not self._parsers['vasprun.xml']:\n return {'parameters': None}\n\n if self._parsers['OUTCAR']:\n output.update_dict(self._parsers['OUTCAR'].output_dict)\n\n if self._parsers['vasprun.xml']:\n output.update_dict({'efermi': self._parsers['vasprun.xml'].efermi})\n\n return {'parameters': output}\n\n def _set_node(self, node_name, node):\n \"\"\"Wrapper for self.add_node, checking whether the Node is None and using the correct linkname\"\"\"\n\n if node is not None:\n self.add_node(LINKNAME_DICT[node_name], node)\n", "sub_path": "aiida_vasp/parsers/vasp.py", "file_name": "vasp.py", "file_ext": "py", "file_size_in_byte": 17200, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "aiida_vasp.io.doscar.DosParser", "line_number": 84, "usage_type": "name"}, {"api_name": "aiida_vasp.io.eigenval.EigParser", "line_number": 89, "usage_type": "name"}, {"api_name": "aiida_vasp.io.kpoints.KpParser", "line_number": 94, "usage_type": "name"}, {"api_name": "aiida_vasp.io.outcar.OutcarParser", "line_number": 99, "usage_type": "name"}, {"api_name": "aiida_vasp.io.vasprun.VasprunParser", "line_number": 104, "usage_type": "name"}, {"api_name": "aiida_vasp.parsers.base.BaseParser", "line_number": 111, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 200, "usage_type": "call"}, {"api_name": "aiida.orm.DataFactory", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 327, "usage_type": "call"}, {"api_name": "aiida_vasp.io.eigenval.EigParser.parse_eigenval", "line_number": 358, "usage_type": "call"}, {"api_name": "aiida_vasp.io.eigenval.EigParser", "line_number": 358, "usage_type": "name"}, {"api_name": "aiida.orm.DataFactory", "line_number": 359, "usage_type": "call"}, {"api_name": "aiida.orm.DataFactory", "line_number": 360, "usage_type": "call"}, {"api_name": "aiida.orm.DataFactory", "line_number": 395, "usage_type": "call"}, {"api_name": "aiida.orm.DataFactory", "line_number": 417, "usage_type": "call"}, {"api_name": "aiida.orm.DataFactory", "line_number": 431, "usage_type": "call"}, {"api_name": "ase.io.read", "line_number": 436, "usage_type": "call"}, {"api_name": "aiida.orm.DataFactory", "line_number": 456, "usage_type": "call"}, {"api_name": "aiida.orm.DataFactory", "line_number": 469, "usage_type": "call"}]} +{"seq_id": "300821301", "text": "import csv\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\n\nthisPath = os.path.dirname(os.path.abspath(__file__))\nfilePath = os.path.join(thisPath, 'pokemon.csv')\n\npoke_data = open(filePath, 'r', encoding='utf-8')\npoke_csv = csv.reader(poke_data, delimiter=',', quotechar='\"')\n\nheader = next(poke_csv)\nheader_position = {}\nfor i in range(len(header)):\n header_position[header[i]] = i\n\n\nweight_pos = header_position['weight_kg']\nname_pos = header_position['name']\npoke_weight_dict = {}\n\nfor line in poke_csv:\n weight = line[weight_pos]\n name = line[name_pos]\n if weight != '':\n weight = float(weight)\n poke_weight_dict[name] = weight\n\npoke_weight_list = list(poke_weight_dict.values())\n\nx = range(len(poke_weight_list))\nplt.scatter(x, poke_weight_list)\nplt.show()", "sub_path": "aula0/graficoMatplotlib.py", "file_name": "graficoMatplotlib.py", "file_ext": "py", "file_size_in_byte": 801, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "os.path.dirname", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}]} +{"seq_id": "89668648", "text": "import fire\nfrom typing import Mapping\nimport yaml\n\n\ndef patch_yaml(input_yaml: str, **kwargs):\n \"\"\"Patch a yaml file using keyword arguments as in\n ./patch_yaml.py config.yaml --api-key=kjasdkjasd --password=blah --section.api=blink\n\n Nested fields must be separated with a dot (section.api -> config[\"section\"][\"api\"])\n\n :param input_yaml:\n :param kwargs:\n :return:\n \"\"\"\n with open(input_yaml) as yaml_file:\n content = yaml.load(yaml_file, Loader=yaml.FullLoader)\n\n if len(kwargs) == 0:\n return\n\n def patch(mapping: dict, update: Mapping):\n \"\"\"Recursively update mapping with stuff from update\n\n :param mapping:\n :param update:\n :return:\n \"\"\"\n for field, value in update.items():\n if \".\" not in field:\n mapping[field] = value\n else:\n fields = field.split(\".\")\n patch(mapping[fields[0]], {\".\".join(fields[1:]): value})\n\n patch(content, kwargs)\n\n # dump the updated file:\n with open(input_yaml, \"w\") as yaml_file:\n yaml.dump(content, yaml_file)\n\n\nif __name__ == \"__main__\":\n fire.Fire(patch_yaml)\n", "sub_path": "tools/patch_yaml.py", "file_name": "patch_yaml.py", "file_ext": "py", "file_size_in_byte": 1169, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "yaml.load", "line_number": 17, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 17, "usage_type": "attribute"}, {"api_name": "typing.Mapping", "line_number": 22, "usage_type": "name"}, {"api_name": "yaml.dump", "line_number": 40, "usage_type": "call"}, {"api_name": "fire.Fire", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "506218067", "text": "from zope.component.hooks import getSite\nfrom Products.CMFCore.utils import getToolByName\n\n# XXX this can probably be removed\ndef getSiteEncoding(default='utf-8'):\n \"\"\"Get the default site encoding\n \"\"\"\n return 'utf-8'\n\ndef getAllowedContentTypes():\n \"\"\"Get a set of allowed MIME types according to the portal_properties\n tool\n \"\"\"\n \n site = getSite()\n if site is None:\n return None\n \n portal_transforms = getToolByName(site, 'portal_transforms', None)\n if portal_transforms is None:\n return None\n \n portal_properties = getToolByName(site, 'portal_properties', None)\n if portal_properties is None:\n return None\n \n site_properties = portal_properties.get('site_properties', None)\n if site_properties is None:\n return None\n \n allowed = set(portal_transforms.listAvailableTextInputs())\n forbidden = set(site_properties.getProperty('forbidden_contenttypes', []))\n \n return allowed - forbidden\n", "sub_path": "workspace/buildout-cache/eggs/plone.app.textfield-1.2.2-py2.7.egg/plone/app/textfield/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 986, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "zope.component.hooks.getSite", "line_number": 15, "usage_type": "call"}, {"api_name": "Products.CMFCore.utils.getToolByName", "line_number": 19, "usage_type": "call"}, {"api_name": "Products.CMFCore.utils.getToolByName", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "246703867", "text": "import os\nimport sys\nimport time\nimport datetime\nimport argparse\nimport subprocess\nimport config as cf\nfrom time import gmtime, strftime\n\ndef run_varcaller(bamfile, outdir,threads, out_log, err_log, reference, vcf, intervals, db, thresh):\n start_time = strftime('%Y-%m-%d %H:%M:%S')\n out_logger = open(out_log, 'a')\n out_logger.write('Unified Genotyper caller started at '+ start_time + '\\n')\n print('Unified Genotyper started at '+ start_time )\n err_logger = open(err_log, 'a')\n err_logger.write('Unified Genotyper started at '+ start_time + '\\n')\n variants = outdir+'/'+os.path.basename(bamfile)[:os.path.basename(bamfile).rfind('.')]+'.vcf'\n run_ug = subprocess.Popen(['java','-jar',cf.gatk,'-T','UnifiedGenotyper','-R',reference,'-I',bamfile,'--dbsnp',vcf,'-out_mode','EMIT_ALL_CONFIDENT_SITES','-L',intervals,'-o',variants],stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=False)\n ug_status = run_ug.communicate()\n ug_exitcode = run_ug.returncode\n\n\n out_logger.write(ug_status[0] + '\\n')\n err_logger.write(ug_status[1] + '\\n')\n end_time = strftime('%Y-%m-%d %H:%M:%S')\n if ug_exitcode:\n out_logger.write('Unified Genotyper crashed at ' +end_time+' with returncode '+str(ug_exitcode)+'. Please check error log for details.\\n')\n print('Unfied Genotyper crashed at ' +end_time+' with returncode '+str(ug_exitcode)+'. Please check error log for details.')\n out_logger.close()\n err_logger.write('Unified Genotyper crashed at '+ end_time+'\\n')\n err_logger.close()\n return(ug_exitcode, variants)\n\n out_logger.write('Unified Genotyper completed successfully at ' +end_time)\n err_logger.write('Unified Genotyper completed successfully at ' +end_time)\n print('Unified Genotyper completed successfully at '+end_time)\n out_logger.close()\n err_logger.close()\n #End of Unified Genotyper and start of annovar\n\n start_time = strftime('%Y-%m-%d %H:%M:%S')\n out_logger = open(out_log, 'a')\n out_logger.write('Annovar started at '+ start_time + '\\n')\n print('Annovar started at '+ start_time )\n err_logger = open(err_log, 'a')\n err_logger.write('Annovar started at '+ start_time + '\\n')\n variants_ann = outdir+'/'+os.path.basename(bamfile)[:os.path.basename(bamfile).rfind('.')]+'_annotated.vcf'\n #add annovar command\n run_annovar = subprocess.Popen(['perl',cf.annovar+'table_annovar.pl',variants,cf.annovar+'humandb/','-buildver','hg19','-out',variants_ann,'-remove','-protocol',db,'-operation','g,r,r,f,f,f,f','-nastring','.','-vcfinput'],stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=False)\n annovar_status = run_annovar.communicate()\n annovar_exitcode = run_annovar.returncode\n\n\n out_logger.write(annovar_status[0] + '\\n')\n err_logger.write(annovar_status[1] + '\\n')\n end_time = strftime('%Y-%m-%d %H:%M:%S')\n if annovar_exitcode:\n out_logger.write('Annovar crashed at ' +end_time+' with returncode '+str(annovar_exitcode)+'. Please check error log for details.\\n')\n print('Annovar crashed at ' +end_time+' with returncode '+str(annovar_exitcode)+'. Please check error log for details.')\n out_logger.close()\n err_logger.write('Annovar crashed at '+ end_time+'\\n')\n err_logger.close()\n return(annovar_exitcode, variants_ann)\n\n out_logger.write('Annovar completed successfully at ' +end_time)\n err_logger.write('Annovar completed successfully at ' +end_time)\n print('Annovar completed successfully at '+end_time)\n out_logger.close()\n err_logger.close()\n return(annovar_exitcode, variants_ann)\n #End of Annovar\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser('This module runs GATK jars to realign indels and recalibrate base quality score from bam files')\n parser.add_argument('-b','--bamfile',type=str,help='Input sam file path')\n parser.add_argument('-o','--outdir',type=str,help='Output directory path')\n parser.add_argument('-r','--reference',type=str,help='Path to reference fasta file')\n parser.add_argument('-t','--threads',type=str,help='Number of threads allocated')\n parser.add_argument('-v','--vcf',type=str,help='Path to known variants file')\n parser.add_argument('-i','--intervals',type=str, help='Path to intervals file')\n parser.add_argument('-d','--db',type=str, nargs='+', help='Databases to annotate variants with')\n parser.add_argument('-q','--thresh', type=str, nargs='+', help='Quality threshold values')\n args = parser.parse_args()\n out_log = args.outdir + '/log.out'\n err_log = args.outdir + '/log.err'\n ret = run_varcaller(args.bamfile, args.outdir, args.threads, out_log, err_log, args.reference, args.vcf, args.intervals, ','.join(args.db), args.thresh)\n\n", "sub_path": "variant_detection.py", "file_name": "variant_detection.py", "file_ext": "py", "file_size_in_byte": 4745, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "time.strftime", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 18, "usage_type": "call"}, {"api_name": "config.gatk", "line_number": 18, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 18, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 25, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 49, "usage_type": "call"}, {"api_name": "config.annovar", "line_number": 49, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 49, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 56, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 74, "usage_type": "call"}]} +{"seq_id": "452304351", "text": "import numpy as np\nfrom typing import List, Tuple, Union\n\nfrom surfaces.surface import Surface\nfrom ray.ray import Ray\nfrom utility.binarytree import Tree\nimport controllers.ray_controller as rc\nfrom ray.abstract_ray import ARay\n\n\n# def restruct_way_point_of_ray(way_point_of_ray: list):\n# if len(way_point_of_ray) == 0:\n# return None\n#\n# shape = len(way_point_of_ray[0])\n# ans = [[]] * shape\n#\n# for i in range(len(way_point_of_ray)):\n# for j in range(shape):\n# ans[j].append(way_point_of_ray[i][j])\n#\n# return ans\n\n\ndef _not_sequence_modeling(ray: Ray, surfaces: List[Surface]):\n min_p = 100000000000000\n # index of nearest surface and intersection point\n index = -1\n for i in range(len(surfaces)):\n t = surfaces[i].ray_surface_intersection(ray.dir, ray.start)\n if len(t) != 0:\n t = t[0]\n else:\n continue\n if t < min_p:\n min_p = t\n index = i\n if index != -1:\n return index, ray.calc_point_of_ray(min_p)\n else:\n return index, None\n\n\ndef deep_modeling(type_polarization: str, ray: Ray, surfaces: list, deep: int,\n ray_const_length: float = 1) -> Tree:\n if not all(isinstance(some, Surface) for some in surfaces):\n raise AttributeError(\n \"Not all elements in surfaces is instance of class Surface %s\" % (\n str([type(some) for some in surfaces]))\n )\n if deep < 1:\n raise AttributeError(\n \"Invalid deep value(%s)\" % (\n str(deep))\n )\n\n if type_polarization != 's' and type_polarization != 'p':\n raise AttributeError(\n \"Enter correct value of type polarization (s or p). You enter: %s\" % (\n type_polarization)\n )\n\n def fill_ray_tree(tree: Tree, surfaces: list, deep: int):\n ray_ = tree.value\n\n # index of nearest surface and intersection point\n index, i_point = _not_sequence_modeling(ray_, surfaces)\n reflect_ray = None\n refract_ray = None\n exit = False\n\n # if intersection is\n if i_point == None:\n tree.left = None\n tree.right = None\n exit = True\n # i_point = ray_.calc_point_of_ray(ray_const_length)\n\n # _append_point_to_path(ray_, ray_._Ray__path_of_ray, i_point)\n if deep < 0:\n return\n\n if exit:\n return\n\n # check total internal refraction\n if rc.is_total_inturnal_refraction(ray_, surfaces[index]):\n reflect_ray = Ray.reflect(ray_, surfaces[index])\n tree.left = Tree(reflect_ray)\n else:\n refract_ray = Ray.refract(ray_, surfaces[index])\n tree.right = Tree(refract_ray)\n reflect_ray = Ray.reflect(ray_, surfaces[index])\n tree.left = Tree(reflect_ray)\n\n point, norm, t = ARay.find_norm_vec_and_point(ray_.dir, ray_.start, surfaces[index])\n n1, n2 = surfaces[index].get_refractive_indexes(ray_.start)\n # , n1, n2\n rc.set_brightness(type_polarization, ray_, refract_ray, reflect_ray, norm, n1, n2)\n\n # следующая итерация рекурсии\n if tree.left is not None:\n fill_ray_tree(tree.left, surfaces, deep - 1)\n else:\n tree.left = Tree(None)\n if tree.right is not None:\n fill_ray_tree(tree.right, surfaces, deep - 1)\n else:\n tree.right = Tree(None)\n\n tree = Tree(ray)\n fill_ray_tree(tree, surfaces, deep)\n return tree\n\n\ndef model_path(ray: Ray, surfaces: list, is_return_ray_list: bool = False, is_have_ray_in_infinity: bool = False,\n length_last_ray: float = 1):\n way_point_of_ray = []\n ray_list = [ray]\n new_ray = ray\n temp = None\n while True:\n min_p = float(np.finfo(float).max)\n # index of nearest surface and intersection point\n # ищем ближайшую поверхность\n index, i_point = -1, None\n index, i_point = _not_sequence_modeling(new_ray, surfaces)\n print(i_point)\n if i_point is None:\n break\n # print(\"Surf \" + str(surfaces[index]))\n if surfaces[index].type == Surface.types.REFLECTING:\n temp = new_ray.reflect(surfaces[index])\n elif surfaces[index].type == Surface.types.REFRACTING:\n temp = new_ray.refract(surfaces[index])\n print(temp)\n _append_point_to_path(new_ray, way_point_of_ray, temp.start)\n # print(new_ray,temp,'\\n')\n if is_return_ray_list:\n ray_list.append(temp)\n new_ray = temp\n if is_have_ray_in_infinity:\n _append_point_to_path(new_ray, way_point_of_ray,\n ARay.calc_point_of_ray_(new_ray.dir, new_ray.start, length_last_ray))\n if is_return_ray_list:\n return way_point_of_ray, ray_list\n return way_point_of_ray\n\n\ndef _append_point_to_path(ray: Ray, way_points_of_ray: list, point: list):\n # if len(point) == 0:\n # raise AttributeError(\"Zero dimensional point\")\n # if len(way_points_of_ray) != 0 and (len(point) != len(way_points_of_ray) or ray.dim != len(point)):\n # raise AttributeError(\n # \"\"\"Iterables objects(point) have different length with ray or way_points_of_ray. len(way_points_of_ray):\n # %d, len(point): %d, ray(%d)\"\"\" % (\n # len(way_points_of_ray), len(point), ray.dim))\n if len(way_points_of_ray) == 0:\n for i in range(ray.dim):\n way_points_of_ray.append([])\n for j in range(ray.dim):\n way_points_of_ray[j].append(ray.start[j])\n for j in range(ray.dim):\n way_points_of_ray[j].append(point[j])\n\n\ndef path_ray_for_drawing(ray: Ray, surfaces: list, is_return_ray_list: bool = False,\n is_have_ray_in_infinity: bool = False):\n if not all(isinstance(some, Surface) for some in surfaces):\n raise AttributeError(\n \"Not all elements in surfaces is instance of class Surface %s\" % (\n str([type(some) for some in surfaces]))\n )\n ans = None\n ray_list = None\n if is_return_ray_list:\n ans, ray_list = model_path(ray, surfaces, is_return_ray_list, True)\n else:\n ans = model_path(ray, surfaces, is_have_ray_in_infinity=True)\n\n if is_return_ray_list:\n return ans, ray_list\n return ans\n", "sub_path": "controllers/modeling_controller.py", "file_name": "modeling_controller.py", "file_ext": "py", "file_size_in_byte": 6427, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "ray.ray.Ray", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 25, "usage_type": "name"}, {"api_name": "surfaces.surface.Surface", "line_number": 25, "usage_type": "name"}, {"api_name": "surfaces.surface", "line_number": 29, "usage_type": "argument"}, {"api_name": "surfaces.surface", "line_number": 30, "usage_type": "name"}, {"api_name": "ray.ray.dir", "line_number": 30, "usage_type": "attribute"}, {"api_name": "ray.ray", "line_number": 30, "usage_type": "name"}, {"api_name": "ray.ray.start", "line_number": 30, "usage_type": "attribute"}, {"api_name": "ray.ray.calc_point_of_ray", "line_number": 39, "usage_type": "call"}, {"api_name": "ray.ray", "line_number": 39, "usage_type": "name"}, {"api_name": "ray.ray.Ray", "line_number": 44, "usage_type": "name"}, {"api_name": "surfaces.surface.Surface", "line_number": 46, "usage_type": "argument"}, {"api_name": "surfaces.surface", "line_number": 46, "usage_type": "name"}, {"api_name": "surfaces.surface", "line_number": 49, "usage_type": "name"}, {"api_name": "utility.binarytree.Tree", "line_number": 63, "usage_type": "name"}, {"api_name": "surfaces.surface", "line_number": 67, "usage_type": "argument"}, {"api_name": "controllers.ray_controller.is_total_inturnal_refraction", "line_number": 87, "usage_type": "call"}, {"api_name": "controllers.ray_controller", "line_number": 87, "usage_type": "name"}, {"api_name": "surfaces.surface", "line_number": 87, "usage_type": "name"}, {"api_name": "ray.ray.Ray.reflect", "line_number": 88, "usage_type": "call"}, {"api_name": "ray.ray.Ray", "line_number": 88, "usage_type": "name"}, {"api_name": "surfaces.surface", "line_number": 88, "usage_type": "name"}, {"api_name": "utility.binarytree.Tree", "line_number": 89, "usage_type": "call"}, {"api_name": "ray.ray.Ray.refract", "line_number": 91, "usage_type": "call"}, {"api_name": "ray.ray.Ray", "line_number": 91, "usage_type": "name"}, {"api_name": "surfaces.surface", "line_number": 91, "usage_type": "name"}, {"api_name": "utility.binarytree.Tree", "line_number": 92, "usage_type": "call"}, {"api_name": "ray.ray.Ray.reflect", "line_number": 93, "usage_type": "call"}, {"api_name": "ray.ray.Ray", "line_number": 93, "usage_type": "name"}, {"api_name": "surfaces.surface", "line_number": 93, "usage_type": "name"}, {"api_name": "utility.binarytree.Tree", "line_number": 94, "usage_type": "call"}, {"api_name": "ray.abstract_ray.ARay.find_norm_vec_and_point", "line_number": 96, "usage_type": "call"}, {"api_name": "ray.abstract_ray.ARay", "line_number": 96, "usage_type": "name"}, {"api_name": "surfaces.surface", "line_number": 96, "usage_type": "name"}, {"api_name": "surfaces.surface", "line_number": 97, "usage_type": "name"}, {"api_name": "controllers.ray_controller.set_brightness", "line_number": 99, "usage_type": "call"}, {"api_name": "controllers.ray_controller", "line_number": 99, "usage_type": "name"}, {"api_name": "surfaces.surface", "line_number": 103, "usage_type": "argument"}, {"api_name": "utility.binarytree.Tree", "line_number": 105, "usage_type": "call"}, {"api_name": "surfaces.surface", "line_number": 107, "usage_type": "argument"}, {"api_name": "utility.binarytree.Tree", "line_number": 109, "usage_type": "call"}, {"api_name": "utility.binarytree.Tree", "line_number": 111, "usage_type": "call"}, {"api_name": "ray.ray", "line_number": 111, "usage_type": "argument"}, {"api_name": "surfaces.surface", "line_number": 112, "usage_type": "argument"}, {"api_name": "utility.binarytree.Tree", "line_number": 45, "usage_type": "name"}, {"api_name": "ray.ray.Ray", "line_number": 116, "usage_type": "name"}, {"api_name": "ray.ray", "line_number": 119, "usage_type": "name"}, {"api_name": "ray.ray", "line_number": 120, "usage_type": "name"}, {"api_name": "numpy.finfo", "line_number": 123, "usage_type": "call"}, {"api_name": "surfaces.surface", "line_number": 127, "usage_type": "argument"}, {"api_name": "surfaces.surface", "line_number": 132, "usage_type": "name"}, {"api_name": "surfaces.surface.Surface.types", "line_number": 132, "usage_type": "attribute"}, {"api_name": "surfaces.surface.Surface", "line_number": 132, "usage_type": "name"}, {"api_name": "surfaces.surface", "line_number": 133, "usage_type": "name"}, {"api_name": "surfaces.surface", "line_number": 134, "usage_type": "name"}, {"api_name": "surfaces.surface.Surface.types", "line_number": 134, "usage_type": "attribute"}, {"api_name": "surfaces.surface.Surface", "line_number": 134, "usage_type": "name"}, {"api_name": "surfaces.surface", "line_number": 135, "usage_type": "name"}, {"api_name": "ray.abstract_ray.ARay.calc_point_of_ray_", "line_number": 144, "usage_type": "call"}, {"api_name": "ray.abstract_ray.ARay", "line_number": 144, "usage_type": "name"}, {"api_name": "ray.ray.Ray", "line_number": 150, "usage_type": "name"}, {"api_name": "ray.ray.dim", "line_number": 159, "usage_type": "attribute"}, {"api_name": "ray.ray", "line_number": 159, "usage_type": "name"}, {"api_name": "ray.ray.dim", "line_number": 161, "usage_type": "attribute"}, {"api_name": "ray.ray", "line_number": 161, "usage_type": "name"}, {"api_name": "ray.ray.start", "line_number": 162, "usage_type": "attribute"}, {"api_name": "ray.ray", "line_number": 162, "usage_type": "name"}, {"api_name": "ray.ray.dim", "line_number": 163, "usage_type": "attribute"}, {"api_name": "ray.ray", "line_number": 163, "usage_type": "name"}, {"api_name": "ray.ray.Ray", "line_number": 167, "usage_type": "name"}, {"api_name": "surfaces.surface.Surface", "line_number": 169, "usage_type": "argument"}, {"api_name": "surfaces.surface", "line_number": 169, "usage_type": "name"}, {"api_name": "surfaces.surface", "line_number": 172, "usage_type": "name"}, {"api_name": "ray.ray", "line_number": 177, "usage_type": "argument"}, {"api_name": "surfaces.surface", "line_number": 177, "usage_type": "argument"}, {"api_name": "ray.ray", "line_number": 179, "usage_type": "argument"}, {"api_name": "surfaces.surface", "line_number": 179, "usage_type": "argument"}]} +{"seq_id": "268662585", "text": "import requests\n\n# Set the API endpoint URL\napi_url = \"https://api.shotgrid.io/v1/reviews\"\n\n# Set the API key for your account\napi_key = \"YOUR_API_KEY\"\n\n# Set the review details\ndata = {\n \"shot_id\": \"SHOT_ID\",\n \"review_type\": \"feedback\",\n \"title\": \"My New Review\",\n \"description\": \"This is a new review that I am creating in Autodesk Shotgrid\",\n \"timeline_time\": 0,\n \"metadata\": {\n \"key1\": \"value1\",\n \"key2\": \"value2\"\n }\n}\n\n# Set the headers for the request\nheaders = {\n \"Content-Type\": \"application/json\",\n \"Authorization\": f\"Bearer {api_key}\"\n}\n\n# Send the request to create the review\nresponse = requests.post(api_url, json=data, headers=headers)\n\n# Check the status code of the response\nif response.status_code == 201:\n # If the request was successful, print the review details\n review = response.json()\n print(f\"Review created with ID: {review['id']}\")\n print(f\"Title: {review['title']}\")\n print(f\"Description: {review['description']}\")\n print(f\"Timeline Time: {review['timeline_time']}\")\n print(f\"Metadata: {review['metadata']}\")\nelse:\n # If the request was not successful, print the error message\n print(f\"Error creating review: {response.json()['message']}\")\n\n", "sub_path": "createReview.py", "file_name": "createReview.py", "file_ext": "py", "file_size_in_byte": 1192, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "requests.post", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "118851011", "text": "# customapplication\n#\n# wrapper class for running app through Gunicorn\n#\nfrom gunicorn.app.base import Application\nfrom gunicorn import util\n\nclass MyCustomApplication(Application):\n '''\n Custom Gunicorn Application\n '''\n\n def __init__(self, options={}):\n '''__init__ method\n\n Load the base config and assign some core attributes.\n '''\n self.usage = None\n self.callable = None\n self.options = options\n self.do_load_config()\n\n def init(self, *args):\n '''init method\n\n Takes our custom options from self.options and creates a config\n dict which specifies custom settings.\n '''\n cfg = {}\n for k, v in self.options.items():\n if k.lower() in self.cfg.settings and v is not None:\n cfg[k.lower()] = v\n return cfg\n\n def load(self):\n '''load method\n\n Imports our application and returns it to be run.\n \n FORMAT: :\n \n If the WSGI object is not named 'application', it must\n be included in the function parameter.\n ''' \n return util.import_app(\"appname.main:app\")\n", "sub_path": "appname/appname/customapplication.py", "file_name": "customapplication.py", "file_ext": "py", "file_size_in_byte": 1186, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "gunicorn.app.base.Application", "line_number": 8, "usage_type": "name"}, {"api_name": "gunicorn.util.import_app", "line_number": 45, "usage_type": "call"}, {"api_name": "gunicorn.util", "line_number": 45, "usage_type": "name"}]} +{"seq_id": "593157952", "text": "import json\nimport logging\nfrom datetime import datetime\n\nimport dateutil\n\nfrom .utils import url_to_filename, expiration_datetime\nfrom .exceptions import SubscriptionExpired, InvalidSubscriptionFormat, SubscriptionMissingExpiration\n\nlogger = logging.getLogger(__name__)\n\n\nclass Id:\n def __init__(self, id):\n self.id = id\n\n def to_key(self, url=''):\n key = self.id\n if url:\n key += '/%s' % url_to_filename(url)\n return key\n\n\nclass Pattern:\n def __init__(self, topic):\n self.topic = topic\n\n def to_key(self, url=''):\n self._validate()\n if self.topic.endswith('.'):\n self.topic = self.topic[:-1]\n if self.topic.endswith('*'):\n self.topic = self.topic[:-1]\n\n parts = self.topic.upper().split('.')\n key = '/'.join([p for p in parts if p]) + '/'\n if url:\n key += url_to_filename(url)\n return key\n\n def _validate(self):\n if not self.topic:\n raise ValueError(\"non-empty topic is required\")\n if '/' in self.topic:\n raise ValueError(\"topic should contain dots, not slashes\")\n if self.topic.endswith('*') and self.topic[-2] != '.':\n raise ValueError(\"* character is supported only after a dot\")\n\n def to_layers(self):\n layers = []\n key = self.to_key()\n split_layers = [layer for layer in key.split(\"/\") if layer]\n for i in range(0, len(split_layers)):\n layers.append(\"/\".join(split_layers[0:i + 1]) + \"/\")\n return layers\n\n\nclass Subscription:\n CALLBACK_KEY = 'c'\n EXPIRATION_KEY = 'e'\n\n def __init__(self, payload, key, now: datetime):\n self.payload = payload\n self.key = key\n self.now = now\n try:\n self.data = self._decode(payload)\n self.is_valid = True\n except (InvalidSubscriptionFormat, SubscriptionExpired) as e:\n self.data = None\n self.is_valid = False\n self.error = str(e)\n\n def _decode(self, payload):\n try:\n data = json.loads(payload.decode('utf-8'))\n except UnicodeError as e:\n raise InvalidSubscriptionFormat(\"data is not UTF-8\") from e\n except ValueError as e:\n logger.warning(\"Tried to decode JSON data %s but failed\", payload)\n raise InvalidSubscriptionFormat(\"data is not a valid JSON\") from e\n\n try:\n data[self.CALLBACK_KEY] # to raise KeyError\n expiration = data.get(self.EXPIRATION_KEY)\n if expiration:\n data[self.EXPIRATION_KEY] = dateutil.parser.parse(expiration)\n self.is_expired = data[self.EXPIRATION_KEY] < self.now\n if self.is_expired:\n raise SubscriptionExpired()\n except KeyError as e:\n raise InvalidSubscriptionFormat(f\"data missing required key:{str(e)}\") from e\n except (TypeError, ValueError) as e:\n raise InvalidSubscriptionFormat(f\"expiration invalid format:{str(data[self.EXPIRATION_KEY])}\") from e\n\n return data\n\n @property\n def callback_url(self):\n if self.data:\n return self.data[self.CALLBACK_KEY]\n else:\n # expired or wrongly initialized subscription\n return None\n\n @classmethod\n def encode_obj(cls, callback, expiration_seconds: int):\n if not expiration_seconds:\n raise SubscriptionMissingExpiration()\n\n expiration = expiration_datetime(expiration_seconds).isoformat()\n\n data = {\n cls.CALLBACK_KEY: callback,\n cls.EXPIRATION_KEY: expiration\n }\n return json.dumps(data).encode('utf-8')\n\n def __hash__(self):\n return hash(self.callback_url) if self.callback_url else 0\n\n def __eq__(self, other):\n if self.callback_url is None or other.callback_url is None:\n return False\n return self.callback_url == other.callback_url\n", "sub_path": "libtrustbridge/websub/domain.py", "file_name": "domain.py", "file_ext": "py", "file_size_in_byte": 3973, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "utils.url_to_filename", "line_number": 20, "usage_type": "call"}, {"api_name": "utils.url_to_filename", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 62, "usage_type": "name"}, {"api_name": "exceptions.InvalidSubscriptionFormat", "line_number": 69, "usage_type": "name"}, {"api_name": "exceptions.SubscriptionExpired", "line_number": 69, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 76, "usage_type": "call"}, {"api_name": "exceptions.InvalidSubscriptionFormat", "line_number": 78, "usage_type": "call"}, {"api_name": "exceptions.InvalidSubscriptionFormat", "line_number": 81, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 87, "usage_type": "call"}, {"api_name": "dateutil.parser", "line_number": 87, "usage_type": "attribute"}, {"api_name": "exceptions.SubscriptionExpired", "line_number": 90, "usage_type": "call"}, {"api_name": "exceptions.InvalidSubscriptionFormat", "line_number": 92, "usage_type": "call"}, {"api_name": "exceptions.InvalidSubscriptionFormat", "line_number": 94, "usage_type": "call"}, {"api_name": "exceptions.SubscriptionMissingExpiration", "line_number": 109, "usage_type": "call"}, {"api_name": "utils.expiration_datetime", "line_number": 111, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 117, "usage_type": "call"}]} +{"seq_id": "335213431", "text": "import os\r\nimport cv2\r\nimport sys\r\nimport glob\r\nimport random\r\nimport numpy as np\r\n\r\n# function to overlay a transparent image on backround.\r\ndef transparentOverlay(src , overlay , pos=(0,0)):\r\n \"\"\"\r\n :param src: Input Color Background Image\r\n :param overlay: transparent Image (BGRA)\r\n :param pos: position where the image to be blit.\r\n :param scale : scale factor of transparent image.\r\n :return: Resultant Image\r\n \"\"\"\r\n #overlay = cv2.resize(overlay,(0,0),fx=scale,fy=scale)\r\n h,w,_ = overlay.shape # Size of pngImg\r\n rows,cols,_ = src.shape # Size of background Image\r\n y,x = pos[0],pos[1] # Position of PngImage\r\n \r\n #loop over all pixels and apply the blending equation\r\n for i in range(h):\r\n for j in range(w):\r\n if x+i >= rows or y+j >= cols:\r\n continue\r\n alpha = float(overlay[i][j][3]/255.0) # read the alpha channel \r\n src[x+i][y+j] = alpha*overlay[i][j][:3]+(1-alpha)*src[x+i][y+j]\r\n return src\r\n\r\n\r\n\r\n# function to overlay a transparent image on backround.\r\ndef transparentOverlay_mask(src , overlay , pos=(0,0)):\r\n \"\"\"\r\n :param src: Input Color Background Image\r\n :param overlay: transparent Image (BGRA)\r\n :param pos: position where the image to be blit.\r\n :param scale : scale factor of transparent image.\r\n :return: Resultant Image\r\n \"\"\"\r\n #overlay = cv2.resize(overlay,(0,0),fx=scale,fy=scale)\r\n h,w,_ = overlay.shape # Size of pngImg\r\n rows,cols,_ = src.shape # Size of background Image\r\n y,x = pos[0],pos[1] # Position of PngImage\r\n \r\n #loop over all pixels and apply the blending equation\r\n for i in range(h):\r\n for j in range(w):\r\n if x+i >= rows or y+j >= cols:\r\n continue\r\n alpha = float(overlay[i][j][3]/255.0) # read the alpha channel \r\n src[x+i][y+j] = alpha*overlay[i][j][:4]+(1-alpha)*src[x+i][y+j]\r\n return src\r\n\r\n\r\n\"\"\" ----------- Read all images --------------------\"\"\"\r\n\r\n#root=\"D:\\\\EVA\\\\S14\\\\overlay_160\"\r\nimages_bg= glob.glob(\"D:\\\\EVA\\\\EVA_S14\\\\bck_rsz_224\\\\*\")\r\n\r\nimages_fg= glob.glob(\"D:\\\\EVA\\\\EVA_S14\\\\for_transparent\\\\*\")\r\n\r\ntarget='D:\\\\EVA\\\\EVA_S14\\\\overlay\\\\'\r\n\r\nmaskpath='D:\\\\EVA\\\\EVA_S14\\\\overlay_mask\\\\'\r\n\r\nimages_bg_sub=images_bg[0:2]\r\nimages_fg_sub=images_fg[0:2]\r\n\r\nfor bg in images_bg_sub:\r\n\tfor fg in images_fg_sub:\t\t\t\r\n\t\tfor i in range(20):\r\n\t\t\t#reading background image\r\n\t\t\tsrc = cv2.imread(bg,cv2.IMREAD_UNCHANGED)\r\n\t\t\th1,w1,_=src.shape\r\n\t\t\tfilename = bg.split('bck_rsz_224\\\\')[1]\r\n\t\t\tfile = filename.split('.jpg')[0]\r\n\t\t\t\r\n\t\t\t#reading overlay image\r\n\t\t\toverlay = cv2.imread(fg,cv2.IMREAD_UNCHANGED)\r\n\t\t\th2,w2,_=overlay.shape\r\n\t\t\tfilenam = fg.split('for_transparent\\\\')[1]\r\n\t\t\tfil = filenam.split('.png')[0]\r\n\t\t\t\r\n\t\t\t#initializing random numbers for position to place overlay image\r\n\t\t\tm=random.randint(1,(h1-h2))\r\n\t\t\tprint(h1,h2,m)\r\n\t\t\tn=random.randint(1,(w1-w2))\r\n\t\t\tprint(w1,w2,n)\r\n\t\t\t\r\n\t\t\t#Overlaying bg and fg\r\n\t\t\tresult = transparentOverlay(src,overlay,(m,n))\r\n\t\t\t\r\n\t\t\tcv2.imwrite(target+file+\"_\"+fil+\"_\"+str(i+1)+\".jpg\", result)\r\n\t\t\t\r\n\t\t\t#rc=result[:,:,0]\r\n\t\t\t\r\n\t\t\t#cv2.imwrite(root+\"\\\\\"+file+\"_\"+fil+\"_\"+str(i+1)+\".png\", rc)\r\n\t\t\t\r\n\t\t\t#bc=result[:,:,1]\r\n\t\t\t\r\n\t\t\t#cv2.imwrite(root+\"B_channel\\\\\"+file+\"_\"+fil+\"_\"+str(i+1)+\".png\", bc)\r\n\t\t\t\r\n\t\t\t#gc=result[:,:,2]\r\n\t\t\t\r\n\t\t\t#cv2.imwrite(root+\"G_channel\\\\\"+file+\"_\"+fil+\"_\"+str(i+1)+\".png\", gc)\r\n\t\t\t\r\n\t\t\t#Creating mask\r\n\t\t\timg_RGBA =np.zeros((224,224,4), np.uint8)\r\n\t\t\t\r\n\t\t\tmask = transparentOverlay_mask(img_RGBA,overlay,(m,n))\r\n\t\t\t\r\n\t\t\talpha_mask=mask[:,:,3]\r\n\t\t\t\r\n\t\t\t\r\n\t\t\tcv2.imwrite(maskpath+file+\"_\"+fil+\"_\"+str(i+1)+\".jpg\", alpha_mask)\r\n\t\t\t\r\n\t\t\tdel result,src,overlay\r\n\tprint(\"Generated images for\",file)\r\n\t\r\n\t\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "sub_path": "overlay_bg_fg.py", "file_name": "overlay_bg_fg.py", "file_ext": "py", "file_size_in_byte": 3744, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "glob.glob", "line_number": 60, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 75, "usage_type": "call"}, {"api_name": "cv2.IMREAD_UNCHANGED", "line_number": 75, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.IMREAD_UNCHANGED", "line_number": 81, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 87, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 89, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 110, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 117, "usage_type": "call"}]} +{"seq_id": "203937121", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Dec 21 10:03:55 2017\r\n\r\n@author: perez\r\n\"\"\"\r\n\r\n\"\"\"\r\nMéthodologie :\r\n1) PRE-TRAITEMENT : Conversion matrice données en une matrice numpy\r\n > 'dataToNumpy' (Entrée = whole Data Set)\r\n2) OPTIMISATION : Regression non-linéaire avec la matrice numpy \r\n > 'optimisation' (Entrée = Training Set)\r\n > 'hypFunction' (Entrée = une commune - numpy)\r\n3) CALCUL RESULTAT: Calcul des résultats à partir des paramètres optimisés - en passant par numpy \r\n > hypFunction (Entrée = une ou des commune(s))\r\n4) POST-TRAITEMENT: Validation de la fonction hypothèse \r\n > fonction4 (Entrée = Validation Set)\r\n \r\nData format classique (instinctif, facile à manipuler) VS Data format numpy (facile pour calculer)\r\n > Ou bien tout le temps classique (nécessite de créer ses propres fonction d'optimisation)\r\n > Ou bien tout le temps numpy (mais besoin de connaître les indices de chaque variable/modalité, pas pratique)\r\n > Ou bien transition de classique à numpy : SOLUTION RETENUE ?!\r\n\"\"\"\r\n\r\n\r\n\r\nimport numpy as np\r\nimport scipy \r\n\r\n\r\n\r\n\"\"\" =================================== PRE-TRAITEMENT =================================== \"\"\"\r\n\r\n\r\n\"\"\"La fonction length renvoit la taille de x si c'est une liste ou 1 si ce n'en est pas une \"\"\"\r\ndef length(x):\r\n if type(x) == list:\r\n return(len(x))\r\n return(1)\r\n\r\n\r\n\"\"\"Le but de la fonction dataToNumpy est de changer le format de la fonction mData pour que l'on puisse faire des opérations dessus.\r\nElle opère donc de façon à mettre les modalités de chaque variable de la ligne i de mData bout-à-bout.\r\nOn renvoit dont deux matrices numpy : l'une avec les informations d'indice iOut, et l'autre avec toutes les autres informations de mData \"\"\"\r\n\r\n# mData : tableau issu de la fonction loadData pour un iData donné et un iDep donné. Dans notre utilisation mData1 représente le résultat de la fonction regroupeParCommune. Il comporte donc une liste de nom de commune et de pourcentage de chaque modalités dans la commune.\r\n # une ligne de mData1 a la forme [ indice de la commune, [%CSP = 0, %CSP = 1, %CSP = 2, %CSP = 3, %CSP = 4], [%indiceLieuTravail = 0, %indiceLieuTravail = 1] ]\r\n# iOut = indices des sorties dans mData\r\n\r\ndef dataToNumpy(mData, iOut = []):\r\n\r\n # Met les modalités des variables bout-à-bout\r\n \"\"\" On calcule les tailles des matrices que l'on veut renvoyer : autant de lignes que mData et autant de coordonnées que l'on souhaite renvoyer d'information dans dataOutNumpy d'une part, et toutes les autres d'autre part \"\"\"\r\n I, M, J, K = len(mData), len(mData[0]), 0, 0 # I : nombre de lignes de mData ; M : nombre de champs sur une ligne de mData\r\n for m in range(1,M): # On parcourt tous les champs d'une ligne de mData\r\n x = mData[0][m] # On va chercher l'information de la commune d'indice m\r\n if m in iOut: # Si l'on cherche l'information d'indice m\r\n K += length(x) \r\n else:\r\n J += length(x)\r\n \r\n dataInNumpy = np.zeros((I,J)) # On crée une matrice avec autant de lignes que mData et chaque ligne comporte autant de champs que les lignes initiales moins celles que l'on veut renvoyer dans iOut\r\n dataOutNumpy = np.zeros((I,K)) # On crée une matrice avec autant de lignes que mData et chaque ligne comporte le nombre de champ que l'on désire renvoyer dans iOut\r\n \r\n \"\"\"On remplit ensuite les matrices dataInNumpy et dataOutNumpy avec les informations de mData correspondantes au format Numpy \"\"\"\r\n for i in range(I): # On parcourt toutes les lignes de mData\r\n j, k = 0, 0\r\n for m in range(1,M): # On parcourt tous les champs de la ligne i\r\n x = mData[i][m] # On s'intéresse au champ d'indice m de la ligne i\r\n n = length(x)\r\n if m in iOut: # Si l'on veut renvoyer cette information\r\n if n == 1: # Si le champ d'indice m de la ligne i est un nombre\r\n dataOutNumpy[i,k] = x # On l'ajoute à dataOutNumpy\r\n k += 1\r\n else:\r\n for y in range(n): # Si le champ d'indice m de la ligne i est une liste\r\n dataOutNumpy[i,k] = x[y] # On ajoute tous les termes de la liste à la suite\r\n k += 1\r\n else: # Si on ne veut pas renvoyer cette information, on fait les mêmes opérations que précédemment mais en ajoutant les données à dataInNumpy \r\n if n ==1:\r\n dataInNumpy[i,j] = x\r\n j += 1\r\n else:\r\n for y in range(n):\r\n dataInNumpy[i,j] = x[y]\r\n j += 1\r\n return(dataInNumpy, dataOutNumpy) # On renvoit les données que l'on veut (dataOutNumpy) et les autres (dataInNumpy)\r\n \r\n \r\n \r\n \r\n\"\"\" =================================== OPTIMISATION =================================== \"\"\"\r\n\r\n\r\n\"\"\" Renvoit une sigmoïd de la forme 1/(1+exp(-x)) \"\"\"\r\ndef sigmoid(X):\r\n return(1./(1+np.exp(-X)))\r\n \r\n \r\n \r\n\"\"\"La fonction hypFunction a pour but de créer un polynome des données de mDataX.\r\nOn modifie donc mDataX de façon à avoir un polynome de chacune de ses données initiales des degrés 0 à degPoly\"\"\"\r\n\r\n# mDataX : tableau issu de la fonction loadData pour un iData donné et un iDep donné. Dans notre utilisation mDataX représente le résultat de la fonction regroupeParCommune après execution de la fonction splitSet. \r\n#Il comporte donc une liste de de données avec uniquement les champs qui nous intéressent du fichier txt original. Chaque ligne de mDataX correspond à une commune\r\n\r\ndef hypFunction(mDataX, Theta, degPoly): ##### Ne marche actuellement pas si mDataX contient une seule commune\r\n X = np.concatenate((np.ones((mDataX.shape[0],1)), mDataX), axis = 1) # On ajoute un 1 devant toutes les lignes de mDataX\r\n for i in range(2, degPoly+1):\r\n X = np.concatenate((X, mDataX**i), axis = 1) # On ajoute à chaque champ de mDataX la suite de ses puissance de 1 à degPoly\r\n return(X.dot(Theta.T)) # On renvoit le produit matriciel entre X et Theta.T\r\n\r\n\r\n\r\n\"\"\"La fonction error a pour but de calculer l'erreur entre le résultat de la fonction hypFunction calculée sur un echantillon de mData par rapport au reste de mData.\r\nAinsi on réalise un calcule polynomial sur un échantillon constitué de 80% de mData que l'on conpare aux reste des 20% de mData.\r\nError renvoit une matrice correspondant au éléments diagonaux de la matrice des moyennes pondérées des résidus de la fonction hypFunction. \"\"\"\r\n\r\n# hypFunction, Theta : voir précédemment\r\n# dataX : tableaux de valeurs issues de mData après exécution de regroupeParCommune et de splitSet. On obtient donc 80% des communes de mData dans dataX. Ce tableau sert au calcul du polynôme de notre modélisation.\r\n# dataY : tableaux de valeurs issues de mData après exécution de regroupeParCommune et de splitSet. On obtient donc 20% des communes de mData dans dataY. Ce tableau sert à la vérification du polynôme sur un échantillon de valeurs vraies.\r\n# degPoly : degré du polynôme d'interpolation choisi\r\n\r\ndef error(hypFunction, Theta, dataX, dataY, degPoly):\r\n n = dataX.shape[0] # Nombre de lignes de mDataX\r\n w = np.zeros(n) # Poids des individus, pondérés par le nombre d'habitants/commune X[i,0]\r\n for i in range(n): # On parcourt toutes les lignes de mDataX\r\n w[i] = dataX[i,0] # dataX[i,0] représente le nombre d'individus résidant dans la commune i\r\n weight = np.diag(w/np.sum(w)) # Poids sous forme matricielle diagonale et normalisés\r\n guess = hypFunction(dataX, Theta, degPoly) # Polynome issu de l'execution de hypFunction\r\n residu = (guess-dataY)/(1E-10+guess+dataY/2) # Ecart entre la fonction calculée et les valeurs vraies issues d'un echantillon de mData : mDataY\r\n square_Erreur = residu.T.dot(weight.dot(residu)) # Moyenne pondérée des carrés des résidus\r\n k = square_Erreur.shape[0] \r\n erreur = np.zeros(k) # Eléments diagonaux de la matrice square_Erreur\r\n for i in range(k):\r\n erreur[i] = square_Erreur[i,i] \r\n return(np.sqrt(erreur))\r\n\r\n \r\n \r\ndef costFunction(Theta, X, y, Lambda):\r\n m = y.shape[0]\r\n J = 0\r\n X_aux = np.dot(X,Theta.T)\r\n J = (0.5/m)*np.dot((X_aux-y).T,X_aux-y)+(Lambda/(2*m))*(np.dot(Theta,Theta.T)-Theta[0]**2)\r\n return(J)\r\n\r\ndef gradientCostFunction(Theta, X, y, Lambda):\r\n m = y.shape[0]\r\n X_aux = np.dot(X,Theta.T)\r\n grad = (1/m)*np.dot(X.T, X_aux-y)+(Lambda/m)*Theta\r\n grad[0] = grad[0] - (Lambda/m)*Theta[0]\r\n return(grad)\r\n\r\n\"\"\" ================================== Ne pas modifier ============================================== \"\"\"\r\ndef optimisation(mDataX, mDataY, degPoly, Lambda = 1.):\r\n # Mise en forme du problème comme d'un système linéaire\r\n K = mDataY.shape[1] # Nombre de fonction hypothesis OneVsAll à entraîner\r\n X = np.concatenate((np.ones((mDataX.shape[0],1)), mDataX), axis = 1)\r\n for i in range(2, degPoly+1):\r\n X = np.concatenate((X, mDataX**i), axis = 1)\r\n \r\n L = X.shape[1]\r\n allTheta = np.ones((K,L)) # Theta(k,:) = paramètres de la fonction hypothesis de la k-ième sortie\r\n for k in range(K):\r\n y = mDataY[:,k]\r\n resOptim = scipy.optimize.minimize(fun=costFunction, x0=np.ones(L), args=(X,y,Lambda), jac = gradientCostFunction)\r\n allTheta[k,:] = resOptim.x\r\n return(allTheta)\r\n\r\n#################################\r\n# NORMALISER LE NOMBRE DE TRAVAIL SUR LA REGION ?? lE DEPARTEMENT ?? ET/OU LOCALEMENT ??\r\n# Car sortie = pourcentage => besoin de données normalisées\r\n \r\n \r\n \r\n\"\"\" \r\nfrom scipy.cluster.vq import kmeans2\r\ncentroids, clust_ind = kmeans2(mFact[:,:2], nb_classes)\r\n \r\n\"\"\" \r\n \r\n \r\n", "sub_path": "ModelisationPro.py", "file_name": "ModelisationPro.py", "file_ext": "py", "file_size_in_byte": 10715, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.zeros", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 165, "usage_type": "call"}, {"api_name": "scipy.optimize.minimize", "line_number": 168, "usage_type": "call"}, {"api_name": "scipy.optimize", "line_number": 168, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 168, "usage_type": "call"}]} +{"seq_id": "284643754", "text": "import matplotlib.pyplot as plt\nimport matplotlib.animation as animation\n\nfig = plt.figure()\nax1 = fig.add_subplot(1, 1, 1)\n\n\ndef mianimate(i):\n print(\"Refreshing Data....\")\n graph_data = open('nomames.txt', 'r').read()\n lines = graph_data.split(\"\\n\")\n xs = []\n ys = []\n for line in lines:\n if len(line) > 1:\n x, y = line.split(',')\n xs.append(int(x))\n ys.append(int(y))\n ax1.clear()\n ax1.plot(x, y)\n\n\nani = animation.FuncAnimation(fig, mianimate, interval=1000)\nplt.show()\n", "sub_path": "random/plots/liveplot.py", "file_name": "liveplot.py", "file_ext": "py", "file_size_in_byte": 538, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 4, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 4, "usage_type": "name"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.animation", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "31784775", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Feb 15 21:26:03 2016\n\n@author: loffb\n\"\"\"\n###############################################################################\n# Import section\n###############################################################################\nimport sqlite3\nimport os\nimport traceback\nimport time as time\n###############################################################################\n# Header\n###############################################################################\n__author__ = 'Benjamin Loeffel'\n__copyright__ = \"Copyright 2016\"\n__credits__ = ['Jonas Lengacher']\n__license__ = 'GPL'\n__version__ = '23_02_2016_15_50'\n__day__ = time.strftime('%d.%m.%Y')\n__time__ = time.strftime('%H:%M:%S')\n__maintainer__ = 'Benjamin Loeffel'\n__email__ = 'benjamin.loeffel@gmx.ch'\n__status__ = 'Developement'\n###############################################################################\n###############################################################################\nclass WhiskyClass(object):\n \"\"\"\n Class for a Whisky Bottle\n \"\"\"\n def __init__(self,Distillery,Name,Region,Age,Body,Smokyness,Price,NLeds=2):\n \"\"\"\n Constructor of Whisky class\n \n Parameters\n ----------\n Distillery : scalar, string\n Name of the distillery\n Name : scalar, string\n Name of the whisky\n Region : scalar, string\n Name of the region\n Age : scalar, int\n Age of the whisky\n Body : scalar, float\n Body (range -100 - 100)\n Smokyness : scalar, float\n Smokyness (range -100 - 100)\n NLeds : scalar, int, optional\n Number of leds the bottle occupies, default = 2\n \"\"\"\n self.region=Region\n self.distillery=Distillery\n self.name=Name\n self.age=Age\n self.body = Body\n self.smokyness = Smokyness\n self.price=Price\n self.nleads=NLeds\n self.startled=False\n self.endled=False\n self.strip=False\n \nif __name__ == '__main__':\n \n Glenfiddich12=WhiskyClass('Glenfiddich','Glenfiddich 12 y','Speyside',12,-30,-50,37.00)\n Glenfiddich125thAnniversary=WhiskyClass('Glenfiddich','Glenfiddich 125th Anniversary 19 y','Speyside',19,-10,-30,135.00)\n Glenmorangie10=WhiskyClass('Glenmorangie','Glenmorangie 10 y','Highlands',10,-4,-42,35.00)\n GlenmorangieQuintaRuban=WhiskyClass('Glenmorangie', 'Glenmorangie Quinta Ruban 12 y', 'Highlands',12,-15,-35,66.00)\n GlenmorangieNectardOr=WhiskyClass('Glenmorangie', 'Glenmorangie Nectar d Or 12 y', 'Highlands',12,-8,3,78.00)\n Glenmorangie18=WhiskyClass('Glenmorangie','Glenmorangie 18 y','Highlands',18,42,0,115.00)\n Talisker10=WhiskyClass('Talisker','Talisker 10 y','Skye',10,-10,70,48.00)\n Lagavulin16=WhiskyClass('Lagavulin','Lagavulin 16 y','Islay',16,85,95,62.00)\n Aberlour10=WhiskyClass('Aberlour','Aberlour 10 y','Highlands',10,50,-50,30.00)\n ArdbegUigeadail=WhiskyClass('Ardbeg','Ardbeg Uigeadail','Islay',00,-10,95,95.00)\n Cardhu12=WhiskyClass('Cardhu','Cardhu 12 y','Speyside',12,-30,-10,39.00)\n Cragganmore12=WhiskyClass('Cragganmore','Cragganmore 12 y','Speyside',12,10,30,49.00)\n CragganmoreDistEdit=WhiskyClass('Cragganmore','Cragganmore Distillers Edition','Speyside',12,18,72,69.00)\n Dalwhinnie15=WhiskyClass('Dalwhinnie','Dalwhinnie 15 y','Highlands',15,-50,-5,49.00)\n Glenkinchie12=WhiskyClass('Glenkinchie','Glenkinchie 12 y','Lowlands',12,-90,-30,49.00)\n Glendronach12=WhiskyClass('Glendronach','Glendronach 12 y','Highlands',12,95,-5,60.00)\n HighlandPark18=WhiskyClass('HighlandPark','Highland Park 18 y','Isle',18,70,55,115.00)\n KilchomanMarchirBay=WhiskyClass('Kilchoman','Kilchoman Marchir Bay','Isle',00,-50,95,65.00)\n Knockando12=WhiskyClass('Knockando','Knockando 12 y','Speyside',12,-55,-70,35.00)\n BruichladdichOctomore5=WhiskyClass('Bruichladdich','Octomore 5 y','Isle',5,-70,98,169.00)\n Macallan12=WhiskyClass('Macallan','Macallan 12 y','Speyside',12,58,-8,89.00)\n Glenrothes1995=WhiskyClass('Glenrothes','Glenrothes 1995','Speyside',15,62,-35,86.00)\n Balvenie12=WhiskyClass('Balvenie','Balvenie 12 y','Speyside',12,70,-25,60.00)\n GlenDronach8=WhiskyClass('GlenDronach','GlenDronach 8 y','Highlands',8,100,100,45.00)\n Jura16=WhiskyClass('Jura','Jura 16 y','Isle',16,100,100,74.00)\n Slyrs3=WhiskyClass('Slyrs','Slyrs 2008 3 y','Deutschland',3,100,100,90.00)\n Auchentoshan12=WhiskyClass('Auchentoshan','Auchentoshan 12 y','Lowlands',12,100,100,59.00)\n CaolIla12=WhiskyClass('CaolIla','Caol Ila 12 y','Isle',12,100,100,49.00)\n ClynelishDistEdit=WhiskyClass('Clynelish','Clynelish Distillers Edition 12 y','Highlands',12,100,100,60.00)\n TomatinCuBocan=WhiskyClass('Tomatin','Cu Bocan','Highlands',00,100,100,55.00)\n Edradour10=WhiskyClass('Edradour','Marsala Wood Finish 10 y','Highlands',10,100,100,79.00)\n FassZination13=WhiskyClass('FassZination','FassZination 13 y','Unbekannt',13,100,100,100.00)\n FortyThree=WhiskyClass('Rugenbräu','Forty Three','Schweiz',00,100,100,64.00)\n JamesonSelectReserve=WhiskyClass('Jameson','Jameson Select Reserve','Irish',00,100,100,40.00)\n JBReserve15=WhiskyClass('J&B','J&B Reserve 15 y','Scotch',15,100,100,40.00)\n RoyalLochnagarDistEdit=WhiskyClass('RoyalLochnagar','Royal Lochnagar Distillers Edition','Highlands',00,100,100,69.00)\n SaentisMaltSilver=WhiskyClass('Säntis Malt','Säntis Malt Silver','Schweiz',5,100,100,52.00)\n SaentisMaltGold=WhiskyClass('Säntis Malt','Säntis Malt Gold','Schweiz',5,100,100,72.00)\n SecretStills=WhiskyClass('GordonMacPhail','Secret Stills 1969','Isle',23,100,100,100.00)\n Speyburn10=WhiskyClass('Speyburn','Speyburn 10 y','Speyside',10,100,100,25.00)\n SpeyburnBradanOrach=WhiskyClass('Speyburn','Speyburn Bradan Orach','Speyside',00,100,100,25.00)\n TestWhisyk=WhiskyClass('TestWhisky','TestWhisky 100 y','Lummerland',100,-100,-100,100.00)\n \n \n StripOneList = []\n StripOneList.append(Glenfiddich12)\n StripOneList.append(Glenfiddich125thAnniversary)\n StripOneList.append(Glenmorangie10)\n StripOneList.append(GlenmorangieQuintaRuban)\n StripOneList.append(GlenmorangieNectardOr)\n StripOneList.append(Glenmorangie18)\n StripOneList.append(Talisker10)\n StripOneList.append(Lagavulin16)\n StripOneList.append(Aberlour10)\n StripOneList.append(ArdbegUigeadail)\n StripOneList.append(Cardhu12)\n StripOneList.append(Cragganmore12)\n StripOneList.append(CragganmoreDistEdit)\n StripOneList.append(Dalwhinnie15)\n StripOneList.append(Glenkinchie12)\n StripOneList.append(Glendronach12)\n StripOneList.append(HighlandPark18)\n StripOneList.append(KilchomanMarchirBay)\n StripOneList.append(Knockando12)\n StripOneList.append(BruichladdichOctomore5)\n StripOneList.append(Macallan12)\n StripOneList.append(Glenrothes1995)\n StripOneList.append(Balvenie12)\n StripOneList.append(GlenDronach8)\n \n StripTwoList=[]\n StripTwoList.append(Jura16)\n StripTwoList.append(Slyrs3)\n StripTwoList.append(Auchentoshan12)\n StripTwoList.append(CaolIla12)\n StripTwoList.append(ClynelishDistEdit)\n StripTwoList.append(TomatinCuBocan)\n StripTwoList.append(Edradour10)\n StripTwoList.append(FassZination13)\n StripTwoList.append(FortyThree)\n StripTwoList.append(JamesonSelectReserve)\n StripTwoList.append(JBReserve15)\n StripTwoList.append(RoyalLochnagarDistEdit)\n StripTwoList.append(SaentisMaltSilver)\n StripTwoList.append(SaentisMaltGold)\n StripTwoList.append(SecretStills)\n StripTwoList.append(Speyburn10)\n StripTwoList.append(SpeyburnBradanOrach)\n StripTwoList.append(TestWhisyk)\n \n \n ###########################################################################\n # Exporting to SQL\n ###########################################################################\n try:\n SQLFile='Database_Whisky.sqlite'\n try:\n os.remove(SQLFile)\n except:\n pass\n Connection = sqlite3.connect(SQLFile)\n Cursor = Connection.cursor()\n \n FieldNames=['Region','Distillery','Name','Age','Body','Smokyness','Price','Strip','StartLed','EndLed']\n FieldTypes=['TEXT','TEXT','TEXT','INTEGER','FLOAT','FLOAT','FlOAT','INTEGER','INTEGER','INTEGER']\n Values=zip(FieldNames,FieldTypes)\n Pairs=[]\n for Value in Values:\n Pairs.append(' '.join(Value))\n Pairs = u','.join(map(str, Pairs)) \n SQLCommand=u'CREATE TABLE Whisky ({pairs})'.format(pairs=Pairs)\n Cursor.execute(SQLCommand)\n \n \n FieldNamesString = ','.join(map(str,FieldNames))\n StripLists=[StripOneList,StripTwoList]\n NLeds=[120,109]\n \n for index,StripList in enumerate(StripLists):\n CurrentLed=0\n for Whisky in StripList:\n Whisky.startled=CurrentLed\n Whisky.endled=CurrentLed+Whisky.nleads-1\n Whisky.strip=index\n CurrentLed=Whisky.endled+1\n \n Data=[Whisky.region,Whisky.distillery,Whisky.name,Whisky.age,Whisky.body,Whisky.smokyness,Whisky.price,Whisky.strip,Whisky.startled,Whisky.endled]\n ValueString=u\"\"\n for Value in Data:\n ValueString=ValueString+u\"'\"+unicode(str(Value),encoding='UTF-8')+u\"',\"\n ValueString=ValueString[:-1]\n SQLCommand=u'INSERT INTO Whisky ({Col}) VALUES ({Val})'.format(Col=FieldNamesString,Val=ValueString)\n Cursor.execute(SQLCommand) \n \n if CurrentLed > NLeds[index]:\n raise ValueError('Not enough leds on strip {index}'.format(index=index+1))\n \n Connection.commit()\n Connection.close()\n print('Database written successfully')\n except:\n Connection.commit()\n Connection.close()\n traceback.print_exc()", "sub_path": "Database_Generator.py", "file_name": "Database_Generator.py", "file_ext": "py", "file_size_in_byte": 9956, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "time.strftime", "line_number": 22, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 23, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 165, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 168, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 211, "usage_type": "call"}]} +{"seq_id": "294762066", "text": "# -*- coding: utf-8 -*-\n\nimport sys\nimport numpy as np\nimport cv2\n\nimg = None\ngray = None\n\ndef onTrackbarSlide(pos):\n global img\n gray = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2GRAY)\n ret, dst = cv2.threshold(gray, int(pos), 255, cv2.THRESH_BINARY)\n contours, hierarchy = cv2.findContours(dst, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n if contours:\n copy_gray = np.array(gray)\n for contour in contours:\n #center, radius = cv2.minEnclosingCircle(contour)\n #cv2.circle(copy_gray, (int(center[0]), int(center[1])), int(radius), (0, 0, 255), 1)\n if len(contour) >= 5:\n ellipse = cv2.fitEllipse(contour)\n cv2.ellipse(copy_gray, ellipse, (0, 0, 255), 1)\n cv2.imshow('Contours', copy_gray)\n\ndef main(args):\n global img\n if len(args) != 2:\n return -1\n img = cv2.imread(args[1])\n cv2.namedWindow('Contours')\n cv2.createTrackbar('Threshold', 'Contours', 100, 255, onTrackbarSlide)\n cv2.imshow('Contours', img)\n\n onTrackbarSlide(0)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n return 0\n\nif __name__ == '__main__':\n args = sys.argv\n main(args)\n", "sub_path": "8/8-5/2/3/sample.py", "file_name": "sample.py", "file_ext": "py", "file_size_in_byte": 1170, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "cv2.cvtColor", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.fitEllipse", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.ellipse", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 36, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 40, "usage_type": "attribute"}]} +{"seq_id": "100665976", "text": "from dataclasses import dataclass, field\nfrom typing import List, Optional\nfrom xml.etree.ElementTree import QName\nfrom .t_root_element import TRootElement\n\n__NAMESPACE__ = \"http://www.omg.org/spec/BPMN/20100524/MODEL\"\n\n\n@dataclass\nclass TPartnerRole(TRootElement):\n class Meta:\n name = \"tPartnerRole\"\n\n participant_ref: List[QName] = field(\n default_factory=list,\n metadata={\n \"name\": \"participantRef\",\n \"type\": \"Element\",\n \"namespace\": \"http://www.omg.org/spec/BPMN/20100524/MODEL\",\n }\n )\n name: Optional[str] = field(\n default=None,\n metadata={\n \"type\": \"Attribute\",\n }\n )\n", "sub_path": "bpmn/models/t_partner_role.py", "file_name": "t_partner_role.py", "file_ext": "py", "file_size_in_byte": 684, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "t_root_element.TRootElement", "line_number": 10, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 14, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.QName", "line_number": 14, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 14, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 22, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 22, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "533567020", "text": "#Homework 6\n#Ryan Webster\n#Collaborators: Sean Cunningham, Madison Walder, Jimmy Lilly\n\n#import modules\nimport numpy as np\nimport astropy.units as u\nfrom astropy.constants import G\nimport matplotlib.pyplot as plt\nimport matplotlib\nfrom ReadFile import Read\nfrom CenterOfMass2 import CenterOfMass\n\n\n\ndef OrbitCOM(galaxy, start, end, n):\n \"\"\"function that loops over all the desired snapshots to compute the COM pos and vel as a function of time.\n inputs:\n galaxy: Name of desired galaxy\n start: Starting Snapshot\n end: Ending SnapShot\n n: Interval when to return COM\n \n returns:\n File of galactic position and velocity over start and end times \n \"\"\"\n \n #composing filename for output\n fileout = \"Orbit_{}.txt\".format(galaxy)\n \n #setting tolerance and VolDec\n if galaxy == 'M33':\n VolDec = 4.0\n delta = 0.1\n else:\n VolDec = 5.0\n delta = 0.1\n #print(galaxy,VolDec,delta)\n #generating the snapshot id sequence \n snap_ids = np.arange(start,end,n)\n #initializing the array for orbital info: t, x, y, z, vx, vy, vz of COM\n orbit = np.zeros([len(snap_ids),7])\n \n for i, snap_id in enumerate(snap_ids):# loop over files\n #composing the data filename \n ilbl = '000' + str(snap_ids[i])\n ilbl = ilbl[-3:]\n filename = \"%s_\"%(galaxy)+\"VLowRes/\"+\"%s_\"%(galaxy) + ilbl + '.txt' #I kept my VLowRes files in a separate folder, \n #to keep my Homework6 directory from being polluted with .txt files\n\n #Initializing CenterOfMass class\n COM = CenterOfMass(filename,2)# Uses disk particles\n #Storing the COM pos and vel\n POS = COM.COM_P(delta,VolDec)\n VEL = COM.COM_V(POS[0],POS[1],POS[2])\n \n #storting t, x, y, z, vx, vy, vz in obrit array\n orbit[i]= COM.time.value/1000, *tuple(POS.value), *tuple(VEL.value)\n \n #print(snap_id)\n \n #Writing out data\n np.savetxt(fileout, orbit, fmt = \"%11.3f\"*7, comments='#',\n header=\"{:>10s}{:>11s}{:>11s}{:>11s}{:>11s}{:>11s}{:>11s}\"\\\n .format('t', 'x', 'y', 'z', 'vx', 'vy', 'vz'))\n\n#Running funtion\n#OrbitCOM('MW', 0, 800, 5)\n#OrbitCOM('M31', 0, 800, 5)\n#OrbitCOM('M33', 0, 800, 5)\n\n#Reading in data\nMW_data = np.genfromtxt(\"Orbit_MW.txt\",dtype=None,names=True)\nM31_data = np.genfromtxt(\"Orbit_M31.txt\",dtype=None,names=True)\nM33_data = np.genfromtxt(\"Orbit_M33.txt\",dtype=None,names=True)\n\n#Function used to calculate magnitude of differences between vectors of different galaxies\ndef mag_diff(data1,data2):\n\n xdiff = data1['x']-data2['x'] #subtracting x positions\n ydiff = data1['y']-data2['y'] #\"\" y \"\"\n zdiff = data1['z']-data2['z'] #\"\" z \"\"\n\n vxdiff = data1['vx']-data2['vx'] #subtracting x velocities\n vydiff = data1['vy']-data2['vy'] #\"\" y \"\"\n vzdiff = data1['vz']-data2['vz'] #\"\" z \"\"\n\n r = np.sqrt(xdiff**2.0+ydiff**2.0+zdiff**2.0) #Calculating magnitude of position vectors\n vr = np.sqrt(vxdiff**2.0+vydiff**2.0+vzdiff**2.0) #\"\" velocity \"\"\n\n return(r,vr)\n\n#Using mag_diff for MW and M31\nMW_M31_r,MW_M31_vr = mag_diff(MW_data,M31_data)\n\n#Using mag_diff for M33 and M31\nM33_M31_r,M33_M31_vr = mag_diff(M33_data,M31_data)\n\n\n# Plot the Orbit of the galaxies \n#################################\n\nplt.figure(1)\nplt.title(\"Local Group Separation (0-11 Gyr)\")\nplt.xlabel('Time (Gyr)')\nplt.ylabel('Separation (kpc)')\nplt.plot(MW_data['t'],MW_M31_r,label='MW M31 Separation')\nplt.plot(MW_data['t'],M33_M31_r,label='M33 M31 Separation')\nplt.semilogy()\nplt.legend()\nplt.show()\n#plt.savefig('local_group_sep.png',dpi=350)\n\n\n# Plot the orbital velocities of the galaxies \n#################################\n\nplt.figure(2)\nplt.title(\"Local Group Velocity (0-11 Gyr)\")\nplt.xlabel('Time (Gyr)')\nplt.ylabel('Velocity (km/s)')\nplt.plot(MW_data['t'],MW_M31_vr,label='M33 M31 Velocity')\nplt.plot(MW_data['t'],M33_M31_vr,label='M33 M31 Velocity')\nplt.semilogy()\nplt.legend()\nplt.show()\n#plt.savefig('local_group_vel.png',dpi=350)\n\n\n#********** Section 4 **********\n\n#Question 1:\n\n#Based on the graph of galaxy separation vs time, MW and M31 will have 3 close encounters.\n#This is based off the fact that the graph gets close to 0 kpc 3 times over the course of\n#the simulation.\n\n#Question 2:\n\n#It appears that when the separation between MW and M31 is a minimum, the velocity is a maximum.\n#This is also seen for M33 and M31. \n\n#Question 3:\n\n#MW and M31 appear to merge after 6.6 Gyr. After the merger, M33 appears to orbit MW and M31.", "sub_path": "FINALREPORT/scripts/OrbitCOM.py", "file_name": "OrbitCOM.py", "file_ext": "py", "file_size_in_byte": 4520, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.arange", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 42, "usage_type": "call"}, {"api_name": "CenterOfMass2.CenterOfMass", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.semilogy", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.semilogy", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}]} +{"seq_id": "178722327", "text": "import boto3\n\n\nclass Route53(object):\n\n def __init__(self):\n self._client = boto3.client('route53')\n\n def create_record(self, hosted_zone_id, record, target, record_type='CNAME', ttl=300):\n change_batch = {}\n change = {\n 'Action': 'UPSERT',\n 'ResourceRecordSet': {\n 'Name': record,\n 'Type': record_type,\n 'TTL': ttl,\n 'ResourceRecords': [\n {'Value': target}\n ]\n }\n }\n change_batch['Changes'] = [change]\n self._client.change_resource_record_sets(HostedZoneId=hosted_zone_id, ChangeBatch=change_batch)\n", "sub_path": "nerdployer/helpers/route53.py", "file_name": "route53.py", "file_ext": "py", "file_size_in_byte": 675, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "boto3.client", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "592262561", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom six.moves import xrange\n\nfrom util import log\nfrom pprint import pprint\n\nfrom input_ops import create_input_ops\n\nimport os\nimport time\nimport tensorflow as tf\nimport numpy as np\nimport tqdm\nimport matplotlib.pyplot as mplot\n\nclass Trainer(object):\n\n @staticmethod\n def get_model_class(model_name):\n if model_name == 'baseline':\n from model_baseline import Model\n elif model_name == 'relational_network':\n from model_rn import Model\n elif model_name == 'attentional_relational_network':\n from model_attentional_rn import Model\n else:\n raise ValueError(model_name)\n return Model\n\n def __init__(self,\n config,\n dataset_train,\n dataset_val,\n dataset_test):\n self.config = config\n hyper_parameter_str = config.dataset_path+'_lr_'+str(config.learning_rate)\n self.train_dir = './train_dir/%s-%s-%s-%s' % (\n config.model,\n config.prefix,\n hyper_parameter_str,\n time.strftime(\"%Y%m%d-%H%M%S\")\n )\n\n if not os.path.exists(self.train_dir):\n os.makedirs(self.train_dir)\n log.infov(\"Train Dir: %s\", self.train_dir)\n\n # --- input ops ---\n self.batch_size = config.batch_size\n\n _, self.batch_train = create_input_ops(dataset_train, self.batch_size,\n is_training=True)\n\n _, self.batch_val = create_input_ops(dataset_val, self.batch_size,\n is_training=False)\n\n _, self.batch_test = create_input_ops(dataset_test, self.batch_size,\n is_training=False)\n self.train_length = len(dataset_train)\n self.val_length = len(dataset_val)\n self.test_length = len(dataset_test)\n\n\n # --- create model ---\n Model = self.get_model_class(config.model)\n log.infov(\"Using Model class : %s\", Model)\n self.model = Model(config)\n\n # --- optimizer ---\n self.global_step = tf.contrib.framework.get_or_create_global_step(graph=None)\n self.learning_rate = config.learning_rate\n if config.lr_weight_decay:\n self.learning_rate = tf.train.exponential_decay(\n self.learning_rate,\n global_step=self.global_step,\n decay_steps=10000,\n decay_rate=0.5,\n staircase=True,\n name='decaying_learning_rate'\n )\n\n self.check_op = tf.no_op()\n\n self.optimizer = tf.contrib.layers.optimize_loss(\n loss=self.model.loss,\n global_step=self.global_step,\n learning_rate=self.learning_rate,\n optimizer=tf.train.AdamOptimizer,\n clip_gradients=20.0,\n name='optimizer_loss'\n )\n\n self.summary_op = tf.summary.merge_all()\n try:\n import tfplot\n self.plot_summary_op = tf.summary.merge_all(key='plot_summaries')\n except:\n pass\n\n self.saver = tf.train.Saver(max_to_keep=5)\n self.best_val_saver = tf.train.Saver(max_to_keep=5)\n self.summary_writer = tf.summary.FileWriter(self.train_dir)\n\n self.checkpoint_secs = 600 # 10 min\n\n self.supervisor = tf.train.Supervisor(\n logdir=self.train_dir,\n is_chief=True,\n saver=None,\n summary_op=None,\n summary_writer=self.summary_writer,\n save_summaries_secs=300,\n save_model_secs=self.checkpoint_secs,\n global_step=self.global_step,\n )\n\n session_config = tf.ConfigProto(\n allow_soft_placement=True,\n # intra_op_parallelism_threads=1,\n # inter_op_parallelism_threads=1,\n gpu_options=tf.GPUOptions(allow_growth=True),\n device_count={'GPU': 1},\n )\n self.session = self.supervisor.prepare_or_wait_for_session(config=session_config)\n\n self.ckpt_path = config.checkpoint\n if self.ckpt_path is not None:\n log.info(\"Checkpoint path: %s\", self.ckpt_path)\n self.saver.restore(self.session, self.ckpt_path)\n log.info(\"Loaded the pretrain parameters from the provided checkpoint path\")\n\n def train(self):\n log.infov(\"Training Starts!\")\n pprint(self.batch_train)\n\n\n step = 0\n output_save_step = 1000\n epoch_train_iter = int(self.train_length/self.batch_size)# * 10\n epoch_val_iter = int(self.val_length/self.batch_size)# * 10\n total_epochs = int(200000 / epoch_train_iter)\n\n best_val_accuracy = 0.\n with tqdm.tqdm(total=total_epochs) as epoch_bar:\n\n for e in range(total_epochs):\n train_loss = []\n train_accuracy = []\n val_loss = []\n val_accuracy = []\n total_train_time = []\n with tqdm.tqdm(total=epoch_train_iter) as train_bar:\n for train_step in range(epoch_train_iter):\n step, accuracy, summary, loss, step_time = \\\n self.run_single_step(self.batch_train, step=step, is_train=True)\n step += 1\n train_loss.append(loss)\n train_accuracy.append(accuracy)\n total_train_time.append(step_time)\n train_bar.update(1)\n train_bar.set_description(\"Train loss: {train_loss}, Train accuracy: {train_accuracy},\"\n \"Train loss mean: {train_loss_mean}, \"\n \"Train accuracy mean: {train_accuracy_mean}\"\n .format(train_loss=loss, train_accuracy=accuracy,\n train_loss_mean=np.mean(train_loss),\n train_accuracy_mean=np.mean(train_accuracy)))\n\n train_loss_mean = np.mean(train_loss)\n train_loss_std = np.std(train_loss)\n train_accuracy_mean = np.mean(train_accuracy)\n train_accuracy_std = np.std(train_accuracy)\n total_train_time = np.sum(total_train_time)\n\n with tqdm.tqdm(total=epoch_val_iter) as val_bar:\n for val_iters in range(epoch_val_iter):\n\n loss, accuracy = \\\n self.run_test(self.batch_val, is_train=False)\n val_loss.append(loss)\n val_accuracy.append(accuracy)\n\n val_bar.update(1)\n val_bar.set_description(\"Val loss: {val_loss}, Val accuracy: {val_accuracy},\"\n \"Val loss mean: {val_loss_mean}, Val accuracy mean: {val_accuracy_mean}\"\n .format(val_loss=loss, val_accuracy=loss,\n val_loss_mean=np.mean(val_loss),\n val_accuracy_mean=np.mean(train_accuracy)))\n\n val_loss_mean = np.mean(val_loss)\n val_loss_std = np.std(val_loss)\n val_accuracy_mean = np.mean(val_accuracy)\n val_accuracy_std = np.std(val_accuracy)\n\n\n\n if val_accuracy_mean >= best_val_accuracy:\n best_val_accuracy = val_accuracy_mean\n val_save_path = self.best_val_saver.save(self.session,\n os.path.join(self.train_dir, 'model'),\n global_step=step)\n print(\"Saved best val model at\", val_save_path)\n\n self.log_step_message(step, train_accuracy_mean, val_loss_mean, val_accuracy_mean, train_loss_mean, total_train_time,\n is_train=True)\n\n self.summary_writer.add_summary(summary, global_step=step)\n\n\n log.infov(\"Saved checkpoint at %d\", step)\n save_path = self.saver.save(self.session,\n os.path.join(self.train_dir, 'model'),\n global_step=step)\n print(\"Saved current train model at\", save_path)\n epoch_bar.update(1)\n\n def run_single_step(self, batch, step=None, is_train=True):\n _start_time = time.time()\n\n batch_chunk = self.session.run(batch)\n\n fetch = [self.global_step, self.model.accuracy, self.summary_op,\n self.model.loss, self.check_op, self.optimizer]\n\n try:\n if step is not None and (step % 100 == 0):\n fetch += [self.plot_summary_op]\n except:\n pass\n\n fetch_values = self.session.run(\n fetch, feed_dict=self.model.get_feed_dict(batch_chunk, step=step, is_training=True)\n )\n [step, accuracy, summary, loss] = fetch_values[:4]\n\n try:\n if self.plot_summary_op in fetch:\n summary += fetch_values[-1]\n except:\n pass\n\n _end_time = time.time()\n\n return step, accuracy, summary, loss, (_end_time - _start_time)\n\n def run_test(self, batch, is_train=False, repeat_times=8):\n\n batch_chunk = self.session.run(batch)\n\n loss, accuracy = self.session.run(\n [self.model.loss, self.model.accuracy], feed_dict=self.model.get_feed_dict(batch_chunk,\n is_training=False)\n )\n\n return loss, accuracy\n\n def log_step_message(self, step, train_accuracy, val_loss, val_accuracy, train_loss, step_time, is_train=True):\n if step_time == 0:\n step_time = 0.001\n log_fn = (is_train and log.info or log.infov)\n log_fn((\" [{split_mode:5s} step {step:4d}] \" +\n \"Train Loss: {train_loss:.5f} \" +\n \"Train Accuracy: {train_accuracy:.2f} \"\n \"Validation Accuracy: {val_accuracy:.2f} \" +\n \"Validation Loss: {val_loss:.2f} \" +\n \"({sec_per_batch:.3f} sec/batch, {instance_per_sec:.3f} instances/sec) \"\n ).format(split_mode=(is_train and 'train' or 'val'),\n step=step,\n train_loss=train_loss,\n train_accuracy=train_accuracy*100,\n val_accuracy=val_accuracy*100,\n val_loss=val_loss,\n sec_per_batch=step_time,\n instance_per_sec=8000 / step_time\n )\n )\n\n\ndef check_data_path(path):\n if os.path.isfile(os.path.join(path, 'data.hy')) \\\n and os.path.isfile(os.path.join(path, 'id.txt')):\n return True\n else:\n return False\n\n\ndef main():\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('--batch_size', type=int, default=16)\n parser.add_argument('--model', type=str, default='relational_network', choices=['relational_network', 'baseline', \"attentional_relational_network\"])\n parser.add_argument('--prefix', type=str, default='default')\n parser.add_argument('--checkpoint', type=str, default=None)\n parser.add_argument('--dataset_path', type=str, default='Sort-of-CLEVR_default')\n parser.add_argument('--learning_rate', type=float, default=2.5e-4)\n parser.add_argument('--lr_weight_decay', action='store_true', default=False)\n config = parser.parse_args()\n\n path = os.path.join('./datasets', config.dataset_path)\n\n if check_data_path(path):\n import sort_of_clevr as dataset\n else:\n raise ValueError(path)\n\n config.data_info = dataset.get_data_info()\n config.conv_info = dataset.get_conv_info()\n dataset_train, dataset_val, dataset_test = dataset.create_default_splits(path)\n\n trainer = Trainer(config,\n dataset_train, dataset_val, dataset_test)\n\n log.warning(\"dataset: %s, learning_rate: %f\",\n config.dataset_path, config.learning_rate)\n trainer.train()\n\nif __name__ == '__main__':\n main()\n", "sub_path": "trainer.py", "file_name": "trainer.py", "file_ext": "py", "file_size_in_byte": 12539, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "model_attentional_rn.Model", "line_number": 31, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 48, "usage_type": "call"}, {"api_name": "util.log.infov", "line_number": 49, "usage_type": "call"}, {"api_name": "util.log", "line_number": 49, "usage_type": "name"}, {"api_name": "input_ops.create_input_ops", "line_number": 54, "usage_type": "call"}, {"api_name": "input_ops.create_input_ops", "line_number": 57, "usage_type": "call"}, {"api_name": "input_ops.create_input_ops", "line_number": 60, "usage_type": "call"}, {"api_name": "model_attentional_rn.Model", "line_number": 68, "usage_type": "name"}, {"api_name": "util.log.infov", "line_number": 69, "usage_type": "call"}, {"api_name": "util.log", "line_number": 69, "usage_type": "name"}, {"api_name": "model_attentional_rn.Model", "line_number": 69, "usage_type": "name"}, {"api_name": "model_attentional_rn.Model", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.contrib.framework.get_or_create_global_step", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 73, "usage_type": "attribute"}, {"api_name": "tensorflow.train.exponential_decay", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tensorflow.no_op", "line_number": 85, "usage_type": "call"}, {"api_name": "tensorflow.contrib.layers.optimize_loss", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 87, "usage_type": "attribute"}, {"api_name": "tensorflow.train", "line_number": 91, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.merge_all", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 96, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.merge_all", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 99, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 103, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 103, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 104, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 105, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 105, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Supervisor", "line_number": 109, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 109, "usage_type": "attribute"}, {"api_name": "tensorflow.ConfigProto", "line_number": 120, "usage_type": "call"}, {"api_name": "tensorflow.GPUOptions", "line_number": 124, "usage_type": "call"}, {"api_name": "util.log.info", "line_number": 131, "usage_type": "call"}, {"api_name": "util.log", "line_number": 131, "usage_type": "name"}, {"api_name": "util.log.info", "line_number": 133, "usage_type": "call"}, {"api_name": "util.log", "line_number": 133, "usage_type": "name"}, {"api_name": "util.log.infov", "line_number": 136, "usage_type": "call"}, {"api_name": "util.log", "line_number": 136, "usage_type": "name"}, {"api_name": "pprint.pprint", "line_number": 137, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 147, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 175, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 202, "usage_type": "call"}, {"api_name": "os.path", "line_number": 202, "usage_type": "attribute"}, {"api_name": "util.log.infov", "line_number": 212, "usage_type": "call"}, {"api_name": "util.log", "line_number": 212, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path", "line_number": 214, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 220, "usage_type": "call"}, {"api_name": "time.time", "line_number": 244, "usage_type": "call"}, {"api_name": "util.log.info", "line_number": 262, "usage_type": "attribute"}, {"api_name": "util.log", "line_number": 262, "usage_type": "name"}, {"api_name": "util.log.infov", "line_number": 262, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 282, "usage_type": "call"}, {"api_name": "os.path", "line_number": 282, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 282, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 283, "usage_type": "call"}, {"api_name": "os.path", "line_number": 283, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 283, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 291, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 301, "usage_type": "call"}, {"api_name": "os.path", "line_number": 301, "usage_type": "attribute"}, {"api_name": "sort_of_clevr.get_data_info", "line_number": 308, "usage_type": "call"}, {"api_name": "sort_of_clevr.get_conv_info", "line_number": 309, "usage_type": "call"}, {"api_name": "sort_of_clevr.create_default_splits", "line_number": 310, "usage_type": "call"}, {"api_name": "{'Model': 'model_attentional_rn.Model', 'tfplot': 'tfplot'}", "line_number": 312, "usage_type": "call"}, {"api_name": "util.log.warning", "line_number": 315, "usage_type": "call"}, {"api_name": "util.log", "line_number": 315, "usage_type": "name"}]} +{"seq_id": "389881423", "text": "import scrapy\nfrom ..items import Fortune500Item\n\n\nclass FortuneSpider(scrapy.Spider):\n name = 'fortune'\n start_urls = [\n 'https://fortune.com/fortune500/2019/walmart'\n ]\n\n def parse(self, response):\n name = response.css(\"#content > div.franchiseHero__wrapper--3PvFt > div.franchiseHero__singleWrapper--2KIbJ > \"\n \"div > h1 > span::text\").extract_first()\n\n industry = response.css(\"#content > div.container__xl--3mlsX.container__\"\n \"container--a5NXK.aboutWrapper__wrapper--3-4PU.container__padding--2mKn5 > \"\n \"div.aboutWrapper__contentWrapper--1MEMC > div.dataTable__wrapper--2Y2vt.\"\n \"dataTable__wrapper--2Y2vt > table > tbody > tr:nth-child(4) > td.dataTable__\"\n \"value--3n5tL.dataTable__valueAlignLeft--3uvNx > div::text\").extract_first()\n\n rank = response.css(\"#content > div.franchiseHero__wrapper--3PvFt > div.franchiseHero__singleWrapper--2KIbJ > \"\n \"div > div.franchiseHero__rankWrapper--3yC5a::text\").extract_first()\n\n hq_location = response.css(\"#content > div.container__xl--3mlsX.container__container--a5NXK.\"\n \"aboutWrapper__wrapper--3-4PU.container__padding--2mKn5 > div.aboutWrapper__\"\n \"contentWrapper--1MEMC > div.dataTable__wrapper--2Y2vt.dataTable__\"\n \"wrapper--2Y2vt > table > tbody > tr:nth-child(5) > td.dataTable__\"\n \"value--3n5tL.dataTable__valueAlignLeft--3uvNx > div::text\").extract_first()\n\n employees = response.css(\"#content > div.container__xl--3mlsX.container__container--a5NXK.aboutWrapper_\"\n \"_wrapper--3-4PU.container__padding--2mKn5 > div.aboutWrapper__contentWrapper\"\n \"--1MEMC > div.dataTable__wrapper--2Y2vt.dataTable__wrapper--2Y2vt > table > \"\n \"tbody > tr:nth-child(8) > td.dataTable__value--3n5tL.dataTable__\"\n \"valueAlignLeft--3uvNx::text\").extract_first()\n\n link_to_next = response.css(\"#content > div.franchiseHero__wrapper--3PvFt > div.franchiseHero__singleWrapper\"\n \"--2KIbJ > div > div.container__xl--3mlsX.container__container--a5NXK.single\"\n \"Pagination__wrapper--13BdV.heroPagination__wrapper--32zCC.container__padding--\"\n \"2mKn5 > a.button__wrapper--1B-uq.button__wrapper--1B-uq.singlePagination__next--\"\n \"3SyYt::attr(href)\").extract_first()\n\n items = Fortune500Item()\n\n items['name'] = name\n items['rank'] = rank\n items['industry'] = industry\n items['hq_location'] = hq_location\n items['employees'] = employees\n\n yield items\n\n next_page = link_to_next\n if int(rank) < 500:\n yield response.follow(next_page, callback=self.parse)\n", "sub_path": "fortune_500/fortune_500/spiders/fortune500.py", "file_name": "fortune500.py", "file_ext": "py", "file_size_in_byte": 3100, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "scrapy.Spider", "line_number": 5, "usage_type": "attribute"}, {"api_name": "items.Fortune500Item", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "629690219", "text": "# -*- encoding: utf-8 -*\n\n\"\"\"\ndelete from bpp_autor_jednostka ;delete from bpp_autor;delete from bpp_bppuser_groups; delete from bpp_bppuser;delete from bpp_jednostka;\n\ndelete from bpp_zrodlo;\n\ndelete from bpp_wydawnictwo_zwarte_autor;delete from bpp_wydawnictwo_zwarte; delete from bpp_wydawnictwo_ciagle_autor; delete from bpp_wydawnictwo_ciagle; delete from bpp_patent_autor; delete from bpp_patent; delete from bpp_praca_doktorska; delete from bpp_praca_habilitacyjna;\n\"\"\"\n\nfrom optparse import make_option\nimport subprocess\nimport sys\n\nfrom django.core.management import BaseCommand\n\n\nclass Command(BaseCommand):\n help = 'Uruchamiam import_bpp na wielu CPU'\n\n def add_arguments(self, parser):\n parser.add_argument(\"--cpu\", action=\"store\", type=int, default=4)\n\n def handle(self, *args, **options):\n cpus = options['cpu']\n\n jednowatkowe = ['uzytkownicy', 'korekty', 'clusters']\n\n for option in ['publikacje']:\n # '['uzytkownicy', 'jednostki', 'autorzy',\n # 'powiazania', 'zrodla', 'korekty', 'publikacje', 'clusters']:\n proc = []\n if option in jednowatkowe:\n ret = subprocess.check_call(\n [sys.executable, sys.argv[1], 'import_bpp',\n '--' + option,\n '--traceback'])\n continue\n\n else:\n for n in range(cpus):\n ret = subprocess.Popen(\n [sys.executable, sys.argv[0], 'import_bpp',\n '--' + option,\n '--initial-offset=%s' % n,\n '--skip=%s' % (cpus-1),\n '--traceback'])\n proc.append(ret)\n\n for elem in proc:\n elem.wait()\n\n proc = []\n for n in range(cpus):\n ret = subprocess.Popen(\n [sys.executable, sys.argv[0], 'rebuild_cache',\n '--initial-offset=%s' % n,\n '--skip=%s' % (cpus-1),\n '--traceback'])\n proc.append(ret)\n\n for elem in proc:\n elem.wait()\n", "sub_path": "src/bpp/management/commands/run_import.py", "file_name": "run_import.py", "file_ext": "py", "file_size_in_byte": 2166, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.core.management.BaseCommand", "line_number": 18, "usage_type": "name"}, {"api_name": "subprocess.check_call", "line_number": 34, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 35, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 35, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 42, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 43, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 43, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 55, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 56, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 56, "usage_type": "attribute"}]} +{"seq_id": "384347286", "text": "\"\"\"Thermostat temperature up command.\"\"\"\nimport asyncio\n\nfrom .. import ack_handler\nfrom ...topics import THERMOSTAT_SET_COOL_SETPOINT\nfrom .direct_command import DirectCommandHandlerBase\n\n\nclass ThermostatCoolSetPointCommand(DirectCommandHandlerBase):\n \"\"\"Manage an outbound THERMOSTAT_SET_COOL_SETPOINT command to a device.\"\"\"\n\n def __init__(self, address):\n \"\"\"Init the TemperatureUpCommand class.\"\"\"\n super().__init__(topic=THERMOSTAT_SET_COOL_SETPOINT, address=address)\n self._degrees = None\n self._zone = None\n self._deadband = None\n\n # pylint: disable=arguments-differ\n async def async_send(self, degrees, zone: int = None, deadband: int = None):\n \"\"\"Send the THERMOSTAT_SET_COOL_SETPOINT command async.\"\"\"\n return await super().async_send(degrees=degrees, zone=zone, deadband=deadband)\n\n @ack_handler\n async def async_handle_ack(self, cmd1, cmd2, user_data):\n \"\"\"Handle the ACK response.\"\"\"\n if user_data[\"d1\"]:\n self._degrees = user_data[\"d1\"] / 2\n self._zone = cmd2\n self._deadband = user_data[\"d2\"] / 2\n else:\n self._degrees = cmd2 / 2\n self._zone = None\n self._deadband = None\n await super().async_handle_ack(cmd1=cmd1, cmd2=cmd2, user_data=user_data)\n await asyncio.sleep(0.2)\n self._degrees = None\n self._zone = None\n self._deadband = None\n\n def _update_subscribers_on_direct_ack(\n self, cmd1, cmd2, target, user_data, hops_left\n ):\n \"\"\"Update subscribers.\"\"\"\n self._call_subscribers(\n degrees=self._degrees, zone=self._zone, deadband=self._deadband\n )\n", "sub_path": "pyinsteon/handlers/to_device/thermostat_cool_set_point.py", "file_name": "thermostat_cool_set_point.py", "file_ext": "py", "file_size_in_byte": 1701, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "direct_command.DirectCommandHandlerBase", "line_number": 9, "usage_type": "name"}, {"api_name": "topics.THERMOSTAT_SET_COOL_SETPOINT", "line_number": 14, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "267711397", "text": "from requests_oauthlib import OAuth1Session\nimport secrets\n\nclient_key = secrets.client_key\nclient_secret = secrets.client_secret\n\n# get a request token\nrequest_token_url = 'https://api.twitter.com/oauth/request_token'\noauth = OAuth1Session(client_key, client_secret=client_secret)\nfetch_response = oauth.fetch_request_token(request_token_url)\nresource_owner_key = fetch_response.get('oauth_token')\nresource_owner_secret = fetch_response.get('oauth_token_secret')\n\n\n# get authorization from user \nbase_authorization_url = 'https://api.twitter.com/oauth/authorize'\n# authorize_url = base_authorization_url + '?oauth_token='\n# authorize_url = authorize_url + resource_owner_key\n# print ('Please go here and authorize,', authorize_url)\n# verifier = input('Please input the verifier')\n\nauthorization_url = oauth.authorization_url(base_authorization_url)\nprint ('Please go here and authorize,', authorization_url)\nverifier = input('Paste the verification code here: ')\n\naccess_token_url = 'https://api.twitter.com/oauth/access_token'\noauth = OAuth1Session(client_key,\n client_secret=client_secret,\n resource_owner_key=resource_owner_key,\n resource_owner_secret=resource_owner_secret,\n verifier=verifier)\noauth_tokens = oauth.fetch_access_token(access_token_url)\n\nresource_owner_key = oauth_tokens.get('oauth_token')\nresource_owner_secret = oauth_tokens.get('oauth_token_secret')\n\nprint(resource_owner_key, resource_owner_secret)\nprotected_url = 'https://api.twitter.com/1.1/account/settings.json'\n\noauth = OAuth1Session(client_key,\n client_secret=client_secret,\n resource_owner_key=resource_owner_key,\n resource_owner_secret=resource_owner_secret)\nr = oauth.get(protected_url)\nprint (r.text)\n\nprotected_url = 'https://api.twitter.com/1.1/search/tweets.json'\nparams = {'q':'food'}\nr = oauth.get(protected_url, params=params)\nprint (r.text)\n", "sub_path": "twitter-oauth1.py", "file_name": "twitter-oauth1.py", "file_ext": "py", "file_size_in_byte": 2009, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "secrets.client_key", "line_number": 4, "usage_type": "attribute"}, {"api_name": "secrets.client_secret", "line_number": 5, "usage_type": "attribute"}, {"api_name": "requests_oauthlib.OAuth1Session", "line_number": 9, "usage_type": "call"}, {"api_name": "requests_oauthlib.OAuth1Session", "line_number": 27, "usage_type": "call"}, {"api_name": "requests_oauthlib.OAuth1Session", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "275639076", "text": "from django.urls import path\nfrom main import views\n\nurlpatterns = [\n path('', views.todos_list, name='todos_list'),\n path('/', views.todo_detail, name='todo_detail'),\n path('new/', views.todo_new, name='todo_new'),\n path('/completed', views.completed_detail, name='completed_detail'),\n path('completed/', views.completed_todos_list, name='completed_todos'),\n path('delete/', views.delete_todos, name='delete_todos'),\n path('delete/completed/', views.delete_completed, name='delete_completed'),\n path('update/', views.update_todo, name='update_todo'),\n path('update-mark/', views.update_mark, name='update_mark'),\n path('delete/', views.delete_todo, name='delete_todo'),\n\n\n]\n\n", "sub_path": "week5/TODO/main/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 746, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "main.views.todos_list", "line_number": 5, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 5, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "main.views.todo_detail", "line_number": 6, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 6, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "main.views.todo_new", "line_number": 7, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "main.views.completed_detail", "line_number": 8, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "main.views.completed_todos_list", "line_number": 9, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "main.views.delete_todos", "line_number": 10, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "main.views.delete_completed", "line_number": 11, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "main.views.update_todo", "line_number": 12, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "main.views.update_mark", "line_number": 13, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "main.views.delete_todo", "line_number": 14, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 14, "usage_type": "name"}]} +{"seq_id": "95147765", "text": "\"\"\" statuspage URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.11/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\n\nfrom django.conf.urls import url\nfrom django.contrib import admin\nfrom lib.records.views import redirectToYellowAntAuthenticationPage, yellowantapi, yellowantRedirecturl, webhook\nfrom lib.web import urls as web_urls\nfrom django.urls import path, include\n\nurlpatterns = [\n\n # For Django Admin\n url(r'^admin/', admin.site.urls),\n\n # For creating new integration\n path(\"create-new-integration/\", redirectToYellowAntAuthenticationPage, \\\n name=\"statuspage-auth-redirect\"),\n\n # For redirecting from yellowant\n path(\"yellowantredirecturl/\", yellowantRedirecturl, \\\n name=\"yellowant-auth-redirect\"),\n\n # For redirecting to yellowant authentication page\n path(\"yellowantauthurl/\", redirectToYellowAntAuthenticationPage, \\\n name=\"yellowant-auth-url\"),\n\n # For getting command specific information from slack on executing a command\n path(\"yellowant-api/\", yellowantapi, name=\"yellowant-api\"),\n\n url('webhook/(?P[^/]+)/', webhook, name='webhook'),\n # Including all web urls\n path('', include(web_urls)),\n]\n", "sub_path": "mailgunapp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1714, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 26, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "lib.records.views.redirectToYellowAntAuthenticationPage", "line_number": 29, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "lib.records.views.yellowantRedirecturl", "line_number": 33, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "lib.records.views.redirectToYellowAntAuthenticationPage", "line_number": 37, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 41, "usage_type": "call"}, {"api_name": "lib.records.views.yellowantapi", "line_number": 41, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 43, "usage_type": "call"}, {"api_name": "lib.records.views.webhook", "line_number": 43, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 45, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 45, "usage_type": "call"}, {"api_name": "lib.web.urls", "line_number": 45, "usage_type": "argument"}]} +{"seq_id": "264536101", "text": "from django.forms import ModelForm\nfrom django.forms import Textarea\nfrom .models import Contact\n\n\nclass ContactForm(ModelForm):\n\n class Meta:\n # Определяем модель, на основе которой создаем форму\n model = Contact\n # Поля, которые будем использовать для заполнения\n fields = ['email','message']\n widgets = {\n 'message': Textarea(\n attrs={\n 'placeholder': 'Напишите Ваше сообщение'\n }\n ),\n 'email': Textarea(\n attrs={\n 'placeholder': 'Напишите Вашу почту',\n 'maxlength': '200',\n 'rows': '2'\n }\n )\n }\n", "sub_path": "forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 846, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.forms.ModelForm", "line_number": 6, "usage_type": "name"}, {"api_name": "models.Contact", "line_number": 10, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 14, "usage_type": "call"}, {"api_name": "django.forms.Textarea", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "398142369", "text": "import serial\nimport numpy as np\nfrom time import time\nimport matplotlib.pyplot as plt\n#from pylab import rcParams\n\n\nM=np.zeros((30,30))\nport = \"COM20\"\nbaud = 115200\ntemp = 0b101111\ndata = 0\nheader = 0\nPressureArray=[]\nX=[x for x in range(30)]\nY=[y for y in range(30)]\nx,y=np.meshgrid(X, Y)\ncmd=\"COM6\"\n#fig=plt.figure()\n#plt.axes().set_aspect('equal')\nrow=0\n \ndef animate():\n df = np.zeros((30,30))\n while 1:\n header=0\n data1=0\n while(header != 255):\n header = int.from_bytes(ser.read(1),'big')\n data1=ser.read(1)\n if(data1==b'\\xff'):\n for i in range(30):\n for j in range(30):\n temp =ser.read(2)\n data=int.from_bytes(temp,byteorder='little')\n df[29-i,j] = data\n #plt.contourf(x,y,df, 50, cmap=plt.cm.jet,vmin=0, vmax=700) \n #plt.show()\n yield df\n\nser = serial.Serial(port, baud, timeout=1)\nif ser.isOpen():\n print(ser.name + ' is open...')\n\nrw=animate()\nM=next(rw)\npast=time()\n\nfor i in range(60000):\n plt.clf()\n M=next(rw)\n plt.contourf(X, Y, M, 50, cmap=plt.cm.jet,vmin=50, vmax=500)\n\t# use plt.contour to add contour lines\n #C = plt.contour(X, Y, M, 8, colors='black', linewidth=.5)\n #plt.clabel(C, inline=True, fontsize=10)\n plt.title('NAMI Pressure Sensor')\n plt.xticks(())\n plt.yticks(())\n plt.figure(\"Nano and Advanced Materials Institue\")\n plt.axes().set_aspect('equal')\n #plt.figure(figsize=(10,10))\n #rcParams['figure.figsize'] = 10, 10\n plt.pause(0.03)\n rightnow=time()\n\n\n", "sub_path": "PressureSensor30x30_PCB3.py", "file_name": "PressureSensor30x30_PCB3.py", "file_ext": "py", "file_size_in_byte": 1628, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.zeros", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 24, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 41, "usage_type": "call"}, {"api_name": "time.time", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 52, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.title", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axes", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "time.time", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "77693899", "text": "import gym\nfrom tqdm import tqdm\nimport custom_bandits\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom agents import pureExploitation, pureExploration, epsilonGreedy, decayingEpsilonGreedy, softmaxExploration, UCB\nfrom utils import smooth_array, create_Earth\n\nif __name__ == \"__main__\":\n\n # parameters\n SEED = 0\n ANSWER_TO_EVERYTHING = 42\n\n timeSteps = 1000\n noOfEnvs = 50\n\n # to store rewards across environments for all 6 agents\n reward_exploitation = np.zeros((noOfEnvs,timeSteps))\n reward_exploration = np.zeros((noOfEnvs,timeSteps))\n reward_epsilonGreedy = np.zeros((noOfEnvs,timeSteps))\n reward_decayingEpsilonGreedy = np.zeros((noOfEnvs,timeSteps))\n reward_softmax = np.zeros((noOfEnvs,timeSteps))\n reward_ucb = np.zeros((noOfEnvs,timeSteps))\n\n # for every env\n for i in tqdm(range(noOfEnvs),ascii=True, unit=\" env \"):\n\n # skip 42\n if SEED==ANSWER_TO_EVERYTHING:\n SEED = SEED + 1\n create_Earth(ANSWER_TO_EVERYTHING) # read hitchhiker's guide to galaxy if not yet read\n\n np.random.seed(SEED)\n\n # generating alpha and beta\n alpha = np.random.uniform()\n beta = np.random.uniform()\n\n # create env\n env = gym.make('twoArm_bandits-v0', alpha=alpha, beta=beta, seed=SEED)\n env.reset()\n\n # store reward history for every env\n _, _, _, reward_exploitation[i], _ = pureExploitation(env, timeSteps)\n _, _, _, reward_exploration[i], _ = pureExploration(env, timeSteps)\n _, _, _, reward_epsilonGreedy[i], _ = epsilonGreedy(env, timeSteps, epsilon=0.1)\n _, _, _, reward_decayingEpsilonGreedy[i], _ = decayingEpsilonGreedy(env, timeSteps, decay_till=timeSteps/2, max_epsilon=1, min_epsilon=1e-6, decay_type='exp')\n _, _, _, reward_softmax[i], _ = softmaxExploration(env, timeSteps, decay_till=timeSteps/2, max_tau=100, min_tau = 0.005, decay_type='exp')\n _, _, _, reward_ucb[i], _ = UCB(env, timeSteps, c=0.1)\n\n print(f' Seed: {SEED} || alpha: {alpha} || beta: {beta}')\n \n # increment seed\n SEED = SEED + 1\n\n # average out results across environments and smooth the values and plot\n episodes = [i for i in range(timeSteps)]\n smooth_window = 50\n avg_reward_exploitation = smooth_array(np.mean(reward_exploitation, axis=0), smooth_window)\n avg_reward_exploration = smooth_array(np.mean(reward_exploration, axis=0), smooth_window)\n avg_reward_epsilonGreedy = smooth_array(np.mean(reward_epsilonGreedy, axis=0), smooth_window)\n avg_reward_decayingEpsilonGreedy = smooth_array(np.mean(reward_decayingEpsilonGreedy, axis=0), smooth_window)\n avg_reward_softmax = smooth_array(np.mean(reward_softmax, axis=0), smooth_window)\n avg_reward_ucb = smooth_array(np.mean(reward_ucb, axis=0), smooth_window)\n\n plt.figure(figsize=(12,8))\n plt.rcParams.update({'font.size': 14})\n plt.plot(episodes, avg_reward_exploitation, label='Pure Exploitation')\n plt.plot(episodes, avg_reward_exploration, label='Pure Exploration')\n plt.plot(episodes, avg_reward_epsilonGreedy, label='Epsilon Greedy')\n plt.plot(episodes, avg_reward_decayingEpsilonGreedy, label='Deacying Epsilon')\n plt.plot(episodes, avg_reward_softmax, label='Softmax')\n plt.plot(episodes, avg_reward_ucb, label='UCB')\n plt.title('Average Reward for 50 Two Arm Bandit Environments')\n plt.xlabel('Time Steps')\n plt.ylabel('Average Reward')\n plt.legend()\n plt.savefig('q1p4.svg')\n plt.savefig('q1p4.jpg', dpi=300)\n plt.show()", "sub_path": "Question 1/Two Arm Bandit/ques1_part4.py", "file_name": "ques1_part4.py", "file_ext": "py", "file_size_in_byte": 3542, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.zeros", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 24, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 27, "usage_type": "call"}, {"api_name": "utils.create_Earth", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 37, "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": "gym.make", "line_number": 41, "usage_type": "call"}, {"api_name": "agents.pureExploitation", "line_number": 45, "usage_type": "call"}, {"api_name": "agents.pureExploration", "line_number": 46, "usage_type": "call"}, {"api_name": "agents.epsilonGreedy", "line_number": 47, "usage_type": "call"}, {"api_name": "agents.decayingEpsilonGreedy", "line_number": 48, "usage_type": "call"}, {"api_name": "agents.softmaxExploration", "line_number": 49, "usage_type": "call"}, {"api_name": "agents.UCB", "line_number": 50, "usage_type": "call"}, {"api_name": "utils.smooth_array", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 60, "usage_type": "call"}, {"api_name": "utils.smooth_array", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 61, "usage_type": "call"}, {"api_name": "utils.smooth_array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 62, "usage_type": "call"}, {"api_name": "utils.smooth_array", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 63, "usage_type": "call"}, {"api_name": "utils.smooth_array", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 64, "usage_type": "call"}, {"api_name": "utils.smooth_array", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams.update", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 68, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}]} +{"seq_id": "345723524", "text": "from flask import Blueprint, abort, jsonify, request\nfrom flask_login import current_user, login_required\n\nfrom ..models import Tag\n\napi_tag_blueprint = Blueprint(\"api_tag\", __name__)\n\n\n@api_tag_blueprint.patch(\"/\")\n@login_required\ndef update(tag_id):\n \"\"\"Update Tag\"\"\"\n tag = Tag.query.get_or_404(tag_id)\n if tag.username != current_user.username:\n abort(403)\n result = tag.rename(request.get_json().get(\"name\"))\n return jsonify(result)\n\n\n@api_tag_blueprint.delete(\"/\")\n@login_required\ndef delete(tag_id):\n tag = current_user.tags.filter_by(id=tag_id).first_or_404()\n return jsonify(tag.delete())\n", "sub_path": "tubee/routes/api_tag.py", "file_name": "api_tag.py", "file_ext": "py", "file_size_in_byte": 642, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask.Blueprint", "line_number": 6, "usage_type": "call"}, {"api_name": "models.Tag.query.get_or_404", "line_number": 13, "usage_type": "call"}, {"api_name": "models.Tag.query", "line_number": 13, "usage_type": "attribute"}, {"api_name": "models.Tag", "line_number": 13, "usage_type": "name"}, {"api_name": "flask_login.current_user.username", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 14, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 17, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 10, "usage_type": "name"}, {"api_name": "flask_login.current_user.tags.filter_by", "line_number": 23, "usage_type": "call"}, {"api_name": "flask_login.current_user.tags", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 24, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "83203215", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 2.4 (62061)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-i686/egg/xml2ddl/downloadXml.py\n# Compiled at: 2005-09-26 21:01:56\nimport re, os\nfrom xml.sax.saxutils import escape\nfrom downloadCommon import getSeqName\nfrom xml.dom.minidom import parse, parseString\nfrom OracleInterface import OracleDownloader\nfrom PostgreSQLInterface import PgDownloader\nfrom MySqlInterface import MySqlDownloader\nfrom FirebirdInterface import FbDownloader\nfrom DdlCommonInterface import g_dbTypes\n__author__ = 'Scott Kirkwood (scott_kirkwood at berlios.com)'\n__keywords__ = ['XML', 'DML', 'SQL', 'Databases', 'Agile DB', 'ALTER', 'CREATE TABLE', 'GPL']\n__licence__ = 'GNU Public License (GPL)'\n__url__ = 'http://xml2dml.berlios.de'\nimport os, sys\nsys.path += ['./tests']\nif os.path.exists('tests/my_conn.py') or os.path.exists('my_conn.py'):\n from my_conn import conn_info\nelse:\n try:\n from connect_info import conn_info\n except:\n pass\n\nclass DownloadXml:\n __module__ = __name__\n\n def __init__(self, downloader, options):\n self.db = downloader\n self.options = options\n if 'tables' not in self.options:\n self.options['tables'] = []\n if 'views' not in self.options:\n self.options['views'] = []\n if 'functions' not in self.options:\n self.options['functions'] = []\n\n def downloadSchema(self, tableList=None, of=sys.stdout):\n tables = self.db.getTables(tableList=self.options['tables'])\n of.write('\\n')\n of.write('\\n')\n for strTableName in tables:\n curTable = {'name': strTableName, 'columns': []}\n desc = self.db.getTableComment(strTableName)\n if desc:\n curTable['desc'] = escape(desc)\n if self.options == None or 'getindexes' not in self.options or self.options['getindexes'] == True:\n curTable['indexes'] = self.db.getTableIndexes(strTableName)\n pkMap = {}\n for index in curTable['indexes']:\n if index[3]:\n for (nIndex, colName) in enumerate(index[1]):\n pkMap[colName] = nIndex + 1\n\n if self.options == None or 'getrelations' not in self.options or self.options['getrelations'] == True:\n curTable['relations'] = self.db.getTableRelations(strTableName)\n for colRow in self.db.getTableColumns(strTableName):\n (strColumnName, type, attlen, precision, attnotnull, default, bAutoIncrement) = colRow\n curCol = {'name': str(strColumnName), 'type': str(type)}\n if attlen:\n curCol['size'] = attlen\n if precision:\n curCol['precision'] = precision\n if attnotnull:\n curCol['null'] = 'no'\n if strColumnName in pkMap:\n curCol['key'] = pkMap[strColumnName]\n if default:\n curCol['default'] = default\n strComment = self.db.getColumnComment(strTableName, strColumnName)\n if strComment:\n curCol['desc'] = escape(strComment)\n if bAutoIncrement:\n curCol['autoincrement'] = 'yes'\n curTable['columns'].append(curCol)\n\n self.dumpTable(curTable, of)\n\n if self.options == None or 'getviews' not in self.options or self.options['getviews'] == True:\n self.getViews(of)\n if self.options == None or 'getfunctions' not in self.options or self.options['getfunctions'] == True:\n self.getFunctions(of)\n of.write('\\n')\n return\n\n def getViews(self, of):\n views = self.db.getViews(self.options['views'])\n for viewName in views:\n definition = self.db.getViewDefinition(viewName)\n info = {'name': viewName, 'definition': definition}\n self.dumpView(info, of)\n\n def dumpView(self, info, of):\n of.write(' \\n' % self.doAttribs(info, ['name']))\n of.write(' %s\\n' % escape(info['definition']))\n of.write(' \\n')\n\n def getFunctions(self, of):\n mangledNames = self.db.getFunctions(self.options['functions'])\n for mangledName in mangledNames:\n (strFuncName, params, strReturn, strLanguage, definition) = self.db.getFunctionDefinition(mangledName)\n info = {'name': strFuncName, 'definition': definition, 'arguments': (', ').join(params), 'returns': strReturn}\n if strLanguage and len(strLanguage) > 0:\n info['language'] = strLanguage\n self.dumpFunction(info, of)\n\n def dumpFunction(self, info, of):\n of.write(' \\n' % self.doAttribs(info, ['name', 'arguments', 'returns', 'language']))\n of.write('%s\\n' % escape(info['definition'].strip()))\n of.write(' \\n')\n\n def dumpTable(self, info, of):\n of.write(' \\n' % self.doAttribs(info, ['name', 'desc']))\n for col in info['columns']:\n of.write(' \\n' % self.doAttribs(col, ['name', 'type', 'size', 'precision', 'null', 'default', 'key', 'desc', 'autoincrement']))\n\n if 'indexes' in info:\n strIndexes = ''\n for index in info['indexes']:\n if not index[3]:\n strIndexes += ' \\n' % (index[0], (',').join(index[1]))\n\n if len(strIndexes) > 0:\n of.write(' \\n')\n of.write(strIndexes)\n of.write(' \\n')\n if 'relations' in info and len(info['relations']) > 0:\n of.write(' \\n')\n for index in info['relations']:\n curInfo = {'name': index[0], 'column': (',').join(index[1]), 'table': index[2], 'fk': (',').join(index[3])}\n if index[4] == 'c':\n curInfo['onupdate'] = 'cascade'\n elif index[4] == 'r':\n curInfo['onupdate'] = 'restrict'\n elif index[4] == 'n':\n curInfo['onupdate'] = 'setnull'\n elif index[4] == 'd':\n curInfo['onupdate'] = 'default'\n if index[5] == 'c':\n curInfo['ondelete'] = 'cascade'\n elif index[5] == 'r':\n curInfo['ondelete'] = 'restrict'\n elif index[5] == 'n':\n curInfo['ondelete'] = 'setnull'\n elif index[5] == 'd':\n curInfo['ondelete'] = 'default'\n of.write(' \\n' % self.doAttribs(curInfo, ['name', 'column', 'table', 'fk', 'ondelete', 'onupdate']))\n\n of.write(' \\n')\n of.write('
\\n')\n\n def doAttribs(self, attribs, nameList):\n ret = []\n for name in nameList:\n if name in attribs:\n ret.append('%s=\"%s\"' % (name, attribs[name]))\n\n return (' ').join(ret)\n\n\ndef createDownloader(dbms, conn=None, info=None, options=None):\n if dbms.startswith('postgres'):\n db = PgDownloader()\n elif dbms.startswith('mysql'):\n db = MySqlDownloader()\n elif dbms.startswith('firebird'):\n db = FbDownloader()\n elif dbms.startswith('oracle'):\n db = OracleDownloader()\n if conn:\n db.useConnection(conn, info['version'])\n elif info:\n db.connect(info)\n else:\n info = conn_info[dbms]\n db.connect(info)\n return DownloadXml(db, options)\n\n\ndef parseCommandLine():\n import optparse\n parser = optparse.OptionParser()\n dbmsDefault = g_dbTypes[0]\n parser.add_option('-b', '--dbms', dest='strDbms', metavar='DBMS', default=dbmsDefault, help='Dowload for which Database Managment System (postgres, mysql, or firebird), defaults to %s' % dbmsDefault)\n parser.add_option('', '--host', dest='strHost', metavar='HOST', default='localhost', help='Hostname or IP of machine')\n parser.add_option('-d', '--dbname', dest='strDbName', metavar='DATABASE', help='Dowload for which named Database')\n parser.add_option('-u', '--user', dest='strUserName', metavar='USER', help='User to login with')\n parser.add_option('-p', '--pass', dest='strPassword', metavar='PASS', help='Password for the user')\n parser.add_option('-t', '--tables', dest='strTables', metavar='TABLES', default=None, help='Comma separated list of tables')\n parser.add_option('-v', '--views', dest='strViews', metavar='VIEWS', default=None, help='Comma separated list of views')\n parser.add_option('-f', '--funcs', dest='strFuncs', metavar='FUNCTIONS', default=None, help='Comma separated list of functions')\n (options, args) = parser.parse_args()\n info = {'dbname': options.strDbName, 'user': options.strUserName, 'pass': options.strPassword, 'host': options.strHost, 'version': 99}\n if options.strTables:\n tables = options.strTables.split(',')\n else:\n tables = None\n if options.strViews:\n views = options.strViews.split(',')\n else:\n views = None\n if options.strFuncs:\n functions = options.strFuncs.split(',')\n else:\n functions = None\n runOptions = {'getfunctions': True, 'getviews': True, 'getrelations': True, 'getindexes': True, 'tables': tables, 'views': views, 'functions': functions}\n if info['dbname'] == None or info['user'] == None:\n parser.print_help()\n sys.exit(-1)\n cd = createDownloader(options.strDbms, info=info, options=runOptions)\n cd.downloadSchema()\n return\n\n\nif __name__ == '__main__':\n parseCommandLine()", "sub_path": "pycfiles/xml2ddl-0.3.1-py2.4/downloadXml.py", "file_name": "downloadXml.py", "file_ext": "py", "file_size_in_byte": 9815, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sys.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 43, "usage_type": "attribute"}, {"api_name": "xml.sax.saxutils.escape", "line_number": 51, "usage_type": "call"}, {"api_name": "xml.sax.saxutils.escape", "line_number": 77, "usage_type": "call"}, {"api_name": "xml.sax.saxutils.escape", "line_number": 100, "usage_type": "call"}, {"api_name": "xml.sax.saxutils.escape", "line_number": 114, "usage_type": "call"}, {"api_name": "PostgreSQLInterface.PgDownloader", "line_number": 168, "usage_type": "call"}, {"api_name": "MySqlInterface.MySqlDownloader", "line_number": 170, "usage_type": "call"}, {"api_name": "FirebirdInterface.FbDownloader", "line_number": 172, "usage_type": "call"}, {"api_name": "OracleInterface.OracleDownloader", "line_number": 174, "usage_type": "call"}, {"api_name": "connect_info.conn_info", "line_number": 180, "usage_type": "name"}, {"api_name": "optparse.OptionParser", "line_number": 187, "usage_type": "call"}, {"api_name": "DdlCommonInterface.g_dbTypes", "line_number": 188, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 214, "usage_type": "call"}]} +{"seq_id": "349229427", "text": "#!/usr/bin/python\n\nimport re\nimport distance\n\n#Return 0 for not ambiguous, 1 for ambiguous\ndef collapse_ambiguous_from_annotations_list(list_of_peptides):\n new_peptide_list = []\n for peptide in list_of_peptides:\n new_peptide = peptide.replace(\"I\", \"L\")\n new_peptide_list.append(new_peptide)\n\n #Let make sure its not just I/L substitutions\n new_peptide_list = list(set(new_peptide_list))\n return new_peptide_list\n\ndef categorize_peptide_distance(annotation1, annotation2):\n #Determining if it is I/L Substitution\n if annotation1.replace(\"I\", \"L\") == annotation2.replace(\"I\", \"L\"):\n #I/L Substitution\n return \"I/L Substitution\"\n\n annotation1_sequence_only = re.sub(r'[0-9.+-]+', '', annotation1)\n annotation2_sequence_only = re.sub(r'[0-9.+-]+', '', annotation2)\n\n string_distance = distance.nlevenshtein(annotation1_sequence_only, annotation2_sequence_only, method=1)\n #Detecting Site locatization of PTMs\n if string_distance < 0.01:\n return \"PTM Localization\"\n\n hamming_distance = 0\n\n if len(annotation1_sequence_only) == len(annotation2_sequence_only):\n hamming_distance = distance.hamming(annotation1_sequence_only, annotation2_sequence_only)\n\n if hamming_distance == 2:\n return \"Double Amino Substitution\"\n\n if hamming_distance == 1:\n #Seeing if it is a deamidation\n annotation1_contains_deamidation = False\n annotation2_contains_deamidation = False\n\n if annotation1.find(\"+0.984\") != -1:\n annotation1_contains_deamidation = True\n if annotation2.find(\"+0.984\") != -1:\n annotation2_contains_deamidation = True\n\n if annotation1_contains_deamidation != annotation2_contains_deamidation:\n #Probably should check for Q->E\n return \"Deamidation\"\n\n\n #Checking for Q->K Substitution\n\n #Determining String Distance\n string_distance = distance.nlevenshtein(annotation1, annotation2, method=1)\n\n return \"UNKNOWN\"\n", "sub_path": "msgf-plus-ambiguity/bin/ming_ambiguity_library.py", "file_name": "ming_ambiguity_library.py", "file_ext": "py", "file_size_in_byte": 2059, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "re.sub", "line_number": 23, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 24, "usage_type": "call"}, {"api_name": "distance.nlevenshtein", "line_number": 26, "usage_type": "call"}, {"api_name": "distance.hamming", "line_number": 34, "usage_type": "call"}, {"api_name": "distance.nlevenshtein", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "212451215", "text": "import schedule\nimport logging\nimport bot_configuration\nimport time\nimport heartbeat\n\nlogging.basicConfig(format=bot_configuration.LOG_FORMAT, filename=bot_configuration.DATA_FOLDER+bot_configuration.LOG_FILE,level=bot_configuration.LOGGING_LEVEL)\n\nconsole = logging.StreamHandler()\nconsole.setLevel(bot_configuration.LOGGING_LEVEL)\nformatter = logging.Formatter(bot_configuration.LOG_FORMAT)\nconsole.setFormatter(formatter)\nlogging.getLogger('').addHandler(console)\nlogging.getLogger(\"schedule\").setLevel(logging.ERROR)\nlogging.getLogger(\"stockstats\").setLevel(logging.ERROR)\n\n#Configure the heartbeat\nschedule.every(1).minutes.do(heartbeat.pulse)\n\nlogging.critical('Bot is online.')\n\n#Pulse forever\nwhile True:\n\tschedule.run_pending()\n\ttime.sleep(1)", "sub_path": "main-scheduler.py", "file_name": "main-scheduler.py", "file_ext": "py", "file_size_in_byte": 751, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.basicConfig", "line_number": 7, "usage_type": "call"}, {"api_name": "bot_configuration.LOG_FORMAT", "line_number": 7, "usage_type": "attribute"}, {"api_name": "bot_configuration.DATA_FOLDER", "line_number": 7, "usage_type": "attribute"}, {"api_name": "bot_configuration.LOG_FILE", "line_number": 7, "usage_type": "attribute"}, {"api_name": "bot_configuration.LOGGING_LEVEL", "line_number": 7, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 9, "usage_type": "call"}, {"api_name": "bot_configuration.LOGGING_LEVEL", "line_number": 10, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 11, "usage_type": "call"}, {"api_name": "bot_configuration.LOG_FORMAT", "line_number": 11, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.ERROR", "line_number": 14, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.ERROR", "line_number": 15, "usage_type": "attribute"}, {"api_name": "schedule.every", "line_number": 18, "usage_type": "call"}, {"api_name": "heartbeat.pulse", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.critical", "line_number": 20, "usage_type": "call"}, {"api_name": "schedule.run_pending", "line_number": 24, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "569355873", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed May 13 10:03:22 2020\n\n@author: fcseidl\n\nVarious helper classes and functions for Koopman mode estimation.\n\"\"\"\n\nimport numpy as np\nfrom scipy.stats import linregress\n\neps = 1e-6\n\n\ndef exponential_regression(x, y):\n r\"\"\"\n Params\n ------\n x, y : array-like\n length-n arrays of x and y values.\n \n Returns\n -------\n a, b : numeric\n such that ae^bx is the (least squares) exponential curve of best fit \n to x and y.\n r2 : numeric\n correlation coefficient (of linear regression to log data)\n \"\"\"\n b, inter, r2, _, __ = linregress(x, np.log(y))\n a = np.e ** inter\n return a, b, r2\n \n\ndef proj(u, v):\n r\"\"\"\n Return projection of u onto v.\n \"\"\"\n return u.dot(v) / v.dot(v) * v\n\n\ndef close(a, b, epsilon=eps):\n r\"\"\"\n Return whether two numbers are close.\n \"\"\"\n return np.abs(a - b) < epsilon\n\n\nif __name__ == \"__main__\":\n u = np.asarray([1, 2, 3])\n v = np.asarray([5, 6, 2])\n print(proj(u, v))\n print(proj(v, u))\n print(proj(u, u))\n print(proj(v, v))", "sub_path": "utilities.py", "file_name": "utilities.py", "file_ext": "py", "file_size_in_byte": 1117, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "scipy.stats.linregress", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.e", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "448328245", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Mar 12 19:17:52 2020\n\n@author: CVPR\n\"\"\"\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\nfrom tensorflow import keras\nfrom tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D\n\nimport cv2\nimport sys\nimport time\nimport numpy as np\nimport tensorflow as tf\n\nclass MyCallback(tf.keras.callbacks.Callback):\n def __init__(self):\n super().__init__()\n self.start_time = time.time()\n\n def on_epoch_end(self, epoch, logs=None):\n if time.time() - self.start_time > 30:\n self.model.stop_training = True\n\n\nclass MnistGUI(QMainWindow):\n def __init__(self, parent = None):\n super(MnistGUI, self).__init__(parent)\n # 초기 모델\n self.model = keras.Sequential()\n self.model.add(Flatten(input_shape=(28, 28, 1)))\n self.model.add(Dense(128, activation = 'sigmoid'))\n self.model.add(Dense(10, activation = 'softmax'))\n \n # Main\n self.setWindowTitle(\"류원정짱\")\n\n # Widget 생성\n wid = QWidget(self)\n self.canvasLabel = QLabel()\n self.canvas = QPixmap(280, 280)\n self.predictButton = QPushButton()\n self.resetButton = QPushButton()\n self.mnist1 = QRadioButton()\n self.mnist2 = QRadioButton()\n self.mnist3 = QRadioButton()\n self.mnist4 = QRadioButton()\n self.mnist5 = QRadioButton()\n\n # Widget text 설정\n self.predictButton.setText(\"predict\")\n self.resetButton.setText(\"reset\")\n self.mnist1.setText(\"1번째\")\n self.mnist2.setText(\"2번째\")\n self.mnist3.setText(\"3번째\")\n self.mnist4.setText(\"4번째\")\n self.mnist5.setText(\"5번째\")\n self.mnist1.setChecked(True)\n\n # Widget 기능 설정\n self.predictButton.clicked.connect(self.predict)\n self.resetButton.clicked.connect(self.reset)\n self.mnist1.clicked.connect(self.radioButtonClicked)\n self.mnist2.clicked.connect(self.radioButtonClicked)\n self.mnist3.clicked.connect(self.radioButtonClicked)\n self.mnist4.clicked.connect(self.radioButtonClicked)\n self.mnist5.clicked.connect(self.radioButtonClicked)\n\n # Widget 크기 설정\n self.mnist1.setSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred)\n self.mnist2.setSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred)\n self.mnist3.setSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred)\n self.mnist4.setSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred)\n self.mnist5.setSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred)\n self.predictButton.setSizePolicy(QSizePolicy.Preferred, QSizePolicy.Maximum)\n self.resetButton.setSizePolicy(QSizePolicy.Preferred, QSizePolicy.Maximum)\n\n # canvas 지정\n self.canvasLabel.setPixmap(self.canvas)\n self.last_x, self.last_y = None, None\n self.reset()\n\n # Widget 위치 설정\n self.setCentralWidget(wid)\n buttonLayout = QHBoxLayout()\n buttonLayout.addWidget(self.predictButton)\n buttonLayout.addWidget(self.resetButton)\n\n radioLayout = QVBoxLayout()\n radioLayout.addWidget(self.mnist1)\n radioLayout.addWidget(self.mnist2)\n radioLayout.addWidget(self.mnist3)\n radioLayout.addWidget(self.mnist4)\n radioLayout.addWidget(self.mnist5)\n\n controlLayout = QVBoxLayout()\n controlLayout.addLayout(radioLayout)\n controlLayout.addLayout(buttonLayout)\n\n WholeLayout = QHBoxLayout()\n WholeLayout.addWidget(self.canvasLabel)\n WholeLayout.addLayout(controlLayout)\n \n wid.setLayout(WholeLayout)\n \n \n # 종료\n def exitCall(self):\n sys.exit(app.exec_())\n\n # Mnist 그리기\n def mouseMoveEvent(self, e):\n if self.last_x is None:\n self.last_x = e.x()\n self.last_y = e.y()\n return\n\n painter = QPainter(self.canvasLabel.pixmap())\n p = painter.pen()\n p.setWidth(20)\n p.setColor(QColor('#FFFFFF'))\n painter.setPen(p)\n painter.drawLine(self.last_x - 10, self.last_y - 10, e.x() - 10, e.y() - 10)\n painter.end()\n self.update()\n\n self.last_x = e.x()\n self.last_y = e.y()\n\n def mouseReleaseEvent(self, e):\n self.last_x = None\n self.last_y = None\n\n def radioButtonClicked(self):\n if self.mnist1.isChecked():\n self.model = keras.Sequential()\n self.model.add(Flatten(input_shape=(28, 28, 1)))\n self.model.add(Dense(128, activation = 'sigmoid'))\n self.model.add(Dense(10, activation = 'softmax'))\n\n elif self.mnist2.isChecked():\n self.model = keras.Sequential()\n self.model.add(Conv2D(32, (3, 3), activation = 'relu', input_shape=(28, 28, 1)))\n self.model.add(MaxPooling2D(pool_size=(2, 2)))\n self.model.add(Flatten())\n self.model.add(Dense(128, activation = 'sigmoid'))\n self.model.add(Dense(10, activation = 'softmax'))\n\n elif self.mnist3.isChecked():\n self.model = keras.Sequential()\n self.model.add(Conv2D(32, (3, 3), activation = 'relu', input_shape=(28, 28, 1)))\n self.model.add(Conv2D(64, (3, 3), activation = 'relu'))\n self.model.add(MaxPooling2D(pool_size=(2, 2)))\n self.model.add(Flatten())\n self.model.add(Dense(128, activation = 'sigmoid'))\n self.model.add(Dense(10, activation = 'softmax'))\n\n elif self.mnist4.isChecked():\n self.model = keras.Sequential()\n self.model.add(Conv2D(32, (3, 3), activation = 'relu', input_shape=(28, 28, 1)))\n self.model.add(MaxPooling2D(pool_size=(2, 2)))\n self.model.add(Conv2D(64, (3, 3), activation = 'relu'))\n self.model.add(MaxPooling2D(pool_size=(2, 2)))\n self.model.add(Flatten())\n self.model.add(Dense(128, activation = 'sigmoid'))\n self.model.add(Dense(10, activation = 'softmax'))\n\n elif self.mnist5.isChecked():\n self.model = keras.Sequential()\n self.model.add(Conv2D(32, (3, 3), activation = 'relu', input_shape=(28, 28, 1)))\n self.model.add(MaxPooling2D(pool_size=(2, 2)))\n self.model.add(Conv2D(64, (3, 3), activation = 'relu'))\n self.model.add(MaxPooling2D(pool_size=(2, 2)))\n self.model.add(Flatten())\n self.model.add(Dense(64, activation = 'sigmoid'))\n self.model.add(Dense(32, activation = 'sigmoid'))\n self.model.add(Dense(10, activation = 'softmax'))\n\n def predict(self):\n self.canvasLabel.pixmap().save(\"SavedImage.jpg\")\n testData = cv2.imread(\"SavedImage.jpg\", cv2.IMREAD_GRAYSCALE)\n testData = cv2.resize(testData, dsize=(28, 28), interpolation=cv2.INTER_CUBIC)\n\n earlystop_callback = tf.keras.callbacks.EarlyStopping(monitor='val_categorical_accuracy', min_delta=0.0, patience=3)\n\n mnist = tf.keras.datasets.mnist\n (x_train, y_train_origin), (x_test, y_test_origin) = mnist.load_data()\n x_train, x_test = x_train / 255.0, x_test / 255.0 # 0과 1 사이의 값으로 변환\n\n testData = testData / 255.0\n testData = testData.reshape((1, 28, 28, 1))\n \n x_train = x_train.reshape((-1, 28, 28, 1))\n x_test = x_test.reshape((-1, 28, 28, 1))\n\n nb_classes = 10\n y_train = keras.utils.to_categorical(y_train_origin, num_classes = nb_classes) # one-hot encoding\n y_test = keras.utils.to_categorical(y_test_origin, num_classes = nb_classes) # one-hot encoding\n\n self.model.compile(optimizer = keras.optimizers.Adam(learning_rate = 0.01), loss = 'categorical_crossentropy', metrics = ['categorical_accuracy'])\n hist = self.model.fit(x_train, y_train, epochs=10000, batch_size=1000, validation_data=(x_test, y_test), callbacks=[earlystop_callback, MyCallback()])\n self.model.evaluate(x_test, y_test)\n\n y_new = self.model.predict(testData)\n \n \n QMessageBox.question(self, '류원정 짱', \"{}\".format(y_new.argmax()), QMessageBox.Cancel, QMessageBox.Cancel)\n\n\n def reset(self):\n painter = QPainter(self.canvasLabel.pixmap())\n painter.setBrush(QColor(0, 0, 0))\n painter.drawRect(0, 0, 280, 280)\n painter.end()\n self.update()\n\n\n# 메인 함수\nif __name__ == \"__main__\":\n # GUI 생성\n app = QApplication(sys.argv)\n\n pannel = MnistGUI()\n pannel.resize(480, 280)\n pannel.show()\n sys.exit(app.exec_())\n", "sub_path": "untitled4.py", "file_name": "untitled4.py", "file_ext": "py", "file_size_in_byte": 8799, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "tensorflow.keras", "line_number": 19, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 22, "usage_type": "call"}, {"api_name": "time.time", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 33, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 36, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 112, "usage_type": "call"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 139, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 139, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 140, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 141, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 145, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 145, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 146, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 147, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 148, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 149, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 150, "usage_type": "call"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 153, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 153, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 154, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 155, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 156, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 157, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 158, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 159, "usage_type": "call"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 162, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 162, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 163, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 164, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 165, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 166, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 167, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 168, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 169, "usage_type": "call"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 172, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 172, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 173, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 174, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 175, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 176, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 177, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 178, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 179, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 180, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 184, "usage_type": "call"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 184, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 185, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 185, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.callbacks.EarlyStopping", "line_number": 187, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 187, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 189, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.utils.to_categorical", "line_number": 200, "usage_type": "call"}, {"api_name": "tensorflow.keras.utils", "line_number": 200, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 200, "usage_type": "name"}, {"api_name": "tensorflow.keras.utils.to_categorical", "line_number": 201, "usage_type": "call"}, {"api_name": "tensorflow.keras.utils", "line_number": 201, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 201, "usage_type": "name"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 203, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers", "line_number": 203, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 203, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 224, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 229, "usage_type": "call"}]} +{"seq_id": "629240027", "text": "# vim: tabstop=4 shiftwidth=4 softtabstop=4\n\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport os\nimport random\nimport uuid\n\nfrom keystone.common.sql import migration\nfrom keystone import config\nfrom keystone import contrib\nfrom keystone.openstack.common import importutils\nfrom keystone.openstack.common import jsonutils\nfrom keystone.openstack.common import log\nfrom keystone.tests import mapping_fixtures\nfrom keystone.tests import test_v3\n\n\nCONF = config.CONF\nLOG = log.getLogger(__name__)\n\n\ndef dummy_validator(*args, **kwargs):\n pass\n\n\nclass FederationTests(test_v3.RestfulTestCase):\n\n EXTENSION_NAME = 'federation'\n EXTENSION_TO_ADD = 'federation_extension'\n\n def setup_database(self):\n super(FederationTests, self).setup_database()\n package_name = \"%s.%s.migrate_repo\" % (contrib.__name__,\n self.EXTENSION_NAME)\n package = importutils.import_module(package_name)\n self.repo_path = os.path.abspath(os.path.dirname(package.__file__))\n migration.db_version_control(version=None, repo_path=self.repo_path)\n migration.db_sync(version=None, repo_path=self.repo_path)\n\n\nclass FederatedIdentityProviderTests(FederationTests):\n \"\"\"A test class for Identity Providers.\"\"\"\n\n idp_keys = ['description', 'enabled']\n\n default_body = {'description': None, 'enabled': True}\n\n def base_url(self, suffix=None):\n if suffix is not None:\n return '/OS-FEDERATION/identity_providers/' + str(suffix)\n return '/OS-FEDERATION/identity_providers'\n\n def _fetch_attribute_from_response(self, resp, parameter,\n assert_is_not_none=True):\n \"\"\"Fetch single attribute from TestResponse object.\"\"\"\n result = resp.result.get(parameter, None)\n if assert_is_not_none:\n self.assertIsNotNone(result)\n return result\n\n def _fetch_attributes_from_response(self, resp, parameters=[],\n assert_is_not_none=True):\n \"\"\"Fetch parameters from the TestResponse object.\"\"\"\n\n result = dict()\n kwargs = {'assert_is_not_none': assert_is_not_none}\n for parameter in parameters:\n value = self._fetch_attribute_from_response(resp, parameter,\n **kwargs)\n result[parameter] = value\n return result\n\n def _create_and_decapsulate_response(self, body=None):\n \"\"\"Create IdP and fetch it's random id along with entity.\"\"\"\n default_resp = self._create_default_idp(body=body)\n idp = self._fetch_attribute_from_response(default_resp,\n 'identity_provider')\n self.assertIsNotNone(idp)\n idp_id = idp.get('id')\n return (idp_id, idp)\n\n def _get_idp(self, idp_id):\n \"\"\"Fetch IdP entity based on it's id.\"\"\"\n url = self.base_url(suffix=idp_id)\n resp = self.get(url)\n return resp\n\n def _create_default_idp(self, body=None):\n \"\"\"Create default IdP.\"\"\"\n url = self.base_url(suffix=uuid.uuid4().hex)\n if body is None:\n body = self._http_idp_input()\n resp = self.put(url, body={'identity_provider': body},\n expected_status=201)\n return resp\n\n def _http_idp_input(self, **kwargs):\n \"\"\"Create default input for IdP data.\"\"\"\n body = None\n if 'body' not in kwargs:\n body = self.default_body.copy()\n body['description'] = uuid.uuid4().hex\n else:\n body = kwargs['body']\n return body\n\n def _assign_protocol_to_idp(self, idp_id=None, proto=None, url=None,\n mapping_id=None, validate=True, **kwargs):\n if url is None:\n url = self.base_url(suffix='%(idp_id)s/protocols/%(protocol_id)s')\n if idp_id is None:\n idp_id, _ = self._create_and_decapsulate_response()\n if proto is None:\n proto = uuid.uuid4().hex\n if mapping_id is None:\n mapping_id = uuid.uuid4().hex\n body = {'mapping_id': mapping_id}\n url = url % {'idp_id': idp_id, 'protocol_id': proto}\n resp = self.put(url, body={'protocol': body}, **kwargs)\n if validate:\n self.assertValidResponse(resp, 'protocol', dummy_validator,\n keys_to_check=['id', 'mapping_id'],\n ref={'id': proto,\n 'mapping_id': mapping_id})\n return (resp, idp_id, proto)\n\n def _get_protocol(self, idp_id, protocol_id):\n url = \"%s/protocols/%s\" % (idp_id, protocol_id)\n url = self.base_url(suffix=url)\n r = self.get(url)\n return r\n\n def test_create_idp(self):\n \"\"\"Creates the IdentityProvider entity.\"\"\"\n\n keys_to_check = self.idp_keys\n body = self._http_idp_input()\n resp = self._create_default_idp(body=body)\n self.assertValidResponse(resp, 'identity_provider', dummy_validator,\n keys_to_check=keys_to_check,\n ref=body)\n\n def test_list_idps(self, iterations=5):\n \"\"\"Lists all available IdentityProviders.\n\n This test collects ids of created IdPs and\n intersects it with the list of all available IdPs.\n List of all IdPs can be a superset of IdPs created in this test,\n because other tests also create IdPs.\n\n \"\"\"\n def get_id(resp):\n r = self._fetch_attribute_from_response(resp,\n 'identity_provider')\n return r.get('id')\n\n ids = []\n for _ in xrange(iterations):\n id = get_id(self._create_default_idp())\n ids.append(id)\n ids = set(ids)\n\n keys_to_check = self.idp_keys\n url = self.base_url()\n resp = self.get(url)\n self.assertValidListResponse(resp, 'identity_providers',\n dummy_validator,\n keys_to_check=keys_to_check)\n entities = self._fetch_attribute_from_response(resp,\n 'identity_providers')\n entities_ids = set([e['id'] for e in entities])\n ids_intersection = entities_ids.intersection(ids)\n self.assertEqual(ids_intersection, ids)\n\n def test_check_idp_uniqueness(self):\n \"\"\"Add same IdP twice.\n\n Expect HTTP 409 code for the latter call.\n\n \"\"\"\n url = self.base_url(suffix=uuid.uuid4().hex)\n body = self._http_idp_input()\n self.put(url, body={'identity_provider': body},\n expected_status=201)\n self.put(url, body={'identity_provider': body},\n expected_status=409)\n\n def test_get_idp(self):\n \"\"\"Create and later fetch IdP.\"\"\"\n body = self._http_idp_input()\n default_resp = self._create_default_idp(body=body)\n default_idp = self._fetch_attribute_from_response(default_resp,\n 'identity_provider')\n idp_id = default_idp.get('id')\n url = self.base_url(suffix=idp_id)\n resp = self.get(url)\n self.assertValidResponse(resp, 'identity_provider',\n dummy_validator, keys_to_check=body.keys(),\n ref=body)\n\n def test_get_nonexisting_idp(self):\n \"\"\"Fetch nonexisting IdP entity.\n\n Expected HTTP 404 status code.\n\n \"\"\"\n idp_id = uuid.uuid4().hex\n self.assertIsNotNone(idp_id)\n\n url = self.base_url(suffix=idp_id)\n self.get(url, expected_status=404)\n\n def test_delete_existing_idp(self):\n \"\"\"Create and later delete IdP.\n\n Expect HTTP 404 for the GET IdP call.\n \"\"\"\n default_resp = self._create_default_idp()\n default_idp = self._fetch_attribute_from_response(default_resp,\n 'identity_provider')\n idp_id = default_idp.get('id')\n self.assertIsNotNone(idp_id)\n url = self.base_url(suffix=idp_id)\n self.delete(url)\n self.get(url, expected_status=404)\n\n def test_delete_nonexisting_idp(self):\n \"\"\"Delete nonexisting IdP.\n\n Expect HTTP 404 for the GET IdP call.\n \"\"\"\n idp_id = uuid.uuid4().hex\n url = self.base_url(suffix=idp_id)\n self.delete(url, expected_status=404)\n\n def test_update_idp_mutable_attributes(self):\n \"\"\"Update IdP's mutable parameters.\"\"\"\n default_resp = self._create_default_idp()\n default_idp = self._fetch_attribute_from_response(default_resp,\n 'identity_provider')\n idp_id = default_idp.get('id')\n url = self.base_url(suffix=idp_id)\n self.assertIsNotNone(idp_id)\n\n _enabled = not default_idp.get('enabled')\n body = {'description': uuid.uuid4().hex, 'enabled': _enabled}\n\n body = {'identity_provider': body}\n resp = self.patch(url, body=body)\n updated_idp = self._fetch_attribute_from_response(resp,\n 'identity_provider')\n body = body['identity_provider']\n for key in body.keys():\n self.assertEqual(body[key], updated_idp.get(key))\n\n resp = self.get(url)\n updated_idp = self._fetch_attribute_from_response(resp,\n 'identity_provider')\n for key in body.keys():\n self.assertEqual(body[key], updated_idp.get(key))\n\n def test_update_idp_immutable_attributes(self):\n \"\"\"Update IdP's immutable parameters.\n\n Expect HTTP 403 code.\n\n \"\"\"\n default_resp = self._create_default_idp()\n default_idp = self._fetch_attribute_from_response(default_resp,\n 'identity_provider')\n idp_id = default_idp.get('id')\n self.assertIsNotNone(idp_id)\n\n body = self._http_idp_input()\n body['id'] = uuid.uuid4().hex\n body['protocols'] = [uuid.uuid4().hex, uuid.uuid4().hex]\n\n url = self.base_url(suffix=idp_id)\n self.patch(url, body={'identity_provider': body}, expected_status=403)\n\n def test_update_nonexistent_idp(self):\n \"\"\"Update nonexistent IdP\n\n Expect HTTP 404 code.\n\n \"\"\"\n idp_id = uuid.uuid4().hex\n url = self.base_url(suffix=idp_id)\n body = self._http_idp_input()\n body['enabled'] = False\n body = {'identity_provider': body}\n\n self.patch(url, body=body, expected_status=404)\n\n def test_assign_protocol_to_idp(self):\n \"\"\"Assign a protocol to existing IdP.\"\"\"\n\n self._assign_protocol_to_idp(expected_status=201)\n\n def test_protocol_composite_pk(self):\n \"\"\"Test whether Keystone let's add two entities with identical\n names, however attached to different IdPs.\n\n 1. Add IdP and assign it protocol with predefined name\n 2. Add another IdP and assign it a protocol with same name.\n\n Expect HTTP 201 code\n\n \"\"\"\n url = self.base_url(suffix='%(idp_id)s/protocols/%(protocol_id)s')\n\n kwargs = {'expected_status': 201}\n self._assign_protocol_to_idp(proto='saml2',\n url=url, **kwargs)\n\n self._assign_protocol_to_idp(proto='saml2',\n url=url, **kwargs)\n\n def test_protocol_idp_pk_uniqueness(self):\n \"\"\"Test whether Keystone checks for unique idp/protocol values.\n\n Add same protocol twice, expect Keystone to reject a latter call and\n return HTTP 409 code.\n\n \"\"\"\n url = self.base_url(suffix='%(idp_id)s/protocols/%(protocol_id)s')\n\n kwargs = {'expected_status': 201}\n resp, idp_id, proto = self._assign_protocol_to_idp(proto='saml2',\n url=url, **kwargs)\n kwargs = {'expected_status': 409}\n resp, idp_id, proto = self._assign_protocol_to_idp(idp_id=idp_id,\n proto='saml2',\n validate=False,\n url=url, **kwargs)\n\n def test_assign_protocol_to_nonexistent_idp(self):\n \"\"\"Assign protocol to IdP that doesn't exist.\n\n Expect HTTP 404 code.\n\n \"\"\"\n\n idp_id = uuid.uuid4().hex\n kwargs = {'expected_status': 404}\n self._assign_protocol_to_idp(proto='saml2',\n idp_id=idp_id,\n validate=False,\n **kwargs)\n\n def test_get_protocol(self):\n \"\"\"Create and later fetch protocol tied to IdP.\"\"\"\n\n resp, idp_id, proto = self._assign_protocol_to_idp(expected_status=201)\n proto_id = self._fetch_attribute_from_response(resp, 'protocol')['id']\n url = \"%s/protocols/%s\" % (idp_id, proto_id)\n url = self.base_url(suffix=url)\n\n resp = self.get(url)\n\n reference = {'id': proto_id}\n self.assertValidResponse(resp, 'protocol',\n dummy_validator,\n keys_to_check=reference.keys(),\n ref=reference)\n\n def test_list_protocols(self):\n \"\"\"Create set of protocols and later list them.\n\n Compare input and output id sets.\n\n \"\"\"\n resp, idp_id, proto = self._assign_protocol_to_idp(expected_status=201)\n iterations = random.randint(0, 16)\n protocol_ids = []\n for _ in xrange(iterations):\n resp, _, proto = self._assign_protocol_to_idp(idp_id=idp_id,\n expected_status=201)\n proto_id = self._fetch_attribute_from_response(resp, 'protocol')\n proto_id = proto_id['id']\n protocol_ids.append(proto_id)\n\n url = \"%s/protocols\" % idp_id\n url = self.base_url(suffix=url)\n resp = self.get(url)\n self.assertValidListResponse(resp, 'protocols',\n dummy_validator,\n keys_to_check=['id'])\n entities = self._fetch_attribute_from_response(resp, 'protocols')\n entities = set([entity['id'] for entity in entities])\n protocols_intersection = entities.intersection(protocol_ids)\n self.assertEqual(protocols_intersection, set(protocol_ids))\n\n def test_update_protocols_attribute(self):\n \"\"\"Update protocol's attribute.\"\"\"\n\n resp, idp_id, proto = self._assign_protocol_to_idp(expected_status=201)\n new_mapping_id = uuid.uuid4().hex\n\n url = \"%s/protocols/%s\" % (idp_id, proto)\n url = self.base_url(suffix=url)\n body = {'mapping_id': new_mapping_id}\n resp = self.patch(url, body={'protocol': body})\n self.assertValidResponse(resp, 'protocol', dummy_validator,\n keys_to_check=['id', 'mapping_id'],\n ref={'id': proto,\n 'mapping_id': new_mapping_id}\n )\n\n def test_delete_protocol(self):\n \"\"\"Delete protocol.\n\n Expect HTTP 404 code for the GET call after the protocol is deleted.\n\n \"\"\"\n url = self.base_url(suffix='/%(idp_id)s/'\n 'protocols/%(protocol_id)s')\n resp, idp_id, proto = self._assign_protocol_to_idp(expected_status=201)\n url = url % {'idp_id': idp_id,\n 'protocol_id': proto}\n self.delete(url)\n self.get(url, expected_status=404)\n\n\nclass MappingCRUDTests(FederationTests):\n \"\"\"A class for testing CRUD operations for Mappings.\"\"\"\n\n MAPPING_URL = '/OS-FEDERATION/mappings/'\n\n def assertValidMappingListResponse(self, resp, *args, **kwargs):\n return self.assertValidListResponse(\n resp,\n 'mappings',\n self.assertValidMapping,\n keys_to_check=[],\n *args,\n **kwargs)\n\n def assertValidMappingResponse(self, resp, *args, **kwargs):\n return self.assertValidResponse(\n resp,\n 'mapping',\n self.assertValidMapping,\n keys_to_check=[],\n *args,\n **kwargs)\n\n def assertValidMapping(self, entity, ref=None):\n self.assertIsNotNone(entity.get('id'))\n self.assertIsNotNone(entity.get('rules'))\n if ref:\n self.assertEqual(jsonutils.loads(entity['rules']), ref['rules'])\n return entity\n\n def _create_default_mapping_entry(self):\n url = self.MAPPING_URL + uuid.uuid4().hex\n resp = self.put(url,\n body={'mapping': mapping_fixtures.MAPPING_LARGE},\n expected_status=201)\n return resp\n\n def _get_id_from_response(self, resp):\n r = resp.result.get('mapping')\n return r.get('id')\n\n def test_mapping_create(self):\n resp = self._create_default_mapping_entry()\n self.assertValidMappingResponse(resp, mapping_fixtures.MAPPING_LARGE)\n\n def test_mapping_list(self):\n url = self.MAPPING_URL\n self._create_default_mapping_entry()\n resp = self.get(url)\n entities = resp.result.get('mappings')\n self.assertIsNotNone(entities)\n self.assertResponseStatus(resp, 200)\n self.assertValidListLinks(resp.result.get('links'))\n self.assertEqual(len(entities), 1)\n\n def test_mapping_delete(self):\n url = self.MAPPING_URL + '%(mapping_id)s'\n resp = self._create_default_mapping_entry()\n mapping_id = self._get_id_from_response(resp)\n url = url % {'mapping_id': str(mapping_id)}\n resp = self.delete(url)\n self.assertResponseStatus(resp, 204)\n self.get(url, expected_status=404)\n\n def test_mapping_get(self):\n url = self.MAPPING_URL + '%(mapping_id)s'\n resp = self._create_default_mapping_entry()\n mapping_id = self._get_id_from_response(resp)\n url = url % {'mapping_id': mapping_id}\n resp = self.get(url)\n self.assertValidMappingResponse(resp, mapping_fixtures.MAPPING_LARGE)\n\n def test_mapping_update(self):\n url = self.MAPPING_URL + '%(mapping_id)s'\n resp = self._create_default_mapping_entry()\n mapping_id = self._get_id_from_response(resp)\n url = url % {'mapping_id': mapping_id}\n resp = self.patch(url,\n body={'mapping': mapping_fixtures.MAPPING_SMALL})\n self.assertValidMappingResponse(resp, mapping_fixtures.MAPPING_SMALL)\n resp = self.get(url)\n self.assertValidMappingResponse(resp, mapping_fixtures.MAPPING_SMALL)\n\n def test_delete_mapping_dne(self):\n url = self.MAPPING_URL + uuid.uuid4().hex\n self.delete(url, expected_status=404)\n\n def test_get_mapping_dne(self):\n url = self.MAPPING_URL + uuid.uuid4().hex\n self.get(url, expected_status=404)\n\n def test_create_mapping_bad_requirements(self):\n url = self.MAPPING_URL + uuid.uuid4().hex\n self.put(url, expected_status=400,\n body={'mapping': mapping_fixtures.MAPPING_BAD_REQ})\n\n def test_create_mapping_no_rules(self):\n url = self.MAPPING_URL + uuid.uuid4().hex\n self.put(url, expected_status=400,\n body={'mapping': mapping_fixtures.MAPPING_NO_RULES})\n\n def test_create_mapping_no_remote_objects(self):\n url = self.MAPPING_URL + uuid.uuid4().hex\n self.put(url, expected_status=400,\n body={'mapping': mapping_fixtures.MAPPING_NO_REMOTE})\n\n def test_create_mapping_bad_value(self):\n url = self.MAPPING_URL + uuid.uuid4().hex\n self.put(url, expected_status=400,\n body={'mapping': mapping_fixtures.MAPPING_BAD_VALUE})\n\n def test_create_mapping_missing_local(self):\n url = self.MAPPING_URL + uuid.uuid4().hex\n self.put(url, expected_status=400,\n body={'mapping': mapping_fixtures.MAPPING_MISSING_LOCAL})\n\n def test_create_mapping_missing_type(self):\n url = self.MAPPING_URL + uuid.uuid4().hex\n self.put(url, expected_status=400,\n body={'mapping': mapping_fixtures.MAPPING_MISSING_TYPE})\n\n def test_create_mapping_wrong_type(self):\n url = self.MAPPING_URL + uuid.uuid4().hex\n self.put(url, expected_status=400,\n body={'mapping': mapping_fixtures.MAPPING_WRONG_TYPE})\n\n def test_create_mapping_extra_remote_properties_not_any_of(self):\n url = self.MAPPING_URL + uuid.uuid4().hex\n mapping = mapping_fixtures.MAPPING_EXTRA_REMOTE_PROPS_NOT_ANY_OF\n self.put(url, expected_status=400, body={'mapping': mapping})\n\n def test_create_mapping_extra_remote_properties_any_one_of(self):\n url = self.MAPPING_URL + uuid.uuid4().hex\n mapping = mapping_fixtures.MAPPING_EXTRA_REMOTE_PROPS_ANY_ONE_OF\n self.put(url, expected_status=400, body={'mapping': mapping})\n\n def test_create_mapping_extra_remote_properties_just_type(self):\n url = self.MAPPING_URL + uuid.uuid4().hex\n mapping = mapping_fixtures.MAPPING_EXTRA_REMOTE_PROPS_JUST_TYPE\n self.put(url, expected_status=400, body={'mapping': mapping})\n\n def test_create_mapping_empty_map(self):\n url = self.MAPPING_URL + uuid.uuid4().hex\n self.put(url, expected_status=400,\n body={'mapping': {}})\n\n def test_create_mapping_extra_rules_properties(self):\n url = self.MAPPING_URL + uuid.uuid4().hex\n self.put(url, expected_status=400,\n body={'mapping': mapping_fixtures.MAPPING_EXTRA_RULES_PROPS})\n", "sub_path": "keystone/tests/test_v3_federation.py", "file_name": "test_v3_federation.py", "file_ext": "py", "file_size_in_byte": 22485, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "keystone.config.CONF", "line_number": 30, "usage_type": "attribute"}, {"api_name": "keystone.config", "line_number": 30, "usage_type": "name"}, {"api_name": "keystone.openstack.common.log.getLogger", "line_number": 31, "usage_type": "call"}, {"api_name": "keystone.openstack.common.log", "line_number": 31, "usage_type": "name"}, {"api_name": "keystone.tests.test_v3.RestfulTestCase", "line_number": 38, "usage_type": "attribute"}, {"api_name": "keystone.tests.test_v3", "line_number": 38, "usage_type": "name"}, {"api_name": "keystone.contrib.__name__", "line_number": 45, "usage_type": "attribute"}, {"api_name": "keystone.contrib", "line_number": 45, "usage_type": "name"}, {"api_name": "keystone.openstack.common.importutils.import_module", "line_number": 47, "usage_type": "call"}, {"api_name": "keystone.openstack.common.importutils", "line_number": 47, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 48, "usage_type": "call"}, {"api_name": "keystone.common.sql.migration.db_version_control", "line_number": 49, "usage_type": "call"}, {"api_name": "keystone.common.sql.migration", "line_number": 49, "usage_type": "name"}, {"api_name": "keystone.common.sql.migration.db_sync", "line_number": 50, "usage_type": "call"}, {"api_name": "keystone.common.sql.migration", "line_number": 50, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 102, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 114, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 126, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 128, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 193, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 219, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 244, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 258, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 287, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 288, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 299, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 356, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 386, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 410, "usage_type": "call"}, {"api_name": "keystone.openstack.common.jsonutils.loads", "line_number": 464, "usage_type": "call"}, {"api_name": "keystone.openstack.common.jsonutils", "line_number": 464, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 468, "usage_type": "call"}, {"api_name": "keystone.tests.mapping_fixtures.MAPPING_LARGE", "line_number": 470, "usage_type": "attribute"}, {"api_name": "keystone.tests.mapping_fixtures", "line_number": 470, "usage_type": "name"}, {"api_name": "keystone.tests.mapping_fixtures.MAPPING_LARGE", "line_number": 480, "usage_type": "attribute"}, {"api_name": "keystone.tests.mapping_fixtures", "line_number": 480, "usage_type": "name"}, {"api_name": "keystone.tests.mapping_fixtures.MAPPING_LARGE", "line_number": 507, "usage_type": "attribute"}, {"api_name": "keystone.tests.mapping_fixtures", "line_number": 507, "usage_type": "name"}, {"api_name": "keystone.tests.mapping_fixtures.MAPPING_SMALL", "line_number": 515, "usage_type": "attribute"}, {"api_name": "keystone.tests.mapping_fixtures", "line_number": 515, "usage_type": "name"}, {"api_name": "keystone.tests.mapping_fixtures.MAPPING_SMALL", "line_number": 516, "usage_type": "attribute"}, {"api_name": "keystone.tests.mapping_fixtures", "line_number": 516, "usage_type": "name"}, {"api_name": "keystone.tests.mapping_fixtures.MAPPING_SMALL", "line_number": 518, "usage_type": "attribute"}, {"api_name": "keystone.tests.mapping_fixtures", "line_number": 518, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 521, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 525, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 529, "usage_type": "call"}, {"api_name": "keystone.tests.mapping_fixtures.MAPPING_BAD_REQ", "line_number": 531, "usage_type": "attribute"}, {"api_name": "keystone.tests.mapping_fixtures", "line_number": 531, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 534, "usage_type": "call"}, {"api_name": "keystone.tests.mapping_fixtures.MAPPING_NO_RULES", "line_number": 536, "usage_type": "attribute"}, {"api_name": "keystone.tests.mapping_fixtures", "line_number": 536, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 539, "usage_type": "call"}, {"api_name": "keystone.tests.mapping_fixtures.MAPPING_NO_REMOTE", "line_number": 541, "usage_type": "attribute"}, {"api_name": "keystone.tests.mapping_fixtures", "line_number": 541, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 544, "usage_type": "call"}, {"api_name": "keystone.tests.mapping_fixtures.MAPPING_BAD_VALUE", "line_number": 546, "usage_type": "attribute"}, {"api_name": "keystone.tests.mapping_fixtures", "line_number": 546, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 549, "usage_type": "call"}, {"api_name": "keystone.tests.mapping_fixtures.MAPPING_MISSING_LOCAL", "line_number": 551, "usage_type": "attribute"}, {"api_name": "keystone.tests.mapping_fixtures", "line_number": 551, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 554, "usage_type": "call"}, {"api_name": "keystone.tests.mapping_fixtures.MAPPING_MISSING_TYPE", "line_number": 556, "usage_type": "attribute"}, {"api_name": "keystone.tests.mapping_fixtures", "line_number": 556, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 559, "usage_type": "call"}, {"api_name": "keystone.tests.mapping_fixtures.MAPPING_WRONG_TYPE", "line_number": 561, "usage_type": "attribute"}, {"api_name": "keystone.tests.mapping_fixtures", "line_number": 561, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 564, "usage_type": "call"}, {"api_name": "keystone.tests.mapping_fixtures.MAPPING_EXTRA_REMOTE_PROPS_NOT_ANY_OF", "line_number": 565, "usage_type": "attribute"}, {"api_name": "keystone.tests.mapping_fixtures", "line_number": 565, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 569, "usage_type": "call"}, {"api_name": "keystone.tests.mapping_fixtures.MAPPING_EXTRA_REMOTE_PROPS_ANY_ONE_OF", "line_number": 570, "usage_type": "attribute"}, {"api_name": "keystone.tests.mapping_fixtures", "line_number": 570, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 574, "usage_type": "call"}, {"api_name": "keystone.tests.mapping_fixtures.MAPPING_EXTRA_REMOTE_PROPS_JUST_TYPE", "line_number": 575, "usage_type": "attribute"}, {"api_name": "keystone.tests.mapping_fixtures", "line_number": 575, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 579, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 584, "usage_type": "call"}, {"api_name": "keystone.tests.mapping_fixtures.MAPPING_EXTRA_RULES_PROPS", "line_number": 586, "usage_type": "attribute"}, {"api_name": "keystone.tests.mapping_fixtures", "line_number": 586, "usage_type": "name"}]} +{"seq_id": "441013751", "text": "# encoding:utf-8\nfrom __future__ import division\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport sys\nreload(sys)\nsys.setdefaultencoding('utf-8')\n\ndef Newton(x_0):\n count = 1\n x_k = x_0\n while(True):\n x_k1 = (2 * (x_k ** 3 + x_k * x_k + 50) / (3 * x_k * x_k + 4 * x_k + 10))\n if (abs(x_k1 - x_k)) < (10 ** (-10)):\\\n break\n count += 1\n x_k = x_k1\n return count\n\ndef xianjie(x_0, x_1):\n count = 1\n x_k = x_1\n x_k_1 = x_0\n while(True):\n x_k1 = x_k - ((x_k ** 3 + 2 * x_k * x_k + 10 * x_k - 100) / (x_k ** 3 + 2 * x_k * x_k + 10 * x_k - x_k_1 ** 3 - 2 * x_k_1 * x_k_1 - 10 * x_k_1)) * (x_k - x_k_1)\n if (abs(x_k1 - x_k)) < (10 ** -10):\n break\n count += 1\n x_k_1 = x_k\n x_k = x_k1\n return count\n\nif __name__ == '__main__':\n x = []\n y_n = []\n y_x = []\n for i in range(100):\n x.append(i)\n y_n.append(Newton(i))\n y_x.append(xianjie(i, i + 1))\n plt.figure()\n plt.subplot(1, 2, 1)\n plt.xlabel('x0')\n plt.ylabel('Convergence times')\n plt.plot(x, y_n)\n plt.title('Newton')\n plt.subplot(1, 2, 2)\n plt.xlabel('x0')\n plt.ylabel('Convergence times')\n plt.plot(x, y_x)\n plt.title('chord cut')\n plt.show()", "sub_path": "MathClass/8.py", "file_name": "8.py", "file_ext": "py", "file_size_in_byte": 1282, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sys.setdefaultencoding", "line_number": 7, "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.subplot", "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.ylabel", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "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.plot", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "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"}]} +{"seq_id": "54681935", "text": "import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.utils.data\nfrom scipy.sparse.linalg import eigs\n\ndef scaled_Laplacian(W):\n '''\n compute \\tilde{L}\n Parameters\n ----------\n W: np.ndarray, shape is (N, N), N is the num of vertices\n Returns\n ----------\n scaled_Laplacian: np.ndarray, shape (N, N)\n '''\n\n assert W.shape[0] == W.shape[1]\n D = np.diag(np.sum(W, axis=1))\n L = D - W\n lambda_max = eigs(L, k=1, which='LR')[0].real\n identity = np.identity(W.shape[0])\n return ((2 * L) / lambda_max - identity)\n\ndef cheb_polynomial(L_tilde, K):\n '''\n compute a list of chebyshev polynomials from T_0 to T_{K-1}\n Parameters\n ----------\n L_tilde: scaled Laplacian, np.ndarray, shape (N, N)\n K: the maximum order of chebyshev polynomials\n Returns\n ----------\n cheb_polynomials: list(np.ndarray), length: K, from T_0 to T_{K-1}\n '''\n\n N = L_tilde.shape[0]\n cheb_polynomials = [np.identity(N), L_tilde.copy()]\n for i in range(2, K):\n cheb_polynomials.append(2 * L_tilde * cheb_polynomials[i - 1] - cheb_polynomials[i - 2])\n return cheb_polynomials", "sub_path": "Bigscity-TrafficDL/trafficdl/lib/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1144, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.diag", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 19, "usage_type": "call"}, {"api_name": "scipy.sparse.linalg.eigs", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "354685099", "text": "import os,datetime,time,win32gui,win32con,subprocess\n#VARIÁVEIS RELEVANTES\ntempo_de_uso = 30 #EM MINUTOS\ntempo_de_descanso = 10 #EM MINUTOS\n\nult_reg_tempo = datetime.datetime.now()\n\ndef verificarSistema():\n process_name='LogonUI.exe'\n callall='TASKLIST'\n outputall=subprocess.check_output(callall)\n outputstringall=str(outputall)\n if process_name in outputstringall:\n return False\n else: \n return True\n \ndef calcular_tempo_rodando(ult_reg_tempo):\n rodando_ha = (datetime.datetime.now() - ult_reg_tempo).total_seconds()\n return rodando_ha\nwhile True:\n rodando_ha = calcular_tempo_rodando(ult_reg_tempo)\n porcentam_ate_o_bloqueio = ((rodando_ha/60)/tempo_de_uso)*100\n print('Rodando programa há:',str(datetime.timedelta(seconds=rodando_ha)).split('.')[0]+'s',str(porcentam_ate_o_bloqueio).split('.')[0]+'%')\n SC_MONITORPOWER = 0xF170\n if verificarSistema():\n if (rodando_ha/60) > tempo_de_uso + tempo_de_descanso:\n ult_reg_tempo = datetime.datetime.now()\n open('t_rodando.txt','w').write(str(ult_reg_tempo))\n elif (rodando_ha/60) > tempo_de_uso:\n print('TELA BLOQUEADA')\n win32gui.SendMessage(win32con.HWND_BROADCAST, win32con.WM_SYSCOMMAND, SC_MONITORPOWER, 2)\n time.sleep(0.5)\n os.system(\"cls\")", "sub_path": "Windows Autologoff/wlogoff.py", "file_name": "wlogoff.py", "file_ext": "py", "file_size_in_byte": 1320, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "datetime.datetime.now", "line_number": 6, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 6, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 24, "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": "win32gui.SendMessage", "line_number": 32, "usage_type": "call"}, {"api_name": "win32con.HWND_BROADCAST", "line_number": 32, "usage_type": "attribute"}, {"api_name": "win32con.WM_SYSCOMMAND", "line_number": 32, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 33, "usage_type": "call"}, {"api_name": "os.system", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "510124492", "text": "#!/usr/bin/python\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport ising\nimport sys\n\"\"\"\nLX=int(sys.argv[1])\nLY=int(sys.argv[2])\nNMCS=int(sys.argv[3])\nT=float(sys.argv[4])\n\"\"\"\nLX=100\nLY=100\nNMCS=100\nT=2.27\n\nt,En,Mag=[],[],[]\n\nspin=ising.Monte(LX,LY,T)\nspin.init()\n\nfor i in range(NMCS):\n spin.Mcs()\n t.append(i)\n En.append(spin.energy)\n Mag.append(spin.magnetization)\n \n\nt,En,Mag=np.array(t),np.array(En),np.array(Mag)\n\nfig=plt.figure(figsize=(12,6))\n\nax1=fig.add_subplot(211)\n\nax1.plot(t,En)\nax1.set_xlabel(\"MCS\")\nax1.set_ylabel(\"Energy\")\n\nax2=fig.add_subplot(212)\nax2.plot(t,Mag)\nax2.set_xlabel(\"MCS\")\nax2.set_ylabel(\"Magnetization\")\n\nplt.show()\nfig.savefig(\"ising.png\")\n", "sub_path": "Ising2D_SWIG/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 694, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "ising.Monte", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}]} +{"seq_id": "325544208", "text": "from hydroserving.httpclient.api import UploadMetadata\nfrom hydroserving.helpers.assembly import assemble_model\nfrom hydroserving.models.model_metadata import ModelMetadata\nimport click\n\n\ndef upload_model(model_api, source, model):\n tar = assemble_model(model)\n model_metadata = ModelMetadata.from_folder_metadata(model)\n\n click.echo(\"Uploading to {}\".format(model_api.connection.remote_addr))\n\n contract = None\n if model_metadata.model_contract is not None:\n contract = model_metadata.model_contract.SerializeToString()\n\n metadata = UploadMetadata(\n model_name=model_metadata.model_name,\n model_type=model_metadata.model_type,\n target_source=source,\n model_contract=contract,\n description=model_metadata.description\n )\n\n with click.progressbar(length=1, label='Uploading model assembly')as bar:\n create_encoder_callback = create_bar_callback_factory(bar)\n result = model_api.upload(tar, metadata, create_encoder_callback)\n return result\n\n\ndef create_bar_callback_factory(bar):\n def create_click_callback(multipart_encoder):\n bar.length = multipart_encoder.len\n\n def callback(monitor):\n bar.update(monitor.bytes_read)\n\n return callback\n\n return create_click_callback\n", "sub_path": "hydroserving/helpers/upload.py", "file_name": "upload.py", "file_ext": "py", "file_size_in_byte": 1288, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "hydroserving.helpers.assembly.assemble_model", "line_number": 8, "usage_type": "call"}, {"api_name": "hydroserving.models.model_metadata.ModelMetadata.from_folder_metadata", "line_number": 9, "usage_type": "call"}, {"api_name": "hydroserving.models.model_metadata.ModelMetadata", "line_number": 9, "usage_type": "name"}, {"api_name": "click.echo", "line_number": 11, "usage_type": "call"}, {"api_name": "hydroserving.httpclient.api.UploadMetadata", "line_number": 17, "usage_type": "call"}, {"api_name": "click.progressbar", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "377055656", "text": "\"\"\"Profile model.\"\"\"\n\n# Django\nfrom django.db import models\nfrom django.dispatch import receiver\nfrom django.db.models.signals import post_save\n\n# Utilities\nfrom apps.utils.models import BaseModel\nfrom apps.utils.images import custom_upload_to\n\n# Models\nfrom .users import User\n\n\nclass Profile(BaseModel):\n \"\"\"Profile model.\n\n A profile holds a user's public data like biography, picture\n and statics\n \"\"\"\n\n user = models.OneToOneField('users.User', on_delete=models.CASCADE)\n\n picture = models.ImageField(\n 'profile picture', \n upload_to=custom_upload_to,\n null=True,\n blank=True\n )\n\n biography = models.TextField(max_length=500, null=True, blank=True)\n\n website = models.URLField(\n max_length=255, \n null=True, \n blank=True, \n verbose_name=\"Website\"\n )\n\n class Meta:\n \"\"\"Meta class.\"\"\"\n\n verbose_name = \"perfil\"\n verbose_name_plural = \"perfiles\"\n ordering = ['user__username', ]\n\n def __str__(self):\n \"\"\"Return user's str representation\"\"\"\n return str(self.user)\n\n\n@receiver(post_save, sender=User)\ndef ensure_profile_exists(sender, instance, **kwargs):\n \"\"\"\n Señal que se encarga de crear un perfil por defecto en caso de que \n el usuario se cree una cuenta (post_save) pero nunca ingrese a su perfil.\n \"\"\"\n if kwargs.get('created', False): # Si acaba de crearse un usuario creamos el perfil\n Profile.objects.get_or_create(user=instance)\n ", "sub_path": "apps/users/models/profiles.py", "file_name": "profiles.py", "file_ext": "py", "file_size_in_byte": 1506, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "apps.utils.models.BaseModel", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.OneToOneField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.db.models.ImageField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "apps.utils.images.custom_upload_to", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.URLField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.dispatch.receiver", "line_number": 53, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 53, "usage_type": "argument"}, {"api_name": "users.User", "line_number": 53, "usage_type": "name"}]} +{"seq_id": "27869382", "text": "from tensorflow import keras\nfrom tensorflow.keras import layers,Sequential\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n#######################\n#Define el modelo\n#######################\nclass Cuadratica(layers.Layer):\n\n def __init__(self):\n super(Cuadratica, self).__init__()\n \n \n def build(self, input_shape):\n self.a = self.add_weight(shape=(input_shape[-1],1),\n initializer='random_normal',\n dtype=tf.float32,\n trainable=True,\n name=\"a\")\n self.b = self.add_weight(shape=(input_shape[-1],1),\n initializer='random_normal',\n dtype=tf.float32,\n trainable=True,\n name=\"b\")\n self.c = self.add_weight(shape=1,\n dtype=tf.float32,\n initializer='zeros',\n trainable=True,\n name=\"c\")\n\n def call(self, inputs):\n x=((inputs**2) * self.a) + (inputs * self.b) + self.c\n return x\n\nmodelo=Sequential()\nmodelo.add(Cuadratica())\n\n#######################\n#Prepara los datos\n#######################\n\n#ruido\nsigma=10\n\n#modelo\na=1\nb=-3\nc=5\n\n#datos\nx=np.arange(-10,10,.1)\nx=x.reshape(-1,1)\ny=a*x**2+b*x+c-.1*x**3\nz=y+np.random.normal(0, sigma,y.shape)\n\n#Representa graficamente los datos\nplt.figure(1)\nlines = plt.plot(x, z)\nplt.setp(lines, color='b', linewidth=2.0)\nplt.title(\"Datos a estimar\")\nplt.show()\n\n\n#######################\n#Entrena\n#######################\n\n#Regulariza los datos\nx_corr=max(abs(x))\nz_corr=max(abs(z))\n\nplt.figure(2)\nlines = plt.plot(x/x_corr, z/z_corr)\nplt.setp(lines, color='b', linewidth=2.0)\nplt.title(\"Datos a estimar (escala)\")\nplt.show()\n\n#Prepara el dataset\ntot=len(x)\ntam_muestra=10\n\ntrain_dataset=tf.data.Dataset.from_tensor_slices(((x/x_corr).astype(np.float32),\n (z/z_corr).astype(np.float32))).repeat().shuffle(tot).batch(tam_muestra)\n\n\n#loop de entrenamiento\n\noptimizer =tf.keras.optimizers.Adam(learning_rate=1e-3)\nloss_fn=tf.keras.losses.MeanSquaredError()\n\nepochs=200\nfor x_batch_train, y_batch_train in train_dataset.take(int(epochs*tot/tam_muestra)):\n with tf.GradientTape() as tape:\n resp_est = modelo(x_batch_train)\n error = loss_fn(y_batch_train, resp_est)\n error+= sum(modelo.losses)\n \n grads = tape.gradient(error, modelo.trainable_weights)\n optimizer.apply_gradients(zip(grads, modelo.trainable_weights))\n\n\n#######################\n#Inferencia\n#######################\n\n#Estima\ny_est=modelo.predict(x/x_corr)*z_corr\n\nplt.figure(3)\nplt.title(\"Estimacion vs Reales\")\nlines = plt.plot(x, y_est)\nlines2 = plt.plot(x,z)\nplt.setp(lines, color='g', linewidth=2.0)\nplt.setp(lines2, color='b', linewidth=2.0)\nplt.show()\n\n\nprint(np.mean(abs(y_est-z))/tot)\nprint(\"a={}\".format(modelo.weights[0]/(x_corr**2)*z_corr))\nprint(\"b={}\".format(modelo.weights[1]/x_corr*z_corr))\nprint(\"c={}\".format(modelo.weights[2]*z_corr))", "sub_path": "AI scripts/Workbench/estimacion v1.py", "file_name": "estimacion v1.py", "file_ext": "py", "file_size_in_byte": 3087, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "tensorflow.keras.layers.Layer", "line_number": 10, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers", "line_number": 10, "usage_type": "name"}, {"api_name": "tensorflow.float32", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 56, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "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.setp", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 84, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 84, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 84, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 85, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 90, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.losses.MeanSquaredError", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 91, "usage_type": "attribute"}, {"api_name": "tensorflow.GradientTape", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 120, "usage_type": "call"}]} +{"seq_id": "114399778", "text": "#\n# Copyright (c) 2014, Prometheus Research, LLC\n#\n\n\nfrom webob.exc import HTTPNotFound, HTTPBadRequest\n\nfrom rex.core import get_settings, StrVal, IntVal\nfrom rex.web import Parameter, authenticate\nfrom rex.instrument import User\n\n\n__all__ = (\n 'BaseResource',\n 'get_instrument_user',\n 'ConstantArg',\n 'FakeRequest',\n)\n\n\ndef get_instrument_user(request):\n login = authenticate(request)\n return User.get_implementation().get_by_login(login)\n\n\nclass ConstantArg(object):\n def __init__(self, name, value):\n self.name = name\n self.value = value\n\n\nclass FakeRequest(object):\n def __init__(self, payload, user):\n self.payload = payload\n self.environ = {\n 'rex.user': user.login\n }\n\n\nclass BaseResource(object):\n base_parameters = (\n Parameter('uid', StrVal(), None),\n Parameter('offset', IntVal(0), 0),\n Parameter('limit', IntVal(1), None),\n )\n\n parameters = (\n Parameter('uid', StrVal()),\n )\n\n interface_name = None\n interface_package = 'instrument'\n extra_properties = []\n\n def get_or_404(self, user, uid):\n instance = user.get_object_by_uid(\n uid,\n self.interface_name,\n package_name=self.interface_package,\n )\n if not instance:\n raise HTTPNotFound()\n return instance\n\n def get_criteria(self, params, allowed_params):\n # pylint: disable=no-self-use\n\n criteria = {}\n for allowed_param in allowed_params:\n if allowed_param in params:\n criteria[allowed_param] = params[allowed_param]\n return criteria\n\n def do_list(self, request, list_criteria=None, **kwargs):\n list_criteria = list_criteria or []\n list_criteria.extend(['limit', 'offset'])\n criteria = self.get_criteria(kwargs, list_criteria)\n\n user = get_instrument_user(request)\n instances = user.find_objects(\n self.interface_name,\n package_name=self.interface_package,\n **criteria\n )\n return [\n instance.as_dict(extra_properties=self.extra_properties)\n for instance in instances\n ]\n\n def do_retrieve(self, request, uid):\n # pylint: disable=no-self-use\n\n user = get_instrument_user(request)\n instance = self.get_or_404(user, uid)\n return instance.as_dict(extra_properties=self.extra_properties)\n\n def get_arg_from_payload(self, arg, payload, required=False):\n # pylint: disable=no-self-use\n\n if isinstance(arg, ConstantArg):\n return arg.name, arg.value\n elif isinstance(arg, tuple):\n name, impl = arg\n else:\n name, impl = arg, None\n\n if name in payload:\n value = payload[name]\n if impl:\n value = impl.get_by_uid(value)\n if not value:\n raise HTTPBadRequest(\n '%s is not the UID of a valid %s' % (\n payload[name],\n name,\n )\n )\n return name, value\n\n elif required:\n raise HTTPBadRequest(\n 'Missing required parameter: %s' % (\n name,\n )\n )\n\n else:\n return None, None\n\n def do_create(self, request, create_args=None, create_kwargs=None):\n create_args = create_args or []\n create_kwargs = create_kwargs or []\n\n cargs = []\n for create_arg in create_args:\n name, value = self.get_arg_from_payload(\n create_arg,\n request.payload,\n required=True,\n )\n cargs.append(value)\n\n ckwargs = {}\n for create_kwarg in create_kwargs:\n name, value = self.get_arg_from_payload(\n create_kwarg,\n request.payload,\n )\n if name and value:\n ckwargs[name] = value\n\n setting = getattr(\n get_settings(),\n '%s_implementation' % self.interface_package,\n )\n impl = getattr(\n setting,\n self.interface_name,\n )\n instance = impl.create(*cargs, **ckwargs)\n return instance.as_dict(extra_properties=self.extra_properties)\n\n def do_update(self, request, uid, properties=None):\n properties = properties or []\n\n user = get_instrument_user(request)\n instance = self.get_or_404(user, uid)\n\n updated = False\n for prop in properties:\n if isinstance(prop, ConstantArg):\n setattr(\n instance,\n prop.name,\n prop.value,\n )\n\n if prop in request.payload:\n setattr(\n instance,\n prop,\n request.payload[prop],\n )\n updated = True\n\n if updated:\n if hasattr(instance, 'modify'):\n instance.modify(user)\n instance.save()\n\n return instance.as_dict(extra_properties=self.extra_properties)\n\n def do_delete(self, request, uid):\n user = get_instrument_user(request)\n instance = self.get_or_404(user, uid)\n instance.delete()\n\n", "sub_path": "src/rex.formbuilder/src/rex/formbuilder/api/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 5381, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "rex.web.authenticate", "line_number": 22, "usage_type": "call"}, {"api_name": "rex.instrument.User.get_implementation", "line_number": 23, "usage_type": "call"}, {"api_name": "rex.instrument.User", "line_number": 23, "usage_type": "name"}, {"api_name": "rex.web.Parameter", "line_number": 42, "usage_type": "call"}, {"api_name": "rex.core.StrVal", "line_number": 42, "usage_type": "call"}, {"api_name": "rex.web.Parameter", "line_number": 43, "usage_type": "call"}, {"api_name": "rex.core.IntVal", "line_number": 43, "usage_type": "call"}, {"api_name": "rex.web.Parameter", "line_number": 44, "usage_type": "call"}, {"api_name": "rex.core.IntVal", "line_number": 44, "usage_type": "call"}, {"api_name": "rex.web.Parameter", "line_number": 48, "usage_type": "call"}, {"api_name": "rex.core.StrVal", "line_number": 48, "usage_type": "call"}, {"api_name": "webob.exc.HTTPNotFound", "line_number": 62, "usage_type": "call"}, {"api_name": "webob.exc.HTTPBadRequest", "line_number": 112, "usage_type": "call"}, {"api_name": "webob.exc.HTTPBadRequest", "line_number": 121, "usage_type": "call"}, {"api_name": "rex.core.get_settings", "line_number": 153, "usage_type": "call"}]} +{"seq_id": "249258880", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.interpolate import interp1d\n\nlist_for_x = list()\nlist_for_y = list()\n\nx1 = np.linspace(0, 50, 1000)\ny1 = -0.7 * x1 + 50\n\nlist_previous_x = [element for element in x1 if element < 26]\nlist_previous_y = [-0.7 * element + 50 for element in list_previous_x]\n\nlist_for_x = [element for element in x1 if element > 25 and element < 61][::100]\nlist_for_y = [-0.7 * element + 50 for element in list_for_x]\n\nx2 = np.linspace(50, 200, 1000)\ny2 = x1 / x1 * 15\n\n[list_for_x.append(element) for element in x2 if element > 60 and element < 70]\n[list_for_y.append(element/element * 15) for element in list_for_x if element > 60 and element < 70]\n\nlist_next_x = [element for element in x2 if element > 70]\nlist_next_y = [element/element * 15 for element in list_next_x]\n\n\nf = interp1d(list_for_x, list_for_y, kind='cubic')\nxnew = np.linspace(list_for_x[0], list_for_x[-1], num = 100, endpoint=True)\n\n\nfig, axes = plt.subplots(1, 2, figsize=(13,8))\naxes[0].plot(x1, y1, '-', x2, y2, '-', color='g')\naxes[1].plot(xnew, f(xnew), '-', list_previous_x, list_previous_y, '-',\\\n list_next_x, list_next_y, '-', color='g')\n\nplt.show()\n", "sub_path": "coner_inerpolation.py", "file_name": "coner_inerpolation.py", "file_ext": "py", "file_size_in_byte": 1174, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.linspace", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 17, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}]} +{"seq_id": "614576911", "text": "import os\nimport sys\nimport ast\nfrom pathlib import Path\n\nORG = \"sakshamarora1\"\nNAME = \"dffml-operations-image\"\nDESCRIPTION = \"DFFML operations image\"\nAUTHOR_NAME = \"Saksham Arora\"\nAUTHOR_EMAIL = \"sakshamarora1001@gmail.com\"\n\nIMPORT_NAME = (\n NAME\n if \"replace_package_name\".upper() != NAME\n else \"replace_import_package_name\".upper()\n).replace(\"-\", \"_\")\n\nSELF_PATH = Path(sys.argv[0]).parent.resolve()\nif not (SELF_PATH / Path(IMPORT_NAME, \"version.py\")).is_file():\n SELF_PATH = os.path.dirname(os.path.realpath(__file__))\n\nVERSION = ast.literal_eval(\n Path(SELF_PATH, IMPORT_NAME, \"version.py\")\n .read_text()\n .split(\"=\")[-1]\n .strip()\n)\n\nREADME = Path(SELF_PATH, \"README.md\").read_text()\n\nKWARGS = dict(\n name=NAME,\n version=VERSION,\n description=DESCRIPTION,\n long_description=README,\n long_description_content_type=\"text/markdown\",\n author=AUTHOR_NAME,\n author_email=AUTHOR_EMAIL,\n maintainer=AUTHOR_NAME,\n maintainer_email=AUTHOR_EMAIL,\n url=f\"https://github.com/{ORG}/{NAME}\",\n license=\"MIT\",\n keywords=[\"dffml\"],\n classifiers=[\n \"Development Status :: 3 - Alpha\",\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: MIT License\",\n \"Natural Language :: English\",\n \"Operating System :: OS Independent\",\n \"Programming Language :: Python :: 3 :: Only\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: Implementation :: CPython\",\n \"Programming Language :: Python :: Implementation :: PyPy\",\n ],\n)\n", "sub_path": "operations/image/setup_common.py", "file_name": "setup_common.py", "file_ext": "py", "file_size_in_byte": 1572, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pathlib.Path", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 20, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 22, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 23, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "84440531", "text": "import math\n\nfrom django.test import TestCase\n\nfrom util import knmidata, geocoding\n\n\nclass MyDirectionTestCase(TestCase):\n LOCATION_START = \"Utrecht, Netherlands\"\n LOCATION_END = \"Nieuwegein, Netherlands\"\n\n def setUp(self):\n print(\"setUp()\")\n\n def test_bearing(self):\n bearing_rad, distance_m = geocoding.get_bearing_between_locations_rad(self.LOCATION_START, self.LOCATION_END)\n bearing_deg = bearing_rad * 180.0 / math.pi\n self.assertAlmostEqual(bearing_deg, 207.4734, places=4)\n print(bearing_deg)\n\n def test_distance(self):\n bearing_rad, distance_m = geocoding.get_bearing_between_locations_rad(self.LOCATION_START, self.LOCATION_END)\n self.assertAlmostEqual(distance_m, 10241.65, places=2)\n print(distance_m)\n\n\nclass KNMIStationTestCase(TestCase):\n def test_get_stations(self):\n knmi_stations = knmidata.KNMIStations()\n self.assertEqual(len(knmi_stations.stations), 35)\n\n def test_calculate_wind_direction(self):\n name = 'test_station'\n lat = 52.10927\n lon = 5.18097\n wind_direction_deg = 10\n wind_speed_ms = 10.0\n wind_direction_to_deg = 190\n test_station = knmidata.KNMIStation(name, lat, lon, wind_direction_deg, wind_speed_ms)\n self.assertAlmostEqual(test_station.get_wind_direction_deg(), wind_direction_deg)\n self.assertAlmostEqual(test_station.get_wind_direction_rad(), wind_direction_deg / 180.0 * math.pi)\n self.assertAlmostEqual(test_station.get_wind_direction_rad(), wind_direction_deg / 180.0 * math.pi)\n self.assertAlmostEqual(test_station.get_wind_direction_to_deg(), wind_direction_to_deg)\n self.assertAlmostEqual(test_station.get_wind_direction_to_rad(), wind_direction_to_deg / 180.0 * math.pi)\n\n\nclass WindDirectionTestCase(TestCase):\n def test_wind_direction(self):\n stations = knmidata.get_actual_stations()\n for station in stations:\n print(station)\n station = stations[0]\n print(station)\n print('wind direction [deg]: ' + str(station.wind_direction_deg))\n print('wind_speed [m/s]: ' + str(station.wind_speed_ms))", "sub_path": "util/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 2170, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.test.TestCase", "line_number": 8, "usage_type": "name"}, {"api_name": "util.geocoding.get_bearing_between_locations_rad", "line_number": 16, "usage_type": "call"}, {"api_name": "util.geocoding", "line_number": 16, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 17, "usage_type": "attribute"}, {"api_name": "util.geocoding.get_bearing_between_locations_rad", "line_number": 22, "usage_type": "call"}, {"api_name": "util.geocoding", "line_number": 22, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 27, "usage_type": "name"}, {"api_name": "util.knmidata.KNMIStations", "line_number": 29, "usage_type": "call"}, {"api_name": "util.knmidata", "line_number": 29, "usage_type": "name"}, {"api_name": "util.knmidata.KNMIStation", "line_number": 39, "usage_type": "call"}, {"api_name": "util.knmidata", "line_number": 39, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 41, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 42, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 44, "usage_type": "attribute"}, {"api_name": "django.test.TestCase", "line_number": 47, "usage_type": "name"}, {"api_name": "util.knmidata.get_actual_stations", "line_number": 49, "usage_type": "call"}, {"api_name": "util.knmidata", "line_number": 49, "usage_type": "name"}]} +{"seq_id": "235128107", "text": "\nimport numpy as np\nimport cv2\nfrom matplotlib import pyplot as plt\nimport os\n\ndef getColorSpaces(image):\n rgb = cv2.cvtColor(image,cv2.COLOR_RGB2BGR)\n gray = cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)\n\n return rgb,gray\n\ndef getImageDimnesion(image):\n height,width = image.shape[:2]\n\n return height,width\n\ndef getImageChannels(image):\n _,_,channels = image.shape\n\n return channels\n\ndef split_into_rgb_channels(image):\n '''Split the target image into its red, green and blue channels.\n image - a numpy array of shape (rows, columns, 3).\n output - three numpy arrays of shape (rows, columns) and dtype same as\n image, containing the corresponding channels. \n '''\n red = image[:,:,2]\n green = image[:,:,1]\n blue = image[:,:,0]\n return red, green, blue\n\n \ndef LinearTransform(im, k1, k2, a, b):\n\n # Update input for use for main use\n k1 = k1\n k2 = int(k2*255)\n a = int(a*255)\n b = int(b*255)\n \n # Get the dimensions of background image \n im = np.array(im)\n \n new_im = k1*im + k2\n new_im[im > b] = 255\n new_im[im < a] = 0\n new_im[new_im > 255] = 255\n new_im[new_im < 0] = 0\n \n return new_im.astype('uint8')\n\n\ndef display_linear_transform(im,k1,k2,a,b):\n \n# Just for displaying purposes\n x = np.arange(0,1,0.01)\n y = k1*x + k2\n y[ x < a] = 0\n y[ x > b] = 1\n y[ y > 1] = 1\n y[ y < 0] = 0\n\n new_im = LinearTransform(im,k1,k2,a,b)\n# plt.imshow(new_im)\n \n# Plot the graph, image\n fig = plt.figure(figsize=(16,16))\n ax1 = fig.add_subplot(1,3,2)\n ax2 = fig.add_subplot(1,3,3)\n ax3 = fig.add_subplot(1,3,1)\n ax1.imshow(np.array(im), cmap='gray')\n ax2.imshow(new_im, cmap='gray')\n ax3.plot(x,y)\n\n ax1.set_title('Old Image')\n ax2.set_title('Transformed Image')\n ax3.set_title('Function')\n ax1.axis('off')\n ax2.axis('off')\n ax3.axis('scaled')\n plt.show()\n\ndef multiple_linear_transform(im,k1_list,k2_list,a_list, b_list):\n # Just for displaying purposes\n x = np.arange(0,1,0.01)\n y_list = []\n im_list = []\n for i in range(len(k1_list)):\n try:\n new_y = k1_list[i]*x + k2_list[i]\n new_y[ x < a_list[i]] = 0\n new_y[ x > b_list[i]] = 1\n \n new_im = LinearTransform(im,k1_list[i],k2_list[i],a_list[i],b_list[i])\n \n y_list.append(new_y)\n im_list.append(new_im)\n \n except Exception as e:\n print(\"Error while producing linear transform:\",e)\n \n im_list = np.array(im_list)\n new_im = np.sum(im_list,axis=0)\n new_im[ new_im > 255] = 255\n new_im[ new_im < 0] = 0\n \n \n y_list = np.array(y_list)\n y = np.sum(y_list,axis=0)\n y[ y > 1] = 1\n y[ y < 0] = 0\n \n \n# Plot the graph, image\n fig = plt.figure(figsize=(16,16))\n ax1 = fig.add_subplot(1,3,2)\n ax2 = fig.add_subplot(1,3,3)\n ax3 = fig.add_subplot(1,3,1)\n# ax1.imshow(np.array(im), cmap='gray')\n# ax2.imshow(np.array(new_im), cmap='gray')\n ax3.plot(x,y)\n ax1.set_title('Old Image')\n ax2.set_title('Transformed Image')\n ax3.set_title('Function')\n ax1.axis('off')\n ax2.axis('off')\n ax3.axis('scaled')\n plt.show()\n \n\nimge_path=r'A1_resources/DIP_2019_A1/squares.jpg'\nim = cv2.imread(imge_path)\n#\n#k1 = 2.0\n#k2 = -0.5\n#a = 0.25\n#b = 0.75\n\n#display_linear_transform(im,k1,k2,a,b)\n\nfrom PIL import Image\n# Example 4\nk1 = [2.0,0.0]\nk2 = [0.0,2.0]\na = [0.2,0.6]\nb = [0.6,0.6]\nim = Image.open(r'A1_resources/DIP_2019_A1/lena.jpg').convert('L')\nmultiple_linear_transform(im,k1,k2,a,b)\n\n#k1 = [0.0,0.0,0.0,0.0]\n#k2 = [0.25,0.25,0.25,0.25]\n#a = [0.0,0.25,0.5,0.75]\n#b = [1.0,1.0,1.0,1.0]\n#im = Image.open(r'A1_resources/DIP_2019_A1/contrast1.jpg').convert('L')\n#multiple_linear_transform(im,k1,k2,a,b)\n\n\n\n\n\n\n\n", "sub_path": "Assignment1/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 3805, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "cv2.cvtColor", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 8, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 9, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 134, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 149, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 149, "usage_type": "name"}]} +{"seq_id": "437538715", "text": "import wiringpi2 as wiring\nfrom utils import UTILS\nfrom settings import SETTINGS\nfrom laserexceptions import BadSetting\nfrom multiprocessing import Process\nfrom threading import Thread\n\nclass GPIO(object):\n \"\"\"\n - abstraction layer on top of wiringpi wrapper\n - provides low level helper functions for controlling the gpio's\n \n \"\"\"\n Utils = UTILS # incorporate utility methods (all class methods (static))\n PULSEID = SETTINGS.PULSEID\n PAUSEID = SETTINGS.PAUSEID\n STARTEXPID = SETTINGS.STARTEXPID\n STARTMODID = SETTINGS.STARTMODID\n \n _VALID_PINS = [0, 1, 2, 3, 4, 5, 6, 7] # valid output pins for laser TTL\n _LASERPIN = 0 # output gpio (wiring format) for laser control pulse (GPIO 17)\n _EXPPIN = 3 # digital output indicating the start of the experiment (GPIO 22)\n _MODPIN = 1 # digital output pin indicating start of protocol modules (GPIO 18)\n _EVENTDUR = 10 # event duration of experiment and module triggers \n \n if not(_LASERPIN in _VALID_PINS):\n raise BadSetting('laser output pin is not a valid output pin must be one in %(pins)s' % {'pins': _VALID_PINS})\n \n _INPUT = 0 # logical value for setting a pin to output mode\n _OUTPUT = 1 # logical value for setting a pint to input mode\n\n _HIGH = 1 # logical value for setting a pin to HIGH (3.3V)\n _LOW = 0 # logical value for setting a pin to LOW (0V)\n \n _maxPULSEDUR = 20 # maximum pulse duration\n _minPULSEDUR = 0. # minumum pulse duration\n \n _PRIORITY = 80 # thread/process priority (99 is highest, 0 is lowest)\n \n def __init__(self):\n \"\"\"\n - initialize gpio setup and configure pins\n - TODO: set thread to realtime\n\n \"\"\"\n self.PULSEDUR = SETTINGS.PULSEDUR # pulse duration in millisecond \n print('pulse duration', self.PULSEDUR) \n\n # initialize the wiringpi interface\n wiring.wiringPiSetup() # setup pins to the wiring numbering gpio 17 = pin 0 etc. and initialize ouput \n wiring.pinMode(GPIO._LASERPIN, GPIO._OUTPUT) # set laser pin to output mode\n wiring.pinMode(GPIO._EXPPIN, GPIO._OUTPUT) # set laser pin to output mode\n wiring.pinMode(GPIO._MODPIN, GPIO._OUTPUT) # set laser pin to output mode\n wiring.piHiPri(GPIO._PRIORITY) # set thread/process priority from 0 (low) to 99(max)\n\n\n def __enter__(self):\n \"\"\"\n context for with statement, thus ensuring the laser is off at protocol start\n \n \"\"\"\n self._LaserOff()\n return self # this class incorporates its own context and returns an instance of itself for use within the context\n \n def __exit__(self, type, value, traceback):\n \"\"\"\n context for with statement, ensuring the laser is turend off when an exception happens\n \n \"\"\"\n self._LaserOff()\n \n if not(type is None):\n print('exception was catched in by exit method')\n print(type, value, traceback)\n \n def Pulse(self, timeStep):\n \"\"\"\n generate pulse of the laser of a set duration (ms)\n \n param:\n timeStep , time step between two pulses in millisecond (integer)\n \n \"\"\"\n exec_delay_correction = 0.0 # intermediate python commands introduce delays, so a basic correction is applied \n waitTime = timeStep - self.PULSEDUR - exec_delay_correction #the wait time after the pulse is the timeStep - the pulse duration\n self._LaserOn() # turn on laser\n self._Delay(self.PULSEDUR) # wait for the pulse duration\n self._LaserOff() # turn laser off\n \n # assert(wiring.digitalRead(GPIO._LASERPIN) == GPIO._LOW) # check whether the laser is off (this causes a delay) \n self._Delay(waitTime) # wait untill start of next pulse \n\n def Wait(self, ms):\n \"\"\"\n - public method: wait a period of time and block execution during this period\n - no pulse is generate, only used for initial pause period or long duration pause during experiment\n\n - wait uses delay in milliseconds, which wraps around in days, not 71 minutes like delayMicroseconds\n\n param:\n ms , milliseconds to wait\n \n \"\"\"\t\n \n us = int(GPIO.Utils.MStoUS(ms)) # convert float milliseconds to integer microSeconds\n wiring.delayMicroseconds(us) # pause execution for a selected amount of microSeconds\n \n \n \n \"\"\"\n Parallel calls for control events \n \n \"\"\"\n \n def StartExp(self):\n \"\"\"\n generate pulse on secondary digital output to indicate the start of the experiment\n \n \"\"\"\n \n def StartExpPulse():\n wiring.digitalWrite(GPIO._EXPPIN, GPIO._HIGH)\n wiring.delayMicroseconds(GPIO._EVENTDUR)\n wiring.digitalWrite(GPIO._EXPPIN, GPIO._LOW)\n \n Th = Thread(target = StartExpPulse)\n Th.start()\n \n def StartMod(self):\n \"\"\"\n trigger digital output indicating a start of a protocol module\n \n \"\"\"\n \n def StartModPulse():\n wiring.digitalWrite(GPIO._MODPIN, GPIO._HIGH)\n wiring.delayMicroseconds(GPIO._EVENTDUR)\n wiring.digitalWrite(GPIO._MODPIN, GPIO._LOW)\n \n Th = Thread(target = StartModPulse)\n Th.start()\n \n \n \"\"\"\n Private methods\n \n \"\"\"\n \n \n def _LaserOn(self):\n \"\"\"\n set laser CMOS to HIGH (3.3V)\n\n \"\"\"\n wiring.digitalWrite(GPIO._LASERPIN, GPIO._HIGH)\n\n def _LaserOff(self):\n \"\"\"\n set laser CMOS to LOW (0V)\n\n \"\"\"\n wiring.digitalWrite(GPIO._LASERPIN, GPIO._LOW)\n\n \n def _Delay(self, ms):\n \"\"\"\n - private method: pause execution for internal use (primarily between laser on and off)\n - pause program for a defined number of milliseconds\n\n param:\n ms , milliseconds to wait\n\n \"\"\"\n #us = int(GPIO.Utils.MStoUS(ms)) # convert from float millisecond to integer microseconds\n us = int(ms * 1000) # faster?\n wiring.delayMicroseconds(us) # pause execution, for a selected number of milliseconds \n \n \n \"\"\"\n validation through property interface for critical parameters\n \n \"\"\"\n \n def _getPULSEDUR(self):\n return self._PULSEDUR\n def _setPULSEDUR(self, duration):\n if duration < self._maxPULSEDUR and duration >= self._minPULSEDUR:\n self._PULSEDUR = duration\n else:\n raise BadSetting('pulse duration should be longer than %(min)s and shorter than %(max)s milliseconds' \\\n % {'min': GPIO._minPULSEDUR, 'max': GPIO._maxPULSEDUR}) \n \n PULSEDUR = property(_getPULSEDUR, _setPULSEDUR, doc = 'critical parameter: pulse duration (ms)') # create property PULSEDUR with protected getter and setter\n \n", "sub_path": "Engine/sysio.py", "file_name": "sysio.py", "file_ext": "py", "file_size_in_byte": 8036, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "utils.UTILS", "line_number": 14, "usage_type": "name"}, {"api_name": "settings.SETTINGS.PULSEID", "line_number": 15, "usage_type": "attribute"}, {"api_name": "settings.SETTINGS", "line_number": 15, "usage_type": "name"}, {"api_name": "settings.SETTINGS.PAUSEID", "line_number": 16, "usage_type": "attribute"}, {"api_name": "settings.SETTINGS", "line_number": 16, "usage_type": "name"}, {"api_name": "settings.SETTINGS.STARTEXPID", "line_number": 17, "usage_type": "attribute"}, {"api_name": "settings.SETTINGS", "line_number": 17, "usage_type": "name"}, {"api_name": "settings.SETTINGS.STARTMODID", "line_number": 18, "usage_type": "attribute"}, {"api_name": "settings.SETTINGS", "line_number": 18, "usage_type": "name"}, {"api_name": "laserexceptions.BadSetting", "line_number": 27, "usage_type": "call"}, {"api_name": "settings.SETTINGS.PULSEDUR", "line_number": 46, "usage_type": "attribute"}, {"api_name": "settings.SETTINGS", "line_number": 46, "usage_type": "name"}, {"api_name": "wiringpi2.wiringPiSetup", "line_number": 50, "usage_type": "call"}, {"api_name": "wiringpi2.pinMode", "line_number": 51, "usage_type": "call"}, {"api_name": "wiringpi2.pinMode", "line_number": 52, "usage_type": "call"}, {"api_name": "wiringpi2.pinMode", "line_number": 53, "usage_type": "call"}, {"api_name": "wiringpi2.piHiPri", "line_number": 54, "usage_type": "call"}, {"api_name": "wiringpi2.delayMicroseconds", "line_number": 106, "usage_type": "call"}, {"api_name": "wiringpi2.digitalWrite", "line_number": 122, "usage_type": "call"}, {"api_name": "wiringpi2.delayMicroseconds", "line_number": 123, "usage_type": "call"}, {"api_name": "wiringpi2.digitalWrite", "line_number": 124, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 126, "usage_type": "call"}, {"api_name": "wiringpi2.digitalWrite", "line_number": 136, "usage_type": "call"}, {"api_name": "wiringpi2.delayMicroseconds", "line_number": 137, "usage_type": "call"}, {"api_name": "wiringpi2.digitalWrite", "line_number": 138, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 140, "usage_type": "call"}, {"api_name": "wiringpi2.digitalWrite", "line_number": 155, "usage_type": "call"}, {"api_name": "wiringpi2.digitalWrite", "line_number": 162, "usage_type": "call"}, {"api_name": "wiringpi2.delayMicroseconds", "line_number": 176, "usage_type": "call"}, {"api_name": "laserexceptions.BadSetting", "line_number": 190, "usage_type": "call"}]} +{"seq_id": "642531662", "text": "\"\"\"\r\nLibrary to read and control the ion pump\r\n\"\"\"\r\n\r\nimport serial\r\nimport os\r\nimport time\r\nimport datetime\r\n\r\nDEFAULT_LOG_FILE_STRING = \"Ion_Pump_Log\" \r\nMPC_DEFAULT_ADDRESS = 5\r\nSPC_DEFAULT_ADDRESS = 1\r\n\r\n\r\n# def get_key(my_dict, val):\r\n# for key, value in my_dict.items():\r\n# if val == value:\r\n# return key\r\n\r\n\r\nclass IonPump:\r\n\r\n\r\n \"\"\"Constructor.\r\n\r\n Args:\r\n pump_label: str, the label describing the type of pump. Supports \"spc\" and \"mpc\".\r\n COM_PORT: str, the string describing the COM port of the pump serial connection, e.g. 'COM7'\r\n address: int, the address of the pump. Default is 1 for spc, 5 for mpc\r\n echo: bool, whether to echo the response after a send command\r\n wait_time: float, the wait time for a read after a send command\r\n sendwidget: Widget; ignore unless making a gui\r\n recvwidget: Widget; ignore unless making a gui\r\n history_file_string: A string path to a history file which the ion pump should log into. If not specified, a default name is chosen\r\n and an empty file created\r\n log_history: bool, whether the ion gauge should log its readings.\r\n overwrite_history: bool, whether the ion gauge should overwrite the specified history file\r\n \"\"\"\r\n def __init__(self, pump_label, COM_PORT, address = None, echo = True, wait_time = 0.1, sendwidget = None, recvwidget = None,\r\n history_file_string = None, log_history = True, overwrite_history = False):\r\n #Default port settings for ion pumps\r\n PORT_SETTINGS = {'baudrate':9600, 'bytesize':serial.EIGHTBITS, 'parity':serial.PARITY_NONE, 'stopbits':serial.STOPBITS_ONE, 'timeout':1}\r\n self.serial_port = serial.Serial(COM_PORT, **PORT_SETTINGS)\r\n self.pump_label = pump_label \r\n self.echo = echo\r\n self.wait_time = wait_time\r\n self.sendwidget = sendwidget\r\n self.recvwidget = recvwidget\r\n self.log_history = log_history\r\n self.overwrite_history = overwrite_history\r\n if(address is None):\r\n if(pump_label == \"mpc\"):\r\n self.address = MPC_DEFAULT_ADDRESS\r\n elif(pump_label == \"spc\"):\r\n self.address = SPC_DEFAULT_ADDRESS\r\n else:\r\n self.address = address\r\n if(history_file_string == None):\r\n label_index = 0 \r\n label_ok = False\r\n while(not label_ok):\r\n attempted_filename = DEFAULT_LOG_FILE_STRING + str(label_index) + \".csv\"\r\n label_ok = not os.path.exists(attempted_filename) \r\n label_index += 1\r\n history_file_string = attempted_filename\r\n if(self.log_history):\r\n if(self.overwrite_history):\r\n self.history_csv_file = open(history_file_string, 'w')\r\n else:\r\n self.history_csv_file = open(history_file_string, 'a')\r\n else:\r\n self.history_csv_file = None \r\n \r\n\r\n \"\"\"\r\n Sends an arbitrary command to the turbo pump\r\n\r\n Args: \r\n command: str, the command to be sent to the turbo pump\r\n add_checksum_and_end: Convenience. If True, the command string has a checksum and carriage return character \r\n appended, following the initial tilde convention\r\n \r\n Returns:\r\n the return value of serial.write() \r\n \"\"\"\r\n\r\n def send(self, command, add_checksum_and_end = False):\r\n if(add_checksum_and_end):\r\n to_be_checked_string = command[1:]\r\n checksum_string = get_checksum_string(to_be_checked_string)\r\n command = command + checksum_string + \"\\r\"\r\n return_value = self.serial_port.write(bytes(command, encoding = \"ASCII\"))\r\n self.serial_port.flush()\r\n if(self.echo):\r\n self.log(command, widget = self.sendwidget)\r\n time.sleep(self.wait_time)\r\n return return_value\r\n\r\n \"\"\"Convenience method which turns on the ion pump\"\"\"\r\n # def turn_on(self):\r\n # ON_COMMAND_STRING = \"\"\r\n # address_string = hex(self.address)[2:]\r\n # address_string = address_string.upper() \r\n # if(self.address < 16):\r\n # address_string = \"0\" + address_string \r\n \r\n \r\n \"\"\"Convenience method which measures the ion pump current, pressure, and voltage\"\"\"\r\n\r\n def measure_all(self, supply_index = 1):\r\n current_value = self.measure_current(supply_index = supply_index) \r\n pressure_value = self.measure_pressure(supply_index = supply_index) \r\n voltage_value = self.measure_voltage(supply_index = supply_index) \r\n return (current_value, pressure_value, voltage_value)\r\n\r\n def measure_current(self, supply_index = 1):\r\n if(self.pump_label == \"spc\"):\r\n data_field = ''\r\n elif(self.pump_label == \"mpc\"):\r\n data_field = str(supply_index) + ' '\r\n address_string = self.get_address_string() \r\n CURRENT_MEASURE_CODE = '0A'\r\n current_measure_command_initial = ' ' + address_string + ' ' + CURRENT_MEASURE_CODE + \" \" + data_field\r\n current_measure_command_checksum = self.get_checksum_string(current_measure_command_initial)\r\n current_measure_command = '~' + current_measure_command_initial + current_measure_command_checksum + \"\\r\"\r\n current_bytes_list = self.send_and_get_response(current_measure_command)\r\n current_value = self.parse_current_bytes(current_bytes_list)\r\n return current_value \r\n\r\n def measure_pressure(self, supply_index = 1):\r\n if(self.pump_label == \"spc\"):\r\n data_field = ''\r\n elif(self.pump_label == \"mpc\"):\r\n data_field = str(supply_index) + ' '\r\n address_string = self.get_address_string() \r\n PRESSURE_MEASURE_CODE = '0B'\r\n pressure_measure_command_initial = ' ' + address_string + ' ' + PRESSURE_MEASURE_CODE + \" \" + data_field\r\n pressure_measure_command_checksum = self.get_checksum_string(pressure_measure_command_initial)\r\n pressure_measure_command = '~' + pressure_measure_command_initial + pressure_measure_command_checksum + \"\\r\"\r\n pressure_bytes_list = self.send_and_get_response(pressure_measure_command)\r\n pressure_value = self.parse_pressure_bytes(pressure_bytes_list) \r\n return pressure_value \r\n\r\n def measure_voltage(self, supply_index = 1):\r\n if(self.pump_label == \"spc\"):\r\n data_field = ''\r\n elif(self.pump_label == \"mpc\"):\r\n data_field = str(supply_index) + ' '\r\n address_string = self.get_address_string() \r\n VOLTAGE_MEASURE_CODE = '0C'\r\n voltage_measure_command_initial = ' ' + address_string + ' ' + VOLTAGE_MEASURE_CODE + \" \" + data_field\r\n voltage_measure_command_checksum = self.get_checksum_string(voltage_measure_command_initial)\r\n voltage_measure_command = '~' + voltage_measure_command_initial + voltage_measure_command_checksum + \"\\r\"\r\n voltage_bytes_list = self.send_and_get_response(voltage_measure_command)\r\n voltage_value = self.parse_voltage_bytes(voltage_bytes_list) \r\n return voltage_value \r\n\r\n @staticmethod\r\n def parse_current_bytes(current_bytes_list):\r\n current_string = current_bytes_list[0].decode(\"ASCII\")\r\n status_code = current_string[3:5]\r\n if(status_code == \"OK\"):\r\n current_value_string = current_string[9:15]\r\n current_value = float(current_value_string)\r\n return current_value\r\n else:\r\n return -1\r\n \r\n @staticmethod\r\n def parse_pressure_bytes(pressure_bytes_list):\r\n pressure_string = pressure_bytes_list[0].decode(\"ASCII\")\r\n status_code = pressure_string[3:5]\r\n if(status_code == \"OK\"):\r\n pressure_value_string = pressure_string[9:15]\r\n pressure_value = float(pressure_value_string) \r\n return pressure_value\r\n else:\r\n return -1\r\n\r\n @staticmethod\r\n def parse_voltage_bytes(voltage_bytes_list):\r\n voltage_string = voltage_bytes_list[0].decode(\"ASCII\")\r\n status_code = voltage_string[3:5]\r\n if(status_code == \"OK\"):\r\n voltage_value_string = voltage_string[9:13]\r\n voltage_value = int(voltage_value_string)\r\n return voltage_value \r\n else:\r\n return -1\r\n\r\n\r\n\r\n def get_address_string(self):\r\n address_string = hex(self.address)[2:]\r\n address_string = address_string.upper() \r\n if(self.address < 16):\r\n address_string = \"0\" + address_string \r\n return address_string \r\n\r\n @staticmethod\r\n def get_checksum_string(checked_string):\r\n checksum_val = 0\r\n for byte in checked_string.encode(\"ASCII\"):\r\n checksum_val += int(byte) \r\n checksum_val = checksum_val % 256\r\n checksum_string = hex(checksum_val)[2:]\r\n checksum_string = checksum_string.upper() \r\n if(checksum_val < 16):\r\n checksum_string = \"0\" + checksum_string\r\n return checksum_string\r\n\r\n def send_and_get_response(self, command, add_checksum_and_end = False):\r\n self.send(command, add_checksum_and_end= add_checksum_and_end)\r\n return self.serial_port.readlines()\r\n\r\n\r\n # def sendrecv(self, cmd):\r\n # \"\"\"Send a command and (optionally) printing the picomotor driver's response.\"\"\"\r\n # res = self.send(cmd)\r\n # if self.echo:\r\n # time.sleep(self.wait)\r\n # ret_str = self.readlines()\r\n # self.log(ret_str, widget=self.recvwidget)\r\n # return res\r\n\r\n\r\n\r\n\r\n # def __init__(self, COM_PORT, echo=True, wait=0.1, sendwidget=None, recvwidget=None):\r\n # \"\"\"\r\n # Args: \r\n # - COM_PORT: str, e.g. 'COM7'\r\n # - FILL OUT:\r\n # \"\"\"\r\n # COM_SETTINGS = dict(baudrate=9600, bytesize=serial.EIGHTBITS,\r\n # parity=serial.PARITY_NONE, stopbits=serial.STOPBITS_ONE,\r\n # timeout=1)\r\n # super.__init__(COM_PORT, **COM_SETTINGS)\r\n # self.echo = echo\r\n # self.wait = wait\r\n # self.sendwidget = sendwidget\r\n # self.recvwidget = recvwidget\r\n # self.history = {}\r\n # for idx in range(1, 4):\r\n # self.history[str(idx)] = {str(idx): OrderedDict()\r\n # for idx in range(0, 3)}\r\n # self.MAX_LENGTH = 1e3\r\n\r\n # def construct_cmd(args):\r\n # \"\"\"Returns bytes object to be sent over COM port to ion pump.\"\"\"\r\n # def calculate_checksum(args):\r\n # pass\r\n # pass\r\n # command_codes = {} # key, val pairs of string descriptor and 2 character hex codes\r\n # self.command_dict = command_dict\r\n\r\n # def send(self, cmd):\r\n # \"\"\"Send a command to the picomotor driver.\"\"\"\r\n # line = cmd + '\\r\\n'\r\n # retval = self.write(bytes(line, encoding='ascii'))\r\n # self.flush()\r\n # if self.echo:\r\n # self.log(cmd, widget=self.sendwidget)\r\n # time.sleep(self.wait)\r\n # return retval\r\n\r\n # def readlines(self):\r\n # \"\"\"Read response from picomotor driver.\"\"\"\r\n # return ''.join([l.decode('ASCII') for l in self.readlines()])\r\n\r\n def log(self, msg, widget=None):\r\n if widget is None:\r\n print(msg, flush=True)\r\n else:\r\n widget.value = msg\r\n\r\n # def sendrecv(self, cmd):\r\n # \"\"\"Send a command and (optionally) printing the picomotor driver's response.\"\"\"\r\n # res = self.send(cmd)\r\n # if self.echo:\r\n # time.sleep(self.wait)\r\n # ret_str = self.readlines()\r\n # self.log(ret_str, widget=self.recvwidget)\r\n # return res\r\n", "sub_path": "instruments/ionpump.py", "file_name": "ionpump.py", "file_ext": "py", "file_size_in_byte": 11739, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "serial.EIGHTBITS", "line_number": 42, "usage_type": "attribute"}, {"api_name": "serial.PARITY_NONE", "line_number": 42, "usage_type": "attribute"}, {"api_name": "serial.STOPBITS_ONE", "line_number": 42, "usage_type": "attribute"}, {"api_name": "serial.Serial", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 96, "usage_type": "call"}]} +{"seq_id": "90253910", "text": "# -*- coding: utf-8 -*-\n#\n# Copyright 2014 Kolab Systems AG (http://www.kolabsys.com)\n#\n# Thomas Bruederli \n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Affero General Public License as\n# published by the Free Software Foundation, either version 3 of the\n# License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU Affero General Public License for more details.\n#\n# You should have received a copy of the GNU Affero General Public License\n# along with this program. If not, see .\n#\n\nfrom flask import Flask, session, request, g, render_template\nfrom flask.ext.sqlalchemy import SQLAlchemy\nfrom flask.ext.bootstrap import Bootstrap\nfrom flask.ext.login import LoginManager\nfrom flask.ext.babel import Babel\nfrom config import config\n\nbootstrap = Bootstrap()\ndb = SQLAlchemy()\nlogin_manager = LoginManager()\nlogin_manager.session_protection = 'strong'\nlogin_manager.login_view = 'auth.login'\n\n\ndef create_app(config_name):\n app = Flask(__name__)\n app.config.from_object(config[config_name])\n config[config_name].init_app(app)\n\n bootstrap.init_app(app)\n db.init_app(app)\n login_manager.init_app(app)\n babel = Babel(app)\n\n # initialize logging\n import logging.config\n logging.config.fileConfig(app.config['CONFIG_DIR'] + '/bonnie-flask.conf')\n\n # add main controller\n from views.root import root\n app.register_blueprint(root)\n\n # add (json) data controller\n from views.data import data\n app.register_blueprint(data)\n\n # add auth controller\n from auth import auth\n app.register_blueprint(auth, url_prefix='/auth')\n\n # add API controller\n from api import api\n app.register_blueprint(api, url_prefix='/api')\n\n # set locale from client headers\n @babel.localeselector\n def get_locale():\n return request.accept_languages.best_match(['de','fr','en'])\n\n # render custom error pages\n @app.errorhandler(403)\n def forbidden(e):\n return render_template('403.html'), 403\n\n @app.errorhandler(404)\n def pagenotfound(e):\n return render_template('404.html'), 404\n\n return app\n\n ", "sub_path": "app/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 2384, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask.ext.bootstrap.Bootstrap", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.ext.sqlalchemy.SQLAlchemy", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.ext.login.LoginManager", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 36, "usage_type": "call"}, {"api_name": "config.config", "line_number": 37, "usage_type": "name"}, {"api_name": "config.config", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.ext.babel.Babel", "line_number": 43, "usage_type": "call"}, {"api_name": "logging.config.config.fileConfig", "line_number": 47, "usage_type": "call"}, {"api_name": "logging.config.config", "line_number": 47, "usage_type": "attribute"}, {"api_name": "logging.config", "line_number": 47, "usage_type": "name"}, {"api_name": "views.root.root", "line_number": 51, "usage_type": "argument"}, {"api_name": "views.data.data", "line_number": 55, "usage_type": "argument"}, {"api_name": "auth.auth", "line_number": 59, "usage_type": "argument"}, {"api_name": "api.api", "line_number": 63, "usage_type": "argument"}, {"api_name": "flask.request.accept_languages.best_match", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.request.accept_languages", "line_number": 68, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 68, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "197355706", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Author: oesteban\n# @Date: 2015-11-19 16:44:27\n# @Last Modified by: oesteban\n# @Last Modified time: 2016-09-21 17:37:25\n\n\"\"\"\n=====\nMRIQC\n=====\n\"\"\"\nfrom __future__ import print_function\nfrom __future__ import division\nfrom __future__ import absolute_import\nfrom __future__ import unicode_literals\n\nimport os\nimport os.path as op\nfrom multiprocessing import cpu_count\nfrom nipype import logging, config as ncfg\nfrom lockfile import LockFile\n\nfrom argparse import ArgumentParser\nfrom argparse import RawTextHelpFormatter\n\nfrom mriqc.workflows import core as mwc\nfrom mriqc import __version__\n\nLOGGER = logging.getLogger('workflow')\n\n\ndef main():\n \"\"\"Entry point\"\"\"\n parser = ArgumentParser(description='MRI Quality Control',\n formatter_class=RawTextHelpFormatter)\n\n parser.add_argument('-v', '--version', action='version',\n version='mriqc v{}'.format(__version__))\n\n parser.add_argument('bids_dir', action='store',\n help='The directory with the input dataset '\n 'formatted according to the BIDS standard.')\n parser.add_argument('output_dir', action='store',\n help='The directory where the output files '\n 'should be stored. If you are running group level analysis '\n 'this folder should be prepopulated with the results of the'\n 'participant level analysis.')\n parser.add_argument('analysis_level', action='store',\n help='Level of the analysis that will be performed. '\n 'Multiple participant level analyses can be run independently '\n '(in parallel) using the same output_dir.',\n choices=['participant', 'group'])\n parser.add_argument('--participant_label', '--subject_list', '-S', action='store',\n help='The label(s) of the participant(s) that should be analyzed. '\n 'The label corresponds to sub- from the '\n 'BIDS spec (so it does not include \"sub-\"). If this parameter '\n 'is not provided all subjects should be analyzed. Multiple '\n 'participants can be specified with a space separated list.',\n nargs=\"*\")\n\n g_input = parser.add_argument_group('mriqc specific inputs')\n g_input.add_argument('-d', '--data-type', action='store', nargs='*',\n choices=['anat', 'func'], default=['anat', 'func'])\n g_input.add_argument('-s', '--session-id', action='store')\n g_input.add_argument('-r', '--run-id', action='store')\n g_input.add_argument('--nthreads', action='store', default=0,\n type=int, help='number of threads')\n g_input.add_argument('--write-graph', action='store_true', default=False,\n help='Write workflow graph.')\n g_input.add_argument('--dry-run', action='store_true', default=False,\n help='Do not run the workflow.')\n g_input.add_argument('--use-plugin', action='store', default=None,\n help='nipype plugin configuration file')\n\n g_input.add_argument('--testing', action='store_true', default=False,\n help='use testing settings for a minimal footprint')\n g_input.add_argument('--hmc-afni', action='store_true', default=False,\n help='Use ANFI 3dvolreg for head motion correction (HMC) and '\n 'frame displacement (FD) estimation')\n\n\n g_outputs = parser.add_argument_group('mriqc specific outputs')\n g_outputs.add_argument('-w', '--work-dir', action='store', default=op.join(os.getcwd(), 'work'))\n g_outputs.add_argument('--report-dir', action='store')\n\n # ANTs options\n g_ants = parser.add_argument_group('specific settings for ANTs registrations')\n g_ants.add_argument('--ants-nthreads', action='store', type=int,\n help='number of threads that will be set in ANTs processes')\n g_ants.add_argument('--ants-settings', action='store',\n help='path to JSON file with settings for ANTS')\n\n opts = parser.parse_args()\n\n\n # Build settings dict\n bids_dir = op.abspath(opts.bids_dir)\n settings = {\n 'bids_dir': bids_dir,\n 'write_graph': opts.write_graph,\n 'testing': opts.testing,\n 'hmc_afni': opts.hmc_afni,\n 'nthreads': opts.nthreads,\n 'output_dir': op.abspath(opts.output_dir),\n 'work_dir': op.abspath(opts.work_dir)\n }\n\n if opts.ants_settings:\n settings['ants_settings'] = opts.ants_settings\n\n if opts.ants_nthreads:\n settings['ants_nthreads'] = opts.ants_nthreads\n\n log_dir = op.join(settings['output_dir'], 'logs')\n\n settings['report_dir'] = opts.report_dir\n if not settings['report_dir']:\n settings['report_dir'] = op.join(settings['work_dir'], 'reports')\n\n with LockFile(op.join(os.getenv('HOME'), '.mriqc-lock')):\n if not op.exists(settings['output_dir']):\n os.makedirs(settings['output_dir'])\n\n if not op.exists(settings['work_dir']):\n os.makedirs(settings['work_dir'])\n\n if not op.exists(log_dir):\n os.makedirs(log_dir)\n\n if not op.exists(settings['report_dir']):\n os.makedirs(settings['report_dir'])\n\n # Set nipype config\n ncfg.update_config({\n 'logging': {'log_directory': log_dir, 'log_to_file': True},\n 'execution': {'crashdump_dir': log_dir}\n })\n\n plugin_settings = {'plugin': 'Linear'}\n if opts.use_plugin is not None:\n from yaml import load as loadyml\n with open(opts.use_plugin) as pfile:\n plugin_settings = loadyml(pfile)\n else:\n # Setup multiprocessing\n if settings['nthreads'] == 0:\n settings['nthreads'] = cpu_count()\n\n if settings['nthreads'] > 1:\n plugin_settings['plugin'] = 'MultiProc'\n plugin_settings['plugin_args'] = {'n_procs': settings['nthreads']}\n\n LOGGER.info(\n 'Running MRIQC-%s (analysis_level=%s, participant_label=%s)\\n\\tSettings=%s',\n __version__, opts.analysis_level, opts.participant_label, settings)\n\n # Set up participant level\n if opts.analysis_level == 'participant':\n for dtype in opts.data_type:\n ms_func = getattr(mwc, 'ms_' + dtype)\n workflow = ms_func(subject_id=opts.participant_label, session_id=opts.session_id,\n run_id=opts.run_id, settings=settings)\n if workflow is None:\n LOGGER.warn('No scans were found for the given inputs')\n continue\n\n workflow.base_dir = settings['work_dir']\n if settings.get('write_graph', False):\n workflow.write_graph()\n\n if not opts.dry_run:\n workflow.run(**plugin_settings)\n\n # Set up group level\n elif opts.analysis_level == 'group':\n from mriqc.reports import MRIQCReportPDF\n\n for dtype in opts.data_type:\n reporter = MRIQCReportPDF(dtype, settings)\n reporter.group_report()\n reporter.individual_report()\n\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "mriqc/utils/mriqc_run.py", "file_name": "mriqc_run.py", "file_ext": "py", "file_size_in_byte": 7406, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "nipype.logging.getLogger", "line_number": 30, "usage_type": "call"}, {"api_name": "nipype.logging", "line_number": 30, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 35, "usage_type": "call"}, {"api_name": "argparse.RawTextHelpFormatter", "line_number": 36, "usage_type": "name"}, {"api_name": "mriqc.__version__", "line_number": 39, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path", "line_number": 106, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "name"}, {"api_name": "lockfile.LockFile", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path", "line_number": 125, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path", "line_number": 131, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 132, "usage_type": "call"}, {"api_name": "nipype.config.update_config", "line_number": 135, "usage_type": "call"}, {"api_name": "nipype.config", "line_number": 135, "usage_type": "name"}, {"api_name": "yaml.load", "line_number": 144, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 148, "usage_type": "call"}, {"api_name": "mriqc.__version__", "line_number": 156, "usage_type": "argument"}, {"api_name": "mriqc.workflows.core", "line_number": 161, "usage_type": "argument"}, {"api_name": "mriqc.reports.MRIQCReportPDF", "line_number": 180, "usage_type": "call"}]} +{"seq_id": "20357290", "text": "# coding=utf-8\n\nimport json\nimport codecs\nimport re\nimport os\nimport sys\nimport subprocess\nimport jieba.analyse\nfrom pyecharts import Bar, Pie, Map, WordCloud, Page\nfrom collections import Counter\nimport logging\n\n\nclass Analysis:\n FileLog = './output/gpla_%s.log'\n FileDate = './output/gpla_%s_date.json'\n FileUser = './output/gpla_%s_user.json'\n FileHour = './output/gpla_%s_hour.json'\n FileMin = './output/gpla_%s_min.json'\n FileWord = './output/gpla_%s_word.json'\n FileGitlogTemplate = './assets/page.template.html'\n FileGitlogHtml = './output/gpla_%s.html'\n\n DataDate = {}\n DataUser = {}\n DataHour = {}\n DataMin = {}\n DataWord = {}\n\n def __init__(self, projectPath):\n self.projectName = os.path.split(projectPath)[1]\n project = self.projectName.lower()\n self.FileLog = self.FileLog % project\n self.FileDate = self.FileDate % project\n self.FileUser = self.FileUser % project\n self.FileHour = self.FileHour % project\n self.FileMin = self.FileMin % project\n self.FileWord = self.FileWord % project\n\n self.FileGitlogHtml = self.FileGitlogHtml % project\n\n # 每次重新拉取处理\n (status, output) = subprocess.getstatusoutput('/bin/sh ./analysis.sh %s %s' % (project, projectPath))\n if output != \"\":\n print('Error:', output)\n return\n\n if os.path.exists(self.FileDate) is True:\n with codecs.open(self.FileDate, encoding='utf-8') as file:\n self.DataDate = json.load(file)\n\n if os.path.exists(self.FileHour) is True:\n with codecs.open(self.FileHour, encoding='utf-8') as file:\n self.DataHour = json.load(file)\n\n if os.path.exists(self.FileMin) is True:\n with codecs.open(self.FileMin, encoding='utf-8') as file:\n self.DataMin = json.load(file)\n\n if os.path.exists(self.FileUser) is True:\n with codecs.open(self.FileUser, encoding='utf-8') as file:\n self.DataUser = json.load(file)\n\n if os.path.exists(self.FileWord) is True:\n with codecs.open(self.FileWord, encoding='utf-8') as file:\n self.DataWord = json.load(file)\n\n self.page = Page(page_title=\"gpla\")\n self.handle()\n return\n\n # 处理数据\n def handle(self):\n if len(self.DataDate) <= 0:\n print('Error:', \"无提交数据\")\n return\n\n # 按日期统计\n keyList, valueList = self.dictToList(self.DataDate)\n dateBar = Bar('日期', title_pos='center')\n dateBar.add('', keyList, valueList)\n self.page.add(dateBar)\n\n # 按小时统计\n if len(self.DataHour) > 0:\n keyList, valueList = self.dictToList(self.DataHour)\n timeBar = Bar('小时', title_pos='center')\n timeBar.add('', keyList, valueList)\n self.page.add(timeBar)\n\n # 按分钟统计\n if len(self.DataMin) > 0:\n keyList, valueList = self.dictToList(self.DataMin)\n timeBar = Bar('分钟', title_pos='center')\n timeBar.add('', keyList, valueList)\n self.page.add(timeBar)\n\n # 按成员统计\n if len(self.DataUser) > 0:\n keyList, valueList = self.dictToList(self.DataUser)\n userPie = Pie('成员', title_pos='center')\n userPie.add('', keyList, valueList, is_label_show=True, is_legend_show=False)\n self.page.add(userPie)\n\n # 关键字词云\n if len(self.DataWord) > 0:\n keyList, valueList = self.dictToList(self.cutWord(self.DataWord))\n if len(keyList) > 0 and len(valueList) > 0:\n wordCloud = WordCloud(width=800, height=800)\n wordCloud.add(\n '',\n keyList,\n valueList,\n # circle, cardioid, diamond, triangle-forward, triangle, pentagon, star'\n shape='circle',\n word_size_range=[10, 100])\n self.page.add(wordCloud)\n\n self.page.render(path=self.FileGitlogHtml, template_name=self.FileGitlogTemplate)\n\n # 处理提交日历\n calData = []\n for key in self.DataDate:\n calData.append({\n 'date': key,\n 'count': self.DataDate[key],\n })\n with open(self.FileGitlogHtml, \"r\", encoding=\"utf-8\") as f:\n lines = f.readlines()\n with open(self.FileGitlogHtml, \"w\", encoding=\"utf-8\") as f_w:\n for line in lines:\n if \"%CalendarData%\" in line:\n line = line.replace(\"%CalendarData%\", json.dumps(calData))\n if \"%ProjectName%\" in line:\n line = line.replace(\"%ProjectName%\", self.projectName)\n f_w.write(line)\n print('Output:', self.FileGitlogHtml)\n return True\n\n def dictToList(self, datas):\n nameList = []\n numList = []\n for key, value in datas.items():\n nameList.append(key)\n numList.append(value)\n return nameList, numList\n\n def cutWord(self, texts):\n tagMap = Counter()\n for text in texts:\n text = re.sub(r\"\", \"\", text)\n tags = jieba.analyse.extract_tags(text)\n for tag in tags:\n if self.filterWord(tag) == False:\n continue\n tagMap[tag] += 1\n return tagMap\n\n # 添加词汇过滤规则\n def filterWord(self, word):\n word = word.lower()\n if word.encode('UTF-8').isalpha():\n return False\n return True\n\n\nif __name__ == '__main__':\n if len(sys.argv) < 2 or os.path.exists(sys.argv[1]) is False:\n print('Error:', 'invalid path')\n exit(0)\n jieba.setLogLevel(logging.INFO)\n Analysis(sys.argv[1])\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5900, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "os.path.split", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "subprocess.getstatusoutput", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "codecs.open", "line_number": 50, "usage_type": "call"}, {"api_name": "json.load", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "codecs.open", "line_number": 54, "usage_type": "call"}, {"api_name": "json.load", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "codecs.open", "line_number": 58, "usage_type": "call"}, {"api_name": "json.load", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "codecs.open", "line_number": 62, "usage_type": "call"}, {"api_name": "json.load", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "codecs.open", "line_number": 66, "usage_type": "call"}, {"api_name": "json.load", "line_number": 67, "usage_type": "call"}, {"api_name": "pyecharts.Page", "line_number": 69, "usage_type": "call"}, {"api_name": "pyecharts.Bar", "line_number": 81, "usage_type": "call"}, {"api_name": "pyecharts.Bar", "line_number": 88, "usage_type": "call"}, {"api_name": "pyecharts.Bar", "line_number": 95, "usage_type": "call"}, {"api_name": "pyecharts.Pie", "line_number": 102, "usage_type": "call"}, {"api_name": "pyecharts.WordCloud", "line_number": 110, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 134, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 150, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 152, "usage_type": "call"}, {"api_name": "jieba.analyse.analyse.extract_tags", "line_number": 153, "usage_type": "call"}, {"api_name": "jieba.analyse.analyse", "line_number": 153, "usage_type": "attribute"}, {"api_name": "jieba.analyse", "line_number": 153, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 169, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path", "line_number": 169, "usage_type": "attribute"}, {"api_name": "jieba.analyse.setLogLevel", "line_number": 172, "usage_type": "call"}, {"api_name": "jieba.analyse", "line_number": 172, "usage_type": "name"}, {"api_name": "logging.INFO", "line_number": 172, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 173, "usage_type": "attribute"}]} +{"seq_id": "206250717", "text": "#!/usr/bin/env python3\n\nfrom datetime import datetime\n\ndef read_temperature(tfile):\n \"\"\"Read the temperature from a given device file.\"\"\"\n # open the file and read its content\n f = open(tfile)\n text = f.read()\n f.close()\n # Keep the time of the measurement\n m_time = datetime.utcnow()\n # read the temperature part from the file (20th argument)\n tempdata = text.split(\" \")[20]\n # temperature from 3rd char in string, convert to float, divide by 1000 for C\n temperature = float(tempdata[2:]) / 1000\n return temperature, m_time\n\ndef main():\n temp01 = (\"/sys/bus/w1/devices/28-031600954bff/w1_slave\")\n temperature, m_time = read_temperature(temp01)\n print (temperature)\n\n\nif __name__ == \"__main__\":\n main()\n\n", "sub_path": "get_temp.py", "file_name": "get_temp.py", "file_ext": "py", "file_size_in_byte": 752, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "datetime.datetime.utcnow", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "325228934", "text": "#####################################\n#help\nfrom typing import Text\nfrom reportlab.lib import colors\n\n\n# def drawMyRuler(pdf):\n# pdf.drawString(100,810, 'x100')\n# pdf.drawString(200,810, 'x200')\n# pdf.drawString(300,810, 'x300')\n# pdf.drawString(400,810, 'x400')\n# pdf.drawString(500,810, 'x500')\n\n# pdf.drawString(10,100, 'y100')\n# pdf.drawString(10,200, 'y200')\n# pdf.drawString(10,300, 'y300')\n# pdf.drawString(10,400, 'y400')\n# pdf.drawString(10,500, 'y500')\n# pdf.drawString(10,600, 'y600')\n# pdf.drawString(10,700, 'y700')\n# pdf.drawString(10,800, 'y800')\n\n\n\n\n \n# ############################################\n#Content\nfilename = 'MyDoc.pdf'\ndocumentTitle = 'Document title!'\ntitle = 'Tasmanian devil'\nsubTitle = 'The largest carnivorous marsupial'\n\ntextLines = [\n'The Tasmanian devil (Sarcophilus harrisii) is',\n'a carnivorous marsupial of the family',\n'Dasyuridae.'\n]\n\nimage = 'tasmanianDevil.jpg'\n\n\n# ######################################################\n# 0)create document\nfrom reportlab.pdfgen import canvas\n\npdf = canvas.Canvas(filename)\npdf.setTitle(documentTitle)\n\n\n\n# drawMyRuler(pdf)\n# ############################################\n# 1) TItle :: Set fonts\n# Print available fonts\n# for font in pdf.getAvailableFonts():\n# print(font)\n\n# Register a new font\nfrom reportlab.pdfbase.ttfonts import TTFont\nfrom reportlab.pdfbase import pdfmetrics\n\npdfmetrics.registerFont(\n TTFont('abc','SakBunderan.ttf')\n)\npdf.setFont('abc',36)\npdf.drawCentredString(300, 770, title)\n\n\n\n\n\n\n\n\n\n# ################################################\n# 2) Sub Title :: Centered String\n\npdf.setFillColorRGB(0, 0, 255)\npdf.setFont(\"Courier-Bold\", 24)\npdf.drawCentredString(290, 720, subTitle)\n\n\n\n\n\n\n# ####################################################\n# 3) Draw a line\npdf.line(30,710, 550, 710)\n\n\n\n\n\n\n\n\n# ####################################################\n# 4) Text object :: for large amounts of text\nfrom reportlab.lib import colors\n\ntext = pdf.beginText(40, 680)\ntext.setFont(\"Courier\", 18)\ntext.setFillColor(colors.red)\nfor line in textLines:\n text.textLine(line)\n\npdf.drawText(text)\n\n\n\n\n\n# #################################################\n# 5) Draw a image\npdf.drawInlineImage(image,130,400)\n\n\n\n\npdf.save()", "sub_path": "pdfExample.py", "file_name": "pdfExample.py", "file_ext": "py", "file_size_in_byte": 2266, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "reportlab.pdfgen.canvas.Canvas", "line_number": 47, "usage_type": "call"}, {"api_name": "reportlab.pdfgen.canvas", "line_number": 47, "usage_type": "name"}, {"api_name": "reportlab.pdfbase.pdfmetrics.registerFont", "line_number": 63, "usage_type": "call"}, {"api_name": "reportlab.pdfbase.pdfmetrics", "line_number": 63, "usage_type": "name"}, {"api_name": "reportlab.pdfbase.ttfonts.TTFont", "line_number": 64, "usage_type": "call"}, {"api_name": "reportlab.lib.colors.red", "line_number": 106, "usage_type": "attribute"}, {"api_name": "reportlab.lib.colors", "line_number": 106, "usage_type": "name"}]} +{"seq_id": "273629480", "text": "from distutils.core import setup\nfrom distutils.extension import Extension\nfrom Cython.Build import cythonize\nimport numpy\nimport os\n\n\ndef find_pyx(path='.'):\n pyx_files = []\n for root, dirs, filenames in os.walk(path):\n for fname in filenames:\n if fname.endswith('.pyx'):\n pyx_files.append(os.path.join(root, fname))\n return pyx_files\n\n\nextensions = [\n Extension(\"cython_pricer.cython_pricer_naive\", [r\"cython_pricer/cython_pricer_naive.pyx\"], include_dirs=[numpy.get_include()],\n extra_compile_args=['/openmp'],\n extra_link_args=['/openmp']),\n Extension(\"cython_pricer.cython_pricer_optimized\", [r\"cython_pricer/cython_pricer_optimized.pyx\"],\n include_dirs=[numpy.get_include()], extra_compile_args=['/openmp'],\n extra_link_args=['/openmp'])\n]\n# os.environ[\"CC\"] = \"g++-9\"\n# os.environ[\"CXX\"] = \"g++-9\"\nsetup(\n name='cython_pricer',\n ext_modules=cythonize(extensions, compiler_directives={'language_level': \"3\"}),#, build_dir=r\"cython_pricer/build\"),\n script_args=[\"build_ext\", \"--inplace\"],\n)\n", "sub_path": "policy_pricer_restr/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1105, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "os.walk", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "distutils.extension.Extension", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.get_include", "line_number": 18, "usage_type": "call"}, {"api_name": "distutils.extension.Extension", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.get_include", "line_number": 22, "usage_type": "call"}, {"api_name": "distutils.core.setup", "line_number": 27, "usage_type": "call"}, {"api_name": "Cython.Build.cythonize", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "304664341", "text": "import configparser\r\nimport sys,os\r\nhome = os.path.expanduser('~')\r\nurl = sys.argv[1]\r\nconfig = configparser.ConfigParser()\r\nconfig[\"DEFAULT\"] = {\r\n\r\n 'url': f'https://sctapi.ftqq.com/{url}.send'\r\n}\r\npath = os.path.join(home,'.push_config')\r\nwith open(path, 'w')as configfile:\r\n config.write(configfile)\r\n", "sub_path": "make_config.py", "file_name": "make_config.py", "file_ext": "py", "file_size_in_byte": 311, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "os.path.expanduser", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 4, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}]} +{"seq_id": "341127929", "text": "#Houses Price Regression model\r\n\r\n#importing libraries\r\nimport pandas as pd\r\nimport numpy as np\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\nimport math\r\nfrom scipy import stats\r\nimport warnings\r\nwarnings.filterwarnings('ignore')\r\nwarnings.filterwarnings('ignore', category = DeprecationWarning)\r\n\r\n\r\n#Get the training data and first look into variables\r\nRawData = pd.read_csv('train.csv')\r\n\r\n#Chcecking the data\r\nRawData.describe()\r\nRawData.dtypes\r\nRawData.hist(bins = 30)\r\n\r\n#looking for nan values\r\nnulls_summary = pd.DataFrame(RawData.isnull().any(), columns = ['Nulls'])\r\nnulls_summary['Number of nans'] = pd.DataFrame(RawData.isnull().sum())\r\nnulls_summary['Percentage of nans'] = round((RawData.isnull().mean()*100),2)\r\nprint(nulls_summary)\r\n\r\n#checking how namy categorical values is in each column\r\nfor col in RawData.select_dtypes(['object']):\r\n print(RawData[col].value_counts())\r\n \r\n#time for some visualisation\r\nsns.stripplot(data = RawData, x = 'SalePrice', y = 'SalePrice')\r\nsns.stripplot(data = RawData, y = 'GarageYrBlt', x = 'YearRemodAdd')\r\nsns.stripplot(data = RawData, y = 'SalePrice', x = 'LotArea')\r\nsns.stripplot(data = RawData, y = 'SalePrice', x = 'MSSubClass')\r\nsns.boxplot(data = RawData, x = 'YearBuilt' , y = 'SalePrice')\r\nsns.boxplot(data = RawData, x = 'YearRemodAdd' , y = 'SalePrice')\r\nsns.boxplot(data = RawData, x = 'OverallQual', y = 'SalePrice')\r\nsns.boxplot(data = RawData, x = 'OverallCond', y = 'SalePrice')\r\nsns.boxplot(data = RawData, x = 'Neighborhood', y = 'SalePrice')\r\nsns.boxplot(data = RawData, x = 'YrSold', y = 'SalePrice')\r\nsns.boxplot(data = RawData, x = 'SaleCondition', y = 'SalePrice')\r\nsns.boxplot(data = RawData, x = 'BsmtQual', y = 'SalePrice')\r\nsns.boxplot(data = RawData, x = 'GarageCond', y = 'SalePrice')\r\nsns.boxplot(data = RawData, x = 'GarageType', y = 'SalePrice')\r\nsns.boxplot(data = RawData, x = 'MSZoning', y = 'SalePrice')\r\nsns.stripplot(data = RawData, x = 'LotFrontage', y = 'SalePrice')\r\n\r\n#get combined Data\r\ndef combined_data():\r\n train = pd.read_csv('train.csv')\r\n train.drop('SalePrice', axis = 1, inplace = True)\r\n \r\n test = pd.read_csv('test.csv')\r\n AllData = train.append(test)\r\n AllData.reset_index(inplace = True)\r\n AllData.drop(['index', 'Id'], axis = 1, inplace = True)\r\n \r\n return AllData\r\nAllData = combined_data()\r\n\r\n#Getting target data\r\nY = RawData.iloc[:, -1]\r\n\r\n#Checking for NaN values in all columns\r\nnulls_summary = pd.DataFrame(AllData.isnull().any(), columns = ['Nulls'])\r\nnulls_summary['Number of nans'] = pd.DataFrame(AllData.isnull().sum())\r\nnulls_summary['Percentage of nans'] = round((AllData.isnull().mean()*100),2)\r\nprint(nulls_summary)\r\n\r\n#Getting away columns with Nan precentage ~50%\r\nAllData.drop(['PoolQC', 'MiscFeature', 'Alley', 'Fence', 'FireplaceQu', 'Street', 'Utilities'], axis = 1, inplace = True)\r\n\r\n\r\n#Dealing with 0-1 values\r\n#Paved drive\r\ndef PavedDrive():\r\n global AllData\r\n AllData['PavedDrive'] = AllData['PavedDrive'].map({'Y':1, 'N':0})\r\n return AllData\r\nAllData = PavedDrive()\r\n\r\n#Central Air\r\ndef CentralAir():\r\n global AllData\r\n AllData['CentralAir'] = AllData['CentralAir'].map({'Y':1, 'N':0})\r\n return AllData\r\nAllData = CentralAir()\r\n\r\ndef HeatingQC():\r\n global AllData\r\n AllData['HeatingQC'] = AllData['HeatingQC'].map({'Y':1, 'N':0})\r\n return AllData\r\nAllData = HeatingQC()\r\n\r\n\r\n#Filling NaN vaues\r\n#At first dealing with the strings values\r\nAllData.GarageFinish.fillna(value = 'No', inplace = True)\r\nAllData.GarageQual.fillna(value = 'No', inplace = True)\r\nAllData.GarageCond.fillna(value = 'No', inplace = True)\r\nAllData.GarageType.fillna(value = 'No', inplace = True)\r\nAllData.BsmtCond.fillna(value = 'No', inplace = True)\r\nAllData.BsmtExposure.fillna(value = 'No', inplace = True)\r\nAllData.BsmtQual.fillna(value = 'No', inplace = True)\r\nAllData.BsmtFinType2.fillna(value = 'No', inplace = True)\r\nAllData.BsmtFinType1.fillna(value = 'No', inplace = True)\r\nAllData.MasVnrType.fillna(value = 'No', inplace = True)\r\n\r\nAllData.MSZoning.fillna(value = 'RL', inplace = True)\r\nAllData.Functional.fillna(value = 'Typ', inplace = True)\r\nAllData.Exterior1st.fillna(value = 'VinylSd', inplace = True)\r\nAllData.Exterior2nd.fillna(value = 'VinylSd', inplace = True)\r\nAllData.Electrical.fillna(value = 'SBrkr', inplace = True)\r\nAllData.KitchenQual.fillna(value = 'TA', inplace = True)\r\nAllData.SaleType.fillna(value = 'WD', inplace = True)\r\n\r\n#Now inplacementing '0'\r\nAllData.MasVnrArea.fillna(value = 0, inplace = True)\r\nAllData.BsmtFinSF1.fillna(value = 0, inplace = True)\r\nAllData.BsmtFinSF2.fillna(value = 0, inplace = True)\r\nAllData.BsmtUnfSF.fillna(value = 0, inplace = True)\r\nAllData.TotalBsmtSF.fillna(value = 0, inplace = True)\r\n\r\n#Fillin with int values\r\nAllData.LotFrontage.fillna(value = AllData['LotFrontage'].median(), inplace = True)\r\nAllData.GarageYrBlt.fillna(value = AllData['YearBuilt'], inplace = True)\r\nAllData.BsmtFullBath.fillna(value = AllData['BsmtFullBath'].median(), inplace = True)\r\nAllData.BsmtHalfBath.fillna(value = AllData['BsmtHalfBath'].median(), inplace = True)\r\nAllData.GarageCars.fillna(value = AllData['GarageCars'].median(), inplace = True)\r\nAllData.GarageArea.fillna(value = AllData['GarageArea'].median(), inplace = True)\r\nAllData.PavedDrive.fillna(value = AllData['PavedDrive'].median(), inplace = True)\r\n\r\n\r\n#Getting dummies\r\ndef FunctionalDummies():\r\n global AllData\r\n functional_dummies = pd.get_dummies(AllData['Functional'], prefix = 'Functional')\r\n AllData = pd.concat([AllData, functional_dummies], axis = 1)\r\n AllData.drop('Functional', axis = 1, inplace = True)\r\n return AllData\r\nAllData = FunctionalDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef KitchenQualDummies():\r\n global AllData\r\n KitchenQual_dummies = pd.get_dummies(AllData['KitchenQual'], prefix = 'KitchenQual')\r\n AllData = pd.concat([AllData, KitchenQual_dummies], axis = 1)\r\n AllData.drop('KitchenQual', axis = 1, inplace = True)\r\n return AllData\r\nAllData = KitchenQualDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef MSZoningDummies():\r\n global AllData\r\n MSZoning_dummies = pd.get_dummies(AllData['MSZoning'], prefix = 'MSZoning')\r\n AllData = pd.concat([AllData, MSZoning_dummies], axis = 1)\r\n AllData.drop('MSZoning', axis = 1, inplace = True)\r\n return AllData\r\nAllData = MSZoningDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef LotShapeDummies():\r\n global AllData\r\n LotShape_dummies = pd.get_dummies(AllData['LotShape'], prefix = 'LotShape')\r\n AllData = pd.concat([AllData, LotShape_dummies], axis = 1)\r\n AllData.drop('LotShape', axis = 1, inplace = True)\r\n return AllData\r\nAllData = LotShapeDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef LandContourDummies():\r\n global AllData\r\n LandContour_dummies = pd.get_dummies(AllData['LandContour'], prefix = 'LandContour')\r\n AllData = pd.concat([AllData, LandContour_dummies], axis = 1)\r\n AllData.drop('LandContour', axis = 1, inplace = True)\r\n return AllData\r\nAllData = LandContourDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef LotConfigDummies():\r\n global AllData\r\n LotConfig_dummies = pd.get_dummies(AllData['LotConfig'], prefix = 'LotConfig')\r\n AllData = pd.concat([AllData, LotConfig_dummies], axis = 1)\r\n AllData.drop('LotConfig', axis = 1, inplace = True)\r\n return AllData\r\nAllData = LotConfigDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef LandSlopeDummies():\r\n global AllData\r\n LandSlope_dummies = pd.get_dummies(AllData['LandSlope'], prefix = 'LandSlope')\r\n AllData = pd.concat([AllData, LandSlope_dummies], axis = 1)\r\n AllData.drop('LandSlope', axis = 1, inplace = True)\r\n return AllData\r\nAllData = LandSlopeDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef NeighborhoodDummies():\r\n global AllData\r\n Neighborhood_dummies = pd.get_dummies(AllData['Neighborhood'], prefix = 'Neighborhood')\r\n AllData = pd.concat([AllData, Neighborhood_dummies], axis = 1)\r\n AllData.drop('Neighborhood', axis = 1, inplace = True)\r\n return AllData\r\nAllData = NeighborhoodDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef Condition1Dummies():\r\n global AllData\r\n Condition1_dummies = pd.get_dummies(AllData['Condition1'], prefix = 'Condition1')\r\n AllData = pd.concat([AllData, Condition1_dummies], axis = 1)\r\n AllData.drop('Condition1', axis = 1, inplace = True)\r\n return AllData\r\nAllData = Condition1Dummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef Condition2Dummies():\r\n global AllData\r\n Condition2_dummies = pd.get_dummies(AllData['Condition2'], prefix = 'Condition2')\r\n AllData = pd.concat([AllData, Condition2_dummies], axis = 1)\r\n AllData.drop('Condition2', axis = 1, inplace = True)\r\n return AllData\r\nAllData = Condition2Dummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef BldgTypeDummies():\r\n global AllData\r\n BldgType_dummies = pd.get_dummies(AllData['BldgType'], prefix = 'BldgType')\r\n AllData = pd.concat([AllData, BldgType_dummies], axis = 1)\r\n AllData.drop('BldgType', axis = 1, inplace = True)\r\n return AllData\r\nAllData = BldgTypeDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef HouseStyleDummies():\r\n global AllData\r\n HouseStyle_dummies = pd.get_dummies(AllData['HouseStyle'], prefix = 'HouseStyle')\r\n AllData = pd.concat([AllData, HouseStyle_dummies], axis = 1)\r\n AllData.drop('HouseStyle', axis = 1, inplace = True)\r\n return AllData\r\nAllData = HouseStyleDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef RoofStyleDummies():\r\n global AllData\r\n RoofStyle_dummies = pd.get_dummies(AllData['RoofStyle'], prefix = 'RoofStyle')\r\n AllData = pd.concat([AllData, RoofStyle_dummies], axis = 1)\r\n AllData.drop('RoofStyle', axis = 1, inplace = True)\r\n return AllData\r\nAllData = RoofStyleDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef RoofMatlDummies():\r\n global AllData\r\n RoofMatl_dummies = pd.get_dummies(AllData['RoofMatl'], prefix = 'RoofMatl')\r\n AllData = pd.concat([AllData, RoofMatl_dummies], axis = 1)\r\n AllData.drop('RoofMatl', axis = 1, inplace = True)\r\n return AllData\r\nAllData = RoofMatlDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef Exterior1stDummies():\r\n global AllData\r\n Exterior1st_dummies = pd.get_dummies(AllData['Exterior1st'], prefix = 'Exterior1st')\r\n AllData = pd.concat([AllData, Exterior1st_dummies], axis = 1)\r\n AllData.drop('Exterior1st', axis = 1, inplace = True)\r\n return AllData\r\nAllData = Exterior1stDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef Exterior2ndDummies():\r\n global AllData\r\n Exterior2nd_dummies = pd.get_dummies(AllData['Exterior2nd'], prefix = 'Exterior2nd')\r\n AllData = pd.concat([AllData, Exterior2nd_dummies], axis = 1)\r\n AllData.drop('Exterior2nd', axis = 1, inplace = True)\r\n return AllData\r\nAllData = Exterior2ndDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef MasVnrTypeDummies():\r\n global AllData\r\n MasVnrType_dummies = pd.get_dummies(AllData['MasVnrType'], prefix = 'MasVnrType')\r\n AllData = pd.concat([AllData, MasVnrType_dummies], axis = 1)\r\n AllData.drop('MasVnrType', axis = 1, inplace = True)\r\n return AllData\r\nAllData = MasVnrTypeDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef ExterQualDummies():\r\n global AllData\r\n ExterQual_dummies = pd.get_dummies(AllData['ExterQual'], prefix = 'ExterQual')\r\n AllData = pd.concat([AllData, ExterQual_dummies], axis = 1)\r\n AllData.drop('ExterQual', axis = 1, inplace = True)\r\n return AllData\r\nAllData = ExterQualDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef ExterCondDummies():\r\n global AllData\r\n ExterCond_dummies = pd.get_dummies(AllData['ExterCond'], prefix = 'ExterCond')\r\n AllData = pd.concat([AllData, ExterCond_dummies], axis = 1)\r\n AllData.drop('ExterCond', axis = 1, inplace = True)\r\n return AllData\r\nAllData = ExterCondDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef FoundationDummies():\r\n global AllData\r\n Foundation_dummies = pd.get_dummies(AllData['Foundation'], prefix = 'Foundation')\r\n AllData = pd.concat([AllData, Foundation_dummies], axis = 1)\r\n AllData.drop('Foundation', axis = 1, inplace = True)\r\n return AllData\r\nAllData = FoundationDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef BsmtQualDummies():\r\n global AllData\r\n BsmtQual_dummies = pd.get_dummies(AllData['BsmtQual'], prefix = 'BsmtQual')\r\n AllData = pd.concat([AllData, BsmtQual_dummies], axis = 1)\r\n AllData.drop('BsmtQual', axis = 1, inplace = True)\r\n return AllData\r\nAllData = BsmtQualDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef BsmtCondDummies():\r\n global AllData\r\n BsmtCond_dummies = pd.get_dummies(AllData['BsmtCond'], prefix = 'BsmtCond')\r\n AllData = pd.concat([AllData, BsmtCond_dummies], axis = 1)\r\n AllData.drop('BsmtCond', axis = 1, inplace = True)\r\n return AllData\r\nAllData = BsmtCondDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef BsmtExposureDummies():\r\n global AllData\r\n BsmtExposure_dummies = pd.get_dummies(AllData['BsmtExposure'], prefix = 'BsmtExposure')\r\n AllData = pd.concat([AllData, BsmtExposure_dummies], axis = 1)\r\n AllData.drop('BsmtExposure', axis = 1, inplace = True)\r\n return AllData\r\nAllData = BsmtExposureDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef BsmtFinType1Dummies():\r\n global AllData\r\n BsmtFinType1_dummies = pd.get_dummies(AllData['BsmtFinType1'], prefix = 'BsmtFinType1')\r\n AllData = pd.concat([AllData, BsmtFinType1_dummies], axis = 1)\r\n AllData.drop('BsmtFinType1', axis = 1, inplace = True)\r\n return AllData\r\nAllData = BsmtFinType1Dummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef BsmtFinType2Dummies():\r\n global AllData\r\n BsmtFinType2_dummies = pd.get_dummies(AllData['BsmtFinType2'], prefix = 'BsmtFinType2')\r\n AllData = pd.concat([AllData, BsmtFinType2_dummies], axis = 1)\r\n AllData.drop('BsmtFinType2', axis = 1, inplace = True)\r\n return AllData\r\nAllData = BsmtFinType2Dummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef HeatingDummies():\r\n global AllData\r\n Heating_dummies = pd.get_dummies(AllData['Heating'], prefix = 'Heating')\r\n AllData = pd.concat([AllData, Heating_dummies], axis = 1)\r\n AllData.drop('Heating', axis = 1, inplace = True)\r\n return AllData\r\nAllData = HeatingDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef ElectricalDummies():\r\n global AllData\r\n Electrical_dummies = pd.get_dummies(AllData['Electrical'], prefix = 'Electrical')\r\n AllData = pd.concat([AllData, Electrical_dummies], axis = 1)\r\n AllData.drop('Electrical', axis = 1, inplace = True)\r\n return AllData\r\nAllData = ElectricalDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef GarageTypeDummies():\r\n global AllData\r\n GarageType_dummies = pd.get_dummies(AllData['GarageType'], prefix = 'GarageType')\r\n AllData = pd.concat([AllData, GarageType_dummies], axis = 1)\r\n AllData.drop('GarageType', axis = 1, inplace = True)\r\n return AllData\r\nAllData = GarageTypeDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef GarageFinishDummies():\r\n global AllData\r\n GarageFinish_dummies = pd.get_dummies(AllData['GarageFinish'], prefix = 'GarageFinish')\r\n AllData = pd.concat([AllData, GarageFinish_dummies], axis = 1)\r\n AllData.drop('GarageFinish', axis = 1, inplace = True)\r\n return AllData\r\nAllData = GarageFinishDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef GarageQualDummies():\r\n global AllData\r\n GarageQual_dummies = pd.get_dummies(AllData['GarageQual'], prefix = 'GarageQual')\r\n AllData = pd.concat([AllData, GarageQual_dummies], axis = 1)\r\n AllData.drop('GarageQual', axis = 1, inplace = True)\r\n return AllData\r\nAllData = GarageQualDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef GarageCondDummies():\r\n global AllData\r\n GarageCond_dummies = pd.get_dummies(AllData['GarageCond'], prefix = 'GarageCond')\r\n AllData = pd.concat([AllData, GarageCond_dummies], axis = 1)\r\n AllData.drop('GarageCond', axis = 1, inplace = True)\r\n return AllData\r\nAllData = GarageCondDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef SaleConditionDummies():\r\n global AllData\r\n SaleCondition_dummies = pd.get_dummies(AllData['SaleCondition'], prefix = 'SaleCondition')\r\n AllData = pd.concat([AllData, SaleCondition_dummies], axis = 1)\r\n AllData.drop('SaleCondition', axis = 1, inplace = True)\r\n return AllData\r\nAllData = SaleConditionDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef SaleTypeDummies():\r\n global AllData\r\n SaleType_dummies = pd.get_dummies(AllData['SaleType'], prefix = 'SaleType')\r\n AllData = pd.concat([AllData, SaleType_dummies], axis = 1)\r\n AllData.drop('SaleType', axis = 1, inplace = True)\r\n return AllData\r\nAllData = SaleTypeDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\ndef HeatingQCDummies():\r\n global AllData\r\n HeatingQC_dummies = pd.get_dummies(AllData['HeatingQC'], prefix = 'HeatingQC')\r\n AllData = pd.concat([AllData, HeatingQC_dummies], axis = 1)\r\n AllData.drop('HeatingQC', axis = 1, inplace = True)\r\n return AllData\r\nAllData = HeatingQCDummies()\r\nAllData = AllData.iloc[:, :-1]\r\n\r\n\r\n#Splitting for test and train set\r\nX = AllData.iloc[:1460, :]\r\nTestData = AllData.iloc[1460:, :]\r\n\r\n\r\n#Scalling without reduction\r\n#Scalling variables\r\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\r\nX_S = StandardScaler()\r\nX = X_S.fit_transform(X)\r\nTestData = X_S.fit_transform(TestData)\r\n\r\nMM = MinMaxScaler(feature_range = (0,1))\r\nX = MM.fit_transform(X)\r\nTestData = MM.fit_transform(TestData)\r\n\r\n#Comming back to dataframe\r\nX = pd.DataFrame(X)\r\nTestData = pd.DataFrame(TestData)\r\n\r\n#Taking data to validation\r\nfrom sklearn.cross_validation import train_test_split\r\nX_train, X_val, Y_train, Y_val = train_test_split(X, Y, test_size = 0.2, random_state = 0)\r\n\r\n#Fitting first regressor\r\nfrom sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\r\nRanReg = RandomForestRegressor()\r\nRanReg.fit(X, Y)\r\n\r\nGBReg = GradientBoostingRegressor()\r\nGBReg.fit(X, Y)\r\n\r\nimport xgboost as xgb\r\nXGBReg = xgb.XGBRegressor()\r\nXGBReg.fit(X, Y)\r\n\r\n#ANN\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense\r\nfrom keras.wrappers.scikit_learn import KerasRegressor\r\nfrom sklearn.model_selection import cross_val_score, KFold\r\n\r\ndef ANNModel():\r\n model = Sequential()\r\n model.add(Dense(output_dim = 238, init = 'normal', activation = 'relu', input_dim = 238))\r\n model.add(Dense(output_dim = 100, init = 'normal', activation = 'relu'))\r\n model.add(Dense(output_dim = 1, init = 'normal'))\r\n model.compile(optimizer = 'adam', loss = 'mean_squared_logarithmic_error')\r\n return model\r\n\r\nseed = 10\r\nnp.random.seed(seed)\r\n\r\nANNReg = KerasRegressor(build_fn = ANNModel, epochs = 100, batch_size = 5, verbose = 1)\r\nkfold = KFold(n_splits=10, random_state=seed)\r\nresults = cross_val_score(ANNReg, X_train, Y_train, cv=kfold)\r\nANNReg.fit(X_train, Y_train)\r\n\r\n\r\n#Prediction\r\nRanRegPred = RanReg.predict(X)\r\nGBRegPred = GBReg.predict(X)\r\nXGBRegPred = XGBReg.predict(X)\r\nANNRegPred = ANNReg.predict(X_val).ravel()\r\n\r\n#Checking the RMSLE\r\ndef rmsle(y, y0):\r\n assert len(y) == len(y0)\r\n return np.sqrt(np.mean(np.power(np.log1p(y)-np.log1p(y0), 2)))\r\nRanReg_Score = rmsle(Y, RanRegPred)\r\nGBReg_Score = rmsle(Y, GBRegPred)\r\nXGBReg_Score = rmsle(Y, XGBRegPred)\r\nANNReg_Score = rmsle(Y_train, ANNRegPred)\r\nprint('RanRegScore = ',RanReg_Score)\r\nprint('GBRegScore = ',GBReg_Score)\r\nprint('XGBRegScore = ',XGBReg_Score)\r\nprint('ANNRegScore = ',ANNReg_Score)\r\n\r\n#chgecking features -> only for dataframes\r\nfeatures = pd.DataFrame()\r\nfeatures['feature'] = X_train.columns\r\nfeatures['importance'] = GBReg.feature_importances_\r\nfeatures.sort_values(by = 'importance', ascending = True, inplace = True)\r\nfeatures.set_index('feature', inplace = True)\r\nfeatures.plot(kind = 'barh')\r\n\r\n#Taking only important features\r\nfrom sklearn.feature_selection import SelectFromModel\r\nmodel = SelectFromModel(GBReg, prefit = True)\r\nX_reduced = model.transform(X)\r\nX_train_reduced = model.transform(X_train)\r\nX_val_reduced = model.transform(X_val)\r\nTestData_reduced = model.transform(TestData)\r\nprint(X_train_reduced.shape)\r\n\r\n#Next Fitting on a reduced dataset on the best alghorytm\r\nGBReg_reduced = GradientBoostingRegressor()\r\nGBReg_reduced.fit(X_reduced, Y)\r\n\r\n#Prediction\r\nGBRegPred_reduced = GBReg_reduced.predict(X_reduced)\r\n\r\n#Checking the RMSLE\r\nGBReg_reduced_Score = rmsle(Y, GBRegPred_reduced)\r\nprint('RanRegScore = ',GBReg_reduced_Score)\r\n\r\n#Taking GridSearch for it\r\nfrom sklearn.model_selection import GridSearchCV\r\nparameters = {'n_estimators':np.arange(100, 500, 100),\r\n 'max_features':['sqrt', 'auto', 'log2'],\r\n 'max_depth':np.arange(1,8),\r\n 'min_samples_split':np.arange(2,5),\r\n 'min_samples_leaf':np.arange(1,4),\r\n 'learning_rate':[0.05, 0.1, 0.5, 1]}\r\ngrid_search = GridSearchCV(GBReg_reduced, scoring = 'neg_mean_squared_log_error', param_grid = parameters, cv = 10, verbose = 1)\r\ngrid_search.fit(X_reduced, Y)\r\nbests = grid_search.best_params_\r\nprint('Best score: {}'.format(grid_search.best_score_))\r\nprint('Best parameters: {}'.format(grid_search.best_params_))\r\n\r\n\r\n\r\n#Making prediction for test data (with the best algorithm)\r\nFinishModel = GradientBoostingRegressor(n_estimators = 100)\r\nFinishModel.fit(X, Y)\r\nPrediction = FinishModel.predict(TestData)\r\n\r\ndf_output = pd.DataFrame()\r\naux = pd.read_csv('test.csv')\r\ndf_output['Id'] = aux['Id']\r\ndf_output['SalePrice'] = Prediction\r\ndf_output[['Id','SalePrice']].to_csv('GBReg_best.csv', index=False)\r\n", "sub_path": "HousePricesAllInOne.py", "file_name": "HousePricesAllInOne.py", "file_ext": "py", "file_size_in_byte": 21406, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "warnings.filterwarnings", "line_number": 11, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 25, "usage_type": "call"}, {"api_name": "seaborn.stripplot", "line_number": 34, "usage_type": "call"}, {"api_name": "seaborn.stripplot", "line_number": 35, "usage_type": "call"}, {"api_name": "seaborn.stripplot", "line_number": 36, "usage_type": "call"}, {"api_name": "seaborn.stripplot", "line_number": 37, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 38, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 39, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 40, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 41, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 42, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 43, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 44, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 45, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 46, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 47, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 48, "usage_type": "call"}, {"api_name": "seaborn.stripplot", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 56, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 68, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 69, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 140, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 141, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 149, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 150, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 158, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 159, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 167, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 168, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 176, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 177, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 185, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 186, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 194, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 195, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 203, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 204, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 212, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 213, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 221, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 222, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 230, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 231, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 239, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 240, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 248, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 249, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 257, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 258, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 266, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 267, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 275, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 276, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 284, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 285, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 293, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 294, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 302, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 303, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 311, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 312, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 320, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 321, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 329, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 330, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 338, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 339, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 347, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 348, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 356, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 357, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 365, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 366, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 374, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 375, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 383, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 384, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 392, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 393, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 401, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 402, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 410, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 411, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 419, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 420, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 428, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 429, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 437, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 438, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 453, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 457, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 462, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 463, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.train_test_split", "line_number": 467, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 471, "usage_type": "call"}, {"api_name": "sklearn.ensemble.GradientBoostingRegressor", "line_number": 474, "usage_type": "call"}, {"api_name": "xgboost.XGBRegressor", "line_number": 478, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 488, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 489, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 490, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 491, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 496, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 496, "usage_type": "attribute"}, {"api_name": "keras.wrappers.scikit_learn.KerasRegressor", "line_number": 498, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 499, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 500, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 513, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 513, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 513, "usage_type": "call"}, {"api_name": "numpy.log1p", "line_number": 513, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 524, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.SelectFromModel", "line_number": 533, "usage_type": "call"}, {"api_name": "sklearn.ensemble.GradientBoostingRegressor", "line_number": 541, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 553, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 555, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 556, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 557, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 559, "usage_type": "call"}, {"api_name": "sklearn.ensemble.GradientBoostingRegressor", "line_number": 568, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 572, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 573, "usage_type": "call"}]} +{"seq_id": "459606259", "text": "from django.shortcuts import render,redirect\nfrom django.http import HttpResponse\nfrom . models import Employee\nfrom . forms import EmployeeForm\nfrom django.contrib import messages\n\n# Create your views here.\ndef home(request):\n return render(request,\"EmployeeApp/home.html\")\n\ndef list_employees(request):\n # Django ORM\n employees = Employee.objects.all()\n context = {\n \"employees\": employees,\n \"hello\":\"Its not hello\"\n }\n return render(request,\"EmployeeApp/employee_list.html\",context)\n\n\ndef add_employee(request):\n form = EmployeeForm()\n if request.method == \"POST\":\n form = EmployeeForm(request.POST)\n if form.is_valid():\n employee = form.save()\n messages.success(request,f\"Successfully added Employee {employee.name}\")\n return redirect('list_employees')\n context = {\n \"form\":form\n }\n return render(request,\"EmployeeApp/employee_form.html\",context)\n\n\ndef update_employee(request,id=None):\n employee = Employee.objects.get(id=id)\n form = EmployeeForm(instance=employee)\n if request.method == \"POST\":\n form = EmployeeForm(request.POST,instance=employee)\n if form.is_valid():\n employee = form.save()\n messages.success(request,f\"Successfully updated Employee {employee.name}\")\n return redirect('list_employees')\n context = {\n \"form\":form\n }\n return render(request,\"EmployeeApp/employee_form.html\",context)\n\n\ndef delete_employee(request,id=None):\n employee = Employee.objects.get(id=id)\n employeename = employee.name\n employee.delete()\n messages.success(request,f\"Successfully deleted Employee {employee}\")\n return redirect('list_employees')\n", "sub_path": "MainApp/EmployeeApp/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1727, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.shortcuts.render", "line_number": 9, "usage_type": "call"}, {"api_name": "models.Employee.objects.all", "line_number": 13, "usage_type": "call"}, {"api_name": "models.Employee.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "models.Employee", "line_number": 13, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 18, "usage_type": "call"}, {"api_name": "forms.EmployeeForm", "line_number": 22, "usage_type": "call"}, {"api_name": "forms.EmployeeForm", "line_number": 24, "usage_type": "call"}, {"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": 32, "usage_type": "call"}, {"api_name": "models.Employee.objects.get", "line_number": 36, "usage_type": "call"}, {"api_name": "models.Employee.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "models.Employee", "line_number": 36, "usage_type": "name"}, {"api_name": "forms.EmployeeForm", "line_number": 37, "usage_type": "call"}, {"api_name": "forms.EmployeeForm", "line_number": 39, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 42, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 42, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 43, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 47, "usage_type": "call"}, {"api_name": "models.Employee.objects.get", "line_number": 51, "usage_type": "call"}, {"api_name": "models.Employee.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "models.Employee", "line_number": 51, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 54, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 54, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "405447271", "text": "import vamp\nimport librosa\nimport struct\nimport argparse\nimport mutagen\nimport base64\nimport textwrap\n\ndef determine_filetype(inputfile):\n audio = mutagen.File(inputfile)\n return audio.mime[0]\n\ndef write_flac(flacfile, beatgridfile, avg_tempo):\n audio = mutagen.File(flacfile)\n print(audio)\n\n beatgrid_fp = open(beatgridfile, 'rb')\n beatgrid = beatgrid_fp.read()\n beatgrid_fp.close()\n\n beatgrid = b'application/octet-stream' + b'\\x00\\x00' + b'Serato BeatGrid' + b'\\x00' + beatgrid\n audio[\"SERATO_BEATGRID\"] = textwrap.fill(base64.b64encode(beatgrid).decode('ascii'), width=72)\n audio[\"bpm\"] = str(avg_tempo)\n audio.save()\n\ndef write_mp3(mp3file, beatgridfile, avg_tempo):\n audio = mutagen.id3.ID3(mp3file)\n\n beatgrid_fp = open(beatgridfile, 'rb')\n beatgrid = beatgrid_fp.read()\n beatgrid_fp.close()\n\n audio['TBPM'] = mutagen.id3.TBPM(\n encoding=0,\n text=str(avg_tempo),\n )\n audio['GEOB:Serato BeatGrid'] = mutagen.id3.GEOB(\n encoding=0,\n mime='application/octet-stream',\n desc='Serato BeatGrid',\n data=beatgrid,\n )\n audio.save()\n\ndef gen_beatgrid(inputfile, outputfile):\n data, rate = librosa.load(inputfile, sr=None)\n params = {}\n# tempo, beats = librosa.beat.beat_track(data, units='time', sr=rate)\n avg_tempo = librosa.beat.tempo(data)[0]\n beats = vamp.collect(data, rate, \"qm-vamp-plugins:qm-barbeattracker\")\n\n# print(beats)\n# exit()\n\n fp = open(outputfile, 'wb')\n\n fp.write(b'\\x01\\x00')\n print(int(len(beats['list']) / 8 + 1))\n\n fp.write(struct.pack('>i', int(len(beats['list']) / 8 + 1))) # only emit a marker every eight beats\n\n lastts = \"\"\n process_beat = True\n\n for count, item in enumerate(beats['list']):\n if count == 1016: # serato only allows 128 markers - leave one space for the termination marker\n break\n# print(f'pb: {process_beat} item: {item} lastts: {lastts}')\n if item['label'] == '1' and not process_beat:\n process_beat = True\n continue\n if item['label'] == '1' and process_beat:\n# print(f\"Writing beat at {item['timestamp']}\")\n fp.write(struct.pack('>f', item['timestamp']))\n fp.write(b'\\x00\\x00\\x00\\x08')\n# print(item['timestamp'])\n lastts = item['timestamp']\n process_beat = False\n continue\n\n print(lastts)\n fp.write(struct.pack('>f', lastts))\n fp.write(struct.pack('>f', avg_tempo))\n fp.write(b'\\x37')\n print(len(beats['list']))\n return avg_tempo\n\nparser = argparse.ArgumentParser()\nparser.add_argument('input_file', metavar='INFILE', help=\"the audio file to generate a variable beatgrid for\")\nparser.add_argument('beatgrid_file', metavar='BGFILE', help=\"the binary file to write the Serato BeatGrid to\")\nargs = parser.parse_args()\n\navg_tempo = gen_beatgrid(args.input_file, args.beatgrid_file)\n\nfiletype = determine_filetype(args.input_file)\n\nprint(filetype)\n\nif filetype == 'audio/flac':\n write_flac(args.input_file, args.beatgrid_file, int(avg_tempo))\nelif filetype == 'audio/mp3':\n write_mp3(args.input_file, args.beatgrid_file, int(avg_tempo))\nelse:\n print(f'Sorry, {filetype} files are not supported')\n", "sub_path": "serato_beatgrid.py", "file_name": "serato_beatgrid.py", "file_ext": "py", "file_size_in_byte": 3243, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "mutagen.File", "line_number": 10, "usage_type": "call"}, {"api_name": "mutagen.File", "line_number": 14, "usage_type": "call"}, {"api_name": "textwrap.fill", "line_number": 22, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 22, "usage_type": "call"}, {"api_name": "mutagen.id3.ID3", "line_number": 27, "usage_type": "call"}, {"api_name": "mutagen.id3", "line_number": 27, "usage_type": "attribute"}, {"api_name": "mutagen.id3.TBPM", "line_number": 33, "usage_type": "call"}, {"api_name": "mutagen.id3", "line_number": 33, "usage_type": "attribute"}, {"api_name": "mutagen.id3.GEOB", "line_number": 37, "usage_type": "call"}, {"api_name": "mutagen.id3", "line_number": 37, "usage_type": "attribute"}, {"api_name": "librosa.load", "line_number": 46, "usage_type": "call"}, {"api_name": "librosa.beat.tempo", "line_number": 49, "usage_type": "call"}, {"api_name": "librosa.beat", "line_number": 49, "usage_type": "attribute"}, {"api_name": "vamp.collect", "line_number": 50, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 60, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 74, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 82, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 83, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 88, "usage_type": "call"}]} +{"seq_id": "457018054", "text": "#!/usr/bin/env python\n\nimport os\nimport sys\nfrom glob import glob\n\nfrom setuptools import Extension, setup\n\nimport versioneer\n\nextra_compile_args = []\nif not sys.platform.startswith('win'):\n extra_compile_args.append('-std=c++11')\n\next_module = Extension(\n \"pytopickle\",\n sources=glob(\"pandahouse/*.cpp\"),\n extra_compile_args=extra_compile_args,\n language=\"c++\",\n)\n\nsetup(name='pandahouse',\n version=versioneer.get_version(),\n cmdclass=versioneer.get_cmdclass(),\n description='Pandas interface for Clickhouse HTTP API',\n url='http://github.com/kszucs/pandahouse',\n maintainer='Krisztian Szucs',\n maintainer_email='szucs.krisztian@gmail.com',\n license='BSD',\n keywords='',\n packages=['pandahouse'],\n tests_require=['pytest'],\n setup_requires=['pytest-runner'],\n install_requires=['pandas', 'requests', 'toolz'],\n long_description=(open('README.rst').read() if os.path.exists('README.rst') else ''),\n zip_safe=False,\n ext_modules=[ext_module])\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1032, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sys.platform.startswith", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 12, "usage_type": "attribute"}, {"api_name": "setuptools.Extension", "line_number": 15, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 17, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 22, "usage_type": "call"}, {"api_name": "versioneer.get_version", "line_number": 23, "usage_type": "call"}, {"api_name": "versioneer.get_cmdclass", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}]} +{"seq_id": "599731036", "text": "from django.urls import path\nfrom . import views\n\nurlpatterns =[\n path('login/',views.loginPage, name=\"login\"),\n path('register/',views.registerPage, name=\"register\"),\n path('logout/',views.logoutUser, name=\"logout\"),\n path('',views.home,name='home'),\n path('room//',views.room,name='room'),\n path('profile//',views.userProfile,name='userProfile'),\n path('createRoom/',views.createRoom,name='createRoom'),\n path('updateRoom//',views.updateRoom,name='updateRoom'),\n path('deleteRoom//',views.deleteRoom,name='deleteRoom'),\n path('deleteMessage//',views.deleteMessage,name='deleteMessage'),\n path('updateUser/',views.updateUser,name='updateUser'),\n path('topics/',views.topicsPage,name='topics'),\n path('activity/',views.activityPage,name='activity'),\n]", "sub_path": "study_bud/base/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 830, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "123009048", "text": "import os\nimport warnings # Ignore warnings\n\nimport pandas as pd\nimport torch\nfrom PIL import Image\nfrom torch.utils.data import Dataset\n\nfrom torchvision import transforms\n\nwarnings.filterwarnings(\"ignore\")\n\n\nclass MiniImageNet(Dataset):\n \"\"\"Mini Image Net dataset.\"\"\"\n\n def __init__(self, csv_file, separator, root_dir):\n \"\"\"\n Args:\n csv_file (string): Path to the csv file with annotations.\n root_dir (string): Directory with all the images.\n transform (callable, optional): Optional transform to be applied\n on a sample.\n \"\"\"\n self.csv_mappings = pd.read_csv(csv_file, sep=separator, header=None, squeeze=True)\n self.root_dir = root_dir\n\n all_class_paths = [os.path.join(self.root_dir, class_name) for class_name in self.csv_mappings]\n self.all_targets = []\n self.all_image_tensors = []\n\n load_file_to_pil = lambda filepath: Image.open(filepath)\n self.transform = transforms.Compose([\n load_file_to_pil,\n transforms.Resize((84, 84)),\n transforms.ToTensor()\n ])\n\n n_class = len(all_class_paths)\n\n for i, current_class_dir in enumerate(all_class_paths):\n print(\"Loading {}/{} on RAM\".format(i + 1, n_class))\n\n full_file_paths_of_class = next(os.walk(current_class_dir))[2]\n full_file_paths_of_class = [os.path.join(current_class_dir, image_filename)\n for image_filename\n in full_file_paths_of_class]\n\n target_of_class = [i] * len(full_file_paths_of_class)\n\n self.all_targets.extend(target_of_class)\n\n image_tensors_from_path = [self.transform(full_path) for full_path in full_file_paths_of_class]\n self.all_image_tensors.extend(image_tensors_from_path)\n\n self.all_targets = torch.LongTensor(self.all_targets)\n self.all_image_tensors = torch.stack(self.all_image_tensors)\n\n def __len__(self):\n return len(self.all_targets)\n\n def __getitem__(self, idx):\n\n x = self.all_image_tensors[idx]\n y = self.all_targets[idx]\n \n return x, y\n", "sub_path": "src/utils/mini_image_net.py", "file_name": "mini_image_net.py", "file_ext": "py", "file_size_in_byte": 2331, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "warnings.filterwarnings", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 14, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 32, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 32, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 33, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 33, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 35, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 35, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 36, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 36, "usage_type": "name"}, {"api_name": "os.walk", "line_number": 44, "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": "torch.LongTensor", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "342569519", "text": "import logging\nimport math\nfrom typing import Tuple, Union, List\n\nimport click\n\nfrom .decorators import layer_processor, global_processor, LayerType\nfrom .model import LineCollection, VectorData\nfrom .utils import Length\nfrom .vpype import cli\n\n\n@cli.command(group=\"Transforms\")\n@click.argument(\"offset\", nargs=2, type=Length(), required=True)\n@layer_processor\ndef translate(lc: LineCollection, offset: Tuple[float, float]):\n \"\"\"\n Translate the geometries. X and Y offsets must be provided. These arguments understand\n supported units.\n \"\"\"\n lc.translate(offset[0], offset[1])\n return lc\n\n\n# noinspection PyShadowingNames\n@cli.command(group=\"Transforms\")\n@click.argument(\"scale\", nargs=2, type=Length())\n@click.option(\n \"-l\",\n \"--layer\",\n type=LayerType(accept_multiple=True),\n default=\"all\",\n help=\"Target layer(s).\",\n)\n@click.option(\n \"--to\",\n \"absolute\",\n is_flag=True,\n help=\"Arguments are interpreted as absolute size instead of (relative) factors.\",\n)\n@click.option(\n \"-p\",\n \"--keep-proportions\",\n is_flag=True,\n help=\"[--to only] Maintain the geometries proportions.\",\n)\n@click.option(\n \"-o\", \"--origin\", \"origin_coords\", nargs=2, type=Length(), help=\"Use a specific origin.\"\n)\n@global_processor\ndef scale(\n vector_data: VectorData,\n scale: Tuple[float, float],\n layer: Union[int, List[int]],\n absolute: bool,\n keep_proportions: bool,\n origin_coords: Tuple[float, float],\n):\n \"\"\"Scale the geometries.\n\n The origin used is the bounding box center, unless the `--origin` option is used.\n\n By default, the arguments are used as relative factors (e.g. `scale 2 2` make the\n geometries twice as big in both dimensions). With `--to`, the arguments are interpreted as\n the final size. In this case, arguments understand the supported units (e.g.\n `scale --to 10cm 10cm`).\n\n By default, act on all layers. If one or more layer IDs are provided with the `--layer`\n option, only these layers will be affected. In this case, the bounding box is that of the\n listed layers.\n \"\"\"\n\n if vector_data.is_empty():\n return vector_data\n\n # these are the layers we want to act on\n layer_ids = LayerType.multiple_to_layer_ids(layer, vector_data)\n bounds = vector_data.bounds(layer_ids)\n\n if absolute:\n factors = (scale[0] / (bounds[2] - bounds[0]), scale[1] / (bounds[3] - bounds[1]))\n\n if keep_proportions:\n factors = (min(factors), min(factors))\n else:\n factors = scale\n\n if len(origin_coords) == 2:\n origin = origin_coords\n else:\n origin = (\n 0.5 * (bounds[0] + bounds[2]),\n 0.5 * (bounds[1] + bounds[3]),\n )\n\n logging.info(f\"scaling factors: {factors}, origin: {origin}\")\n\n for vid in layer_ids:\n lc = vector_data[vid]\n lc.translate(-origin[0], -origin[1])\n lc.scale(factors[0], factors[1])\n lc.translate(origin[0], origin[1])\n\n return vector_data\n\n\n@cli.command(group=\"Transforms\")\n@click.argument(\"angle\", required=True, type=float)\n@click.option(\n \"-l\",\n \"--layer\",\n type=LayerType(accept_multiple=True),\n default=\"all\",\n help=\"Target layer(s).\",\n)\n@click.option(\"-r\", \"--radian\", is_flag=True, help=\"Angle is in radians.\")\n@click.option(\n \"-o\", \"--origin\", \"origin_coords\", nargs=2, type=Length(), help=\"Use a specific origin.\"\n)\n@global_processor\ndef rotate(\n vector_data: VectorData,\n angle: float,\n layer: Union[int, List[int]],\n radian: bool,\n origin_coords: Tuple[float, float],\n):\n \"\"\"\n Rotate the geometries (clockwise positive).\n\n The origin used is the bounding box center, unless the `--origin` option is used.\n\n By default, act on all layers. If one or more layer IDs are provided with the `--layer`\n option, only these layers will be affected. In this case, the bounding box is that of the\n listed layers.\n \"\"\"\n if vector_data.is_empty():\n return vector_data\n\n if not radian:\n angle *= math.pi / 180.0\n\n # these are the layers we want to act on\n layer_ids = LayerType.multiple_to_layer_ids(layer, vector_data)\n\n bounds = vector_data.bounds(layer_ids)\n if len(origin_coords) == 2:\n origin = origin_coords\n else:\n origin = (\n 0.5 * (bounds[0] + bounds[2]),\n 0.5 * (bounds[1] + bounds[3]),\n )\n\n logging.info(f\"rotating origin: {origin}\")\n\n for vid in layer_ids:\n lc = vector_data[vid]\n lc.translate(-origin[0], -origin[1])\n lc.rotate(angle)\n lc.translate(origin[0], origin[1])\n\n return vector_data\n\n\n@cli.command(group=\"Transforms\")\n@click.argument(\"angles\", required=True, nargs=2, type=float)\n@click.option(\n \"-l\",\n \"--layer\",\n type=LayerType(accept_multiple=True),\n default=\"all\",\n help=\"Target layer(s).\",\n)\n@click.option(\"-r\", \"--radian\", is_flag=True, help=\"Angle is in radians.\")\n@click.option(\n \"-o\", \"--origin\", \"origin_coords\", nargs=2, type=Length(), help=\"Use a specific origin.\"\n)\n@global_processor\ndef skew(\n vector_data: VectorData,\n layer: Union[int, List[int]],\n angles: Tuple[float, float],\n radian: bool,\n origin_coords: Tuple[float, float],\n):\n \"\"\"\n Skew the geometries.\n\n The geometries are sheared by the provided angles along X and Y dimensions.\n\n The origin used in the bounding box center, unless the `--centroid` or `--origin` options\n are used.\n \"\"\"\n if vector_data.is_empty():\n return vector_data\n\n # these are the layers we want to act on\n layer_ids = LayerType.multiple_to_layer_ids(layer, vector_data)\n\n bounds = vector_data.bounds(layer_ids)\n if len(origin_coords) == 2:\n origin = origin_coords\n else:\n origin = (\n 0.5 * (bounds[0] + bounds[2]),\n 0.5 * (bounds[1] + bounds[3]),\n )\n\n if not radian:\n angles = tuple(a * math.pi / 180.0 for a in angles)\n\n logging.info(f\"skewing origin: {origin}\")\n\n for vid in layer_ids:\n lc = vector_data[vid]\n lc.translate(-origin[0], -origin[1])\n lc.skew(angles[0], angles[1])\n lc.translate(origin[0], origin[1])\n\n return vector_data\n", "sub_path": "vpype/transforms.py", "file_name": "transforms.py", "file_ext": "py", "file_size_in_byte": 6188, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "model.LineCollection", "line_number": 16, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 16, "usage_type": "name"}, {"api_name": "vpype.cli.command", "line_number": 13, "usage_type": "call"}, {"api_name": "vpype.cli", "line_number": 13, "usage_type": "name"}, {"api_name": "click.argument", "line_number": 14, "usage_type": "call"}, {"api_name": "utils.Length", "line_number": 14, "usage_type": "call"}, {"api_name": "decorators.layer_processor", "line_number": 15, "usage_type": "name"}, {"api_name": "model.VectorData", "line_number": 52, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 53, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 54, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 54, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 57, "usage_type": "name"}, {"api_name": "decorators.LayerType.multiple_to_layer_ids", "line_number": 77, "usage_type": "call"}, {"api_name": "decorators.LayerType", "line_number": 77, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 96, "usage_type": "call"}, {"api_name": "vpype.cli.command", "line_number": 26, "usage_type": "call"}, {"api_name": "vpype.cli", "line_number": 26, "usage_type": "name"}, {"api_name": "click.argument", "line_number": 27, "usage_type": "call"}, {"api_name": "utils.Length", "line_number": 27, "usage_type": "call"}, {"api_name": "click.option", "line_number": 28, "usage_type": "call"}, {"api_name": "decorators.LayerType", "line_number": 31, "usage_type": "call"}, {"api_name": "click.option", "line_number": 35, "usage_type": "call"}, {"api_name": "click.option", "line_number": 41, "usage_type": "call"}, {"api_name": "click.option", "line_number": 47, "usage_type": "call"}, {"api_name": "utils.Length", "line_number": 48, "usage_type": "call"}, {"api_name": "decorators.global_processor", "line_number": 50, "usage_type": "name"}, {"api_name": "model.VectorData", "line_number": 122, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 124, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 124, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 126, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 141, "usage_type": "attribute"}, {"api_name": "decorators.LayerType.multiple_to_layer_ids", "line_number": 144, "usage_type": "call"}, {"api_name": "decorators.LayerType", "line_number": 144, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 155, "usage_type": "call"}, {"api_name": "vpype.cli.command", "line_number": 107, "usage_type": "call"}, {"api_name": "vpype.cli", "line_number": 107, "usage_type": "name"}, {"api_name": "click.argument", "line_number": 108, "usage_type": "call"}, {"api_name": "click.option", "line_number": 109, "usage_type": "call"}, {"api_name": "decorators.LayerType", "line_number": 112, "usage_type": "call"}, {"api_name": "click.option", "line_number": 116, "usage_type": "call"}, {"api_name": "click.option", "line_number": 117, "usage_type": "call"}, {"api_name": "utils.Length", "line_number": 118, "usage_type": "call"}, {"api_name": "decorators.global_processor", "line_number": 120, "usage_type": "name"}, {"api_name": "model.VectorData", "line_number": 181, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 182, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 182, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 183, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 185, "usage_type": "name"}, {"api_name": "decorators.LayerType.multiple_to_layer_ids", "line_number": 199, "usage_type": "call"}, {"api_name": "decorators.LayerType", "line_number": 199, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 211, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 213, "usage_type": "call"}, {"api_name": "vpype.cli.command", "line_number": 166, "usage_type": "call"}, {"api_name": "vpype.cli", "line_number": 166, "usage_type": "name"}, {"api_name": "click.argument", "line_number": 167, "usage_type": "call"}, {"api_name": "click.option", "line_number": 168, "usage_type": "call"}, {"api_name": "decorators.LayerType", "line_number": 171, "usage_type": "call"}, {"api_name": "click.option", "line_number": 175, "usage_type": "call"}, {"api_name": "click.option", "line_number": 176, "usage_type": "call"}, {"api_name": "utils.Length", "line_number": 177, "usage_type": "call"}, {"api_name": "decorators.global_processor", "line_number": 179, "usage_type": "name"}]} +{"seq_id": "182990769", "text": "import ast\nfrom typing import Dict, Tuple\n\nfrom boa3.compiler.codegenerator import CodeGenerator\nfrom boa3.model.method import Method\nfrom boa3.model.operation.binary.binaryoperation import BinaryOperation\nfrom boa3.model.operation.binaryop import BinaryOp\nfrom boa3.model.operation.unary.unaryoperation import UnaryOperation\nfrom boa3.model.symbol import ISymbol\nfrom boa3.model.type.itype import IType\nfrom boa3.model.type.type import Type\nfrom boa3.model.variable import Variable\n\n\nclass VisitorCodeGenerator(ast.NodeVisitor):\n \"\"\"\n This class is responsible for walk through the ast.\n\n The methods with the name starting with 'visit_' are implementations of methods from the :class:`NodeVisitor` class.\n These methods are used to walk through the Python abstract syntax tree.\n\n :ivar generator:\n \"\"\"\n\n def __init__(self, generator: CodeGenerator):\n self.generator = generator\n\n @property\n def symbols(self) -> Dict[str, ISymbol]:\n return self.generator.symbol_table\n\n def visit_to_generate(self, node: ast.AST):\n \"\"\"\n Visitor to generate the nodes that the primary visitor is used to retrieve value\n\n :param node: an ast node\n \"\"\"\n result = self.visit(node)\n\n # the default return of the name visitor is the name string\n if isinstance(node, ast.Name):\n # TODO: validate function calls\n self.generator.convert_load_symbol(result)\n\n def visit_FunctionDef(self, function: ast.FunctionDef):\n \"\"\"\n Visitor of the function definition node\n\n Generates the Neo VM code for the function\n\n :param function: the python ast function definition node\n \"\"\"\n method = self.symbols[function.name]\n if function.returns is not None:\n fun_rtype_id: str = self.visit(function.returns)\n else:\n fun_rtype_id: str = Type.none.identifier\n\n symbol: ISymbol = self.generator.get_symbol(fun_rtype_id)\n if isinstance(method, Method) and isinstance(symbol, IType):\n fun_return: IType = symbol\n method.return_type = fun_return\n\n self.generator.convert_begin_method(method)\n\n for stmt in function.body:\n self.visit(stmt)\n\n self.generator.convert_end_method()\n\n def visit_arguments(self, arguments: ast.arguments) -> Dict[str, Variable]:\n \"\"\"\n Visitor of the function arguments node\n\n :param arguments: the python ast function arguments node\n :return: a dictionary that maps each argument to its identifier\n \"\"\"\n args: Dict[str, Variable] = {}\n\n for arg in arguments.args:\n var_id, var = self.visit_arg(arg) # Tuple[str, Variable]\n args[var_id] = var\n return args\n\n def visit_arg(self, arg: ast.arg) -> Tuple[str, Variable]:\n \"\"\"\n Visitor of a function argument node\n\n :param arg: the python ast arg node\n :return: a tuple with the identifier and the argument\n \"\"\"\n var_id = arg.arg\n var_type = self.visit(arg.annotation)\n\n return var_id, Variable(var_type)\n\n def visit_Return(self, ret: ast.Return):\n \"\"\"\n Visitor of a function return node\n\n :param ret: the python ast return node\n \"\"\"\n if ret.value is not None:\n self.visit_to_generate(ret.value)\n\n def store_variable(self, var_id: str, value: ast.AST, index: ast.AST = None):\n # if the value is None, it is a variable declaration\n if value is not None:\n if index is None:\n # if index is None, then it is a variable assignment\n self.visit_to_generate(value)\n self.generator.convert_store_variable(var_id)\n else:\n # if not, it is an array assignment\n self.generator.convert_load_symbol(var_id)\n self.visit_to_generate(index)\n self.visit_to_generate(value)\n self.generator.convert_set_array_item()\n\n def visit_AnnAssign(self, ann_assign: ast.AnnAssign):\n \"\"\"\n Visitor of an annotated assignment node\n\n :param ann_assign: the python ast variable assignment node\n \"\"\"\n var_id = self.visit(ann_assign.target)\n self.store_variable(var_id, ann_assign.value)\n\n def visit_Assign(self, assign: ast.Assign):\n \"\"\"\n Visitor of an assignment node\n\n :param assign: the python ast variable assignment node\n \"\"\"\n var_index = None\n var_id = self.visit(assign.targets[0])\n\n # if it is a tuple, then it is an array assignment\n if isinstance(var_id, tuple):\n var_index = var_id[1]\n var_id: str = var_id[0]\n\n self.store_variable(var_id, assign.value, var_index)\n\n def visit_Subscript(self, subscript: ast.Subscript):\n \"\"\"\n Visitor of a subscript node\n\n :param subscript: the python ast subscript node\n \"\"\"\n if isinstance(subscript.ctx, ast.Load):\n # get item\n self.visit_to_generate(subscript.value)\n self.visit_to_generate(subscript.slice.value)\n self.generator.convert_get_array_item()\n else:\n # set item\n var_id = self.visit(subscript.value)\n return var_id, subscript.slice.value\n\n def visit_BinOp(self, bin_op: ast.BinOp):\n \"\"\"\n Visitor of a binary operation node\n\n :param bin_op: the python ast binary operation node\n \"\"\"\n if isinstance(bin_op.op, BinaryOperation):\n self.visit_to_generate(bin_op.left)\n self.visit_to_generate(bin_op.right)\n self.generator.convert_operation(bin_op.op)\n\n def visit_UnaryOp(self, un_op: ast.UnaryOp):\n \"\"\"\n Visitor of a binary operation node\n\n :param un_op: the python ast binary operation node\n \"\"\"\n if isinstance(un_op.op, UnaryOperation):\n self.visit_to_generate(un_op.operand)\n self.generator.convert_operation(un_op.op)\n\n def visit_Compare(self, compare: ast.Compare):\n \"\"\"\n Visitor of a compare operation node\n\n :param compare: the python ast compare operation node\n \"\"\"\n converted: bool = False\n left = compare.left\n for index, op in enumerate(compare.ops):\n right = compare.comparators[index]\n if isinstance(op, BinaryOperation):\n self.visit_to_generate(left)\n self.visit_to_generate(right)\n self.generator.convert_operation(op)\n # if it's more than two comparators, must include AND between the operations\n if not converted:\n converted = True\n else:\n self.generator.convert_operation(BinaryOp.And)\n left = right\n\n def visit_BoolOp(self, bool_op: ast.BoolOp):\n \"\"\"\n Visitor of a compare operation node\n\n :param bool_op: the python ast boolean operation node\n \"\"\"\n if isinstance(bool_op.op, BinaryOperation):\n left = bool_op.values[0]\n self.visit_to_generate(left)\n for index, right in enumerate(bool_op.values[1:]):\n self.visit_to_generate(right)\n self.generator.convert_operation(bool_op.op)\n\n def visit_While(self, while_node: ast.While):\n \"\"\"\n Verifies if the type of while test is valid\n\n :param while_node: the python ast while statement node\n \"\"\"\n start_addr: int = self.generator.convert_begin_while()\n for stmt in while_node.body:\n self.visit_to_generate(stmt)\n\n test_address: int = self.generator.address\n self.visit_to_generate(while_node.test)\n self.generator.convert_end_while(start_addr, test_address)\n\n for stmt in while_node.orelse:\n self.visit_to_generate(stmt)\n\n def visit_If(self, if_node: ast.If):\n \"\"\"\n Verifies if the type of if test is valid\n\n :param if_node: the python ast if statement node\n \"\"\"\n self.visit_to_generate(if_node.test)\n\n start_addr: int = self.generator.convert_begin_if()\n for stmt in if_node.body:\n self.visit_to_generate(stmt)\n\n if len(if_node.orelse) > 0:\n start_addr = self.generator.convert_begin_else(start_addr)\n for stmt in if_node.orelse:\n self.visit_to_generate(stmt)\n\n self.generator.convert_end_if(start_addr)\n\n def visit_IfExp(self, if_node: ast.IfExp):\n \"\"\"\n Verifies if the type of if test is valid\n\n :param if_node: the python ast if statement node\n \"\"\"\n self.visit_to_generate(if_node.test)\n\n start_addr: int = self.generator.convert_begin_if()\n self.visit_to_generate(if_node.body)\n\n start_addr = self.generator.convert_begin_else(start_addr)\n self.visit_to_generate(if_node.orelse)\n\n self.generator.convert_end_if(start_addr)\n\n def visit_Name(self, name: ast.Name) -> str:\n \"\"\"\n Visitor of a name node\n\n :param name: the python ast name identifier node\n :return: the identifier of the name\n \"\"\"\n return name.id\n\n def visit_NameConstant(self, constant: ast.NameConstant):\n \"\"\"\n Visitor of constant names node\n\n :param constant: the python ast name constant node\n :return: the value of the constant\n \"\"\"\n self.generator.convert_literal(constant.value)\n\n def visit_Num(self, num: ast.Num):\n \"\"\"\n Visitor of literal number node\n\n :param num: the python ast number node\n \"\"\"\n self.generator.convert_literal(num.n)\n\n def visit_Str(self, str: ast.Str):\n \"\"\"\n Visitor of literal string node\n\n :param str: the python ast string node\n \"\"\"\n self.generator.convert_literal(str.s)\n\n def visit_Tuple(self, tup_node: ast.Tuple):\n \"\"\"\n Visitor of literal tuple node\n\n :param tup_node: the python ast string node\n :return: the value of the tuple\n \"\"\"\n tup = tuple([value for value in tup_node.elts])\n length = len(tup_node.elts)\n self.generator.convert_new_array(length)\n for index, value in enumerate(tup_node.elts):\n self.generator.convert_set_new_array_item_at(index)\n self.visit_to_generate(value)\n self.generator.convert_set_array_item()\n", "sub_path": "boa3/compiler/codegeneratorvisitor.py", "file_name": "codegeneratorvisitor.py", "file_ext": "py", "file_size_in_byte": 10508, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "ast.NodeVisitor", "line_number": 15, "usage_type": "attribute"}, {"api_name": "boa3.compiler.codegenerator.CodeGenerator", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 29, "usage_type": "name"}, {"api_name": "boa3.model.symbol.ISymbol", "line_number": 29, "usage_type": "name"}, {"api_name": "ast.AST", "line_number": 32, "usage_type": "attribute"}, {"api_name": "ast.Name", "line_number": 41, "usage_type": "attribute"}, {"api_name": "ast.FunctionDef", "line_number": 45, "usage_type": "attribute"}, {"api_name": "boa3.model.type.type.Type.none", "line_number": 57, "usage_type": "attribute"}, {"api_name": "boa3.model.type.type.Type", "line_number": 57, "usage_type": "name"}, {"api_name": "boa3.model.symbol.ISymbol", "line_number": 59, "usage_type": "name"}, {"api_name": "boa3.model.method.Method", "line_number": 60, "usage_type": "argument"}, {"api_name": "boa3.model.type.itype.IType", "line_number": 60, "usage_type": "argument"}, {"api_name": "boa3.model.type.itype.IType", "line_number": 61, "usage_type": "name"}, {"api_name": "ast.arguments", "line_number": 71, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 78, "usage_type": "name"}, {"api_name": "boa3.model.variable.Variable", "line_number": 78, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 71, "usage_type": "name"}, {"api_name": "boa3.model.variable.Variable", "line_number": 71, "usage_type": "name"}, {"api_name": "ast.arg", "line_number": 85, "usage_type": "attribute"}, {"api_name": "boa3.model.variable.Variable", "line_number": 95, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 85, "usage_type": "name"}, {"api_name": "boa3.model.variable.Variable", "line_number": 85, "usage_type": "name"}, {"api_name": "ast.Return", "line_number": 97, "usage_type": "attribute"}, {"api_name": "ast.AST", "line_number": 106, "usage_type": "attribute"}, {"api_name": "ast.AnnAssign", "line_number": 120, "usage_type": "attribute"}, {"api_name": "ast.Assign", "line_number": 129, "usage_type": "attribute"}, {"api_name": "ast.Subscript", "line_number": 145, "usage_type": "attribute"}, {"api_name": "ast.Load", "line_number": 151, "usage_type": "attribute"}, {"api_name": "ast.BinOp", "line_number": 161, "usage_type": "attribute"}, {"api_name": "boa3.model.operation.binary.binaryoperation.BinaryOperation", "line_number": 167, "usage_type": "argument"}, {"api_name": "ast.UnaryOp", "line_number": 172, "usage_type": "attribute"}, {"api_name": "boa3.model.operation.unary.unaryoperation.UnaryOperation", "line_number": 178, "usage_type": "argument"}, {"api_name": "ast.Compare", "line_number": 182, "usage_type": "attribute"}, {"api_name": "boa3.model.operation.binary.binaryoperation.BinaryOperation", "line_number": 192, "usage_type": "argument"}, {"api_name": "boa3.model.operation.binaryop.BinaryOp.And", "line_number": 200, "usage_type": "attribute"}, {"api_name": "boa3.model.operation.binaryop.BinaryOp", "line_number": 200, "usage_type": "name"}, {"api_name": "ast.BoolOp", "line_number": 203, "usage_type": "attribute"}, {"api_name": "boa3.model.operation.binary.binaryoperation.BinaryOperation", "line_number": 209, "usage_type": "argument"}, {"api_name": "ast.While", "line_number": 216, "usage_type": "attribute"}, {"api_name": "ast.If", "line_number": 233, "usage_type": "attribute"}, {"api_name": "ast.IfExp", "line_number": 252, "usage_type": "attribute"}, {"api_name": "ast.Name", "line_number": 268, "usage_type": "attribute"}, {"api_name": "ast.NameConstant", "line_number": 277, "usage_type": "attribute"}, {"api_name": "ast.Num", "line_number": 286, "usage_type": "attribute"}, {"api_name": "ast.Str", "line_number": 294, "usage_type": "attribute"}, {"api_name": "ast.Tuple", "line_number": 302, "usage_type": "attribute"}]} +{"seq_id": "527344620", "text": "from django.shortcuts import render, redirect, get_object_or_404\nfrom django.conf.urls import url\nfrom .models import Place\nfrom .forms import NewPlaceForm, TripReviewForm\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponseForbidden\nfrom django.contrib import messages\n\n# Create your views here.\n\n@login_required\ndef place_list(request):\n\n if request.method == 'POST':\n form = NewPlaceForm(request.POST)\n place = form.save(commit=False)\n place.user = request.user\n if form.is_valid():\n place.save()\n return redirect('place_list')\n\n places = Place.objects.filter(user=request.user).filter(visited=False).order_by('name')\n new_place_form = NewPlaceForm()\n return render(request, 'travel_wishlist/wishlist.html', {'places': places, 'new_place_form': new_place_form})\n\n@login_required\ndef places_visited(request):\n visited = Place.objects.filter(user=request.user).filter(visited=True).order_by('name')\n return render(request, 'travel_wishlist/visited.html', {'visited': visited})\n\n@login_required\ndef place_was_visited(request, place_pk):\n if request.method == 'POST':\n place = get_object_or_404(Place, pk=place_pk)\n if place.user == request.user:\n place.visited = True\n place.save()\n else:\n return HttpResponseForbidden()\n\n # Making new call\n return redirect('place_list')\n\n@login_required\ndef delete_place(request, place_pk):\n place = get_object_or_404(Place, pk=place_pk)\n if place.user == request.user:\n place.delete()\n return redirect('place_list')\n else:\n return HttpResponseForbidden()\n\n@login_required\ndef place_details(request, place_pk):\n\n place = get_object_or_404(Place, pk=place_pk)\n \n # Does this place belong to current user?\n if place.user != request.user:\n # messages.error(request, 'Not Allowed')\n return HttpResponseForbidden()\n\n # Is GET or POST request?\n\n # If POST, validate form data and update.\n if request.method == 'POST':\n form = TripReviewForm(request.POST, request.FILES, instance=place)\n\n if form.is_valid():\n form.save()\n messages.info(request, 'Trip information updated')\n else:\n messages.error(request, form.errors)\n\n return redirect('place_details', place_pk=place_pk)\n\n else:\n # If GET request, show Place info and form\n # If place is visited, show form; if place is not visited, no form.\n if place.visited:\n review_form = TripReviewForm(instance=place)\n return render(request, 'travel_wishlist/place_details.html', {'place': place, 'review_form': review_form} )\n else:\n return render(request, 'travel_wishlist/place_details.html', {'place': place} )\n", "sub_path": "wishlist/travel_wishlist/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2851, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "forms.NewPlaceForm", "line_number": 15, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Place.objects.filter", "line_number": 22, "usage_type": "call"}, {"api_name": "models.Place.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "models.Place", "line_number": 22, "usage_type": "name"}, {"api_name": "forms.NewPlaceForm", "line_number": 23, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 24, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 11, "usage_type": "name"}, {"api_name": "models.Place.objects.filter", "line_number": 28, "usage_type": "call"}, {"api_name": "models.Place.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "models.Place", "line_number": 28, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 29, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 26, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 34, "usage_type": "call"}, {"api_name": "models.Place", "line_number": 34, "usage_type": "argument"}, {"api_name": "django.http.HttpResponseForbidden", "line_number": 39, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 42, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 31, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 46, "usage_type": "call"}, {"api_name": "models.Place", "line_number": 46, "usage_type": "argument"}, {"api_name": "django.shortcuts.redirect", "line_number": 49, "usage_type": "call"}, {"api_name": "django.http.HttpResponseForbidden", "line_number": 51, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 44, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 56, "usage_type": "call"}, {"api_name": "models.Place", "line_number": 56, "usage_type": "argument"}, {"api_name": "django.http.HttpResponseForbidden", "line_number": 61, "usage_type": "call"}, {"api_name": "forms.TripReviewForm", "line_number": 67, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 71, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 71, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 73, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 73, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 75, "usage_type": "call"}, {"api_name": "forms.TripReviewForm", "line_number": 81, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 82, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 84, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 53, "usage_type": "name"}]} +{"seq_id": "178799383", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Oct 15 13:22:19 2019\n\n@author: Camille\n\"\"\"\n\nimport qutip as qt\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.integrate as integrate\nplt.close('all')\nfrom qutip.ui.progressbar import TextProgressBar\nfrom compute_Wigner_class import compute_Wigner\n\n\"Parameters\"\nNa=50 # Truncature\n\nk2 = 1 # k2 comes from the combination of g2 and kb\nk1=k2/1000 # single-photon loss rate\nalpha_inf_abs= 4 #alpha stationnary\ndelta = alpha_inf_abs**2*k2/2 #speed rate of the rotating pumping\nalpha=4 #alpha of initial state\n\n#Times \nT=np.pi /delta \nT1 =0.5*T\nT2 =T1+T\nT_final=T2+3*T\nn_t = 1001 #number of points from 0 to T_final\n#from 0 to T1 : \"free\" evolution (with 2 photon drive H)\n#from T1 to T2 : gate NOT\n#from T2 to T_final : \"free\" evolution (with 2 photon drive H)\n\nnbWignerPlot = 15\nnbCols=4\n\n\n\"Local Parameters\"\nIa = qt.identity(Na) # identity\na = qt.destroy(Na) # lowering operator\nn_a = a.dag()*a # photon number\n\neps_2=alpha_inf_abs**2*k2/2 #cf Mirrahimi NJP 2014\n\n\n\"Catstates\"\nC_alpha_plus = qt.coherent(Na, alpha)+qt.coherent(Na, -alpha)\nC_alpha_plus = C_alpha_plus/C_alpha_plus.norm()\nC_alpha_minus = qt.coherent(Na, alpha)-qt.coherent(Na, -alpha)\nC_alpha_minus = C_alpha_minus/C_alpha_minus.norm()\n\nC_y_alpha=qt.coherent(Na,alpha)+ 1j* qt.coherent(Na, -alpha)\nC_y_alpha=C_y_alpha/C_y_alpha.norm()\n\n\n\"Calculates the coefficient of the hamiltonian time-dependant terms\"\ndef coef_eps(t,args):\n return(-1j*eps_2*np.exp(1j*2*delta*(t-T1)*(t>=T1 and t<=T2)))\n\ndef coef_eps_conj(t,args):\n return(np.conjugate(coef_eps(t,args)))\n \n\nH_NOT=[[a**2,coef_eps],[a.dag()**2, coef_eps_conj]]\n#H=-1j*(a**2*eps_2-a.dag()**2*np.conjugate(eps_2))\ncops=[k1**0.5*a,k2**0.5*a**2]\n\n\"Resolution of the equation over time with mesolve\"\ninit_state=C_alpha_plus#initial state\ntlist = np.linspace(0, T_final, n_t)\nres_NOT = qt.mesolve(H_NOT, init_state, tlist, cops, progress_bar=TextProgressBar())\n\n\"Resolution of the equation without NOT\" #to have the theoretical fidelity (no analytical formula)\nH_free=[-1j*eps_2*a**2 + 1j*eps_2*a.dag()**2] #eps_2 is real\nres_free= qt.mesolve(H_free, init_state, tlist, cops, progress_bar=TextProgressBar())\n\n\n#Wigner\nres_NOT_Wigner = compute_Wigner([-6, 6, 51], nbWignerPlot,nbCols, n_t,-1)\nres_NOT_Wigner.draw_Wigner(res_NOT.states, title='Simulations with NOT')\nres_free_Wigner= compute_Wigner([-6,6, 51], nbWignerPlot,nbCols, n_t,-1)\nres_free_Wigner.draw_Wigner(res_free.states, title='Simulations without NOT')\n\n \n\"Plot the evolution of fidelity over time\"\ntarget_res=[] #to check the Wigner\nfidelity_NOT_list=[]\nfidelity_free_list=[]\nfor ii,t in enumerate(tlist):\n if t<=T1:\n current_theta=0\n elif (t>T1) and (t-1):\n pg_info = pg_info.split(\"curPage=\")[1]\n pgNum = int(pg_info)\n\n #根据总页数，拼成分页时使用的url\n i = 1\n url = self.liepin_urlpatten\n for i in range(pgNum):\n each_url = url.format(KEYWORD=keyword, CURR_PAGE=i+1)\n #调用分页后的页面\n yield Request(each_url,callback=self.get_joburls_bypage)\n\n #解析职位检索结果页面上的所有职位的链接，插入表中 \n def get_joburls_bypage(self, response):\n hxs = HtmlXPathSelector(response)\n links = hxs.select('//ul[@class=\"sojob-result-list\"]/li/a/@href').extract()\n links_jobdate = hxs.select('//ul[@class=\"sojob-result-list\"]/li/a/dl/dt[@class=\"date\"]/span/text()').extract()\n today = self.getYYYYMMDD()\n today2 = self.getYYYYMMDD2()\n # 找到每个职位的发布日期，如果发布日期是当天的，就入库\n for idx,link in enumerate(links):\n if (links_jobdate[idx].find(today2)>-1):\t\t\t\t\n open('../output/link_output/link.txt', 'ab').write(link+'\\r\\n')\n\n #得到yyyymmdd格式的当期日期\n def getYYYYMMDD(self):\n return datetime.datetime.now().strftime('%Y%m%d')\n\t\t\t\t\t\t\n #得到yyyy-mm-dd格式的当期日期\n def getYYYYMMDD2(self):\n return datetime.datetime.now().strftime('%Y-%m-%d')\n", "sub_path": "Scrapy/liepin/link/link/spiders/link_spider.py", "file_name": "link_spider.py", "file_ext": "py", "file_size_in_byte": 2858, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "scrapy.spider.BaseSpider", "line_number": 11, "usage_type": "name"}, {"api_name": "scrapy.selector.HtmlXPathSelector", "line_number": 32, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 52, "usage_type": "call"}, {"api_name": "scrapy.selector.HtmlXPathSelector", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 68, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 68, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 72, "usage_type": "attribute"}]} +{"seq_id": "613383949", "text": "#coding=utf-8\n#Date: 11-12-8\n#Time: 下午10:28\nimport base64\nimport urllib\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponse, Http404\nimport json, datetime\nfrom django.views.decorators.csrf import csrf_exempt\nfrom zt.settings import MEDIA_ROOT\nfrom zt import xlwt\nfrom zt.xlwt import Font, Alignment\nfrom PIL import Image\nimport uuid\n__author__ = u'王健'\n\ndef gsdate(date):\n return u'%s月%s日'%(date[4:6],date[6:8])\n\n\n@login_required\n@csrf_exempt\ndef uploadImage(request):\n\n data = request.read()\n if data:\n try:\n data = json.loads(data)\n except :\n s = open('d:/ztweb/zt/static/logging/%s_%s.txt'%(datetime.datetime.now().strftime('%Y%m%d'),str(uuid.uuid4())),'w')\n s.write(data)\n s.close()\n return HttpResponse(u'没有数据')\n else:\n raise Http404()\n img1name=MEDIA_ROOT+'/excel/'+str(uuid.uuid4())\n img2name=MEDIA_ROOT+'/excel/'+str(uuid.uuid4())\n img1=img1name+'.png'\n img2=img2name+'.png'\n img1bmp=img1name+'.bmp'\n img2bmp=img2name+'.bmp'\n\n\n\n s=str(data.get('img1','').replace(' ','+'))\n with open(img1,'wb') as f:\n f.write(base64.decodestring(s))\n\n\n\n s=str(data.get('img2','').replace(' ','+'))\n with open(img2,'wb') as f:\n f.write(base64.decodestring(s))\n try:\n Image.open(img1).convert(\"RGB\").save(img1bmp)\n Image.open(img2).convert(\"RGB\").save(img2bmp)\n except:\n import os\n command ='c:/python27/python.exe %s/img.py '%MEDIA_ROOT\n os.system(command)\n\n response = HttpResponse(mimetype=u'application/ms-excel')\n excelname = data.get('excelname','')\n sheetname = data.get('sheetname','')\n scx = data.get('scx','')\n site = data.get('site','')\n code = data.get('ismain','')\n\n filename = u'%s.xls'%urllib.quote(excelname.encode('utf-8'))\n if hasattr(request,'META') and request.META.has_key('HTTP_USER_AGENT'):\n if request.META['HTTP_USER_AGENT'].find(\"Firefox\")!=-1:\n filename = u'%s.xls'%excelname\n\n response['Content-Disposition'] = ('attachment;filename=%s' % filename).encode('utf-8')\n\n\n style1=xlwt.XFStyle()\n font1=Font()\n font1.height=220\n style1.font=font1\n algn=Alignment()\n algn.horz=Alignment.HORZ_CENTER\n algn.vert=Alignment.VERT_CENTER\n style1.alignment=algn\n style0=xlwt.XFStyle()\n algn0=Alignment()\n algn0.horz=Alignment.HORZ_CENTER\n algn0.vert=Alignment.VERT_CENTER\n font=Font()\n font.height=280\n font.bold=True\n style0.alignment=algn0\n style0.font=font\n\n wb = xlwt.Workbook()\n ws = wb.add_sheet(u\"%s\"%sheetname, cell_overwrite_ok=True)\n ws.write_merge(0,1,0,2,u'作者：%s'%request.user.last_name,style0)\n ws.write_merge(2,3,0,1,u'数据',style0)\n ws.write_merge(4,5,0,1,u'项目',style0)\n ws.write_merge(6,7,0,1,u'本期应达成项数',style0)\n ws.write_merge(8,9,0,1,u'本期应达成件数',style0)\n ws.write_merge(10,11,0,1,u'前期应追项数',style0)\n ws.write_merge(12,13,0,1,u'前期应追件数',style0)\n ws.write_merge(14,15,0,1,u'提前完成项数',style0)\n ws.write_merge(16,17,0,1,u'提前完成件数',style0)\n ws.write_merge(18,19,0,1,u'件数日达成率',style0)\n ws.write_merge(20,21,0,1,u'项数日达成率',style0)\n ws.write_merge(22,26,0,1,u'计划\\n\\t未完成分析\\n\\t以及解决措施',style0)\n ws.write_merge(27,30,0,1,u'计划员总结',style0)\n ws.col(0).width = 256 * 12\n ws.col(1).width = 256 * 12\n\n\n\n rownum=2\n datadict={\"bzxiangjh\":0,'bzxiangsj':0,'bzjianjh':0,'bzjiansj':0,'xiangdc':'0%','jiandc':'0%'}\n arrdata=json.loads(data.get('data',''))\n for row,d in enumerate(arrdata):\n\n ws.write_merge(2, 3, rownum+row*2, rownum+row*2+1, gsdate(d.get(\"date\",\"\")), style1)\n ws.write_merge(4, 5, rownum+row*2, rownum+row*2, u'计划', style1)\n ws.write_merge(4, 5, rownum+row*2+1, rownum+row*2+1, u'实际', style1)\n ws.write_merge(6, 7, rownum+row*2, rownum+row*2, d.get(\"bzxiangjh\",\"\"), style1)\n ws.write_merge(6, 7, rownum+row*2+1, rownum+row*2+1, d.get(\"bzxiangsj\",\"\"), style1)\n ws.write_merge(8, 9, rownum+row*2, rownum+row*2, d.get(\"bzjianjh\",\"\"), style1)\n ws.write_merge(8, 9, rownum+row*2+1, rownum+row*2+1, d.get(\"bzjiansj\",\"\"), style1)\n ws.write_merge(10, 11, rownum+row*2+1, rownum+row*2+1, d.get(\"qqxiangsj\",\"\"), style1)\n ws.write_merge(12,13, rownum+row*2+1, rownum+row*2+1, d.get(\"qqjiansj\",\"\"), style1)\n ws.write_merge(14,15, rownum+row*2+1, rownum+row*2+1, d.get(\"tqxiangsj\",\"\"), style1)\n ws.write_merge(16,17, rownum+row*2+1, rownum+row*2+1, d.get(\"tqjiansj\",\"\"), style1)\n ws.write_merge(18,19, rownum+row*2, rownum+row*2+1, '%s%%'%d.get(\"jianri\",\"\"), style1)\n ws.write_merge(20,21, rownum+row*2, rownum+row*2+1, '%s%%'%d.get(\"xiangri\",\"\"), style1)\n\n ws.write_merge(22,26, rownum+row*2, rownum+row*2+1, '', style1)\n ws.write_merge(27,30, rownum+row*2, rownum+row*2+1, '', style1)\n datadict['bzxiangjh']+=d.get('bzxiangjh')\n datadict['bzxiangsj']+=d.get('bzxiangsj')\n datadict['bzjianjh']+=d.get('bzjianjh')\n datadict['bzjiansj']+=d.get('bzjiansj')\n ws.col(rownum+row*2).width = 256 * 12\n ws.col(rownum+row*2+1).width = 256 * 12\n if datadict['bzxiangjh']>0:\n datadict['xiangdc']='%.2f%%'%((float(datadict['bzxiangsj'])/datadict['bzxiangjh'])*100,)\n if datadict['bzjianjh']>0:\n datadict['jiandc']='%.2f%%'%((float(datadict['bzjiansj'])/datadict['bzjianjh'])*100,)\n length=len(arrdata)*2\n ws.write_merge(0,1,3,rownum+length,u'生产线:%s 作业区:%s 主配件:%s %s'%(scx,site,code,excelname),style0)\n ws.write_merge(0,1,rownum+length+1,rownum+length+3,u'考核期：%s——%s'%(gsdate(excelname.split('-')[1]),gsdate(excelname.split('-')[2])),style0)\n ws.write_merge(2, 3, rownum+length, rownum+length+1, u'合计', style1)\n ws.write_merge(2, 5, rownum+length+2, rownum+length+3, u'累计达成率', style1)\n ws.write_merge(4, 5, rownum+length, rownum+length, u'计划', style1)\n ws.write_merge(4, 5, rownum+length+1, rownum+length+1, u'实际', style1)\n ws.write_merge(6, 7, rownum+length, rownum+length, datadict['bzxiangjh'], style1)\n ws.write_merge(6, 7, rownum+length+1, rownum+length+1, datadict['bzxiangsj'], style1)\n ws.write_merge(6, 7, rownum+length+2, rownum+length+3, datadict['xiangdc'], style1)\n ws.write_merge(8, 9, rownum+length, rownum+length, datadict['bzjianjh'], style1)\n ws.write_merge(8, 9, rownum+length+1, rownum+length+1, datadict['bzjiansj'], style1)\n ws.write_merge(8, 9, rownum+length+2, rownum+length+3, datadict['jiandc'], style1)\n\n ws.col(rownum+length).width = 256 * 12\n ws.col(rownum+length+1).width = 256 * 12\n ws.col(rownum+length+2).width = 256 * 12*2\n ws.col(rownum+length+3).width = 256 * 12*2\n\n ws.write_merge(10, 21, rownum+length, rownum+length+3, '', style1)\n ws.insert_bitmap(img1bmp, 10, rownum+length, 21, rownum+length+3, scale_x=1, scale_y=1)\n ws.write_merge(22, 30, rownum+length, rownum+length+3, '', style1)\n ws.insert_bitmap(img2bmp, 22, rownum+length, 30, rownum+length+3, scale_x=1, scale_y=1)\n\n wb.save(response)\n return response\n\n@login_required\n@csrf_exempt\ndef getExcelByData(request):\n data = request.read()\n if data:\n try:\n data = json.loads(data)\n except :\n s = open('d:/ztweb/zt/static/logging/%s_%s.txt'%(datetime.datetime.now().strftime('%Y%m%d'),str(uuid.uuid4())),'w')\n s.write(data)\n s.close()\n return HttpResponse(u'没有数据')\n else:\n raise Http404()\n\n response = HttpResponse(mimetype=u'application/ms-excel')\n excelname = data.get('excelname','')\n sheetname = data.get('sheetname','')\n\n filename = u'%s.xls'%urllib.quote(excelname.encode('utf-8'))\n if hasattr(request,'META') and request.META.has_key('HTTP_USER_AGENT'):\n if request.META['HTTP_USER_AGENT'].find(\"Firefox\")!=-1:\n filename = u'%s.xls'%excelname\n\n response['Content-Disposition'] = ('attachment;filename=%s' % filename).encode('utf-8')\n\n\n style1=xlwt.XFStyle()\n font1=Font()\n font1.height=220\n style1.font=font1\n algn=Alignment()\n algn.horz=Alignment.HORZ_RIGHT\n style1.alignment=algn\n style0=xlwt.XFStyle()\n algn0=Alignment()\n algn0.horz=Alignment.HORZ_CENTER\n font=Font()\n font.height=280\n font.bold=True\n style0.alignment=algn0\n style0.font=font\n\n wb = xlwt.Workbook()\n ws = wb.add_sheet(u\"%s\"%sheetname, cell_overwrite_ok=True)\n rownum = 0\n for i,index in enumerate(data.get('index',[])):\n ws.write_merge(rownum, rownum, i, i, data.get('head',{}).get(index,\"\"), style0)\n ws.col(i).width = 256 * 17\n rownum+=1\n for row,d in enumerate(data.get('data',[])):\n for i,index in enumerate(data.get('index',[])):\n ws.write_merge(rownum+row, rownum+row, i, i, d.get(index,\"\"), style1)\n rownum+=1\n wb.save(response)\n return response\n\n@login_required\n@csrf_exempt\ndef getExcelByGroupData(request):\n data = request.read()\n if data:\n try:\n data = json.loads(data)\n except :\n s = open('d:/ztweb/zt/static/logging/%s_%s.txt'%(datetime.datetime.now().strftime('%Y%m%d'),str(uuid.uuid4())),'w')\n s.write(data)\n s.close()\n return HttpResponse(u'没有数据')\n else:\n raise Http404()\n\n response = HttpResponse(mimetype=u'application/ms-excel')\n excelname = data.get('excelname','')\n sheetname = data.get('sheetname','')\n\n filename = u'%s.xls'%urllib.quote(excelname.encode('utf-8'))\n if hasattr(request,'META') and request.META.has_key('HTTP_USER_AGENT'):\n if request.META['HTTP_USER_AGENT'].find(\"Firefox\")!=-1:\n filename = u'%s.xls'%excelname\n\n response['Content-Disposition'] = ('attachment;filename=%s' % filename).encode('utf-8')\n\n\n style1=xlwt.XFStyle()\n font1=Font()\n font1.height=220\n style1.font=font1\n algn=Alignment()\n algn.horz=Alignment.HORZ_RIGHT\n style1.alignment=algn\n style0=xlwt.XFStyle()\n algn0=Alignment()\n algn0.horz=Alignment.HORZ_CENTER\n font=Font()\n font.height=280\n font.bold=True\n style0.alignment=algn0\n style0.font=font\n\n wb = xlwt.Workbook()\n ws = wb.add_sheet(u\"%s\"%sheetname, cell_overwrite_ok=True)\n rownum = 0\n for i,head in enumerate(data.get('head',[])):\n ws.write_merge(rownum+head.get('top',0), rownum+head.get('height',0)+head.get('top',0), head.get('left',0), head.get('left',0)+head.get('width',0), head.get('text',''), style0)\n ws.col(i).width = 256 * 17\n rownum+=3\n for row,d in enumerate(data.get('data',[])):\n for i,index in enumerate(data.get('index',[])):\n ws.write_merge(rownum+row, rownum+row, i, i, d.get(index,\"\"), style1)\n rownum+=1\n wb.save(response)\n return response\n\n", "sub_path": "ztmanage/dataPrint.py", "file_name": "dataPrint.py", "file_ext": "py", "file_size_in_byte": 10993, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "json.loads", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 30, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 33, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 35, "usage_type": "call"}, {"api_name": "zt.settings.MEDIA_ROOT", "line_number": 36, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 36, "usage_type": "call"}, {"api_name": "zt.settings.MEDIA_ROOT", "line_number": 37, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 37, "usage_type": "call"}, {"api_name": "base64.decodestring", "line_number": 47, "usage_type": "call"}, {"api_name": "base64.decodestring", "line_number": 53, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 55, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 55, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 56, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 56, "usage_type": "name"}, {"api_name": "zt.settings.MEDIA_ROOT", "line_number": 59, "usage_type": "name"}, {"api_name": "os.system", "line_number": 60, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 62, "usage_type": "call"}, {"api_name": "urllib.quote", "line_number": 69, "usage_type": "call"}, {"api_name": "zt.xlwt.XFStyle", "line_number": 77, "usage_type": "call"}, {"api_name": "zt.xlwt", "line_number": 77, "usage_type": "name"}, {"api_name": "zt.xlwt.Font", "line_number": 78, "usage_type": "call"}, {"api_name": "zt.xlwt.Alignment", "line_number": 81, "usage_type": "call"}, {"api_name": "zt.xlwt.Alignment.HORZ_CENTER", "line_number": 82, "usage_type": "attribute"}, {"api_name": "zt.xlwt.Alignment", "line_number": 82, "usage_type": "name"}, {"api_name": "zt.xlwt.Alignment.VERT_CENTER", "line_number": 83, "usage_type": "attribute"}, {"api_name": "zt.xlwt.Alignment", "line_number": 83, "usage_type": "name"}, {"api_name": "zt.xlwt.XFStyle", "line_number": 85, "usage_type": "call"}, {"api_name": "zt.xlwt", "line_number": 85, "usage_type": "name"}, {"api_name": "zt.xlwt.Alignment", "line_number": 86, "usage_type": "call"}, {"api_name": "zt.xlwt.Alignment.HORZ_CENTER", "line_number": 87, "usage_type": "attribute"}, {"api_name": "zt.xlwt.Alignment", "line_number": 87, "usage_type": "name"}, {"api_name": "zt.xlwt.Alignment.VERT_CENTER", "line_number": 88, "usage_type": "attribute"}, {"api_name": "zt.xlwt.Alignment", "line_number": 88, "usage_type": "name"}, {"api_name": "zt.xlwt.Font", "line_number": 89, "usage_type": "call"}, {"api_name": "zt.xlwt.Workbook", "line_number": 95, "usage_type": "call"}, {"api_name": "zt.xlwt", "line_number": 95, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 117, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 21, "usage_type": "name"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 22, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 179, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 181, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 181, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 181, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 184, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 186, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 188, "usage_type": "call"}, {"api_name": "urllib.quote", "line_number": 192, "usage_type": "call"}, {"api_name": "zt.xlwt.XFStyle", "line_number": 200, "usage_type": "call"}, {"api_name": "zt.xlwt", "line_number": 200, "usage_type": "name"}, {"api_name": "zt.xlwt.Font", "line_number": 201, "usage_type": "call"}, {"api_name": "zt.xlwt.Alignment", "line_number": 204, "usage_type": "call"}, {"api_name": "zt.xlwt.Alignment.HORZ_RIGHT", "line_number": 205, "usage_type": "attribute"}, {"api_name": "zt.xlwt.Alignment", "line_number": 205, "usage_type": "name"}, {"api_name": "zt.xlwt.XFStyle", "line_number": 207, "usage_type": "call"}, {"api_name": "zt.xlwt", "line_number": 207, "usage_type": "name"}, {"api_name": "zt.xlwt.Alignment", "line_number": 208, "usage_type": "call"}, {"api_name": "zt.xlwt.Alignment.HORZ_CENTER", "line_number": 209, "usage_type": "attribute"}, {"api_name": "zt.xlwt.Alignment", "line_number": 209, "usage_type": "name"}, {"api_name": "zt.xlwt.Font", "line_number": 210, "usage_type": "call"}, {"api_name": "zt.xlwt.Workbook", "line_number": 216, "usage_type": "call"}, {"api_name": "zt.xlwt", "line_number": 216, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 173, "usage_type": "name"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 174, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 236, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 238, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 238, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 238, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 241, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 243, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 245, "usage_type": "call"}, {"api_name": "urllib.quote", "line_number": 249, "usage_type": "call"}, {"api_name": "zt.xlwt.XFStyle", "line_number": 257, "usage_type": "call"}, {"api_name": "zt.xlwt", "line_number": 257, "usage_type": "name"}, {"api_name": "zt.xlwt.Font", "line_number": 258, "usage_type": "call"}, {"api_name": "zt.xlwt.Alignment", "line_number": 261, "usage_type": "call"}, {"api_name": "zt.xlwt.Alignment.HORZ_RIGHT", "line_number": 262, "usage_type": "attribute"}, {"api_name": "zt.xlwt.Alignment", "line_number": 262, "usage_type": "name"}, {"api_name": "zt.xlwt.XFStyle", "line_number": 264, "usage_type": "call"}, {"api_name": "zt.xlwt", "line_number": 264, "usage_type": "name"}, {"api_name": "zt.xlwt.Alignment", "line_number": 265, "usage_type": "call"}, {"api_name": "zt.xlwt.Alignment.HORZ_CENTER", "line_number": 266, "usage_type": "attribute"}, {"api_name": "zt.xlwt.Alignment", "line_number": 266, "usage_type": "name"}, {"api_name": "zt.xlwt.Font", "line_number": 267, "usage_type": "call"}, {"api_name": "zt.xlwt.Workbook", "line_number": 273, "usage_type": "call"}, {"api_name": "zt.xlwt", "line_number": 273, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 230, "usage_type": "name"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 231, "usage_type": "name"}]} +{"seq_id": "134052147", "text": "# -*- coding: utf-8 -*-\n\nimport re\n\nfrom crc import CRC8\n\n\nFEND = '\\xc0' # Frame End\nFESCP = '\\xdb' # Frame Escape\nTFEND = '\\xdc' # Transposed Frame End\nTFESC = '\\xdd' # Transposed Frame Escape\n\nFEND_ESCAPE = FESCP + TFEND\nFESCP_ESCAPE = FESCP + TFESC\n\nADDR_RANGE = '\\x80-\\xff' # 0b1*******\nCMD_RANGE = '\\x00-\\x7f' # 0b0*******\nN_RANGE = DATA_RANGE = CRC_RANGE = '\\x00-\\xff'\n\nADDR_BROADCAST = '\\x80'\n\n\nclass WakeError(Exception):\n pass\n\n\nclass WakePacket(object):\n \"\"\"Work with WAKE packet\"\"\"\n\n __slots__ = '__FEND', '__ADDR', '__CMD', '__N', '__DATA', '__CRC',\n\n __FEND_RE = r'(?P{:s})'.format(FEND)\n __ADDR_RE = r'(?P[{:s}]?)'.format(ADDR_RANGE)\n __CMD_RE = r'(?P[{:s}])'.format(CMD_RANGE)\n __N_RE = r'(?P[{:s}])'.format(N_RANGE)\n __DATA_RE = r'(?P[%s]{0,255})' % DATA_RANGE\n __CRC_RE = r'(?P[{:s}])'.format(CRC_RANGE)\n\n __WAKE_PACKET_TEMPLATE = re.compile(__FEND_RE + __ADDR_RE + __CMD_RE + __N_RE + __DATA_RE + __CRC_RE)\n __ADDR_RE_COMPILE = re.compile(__ADDR_RE)\n __CMD_RE_COMPILE = re.compile(__CMD_RE)\n __DATA_RE_COMPILE = re.compile(__DATA_RE)\n\n def __init__(self, wake_packet=None):\n self.__FEND = None\n self.__ADDR = None\n self.__CMD = None\n self.__N = None\n self.__DATA = None\n self.__CRC = None\n if wake_packet:\n self.loads(wake_packet)\n\n FEND = property(lambda self: self.__FEND)\n\n # --- Descriptors ADDR ---\n\n def __addr_set(self, value):\n value = self.unescape(value)\n if self.valid_addr(value):\n self.__ADDR = value\n\n def __addr_del(self):\n self.__ADDR = ADDR_BROADCAST\n\n ADDR = property(lambda self: self.__ADDR, __addr_set, __addr_del)\n\n # --- Descriptors CMD ---\n\n def __cmd_set(self, value):\n \"\"\"\"\"\"\n value = self.unescape(value)\n if self.valid_cmd(value):\n self.__CMD = value\n\n CMD = property(lambda self: self.__CMD, __cmd_set)\n\n # --- Descriptors DATA ---\n\n def __data_set(self, value):\n \"\"\"\"\"\"\n value = self.unescape(value)\n if self.valid_data(value):\n self.__DATA = value\n self.__len_data()\n\n def __data_del(self):\n self.__DATA = ''\n self.__len_data()\n\n DATA = property(lambda self: self.__DATA, __data_set, __data_del)\n\n N = property(lambda self: self.__N)\n\n CRC = property(lambda self: self.__CRC)\n\n def __len_data(self):\n self.__N = len(self.__DATA)\n\n def valid_addr(self, addr):\n \"\"\"\n Address validation\n :type addr: str\n \"\"\"\n if not self.__ADDR_RE_COMPILE.match(addr):\n raise WakeError(\"ADDR is out of range {:s}\".format(ADDR_RANGE))\n return True\n\n def valid_cmd(self, cmd):\n \"\"\"\n Command validation\n :type cmd: str\n \"\"\"\n if not self.__CMD_RE_COMPILE.match(cmd):\n raise WakeError(\"CMD is out of range {:s}\".format(CMD_RANGE))\n return True\n\n def valid_data(self, data):\n \"\"\"\n Data validation\n :type data: str\n \"\"\"\n if not self.__DATA_RE_COMPILE.match(data):\n raise WakeError(\"DATA is out of range {:s} or the length of more than 255\".format(DATA_RANGE))\n return True\n\n def __check_wake_packet_integrity(self):\n \"\"\"Check the integrity of the WAKE package\"\"\"\n pass\n\n @staticmethod\n def escape(wake_packet):\n \"\"\"\n Byte stuffing\n :type wake_packet: str\n \"\"\"\n wake_packet_list = list(wake_packet)\n for i, char in enumerate(wake_packet_list[1:], start=1):\n if char == FEND:\n wake_packet_list[i] = FEND_ESCAPE\n elif char == FESCP:\n wake_packet_list[i] = FESCP_ESCAPE\n return ''.join(wake_packet_list)\n\n @staticmethod\n def unescape(wake_packet):\n \"\"\"\n Reverse operation of the byte stuffing\n :type wake_packet: str\n \"\"\"\n return wake_packet.replace(FEND_ESCAPE, FEND).replace(FESCP_ESCAPE, FESCP)\n\n def create(self, cmd, data='', addr=ADDR_BROADCAST):\n \"\"\"\n Create WAKE ``obj``\n :type cmd: str\n :type data: str\n :type addr: str\n \"\"\"\n # TODO исправить через дескрипторы\n addr = self.unescape(addr)\n cmd = self.unescape(cmd)\n data = self.unescape(data)\n if self.valid_addr(addr) and self.valid_cmd(cmd) and self.valid_data(data):\n self.__ADDR = addr\n self.__CMD = cmd\n self.__DATA = data\n\n def loads(self, wake_packet):\n \"\"\"\n Create WAKE ``obj`` from the string\n :type wake_packet: str\n \"\"\"\n wake_packet = self.unescape(wake_packet)\n (self.__FEND, self.__ADDR, self.__CMD,\n self.__N, self.__DATA, self.__CRC) = self.__WAKE_PACKET_TEMPLATE.match(wake_packet).groups()\n if not self.__ADDR:\n self.__ADDR = ADDR_BROADCAST\n\n def dumps(self):\n \"\"\"Serialize WAKE ``obj`` to a WAKE formatted ``str``\"\"\"\n addr = self.__ADDR if self.__ADDR != ADDR_BROADCAST else ''\n wake_packet = self.__FEND + addr + self.__CMD + self.__N + self.__DATA\n wake_packet += CRC8().crc(wake_packet)\n return self.escape(wake_packet)\n\n\ndef fromstring(wake_packet):\n \"\"\"\"\"\"\n return WakePacket(wake_packet)\n", "sub_path": "pywake/wake.py", "file_name": "wake.py", "file_ext": "py", "file_size_in_byte": 5391, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "re.compile", "line_number": 39, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 40, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 41, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 42, "usage_type": "call"}, {"api_name": "crc.CRC8", "line_number": 184, "usage_type": "call"}]} +{"seq_id": "362078329", "text": "import cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimg = cv2.imread('1.jpeg', 0)\n#kernel = np.array([0, -1, 0, -1, 5, -1, 0, -1, 0]).reshape((3, 3))\n#kernel = np.array([0, 1, 0, 1, -5, 1, 0, 1, 0]).reshape((3, 3))\n#kernel = np.array([1, 1, 1, 1, -8, 1, 1, 1, 1]).reshape((3, 3))\nkernel = np.array([-1, -1, -1, -1, 9, -1, -1, -1, -1]).reshape((3, 3))\nfilter_img = cv2.filter2D(img, -1, kernel)\n\nplt.subplot(1, 2, 1)\nplt.imshow(img, 'gray')\nplt.subplot(1, 2, 2)\nplt.imshow(filter_img, 'gray')\nplt.show()\nprint('end')", "sub_path": "learn-opencv/feature_test/06_lap_filter.py", "file_name": "06_lap_filter.py", "file_ext": "py", "file_size_in_byte": 526, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "cv2.imread", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.filter2D", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "471383963", "text": "from django.test import TestCase, Client\n\n# Create your tests here.\nclass TestUrls(TestCase):\n\n def test_default_route_return_http200(self):\n c = Client()\n response = c.post(f\"/\")\n self.assertEqual(response.status_code, 200)\n\n def test_default_route_render_index(self):\n c = Client()\n response = c.post(f\"/\")\n self.assertTemplateUsed(response, 'policon/index.html')\n\nif __name__ == \"__main__\":\n unittest.main()\n", "sub_path": "policon/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 462, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.test.TestCase", "line_number": 4, "usage_type": "name"}, {"api_name": "django.test.Client", "line_number": 7, "usage_type": "call"}, {"api_name": "django.test.Client", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "326403836", "text": "import PIL.Image as Image\r\n\r\n\r\ndef toSVG(infile, outfile):\r\n image = Image.open(infile).convert('RGBA')\r\n data = image.load()\r\n width, height = image.size\r\n out = open(outfile, \"w\")\r\n out.write('\\n')\r\n out.write('\\n' % \\\r\n {'x': width, 'y': height})\r\n\r\n for y in range(height):\r\n for x in range(width):\r\n rgba = data[x, y]\r\n rgb = '#%02x%02x%02x' % rgba[:3]\r\n if rgba[3] > 0:\r\n out.write('\\n' % (x, y, rgb, rgba[3] / 255.0))\r\n out.write('\\n')\r\n out.close()\r\n\r\n\r\ntoSVG(r'E:\\work\\segmentation\\y.png',\r\n r'E:\\work\\segmentation\\y1.svg')\r\n", "sub_path": "test7 - 副本.py", "file_name": "test7 - 副本.py", "file_ext": "py", "file_size_in_byte": 928, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "PIL.Image.open", "line_number": 5, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 5, "usage_type": "name"}]} +{"seq_id": "29277352", "text": "import cv2\n\n# face classifier\nface_classifier = cv2.CascadeClassifier('/home/sadabrata/transfer_learning_opencv/files/haarcascade_frontalface_default.xml')\n\n# detect face and return the cropped face\ndef face_extractor(img):\n faces = face_classifier.detectMultiScale(img, 1.3, 5)\n if faces is ():\n return None\n # crop all faces found\n for (x,y,w,h) in faces:\n # x=x-10\n # y=y-10\n cropped_face = img[y-10:y+h+50, x-10:x+w+50]\n return cropped_face\n\n# initialize Webcam\ncap = cv2.VideoCapture(0)\ncount = 0\n\n# collecting 200 samples of face from webcam\nwhile True:\n ret, frame = cap.read()\n temp = face_extractor(frame)\n if temp is not None:\n count += 1\n face = cv2.resize(temp, (400, 400))\n # saving file in specified directory with unique name\n file_name_path = '/home/sadabrata/transfer_learning_opencv/dataset/train/sadabrata/' + str(count) + '.jpg'\n cv2.imwrite(file_name_path, face)\n # displaying live count on images\n cv2.putText(face, str(count), (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)\n cv2.imshow('Face Cropper', face)\n else:\n print(\"Face not found\")\n pass\n if cv2.waitKey(1) == 13 or count == 200: #13 is the Enter Key\n break\n \ncap.release()\ncv2.destroyAllWindows() \nprint(\"Collecting Samples Complete\")\n", "sub_path": "Face Classification using pretrained networks and OpenCV/create_dataset_webcam.py", "file_name": "create_dataset_webcam.py", "file_ext": "py", "file_size_in_byte": 1370, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "cv2.CascadeClassifier", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_COMPLEX", "line_number": 33, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "135839844", "text": "###############################################################################\n#\n# Copyright 2012 Pants Developers (see AUTHORS.txt)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n###############################################################################\n\nimport socket\nimport unittest\n\nimport pants\n\nfrom pants.test._pants_util import *\n\nclass Echo(pants.Stream):\n def on_read(self, data):\n self.write(data)\n\nclass TestEcho(PantsTestCase):\n def setUp(self):\n self.server = pants.Server(ConnectionClass=Echo).listen(('127.0.0.1', 4040))\n PantsTestCase.setUp(self)\n\n def test_echo_with_one_client(self):\n sock = socket.socket()\n sock.settimeout(1.0)\n sock.connect(('127.0.0.1', 4040))\n request = repr(sock)\n sock.send(request)\n response = sock.recv(1024)\n self.assertEqual(response, request)\n sock.close()\n\n def test_echo_with_two_sequential_clients(self):\n sock1 = socket.socket()\n sock1.settimeout(1.0)\n sock1.connect(('127.0.0.1', 4040))\n request1 = repr(sock1)\n sock1.send(request1)\n response1 = sock1.recv(1024)\n self.assertEqual(response1, request1)\n sock1.close()\n\n sock2 = socket.socket()\n sock2.settimeout(1.0)\n sock2.connect(('127.0.0.1', 4040))\n request2 = repr(sock2)\n sock2.send(request2)\n response2 = sock2.recv(1024)\n self.assertEqual(response2, request2)\n sock2.close()\n\n def test_echo_with_two_concurrent_clients(self):\n sock1 = socket.socket()\n sock1.settimeout(1.0)\n sock2 = socket.socket()\n sock2.settimeout(1.0)\n sock1.connect(('127.0.0.1', 4040))\n sock2.connect(('127.0.0.1', 4040))\n request1 = repr(sock1)\n request2 = repr(sock2)\n sock1.send(request1)\n sock2.send(request2)\n response1 = sock1.recv(1024)\n response2 = sock2.recv(1024)\n self.assertEqual(response1, request1)\n self.assertEqual(response2, request2)\n sock1.close()\n sock2.close()\n\n def tearDown(self):\n PantsTestCase.tearDown(self)\n self.server.close()\n", "sub_path": "pants/test/core/test_echo.py", "file_name": "test_echo.py", "file_ext": "py", "file_size_in_byte": 2677, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pants.Stream", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pants.Server", "line_number": 32, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 36, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 46, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 55, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 65, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "242652670", "text": "from django.contrib import messages\nfrom django.shortcuts import render, redirect\n\nfrom result import Result, ResultFlag\nfrom sounds import data\n\n\ndef sounds(request):\n page_data = data.sounds(request)\n if page_data.isOk():\n return render(request, 'sounds/sound/sounds.html', page_data.get())\n\n\ndef sound(request, sound_code):\n page_data = data.sound(request, sound_code)\n for r in page_data.get()['requests']:\n print(r.serialize())\n if page_data.isOk():\n return render(request, 'sounds/sound/sound.html', page_data.get())\n\n\ndef sound_search(request):\n query = data.sound_search(request)\n if query.isOk():\n request.session['search'] = query.get()\n return redirect('sounds')\n\n\ndef sound_rate(request, sound_code):\n page_data = data.sound_rate(request, sound_code)\n if page_data.hasErrors():\n for err in page_data.getErrors():\n messages.error(request, err)\n return redirect(reverse('sounds')) # We don't want to use bookmarking, because want the user to see the error\n # messages at the top of the page.\n\n # Not using the name here because we want to use the bookmarking capabilities of the URL\n return redirect('/sounds/#' + sound_code)\n", "sub_path": "sounds/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1277, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sounds.data.sounds", "line_number": 9, "usage_type": "call"}, {"api_name": "sounds.data", "line_number": 9, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 11, "usage_type": "call"}, {"api_name": "sounds.data.sound", "line_number": 15, "usage_type": "call"}, {"api_name": "sounds.data", "line_number": 15, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 19, "usage_type": "call"}, {"api_name": "sounds.data.sound_search", "line_number": 23, "usage_type": "call"}, {"api_name": "sounds.data", "line_number": 23, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 26, "usage_type": "call"}, {"api_name": "sounds.data.sound_rate", "line_number": 30, "usage_type": "call"}, {"api_name": "sounds.data", "line_number": 30, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 33, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 33, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 34, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "372949525", "text": "import numpy as np\nimport pywt\nfrom scipy.fftpack import dct\n\nimport data\nfrom utils.feature_utils import *\n\nNFFT = 512\n\npath_whole_data = 'data/validator.csv'\n\ndef get_frames(sample_rate, signal):\n\talpha = 0.97\n\temphasized_signal = np.append(signal[0], signal[1:] - alpha * signal[:-1])\n\n\tframe_size = 0.025\n\tframe_stride = 0.01\n\n\tframe_length, frame_step = frame_size * sample_rate, frame_stride * sample_rate # Convert from seconds to samples\n\tsignal_length = len(emphasized_signal)\n\tframe_length = int(round(frame_length))\n\tframe_step = int(round(frame_step))\n\tnum_frames = int(np.ceil(float(np.abs(signal_length - frame_length)) / frame_step)) # Make sure that we have at least 1 frame\n\n\tpad_signal_length = num_frames * frame_step + frame_length\n\tz = np.zeros((pad_signal_length - signal_length))\n\tpad_signal = np.append(emphasized_signal, z) # Pad Signal to make sure that all frames have equal number of samples without truncating any samples from the original signal\n\n\tindices = np.tile(np.arange(0, frame_length), (num_frames, 1)) + np.tile(np.arange(0, num_frames * frame_step, frame_step), (frame_length, 1)).T\n\tframes = pad_signal[indices.astype(np.int32, copy=False)]\n\tframes *= np.hamming(frame_length)\n\n\treturn frames\n\ndef power_spectrum_fft(sample_rate, signal): #gives fft power spectrum\n\tframes = get_frames(sample_rate, signal)\n\tmag_frames = np.absolute(np.fft.rfft(frames, NFFT)) # Magnitude of the FFT\n\tpow_frames = ((1.0 / NFFT) * ((mag_frames) ** 2)) #power spectrum\n\treturn pow_frames\n\ndef power_spectrum_wavelet(sample_rate, signal): #gives wavelet power spectrum (I hope!)\n\tframes = get_frames(sample_rate, signal)\n\t(x, y) = pywt.dwt(frames, 'haar')\n\t# return np.append(x, y)\n\treturn x\n\ndef get_spectrums(sample_rate, signal): #give both spectrums\n\tprint(sample_rate, signal.ndim, signal.shape)\n\treturn (power_spectrum_fft(sample_rate, signal), power_spectrum_wavelet(sample_rate, signal))\n\nMAX_FFT_SIZE = 3000\nMAX_WAVELET_SIZE = 3000\n\ndef get_sizes():\n\tprint('Combining wavs with csvs...')\n\n\t_, df = data.combine_all_wavs_and_trans_from_csvs(path_whole_data)\n\n\tprint('Done')\n\n\tprint('Loading audio...')\n\n\tindices = [ i for i in range(len(df)) ]\n\tsignals, transcript, sr = load_audio(df, indices)\n\n\tprint('Done')\n\n\tassert(len(signals) == len(transcript))\n\n\tprint('Proceed to get maximum sizes')\n\n\tmax_len_fft = 0\n\tmax_len_wavelet = 0\n\n\tfor i in range(len(signals)):\n\t\tprint('Calculating spectrums of', i, 'audio file named', df.iloc[i]['filename'])\n\n\t\tfft, wavelet = get_spectrums(sr, signals[i])\n\n\t\tmax_len_fft = max(max_len_fft, len(fft))\n\t\tmax_len_wavelet = max(max_len_wavelet, len(wavelet))\n\n\tprint('Done')\n\n\treturn (max_len_fft, max_len_wavelet)\n\nif __name__ == '__main__':\n\tx, y = get_sizes()\n\tprint('fft max size', x, 'wavelet max size', y)", "sub_path": "get_features.py", "file_name": "get_features.py", "file_ext": "py", "file_size_in_byte": 2778, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.append", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.hamming", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.fft.rfft", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pywt.dwt", "line_number": 43, "usage_type": "call"}, {"api_name": "data.combine_all_wavs_and_trans_from_csvs", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "453903067", "text": "MINUTE = 60\nHOUR = 60 * MINUTE\nDAY = 24 * HOUR\nWEEK = 7 * DAY\n\ntry:\n from tornado.options import options\n DEFAULT_TIMEOUT = options.CACHE_DEFAULT_TIMEOUT\nexcept Exception:\n DEFAULT_TIMEOUT = 2 * DAY # Domyslny czas na jaki keszujemy\n\n \nMEMCACHE_NONE = '⪔Noneद⪣' # Special value to have a distinction between\n # real None and miss", "sub_path": "kalmoize/constants.py", "file_name": "constants.py", "file_ext": "py", "file_size_in_byte": 415, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "tornado.options.options.CACHE_DEFAULT_TIMEOUT", "line_number": 8, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 8, "usage_type": "name"}]} +{"seq_id": "4927436", "text": "from django.shortcuts import render, redirect, get_object_or_404\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponseRedirect\nfrom django.http import HttpResponseNotFound\n\nfrom .models import Topic, Entry\nfrom .forms import TopicForm, EntryForm, EditForm\nfrom django.http import Http404\n\n# Здесь представлены представления\n\n\ndef index(request):\n \"\"\"Домашняя страница приложения learning_log\"\"\"\n return render(request, 'learning_logs/index.html')\n\n\ndef check_topic_owner(topic, request):\n \"\"\"Проверка того, что тема принадлежит текущему пользователю\"\"\"\n if topic.owner != request.user:\n raise Http404\n\n\ndef check_entry_owner(entry, request):\n \"\"\"Проверка того, что тема принадлежит текущему пользователю\"\"\"\n if entry.owner != request.user:\n raise Http404\n\n\n@login_required\ndef topics(request):\n \"\"\"выводит список тем\"\"\"\n topics = Topic.objects.filter(owner=request.user).order_by('date_added')\n context = {'topics': topics}\n return render(request, 'learning_logs/topics.html', context)\n\n\n@login_required\ndef topic(request, topic_id):\n \"\"\"выводит одну тему и все ее записи\"\"\"\n topic = get_object_or_404(Topic, id=topic_id)\n # Проверка того, что тема принадлежит текущему пользователю\n # if topic.owner != request.user:\n # raise Http404\n check_topic_owner(topic, request)\n entries = topic.entry_set.order_by('-date_added')\n context = {'topic': topic, 'entries': entries}\n return render(request, 'learning_logs/topic.html', context)\n\n\n@login_required\ndef new_topic(request):\n \"\"\"определяет новую тему\"\"\"\n if request.method != 'POST':\n #данные не отправлялись; создается пустая форма.\n form = TopicForm()\n else:\n #отправлены данные POST; обработать данные.\n form = TopicForm(data=request.POST)\n if form.is_valid():\n new_topic = form.save(commit=False)\n new_topic.owner = request.user\n new_topic.save()\n return redirect('learning_logs:topics')\n #вывести пустую или недействительную форму\n context = {'form': form}\n return render(request, 'learning_logs/new_topic.html', context)\n\n\n@login_required\ndef new_entry(request, topic_id):\n \"\"\"добавляет новую запись по конкретной теме\"\"\"\n topic = get_object_or_404(Topic, id=topic_id)\n check_topic_owner(topic, request)\n if request.method != 'POST':\n #данные не отправлялиссь, создается пустая форма\n form = EntryForm()\n else:\n #данные отправлены, обработать данные\n form = EntryForm(data=request.POST)\n if form.is_valid():\n new_entry = form.save(commit=False)\n new_entry.topic = topic\n new_entry.owner = request.user\n new_entry.save()\n return redirect('learning_logs:topic',topic_id=topic_id)\n\n #вывести пустую или недействительную форму\n context = {'topic': topic, 'form': form}\n return render(request, 'learning_logs/new_entry.html', context)\n\n\n@login_required\ndef edit_entry(request, entry_id):\n \"\"\"редактирует существующую запись\"\"\"\n entry = get_object_or_404(Entry, id=entry_id)\n topic = entry.topic\n # Проверка того, что тема принадлежит текущему пользователю\n check_topic_owner(topic, request)\n # check_entry_owner(entry, request)\n if request.method != 'POST':\n #Исходный запрос; форма заполняется данными текущей запсиси.\n form = EditForm(instance=entry)\n else:\n #отправка данных POST; обработать данные.\n form = EditForm(instance=entry, data=request.POST)\n if form.is_valid():\n form.save()\n return redirect('learning_logs:topic', topic_id=topic.id)\n\n context = {'entry': entry, 'topic': topic, 'form': form}\n return render(request, 'learning_logs/edit_entry.html', context)\n\n\ndef delete_topic(request, topic_id):\n \"\"\"удаляет тему\"\"\"\n try:\n topic = Topic.objects.get(id=topic_id)\n context = {'topic': topic}\n check_topic_owner(topic, request)\n topic.delete()\n return HttpResponseRedirect(\"/topics\")\n\n except Topic.DoesNotExist:\n return HttpResponseNotFound(\"

Тема не найдена

\")\n\n\ndef delete_entry(request, entry_id):\n \"\"\"удаляет запись\"\"\"\n try:\n entry = Entry.objects.get(id=entry_id)\n topic = entry.topic\n context = {'entry': entry}\n check_topic_owner(topic, request)\n entry.delete()\n return redirect('learning_logs:topic', topic_id=topic.id)\n\n except Topic.DoesNotExist:\n return HttpResponseNotFound(\"

Запись не найдена

\")\n", "sub_path": "learning_logs/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5343, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 21, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 27, "usage_type": "name"}, {"api_name": "models.Topic.objects.filter", "line_number": 33, "usage_type": "call"}, {"api_name": "models.Topic.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "models.Topic", "line_number": 33, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 35, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 30, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 41, "usage_type": "call"}, {"api_name": "models.Topic", "line_number": 41, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 48, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 38, "usage_type": "name"}, {"api_name": "forms.TopicForm", "line_number": 56, "usage_type": "call"}, {"api_name": "forms.TopicForm", "line_number": 59, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 64, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 67, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 51, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 73, "usage_type": "call"}, {"api_name": "models.Topic", "line_number": 73, "usage_type": "argument"}, {"api_name": "forms.EntryForm", "line_number": 77, "usage_type": "call"}, {"api_name": "forms.EntryForm", "line_number": 80, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 86, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 90, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 70, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 96, "usage_type": "call"}, {"api_name": "models.Entry", "line_number": 96, "usage_type": "argument"}, {"api_name": "forms.EditForm", "line_number": 103, "usage_type": "call"}, {"api_name": "forms.EditForm", "line_number": 106, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 109, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 112, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 93, "usage_type": "name"}, {"api_name": "models.Topic.objects.get", "line_number": 118, "usage_type": "call"}, {"api_name": "models.Topic.objects", "line_number": 118, "usage_type": "attribute"}, {"api_name": "models.Topic", "line_number": 118, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 122, "usage_type": "call"}, {"api_name": "models.Topic.DoesNotExist", "line_number": 124, "usage_type": "attribute"}, {"api_name": "models.Topic", "line_number": 124, "usage_type": "name"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 125, "usage_type": "call"}, {"api_name": "models.Entry.objects.get", "line_number": 131, "usage_type": "call"}, {"api_name": "models.Entry.objects", "line_number": 131, "usage_type": "attribute"}, {"api_name": "models.Entry", "line_number": 131, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 136, "usage_type": "call"}, {"api_name": "models.Topic.DoesNotExist", "line_number": 138, "usage_type": "attribute"}, {"api_name": "models.Topic", "line_number": 138, "usage_type": "name"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 139, "usage_type": "call"}]} +{"seq_id": "50268031", "text": "''' makin' freinds inthe ap json format '''\nfrom uuid import uuid4\n\nfrom fedireads.settings import DOMAIN\n\n\ndef get_follow_request(user, to_follow):\n ''' a local user wants to follow someone '''\n uuid = uuid4()\n return {\n '@context': 'https://www.w3.org/ns/activitystreams',\n 'id': 'https://%s/%s' % (DOMAIN, str(uuid)),\n 'summary': '',\n 'type': 'Follow',\n 'actor': user.remote_id,\n 'object': to_follow.remote_id,\n }\n\ndef get_unfollow(relationship):\n ''' undo that precious bond of friendship '''\n return {\n '@context': 'https://www.w3.org/ns/activitystreams',\n 'id': '%s/undo' % relationship.remote_id,\n 'type': 'Undo',\n 'actor': relationship.user_subject.remote_id,\n 'object': {\n 'id': relationship.relationship_id,\n 'type': 'Follow',\n 'actor': relationship.user_subject.remote_id,\n 'object': relationship.user_object.remote_id,\n }\n }\n\n\ndef get_accept(user, relationship):\n ''' accept a follow request '''\n return {\n '@context': 'https://www.w3.org/ns/activitystreams',\n 'id': '%s#accepts/follows/' % user.remote_id,\n 'type': 'Accept',\n 'actor': user.remote_id,\n 'object': {\n 'id': relationship.relationship_id,\n 'type': 'Follow',\n 'actor': relationship.user_subject.remote_id,\n 'object': relationship.user_object.remote_id,\n }\n }\n\n\ndef get_reject(user, relationship):\n ''' reject a follow request '''\n return {\n '@context': 'https://www.w3.org/ns/activitystreams',\n 'id': '%s#rejects/follows/' % user.remote_id,\n 'type': 'Reject',\n 'actor': user.remote_id,\n 'object': {\n 'id': relationship.relationship_id,\n 'type': 'Follow',\n 'actor': relationship.user_subject.remote_id,\n 'object': relationship.user_object.remote_id,\n }\n }\n\n\ndef get_followers(user, page, follow_queryset):\n ''' list of people who follow a user '''\n id_slug = '%s/followers' % user.remote_id\n return get_follow_info(id_slug, page, follow_queryset)\n\n\ndef get_following(user, page, follow_queryset):\n ''' list of people who follow a user '''\n id_slug = '%s/following' % user.remote_id\n return get_follow_info(id_slug, page, follow_queryset)\n\n\ndef get_follow_info(id_slug, page, follow_queryset):\n ''' a list of followers or following '''\n if page:\n return get_follow_page(follow_queryset, id_slug, page)\n count = follow_queryset.count()\n return {\n '@context': 'https://www.w3.org/ns/activitystreams',\n 'id': id_slug,\n 'type': 'OrderedCollection',\n 'totalItems': count,\n 'first': '%s?page=1' % id_slug,\n }\n\n\ndef get_follow_page(user_list, id_slug, page):\n ''' format a list of followers/following '''\n page = int(page)\n page_length = 10\n start = (page - 1) * page_length\n end = start + page_length\n follower_page = user_list.all()[start:end]\n data = {\n '@context': 'https://www.w3.org/ns/activitystreams',\n 'id': '%s?page=%d' % (id_slug, page),\n 'type': 'OrderedCollectionPage',\n 'totalItems': user_list.count(),\n 'partOf': id_slug,\n 'orderedItems': [u.remote_id for u in follower_page],\n }\n if end <= user_list.count():\n # there are still more pages\n data['next'] = '%s?page=%d' % (id_slug, page + 1)\n if start > 0:\n data['prev'] = '%s?page=%d' % (id_slug, page - 1)\n return data\n", "sub_path": "fedireads/activitypub/follow.py", "file_name": "follow.py", "file_ext": "py", "file_size_in_byte": 3560, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "uuid.uuid4", "line_number": 9, "usage_type": "call"}, {"api_name": "fedireads.settings.DOMAIN", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "243908266", "text": "\"\"\"Model Based Value Expansion Algorithm.\"\"\"\nimport torch\n\nfrom rllib.dataset.datatypes import Loss\nfrom rllib.util.value_estimation import discount_cumsum, mc_return\nfrom rllib.value_function import NNEnsembleQFunction\n\nfrom .dyna import Dyna\n\n\nclass MVE(Dyna):\n \"\"\"Derived Algorithm using MVE to calculate targets.\n\n References\n ----------\n Feinberg, V., et. al. (2018).\n Model-based value estimation for efficient model-free reinforcement learning.\n arXiv.\n \"\"\"\n\n def __init__(self, td_k=False, lambda_=1.0, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.td_k = td_k\n self.lambda_ = lambda_\n\n def forward(self, observation):\n \"\"\"Rollout model and call base algorithm with transitions.\"\"\"\n self.base_algorithm.reset_info()\n loss = Loss()\n loss += self.base_algorithm.actor_loss(observation)\n loss += self.model_augmented_critic_loss(observation)\n loss += self.base_algorithm.regularization_loss(observation)\n return loss\n\n def model_augmented_critic_loss(self, observation):\n \"\"\"Get Model-Based critic-loss.\"\"\"\n with torch.no_grad():\n state, action = observation.state[..., 0, :], observation.action[..., 0, :]\n sim_observation = self.simulate(\n state, self.policy, initial_action=action, stack_obs=True\n )\n\n if not self.td_k:\n sim_observation.state = observation.state[..., :1, :]\n sim_observation.action = observation.action[..., :1, :]\n\n pred_q = self.base_algorithm.get_value_prediction(sim_observation)\n\n # Get target_q with semi-gradients.\n with torch.no_grad():\n target_q = self.get_value_target(sim_observation)\n if not self.td_k:\n target_q = target_q.reshape(self.num_samples, *pred_q.shape[:2]).mean(0)\n if pred_q.shape != target_q.shape: # Reshape in case of ensembles.\n assert isinstance(self.critic, NNEnsembleQFunction)\n target_q = target_q.unsqueeze(-1).repeat_interleave(\n self.critic.num_heads, -1\n )\n\n critic_loss = self.base_algorithm.criterion(pred_q, target_q)\n\n return Loss(critic_loss=critic_loss)\n\n def get_value_target(self, observation):\n \"\"\"Rollout model and call base algorithm with transitions.\"\"\"\n if self.td_k:\n final_state = observation.next_state[..., -1, :]\n done = observation.done[..., -1]\n final_value = self.base_algorithm.value_function(final_state)\n\n if final_value.ndim == observation.reward.ndim: # It is an ensemble.\n final_min = final_value.min(-1)[0]\n final_max = final_value.max(-1)[0]\n lambda_ = self.critic_ensemble_lambda\n final_value = lambda_ * final_min + (1.0 - lambda_) * final_max\n tau = self.base_algorithm.entropy_loss.eta.item()\n reward = observation.reward + tau * observation.entropy\n rewards = torch.cat(\n (reward, (final_value * (1 - done)).unsqueeze(-1)), dim=-1\n )\n sim_target = discount_cumsum(\n rewards,\n self.base_algorithm.gamma,\n self.base_algorithm.reward_transformer,\n )[..., :-1]\n else:\n sim_target = mc_return(\n observation,\n gamma=self.base_algorithm.gamma,\n lambda_=self.lambda_,\n value_function=self.base_algorithm.value_function,\n reward_transformer=self.base_algorithm.reward_transformer,\n entropy_regularization=self.base_algorithm.entropy_loss.eta.item(),\n reduction=\"min\",\n ).unsqueeze(-1)\n\n return sim_target\n", "sub_path": "rllib/algorithms/mve.py", "file_name": "mve.py", "file_ext": "py", "file_size_in_byte": 3833, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "dyna.Dyna", "line_number": 11, "usage_type": "name"}, {"api_name": "rllib.dataset.datatypes.Loss", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 50, "usage_type": "call"}, {"api_name": "rllib.value_function.NNEnsembleQFunction", "line_number": 55, "usage_type": "argument"}, {"api_name": "rllib.dataset.datatypes.Loss", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 78, "usage_type": "call"}, {"api_name": "rllib.util.value_estimation.discount_cumsum", "line_number": 81, "usage_type": "call"}, {"api_name": "rllib.util.value_estimation.mc_return", "line_number": 87, "usage_type": "call"}]} +{"seq_id": "139301908", "text": "from aiogram.types import Message\n\nimport zmanim_bot.keyboards.menus\nfrom ...misc import dp\nfrom ...tracking import track\nfrom ...texts.single import buttons, messages\nfrom ..forms import ConverterGregorianDateState, ConverterJewishDateState\nfrom ...utils import chat_action\n\n\n@dp.message_handler(commands=['converter'])\n@dp.message_handler(text=buttons.mm_converter)\n@chat_action('text')\n@track('Entry to converter')\nasync def handle_converter_entry(msg: Message):\n kb = zmanim_bot.keyboards.menus.get_converter_menu()\n await msg.reply(messages.init_converter, reply_markup=kb)\n\n\n@dp.message_handler(text=buttons.conv_greg_to_jew)\n@chat_action('text')\n@track('Converter - gregorian -> jewish')\nasync def start_greg_to_jew_converter(msg: Message):\n await ConverterGregorianDateState().waiting_for_gregorian_date.set()\n kb = zmanim_bot.keyboards.menus.get_cancel_keyboard()\n await msg.reply(messages.greg_date_request, reply_markup=kb)\n\n\n@dp.message_handler(text=buttons.conv_jew_to_greg)\n@chat_action('text')\n@track('Converter - jewish -> gregorian')\nasync def start_jew_to_greg_converter(msg: Message):\n await ConverterJewishDateState.waiting_for_jewish_date.set()\n kb = zmanim_bot.keyboards.menus.get_cancel_keyboard()\n await msg.reply(messages.jew_date_request, reply_markup=kb)\n", "sub_path": "zmanim_bot/handlers/text/converter.py", "file_name": "converter.py", "file_ext": "py", "file_size_in_byte": 1306, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "aiogram.types.Message", "line_number": 15, "usage_type": "name"}, {"api_name": "zmanim_bot.keyboards.menus.keyboards.menus.get_converter_menu", "line_number": 16, "usage_type": "call"}, {"api_name": "zmanim_bot.keyboards.menus.keyboards", "line_number": 16, "usage_type": "attribute"}, {"api_name": "zmanim_bot.keyboards.menus", "line_number": 16, "usage_type": "name"}, {"api_name": "texts.single.messages.init_converter", "line_number": 17, "usage_type": "attribute"}, {"api_name": "texts.single.messages", "line_number": 17, "usage_type": "name"}, {"api_name": "misc.dp.message_handler", "line_number": 11, "usage_type": "call"}, {"api_name": "misc.dp", "line_number": 11, "usage_type": "name"}, {"api_name": "misc.dp.message_handler", "line_number": 12, "usage_type": "call"}, {"api_name": "misc.dp", "line_number": 12, "usage_type": "name"}, {"api_name": "texts.single.buttons.mm_converter", "line_number": 12, "usage_type": "attribute"}, {"api_name": "texts.single.buttons", "line_number": 12, "usage_type": "name"}, {"api_name": "utils.chat_action", "line_number": 13, "usage_type": "call"}, {"api_name": "tracking.track", "line_number": 14, "usage_type": "call"}, {"api_name": "aiogram.types.Message", "line_number": 23, "usage_type": "name"}, {"api_name": "forms.ConverterGregorianDateState", "line_number": 24, "usage_type": "call"}, {"api_name": "zmanim_bot.keyboards.menus.keyboards.menus.get_cancel_keyboard", "line_number": 25, "usage_type": "call"}, {"api_name": "zmanim_bot.keyboards.menus.keyboards", "line_number": 25, "usage_type": "attribute"}, {"api_name": "zmanim_bot.keyboards.menus", "line_number": 25, "usage_type": "name"}, {"api_name": "texts.single.messages.greg_date_request", "line_number": 26, "usage_type": "attribute"}, {"api_name": "texts.single.messages", "line_number": 26, "usage_type": "name"}, {"api_name": "misc.dp.message_handler", "line_number": 20, "usage_type": "call"}, {"api_name": "misc.dp", "line_number": 20, "usage_type": "name"}, {"api_name": "texts.single.buttons.conv_greg_to_jew", "line_number": 20, "usage_type": "attribute"}, {"api_name": "texts.single.buttons", "line_number": 20, "usage_type": "name"}, {"api_name": "utils.chat_action", "line_number": 21, "usage_type": "call"}, {"api_name": "tracking.track", "line_number": 22, "usage_type": "call"}, {"api_name": "aiogram.types.Message", "line_number": 32, "usage_type": "name"}, {"api_name": "forms.ConverterJewishDateState.waiting_for_jewish_date.set", "line_number": 33, "usage_type": "call"}, {"api_name": "forms.ConverterJewishDateState.waiting_for_jewish_date", "line_number": 33, "usage_type": "attribute"}, {"api_name": "forms.ConverterJewishDateState", "line_number": 33, "usage_type": "name"}, {"api_name": "zmanim_bot.keyboards.menus.keyboards.menus.get_cancel_keyboard", "line_number": 34, "usage_type": "call"}, {"api_name": "zmanim_bot.keyboards.menus.keyboards", "line_number": 34, "usage_type": "attribute"}, {"api_name": "zmanim_bot.keyboards.menus", "line_number": 34, "usage_type": "name"}, {"api_name": "texts.single.messages.jew_date_request", "line_number": 35, "usage_type": "attribute"}, {"api_name": "texts.single.messages", "line_number": 35, "usage_type": "name"}, {"api_name": "misc.dp.message_handler", "line_number": 29, "usage_type": "call"}, {"api_name": "misc.dp", "line_number": 29, "usage_type": "name"}, {"api_name": "texts.single.buttons.conv_jew_to_greg", "line_number": 29, "usage_type": "attribute"}, {"api_name": "texts.single.buttons", "line_number": 29, "usage_type": "name"}, {"api_name": "utils.chat_action", "line_number": 30, "usage_type": "call"}, {"api_name": "tracking.track", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "466413754", "text": "import csv\nfrom collections import Counter\n\nfilename = 'shkib.csv'\nsrc_users = 1\noutput_bytes = 8\nmost_number_users = 5\n\nwith open(filename, 'r') as str:\n reader = csv.reader(str)\n users = {}\n for row in reader:\n if row[src_users] and row[output_bytes].isdigit():\n get_user_bytes = users.get(row[src_users])\n if not get_user_bytes:\n users[row[src_users]] = int(row[output_bytes])\n else:\n users[row[src_users]] = users.get(row[src_users]) + int(row[output_bytes])\n\n count_users = Counter(users).most_common(most_number_users)\n print(\"Пользователь : Количество данных\")\n print('-----------------------------------------------------')\n for user in count_users:\n print(\"{User}: {Count}\".format(User=user[0], Count=user[1]))\n", "sub_path": "sub2.py", "file_name": "sub2.py", "file_ext": "py", "file_size_in_byte": 865, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "csv.reader", "line_number": 10, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "326118280", "text": "from django import template\nfrom django.utils.html import mark_safe\nfrom markdown import Markdown\nfrom bs4 import BeautifulSoup\nimport re\n\nregister = template.Library()\n\nLEAD_LENGTH = 40\n\n@register.filter(name='blog_lead')\ndef blog_lead(value):\n value = re.sub(r'#{1,6}', '', value)\n value = re.sub(r'\\![\\w*](\\w*)', '', value)\n return value\n\n@register.filter(name='blog')\ndef blog(value):\n extensions = ['fenced_code', ]\n md = Markdown(extensions=extensions).convert(value)\n bs = BeautifulSoup(md, \"html.parser\")\n h1s = bs.find_all('h1')\n h2s = bs.find_all('h2')\n h3s = bs.find_all('h3')\n for h1 in h1s:\n h1[\"class\"] = \"blog-h1\"\n for h2 in h2s:\n h2[\"class\"] = \"blog-h2\"\n for h3 in h3s:\n h3[\"class\"] = \"blog-h3\"\n\n pres = bs.find_all('pre')\n for pre in pres:\n pre[\"class\"] = \"prettyprint linenums\"\n\n imgs = bs.find_all('img')\n for img in imgs:\n img[\"class\"] = \"img-fluid\"\n return mark_safe(bs.prettify())\n", "sub_path": "microblog/blog/templatetags/blog_tag.py", "file_name": "blog_tag.py", "file_ext": "py", "file_size_in_byte": 989, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.template.Library", "line_number": 7, "usage_type": "call"}, {"api_name": "django.template", "line_number": 7, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 13, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 14, "usage_type": "call"}, {"api_name": "markdown.Markdown", "line_number": 20, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 21, "usage_type": "call"}, {"api_name": "django.utils.html.mark_safe", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "110405729", "text": "# %%\r\nfrom nltk.corpus import wordnet as wn\r\n# %%\r\nwn.synset('dog.n.01')\r\n# %%\r\nprint(wn.synset('dog.n.01'))\r\n# %%\r\ndog = wn.synset('dog.n.01')\r\n# %%\r\ndog\r\n# %%\r\nimport nltk\r\n# %%\r\nnltk.download('wordnet')\r\n# %%\r\nanimal = wn.synset('animal.n.01')\r\ncat = wn.synset('cat.n.01')\r\n# %%\r\nanimal.path_similarity(cat)\r\n# %%\r\nprint(nltk.tokenize(\"\"\"I am tired\"\"\"))\r\n# %%\r\nfrom nltk import NLTKWordTokenizer as tokenize\r\n# %%\r\ntokenize.tokenize(\"\"\"I am tired\"\"\")\r\n# %%\r\n", "sub_path": "wordnet.py", "file_name": "wordnet.py", "file_ext": "py", "file_size_in_byte": 461, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "nltk.corpus.wordnet.synset", "line_number": 4, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet", "line_number": 4, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.synset", "line_number": 6, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet", "line_number": 6, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.synset", "line_number": 8, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet", "line_number": 8, "usage_type": "name"}, {"api_name": "nltk.download", "line_number": 14, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet.synset", "line_number": 16, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet", "line_number": 16, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.synset", "line_number": 17, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet", "line_number": 17, "usage_type": "name"}, {"api_name": "nltk.tokenize", "line_number": 21, "usage_type": "call"}, {"api_name": "nltk.NLTKWordTokenizer.tokenize", "line_number": 25, "usage_type": "call"}, {"api_name": "nltk.NLTKWordTokenizer", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "292696200", "text": "from django.shortcuts import render , redirect\n\nfrom django.contrib import messages\nfrom .forms import UserRegisterForm\n\n\n# Create your views here.\ndef register(request):\n\tif request.method== 'POST':\n\t\tforms=UserRegisterForm(request.POST)\n\t\tif forms.is_valid():\n\t\t\tforms.save()\n\t\t\tusername=forms.cleaned_data.get('username')\n\t\t\tmessages.success(request, f'Account Created for {username}!')\n\t\t\t\n\n\n\n\telse:\n\t\tforms=UserRegisterForm()\n\treturn render(request, 'users/register.html' , {'form':forms})\n\n\n\n\n", "sub_path": "users/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 499, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "forms.UserRegisterForm", "line_number": 10, "usage_type": "call"}, {"api_name": "forms.is_valid", "line_number": 11, "usage_type": "call"}, {"api_name": "forms.save", "line_number": 12, "usage_type": "call"}, {"api_name": "forms.cleaned_data.get", "line_number": 13, "usage_type": "call"}, {"api_name": "forms.cleaned_data", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.contrib.messages.success", "line_number": 14, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 14, "usage_type": "name"}, {"api_name": "forms.UserRegisterForm", "line_number": 20, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "618885771", "text": "# -*- coding: utf-8 -*-\n\nimport requests\nimport gunpg\nfrom flask_restful import Resource, reqparse\nfrom flask import request\n\nBASEURL = \"https://api.stackexchange.com/2.2/users/\"\n\nparams = {\n \"site\": \"stackoverflow\",\n \"sort\": \"creation\"\n}\n\n\nclass GetUserPosts(Resource):\n\n def post(self):\n parser = reqparse.RequestParser(bundle_errors=True)\n parser.add_argument('userId', type=str, required=True, location='json')\n args = parser.parse_args(strict=True)\n targetUrl = BASEURL + args['userId'] + '/posts'\n r = requests.get(targetUrl, params)\n return r.json()\n", "sub_path": "stackoverflow_posts/resources/user_posts.py", "file_name": "user_posts.py", "file_ext": "py", "file_size_in_byte": 608, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask_restful.Resource", "line_number": 16, "usage_type": "name"}, {"api_name": "flask_restful.reqparse.RequestParser", "line_number": 19, "usage_type": "call"}, {"api_name": "flask_restful.reqparse", "line_number": 19, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "235476311", "text": "from flask import Flask, render_template\nimport requests\n\napp = Flask(__name__)\n\n@app.route('/')\ndef home():\n return render_template('index.html')\n\n@app.route('/org/')\ndef org(org_name):\n org_url = f'https://api.github.com/orgs/{org_name}/repos?per_page=100'\n headers = {\"user-agent\": \"jahankhan\"}\n response = requests.get(org_url, headers=headers)\n forked = response.json()\n starred = forked.copy()\n forked.sort(key=lambda repo: repo[\"forks\"], reverse=True)\n starred.sort(key=lambda repo: repo[\"stargazers_count\"], reverse=True)\n contributed_to = dict()\n contributors = dict()\n # Go through each repo and count contributors to a repo and overall contributions\n # (api rate limit causes a lot of problems here)\n for repo in starred:\n contributors_url = f'https://api.github.com/repos/{org_name}/{repo[\"name\"]}/contributors?per_page=100'\n response2 = requests.get(contributors_url, headers=headers)\n response2 = response2.json()\n contributed_to[f'{repo[\"name\"]}'] = contributed_to.get(f'{repo[\"name\"]}', 0) + len(response2)\n stats_url = f'https://api.github.com/repos/{org_name}/{repo[\"name\"]}/stats/contributors'\n response3 = requests.get(stats_url, headers=headers)\n response3 = response3.json()\n for contributor in response3:\n # check if we hit api rate limit\n if contributor == 'message':\n break\n contributors[f'{contributor[\"author\"][\"login\"]}'] = contributors.get(f'{contributor[\"author\"][\"login\"]}', 0) + contributor[\"total\"]\n\n contributed_to = sorted(contributed_to.items(), key=lambda repo: repo[1], reverse=True)\n contributors = sorted(contributors.items(), key=lambda contributor: contributor[1], reverse=True)\n\n return render_template('org.html', org=org_name, forked=forked, starred=starred, contributed_to=contributed_to, contributors=contributors)\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1924, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 8, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "111216434", "text": "\"\"\"\n# wxpy 接入图灵机器人相当方便，我们首先需要到图灵近期人官网进行注册，http://www.tuling123.com/member/robot/1379862/center/frame.jhtml?page=0&child=0\n# 通过注册Tuling 对象，当我们接收到消息的时候，可以直接使用tuling机器人来帮我们进行答复。其他的业务需求各位可以根据自己的需求来完成相应的逻辑。\n# bot = Bot()\n# # 获取好友\n# dear = bot.friends().search('被单')[0]\n# # 注册获得个人的图灵机器人key 填入\n# tuling = Tuling(api_key='232fe069b3c94ad087d3031c370a2067')\n# # 使用图灵机器人自动与指定好友聊天\n# @bot.register(dear)\n# def reply_my_friend(msg):\n# print(msg)\n# tuling.do_reply(msg)\n# embed()\n\"\"\"\nfrom wxpy import *\nfrom aliyunsdkcore.client import AcsClient\nfrom aliyunsdkchatbot.request.v20171011 import ChatRequest\nimport json\n\nbot = Bot(cache_path=True)\n\n# 获取好友\nmy_friend = bot.friends().search(u'零林')[0] # 你朋友的微信名称，不是备注，也不是微信帐号。\n# my_friend = bot.friends().search(u'妹')[0]\n\n# 注册获得个人的图灵机器人key 填入\ntuling = Tuling(api_key='232fe069b3c94ad087d3031c370a2067')\n\n# 创建AcsClient实例\nclient = AcsClient(\n \"LTAIjgiPhdMbT2F9\",\n \"n1RHkNGL7KnVPDRjWbKfr5qK7YKS2G\",\n \"cn-shanghai\"\n);\n\n\n# 使用图灵机器人自动与指定好友聊天\n@bot.register(my_friend)\ndef reply_my_friend(msg):\n # 创建request，并设置参数\n print(msg)\n print(msg.chat.name)\n print(msg.text)\n # print(msg.member.name)\n # tuling.do_reply(msg)\n sessionId = \"4796a580eb2e4ea1a8e3db9d780a1d6c\"\n\n request = ChatRequest.ChatRequest()\n request.set_SessionId(sessionId)\n request.set_InstanceId(\"chatbot-cn-mp90vh2if0005g\")\n # request.set_Utterance(\"订餐厅\")\n request.set_Utterance(msg.text)\n # 发起API请求并显示返回值\n response = client.do_action_with_exception(request)\n # bytes to str\n bs = str(response, encoding=\"utf8\")\n print(bs)\n chat_bot_msg = json.loads(bs)\n sessionId = chat_bot_msg[\"SessionId\"]\n messages = chat_bot_msg[\"Messages\"]\n for message in messages:\n text = message[\"Text\"]\n content = text[\"Content\"]\n print(content)\n return content\n\n\nembed()\n", "sub_path": "service/menu/微信自动聊天机器人.py", "file_name": "微信自动聊天机器人.py", "file_ext": "py", "file_size_in_byte": 2275, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "aliyunsdkcore.client.AcsClient", "line_number": 31, "usage_type": "call"}, {"api_name": "aliyunsdkchatbot.request.v20171011.ChatRequest.ChatRequest", "line_number": 49, "usage_type": "call"}, {"api_name": "aliyunsdkchatbot.request.v20171011.ChatRequest", "line_number": 49, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "96658709", "text": "import sys\nimport os\nimport pickle\nimport logging.config\nfrom time import time\nfrom math import sqrt\nfrom shutil import copyfile, move, rmtree\n\nimport numpy as np\n\nimport pandas as pd\n\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\nfrom sklearn.model_selection import ShuffleSplit\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.preprocessing import StandardScaler\n\nfrom keras.callbacks import Callback, LearningRateScheduler\nfrom keras.layers import Input, Dense, CuDNNGRU, Bidirectional, GaussianNoise\nfrom keras.models import Model, load_model\nfrom keras.utils.vis_utils import plot_model\n\n\nDEFAULT_LOGGING = {\n 'version': 1,\n 'disable_existing_loggers': False,\n 'formatters': { \n 'standard': { \n 'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n },\n },\n 'handlers': { \n 'default': { \n 'level': 'DEBUG',\n 'formatter': 'standard',\n 'class': 'logging.StreamHandler',\n },\n },\n 'loggers': { \n 'root': { \n 'handlers': ['default'],\n 'level': 'INFO'\n },\n 'RiskM': {\n 'handlers': ['default'],\n 'level': 'INFO',\n 'propagate': False\n },\n } \n}\n\n\nlogging.config.dictConfig(DEFAULT_LOGGING)\n\n\nlogger = logging.getLogger('RiskM')\n\n\nclass RMC:\n PROJECT_ROOT_DIR = os.environ['RM_ROOT_DIR']\n INPUT_DIR = os.path.join(PROJECT_ROOT_DIR, 'input')\n OUTPUT_DIR = os.path.join(PROJECT_ROOT_DIR, 'output')\n SRC_DIR = os.path.join(PROJECT_ROOT_DIR, 'src')\n THIS_FILE = 'riskm_all'\n\n PROPHET_INPUT_ALL = '201709_SD_MC_EUR_Basis10k_10001_60_1'\n PROPHET_INPUT_ALL_PROPER_HEADER = '201709_SD_MC_EUR_Basis10k_10001_60_1 (proper header)'\n PROPHET_INPUT = '201709_SD_MC_EUR_Basis10k_10001_60_1 (2018)'\n PROPHET_OUTPUT = '10k_Daten_fuer_Training_v01_fix'\n\n TRAIN_X_DATA_FILE = 'train_x_data'\n TRAIN_Y_DATA_FILE = 'train_y_data'\n VAL_X_DATA_FILE = 'val_x_data'\n VAL_Y_DATA_FILE = 'val_y_data'\n TEST_X_DATA_FILE = 'test_x_data'\n TEST_Y_DATA_FILE = 'test_y_data'\n\n SCEN_ID_COL = 'SCENARIO'\n MONTH_COL = 'MONTH'\n\n INPUT_LEN = 13\n INPUT_DIM = 78\n OUTPUT_DIM = 1\n\n TRAIN_SIZE = 0.95\n VAL_SIZE = 0.05\n TEST_SIZE = 0.00\n DP = 'DP02R00'\n\n MV = 'MV03R00'\n\n BATCH_SIZE = 32\n OV = 'OV01R00'\n\n START_EP = 0\n END_EP = 400\n LOAD_MODEL = 'TR010_MV03R00_OV01R00_DP02R00'\n TRN = 'TR011'\n \n\ndef build_keras_model():\n input = Input(shape=(RMC.INPUT_LEN, RMC.INPUT_DIM), name='Input_Sequence')\n output = \\\n Bidirectional(CuDNNGRU(units=300, return_sequences=True, name='RNN_1'))(input)\n output = \\\n Bidirectional(CuDNNGRU(units=300, return_sequences=True, name='RNN_2'))(output)\n output = \\\n Bidirectional(CuDNNGRU(units=300, name='RNN_3'))(output)\n output = Dense(300, name='Dense_1')(output)\n output = Dense(200, name='Dense_2')(output)\n output = Dense(100, name='Dense_3')(output)\n output = Dense(1, name='Prediction')(output)\n\n model = Model(input, output)\n\n return model\n\n\ndef lr_schedule(ep):\n lr = 0.001\n\n lr = lr / (ep // 10 + 1)\n\n logger.info('New learning rate: %01.10f', lr)\n\n return lr\n\n\ndef compile_keras_model(model):\n #adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, decay=0.00, clipnorm=1.0) #epsilon=None (doesn't work)\n\n model.compile(optimizer='adam', loss='mse', metrics=['mape'])\n\n return model\n\n\ndef time_it(start, end):\n h, r = divmod(end - start, 3600)\n m, s = divmod(r, 60)\n \n return \"{:0>2}:{:0>2}:{:06.3f}\".format(int(h), int(m), s)\n\n\ndef load_x_data(file_name, init=False):\n if init:\n data = pd.read_csv(filepath_or_buffer=os.path.join(RMC.OUTPUT_DIR, file_name + '.csv'))\n else:\n file_name += '_' + RMC.DP + '.csv'\n data = pd.read_csv(filepath_or_buffer=os.path.join(RMC.OUTPUT_DIR, file_name))\n print(data.shape)\n data.set_index([RMC.SCEN_ID_COL, RMC.MONTH_COL], inplace=True)\n print(data.shape)\n data.sort_index(inplace=True)\n\n return data\n\n\ndef load_y_data(file_name, init=False):\n if init:\n data = pd.read_csv(filepath_or_buffer=os.path.join(RMC.INPUT_DIR, file_name + '.csv'),\n sep=';', thousands='.', decimal=',', header=0)\n else:\n file_name += '_' + RMC.DP + '.csv'\n data = pd.read_csv(filepath_or_buffer=os.path.join(RMC.OUTPUT_DIR, file_name))\n data.set_index(RMC.SCEN_ID_COL, inplace=True)\n data.sort_index(inplace=True)\n\n return data\n\n\ndef load_all_data(train_set, val_set, test_set, init):\n train_x = None\n train_y = None\n val_x = None\n val_y = None\n test_x = None\n test_y = None\n\n if train_set:\n logger.info(\"Loading training data ...\")\n\n if init:\n train_x = load_x_data(file_name=RMC.PROPHET_INPUT, init=True)\n train_y = load_y_data(file_name=RMC.PROPHET_OUTPUT, init=True)\n else:\n train_x = load_x_data(file_name=RMC.TRAIN_X_DATA_FILE)\n train_y = load_y_data(file_name=RMC.TRAIN_Y_DATA_FILE)\n\n logger.info(\"Loading training data done.\")\n\n if val_set:\n logger.info(\"Loading prepared validation data ...\")\n\n val_x = load_x_data(file_name=RMC.VAL_X_DATA_FILE)\n val_y = load_y_data(file_name=RMC.VAL_Y_DATA_FILE)\n\n logger.info(\"Loading prepared validation data done.\")\n\n if test_set:\n logger.info(\"Loading prepared test data ...\")\n\n test_x = load_x_data(file_name=RMC.TEST_X_DATA_FILE)\n test_y = load_y_data(file_name=RMC.TEST_Y_DATA_FILE)\n\n logger.info(\"Loading prepared test data done.\")\n\n return train_x, train_y, val_x, val_y, test_x, test_y\n\n\ndef save_data(data, file_name):\n file_name += '_' + RMC.DP + '.csv'\n\n data.to_csv(path_or_buf=os.path.join(RMC.OUTPUT_DIR, file_name))\n\n\ndef save_all_prepared_data(train_x, train_y, val_x, val_y, test_x, test_y):\n if train_x is not None and train_y is not None:\n logger.info(\"Saving prepared training data ...\")\n\n save_data(train_x, RMC.TRAIN_X_DATA_FILE)\n save_data(train_y, RMC.TRAIN_Y_DATA_FILE)\n\n logger.info(\"Saving prepared training data done.\")\n\n if val_x is not None and val_y is not None:\n logger.info(\"Saving prepared validation data ...\")\n\n save_data(val_x, RMC.VAL_X_DATA_FILE)\n save_data(val_y, RMC.VAL_Y_DATA_FILE)\n\n logger.info(\"Saving prepared validation data done.\")\n\n if test_x is not None and test_y is not None:\n logger.info(\"Saving prepared test data ...\")\n\n save_data(test_x, RMC.TEST_X_DATA_FILE)\n save_data(test_y, RMC.TEST_Y_DATA_FILE)\n\n logger.info(\"Saving prepared test data done.\")\n\n\ndef prepare_x_data(data):\n data.set_index(['SCENARIO', 'ECONOMY', 'CLASS', 'MEASURE', 'OS_TERM'], inplace=True)\n data = pd.DataFrame(data.stack())\n\n data.columns = ['VALUE']\n data.index.names = ['SCENARIO', 'ECONOMY', 'CLASS', 'MEASURE', 'OS_TERM', 'MONTH']\n data.reset_index(inplace=True)\n\n data['EC_CL_MS_OS'] = data['ECONOMY'] + '_' + data['CLASS'] + '_' + data['MEASURE'] + '_' + data.OS_TERM.map(str)\n data.drop(columns=['ECONOMY', 'CLASS', 'MEASURE', 'OS_TERM'], inplace=True)\n data.set_index(['SCENARIO', 'EC_CL_MS_OS', 'MONTH'], inplace=True)\n\n data = data.unstack(1)\n data.columns = data.columns.droplevel()\n\n data.sort_index(inplace=True)\n\n return data\n\n\ndef prepare_y_data(data):\n data.set_index('SCENARIO', inplace=True)\n data.sort_index(inplace=True)\n data.drop(columns='OWN_FUNDS_40', inplace=True)\n\n return data\n\n\ndef create_feature_prep_pipeline():\n return StandardScaler()\n\n\ndef load_feature_prep_pipeline(model_dir, model_file):\n fpp = pickle.load(open(os.path.join(model_dir, model_file + '_fpp.p'), 'rb'))\n\n return fpp\n\n\ndef save_feature_prep_pipeline(fpp, model_dir, model_file):\n pickle.dump(fpp, open(os.path.join(model_dir, model_file + '_fpp.p'), 'wb'))\n\n\ndef get_data_packages(x_data, y_data, fpp, fit):\n x = x_data.as_matrix()\n y = y_data.as_matrix()\n\n if fit:\n x = fpp.fit_transform(x)\n else:\n x = fpp.transform(x)\n\n x = x.reshape(-1, RMC.INPUT_LEN, RMC.INPUT_DIM)\n\n return x, y\n\n\ndef split_data(x, y, train_size, val_size, test_size):\n if val_size + test_size > 0:\n x_tr,y_tr, x_v, y_v = split_train_test(x, y, train_size, val_size + test_size)\n\n if val_size == 0:\n x_te = x_v\n y_te = y_v\n x_v = None\n y_v = None\n elif test_size == 0:\n x_te = None\n y_te = None\n else:\n x_v, y_v, x_te, y_te = split_train_test(x_v, y_v,\n val_size / (val_size + test_size),\n test_size / (val_size + test_size))\n else:\n x_tr = x\n y_tr = y\n x_v = None\n y_v = None\n x_te = None\n y_te = None\n\n return x_tr, y_tr, x_v, y_v, x_te, y_te\n\n\ndef split_train_test(x, y, train_size, test_size):\n split = ShuffleSplit(n_splits=1, train_size=train_size, test_size=test_size, random_state=None)\n res = split.split(y)\n print(len(y))\n x_tr = None\n y_tr = None\n x_te = None\n y_te = None\n print(train_size, test_size)\n for train_i, test_i in res:\n print(np.max(train_i))\n print(np.min(train_i))\n print(len(train_i))\n x_tr = pd.DataFrame(x.loc[x.index.levels[0][train_i].values])\n y_tr = pd.DataFrame(y.iloc[train_i])\n x_te = pd.DataFrame(x.loc[x.index.levels[0][test_i].values])\n y_te = pd.DataFrame(y.iloc[test_i])\n\n x_tr.reset_index(inplace=True)\n x_tr.set_index(['SCENARIO', 'MONTH'], inplace=True)\n x_tr.sort_index(inplace=True)\n\n y_tr.sort_index(inplace=True)\n\n x_te.reset_index(inplace=True)\n x_te.set_index(['SCENARIO', 'MONTH'], inplace=True)\n x_te.sort_index(inplace=True)\n\n y_te.sort_index(inplace=True)\n\n return x_tr, y_tr, x_te, y_te\n\n\ndef previous_keras_model_file_exists(model_dir, model_file_name):\n return os.path.exists(os.path.join(model_dir, model_file_name + '_model.h5'))\n\n\ndef load_keras_model(model_dir, model_file_name):\n model = load_model(os.path.join(model_dir, model_file_name + '_model.h5'))\n\n return model\n\n\ndef save_keras_model(model, model_dir, model_file_name):\n model.save(os.path.join(model_dir, model_file_name + '_model.h5'))\n \n\ndef save_training_history(history, model_dir, model_file_name):\n hist = pd.DataFrame.from_dict(history.history)\n hist['epoch'] = [i + 1 for i in range(len(hist))]\n hist.set_index('epoch', inplace=True)\n hist.to_csv(path_or_buf=os.path.join(model_dir, model_file_name + '_history.csv'))\n\n plt.plot(hist['loss'])\n plt.plot(hist['val_loss'])\n plt.yscale('log')\n plt.title('Model Loss')\n plt.ylabel('Loss')\n plt.xlabel('Epoch')\n plt.legend(['Train', 'Test'], loc='upper right')\n\n fig = plt.gcf()\n fig.set_size_inches(12, 8)\n fig.savefig(os.path.join(model_dir, model_file_name + '_history.png'), dpi=100)\n\n\ndef save_model_graph_and_summary(model, model_dir, model_file_name):\n plot_model(model, to_file=os.path.join(model_dir, model_file_name + '_model.png'), show_shapes=True)\n\n with open(os.path.join(model_dir, model_file_name + '_model.txt'), 'w') as fh:\n model.summary(print_fn=lambda x: fh.write(x + '\\n'))\n\n\ndef copy_this_file(model_dir, model_file_name):\n this_file_name = os.path.join(RMC.SRC_DIR, RMC.THIS_FILE + '.py')\n copy_file_name = os.path.join(model_dir, model_file_name + '_' + RMC.THIS_FILE + '.py')\n\n copyfile(this_file_name, copy_file_name)\n\n\nclass Model_Tracker(Callback):\n def __init__(self, model_dir, model_file_name, model):\n super(Callback, self).__init__()\n\n self.model = model\n self.file_name = model_file_name\n self.dir = model_dir\n self.best_epoch = None\n self.best_val_loss = None\n\n\n def on_epoch_end(self, epoch, logs=None):\n val_loss = logs['val_loss']\n\n if self.best_val_loss is None or self.best_val_loss > val_loss:\n self.best_epoch = epoch\n self.best_val_loss = val_loss\n\n save_keras_model(self.model, self.dir, self.file_name)\n\n print(\"New model version saved - val_rmse ({:.6f})\".format(sqrt(val_loss)))\n\n\ndef execute_pre_init():\n from pyspark import SparkContext\n from pyspark.sql import SparkSession\n\n logger.info(\"Pre-initial data preparation ...\")\n\n then = time()\n\n logger.info(\"Creating proper header ...\")\n\n fac = os.path.join(RMC.INPUT_DIR, RMC.PROPHET_INPUT + '.fac')\n csv = os.path.join(RMC.OUTPUT_DIR, RMC.PROPHET_INPUT_PROPER_HEADER + '.csv')\n\n with open(fac, 'r') as orig:\n with open(csv, 'w') as copy:\n i = 0\n for line in orig:\n if i >= 2:\n copy.write(line)\n\n i += 1\n\n if i % 10000 == 0:\n logger.info(\"Creating proper header ... %3.2f%%\", (i * 100.0 / 780081))\n\n logger.info(\"Creating proper header done.\")\n\n logger.info(\"Extracting 2018 data ...\")\n\n sc = SparkContext(\"local[3]\", \"test\")\n spark = SparkSession(sc)\n\n logger.info(\"Reading data file ...\")\n\n df = spark.read.csv(path=os.path.join(RMC.OUTPUT_DIR, RMC.PROPHET_INPUT_PROPER_HEADER + '.csv'), header=True, inferSchema=True)\n\n logger.info(\"Reading data file done.\")\n\n logger.info(\"Selecting 2018 data ...\")\n\n df = df.select(RMC.SCEN_ID_COL, 'ECONOMY', 'CLASS', 'MEASURE', 'OS_TERM', '201712', '201801', '201802', '201803',\n '201804', '201805', '201806', '201807', '201808', '201809', '201810', '201811', '201812')\n\n logger.info(\"Selecting 2018 data done.\")\n\n logger.info(\"Saving 2018 data file ...\")\n\n df.coalesce(1).write.csv(path=os.path.join(RMC.OUTPUT_DIR, 'tmp.csv'), mode='overwrite', header=True)\n\n for file in os.listdir(os.path.join(RMC.OUTPUT_DIR, 'tmp.csv')):\n if file.endswith('.csv'):\n move(src=os.path.join(RMC.OUTPUT_DIR, 'tmp.csv', file),\n dst=os.path.join(RMC.OUTPUT_DIR, RMC.PROPHET_INPUT_2018_ONLY + '.csv'))\n break\n\n rmtree(os.path.join(RMC.OUTPUT_DIR, 'tmp.csv'))\n\n logger.info(\"Saving 2018 data file done.\")\n\n sc.stop()\n\n logger.info(\"Extracting 2018 data done.\")\n\n logger.info(\"Pre-initial data preparation done in %s.\", time_it(then, time()))\n\n\ndef execute_init(train_x, train_y):\n logger.info(\"Starting initial data preparation ...\")\n\n val_x = None\n val_y = None\n test_x = None\n test_y = None\n\n if train_x is not None and train_y is not None:\n logger.info(\"Preparing training data ...\")\n\n train_x = prepare_x_data(train_x)\n train_y = prepare_y_data(train_y)\n\n logger.info(\"Preparing training data done.\")\n\n logger.info(\"Splitting prepared training data ...\")\n\n train_x_as_test = None\n train_y_as_test = None\n\n if RMC.TEST_SIZE == 0:\n train_x_as_test = train_x\n train_y_as_test = train_y\n\n train_x, train_y, val_x, val_y, test_x, test_y = split_data(x=train_x, y=train_y,\n train_size=RMC.TRAIN_SIZE,\n val_size=RMC.VAL_SIZE,\n test_size=RMC.TEST_SIZE)\n\n if RMC.TEST_SIZE == 0:\n test_x = train_x_as_test\n test_y = train_y_as_test\n\n logger.info(\"Splitting prepared training data done.\")\n\n return train_x, train_y, val_x, val_y, test_x, test_y\n\n\ndef execute_train(model_dir, model_file_name, start_epoch, end_epoch, fpp, build_on_model, train_x, train_y, val_x, val_y):\n if fpp is None:\n fpp = create_feature_prep_pipeline()\n fit = True\n else:\n fit = False\n\n x_t, y_t = get_data_packages(x_data=train_x, y_data=train_y, fpp=fpp, fit=fit)\n x_v, y_v = get_data_packages(x_data=val_x, y_data=val_y, fpp=fpp, fit=False)\n\n logger.info('Building/compiling model ...')\n\n if build_on_model is None:\n model = build_keras_model()\n else:\n model = build_on_model\n\n model = compile_keras_model(model)\n\n callbacks = [LearningRateScheduler(lr_schedule)]\n\n mt_callback = None\n\n if model_file_name is not None:\n mt_callback = Model_Tracker(model_dir, model_file_name, model=model)\n\n callbacks.append(mt_callback)\n\n save_model_graph_and_summary(model, model_dir, model_file_name)\n save_feature_prep_pipeline(fpp, model_dir, model_file_name)\n copy_this_file(model_dir, model_file_name)\n\n logger.info('Building/compiling model done.')\n\n logger.info('Fitting model ...')\n\n history = model.fit(\n x=[x_t], y=y_t,\n batch_size=RMC.BATCH_SIZE,\n epochs=end_epoch,\n verbose=1,\n callbacks=callbacks,\n shuffle=True,\n initial_epoch=start_epoch,\n steps_per_epoch=None,\n validation_data=[[x_v], y_v])\n\n if model_file_name is not None:\n save_training_history(history, model_dir, model_file_name)\n\n y_p = model.predict(x_v, verbose=1)\n\n y = np.reshape(a=y_v, newshape=(len(y_v),))\n y_p = np.reshape(a=y_p, newshape=(len(y_v),))\n\n test_result = pd.DataFrame(\n {RMC.SCEN_ID_COL: val_x.index.levels[0], 'y': y, 'y_pred': y_p, 'Difference': y - y_p, 'Deviation': (y - y_p) * 100 / y})\n test_result.set_index(RMC.SCEN_ID_COL, inplace=True)\n test_result.sort_index(inplace=True)\n\n skl_mse = mean_squared_error(y, y_p)\n skl_rmse = sqrt(skl_mse)\n\n if model_file_name is not None:\n with open(os.path.join(model_dir, model_file_name + '_train_results.csv'), \"w\") as file:\n file.write(\"Best Epoch: {0}, Val MSE: {1}, Val RMSE: {2}\\n\".format(mt_callback.best_epoch, skl_mse, skl_rmse))\n file.write(\"\\n\")\n test_result.to_csv(path_or_buf=file, columns=['y', 'y_pred', 'Difference', 'Deviation'])\n file.write(\",,,, {0}\\n\".format(np.mean(np.absolute(y - y_p) * 100 / y)))\n\n logger.info('Fitting model done.')\n\n return fpp, model\n\n\ndef execute_test(fpp, model, test_x, test_y, model_dir, model_file_name):\n logger.info(\"Testing model ...\")\n\n x, y = get_data_packages(x_data=test_x, y_data=test_y, fpp=fpp, fit=False)\n\n y_p = model.predict(x, verbose=1)\n\n y = np.reshape(a=y, newshape=(len(y),))\n y_p = np.reshape(a=y_p, newshape=(len(y),))\n\n test_result = pd.DataFrame(\n {RMC.SCEN_ID_COL: test_x.index.levels[0], 'y': y, 'y_pred': y_p, 'Difference': y - y_p, 'Deviation': (y - y_p) * 100 / y})\n test_result.set_index(RMC.SCEN_ID_COL, inplace=True)\n test_result.sort_index(inplace=True)\n\n skl_mse = mean_squared_error(y, y_p)\n skl_rmse = sqrt(skl_mse)\n\n print(\" - test_skl_mse ({:.6f}), test_skl_rmse ({:.6f})\".format(skl_mse, skl_rmse))\n print('\\n')\n\n if model_dir is not None:\n with open(os.path.join(model_dir, model_file_name + '_test_results.csv'), \"w\") as file:\n file.write(\"Test MSE: {0}, Test RMSE: {1}\\n\".format(skl_mse, skl_rmse))\n file.write(\"\\n\")\n test_result.to_csv(path_or_buf=file, columns=['y', 'y_pred', 'Difference', 'Deviation'])\n file.write(\",,,, {0}\\n\".format(np.mean(np.absolute(y - y_p) * 100 / y)))\n\n\ndef main():\n overall = time()\n\n logger.info(\"Main script started ...\")\n\n pre_init = False\n init = False\n train = False\n test = False\n\n fpp = None\n model = None\n model_file_name = None\n model_dir = None\n\n for arg in sys.argv[1:]:\n if arg == 'pre_init':\n pre_init = True\n if arg == 'init':\n initialization = True\n elif arg == 'train':\n train = True\n elif arg == 'test':\n test = True\n\n if not pre_init and not init and not train and not test:\n init = True\n train = True\n\n if pre_init:\n execute_pre_init()\n\n train_x, train_y, val_x, val_y, test_x, test_y = load_all_data(\n train_set=(train or init),\n val_set=(train and not init),\n test_set=(test and not init),\n init=init)\n\n if init:\n train_x, train_y, val_x, val_y, test_x, test_y = execute_init(\n train_x=train_x, train_y=train_y)\n\n save_all_prepared_data(\n train_x=train_x,\n train_y=train_y,\n val_x=val_x,\n val_y=val_y,\n test_x=test_x,\n test_y=test_y)\n\n if train or test:\n if RMC.TRN is not None:\n model_file_name = '{0}_{1}_{2}_{3}'.format(RMC.TRN, RMC.MV, RMC.OV, RMC.DP)\n model_dir = os.path.join(RMC.OUTPUT_DIR, model_file_name)\n\n if not os.path.exists(model_dir) and train:\n os.makedirs(model_dir)\n\n if previous_keras_model_file_exists(model_dir, model_file_name):\n logger.info(\"Loading model ...\")\n\n fpp = load_feature_prep_pipeline(model_dir, model_file_name)\n model = load_keras_model(model_dir, model_file_name)\n\n logger.info(\"Loading model done.\")\n\n if train:\n fpp, model = execute_train(model_dir, model_file_name,\n start_epoch=RMC.START_EP, end_epoch=RMC.END_EP,\n fpp=fpp, build_on_model=model,\n train_x=train_x, train_y=train_y, val_x=val_x, val_y=val_y)\n\n if test:\n execute_test(fpp, model, test_x, test_y, model_dir, model_file_name)\n\n logger.info(\"Main script finished in %s.\", time_it(overall, time()))\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "trainings/Baseline_RNN_Y1_EXT/TR011_MV03R00_OV01R00_DP02R00_riskm_all.py", "file_name": "TR011_MV03R00_OV01R00_DP02R00_riskm_all.py", "file_ext": "py", "file_size_in_byte": 21733, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "matplotlib.use", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.config.config.dictConfig", "line_number": 56, "usage_type": "call"}, {"api_name": "logging.config.config", "line_number": 56, "usage_type": "attribute"}, {"api_name": "logging.config", "line_number": 56, "usage_type": "name"}, {"api_name": "logging.config.getLogger", "line_number": 59, "usage_type": "call"}, {"api_name": "logging.config", "line_number": 59, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 63, "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": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "keras.layers.Input", "line_number": 105, "usage_type": "call"}, {"api_name": "keras.layers.Bidirectional", "line_number": 107, "usage_type": "call"}, {"api_name": "keras.layers.CuDNNGRU", "line_number": 107, "usage_type": "call"}, {"api_name": "keras.layers.Bidirectional", "line_number": 109, "usage_type": "call"}, {"api_name": "keras.layers.CuDNNGRU", "line_number": 109, "usage_type": "call"}, {"api_name": "keras.layers.Bidirectional", "line_number": 111, "usage_type": "call"}, {"api_name": "keras.layers.CuDNNGRU", "line_number": 111, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 112, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 114, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 115, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 117, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path", "line_number": 152, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path", "line_number": 167, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 216, "usage_type": "call"}, {"api_name": "os.path", "line_number": 216, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 247, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 274, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 278, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 278, "usage_type": "call"}, {"api_name": "os.path", "line_number": 278, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 284, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 284, "usage_type": "call"}, {"api_name": "os.path", "line_number": 284, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.ShuffleSplit", "line_number": 329, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 339, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 341, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 342, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 343, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 344, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 362, "usage_type": "call"}, {"api_name": "os.path", "line_number": 362, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 362, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 366, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 366, "usage_type": "call"}, {"api_name": "os.path", "line_number": 366, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 372, "usage_type": "call"}, {"api_name": "os.path", "line_number": 372, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 376, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 376, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 379, "usage_type": "call"}, {"api_name": "os.path", "line_number": 379, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 381, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 381, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 382, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 382, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "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": "matplotlib.pyplot.ylabel", "line_number": 385, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 385, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 386, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 386, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 387, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 387, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 389, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 389, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 391, "usage_type": "call"}, {"api_name": "os.path", "line_number": 391, "usage_type": "attribute"}, {"api_name": "keras.utils.vis_utils.plot_model", "line_number": 395, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 395, "usage_type": "call"}, {"api_name": "os.path", "line_number": 395, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 397, "usage_type": "call"}, {"api_name": "os.path", "line_number": 397, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 402, "usage_type": "call"}, {"api_name": "os.path", "line_number": 402, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 403, "usage_type": "call"}, {"api_name": "os.path", "line_number": 403, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 405, "usage_type": "call"}, {"api_name": "keras.callbacks.Callback", "line_number": 408, "usage_type": "name"}, {"api_name": "keras.callbacks.Callback", "line_number": 410, "usage_type": "argument"}, {"api_name": "math.sqrt", "line_number": 428, "usage_type": "call"}, {"api_name": "time.time", "line_number": 437, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 441, "usage_type": "call"}, {"api_name": "os.path", "line_number": 441, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 442, "usage_type": "call"}, {"api_name": "os.path", "line_number": 442, "usage_type": "attribute"}, {"api_name": "pyspark.SparkContext", "line_number": 460, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 461, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 465, "usage_type": "call"}, {"api_name": "os.path", "line_number": 465, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 478, "usage_type": "call"}, {"api_name": "os.path", "line_number": 478, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 480, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 480, "usage_type": "call"}, {"api_name": "os.path", "line_number": 480, "usage_type": "attribute"}, {"api_name": "shutil.move", "line_number": 482, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 482, "usage_type": "call"}, {"api_name": "os.path", "line_number": 482, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 483, "usage_type": "call"}, {"api_name": "os.path", "line_number": 483, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 486, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 486, "usage_type": "call"}, {"api_name": "os.path", "line_number": 486, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 494, "usage_type": "call"}, {"api_name": "keras.callbacks.LearningRateScheduler", "line_number": 555, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 588, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 589, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 591, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 596, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 597, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 600, "usage_type": "call"}, {"api_name": "os.path", "line_number": 600, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 604, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 604, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 618, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 619, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 621, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 626, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 627, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 633, "usage_type": "call"}, {"api_name": "os.path", "line_number": 633, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 637, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 637, "usage_type": "call"}, {"api_name": "time.time", "line_number": 641, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 655, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 693, "usage_type": "call"}, {"api_name": "os.path", "line_number": 693, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 695, "usage_type": "call"}, {"api_name": "os.path", "line_number": 695, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 696, "usage_type": "call"}, {"api_name": "time.time", "line_number": 715, "usage_type": "call"}]} +{"seq_id": "475954836", "text": "from itertools import permutations\r\n\r\ncal = 0 # 0:+, 1:- 2:*, 3://\r\nN = int(input())\r\narr_num = list(map(int, input().split()))\r\narr_cal_num = list(map(int, input().split()))\r\n\r\n# 연산자 리스트 만들기\r\narr_cal = []\r\nfor i in arr_cal_num:\r\n arr_cal += [cal]*i\r\n cal += 1\r\n\r\n# 조합 을 이용해 중복없는 리스트 만들기\r\narr_cal_set = set(permutations(arr_cal))\r\n\r\n# 각 숫자별 계산\r\ntmp_arr = []\r\nfor arr_cal_set_num in arr_cal_set:\r\n tmp = arr_num[0]\r\n for i in range(N-1):\r\n if arr_cal_set_num[i] == 0:\r\n tmp += arr_num[i+1]\r\n elif arr_cal_set_num[i] == 1:\r\n tmp -= arr_num[i+1]\r\n elif arr_cal_set_num[i] == 2:\r\n tmp *= arr_num[i+1]\r\n elif arr_cal_set_num[i] == 3:\r\n if tmp < 0:\r\n tmp = -(-tmp // arr_num[i+1])\r\n else:\r\n tmp //= arr_num[i+1]\r\n tmp_arr.append(tmp)\r\n\r\nprint(max(tmp_arr))\r\nprint(min(tmp_arr)) \r\n\r\n \r\n\r\n\r\n\r\n", "sub_path": "#02. 알고리즘 이론/13. BackTracking/조민준_boj#14888.py", "file_name": "조민준_boj#14888.py", "file_ext": "py", "file_size_in_byte": 986, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "itertools.permutations", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "444740327", "text": "import cv2\nimport pickle\n\n\ndef unpickle(file):\n with open(file, 'rb') as fo:\n data = pickle.load(fo, encoding='bytes')\n return data\n\n\ndef display_image(file_path, delay=0):\n img = cv2.imread(file_path, cv2.IMREAD_COLOR)\n cv2.imshow('img', img)\n cv2.waitKey(delay)\n\n\ndef read_resize_image(file_path, image_size):\n\n image = cv2.imread(file_path, cv2.IMREAD_COLOR)\n\n s = 0\n if image.shape[0] < image.shape[1]:\n s = (int(round(float(image.shape[0]) * image_size) / image.shape[1]), image_size)\n else:\n s = (image_size, int(round(float(image.shape[1]) * image_size) / image.shape[0]))\n\n image = cv2.resize(image, (s[1], s[0]), interpolation=cv2.INTER_CUBIC)\n image = cv2.copyMakeBorder(image, 0, image_size - image.shape[0], 0, image_size - image.shape[1],\n cv2.BORDER_CONSTANT, value=(0, 0, 0))\n\n return image[:, :, ::-1]\n", "sub_path": "util/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 905, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pickle.load", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cv2.copyMakeBorder", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.BORDER_CONSTANT", "line_number": 29, "usage_type": "attribute"}]} +{"seq_id": "218126017", "text": "from comicapi.genericmetadata import GenericMetadata\nimport os\nimport re\nfrom PIL import Image\nfrom comicapi import comicinfoxml, filenameparser\nimport tempfile\nimport shutil\nimport io\nimport xml.etree.ElementTree as ET\nimport fitz # PyMuPDF\nimport config\nimport archiveutil\n\nSETTINGS = config.get_config() \n\nscene_groups = ['zWater','zzz-mephisto','zSoU-Nerd','zzoronewtag10','zzoroboros','zzzGlorithSolo','ZZZZZ','zzGGtag','zDream','zzorostnick11','zEmpire-DrDoom','zzzNeverAngel-Empire','zzzDQzzz','zzForsythe']\nmetadata_files = [\"ComicInfo.xml\",\"ComicBookInfo.json\",\"CoMet.xml\"]\n\n\ndef remove_issue_number(filename):\n name = filename.partition('#')[0]\n return name\n\ndef pad_to_length(number,length=3):\n return number.zfill(length)\n\ndef stripBadChars(string):\n return string.replace(': ',' - ').replace('/','-').replace(' ',' ').strip()\n\ndef getFileExtension(filename):\n return os.path.splitext(filename)\n\ndef get_comic_details(name):\n fnp = filenameparser.FileNameParser()\n fnp.parseFilename(name)\n return fnp\n\ndef get_series_name(name):\n fnp = filenameparser.FileNameParser()\n fnp.parseFilename(name)\n return fnp.series\n\ndef getYearFromName(name):\n #return re.search('\\d{4}',name).group()\n return re.findall('\\d{4}',name)[-1]\n\ndef getYearFromVolume(name):\n try:\n #return re.search('\\d{4}',name).group()\n return re.findall('\\d{4}',name)[0]\n except:\n return \"\"\n\ndef is_image(file):\n try:\n Image.open(file)\n except IOError:\n return False\n return True\n\ndef get_meta_from_dir(dir):\n metas = {\"ComicInfo.xml\":\"\"}\n for subdir, dirs, files in os.walk(dir):\n for file in files:\n filepath = subdir + os.sep + file\n basename = os.path.basename(filepath)\n if basename != \"\" and basename in metadata_files:\n if basename == \"ComicInfo.xml\":\n meta = comicinfoxml.ComicInfoXml().readFromExternalFile(filepath)\n metas[\"ComicInfo.xml\"] = meta\n return metas\n\ndef merge_meta_xml(xml1,xml2,priority):\n xml1 = ET.fromstring(xml1)\n xml2 = ET.fromstring(xml2)\n\n if priority == \"xml1\":\n for i in xml1:\n found = xml2.find(i.tag)\n if found != None:\n found.text = i.text\n else:\n xml2.append(i)\n return ET.tostring(xml2, encoding='utf8', method='xml')\n else:\n for i in xml2:\n found = xml1.find(i.tag)\n if found != None:\n found.text = i.text\n else:\n xml1.append(i)\n return ET.tostring(xml1, encoding='utf8', method='xml')\n\ndef write_meta_to_dir(metadata,dir,type):\n try:\n if type == \"ComicInfo.xml\":\n writer = comicinfoxml.ComicInfoXml()\n file = os.path.join(dir,\"ComicInfo.xml\")\n writer.writeToExternalFile(file,metadata)\n return True\n except:\n return False\n\ndef comicdb_to_meta(results):\n metadata = GenericMetadata()\n md = metadata\n if \"genres\" in results:\n md.genre = results[\"genres\"]\n if results[\"description\"] != \"\":\n md.comments = results[\"description\"]\n if results[\"page_count\"] != \"\":\n md.pageCount = results[\"page_count\"]\n md.credits = results[\"credits\"]\n md.webLink = results[\"issueLink\"]\n md.title = results[\"issueName\"]\n md.issue = results[\"issueNumber\"]\n md.series = results[\"name\"]\n #md.issueCount = results[\"issue_count\"]\n md.publisher = results[\"publisher\"]\n metadata.isEmpty = False\n return metadata\n\ndef get_possible_promos_from_dir(dir):\n promos = []\n totallength = 0\n non_empty_files = []\n for subdir, dirs, files in os.walk(dir):\n for file in files:\n filepath = subdir + os.sep + file\n basename = os.path.basename(filepath)\n if basename != \"\" and basename not in metadata_files:\n totallength += len(basename)\n non_empty_files.append(basename)\n for file in non_empty_files:\n if len(file) < totallength / len(non_empty_files):\n promos.append(os.path.splitext(file)[0])\n return promos\n\ndef remove_promos_from_dir(dir):\n found = False\n promos = []\n try:\n for subdir, dirs, files in os.walk(dir):\n for file in files:\n filepath = subdir + os.sep + file\n base = os.path.basename(filepath)\n if base != \"\" and os.path.splitext(base)[0] in scene_groups:\n os.remove(filepath)\n print(f\"Removing promo: {os.path.splitext(base)[0]}\")\n found = True\n if not found:\n print(\"No scene promos found\")\n promos = get_possible_promos_from_dir(dir) \n print(f\"Possible scene promos: {' '.join(map(str,promos))}\") \n return found\n return found\n except Exception as e:\n print(f\"Failed to remove promos from directory: {e}\")\n return found\n\ndef remove_promos_from_file(filename,pdf_zoom):\n tmp = \"\"\n try:\n tmp = archiveutil.extract_to_temp(filename,pdf_zoom)\n except Exception as e:\n print(\"Extract error: \",e)\n shutil.rmtree(tmp)\n return False\n promos_found = False\n try:\n promos_found = remove_promos_from_dir(tmp)\n except Exception as e:\n print(f\"Error removing promos: {e}\")\n shutil.rmtree(tmp)\n return False\n if not promos_found:\n shutil.rmtree(tmp)\n return False\n try:\n return archiveutil.dir_to_archive(tmp,filename)\n except Exception as e:\n print(\"Archive error: \",e)\n shutil.rmtree(tmp)\n return False\n\ndef remove_comixology_meta_from_dir(dir):\n for subdir, dirs, files in os.walk(dir):\n for file in files:\n filepath = subdir + os.sep + file\n base = os.path.basename(filepath)\n if base == \".meta.asc\":\n os.remove(filepath)\n print(\"Removing Comixology meta.asc file\")\n\ndef get_issue_number(issue):\n fnp = filenameparser.FileNameParser()\n fnp.parseFilename(issue)\n return fnp.issue\n\ndef get_cover_from_dir(dir):\n cover = \"\"\n for subdir, dirs, files in os.walk(dir):\n for file in files:\n filepath = subdir + os.sep + file\n if is_image(filepath):\n cover = filepath\n break\n img = Image.open(cover).convert('RGB')\n return img\n\nif __name__ == \"__main__\":\n pass", "sub_path": "comicutil.py", "file_name": "comicutil.py", "file_ext": "py", "file_size_in_byte": 6509, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "config.get_config", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "comicapi.filenameparser.FileNameParser", "line_number": 34, "usage_type": "call"}, {"api_name": "comicapi.filenameparser", "line_number": 34, "usage_type": "name"}, {"api_name": "comicapi.filenameparser.FileNameParser", "line_number": 39, "usage_type": "call"}, {"api_name": "comicapi.filenameparser", "line_number": 39, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 45, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 50, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 56, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 56, "usage_type": "name"}, {"api_name": "os.walk", "line_number": 63, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "comicapi.comicinfoxml.ComicInfoXml", "line_number": 69, "usage_type": "call"}, {"api_name": "comicapi.comicinfoxml", "line_number": 69, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.fromstring", "line_number": 74, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 74, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.fromstring", "line_number": 75, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 75, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.tostring", "line_number": 84, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 84, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.tostring", "line_number": 92, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 92, "usage_type": "name"}, {"api_name": "comicapi.comicinfoxml.ComicInfoXml", "line_number": 97, "usage_type": "call"}, {"api_name": "comicapi.comicinfoxml", "line_number": 97, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "comicapi.genericmetadata.GenericMetadata", "line_number": 105, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 127, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 129, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 143, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 145, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "archiveutil.extract_to_temp", "line_number": 164, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 167, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 174, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 177, "usage_type": "call"}, {"api_name": "archiveutil.dir_to_archive", "line_number": 180, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 183, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 187, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 189, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 192, "usage_type": "call"}, {"api_name": "comicapi.filenameparser.FileNameParser", "line_number": 196, "usage_type": "call"}, {"api_name": "comicapi.filenameparser", "line_number": 196, "usage_type": "name"}, {"api_name": "os.walk", "line_number": 202, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 204, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 208, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 208, "usage_type": "name"}]} +{"seq_id": "114517161", "text": "\nimport argparse\nfrom random import randint\n\n# Define an argument parser object\nparser = argparse.ArgumentParser()\n\n# Add positional arguments\nparser.add_argument('count', type = int, help = 'Count of random integers to be generated')\n\n# Add optional arguments\nparser.add_argument('-r', '--range', metavar = ('lower', 'upper'), nargs = 2, type = int, default = [0, 10], help = 'Integer range [a, b] from which the random numbers will be chosen')\n\nparser.add_argument('-v', '--verbose', action = 'store_true', help = 'Verbose mode')\n\n# Parse command-line arguments\nargs = parser.parse_args()\n\n# Program\nif args.verbose:\n print(\"Generating {:d} random integer in the range [{:d}, {:d}]\".format(args.count, args.range[0], args.range[1]))\n \nfor i in range(args.count):\n print(randint(args.range[0], args.range[1]))\n", "sub_path": "Python Scripting Academy Unit 3/Module 4/rand.py", "file_name": "rand.py", "file_ext": "py", "file_size_in_byte": 828, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 6, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "113090569", "text": "from json import dumps\n\nfrom db import db\n\n\nclass EmployeeTSModel(db.Model):\n \"\"\"\n Employees Model\n \"\"\"\n __tablename__ = \"employeets\"\n employee_ts_id = db.Column(db.Integer, primary_key=True)\n workdate = db.Column(db.DATE)\n employee_id = db.Column(db.Integer, db.ForeignKey(\"employees.employee_id\"))\n firstname = db.Column(db.String(80))\n lastname = db.Column(db.String(80))\n fullname = db.Column(db.String(80))\n team_id = db.Column(db.Integer, db.ForeignKey(\"teams.team_id\"))\n team_name = db.Column(db.String(80))\n location_id = db.Column(db.Integer, db.ForeignKey(\"locations.location_id\"))\n location_name = db.Column(db.String(80))\n prod_hours = db.Column(db.Float(precision=2))\n sales = db.Column(db.Float(precision=2))\n avg_speed_answer = db.Column(db.Float(precision=2))\n avg_handle = db.Column(db.Float(precision=2))\n first_call_resolution = db.Column(db.Float(precision=2))\n customer_satisfaction = db.Column(db.Float(precision=2))\n absenteeism = db.Column(db.Float(precision=2))\n input_data_error = db.Column(db.Float(precision=2))\n contact_quality = db.Column(db.Float(precision=2))\n ratings = db.Column(db.Float(precision=2))\n # define foreign key\n employee = db.relationship(\"EmployeeModel\")\n location = db.relationship(\"LocationModel\")\n team = db.relationship(\"TeamModel\")\n\n def __init__(self, employee_ts_id, workdate, employee_id,\n firstname, lastname, fullname, team_id, team_name,\n location_id, location_name, prod_hours, sales, avg_speed_answer,\n avg_handle, first_call_resolution, customer_satisfaction,\n absenteeism, input_data_error, contact_quality, ratings):\n self.employee_ts_id = employee_ts_id\n self.workdate = workdate\n self.employee_id = employee_id\n self.firstname = firstname\n self.lastname = lastname\n self.fullname = fullname\n self.team_id = team_id\n self.team_name = team_name\n self.location_id = location_id\n self.location_name = location_name\n self.prod_hours = prod_hours\n self.sales = sales\n self.avg_speed_answer = avg_speed_answer\n self.avg_handle = avg_handle\n self.first_call_resolution = first_call_resolution\n self.customer_satisfaction = customer_satisfaction\n self.absenteeism = absenteeism\n self.input_data_error = input_data_error\n self.contact_quality = contact_quality\n self.ratings = ratings\n\n def json(self):\n return {\n \"employee_ts_id\": self.employee_ts_id,\n \"workdate\": dumps(self.workdate, default=str),\n \"employee_id\": self.employee_id,\n \"firstname\": self.firstname.strip() if self.firstname else None,\n \"lastname\": self.lastname.strip() if self.lastname else None,\n # \"fullname\": self.firstname.strip() + \" \" + self.lastname.strip(),\n \"team_id\": self.team_id,\n \"team_name\": self.team_name,\n \"location_id\": self.location_id,\n \"location_name\": self.location_name,\n \"sales\": self.sales,\n \"prod_hours\": self.prod_hours,\n \"avg_speed_answer\": self.avg_speed_answer,\n \"avg_handle\": self.avg_handle,\n \"first_call_resolution\": self.first_call_resolution,\n \"customer_satisfaction\": self.customer_satisfaction,\n \"absenteeism\": self.absenteeism,\n \"input_data_error\": self.input_data_error,\n \"contact_quality\": self.contact_quality,\n \"ratings\": self.ratings,\n }\n\n @classmethod\n def find_by_id(cls, employee_id):\n return cls.query.filter_by(employee_id=employee_id).all()\n\n @classmethod\n def find_by_fullname(cls, fullname):\n return cls.query.filter_by(fullname=fullname).first()\n\n @classmethod\n def find_by_firstname(cls, firstname):\n return cls.query.filter_by(firstname=firstname).all()\n\n @classmethod\n def find_by_lastname(cls, lastname):\n return cls.query.filter_by(lastname=lastname).all()\n\n def save_to_db(self):\n db.session.add(self)\n db.session.commit()\n\n def delete_from_db(self):\n db.session.delete(self)\n db.session.commit()\n", "sub_path": "models/employeets.py", "file_name": "employeets.py", "file_ext": "py", "file_size_in_byte": 4292, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "db.db.Model", "line_number": 6, "usage_type": "attribute"}, {"api_name": "db.db", "line_number": 6, "usage_type": "name"}, {"api_name": "db.db.Column", "line_number": 11, "usage_type": "call"}, {"api_name": "db.db", "line_number": 11, "usage_type": "name"}, {"api_name": "db.db.Integer", "line_number": 11, "usage_type": "attribute"}, {"api_name": "db.db.Column", "line_number": 12, "usage_type": "call"}, {"api_name": "db.db", "line_number": 12, "usage_type": "name"}, {"api_name": "db.db.DATE", "line_number": 12, "usage_type": "attribute"}, {"api_name": "db.db.Column", "line_number": 13, "usage_type": "call"}, {"api_name": "db.db", "line_number": 13, "usage_type": "name"}, {"api_name": "db.db.Integer", "line_number": 13, "usage_type": "attribute"}, {"api_name": "db.db.ForeignKey", "line_number": 13, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 14, "usage_type": "call"}, {"api_name": "db.db", "line_number": 14, "usage_type": "name"}, {"api_name": "db.db.String", "line_number": 14, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 15, "usage_type": "call"}, {"api_name": "db.db", "line_number": 15, "usage_type": "name"}, {"api_name": "db.db.String", "line_number": 15, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 16, "usage_type": "call"}, {"api_name": "db.db", "line_number": 16, "usage_type": "name"}, {"api_name": "db.db.String", "line_number": 16, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 17, "usage_type": "call"}, {"api_name": "db.db", "line_number": 17, "usage_type": "name"}, {"api_name": "db.db.Integer", "line_number": 17, "usage_type": "attribute"}, {"api_name": "db.db.ForeignKey", "line_number": 17, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 18, "usage_type": "call"}, {"api_name": "db.db", "line_number": 18, "usage_type": "name"}, {"api_name": "db.db.String", "line_number": 18, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 19, "usage_type": "call"}, {"api_name": "db.db", "line_number": 19, "usage_type": "name"}, {"api_name": "db.db.Integer", "line_number": 19, "usage_type": "attribute"}, {"api_name": "db.db.ForeignKey", "line_number": 19, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 20, "usage_type": "call"}, {"api_name": "db.db", "line_number": 20, "usage_type": "name"}, {"api_name": "db.db.String", "line_number": 20, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "db.db", "line_number": 21, "usage_type": "name"}, {"api_name": "db.db.Float", "line_number": 21, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "db.db", "line_number": 22, "usage_type": "name"}, {"api_name": "db.db.Float", "line_number": 22, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "db.db", "line_number": 23, "usage_type": "name"}, {"api_name": "db.db.Float", "line_number": 23, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "db.db", "line_number": 24, "usage_type": "name"}, {"api_name": "db.db.Float", "line_number": 24, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "db.db", "line_number": 25, "usage_type": "name"}, {"api_name": "db.db.Float", "line_number": 25, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 26, "usage_type": "call"}, {"api_name": "db.db", "line_number": 26, "usage_type": "name"}, {"api_name": "db.db.Float", "line_number": 26, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 27, "usage_type": "call"}, {"api_name": "db.db", "line_number": 27, "usage_type": "name"}, {"api_name": "db.db.Float", "line_number": 27, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 28, "usage_type": "call"}, {"api_name": "db.db", "line_number": 28, "usage_type": "name"}, {"api_name": "db.db.Float", "line_number": 28, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 29, "usage_type": "call"}, {"api_name": "db.db", "line_number": 29, "usage_type": "name"}, {"api_name": "db.db.Float", "line_number": 29, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 30, "usage_type": "call"}, {"api_name": "db.db", "line_number": 30, "usage_type": "name"}, {"api_name": "db.db.Float", "line_number": 30, "usage_type": "call"}, {"api_name": "db.db.relationship", "line_number": 32, "usage_type": "call"}, {"api_name": "db.db", "line_number": 32, "usage_type": "name"}, {"api_name": "db.db.relationship", "line_number": 33, "usage_type": "call"}, {"api_name": "db.db", "line_number": 33, "usage_type": "name"}, {"api_name": "db.db.relationship", "line_number": 34, "usage_type": "call"}, {"api_name": "db.db", "line_number": 34, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 65, "usage_type": "call"}, {"api_name": "db.db.session.add", "line_number": 103, "usage_type": "call"}, {"api_name": "db.db.session", "line_number": 103, "usage_type": "attribute"}, {"api_name": "db.db", "line_number": 103, "usage_type": "name"}, {"api_name": "db.db.session.commit", "line_number": 104, "usage_type": "call"}, {"api_name": "db.db.session", "line_number": 104, "usage_type": "attribute"}, {"api_name": "db.db", "line_number": 104, "usage_type": "name"}, {"api_name": "db.db.session.delete", "line_number": 107, "usage_type": "call"}, {"api_name": "db.db.session", "line_number": 107, "usage_type": "attribute"}, {"api_name": "db.db", "line_number": 107, "usage_type": "name"}, {"api_name": "db.db.session.commit", "line_number": 108, "usage_type": "call"}, {"api_name": "db.db.session", "line_number": 108, "usage_type": "attribute"}, {"api_name": "db.db", "line_number": 108, "usage_type": "name"}]} +{"seq_id": "470326111", "text": "import os\nimport enum\nimport weakref\nfrom pathlib import Path\n\nDB_DIR = Path(\"storage\")\nDB_DIR_TRAINED_WEIGHT = DB_DIR / \"trained_weight\"\nDB_DIR_PRETRAINED_WEIGHT = DB_DIR / \"pretrained_weight\"\n\nDATASET_DIR = Path(\"datasrc\")\nDATASET_IMG_DIR = DATASET_DIR / \"img\"\nDATASET_LABEL_DIR = DATASET_DIR / \"label\"\nDATASET_LABEL_CLASSIFICATION_DIR = DATASET_LABEL_DIR / \"classification\"\nDATASET_LABEL_DETECTION_DIR = DATASET_LABEL_DIR / \"detection\"\nDATASET_LABEL_SEGMENTATION_DIR = DATASET_LABEL_DIR / \"segmentation\"\n\nDATASET_PREDICTION_DIR = DATASET_DIR / \"prediction_set\"\nDATASET_PREDICTION_IMG_DIR = DATASET_PREDICTION_DIR / \"img\"\n\nMAX_THREAD_NUM = 1\n\nDATASET_NAME_MAX_LENGTH = 128\nDATASET_NAME_MIN_LENGTH = 1\nDATASET_DESCRIPTION_MAX_LENGTH = 1024\nDATASET_DESCRIPTION_MIN_LENGTH = 0\nDATASET_RATIO_MAX = 0.99\nDATASET_RATIO_MIN = 0.3\n\nEPOCH_MAX = 1000\nEPOCH_MIN = 1\nBATCH_MAX = 128\nBATCH_MIN = 1\nCELL_MAX = 10\nCELL_MIN = 2\nBBOX_MAX = 5\nBBOX_MIN = 1\n\n\ndef create_directories():\n dirs = [\n DB_DIR, DB_DIR_TRAINED_WEIGHT, DB_DIR_PRETRAINED_WEIGHT,\n DATASET_IMG_DIR, DATASET_LABEL_DIR, DATASET_LABEL_CLASSIFICATION_DIR,\n DATASET_LABEL_DETECTION_DIR, DATASET_LABEL_SEGMENTATION_DIR,\n DATASET_PREDICTION_DIR, DATASET_PREDICTION_IMG_DIR\n ]\n for d in dirs:\n d.mkdir(parents=True, exist_ok=True)\n\n\nclass Task(enum.Enum):\n CLASSIFICATION = 0\n DETECTION = 1\n SEGMENTATION = 2\n\n\nclass State(enum.Enum):\n CREATED = 0\n RESERVED = 1\n STARTED = 2\n STOPPED = 3\n\n PRED_CREATED = 4\n PRED_RESERVED = 5\n PRED_STARTED = 6\n\n\nclass RunningState(enum.Enum):\n PREPARING = 0\n TRAINING = 1\n VALIDATING = 2\n PREDICTING = 3\n STARTING = 4\n STOPPING = 5\n WEIGHT_DOWNLOADING = 6\n\n\nclass Algorithm(enum.Enum):\n RESNET = 1\n RESNEXT = 2\n DENSENET = 3\n VGG = 4\n INCEPTION = 5\n\n YOLOV1 = 30\n YOLOV2 = 31\n SSD = 32\n\n UNET = 60\n FCN = 61\n DEEPLABV3PLUS = 62\n\n\nTASK_ID_BY_NAME = {\n \"classification\": Task.CLASSIFICATION.value,\n \"detection\": Task.DETECTION.value,\n \"segmentation\": Task.SEGMENTATION.value,\n}\n\n# name, description, min, max\nERROR_MESSAGE_TEMPLATE = \"{} {}. Please input {} ~ {}.\"\n", "sub_path": "renom_img/server/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 2181, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pathlib.Path", "line_number": 6, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 10, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 50, "usage_type": "attribute"}, {"api_name": "enum.Enum", "line_number": 56, "usage_type": "attribute"}, {"api_name": "enum.Enum", "line_number": 67, "usage_type": "attribute"}, {"api_name": "enum.Enum", "line_number": 77, "usage_type": "attribute"}]} +{"seq_id": "52359888", "text": "\"\"\"column\n\nRevision ID: 2fd1192706e7\nRevises: 83dc9267d154\nCreate Date: 2018-10-15 16:38:16.965150\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '2fd1192706e7'\ndown_revision = '83dc9267d154'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('user', sa.Column('height', sa.String(length=64), nullable=True))\n op.add_column('user', sa.Column('width', sa.String(length=64), nullable=True))\n op.drop_index('ix_user_email', table_name='user')\n op.drop_column('user', 'email')\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('user', sa.Column('email', sa.VARCHAR(length=120), nullable=True))\n op.create_index('ix_user_email', 'user', ['email'], unique=1)\n op.drop_column('user', 'width')\n op.drop_column('user', 'height')\n # ### end Alembic commands ###\n", "sub_path": "migrations/versions/2fd1192706e7_column.py", "file_name": "2fd1192706e7_column.py", "file_ext": "py", "file_size_in_byte": 1009, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "alembic.op.add_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op.drop_index", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 23, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 24, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 24, "usage_type": "name"}, {"api_name": "alembic.op.add_column", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 30, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op.create_index", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 31, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 32, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 32, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 33, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 33, "usage_type": "name"}]} +{"seq_id": "321382062", "text": "# pylint: disable=invalid-name\n\"\"\"\nSAS generic computation and sld file readers\n\"\"\"\nfrom __future__ import print_function\n\nimport os\nimport sys\nimport copy\nimport logging\n\nfrom periodictable import formula\nfrom periodictable import nsf\nimport numpy as np\n\nfrom . import _sld2i\nfrom .BaseComponent import BaseComponent\n\nlogger = logging.getLogger(__name__)\n\nif sys.version_info[0] < 3:\n def decode(s):\n return s\nelse:\n def decode(s):\n return s.decode() if isinstance(s, bytes) else s\n\nMFACTOR_AM = 2.853E-12\nMFACTOR_MT = 2.3164E-9\nMETER2ANG = 1.0E+10\n#Avogadro constant [1/mol]\nNA = 6.02214129e+23\n\ndef mag2sld(mag, v_unit=None):\n \"\"\"\n Convert magnetization to magnatic SLD\n sldm = Dm * mag where Dm = gamma * classical elec. radius/(2*Bohr magneton)\n Dm ~ 2.853E-12 [A^(-2)] ==> Shouldn't be 2.90636E-12 [A^(-2)]???\n \"\"\"\n if v_unit == \"A/m\":\n factor = MFACTOR_AM\n elif v_unit == \"mT\":\n factor = MFACTOR_MT\n else:\n raise ValueError(\"Invalid valueunit\")\n sld_m = factor * mag\n return sld_m\n\ndef transform_center(pos_x, pos_y, pos_z):\n \"\"\"\n re-center\n :return: posx, posy, posz [arrays]\n \"\"\"\n posx = pos_x - (min(pos_x) + max(pos_x)) / 2.0\n posy = pos_y - (min(pos_y) + max(pos_y)) / 2.0\n posz = pos_z - (min(pos_z) + max(pos_z)) / 2.0\n return posx, posy, posz\n\nclass GenSAS(BaseComponent):\n \"\"\"\n Generic SAS computation Model based on sld (n & m) arrays\n \"\"\"\n def __init__(self):\n \"\"\"\n Init\n :Params sld_data: MagSLD object\n \"\"\"\n # Initialize BaseComponent\n BaseComponent.__init__(self)\n self.sld_data = None\n self.data_pos_unit = None\n self.data_x = None\n self.data_y = None\n self.data_z = None\n self.data_sldn = None\n self.data_mx = None\n self.data_my = None\n self.data_mz = None\n self.data_vol = None #[A^3]\n self.is_avg = False\n ## Name of the model\n self.name = \"GenSAS\"\n ## Define parameters\n self.params = {}\n self.params['scale'] = 1.0\n self.params['background'] = 0.0\n self.params['solvent_SLD'] = 0.0\n self.params['total_volume'] = 1.0\n self.params['Up_frac_in'] = 1.0\n self.params['Up_frac_out'] = 1.0\n self.params['Up_theta'] = 0.0\n self.description = 'GenSAS'\n ## Parameter details [units, min, max]\n self.details = {}\n self.details['scale'] = ['', 0.0, np.inf]\n self.details['background'] = ['[1/cm]', 0.0, np.inf]\n self.details['solvent_SLD'] = ['1/A^(2)', -np.inf, np.inf]\n self.details['total_volume'] = ['A^(3)', 0.0, np.inf]\n self.details['Up_frac_in'] = ['[u/(u+d)]', 0.0, 1.0]\n self.details['Up_frac_out'] = ['[u/(u+d)]', 0.0, 1.0]\n self.details['Up_theta'] = ['[deg]', -np.inf, np.inf]\n # fixed parameters\n self.fixed = []\n\n def set_pixel_volumes(self, volume):\n \"\"\"\n Set the volume of a pixel in (A^3) unit\n :Param volume: pixel volume [float]\n \"\"\"\n if self.data_vol is None:\n raise TypeError(\"data_vol is missing\")\n self.data_vol = volume\n\n def set_is_avg(self, is_avg=False):\n \"\"\"\n Sets is_avg: [bool]\n \"\"\"\n self.is_avg = is_avg\n\n def _gen(self, qx, qy):\n \"\"\"\n Evaluate the function\n :Param x: array of x-values\n :Param y: array of y-values\n :Param i: array of initial i-value\n :return: function value\n \"\"\"\n pos_x = self.data_x\n pos_y = self.data_y\n pos_z = self.data_z\n if self.is_avg is None:\n pos_x, pos_y, pos_z = transform_center(pos_x, pos_y, pos_z)\n sldn = copy.deepcopy(self.data_sldn)\n sldn -= self.params['solvent_SLD']\n # **** WARNING **** new_GenI holds pointers to numpy vectors\n # be sure that they are contiguous double precision arrays and make \n # sure the GC doesn't eat them before genicom is called.\n # TODO: rewrite so that the parameters are passed directly to genicom\n args = (\n (1 if self.is_avg else 0),\n pos_x, pos_y, pos_z,\n sldn, self.data_mx, self.data_my,\n self.data_mz, self.data_vol,\n self.params['Up_frac_in'],\n self.params['Up_frac_out'],\n self.params['Up_theta'])\n model = _sld2i.new_GenI(*args)\n if len(qy):\n qx, qy = _vec(qx), _vec(qy)\n I_out = np.empty_like(qx)\n #print(\"npoints\", qx.shape, \"npixels\", pos_x.shape)\n _sld2i.genicomXY(model, qx, qy, I_out)\n #print(\"I_out after\", I_out)\n else:\n qx = _vec(qx)\n I_out = np.empty_like(qx)\n _sld2i.genicom(model, qx, I_out)\n vol_correction = self.data_total_volume / self.params['total_volume']\n result = (self.params['scale'] * vol_correction * I_out\n + self.params['background'])\n return result\n\n def set_sld_data(self, sld_data=None):\n \"\"\"\n Sets sld_data\n \"\"\"\n self.sld_data = sld_data\n self.data_pos_unit = sld_data.pos_unit\n self.data_x = _vec(sld_data.pos_x)\n self.data_y = _vec(sld_data.pos_y)\n self.data_z = _vec(sld_data.pos_z)\n self.data_sldn = _vec(sld_data.sld_n)\n self.data_mx = _vec(sld_data.sld_mx)\n self.data_my = _vec(sld_data.sld_my)\n self.data_mz = _vec(sld_data.sld_mz)\n self.data_vol = _vec(sld_data.vol_pix)\n self.data_total_volume = sum(sld_data.vol_pix)\n self.params['total_volume'] = sum(sld_data.vol_pix)\n\n def getProfile(self):\n \"\"\"\n Get SLD profile\n : return: sld_data\n \"\"\"\n return self.sld_data\n\n def run(self, x=0.0):\n \"\"\"\n Evaluate the model\n :param x: simple value\n :return: (I value)\n \"\"\"\n if isinstance(x, list):\n if len(x[1]) > 0:\n msg = \"Not a 1D.\"\n raise ValueError(msg)\n # 1D I is found at y =0 in the 2D pattern\n out = self._gen(x[0], [])\n return out\n else:\n msg = \"Q must be given as list of qx's and qy's\"\n raise ValueError(msg)\n\n def runXY(self, x=0.0):\n \"\"\"\n Evaluate the model\n :param x: simple value\n :return: I value\n :Use this runXY() for the computation\n \"\"\"\n if isinstance(x, list):\n return self._gen(x[0], x[1])\n else:\n msg = \"Q must be given as list of qx's and qy's\"\n raise ValueError(msg)\n\n def evalDistribution(self, qdist):\n \"\"\"\n Evaluate a distribution of q-values.\n\n :param qdist: ndarray of scalar q-values (for 1D) or list [qx,qy]\n where qx,qy are 1D ndarrays (for 2D).\n \"\"\"\n if isinstance(qdist, list):\n return self.run(qdist) if len(qdist[1]) < 1 else self.runXY(qdist)\n else:\n mesg = \"evalDistribution is expecting an ndarray of \"\n mesg += \"a list [qx,qy] where qx,qy are arrays.\"\n raise RuntimeError(mesg)\n\ndef _vec(v):\n return np.ascontiguousarray(v, 'd')\n\nclass OMF2SLD(object):\n \"\"\"\n Convert OMFData to MAgData\n \"\"\"\n def __init__(self):\n \"\"\"\n Init\n \"\"\"\n self.pos_x = None\n self.pos_y = None\n self.pos_z = None\n self.mx = None\n self.my = None\n self.mz = None\n self.sld_n = None\n self.vol_pix = None\n self.output = None\n self.omfdata = None\n\n def set_data(self, omfdata, shape='rectangular'):\n \"\"\"\n Set all data\n \"\"\"\n self.omfdata = omfdata\n length = int(omfdata.xnodes * omfdata.ynodes * omfdata.znodes)\n pos_x = np.arange(omfdata.xmin,\n omfdata.xnodes*omfdata.xstepsize + omfdata.xmin,\n omfdata.xstepsize)\n pos_y = np.arange(omfdata.ymin,\n omfdata.ynodes*omfdata.ystepsize + omfdata.ymin,\n omfdata.ystepsize)\n pos_z = np.arange(omfdata.zmin,\n omfdata.znodes*omfdata.zstepsize + omfdata.zmin,\n omfdata.zstepsize)\n self.pos_x = np.tile(pos_x, int(omfdata.ynodes * omfdata.znodes))\n self.pos_y = pos_y.repeat(int(omfdata.xnodes))\n self.pos_y = np.tile(self.pos_y, int(omfdata.znodes))\n self.pos_z = pos_z.repeat(int(omfdata.xnodes * omfdata.ynodes))\n self.mx = omfdata.mx\n self.my = omfdata.my\n self.mz = omfdata.mz\n self.sld_n = np.zeros(length)\n\n if omfdata.mx is None:\n self.mx = np.zeros(length)\n if omfdata.my is None:\n self.my = np.zeros(length)\n if omfdata.mz is None:\n self.mz = np.zeros(length)\n\n self._check_data_length(length)\n self.remove_null_points(False, False)\n mask = np.ones(len(self.sld_n), dtype=bool)\n if shape.lower() == 'ellipsoid':\n try:\n # Pixel (step) size included\n x_c = max(self.pos_x) + min(self.pos_x)\n y_c = max(self.pos_y) + min(self.pos_y)\n z_c = max(self.pos_z) + min(self.pos_z)\n x_d = max(self.pos_x) - min(self.pos_x)\n y_d = max(self.pos_y) - min(self.pos_y)\n z_d = max(self.pos_z) - min(self.pos_z)\n x_r = (x_d + omfdata.xstepsize) / 2.0\n y_r = (y_d + omfdata.ystepsize) / 2.0\n z_r = (z_d + omfdata.zstepsize) / 2.0\n x_dir2 = ((self.pos_x - x_c / 2.0) / x_r)\n x_dir2 *= x_dir2\n y_dir2 = ((self.pos_y - y_c / 2.0) / y_r)\n y_dir2 *= y_dir2\n z_dir2 = ((self.pos_z - z_c / 2.0) / z_r)\n z_dir2 *= z_dir2\n mask = (x_dir2 + y_dir2 + z_dir2) <= 1.0\n except Exception as exc:\n logger.error(exc)\n self.output = MagSLD(self.pos_x[mask], self.pos_y[mask],\n self.pos_z[mask], self.sld_n[mask],\n self.mx[mask], self.my[mask], self.mz[mask])\n self.output.set_pix_type('pixel')\n self.output.set_pixel_symbols('pixel')\n\n def get_omfdata(self):\n \"\"\"\n Return all data\n \"\"\"\n return self.omfdata\n\n def get_output(self):\n \"\"\"\n Return output\n \"\"\"\n return self.output\n\n def _check_data_length(self, length):\n \"\"\"\n Check if the data lengths are consistent\n :Params length: data length\n \"\"\"\n parts = (self.pos_x, self.pos_y, self.pos_z, self.mx, self.my, self.mz)\n if any(len(v) != length for v in parts):\n raise ValueError(\"Error: Inconsistent data length.\")\n\n def remove_null_points(self, remove=False, recenter=False):\n \"\"\"\n Removes any mx, my, and mz = 0 points\n \"\"\"\n if remove:\n is_nonzero = (np.fabs(self.mx) + np.fabs(self.my) +\n np.fabs(self.mz)).nonzero()\n if len(is_nonzero[0]) > 0:\n self.pos_x = self.pos_x[is_nonzero]\n self.pos_y = self.pos_y[is_nonzero]\n self.pos_z = self.pos_z[is_nonzero]\n self.sld_n = self.sld_n[is_nonzero]\n self.mx = self.mx[is_nonzero]\n self.my = self.my[is_nonzero]\n self.mz = self.mz[is_nonzero]\n if recenter:\n self.pos_x -= (min(self.pos_x) + max(self.pos_x)) / 2.0\n self.pos_y -= (min(self.pos_y) + max(self.pos_y)) / 2.0\n self.pos_z -= (min(self.pos_z) + max(self.pos_z)) / 2.0\n\n def get_magsld(self):\n \"\"\"\n return MagSLD\n \"\"\"\n return self.output\n\n\nclass OMFReader(object):\n \"\"\"\n Class to load omf/ascii files (3 columns w/header).\n \"\"\"\n ## File type\n type_name = \"OMF ASCII\"\n\n ## Wildcards\n type = [\"OMF files (*.OMF, *.omf)|*.omf\"]\n ## List of allowed extensions\n ext = ['.omf', '.OMF']\n\n def read(self, path):\n \"\"\"\n Load data file\n :param path: file path\n :return: x, y, z, sld_n, sld_mx, sld_my, sld_mz\n \"\"\"\n desc = \"\"\n mx = np.zeros(0)\n my = np.zeros(0)\n mz = np.zeros(0)\n try:\n input_f = open(path, 'rb')\n buff = decode(input_f.read())\n lines = buff.split('\\n')\n input_f.close()\n output = OMFData()\n valueunit = None\n for line in lines:\n line = line.strip()\n # Read data\n if line and not line.startswith('#'):\n try:\n toks = line.split()\n _mx = float(toks[0])\n _my = float(toks[1])\n _mz = float(toks[2])\n _mx = mag2sld(_mx, valueunit)\n _my = mag2sld(_my, valueunit)\n _mz = mag2sld(_mz, valueunit)\n mx = np.append(mx, _mx)\n my = np.append(my, _my)\n mz = np.append(mz, _mz)\n except Exception as exc:\n # Skip non-data lines\n logger.error(str(exc)+\" when processing %r\"%line)\n #Reading Header; Segment count ignored\n s_line = line.split(\":\", 1)\n if s_line[0].lower().count(\"oommf\") > 0:\n oommf = s_line[1].lstrip()\n if s_line[0].lower().count(\"title\") > 0:\n title = s_line[1].lstrip()\n if s_line[0].lower().count(\"desc\") > 0:\n desc += s_line[1].lstrip()\n desc += '\\n'\n if s_line[0].lower().count(\"meshtype\") > 0:\n meshtype = s_line[1].lstrip()\n if s_line[0].lower().count(\"meshunit\") > 0:\n meshunit = s_line[1].lstrip()\n if meshunit.count(\"m\") < 1:\n msg = \"Error: \\n\"\n msg += \"We accept only m as meshunit\"\n raise ValueError(msg)\n if s_line[0].lower().count(\"xbase\") > 0:\n xbase = s_line[1].lstrip()\n if s_line[0].lower().count(\"ybase\") > 0:\n ybase = s_line[1].lstrip()\n if s_line[0].lower().count(\"zbase\") > 0:\n zbase = s_line[1].lstrip()\n if s_line[0].lower().count(\"xstepsize\") > 0:\n xstepsize = s_line[1].lstrip()\n if s_line[0].lower().count(\"ystepsize\") > 0:\n ystepsize = s_line[1].lstrip()\n if s_line[0].lower().count(\"zstepsize\") > 0:\n zstepsize = s_line[1].lstrip()\n if s_line[0].lower().count(\"xnodes\") > 0:\n xnodes = s_line[1].lstrip()\n if s_line[0].lower().count(\"ynodes\") > 0:\n ynodes = s_line[1].lstrip()\n if s_line[0].lower().count(\"znodes\") > 0:\n znodes = s_line[1].lstrip()\n if s_line[0].lower().count(\"xmin\") > 0:\n xmin = s_line[1].lstrip()\n if s_line[0].lower().count(\"ymin\") > 0:\n ymin = s_line[1].lstrip()\n if s_line[0].lower().count(\"zmin\") > 0:\n zmin = s_line[1].lstrip()\n if s_line[0].lower().count(\"xmax\") > 0:\n xmax = s_line[1].lstrip()\n if s_line[0].lower().count(\"ymax\") > 0:\n ymax = s_line[1].lstrip()\n if s_line[0].lower().count(\"zmax\") > 0:\n zmax = s_line[1].lstrip()\n if s_line[0].lower().count(\"valueunit\") > 0:\n valueunit = s_line[1].lstrip().rstrip()\n if s_line[0].lower().count(\"valuemultiplier\") > 0:\n valuemultiplier = s_line[1].lstrip()\n if s_line[0].lower().count(\"valuerangeminmag\") > 0:\n valuerangeminmag = s_line[1].lstrip()\n if s_line[0].lower().count(\"valuerangemaxmag\") > 0:\n valuerangemaxmag = s_line[1].lstrip()\n if s_line[0].lower().count(\"end\") > 0:\n output.filename = os.path.basename(path)\n output.oommf = oommf\n output.title = title\n output.desc = desc\n output.meshtype = meshtype\n output.xbase = float(xbase) * METER2ANG\n output.ybase = float(ybase) * METER2ANG\n output.zbase = float(zbase) * METER2ANG\n output.xstepsize = float(xstepsize) * METER2ANG\n output.ystepsize = float(ystepsize) * METER2ANG\n output.zstepsize = float(zstepsize) * METER2ANG\n output.xnodes = float(xnodes)\n output.ynodes = float(ynodes)\n output.znodes = float(znodes)\n output.xmin = float(xmin) * METER2ANG\n output.ymin = float(ymin) * METER2ANG\n output.zmin = float(zmin) * METER2ANG\n output.xmax = float(xmax) * METER2ANG\n output.ymax = float(ymax) * METER2ANG\n output.zmax = float(zmax) * METER2ANG\n output.valuemultiplier = valuemultiplier\n output.valuerangeminmag = mag2sld(float(valuerangeminmag), \\\n valueunit)\n output.valuerangemaxmag = mag2sld(float(valuerangemaxmag), \\\n valueunit)\n output.set_m(mx, my, mz)\n return output\n except Exception:\n msg = \"%s is not supported: \\n\" % path\n msg += \"We accept only Text format OMF file.\"\n raise RuntimeError(msg)\n\nclass PDBReader(object):\n \"\"\"\n PDB reader class: limited for reading the lines starting with 'ATOM'\n \"\"\"\n type_name = \"PDB\"\n ## Wildcards\n type = [\"pdb files (*.PDB, *.pdb)|*.pdb\"]\n ## List of allowed extensions\n ext = ['.pdb', '.PDB']\n\n def read(self, path):\n \"\"\"\n Load data file\n\n :param path: file path\n :return: MagSLD\n :raise RuntimeError: when the file can't be opened\n \"\"\"\n pos_x = np.zeros(0)\n pos_y = np.zeros(0)\n pos_z = np.zeros(0)\n sld_n = np.zeros(0)\n sld_mx = np.zeros(0)\n sld_my = np.zeros(0)\n sld_mz = np.zeros(0)\n vol_pix = np.zeros(0)\n pix_symbol = np.zeros(0)\n x_line = []\n y_line = []\n z_line = []\n x_lines = []\n y_lines = []\n z_lines = []\n try:\n input_f = open(path, 'rb')\n buff = decode(input_f.read())\n lines = buff.split('\\n')\n input_f.close()\n num = 0\n for line in lines:\n try:\n # check if line starts with \"ATOM\"\n if line[0:6].strip().count('ATM') > 0 or \\\n line[0:6].strip() == 'ATOM':\n # define fields of interest\n atom_name = line[12:16].strip()\n try:\n float(line[12])\n atom_name = atom_name[1].upper()\n except Exception:\n if len(atom_name) == 4:\n atom_name = atom_name[0].upper()\n elif line[12] != ' ':\n atom_name = atom_name[0].upper() + \\\n atom_name[1].lower()\n else:\n atom_name = atom_name[0].upper()\n _pos_x = float(line[30:38].strip())\n _pos_y = float(line[38:46].strip())\n _pos_z = float(line[46:54].strip())\n pos_x = np.append(pos_x, _pos_x)\n pos_y = np.append(pos_y, _pos_y)\n pos_z = np.append(pos_z, _pos_z)\n try:\n val = nsf.neutron_sld(atom_name)[0]\n # sld in Ang^-2 unit\n val *= 1.0e-6\n sld_n = np.append(sld_n, val)\n atom = formula(atom_name)\n # cm to A units\n vol = 1.0e+24 * atom.mass / atom.density / NA\n vol_pix = np.append(vol_pix, vol)\n except Exception:\n logger.error(\"Error: set the sld of %s to zero\"% atom_name)\n sld_n = np.append(sld_n, 0.0)\n sld_mx = np.append(sld_mx, 0)\n sld_my = np.append(sld_my, 0)\n sld_mz = np.append(sld_mz, 0)\n pix_symbol = np.append(pix_symbol, atom_name)\n elif line[0:6].strip().count('CONECT') > 0:\n toks = line.split()\n num = int(toks[1]) - 1\n val_list = []\n for val in toks[2:]:\n try:\n int_val = int(val)\n except Exception:\n break\n if int_val == 0:\n break\n val_list.append(int_val)\n #need val_list ordered\n for val in val_list:\n index = val - 1\n if (pos_x[index], pos_x[num]) in x_line and \\\n (pos_y[index], pos_y[num]) in y_line and \\\n (pos_z[index], pos_z[num]) in z_line:\n continue\n x_line.append((pos_x[num], pos_x[index]))\n y_line.append((pos_y[num], pos_y[index]))\n z_line.append((pos_z[num], pos_z[index]))\n if len(x_line) > 0:\n x_lines.append(x_line)\n y_lines.append(y_line)\n z_lines.append(z_line)\n except Exception as exc:\n logger.error(exc)\n\n output = MagSLD(pos_x, pos_y, pos_z, sld_n, sld_mx, sld_my, sld_mz)\n output.set_conect_lines(x_line, y_line, z_line)\n output.filename = os.path.basename(path)\n output.set_pix_type('atom')\n output.set_pixel_symbols(pix_symbol)\n output.set_nodes()\n output.set_pixel_volumes(vol_pix)\n output.sld_unit = '1/A^(2)'\n return output\n except Exception:\n raise RuntimeError(\"%s is not a sld file\" % path)\n\n def write(self, path, data):\n \"\"\"\n Write\n \"\"\"\n print(\"Not implemented... \")\n\nclass SLDReader(object):\n \"\"\"\n Class to load ascii files (7 columns).\n \"\"\"\n ## File type\n type_name = \"SLD ASCII\"\n ## Wildcards\n type = [\"sld files (*.SLD, *.sld)|*.sld\",\n \"txt files (*.TXT, *.txt)|*.txt\",\n \"all files (*.*)|*.*\"]\n ## List of allowed extensions\n ext = ['.sld', '.SLD', '.txt', '.TXT', '.*']\n def read(self, path):\n \"\"\"\n Load data file\n :param path: file path\n :return MagSLD: x, y, z, sld_n, sld_mx, sld_my, sld_mz\n :raise RuntimeError: when the file can't be opened\n :raise ValueError: when the length of the data vectors are inconsistent\n \"\"\"\n try:\n pos_x = np.zeros(0)\n pos_y = np.zeros(0)\n pos_z = np.zeros(0)\n sld_n = np.zeros(0)\n sld_mx = np.zeros(0)\n sld_my = np.zeros(0)\n sld_mz = np.zeros(0)\n try:\n # Use numpy to speed up loading\n input_f = np.loadtxt(path, dtype='float', skiprows=1,\n ndmin=1, unpack=True)\n pos_x = np.array(input_f[0])\n pos_y = np.array(input_f[1])\n pos_z = np.array(input_f[2])\n sld_n = np.array(input_f[3])\n sld_mx = np.array(input_f[4])\n sld_my = np.array(input_f[5])\n sld_mz = np.array(input_f[6])\n ncols = len(input_f)\n if ncols == 8:\n vol_pix = np.array(input_f[7])\n elif ncols == 7:\n vol_pix = None\n except Exception:\n # For older version of numpy\n input_f = open(path, 'rb')\n buff = decode(input_f.read())\n lines = buff.split('\\n')\n input_f.close()\n for line in lines:\n toks = line.split()\n try:\n _pos_x = float(toks[0])\n _pos_y = float(toks[1])\n _pos_z = float(toks[2])\n _sld_n = float(toks[3])\n _sld_mx = float(toks[4])\n _sld_my = float(toks[5])\n _sld_mz = float(toks[6])\n pos_x = np.append(pos_x, _pos_x)\n pos_y = np.append(pos_y, _pos_y)\n pos_z = np.append(pos_z, _pos_z)\n sld_n = np.append(sld_n, _sld_n)\n sld_mx = np.append(sld_mx, _sld_mx)\n sld_my = np.append(sld_my, _sld_my)\n sld_mz = np.append(sld_mz, _sld_mz)\n try:\n _vol_pix = float(toks[7])\n vol_pix = np.append(vol_pix, _vol_pix)\n except Exception as exc:\n vol_pix = None\n except Exception as exc:\n # Skip non-data lines\n logger.error(exc)\n output = MagSLD(pos_x, pos_y, pos_z, sld_n,\n sld_mx, sld_my, sld_mz)\n output.filename = os.path.basename(path)\n output.set_pix_type('pixel')\n output.set_pixel_symbols('pixel')\n if vol_pix is not None:\n output.set_pixel_volumes(vol_pix)\n return output\n except Exception:\n raise RuntimeError(\"%s is not a sld file\" % path)\n\n def write(self, path, data):\n \"\"\"\n Write sld file\n :Param path: file path\n :Param data: MagSLD data object\n \"\"\"\n if path is None:\n raise ValueError(\"Missing the file path.\")\n if data is None:\n raise ValueError(\"Missing the data to save.\")\n x_val = data.pos_x\n y_val = data.pos_y\n z_val = data.pos_z\n vol_pix = data.vol_pix\n length = len(x_val)\n sld_n = data.sld_n\n if sld_n is None:\n sld_n = np.zeros(length)\n sld_mx = data.sld_mx\n if sld_mx is None:\n sld_mx = np.zeros(length)\n sld_my = np.zeros(length)\n sld_mz = np.zeros(length)\n else:\n sld_my = data.sld_my\n sld_mz = data.sld_mz\n out = open(path, 'w')\n # First Line: Column names\n out.write(\"X Y Z SLDN SLDMx SLDMy SLDMz VOLUMEpix\")\n for ind in range(length):\n out.write(\"\\n%g %g %g %g %g %g %g %g\" % \\\n (x_val[ind], y_val[ind], z_val[ind], sld_n[ind],\n sld_mx[ind], sld_my[ind], sld_mz[ind], vol_pix[ind]))\n out.close()\n\n\nclass OMFData(object):\n \"\"\"\n OMF Data.\n \"\"\"\n _meshunit = \"A\"\n _valueunit = \"A^(-2)\"\n def __init__(self):\n \"\"\"\n Init for mag SLD\n \"\"\"\n self.filename = 'default'\n self.oommf = ''\n self.title = ''\n self.desc = ''\n self.meshtype = ''\n self.meshunit = self._meshunit\n self.valueunit = self._valueunit\n self.xbase = 0.0\n self.ybase = 0.0\n self.zbase = 0.0\n self.xstepsize = 6.0\n self.ystepsize = 6.0\n self.zstepsize = 6.0\n self.xnodes = 10.0\n self.ynodes = 10.0\n self.znodes = 10.0\n self.xmin = 0.0\n self.ymin = 0.0\n self.zmin = 0.0\n self.xmax = 60.0\n self.ymax = 60.0\n self.zmax = 60.0\n self.mx = None\n self.my = None\n self.mz = None\n self.valuemultiplier = 1.\n self.valuerangeminmag = 0\n self.valuerangemaxmag = 0\n\n def __str__(self):\n \"\"\"\n doc strings\n \"\"\"\n _str = \"Type: %s\\n\" % self.__class__.__name__\n _str += \"File: %s\\n\" % self.filename\n _str += \"OOMMF: %s\\n\" % self.oommf\n _str += \"Title: %s\\n\" % self.title\n _str += \"Desc: %s\\n\" % self.desc\n _str += \"meshtype: %s\\n\" % self.meshtype\n _str += \"meshunit: %s\\n\" % str(self.meshunit)\n _str += \"xbase: %s [%s]\\n\" % (str(self.xbase), self.meshunit)\n _str += \"ybase: %s [%s]\\n\" % (str(self.ybase), self.meshunit)\n _str += \"zbase: %s [%s]\\n\" % (str(self.zbase), self.meshunit)\n _str += \"xstepsize: %s [%s]\\n\" % (str(self.xstepsize),\n self.meshunit)\n _str += \"ystepsize: %s [%s]\\n\" % (str(self.ystepsize),\n self.meshunit)\n _str += \"zstepsize: %s [%s]\\n\" % (str(self.zstepsize),\n self.meshunit)\n _str += \"xnodes: %s\\n\" % str(self.xnodes)\n _str += \"ynodes: %s\\n\" % str(self.ynodes)\n _str += \"znodes: %s\\n\" % str(self.znodes)\n _str += \"xmin: %s [%s]\\n\" % (str(self.xmin), self.meshunit)\n _str += \"ymin: %s [%s]\\n\" % (str(self.ymin), self.meshunit)\n _str += \"zmin: %s [%s]\\n\" % (str(self.zmin), self.meshunit)\n _str += \"xmax: %s [%s]\\n\" % (str(self.xmax), self.meshunit)\n _str += \"ymax: %s [%s]\\n\" % (str(self.ymax), self.meshunit)\n _str += \"zmax: %s [%s]\\n\" % (str(self.zmax), self.meshunit)\n _str += \"valueunit: %s\\n\" % self.valueunit\n _str += \"valuemultiplier: %s\\n\" % str(self.valuemultiplier)\n _str += \"ValueRangeMinMag:%s [%s]\\n\" % (str(self.valuerangeminmag),\n self.valueunit)\n _str += \"ValueRangeMaxMag:%s [%s]\\n\" % (str(self.valuerangemaxmag),\n self.valueunit)\n return _str\n\n def set_m(self, mx, my, mz):\n \"\"\"\n Set the Mx, My, Mz values\n \"\"\"\n self.mx = mx\n self.my = my\n self.mz = mz\n\nclass MagSLD(object):\n \"\"\"\n Magnetic SLD.\n \"\"\"\n pos_x = None\n pos_y = None\n pos_z = None\n sld_n = None\n sld_mx = None\n sld_my = None\n sld_mz = None\n # Units\n _pos_unit = 'A'\n _sld_unit = '1/A^(2)'\n _pix_type = 'pixel'\n\n def __init__(self, pos_x, pos_y, pos_z, sld_n=None,\n sld_mx=None, sld_my=None, sld_mz=None, vol_pix=None):\n \"\"\"\n Init for mag SLD\n :params : All should be numpy 1D array\n \"\"\"\n self.is_data = True\n self.filename = ''\n self.xstepsize = 6.0\n self.ystepsize = 6.0\n self.zstepsize = 6.0\n self.xnodes = 10.0\n self.ynodes = 10.0\n self.znodes = 10.0\n self.has_stepsize = False\n self.has_conect = False\n self.pos_unit = self._pos_unit\n self.sld_unit = self._sld_unit\n self.pix_type = 'pixel'\n self.pos_x = pos_x\n self.pos_y = pos_y\n self.pos_z = pos_z\n self.sld_n = sld_n\n self.line_x = None\n self.line_y = None\n self.line_z = None\n self.sld_mx = sld_mx\n self.sld_my = sld_my\n self.sld_mz = sld_mz\n self.vol_pix = vol_pix\n self.sld_m = None\n self.sld_phi = None\n self.sld_theta = None\n self.pix_symbol = None\n if sld_mx is not None and sld_my is not None and sld_mz is not None:\n self.set_sldms(sld_mx, sld_my, sld_mz)\n self.set_nodes()\n\n def __str__(self):\n \"\"\"\n doc strings\n \"\"\"\n _str = \"Type: %s\\n\" % self.__class__.__name__\n _str += \"File: %s\\n\" % self.filename\n _str += \"Axis_unit: %s\\n\" % self.pos_unit\n _str += \"SLD_unit: %s\\n\" % self.sld_unit\n return _str\n\n def set_pix_type(self, pix_type):\n \"\"\"\n Set pixel type\n :Param pix_type: string, 'pixel' or 'atom'\n \"\"\"\n self.pix_type = pix_type\n\n def set_sldn(self, sld_n):\n \"\"\"\n Sets neutron SLD\n \"\"\"\n if sld_n.__class__.__name__ == 'float':\n if self.is_data:\n # For data, put the value to only the pixels w non-zero M\n is_nonzero = (np.fabs(self.sld_mx) +\n np.fabs(self.sld_my) +\n np.fabs(self.sld_mz)).nonzero()\n self.sld_n = np.zeros(len(self.pos_x))\n if len(self.sld_n[is_nonzero]) > 0:\n self.sld_n[is_nonzero] = sld_n\n else:\n self.sld_n.fill(sld_n)\n else:\n # For non-data, put the value to all the pixels\n self.sld_n = np.ones(len(self.pos_x)) * sld_n\n else:\n self.sld_n = sld_n\n\n def set_sldms(self, sld_mx, sld_my, sld_mz):\n r\"\"\"\n Sets mx, my, mz and abs(m).\n \"\"\" # Note: escaping\n if sld_mx.__class__.__name__ == 'float':\n self.sld_mx = np.ones(len(self.pos_x)) * sld_mx\n else:\n self.sld_mx = sld_mx\n if sld_my.__class__.__name__ == 'float':\n self.sld_my = np.ones(len(self.pos_x)) * sld_my\n else:\n self.sld_my = sld_my\n if sld_mz.__class__.__name__ == 'float':\n self.sld_mz = np.ones(len(self.pos_x)) * sld_mz\n else:\n self.sld_mz = sld_mz\n\n sld_m = np.sqrt(sld_mx * sld_mx + sld_my * sld_my + \\\n sld_mz * sld_mz)\n self.sld_m = sld_m\n\n def set_pixel_symbols(self, symbol='pixel'):\n \"\"\"\n Set pixel\n :Params pixel: str; pixel or atomic symbol, or array of strings\n \"\"\"\n if self.sld_n is None:\n return\n if symbol.__class__.__name__ == 'str':\n self.pix_symbol = np.repeat(symbol, len(self.sld_n))\n else:\n self.pix_symbol = symbol\n\n def set_pixel_volumes(self, vol):\n \"\"\"\n Set pixel volumes\n :Params pixel: str; pixel or atomic symbol, or array of strings\n \"\"\"\n if self.sld_n is None:\n return\n if vol.__class__.__name__ == 'ndarray':\n self.vol_pix = vol\n elif vol.__class__.__name__.count('float') > 0:\n self.vol_pix = np.repeat(vol, len(self.sld_n))\n else:\n self.vol_pix = None\n\n def get_sldn(self):\n \"\"\"\n Returns nuclear sld\n \"\"\"\n return self.sld_n\n\n def set_nodes(self):\n \"\"\"\n Set xnodes, ynodes, and znodes\n \"\"\"\n self.set_stepsize()\n if self.pix_type == 'pixel':\n try:\n xdist = (max(self.pos_x) - min(self.pos_x)) / self.xstepsize\n ydist = (max(self.pos_y) - min(self.pos_y)) / self.ystepsize\n zdist = (max(self.pos_z) - min(self.pos_z)) / self.zstepsize\n self.xnodes = int(xdist) + 1\n self.ynodes = int(ydist) + 1\n self.znodes = int(zdist) + 1\n except Exception:\n self.xnodes = None\n self.ynodes = None\n self.znodes = None\n else:\n self.xnodes = None\n self.ynodes = None\n self.znodes = None\n\n def set_stepsize(self):\n \"\"\"\n Set xtepsize, ystepsize, and zstepsize\n \"\"\"\n if self.pix_type == 'pixel':\n try:\n xpos_pre = self.pos_x[0]\n ypos_pre = self.pos_y[0]\n zpos_pre = self.pos_z[0]\n for x_pos in self.pos_x:\n if xpos_pre != x_pos:\n self.xstepsize = np.fabs(x_pos - xpos_pre)\n break\n for y_pos in self.pos_y:\n if ypos_pre != y_pos:\n self.ystepsize = np.fabs(y_pos - ypos_pre)\n break\n for z_pos in self.pos_z:\n if zpos_pre != z_pos:\n self.zstepsize = np.fabs(z_pos - zpos_pre)\n break\n #default pix volume\n self.vol_pix = np.ones(len(self.pos_x))\n vol = self.xstepsize * self.ystepsize * self.zstepsize\n self.set_pixel_volumes(vol)\n self.has_stepsize = True\n except Exception:\n self.xstepsize = None\n self.ystepsize = None\n self.zstepsize = None\n self.vol_pix = None\n self.has_stepsize = False\n else:\n self.xstepsize = None\n self.ystepsize = None\n self.zstepsize = None\n self.has_stepsize = True\n return self.xstepsize, self.ystepsize, self.zstepsize\n\n def set_conect_lines(self, line_x, line_y, line_z):\n \"\"\"\n Set bonding line data if taken from pdb\n \"\"\"\n if line_x.__class__.__name__ != 'list' or len(line_x) < 1:\n return\n if line_y.__class__.__name__ != 'list' or len(line_y) < 1:\n return\n if line_z.__class__.__name__ != 'list' or len(line_z) < 1:\n return\n self.has_conect = True\n self.line_x = line_x\n self.line_y = line_y\n self.line_z = line_z\n\ndef _get_data_path(*path_parts):\n from os.path import realpath, join as joinpath, dirname, abspath\n # in sas/sascalc/calculator; want sas/sasview/test\n return joinpath(dirname(realpath(__file__)),\n '..', '..', 'sasview', 'test', *path_parts)\n\ndef test_load():\n \"\"\"\n Test code\n \"\"\"\n from mpl_toolkits.mplot3d import Axes3D\n tfpath = _get_data_path(\"1d_data\", \"CoreXY_ShellZ.txt\")\n ofpath = _get_data_path(\"coordinate_data\", \"A_Raw_Example-1.omf\")\n if not os.path.isfile(tfpath) or not os.path.isfile(ofpath):\n raise ValueError(\"file(s) not found: %r, %r\"%(tfpath, ofpath))\n reader = SLDReader()\n oreader = OMFReader()\n output = reader.read(tfpath)\n ooutput = oreader.read(ofpath)\n foutput = OMF2SLD()\n foutput.set_data(ooutput)\n\n import matplotlib.pyplot as plt\n fig = plt.figure()\n ax = Axes3D(fig)\n ax.plot(output.pos_x, output.pos_y, output.pos_z, '.', c=\"g\",\n alpha=0.7, markeredgecolor='gray', rasterized=True)\n gap = 7\n max_mx = max(output.sld_mx)\n max_my = max(output.sld_my)\n max_mz = max(output.sld_mz)\n max_m = max(max_mx, max_my, max_mz)\n x2 = output.pos_x+output.sld_mx/max_m * gap\n y2 = output.pos_y+output.sld_my/max_m * gap\n z2 = output.pos_z+output.sld_mz/max_m * gap\n x_arrow = np.column_stack((output.pos_x, x2))\n y_arrow = np.column_stack((output.pos_y, y2))\n z_arrow = np.column_stack((output.pos_z, z2))\n unit_x2 = output.sld_mx / max_m\n unit_y2 = output.sld_my / max_m\n unit_z2 = output.sld_mz / max_m\n color_x = np.fabs(unit_x2 * 0.8)\n color_y = np.fabs(unit_y2 * 0.8)\n color_z = np.fabs(unit_z2 * 0.8)\n colors = np.column_stack((color_x, color_y, color_z))\n plt.show()\n\ndef test_save():\n ofpath = _get_data_path(\"coordinate_data\", \"A_Raw_Example-1.omf\")\n if not os.path.isfile(ofpath):\n raise ValueError(\"file(s) not found: %r\"%(ofpath,))\n oreader = OMFReader()\n omfdata = oreader.read(ofpath)\n omf2sld = OMF2SLD()\n omf2sld.set_data(omfdata)\n writer = SLDReader()\n writer.write(\"out.txt\", omf2sld.output)\n\ndef test():\n \"\"\"\n Test code\n \"\"\"\n ofpath = _get_data_path(\"coordinate_data\", \"A_Raw_Example-1.omf\")\n if not os.path.isfile(ofpath):\n raise ValueError(\"file(s) not found: %r\"%(ofpath,))\n oreader = OMFReader()\n omfdata = oreader.read(ofpath)\n omf2sld = OMF2SLD()\n omf2sld.set_data(omfdata)\n model = GenSAS()\n model.set_sld_data(omf2sld.output)\n x = np.linspace(0, 0.1, 11)[1:]\n return model.runXY([x, x])\n\nif __name__ == \"__main__\":\n #test_load()\n #test_save()\n #print(test())\n test()\n", "sub_path": "sas/sascalc/calculator/sas_gen.py", "file_name": "sas_gen.py", "file_ext": "py", "file_size_in_byte": 41209, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 21, "usage_type": "attribute"}, {"api_name": "BaseComponent.BaseComponent", "line_number": 59, "usage_type": "name"}, {"api_name": "BaseComponent.BaseComponent.__init__", "line_number": 69, "usage_type": "call"}, {"api_name": "BaseComponent.BaseComponent", "line_number": 69, "usage_type": "name"}, {"api_name": "numpy.inf", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 97, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 101, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.empty_like", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.empty_like", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.ascontiguousarray", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 265, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 278, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.fabs", "line_number": 340, "usage_type": "call"}, {"api_name": "numpy.fabs", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 381, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 382, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 383, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 403, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 404, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 405, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 465, "usage_type": "call"}, {"api_name": "os.path", "line_number": 465, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 515, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 516, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 517, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 518, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 519, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 520, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 521, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 522, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 523, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 557, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 558, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 559, "usage_type": "call"}, {"api_name": "periodictable.nsf.neutron_sld", "line_number": 561, "usage_type": "call"}, {"api_name": "periodictable.nsf", "line_number": 561, "usage_type": "name"}, {"api_name": "numpy.append", "line_number": 564, "usage_type": "call"}, {"api_name": "periodictable.formula", "line_number": 565, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 568, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 571, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 572, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 573, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 574, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 575, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 607, "usage_type": "call"}, {"api_name": "os.path", "line_number": 607, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 644, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 645, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 646, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 647, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 648, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 649, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 650, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 653, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 655, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 656, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 657, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 658, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 659, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 660, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 661, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 664, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 683, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 684, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 685, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 686, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 687, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 688, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 689, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 692, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 700, "usage_type": "call"}, {"api_name": "os.path", "line_number": 700, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 726, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 729, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 730, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 731, "usage_type": "call"}, {"api_name": "numpy.fabs", "line_number": 907, "usage_type": "call"}, {"api_name": "numpy.fabs", "line_number": 908, "usage_type": "call"}, {"api_name": "numpy.fabs", "line_number": 909, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 910, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 917, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 926, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 930, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 934, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 938, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 950, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 964, "usage_type": "call"}, {"api_name": "numpy.fabs", "line_number": 1007, "usage_type": "call"}, {"api_name": "numpy.fabs", "line_number": 1011, "usage_type": "call"}, {"api_name": "numpy.fabs", "line_number": 1015, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 1018, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1053, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 1053, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 1053, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 1063, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1063, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 1073, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1073, "usage_type": "name"}, {"api_name": "mpl_toolkits.mplot3d.Axes3D", "line_number": 1074, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 1085, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 1086, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 1087, "usage_type": "call"}, {"api_name": "numpy.fabs", "line_number": 1091, "usage_type": "call"}, {"api_name": "numpy.fabs", "line_number": 1092, "usage_type": "call"}, {"api_name": "numpy.fabs", "line_number": 1093, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 1094, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 1095, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1095, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 1099, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1099, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 1113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1113, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 1121, "usage_type": "call"}]} +{"seq_id": "193465815", "text": "import json\nimport unittest\nimport models\nfrom models.recipe import Recipe\nfrom os import environ\nfrom datetime import datetime\n\n\nfrom api.fetcher import Fetcher\n\n\nclass test_app_backend(unittest.TestCase):\n\n @classmethod\n def setUpClass(cls) -> None:\n cls.fetcher = Fetcher()\n\n\n def tearDown(self) -> None:\n models.storage.reload()\n models.storage.reload(\"favorites\")\n\n def test_dotenv_rapidAPI_host(self):\n self.assertIsNotNone(environ.get(\"X-RAPIDAPI-HOST\", None))\n\n def test_dotenv_rapidAPI_key(self):\n self.assertIsNotNone(environ.get(\"X-RAPIDAPI-KEY\", None))\n\n # @unittest.skip(\"Saving requests\")\n def test_fetcher_get_recipe(self):\n self.fetcher.get_recipe(**{'number': 20, 'tags': \"vegetarian,dessert\"})\n self.assertEqual(self.fetcher.status_code, 200)\n self.assertEqual(type(self.fetcher.json), dict)\n with open('test_data_1.json', 'w+') as j:\n json.dump(self.fetcher.json, j)\n self.fetcher.create_recipe()\n\n @unittest.skip(\"want to use API call\")\n def test_create_recipe_cache(self):\n fetch = self.fetcher\n with open('test_data_0.json', 'r') as f:\n j = json.load(f)\n fetch.create_recipe(j)\n models.storage.reload(\"objects\")\n print(models.storage.all())\n\n # def test_create_recipe_cache(self):\n # self.fetcher.create_recipe()\n # models.storage.reload(\"objects\")\n # print(models.storage.all())\n\n @unittest.skip(\"not test this now\")\n def test_create_recipe_fav(self):\n fetch = self.fetcher\n with open('test_data_0.json', 'r') as f:\n j = json.load(f)\n print(j)\n fetch.create_recipe(j)\n models.storage.save(\"favorites\")\n models.storage.reload(\"favorites\")\n\n def test_like_recipe(self):\n new_r = dict(\n id=\"abcd\",\n title=\"a new recipe\"\n )\n r = Recipe(**new_r)\n r.save()\n r.like()\n self.assertIsNotNone(models.storage.get(\"Recipe\", \"abcd\", \"fav\"))\n def test_Dislike_recipe(self):\n new_r = dict(\n id=\"xyz\",\n title=\"I don't like you!\"\n )\n r = Recipe(**new_r)\n r.save()\n r.dislike()\n self.assertIsNone(models.storage.get(\"Recipe\", \"xzy\"))\n self.assertIsNone(models.storage.get(\"Recipe\", \"xzy\", \"fav\"))\n\n def test_delete_from_like_recipe(self):\n new_r = dict(\n id=\"acbeasyas123\",\n title=\"a new recipe\"\n )\n r = Recipe(**new_r)\n r.save()\n r.like()\n self.assertIsNotNone(models.storage.get(\"Recipe\", \"acbeasyas123\", \"fav\"))\n r.delete_from_fav()\n self.assertIsNone(models.storage.get(\"Recipe\", \"acbeasyas123\", \"fav\"))\n\n", "sub_path": "tests/test_app.py", "file_name": "test_app.py", "file_ext": "py", "file_size_in_byte": 2801, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "unittest.TestCase", "line_number": 12, "usage_type": "attribute"}, {"api_name": "api.fetcher.Fetcher", "line_number": 16, "usage_type": "call"}, {"api_name": "models.storage.reload", "line_number": 20, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.storage.reload", "line_number": 21, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 24, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 24, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 27, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 27, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 35, "usage_type": "call"}, {"api_name": "json.load", "line_number": 42, "usage_type": "call"}, {"api_name": "models.storage.reload", "line_number": 44, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 44, "usage_type": "attribute"}, {"api_name": "models.storage.all", "line_number": 45, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 45, "usage_type": "attribute"}, {"api_name": "unittest.skip", "line_number": 38, "usage_type": "call"}, {"api_name": "json.load", "line_number": 56, "usage_type": "call"}, {"api_name": "models.storage.save", "line_number": 59, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 59, "usage_type": "attribute"}, {"api_name": "models.storage.reload", "line_number": 60, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 60, "usage_type": "attribute"}, {"api_name": "unittest.skip", "line_number": 52, "usage_type": "call"}, {"api_name": "models.recipe.Recipe", "line_number": 67, "usage_type": "call"}, {"api_name": "models.storage.get", "line_number": 70, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 70, "usage_type": "attribute"}, {"api_name": "models.recipe.Recipe", "line_number": 76, "usage_type": "call"}, {"api_name": "models.storage.get", "line_number": 79, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 79, "usage_type": "attribute"}, {"api_name": "models.storage.get", "line_number": 80, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 80, "usage_type": "attribute"}, {"api_name": "models.recipe.Recipe", "line_number": 87, "usage_type": "call"}, {"api_name": "models.storage.get", "line_number": 90, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 90, "usage_type": "attribute"}, {"api_name": "models.storage.get", "line_number": 92, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 92, "usage_type": "attribute"}]} +{"seq_id": "295509945", "text": "# coding: utf-8\n\nfrom django import forms\nfrom django.forms import ModelForm # , BaseModelFormSet, BaseInlineFormSet\nfrom django.forms.utils import ErrorList\nfrom django.contrib.admin.widgets import AdminDateWidget\n# from pip._vendor.webencodings import labels\nfrom .models import *\n\n\n# class BootsTrapErrorList(ErrorList):\n# \"\"\"Кастомный класс вывода списка ошибок в BootsTrap.\"\"\"\n# def __str__(self):\n# return self.as_divs()\n#\n# def as_divs(self):\n# if not self: return ''\n# return '
%s
' % ''.join(['
%s
' % e for e in self])\n\n\nclass BankForm(ModelForm):\n \"\"\"\n Форма для справочника банков\n \"\"\"\n\n class Meta:\n model = Bank\n fields = ['name', 'commission']\n\n def clean_commission(self):\n \"\"\"\n Проверка, что комиссия не отридцательна и не более 100%\n :return: поле commission\n \"\"\"\n commission = self.cleaned_data['commission']\n if commission < 0:\n raise forms.ValidationError(\"Комісія не може бути від'ємною\")\n elif commission > 100:\n raise forms.ValidationError(\"Комісія не може бути більше 100%\")\n return commission\n\n def clean_DELETE(self):\n \"\"\"\n Проверка, что к удаляемому банку не привязаны оплаты\n :return: поле DELETE\n \"\"\"\n DELETE = self.cleaned_data['DELETE']\n if DELETE and Bank.objects.get(id=self.cleaned_data['id'].id).payments_set.filter(del_mark=False):\n raise forms.ValidationError(\"Банк не можливо видалити, тому що до нього прив'язані не виделені оплати\")\n return DELETE\n\n\nclass DiscountForm(ModelForm):\n \"\"\"\n Форма для справочника скидок\n \"\"\"\n\n class Meta:\n model = Discount\n fields = ['tariff', 'name', 'is_fixed', 'percent', 'fixed_value']\n\n def clean_percent(self):\n \"\"\"\n Проверка, что процентная доля не отридцательна и не более 100%\n :return: поле percent\n \"\"\"\n percent = self.cleaned_data['percent']\n if percent < 0:\n raise forms.ValidationError(\"Відсоток не може бути від'ємним\")\n elif percent > 100:\n raise forms.ValidationError(\"Відсоток не може бути більше 100%\")\n return percent\n\n def clean_fixed_value(self):\n \"\"\"\n Проверка, что абсолютная доля не отридцательна и не более 100%\n :return: поле fixed_value\n \"\"\"\n fixed_value = self.cleaned_data['fixed_value']\n if fixed_value < 0:\n raise forms.ValidationError(\"Фіксоване значення не може бути від'ємним\")\n return fixed_value\n\n def clean_DELETE(self):\n \"\"\"\n Проверка, что к удаляемой записи не привязаны оплаты\n :return: поле DELETE\n \"\"\"\n DELETE = self.cleaned_data['DELETE']\n if DELETE and Discount.objects.get(id=self.cleaned_data['id'].id).discountinstance_set.filter(del_mark=False):\n raise forms.ValidationError(\"Знижку не можливо видалити, тому що вона надана деяким квартирам\")\n return DELETE\n\n\nclass ServiceForm(ModelForm):\n \"\"\"\n Форма для справочника сервисов\n \"\"\"\n\n class Meta:\n model = Service\n fields = ['name', 'constant', 'tariffable', 'seasonality', 'dimension']\n\n def clean_DELETE(self):\n \"\"\"\n Проверка, что к удаляемой записи не привязаны оплаты\n :return: поле DELETE\n \"\"\"\n DELETE = self.cleaned_data['DELETE']\n if DELETE and Service.objects.get(id=self.cleaned_data['id'].id).serviceinstance_set.filter(del_mark=False):\n raise forms.ValidationError(\"Послугу не можливо видалити, тому що вона надана деяким квартирам\")\n elif DELETE and Service.objects.get(id=self.cleaned_data['id'].id).discount_set.filter(del_mark=False):\n raise forms.ValidationError(\"Послугу не можливо видалити, тому що до неї прив'язана льгота\")\n return DELETE\n\n\nclass TariffForm(ModelForm):\n \"\"\"\n Форма для справочника сервисов\n \"\"\"\n\n class Meta:\n model = Tariff\n fields = ['service', 'value', 'service_volume', 'season_begin', 'season_end', 'date_begin', 'date_end']\n widgets = {\n 'season_begin': AdminDateWidget,\n 'season_end': AdminDateWidget,\n 'date_begin': AdminDateWidget,\n 'date_end': AdminDateWidget\n }\n\n def clean_value(self):\n \"\"\"\n Проверка, что значение не отридцательное\n :return: поле value\n \"\"\"\n value = self.cleaned_data['value']\n if value < 0:\n raise forms.ValidationError(\"Значення тарифу не може бути від'ємним\")\n return value\n\n def clean_service_volume(self):\n \"\"\"\n Проверка, что постоянное значение не отридцательное\n :return: поле value\n \"\"\"\n value = self.cleaned_data['service_volume']\n if value < 0:\n raise forms.ValidationError(\"Фіксоване значення не може бути від'ємним\")\n return value\n\n\nclass PaymentForm(ModelForm):\n \"\"\"\n Форма для ввода оплат в деталях сальдовой карточки\n \"\"\"\n\n # error_class = BootsTrapErrorList\n # error_css_class = 'error'\n # required_css_class = 'required'\n # pay_date = forms.DateField(\n # widget=forms.TextInput(attrs={'class': 'datepicker'}),\n # input_formats=['%d.%m.%Y']\n # )\n\n class Meta:\n model = Payment\n fields = ['pay_date', 'value', 'bank', 'saldo_card', 'flat']\n widgets = {\n 'saldo_card': forms.HiddenInput,\n 'flat': forms.HiddenInput,\n # 'pay_date': forms.TextInput(\n # attrs={'class': 'datepicker'},\n # ),\n # 'value': forms.TextInput(attrs={'class': 'form-control'}),\n # 'bank': forms.ModelChoiceField(queryset=Bank.safe_objects.all(), to_field_name='bank', empty_label=''),\n }\n\n\nclass TrafficForm(ModelForm):\n \"\"\"\n Форма для ввода значений трафика в деталях сальдовой карточки\n \"\"\"\n\n class Meta:\n model = Traffic\n fields = ['saldo_card', 'service_instance', 'value', 'registered_on']\n widgets = {\n 'saldo_card': forms.HiddenInput,\n 'registered_on': forms.HiddenInput,\n }\n\n # def __init__(self, *args, **kwargs):\n # super(TrafficForm, self).__init__(*args, **kwargs)\n # self.parent_saldo_card = kwargs['saldo_card']\n\n # def clean(self):\n # if len(self.changed_data) != 0:\n # # a = dir(self)\n # b = self.cleaned_data\n # if self.cleaned_data['service_instance'] is not None or self.cleaned_data['value'] is not None:\n # self.cleaned_data['saldo_card'] = self.parent_saldo_card\n # raise 0\n # super(TrafficForm, self).clean()\n\n # def clean_saldo_card(self, *args, **kwargs):\n # \"\"\"\n # Очистка значения saldo_card если service_instance не задан\n # :return: поле value\n # \"\"\"\n # saldo_card = self.cleaned_data['saldo_card']\n # raise 0\n # if self.fields.get('saldo_card'):\n # debug = dir(self.fields['value'].prepare_value)\n # val = self.fields['value'].prepare_value.__get__\n # raise 0\n # return saldo_card\n # else:\n # value = self.fields['value']\n # service_instance = self.fields['service_instance']\n # raise 0\n", "sub_path": "accounting/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 8460, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.forms.ModelForm", "line_number": 21, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 37, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 37, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 39, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 39, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 49, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 49, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 53, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 69, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 69, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 71, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 71, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 81, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 81, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 91, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 91, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 95, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 111, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 111, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 113, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 113, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 117, "usage_type": "name"}, {"api_name": "django.contrib.admin.widgets.AdminDateWidget", "line_number": 126, "usage_type": "name"}, {"api_name": "django.contrib.admin.widgets.AdminDateWidget", "line_number": 127, "usage_type": "name"}, {"api_name": "django.contrib.admin.widgets.AdminDateWidget", "line_number": 128, "usage_type": "name"}, {"api_name": "django.contrib.admin.widgets.AdminDateWidget", "line_number": 129, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 139, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 139, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 149, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 149, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 153, "usage_type": "name"}, {"api_name": "django.forms.HiddenInput", "line_number": 170, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 170, "usage_type": "name"}, {"api_name": "django.forms.HiddenInput", "line_number": 171, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 171, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 180, "usage_type": "name"}, {"api_name": "django.forms.HiddenInput", "line_number": 189, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 189, "usage_type": "name"}, {"api_name": "django.forms.HiddenInput", "line_number": 190, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 190, "usage_type": "name"}]} +{"seq_id": "383296814", "text": "import sqlite3\n\ndef create_table(db_name,table_name, sql):\n with sqlite3.connect(db_name) as db:\n cursor = db.cursor()\n cursor.execute(\"select name from sqlite_master where name=?\",(table_name,))\n result = cursor.fetchall()\n keep_table = True\n if len(result) == 1:\n response = input(\"The table {0} already exists, do you wish to re-create it (y/n):\".format(table_name))\n if response == 'y':\n keep_table = False\n print(\"The {0} table will be recreated - all existing data will be deleted\".format(table_name))\n cursor.execute(\"drop table if exists {0}\".format(table_name))\n db.commit()\n else:\n print(\"The existing table was kept\")\n else:\n keep_table = False\n if not keep_table:\n cursor.execute(sql)\n db.commit()\n\ndef create_product_table():\n sql = \"\"\"create table Product\n (ProductID integer,\n ProductName text,\n Size string,\n Price real,\n Primary Key(ProductID))\"\"\"\n create_table(db_name, \"Product\", sql)\n\n\ndef create_employee_table():\n sql = \"\"\"create table Employee\n (EmployeeID integer,\n EmployeeFirstName text,\n EmployeeLastName text,\n Password text,\n EmployeeEmail float,\n Primary Key(EmployeeID))\"\"\"\n create_table(db_name, \"Employee\", sql)\n\ndef create_member_table():\n sql = \"\"\"create table Member\n (MemberID integer,\n Title string,\n MemberFirstName text,\n MemberLastName text,\n HouseNo interger,\n Street text,\n Town text,\n City text,\n County text,\n Postcode text,\n TelephoneNo integer,\n MemberEmail text,\n Primary Key(MemberID))\"\"\"\n create_table(db_name, \"Member\", sql)\n\ndef create_order_table():\n sql = \"\"\"create table CustomerOrder\n (OrderID integer,\n MemberID integer,\n EmployeeID integer,\n DateTime text,\n Primary Key(OrderID),\n foreign key(MemberID) references Member(MemberID),\n foreign key(EmployeeID) references Employee(EmployeeID))\"\"\" \n create_table(db_name,\"CustomerOrder\", sql)\n\ndef create_product_order_table():\n sql = \"\"\"create table ProductOrder\n (ProductOrderID integer,\n ProductID interger,\n OrderID integer,\n Quantity integer,\n Primary Key(ProductOrderID),\n foreign key(ProductID) references Product(ProductID),\n foreign key(OrderID) references CustomerOrder(OrderID))\"\"\" \n create_table(db_name,\"ProductOrder\", sql)\n\ndef create_location_table():\n sql = \"\"\"create table Location\n (LocationID integer,\n LocationName string,\n Primary Key(LocationID))\"\"\" \n create_table(db_name,\"Location\", sql)\n\ndef create_product_location_table():\n sql = \"\"\"create table ProductLocation\n (ProductID integer,\n LocationID integer,\n Primary Key(ProductID),\n Foreign Key(LocationID) references Location(LocationID))\"\"\" \n create_table(db_name,\"ProductLocation\", sql)\n\nif __name__ == \"__main__\":\n db_name = \"ProductDatabase.db\"\n create_product_table()\n create_product_order_table()\n create_location_table()\n create_product_location_table()\n create_employee_table()\n create_member_table()\n create_order_table()\n", "sub_path": "Implementation/Database/Creating_Table.py", "file_name": "Creating_Table.py", "file_ext": "py", "file_size_in_byte": 3639, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sqlite3.connect", "line_number": 4, "usage_type": "call"}]} +{"seq_id": "43341317", "text": "import os\nimport sys\nfrom setuptools import find_packages, setup\nfrom setuptools.command.install import install\n\n\npackage_name = \"dbt-helper\"\nVERSION = \"0.2.0\"\ndescription = \"\"\"dbt-helper is a command line tool to help ease dbt development and database management\"\"\"\n\n\nclass VerifyVersionCommand(install):\n \"\"\"\n Custom command to verify that the git tag matches our version\n https://circleci.com/blog/continuously-deploying-python-packages-to-pypi-with-circleci/\n \"\"\"\n\n description = \"verify that the git tag matches our version\"\n\n def run(self):\n tag = os.getenv(\"CIRCLE_TAG\")\n\n if tag != VERSION:\n info = \"Git tag: {0} does not match the version of this app: {1}\".format(\n tag, VERSION\n )\n sys.exit(info)\n\n\nsetup(\n name=package_name,\n version=VERSION,\n description=description,\n author=\"Michael Kaminsky\",\n author_email=\"michael@kaminsky.rocks\",\n url=\"https://github.com/mikekaminsky/dbt-helper\",\n packages=find_packages(),\n package_data={},\n test_suite=\"test\",\n entry_points={\"console_scripts\": [\"dbt-helper = core.main:main\"]},\n scripts=[],\n install_requires=[\"dbt\"],\n cmdclass={\"verify\": VerifyVersionCommand},\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1237, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "setuptools.command.install.install", "line_number": 12, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 27, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 30, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "593365862", "text": "from flask_bootstrap import Bootstrap\r\nfrom flask import (Flask, request, redirect, url_for, render_template)\r\nfrom libs.GroupBotORM import *\r\nfrom libs import config_bot as config\r\n\r\nbootstrap = Bootstrap()\r\n\r\napp = Flask(__name__)\r\nbootstrap.init_app(app)\r\n\r\n\r\n@app.route('/')\r\ndef show_topics():\r\n topics = QATopic.select()\r\n # topics = (Topic\r\n # .select(Topic, fn.Count(Ask.id).alias('ask_count'), fn.Count(Reply.id).alias('reply_count'))\r\n # .join(Ask, JOIN.LEFT_OUTER)\r\n # .switch(Topic)\r\n # .join(Reply, JOIN.LEFT_OUTER)\r\n # .group_by(Topic)\r\n # )\r\n return render_template('topics.html', session_name=config.BOT_SESSION_NAME, topics=topics)\r\n\r\n\r\n@app.route('/topic/')\r\ndef show_topic(topic_id):\r\n topic = QATopic.get(id=topic_id)\r\n return render_template('topic.html', session_name=config.BOT_SESSION_NAME, topic=topic)\r\n\r\n\r\n@app.route('/add-topic', methods=['POST'])\r\ndef add_topic():\r\n if len(request.form['title'].strip()) > 0:\r\n QATopic.create(\r\n active=request.form['active'] == 'True',\r\n use_reply=request.form['useReply'] == 'True',\r\n show_title=request.form['showTitle'] == 'True',\r\n title=request.form['title'].strip(),\r\n remark=request.form['remark'].strip(),\r\n )\r\n return redirect(request.referrer)\r\n\r\n\r\n@app.route('/toggle-topic/', methods=['GET'])\r\ndef toggle_topic(topic_id):\r\n topic = QATopic.get(id=topic_id)\r\n topic.active = not topic.active\r\n topic.save()\r\n return redirect(request.referrer)\r\n\r\n\r\n@app.route('/update-topic/', methods=['POST'])\r\ndef update_topic(topic_id):\r\n topic = QATopic.get(id=topic_id)\r\n topic.active = request.form['active'] == 'True'\r\n topic.use_reply = request.form['useReply'] == 'True'\r\n topic.show_title = request.form['showTitle'] == 'True'\r\n topic.title = request.form['title'].strip()\r\n topic.remark = request.form['remark'].strip()\r\n topic.save()\r\n return redirect(request.referrer)\r\n\r\n\r\n@app.route('/add-tag/', methods=['POST'])\r\ndef add_tag(topic_id):\r\n topic = QATopic.get(id=topic_id)\r\n\r\n if len(request.form['title'].strip()) > 0:\r\n QATag.create(\r\n topic=topic,\r\n active=True,\r\n title=request.form['title'].strip().strip('#').lower(),\r\n )\r\n return redirect(request.referrer)\r\n\r\n\r\n@app.route('/toggle-tag/', methods=['GET'])\r\ndef toggle_tag(tag_id):\r\n tag = QATag.get(id=tag_id)\r\n tag.active = not tag.active\r\n tag.save()\r\n return redirect(request.referrer)\r\n\r\n\r\n@app.route('/add-ask/', methods=['POST'])\r\ndef add_ask(topic_id):\r\n topic = QATopic.get(id=topic_id)\r\n\r\n if len(request.form['words'].strip()) > 0:\r\n QAAsk.create(\r\n topic=topic,\r\n active=request.form['active'] == 'True',\r\n mode=int(request.form['mode'].strip()),\r\n words=request.form['words'].strip().lower(),\r\n max=int(request.form['max'].strip()),\r\n remark=request.form['remark'].strip(),\r\n )\r\n return redirect(request.referrer)\r\n\r\n\r\n@app.route('/update-ask/', methods=['POST'])\r\ndef update_ask(ask_id):\r\n ask = QAAsk.get(id=ask_id)\r\n ask.active = request.form['active'] == 'True'\r\n ask.mode = int(request.form['mode'].strip())\r\n ask.words = request.form['words'].strip().lower()\r\n ask.max = int(request.form['max'].strip())\r\n ask.remark = request.form['remark'].strip()\r\n ask.save()\r\n return redirect(request.referrer)\r\n\r\n\r\n@app.route('/add-reply/', methods=['POST'])\r\ndef add_reply(topic_id):\r\n topic = QATopic.get(id=topic_id)\r\n\r\n if len(request.form['text'].strip()) > 0:\r\n QAReply.create(\r\n topic=topic,\r\n active=request.form['active'] == 'True',\r\n text=request.form['text'].strip(),\r\n trigger=request.form['trigger'].strip(),\r\n remark=request.form['remark'].strip(),\r\n )\r\n return redirect(request.referrer)\r\n\r\n\r\n@app.route('/update-reply/', methods=['POST'])\r\ndef update_reply(reply_id):\r\n reply = QAReply.get(id=reply_id)\r\n reply.active = request.form['active'] == 'True'\r\n reply.text = request.form['text'].strip()\r\n reply.trigger = request.form['trigger'].strip()\r\n reply.remark = request.form['remark'].strip()\r\n reply.save()\r\n return redirect(request.referrer)\r\n\r\n\r\n@app.route('/kvs')\r\ndef show_kvs():\r\n kvs = KeyValue.select()\r\n return render_template('kvs.html', session_name=config.BOT_SESSION_NAME, kvs=kvs)\r\n\r\n\r\n@app.route('/update-kv/', methods=['POST'])\r\ndef update_kv(kv_id):\r\n kv = KeyValue.get(id=kv_id)\r\n kv.key = request.form['key'].strip()\r\n kv.value = request.form['value'].strip()\r\n kv.save()\r\n return redirect(request.referrer)\r\n\r\n\r\napp.run(debug=config.DEBUG_MODE)\r\n", "sub_path": "qa_mgr.py", "file_name": "qa_mgr.py", "file_ext": "py", "file_size_in_byte": 4920, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask_bootstrap.Bootstrap", "line_number": 6, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 22, "usage_type": "call"}, {"api_name": "libs.config_bot.BOT_SESSION_NAME", "line_number": 22, "usage_type": "attribute"}, {"api_name": "libs.config_bot", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 28, "usage_type": "call"}, {"api_name": "libs.config_bot.BOT_SESSION_NAME", "line_number": 28, "usage_type": "attribute"}, {"api_name": "libs.config_bot", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 33, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.request.referrer", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.request.referrer", "line_number": 49, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 49, "usage_type": "name"}, {"api_name": "flask.request.form", "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": "flask.request.form", "line_number": 57, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.request.referrer", "line_number": 61, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 61, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 68, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 68, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 72, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.request.referrer", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 82, "usage_type": "call"}, {"api_name": "flask.request.referrer", "line_number": 82, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 82, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 89, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 89, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 92, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 92, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 93, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 93, "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", "line_number": 95, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 95, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 96, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 96, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.request.referrer", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 104, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 104, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 105, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 105, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 106, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 106, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 107, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 107, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 108, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 108, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 110, "usage_type": "call"}, {"api_name": "flask.request.referrer", "line_number": 110, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 110, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 117, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 117, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 120, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 120, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 121, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 121, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 122, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 122, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 123, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 123, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 125, "usage_type": "call"}, {"api_name": "flask.request.referrer", "line_number": 125, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 125, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 131, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 131, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 132, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 132, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 133, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 133, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 134, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 134, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 136, "usage_type": "call"}, {"api_name": "flask.request.referrer", "line_number": 136, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 136, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 142, "usage_type": "call"}, {"api_name": "libs.config_bot.BOT_SESSION_NAME", "line_number": 142, "usage_type": "attribute"}, {"api_name": "libs.config_bot", "line_number": 142, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 148, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 148, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 149, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 149, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 151, "usage_type": "call"}, {"api_name": "flask.request.referrer", "line_number": 151, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 151, "usage_type": "name"}, {"api_name": "libs.config_bot.DEBUG_MODE", "line_number": 154, "usage_type": "attribute"}, {"api_name": "libs.config_bot", "line_number": 154, "usage_type": "name"}]} +{"seq_id": "605270330", "text": "\"\"\" Compiled: 2020-09-18 10:38:49 \"\"\"\n\n#__src_file__ = \"extensions/settlement/etc/FSettlementSWIFTDocumentCreator.py\"\nimport FOperationsDocumentService as DocumentMod\nimport FSwiftMessageTypeExtractor as ExtractorMod\nimport FSettlementSwiftXMLSpecifier\nfrom tempfile import gettempdir\nimport FOperationsDocumentXMLCreator as XmlCreator\nimport ael\nimport acm\nimport FOperationsUtils as UtilsMod\nfrom FOperationsExceptions import WrapperException\nfrom FSwiftExceptions import SwiftWriterAPIException\n\n\nael_variables = [('settlement_oid', 'Settlement Oid', 'int', [], '0', 0)]\n\ndef ael_main(dictionary):\n ''' This function is called from FSettlement.cpp (CreateSWIFTDocuments)\n when doing preview of the document from the settlement sheet.'''\n\n settlement_oid = int(dictionary['settlement_oid'])\n createdDocuments = acm.FArray()\n\n import FDocumentationParameters as Params\n try:\n docService = DocumentMod.CreateDocumentService(Params)\n\n rec = ael.Settlement[int(settlement_oid)]\n if not rec:\n UtilsMod.LogVerbose('Could not find Settlement (seqnbr=%s), no document will be fetched' % (str(settlement_oid)))\n else:\n xmlSpecifier = FSettlementSwiftXMLSpecifier.SettlementSwiftXMLSpecifier(\"\", rec)\n if docService.IsConnected():\n mtExtractor = ExtractorMod.FSwiftMessageTypeExtractor(docService)\n xml2 = XmlCreator.ToXml(xmlSpecifier)\n docIds = docService.CreateDocument(xml2)\n xmlDirectory = gettempdir()\n if xmlDirectory[-1] != \"\\\\\":\n xmlDirectory = xmlDirectory + \"\\\\\"\n\n xmldata = docService.GetXML(xml2)\n XmlCreator.SaveXml(xmldata, xmlDirectory, xmlSpecifier.GetUniqueFilename())\n\n for docId in docIds:\n pair = acm.FPair()\n pair.First(docId)\n pair.Second(mtExtractor.Extract(docId))\n createdDocuments.Add(pair)\n\n else:\n UtilsMod.LogVerbose('Could not create document: No connection to document service.')\n except (WrapperException, SwiftWriterAPIException) as e:\n UtilsMod.LogAlways('Could not do create document for settlement {}: {}'.format(settlement_oid, e))\n createdDocuments.Clear()\n return createdDocuments\n\n", "sub_path": "Extensions/Settlement/FPythonCode/FSettlementSWIFTDocumentCreator.py", "file_name": "FSettlementSWIFTDocumentCreator.py", "file_ext": "py", "file_size_in_byte": 2355, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "acm.FArray", "line_number": 23, "usage_type": "call"}, {"api_name": "FOperationsDocumentService.CreateDocumentService", "line_number": 27, "usage_type": "call"}, {"api_name": "ael.Settlement", "line_number": 29, "usage_type": "attribute"}, {"api_name": "FOperationsUtils.LogVerbose", "line_number": 31, "usage_type": "call"}, {"api_name": "FSettlementSwiftXMLSpecifier.SettlementSwiftXMLSpecifier", "line_number": 33, "usage_type": "call"}, {"api_name": "FSwiftMessageTypeExtractor.FSwiftMessageTypeExtractor", "line_number": 35, "usage_type": "call"}, {"api_name": "FOperationsDocumentXMLCreator.ToXml", "line_number": 36, "usage_type": "call"}, {"api_name": "tempfile.gettempdir", "line_number": 38, "usage_type": "call"}, {"api_name": "FOperationsDocumentXMLCreator.SaveXml", "line_number": 43, "usage_type": "call"}, {"api_name": "acm.FPair", "line_number": 46, "usage_type": "call"}, {"api_name": "FOperationsUtils.LogVerbose", "line_number": 52, "usage_type": "call"}, {"api_name": "FOperationsExceptions.WrapperException", "line_number": 53, "usage_type": "name"}, {"api_name": "FSwiftExceptions.SwiftWriterAPIException", "line_number": 53, "usage_type": "name"}, {"api_name": "FOperationsUtils.LogAlways", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "2164046", "text": "from __future__ import unicode_literals\n\nimport logging\n\nfrom django.db import models\nfrom django.apps import apps\nfrom django.core.exceptions import ValidationError\n\nfrom fullcalendar.managers import EventCategoryManager, EventManager\nfrom fullcalendar.messages import Messages as msgs\n\nconfig = apps.get_app_config('fullcalendar')\nlogger = logging.getLogger(__name__)\n\n\nclass MetaModelMixin(models.Model):\n created = models.DateTimeField(\n auto_now_add=True,\n verbose_name=msgs.BASE_MODEL_CREATED)\n\n modified = models.DateTimeField(\n auto_now=True,\n verbose_name=msgs.BASE_MODEL_MODIFIED)\n\n is_published = models.BooleanField(\n default=True,\n verbose_name=msgs.BASE_MODEL_IS_PUBLISHED)\n\n class Meta:\n abstract = True\n\n\nclass EventCategory(MetaModelMixin):\n title = models.CharField(\n max_length=255,\n unique=True,\n verbose_name=msgs.EVENT_CATEGORY_TITLE)\n\n description = models.TextField(\n blank=True,\n null=True,\n verbose_name=msgs.EVENT_CATEGORY_DESCRIPTION)\n\n color = models.CharField(\n max_length=7,\n default=config.EVENT_CATEGORY_COLOR_DEFAULT,\n verbose_name=msgs.EVENT_CATEGORY_COLOR,\n help_text=msgs.EVENT_CATEGORY_COLOR_HELP)\n\n objects = EventCategoryManager()\n\n class Meta:\n unique_together = (('title', 'description', 'color'),)\n verbose_name = msgs.EVENT_CATEGORY\n verbose_name_plural = msgs.EVENT_CATEGORY_PLURAL\n\n def __unicode__(self):\n return u'{title}'.format(\n title=self.title)\n\n\nclass Event(MetaModelMixin):\n category = models.ForeignKey(\n EventCategory,\n on_delete=models.CASCADE,\n related_name='events',\n verbose_name=msgs.EVENT_CATEGORY)\n\n title = models.CharField(\n max_length=255,\n verbose_name=msgs.EVENT_TITLE)\n\n description = models.TextField(\n blank=True,\n null=True,\n verbose_name=msgs.EVENT_DESCRIPTION)\n\n start = models.DateTimeField(\n verbose_name=msgs.EVENT_START_TIME)\n\n end = models.DateTimeField(\n verbose_name=msgs.EVENT_END_TIME)\n\n all_day = models.BooleanField(\n default=False,\n verbose_name=msgs.EVENT_ALL_DAY)\n\n objects = EventManager()\n\n class Meta:\n verbose_name = msgs.EVENT\n verbose_name_plural = msgs.EVENT_PLURAL\n\n def __unicode__(self):\n return u'{title}'.format(\n title=self.title)\n\n def clean(self, *args, **kwargs):\n if self.start >= self.end:\n raise ValidationError({\n 'end': ValidationError(msgs.EVENT_TIME_VALIDATION_ERROR, code='invalid')\n })\n\n def get_start_date_time(self, format=None):\n return self.start.strftime(format) if format else self.start\n\n def get_end_date_time(self, format=None):\n return self.end.strftime(format) if format else self.end\n", "sub_path": "fullcalendar/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2915, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.apps.apps.get_app_config", "line_number": 12, "usage_type": "call"}, {"api_name": "django.apps.apps", "line_number": 12, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "fullcalendar.messages.Messages.BASE_MODEL_CREATED", "line_number": 19, "usage_type": "attribute"}, {"api_name": "fullcalendar.messages.Messages", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "fullcalendar.messages.Messages.BASE_MODEL_MODIFIED", "line_number": 23, "usage_type": "attribute"}, {"api_name": "fullcalendar.messages.Messages", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "fullcalendar.messages.Messages.BASE_MODEL_IS_PUBLISHED", "line_number": 27, "usage_type": "attribute"}, {"api_name": "fullcalendar.messages.Messages", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "fullcalendar.messages.Messages.EVENT_CATEGORY_TITLE", "line_number": 37, "usage_type": "attribute"}, {"api_name": "fullcalendar.messages.Messages", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "fullcalendar.messages.Messages.EVENT_CATEGORY_DESCRIPTION", "line_number": 42, "usage_type": "attribute"}, {"api_name": "fullcalendar.messages.Messages", "line_number": 42, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 44, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 44, "usage_type": "name"}, {"api_name": "fullcalendar.messages.Messages.EVENT_CATEGORY_COLOR", "line_number": 47, "usage_type": "attribute"}, {"api_name": "fullcalendar.messages.Messages", "line_number": 47, "usage_type": "name"}, {"api_name": "fullcalendar.messages.Messages.EVENT_CATEGORY_COLOR_HELP", "line_number": 48, "usage_type": "attribute"}, {"api_name": "fullcalendar.messages.Messages", "line_number": 48, "usage_type": "name"}, {"api_name": "fullcalendar.managers.EventCategoryManager", "line_number": 50, "usage_type": "call"}, {"api_name": "fullcalendar.messages.Messages.EVENT_CATEGORY", "line_number": 54, "usage_type": "attribute"}, {"api_name": "fullcalendar.messages.Messages", "line_number": 54, "usage_type": "name"}, {"api_name": "fullcalendar.messages.Messages.EVENT_CATEGORY_PLURAL", "line_number": 55, "usage_type": "attribute"}, {"api_name": "fullcalendar.messages.Messages", "line_number": 55, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 63, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 63, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 65, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 65, "usage_type": "name"}, {"api_name": "fullcalendar.messages.Messages.EVENT_CATEGORY", "line_number": 67, "usage_type": "attribute"}, {"api_name": "fullcalendar.messages.Messages", "line_number": 67, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 69, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 69, "usage_type": "name"}, {"api_name": "fullcalendar.messages.Messages.EVENT_TITLE", "line_number": 71, "usage_type": "attribute"}, {"api_name": "fullcalendar.messages.Messages", "line_number": 71, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 73, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 73, "usage_type": "name"}, {"api_name": "fullcalendar.messages.Messages.EVENT_DESCRIPTION", "line_number": 76, "usage_type": "attribute"}, {"api_name": "fullcalendar.messages.Messages", "line_number": 76, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 78, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 78, "usage_type": "name"}, {"api_name": "fullcalendar.messages.Messages.EVENT_START_TIME", "line_number": 79, "usage_type": "attribute"}, {"api_name": "fullcalendar.messages.Messages", "line_number": 79, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 81, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 81, "usage_type": "name"}, {"api_name": "fullcalendar.messages.Messages.EVENT_END_TIME", "line_number": 82, "usage_type": "attribute"}, {"api_name": "fullcalendar.messages.Messages", "line_number": 82, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 84, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 84, "usage_type": "name"}, {"api_name": "fullcalendar.messages.Messages.EVENT_ALL_DAY", "line_number": 86, "usage_type": "attribute"}, {"api_name": "fullcalendar.messages.Messages", "line_number": 86, "usage_type": "name"}, {"api_name": "fullcalendar.managers.EventManager", "line_number": 88, "usage_type": "call"}, {"api_name": "fullcalendar.messages.Messages.EVENT", "line_number": 91, "usage_type": "attribute"}, {"api_name": "fullcalendar.messages.Messages", "line_number": 91, "usage_type": "name"}, {"api_name": "fullcalendar.messages.Messages.EVENT_PLURAL", "line_number": 92, "usage_type": "attribute"}, {"api_name": "fullcalendar.messages.Messages", "line_number": 92, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 100, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 101, "usage_type": "call"}, {"api_name": "fullcalendar.messages.Messages.EVENT_TIME_VALIDATION_ERROR", "line_number": 101, "usage_type": "attribute"}, {"api_name": "fullcalendar.messages.Messages", "line_number": 101, "usage_type": "name"}]} +{"seq_id": "35895723", "text": "import importlib\n\nclass CRUDRunner(object):\n \"\"\"Runner executes provider crud operations.\"\"\"\n\n def __init__(self, ctx, provider, obj_name):\n super(CRUDRunner, self).__init__()\n self.ctx = ctx\n self.providers = self._load_providers(ctx, provider, obj_name)\n\n def execute(self, operation_name, *args, **kwargs):\n if len(self.providers) == 0:\n raise Exception(\n 'Please specify provider(s) to run %s' % operation_name)\n\n for p in self.providers:\n try:\n if isinstance(p, str):\n self.ctx.log(\n 'Can\\'t load %s to run \\'%s\\'' % (p, operation_name))\n else:\n return getattr(p, operation_name)(*args, **kwargs)\n except AttributeError:\n p.ctx.log('%s not found', operation_name)\n\n def _load_providers(self, ctx, provider, obj_name):\n def get_instance(p):\n try:\n module = importlib.import_module(\n 'a2ml.api.%s.%s' % (p, obj_name))\n provider_class = getattr(module, '%s%s' % \\\n (p.capitalize(),obj_name.capitalize()))\n return provider_class(ctx.copy(p))\n except:\n if self.ctx.debug:\n import traceback\n traceback.print_exc()\n return '%s%s' % (p.capitalize(),obj_name.capitalize())\n providers = [provider] if provider else ctx.get_providers()\n return [get_instance(p) for p in providers]\n", "sub_path": "a2ml/api/utils/crud_runner.py", "file_name": "crud_runner.py", "file_ext": "py", "file_size_in_byte": 1570, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "importlib.import_module", "line_number": 29, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "372347992", "text": "from django.shortcuts import render, HttpResponse, redirect\nfrom django.contrib import messages\nfrom datetime import date, datetime\nfrom dateutil.parser import parse as parse_date\nfrom .. login.models import User\nfrom .models import Appointment\n# Create your views here.\n\ndef index(request):\n if not \"user_id\" in request.session:\n messages.error(request, \"Must be logged in to continue\")\n return redirect(\"login:index\")\n now = datetime.today()\n Appointment.objects.update_status()\n #pass over my user to make it accessable in my template\n current_user = User.objects.get(id = request.session[\"user_id\"])\n #some logic to filter by date likely start with all appointments and then filter stuff out before shoving it into a context variable\n todays_appointments = Appointment.objects.filter(start_date = now, created_by = current_user ).order_by('start_time')\n #some logic to filter by date\n other_appointments = Appointment.objects.filter(created_by = current_user).exclude(start_date = now, ).order_by('start_date', 'start_time')\n\n context = {\n \"current_user\" : current_user,\n \"todays_appointments\" : todays_appointments,\n \"other_appointments\" : other_appointments,\n \"now\" : now\n }\n return render(request, \"main/index.html\", context)\n\ndef add_appointment(request):\n if not \"user_id\" in request.session:\n return redirect (\"login:index\")\n if not request.method == \"POST\":\n return redirect (\"main:index\")\n\n responce_from_model = Appointment.objects.create_appointment(request.POST, request.session[\"user_id\"])\n\n if responce_from_model[\"status\"]:\n messages.error(request, \"You added an Appointment\")\n return redirect(\"main:index\")\n else:\n for error in responce_from_model[\"errors\"]:\n messages.error(request, error)\n return redirect(\"main:index\")\n\ndef edit_appointment(request, appointment_id):\n if not \"user_id\" in request.session:\n return redirect (\"login:index\")\n if not request.method == \"POST\":\n return redirect (\"main:index\")\n appointment = Appointment.objects.get(id = appointment_id)\n context = {\n \"appointment\" : appointment\n }\n\n return render(request, \"main/edit_appointment.html\", context)\n\ndef confirm_edit_appointment(request, appointment_id):\n if not \"user_id\" in request.session:\n return redirect (\"login:index\")\n if not request.method == \"POST\":\n return redirect (\"main:index\")\n appointment = Appointment.objects.get(id = appointment_id)\n responce_from_model = Appointment.objects.edit_appointment(request.POST, appointment_id, request.session[\"user_id\"])\n context = {\n \"appointment\" : appointment\n }\n if responce_from_model[\"status\"]:\n messages.error(request, \"You updated an Appointment\")\n return redirect(\"main:index\")\n else:\n for error in responce_from_model[\"errors\"]:\n messages.error(request, error)\n return render(request, \"main/edit_appointment.html\", context)\n\n\n\ndef delete_appointment(request, appointment_id):\n if not request.method == \"POST\":\n return redirect (\"main:index\")\n\n Appointment.objects.delete_appointment(appointment_id)\n return redirect(\"main:index\")\n", "sub_path": "Belt_Exam/apps/main/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3270, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.contrib.messages.error", "line_number": 11, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 11, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "name"}, {"api_name": "models.Appointment.objects.update_status", "line_number": 14, "usage_type": "call"}, {"api_name": "models.Appointment.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "models.Appointment", "line_number": 14, "usage_type": "name"}, {"api_name": "login.models.User.objects.get", "line_number": 16, "usage_type": "call"}, {"api_name": "login.models.User.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "login.models.User", "line_number": 16, "usage_type": "name"}, {"api_name": "models.Appointment.objects.filter", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Appointment.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.Appointment", "line_number": 18, "usage_type": "name"}, {"api_name": "models.Appointment.objects.filter", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Appointment.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.Appointment", "line_number": 20, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 28, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 32, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 34, "usage_type": "call"}, {"api_name": "models.Appointment.objects.create_appointment", "line_number": 36, "usage_type": "call"}, {"api_name": "models.Appointment.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "models.Appointment", "line_number": 36, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 39, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 39, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 40, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 43, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 43, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 44, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 48, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 50, "usage_type": "call"}, {"api_name": "models.Appointment.objects.get", "line_number": 51, "usage_type": "call"}, {"api_name": "models.Appointment.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "models.Appointment", "line_number": 51, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 56, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 60, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 62, "usage_type": "call"}, {"api_name": "models.Appointment.objects.get", "line_number": 63, "usage_type": "call"}, {"api_name": "models.Appointment.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "models.Appointment", "line_number": 63, "usage_type": "name"}, {"api_name": "models.Appointment.objects.edit_appointment", "line_number": 64, "usage_type": "call"}, {"api_name": "models.Appointment.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "models.Appointment", "line_number": 64, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 69, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 69, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 70, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 73, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 73, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 74, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 80, "usage_type": "call"}, {"api_name": "models.Appointment.objects.delete_appointment", "line_number": 82, "usage_type": "call"}, {"api_name": "models.Appointment.objects", "line_number": 82, "usage_type": "attribute"}, {"api_name": "models.Appointment", "line_number": 82, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "34333499", "text": "import itertools\nimport time\nimport asyncio\nimport sys\n\n@asyncio.coroutine\ndef spin():\n write, flush = sys.stdout.write, sys.stdout.flush\n for char in itertools.cycle('|/-\\\\'):\n status = char + ' calculating!'\n write(status)\n flush()\n write('\\x08' * len(status))\n try:\n # 这个小休眠会将 CPU 释放，转而运行外层协程 supervisor 中的代码\n yield from asyncio.sleep(.1)\n except asyncio.CancelledError:\n break\n write(' ' * len(status) + '\\x08' * len(status))\n\n@asyncio.coroutine\ndef slow():\n # time.sleep 会阻塞整个进程\n # asyncio.sleep 仅阻塞当前协程，不会阻塞 loop 事件循环\n yield from asyncio.sleep(3) \n return 123\n\n@asyncio.coroutine\ndef supervisor():\n print('开始转���...')\n # asyncio.ensure_future 接收协程作为参数返回任务对象\n # 将协程 spin 注入 future 生成任务\n spinner = asyncio.ensure_future(spin()) \n # 休眠 3 秒后，将协程 slow 的返回值赋值给 result\n # 在休眠过程中，CPU 被释放，处理 spinner 任务\n result = yield from slow() \n # 任务的 cancel 方法触发 asyncio.CancelledError 异常\n # 在 spin 协程中捕获该异常，这就相当于上一版关闭协程的信号\n spinner.cancel() \n return result\n\ndef main():\n start = time.time()\n loop = asyncio.get_event_loop() # 创建事件循环\n # 将协程 supervisor 注入事件循环生成任务对象并启动\n # 将协程 supervisor 的返回值赋值给 result\n result = loop.run_until_complete(supervisor()) \n print('Answer: {}'.format(result))\n print('耗时 {:.2f}s'.format(time.time()-start))\n\nif __name__ == '__main__':\n # 整个程序中，main 启动事件循环，里面就 supervisor 一件事\n # supervisor 里一共就两件事，spinner 任务和 slow 休眠\n # \n main()\n", "sub_path": "协程/b.py", "file_name": "b.py", "file_ext": "py", "file_size_in_byte": 1949, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sys.stdout", "line_number": 8, "usage_type": "attribute"}, {"api_name": "itertools.cycle", "line_number": 9, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 16, "usage_type": "call"}, {"api_name": "asyncio.CancelledError", "line_number": 17, "usage_type": "attribute"}, {"api_name": "asyncio.coroutine", "line_number": 6, "usage_type": "attribute"}, {"api_name": "asyncio.sleep", "line_number": 25, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 21, "usage_type": "attribute"}, {"api_name": "asyncio.ensure_future", "line_number": 33, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 28, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 43, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 44, "usage_type": "call"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "80180977", "text": "import emoji\nfrom django import template\nfrom django.template.defaultfilters import stringfilter\nfrom emoji import EMOJI_ALIAS_UNICODE\n\nregister = template.Library()\n\n# Update emoji mapping.\nEMOJI_ALIAS_UNICODE.update({\n # Smileys\n ':zipper_mouth_face:': '🤐',\n ':upside_down_face:': '🙃',\n ':money_mouth_face:': '🤑',\n ':face_with_head_bandage:': '🤕',\n ':face_with_cowboy_hat:': '🤠',\n\n # Gestures\n ':spock-hand:': '🖖',\n ':the_horns:': '🤘',\n ':i_love_you_hand_sign:': '🤟',\n})\n\n\n@register.filter(is_safe=True)\n@stringfilter\ndef emojify(val):\n \"\"\"\n 'Python is :thumbs_up:' => 'Python is 👍'\n\n Unfortunately not all Slack emojis are supported.\n You can add them yourself with `EMOJI_ALIAS_UNICODE.update`.\n\n :param val: (string)\n :return: (string)\n \"\"\"\n return emoji.emojize(val, use_aliases=True)\n", "sub_path": "website/templatetags/website_tags.py", "file_name": "website_tags.py", "file_ext": "py", "file_size_in_byte": 872, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.template.Library", "line_number": 6, "usage_type": "call"}, {"api_name": "django.template", "line_number": 6, "usage_type": "name"}, {"api_name": "emoji.EMOJI_ALIAS_UNICODE.update", "line_number": 9, "usage_type": "call"}, {"api_name": "emoji.EMOJI_ALIAS_UNICODE", "line_number": 9, "usage_type": "name"}, {"api_name": "emoji.emojize", "line_number": 36, "usage_type": "call"}, {"api_name": "django.template.defaultfilters.stringfilter", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "17618952", "text": "import requests\nimport time\nimport json\nimport random\nimport urllib3\n\nfrom fake_useragent import UserAgent\n\n\nclass ITJuZi(object):\n def __init__(self, account, password):\n # 登陆url\n self.login_url = '''https://www.itjuzi.com/api/authorizations'''\n # 事件url\n self.request_url = '''https://www.itjuzi.com/api/investevents'''\n # 总数\n self.pagetotal = 0\n # 页码\n self.page_num = 1\n # 打开json文件\n self.file = open('IT桔子.json', 'a', encoding='utf-8')\n # token，验证用户时候登陆\n self.token = ''\n # 登陆用户名\n self.account = account\n # 登陆密码\n self.password = password\n\n def login_itjuzi(self):\n \"\"\"\n 登陆IT桔子\n :return: token，验证用户是否登陆\n \"\"\"\n data_post = {\n \"account\": self.account,\n \"password\": self.password,\n \"type\": \"pswd\"\n }\n headers = {\n 'Origin': 'https://www.itjuzi.com',\n 'Host': 'www.itjuzi.com',\n 'User-Agent': UserAgent(verify_ssl=False).random\n }\n\n verify_login = requests.post(self.login_url, data=data_post, headers=headers, verify=False)\n if verify_login.status_code == 200:\n verify_login = verify_login.json()\n return verify_login['data']['token']\n else:\n return ''\n\n def get_data(self):\n \"\"\"\n 获取事件json数据\n :return:\n \"\"\"\n while True:\n headers = {\n 'cookie': 'Hm_lvt_1c587ad486cdb6b962e94fc2002edf89=1592639887; _ga=GA1.2.423241089.1592639889; _gid=GA1.2.1735206124.1592639889; juzi_user=684879; juzi_token=bearer eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpc3MiOiJodHRwczpcL1wvd3d3Lml0anV6aS5jb21cL2FwaVwvYXV0aG9yaXphdGlvbnMiLCJpYXQiOjE1OTI2NDAxMTksImV4cCI6MTU5MjY0MzcxOSwibmJmIjoxNTkyNjQwMTE5LCJqdGkiOiJydjZsUlpobnU5WkpNR1B0Iiwic3ViIjo2ODQ4NzksInBydiI6IjIzYmQ1Yzg5NDlmNjAwYWRiMzllNzAxYzQwMDg3MmRiN2E1OTc2ZjciLCJ1dWlkIjoiTURNRzJoIn0.JxpQw3q5OKb8hetn1KJNnhALs23H48C2NyjbFjaGwP0; Hm_lpvt_1c587ad486cdb6b962e94fc2002edf89=1592640119',\n 'origin': 'https://www.itjuzi.com',\n 'referer': 'https://www.itjuzi.com/investevent',\n 'user-agent': UserAgent(verify_ssl=False).random,\n 'authorization': self.token\n }\n # post数据\n post_data = {\n # 总条数\n \"pagetotal\": self.pagetotal,\n \"total\": 0,\n \"per_page\": 20,\n # 请求页码数\n \"page\": self.page_num,\n \"type\": 1,\n \"scope\": \"\",\n \"sub_scope\": \"\",\n \"round\": [],\n \"valuation\": [],\n \"valuations\": \"\",\n \"ipo_platform\": \"\",\n \"equity_ratio\": \"\",\n \"status\": \"\",\n \"prov\": \"\",\n \"city\": [],\n \"time\": [],\n \"selected\": \"\",\n \"location\": \"\",\n \"hot_city\": \"\",\n \"currency\": [],\n \"keyword\": \"\"\n }\n item = {}\n try:\n datas_get = requests.post(self.request_url, headers=headers, data=post_data, verify=False)\n datas = datas_get.json()\n data_jsons = datas['data']['data']\n for data in data_jsons:\n item['id'] = data['id']\n item['com_id'] = data['com_id']\n # 日期\n item['date'] = data['agg_time']\n # 公司名称\n item['name'] = data['name']\n item['logo'] = data['logo']\n # 行业\n item['com_scope'] = data['com_scope']\n # 分类\n item['com_sub_scope'] = data['com_sub_scope']\n # 轮次\n item['round'] = data['round']\n # 金额\n item['money'] = data['money']\n # 最新估值(估算)\n item['valuation'] = data['valuation'] * 10000\n # 地区\n item['prov'] = data['prov'] + data['city']\n # 注册公司名\n item['com_registered_name'] = data['com_registered_name']\n # 公司描述\n item['com_des'] = data['com_des']\n # 投资者\n investors = data['investor']\n investor_list = []\n for investor in investors:\n investor_list.append(investor['name'])\n item['investors'] = '|'.join(investor_list)\n\n print(item)\n self.save_json(item)\n\n except Exception as e:\n print(f'请求退出！{e} {datas}')\n break\n else:\n if datas_get.status_code == 200:\n self.pagetotal = datas['data']['page']['total']\n self.page_num += 1\n time.sleep(random.uniform(1, 3))\n else:\n time.sleep(1)\n self.file.close()\n break\n\n def save_json(self, item):\n \"\"\"\n 保存json到本地\n :param item: 要保存的数据\n :return:\n \"\"\"\n self.file.write(json.dumps(item, ensure_ascii=False) + '\\n')\n\n def main(self):\n self.token = self.login_itjuzi()\n if self.token:\n print('登陆成功！')\n self.get_data()\n else:\n print('登陆失败！')\n\n\nif __name__ == '__main__':\n urllib3.disable_warnings()\n\n phone = input('请输入用户登陆名(手机号)：')\n passwd = input('请输入密码：')\n\n it = ITJuZi(phone, passwd)\n it.main()\n", "sub_path": "IT桔子网/itjuzi.py", "file_name": "itjuzi.py", "file_ext": "py", "file_size_in_byte": 5991, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "fake_useragent.UserAgent", "line_number": 42, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 45, "usage_type": "call"}, {"api_name": "fake_useragent.UserAgent", "line_number": 62, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 93, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 137, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 137, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 139, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 149, "usage_type": "call"}, {"api_name": "urllib3.disable_warnings", "line_number": 161, "usage_type": "call"}]} +{"seq_id": "282295793", "text": "def find_best_weight(preds, target):\n from scipy.optimize import minimize\n def _validate_func(weights):\n \n final_prediction = 0\n for weight, prediction in zip(weights, preds):\n final_prediction += weight * prediction\n return np.sqrt(mean_squared_error(final_prediction, target))\n\n starting_values = [0.5]*len(preds)\n cons = ({'type':'eq','fun':lambda w: 1-sum(w)})\n bounds = [(0, 1)] * len(preds)\n \n res = minimize(_validate_func, starting_values, method='Nelder-Mead', bounds=bounds, constraints=cons)\n \n print('Ensemble Score: {best_score}'.format(best_score=(1-res['fun'])))\n print('Best Weights: {weights}'.format(weights=res['x']))\n \n return res\n", "sub_path": "blending_find_best_weight.py", "file_name": "blending_find_best_weight.py", "file_ext": "py", "file_size_in_byte": 730, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "scipy.optimize.minimize", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "105035440", "text": "import requests\nfrom bs4 import BeautifulSoup\nimport time\nimport random\n\nlink = \"http://www.santostang.com/\"\n\n\ndef scrapy(link):\n headers = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'\n }\n r = requests.get(link, headers=headers)\n html = r.text\n soup = BeautifulSoup(html, 'lxml')\n return soup\n\n\nsoup = scrapy(link)\ntitle_list = soup.find_all(\"h1\", class_=\"post-title\")\n\nfor eachone in title_list:\n url = eachone.a['href']\n print('开始爬去这篇博客:', url)\n soup_article = scrapy(url)\n title = soup_article.find(\"h1\", class_=\"view-title\").text.strip()\n print('这篇博客的标题为:', title)\n sleep_time = random.randint(0, 2) + random.random()\n print('开始休息', sleep_time, '秒')\n time.sleep(sleep_time)\n", "sub_path": "37.py", "file_name": "37.py", "file_ext": "py", "file_size_in_byte": 853, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 15, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 28, "usage_type": "call"}, {"api_name": "random.random", "line_number": 28, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "144862512", "text": "from pyspark import SparkContext\n\nsc = SparkContext(master=\"spark://192.168.5.153:7077\", appName=\"test_app\")\n\nlicense_files = sc.textFile(\"hdfs://192.168.5.153:9000/licenses\")\n\nwords = license_files.flatMap(lambda x:x.split(' '))\nwords.take(5)\n# ['The', 'MIT', 'License', '(MIT)', '']\n\nlowercase = words.map(lambda x:x.lower())\nlowercase.take(5)\n# ['the', 'mit', 'license', '(mit)', '']\n\nlongwords = words.filter(lambda x:len(x)>12)\nlongwords.take(3)\n# ['documentation', 'MERCHANTABILITY,', 'NONINFRINGEMENT.']\n", "sub_path": "learning_spark/common_transformations.py", "file_name": "common_transformations.py", "file_ext": "py", "file_size_in_byte": 511, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pyspark.SparkContext", "line_number": 3, "usage_type": "call"}]} +{"seq_id": "7736489", "text": "# -*- coding: utf-8 -*-\n\n\"\"\"\n\nBuilding a simple TB model using primarily dictionary format\ntime: years\n\n\"\"\"\n\nimport math\nfrom scipy.integrate import odeint\nimport matplotlib.pylab as pylab\n\n#Define the compartment labels using a list of strings\ncompartment_labels = [\n \"susceptibles\", \n \"early_latents\",\n \"late_latents\",\n \"infectious\",\n \"under_treatment\"\n]\n\n#Initialise the compartments\ninit_compartments = {\n \"susceptibles\": 1e6,\n \"early_latents\": 0.,\n \"late_latents\": 0.,\n \"infectious\": 1.,\n \"under_treatment\": 0.,\n}\n\n#Define parameters\nparams_fixed = {\n \"rate_pop_birth\": 20. / 1e3,\n \"rate_pop_death\": 1. / 65,\n #tbbiol refers to parameters thought to be fundamental to the biology of TB\n \"multiplier_tbbiol_contact\": 10.,\n \"rate_tbbiol_early_progress\": .1 / .5,\n \"rate_tbbiol_late_progress\": .1 / 100.,\n \"rate_tbbiol_stabilise\": .9 / .5,\n \"rate_tbbiol_recover\": .6 / 3.,\n \"rate_tbbiol_death\": .4 / 3.,\n \n \"rate_detect\": 1.,\n \"time_treatment\": .5,\n\n \"prop_success\": .9,\n \"prop_death\": .05,\n}\nparams_fixed[\"prop_default\"] \\\n = params_fixed[\"prop_success\"] - params_fixed[\"prop_death\"]\nparams_fixed[\"rate_treatment_completion\"] \\\n = params_fixed[\"prop_success\"] / params_fixed[\"time_treatment\"]\nparams_fixed[\"rate_treatment_default\"] \\\n = params_fixed[\"prop_default\"] / params_fixed[\"time_treatment\"]\nparams_fixed[\"rate_treatment_death\"] \\\n = params_fixed[\"prop_death\"] / params_fixed[\"time_treatment\"]\n\n#Create stand-alone functions to convert between lists and compartments\ndef convert_list_to_compartments(vec, labels):\n return {l:vec[i] for i, l in enumerate(labels)}\ndef convert_compartments_to_list(compartments, labels):\n return [compartments[l] for l in labels]\n\n#Define function to calculate variable parameters\ndef calculate_params_variable(params_fixed, compartments):\n#Initialise an empty dictionary\n params_variable = {}\n #Then populate\n params_variable[\"pop_total\"] = sum(compartments.values())\n params_variable[\"rate_force\"] = \\\n params_fixed[\"multiplier_tbbiol_contact\"] * compartments[\"infectious\"] \\\n / params_variable[\"pop_total\"]\n\n return params_variable\n\n#Define function for calculating inter-compartmental flows\ndef calculate_flows(params_fixed, params_variable, compartments):\n flows = {}\n flows[\"susceptibles\"] = \\\n params_fixed[\"rate_pop_birth\"] * params_variable[\"pop_total\"] \\\n + compartments[\"late_latents\"] \\\n * params_fixed[\"rate_treatment_completion\"] \\\n - compartments[\"susceptibles\"] \\\n * ( params_variable[\"rate_force\"] \\\n + params_fixed[\"rate_pop_death\"]) \n \n flows[\"early_latents\"] = \\\n compartments[\"susceptibles\"] * params_variable[\"rate_force\"] \\\n - compartments[\"early_latents\"] \\\n * ( params_fixed[\"rate_tbbiol_early_progress\"] \\\n + params_fixed[\"rate_tbbiol_stabilise\"] \\\n + params_fixed[\"rate_pop_death\"])\n \n flows[\"late_latents\"] = \\\n compartments[\"early_latents\"] * params_fixed[\"rate_tbbiol_stabilise\"] \\\n + compartments[\"infectious\"] * params_fixed[\"rate_tbbiol_recover\"] \\\n - compartments[\"late_latents\"] \\\n * ( params_fixed[\"rate_tbbiol_late_progress\"] \n + params_fixed[\"rate_pop_death\"]) \n\n flows[\"infectious\"] = \\\n compartments[\"early_latents\"] \\\n * params_fixed[\"rate_tbbiol_early_progress\"] \\\n + compartments[\"late_latents\"] \\\n * params_fixed[\"rate_tbbiol_late_progress\"] \\\n - compartments[\"infectious\"] \\\n * ( params_fixed[\"rate_detect\"] \\\n + params_fixed[\"rate_tbbiol_recover\"] \\\n + params_fixed[\"rate_tbbiol_death\"] \\\n + params_fixed[\"rate_pop_death\"]) \n \n flows[\"under_treatment\"] = \\\n compartments[\"infectious\"] * params_fixed[\"rate_detect\"] \\\n - compartments[\"under_treatment\"] \\\n * ( params_fixed[\"rate_treatment_default\"] \n + params_fixed[\"rate_treatment_death\"] \\\n + params_fixed[\"rate_pop_death\"] \\\n + params_fixed[\"rate_treatment_completion\"])\n \n return flows\n\n#Create the anonymous function to feed into the solver\ndef make_derivative_fn(params_fixed, component_labels):\n def derivative_fn(y, t):\n compartments = convert_list_to_compartments(y, component_labels)\n params_variable = calculate_params_variable(params_fixed, compartments)\n flows = calculate_flows(params_fixed, params_variable, compartments)\n return convert_compartments_to_list(flows, component_labels)\n\n return derivative_fn\n\n#Plot\ndef time_plots(plot_labels, soln, times, png=None):\n n_row = int(math.ceil(len(plot_labels) / 2.))\n\n for i_plot, plot_label in enumerate(plot_labels):\n i_label = component_labels.index(plot_label)\n vals = soln[:,i_label]\n pylab.subplot(n_row, 2, i_plot+1)\n pylab.plot(times, vals)\n pylab.ylabel(component_labels[i_label])\n\n pylab.xlabel('time')\n pylab.tight_layout()\n\n if png is None:\n pylab.show()\n else:\n pylab.savefig(png)\n\n#Analysis points\ndef make_times(start, end, step):\n times = []\n time = start\n while time < end:\n times.append(time)\n time += step\n return times\ntimes = make_times(0, 50, 0.001)\n\n#Run model\nderivative_fn = make_derivative_fn(params_fixed, component_labels)\ninit_y = convert_compartments_to_list(init_compartments, component_labels)\nsoln = odeint(derivative_fn, init_y, times)\ntime_plots(component_labels, soln, times)\n\n\n", "sub_path": "simple_tb/withdict.py", "file_name": "withdict.py", "file_ext": "py", "file_size_in_byte": 5665, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "math.ceil", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pylab.subplot", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pylab.plot", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pylab.ylabel", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pylab.xlabel", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pylab.tight_layout", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pylab.show", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pylab.savefig", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 150, "usage_type": "name"}, {"api_name": "scipy.integrate.odeint", "line_number": 165, "usage_type": "call"}]} +{"seq_id": "234927140", "text": "########################################################################\n#Auteur: Ferreira Guillaume\n#Date: 01/06/2018\n#Titre : Projet 7 Papy Robot\n#Objectif: Develop a program using Google APIs and Media wiki\n########################################################################\n\"\"\"\n\n app.py allows to find a place georeferenced by Google\n as well as the story if it is present on wikipedia,\n from a sentence given by the user.\n\n\"\"\"\nimport re\nimport os\nimport requests\nfrom flask import Flask, render_template, request, jsonify\nfrom flask_bootstrap import Bootstrap\n\n\nAPP = Flask(__name__)\nBootstrap(APP)\n\ndef sentence_analysis(sentence):\n \"\"\"\n\n Function that allows interaction with Papy robot\n\n \"\"\"\n liste_init = ['Bonjour', 'Bonjour Papy robot', 'bonjour', 'Salut', 'salut', 'Salut GrandPy !']\n liste_looking_for_place = ['situé', 'se trouve', 'adresse', 'situe', 'trouver']\n liste_word_liaison = ['de','le','la','les','d\\'','du','des','en','l\\'']\n word = re.search(r'(.+)', sentence)\n element_not_found = True\n i = 0\n while element_not_found and i < len(liste_looking_for_place) - 1:\n\n if sentence.find(liste_looking_for_place[i]) >= 0:\n element_not_found = False\n else:\n i = i + 1\n item_to_search = re.search(r'.*'+liste_looking_for_place[i]+' (\\w+) ?(\\w+)? ?(\\w+)? ?(.+)?', sentence)\n #item_to_search = re.search(r'.*'+liste_looking_for_place[i]+' [de]?[le]?[la]?[les]?[d\\']?[du]?[des]?[en]?[l\\']? ?(\\w+) ?[de]?[le]?[la]?[les]?[d\\']?[du]?[des]?[en]?[l\\']? ?(\\w+)?', sentence)\n item=\"\"\n try:\n\t\n try:\n if item_to_search.group(1) not in liste_word_liaison:\n item = item_to_search.group(1)\n except:\n pass\n\n try:\n if item_to_search.group(2) not in liste_word_liaison:\n item = item + \" \" +item_to_search.group(2)\n except:\n pass\n \n try:\n if item_to_search.group(3) not in liste_word_liaison:\n item = item + \" \" +item_to_search.group(3)\n except:\n pass\n\n try:\n if item_to_search.group(4) not in liste_word_liaison:\n item = item + \" \" +item_to_search.group(4)\n except:\n pass\n\n \n if item !=\"\":\n return 'Voici l\\'histoire de ' + item + ' : ', 1, sentence, item\n else:\n return'Je ne comprends pas ce que vous voulez me dire', 0, sentence, ''\n except:\n return'Je ne comprends pas ce que vous voulez me dire, votre demande est trop complexe', 0, sentence, ''\n\n\"\"\"\n try:\n return 'Voici l\\'histoire de ' + item_to_search.group(1) + \" \" + item_to_search.group(2) + ' : ', 1, sentence, item_to_search.group(1) + \" \" + item_to_search.group(2)\n except:\n try:\n return 'Voici l\\'histoire de ' + item_to_search.group(1) + ' : ', 1, sentence, item_to_search.group(1)\n except AttributeError:\n if word.group(1) in liste_init:\n return 'Bonjour, que voulez vous savoir ?', 0, sentence, ''\n return'Je ne comprends pas ce que vous voulez me dire', 0, sentence, ''\n else:\n return'Je ne comprends pas ce que vous voulez me dire', 0, sentence, ''\n else:\n try:\n return 'Voici l\\'histoire de ' + item_to_search.group(1) + ' : ', 1, sentence, item_to_search.group(1)\n except AttributeError:\n if word.group(1) in liste_init:\n return 'Bonjour, que voulez vous savoir ?', 0, sentence, ''\n return'Je ne comprends pas ce que vous voulez me dire', 0, sentence, ''\n else:\n return'Je ne comprends pas ce que vous voulez me dire', 0, sentence, ''\n\"\"\"\n\ndef history_travel(travel):\n \"\"\"\n\n Function that returns the description wikipedia API\n\n \"\"\"\n try:\n url = \"https://fr.wikipedia.org/w/api.php?action=opensearch&search={0} \\\n &format=json\".format(travel)\n content = requests.get(url)\n data = content.json()\n return data[2][0]\n except:\n return \"\"\n\n\n\ndef geolocate_address(address):\n \"\"\"\n\n Function that returns coordinates in WGS84 according to an address (String),\n with the Google geocode API\n\n \"\"\"\n url = \"https://maps.googleapis.com/maps/api/geocode/json?address={0} \\\n &key=AIzaSyB46oeHhGtkM0inOkFEeN5pai36RyPU0UA\".format(address)\n content = requests.get(url)\n data = content.json()\n products = data['results']\n lat = 0.0\n long = 0.0\n for prod in products:\n for geo in prod['geometry']['location'].items():\n if geo[0] == \"lat\":\n lat = geo[1]\n elif geo[0] == \"lng\":\n long = geo[1]\n else:\n print(\"autre parametre erreur\")\n\n coord = [lat, long]\n address = \"\"\n try:\n for prod in products:\n address = address + \" \" + str(prod['address_components'][0]['long_name'])\n address = address + \" \" + str(prod['address_components'][1]['long_name'])\n address = address + \" \" + str(prod['address_components'][2]['long_name'])\n address = address + \" \" + str(prod['address_components'][3]['long_name'])\n except AttributeError:\n pass\n\n\n return coord, address\n\n\n@APP.route('/')\ndef index():\n \"\"\"\n Returns the html template\n \"\"\"\n return render_template('form.html')\n\n@APP.route('/process', methods=['POST'])\ndef process():\n \"\"\"\n Retrieves the post variable and returns the variables retrieved from the APIs \n to the form.html page\n \"\"\"\n address = request.form['name']\n grandpy_answer, find_adress, question, address = sentence_analysis(address)\n name = \"\"\n address_place = \"\"\n wiki = \"\"\n if find_adress == 1:\n try:\n coord, address_place = geolocate_address(address)\n name = str(coord[0]) + \",,\" + str(coord[1])\n\n except AttributeError:\n name = name + \"0,,0\"\n try:\n wiki = history_travel(address)\n if wiki == \"\":\n name = name + \",,Malheuresement je ne connais pas tout.\"\n else:\n name = name + \",,\" + wiki\n except AttributeError:\n name = name + \",,Malheuresement je ne connais pas tout.\"\n print(name) \n\n return jsonify({'name' : name, 'trouver': find_adress, 'reponse_papy': grandpy_answer \\\n , 'question':question, 'lieu': address_place})\n\n\n\nif __name__ == '__main__':\n PORT = int(os.environ.get('PORT', 5000))\n APP.run(host='0.0.0.0', port=PORT, debug=True)\n", "sub_path": "PapyRob/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 6642, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask.Flask", "line_number": 21, "usage_type": "call"}, {"api_name": "flask_bootstrap.Bootstrap", "line_number": 22, "usage_type": "call"}, {"api_name": "re.search", "line_number": 33, "usage_type": "call"}, {"api_name": "re.search", "line_number": 42, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 111, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 128, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 162, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 170, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 170, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 192, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 198, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 198, "usage_type": "attribute"}]} +{"seq_id": "273171934", "text": "\"\"\"\n作者：desklee\n日期：2019/7/29\n功能：计算对象\n版本：2.0\n\"\"\"\nimport json\nimport numpy as np\nclass calculte():\n def __init__(self, data, n_x, n_y, t_s, morning_time, afternoon_time):\n self.data = data\n self.n_x = n_x\n self.n_y = n_y\n self.t_s = t_s\n self.morning_time = morning_time\n self.afternoon_time = afternoon_time\n\n def _process_data_(self, num):\n list_patientID = np.array(self.data['就诊号'])[:]\n list_doctID = np.array(self.data['医生'])[:]\n list_sleepy = np.array(self.data['麻醉方式'])[:]\n list_operation = np.array(self.data['time'])[:]\n list_clean = np.array(self.data['手术级别'])[:]\n list_start = np.array(self.data['开始时间'])[:]\n list_index_or = np.array(self.data['手术室号'])[:]\n# list_patientID = np.array(self.data[(np.where(self.data == '就诊号')[1][0])])[1:]\n# list_doctID = np.array(self.data[(np.where(self.data == '医生名称')[1][0])])[1:]\n# list_sleepy = np.array(self.data[(np.where(self.data == '麻醉方式')[1][0])])[1:]\n# list_operation = np.array(self.data[(np.where(self.data == '手术时长(分钟)')[1][0])])[1:]\n# list_operation = (np.array(self.data[(np.where(self.data == '手术时长(分钟)')[1][0])])[1:]).astype(np.float64)\n list_operation = (np.ceil(list_operation / 5) * 5).astype(np.int)\n # list_clean = np.array(self.data[(np.where(self.data == '手术级别')[1][0])])[1:]\n list_sleepy.reshape((num, 1))\n for i in range(num):\n b = list_sleepy[i]\n if (b == '全身麻醉' or b == '全身麻醉(喉罩)'):\n tb = 60\n else:\n tb = 0\n list_sleepy[i] = tb\n list_clean.reshape((num, 1))\n for i in range(num):\n a = list_clean[i]\n if a == '1.0':\n tp = 10\n elif a == '2.0' or a == '3.0':\n tp = 20\n else:\n tp = 30\n list_clean[i] = tp\n c = np.vstack((list_doctID, list_patientID, list_operation, list_sleepy, list_clean, list_start, list_index_or))\n key = [i + 1 for i in range(num)]\n e = [] #存储了所有信息的列表，每一个列表的内容是一个字典\n for i in range(num):\n f = dict()\n d = c[:, i]\n f[key[i]] = d\n e.append(f)\n return list_doctID, list_patientID, list_operation, list_sleepy, list_clean, list_start, list_index_or, e\n\n def _best_result_(self, chrom, Num, t_s, list_doctID, r_time, o_time, c_time):\n \"\"\"\n Created on Mon Jul 29 09:55:36 2019\n @author: lxw\n \"\"\"\n \"\"\"模拟手术室整个工作流程，每5分钟检查一次，得到最终优化目标结果\"\"\"\n # import numpy as np\n # n_o = 3\n # n_r = 2\n # chrom = np.array([1, 2, 3, 2, 1, 3])\n # o_time = np.array([60, 40, 35, 30, 45, 50])\n # c_time = np.array([20, 20, 20, 20, 20, 20])\n # r_time = np.array([60, 60, 60, 60, 60, 60])\n # num是病人数量\n\n # n_o为手术室数量，n_r为复苏室数量, chrom为染色体[1,3,2]表示第一台\n # 手术在1号手术室在1号手术室内做，o_time[30,100,60]表示第一台手术时长30分钟，\n # c_time表示清洁时长，r_time表示复苏时长（0或自定义最小复苏时长默认为60min）\n r_time_max = self.t_s # 最小复苏时长\n o_o_state = np.zeros(self.n_x, dtype=np.bool) # 手术室是否进行手术\n o_c_state = np.zeros(self.n_x, dtype=np.bool) # 手术室是否进行清洁\n o_r_state = np.zeros(self.n_x, dtype=np.bool) # 手术室是否进行复苏\n o_end_state = np.zeros(self.n_x, dtype=np.bool) # 手术室是否结束工作\n r_state = np.zeros(self.n_y, dtype=int) # 复苏室内状态，0表示空置，大于0表示在使用\n# r_empty_num = self.n_y # 有几个复苏室空床位\n o_total_time = np.zeros(self.n_x, dtype=int) # 各手术室内工作总时长（直到最后一台手术完成清洁）\n o_total_r_time = np.zeros(self.n_x, dtype=int) # 各手术室内复苏总时长\n o_total_empty_time = np.zeros(self.n_x, dtype=int) # 各手术室内日常工作时段的闲置总时长（既不做手术，也不清洁，不复苏）\n overtime_work = np.zeros(self.n_x, dtype=int)\n o_dict = {} # 一个存放每个手术室室染色体上第几台手术的字典，如{1：[2,4,5]表示第一号手术室按顺序做染色体上第2，4，5台手术\n o_order = np.zeros(self.n_x, dtype=int) # 存放目前该手术室正在进行第几台手术\n o_len = np.zeros(self.n_x, dtype=int) # 存放每个手术室有几台手术\n o_o_time = np.zeros(self.n_x, dtype=int) # 目前手术室内手术还需要多长时间\n o_c_time = np.zeros(self.n_x, dtype=int) # 目前手术室内清洁还需要多长时间\n o_r_time = np.zeros(self.n_x, dtype=int) # 目前手术室内已复苏了多长时间\n work_time = (self.afternoon_time - self.morning_time) * 60 // 5 # 日常工作时长有多少个5分钟\n result = {}\n for o in range(self.n_x):\n o_dict[o] = np.where(chrom == o + 1)[0]\n o_len[o] = o_dict[o].shape[0]\n result[o] = []\n\n for t in range(288):\n # 一天排班最多24小时，每5分钟检查一次\n if o_end_state.sum() == self.n_x: # 如果所有手术室均结束工作，跳出循环\n break\n\n r_empty_num = r_state[r_state == 0].size # 更新复苏室空床位数\n if o_r_state[o_r_state == True].size > 0 and r_empty_num > 0: # 当复苏室有空床位，又有手术室内复苏\n o_r_time_sort = np.argsort(-o_r_time) # 早做完手术的优先进入复苏室\n r_state_sort = np.argsort(r_state)\n for r in range(r_empty_num):\n o_room = o_r_time_sort[r]\n r_bed = r_state_sort[r]\n if o_r_time[o_room] == 0: # 没有还在手术室内复苏的就跳出循环\n break\n o_r_state[o_room] = False # 更改手术室内复苏状态\n o_total_r_time[o_room] += o_r_time[o_room]\n o_total_time[o_room] += o_r_time[o_room]\n result[o_room][o_order[o_room]].append(o_r_time[o_room])\n r_state[r_bed] = r_time_max - o_r_time[o_room] # 将剩余的复苏时间填入复苏室\n o_r_time[o_room] = 0 # 将手术室内复苏时间设为0\n o_c_state[o_room] = True # 手术室进入清洁状态\n o_c_time[o_room] = c_time[o_dict[o_room][o_order[o_room]]] # 填入需清洁的时间\n r_empty_num -= 1 # 复苏室空床位数减一\n r_state[r_state > 0] -= 5 # 更新复苏室床位状态，大于0的就减5\n\n for o in range(self.n_x):\n # 对每个手术室状态进行检查\n\n if o_end_state[o] == True: # 如果已结束当天所有工作，进入下一个手术室循环\n continue\n\n if o_len[o] == 0: # 考虑手术室一台手术都没排\n o_total_empty_time[o] += work_time * 5\n o_end_state[o] = True\n continue\n\n if o_o_state[o] == False and o_c_state[o] == False and o_r_state[o] == False: # 手术室空闲状态\n o_o_state[o] = True # 开始手术\n o_o_time[o] = o_time[o_dict[o][o_order[o]]] - 5 # 将第i台的手术时长填入减5\n\n\n elif o_o_state[o] == True: # 手术室进行手术状态\n if o_o_time[o] == 0:\n o_o_state[o] = False\n result[o].append([o_dict[o][o_order[o]], o_time[o_dict[o][o_order[o]]]])\n o_total_time[o] += o_time[o_dict[o][o_order[o]]] # 将这一台手术时长计入工作总时间\n if r_time[o_dict[o][o_order[o]]] == 0: # 若不需要复苏\n result[o][o_order[o]].append(0)\n o_c_state[o] = True\n o_c_time[o] = c_time[o_dict[o][o_order[o]]] - 5\n else:\n if r_empty_num == 0:\n o_r_state[o] = True\n o_r_time[o] += 5\n else:\n result[o][o_order[o]].append(0)\n r_state[np.where(r_state == 0)[0][0]] += r_time_max - 5\n r_empty_num -= 1\n o_c_state[o] = True\n o_c_time[o] = c_time[o_dict[o][o_order[o]]] - 5\n else:\n o_o_time[o] -= 5\n\n\n elif o_c_state[o] == True: # 手术室处于清洁状态\n if o_c_time[o] == 0:\n o_total_time[o] += c_time[o_dict[o][o_order[o]]] # 将这一台手术清洁时间计入手术室工作总时间\n o_c_state[o] = False\n result[o][o_order[o]].append(c_time[o_dict[o][o_order[o]]])\n\n o_order[o] += 1\n if o_order[o] < o_len[o]: # 推入下一台手术\n o_o_state[o] = True\n o_o_time[o] = o_time[o_dict[o][o_order[o]]] - 5\n else:\n o_end_state[o] = True # 手术室工作结束\n if t + 1 < work_time: # 将闲置时间累加\n o_total_empty_time[o] += (work_time - t) * 5\n else:\n overtime_work[o] += (t - work_time) * 5\n else:\n o_c_time[o] -= 5\n\n elif o_r_state[o] == True: # 手术室处于复苏状态\n o_r_time[o] += 5\n if o_r_time[o] == r_time_max:\n result[o][o_order[o]].append(r_time_max)\n o_total_r_time[o] += r_time_max\n o_total_time[o] += r_time_max\n o_r_time[o] = 0\n o_r_state[o] = False\n o_c_state[o] = True\n o_c_time[o] = c_time[o_dict[o][o_order[o]]]\n\n return o_total_time.sum(), o_total_r_time.sum(), o_total_empty_time.sum(), overtime_work.sum(), result\n\n def _get_list_(self,a):\n key = []\n dic = {}\n key_2 = ['time_of_operation', 'time_of_sleep', 'time_of_clean']\n for i in range(self.n_x):\n c = a[i]\n key.append('手术室{}'.format(i+1))\n x = []\n for j in range(int(len(c) / 3)):\n e = 3 * j\n d = c[e:e + 3]\n f = dict(zip(key_2, d))\n x.append(f)\n dic[key[i]] = x\n return dic\n\n def _output_date_(self,output_1):\n f = open('output.json', 'w', encoding='utf-8')\n json.dump(output_1, f, ensure_ascii=False, indent=4)\n f.close()\n\n\n\n\n# def output(self,):\n# return\n", "sub_path": "ors_backend/model/schedule/calculation.py", "file_name": "calculation.py", "file_ext": "py", "file_size_in_byte": 11451, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.vstack", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 81, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 82, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 83, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 92, "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.zeros", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 159, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 217, "usage_type": "call"}]} +{"seq_id": "93385860", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nfrom HtmlTestRunner import HTMLTestRunner\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\nfrom email.header import Header\nimport smtplib, getpass, time, os, unittest\n\ndef attach_files(msg, file_new):\n for i, v in enumerate(file_new):\n with open(v, 'rb') as fp:\n content = fp.read()\n html_attach = MIMEText(content, 'html', 'utf-8')\n # html_attach[\"Content-Disposition\"] = 'attachment; filename=\"attach' + str(i + 1) + '.html\"'\n name = v.split('/')[-1]\n html_attach[\"Content-Disposition\"] = 'attachment; filename=\"' + name + '\"'\n msg.attach(html_attach)\n return msg\n\ndef send_mail(file_new):\n smtpserver = input('Enter your smtp server:')\n user_account = input(\"Enter your user account:\")\n pw = getpass.getpass(\"Enter your password:\")\n sender = user_account\n receiver = input(\"Enter your receiver:\")\n\n # msg = MIMEText(content, 'html', 'utf-8')\n msg = MIMEMultipart('mixed')\n \n # with open(file_new, 'rb') as fp:\n # content = fp.read()\n # html_attach = MIMEText(content, 'html', 'utf-8')\n # html_attach[\"Content-Disposition\"] = 'attachment; filename=\"attach.html\"'\n # msg.attach(html_attach) \n msg = attach_files(msg, file_new)\n\n # https://docs.python.org/3/library/time.html#time.strftime\n # time.strftime(format[, t])\n # If t is not provided, the current time as returned by localtime() is used. \n subject ='自動化測試報告' + time.strftime(\" %Y-%m-%d %H:%M:%S\")\n\n text = 'Hi你好,\\n\\t請見測試附檔'\n text_plain = MIMEText(text, 'plain', 'utf-8') \n msg.attach(text_plain)\n \n msg['Subject'] = Header(subject, 'utf-8')\n # msg['From'] = sender\n msg['From'] = Header(\"{0}\".format(\"測試者\"), 'utf-8') # sender's name\n print(msg['From'])\n msg['To'] = receiver\n # msg['To'] = Header(\"{0} <{1}>\".format(\"我是誰\", receiver), 'utf-8') # not working\n # msg['To'] = Header(\"{0}\".format(\"我是誰\"), 'utf-8') # only \"我是誰\"\n print(msg['To'])\n # print(msg)\n\n smtp = smtplib.SMTP()\n smtp.connect(smtpserver, 587) # 傳輸層安全性 (TLS)/STARTTLS 通訊埠：587\n \n # Perform the login step after you've started TLS.\n smtp.ehlo()\n smtp.starttls()\n try:\n smtp.login(user_account, pw)\n smtp.sendmail(sender, receiver, msg.as_string())\n smtp.quit()\n except:\n raise\n else:\n print('email has send out')\n\ndef new_report(folder):\n lists = os.listdir(folder)\n #重新按時間對目錄下的檔進行排列\n lists.sort(key=lambda fn: os.path.getmtime(folder+\"/\"+fn))\n print('最新的文件為： ' + lists[-1])\n file_latest = os.path.join(folder, lists[-1])\n print('最新的文件為： ' + file_latest)\n return file_latest\n\n# def files_in_a_period_of_time(folder, lists, seconds):\ndef files_in_a_period_of_time(folder, seconds):\n lists = os.listdir(folder)\n ret = []\n print('folder', folder)\n print()\n print('lists', lists)\n print()\n for f in lists:\n st = os.stat(folder + \"/\" + f)\n # st = os.stat(os.path.join(folder, f))\n mtime = st.st_mtime\n if time.time() - mtime < seconds:\n f = folder + \"/\" + f\n # f = os.path.join(folder, f)\n ret.append(f)\n return ret\n\ndef run_test():\n test_dir = './test_project/test_case'\n discover = unittest.defaultTestLoader.discover(test_dir, pattern='test*.py')\n # runner = unittest.TextTestRunner()\n runner = HTMLTestRunner(output='example_suite') # https://github.com/oldani/HtmlTestRunner\n # Just import HtmlTestRunner from package, then pass it to unittest.main with the testRunner keyword. \n # This class have only one required parameter, with is output this is use to place the report of the TestCase, \n # this is saved under a reports directory.\n\n runner.run(discover)\n\nif __name__ == '__main__':\n run_test()\n result_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), os.path.pardir, \"reports\", \"example_suite\")\n print(result_dir)\n result_dir = result_dir.replace('\\\\', '/')\n print(result_dir)\n # file_new = new_report(result_dir)\n file_new = files_in_a_period_of_time(result_dir, 60)\n print()\n print('file_new', file_new)\n send_mail(file_new)\n\n # test_dir = './test_project/test_case'\n # discover = unittest.defaultTestLoader.discover(test_dir, pattern='test*.py')\n # runner = HTMLTestRunner(output='example_suite') # https://github.com/oldani/HtmlTestRunner\n # runner.run(discover)\n\n # (C:\\Users\\Han\\Anaconda3) C:\\Users\\Han\\selenium_test\\ch08>python test_project\\runtest_send_mail_multi-attachment.py\n \n # Running tests...\n # ----------------------------------------------------------------------\n # test_baidu (test_baidu.MyTest) ... OK (10.244832)s\n # test_youdao (test_youdao.MyTest) ... OK (11.589231)s\n \n # ----------------------------------------------------------------------\n # Ran 2 tests in 0:00:21\n \n # OK\n \n \n \n # Generating HTML reports...\n # C:\\Users\\Han\\selenium_test\\ch08\\test_project\\..\\reports\\example_suite\n # C:/Users/Han/selenium_test/ch08/test_project/../reports/example_suite\n # folder C:/Users/Han/selenium_test/ch08/test_project/../reports/example_suite\n \n # lists ['Test_test_baidu.MyTest_2018-04-05_02-56-45.html', 'Test_test_baidu.MyTest_2018-04-05_02-58-59.html', 'Test_test_youdao.MyTest_2018-04-05_02-56-45.html', 'Test_test_youdao.MyTest_2018-04-05_02-58-59.html']\n \n \n # file_new ['C:/Users/Han/selenium_test/ch08/test_project/../reports/example_suite/Test_test_baidu.MyTest_2018-04-05_02-58-59.html', 'C:/Users/Han/selenium_test/ch08/test_project/../reports/example_suite/Test_test_youdao.MyTest_2018-04-05_02-58-59.html']\n # Enter your smtp server:\n # Enter your user account:\n # Enter your password:\n # Enter your receiver:", "sub_path": "ch08/test_project/runtest_send_mail_multi-attachment.py", "file_name": "runtest_send_mail_multi-attachment.py", "file_ext": "py", "file_size_in_byte": 5976, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "email.mime.text.MIMEText", "line_number": 13, "usage_type": "call"}, {"api_name": "getpass.getpass", "line_number": 23, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 28, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 40, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 43, "usage_type": "call"}, {"api_name": "email.header.Header", "line_number": 46, "usage_type": "call"}, {"api_name": "email.header.Header", "line_number": 48, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 56, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.getmtime", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 82, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 89, "usage_type": "call"}, {"api_name": "time.time", "line_number": 92, "usage_type": "call"}, {"api_name": "unittest.defaultTestLoader.discover", "line_number": 100, "usage_type": "call"}, {"api_name": "unittest.defaultTestLoader", "line_number": 100, "usage_type": "attribute"}, {"api_name": "HtmlTestRunner.HTMLTestRunner", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 111, "usage_type": "call"}]} +{"seq_id": "629526695", "text": "from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n url(r'^login/$', views.login_page, name='login_page'),\n url(r'^logout/$', views.logout_page, name='logout_page'),\n url(r'^register/$', views.register_page, name='register_page'),\n url(r'^requests/$', views.requests_page, name='requests_page'),\n url(r'^$', views.index, name='index'),\n]\n", "sub_path": "eco_test/apps/user_app/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 372, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "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": "539888383", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Sep 14 19:31:52 2016\n\n@author: luisalejo\n\"\"\"\n\nimport numpy as np\nfrom matplotlib import pyplot\n\nT = 100.\ndt = 0.02\nN = int(T/dt) + 1\nt = np.linspace(0.0, T, N)\n\nz0 = 100.\nb0 = 10.\nzt = 100.\ng = 9.81\nu = np.array([z0, b0])\nz = np.zeros(N)\nz[0] = z0\nb = np.zeros(N)\nb[0] = b0\n\nfor n in range(1, N):\n u = u + dt*np.array([u[1], g*(1-u[0]/zt)])\n z[n] = u[0]\n b[n] = u[1]\n\npyplot.figure(figsize=(10,4)) #set plot size\npyplot.ylim(40,160) #y-axis plot limits\npyplot.tick_params(axis='both', labelsize=14) #increase font size for ticks\npyplot.xlabel('t (s)', fontsize=14) #x label\npyplot.ylabel('z (m)', fontsize=14) #y label\npyplot.plot(t,z, 'r-');\n\npyplot.figure(figsize=(10,4)) #set plot size\npyplot.ylim(-15,15) #y-axis plot limits\npyplot.tick_params(axis='both', labelsize=14) #increase font size for ticks\npyplot.xlabel('t (s)', fontsize=14) #x label\npyplot.ylabel('b (m/s)', fontsize=14) #y label\npyplot.plot(t,b, 'g-');\n\n\n# Definining the different values for dt\ndt_values = np.array([0.1, 0.05, 0.01, 0.005, 0.001, 0.0001])\n\n# Array containing the z solutions for each dt (for each grid).\nz_values = np.empty_like(dt_values, dtype=np.ndarray)\n\nfor i, dt in enumerate(dt_values):\n N = int(T/dt)+1 #Number of time-steps\n t = np.linspace(0.0, T, N)\n \n # initila conditions\n u = np.array([z0, b0])\n z = np.empty_like(t)\n z[0] = z0\n \n # time loop\n for n in range(1,N):\n u = u +dt*np.array([u[1], g*(1-u[0]/zt)])\n z[n] = u[0]\n \n z_values[i] = z.copy()\n", "sub_path": "working/01_Phugoid Model/02_Phugoid Oscillations work.py", "file_name": "02_Phugoid Oscillations work.py", "file_ext": "py", "file_size_in_byte": 1583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.linspace", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.empty_like", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.empty_like", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "320689249", "text": "from objects import Session, RaceEntry, POINT_OF_CALL, POS, Race\nfrom charts import FURLONG_DISTANCE\nimport copy\n\nfrom scipy.interpolate import interp1d\n\n \nsession = Session()\n\nSTART = 0\nSTRETCH = -2\nFINAL = -3\n\nPOINT_OF_CALL_DIST_MULT = {\n 1: None,\n 11: (START, .1875, .375, STRETCH, FINAL),\n 12: (START, .25, .375, STRETCH, FINAL),\n 13: (START, .25, .5, STRETCH, FINAL),\n 14: (START, .25, .5, STRETCH, FINAL),\n 15: (START, .25, .5, STRETCH, FINAL),\n 16: (START, .25, .5, .75, STRETCH, FINAL),\n 17: (START, .25, .5, .75, STRETCH, FINAL),\n 18: (START, .25, .5, .75, STRETCH, FINAL),\n 19: (START, .25, .5, .75, STRETCH, FINAL),\n 20: (.25, .5, .75, 1, STRETCH, FINAL),\n 21: (START, .25, .5, .75, STRETCH, FINAL),\n 22: (.25, .5, 1, 1.25, STRETCH, FINAL),\n 23: (START, .25, .5, .75, STRETCH, FINAL),\n 24: (.25, .5, 1, 1.375, STRETCH, FINAL),\n 25: (START, .25, .5, .75, STRETCH, FINAL),\n 26: (.25, .5, .75, 1, STRETCH, FINAL),\n 27: (.25, .5, .75, 1, STRETCH, FINAL)}\n\n\ndef running_time(race_entry):\n race = race_entry.race\n DISTANCE = FURLONG_DISTANCE[race.distance]\n poc_dists_mult = POINT_OF_CALL_DIST_MULT[race.distance]\n if poc_dists_mult == None:\n# print(\"No Distance\")\n return None\n\n # Get winning poc\n i = 1\n x = []\n y = []\n for poc_dist in poc_dists_mult:\n if poc_dist == START:\n for cur_race_entry in race.entries:\n if cur_race_entry.result.fin_pos == 1:\n start = race_entry.result.start\n result = start\n break\n elif poc_dist == STRETCH:\n #TODO - what is stretch for track\n continue\n elif poc_dist == FINAL:\n result = race.result.final_call\n poc_dist = DISTANCE/8\n else:\n attr = \"%s_call\"%(POINT_OF_CALL[i])\n result = getattr(race.result, attr)\n if result == None:\n# print(f\"No POC, {attr}\")\n continue\n\n furlong = poc_dist * 8\n\n x.append(furlong/DISTANCE)\n y.append(result)\n\n i+=1\n\n# print(x, y)\n f = interp1d(x, y, fill_value=\"extrapolate\")\n\n POS_mult = {}\n\n for poc_name, dist in POS.items():\n dist_furlong = dist * 8\n dist = dist_furlong/DISTANCE\n if dist >= 1:\n continue\n\n POS_mult[poc_name] = dist\n\n POS_mult['fin'] = 1\n# print(POS_mult)\n\n for poc_name, dist in POS_mult.items():\n # Find dist in RaceEntryResult\n # Get position\n position = {}\n for cur_race_entry in race.entries:\n poc_pos = getattr(cur_race_entry.result, f'{poc_name}_pos')\n if not poc_pos:\n continue\n\n position[poc_pos] = cur_race_entry\n\n# print(poc_name, position)\n reversed(sorted(position.keys()))\n\n behind_total = 0\n behind = 0\n results = {}\n for key in sorted(position.keys()):\n cur_race_entry = position[key]\n if behind == None:\n behind = 0\n behind_total += behind\n\n feet = behind_total * 8\n dist = dist + (feet/5280)\n\n# print(key, f(dist))\n behind = getattr(cur_race_entry.result, f'{poc_name}_behind') \n results[cur_race_entry] = f(dist)\n\n return results[race_entry]\n", "sub_path": "running_time.py", "file_name": "running_time.py", "file_ext": "py", "file_size_in_byte": 3524, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "objects.Session", "line_number": 8, "usage_type": "call"}, {"api_name": "charts.FURLONG_DISTANCE", "line_number": 37, "usage_type": "name"}, {"api_name": "objects.POINT_OF_CALL", "line_number": 61, "usage_type": "name"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 75, "usage_type": "call"}, {"api_name": "objects.POS.items", "line_number": 79, "usage_type": "call"}, {"api_name": "objects.POS", "line_number": 79, "usage_type": "name"}]} +{"seq_id": "169529873", "text": "import itertools\n\n# divisible_by_three(4311)\ndef divisible_by_three(x):\n if x % 3 == 0:\n return True\n else:\n return False\n\n# x = l\n# n = 4\ndef get_combinations(x, n):\n res = [y for y in itertools.permutations(x, n)]\n return res\n\n# x = (4, 1)\n# input = res\ndef collapse_tuples(input):\n outer = []\n for x in input:\n inner = [] \n for y in x: \n temp = str(y)\n inner.append(temp)\n outer.append(int(''.join(inner)))\n return outer\n\ndef validate_combination(combos):\n # combos = res \n combos = collapse_tuples(combos)\n is_divisible_by_three = [divisible_by_three(combo) for combo in combos] \n\n if any(is_divisible_by_three):\n res = list(itertools.compress(combos, is_divisible_by_three))\n return max(res)\n else:\n return 0\n\n# input: a list of digits \n# return: largest number that can be made by some or all of the digits AND\n# is divisible by 3 \n# if not possible: return 0\n# constraints: each element can be used only once\n#\n# solution.solution([3, 1, 4, 1])\n# Output:\n# 4311\n\n# Input:\n# solution.solution([3, 1, 4, 1, 5, 9])\n# Output:\n# 94311\ndef solution(l):\n # find all combinations of num_length entries\n res = []\n seq_lengths = [x + 1 for x in range(len(l))]\n for seq_length in seq_lengths:\n # seq_length = 2\n combos = get_combinations(l, seq_length)\n res.append(validate_combination(combos))\n\n return max(res)\n", "sub_path": "02_task.py", "file_name": "02_task.py", "file_ext": "py", "file_size_in_byte": 1498, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "itertools.permutations", "line_number": 13, "usage_type": "call"}, {"api_name": "itertools.compress", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "460618460", "text": "#-*-encoding:utf-8-*-\nfrom smart_open import sopen\nimport re\ndef trim(inputFile, outputFile, lines):\n\twith open(inputFile, 'r') as input, sopen(outputFile) as output:\n\t\ttext = input.readlines()\n\t\tfor line in text:\n\t\t\tif not line:\n\t\t\t\tbreak\n\t\t\tif lines: #trim blank lines\n\t\t\t\tif not re.search('^$', line):\n\t\t\t\t\toutput.write(line)\n\t\t\t\n\t\t\telse:\n\t\t\t\tline = line.strip()\n\t\t\t\toutput.write(line+'\\n')\n\t\t\t\t\n", "sub_path": "build/lib/pyTextUtil/trim.py", "file_name": "trim.py", "file_ext": "py", "file_size_in_byte": 399, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "smart_open.sopen", "line_number": 5, "usage_type": "call"}, {"api_name": "re.search", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "346234921", "text": "import http.cookiejar\nfrom urllib import request\nfrom bs4 import BeautifulSoup\nheaders={'Referer':'Referer: https://accounts.douban.com/login?redir=https://www.douban.com/&source=index_nav',\n 'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:63.0) Gecko/20100101 Firefox/63.0'}\nname='cookie.text'\ncookie=http.cookiejar.MozillaCookieJar(name)\ntry:\n cookie.load(ignore_discard=True,ignore_expires=True)\nexcept:\n print('cannot load cookie')\nopener=request.build_opener(request.HTTPCookieProcessor(cookie))\nopener.addheaders=[(key,value)for key,value in headers.items()]\nurl='https://www.douban.com/people/148806586/'\nresponse=opener.open(url)\nprint(type(response))\nstatus=response.code\nreq=response.read()\nresponse=req.decode('utf-8')\nsoup=BeautifulSoup(response,'lxml')\na=soup.find_all('p',attrs={\"class\":'text'})[0]\ntext=a.find('a',attrs={'target':'_blank'}).text.split(' ')[0]\nprint(status)\nprint(text)\n", "sub_path": "asisthaven.py", "file_name": "asisthaven.py", "file_ext": "py", "file_size_in_byte": 926, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "http.cookiejar.cookiejar.MozillaCookieJar", "line_number": 7, "usage_type": "call"}, {"api_name": "http.cookiejar.cookiejar", "line_number": 7, "usage_type": "attribute"}, {"api_name": "http.cookiejar", "line_number": 7, "usage_type": "name"}, {"api_name": "urllib.request.build_opener", "line_number": 12, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 12, "usage_type": "name"}, {"api_name": "urllib.request.HTTPCookieProcessor", "line_number": 12, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "513646998", "text": "#!/usr/bin/env python3\nimport argparse\nimport ast\nimport configparser\nimport csv\nimport json\nimport os\nimport re\n\nimport fields\nimport dataHandlers\nimport MARCmapper\n\nclass Record:\n\t'''\n\tTake in a dict of fieldedData and parse out a MARC record in JSON....\n\tfieldedData looks like { UUID : {field:value,field:value,etc.} }\n\t'''\n\tdef __init__(\n\t\tself,\n\t\tfieldedData,\n\t\tcustomProperties=None\n\t\t):\n\t\tself.fieldedData = fieldedData\n\t\tself.customProperties = customProperties\n\n\t\tself.dataFields = []\n\n\t\tself.leader = None\n\t\tself.ohOhSix = None\n\t\tself.ohOhEight = None\n\t\tself.ohOhSeven = None\n\t\tself.originalYear = None\n\t\tself.reproductionYear = None\n\n\t\tself.asJSON = {}\n\n\tdef to_json(self):\n\t\t# YEAH ITS A DICT NOT JSON, SHUT UP\n\t\ttemp = {\n\t\t\t\"leader\":self.leader,\n\t\t\t\"fields\": [\n\t\t\t\t{\"007\":self.ohOhSeven},\n\t\t\t\t{\"008\":self.ohOhEight}\n\t\t\t]\n\t\t}\n\n\t\tfor field in self.dataFields:\n\t\t\tfieldDict = {\n\t\t\t\tfield.tag:{\n\t\t\t\t\t\"ind1\":field.ind1,\n\t\t\t\t\t\"ind2\":field.ind2,\n\t\t\t\t\t\"subfields\":[]\n\t\t\t\t}\n\t\t\t}\n\t\t\tfor subfield in field.subfields:\n\t\t\t\tfieldDict[field.tag][\"subfields\"].append(\n\t\t\t\t\t{subfield.subfieldCharacter:subfield.value}\n\t\t\t\t\t)\n\t\t\ttemp['fields'].append(fieldDict)\n\n\t\t# SORT THE FIELDS BY TAG NUMBER\n\t\ttemp['fields'] = sorted(temp['fields'], key= lambda t: list(t.keys())[0])\n\n\t\tself.asJSON = temp\n\nclass Collection:\n\t'''\n\tJust a list of Record objects\n\t'''\n\tdef __init__(self):\n\t\tself.records = []\n\ndef parse_csv_data(Record):\n\tfor field,elements in MARCmapper.MARCmapper.items():\n\t\tif not elements['instructions']:\n\t\t\t# i.e., if there are not separate processing instructions\n\t\t\tif field in Record.fieldedData and Record.fieldedData[field] not in (None,\"None\",\"\",\" \"):\n\t\t\t\t# i.e., if there is actually data in the CSV\n\t\t\t\ttheValue = Record.fieldedData[field]\n\t\t\t\tmarcField = fields.DataField(\n\t\t\t\t\telements['tag'],\n\t\t\t\t\telements['ind1'],\n\t\t\t\t\telements['ind2']\n\t\t\t\t\t)\n\t\t\t\t# PARSE OUT ALL THE SUBFIELDS\n\t\t\t\tfor subfieldDict in elements['subfields']:\n\t\t\t\t\tif 'prefix' in subfieldDict.keys():\n\t\t\t\t\t\ttheValue = subfieldDict['prefix']+theValue\n\t\t\t\t\tif 'suffix' in subfieldDict.keys():\n\t\t\t\t\t\ttheValue = theValue+subfieldDict['suffix']\n\t\t\t\t\tfor key,value in subfieldDict.items():\n\t\t\t\t\t\tif not key in ('prefix','suffix'):\n\t\t\t\t\t\t\tif value == 'value':\n\t\t\t\t\t\t\t\tsfContent = theValue\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tsfContent = value\n\n\t\t\t\t\t\t\ttheSF = fields.Subfield(key,sfContent)\n\t\t\t\t\t\t\tmarcField.subfields.append(theSF)\n\t\t\t\t# ADD THE FIELD TO THE RECORD\n\t\t\t\tRecord.dataFields.append(marcField)\n\ndef set_fixed_field(Record,config):\n\t'''\n\tBASED ON THE CUSTOM STUFF SET IN RECORD.customProperties,\n\tCREATE LDR AND 008 FIELDS.\n\tALSO, PARSE AN 007 FROM THE 'FORMAT' PROPERTY AND THE\n\tCUSTOM VALUES IN MARCmapper.set_ohOhSeven()\n\t'''\n\tffBytes = fields.ItemBytes(\n\t\tformat=Record.customProperties['format'],\n\t\tBLvl=Record.customProperties['BLvl'],\n\t\tCtry=Record.customProperties['Ctry'],\n\t\tDates=Record.customProperties['Dates'],\n\t\tDtSt=Record.customProperties['DtSt'],\n\t\tELvl=Record.customProperties['ELvl'],\n\t\tForm=Record.customProperties['Form'],\n\t\tLang=Record.customProperties['Lang'],\n\t\tLTxt=Record.customProperties['LTxt'],\n\t\tTime=Record.customProperties['Time'],\n\t\tType=Record.customProperties['Type']\n\t\t)\n\tif Record.fieldedData['year'] not in (\"\",\" \"):\n\t\tyear = Record.fieldedData['year']\n\t\t# print(re.sub(\"(.+)(\\\\\\\\\\\\\\\\)$\",\"\\1\"+year,ffBytes.Dates))\n\t\tffBytes.Dates = re.sub(r\"(.*)(19uu)$\",r\"\\1_\"+year,ffBytes.Dates)\n\t\tffBytes.Dates = ffBytes.Dates.replace(\"_\",\"\")\n\t\t# print(ffBytes.Dates)\n\n\tffBytes.set_008_bytes()\n\tif ffBytes:\n\t\tRecord.leader = fields.Leader(ffBytes).data\n\t\tRecord.ohOhEight = fields.OhOhEight(ffBytes).data\n\n\tRecord.ohOhSeven = MARCmapper.set_ohOhSeven(Record,config)\n\ndef read_config():\n\tscriptDirectory = os.path.dirname(os.path.abspath(__file__))\n\tconfigPath = os.path.join(scriptDirectory,'config.ini')\n\tconfig = configparser.SafeConfigParser()\n\tconfig.read(configPath)\n\n\treturn config\n\ndef set_args():\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument(\n\t\t'-d','--dataPath',\n\t\thelp='path path to data CSV file',\n\t\trequired=True\n\t\t)\n\t# parser.add_argument(\n\t# \t'-r','--recordType',\n\t# \thelp=(\n\t# \t\t'3-letter MARC code for record type (BKS,REC,VIS,etc.)'\n\t# \t\t'This code should apply to all the records in the CSV...'\n\t# \t\t),\n\t# \trequired=True\n\t# \t)\n\tparser.add_argument(\n\t\t'-c','--configProperties',\n\t\thelp=(\n\t\t\t'This should correspond the name of a dict defined in config.ini '\n\t\t\t'which will define properties used in fixed field, 007, 300, etc.'\n\t\t\t),\n\t\trequired=True\n\t\t)\n\tparser.add_argument(\n\t\t'-o','--outputPath',\n\t\thelp=(\n\t\t\t'Path to directory where you want the output JSON file to live. '\n\t\t\t'Default is in the ./data directory under this folder.'\n\t\t\t),\n\t\tdefault='./data/'\n\t\t)\n\n\treturn parser.parse_args()\n\ndef main():\n\targs = set_args()\n\tdataPath = args.dataPath\n\t# recordType = args.recordType\n\tprint(dataPath)\n\tconfigProperties = args.configProperties\n\n\n\tconfig = read_config()\n\tcustomProperties = config['customProperties'][configProperties]\n\t# print(customProperties)\n\tcustomProperties = ast.literal_eval(customProperties)\n\n\tcollectionDict = dataHandlers.main(dataPath)\n\n\tmyCollection = Collection()\n\n\t# counter = 0\n\tfor recordUUID,data in collectionDict.items():\n\t\tonerecord = Record(data,customProperties)\n\t\tMARCmapper.main(onerecord)\n\t\tparse_csv_data(onerecord)\n\t\tset_fixed_field(onerecord,config)\n\t\tMARCmapper.set_nonfiling_indicator(onerecord)\n\t\tMARCmapper.set_duration(onerecord)\n\t\tonerecord.to_json()\n\t\t# print(onerecord.customProperties['yyyymmdd'])\n\t\tprint(onerecord.asJSON)\n\t\tmyCollection.records.append(onerecord.asJSON)\n\t\t# counter += 1\n\t\t# if counter > 4:\n\t\t# \tbreak\n\n\twith open('data/output.json','w') as f:\n\t\tjson.dump(myCollection.records,f)\n\nif __name__ == \"__main__\":\n\tmain()\n", "sub_path": "magickMARCer.py", "file_name": "magickMARCer.py", "file_ext": "py", "file_size_in_byte": 5712, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "MARCmapper.MARCmapper.items", "line_number": 75, "usage_type": "call"}, {"api_name": "MARCmapper.MARCmapper", "line_number": 75, "usage_type": "attribute"}, {"api_name": "fields.DataField", "line_number": 81, "usage_type": "call"}, {"api_name": "fields.Subfield", "line_number": 99, "usage_type": "call"}, {"api_name": "fields.ItemBytes", "line_number": 111, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 127, "usage_type": "call"}, {"api_name": "fields.Leader", "line_number": 133, "usage_type": "call"}, {"api_name": "fields.OhOhEight", "line_number": 134, "usage_type": "call"}, {"api_name": "MARCmapper.set_ohOhSeven", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "configparser.SafeConfigParser", "line_number": 141, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 147, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 191, "usage_type": "call"}, {"api_name": "dataHandlers.main", "line_number": 193, "usage_type": "call"}, {"api_name": "MARCmapper.main", "line_number": 200, "usage_type": "call"}, {"api_name": "MARCmapper.set_nonfiling_indicator", "line_number": 203, "usage_type": "call"}, {"api_name": "MARCmapper.set_duration", "line_number": 204, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 214, "usage_type": "call"}]} +{"seq_id": "35293335", "text": "#!/usr/bin/python3\n\"\"\" new view for State objects \"\"\"\nfrom api.v1.views import app_views\nfrom flask import request, jsonify, abort\nfrom models import storage\nfrom models.city import City\nfrom models.state import State\n\n\n@app_views.route('/states//cities', methods=['GET'],\n strict_slashes=False)\ndef allcities(state_id):\n \"\"\" GET all City objects of a State \"\"\"\n state = storage.get(State, state_id)\n if state is None:\n abort(404)\n c_list = state.cities\n res = []\n for i in c_list:\n res.append(i.to_dict())\n return jsonify(res)\n\n\n@app_views.route('cities/', methods=['GET'], strict_slashes=False)\ndef getcity(city_id):\n \"\"\" GET a city object \"\"\"\n if city_id is None:\n abort(404)\n c = storage.get(City, city_id)\n if c is None:\n abort(404)\n return jsonify(c.to_dict())\n\n\n@app_views.route('/cities/', methods=['DELETE'],\n strict_slashes=False)\ndef deletecity(city_id=None):\n \"\"\" DELETE a city \"\"\"\n c = storage.get(City, city_id)\n if c is None:\n abort(404)\n else:\n storage.delete(c)\n storage.save()\n return jsonify({}), 200\n\n\n@app_views.route('/states//cities', methods=['POST'],\n strict_slashes=False)\ndef createcity(state_id):\n \"\"\" CREATE a city \"\"\"\n c = request.get_json(silent=True)\n state = storage.get(State, state_id)\n if c is None:\n abort(400, \"Not a Json\")\n elif state is None:\n abort(404)\n elif \"name\" not in c.keys():\n abort(400, \"Missing name\")\n else:\n c[\"state_id\"] = state_id\n # ^ setting the state_id attr of the particular city equal to\n # the state_id of the state passed in\n new_c = City(**c)\n storage.new(new_c)\n storage.save()\n return jsonify(new_c.to_dict()), 201\n\n\n@app_views.route('/cities/', methods=['PUT'], strict_slashes=False)\ndef updatecity(city_id):\n \"\"\" update city with PUT \"\"\"\n obj = storage.get(City, city_id)\n if obj is None:\n abort(404)\n c = request.get_json(silent=True)\n if c is None:\n abort(400, \"Not a JSON\")\n for key, value in c.items():\n list_ignore = [\"id\", \"state_id\", \"created_at\", \"updated_at\"]\n if key not in list_ignore:\n setattr(obj, key, value)\n # setting attribute to be what's passed in\n obj.save()\n return jsonify(obj.to_dict()), 200\n", "sub_path": "api/v1/views/cities.py", "file_name": "cities.py", "file_ext": "py", "file_size_in_byte": 2447, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "models.storage.get", "line_number": 14, "usage_type": "call"}, {"api_name": "models.state.State", "line_number": 14, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 14, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 21, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 10, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 10, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 28, "usage_type": "call"}, {"api_name": "models.storage.get", "line_number": 29, "usage_type": "call"}, {"api_name": "models.city.City", "line_number": 29, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 32, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 24, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 24, "usage_type": "name"}, {"api_name": "models.storage.get", "line_number": 39, "usage_type": "call"}, {"api_name": "models.city.City", "line_number": 39, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 41, "usage_type": "call"}, {"api_name": "models.storage.delete", "line_number": 43, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 43, "usage_type": "name"}, {"api_name": "models.storage.save", "line_number": 44, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 44, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 45, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 35, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 52, "usage_type": "name"}, {"api_name": "models.storage.get", "line_number": 53, "usage_type": "call"}, {"api_name": "models.state.State", "line_number": 53, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 59, "usage_type": "call"}, {"api_name": "models.city.City", "line_number": 64, "usage_type": "call"}, {"api_name": "models.storage.new", "line_number": 65, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 65, "usage_type": "name"}, {"api_name": "models.storage.save", "line_number": 66, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 66, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 67, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 48, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 48, "usage_type": "name"}, {"api_name": "models.storage.get", "line_number": 73, "usage_type": "call"}, {"api_name": "models.city.City", "line_number": 73, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 73, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 75, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 85, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 70, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 70, "usage_type": "name"}]} +{"seq_id": "337638840", "text": "\"\"\"Usage\n$ python inference.py --data_dir data \\\n --model_dir model \\\n --output_dir output \\\n [args..]\n\"\"\"\nimport argparse\nimport os\nimport tarfile\n\nimport torch\nfrom dataloader import KlueStsDataLoaderFetcher\nfrom transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig\n\n\ndef load_model_and_type(model_dir, model_tar_file):\n \"\"\"load model and model type from tar file pre-fetched from s3\n\n Args:\n model_dir: str: the directory of tar file\n model_tar_path: str: the name of tar file\n \"\"\"\n tarpath = os.path.join(model_dir, model_tar_file)\n tar = tarfile.open(tarpath, \"r:gz\")\n tar.extractall(path=model_dir)\n model = AutoModelForSequenceClassification.from_pretrained(model_dir)\n config = AutoConfig.from_pretrained(model_dir)\n return model, config.model_type\n\n\n@torch.no_grad()\ndef inference(data_dir, model_dir, output_dir, args) -> None:\n # configure gpu\n num_gpus = torch.cuda.device_count()\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n # load model\n model, model_type = load_model_and_type(model_dir, args.model_tar_file)\n model.to(device)\n if num_gpus > 1:\n model = torch.nn.DataParallel(model)\n model.eval()\n\n # load tokenizer\n tokenizer = AutoTokenizer.from_pretrained(model_dir)\n\n # get test_data_loader\n klue_sts_dataloader_fetcher = KlueStsDataLoaderFetcher(tokenizer, args.max_length)\n kwargs = (\n {\"num_workers\": num_gpus, \"pin_memory\": True}\n if torch.cuda.is_available()\n else {}\n )\n klue_sts_test_loader = klue_sts_dataloader_fetcher.get_dataloader(\n file_path=os.path.join(data_dir, args.test_filename),\n batch_size=args.batch_size,\n **kwargs,\n )\n\n # infer\n output_file = open(os.path.join(output_dir, args.output_filename), \"w\")\n for out in klue_sts_test_loader:\n input_ids, attention_mask, token_type_ids, labels = [o.to(device) for o in out]\n if model_type == 'roberta':\n output = model(input_ids, attention_mask=attention_mask)[0]\n else:\n output = model(\n input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask\n )[0]\n\n preds = output.detach().cpu().numpy()\n\n for p in preds:\n score = p[0]\n output_file.write(f\"{score}\\n\")\n\n output_file.close()\n\n\ndef main():\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--batch_size\",\n type=int,\n default=32,\n metavar=\"N\",\n help=\"input batch size for inference (default: 64)\",\n )\n parser.add_argument(\n \"--data_dir\", type=str, default=os.environ.get(\"SM_CHANNEL_EVAL\", \"/data\")\n )\n parser.add_argument(\n \"--model_dir\", type=str, default=\"./model\"\n )\n parser.add_argument(\n \"--model_tar_file\",\n type=str,\n default=\"klue-sts.tar.gz\",\n help=\"it needs to include all things for loading baseline model & tokenizer, \\\n only supporting transformers.AutoModelForSequenceClassification as a model \\\n transformers.XLMRobertaTokenizer or transformers.BertTokenizer as a tokenizer\",\n )\n parser.add_argument(\n \"--output_dir\", type=str, default=os.environ.get(\"SM_OUTPUT_DATA_DIR\", \"/output\")\n )\n parser.add_argument(\n \"--max_length\",\n type=int,\n default=510,\n help=\"maximum sequence length (default: 510)\",\n )\n parser.add_argument(\n \"--output_filename\",\n type=str,\n default=\"output.csv\",\n help=\"filename of the inference output (default: output.csv)\",\n )\n parser.add_argument(\n \"--test_filename\",\n default=\"klue-sts-v1.1_test.json\",\n type=str,\n help=\"Name of the test file (default: klue-sts-v1.1_test.json)\",\n )\n\n args = parser.parse_args()\n\n data_dir = args.data_dir\n model_dir = args.model_dir\n output_dir = args.output_dir\n\n inference(data_dir, model_dir, output_dir, args)\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "inference/klue-sts/inference.py", "file_name": "inference.py", "file_ext": "py", "file_size_in_byte": 4122, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "tarfile.open", "line_number": 24, "usage_type": "call"}, {"api_name": "transformers.AutoModelForSequenceClassification.from_pretrained", "line_number": 26, "usage_type": "call"}, {"api_name": "transformers.AutoModelForSequenceClassification", "line_number": 26, "usage_type": "name"}, {"api_name": "transformers.AutoConfig.from_pretrained", "line_number": 27, "usage_type": "call"}, {"api_name": "transformers.AutoConfig", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.cuda.device_count", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 34, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.nn.DataParallel", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "attribute"}, {"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 45, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 45, "usage_type": "name"}, {"api_name": "dataloader.KlueStsDataLoaderFetcher", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 31, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 81, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 91, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 91, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 105, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 105, "usage_type": "attribute"}]} +{"seq_id": "545065608", "text": "from google.appengine.api import mail\nimport logging\n\nfrom flask import get_template_attribute, current_app as app\n\n\ndef send_message(address, macro_name, **kwargs):\n # get message body\n body = get_template_attribute('email_messages.html', macro_name)(**kwargs)\n \n # try to get message subject\n try:\n subject = get_template_attribute('email_messages.html', macro_name+\"_subject\")(**kwargs)\n except AttributeError:\n subject = \"Notification\"\n\n logging.debug(\"MESSAGE SUBJECT: \" + subject)\n logging.debug(\"MESSAGE BODY: \" + body)\n\n if address == 'ADMINS':\n # send to admins\n mail.send_mail_to_admins(sender=app.config[\"SERVER_EMAIL\"],\n subject=subject,\n body=body)\n else:\n # send mail\n mail.send_mail(sender=app.config[\"SERVER_EMAIL\"],\n to=address,\n subject=subject,\n body=body)\n\n \n", "sub_path": "src/utils/email_messages.py", "file_name": "email_messages.py", "file_ext": "py", "file_size_in_byte": 964, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask.get_template_attribute", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.get_template_attribute", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 18, "usage_type": "call"}, {"api_name": "google.appengine.api.mail.send_mail_to_admins", "line_number": 22, "usage_type": "call"}, {"api_name": "google.appengine.api.mail", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 22, "usage_type": "name"}, {"api_name": "google.appengine.api.mail.send_mail", "line_number": 27, "usage_type": "call"}, {"api_name": "google.appengine.api.mail", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "6818138", "text": "import optparse, pickle\nimport numpy as np\nimport scipy.io as sio\nfrom tests_marco.rf.train_RF import train_RF\nfrom analyse_RF import measure_FoM\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.cross_validation import KFold\nfrom sklearn import preprocessing\nfrom tests_marco.config import config\n\ndef main():\n\n parser = optparse.OptionParser(\"[!] usage: python cross_validate_RF.py -F \")\n\n parser.add_option(\"-F\", dest=\"dataFile\", type=\"string\", \\\n help=\"specify data file to analyse\")\n\n (options, args) = parser.parse_args()\n dataFile = options.dataFile\n\n cfg = config.Config()\n data_path = cfg.paths['data']\n\n dataFile = data_path + \"3pi_20x20_skew2_signPreserveNorm.mat\"\n\n if dataFile == None:\n print(parser.usage)\n exit(0)\n\n data = sio.loadmat(dataFile)\n #scaler = preprocessing.StandardScaler().fit(data[\"X\"])\n\n #X = scaler.transform(np.concatenate((data[\"X\"], data[\"validX\"])))\n X = np.nan_to_num(data[\"X\"])\n m,n = np.shape(X)\n y = np.squeeze(data[\"y\"])\n #y = np.squeeze(np.concatenate((data[\"y\"], data[\"validy\"])))\n n_estimators_grid = [100, 10]\n max_features_grid = [10, 25]\n min_samples_leaf_grid = [1, 2, 5]\n\n kf = KFold(m, n_folds=5)\n fold = 1\n for n_estimators in n_estimators_grid:\n for max_features in max_features_grid:\n for min_samples_leaf in min_samples_leaf_grid:\n fold=1\n FoMs = []\n for train, test in kf:\n print(\"[*]\", fold, n_estimators, max_features, min_samples_leaf)\n file = data_path + \"classifiers/cv/RF_n_estimators\"+str(n_estimators)+\"_max_features\"+str(max_features)+\\\n \"_min_samples_leaf\"+str(min_samples_leaf)+\"_\"+dataFile.split(\"/\")[-1].split(\".\")[0]+\\\n \"_fold\"+str(fold)+\".pkl\"\n try:\n rf = pickle.load(open(file,\"rb\"))\n except IOError:\n train_x, train_y = X[train], y[train]\n rf = train_RF(train_x, train_y, n_estimators, max_features, min_samples_leaf)\n outputFile = open(file, \"wb\")\n pickle.dump(rf, outputFile)\n FoM, threshold = measure_FoM(X[test], y[test], rf, False)\n fold+=1\n FoMs.append(FoM)\n print(\"[+] mean FoM: %.3lf\" % (np.mean(np.array(FoMs))))\n print()\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "machine-learning/tests_marco/rf/cross_validate_RF.py", "file_name": "cross_validate_RF.py", "file_ext": "py", "file_size_in_byte": 2559, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "optparse.OptionParser", "line_number": 13, "usage_type": "call"}, {"api_name": "tests_marco.config.config.Config", "line_number": 21, "usage_type": "call"}, {"api_name": "tests_marco.config.config", "line_number": 21, "usage_type": "name"}, {"api_name": "scipy.io.loadmat", "line_number": 30, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 30, "usage_type": "name"}, {"api_name": "numpy.nan_to_num", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 36, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.KFold", "line_number": 42, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 55, "usage_type": "call"}, {"api_name": "tests_marco.rf.train_RF.train_RF", "line_number": 58, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 60, "usage_type": "call"}, {"api_name": "analyse_RF.measure_FoM", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "326562109", "text": "import logging\nimport threading\nimport time\n\n\ndef foo(thread_name):\n logging.info(f\"Run by {thread_name}\")\n\n # Just halt this thread for 2 seconds\n time.sleep(2)\n logging.info(f\"Internal identity: {threading.get_ident()}\")\n logging.info(f\"{thread_name} ends\")\n\n\nif __name__ == \"__main__\":\n format = \"%(asctime)s >> %(message)s\"\n logging.basicConfig(format=format, level=logging.INFO,\n datefmt=\"%H:%M:%S.%U\")\n\n logging.info(\"Starting the main thread\")\n\n threads = []\n\n for i in range(5):\n t = threading.Thread(target=foo, args=(f'thread {i}',))\n logging.info(\n f\"Creating thread {i} from main thread and pushing it to the pool\")\n threads.append(t)\n\n logging.info('Starting all threads from the pool')\n\n for t in threads:\n t.start()\n\n logging.info('Joining all threads to the main')\n logging.info(f'Main thread is: {threading.get_ident()}')\n\n for t in threads:\n t.join()\n\n time.sleep(20)\n\n logging.info(\"Exiting the main thread\")\n", "sub_path": "mt03.py", "file_name": "mt03.py", "file_ext": "py", "file_size_in_byte": 1048, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.info", "line_number": 7, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 11, "usage_type": "call"}, {"api_name": "threading.get_ident", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 17, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 20, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 35, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 36, "usage_type": "call"}, {"api_name": "threading.get_ident", "line_number": 36, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 41, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "458623887", "text": "#Project will be using printtouch/self touch basically allow user to use their fingers\n#print will show the information to the user, depending on where the user has clicked on\n#then draw an ellipse with color, pos x & y\n#remember width, height\n\n#THIS IS A CONTINUATION FROM PROJECT_1\n\nfrom kivy.app import App\nfrom kivy.uix.widget import Widget\nfrom kivy.graphics import Color,Ellipse,Line\nfrom random import random\n\nclass PaintAppWidget(Widget):\n def on_touch_down(self, touch):\n #print(touch)\n color = (random(), random(), random())\n with self.canvas:\n Color(*color)\n parameter = 100 #size of your circles\n Ellipse(pos=(touch.x - parameter/2, touch.y - parameter/2),\n size=(parameter,parameter))\n #Import Line for it to function properly\n touch.ud['Line'] = Line(points=(touch.x, touch.y))\n def on_touch_move(self, touch):\n touch.ud['Line'].points +=[touch.x, touch.y]\n #Now your able to create a line\n #click on the window while at the same time holding it.\n #Now drag to outside of the circle, you shall bring a line of the same color\n\n\nclass PrintApp(App):\n def build(self):\n return PaintAppWidget()\n\nif __name__ == '__main__':\n PrintApp().run()", "sub_path": "Kivy_tutorial_files/Kivy_App_Tutorial_00/Kivy_App_Tutorial/Example_Projects/Project_2.py", "file_name": "Project_2.py", "file_ext": "py", "file_size_in_byte": 1292, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "kivy.uix.widget.Widget", "line_number": 13, "usage_type": "name"}, {"api_name": "random.random", "line_number": 16, "usage_type": "call"}, {"api_name": "kivy.graphics.Color", "line_number": 18, "usage_type": "call"}, {"api_name": "kivy.graphics.Ellipse", "line_number": 20, "usage_type": "call"}, {"api_name": "kivy.graphics.Line", "line_number": 23, "usage_type": "call"}, {"api_name": "kivy.app.App", "line_number": 31, "usage_type": "name"}]} +{"seq_id": "375246626", "text": "import matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.optimize import curve_fit\n\ndef f(x):\n return [x for ind, x in enumerate(x) if True]\n\nr, Δr, Θ_deg, ΔΘ_deg, Θ_rad, ΔΘ_rad, d, Δd, d1di, Δd1di, N, bcc, diffbcc, Δdiffbcc, a, Δa, c2, Δc2 = np.genfromtxt('data_salt2.txt', unpack = True)\nx = np.linspace(0,11,12)\n\nplt.errorbar(diffbcc[1:], x[1:], xerr=Δdiffbcc[1:], fmt='rx', label='Differenzen')\nplt.grid()\nplt.xlabel(r\"Ein x-Label\")\nplt.ylabel(r\"Ein y-Label\")\nplt.legend(loc='best')\nplt.xlim(-0.3,0.3)\nplt.tight_layout()\nplt.savefig(\"../build/plot_salt2_1.pdf\")\nplt.close()\n\ndef g(x, m, b):\n return m*x+b\n\nparams, cov = curve_fit(g, f(c2), f(a))\nm = params[0]\nΔm = np.sqrt(cov[0][0])\nb = params[1]\nΔb = np.sqrt(cov[1][1])\n\nprint(\"m={}+-{}\".format(m,Δm))\nprint(\"b={}+-{}\".format(b,Δb))\n\nx = np.linspace(0,1,2)\nplt.errorbar(f(c2), f(a), xerr=f(Δc2), yerr=f(Δa), fmt='kx', label='Messpunkte')\nplt.plot(x, g(x, m, b), 'b-', label=\"Ausgleichskurve\")\nplt.grid()\nplt.xlabel(r\"$\\cos^2(\\theta)$\")\nplt.ylabel(r\"$a$ in $10^{-10}$m\")\nplt.legend(loc='best')\nplt.tight_layout()\nplt.savefig(\"../build/plot_salt2_2.pdf\")\nplt.close()\n", "sub_path": "V41 Debye-Scherrer-Aufnahmen/auswertung/plot_salt2.py", "file_name": "plot_salt2.py", "file_ext": "py", "file_size_in_byte": 1176, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.genfromtxt", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "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": "matplotlib.pyplot.close", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "scipy.optimize.curve_fit", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "126315676", "text": "from __future__ import absolute_import\nfrom collections import OrderedDict\n\nimport torch\nfrom torch.autograd import Variable\n\nfrom ..utils import to_torch\n\ndef normalize(output):\n return output / output.norm(dim=1).reshape(-1, 1)\n\ndef extract_cnn_feature(model, inputs, modules=None, normalize_features=False):\n model.eval()\n inputs = to_torch(inputs).cuda()\n with torch.no_grad():\n if modules is None:\n outputs = model(inputs)\n if normalize_features:\n outputs = normalize(outputs)\n outputs = outputs.cpu()\n return outputs\n # Register forward hook for each module\n outputs = OrderedDict()\n handles = []\n for m in modules:\n outputs[id(m)] = None\n def func(m, i, o):\n if normalize_features:\n o = normalize(o)\n outputs[id(m)] = o.cpu()\n handles.append(m.register_forward_hook(func))\n model(inputs)\n for h in handles:\n h.remove()\n return list(outputs.values())\n", "sub_path": "reid/feature_extraction/cnn.py", "file_name": "cnn.py", "file_ext": "py", "file_size_in_byte": 1077, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "utils.to_torch", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 15, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "285176570", "text": "#!/usr/bin/env python\nimport json\nfrom kafka import KafkaProducer\nfrom flask import Flask, request\n\napp = Flask(__name__)\nproducer = KafkaProducer(bootstrap_servers='kafka:29092')\n\n\ndef log_to_kafka(topic, event):\n event.update(request.headers)\n producer.send(topic, json.dumps(event).encode())\n\n\n@app.route(\"/\")\ndef default_response():\n default_event = {'event_type': 'default'}\n log_to_kafka('events', default_event)\n return \"This is the default response!\\n\"\n\n\n@app.route(\"/purchase_sword/\")\ndef purchase_a_sword(sword):\n purchase_sword_event = {'event_type': 'purchase_sword', 'sword': sword}\n log_to_kafka('events', purchase_sword_event)\n return \"Sword Purchased!\\n\"\n\n@app.route(\"/play_hrs/\")\ndef play_hrs(hrs):\n play_hrs_event = {'event_type': 'play_hrs', 'total_hrs': hrs}\n log_to_kafka('events', play_hrs_event)\n return \"Hours logged!\\n\"\n\n@app.route(\"/ref_count/\")\ndef ref_count(ref):\n ref_count_event = {'event_type': 'ref_count', 'total_ref_count': ref}\n log_to_kafka('events', ref_count_event)\n return \"ref_count logged!\\n\"\n\n@app.route(\"/sub_count/\")\ndef sub_sub(sub_sub):\n sub_event = {'event_type': 'sub_count', 'total_sub': sub_sub}\n log_to_kafka('events', sub_event)\n return \"sub_event logged!\\n\"\n\n@app.route(\"/money_paid/\")\ndef money(money):\n money_paid_event = {'event_type': 'money_paid', 'money_paid_amount': money}\n log_to_kafka('events', money_paid_event)\n return \"money_paid_event logged!\\n\"\n\n@app.route(\"/account_open/\")\ndef account_open(acctopen):\n account_open_event = {'event_type': 'account_open', 'account_open': acctopen}\n log_to_kafka('events', account_open_event)\n return \"account_open_event logged!\\n\"\n\n@app.route(\"/join_guild/\")\ndef join_guild(guild):\n join_guild_event = {'event_type': 'join_guild', 'join_guild': guild}\n log_to_kafka('events', join_guild_event)\n return \"join_guild_event logged!\\n\"\n\n@app.route(\"/sw_a_g/\")\ndef sw_a_g(swag):\n swag_event = {'event_type': 'sw_a_g', 'swag': swag}\n log_to_kafka('events', swag_event)\n return \"swag_event logged!\\n\"", "sub_path": "game_api2 (3).py", "file_name": "game_api2 (3).py", "file_ext": "py", "file_size_in_byte": 2137, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "kafka.KafkaProducer", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 11, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 11, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "370785208", "text": "## ---------------------------- ##\n##\n## Aileen Benedict :D\n## \n## Example student submission code for autonomous driving challenge.\n## You must modify the train and predict methods and the NeuralNetwork class. \n## \n## ---------------------------- ##\n\nimport numpy as np\nimport cv2\nfrom tqdm import tqdm\nimport time\n\n# Some constants\nBIN_MIN = -180\nBIN_MAX = 180\nNUM_BINS = 128\n\ndef train(path_to_images, csv_file):\n '''\n First method you need to complete. \n Args: \n path_to_images = path to jpg image files\n csv_file = path and filename to csv file containing frame numbers and steering angles. \n Returns: \n NN = Trained Neural Network object \n '''\n\n # Import Steering Angles CSV\n #frame_nums, steering_angles = read_data()\n data = np.genfromtxt(csv_file, delimiter = ',')\n frame_nums = data[:,0]\n steering_angles = data[:,1]\n\n # X --> Our images. Will be used as input data\n X = get_X(frame_nums, path_to_images)\n # y --> bins of steering angles. Will be used as targets.\n # bin_ranges --> used as reference for which bins relate to which angles\n y, bin_ranges = create_bins(steering_angles)\n\n # Train your network here. You'll probably need some weights and gradients!\n NN = NeuralNetwork(input_size = 3200, hidden_layers = 30, output_size = NUM_BINS)\n NN.adamOptimizer(X, y)\n \n return NN\n\n\ndef predict(NN, image_file):\n '''\n Second method you need to complete. \n Given an image filename, load image, make and return predicted steering angle in degrees. \n '''\n im_full = cv2.imread(image_file)\n\n ## Perform inference using your Neural Network (NN) here.\n \n ## Perform same steps on the image as the training process\n im = update_image(im_full)\n \n ## yHat --> all the bins with the probabilities\n yHat = NN.forward(im)\n \n ## Find the bin with the highest probability\n max_index = np.argmax(yHat) # highest index\n\n # Get bin_ranges\n bin_ranges = np.linspace(BIN_MIN, BIN_MAX, NUM_BINS)\n \n ## Return that angle\n return bin_ranges[max_index]\n\n\ndef read_data(path_to_images = 'data/training/images', csv_file = 'data/training/steering_angles.csv'):\n data = np.genfromtxt(csv_file, delimiter = ',')\n frame_nums = data[:,0]\n steering_angles = data[:,1]\n \n return frame_nums, steering_angles\n\ndef get_X(frame_nums, path_to_images):\n # one iteration to get size of a ;-;\n frame_num = int(frame_nums[0])\n im_full = cv2.imread(path_to_images + '/' + str(int(frame_num)).zfill(4) + '.jpg')\n\n #im_full = im_full[::30, ::30]\n im_full = cv2.resize(im_full,(50,64))\n im_full = convert_to_grayscale(im_full)\n im_full = im_full / 255.\n a = im_full.ravel()\n\n # initialize this thing\n images = np.zeros((len(frame_nums),len(a)))\n images[0] = np.array(a)\n\n # now loop through the rest of the images:\n for i in range(1, len(frame_nums)):\n frame_num = int(frame_nums[i])\n im_full = cv2.imread(path_to_images + '/' + str(int(frame_num)).zfill(4) + '.jpg')\n\n #im_full = im_full[::30, ::30]\n im_full = cv2.resize(im_full,(50,64))\n im_full = convert_to_grayscale(im_full)\n im_full = im_full / 255.\n a = im_full.ravel()\n\n images[i] = np.array(a)\n\n return images\n\ndef update_image(im):\n\n #im_full = im[::30, ::30]\n im_full = cv2.resize(im,(50,64))\n im_full = convert_to_grayscale(im_full)\n im_full = im_full / 255.\n a = im_full.ravel()\n return a\n\n# Taken from first challenge :') heheh\ndef convert_to_grayscale(im):\n '''\n Convert color image to grayscale.\n Args: im = (nxmx3) floating point color image scaled between 0 and 1\n Returns: (nxm) floating point grayscale image scaled between 0 and 1\n '''\n return np.mean(im, axis = 2)\n\n# create what we will use for y\ndef create_bins(steering_angles, num_bins = NUM_BINS):\n # The bins for ALL the steering angles (this will be used for our y)\n bins = np.zeros((len(steering_angles), num_bins))\n\n # Create the ranges\n #bin_ranges = create_bin_ranges(num_bins)\n bin_ranges = np.linspace(BIN_MIN, BIN_MAX, num_bins)\n \n # Loop over each steering angle\n for i in range(len(steering_angles)):\n # We want to put this in the correct bin for bins[i][N] (i is this angle, N is the bin index)\n angle = steering_angles[i]\n index = find_nearest_index(bin_ranges, angle)\n bins[i][index] = 1 # set this bin to 1 (will need to do distribution later)\n\n # Attempting to make distribution ;-;\n if index >= 1:\n bins[i, index-1] = 0.89\n if index < len(bin_ranges) - 1:\n bins[i, index+1] = 0.89\n if index >= 2:\n bins[i, index-2] = 0.61\n if index < len(bin_ranges) - 2:\n bins[i, index+2] = 0.61\n if index >= 3:\n bins[i, index-3] = 0.33\n if index < len(bin_ranges) - 3:\n bins[i, index+3] = 0.33\n if index >= 4:\n bins[i, index-4] = 0.1\n if index < len(bin_ranges) - 4:\n bins[i, index+4] = 0.1\n\n return bins, bin_ranges\n\ndef find_nearest_index(array, value):\n array = np.asarray(array)\n i = (np.abs(array - value)).argmin()\n return i\n \n\nclass NeuralNetwork(object):\n def __init__(self, input_size = 2, hidden_layers = 3, output_size = 1): \n '''\n Neural Network Class, you may need to make some modifications here!\n '''\n #These are constants\n self.inputLayerSize = input_size\n self.outputLayerSize = output_size\n self.hiddenLayerSize = hidden_layers\n \n #These are learned by the neural network\n #Weights (parameters)\n limit = np.sqrt(6 / (self.inputLayerSize + self.hiddenLayerSize))\n self.W1 = np.random.uniform(-limit, limit, (self.inputLayerSize, self.hiddenLayerSize))\n\n limit = np.sqrt(6 / (self.hiddenLayerSize + self.outputLayerSize))\n self.W2 = np.random.uniform(-limit, limit, (self.hiddenLayerSize, self.outputLayerSize))\n \n def forward(self, X):\n #Propogate inputs though network\n self.z2 = np.dot(X, self.W1)\n self.a2 = self.sigmoid(self.z2)\n self.z3 = np.dot(self.a2, self.W2)\n yHat = self.sigmoid(self.z3) \n return yHat\n \n def sigmoid(self, z):\n #Apply sigmoid activation function to scalar, vector, or matrix\n return 1/(1+np.exp(-z))\n \n def sigmoidPrime(self,z):\n #Gradient of sigmoid\n return np.exp(-z)/((1+np.exp(-z))**2)\n \n def costFunction(self, X, y):\n #Compute cost for given X,y, use weights already stored in class.\n self.yHat = self.forward(X)\n J = 0.5*sum((y-self.yHat)**2)\n return J\n \n def costFunctionPrime(self, X, y):\n #Compute derivative with respect to W and W2 for a given X and y:\n self.yHat = self.forward(X)\n \n delta3 = np.multiply(-(y-self.yHat), self.sigmoidPrime(self.z3))\n dJdW2 = np.dot(self.a2.T, delta3)\n \n delta2 = np.dot(delta3, self.W2.T)*self.sigmoidPrime(self.z2)\n dJdW1 = np.dot(X.T, delta2) \n \n return dJdW1, dJdW2\n \n #Helper Functions for interacting with other classes:\n def getParams(self):\n #Get W1 and W2 unrolled into vector:\n params = np.concatenate((self.W1.ravel(), self.W2.ravel()))\n return params\n \n def setParams(self, params):\n #Set W1 and W2 using single paramater vector.\n W1_start = 0\n W1_end = self.hiddenLayerSize * self.inputLayerSize\n self.W1 = np.reshape(params[W1_start:W1_end], (self.inputLayerSize , self.hiddenLayerSize))\n W2_end = W1_end + self.hiddenLayerSize*self.outputLayerSize\n self.W2 = np.reshape(params[W1_end:W2_end], (self.hiddenLayerSize, self.outputLayerSize))\n \n def computeGradients(self, X, y):\n dJdW1, dJdW2 = self.costFunctionPrime(X, y)\n return np.concatenate((dJdW1.ravel(), dJdW2.ravel()))\n\n def adamOptimizer(self, X, y):\n # What happens if I change all these :') ? ~\n num_iterations = 600\n alpha = 1e-4\n #alpha = 1e-3\n #beta1= 0.9\n beta1 = 0.8\n beta2= 0.999\n epsilon= 1e-08\n\n grads_length = self.getParams().shape[0]\n \n m0 = np.zeros(grads_length) #Initialize first moment vector\n v0 = np.zeros(grads_length) #Initialize second moment vector\n t = 0.0\n\n losses = [] #For visualization\n mt = m0\n vt = v0\n \n batch_size = 128\n num_batches = int(np.ceil(X.shape[0] / batch_size))\n for i in tqdm(range(num_iterations)):\n loss = 0\n for j in range(num_batches):\n t += 1\n grads = self.computeGradients(X=X, y=y)\n mt = beta1 * mt + (1 - beta1) * grads\n vt = beta2 * vt + (1 - beta2) * grads ** 2\n mt_hat = mt / (1 - beta1 ** t)\n vt_hat = vt / (1 - beta2 ** t)\n\n params = self.getParams()\n new_params = params - alpha * mt_hat / (np.sqrt(vt_hat) + epsilon)\n self.setParams(new_params)\n\n b_x = X[j * batch_size:(j + 1) * batch_size]\n b_y = y[j * batch_size:(j + 1) * batch_size]\n loss += self.costFunction(X=b_x, y=b_y)\n losses.append(loss)", "sub_path": "4. Autonomous Driving/challenge/aileen_benedict.py", "file_name": "aileen_benedict.py", "file_ext": "py", "file_size_in_byte": 9387, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.genfromtxt", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 75, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 84, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 94, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 99, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 107, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 184, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 187, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 262, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 274, "usage_type": "call"}]} +{"seq_id": "549075546", "text": "import torch\nimport torch.nn\tas nn\nfrom torch.autograd\timport Variable\nimport torch.optim as optim\nimport numpy as\tnp\nimport util\nfrom sklearn.preprocessing import MinMaxScaler \nimport sys\n\nclass CLASSIFIER:\n\t# train_Y is interger \n\tdef __init__(self,\t_train_X, _train_Y, _test_X,_test_Y,_testseenclasses,_testunseenclasses,_test_seen_num,\n\t\t\t\t data_loader, _nclass, _cuda,\n _lr=0.001,\t_beta1=0.5,\t_nepoch=20,\t_batch_size=100, generalized=True):\n\t\t\n\t\tself.train_X = _train_X \n\t\tself.train_Y = _train_Y \n\t\tself.test_X = _test_X\n\t\tself.test_Y = _test_Y\n\t\tself.testseenclasses = _testseenclasses\n\t\tself.testunseenclasses = _testunseenclasses\n\t\tself.test_seen_num = _test_seen_num\n\t\t\n\t\tself.test_seen_feature = data_loader.test_seen_feature\n\t\tself.test_seen_label = data_loader.test_seen_label \n\t\tself.test_unseen_feature = data_loader.test_unseen_feature\n\t\tself.test_unseen_label = data_loader.test_unseen_label \n\t\tself.seenclasses = data_loader.seenclasses\n\t\tself.unseenclasses = data_loader.unseenclasses\n\t\tself.batch_size =\t_batch_size\n\t\tself.nepoch =\t_nepoch\n\t\tself.nclass =\t_nclass\n\t\tself.input_dim = _train_X.size(1)\n\t\tself.cuda\t= _cuda\n\t\tself.model =\tLINEAR_LOGSOFTMAX(self.input_dim, self.nclass)\n\t\tself.model.apply(util.weights_init)\n\t\tself.criterion = nn.NLLLoss()\n\t\t#self.test = test\n\t\t\n\t\tself.input = torch.FloatTensor(_batch_size, self.input_dim) \n\t\tself.label = torch.LongTensor(_batch_size) \n\t\t\n\t\tself.lr =\t_lr\n\t\tself.beta1 = _beta1\n\t\t#\tsetup optimizer\n\t\tself.optimizer = optim.Adam(self.model.parameters(), lr=_lr, betas=(_beta1, 0.999))\n\n\t\tif self.cuda:\n\t\t\tself.model.cuda()\n\t\t\tself.criterion.cuda()\n\t\t\tself.input =\tself.input.cuda()\n\t\t\tself.label =\tself.label.cuda()\n\n\t\tself.index_in_epoch =\t0\n\t\tself.epochs_completed\t= 0\n\t\tself.ntrain =\tself.train_X.size()[0]\n\n\t\tif generalized:\n\t\t\tself.acc_seen_test,self.acc_unseen_test, self.H_test,self.acc_seen, self.acc_unseen, self.H =\tself.fit()\n\t\t\t#print('Final: acc_seen=%.4f, acc_unseen=%.4f, h=%.4f' %\t(self.acc_seen,\tself.acc_unseen, self.H))\n\t\telse:\n\t\t\tself.acc_test,self.acc\t= self.fit_zsl() \n\t\t\t#print('acc=%.4f' % (self.acc))\n\n\t\n\tdef fit_zsl(self):\n\t\tbest_acc = 0\n\t\tbest_acc_test=0\n\t\tmean_loss\t= 0\n\t\tlast_loss_epoch =\t1e8\t\n\t\tfor epoch\tin range(self.nepoch):\n\t\t\tfor i in\trange(0, self.ntrain, self.batch_size):\t\t \n\t\t\t\tself.model.zero_grad()\n\t\t\t\tbatch_input, batch_label = self.next_batch(self.batch_size)\t\n\t\t\t\tself.input.copy_(batch_input)\n\t\t\t\tself.label.copy_(batch_label)\n\t\t\t\t \n\t\t\t\tinputv = Variable(self.input)\n\t\t\t\tlabelv = Variable(self.label)\n\t\t\t\toutput = self.model(inputv)\n\t\t\t\tloss = self.criterion(output, labelv)\n\t\t\t\tmean_loss += loss\n\t\t\t\tloss.backward()\n\t\t\t\tself.optimizer.step()\n\t\t\t\t#print('Training classifier\tloss= ', loss.data[0])\n\t\t\t\n\n\t\t\tacc_test = self.val(self.test_X, self.test_Y,self.unseenclasses)\t\t\n\t\t\tprint('acc_test',acc_test)\n\t\t\tacc = self.val(self.test_unseen_feature, self.test_unseen_label,\tself.unseenclasses)\n\t\t\tprint('acc',acc)\n\t\t\t\n\t\t\t#print('acc %.4f' % (acc))\n\t\t\tif acc >\tbest_acc:\n\t\t\t\tbest_acc = acc\n\t\t\tif acc_test >\tbest_acc_test:\n\t\t\t\tbest_acc_test = acc_test\t\t\t\t\n\t\treturn best_acc_test,best_acc \n\n\tdef fit(self):\n\t\tbest_H = 0\n\t\tbest_seen\t= 0\n\t\tbest_unseen =\t0\n\t\n\t\tbest_H_test = 0\n\t\tbest_seen_test\t= 0\n\t\tbest_unseen_test =\t0\t\n\t\t\n\t\ttest_feat_seen = self.test_X[:self.test_seen_num]\n\t\ttest_label_seen = self.test_Y[:self.test_seen_num]\n\t\ttest_feat_unseen = self.test_X[self.test_seen_num:]\n\t\ttest_label_unseen = self.test_Y[self.test_seen_num:]\n\t\t\n\t\tfor epoch\tin range(self.nepoch):\n\t\t\tfor i in\trange(0, self.ntrain, self.batch_size):\t\t \n\t\t\t\tself.model.zero_grad()\n\t\t\t\tbatch_input, batch_label = self.next_batch(self.batch_size)\t\n\t\t\t\tself.input.copy_(batch_input)\n\t\t\t\tself.label.copy_(batch_label)\n\t\t\t\t \n\t\t\t\tinputv = Variable(self.input)\n\t\t\t\tlabelv = Variable(self.label)\n\t\t\t\toutput = self.model(inputv)\n\t\t\t\tloss = self.criterion(output, labelv)\n\t\t\t\tloss.backward()\n\t\t\t\tself.optimizer.step()\n\t\t\t\t#print('Training classifier\tloss= ', loss.data[0])\\\n\t\t\t\t\t\t\n\t\t\tacc_seen\t= 0\n\t\t\tacc_unseen =\t0\n\t\t\tacc_seen_test\t= 0\n\t\t\tacc_unseen_test =\t0\n\t\t\t\n\t\t\tacc_seen_test\t= self.val_gzsl(test_feat_seen,\ttest_label_seen, self.testseenclasses)\n\t\t\tacc_unseen_test =\tself.val_gzsl(test_feat_unseen,\ttest_label_unseen,\tself.testunseenclasses)\t\t\n\n\t\t\t#print('acc_seen_test',acc_seen_test)\n\t\t\t#print('acc_unseen_test',acc_unseen_test)\n\n\t\t\tacc_seen\t= self.val_gzsl(self.test_seen_feature,\tself.test_seen_label, self.seenclasses)\n\t\t\tacc_unseen =\tself.val_gzsl(self.test_unseen_feature,\tself.test_unseen_label,\tself.unseenclasses)\n\t\t\t#print('acc_seen',acc_seen)\n\t\t\t#print('acc_unseen',acc_unseen)\n\n\t\t\tif acc_seen_test ==0 and acc_unseen_test==0:\n\t\t\t\tH_test = 0\n\t\t\telse:\n\t\t\t\tH_test = 2*acc_seen_test*acc_unseen_test / (acc_seen_test + acc_unseen_test)\n\t\t\tif H_test\t> best_H_test:\n\t\t\t\tbest_seen_test =\tacc_seen_test\n\t\t\t\tbest_unseen_test\t= acc_unseen_test\n\t\t\t\tbest_H_test = H_test\n\t\t\t\t\n\t\t\tif acc_seen ==0 and acc_unseen==0:\n\t\t\t\tH = 0\n\t\t\telse:\n\t\t\t\tH = 2*acc_seen*acc_unseen / (acc_seen+acc_unseen)\n\t\t\tif H\t> best_H:\n\t\t\t\tbest_seen =\tacc_seen\n\t\t\t\tbest_unseen\t= acc_unseen\n\t\t\t\tbest_H = H\t\n\t\t\t\t\n\t\t\t#print('acc_seen=%.4f,\tacc_unseen=%.4f, H=%.4f ' % (acc_seen, acc_unseen, H))\n\t\treturn best_seen_test,\tbest_unseen_test, best_H_test, best_seen,\tbest_unseen, best_H\n\t\t\t\t\t \n\tdef next_batch(self, batch_size):\n\t\tstart\t= self.index_in_epoch\n\t\t#\tshuffle\tthe\tdata at\tthe\tfirst epoch\n\t\tif self.epochs_completed == 0\tand\tstart == 0:\n\t\t\tperm\t= torch.randperm(self.ntrain)\n\t\t\tself.train_X\t= self.train_X[perm]\n\t\t\tself.train_Y\t= self.train_Y[perm]\n\t\t#\tthe\tlast batch\n\t\tif start + batch_size\t> self.ntrain:\n\t\t\tself.epochs_completed +=\t1\n\t\t\trest_num_examples = self.ntrain - start\n\t\t\tif rest_num_examples\t> 0:\n\t\t\t\tX_rest_part\t= self.train_X[start:self.ntrain]\n\t\t\t\tY_rest_part\t= self.train_Y[start:self.ntrain]\n\t\t\t# shuffle the data\n\t\t\tperm\t= torch.randperm(self.ntrain)\n\t\t\tself.train_X\t= self.train_X[perm]\n\t\t\tself.train_Y\t= self.train_Y[perm]\n\t\t\t# start next\tepoch\n\t\t\tstart = 0\n\t\t\tself.index_in_epoch = batch_size\t- rest_num_examples\n\t\t\tend = self.index_in_epoch\n\t\t\tX_new_part =\tself.train_X[start:end]\n\t\t\tY_new_part =\tself.train_Y[start:end]\n\t\t\t#print(start, end)\n\t\t\tif rest_num_examples\t> 0:\n\t\t\t\treturn torch.cat((X_rest_part, X_new_part),\t0) , torch.cat((Y_rest_part, Y_new_part), 0)\n\t\t\telse:\n\t\t\t\treturn X_new_part, Y_new_part\n\t\telse:\n\t\t\tself.index_in_epoch += batch_size\n\t\t\tend = self.index_in_epoch\n\t\t\t#print(start, end)\n\t\t\t# from index\tstart to index end-1\n\t\t\treturn self.train_X[start:end], self.train_Y[start:end]\n\n\n\tdef val_gzsl(self,\ttest_X,\ttest_label,\ttarget_classes): \n\t\tstart\t= 0\n\t\tntest\t= test_X.size()[0]\n\t\tpredicted_label =\ttorch.LongTensor(test_label.size())\n\t\tfor i\tin range(0,\tntest, self.batch_size):\n\t\t\tend = min(ntest,\tstart+self.batch_size)\n\t\t\tif self.cuda:\n\t\t\t\toutput = self.model(Variable(test_X[start:end].cuda(), requires_grad=False)) \n\t\t\telse:\n\t\t\t\toutput = self.model(Variable(test_X[start:end],\trequires_grad=False))\t\n\t\t\t_, predicted_label[start:end] = torch.max(output.data, 1)\n\t\t\tstart = end\n\n\t\tacc =\tself.compute_per_class_acc_gzsl(util.map_label(test_label, target_classes), predicted_label,\ttarget_classes.size(0))\n\n\t\t#acc =\tself.compute_per_class_acc_gzsl(test_label,\tpredicted_label, target_classes)\n\t\treturn acc\n\t\n\t'''\n\tdef compute_per_class_acc_gzsl(self, test_label, predicted_label, target_classes):\n\t\tacc_per_class\t= 0\n\t\tfor i\tin target_classes:\n\t\t\tidx = (test_label ==\ti)\n\t\t\tprint(print('idx',idx))\n\t\t\tprint('torch.sum(test_label[idx]==predicted_label[idx])',torch.sum(test_label[idx]==predicted_label[idx]))\n\t\t\tprint('torch.sum(idx)',torch.sum(idx))\n\t\t\tacc_per_class +=\ttorch.sum(test_label[idx]==predicted_label[idx]).float() / torch.sum(idx).float()\n\t\t\tprint('acc_per_class for class %d = %.4f '%(i,acc_per_class))\n\t\tacc_per_class\t/= target_classes.size(0)\n\t\treturn acc_per_class \n\t'''\t\n\t\n\tdef compute_per_class_acc_gzsl(self, test_label, predicted_label, target_classes):\n\t\tacc_per_class\t= torch.FloatTensor(target_classes).fill_(0)\n\t\tacc_per_class= acc_per_class.cuda()\t\n\t\ttest_label=test_label.cuda()\n\t\tpredicted_label=predicted_label.cuda()\n\t\tfor i\tin range(target_classes):\n\t\t\tidx = (test_label ==\ti)\n\t\t\t#print(print('idx',idx))\n\t\t\t#print('torch.sum(test_label[idx]==predicted_label[idx])',torch.sum(test_label[idx]==predicted_label[idx]))\n\t\t\t#print('torch.sum(idx)',torch.sum(idx))\n\t\t\tacc_per_class [i]=\ttorch.sum(test_label[idx]==predicted_label[idx]).float() / torch.sum(idx).float()\n\t\t\t#print('acc_per_class for class %d = %.4f '%(i,acc_per_class [i]))\n\t\t#print('acc_per_class:',acc_per_class)\n\n\t\tacc_per_class_mean = acc_per_class.mean() \n\t\t#acc_per_class\t/= target_classes.size(0)\n\t\treturn acc_per_class_mean\n\t\n\t\n\t# test_label is integer \n\tdef val(self, test_X, test_label, target_classes):\t\n\t\tstart\t= 0\n\t\tntest\t= test_X.size()[0]\n\t\tpredicted_label =\ttorch.LongTensor(test_label.size())\n\t\tfor i\tin range(0,\tntest, self.batch_size):\n\t\t\tend = min(ntest,\tstart+self.batch_size)\n\t\t\tif self.cuda:\n\t\t\t\toutput = self.model(Variable(test_X[start:end].cuda(),requires_grad=False)) \n\t\t\telse:\n\t\t\t\toutput = self.model(Variable(test_X[start:end],requires_grad=False))\t\n\t\t\t_, predicted_label[start:end] = torch.max(output.data, 1)\n\t\t\tstart = end\n\t\t#print('test_label',test_label)\n\t\t#print('target_classes',target_classes)\n\t\t#print('util.map_label(test_label, target_classes',util.map_label(test_label, target_classes\t))\n\t\t#print('predicted_label',predicted_label)\n\t\tacc =\tself.compute_per_class_acc(util.map_label(test_label, target_classes), predicted_label,\ttarget_classes.size(0))\n\t\treturn acc\n\n\tdef compute_per_class_acc(self, test_label, predicted_label, nclass):\n\t\tacc_per_class\t= torch.FloatTensor(nclass).fill_(0)\n\t\tacc_per_class= acc_per_class.cuda()\n\t\ttest_label=test_label.cuda()\n\t\tpredicted_label=predicted_label.cuda()\n\t\t\n \n\t\tfor i\tin range(nclass):\n\t\t\tidx = (test_label ==\ti)\n\t\t\t#print('torch.sum(test_label[idx]==predicted_label[idx])',torch.sum(test_label[idx]==predicted_label[idx]))\n\t\t\t#print('torch.sum(idx)',torch.sum(idx))\n\t\t\tacc_per_class[i]\t= torch.sum(test_label[idx]==predicted_label[idx]).float() / torch.sum(idx).float()\n\t\t\tprint('acc_per_class for class %d = %.4f '%(i,acc_per_class[i]))\n\t\tprint('acc_per_class:',acc_per_class)\n\t\treturn acc_per_class.mean() \n\nclass LINEAR_LOGSOFTMAX(nn.Module):\n\tdef __init__(self,\tinput_dim, nclass):\n\t\tsuper(LINEAR_LOGSOFTMAX, self).__init__()\n\t\tself.fc =\tnn.Linear(input_dim, nclass)\n\t\tself.logic = nn.LogSoftmax(dim=1)\n\tdef forward(self, x): \n\t\to\t= self.logic(self.fc(x))\n\t\treturn o\t\n", "sub_path": "classifier2.py", "file_name": "classifier2.py", "file_ext": "py", "file_size_in_byte": 10467, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "util.weights_init", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.nn.NLLLoss", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 46, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.randperm", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.randperm", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 210, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 212, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 213, "usage_type": "call"}, {"api_name": "util.map_label", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 236, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 245, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 258, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 264, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 265, "usage_type": "call"}, {"api_name": "util.map_label", "line_number": 271, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 275, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 285, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 290, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 290, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 293, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 293, "usage_type": "name"}, {"api_name": "torch.nn.LogSoftmax", "line_number": 294, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 294, "usage_type": "name"}]} +{"seq_id": "376789856", "text": "# -*- coding:utf-8 -*\nimport os\nimport sys\nimport time\n#from multiprocessing import Pool\n#from multiprocessing.dummy import Pool as ThreadPool\nimport multiprocessing\nimport progressbar\n\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.contrib.tensorboard.plugins import projector\nimport tensorflow.contrib.rnn as rnn_cell\nfrom tensorflow.contrib import legacy_seq2seq as seq2seq\n\nimport pickle\nimport argparse\nimport random\nfrom data_engine import Stock_engine\nimport io\nimport matplotlib.pyplot as plt\nimport matplotlib.finance as mpf\nimport utils\nplt.switch_backend('agg')\n\n\n\nclass Params():\n batch_size = 256 \n infer_size = 1 \n n_epoch =1000 \n per_save = 10\n per_validate= 1\n learning_rate = 0.0001\n decay_steps = 1000\n decay_rate = 0.95 \n grad_clip = 5\n\n state_size = 512 \n num_layers = 1\n seq_length = 20\n log_dir = './logs'\n metadata = 'metadata.tsv'\n gen_num = 500 # how many chars to generate\n interface = {\n \n }\n\n\ntmp_path = '/dev/shm'\n\ndef file_compute_vector(x):\n file_name,func = x\n with open(file_name, 'rb') as f:\n data_list = pickle.load(f)\n vecter_list = []\n for item in data_list:\n vector = func(item)\n vecter_list.append(vector)\n print(len(vecter_list))\n return vecter_list;\n\n cores = multiprocessing.cpu_count()\n pool = multiprocessing.Pool(processes=cores)\n #def f(x):\n # return self.data2vector(x)[0];\n vecter_list = pool.map(func,data_list)\n print('vecter len=%d'%len(vector_list))\n pool.close()\n pool.join()\n return vecter_list\n\nclass Model():\n def __init__(self,args,data_engine):\n self.name = None\n self.args = args\n self.data_engine = data_engine\n self.train_dataset = None\n self.validate_dataset = None\n self.tmp_path = \"/dev/shm\"\n #self.seq_columns = None #store seq info\n #self.vec_columns = None #store vector info\n self.meta = None\n self.log_dir = './logs' \n self.image_dir = None \n self.train_log_dir=''\n self.validate_log_dir=''\n return \n def build_graph(self):\n self._import_data()\n self._create_model()\n self._create_loss()\n self._create_optimizer()\n self._create_summaries()\n self.visualize()\n\n def create_log(self):\n dir_name = os.path.join(self.log_dir,self.name)\n if not os.path.isdir(dir_name):\n os.mkdir(dir_name)\n image_dir = os.path.join(self.log_dir,self.name,'image')\n if not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n train_log_dir = os.path.join(self.log_dir,self.name,'train')\n if not os.path.isdir(train_log_dir):\n os.mkdir(train_log_dir)\n validate_log_dir = os.path.join(self.log_dir,self.name,'validate')\n if not os.path.isdir(validate_log_dir):\n os.mkdir(validate_log_dir)\n self.log_dir = dir_name\n self.image_dir = image_dir\n self.train_log_dir =train_log_dir \n self.validate_log_dir=validate_log_dir \n\n def load_meta(self):\n try:\n file_name = os.path.join(tmp_path,self.name+'_model_vector_info.bin')\n with open(file_name, 'rb') as f:\n self.meta= pickle.load(f)\n print(self.meta)\n #self.seq_columns = self.meta['seq_columns']\n #self.vec_columns = self.meta['vec_columns']\n except:\n print('stock_engine_meta not found!')\n pass \n\n def save_meta(self):\n file_name = os.path.join(tmp_path,self.name+'_model_vector_info.bin')\n with open(file_name, 'wb') as f:\n pickle.dump(self.meta,f)\n \n #load train dataset from data_engine and convert to vector\n def load_train_tensor(self,func=None):\n if func is None:\n func = self.data2vector\n file_name = os.path.join(self.tmp_path,self.name + '_train_vector.bin')\n try:\n print('Load train vector from cache')\n with open(file_name, 'rb') as f:\n self.train_dataset = pickle.load(f)\n print('Load train vector ok!')\n except:\n print('Gen train vector from dataset file ')\n train_dir = '/dev/shm/train'\n files = list(map(lambda x:os.path.join(train_dir,x),os.listdir(train_dir)))\n print(files)\n cores = multiprocessing.cpu_count()\n pool = multiprocessing.Pool(processes=cores)\n #def f(x):\n # return self.data2vector(x)[0];\n v_list = pool.map(file_compute_vector,zip(files,[self.data2vector]*len(files)))\n print('vecter len=%d'%len(v_list))\n train_dataset =[];\n for l in v_list:\n train_dataset = train_dataset + l\n print(len(train_dataset))\n \n pool.close()\n pool.join()\n meta = train_dataset[0][1]\n if self.meta is None:\n print('dump meta')\n self.meta = meta\n self.save_meta();\n else:\n #check columns info consistency\n assert self.meta == meta\n self.train_dataset = list(map(lambda x:x[0],train_dataset));\n print('start dump vector')\n with open(file_name, 'wb') as f:\n pickle.dump(self.train_dataset,f)\n print(self.train_dataset[0])\n return\n #if self.data_engine.batch_list is None:\n # self.data_engine.gen_train_dataset()\n #self.train_dataset = []\n #self.data_list2vector_list(self.data_engine.batch_list,self.train_dataset,func)\n #with open(file_name, 'wb') as f:\n # pickle.dump(self.train_dataset,f)\n\n #load validate dataset from data_engine and convert to vector\n def load_validate_tensor(self,func=None):\n if func is None:\n func = self.data2vector\n file_name = os.path.join(self.tmp_path,self.name + '_validate_vector.bin')\n try:\n print('Load validate vector from cache')\n with open(file_name, 'rb') as f:\n self.validate_dataset = pickle.load(f)\n print('Load validate vector ok!')\n except:\n print('Gen validate vector ')\n if self.data_engine.validate_list is None:\n self.data_engine.gen_validate_dataset()\n self.validate_dataset = []\n self.data_list2vector_list(self.data_engine.validate_list,self.validate_dataset,func)\n with open(file_name, 'wb') as f:\n pickle.dump(self.validate_dataset,f)\n \n def data2vector(self,data):\n print('Must provice data-2vector method in each specific model, Never go here')\n exit(0);\n\n\n\n def __data2vector(self,x):\n return self.data2vector(x)[0];\n print('Must provice data-2vector method in each specific model, Never go here')\n exit(0);\n \n def data_list2vector_list(self,data_list,vector_list,func):\n print(data_list[0]['df_d'].describe())\n vector,meta = self.data2vector(data_list[0])\n #save column info\n if self.meta is None:\n print('dump meta')\n self.meta = meta\n self.save_meta();\n else:\n #check columns info consistency\n assert self.meta == meta\n #pool = ThreadPool(12)\n '''\n cores = multiprocessing.cpu_count()\n pool = multiprocessing.Pool(processes=cores)\n #def f(x):\n # return self.data2vector(x)[0];\n vecter_list = pool.map(func,data_list)\n print('vecter len=%d'%len(vector_list))\n pool.close()\n pool.join()\n return;\n '''\n p = progressbar.ProgressBar()\n p.start()\n N = len(data_list)\n i=0\n print('total trainset:%d'%N)\n for item in data_list:\n vector,meta = self.data2vector(item)\n #save column info\n #if self.meta is None:\n # print('dump meta')\n # self.meta = meta\n # self.save_meta();\n #else:\n #check columns info consistency\n # assert self.meta == meta\n vector_list.append(vector)\n i=i+1\n p.update(min(100,int((i / (N- 1)) * 100)))\n p.finish()\n\n #get train_dataset from data_engine and provde batches for training\n # x history\n # y label\n # z future\n # o info\n # z,o are not for learning, but for human\n\n def split_data_to_batches(self,dataset,batch_size=64):\n if batch_size>=0:\n n_chunk = len(dataset) // batch_size\n else:\n n_chunk = 1\n batch_size = len(dataset)\n section_num = len(dataset[0])\n dim_batches = [[] for x in range(section_num)]\n for i in range(n_chunk):\n start_index = i * batch_size\n end_index = start_index + batch_size\n tmp_batches = [[] for x in range(section_num)]\n batch = dataset[start_index:end_index]\n for item in batch:\n for j in range(len(item)):\n tmp_batches[j].append(item[j])\n for j in range(section_num):\n dim_batches[j].append(np.array(tmp_batches[j]))\n #print(len(dim_batches[0]))\n return dim_batches\n \n\n def all_batches(self,batch_size=64,func=None):\n if self.train_dataset is None:\n self.load_train_tensor(func)\n #self.train_dataset = self.data_engine.dataset\n random.shuffle(self.train_dataset)\n return self.split_data_to_batches(self.train_dataset,batch_size)\n \n #get validate_dataset from data_engine and provde batches for validating \n # x history\n # y label\n # z future\n # o info\n # z,o are not for learning, but for human\n def random_validate(self,batch_size=64):\n if self.validate_dataset is None:\n self.load_validate_tensor()\n #self.validate_dataset = self.data_engine.validate_dataset\n random.shuffle(self.validate_dataset)\n print('validate len=%d,batch=%d'%(len(self.validate_dataset),batch_size))\n return list(map(lambda x:x[0],self.split_data_to_batches(self.validate_dataset,batch_size)))\n\n def train(self):\n args = self.args\n self.all_batches(batch_size=args.batch_size)\n #self.data_engine.load_train_tensor()\n self.args.seq_length=None\n n_epoch = self.args.n_epoch\n dataset = self.train_dataset\n #x,y,z,p = dataset[0]\n #print(x.shape)\n #print(y.shape)\n #self.seq_length,self.vector_size = x.shape\n #self.output_size = y.shape[0]\n self.check_dataset(dataset)\n self.build_graph();\n with tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n saver = tf.train.Saver()\n train_writer = tf.summary.FileWriter(self.train_log_dir, sess.graph)\n validate_writer = tf.summary.FileWriter(self.validate_log_dir )\n\n start_epoch = 0\n checkpoint = tf.train.latest_checkpoint(self.log_dir)\n if checkpoint:\n saver.restore(sess, checkpoint)\n print(\"## restore from the checkpoint {0}\".format(checkpoint))\n start_epoch += int(checkpoint.split('-')[-1])\n print('## start training...')\n\n try:\n for epoch in range(start_epoch,n_epoch): \n start_time = time.time()\n train_loss,summary = self.train_in_turn(sess,train_writer)\n train_writer.add_summary(summary, global_step=epoch)\n train_writer.flush()\n end_time = time.time()\n print('Epoch:%d Batch:%d/%d, training_loss:%f time:%d'%(epoch,0,0, train_loss,end_time-start_time))\n if epoch % args.per_save==0:\n saver.save(sess, os.path.join(self.log_dir, self.name+'_model.ckpt'), global_step=epoch)\n if epoch % args.per_validate==0:\n validate_loss = self.validate_in_turn(sess)\n print(validate_loss)\n summary = sess.run(self.loss_summary, {self.loss: validate_loss})\n validate_writer.add_summary(summary, global_step=epoch)\n validate_writer.flush()\n except KeyboardInterrupt:\n print('## Interrupt manually, try saving checkpoint for now...')\n saver.save(sess, os.path.join(self.log_dir, self.name+'_model.ckpt'), global_step=epoch)\n print('## Last epoch were saved, next time will start from epoch {}.'.format(epoch))\n\n def train_in_turn(self,sess):\n print('Must provice train_in_turn method in each specific model, Never go here')\n exit(0);\n \n def validate_in_turn(self,sess):\n return\n\n def validate_all(self,sess):\n print('Must provice validate_all method in each specific model, Never go here')\n exit(0);\n\n \n def validate(self):\n self.random_validate()\n #self.data_engine.load_validate_tensor()\n dataset = self.validate_dataset\n #x,y,z,o = dataset[0]\n #print(x.shape)\n # print(y.shape)\n #print(z.shape)\n #self.seq_length,self.vector_size = x.shape\n #self.output_size = y.shape[0]\n self.check_dataset(dataset)\n validate_num = len(dataset)\n self.args.batch_size = validate_num\n self.build_graph();\n saver = tf.train.Saver()\n args = self.args\n data_engine=self.data_engine\n with tf.Session() as sess:\n print(self.log_dir)\n ckpt = tf.train.latest_checkpoint(self.log_dir)\n print(ckpt)\n saver.restore(sess, ckpt)\n self.validate_all(sess)\n return\n\n def test(self,code,date=-1,num=10):\n test_list = self.data_engine.gen_test_dataset(code) \n test_dataset = [] \n self.data_list2vector_list(test_list,test_dataset,self.data2vector)\n self.check_dataset(test_dataset)\n test_num = len(test_dataset)\n self.args.batch_size = test_num \n self.build_graph();\n saver = tf.train.Saver()\n args = self.args\n data_engine=self.data_engine\n test_batches = list(map(lambda x:x[0],self.split_data_to_batches(test_dataset,-1)))\n with tf.Session() as sess:\n print(self.log_dir)\n ckpt = tf.train.latest_checkpoint(self.log_dir)\n print(ckpt)\n saver.restore(sess, ckpt)\n self.test_stock(sess,code,test_batches)\n return\n\n #check dataset shape to set the dim info of the graph\n def check_dataset(self,dataset):\n print('Should provice check_dataset method in each specific model ')\n \n\n def draw_stock(self,prefix,columns,df_d,df_judge,y):\n pos = dict(zip(columns,range(len(columns))))\n curves = {\n 'stock':[],\n 'sh':[],\n 'sz':[],\n 'cy':[],\n } \n for i in range(len(df_d)):\n for k in curves.keys():\n if k=='stock':\n suffix=''\n else:\n suffix = '_'+k\n curves[k].append((i,df_d[i][pos['open'+suffix]],df_d[i][pos['high'+suffix]],df_d[i][pos['low'+suffix]],df_d[i][pos['close'+suffix]]))\n for i in range(len(df_judge)):\n for k in curves.keys():\n if k=='stock':\n suffix=''\n else:\n suffix = '_'+k\n curves[k].append((i+len(df_d),df_judge[i][pos['open'+suffix]],df_judge[i][pos['high'+suffix]],df_judge[i][pos['low'+suffix]],df_judge[i][pos['close'+suffix]]))\n\n return self.draw_k(prefix,curves,{},len(df_d),{'1':1.0+y[0]/100,'5':1.0+y[1]/100,'10':1.0+y[2]/100})\n\n \n def draw_k(self,prefix,price_curve_dict,volume_curve_dict,days,pred):\n fig, ax = plt.subplots()\n fig.subplots_adjust(bottom=0.2)\n plt.xticks(rotation=45)\n plt.yticks()\n colors = {\n 'stock':('r','g'),\n 'sh':('orange','yellow'),\n 'sz':('blue','gray'),\n 'cy':('black','gray'),\n }\n for k in ['cy','sz','sh','stock']:\n curve = price_curve_dict.get(k)\n if curve is None:\n continue\n mpf.candlestick_ohlc(ax,curve,width=0.2,colorup=colors.get(k)[0],colordown=colors.get(k)[1])\n\n for k in pred: \n plt.plot(days+int(k)-1,pred[k],'*')\n\n image_name =os.path.join(self.image_dir,prefix+'.png')\n plt.savefig(image_name)\n return image_name\n\n def display_image(self,sess,writer,image_name):\n with open(image_name, 'rb') as f:\n data = f.read()\n image = tf.image.decode_png(data, channels=4)\n image = tf.expand_dims(image, 0)\n summary_op = tf.summary.image(image_name, image)\n summary = sess.run(summary_op)\n writer.add_summary(summary)\n return\n\n writer.close()\n sess.close()\n\n file = open(image_name, 'rb')\n data = file.read()\n file.close()\n image = tf.image.decode_png(data, channels=4)\n image = tf.expand_dims(image, 0)\n sess = tf.Session()\n writer = tf.summary.FileWriter('logs')\n summary_op = tf.summary.image(\"image1\", image)\n summary = sess.run(summary_op)\n writer.add_summary(summary)\n writer.close()\n sess.close()\n\n def visualize(self):\n output_to_logging=False\n output_detail=True\n total_parameters = 0\n parameters_string = \"\"\n\n for variable in tf.trainable_variables():\n\n shape = variable.get_shape()\n variable_parameters = 1\n for dim in shape:\n variable_parameters *= dim.value\n total_parameters += variable_parameters\n if len(shape) == 1:\n parameters_string += (\"%s %d, \" % (variable.name, variable_parameters))\n else:\n parameters_string += (\"%s %s=%d, \" % (variable.name, str(shape), variable_parameters))\n\n if output_to_logging:\n if output_detail:\n logging.info(parameters_string)\n logging.info(\"Total %d variables, %s params\" % (len(tf.trainable_variables()), \"{:,}\".format(total_parameters)))\n else:\n if output_detail:\n print(parameters_string)\n print(\"Total %d variables, %s params\" % (len(tf.trainable_variables()), \"{:,}\".format(total_parameters)))\n\n\n\nif __name__ == '__main__':\n params = Params()\n stock_engine = Stock_engine()\n stock_engine.load_stock_info()\n stock_engine.load_index_data()\n stock_engine.load_all_stock_data()\n stock_engine.param[\"stock_num\"] = 1000 \n stock_engine.param[\"per_stock\"] = 3 \n stock_engine.param[\"window\"] = 100 \n #stock_engine.load_train_tensor()\n #data_engine.all_batches()\n #print(len(stock_engine.batch_list))\n model = Model(params, stock_engine)\n #model.build_graph()\n #model.visualize();\n #model.train(args)\n model.validate()\n\n", "sub_path": "model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 19312, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "matplotlib.pyplot.switch_backend", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 55, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 63, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 64, "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.isdir", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "os.mkdir", "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.isdir", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 109, "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": "pickle.load", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path", "line_number": 145, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 145, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 147, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 148, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path", "line_number": 185, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 189, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 198, "usage_type": "call"}, {"api_name": "progressbar.ProgressBar", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 278, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 287, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 300, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 318, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 319, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 320, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 320, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 321, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 321, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 322, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 322, "usage_type": "attribute"}, {"api_name": "tensorflow.train.latest_checkpoint", "line_number": 325, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 325, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 334, "usage_type": "call"}, {"api_name": "time.time", "line_number": 338, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 341, "usage_type": "call"}, {"api_name": "os.path", "line_number": 341, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 350, "usage_type": "call"}, {"api_name": "os.path", "line_number": 350, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 379, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 379, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 382, "usage_type": "call"}, {"api_name": "tensorflow.train.latest_checkpoint", "line_number": 384, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 384, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 398, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 398, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 402, "usage_type": "call"}, {"api_name": "tensorflow.train.latest_checkpoint", "line_number": 404, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 404, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 442, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 442, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 444, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 444, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 445, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 445, "usage_type": "name"}, {"api_name": "matplotlib.finance.candlestick_ohlc", "line_number": 456, "usage_type": "call"}, {"api_name": "matplotlib.finance", "line_number": 456, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 459, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 459, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 461, "usage_type": "call"}, {"api_name": "os.path", "line_number": 461, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 462, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 462, "usage_type": "name"}, {"api_name": "tensorflow.image.decode_png", "line_number": 468, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 468, "usage_type": "attribute"}, {"api_name": "tensorflow.expand_dims", "line_number": 469, "usage_type": "call"}, {"api_name": "tensorflow.summary.image", "line_number": 470, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 470, "usage_type": "attribute"}, {"api_name": "tensorflow.image.decode_png", "line_number": 481, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 481, "usage_type": "attribute"}, {"api_name": "tensorflow.expand_dims", "line_number": 482, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 483, "usage_type": "call"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 484, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 484, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.image", "line_number": 485, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 485, "usage_type": "attribute"}, {"api_name": "tensorflow.trainable_variables", "line_number": 497, "usage_type": "call"}, {"api_name": "tensorflow.trainable_variables", "line_number": 512, "usage_type": "call"}, {"api_name": "tensorflow.trainable_variables", "line_number": 516, "usage_type": "call"}, {"api_name": "data_engine.Stock_engine", "line_number": 522, "usage_type": "call"}]} +{"seq_id": "120548877", "text": "import json\n\ndef getSumOfData(historyDataList):\n sum = 0\n for d in historyDataList:\n Number1 = json.loads(d['data'])['number1'] \n Number2 = json.loads(d['data'])['number2'] \n sum = sum + Number1 + Number2\n return sum \n \ndef formatTheResultForDB(sum):\n data = {'data': sum}\n data = json.dumps(data)\n return data \n ", "sub_path": "BuisnessLayer/Utilities/HelpFunctions.py", "file_name": "HelpFunctions.py", "file_ext": "py", "file_size_in_byte": 372, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "json.loads", "line_number": 6, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 7, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "47726794", "text": "from mpl_toolkits.mplot3d import Axes3D\r\nimport matplotlib.pyplot as plt\r\nimport random\r\n\r\naS = []\r\nbS = []\r\ncS = []\r\n\r\ndef graph(title, x_label, y_label, x_data, y_data):\r\n plt.title(title)\r\n plt.scatter(x_data, y_data)\r\n plt.xlabel(x_label)\r\n plt.ylabel(y_label)\r\n plt.show()\r\n\r\ndef generate(m,n):\r\n a = 2*m*n\r\n b = (m**2) - (n**2)\r\n c = (m**2) + (n**2)\r\n aS.append(abs(a))\r\n bS.append(abs(b))\r\n cS.append(abs(c))\r\n\r\nnum = 50\r\n\r\nfor m in range(num):\r\n for n in range(num):\r\n generate(m,n)\r\n\r\ngraph('bS vs aS', 'aS', 'bS', aS, bS)\r\ngraph('cS vs aS', 'aS', 'cS', aS, cS)\r\ngraph('cS vs bS', 'bS', 'cS', bS, cS)\r\n\r\nfig = plt.figure()\r\nax = fig.add_subplot(111, projection='3d')\r\n\r\nax.scatter(aS,bS,cS)\r\nplt.title('3d plot of aS, bS, and cS')\r\nax.set_xlabel('aS')\r\nax.set_ylabel('bS')\r\nax.set_zlabel('cS')\r\nplt.show()\r\n", "sub_path": "pythag_triples.py", "file_name": "pythag_triples.py", "file_ext": "py", "file_size_in_byte": 859, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "matplotlib.pyplot.title", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "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": "117413784", "text": "from collections import deque\n\n\nclass AdventOfCode:\n\n def __init__(self, filename):\n with open(filename) as f:\n self.input = f.read().splitlines()\n\n self.state = self.input.pop(0).split()[2]\n self.input.pop(0)\n self.first=0\n\n self.rules = {}\n for line in self.input:\n key, val = line.split(' => ')\n self.rules[key] = val\n\n def next_state(self, f_input):\n next_st = []\n old_st = deque(f_input)\n \n neighbors = deque(['.','.','.','.'])\n old_st.extend(['.','.','.','.'])\n\n while len(old_st) > 0:\n neighbors.append(old_st.popleft())\n next_st.append(self.rules[''.join(neighbors)])\n neighbors.popleft()\n\n left_shift = -2\n for char in next_st:\n if char == '.':\n left_shift += 1\n else:\n break\n self.first += left_shift\n\n next_st = ''.join(next_st)\n next_st = next_st.strip('.')\n\n return next_st\n\n def my_sum(self, state, first_val):\n val = first_val\n tot_val = 0\n for char in state:\n if char == '#':\n tot_val += val\n val += 1\n return tot_val\n\n def row(self, state):\n if self.first > 0:\n prepend = ['_']\n for i in range(self.first-1):\n prepend.append('.')\n return ''.join(prepend) + state\n if self.first == 0:\n return ('@' if state[0] == '#' else '_') + state[1:]\n \n return state[0:-self.first] + ('@' if state[-self.first] == '#' else '_') + state[-self.first+1:]\n\n def part1(self):\n state = self.state\n for i in range(20):\n state = self.next_state(state)\n return self.my_sum(state, self.first)\n\n def part2(self):\n state = self.state\n self.first = 0\n\n lim = 50000000000\n \n total = self.my_sum(state, self.first)\n d_total = 0\n last_run_smooth = False\n\n for i in range(lim):\n state = self.next_state(state)\n new_tot = self.my_sum(state, self.first)\n\n if new_tot - total == d_total:\n if last_run_smooth:\n return new_tot + d_total * (lim - i - 1)\n last_run_smooth = True\n\n d_total = new_tot - total\n total = new_tot\n\n return self.my_sum(state, self.first)\n", "sub_path": "py/2018/12.py", "file_name": "12.py", "file_ext": "py", "file_size_in_byte": 2457, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "collections.deque", "line_number": 21, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "502486883", "text": "from django.test import TestCase\nfrom bangazon_ultra.models.paymenttype_model import *\n\nclass PaymentTypeModelTests(TestCase):\n \"\"\"\n This class tests all aspects of the PaymentType model, which pertains to the creation of new payment types and getting existing payment types from the database.\n\n Methods:\n test_payment_type_model - Tests the PaymentType model's ability to create a new instance of payment type to add to the database.\n\n Author: Steven Holmes (Main Bananas)\n \"\"\"\n\n def test_paymenttype_model(self):\n self.payment = PaymentType.objects.create(payment_name='Visa', account_number=1234567890, expiration_date='12-12-01', billing_address='123 Test Way')\n self.db_acct = PaymentType.objects.get(pk=1)\n self.assertEqual(self.payment.id, self.db_acct.id)", "sub_path": "bangazon_site/bangazon_ultra/tests/test_payment_type_model.py", "file_name": "test_payment_type_model.py", "file_ext": "py", "file_size_in_byte": 825, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.test.TestCase", "line_number": 4, "usage_type": "name"}]} +{"seq_id": "391346590", "text": "import gensim\nimport pandas as pd\nimport smart_open\nimport random\nimport sqlite3\n\ncon = sqlite3.connect(\"database.sqlite\")\ndataframe = pd.read_sql_query(\"select * from content join artists where artists.reviewid = content.reviewid\", con)\n\ndef read_corpus(documents):\n\tfor i, text in enumerate(documents):\n\t\tyield gensim.models.doc2vec.TaggedDocument(gensim.utils.simple_preprocess(text), [dataframe.artist[i]])\n\n\ntrain_corpus = list(read_corpus(dataframe.text))\n\nmodel = gensim.models.doc2vec.Doc2Vec(size=50, min_count=2, iter=55)\nmodel.build_vocab(train_corpus)\nprint(\"training model\")\nmodel.train(train_corpus, total_examples=model.corpus_count, epochs=model.iter)\n\nmodel.save('doc_tensor_news.doc2vec')\nprint(\"model saved\")\n\n\n", "sub_path": "build_docvecs.py", "file_name": "build_docvecs.py", "file_ext": "py", "file_size_in_byte": 730, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sqlite3.connect", "line_number": 7, "usage_type": "call"}, {"api_name": "pandas.read_sql_query", "line_number": 8, "usage_type": "call"}, {"api_name": "gensim.models.doc2vec.TaggedDocument", "line_number": 12, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 12, "usage_type": "attribute"}, {"api_name": "gensim.utils.simple_preprocess", "line_number": 12, "usage_type": "call"}, {"api_name": "gensim.utils", "line_number": 12, "usage_type": "attribute"}, {"api_name": "gensim.models.doc2vec.Doc2Vec", "line_number": 17, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 17, "usage_type": "attribute"}]} +{"seq_id": "147044463", "text": "#!/usr/bin/env python3\n\n\"\"\"\nProblem C. Bathroom Stalls\n\"\"\"\n\nimport argparse\nfrom heapq import heappush, heappop\n\n\ndef main(n, k):\n if n == k:\n return [0, 0]\n\n heap = [-n]\n for _ in range(k):\n max_item = -heappop(heap)\n max_item = max_item - 1\n i = max_item // 2\n j = max_item - i\n heappush(heap, -i)\n heappush(heap, -j)\n return sorted([i, j], reverse=True)\n\n\ndef parse_input():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-f\", \"--file\", help=\"Path to input file\", required=True)\n args = parser.parse_args()\n with open(args.file) as fd:\n t = int(fd.readline())\n for i in range(1, t + 1):\n yield i, fd.readline().strip()\n\n\nif __name__ == '__main__':\n for i, line in parse_input():\n n, k = map(int, line.split())\n r = main(n, k)\n print(\"Case #{}: {} {}\".format(i, *r))\n", "sub_path": "codejam/bathroom_stalls.py", "file_name": "bathroom_stalls.py", "file_ext": "py", "file_size_in_byte": 902, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "heapq.heappop", "line_number": 17, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 21, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 22, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "84775246", "text": "from functools import total_ordering\n\n\n@total_ordering\nclass Version:\n def __init__(self, version):\n version = version.replace('-', '.').split('.')\n self.version = transform(version)\n self.digit_version = self.version[:3]\n self.remains = self.version[3:]\n\n def __eq__(self, other):\n return self.version == other\n\n def __lt__(self, other):\n if self.digit_version == other and self.remains:\n return self.version > other\n return self.version < other\n\n\ndef transform(version_list):\n for i in range(3):\n try:\n if version_list[i].isdigit():\n version_list[i] = int(version_list[i])\n else:\n digits_in_str = ''.join([item for item in version_list[i] if item.isdigit()])\n version_list[i] = int(digits_in_str)\n except IndexError:\n return version_list\n return version_list\n\n\ndef main():\n to_test = [\n ('1.0.0', '2.0.0'),\n ('1.0.0', '1.42.0'),\n ('1.2.0', '1.2.42'),\n ('1.1.0-alpha', '1.2.0-alpha.1'),\n ('1.0.1b', '1.0.10-alpha.beta'),\n ('1.0.0-rc.1', '1.0.0'),\n ]\n\n for version_1, version_2 in to_test:\n assert Version(version_1) <= Version(version_2), f'le failed {version_2} {version_1}'\n assert Version(version_2) > Version(version_1), f'ge failed {version_2} {version_1}'\n assert Version(version_2) != Version(version_1), 'neq failed'\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "compare_task2/compare.py", "file_name": "compare.py", "file_ext": "py", "file_size_in_byte": 1505, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "functools.total_ordering", "line_number": 4, "usage_type": "name"}]} +{"seq_id": "492817092", "text": "import face_recognition\r\nimport os\r\nimport cv2\r\n\r\nKnown_faces_dir =\"known_faces\"\r\nUnknown_faces_dir=\"unknown_faces\"\r\nTolerance=0.5\r\nModel=\"hog\"\r\n\r\nvideo=cv2.VideoCapture(0)\r\nprint(\"loading known faces\")\r\n\r\nknown_faces=[]\r\nknown_names=[]\r\n\r\nfor name in os.listdir(Known_faces_dir):\r\n for filename in os.listdir(f\"{Known_faces_dir}/{name}\"):\r\n image= face_recognition.load_image_file(f\"{Known_faces_dir}/{name}/{filename}\")\r\n encoding=face_recognition.face_encodings(image)[0]\r\n known_faces.append(encoding)\r\n known_names.append(name)\r\n\r\nprint(\"Processing unknown faces\")\r\nfor filename in os.listdir(Unknown_faces_dir):\r\n print(filename)\r\n image=face_recognition.load_image_file(f\"{Unknown_faces_dir}/{filename}\")\r\n img=cv2.resize(image,(640,480))\r\n # img1=cv2.resize(image,(512,512))\r\n locations=face_recognition.face_locations(img,model=Model)\r\n encodings=face_recognition.face_encodings(img,locations)\r\n img=cv2.cvtColor(img,cv2.COLOR_RGB2BGR)\r\n\r\n for face_encoding,face_location in zip(encodings,locations):\r\n results = face_recognition.compare_faces(known_faces,face_encoding,Tolerance)\r\n match =None\r\n if True in results:\r\n match =known_names[results.index(True)]\r\n print(f\"Match found: {match}\")\r\n top_left=(face_location[3],face_location[0])\r\n bottom_right=(face_location[1],face_location[2]+22)\r\n cv2.rectangle(img,top_left,bottom_right,(0,0,255))\r\n cv2.putText(img,match,(face_location[3]+10,face_location[2]+15),cv2.FONT_HERSHEY_COMPLEX,0.5,(200,200,200))\r\n cv2.imshow(filename,img)\r\n \r\n if cv2.waitKey(1000)&0Xff ==ord(\"q\"):\r\n break\r\n\r\ncv2.destroyAllWindows()\r\n", "sub_path": "Face Recognition/face_reg_img.py", "file_name": "face_reg_img.py", "file_ext": "py", "file_size_in_byte": 1728, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "cv2.VideoCapture", "line_number": 10, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 16, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 17, "usage_type": "call"}, {"api_name": "face_recognition.load_image_file", "line_number": 18, "usage_type": "call"}, {"api_name": "face_recognition.face_encodings", "line_number": 19, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 24, "usage_type": "call"}, {"api_name": "face_recognition.load_image_file", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 27, "usage_type": "call"}, {"api_name": "face_recognition.face_locations", "line_number": 29, "usage_type": "call"}, {"api_name": "face_recognition.face_encodings", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 31, "usage_type": "attribute"}, {"api_name": "face_recognition.compare_faces", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_COMPLEX", "line_number": 42, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "183788740", "text": "import torch\nfrom auto_encoder2 import VQ_CVAE\nfrom torchvision import transforms\nimport fpa_dataset\nimport argparse\nimport visualize as vis\nimport io_image\nimport numpy as np\nimport fpa_io\n\nuse_cuda = True\nresults_dir = './results/'\nk = 512\nhidden = 256\nnum_channels_in = 1\nnum_channels_out = 1\nimg_path = 'C:/Users/Administrator/Documents/Datasets/ycb_unreal_colour (493).png'\nimg_save_path = results_dir + 'output_img.png'\nimg_res = (200, 200)\n\nparser = argparse.ArgumentParser(description='Variational AutoEncoders')\nparser.add_argument('--data-dir', default='/home/paulo/datasets/',\n help='directory containing the dataset')\nparser.add_argument('--split-filename', default='', help='Dataset split filename')\nargs = parser.parse_args()\n\ntransforms = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize(mean=[0.5], std=[0.5])])\n\ntrain_loader = fpa_dataset.DataLoaderReconstruction(root_folder=args.data_dir,\n type='train', transform_color=None,\n transform_depth=transforms,\n batch_size=1,\n split_filename=args.split_filename,\n for_autoencoding=True,\n input_type=\"depth\",)\ntest_loader = fpa_dataset.DataLoaderReconstruction(root_folder=args.data_dir,\n type='test', transform_color=None,\n transform_depth=transforms,\n batch_size=1,\n split_filename=args.split_filename,\n for_autoencoding=True,\n input_type=\"depth\")\n\n# load model\nresults_rootpath = 'C:/Users/Administrator/Documents/GitHub/VQ-VAE/results/2018-11-02_19-04-06_cvpr2019/'\ncheckpoint = torch.load(results_rootpath + 'ycb_checkpoint.pth.tar')\nargs = checkpoint['args']\nepoch = checkpoint['epoch']\nmodel = VQ_CVAE(d=hidden, k=k, num_channels_in=num_channels_in, num_channels_out=num_channels_out)\nmodel.load_state_dict(checkpoint['state_dict'])\nif use_cuda:\n print('Using Cuda')\n model.cuda()\n\nfor batch_idx, (data, label_img) in enumerate(train_loader):\n\n subpath, file_num = train_loader.dataset.get_subpath_and_file_num(batch_idx)\n\n if use_cuda:\n data = data.cuda()\n label_img = label_img.cuda()\n outputs = model(data)\n\n save_img_path = train_loader.dataset.root_folder +\\\n train_loader.dataset.gen_obj_folder +\\\n subpath + str(int(file_num)) + '_recon.npy'\n\n #vis.plot_image(data.cpu().numpy().reshape((200, 200)), title=subpath + '/' + str(file_num))\n #vis.show()\n\n output_img = outputs[0][0, 0:3, :, :].detach().cpu().numpy().reshape((200, 200))\n output_img *= train_loader.dataset.normalise_const_max_depth\n np.save(save_img_path, output_img)\n\n print('Train loader {} / {} ; {}'.format(batch_idx, len(train_loader), save_img_path))\n\n #output_img_loaded = np.load(save_img_path)\n\n #vis.plot_image(output_img_loaded)\n #vis.show()\n #vis.plot_image(output_img, title=subpath + '/' + str(file_num))\n #vis.show()\n\nfor batch_idx, (data, label_img) in enumerate(test_loader):\n\n subpath, file_num = train_loader.dataset.get_subpath_and_file_num(batch_idx)\n\n if use_cuda:\n data = data.cuda()\n label_img = label_img.cuda()\n outputs = model(data)\n\n save_img_path = train_loader.dataset.root_folder + \\\n train_loader.dataset.gen_obj_folder + \\\n subpath + str(int(file_num)) + '_recon.npy'\n\n output_img = outputs[0][0, 0:3, :, :].detach().cpu().numpy().reshape((200, 200))\n output_img *= train_loader.dataset.normalise_const_max_depth\n np.save(save_img_path, output_img)\n\n print('Test loader {} / {} ; {}'.format(batch_idx, len(train_loader), save_img_path))\n\n\n", "sub_path": "test_fpa.py", "file_name": "test_fpa.py", "file_ext": "py", "file_size_in_byte": 4153, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 21, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 27, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 27, "usage_type": "call"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 27, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 28, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 28, "usage_type": "name"}, {"api_name": "fpa_dataset.DataLoaderReconstruction", "line_number": 30, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 32, "usage_type": "name"}, {"api_name": "fpa_dataset.DataLoaderReconstruction", "line_number": 37, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 47, "usage_type": "call"}, {"api_name": "auto_encoder2.VQ_CVAE", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 100, "usage_type": "call"}]} +{"seq_id": "388273331", "text": "\"\"\"\nRepresents Lambda debug entrypoints.\n\"\"\"\n\nimport json\nfrom collections import namedtuple\n\nfrom samcli.local.docker.lambda_image import Runtime\n\n\nclass DebuggingNotSupported(Exception):\n pass\n\n\nDebugSettings = namedtuple(\"DebugSettings\", [\"entrypoint\", \"debug_env_vars\"])\n\n\nclass LambdaDebugSettings:\n @staticmethod\n def get_debug_settings(debug_port, debug_args_list, runtime, options):\n \"\"\"\n Get Debug settings based on the Runtime\n\n Parameters\n ----------\n debug_port int\n Port to open for debugging in the container\n debug_args_list list(str)\n Additional debug args\n runtime str\n Lambda Function runtime\n options dict\n Additonal options needed (i.e delve Path)\n\n Returns\n -------\n tuple:DebugSettings (list, dict)\n Tuple of debug entrypoint and debug env vars\n\n \"\"\"\n\n entrypoint_mapping = {\n Runtime.java8.value: DebugSettings(\n entrypoint=[\"/usr/bin/java\"]\n + debug_args_list\n + [\n \"-agentlib:jdwp=transport=dt_socket,server=y,suspend=y,quiet=y,address=\" + str(debug_port),\n \"-XX:MaxHeapSize=2834432k\",\n \"-XX:MaxMetaspaceSize=163840k\",\n \"-XX:ReservedCodeCacheSize=81920k\",\n \"-XX:+UseSerialGC\",\n # \"-Xshare:on\", doesn't work in conjunction with the debug options\n \"-XX:-TieredCompilation\",\n \"-Djava.net.preferIPv4Stack=true\",\n \"-jar\",\n \"/var/runtime/lib/LambdaJavaRTEntry-1.0.jar\",\n ],\n debug_env_vars={},\n ),\n Runtime.java11.value: DebugSettings(\n None,\n debug_env_vars={\n \"_JAVA_OPTIONS\": f\"-agentlib:jdwp=transport=dt_socket,server=y,suspend=y,quiet=y,address=*:{debug_port} -XX:MaxHeapSize=2834432k -XX:MaxMetaspaceSize=163840k -XX:ReservedCodeCacheSize=81920k -XX:+UseSerialGC -XX:-TieredCompilation -Djava.net.preferIPv4Stack=true\"\n + \" \".join(debug_args_list)\n },\n ),\n Runtime.dotnetcore20.value: DebugSettings(\n [\"/var/lang/bin/dotnet\"]\n + debug_args_list\n + [\"/var/runtime/MockBootstraps.dll\", \"--debugger-spin-wait\"],\n debug_env_vars={},\n ),\n Runtime.dotnetcore21.value: DebugSettings(\n [\"/var/lang/bin/dotnet\"]\n + debug_args_list\n + [\"/var/runtime/MockBootstraps.dll\", \"--debugger-spin-wait\"],\n debug_env_vars={},\n ),\n Runtime.go1x.value: DebugSettings(\n [\"/var/runtime/aws-lambda-go\"]\n + debug_args_list\n + [\"-debug=true\", \"-delvePort=\" + str(debug_port), \"-delvePath=\" + options.get(\"delvePath\")],\n debug_env_vars={},\n ),\n Runtime.nodejs.value: DebugSettings(\n [\"/usr/bin/node\"]\n + debug_args_list\n + [\n \"--debug-brk=\" + str(debug_port),\n \"--nolazy\",\n \"--max-old-space-size=1229\",\n \"--max-new-space-size=153\",\n \"--max-executable-size=153\",\n \"--expose-gc\",\n \"/var/runtime/node_modules/awslambda/bin/awslambda\",\n ],\n debug_env_vars={},\n ),\n Runtime.nodejs43.value: DebugSettings(\n [\"/usr/local/lib64/node-v4.3.x/bin/node\"]\n + debug_args_list\n + [\n \"--debug-brk=\" + str(debug_port),\n \"--nolazy\",\n \"--max-old-space-size=2547\",\n \"--max-semi-space-size=150\",\n \"--max-executable-size=160\",\n \"--expose-gc\",\n \"/var/runtime/node_modules/awslambda/index.js\",\n ],\n debug_env_vars={},\n ),\n Runtime.nodejs610.value: DebugSettings(\n [\"/var/lang/bin/node\"]\n + debug_args_list\n + [\n \"--debug-brk=\" + str(debug_port),\n \"--nolazy\",\n \"--max-old-space-size=2547\",\n \"--max-semi-space-size=150\",\n \"--max-executable-size=160\",\n \"--expose-gc\",\n \"/var/runtime/node_modules/awslambda/index.js\",\n ],\n debug_env_vars={},\n ),\n Runtime.nodejs810.value: DebugSettings(\n [\"/var/lang/bin/node\"]\n + debug_args_list\n + [\n # Node8 requires the host to be explicitly set in order to bind to localhost\n # instead of 127.0.0.1. https://github.com/nodejs/node/issues/11591#issuecomment-283110138\n \"--inspect-brk=0.0.0.0:\" + str(debug_port),\n \"--nolazy\",\n \"--expose-gc\",\n \"--max-semi-space-size=150\",\n \"--max-old-space-size=2707\",\n \"/var/runtime/node_modules/awslambda/index.js\",\n ],\n debug_env_vars={},\n ),\n Runtime.nodejs10x.value: DebugSettings(\n [\n \"/var/rapid/init\",\n \"--bootstrap\",\n \"/var/lang/bin/node\",\n \"--bootstrap-args\",\n json.dumps(\n debug_args_list\n + [\n \"--inspect-brk=0.0.0.0:\" + str(debug_port),\n \"--nolazy\",\n \"--expose-gc\",\n \"--max-http-header-size\",\n \"81920\",\n \"/var/runtime/index.js\",\n ]\n ),\n ],\n debug_env_vars={\n \"NODE_PATH\": \"/opt/nodejs/node_modules:/opt/nodejs/node10/node_modules:/var/runtime/node_modules\"\n },\n ),\n Runtime.nodejs12x.value: DebugSettings(\n [\n \"/var/rapid/init\",\n \"--bootstrap\",\n \"/var/lang/bin/node\",\n \"--bootstrap-args\",\n json.dumps(\n debug_args_list\n + [\n \"--inspect-brk=0.0.0.0:\" + str(debug_port),\n \"--nolazy\",\n \"--expose-gc\",\n \"--max-http-header-size\",\n \"81920\",\n \"/var/runtime/index.js\",\n ]\n ),\n ],\n debug_env_vars={\n \"NODE_PATH\": \"/opt/nodejs/node_modules:/opt/nodejs/node12/node_modules:/var/runtime/node_modules\"\n },\n ),\n Runtime.python27.value: DebugSettings(\n [\"/usr/bin/python2.7\"] + debug_args_list + [\"/var/runtime/awslambda/bootstrap.py\"], debug_env_vars={}\n ),\n Runtime.python36.value: DebugSettings(\n [\"/var/lang/bin/python3.6\"] + debug_args_list + [\"/var/runtime/awslambda/bootstrap.py\"],\n debug_env_vars={},\n ),\n Runtime.python37.value: DebugSettings(\n [\n \"/var/rapid/init\",\n \"--bootstrap\",\n \"/var/lang/bin/python3.7\",\n \"--bootstrap-args\",\n json.dumps(debug_args_list + [\"/var/runtime/bootstrap\"]),\n ],\n debug_env_vars={},\n ),\n Runtime.python38.value: DebugSettings(\n [\n \"/var/rapid/init\",\n \"--bootstrap\",\n \"/var/lang/bin/python3.8\",\n \"--bootstrap-args\",\n json.dumps(debug_args_list + [\"/var/runtime/bootstrap\"]),\n ],\n debug_env_vars={},\n ),\n }\n try:\n return entrypoint_mapping[runtime]\n except KeyError:\n raise DebuggingNotSupported(\"Debugging is not currently supported for {}\".format(runtime))\n", "sub_path": "samcli/local/docker/lambda_debug_settings.py", "file_name": "lambda_debug_settings.py", "file_ext": "py", "file_size_in_byte": 8516, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "collections.namedtuple", "line_number": 15, "usage_type": "call"}, {"api_name": "samcli.local.docker.lambda_image.Runtime.java8", "line_number": 43, "usage_type": "attribute"}, {"api_name": "samcli.local.docker.lambda_image.Runtime", "line_number": 43, "usage_type": "name"}, {"api_name": "samcli.local.docker.lambda_image.Runtime.java11", "line_number": 60, "usage_type": "attribute"}, {"api_name": "samcli.local.docker.lambda_image.Runtime", "line_number": 60, "usage_type": "name"}, {"api_name": "samcli.local.docker.lambda_image.Runtime.dotnetcore20", "line_number": 67, "usage_type": "attribute"}, {"api_name": "samcli.local.docker.lambda_image.Runtime", "line_number": 67, "usage_type": "name"}, {"api_name": "samcli.local.docker.lambda_image.Runtime.dotnetcore21", "line_number": 73, "usage_type": "attribute"}, {"api_name": "samcli.local.docker.lambda_image.Runtime", "line_number": 73, "usage_type": "name"}, {"api_name": "samcli.local.docker.lambda_image.Runtime.go1x", "line_number": 79, "usage_type": "attribute"}, {"api_name": "samcli.local.docker.lambda_image.Runtime", "line_number": 79, "usage_type": "name"}, {"api_name": "samcli.local.docker.lambda_image.Runtime.nodejs", "line_number": 85, "usage_type": "attribute"}, {"api_name": "samcli.local.docker.lambda_image.Runtime", "line_number": 85, "usage_type": "name"}, {"api_name": "samcli.local.docker.lambda_image.Runtime.nodejs43", "line_number": 99, "usage_type": "attribute"}, {"api_name": "samcli.local.docker.lambda_image.Runtime", "line_number": 99, "usage_type": "name"}, {"api_name": "samcli.local.docker.lambda_image.Runtime.nodejs610", "line_number": 113, "usage_type": "attribute"}, {"api_name": "samcli.local.docker.lambda_image.Runtime", "line_number": 113, "usage_type": "name"}, {"api_name": "samcli.local.docker.lambda_image.Runtime.nodejs810", "line_number": 127, "usage_type": "attribute"}, {"api_name": "samcli.local.docker.lambda_image.Runtime", "line_number": 127, "usage_type": "name"}, {"api_name": "samcli.local.docker.lambda_image.Runtime.nodejs10x", "line_number": 142, "usage_type": "attribute"}, {"api_name": "samcli.local.docker.lambda_image.Runtime", "line_number": 142, "usage_type": "name"}, {"api_name": "samcli.local.docker.lambda_image.Runtime.nodejs12x", "line_number": 164, "usage_type": "attribute"}, {"api_name": "samcli.local.docker.lambda_image.Runtime", "line_number": 164, "usage_type": "name"}, {"api_name": "samcli.local.docker.lambda_image.Runtime.python27", "line_number": 186, "usage_type": "attribute"}, {"api_name": "samcli.local.docker.lambda_image.Runtime", "line_number": 186, "usage_type": "name"}, {"api_name": "samcli.local.docker.lambda_image.Runtime.python36", "line_number": 189, "usage_type": "attribute"}, {"api_name": "samcli.local.docker.lambda_image.Runtime", "line_number": 189, "usage_type": "name"}, {"api_name": "samcli.local.docker.lambda_image.Runtime.python37", "line_number": 193, "usage_type": "attribute"}, {"api_name": "samcli.local.docker.lambda_image.Runtime", "line_number": 193, "usage_type": "name"}, {"api_name": "samcli.local.docker.lambda_image.Runtime.python38", "line_number": 203, "usage_type": "attribute"}, {"api_name": "samcli.local.docker.lambda_image.Runtime", "line_number": 203, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 148, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 170, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 199, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 209, "usage_type": "call"}]} +{"seq_id": "223019096", "text": "import matplotlib.pyplot as plt\nimport csv\n\nclass House():\n def _init_(self, x, y, output):\n self.x = x\n self.y = y\n self.output = output\n\nclass Battery():\n def _init_(self, x, y, capacity):\n self.x = x\n self.y = y\n self.capacity = capacity\n\nclass District():\n def _init_(self, number):\n self.number = number\n self.path = f'data/district_{number}/district-{number}_'\n self.batteries = []\n self.houses = []\n self.load() # laad alle huizen en batterijen in en sla op in self.batteries en self.houses\n self.connections = {battery_obj: None for battery_obj in self.batteries}\n\n def load(self):\n files = [self.path + 'batteries.csv', self.path + 'houses.csv'] # de twee bestanden opslaan in lijst\n for filename in files:\n with open(filename, 'r') as file:\n data = file.readlines()[1:]\n # per regel check welke elementen de coordinaten zijn en welke de output/capacity\n for row in data:\n row = row.replace('\"', '').split(',')\n x, y, OC = int(row[0]), int(row[1]), float(row[2].rstrip())\n # als de lengte van een bestand klein is dan is dat het bestand van de batterijen\n if len(data) == 5:\n # maak per x, y en capacity van elke regel een battery object aan en geef deze waarden mee\n self.batteries.append(Battery(x, y, OC))\n # andere bestand is vd huizen\n else:\n # maak per x, y en output van elke regel een house object aan en geef deze waarden mee\n self.houses.append(House(x, y, OC))\n \n def allocate(self):\n # vul de dictionary self.connections in\n # de keys zijn al ingevuld, dat zijn alle batterij objecten\n # de values zijn de huizen die aan een batterij gekoppeld zijn\n # hoe kiezen we welke huizen we gebruiken?\n pass\n\n def connect(self):\n for battery, houses in self.connections.items():\n for house in houses:\n xsteps = [battery.x, house.x, house.x]\n ysteps = [battery.y, battery.y, house.y]\n plt.plot(xsteps, ysteps)\n plt.show()\n\n # def visualise(self):\n # pass\n # # laat plot zien? of is dat al voldoende bij connect\n # # we moeten ook eigenlijk de huizen en de batterijen zelf als scatter laten zien\n # # for battery in self.batteries:\n # # plt.scatter is iets geloof ik dat je de x en y coordinaten meegeeft\n # # for house in self.houses:\n # # plt.scatter\n # # in welke volgorde visualiseren? denk eerst handig als we een scatter van huizen en batterijen zien\n\n def costs(self):\n pass\n # bereken kosten van de lengte van elke connectie tussen huis en batterij\n # self.fixedcosts = 5000 * len(self.batteries)\n # for battery, houses in self.connections.items():\n # for house in houses:\n # manhattan_distance = abs(battery.x - house.x) + abs(battery.y - house.y)\n # self.varcosts += 9 * manhattan_distance\n # return self.fixedcosts + self.varcosts", "sub_path": "SomeClass.py", "file_name": "SomeClass.py", "file_ext": "py", "file_size_in_byte": 3283, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "matplotlib.pyplot.plot", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}]} +{"seq_id": "645314352", "text": "import numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom model_funcs_odeint_no_pore import surfaceTempFromClimateModel\nimport scipy.stats as stats\n\ndef plotComparison():\n co2s = np.logspace(-4, 1, 50)\n\n pCO2_mod = 288E-6\n co2_w_min = (co2s/pCO2_mod)**0.1\n co2_w_max = (co2s/pCO2_mod)**0.5\n\n temp_min = np.zeros_like(co2s)\n temp_max = np.zeros_like(co2s)\n for i in range(len(co2s)):\n temp = surfaceTempFromClimateModel(co2s[i], 0.7) - 289\n temp_max[i] = np.exp(temp/10)\n temp_min[i] = np.exp(temp/40)\n\n fig, axs = plt.subplots(2, 1)\n\n axs[0].plot(co2s, co2_w_min, label=r'CO$_{2}$ min (exp=0.1)')\n axs[0].plot(co2s, co2_w_max, label=r'CO$_{2}$ max (exp=0.5)')\n\n axs[0].plot(co2s, temp_min, label=\"Temp min (Te=40)\")\n axs[0].plot(co2s, temp_max, label=\"Temp max (Te=10)\")\n\n axs[0].set_xscale('log')\n axs[0].set_yscale('log')\n axs[0].set_ylabel('Weathering factor')\n\n axs[0].legend()\n axs[0].set_xlim(10E-4, 10)\n\n\n axs[1].plot(co2s, temp_max*co2_w_min, label=\"T_max*C_min\")\n axs[1].plot(co2s, temp_max*co2_w_max, label=\"T_max*C_max\")\n axs[1].plot(co2s, temp_min*co2_w_max, label=\"T_min*C_max\")\n axs[1].plot(co2s, temp_min*co2_w_min, label=\"T_min*C_min\")\n\n axs[1].set_xscale('log')\n axs[1].set_yscale('log')\n axs[1].legend()\n\n axs[1].set_xlabel(r'pCO$_{2}$ [bar]')\n\n axs[1].plot([0, 100], [1, 1], 'C7')\n plt.xlim(10E-4, 10)\n\n\n plt.show()\n\ndef plotColormap():\n\n #set the font size\n font = {'family' : 'DejaVu Sans',\n 'size' : 12}\n\n matplotlib.rc('font', **font)\n\n num_co2s = 50\n num_fluxes = 50\n co2s = np.logspace(-4, 1, num_co2s)\n fluxes = np.linspace(0.35, 1.05, num_fluxes)\n\n pCO2_mod = 288E-6\n co2_w_min = (co2s/pCO2_mod)**0.1\n co2_w_max = (co2s/pCO2_mod)**0.5\n\n temp_min = np.zeros_like(co2s)\n temp_max = np.zeros_like(co2s)\n\n temp_min_c_min = np.zeros((num_co2s, num_fluxes)) \n temp_max_c_min = np.zeros((num_co2s, num_fluxes))\n temp_min_c_max = np.zeros((num_co2s, num_fluxes))\n temp_max_c_max = np.zeros((num_co2s, num_fluxes))\n\n\n for i in range(num_co2s):\n for j in range(num_fluxes):\n delta_temp = surfaceTempFromClimateModel(co2s[i], fluxes[j]) - 289\n t_max = np.exp(delta_temp/10)\n t_min = np.exp(delta_temp/40)\n c_max = (co2s[i]/pCO2_mod)**0.5\n c_min = (co2s[i]/pCO2_mod)**0.1\n\n temp_max_c_max[i][j] = np.log10(t_max*c_max)\n temp_max_c_min[i][j] = np.log10(t_max*c_min)\n temp_min_c_max[i][j] = np.log10(t_min*c_max)\n temp_min_c_min[i][j] = np.log10(t_min*c_min)\n\n #find colorbar min and max\n cbar_min = np.min([np.min(temp_min_c_max),\n np.min(temp_min_c_min),\n np.min(temp_max_c_max),\n np.min(temp_max_c_min)])\n\n cbar_max = np.max([np.max(temp_min_c_max),\n np.max(temp_min_c_min),\n np.max(temp_max_c_max),\n np.max(temp_max_c_min)])\n\n\n NUM_COLORS = 12 \n CMAP_COLOR = 'viridis'\n fig, axs = plt.subplots(2, 2)\n\n name = ['A', 'B', 'C', 'D']\n data = [temp_max_c_max, temp_min_c_max, temp_max_c_min, temp_min_c_min]\n vals = [(10, 0.5), (40, 0.5), (10, 0.1), (40, 0.1)]\n\n ind = 0\n for ax in axs.flatten():\n #scale and flip the axes\n ax.set_yscale('log')\n ax.set_xlim(1.05, 0.35)\n\n #plot the Earth point and label\n ax.text(1.04, 5, name[ind], verticalalignment='center')\n ax.plot([1], [280E-6], 'sk', zorder=11)\n ax.text(0.97, 10**(np.log10(280E-6)+0.35), \"Earth\", color=\"black\", \n horizontalalignment='center', verticalalignment='center', zorder=12)\n\n #plot the contours and add axis labels as needed\n dat = data[ind]\n Te, co2_d = vals[ind]\n\n ax.contourf(fluxes, co2s, dat, \n cmap=plt.cm.get_cmap(CMAP_COLOR, NUM_COLORS), \n vmin=cbar_min, vmax=cbar_max)\n cs = ax.contour(fluxes, co2s, dat, [0], \n colors=['black'], linestyles=['solid'])\n\n ax.text((1.05+0.35)/2, 18, r'$T_{e}=%d$ K, $d$=%0.1f'%(Te, co2_d), \n horizontalalignment='center', verticalalignment='center')\n\n ax.set_yticks([1.0e-4, 1.0e-3, 1.0e-2, 0.1, 1.0, 10])\n\n #label axes as needed\n if not ind%2:\n ax.set_ylabel(r'pCO$_{2}$ [bar]')\n else:\n ax.set_yticklabels([])\n if ind > 1: \n ax.set_xlabel(r'Incident Flux [$S/S_{\\oplus}$]')\n else:\n ax.set_xticklabels([])\n\n #get the r^2 value for the best fit line\n verts = cs.collections[0].get_paths()[0].vertices\n cont_fluxes = verts[:, 0]\n cont_co2s = verts[:, 1]\n slope, intercept, r_value, p_value, std_err = stats.linregress(cont_fluxes, \n np.log10(cont_co2s))\n print(\"Plot %s has r^2=%0.4f\"%(name[ind], r_value**2))\n\n\n\n ind += 1\n\n\n\n m = plt.cm.ScalarMappable(cmap=plt.cm.get_cmap(CMAP_COLOR))\n m.set_array([cbar_min, cbar_max])\n m.set_clim(cbar_min, cbar_max)\n\n cbar_ax = fig.add_axes([0.85, 0.15, 0.03, 0.7])\n cbar = fig.colorbar(m, boundaries=np.linspace(np.floor(cbar_min), \n np.ceil(cbar_max), NUM_COLORS), cax=cbar_ax)\n cbar.set_label(r'log$_{10}$(relative weathering)')\n\n\n\n plt.subplots_adjust(hspace=0.18, wspace=0.07, right=0.8)\n plt.savefig(\"weathering_compared.png\", dpi=600)\n plt.show()\n \n \n\n\n#plotComparison()\nplotColormap()\n", "sub_path": "co2_flux_funcs_compare.py", "file_name": "co2_flux_funcs_compare.py", "file_ext": "py", "file_size_in_byte": 5555, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.logspace", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 15, "usage_type": "call"}, {"api_name": "model_funcs_odeint_no_pore.surfaceTempFromClimateModel", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "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.rc", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.logspace", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 77, "usage_type": "call"}, {"api_name": "model_funcs_odeint_no_pore.surfaceTempFromClimateModel", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "numpy.log10", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm.get_cmap", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 130, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "scipy.stats.linregress", "line_number": 154, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 154, "usage_type": "name"}, {"api_name": "numpy.log10", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm.ScalarMappable", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 164, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm.get_cmap", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 176, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}]} +{"seq_id": "575640068", "text": "import sys\r\n\r\n\r\nfrom PyQt5.QtWidgets import QApplication, QWidget, QSplitter, QGroupBox, QDateEdit, QLineEdit, QLabel, QVBoxLayout, QGridLayout\r\nfrom PyQt5.QtCore import Qt\r\n\r\n\r\n\r\nclass Window(QWidget):\r\n \r\n def __init__(self):\r\n \r\n super().__init__()\r\n self.initUi()\r\n \r\n self.show()\r\n \r\n \r\n def initUi(self):\r\n \r\n\r\n layout = QGridLayout()\r\n \r\n horizontal_splitter = QSplitter(Qt.Horizontal)\r\n \r\n left_vertical_group = QGroupBox()\r\n \r\n date_edit = QDateEdit()\r\n line_edit = QLineEdit()\r\n \r\n left_vertical_layout = QVBoxLayout()\r\n \r\n left_vertical_layout.addWidget(date_edit)\r\n left_vertical_layout.addWidget(line_edit)\r\n \r\n left_vertical_group.setLayout(left_vertical_layout)\r\n \r\n right_vertical_group = QGroupBox()\r\n \r\n label_date = QLabel()\r\n label_text = QLabel()\r\n \r\n right_vertical_layout = QVBoxLayout()\r\n \r\n right_vertical_layout.addWidget(label_date)\r\n right_vertical_layout.addWidget(label_text)\r\n \r\n right_vertical_group.setLayout(right_vertical_layout)\r\n \r\n horizontal_splitter.addWidget(left_vertical_group)\r\n horizontal_splitter.addWidget(right_vertical_group)\r\n \r\n horizontal_splitter.addWidget(left_vertical_group)\r\n horizontal_splitter.addWidget(right_vertical_group)\r\n layout.addWidget(horizontal_splitter)\r\n \r\n date_edit.dateChanged.connect(\r\n lambda: self.on_date_changed(date_edit, label_date))\r\n \r\n line_edit.textChanged.connect(\r\n lambda: self.on_text_changed(line_edit, label_text)) \r\n \r\n self.setLayout(layout) \r\n \r\n \r\n def on_date_changed(self, sender, label):\r\n \r\n date = sender.date().toString()\r\n label.setText(date)\r\n \r\n \r\n def on_text_changed(self, sender, label):\r\n \r\n text = sender.text()\r\n label.setText(text)\r\n \r\n\r\n\r\ndef main(args):\r\n \r\n app = QApplication(args)\r\n window = Window()\r\n sys.exit(app.exec_())\r\n \r\n\r\nif __name__ == '__main__':\r\n main(sys.argv)\r\n", "sub_path": "PyQt5-Examples/09_splitters/horizontal_splitter.py", "file_name": "horizontal_splitter.py", "file_ext": "py", "file_size_in_byte": 2309, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 9, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGridLayout", "line_number": 22, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QSplitter", "line_number": 24, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 24, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 24, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QDateEdit", "line_number": 28, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 31, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 40, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 41, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 43, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 81, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 83, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 87, "usage_type": "attribute"}]} +{"seq_id": "644050080", "text": "#!/usr/local/bin/python3\n\nfrom Utils.OxUtil import OxUtil\nfrom bs4 import BeautifulSoup\nimport csv\n\n\nclass BooHeeClub:\n def __init__(self):\n self.__group_data = []\n self.__src_url = \"http://www.boohee.com\"\n\n def get_group_list(self):\n home_url = self.__src_url + \"/food\"\n rsp = OxUtil.html_request_get(home_url)\n soup = BeautifulSoup(rsp.text, \"html.parser\")\n temp_list = soup.select(\"h3\")\n for item in temp_list:\n first_a = item.select('a')[0]\n data = {\"name\": first_a.get_text(), \"url\": self.__src_url + first_a.get('href'), \"list\": []}\n print(OxUtil.get_local_time(), data)\n self.__group_data.append(data)\n\n def get_element_page(self, url):\n \"\"\"\n 获取单页信息\n :param url:\n :return:\n \"\"\"\n item_list = []\n next_page = None\n rsp = OxUtil.html_request_get(url)\n soup = BeautifulSoup(rsp.text, \"html.parser\")\n temp_next = soup.select('a.next_page')\n if len(temp_next) > 0:\n if temp_next[0].has_attr('href'):\n next_page = self.__src_url + temp_next[0].get('href')\n temp_list = soup.select(\"h4\")\n for item in temp_list:\n first_a = item.select('a')[0]\n item_list.append({\"name\": first_a.get_text(),\n \"url\": self.__src_url + first_a.get('href')})\n return item_list, next_page\n\n def get_item_list(self):\n \"\"\"\n 获取所有食材信息\n :return:\n \"\"\"\n for element in self.__group_data:\n if element[\"name\"] == \"菜肴\":\n continue\n print(OxUtil.get_local_time(), \"开始获取分页信息\", element[\"name\"])\n print(OxUtil.get_local_time(), \"获取分页\", element[\"url\"])\n temp_list, next_url = self.get_element_page(element[\"url\"])\n element[\"list\"].extend(temp_list)\n while next_url:\n print(OxUtil.get_local_time(), \"获取分页\", next_url)\n temp_list, next_url = self.get_element_page(next_url)\n element[\"list\"].extend(temp_list)\n\n def get_single_page(self, url):\n food_detail = dict()\n rsp = OxUtil.html_request_get(url)\n soup = BeautifulSoup(rsp.text, \"html.parser\")\n item_main = soup.select(\"div.widget-food-detail.pull-left\")\n if len(item_main) > 0:\n main_data = item_main[0].select(\"div.nutr-tag.margin10\")\n if len(main_data) > 0:\n rows = main_data[0].select(\"dl>dd\")\n for dd in rows:\n span_info = dd.select(\"span\")\n if len(span_info) == 2:\n food_detail[span_info[0].get_text()] = span_info[1].get_text()\n return food_detail\n\n def get_item_data(self, filename):\n for element in self.__group_data:\n print(OxUtil.get_local_time(), \"开始获取食品信息\", element[\"name\"])\n for item in element[\"list\"]:\n item[\"data\"] = self.get_single_page(item[\"url\"])\n with open(filename, 'a+', errors='ignore') as file:\n file_csv = csv.writer(file)\n file_csv.writerow([element[\"name\"], item[\"name\"], item[\"url\"], item[\"data\"]])\n\n\nif __name__ == \"__main__\":\n catcher = BooHeeClub()\n catcher.get_group_list()\n catcher.get_item_list()\n catcher.get_item_data(\"E:\\\\food.csv\")\n\n", "sub_path": "crawler/food.py", "file_name": "food.py", "file_ext": "py", "file_size_in_byte": 3475, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "Utils.OxUtil.OxUtil.html_request_get", "line_number": 15, "usage_type": "call"}, {"api_name": "Utils.OxUtil.OxUtil", "line_number": 15, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 16, "usage_type": "call"}, {"api_name": "Utils.OxUtil.OxUtil.get_local_time", "line_number": 21, "usage_type": "call"}, {"api_name": "Utils.OxUtil.OxUtil", "line_number": 21, "usage_type": "name"}, {"api_name": "Utils.OxUtil.OxUtil.html_request_get", "line_number": 32, "usage_type": "call"}, {"api_name": "Utils.OxUtil.OxUtil", "line_number": 32, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 33, "usage_type": "call"}, {"api_name": "Utils.OxUtil.OxUtil.get_local_time", "line_number": 53, "usage_type": "call"}, {"api_name": "Utils.OxUtil.OxUtil", "line_number": 53, "usage_type": "name"}, {"api_name": "Utils.OxUtil.OxUtil.get_local_time", "line_number": 54, "usage_type": "call"}, {"api_name": "Utils.OxUtil.OxUtil", "line_number": 54, "usage_type": "name"}, {"api_name": "Utils.OxUtil.OxUtil.get_local_time", "line_number": 58, "usage_type": "call"}, {"api_name": "Utils.OxUtil.OxUtil", "line_number": 58, "usage_type": "name"}, {"api_name": "Utils.OxUtil.OxUtil.html_request_get", "line_number": 64, "usage_type": "call"}, {"api_name": "Utils.OxUtil.OxUtil", "line_number": 64, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 65, "usage_type": "call"}, {"api_name": "Utils.OxUtil.OxUtil.get_local_time", "line_number": 79, "usage_type": "call"}, {"api_name": "Utils.OxUtil.OxUtil", "line_number": 79, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "438747867", "text": "import gym\nimport numpy as np\nimport pybullet as p\n\nfrom gibson2.external.pybullet_tools.utils import joints_from_names, set_joint_positions\nfrom gibson2.robots.robot_locomotor import LocomotorRobot\n\n\nclass Tiago_Dual(LocomotorRobot):\n def __init__(self, config):\n # TODO: update config\n self.wheel_velocity = config.get('wheel_velocity', 1.0)\n self.torso_lift_velocity = config.get('torso_lift_velocity', 1.0)\n self.head_velocity = config.get('head_velocity', 1.0)\n self.arm_left_velocity = config.get('arm_left_velocity', 1.0)\n self.arm_right_velocity = config.get('arm_right_velocity', 1.0)\n self.gripper_velocity = config.get('gripper_velocity', 1.0)\n self.hand_velocity = config.get('hand_velocity', 1.0)\n self.wheel_dim = 2\n self.torso_lift_dim = 1\n self.head_dim = 2\n self.arm_left_dim = 7\n self.arm_right_dim = 7\n self.gripper_dim = 2\n self.hand_dim = 0 # TODO\n self.rest_position = [0, 0, 0, 0, 0,\n -np.pi/6, np.pi/2, 2*np.pi/3, np.pi/2, 0, -np.pi/3, 0,\n 0, 0,\n -np.pi/6, np.pi/2, 2*np.pi/3, np.pi/2, 0, 0, 0\n ]\n\n action_dim = self.wheel_dim \\\n + self.torso_lift_dim + self.head_dim + self.arm_left_dim\\\n + self.arm_right_dim + self.gripper_dim + self.hand_dim\n LocomotorRobot.__init__(self,\n \"tiago/tiago_dual_nohand.urdf\",\n action_dim=action_dim,\n scale=config.get(\"robot_scale\", 1.0),\n is_discrete=config.get(\"is_discrete\", False),\n control=\"velocity\",\n self_collision=True)\n\n def set_up_continuous_action_space(self):\n self.action_high = np.array(\n [self.wheel_velocity] * self.wheel_dim +\n [self.torso_lift_velocity] * self.torso_lift_dim +\n [self.head_velocity] * self.head_dim +\n [self.arm_left_velocity] * self.arm_left_dim +\n [self.gripper_velocity] * self.gripper_dim +\n [self.arm_right_velocity] * self.arm_right_dim +\n [self.hand_velocity] * self.hand_dim\n )\n self.action_low = -self.action_high\n self.action_space = gym.spaces.Box(shape=(self.action_dim,),\n low=-1.0,\n high=1.0,\n dtype=np.float32)\n\n def set_up_discrete_action_space(self):\n assert False, \"Tiago_Dual does not support discrete actions\"\n\n def robot_specific_reset(self):\n super(Tiago_Dual, self).robot_specific_reset()\n\n # roll the arm to its body\n robot_id = self.robot_ids[0]\n torso_joints = joints_from_names(robot_id, ['head_1_joint', 'head_2_joint', 'torso_lift_joint'])\n arm_left_joints = joints_from_names(robot_id,\n [\n 'arm_left_1_joint',\n 'arm_left_2_joint',\n 'arm_left_3_joint',\n 'arm_left_4_joint',\n 'arm_left_5_joint',\n 'arm_left_6_joint',\n 'arm_left_7_joint',\n ])\n\n arm_right_joints = joints_from_names(robot_id,\n [\n 'arm_right_1_joint',\n 'arm_right_2_joint',\n 'arm_right_3_joint',\n 'arm_right_4_joint',\n 'arm_right_5_joint',\n 'arm_right_6_joint',\n 'arm_right_7_joint',\n ])\n\n rest_pos_torso = [-0.07, -0.80, 0.33]\n rest_pos_left = [0.22, 0.48, 1.52, 1.76, 0.04, -0.49, 0]\n #rest_pos_left = [-np.pi/6, np.pi/2, 2*np.pi/3, np.pi/2, 0, -np.pi/3, 0]\n rest_pos_right = [-np.pi/6, np.pi/2, 2*np.pi/3, np.pi/2, 0, 0, 0]\n\n set_joint_positions(robot_id, torso_joints, rest_pos_torso)\n set_joint_positions(robot_id, arm_left_joints, rest_pos_left)\n set_joint_positions(robot_id, arm_right_joints, rest_pos_right)\n\n def get_end_effector_position(self):\n return self.parts['gripper_left_grasping_frame'].get_position()\n\n def get_end_effector_index(self):\n return self.parts['gripper_left_grasping_frame'].body_part_index\n\n def load(self):\n ids = super(Tiago_Dual, self).load()\n robot_id = self.robot_ids[0]\n\n # get problematic links\n moving_parts = [\"arm\", \"gripper\", \"wrist\"]\n problem_links = []\n for part in self.parts:\n idx = self.parts[part].body_part_index\n for x in moving_parts:\n if not x in part:\n problem_links.append(idx)\n\n # disable self collision\n for a in problem_links:\n for b in problem_links:\n p.setCollisionFilterPair(robot_id, robot_id, a, b, 0)\n\n return ids\n", "sub_path": "gibson2/robots/tiago_dual_robot.py", "file_name": "tiago_dual_robot.py", "file_ext": "py", "file_size_in_byte": 5505, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "gibson2.robots.robot_locomotor.LocomotorRobot", "line_number": 9, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 29, "usage_type": "attribute"}, {"api_name": "gibson2.robots.robot_locomotor.LocomotorRobot.__init__", "line_number": 35, "usage_type": "call"}, {"api_name": "gibson2.robots.robot_locomotor.LocomotorRobot", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "gym.spaces.Box", "line_number": 54, "usage_type": "call"}, {"api_name": "gym.spaces", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 57, "usage_type": "attribute"}, {"api_name": "gibson2.external.pybullet_tools.utils.joints_from_names", "line_number": 67, "usage_type": "call"}, {"api_name": "gibson2.external.pybullet_tools.utils.joints_from_names", "line_number": 68, "usage_type": "call"}, {"api_name": "gibson2.external.pybullet_tools.utils.joints_from_names", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 93, "usage_type": "attribute"}, {"api_name": "gibson2.external.pybullet_tools.utils.set_joint_positions", "line_number": 95, "usage_type": "call"}, {"api_name": "gibson2.external.pybullet_tools.utils.set_joint_positions", "line_number": 96, "usage_type": "call"}, {"api_name": "gibson2.external.pybullet_tools.utils.set_joint_positions", "line_number": 97, "usage_type": "call"}, {"api_name": "pybullet.setCollisionFilterPair", "line_number": 121, "usage_type": "call"}]} +{"seq_id": "127456621", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport deviceHelper\nfrom uiautomatorHelper import UIAutomatorHelper\nfrom testcase import TestCase\nfrom signalbasic import TestSignal\nfrom pdu import PduHelper\nimport time\nfrom utils import Log\nimport os\nimport re\nfrom logStat import LogAnalysisTools\nimport pexpect\n\nclass TestHDMI2ACOnOff(TestSignal):\n\n\n def __init__(self, doc, level, owner):\n super(TestHDMI2ACOnOff, self).__init__(doc, level, owner)\n self.package_name = \"cn.whaley.cases.Helios.signalSource.device.HDMI\"\n self.test_content1 = \"testHdmi2_14\"\n self.logFolderPath = os.path.join(self.out,'LOG')\n self.signalPicPath = os.path.join(self.logFolderPath,'test_signal_HDMI2_14_ACOnOff')\n self.pdu_addr = self.plan.pdu.split(\":\")[0]\n self.pdu_port = self.plan.pdu.split(\":\")[1]\n Log().debug(self.pdu_addr)\n Log().debug(self.pdu_port)\n self.pduHelper = PduHelper(self.pdu_addr)\n\n def mkdir(self):\n if not os.path.exists(self.signalPicPath):\n os.mkdir(self.signalPicPath)\n\n\n def execute(self):\n self.mkdir()\n Log().info(self._plan.target)\n result = []\n uiautomatorH = UIAutomatorHelper(\n self._plan.target, package_name=self.package_name)\n if self.connection:\n Log().info(\"SERIAL_MODE\")\n version=self.product_model\n Log().info(version)\n self.run_shell_and_ret('rm -r /data/local/tmp/TAP/*.png')\n self.run_shell_and_ret('rm -r /data/local/tmp/LOGCAT/*.png')\n self.kill_sh_process()\n self.start_log()\n self.AC_OnOff()\n self.run_shell_and_ret('chmod -R 777 /data/local/tmp/*')\n self.run_uiautomator(package_name=self.package_name,test_content=self.test_content1,timeout=1000)\n self.run_shell_and_ret('chmod -R 777 /data/local/tmp/*')\n Log().info(\"******Begin-Pull-Picture*****\")\n deviceHelper.adb_pull('/data/local/tmp/TAP/',self.signalPicPath,self._plan.target)\n self.stop_log()\n else:\n Log().info(\"ADB_MODE\")\n deviceHelper.adb_cmd('shell rm -r /data/local/tmp/TAP/*',self._plan.target)\n version=self.product_model\n Log().info(version)\n self.AC_OnOff()\n deviceHelper.adb_cmd('shell chmod -R 777 /data/local/tmp/',self._plan.target)\n self._status, result = uiautomatorH.uiautomator_test_result(self.test_content1, timeout=1000)\n for line in result:\n Log().info(line)\n deviceHelper.adb_pull('/data/local/tmp/TAP/',self.signalPicPath,self._plan.target)\n\nif __name__ == \"__main__\":\n TestHDMI2ACOnOff(\"AC On Off Switch HDMI2\", 'p1', \"Lijun\").run()\n", "sub_path": "Python_Java_UIautomator/case/platform/signal/test_signal_HDMI2_14_ACOnOff.py", "file_name": "test_signal_HDMI2_14_ACOnOff.py", "file_ext": "py", "file_size_in_byte": 2763, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "signalbasic.TestSignal", "line_number": 16, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "utils.Log", "line_number": 27, "usage_type": "call"}, {"api_name": "utils.Log", "line_number": 28, "usage_type": "call"}, {"api_name": "pdu.PduHelper", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 33, "usage_type": "call"}, {"api_name": "utils.Log", "line_number": 38, "usage_type": "call"}, {"api_name": "uiautomatorHelper.UIAutomatorHelper", "line_number": 40, "usage_type": "call"}, {"api_name": "utils.Log", "line_number": 43, "usage_type": "call"}, {"api_name": "utils.Log", "line_number": 45, "usage_type": "call"}, {"api_name": "utils.Log", "line_number": 54, "usage_type": "call"}, {"api_name": "deviceHelper.adb_pull", "line_number": 55, "usage_type": "call"}, {"api_name": "utils.Log", "line_number": 58, "usage_type": "call"}, {"api_name": "deviceHelper.adb_cmd", "line_number": 59, "usage_type": "call"}, {"api_name": "utils.Log", "line_number": 61, "usage_type": "call"}, {"api_name": "deviceHelper.adb_cmd", "line_number": 63, "usage_type": "call"}, {"api_name": "utils.Log", "line_number": 66, "usage_type": "call"}, {"api_name": "deviceHelper.adb_pull", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "440779221", "text": "\nfrom django.urls import path, include\nfrom . import views\nurlpatterns = [\n path('', views.index),\n path('sobre', views.sobre),\n path('login', views.login),\n path('ideias', views.cadastrar_ideia),\n path('deletar_ideia/', views.deletar_ideia)\n]", "sub_path": "website/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 266, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "26567262", "text": "from flask import(Flask, Response, redirect, url_for, render_template)\nimport pandas as pd\nimport json\nimport os\n\napp = Flask(__name__)\n\n\n# heat page\n@app.route('/')\ndef index():\n # This function returns the index page\n return render_template('heat-index.html')\n\n\n# ipv6 coordinates REST endpoint\n@app.route('/ip-points', methods=['GET'])\ndef ip_points():\n\n # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n # FOR LOCAL DEVELOPMENT USE static/csv/GeoLiteCityv6.csv.gz'\n CSV_PATH = 'static/file/GeoLiteCityv6.csv.gz'\n # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\n # Check if the csv file exists in the path on Openshift Online environment\n # CSV_PATH = 'app-root/repo/static/GeoLiteCityv6.csv.gz'\n if os.path.exists(CSV_PATH):\n # using Pandas read the csv with appropriate parameters\n data = pd.read_csv(CSV_PATH, compression='gzip', header=None)\n # setup DataFrame with indexed columns\n data_frame = pd.DataFrame(data, columns=[7, 8])\n # group by unique column pairs and turn into a list. return size of groups and rest index on the DataFrame\n generated_dataframe = data_frame.groupby(data_frame.columns.tolist()).size().reset_index()\n\n # build the response using JSend specifications\n json_response = {\n 'status': 'success',\n 'data': {\n # set the list of data values as the ip-points\n 'dataPoints': generated_dataframe.values.tolist()\n }\n }\n return Response(response=json.dumps(json_response),\n content_type='application/json')\n else:\n # error with message\n error = {\n 'status': 'error',\n 'message': 'Please provide a GeoLiteCityv6.csv.gz in the static/csv directory and try again'\n }\n return Response(response=json.dumps(error),\n content_type='application/json')\n\n\n@app.errorhandler(404)\ndef not_found(exc):\n return redirect('/')\n\n\nif __name__ == '__main__':\n app.run()\n", "sub_path": "heat.py", "file_name": "heat.py", "file_ext": "py", "file_size_in_byte": 2067, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 43, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 51, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "284511737", "text": "import numpy as np\nimport csv\nimport operator\nfrom numpy import genfromtxt\nfrom numpy import linalg\nfrom collections import OrderedDict\n\ndef loadData(filename, dataSet = []): \n #split csv-rows up; array[0] = size 1; array[1] = size 784\n y = 0\n csv_array = genfromtxt(filename, delimiter=',', dtype=float)\n for y in range(0, len(csv_array-1)):\n split_array = np.array_split(csv_array[y], [1])\n dataSet.append(split_array)\n y += 1\n\ndef euclideanDistance(instance1, instance2):\n a = np.array(instance1)\n b = np.array(instance2)\n distance = linalg.norm(a-b)\n return distance\n\ndef manhattanDistance(instance1, instance2):\n a = np.array(instance1)\n b = np.array(instance2)\n distance = linalg.norm((a-b), ord=1)\n return distance\n\ndef getNeighbourEuclidean(testSet, trainingSet, k):\n dist = []\n for x in range(len(trainingSet)):\n eDist = euclideanDistance(testSet[1], trainingSet[x][1])\n dist.append((trainingSet[x], eDist))\n #sorts list of euclidean\n dist.sort(key=operator.itemgetter(1))\n #adds k-best results to new array\n neighbours = []\n for x in range(k):\n neighbours.append(dist[x][0])\n neighbours = np.array(neighbours)\n return neighbours\n\ndef getNeighbourManhattan(testSet, trainingSet, k):\n dist = []\n for x in range(len(trainingSet)):\n mDist = manhattanDistance(testSet[1], trainingSet[x][1])\n dist.append((trainingSet[x], mDist))\n #sorts list of euclidean\n dist.sort(key=operator.itemgetter(1))\n #adds k-best results to new array\n neighbours = []\n for x in range(k):\n neighbours.append(dist[x][0])\n neighbours = np.array(neighbours)\n return neighbours\n\ndef getResponse(neighbours):\n #dictionary\n classVotes = {}\n for x in range(len(neighbours)):\n response = neighbours[x][0][0]\n if response in classVotes:\n classVotes[response] += 1\n else:\n classVotes[response] = 1\n sortedVotes = OrderedDict(sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True))\n return next(iter(sortedVotes))\n\ndef getAccuracy(testSet, predictions):\n correctlyGuessed = 0\n for x in range(len(testSet)-1):\n if testSet[x][0] == predictions[x]:\n correctlyGuessed += 1\n return (correctlyGuessed/float(len(testSet))) * 100.00\n\ndef main():\n #get data\n testSet = []\n trainSet = []\n loadData('simple_train.csv', trainSet)\n trainSet = np.array(trainSet)\n loadData('simple_test.csv', testSet)\n testSet = np.array(testSet)\n #k = [1,3,5,10,15]\n k = [1,3,5,10]\n for y in range(len(k)):\n #generate predictions\n predictionsEuc = []\n predictionsMan = []\n for x in range(len(testSet)):\n neighboursEuc = getNeighbourEuclidean(testSet[x], trainSet, k[y])\n neighboursMan = getNeighbourManhattan(testSet[x], trainSet, k[y])\n resultsEuc = getResponse(neighboursEuc)\n resultsMan = getResponse(neighboursMan)\n predictionsEuc.append(resultsEuc)\n predictionsMan.append(resultsMan)\n accuracyEuc = getAccuracy(testSet, predictionsEuc)\n accuarcyMan = getAccuracy(testSet, predictionsMan)\n print('Euclidean Accuracy with k = ' + str(k[y]) + ' got: ' + repr(accuracyEuc) + '%')\n print('Manhattan Accuracy with k = ' + str(k[y]) + ' got: ' + repr(accuarcyMan) + '%')\n\n \nmain()", "sub_path": "knn.py", "file_name": "knn.py", "file_ext": "py", "file_size_in_byte": 3430, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.genfromtxt", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.array_split", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 26, "usage_type": "name"}, {"api_name": "operator.itemgetter", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 66, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "283039683", "text": "from datetime import datetime\nimport json\n\nXP_PER_LVL = 300\nPLACEHOLDER_TOKEN = \"YOUR DISCORD TOKEN HERE\"\nDEFAULT_CONFIG_SETTING = 0\n\n# Read values from config file\nwith open('private/config.json') as config_file:\n cfg = json.load(config_file)\n\nDISCORD_KEY = cfg['discord']\nif DISCORD_KEY == PLACEHOLDER_TOKEN:\n print(\"Don't forget to set the 'discord' field in 'private/config.json' with your Discord bot key!\")\n\nDB_PATH = cfg['db_path']\nCMD_PREFIX = cfg['command_prefix']\nADMIN_ACCESS = cfg['roles']['admin_access']\nSERVER_URL = cfg['server_url']\nOWNER = cfg['owner']\nif OWNER == DEFAULT_CONFIG_SETTING:\n print(\"Don't forget to set the 'owner' field in 'private/config.json'!\")\n\nDEBUG_BOT = (cfg['debug'].upper() == \"TRUE\")\n\nLVL_CHANS = cfg['channels']['lvl_allowed']\nNO_SLOWMODE = cfg['channels']['slowmode_disabled']\nXP_OFF = cfg['channels']['xp_disabled']\n\nGAME_ANNOUNCEMENT_CHANNEL = cfg['games']['announcement_channel']\nif GAME_ANNOUNCEMENT_CHANNEL == DEFAULT_CONFIG_SETTING:\n print(\"Don't forget to set the 'announcement_channel' field in 'private/config.json'!\")\n\nGAME_ANNOUNCE_TIME = datetime.strptime(cfg['games']['announcement_time'], \"%I:%M %p\")\n\n# Import ranks from their configuration\nwith open(cfg['ranks_path']) as ranks_file:\n RANKS = json.load(ranks_file)['ranks']\n", "sub_path": "src/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 1299, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "json.load", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "name"}, {"api_name": "json.load", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "350360563", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Distributed under the terms of the MIT License.\n\n\"\"\"\nScript to run CG model analysis.\n\nAuthor: Andrew Tarzia\n\nDate Created: 17 Feb 2022\n\n\"\"\"\n\nimport sys\nimport stk\nimport numpy as np\nimport rdkit.Chem.AllChem as rdkit\nimport os\nimport re\nfrom matplotlib.lines import Line2D\nimport matplotlib.pyplot as plt\nimport json\nfrom string import digits\n\nimport stk\n\nfrom env_set import gulp_path, shape_path\nfrom symmetries import M8L6_Symmetry\nfrom utilities import (\n reorient_linker,\n run_shape,\n ref_shape_dict,\n collect_all_shape_values,\n convert_symm_names,\n read_lib,\n angle_between,\n get_atom_distance,\n)\nfrom facebuildingblock import FaceBuildingBlock\n\n\ndef get_all_angles(molecule):\n\n paths = rdkit.FindAllPathsOfLengthN(\n mol=molecule.to_rdkit_mol(),\n length=3,\n useBonds=False,\n useHs=True,\n )\n angles = []\n for atom_ids in paths:\n atoms = list(\n molecule.get_atoms(atom_ids=[i for i in atom_ids])\n )\n atom1 = atoms[0]\n atom2 = atoms[1]\n atom3 = atoms[2]\n angles.append((atom1, atom2, atom3))\n\n return angles\n\n\nclass CGGulpOptimizer:\n\n def __init__(\n self,\n fileprefix,\n output_dir,\n anisotropy,\n ortho_k,\n o_angle_k,\n ):\n self._fileprefix = fileprefix\n self._output_dir = output_dir\n self._anisotropy = anisotropy\n self._ortho_k = ortho_k\n self._o_angle_k = o_angle_k\n self._gulp_in = os.path.join(\n self._output_dir, f'{self._fileprefix}.gin'\n )\n self._gulp_out = os.path.join(\n self._output_dir, f'{self._fileprefix}.ginout'\n )\n self._output_xyz = os.path.join(\n self._output_dir, f'{self._fileprefix}_final.xyz'\n )\n self._mass = 1\n self._bond_cutoff = 30\n self._angle_cutoff = 30\n\n def _run_gulp(self):\n os.system(\n f'{gulp_path()} < {self._gulp_in} > {self._gulp_out}'\n )\n\n def _extract_gulp(self):\n with open(self._gulp_out, 'r') as f:\n lines = f.readlines()\n\n nums = re.compile(r\"[+-]?\\d+(?:\\.\\d+)?(?:[eE][+-]?\\d+)?\")\n run_data = {'traj': {}}\n for line in lines:\n if 'Cycle' in line:\n splits = line.rstrip().split()\n run_data['traj'][int(splits[1])] = {\n 'energy': float(splits[3]),\n 'gnorm': float(splits[5]),\n }\n\n if 'Final energy' in line:\n string = nums.search(line.rstrip()).group(0)\n energy = float(string)\n run_data['final_energy'] = energy\n\n if 'Final Gnorm' in line:\n string = nums.search(line.rstrip()).group(0)\n gnorm = float(string)\n run_data['final_gnorm'] = gnorm\n\n return run_data\n\n def define_bond_potentials(self):\n bond_ks_ = {\n ('C', 'C'): 10,\n ('B', 'B'): 10,\n ('B', 'C'): self._ortho_k,\n\n ('C', 'Zn'): 10,\n ('B', 'Zn'): 10,\n\n ('Fe', 'Fe'): 10,\n ('Fe', 'Zn'): 10,\n ('Zn', 'Zn'): 10,\n }\n _base_length = 4\n _ortho_length = self._anisotropy*_base_length\n bond_rs_ = {\n ('C', 'C'): _base_length,\n ('B', 'B'): _base_length,\n ('B', 'C'): _ortho_length,\n\n ('C', 'Zn'): 4,\n ('B', 'Zn'): 4,\n\n ('Fe', 'Fe'): 4,\n ('Fe', 'Zn'): 4,\n ('Zn', 'Zn'): 4,\n }\n return bond_ks_, bond_rs_\n\n def define_angle_potentials(self):\n angle_ks_ = {\n ('B', 'C', 'C'): self._o_angle_k,\n ('B', 'B', 'C'): self._o_angle_k,\n\n ('Fe', 'Fe', 'Fe'): 20,\n # ('Fe', 'Fe', 'Zn'): 10,\n ('Fe', 'Zn', 'Zn'): 20,\n ('Zn', 'Zn', 'Zn'): 20,\n\n ('B', 'C', 'Zn'): self._o_angle_k,\n ('B', 'B', 'Zn'): self._o_angle_k,\n ('C', 'C', 'Zn'): self._o_angle_k,\n\n ('C', 'Fe', 'Zn'): 20,\n ('B', 'Fe', 'Zn'): 20,\n }\n angle_thetas_ = {\n ('B', 'C', 'C'): 90,\n ('B', 'B', 'C'): 90,\n\n ('Fe', 'Fe', 'Fe'): 60,\n # ('Fe', 'Fe', 'Zn'): 60,\n # This requires a special rule.\n ('Fe', 'Zn', 'Zn'): (\n 'check',\n {'cut': 70, 'min': 60, 'max': 90},\n ),\n ('Zn', 'Zn', 'Zn'): 60,\n\n ('B', 'C', 'Zn'): 135,\n ('B', 'B', 'Zn'): 135,\n ('C', 'C', 'Zn'): 135,\n\n ('C', 'Fe', 'Zn'): 180,\n ('B', 'Fe', 'Zn'): 180,\n }\n return angle_ks_, angle_thetas_\n\n def _get_coord_mass_string(self, mol):\n coord_string = 'cartesian\\n'\n mass_string = ''\n\n pos_mat = mol.get_position_matrix()\n atoms = list(mol.get_atoms())\n for atom, pos_ in zip(atoms, pos_mat):\n name = f'{atom.__class__.__name__}{atom.get_id()+1}'\n coord_string += (\n f'{name} {round(pos_[0], 2)} {round(pos_[1], 2)} '\n f'{round(pos_[2], 2)}\\n'\n )\n mass_string += f'mass {name} {self._mass}\\n'\n\n return coord_string, mass_string\n\n def _get_bond_string(self, mol):\n bond_ks_, bond_rs_ = self.define_bond_potentials()\n bond_string = 'harm\\n'\n bonds = list(mol.get_bonds())\n\n for bond in bonds:\n atom1 = bond.get_atom1()\n name1 = f'{atom1.__class__.__name__}{atom1.get_id()+1}'\n atom2 = bond.get_atom2()\n name2 = f'{atom2.__class__.__name__}{atom2.get_id()+1}'\n table = str.maketrans('', '', digits)\n sorted_name = tuple(sorted(\n [\n i.translate(table)\n for i in (name1, name2)\n ]\n ))\n\n try:\n bond_k = bond_ks_[sorted_name]\n bond_r = bond_rs_[sorted_name]\n except KeyError:\n continue\n\n bond_string += (\n f'{name1} {name2} {bond_k} {bond_r} '\n f'{self._bond_cutoff}\\n'\n )\n return bond_string\n\n def _get_angle_string(self, mol):\n angle_string = 'three\\n'\n angle_ks_, angle_thetas_ = self.define_angle_potentials()\n angles = get_all_angles(mol)\n pos_mat = mol.get_position_matrix()\n\n for angle in angles:\n atom1, atom2, atom3 = angle\n name1 = f'{atom1.__class__.__name__}{atom1.get_id()+1}'\n name2 = f'{atom2.__class__.__name__}{atom2.get_id()+1}'\n name3 = f'{atom3.__class__.__name__}{atom3.get_id()+1}'\n table = str.maketrans('', '', digits)\n sorted_name = tuple(sorted(\n [\n i.translate(table)\n for i in (name1, name2, name3)\n ]\n ))\n\n try:\n angle_k = angle_ks_[sorted_name]\n angle_theta = angle_thetas_[sorted_name]\n if isinstance(angle_theta, int):\n pass\n elif angle_theta[0] == 'check':\n a1id = atom1.get_id()\n a2id = atom2.get_id()\n a3id = atom3.get_id()\n vector1 = pos_mat[a2id]-pos_mat[a1id]\n vector2 = pos_mat[a2id]-pos_mat[a3id]\n curr_angle = np.degrees(\n angle_between(vector1, vector2)\n )\n if curr_angle < angle_theta[1]['cut']:\n angle_theta = angle_theta[1]['min']\n elif curr_angle >= angle_theta[1]['cut']:\n angle_theta = angle_theta[1]['max']\n\n except KeyError:\n continue\n\n angle_string += (\n f'{name2} {name1} {name3} {angle_k} {angle_theta} '\n f'{self._angle_cutoff} {self._angle_cutoff} '\n f'{self._angle_cutoff} \\n'\n )\n\n return angle_string\n\n def _write_gulp_input(self, mol):\n top_string = 'opti conv cartesian\\n'\n coord_string, mass_string = self._get_coord_mass_string(mol)\n bond_string = self._get_bond_string(mol)\n angle_string = self._get_angle_string(mol)\n settings_string = (\n '\\nmaxcyc 500\\n'\n # f'output xyz movie {filename}_traj.xyz\\n'\n f'output xyz {self._output_xyz}\\n'\n )\n\n with open(self._gulp_in, 'w') as f:\n f.write(top_string)\n f.write(coord_string)\n f.write(mass_string)\n f.write(bond_string)\n f.write(angle_string)\n f.write(settings_string)\n\n def optimize(self, molecule):\n self._write_gulp_input(mol=molecule)\n self._run_gulp()\n return self._extract_gulp()\n\n\ndef symmetries(cdelta, clambda, plane):\n symm_list = {}\n # Predefined list of symmetries.\n symm_c = M8L6_Symmetry(\n D_complex=cdelta,\n L_complex=clambda,\n linker=plane,\n )\n symm_list['d2'] = symm_c.d2()\n symm_list['th1'] = symm_c.th1()\n symm_list['th2'] = symm_c.th2()\n symm_list['td'] = symm_c.td()\n symm_list['tl'] = symm_c.tl()\n symm_list['s41'] = symm_c.s41()\n symm_list['s42'] = symm_c.s42()\n symm_list['s61'] = symm_c.s61()\n symm_list['s62'] = symm_c.s62()\n symm_list['d31'] = symm_c.d31()\n symm_list['d32'] = symm_c.d32()\n symm_list['d31n'] = symm_c.d31n()\n symm_list['d32n'] = symm_c.d32n()\n symm_list['c2v'] = symm_c.c2v()\n symm_list['c2h'] = symm_c.c2h()\n\n return symm_list\n\n\nclass CGM8L6Cube(stk.cage.M8L6Cube):\n\n def _get_scale(self, building_block_vertices):\n return 10\n\n\ndef write_cg_shape_input_file(\n input_file,\n structure_string,\n num_vertices,\n central_atom_id,\n ref_shapes,\n):\n \"\"\"\n Write input file for shape.\n\n \"\"\"\n\n title = '$shape run by Andrew Tarzia.\\n'\n size_of_poly = f'{num_vertices} {central_atom_id}\\n'\n codes = ' '.join(ref_shapes)+'\\n'\n\n string = title+size_of_poly+codes+structure_string\n\n with open(input_file, 'w') as f:\n f.write(string)\n\n\ndef calculate_cgcube_shape_measure(name, structure_string):\n \"\"\"\n Calculate the shape of an 8 atom molecule.\n\n Shape: http://www.ee.ub.edu/index.php?option=com_content&view=\n article&id=575:shape-available&catid=80:news&Itemid=466\n\n \"\"\"\n\n shape_dicts = (ref_shape_dict()['cube'], )\n n_verts = list(set([i['vertices'] for i in shape_dicts]))\n if len(n_verts) != 1:\n raise ValueError('Different vertex shapes selected.')\n\n input_file = f'{name}_shp.dat'\n std_out = f'{name}_shp.out'\n output_file = f'{name}_shp.tab'\n write_cg_shape_input_file(\n input_file=input_file,\n structure_string=structure_string,\n num_vertices=n_verts[0],\n central_atom_id=0,\n ref_shapes=[i['code'] for i in shape_dicts],\n )\n\n run_shape(input_file, shape_path(), std_out)\n shapes = collect_all_shape_values(output_file)\n return shapes\n\n\ndef prepare_precursors(precursor_dir):\n delta_bb = stk.BuildingBlock.init_from_file(\n path=os.path.join(precursor_dir, 'corner_delta.mol'),\n functional_groups=(stk.BromoFactory(), )\n )\n lambda_bb = stk.BuildingBlock.init_from_file(\n path=os.path.join(precursor_dir, 'corner_lambda.mol'),\n functional_groups=(stk.BromoFactory(), )\n )\n\n plane_bb = FaceBuildingBlock.init_from_file(\n path=os.path.join(precursor_dir, 'plane.mol'),\n functional_groups=(stk.BromoFactory(), )\n )\n\n temp_plane = reorient_linker(plane_bb)\n\n # Set functional group ordering based on long axis.\n fg_centroids = tuple(\n temp_plane.get_centroid(\n atom_ids=fg.get_placer_ids(),\n ) for fg in temp_plane.get_functional_groups()\n )\n plus_minus_fg_id = tuple(\n i for i, cent in enumerate(fg_centroids)\n if cent[0] > 0 and cent[1] < 0\n )[0]\n fg1_id = plus_minus_fg_id\n fg2_id, fg3_id, fg4_id = tuple(\n i\n for i in range(temp_plane.get_num_functional_groups())\n if i != fg1_id\n )\n new_fgs = tuple(temp_plane.get_functional_groups())\n plane_bb = temp_plane.with_functional_groups(\n functional_groups=(\n new_fgs[fg1_id],\n new_fgs[fg2_id],\n new_fgs[fg3_id],\n new_fgs[fg4_id],\n )\n )\n\n return delta_bb, lambda_bb, plane_bb\n\n\ndef get_shape_measure(cage, run_prefix, output_dir):\n Zn_bb_ids = {}\n for ai in cage.get_atom_infos():\n aibbid = ai.get_building_block_id()\n if ai.get_atom().get_atomic_number() == 30:\n if aibbid not in Zn_bb_ids:\n Zn_bb_ids[aibbid] = []\n Zn_bb_ids[aibbid].append(\n ai.get_atom().get_id()\n )\n\n Zn_centroids = []\n for n in Zn_bb_ids:\n Zn_centroids.append(cage.get_centroid(\n atom_ids=Zn_bb_ids[n]\n ))\n with open(\n os.path.join(output_dir, f'{run_prefix}_cents.xyz'), 'w'\n ) as f:\n f.write('8\\n\\n')\n for c in Zn_centroids:\n f.write(f'Zn {c[0]} {c[1]} {c[2]}\\n')\n\n # Run calculations.\n s_string = f'{run_prefix}\\n'\n for c in Zn_centroids:\n s_string += f'Zn {c[0]} {c[1]} {c[2]}\\n'\n\n cu8_measure = calculate_cgcube_shape_measure(\n name=os.path.join(output_dir, f'{run_prefix}'),\n structure_string=s_string,\n )\n return cu8_measure\n\n\ndef get_distances(optimizer, cage):\n bond_ks_, __ = optimizer.define_bond_potentials()\n set_ks = tuple(bond_ks_.keys())\n distances = {''.join(i): [] for i in set_ks}\n for bond in cage.get_bonds():\n a1 = bond.get_atom1()\n a2 = bond.get_atom2()\n a1name = a1.__class__.__name__\n a2name = a2.__class__.__name__\n pair = tuple(sorted([a1name, a2name]))\n if pair in set_ks:\n a1id = a1.get_id()\n a2id = a2.get_id()\n distances[''.join(pair)].append(\n get_atom_distance(cage, a1id, a2id)\n )\n\n return distances\n\n\ndef get_angles(optimizer, cage):\n angle_ks_, __ = optimizer.define_angle_potentials()\n set_ks = tuple(angle_ks_.keys())\n angles = {''.join(i): [] for i in set_ks}\n pos_mat = cage.get_position_matrix()\n\n angle_atoms = get_all_angles(cage)\n for angle_trip in angle_atoms:\n triplet = tuple(\n sorted([i.__class__.__name__ for i in angle_trip])\n )\n if triplet in set_ks:\n a1id = angle_trip[0].get_id()\n a2id = angle_trip[1].get_id()\n a3id = angle_trip[2].get_id()\n vector1 = pos_mat[a2id]-pos_mat[a1id]\n vector2 = pos_mat[a2id]-pos_mat[a3id]\n angles[''.join(triplet)].append(np.degrees(\n angle_between(vector1, vector2)\n ))\n\n return angles\n\n\ndef run_aniso_optimisation(\n cage,\n aniso,\n symm,\n flex,\n ortho_k,\n o_angle_k,\n output_dir,\n):\n\n run_prefix = f'{symm}_{aniso}_{flex}'\n output_file = os.path.join(\n output_dir, f'{run_prefix}_res.json'\n )\n\n if os.path.exists(output_file):\n with open(output_file, 'r') as f:\n res_dict = json.load(f)\n else:\n print(f': running optimisation of {run_prefix}')\n opt = CGGulpOptimizer(\n fileprefix=run_prefix,\n output_dir=output_dir,\n anisotropy=aniso,\n ortho_k=ortho_k,\n o_angle_k=o_angle_k,\n )\n run_data = opt.optimize(cage)\n\n # Get cube shape measure.\n opted = cage.with_structure_from_file(\n path=os.path.join(output_dir, f'{run_prefix}_final.xyz'),\n )\n opted.write(\n os.path.join(output_dir, f'{run_prefix}_final.mol')\n )\n\n cu8_measure = get_shape_measure(opted, run_prefix, output_dir)\n distances = get_distances(optimizer=opt, cage=opted)\n angles = get_angles(optimizer=opt, cage=opted)\n\n num_steps = len(run_data['traj'])\n fin_energy = run_data['final_energy']\n fin_gnorm = run_data['final_gnorm']\n traj_data = run_data['traj']\n print(\n f'{run_prefix}: {num_steps} {fin_energy} {fin_gnorm} '\n f'{cu8_measure}'\n )\n res_dict = {\n 'fin_energy': fin_energy,\n 'cu8': cu8_measure,\n 'traj': traj_data,\n 'distances': distances,\n 'angles': angles,\n }\n with open(output_file, 'w') as f:\n json.dump(res_dict, f)\n\n return res_dict\n\n\ndef scatter(\n symm_to_c,\n results,\n ylabel,\n output_dir,\n filename,\n flex,\n):\n\n fig, ax = plt.subplots(figsize=(8, 5))\n for aniso in results:\n da = results[aniso]\n for symm in da:\n if ylabel == 'energy (eV)':\n ys = da[symm]['fin_energy']\n elif ylabel == 'CU-8':\n ys = da[symm]['cu8']['CU-8']\n ax.axhline(y=0, lw=2, c='k')\n\n ax.scatter(\n aniso,\n ys,\n c=symm_to_c[symm][1],\n marker=symm_to_c[symm][0],\n edgecolor='k',\n s=80,\n alpha=0.5,\n )\n\n legend_elements = []\n for s in symm_to_c:\n legend_elements.append(\n Line2D(\n [0],\n [0],\n color='w',\n marker=symm_to_c[s][0],\n label=convert_symm_names(s),\n markerfacecolor=symm_to_c[s][1],\n markersize=10,\n markeredgecolor='k',\n )\n )\n\n ax.legend(handles=legend_elements, fontsize=16, ncol=3)\n\n ax.tick_params(axis='both', which='major', labelsize=16)\n ax.set_xlabel('anisotropy', fontsize=16)\n ax.set_ylabel(ylabel, fontsize=16)\n ax.set_title(f'flex: {flex}', fontsize=16)\n\n fig.tight_layout()\n fig.savefig(\n os.path.join(output_dir, filename),\n dpi=720,\n bbox_inches='tight',\n )\n plt.close()\n\n\ndef ey_vs_shape(\n results,\n output_dir,\n filename,\n):\n\n _to_plot = {\n 'd2': ('o', 'k'),\n 'th2': ('X', 'r'),\n 's62': ('D', 'gold'),\n 'd32': ('o', 'skyblue'),\n }\n\n fig, ax = plt.subplots(figsize=(8, 5))\n for symm in _to_plot:\n x_vals = []\n y_vals = []\n for aniso in results:\n da = results[aniso]\n x_vals.append(da[symm]['cu8']['CU-8'])\n y_vals.append(da[symm]['fin_energy'])\n\n ax.scatter(\n x_vals,\n y_vals,\n c=_to_plot[symm][1],\n marker=_to_plot[symm][0],\n edgecolor='k',\n s=100,\n alpha=1.0,\n label=convert_symm_names(symm),\n )\n\n ax.legend(fontsize=16)\n ax.tick_params(axis='both', which='major', labelsize=16)\n ax.set_xlabel('CU-8', fontsize=16)\n ax.set_ylabel('energy (eV)', fontsize=16)\n ax.set_xlim(0, 2)\n\n fig.tight_layout()\n fig.savefig(\n os.path.join(output_dir, filename),\n dpi=720,\n bbox_inches='tight',\n )\n plt.close()\n\n\ndef comp_scatter(\n symm_to_c,\n symm_set,\n results,\n ylabel,\n output_dir,\n filename,\n flex,\n ylim,\n):\n\n fig, ax = plt.subplots(figsize=(8, 5))\n for aniso in results:\n da = results[aniso]\n for symm in da:\n if symm not in symm_set:\n continue\n if ylabel == 'energy (eV)':\n ys = da[symm]['fin_energy']\n elif ylabel == 'CU-8':\n ys = da[symm]['cu8']['CU-8']\n ax.axhline(y=0, lw=2, c='k')\n\n ax.scatter(\n aniso,\n ys,\n c=symm_to_c[symm][1],\n marker=symm_to_c[symm][0],\n edgecolor='k',\n s=120,\n )\n\n legend_elements = []\n for s in symm_to_c:\n if s not in symm_set:\n continue\n legend_elements.append(\n Line2D(\n [0],\n [0],\n color='w',\n marker=symm_to_c[s][0],\n label=convert_symm_names(s),\n markerfacecolor=symm_to_c[s][1],\n markersize=12,\n markeredgecolor='k',\n )\n )\n\n ax.legend(handles=legend_elements, fontsize=16, ncol=2)\n\n ax.tick_params(axis='both', which='major', labelsize=16)\n ax.set_xlabel('anisotropy', fontsize=16)\n ax.set_ylabel(ylabel, fontsize=16)\n ax.set_title(f'flex: {flex}', fontsize=16)\n ax.set_ylim(ylim)\n\n fig.tight_layout()\n fig.savefig(\n os.path.join(output_dir, filename),\n dpi=720,\n bbox_inches='tight',\n )\n plt.close()\n\n\ndef merge_bond_types(s):\n\n translation = {\n 'CC': 'face',\n 'BB': 'face',\n 'CZn': 'face-metal',\n 'BZn': 'face-metal',\n 'FeFe': 'metal',\n 'FeZn': 'metal',\n 'ZnZn': 'metal',\n }\n\n return translation[s]\n\n\ndef merge_angle_types(s):\n\n translation = {\n 'BCC': 'face',\n 'BBC': 'face',\n 'BCZn': 'face-metal',\n 'BBZn': 'face-metal',\n 'CCZn': 'face-metal',\n 'BFeZn': 'face-metal',\n 'CFeZn': 'face-metal',\n 'FeZnZn': 'metal',\n 'FeFeFe': 'metal',\n 'ZnZnZn': 'metal',\n }\n\n return translation[s]\n\n\ndef geom_distributions(\n results,\n output_dir,\n filename,\n):\n\n # Collect all values for each bond and angle type.\n distance_by_type = {}\n angle_by_type = {}\n for aniso in results:\n da = results[aniso]\n for symm in da:\n dists = da[symm]['distances']\n angles = da[symm]['angles']\n for d in dists:\n if d == 'BC':\n dd = f'{d}{aniso}'\n else:\n dd = merge_bond_types(d)\n if dd in distance_by_type:\n distance_by_type[dd].extend(dists[d])\n else:\n distance_by_type[dd] = dists[d]\n for a in angles:\n aa = merge_angle_types(a)\n if aa in angle_by_type:\n angle_by_type[aa].extend(angles[a])\n else:\n angle_by_type[aa] = angles[a]\n\n fig, axs = plt.subplots(\n nrows=3,\n ncols=1,\n figsize=(8, 8),\n )\n # Plot distributions of each bond type.\n for btype in distance_by_type:\n if 'BC' in btype:\n continue\n data = distance_by_type[btype]\n axs[0].hist(\n x=data,\n bins=50,\n range=(3.6, 4.4),\n density=True,\n histtype='step',\n # color='',\n label=btype,\n lw=3,\n )\n axs[0].tick_params(axis='both', which='major', labelsize=16)\n axs[0].set_xlabel('distance [$\\mathrm{\\AA}}$]', fontsize=16)\n axs[0].set_ylabel('frequency', fontsize=16)\n axs[0].legend(fontsize=16, ncol=1)\n\n # Plot distributions of each variable bond type.\n for btype in distance_by_type:\n if 'BC' not in btype:\n continue\n data = distance_by_type[btype]\n aniso = float(btype.replace('BC', ''))\n axs[1].scatter(\n x=[aniso for i in data],\n y=data,\n color='gray',\n s=30,\n alpha=0.3,\n rasterized=True,\n )\n axs[1].tick_params(axis='both', which='major', labelsize=16)\n axs[1].set_xlabel('anisotropy', fontsize=16)\n axs[1].set_ylabel('F1-F2 distance [$\\mathrm{\\AA}}$]', fontsize=16)\n\n # Plot distributions of each angle type.\n for atype in angle_by_type:\n data = angle_by_type[atype]\n axs[2].hist(\n x=data,\n bins=50,\n range=(20, 182),\n density=True,\n histtype='step',\n # color='',\n label=atype,\n lw=3,\n )\n axs[2].tick_params(axis='both', which='major', labelsize=16)\n axs[2].set_xlabel('angle [degrees]', fontsize=16)\n axs[2].set_ylabel('frequency', fontsize=16)\n axs[2].legend(fontsize=16, ncol=1)\n\n fig.tight_layout()\n fig.savefig(\n os.path.join(output_dir, filename),\n dpi=720,\n bbox_inches='tight',\n )\n plt.close()\n\n\ndef heatmap(\n symm_to_c,\n results,\n output_dir,\n filename,\n vmin,\n vmax,\n clabel,\n flex,\n expt_data,\n ligand_ars,\n):\n\n fig, ax = plt.subplots(figsize=(8, 8))\n maps = np.zeros((len(symm_to_c), len(results)))\n for j, aniso in enumerate(results):\n da = results[aniso]\n for i, symm in enumerate(symm_to_c):\n if clabel == 'energy (eV)':\n maps[i][j] = da[symm]['fin_energy']\n elif clabel == 'CU-8':\n maps[i][j] = da[symm]['cu8']['CU-8']\n\n im = ax.imshow(maps, vmin=vmin, vmax=vmax, cmap='Purples_r')\n # Create colorbar\n cbar = ax.figure.colorbar(im, ax=ax, shrink=0.4)\n cbar.ax.set_ylabel(clabel, rotation=-90, va=\"bottom\", fontsize=16)\n cbar.ax.tick_params(labelsize=16)\n\n # Turn spines off and create white grid.\n ax.spines[:].set_visible(False)\n ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=1)\n # ax.set_xticks(np.arange(maps.shape[1]+1)-.5, minor=True)\n ax.set_yticks(np.arange(maps.shape[0]+1)-.5, minor=True)\n ax.tick_params(which=\"minor\", bottom=False, left=False)\n\n # Scatter points for where experiments land.\n for expt in expt_data:\n da = expt_data[expt]\n # Get symm position.\n symm_position = symm_to_c[da['symmetry']][2]\n known_aniso = ligand_ars[da['ligand_name']]['N']\n sub_arr = [(a-known_aniso)**2 for a in results]\n matched_x_position = np.argmin(sub_arr)\n ax.scatter(\n x=matched_x_position,\n y=symm_position,\n c='red',\n edgecolors='k',\n marker='o',\n s=80,\n )\n\n # Scatter points for lowest/highest energy for each symm.\n if clabel == 'energy (eV)':\n # Min of each row.\n index_min = np.argmin(maps, axis=1)\n ax.scatter(\n x=index_min,\n y=[symm_to_c[symm][2] for symm in symm_to_c],\n c='white',\n marker='P',\n edgecolors='k',\n s=80,\n )\n # # Max of each row.\n # index_max = np.argmax(maps, axis=1)\n # ax.scatter(\n # x=index_max,\n # y=[symm_to_c[symm][2] for symm in symm_to_c],\n # c='white',\n # edgecolors='k',\n # marker='X',\n # s=40,\n # )\n\n ax.tick_params(axis='both', which='major', labelsize=16)\n ax.set_xlabel('anisotropy', fontsize=16)\n ax.set_ylabel('symmetry', fontsize=16)\n # Show all ticks and label them with the respective lists.\n ax.set_xticks([i for i in range(len(results))])\n ax.set_xticklabels([a for a in results])\n ax.set_yticks([symm_to_c[symm][2] for symm in symm_to_c])\n ax.set_yticklabels([\n convert_symm_names(symm) for symm in symm_to_c\n ])\n\n # Rotate the tick labels and set their alignment.\n plt.setp(\n ax.get_xticklabels(),\n rotation=45,\n ha=\"right\",\n rotation_mode=\"anchor\",\n )\n ax.set_title(f'flex: {flex}', fontsize=16)\n\n fig.tight_layout()\n fig.savefig(\n os.path.join(output_dir, filename),\n dpi=720,\n bbox_inches='tight',\n )\n plt.close()\n\n\ndef convergence(\n results,\n output_dir,\n filename,\n):\n\n # Pick examples to plot.\n _to_plot = (\n 'td_2.0',\n # 'c2v_1.65',\n 'c2v_1.5',\n # 'th2_1.6',\n 'th2_1.1',\n # 's61_1.85',\n 's61_1.3',\n 's42_1.05',\n )\n\n fig, axs = plt.subplots(\n nrows=len(_to_plot),\n ncols=1,\n sharex=True,\n figsize=(8, 10),\n )\n\n for name, ax in zip(_to_plot, axs):\n symm, aniso = name.split('_')\n da = results[float(aniso)]\n traj = da[symm]['traj']\n traj_x = [i for i in traj]\n traj_e = [traj[i]['energy'] for i in traj]\n traj_g = [traj[i]['gnorm'] for i in traj]\n\n color = 'tab:red'\n ax.plot(\n traj_x,\n traj_e,\n lw=5,\n # marker='o',\n # markersize=12,\n color=color,\n )\n ax.tick_params(axis='y', labelcolor=color, labelsize=16)\n ax.set_yscale('log')\n\n # instantiate a second axes that shares the same x-axis\n ax2 = ax.twinx()\n color = 'tab:blue'\n ax2.plot(\n traj_x,\n traj_g,\n lw=5,\n # marker='X',\n # markersize=12,\n color=color,\n )\n ax2.tick_params(axis='y', labelcolor=color, labelsize=16)\n ax2.set_yscale('log')\n\n ax.tick_params(axis='both', which='major', labelsize=16)\n ax.text(\n x=340, y=1100,\n s=f'{convert_symm_names(symm)}, aniso={aniso}',\n fontsize=16,\n )\n if name == 'th2_1.1':\n ax.set_ylabel('energy [eV]', fontsize=16)\n ax2.set_ylabel('Gnorm', fontsize=16)\n\n ax.set_xlabel('step', fontsize=16)\n ax.set_xticks(range(0, 501, 50))\n ax.set_xticklabels([str(i) for i in range(0, 501, 50)])\n\n fig.tight_layout()\n fig.savefig(\n os.path.join(output_dir, filename),\n dpi=720,\n bbox_inches='tight',\n )\n plt.close()\n\n\ndef get_ligand_ars(ligand_directory):\n json_file = os.path.join(ligand_directory, 'ligand_ARs.json')\n with open(json_file, 'r') as f:\n ar_data = json.load(f)\n return ar_data\n\n\ndef main():\n first_line = (\n 'Usage: run_cg_model.py precursor_dir output_dir'\n ' expt_lib_file ligand_directory'\n )\n if (not len(sys.argv) == 5):\n print(f\"\"\"\n{first_line}\n\n precursor_dir : (str)\n Directrory containing precursor structures.\n\n output_dir : (str)\n Directrory to output files to.\n\n expt_lib_file : (str)\n File containing experimental symmetry information (XXXXX).\n\n ligand_directory : (str)\n Directory with required ligand structures.\n\n \"\"\")\n sys.exit()\n else:\n precursor_dir = sys.argv[1]\n output_dir = sys.argv[2]\n expt_lib_file = sys.argv[3]\n ligand_directory = sys.argv[4]\n\n expt_data = read_lib(expt_lib_file)\n\n # Get ligand aspect ratios.\n ligand_ars = get_ligand_ars(ligand_directory)\n\n if not os.path.exists(output_dir):\n os.mkdir(output_dir)\n\n delta_bb, lambda_bb, plane_bb = prepare_precursors(\n precursor_dir=precursor_dir,\n )\n\n # Make cage of each symmetry.\n symms = symmetries(delta_bb, lambda_bb, plane_bb)\n anisotropies = np.arange(1.0, 2.01, 0.05)\n # results = {i: {} for i in symms}\n flexes = {\n 'low': (10, 20),\n 'high': (0.1, 2.0),\n }\n for flex in flexes:\n results = {round(i, 2): {} for i in anisotropies}\n for symm in symms:\n topology_graph = CGM8L6Cube(\n building_blocks=symms[symm]['building_blocks'],\n vertex_alignments=symms[symm]['vertex_alignments'],\n num_processes=1,\n )\n cage = stk.ConstructedMolecule(topology_graph)\n cage.write(os.path.join(output_dir, f'{symm}_unopt.mol'))\n\n for aniso in anisotropies:\n aniso = round(aniso, 2)\n res_dict = run_aniso_optimisation(\n cage=cage,\n aniso=aniso,\n symm=symm,\n flex=flex,\n ortho_k=flexes[flex][0],\n o_angle_k=flexes[flex][1],\n output_dir=output_dir,\n )\n results[aniso][symm] = res_dict\n\n symm_to_c = {\n 'd2': ('o', 'k', 0),\n 'th1': ('D', 'r', 1),\n 'th2': ('X', 'r', 2),\n 'td': ('o', 'r', 3),\n 'tl': ('P', 'r', 4),\n 's61': ('X', 'gold', 5),\n 's62': ('D', 'gold', 6),\n 's41': ('X', 'gray', 7),\n 's42': ('D', 'gray', 8),\n 'd31': ('P', 'skyblue', 9),\n 'd32': ('o', 'skyblue', 10),\n 'd31n': ('P', 'b', 11),\n 'd32n': ('o', 'b', 12),\n 'c2h': ('o', 'green', 13),\n 'c2v': ('X', 'green', 14),\n }\n\n convergence(\n results=results,\n output_dir=output_dir,\n filename=f'convergence_{flex}.pdf',\n )\n\n ey_vs_shape(\n results=results,\n output_dir=output_dir,\n filename=f'e_vs_shape_{flex}.pdf',\n )\n\n geom_distributions(\n results=results,\n output_dir=output_dir,\n filename=f'dist_{flex}.pdf',\n )\n\n heatmap(\n symm_to_c=symm_to_c,\n results=results,\n output_dir=output_dir,\n filename=f'energy_map_{flex}.pdf',\n vmin=0,\n vmax=45,\n clabel='energy (eV)',\n flex=flex,\n expt_data=expt_data,\n ligand_ars=ligand_ars,\n )\n\n heatmap(\n symm_to_c=symm_to_c,\n results=results,\n output_dir=output_dir,\n filename=f'energy_map_flat_{flex}.pdf',\n vmin=0,\n vmax=10,\n clabel='energy (eV)',\n flex=flex,\n expt_data=expt_data,\n ligand_ars=ligand_ars,\n )\n\n heatmap(\n symm_to_c=symm_to_c,\n results=results,\n output_dir=output_dir,\n filename=f'shape_map_{flex}.pdf',\n vmin=0,\n vmax=2.2,\n clabel='CU-8',\n flex=flex,\n expt_data=expt_data,\n ligand_ars=ligand_ars,\n )\n\n scatter(\n symm_to_c=symm_to_c,\n results=results,\n output_dir=output_dir,\n filename=f'energy_{flex}.pdf',\n ylabel='energy (eV)',\n flex=flex,\n )\n scatter(\n symm_to_c=symm_to_c,\n results=results,\n output_dir=output_dir,\n filename=f'shape_{flex}.pdf',\n ylabel='CU-8',\n flex=flex,\n )\n\n comp_sets = {\n 'ts': ('th1', 'th2', 'td', 'tl'),\n 'ds': ('d31', 'd32', 'd31n', 'd32n'),\n 'ss': ('s61', 's62', 's41', 's42'),\n 'expt': ('d2', 'tl', 's62', 'th2', 'd32'),\n }\n for key, values in comp_sets.items():\n if flex == 'high':\n eylim = (0, 10)\n else:\n eylim = (0, 45)\n\n comp_scatter(\n symm_to_c=symm_to_c,\n symm_set=values,\n results=results,\n output_dir=output_dir,\n filename=f'comp_energy_{flex}_{key}.pdf',\n ylabel='energy (eV)',\n flex=flex,\n ylim=eylim,\n )\n comp_scatter(\n symm_to_c=symm_to_c,\n symm_set=values,\n results=results,\n output_dir=output_dir,\n filename=f'comp_shape_{flex}_{key}.pdf',\n ylabel='CU-8',\n flex=flex,\n ylim=(0, 2),\n )\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "scripts/run_cg_model.py", "file_name": "run_cg_model.py", "file_ext": "py", "file_size_in_byte": 35462, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "rdkit.Chem.AllChem.FindAllPathsOfLengthN", "line_number": 44, "usage_type": "call"}, {"api_name": "rdkit.Chem.AllChem", "line_number": 44, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 92, "usage_type": "call"}, {"api_name": "env_set.gulp_path", "line_number": 93, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 100, "usage_type": "call"}, {"api_name": "string.digits", "line_number": 216, "usage_type": "argument"}, {"api_name": "string.digits", "line_number": 247, "usage_type": "argument"}, {"api_name": "numpy.degrees", "line_number": 266, "usage_type": "call"}, {"api_name": "utilities.angle_between", "line_number": 267, "usage_type": "call"}, {"api_name": "symmetries.M8L6_Symmetry", "line_number": 313, "usage_type": "call"}, {"api_name": "stk.cage", "line_number": 337, "usage_type": "attribute"}, {"api_name": "utilities.ref_shape_dict", "line_number": 374, "usage_type": "call"}, {"api_name": "utilities.run_shape", "line_number": 390, "usage_type": "call"}, {"api_name": "env_set.shape_path", "line_number": 390, "usage_type": "call"}, {"api_name": "utilities.collect_all_shape_values", "line_number": 391, "usage_type": "call"}, {"api_name": "stk.BuildingBlock.init_from_file", "line_number": 396, "usage_type": "call"}, {"api_name": "stk.BuildingBlock", "line_number": 396, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 397, "usage_type": "call"}, {"api_name": "os.path", "line_number": 397, "usage_type": "attribute"}, {"api_name": "stk.BromoFactory", "line_number": 398, "usage_type": "call"}, {"api_name": "stk.BuildingBlock.init_from_file", "line_number": 400, "usage_type": "call"}, {"api_name": "stk.BuildingBlock", "line_number": 400, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 401, "usage_type": "call"}, {"api_name": "os.path", "line_number": 401, "usage_type": "attribute"}, {"api_name": "stk.BromoFactory", "line_number": 402, "usage_type": "call"}, {"api_name": "facebuildingblock.FaceBuildingBlock.init_from_file", "line_number": 405, "usage_type": "call"}, {"api_name": "facebuildingblock.FaceBuildingBlock", "line_number": 405, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 406, "usage_type": "call"}, {"api_name": "os.path", "line_number": 406, "usage_type": "attribute"}, {"api_name": "stk.BromoFactory", "line_number": 407, "usage_type": "call"}, {"api_name": "utilities.reorient_linker", "line_number": 410, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 458, "usage_type": "call"}, {"api_name": "os.path", "line_number": 458, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 470, "usage_type": "call"}, {"api_name": "os.path", "line_number": 470, "usage_type": "attribute"}, {"api_name": "utilities.get_atom_distance", "line_number": 490, "usage_type": "call"}, {"api_name": "numpy.degrees", "line_number": 513, "usage_type": "call"}, {"api_name": "utilities.angle_between", "line_number": 514, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 531, "usage_type": "call"}, {"api_name": "os.path", "line_number": 531, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 535, "usage_type": "call"}, {"api_name": "os.path", "line_number": 535, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 537, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 551, "usage_type": "call"}, {"api_name": "os.path", "line_number": 551, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 554, "usage_type": "call"}, {"api_name": "os.path", "line_number": 554, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 577, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 591, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 591, "usage_type": "name"}, {"api_name": "matplotlib.lines.Line2D", "line_number": 614, "usage_type": "call"}, {"api_name": "utilities.convert_symm_names", "line_number": 619, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 635, "usage_type": "call"}, {"api_name": "os.path", "line_number": 635, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 639, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 639, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 655, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 655, "usage_type": "name"}, {"api_name": "utilities.convert_symm_names", "line_number": 672, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 683, "usage_type": "call"}, {"api_name": "os.path", "line_number": 683, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 687, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 687, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 701, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 701, "usage_type": "name"}, {"api_name": "matplotlib.lines.Line2D", "line_number": 727, "usage_type": "call"}, {"api_name": "utilities.convert_symm_names", "line_number": 732, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 749, "usage_type": "call"}, {"api_name": "os.path", "line_number": 749, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 753, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 753, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 819, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 819, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 882, "usage_type": "call"}, {"api_name": "os.path", "line_number": 882, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 886, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 886, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 902, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 902, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 903, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 922, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 932, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 945, "usage_type": "call"}, {"api_name": "utilities.convert_symm_names", "line_number": 973, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 977, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 977, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 987, "usage_type": "call"}, {"api_name": "os.path", "line_number": 987, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 991, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 991, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 1012, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1012, "usage_type": "name"}, {"api_name": "utilities.convert_symm_names", "line_number": 1056, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1069, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1069, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 1073, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1073, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 1077, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1077, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 1079, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 1088, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 1105, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 1107, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 1108, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 1109, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 1110, "usage_type": "attribute"}, {"api_name": "utilities.read_lib", "line_number": 1112, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 1117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1117, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 1118, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 1126, "usage_type": "call"}, {"api_name": "stk.ConstructedMolecule", "line_number": 1140, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1141, "usage_type": "attribute"}]} +{"seq_id": "160109879", "text": "from sanic import Sanic\nfrom sanic.response import json\nfrom sanic.exceptions import abort\nimport uuid\nfrom queue import Empty\nimport asyncio\n\napp = Sanic()\noutputs = dict()\nprov = None\n\n\ndef _check_finished():\n global prov\n in_q = app.config['in_q']\n try:\n # Flush the queue\n while True:\n [mtype, fjob_id, output] = in_q.get(block=False)\n if mtype == 'output':\n outputs[fjob_id] = output\n elif mtype == 'prov':\n prov = output\n except Empty:\n pass\n\n\nasync def _process_rpc(data, token):\n (module, method) = data['method'].split('.')\n # async submi job\n if method.startswith('_') and method.endswith('_submit'):\n if token != app.config.get('token'):\n abort(401)\n job_id = str(uuid.uuid1())\n data['method'] = '%s.%s' % (module, method[1:-7])\n app.config['out_q'].put(['submit', job_id, data])\n return {'result': job_id}\n # check job\n elif method.startswith('_check_job'):\n if 'params' not in data:\n abort(404)\n job_id = data['params'][0]\n _check_finished()\n resp = {'finished': False}\n if job_id in outputs:\n resp = outputs[job_id]\n resp['finished'] = True\n return {'result': [resp]}\n # Provenance\n elif method.startswith('get_provenance'):\n _check_finished()\n return {'result': [prov]}\n else:\n if token != app.config.get('token'):\n abort(401)\n job_id = str(uuid.uuid1())\n data['method'] = '%s.%s' % (module, method[1:-7])\n app.config['out_q'].put(['submit', job_id, data])\n try:\n while True:\n _check_finished()\n if job_id in outputs:\n resp = outputs[job_id]\n resp['finished'] = True\n return resp\n await asyncio.sleep(1)\n except Exception:\n return {'error': 'Timeout'}\n\n\n@app.route(\"/\", methods=['GET', 'POST'])\nasync def root(request):\n data = request.json\n if request.method == 'POST' and data is not None and 'method' in data:\n token = request.headers.get('Authorization')\n return json(await _process_rpc(data, token))\n return json({})\n\n\ndef start_callback_server(ip, port, out_queue, in_queue, token):\n conf = {\n 'token': token,\n 'out_q': out_queue,\n 'in_q': in_queue\n }\n app.config.update(conf)\n app.run(host=ip, port=port, debug=False, access_log=False)\n", "sub_path": "JobRunner/callback_server.py", "file_name": "callback_server.py", "file_ext": "py", "file_size_in_byte": 2548, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sanic.Sanic", "line_number": 8, "usage_type": "call"}, {"api_name": "queue.Empty", "line_number": 24, "usage_type": "name"}, {"api_name": "sanic.exceptions.abort", "line_number": 33, "usage_type": "call"}, {"api_name": "uuid.uuid1", "line_number": 34, "usage_type": "call"}, {"api_name": "sanic.exceptions.abort", "line_number": 41, "usage_type": "call"}, {"api_name": "sanic.exceptions.abort", "line_number": 55, "usage_type": "call"}, {"api_name": "uuid.uuid1", "line_number": 56, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 66, "usage_type": "call"}, {"api_name": "sanic.response.json", "line_number": 76, "usage_type": "call"}, {"api_name": "sanic.response.json", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "247078193", "text": "from joblib import Parallel, delayed\r\nimport pickle\r\nimport glob\r\nfrom random import sample\r\nfrom sklearn.utils import shuffle\r\nfrom itertools import combinations\r\nimport numpy as np\r\nfrom PIL import Image\r\nfrom my_data_loader import *\r\n\r\n\r\n# dump result to annot_img_{}.pkl\r\n# result = input_images, input_depths\r\n# for validation\r\ndef main_data_sample(data_type='valid', debug=False):\r\n assert data_type in ['train', 'valid']\r\n\r\n train_annot = load_dataset(data_type)\r\n nsamples = train_annot[\"nsamples\"]\r\n print(\"dataset and counts:\")\r\n print(np.unique(list(map(lambda x: x.split(\"_\")[0], train_annot['images'])), return_counts=True))\r\n\r\n def produce_single_img_and_heatmap(i):\r\n image_path = train_annot[\"images\"][i]\r\n im = Image.open(r\"data/h36m/images/{}\".format(image_path))\r\n center = train_annot[\"center\"][i]\r\n scale = train_annot[\"scale\"][i]\r\n im = np.array(np.transpose(im, [2, 0, 1]))\r\n inp = crop(im, center, scale, 0, 256)\r\n input_img = inp.astype(np.uint8)\r\n if debug:\r\n Image.fromarray(input_img).show()\r\n print(input_img.shape)\r\n return input_img\r\n\r\n def result_gen(i_range):\r\n i_list = list(i_range)\r\n input_img_list = []\r\n for i in i_list:\r\n input_img = produce_single_img_and_heatmap(i)\r\n input_img_list.append(input_img)\r\n\r\n input_depth = train_annot['S'][i_list, :, -1]\r\n input_xy = train_annot['S'][i_list, :, :2]\r\n return np.stack(input_img_list, axis=0), input_xy, input_depth\r\n\r\n nest_i_list = []\r\n inner_list_size = 10\r\n for i in range(nsamples):\r\n if i % inner_list_size == 0:\r\n nest_i_list.append([])\r\n nest_i_list[-1].append(i)\r\n\r\n gap = 30\r\n gap_list = [i for i in range(0, len(nest_i_list), gap)]\r\n for i in range(len(gap_list) - 1):\r\n start = gap_list[i]\r\n end = gap_list[i + 1]\r\n result = Parallel(n_jobs=12)(delayed(result_gen)(i,) for i in map(lambda x: nest_i_list[x], range(start, end)))\r\n with open(r\"data/h36m/images/{}_pkl_files/annot_img_{}.pkl\".format(data_type, i), \"wb\") as f:\r\n pickle.dump(result, f)\r\n print(\"dump {} end\".format(i))\r\n\r\n\r\n# keep generating batches\r\ndef batch_data_loader(data_type='train', batch_num=4, is_shuffle=True,\r\n num_joints=17, equal_tolerance=50):\r\n assert data_type in [\"train\", \"valid\"]\r\n ordered_comb_idx_list = sorted(list(map(list, combinations(range(num_joints), 2))))\r\n\r\n def map_17_to_comb(input_17, equal_tolerance=equal_tolerance):\r\n req = []\r\n for i, j in ordered_comb_idx_list:\r\n zi, zj = input_17[i], input_17[j]\r\n if np.abs(zi - zj) < equal_tolerance:\r\n req.append(0)\r\n elif zi > zj:\r\n req.append(-1)\r\n else:\r\n req.append(1)\r\n return np.asarray(req, dtype=np.int32)\r\n\r\n def read_pkl_file(file_path):\r\n with open(file_path, 'rb') as f:\r\n return pickle.load(f)\r\n\r\n all_pkl_files = glob.glob(r\"data/h36m/images/{}_pkl_files/*\".format(data_type))\r\n if is_shuffle:\r\n all_pkl_files = sample(all_pkl_files, len(all_pkl_files))\r\n\r\n input_img = np.zeros(shape=[batch_num, 256, 256, 3], dtype=np.float32)\r\n xy_input = np.zeros(shape=[batch_num, num_joints, 2], dtype=np.float32)\r\n comb_condition_input = np.zeros(shape=[batch_num, len(ordered_comb_idx_list)], dtype=np.int32)\r\n start_idx = 0\r\n now_file_list = None\r\n now_file = None\r\n now_file_0, now_file_1, now_file_2 = None\r\n while True:\r\n if now_file_list is None:\r\n if all_pkl_files:\r\n req_file = all_pkl_files.pop()\r\n print(\"load file :{}\".format(req_file))\r\n now_file_list = read_pkl_file(req_file)\r\n if is_shuffle:\r\n now_file_list = sample(now_file_list, len(now_file_list))\r\n else:\r\n print(\"all {} file read end, will return\".format(data_type))\r\n yield None\r\n return\r\n\r\n if now_file is None:\r\n now_file = now_file_list.pop()\r\n if now_file_0 is None:\r\n now_file_0 = now_file[0]\r\n if now_file_1 is None:\r\n now_file_1 = now_file[1]\r\n if now_file_2 is None:\r\n now_file_2 = now_file[2]\r\n\r\n if isinstance(now_file_0, np.ndarray):\r\n now_file_0 = now_file_0.tolist()\r\n if isinstance(now_file_1, np.ndarray):\r\n now_file_1 = now_file_1.tolist()\r\n if isinstance(now_file_2, np.ndarray):\r\n now_file_2 = now_file_2.tolist()\r\n\r\n input_img[start_idx] = np.asarray(now_file_0.pop(), dtype=np.float32)\r\n xy_input = np.asarray(now_file_1.pop(), dtype=np.float32)\r\n comb_condition_input[start_idx] = map_17_to_comb(now_file_2.pop())\r\n\r\n start_idx += 1\r\n if not now_file_list:\r\n now_file_list = None\r\n if not now_file:\r\n now_file = None\r\n if not now_file_0:\r\n now_file_0 = None\r\n if not now_file_1:\r\n now_file_1 = None\r\n if not now_file_2:\r\n now_file_2 = None\r\n\r\n if start_idx == batch_num:\r\n if is_shuffle:\r\n input_img, comb_condition_input = shuffle(input_img, comb_condition_input)\r\n yield input_img, xy_input, comb_condition_input\r\n input_img = np.zeros(shape=[batch_num, 256, 256, 3], dtype=np.float32)\r\n xy_input = np.zeros(shape=[batch_num, num_joints, 2], dtype=np.float32)\r\n comb_condition_input = np.zeros(shape=[batch_num, len(ordered_comb_idx_list)], dtype=np.int32)\r\n start_idx = 0\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n pass\r\n", "sub_path": "my_batch_loader.py", "file_name": "my_batch_loader.py", "file_ext": "py", "file_size_in_byte": 5942, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.unique", "line_number": 21, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 25, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 25, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 30, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 32, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 32, "usage_type": "name"}, {"api_name": "numpy.stack", "line_number": 45, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 59, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 59, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 61, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 85, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 87, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 92, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 93, "usage_type": "attribute"}, {"api_name": "random.sample", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 120, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 124, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 127, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 128, "usage_type": "attribute"}, {"api_name": "sklearn.utils.shuffle", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 147, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 148, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 149, "usage_type": "attribute"}]} +{"seq_id": "391241548", "text": "\"\"\"\nAPI request handlers for the jobs module\n\"\"\"\n\nimport json\nimport StringIO\nimport gear_tools\nfrom jsonschema import Draft4Validator, ValidationError\n\nfrom ..dao.containerutil import create_filereference_from_dictionary, create_containerreference_from_dictionary, create_containerreference_from_filereference, ContainerReference\nfrom .. import base\nfrom .. import config\n\nfrom .gears import get_gears, get_gear_by_name, get_invocation_schema, remove_gear, upsert_gear, suggest_container\nfrom .jobs import Job\nfrom .queue import Queue\n\n\nclass GearsHandler(base.RequestHandler):\n\n \"\"\"Provide /gears API routes.\"\"\"\n\n def get(self):\n \"\"\"List all gears.\"\"\"\n\n if self.public_request:\n self.abort(403, 'Request requires login')\n\n fields = self.request.GET.getall('fields')\n if 'all' in fields:\n fields = None\n\n return get_gears(fields)\n\nclass GearHandler(base.RequestHandler):\n \"\"\"Provide /gears/x API routes.\"\"\"\n\n def get(self, _id):\n \"\"\"Detail a gear.\"\"\"\n\n if self.public_request:\n self.abort(403, 'Request requires login')\n\n return get_gear_by_name(_id)\n\n def get_invocation(self, _id):\n\n if self.public_request:\n self.abort(403, 'Request requires login')\n\n gear = get_gear_by_name(_id)\n return get_invocation_schema(gear)\n\n def suggest(self, _id, cont_name, cid):\n\n if self.public_request:\n self.abort(403, 'Request requires login')\n\n cr = ContainerReference(cont_name, cid)\n if not self.superuser_request:\n cr.check_access(self.uid, 'ro')\n\n gear = get_gear_by_name(_id)\n return suggest_container(gear, cont_name+'s', cid)\n\n def post(self, _id):\n \"\"\"Upsert an entire gear document.\"\"\"\n\n if not self.superuser_request:\n self.abort(403, 'Request requires superuser')\n\n doc = self.request.json\n\n if _id != doc.get('gear', {}).get('name', ''):\n self.abort(400, 'Name key must be present and match URL')\n\n try:\n upsert_gear(self.request.json)\n except ValidationError as err:\n key = None\n if len(err.relative_path) > 0:\n key = err.relative_path[0]\n\n self.response.set_status(400)\n return {\n 'reason': 'Gear manifest does not match schema',\n 'error': err.message.replace(\"u'\", \"'\"),\n 'key': key\n }\n\n return { 'name': _id }\n\n def delete(self, _id):\n \"\"\"Delete a gear. Generally not recommended.\"\"\"\n\n if not self.superuser_request:\n self.abort(403, 'Request requires superuser')\n\n return remove_gear(_id)\n\n\nclass RulesHandler(base.RequestHandler):\n\n \"\"\"Provide /rules API routes.\"\"\"\n\n def get(self):\n \"\"\"List rules\"\"\"\n if not self.superuser_request:\n self.abort(403, 'Request requires superuser')\n\n return config.db.singletons.find_one({\"_id\" : \"rules\"})['rule_list']\n\n def post(self):\n \"\"\"Upsert all rules\"\"\"\n if not self.superuser_request:\n self.abort(403, 'Request requires superuser')\n\n doc = self.request.json\n config.db.singletons.replace_one({\"_id\" : \"rules\"}, {'rule_list': doc}, upsert=True)\n\n\nclass JobsHandler(base.RequestHandler):\n \"\"\"Provide /jobs API routes.\"\"\"\n def get(self):\n \"\"\"List all jobs.\"\"\"\n if not self.superuser_request:\n self.abort(403, 'Request requires superuser')\n\n return list(config.db.jobs.find())\n\n def add(self):\n \"\"\"Add a job to the queue.\"\"\"\n submit = self.request.json\n\n gear_name = submit['gear']\n\n # Translate maps to FileReferences\n inputs = {}\n for x in submit['inputs'].keys():\n input_map = submit['inputs'][x]\n inputs[x] = create_filereference_from_dictionary(input_map)\n\n # Add job tags, config, attempt number, and/or previous job ID, if present\n tags = submit.get('tags', None)\n config_ = submit.get('config', {})\n attempt_n = submit.get('attempt_n', 1)\n previous_job_id = submit.get('previous_job_id', None)\n now_flag = submit.get('now', False) # A flag to increase job priority\n\n # Add destination container, or select one\n destination = None\n if submit.get('destination', None) is not None:\n destination = create_containerreference_from_dictionary(submit['destination'])\n else:\n key = inputs.keys()[0]\n destination = create_containerreference_from_filereference(inputs[key])\n\n # Permission check\n if not self.superuser_request:\n for x in inputs:\n inputs[x].check_access(self.uid, 'ro')\n destination.check_access(self.uid, 'rw')\n now_flag = False # Only superuser requests are allowed to set \"now\" flag\n\n # Config manifest check\n gear = get_gear_by_name(gear_name)\n if len(gear.get('manifest', {}).get('config', {})) > 0:\n\n invocation = gear_tools.derive_invocation_schema(gear['manifest'])\n ci = gear_tools.isolate_config_invocation(invocation)\n validator = Draft4Validator(ci)\n\n try:\n validator.validate(config_)\n except ValidationError as err:\n key = None\n if len(err.relative_path) > 0:\n key = err.relative_path[0]\n\n self.response.set_status(422)\n return {\n 'reason': 'config did not match manifest',\n 'error': err.message.replace(\"u'\", \"'\"),\n 'key': key\n }\n\n job = Job(gear_name, inputs, destination=destination, tags=tags, config_=config_, now=now_flag, attempt=attempt_n, previous_job_id=previous_job_id, origin=self.origin)\n result = job.insert()\n\n return { \"_id\": result }\n\n def stats(self):\n if not self.superuser_request:\n self.abort(403, 'Request requires superuser')\n\n return Queue.get_statistics()\n\n def next(self):\n if not self.superuser_request:\n self.abort(403, 'Request requires superuser')\n\n tags = self.request.GET.getall('tags')\n if len(tags) <= 0:\n tags = None\n\n job = Queue.start_job(tags=tags)\n\n if job is None:\n self.abort(400, 'No jobs to process')\n else:\n return job\n\n def reap_stale(self):\n if not self.superuser_request:\n self.abort(403, 'Request requires superuser')\n\n count = Queue.scan_for_orphans()\n return { 'orphaned': count }\n\n\nclass JobHandler(base.RequestHandler):\n \"\"\"Provides /Jobs/ routes.\"\"\"\n\n def get(self, _id):\n if not self.superuser_request:\n self.abort(403, 'Request requires superuser')\n\n return Job.get(_id)\n\n def get_config(self, _id):\n \"\"\"Get a job's config\"\"\"\n if not self.superuser_request:\n self.abort(403, 'Request requires superuser')\n\n c = Job.get(_id).config\n if c is None:\n c = {}\n\n self.response.headers['Content-Type'] = 'application/octet-stream'\n self.response.headers['Content-Disposition'] = 'attachment; filename=\"config.json\"'\n\n # Serve config as formatted json file\n encoded = json.dumps({\"config\": c}, sort_keys=True, indent=4, separators=(',', ': ')) + '\\n'\n self.response.app_iter = StringIO.StringIO(encoded)\n\n def put(self, _id):\n \"\"\"\n Update a job. Updates timestamp.\n Enforces a valid state machine transition, if any.\n Rejects any change to a job that is not currently in 'pending' or 'running' state.\n \"\"\"\n if not self.superuser_request:\n self.abort(403, 'Request requires superuser')\n\n j = Job.get(_id)\n Queue.mutate(j, self.request.json)\n\n def retry(self, _id):\n \"\"\" Retry a job.\n\n The job must have a state of 'failed', and must not have already been retried.\n Returns the id of the new, generated job.\n \"\"\"\n j = Job.get(_id)\n\n # Permission check\n if not self.superuser_request:\n for x in j.inputs:\n j.inputs[x].check_access(self.uid, 'ro')\n j.destination.check_access(self.uid, 'rw')\n\n new_id = Queue.retry(j, force=True)\n return { \"_id\": new_id }\n", "sub_path": "api/jobs/handlers.py", "file_name": "handlers.py", "file_ext": "py", "file_size_in_byte": 8492, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "gears.get_gears", "line_number": 33, "usage_type": "call"}, {"api_name": "gears.get_gear_by_name", "line_number": 44, "usage_type": "call"}, {"api_name": "gears.get_gear_by_name", "line_number": 51, "usage_type": "call"}, {"api_name": "gears.get_invocation_schema", "line_number": 52, "usage_type": "call"}, {"api_name": "dao.containerutil.ContainerReference", "line_number": 59, "usage_type": "call"}, {"api_name": "gears.get_gear_by_name", "line_number": 63, "usage_type": "call"}, {"api_name": "gears.suggest_container", "line_number": 64, "usage_type": "call"}, {"api_name": "gears.upsert_gear", "line_number": 78, "usage_type": "call"}, {"api_name": "jsonschema.ValidationError", "line_number": 79, "usage_type": "name"}, {"api_name": "gears.remove_gear", "line_number": 99, "usage_type": "call"}, {"api_name": "dao.containerutil.create_filereference_from_dictionary", "line_number": 141, "usage_type": "call"}, {"api_name": "dao.containerutil.create_containerreference_from_dictionary", "line_number": 153, "usage_type": "call"}, {"api_name": "dao.containerutil.create_containerreference_from_filereference", "line_number": 156, "usage_type": "call"}, {"api_name": "gears.get_gear_by_name", "line_number": 166, "usage_type": "call"}, {"api_name": "gear_tools.derive_invocation_schema", "line_number": 169, "usage_type": "call"}, {"api_name": "gear_tools.isolate_config_invocation", "line_number": 170, "usage_type": "call"}, {"api_name": "jsonschema.Draft4Validator", "line_number": 171, "usage_type": "call"}, {"api_name": "jsonschema.ValidationError", "line_number": 175, "usage_type": "name"}, {"api_name": "jobs.Job", "line_number": 187, "usage_type": "call"}, {"api_name": "queue.Queue.get_statistics", "line_number": 196, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 196, "usage_type": "name"}, {"api_name": "queue.Queue.start_job", "line_number": 206, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 206, "usage_type": "name"}, {"api_name": "queue.Queue.scan_for_orphans", "line_number": 217, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 217, "usage_type": "name"}, {"api_name": "jobs.Job.get", "line_number": 228, "usage_type": "call"}, {"api_name": "jobs.Job", "line_number": 228, "usage_type": "name"}, {"api_name": "jobs.Job.get", "line_number": 235, "usage_type": "call"}, {"api_name": "jobs.Job", "line_number": 235, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 243, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 244, "usage_type": "call"}, {"api_name": "jobs.Job.get", "line_number": 255, "usage_type": "call"}, {"api_name": "jobs.Job", "line_number": 255, "usage_type": "name"}, {"api_name": "queue.Queue.mutate", "line_number": 256, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 256, "usage_type": "name"}, {"api_name": "jobs.Job.get", "line_number": 264, "usage_type": "call"}, {"api_name": "jobs.Job", "line_number": 264, "usage_type": "name"}, {"api_name": "queue.Queue.retry", "line_number": 272, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 272, "usage_type": "name"}]} +{"seq_id": "120935029", "text": "from django.urls import path\n\nfrom .views import IndexView, CreateProdutoView, UpdateProdutoView, DeleteProdutoView\n\n\nurlpatterns = [\n path('', IndexView.as_view(), name='index'),\n path('add/', CreateProdutoView.as_view(), name='add_produto'),\n path('update//', UpdateProdutoView.as_view(), name='upd_produto'),\n path('delete//', DeleteProdutoView.as_view(), name='del_produto'),\n]", "sub_path": "seguranca/core/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 409, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "views.IndexView.as_view", "line_number": 7, "usage_type": "call"}, {"api_name": "views.IndexView", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "views.CreateProdutoView.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "views.CreateProdutoView", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "views.UpdateProdutoView.as_view", "line_number": 9, "usage_type": "call"}, {"api_name": "views.UpdateProdutoView", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "views.DeleteProdutoView.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "views.DeleteProdutoView", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "18388937", "text": "import copy\nimport json\n\nimport pytest\nfrom web3 import Web3\nfrom web3.providers.eth_tester import EthereumTesterProvider\n\nfrom ethpm import V2_PACKAGES_DIR\nfrom ethpm.utils.chains import create_block_uri, get_chain_id\n\nPACKAGE_NAMES = [\n \"escrow\",\n \"owned\",\n \"piper-coin\",\n \"safe-math-lib\",\n \"standard-token\",\n \"transferable\",\n \"wallet-with-send\",\n \"wallet\",\n]\n\n\n@pytest.fixture()\ndef w3():\n w3 = Web3(EthereumTesterProvider())\n w3.eth.defaultAccount = w3.eth.accounts[0]\n return w3\n\n\n@pytest.fixture()\ndef all_manifests():\n manifests = []\n for pkg in PACKAGE_NAMES:\n with open(str(V2_PACKAGES_DIR / pkg / \"1.0.0.json\")) as file_obj:\n manifest = json.load(file_obj)\n manifests.append(manifest)\n return manifests\n\n@pytest.fixture()\ndef get_manifest():\n def _get_manifest(name):\n with open(str(V2_PACKAGES_DIR / name / \"1.0.0.json\")) as file_obj:\n return json.load(file_obj)\n return _get_manifest\n\n\n# safe-math-lib currently used as default manifest for testing\n# should be extended to all_manifest_types asap\n@pytest.fixture\ndef safe_math_manifest(get_manifest):\n return get_manifest('safe-math-lib')\n\n\n# standalone = no `build_dependencies` which aren't fully supported yet\n@pytest.fixture\ndef all_standalone_manifests(all_manifests):\n standalone_manifests = [\n mnfst for mnfst in all_manifests if \"build_dependencies\" not in mnfst\n ]\n return standalone_manifests\n\n\n@pytest.fixture()\ndef invalid_manifest(safe_math_manifest):\n safe_math_manifest[\"manifest_version\"] = 1\n return safe_math_manifest\n\n\n@pytest.fixture\ndef manifest_with_no_deployments(safe_math_manifest):\n manifest = copy.deepcopy(safe_math_manifest)\n manifest.pop(\"deployments\")\n return manifest\n\n\n@pytest.fixture\ndef manifest_with_empty_deployments(tmpdir, safe_math_manifest):\n manifest = copy.deepcopy(safe_math_manifest)\n manifest[\"deployments\"] = {}\n return manifest\n\n\n@pytest.fixture\ndef manifest_with_matching_deployment(w3, tmpdir, safe_math_manifest):\n w3.testing.mine(5)\n chain_id = get_chain_id(w3)\n block = w3.eth.getBlock(\"earliest\")\n block_uri = create_block_uri(w3.toHex(chain_id), w3.toHex(block.hash))\n manifest = copy.deepcopy(safe_math_manifest)\n manifest[\"deployments\"] = {}\n manifest[\"deployments\"][block_uri] = {\n \"SafeMathLib\": {\n \"contract_type\": \"SafeMathLib\",\n \"address\": \"0x8d2c532d7d211816a2807a411f947b211569b68c\",\n \"transaction\": \"0xaceef751507a79c2dee6aa0e9d8f759aa24aab081f6dcf6835d792770541cb2b\",\n \"block\": \"0x420cb2b2bd634ef42f9082e1ee87a8d4aeeaf506ea5cdeddaa8ff7cbf911810c\",\n }\n }\n return manifest\n\n\n@pytest.fixture\ndef manifest_with_no_matching_deployments(w3, tmpdir, safe_math_manifest):\n w3.testing.mine(5)\n incorrect_chain_id = b\"\\x00\" * 31 + b\"\\x01\"\n block = w3.eth.getBlock(\"earliest\")\n block_uri = create_block_uri(w3.toHex(incorrect_chain_id), w3.toHex(block.hash))\n manifest = copy.deepcopy(safe_math_manifest)\n manifest[\"deployments\"][block_uri] = {\n \"SafeMathLib\": {\n \"contract_type\": \"SafeMathLib\",\n \"address\": \"0x8d2c532d7d211816a2807a411f947b211569b68c\",\n \"transaction\": \"0xaceef751507a79c2dee6aa0e9d8f759aa24aab081f6dcf6835d792770541cb2b\",\n \"block\": \"0x420cb2b2bd634ef42f9082e1ee87a8d4aeeaf506ea5cdeddaa8ff7cbf911810c\",\n }\n }\n return manifest\n\n\n@pytest.fixture\ndef manifest_with_multiple_matches(w3, tmpdir, safe_math_manifest):\n w3.testing.mine(5)\n chain_id = get_chain_id(w3)\n block = w3.eth.getBlock(\"latest\")\n block_uri = create_block_uri(w3.toHex(chain_id), w3.toHex(block.hash))\n w3.testing.mine(1)\n second_block = w3.eth.getBlock(\"latest\")\n second_block_uri = create_block_uri(w3.toHex(chain_id), w3.toHex(second_block.hash))\n manifest = copy.deepcopy(safe_math_manifest)\n manifest[\"deployments\"][block_uri] = {\n \"SafeMathLib\": {\n \"contract_type\": \"SafeMathLib\",\n \"address\": \"0x8d2c532d7d211816a2807a411f947b211569b68c\",\n \"transaction\": \"0xaceef751507a79c2dee6aa0e9d8f759aa24aab081f6dcf6835d792770541cb2b\",\n \"block\": \"0x420cb2b2bd634ef42f9082e1ee87a8d4aeeaf506ea5cdeddaa8ff7cbf911810c\",\n }\n }\n manifest[\"deployments\"][second_block_uri] = {\n \"SafeMathLib\": {\n \"contract_type\": \"SafeMathLib\",\n \"address\": \"0x8d2c532d7d211816a2807a411f947b211569b68c\",\n \"transaction\": \"0xaceef751507a79c2dee6aa0e9d8f759aa24aab081f6dcf6835d792770541cb2b\",\n \"block\": \"0x420cb2b2bd634ef42f9082e1ee87a8d4aeeaf506ea5cdeddaa8ff7cbf911810c\",\n }\n }\n return manifest\n\n\n@pytest.fixture\ndef manifest_with_conflicting_deployments(tmpdir, safe_math_manifest):\n manifest = copy.deepcopy(safe_math_manifest)\n manifest[\"deployments\"][\n \"blockchain://41941023680923e0fe4d74a34bdac8141f2540e3ae90623718e47d66d1ca4a2d/block/1e96de11320c83cca02e8b9caf3e489497e8e432befe5379f2f08599f8aecede\"\n ] = {\n \"WrongNameLib\": {\n \"contract_type\": \"WrongNameLib\",\n \"address\": \"0x8d2c532d7d211816a2807a411f947b211569b68c\",\n \"transaction\": \"0xaceef751507a79c2dee6aa0e9d8f759aa24aab081f6dcf6835d792770541cb2b\",\n \"block\": \"0x420cb2b2bd634ef42f9082e1ee87a8d4aeeaf506ea5cdeddaa8ff7cbf911810c\",\n }\n }\n return manifest\n", "sub_path": "tests/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 5422, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "web3.Web3", "line_number": 25, "usage_type": "call"}, {"api_name": "web3.providers.eth_tester.EthereumTesterProvider", "line_number": 25, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 23, "usage_type": "call"}, {"api_name": "ethpm.V2_PACKAGES_DIR", "line_number": 34, "usage_type": "name"}, {"api_name": "json.load", "line_number": 35, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 30, "usage_type": "call"}, {"api_name": "ethpm.V2_PACKAGES_DIR", "line_number": 42, "usage_type": "name"}, {"api_name": "json.load", "line_number": 43, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 39, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 63, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 71, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 69, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 78, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 76, "usage_type": "attribute"}, {"api_name": "ethpm.utils.chains.get_chain_id", "line_number": 86, "usage_type": "call"}, {"api_name": "ethpm.utils.chains.create_block_uri", "line_number": 88, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 89, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 83, "usage_type": "attribute"}, {"api_name": "ethpm.utils.chains.create_block_uri", "line_number": 107, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 108, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 102, "usage_type": "attribute"}, {"api_name": "ethpm.utils.chains.get_chain_id", "line_number": 123, "usage_type": "call"}, {"api_name": "ethpm.utils.chains.create_block_uri", "line_number": 125, "usage_type": "call"}, {"api_name": "ethpm.utils.chains.create_block_uri", "line_number": 128, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 129, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 120, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 151, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 149, "usage_type": "attribute"}]} +{"seq_id": "150749861", "text": "from django.http import HttpResponse, HttpResponsePermanentRedirect\nfrom django.utils import html\nfrom django.shortcuts import render_to_response\nfrom topology.models import Entity, Entityrelationship\nfrom glue.models import gluesite\nfrom core.utils import countCPUsInSite, countStoragesInSite\nimport random\nfrom geo import countryInfo\n\nknown_types = ['GRID',\n 'EGEE_ROC',\n 'WLCG_TIER',\n 'Country']\n\npredicates = {'GRID': 'SiteGrid',\n 'EGEE_ROC': 'SiteEgeeRoc',\n 'WLCG_TIER': 'SiteWlcgTier',\n 'Country': 'SiteCountry'}\n\n# Stable view\ndef index(request):\n #return render_to_response('geo-index.html', {'geo_active': 1})\n return HttpResponsePermanentRedirect('/gstat/geo/openlayers')\n\n# Stable view\ndef openlayers(request):\n # Restore permalinks values or use default ones\n zoom = 2\n if 'zoom' in request.GET:\n zoom = request.GET['zoom'] \n\n lon = 5\n if 'lon' in request.GET:\n lon = request.GET['lon'] \n\n lat = 15\n if 'lat' in request.GET:\n lat = request.GET['lat'] \n\n layers = 'B000T'\n if 'layers' in request.GET:\n layers = request.GET['layers'] \n\n # Render\n return render_to_response('openlayers.html',\n {'zoom':zoom, 'lon':lon, 'lat':lat, 'layers':layers, 'geo_active': 1,\n 'filters_enabled': True})\n\n# Stable view\ndef fullscreen(request):\n # Restore permalinks values or use default ones\n zoom = 2\n if 'zoom' in request.GET:\n zoom = request.GET['zoom'] \n\n lon = 5\n if 'lon' in request.GET:\n lon = request.GET['lon'] \n\n lat = 15\n if 'lat' in request.GET:\n lat = request.GET['lat'] \n\n layers = 'B000T'\n if 'layers' in request.GET:\n layers = request.GET['layers']\n \n kml = '/gstat/geo/kml'\n if 'kml' in request.GET:\n kml = request.GET['kml']\n\n overlay = ''\n if 'overlay' in request.GET:\n overlay = request.GET['overlay']\n\n # Render\n return render_to_response('fullscreen.html',\n {'zoom':zoom, 'lon':lon, 'lat':lat, 'layers':layers, 'kml':kml,\n 'overlay':overlay})\n\n\n# Stable view\ndef gmap(request):\n return render_to_response('gmap.html', {'geo_active': 1})\n\n# Stable view\ndef kml(request, type='', value=''):\n sites = []\n if (type == ''):\n sites = gluesite.objects.all();\n if (type in known_types):\n # Get the entities for your type\n if (value == 'ALL'):\n entities = Entity.objects.filter(type = type)\n else:\n entities = Entity.objects.filter(uniqueid__iexact = value, \n type = type)\n # Find the related entities\n related_sites = []\n for entity in entities:\n more = [er.subject for er in Entityrelationship.objects.filter(\n predicate = predicates[type],\n object = entity)]\n related_sites.extend(more)\n \n # Get the equivalent GLUE site for each entity\n for related_site in related_sites:\n more = gluesite.objects.filter(uniqueid__iexact = related_site.uniqueid)\n sites.extend(more)\n \n sites_list = []\n for site in sites:\n sites_list.append([html.escape(site.name),\n html.escape(site.description),\n site.longitude,\n site.latitude])\n\n response = render_to_response('kml', {'sites': sites_list})\n response['Content-Type'] = 'application/vnd.google-earth.kml+xml'\n response['Content-Disposition'] = 'attachment; filename=gstat-map.kml'\n response['Content-Description'] = 'KML of The Grid sites'\n return response\n\n# Development view\ndef overlay(request, type=''):\n if (type == 'egee-europe'):\n popups = []\n countries = Entity.objects.filter(type = 'Country')\n egeeEntity = Entity.objects.filter(uniqueid = 'EGEE')[0]\n for country in countries:\n if (country.uniqueid in countryInfo.countriesInEgeeEurope):\n site_list = []\n for er in Entityrelationship.objects.filter(\n predicate = 'SiteCountry', object = country):\n if (len(Entityrelationship.objects.filter(\n predicate = 'SiteGrid', object = egeeEntity,\n subject = er.subject)) > 0):\n site_list.append(er.subject)\n if (len(site_list) > 0):\n logicalcpus = 0\n physicalcpus = 0\n totalsize = 0\n usedsize = 0\n for site in site_list:\n (logicalcpus_, physicalcpus_) = countCPUsInSite(site)\n (totalonlinesize_, usedonlinesize_, totalnearlinesize_, usednearlinesize_) = countStoragesInSite(site)\n logicalcpus += logicalcpus_\n physicalcpus += physicalcpus_\n totalsize += totalonlinesize_ + totalnearlinesize_\n usedsize += usedonlinesize_ + usednearlinesize_\n totalsize = int(totalsize / 1024) # TB\n usedsize = int(usedsize / 1024) # TB\n html = \"\"\n html += countryInfo.countriesInEgeeEurope[country.uniqueid][2]\n html += \"
s:\" + str(len(site_list))\n html += \"
c:\" + str(physicalcpus)\n html += \"
t:\" + str(totalsize)\n html += \"\"\n popups.append([country.uniqueid,\n countryInfo.countriesInEgeeEurope[country.uniqueid][0],\n countryInfo.countriesInEgeeEurope[country.uniqueid][1],\n html])\n\n response = render_to_response('overlay-egee-europe.js',\n {'popups': popups})\n\n return response", "sub_path": "gstat-web/tags/R_0_0_18/apps/geo/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 6109, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.http.HttpResponsePermanentRedirect", "line_number": 23, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 45, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 77, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 84, "usage_type": "call"}, {"api_name": "glue.models.gluesite.objects.all", "line_number": 90, "usage_type": "call"}, {"api_name": "glue.models.gluesite.objects", "line_number": 90, "usage_type": "attribute"}, {"api_name": "glue.models.gluesite", "line_number": 90, "usage_type": "name"}, {"api_name": "topology.models.Entity.objects.filter", "line_number": 94, "usage_type": "call"}, {"api_name": "topology.models.Entity.objects", "line_number": 94, "usage_type": "attribute"}, {"api_name": "topology.models.Entity", "line_number": 94, "usage_type": "name"}, {"api_name": "topology.models.Entity.objects.filter", "line_number": 96, "usage_type": "call"}, {"api_name": "topology.models.Entity.objects", "line_number": 96, "usage_type": "attribute"}, {"api_name": "topology.models.Entity", "line_number": 96, "usage_type": "name"}, {"api_name": "topology.models.Entityrelationship.objects.filter", "line_number": 101, "usage_type": "call"}, {"api_name": "topology.models.Entityrelationship.objects", "line_number": 101, "usage_type": "attribute"}, {"api_name": "topology.models.Entityrelationship", "line_number": 101, "usage_type": "name"}, {"api_name": "glue.models.gluesite.objects.filter", "line_number": 108, "usage_type": "call"}, {"api_name": "glue.models.gluesite.objects", "line_number": 108, "usage_type": "attribute"}, {"api_name": "glue.models.gluesite", "line_number": 108, "usage_type": "name"}, {"api_name": "django.utils.html.escape", "line_number": 113, "usage_type": "call"}, {"api_name": "django.utils.html", "line_number": 113, "usage_type": "name"}, {"api_name": "django.utils.html.escape", "line_number": 114, "usage_type": "call"}, {"api_name": "django.utils.html", "line_number": 114, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 118, "usage_type": "call"}, {"api_name": "topology.models.Entity.objects.filter", "line_number": 128, "usage_type": "call"}, {"api_name": "topology.models.Entity.objects", "line_number": 128, "usage_type": "attribute"}, {"api_name": "topology.models.Entity", "line_number": 128, "usage_type": "name"}, {"api_name": "topology.models.Entity.objects.filter", "line_number": 129, "usage_type": "call"}, {"api_name": "topology.models.Entity.objects", "line_number": 129, "usage_type": "attribute"}, {"api_name": "topology.models.Entity", "line_number": 129, "usage_type": "name"}, {"api_name": "geo.countryInfo.countriesInEgeeEurope", "line_number": 131, "usage_type": "attribute"}, {"api_name": "geo.countryInfo", "line_number": 131, "usage_type": "name"}, {"api_name": "topology.models.Entityrelationship.objects.filter", "line_number": 133, "usage_type": "call"}, {"api_name": "topology.models.Entityrelationship.objects", "line_number": 133, "usage_type": "attribute"}, {"api_name": "topology.models.Entityrelationship", "line_number": 133, "usage_type": "name"}, {"api_name": "topology.models.Entityrelationship.objects.filter", "line_number": 135, "usage_type": "call"}, {"api_name": "topology.models.Entityrelationship.objects", "line_number": 135, "usage_type": "attribute"}, {"api_name": "topology.models.Entityrelationship", "line_number": 135, "usage_type": "name"}, {"api_name": "core.utils.countCPUsInSite", "line_number": 145, "usage_type": "call"}, {"api_name": "core.utils.countStoragesInSite", "line_number": 146, "usage_type": "call"}, {"api_name": "django.utils.html", "line_number": 153, "usage_type": "name"}, {"api_name": "django.utils.html", "line_number": 154, "usage_type": "name"}, {"api_name": "geo.countryInfo.countriesInEgeeEurope", "line_number": 154, "usage_type": "attribute"}, {"api_name": "geo.countryInfo", "line_number": 154, "usage_type": "name"}, {"api_name": "django.utils.html", "line_number": 155, "usage_type": "name"}, {"api_name": "django.utils.html", "line_number": 156, "usage_type": "name"}, {"api_name": "django.utils.html", "line_number": 157, "usage_type": "name"}, {"api_name": "django.utils.html", "line_number": 158, "usage_type": "name"}, {"api_name": "geo.countryInfo.countriesInEgeeEurope", "line_number": 160, "usage_type": "attribute"}, {"api_name": "geo.countryInfo", "line_number": 160, "usage_type": "name"}, {"api_name": "geo.countryInfo.countriesInEgeeEurope", "line_number": 161, "usage_type": "attribute"}, {"api_name": "geo.countryInfo", "line_number": 161, "usage_type": "name"}, {"api_name": "django.utils.html", "line_number": 162, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 164, "usage_type": "call"}]} +{"seq_id": "144544166", "text": "import hashlib, json, sys\nunicode = lambda a,b: a\ndef hashMe(msg=\"\"):\n # For convenience, this is a helper function that wraps our hashing algorithm\n if type(msg)!=str:\n msg = json.dumps(msg,sort_keys=True) # If we don't sort keys, we can't guarantee repeatability!\n \n if sys.version_info.major == 2:\n return unicode(hashlib.sha256(msg).hexdigest(),'utf-8')\n else:\n return hashlib.sha256(str(msg).encode('utf-8')).hexdigest()\nimport random\nrandom.seed(0)\n\ndef makeTransaction(from_=None,to=None,amount=1):\n # This will create valid transactions in the range of (1,maxValue)\n # By construction, this will always return transactions that respect the conservation of tokens.\n # However, note that we have not done anything to check whether these overdraft an account\n d = {}\n d[from_] = amount * -1\n d[to] = d[from_] * -1\n return d\ndef updateState(txn, state):\n # Inputs: txn, state: dictionaries keyed with account names, holding numeric values for transfer amount (txn) or account balance (state)\n # Returns: Updated state, with additional users added to state if necessary\n # NOTE: This does not not validate the transaction- just updates the state!\n \n # If the transaction is valid, then update the state\n state = state.copy() # As dictionaries are mutable, let's avoid any confusion by creating a working copy of the data.\n for key in txn:\n if key in state.keys():\n state[key] += txn[key]\n else:\n state[key] = txn[key]\n return state\n\n\ndef isValidTxn(txn,state):\n # Assume that the transaction is a dictionary keyed by account names\n\n # Check that the sum of the deposits and withdrawals is 0\n if sum(txn.values()) is not 0:\n return False\n \n # Check that the transaction does not cause an overdraft\n for key in txn.keys():\n if key in state.keys(): \n acctBalance = state[key]\n else:\n acctBalance = 0\n if (acctBalance + txn[key]) < 0:\n return False\n \n return True\n\nstate = {'admin': 100000} # Define the initial state\ngenesisBlockTxns = [state]\ngenesisBlockContents = {u'blockNumber':0,u'parentHash':None,u'txnCount':1,u'txns':genesisBlockTxns}\ngenesisHash = hashMe( genesisBlockContents )\ngenesisBlock = {u'hash':genesisHash,u'contents':genesisBlockContents}\ngenesisBlockStr = json.dumps(genesisBlock, sort_keys=True)\n\n\n\ndef makeBlock(txns,chain):\n parentBlock = chain[-1]\n parentHash = parentBlock[u'hash']\n blockNumber = parentBlock[u'contents'][u'blockNumber'] + 1\n blockContents = {u'blockNumber':blockNumber,u'parentHash':parentHash,u'txnCount':len(txns),'txns':txns}\n blockHash = hashMe( blockContents )\n block = {u'hash':blockHash,u'contents':blockContents}\n \n return block\n\n\n\ndef checkBlockHash(block):\n # Raise an exception if the hash does not match the block contents\n expectedHash = hashMe( block['contents'] )\n if block['hash']!=expectedHash:\n raise Exception('Hash does not match contents of block %s'%\n block['contents']['blockNumber'])\n return\n\ndef checkBlockValidity(block,parent,state): \n # We want to check the following conditions:\n # - Each of the transactions are valid updates to the system state\n # - Block hash is valid for the block contents\n # - Block number increments the parent block number by 1\n # - Accurately references the parent block's hash\n parentNumber = parent['contents']['blockNumber']\n parentHash = parent['hash']\n blockNumber = block['contents']['blockNumber']\n \n # Check transaction validity; throw an error if an invalid transaction was found.\n for txn in block['contents']['txns']:\n if isValidTxn(txn,state):\n state = updateState(txn,state)\n else:\n raise Exception('Invalid transaction in block %s: %s'%(blockNumber,txn))\n\n checkBlockHash(block) # Check hash integrity; raises error if inaccurate\n\n if blockNumber!=(parentNumber+1):\n raise Exception('Hash does not match contents of block %s'%blockNumber)\n\n if block['contents']['parentHash'] != parentHash:\n raise Exception('Parent hash not accurate at block %s'%blockNumber)\n \n return state\n\n\ndef checkChain(chain):\n # Work through the chain from the genesis block (which gets special treatment), \n # checking that all transactions are internally valid,\n # that the transactions do not cause an overdraft,\n # and that the blocks are linked by their hashes.\n # This returns the state as a dictionary of accounts and balances,\n # or returns False if an error was detected\n\n \n ## Data input processing: Make sure that our chain is a list of dicts\n if type(chain)==str:\n try:\n chain = json.loads(chain)\n assert( type(chain)==list)\n except: # This is a catch-all, admittedly crude\n return False\n elif type(chain)!=list:\n return False\n \n state = {}\n ## Prime the pump by checking the genesis block\n # We want to check the following conditions:\n # - Each of the transactions are valid updates to the system state\n # - Block hash is valid for the block contents\n\n for txn in chain[0]['contents']['txns']:\n state = updateState(txn,state)\n checkBlockHash(chain[0])\n parent = chain[0]\n \n ## Checking subsequent blocks: These additionally need to check\n # - the reference to the parent block's hash\n # - the validity of the block number\n for block in chain[1:]:\n state = checkBlockValidity(block,parent,state)\n parent = block\n \n return state\n\n", "sub_path": "src/pyblk/pyblk.py", "file_name": "pyblk.py", "file_ext": "py", "file_size_in_byte": 5681, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "json.dumps", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 8, "usage_type": "attribute"}, {"api_name": "hashlib.sha256", "line_number": 9, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 11, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 13, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 61, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 125, "usage_type": "call"}]} +{"seq_id": "324437511", "text": "#########################################################################\n# Copyright 2011 Cloud Sidekick\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#########################################################################\n\nfrom catotaskengine import classes\ntry:\n import boto\n from boto.s3.key import Key\n from boto.s3.acl import CannedACLStrings\n import boto.exception\nexcept ImportError as e:\n msg = \"The AWS S3 require the Boto python library.\"\n raise Exception(msg)\nexcept Exception as e:\n raise Exception(e)\n\ndef create_bucket(c, name):\n\n b = c.create_bucket(name)\n return b\n\n\ndef copy_file(c, from_file, from_bucket, to_file=None, to_bucket=None, storage=\"STANDARD\", canned_acl=None):\n\n create = True\n if canned_acl and canned_acl not in CannedACLStrings:\n raise Exception(\"%s is not one of the canned S3 ACL strings\" % (canned_acl))\n if storage not in [\"STANDARD\", \"REDUCED_REDUNDANCY\"]:\n raise Exception(\"%s is not a valid S3 storage class\" % (storage))\n\n if not to_file:\n # use the same name as the from_file\n to_file = from_file\n if not to_bucket:\n # copy to the same bucket\n to_bucket = from_bucket\n\n try:\n b = c.get_bucket(to_bucket)\n except Exception as e:\n if e.error_code == \"NoSuchBucket\" and create is True:\n b = create_bucket(c, to_bucket)\n elif e.error_code == \"AccessDenied\":\n raise Exception(\"Access Denied attempting to connect to S3, check access key and secret key credentials\")\n else:\n raise Exception(e)\n try:\n k = b.copy_key(to_file, from_bucket, from_file, storage_class=storage)\n except Exception as e:\n if e.error_code == \"NoSuchBucket\":\n raise Exception(\"From bucket %s does not exist\" % (from_bucket))\n elif e.error_code == \"NoSuchKey\":\n raise Exception(\"From file named %s does not exist in bucket %s\" % (from_file, from_bucket))\n else:\n raise Exception(e)\n if canned_acl:\n if canned_acl not in CannedACLStrings:\n raise Exception(\"%s is not one of the canned ACL strings\" % (canned_acl))\n k.set_canned_acl(canned_acl)\n\ndef aws_s3_connect(TE):\n\n cloud_name = \"us-east-1\"\n try:\n cloud = TE.cloud_conns[cloud_name]\n except KeyError as ex:\n cloud = classes.Cloud(cloud_name)\n TE.cloud_conns[cloud_name] = cloud\n\n if not cloud.conn:\n cloud.conn = boto.connect_s3(TE.cloud_login_id, TE.cloud_login_password)\n\n return cloud\n\n\ndef s3_copy_file(te, step):\n\n from_file, from_bucket, to_file, to_bucket, storage, acl = te.get_command_params(step.command, \"from_file\", \"from_bucket\",\n \"to_file\", \"to_bucket\", \"storage\", \"acl\")[:]\n from_file = te.replace_variables(from_file)\n from_bucket = te.replace_variables(from_bucket)\n to_file = te.replace_variables(to_file)\n to_bucket = te.replace_variables(to_bucket)\n cloud = aws_s3_connect(te)\n\n if not len(from_file): \n raise Exception(\"S3 Copy File error: From File cannot be blank\")\n if not len(from_bucket): \n raise Exception(\"S3 Copy File error: From Bucket cannot be blank\")\n if not len(acl):\n acl = None\n if not len(to_file): \n to_file = from_file\n if not len(to_bucket): \n to_bucket = from_bucket\n if not len(storage): \n storage = \"STANDARD\"\n\n copy_file(cloud.conn, from_file, from_bucket, to_file, to_bucket, storage, acl)\n\n\n msg = \"AWS S3 file %s copied from bucket %s to bucket %s to filename %s\" % (from_file, from_bucket, to_bucket, to_file)\n te.insert_audit(step.function_name, msg, \"\")\n", "sub_path": "extensions/aws_s3/aws_s3.py", "file_name": "aws_s3.py", "file_ext": "py", "file_size_in_byte": 4152, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "boto.s3.acl.CannedACLStrings", "line_number": 38, "usage_type": "name"}, {"api_name": "boto.s3.acl.CannedACLStrings", "line_number": 69, "usage_type": "name"}, {"api_name": "catotaskengine.classes.Cloud", "line_number": 79, "usage_type": "call"}, {"api_name": "catotaskengine.classes", "line_number": 79, "usage_type": "name"}, {"api_name": "boto.connect_s3", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "95116445", "text": "from . import util\n\nfrom .interface import BaseClient\nfrom .register import get_type\n\nfrom .commands import HelpCommand\nfrom .commands import PlayCommand\nfrom .commands import WrapperCommand\n\n\nclass MRClient(BaseClient):\n \"\"\"\n This class is one of the only interfaces\n between the FW and the server.\n \"\"\"\n\n fw_cmds = {\n 'help' : HelpCommand,\n 'play' : PlayCommand,\n }\n\n\n def __init__(self, handler, name):\n BaseClient.__init__(self, handler, name)\n self.player = None\n\n def available_cmds(self):\n \"\"\"\n Looks for commands in (in this order):\n - client\n - player\n - room\n - room contents\n \"\"\"\n\n def add_cmds(cmds, obj):\n for k, v in obj.cmds.items():\n cmds[k] = WrapperCommand(getattr(obj, v, None), self.player)\n\n def add_power_cmds(cmds, power):\n for k, v in power.fw_cmds.items():\n cmd = getattr(power.__class__, v).__get__(self.player,\n get_type('player'))\n cmds[k] = WrapperCommand(lambda _, x: cmd(x))\n\n cmds = self.cmds.copy()\n if self.player:\n add_cmds(cmds, self.player)\n for p in self.player.powers:\n add_power_cmds(cmds, p)\n if self.player.room:\n add_cmds(cmds, self.player.room)\n for o in self.player.room.contents:\n if isinstance(o, get_type('thing')):\n add_cmds(cmds, o)\n return cmds\n\n\n def handle_input(self, data):\n \"\"\"\n Basic handler for commands\n \"\"\"\n cmds = self.available_cmds()\n words = data.split()\n match = [x for x in list(cmds.keys()) if util.match_name(words[0], x)]\n if len(match) != 1:\n self.send(\"Huh?\")\n return\n cmd = cmds[match[0]]\n cmd.call(self, words[0], ' '.join(words[1:]))\n\n def on_disconnect(self):\n if self.player is not None:\n self.player.client = None\n\n", "sub_path": "fw/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 2093, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "interface.BaseClient", "line_number": 11, "usage_type": "name"}, {"api_name": "commands.HelpCommand", "line_number": 18, "usage_type": "name"}, {"api_name": "commands.PlayCommand", "line_number": 19, "usage_type": "name"}, {"api_name": "interface.BaseClient.__init__", "line_number": 24, "usage_type": "call"}, {"api_name": "interface.BaseClient", "line_number": 24, "usage_type": "name"}, {"api_name": "commands.WrapperCommand", "line_number": 38, "usage_type": "call"}, {"api_name": "register.get_type", "line_number": 43, "usage_type": "call"}, {"api_name": "commands.WrapperCommand", "line_number": 44, "usage_type": "call"}, {"api_name": "register.get_type", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "606553551", "text": "from flask import request, redirect, url_for, render_template, flash, session\nfrom natsugash import app, getTwitter\nimport natsugash.config as config\nimport os, pyrebase, json, pprint\nimport collections as cl\n\nfirebase = pyrebase.initialize_app(config.FIREBASE_CONFIG)\ndb = firebase.database()\napp.secret_key = config.SECRET_KEY\n\n# Root\n@app.route('/')\ndef show_index():\n if os.path.isfile('assorted_tweets'):\n os.remove('assorted_tweets.json')\n oauth_url = getTwitter.oath_twitter()\n if oauth_url:\n return render_template('oauth.html', title=\"ツイートパック\", oauth_url=oauth_url)\n else:\n return render_template('errorpage.html', title=\"エラーページ\")\n\n\n@app.route('/paci')\ndef show_paci():\n access_token = getTwitter.get_access_token()\n if not session.get('access_token'):\n session['access_token'] = access_token\n if session.get('access_token'):\n getTweets = getTwitter.get_tweets(session.get('access_token'))\n if getTweets:\n tweets = getTwitter.assort_tweets(getTweets)\n fw = open('assorted_tweets.json','w')\n json.dump(tweets,fw,indent=2)\n return render_template('mainpage.html', tweets=tweets, title=\"ついーとぱっく\")\n else:\n return render_template('errorpage.html')\n else:\n return render_template('errorpage.html')\n\n# select\n@app.route('/selectTweets', methods=[\"POST\"])\ndef show_select_tweets():\n tweets = cl.OrderedDict()\n delTweets = cl.OrderedDict()\n selectTweetsList = request.form.getlist('select_tweets')\n\n with open('assorted_tweets.json') as f:\n tweets = json.load(f)\n\n for k, v in tweets.items():\n if k in selectTweetsList:\n delTweets[k] = v\n session['delTweets'] = delTweets\n return render_template('selectTweets.html', delTweets=delTweets)\n\n# delpac\n@app.route('/delpac')\ndef show_del_tweets():\n delTweets = session.get('delTweets')\n getTwitter.del_tweets(delTweets, session['access_token'])\n\n db.child(\"tweets\").push(delTweets)\n session.clear()\n return render_template('delpac.html')\n\n\n@app.context_processor\ndef override_url_for():\n return dict(url_for=dated_url_for)\n\ndef dated_url_for(endpoint, **values):\n if endpoint == 'static':\n filename = values.get('filename', None)\n if filename:\n file_path = os.path.join(app.root_path,\n endpoint, filename)\n values['q'] = int(os.stat(file_path).st_mtime)\n return url_for(endpoint, **values)\n", "sub_path": "natsugash/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2552, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pyrebase.initialize_app", "line_number": 7, "usage_type": "call"}, {"api_name": "natsugash.config.FIREBASE_CONFIG", "line_number": 7, "usage_type": "attribute"}, {"api_name": "natsugash.config", "line_number": 7, "usage_type": "name"}, {"api_name": "natsugash.app.secret_key", "line_number": 9, "usage_type": "attribute"}, {"api_name": "natsugash.app", "line_number": 9, "usage_type": "name"}, {"api_name": "natsugash.config.SECRET_KEY", "line_number": 9, "usage_type": "attribute"}, {"api_name": "natsugash.config", "line_number": 9, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 15, "usage_type": "call"}, {"api_name": "natsugash.getTwitter.oath_twitter", "line_number": 16, "usage_type": "call"}, {"api_name": "natsugash.getTwitter", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 20, "usage_type": "call"}, {"api_name": "natsugash.app.route", "line_number": 12, "usage_type": "call"}, {"api_name": "natsugash.app", "line_number": 12, "usage_type": "name"}, {"api_name": "natsugash.getTwitter.get_access_token", "line_number": 25, "usage_type": "call"}, {"api_name": "natsugash.getTwitter", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 28, "usage_type": "name"}, {"api_name": "natsugash.getTwitter.get_tweets", "line_number": 29, "usage_type": "call"}, {"api_name": "natsugash.getTwitter", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 29, "usage_type": "name"}, {"api_name": "natsugash.getTwitter.assort_tweets", "line_number": 31, "usage_type": "call"}, {"api_name": "natsugash.getTwitter", "line_number": 31, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 38, "usage_type": "call"}, {"api_name": "natsugash.app.route", "line_number": 23, "usage_type": "call"}, {"api_name": "natsugash.app", "line_number": 23, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 43, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.request.form.getlist", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 45, "usage_type": "name"}, {"api_name": "json.load", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 54, "usage_type": "call"}, {"api_name": "natsugash.app.route", "line_number": 41, "usage_type": "call"}, {"api_name": "natsugash.app", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 59, "usage_type": "name"}, {"api_name": "natsugash.getTwitter.del_tweets", "line_number": 60, "usage_type": "call"}, {"api_name": "natsugash.getTwitter", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.session.clear", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 63, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 64, "usage_type": "call"}, {"api_name": "natsugash.app.route", "line_number": 57, "usage_type": "call"}, {"api_name": "natsugash.app", "line_number": 57, "usage_type": "name"}, {"api_name": "natsugash.app.context_processor", "line_number": 67, "usage_type": "attribute"}, {"api_name": "natsugash.app", "line_number": 67, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "natsugash.app.root_path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "natsugash.app", "line_number": 75, "usage_type": "name"}, {"api_name": "os.stat", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "447963958", "text": "from django.contrib import admin\nfrom emtools.emauth.models import Profile\n\nclass ProfileAdmin(admin.ModelAdmin):\n fieldsets = (\n (\"MyBB Information\", {'fields': ['mybb_uid', 'mybb_username']}),\n (\"API Overrides\", {'fields': ['usertitle']}),\n (\"API Information\", {'fields': ['name', 'characterid',\n 'corp', 'corpid',\n 'alliance', 'allianceid']}),\n (\"Maintenance\", {'fields': ['last_check', 'active']})\n )\n readonly_fields = ['mybb_uid', 'mybb_username']\n model = Profile\n max_num = 1\n can_delete = False\n\nadmin.site.register(Profile, ProfileAdmin)\n", "sub_path": "lib/emtools/emauth/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 672, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "django.contrib.admin.ModelAdmin", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 4, "usage_type": "name"}, {"api_name": "emtools.emauth.models.Profile", "line_number": 14, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 18, "usage_type": "call"}, {"api_name": "emtools.emauth.models.Profile", "line_number": 18, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 18, "usage_type": "name"}]} +{"seq_id": "281979217", "text": "from sympy import simplify, srepr, Add, Mul, Pow, Rational, pi, sqrt, Symbol\nfrom latex2sympy.latex2sympy import process_sympy\nimport sys\nimport os\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\n\nx = Symbol('x', real=True)\ny = Symbol('y', real=True)\n\n# shorthand definitions\n\n\ndef _Add(a, b):\n return Add(a, b, evaluate=False)\n\n\ndef _Mul(a, b):\n return Mul(a, b, evaluate=False)\n\n\ndef _Pow(a, b):\n return Pow(a, b, evaluate=False)\n\n\ndef get_simple_examples(func):\n '''\n Returns an array of tuples, containing the string `input`, sympy `output` using the provided sympy `func`, and `symbolically` boolean\n for calling `compare`.\n '''\n return [\n (\"1.1\", func(1.1), False),\n (\"6.9\", func(6.9), False),\n (\"3.5\", func(3.5), False),\n (\"8\", func(8), False),\n (\"0\", func(0), False),\n (\"290348E32\", func(Rational('290348E32')), False),\n (\"1237.293894239480234\", func(Rational('1237.293894239480234')), False),\n (\"8623.4592104E-2\", func(Rational('8623.4592104E-2')), False),\n (\"\\\\pi \", func(pi), False),\n (\"\\\\sqrt{100}\", func(sqrt(100)), False),\n (\"12,123.4\", func(Rational('12123.4')), False),\n (\"-9.4\", func(-9.4), False),\n (\"-35.9825\", func(-35.9825), False),\n (\"-\\\\sqrt{5}\", func(-sqrt(5)), False),\n (\"-324E-3\", func(Rational('-324E-3')), False),\n (\"-0.23\", func(-0.23), False),\n (\"\\\\frac{1}{2}\", func(Rational('1/2')), False),\n (\"\\\\frac{6}{2}\", func(Rational('6/2')), False),\n (\"\\\\frac{9}{5}\", func(Rational('9/5')), False),\n (\"\\\\frac{-42}{6}\", func(Rational('-42/6')), False),\n (\"-\\\\frac{325}{3}\", func(Rational('-325/3')), False),\n (\"\\\\frac{\\\\pi }{2}\", func(pi / 2), False),\n (\"(1+6)/3\", func(Rational(1 + 6, 3)), False),\n (\"1+6/3\", func(1 + Rational('6/3')), False),\n (\"7*4/5\", func(7 * 4 / 5), False),\n (\"15-2.3\", func(15 - Rational('2.3')), False),\n (\"x\", func(x), True),\n (\"x + y\", func(x + y), True),\n (\"\\\\frac{9x}{4}\", func(9 * x / 4), True),\n (\"y\\\\pi\", func(y * pi), True),\n (\"2y-y-y\", func(2 * y - y - y), True)\n ]\n\n\ndef compare(actual, expected, symbolically=False):\n if symbolically:\n assert simplify(actual - expected) == 0\n else:\n actual_exp_tree = srepr(actual)\n expected_exp_tree = srepr(expected)\n try:\n assert actual_exp_tree == expected_exp_tree\n except Exception:\n if isinstance(actual, int) or isinstance(actual, float) or actual.is_number and isinstance(expected, int) or isinstance(expected, float) or expected.is_number:\n assert actual == expected or actual - expected == 0 or simplify(actual - expected) == 0\n else:\n print('expected_exp_tree = ', expected_exp_tree)\n print('actual exp tree = ', actual_exp_tree)\n raise\n\n\ndef assert_equal(latex, expr, variable_values={}, symbolically=False):\n parsed = process_sympy(latex, variable_values)\n compare(parsed, expr, symbolically)\n", "sub_path": "tests/context.py", "file_name": "context.py", "file_ext": "py", "file_size_in_byte": 3117, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sys.path.insert", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 5, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 7, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 8, "usage_type": "call"}, {"api_name": "sympy.Add", "line_number": 14, "usage_type": "call"}, {"api_name": "sympy.Mul", "line_number": 18, "usage_type": "call"}, {"api_name": "sympy.Pow", "line_number": 22, "usage_type": "call"}, {"api_name": "sympy.Rational", "line_number": 36, "usage_type": "call"}, {"api_name": "sympy.Rational", "line_number": 37, "usage_type": "call"}, {"api_name": "sympy.Rational", "line_number": 38, "usage_type": "call"}, {"api_name": "sympy.pi", "line_number": 39, "usage_type": "argument"}, {"api_name": "sympy.sqrt", "line_number": 40, "usage_type": "call"}, {"api_name": "sympy.Rational", "line_number": 41, "usage_type": "call"}, {"api_name": "sympy.sqrt", "line_number": 44, "usage_type": "call"}, {"api_name": "sympy.Rational", "line_number": 45, "usage_type": "call"}, {"api_name": "sympy.Rational", "line_number": 47, "usage_type": "call"}, {"api_name": "sympy.Rational", "line_number": 48, "usage_type": "call"}, {"api_name": "sympy.Rational", "line_number": 49, "usage_type": "call"}, {"api_name": "sympy.Rational", "line_number": 50, "usage_type": "call"}, {"api_name": "sympy.Rational", "line_number": 51, "usage_type": "call"}, {"api_name": "sympy.pi", "line_number": 52, "usage_type": "name"}, {"api_name": "sympy.Rational", "line_number": 53, "usage_type": "call"}, {"api_name": "sympy.Rational", "line_number": 54, "usage_type": "call"}, {"api_name": "sympy.Rational", "line_number": 56, "usage_type": "call"}, {"api_name": "sympy.pi", "line_number": 60, "usage_type": "name"}, {"api_name": "sympy.simplify", "line_number": 67, "usage_type": "call"}, {"api_name": "sympy.srepr", "line_number": 69, "usage_type": "call"}, {"api_name": "sympy.srepr", "line_number": 70, "usage_type": "call"}, {"api_name": "sympy.simplify", "line_number": 75, "usage_type": "call"}, {"api_name": "latex2sympy.latex2sympy.process_sympy", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "318416319", "text": "import geneExpression as gx\nimport numpy as np\nimport sys\nfrom scipy.stats import gmean\nimport abc\nimport copy\nimport _pickle as pck \n#import cPickle as pck\n\n'''Read files with .cemod extension and produce a dictionary with simulation parameters'''\n\nclass modelReader(object):\n\tdef readModelFile(self, model_def_file = None):\n\t\tassert(model_def_file.endswith('.cemod'))\n\t\timport re\n\t\td = {}\n\t\twith open(model_def_file) as f:\n\t\t\tfor line in f:\n\t\t\t\tline = line.rstrip('\\n')\n\t\t\t\tif(line[0] != '#' and line != ''):\n\t\t\t\t\t(key, val) = line.split(':')\n\t\t\t\t\tval = self.__processLineVal(key, val)\n\t\t\t\t\td[key] = val\n\t\tkeys = d.keys()\n\t\tfor k in reversed(list(d.keys())):\n\t\t\td[k]= self.__elementReplace(d[k], d)\n\t\tfor k in list(d.keys()):\n\t\t\tif(k[0] == '$' or k[0] == '_'):\n\t\t\t\tdel d[k]\n\t\treturn d\n\tdef __elementReplace(self, el, source):\n\t\tif(type(el) is list):\n\t\t\tfor k in range(len(el)):\n\t\t\t\tif(type(el[k]) is not str):\n\t\t\t\t\tcontinue\n\t\t\t\telif(el[k][0] == '_' or el[k][0] == '$'):\n\t\t\t\t\tel[k] = source[el[k]]\n\t\telif(type(el) is dict):\n\t\t\tfor k in el.keys():\n\t\t\t\tif(type(el[k]) is not str):\n\t\t\t\t\tcontinue\n\t\t\t\tif(el[k][0] == '_' or el[k][0] == '$'):\n\t\t\t\t\tel[k] = source[el[k]]\n\t\telif(type(el) is str):\n\t\t\tif(el[0] == '_' or el[0] == '$'):\n\t\t\t\tel = source[el]\n\t\telse:\n\t\t\tpass\n\t\treturn el\n\tdef __processLineVal(self, key, val):\n\t\tif(key[0] != '$' and key[0] != '_'):\n\t\t\tval = self.__setStringValue(val)\n\t\telif(key[0] == '$'):\n\t\t\tval = [self.__setStringValue(i) for i in val.split()]\n\t\telif(key[0] == '_'):\n\t\t\tnewdict = {}\n\t\t\tfor s in val.split(','):\n\t\t\t\t(newkey, newval) = s.split()\n\t\t\t\tnewval = self.__setStringValue(newval)\n\t\t\t\tnewdict[newkey] = newval\n\t\t\tval = newdict\n\t\treturn val\n\tdef __setStringValue(self, val):\n\t\tif(val.isdigit()):\n\t\t\tval = int(val)\t\n\t\telif(self.__is_numeric(val)):\n\t\t\tval = float(val)\n\t\telif(type(val) is str):\n\t\t\tif(val == 'True'):\n\t\t\t\tval = True\n\t\t\telif(val == 'False'):\n\t\t\t\tval = False\n\t\t\telif(val == 'None'):\n\t\t\t\tval = None\n\t\treturn val\n\t@staticmethod\n\tdef __is_numeric(s):\n\t try:\n\t float(s)\n\t return True\n\t except (ValueError, TypeError):\n\t return False\n\n'''This class is only to evolve 1 DNA sequence; it works fine but it is useless for the rest of the model'''\nclass singleSeqEvolver():\n\tdef __init__(self, N, length, background):\n\t\tself.N = N\n\t\tself.length = length\n\t\tself.a_priori = background\n\t\tself.seqs = self.generateRandomSeqs(N,length, background)\n\tdef generateRandomSeqs(self, N,length, bck):\n\t\talphabet = list(bck.keys())\n\t\tprobs = list(bck.values())\n\t\tletters = np.random.choice(alphabet, N*length, replace=True, p = probs).reshape(N, length)\n\t\tseqs = [''.join(r) for r in letters]\n\t\treturn np.array(seqs)\n\tdef newGeneration(self, probs, mut_rate = 0.1, exp = 2):\n\t\tassert(len(probs) == len(self.seqs))\n\t\tif(exp>1):\n\t\t\tprobs = probs**exp\n\t\tprobs = probs/(np.sum(probs))\n\t\tnewseqs = np.random.choice(self.seqs, len(self.seqs), replace = True, p = probs)\n\t\tif(mut_rate > 0):\n\t\t\tnewseqs = self.mutateSeqs(newseqs, mut_rate)\n\t\tself.seqs = newseqs\n\tdef mutateSeqs(self, seqs, mut_rate = 0.25):\n\t\tcharlist = [list(s) for s in seqs]\n\n\t\tmutate = np.random.choice(range(0, self.N*self.length), round(mut_rate*self.N*self.length), replace=False)\n\n\t\trows = np.floor_divide(mutate, self.length)\n\t\tcols = np.mod(mutate, self.length)\n\n\t\talphabet = list(self.a_priori.keys())\n\t\tprobs = list(self.a_priori.values())\n\n\t\tnewchars = np.random.choice(alphabet, len(mutate), replace=True, p = probs)\n\t\tfor i in range(len(mutate)):\n\t\t\tcharlist[rows[i]][cols[i]] = newchars[i]\n\t\treturn np.array([''.join(r) for r in charlist])\n\n''' Uses singleSeqEvolver to evolve a DNA sequence'''\ndef evolveSingleSeq():\n\tN = 6\n\tlength = 600\n\tbck = {'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25}\n\ttfs = gx.TFSet(range(0, 10), alphasbtm = 3, interactions = [gx.interactionTuple(0, 1, 3), gx.interactionTuple(2, 3, 3)])\n\tex = gx.ExpressionCalculator()\n\tss = singleSeqEvolver(N, length, bck)\n\tann=tfs.annotate(ss.seqs)\n\tfinal_value = 2\n\n\te1 = np.zeros(len(ann))\n\tgenmean = 0\n\tallExpr = []\n\tgen = 0\n\twhile(genmean < final_value):\n\t\tfor i in range(len(ann)):\n\t\t\te1[i] = ex.activate(ann[i], interactions = True)\n\t\tss.newGeneration(probs=e1, mut_rate = 0.005, exp=4)\n\t\tallExpr.append(e1)\n\t\tann=tfs.annotate(ss.seqs)\n\t\tgenmean = np.mean(e1)\n\t\tprint('gen ', gen, ' mean: ', genmean, ' sd: ', np.std(e1),'\\n')\n\t\tgen+=1\n\n\n\n\n\nclass CellBasic():\n\tdef __init__(self, cellname, subtype, initial_concentrations):\n\t\tself.name = cellname\n\t\tself.subtype = subtype\n\t\tself.initial_concentrations = initial_concentrations\n\n'''This is a template class which implementations should specify a way to evaluate an organism fitness'''\nclass PhenotypeEvaluator():\n\tdef __init__(self, args):\n\t\tself.args = args\n\t\tprint('Instance of abstract class created')\t\t\n\t@abc.abstractmethod\n\tdef generateCells():\n\t\treturn None\n\t@abc.abstractmethod\n\tdef evaluateOrganism():\n\t\treturn None \n\n'''In this class I define a way to evaluate the fitness of individuals based on the squared error between a predetermined level of expression in each cell and the actual level of expression.\nWhen an instance is created, a vector of cells is generated, each with its predetermined optimal expression levels. This class depends strongly on the OrganismTemplate class'''\nclass SimplePhenotypeEvaluator(PhenotypeEvaluator):\n\tINITIAL_CONCENTRATIONS = 1\n\tOPTIMAL_CONCENTRATIONS = 3\n\tdef __init__(self, model_file = None, template = None, args = None):\n\t\tself.organism_template = template\n\t\tself.pan_genes, self.specific_genes, self.ntypes, self.cells_per_type, cellstrings = args\n\t\tself.cells = self.generateCells(cellstrings)\n\tdef generateCells(self, cell_strings = None):\n\t\tcells = []\n\t\tif(cell_strings is None):\n\t\t\tassert(self.organism_template.tf_num + self.pan_genes + self.specific_genes*self.ntypes <= self.organism_template.genome_size and len(self.organism_template.tf_lineage) >= self.organism_template.ncells)\n\t\t\tpan_genes_index = range(self.organism_template.tf_num, self.organism_template.tf_num + self.pan_genes)\n\t\t\tcell_id = 0\n\t\t\tspecific_so_far = 0\n\t\t\tfor t in range(len(self.cells_per_type)):\n\t\t\t\tfor i in range(self.cells_per_type[t]):\n\t\t\t\t\tinitial = np.zeros(self.organism_template.genome_size)\n\t\t\t\t\tinitial_vals = [self.INITIAL_CONCENTRATIONS if i == cell_id else 0 for i in self.organism_template.tf_lineage]\n\t\t\t\t\tinitial[self.organism_template.tf_lineage] = initial_vals\n\t\t\t\t\toptimal = np.zeros(self.organism_template.genome_size)\n\t\t\t\t\toptimal[pan_genes_index] = self.OPTIMAL_CONCENTRATIONS\n\t\t\t\t\tif(self.specific_genes > 0):\n\t\t\t\t\t\tspecific_genes = range(self.organism_template.tf_num + self.pan_genes + specific_so_far, self.organism_template.tf_num + self.pan_genes + specific_so_far + self.specific_genes)\n\t\t\t\t\t\toptimal[specific_genes] = self.OPTIMAL_CONCENTRATIONS\n\t\t\t\t\t\tspecific_so_far += self.specific_genes #cells of the same kind will have the same specific genes and differet lineage_tf\n\t\t\t\t\tcells.append(self.SimpleCell(cell_id, t, initial ,optimal))\n\t\t\t\t\tcell_id +=1\n\t\t\t\t\n\t\telse:\n\t\t\tfor c in range(sum(self.cells_per_type)):\n\t\t\t\tsubt = cell_strings[2][c]\n\t\t\t\tinitial = np.array(cell_strings[0][c])\n\t\t\t\toptimal = np.array(cell_strings[1][c])\n\t\t\t\tcells.append(self.SimpleCell(cellname = c, subtype = subt, initial_concentrations = initial, optimal_concentrations = optimal))\n\t\treturn cells\n\tdef getInitialConcentrations(self, i):\n\t\treturn self.cells[i].initial_concentrations\n\tdef evaluateOrganism(self, organism):\n\t\t#assert(self.organism_template == organism.template)\n\t\terror = np.zeros(self.organism_template.ncells)\n\t\tfor cell in range(organism.expression.shape[1]):\n\t\t\terror[cell] = self.squared_error(self.cells[cell].optimal_concentrations[self.organism_template.tf_num:], organism.expression[self.organism_template.tf_num:, cell])\n\t\t#return gmean(error + 0.0001)\n\t\treturn np.mean(error)\n\t\t#return np.max(error)\n\t@staticmethod\n\tdef squared_error(a, b):\n\t\ta = a.reshape(a.shape[0])\n\t\tb = b.reshape(b.shape[0])\n\t\terr = np.sum(np.square(a - b))/a.shape[0]\n\t\treturn err\n\tclass SimpleCell(CellBasic):\n\t\tdef __init__(self, cellname, subtype, initial_concentrations, optimal_concentrations):\n\t\t\tsuper().__init__(cellname, subtype, initial_concentrations)\n\t\t\tself.optimal_concentrations = optimal_concentrations\n\t\t \n'''OrganismTemplate implements these methods'''\nclass AbstractOrganismTemplate():\n\t@abc.abstractmethod\t\n\tdef evaluateOrganism():\n\t\treturn None\n\t@abc.abstractmethod\n\tdef generateRandomSeqs(N, length, bck):\n\t\treturn None\n\t@abc.abstractmethod\n\tdef __mutateSeqs(mut_rate):\n\t\treturn None\n\t@abc.abstractmethod\n\tdef __recombine(parent1, parent2, recomb_rate):\n\t\treturn None\n\t@abc.abstractmethod\n\tdef reproduce():\n\t\treturn None\n\t@abc.abstractmethod\n\tdef generatePopulation(self, indclass, Ninds):\n\t\treturn None\n\n\n'''OrganismTemplate class holds all the information that is common to all the organisms (i.e., species-level information) and that does not vary during a simulation: genome size, number of Transcription Factors and an instance of geneExpression.TFSet which holds PWMs, Kmax, alphasBtm, and other relevant information about this species transcription factors. This class is used to avoid replicating all the data in each instance of Organism'''\nclass OrganismTemplate(AbstractOrganismTemplate):\n\tDEFAULT_RECOMBINATION_PERKB = 0.2\n\tDEFAULT_MUTATION_RATE = 0.005\n\tdef __init__(self, cell_def_mode = None, model_def_file = None, pheno_class = None):\n\t\toptions = {\n\t\t\t0:self.readModelFile,\n\t\t\t1:self.typeAModel,\n\t\t\t2:self.typeBModel,\n\t\t\t3:self.typeZeroModel,\n\t\t\t4:self.useModelDirectly\n\t\t}\n\t\tself.cell_def_mode = cell_def_mode\n\t\tself.model_file = model_def_file\n\t\tself.pheno_class = pheno_class\n\t\tmodel = options[cell_def_mode](model_def_file)\n\t\tself.genome_size = model['gsize']\n\t\tself.seq_length = model['seqlen']\n\t\tself.tf_num = model['tf_num']\n\t\tself.tfs = gx.TFSet(range(0, model['tf_num']),direction = model['tf_dir'], kmax = model['tf_kmax'], alphasbtm = model['tf_alphasbtm'], interactions = model['tf_interactions'], difKmax = model['tf_difKmax'], min_inhibitor_index = model['tf_lineage'])\n\t\tself.tf_lineage = range(0, model['tf_lineage'])\n\t\tself.ncells = model['num_cells']\n\t\tself.background = model['background']\n\t\tself.mut_rate = model['mut_rate']\n\t\tself.rec_rate = model['rec_rate']\n\t\tself.environment = self.pheno_class(None, template = self, args = model['selection_args'])\n\tdef readModelFile(self, model_def_file = None):\n\t\treturn modelReader().readModelFile(model_def_file)\n\tdef getInitialConcentrations(self, i):\n\t\tassert(i < self.ncells)\n\t\treturn self.environment.getInitialConcentrations(i)\n\tdef typeAModel(self, model_def_file = None):\n\t\tmodel = {'gsize':40,\n\t\t\t 'seqlen':150,\n\t\t\t 'tf_num':15,\n\t\t\t 'tf_dir':None,\n\t\t\t 'tf_kmax':None,\n\t\t\t 'tf_alphasbtm':None,\n\t\t\t 'tf_interactions':None, #[gx.interactionTuple(10, 11, 3), gx.interactionTuple(12, 13, 3)],\n\t\t\t 'tf_difKmax':False,\n\t\t\t 'tf_lineage':4,\n\t\t\t 'num_cells':4,\n\t\t\t 'mut_rate':self.DEFAULT_MUTATION_RATE,\n\t\t\t 'rec_rate':self.DEFAULT_RECOMBINATION_PERKB,\n\t\t\t 'background':{'A':0.25,'C':0.25,'G':0.25,'T':0.25},\n\t\t\t 'selection_args':[5, 5, 4, [1, 1, 1, 1], None]\n\t\t\t}\n\t\treturn model\n\tdef typeZeroModel(self, model_def_file = None):\n\t\tmodel = {'gsize':40,\n\t\t\t 'seqlen':150,\n\t\t\t 'tf_num':15,\n\t\t\t 'tf_dir':None,\n\t\t\t 'tf_kmax':None,\n\t\t\t 'tf_alphasbtm':None,\n\t\t\t 'tf_interactions':None,#[gx.interactionTuple(0, 1, 3), gx.interactionTuple(2, 3, 3)],\n\t\t\t 'tf_difKmax':False,\n\t\t\t 'tf_lineage':4,\n\t\t\t 'num_cells':1,\n\t\t\t 'mut_rate':self.DEFAULT_MUTATION_RATE,\n\t\t\t 'rec_rate':self.DEFAULT_RECOMBINATION_PERKB,\n\t\t\t 'background':{'A':0.25,'C':0.25,'G':0.25,'T':0.25},\n\t\t\t 'selection_args':[15, 0, 1, [1], None] #pan_genes, specific_genes, celltypes, cells_per_type, cellstrings \n\t\t\t}\n\t\treturn model\n\tdef typeBModel(self, model_def_file = None):\n\t\tmodel = {'gsize':400,\n\t\t\t 'seqlen':200,\n\t\t\t 'tf_num':40,\n\t\t\t 'tf_dir':None,\n\t\t\t 'tf_kmax':None,\n\t\t\t 'tf_alphasbtm':None,\n\t\t\t 'tf_interactions':None, #[gx.interactionTuple(0, 1, 3), gx.interactionTuple(2, 3, 3),gx.interactionTuple(4, 5, 3),gx.interactionTuple(6, 7, 3),gx.interactionTuple(7, 8, 3)],\n\t\t\t 'tf_difKmax':True,\n\t\t\t 'tf_lineage':8,\n\t\t\t 'num_cells':8,\n\t\t\t 'background':{'A':0.25,'C':0.25,'G':0.25,'T':0.25},\n\t\t\t 'selection_args':[80, 80, 4, [2, 2, 1, 3], None]\n\t\t\t}\n\t\treturn model\n\tdef useModelDirectly(self, model):\n\t\treturn model\n\tdef evaluateOrganism(self, organism):\n\t\tself.environment.evaluateOrganism(organism)\n\tdef reproduce(self, indcls, parent1, parent2 = None, recomb_rate = None, mut_rate = None, intra_seq_recomb = True):\n\t\tif(recomb_rate is None):\n\t\t\trecomb_rate = self.rec_rate\n\t\tif(mut_rate is None):\n\t\t\tmut_rate = self.mut_rate\n\t\tif(parent2 is None):\n\t\t\toffspringSeqs = self.__mutateSeqs(parent1.seqs, mut_rate)\n\t\telse:\n\t\t\t#assert(parent1.template == parent2.template)#for some reason self was different\n\t\t\toffspringSeqs = self.__mutateSeqs(self.__recombine(parent1.seqs, parent2.seqs, recomb_rate, intra_seq_recomb), mut_rate)\t\t\n\t\treturn indcls(offspringSeqs, self, mut_rate)\n\tdef generatePopulation(self, indclass, Ninds):\n\t\tindividuals = []\n\t\tfor i in range(Ninds):\n\t\t\tindividuals.append(indclass(self.generateRandomSeqs(self.genome_size, self.seq_length, self.background), self))\n\t\treturn individuals\n\t@staticmethod\n\tdef generateRandomSeqs(N, length, bck):\n\t\talphabet = list(bck.keys())\n\t\tprobs = list(bck.values())\n\t\tletters = np.random.choice(alphabet, N*length, replace=True, p = probs).reshape(N, length)\n\t\tseqs = [''.join(r) for r in letters]\n\t\treturn np.array(seqs)\n\tdef __mutateSeqs(self, seqs, mut_rate = 0.25):\n\t\tcharlist = [list(s) for s in seqs]\n\n\t\tmutate = np.random.choice(range(0, self.genome_size * self.seq_length), int(mut_rate * self.genome_size * self.seq_length), replace=False)\n\n\t\trows = np.floor_divide(mutate, self.seq_length)\n\t\tcols = np.mod(mutate, self.seq_length)\n\n\t\talphabet = list(self.background.keys())\n\t\tprobs = list(self.background.values())\n\n\t\tnewchars = np.random.choice(alphabet, len(mutate), replace=True, p = probs)\n\t\tfor i in range(len(mutate)):\n\t\t\tcharlist[rows[i]][cols[i]] = newchars[i]\n\t\treturn np.array([''.join(r) for r in charlist])\n\n\tdef __recombine(self, parent1, parent2, recomb_rate, intraseq = True):\n\t\tfrom_p1 = np.random.choice(range(0, self.genome_size), np.floor_divide(self.genome_size, 2), replace = False)\n\t\tnewseqs = copy.deepcopy(parent2)\n\t\tnewseqs[from_p1] = copy.deepcopy(parent1[from_p1])\n\t\tif(intraseq):\n\t\t\tnum_rec_points = np.random.poisson(lam = 0.001*recomb_rate*self.genome_size*self.seq_length, size = 1)\n\t\t\trec_points = np.random.choice(range(0, self.genome_size * self.seq_length), num_rec_points)\n\t\t\tprevious_seq = None\n\t\t\tfor i in rec_points:\n\t\t\t\tnum_seq = np.floor_divide(i, self.seq_length)\n\t\t\t\tposition = cols = np.mod(i, self.seq_length)\t\t\t\t\n\t\t\t\tif(position > 0 and position < self.seq_length - 1 and num_seq != previous_seq):\n\t\t\t\t\tnewseqs[num_seq] = parent1[num_seq][:position] + parent2[num_seq][position:]\n\t\t\t\t\tprevious_seq = num_seq\n\t\treturn newseqs\n\n''' Each instance of this class represents an individual. Holds information that is different between individuals and that changes during the simulations: a matrix of size genome_size x number_of_cells, whith the expression levels of each gene, the DNA sequeneces, TFBS annotations and the error (which is calculated by a PhenotypeEvaluator instance). Also holds a reference to the OrganismTemplate class to which the individual belongs. '''\nclass Organism():\t\n\tdef __init__(self, seqs = None, template = None, mut_rate = None):\n\t\tself.template = template\n\t\tself.expression = np.zeros((self.template.genome_size, self.template.ncells))\n\t\tself.seqs = seqs\n\t\tself.ann = None\n\t\tself.error = 999999\n\t\tself.mut_rate = template.mut_rate if mut_rate is None else mut_rate\n\tdef getAnnotation(self):\n\t\tself.ann = self.template.tfs.annotate(self.seqs)\n\tdef equilibriumExpression(self, ode_calculator, storeCon = False):\n\t\tself.getAnnotation()\n\t\tfor cell in range(self.template.ncells):\n\t\t\tself.expression[:,cell] = ode_calculator.run(self.ann, self.template.getInitialConcentrations(cell), storeCon = storeCon)\n\tdef timeExpression(self, ode_calculator, time_steps = 10):\n\t\tself.getAnnotation()\n\t\tself.expression = np.zeros((self.template.genome_size, time_steps, self.template.ncells))\n\t\tfor cell in range(self.template.ncells):\n\t\t\tself.expression[:,:,cell] = ode_calculator.run(self.ann, self.template.getInitialConcentrations(cell), storeCon = True)\n\tdef mutantExpression(self, ode_calculator, tf_ind):\n\t\tself.getAnnotation()\n\t\tfor g in range(self.template.tf_num, self.template.genome_size):\n\t\t\tself.ann[g].Qonpartial[self.ann[g].sites.getTFs() == tf_ind] = 0\n\t\t\tself.ann[g].Qoffpartial[self.ann[g].sites.getTFs() == tf_ind] = 0\n\t\tself.expression = np.zeros((self.template.genome_size, self.template.ncells))\n\t\tfor cell in range(self.template.ncells):\n\t\t\tself.expression[:,cell] = ode_calculator.run(self.ann, self.template.getInitialConcentrations(cell), storeCon = False)\n\tdef getError(self):\n\t\tself.error = self.template.environment.evaluateOrganism(self)\n\t\tself.mut_rate = np.power(self.error, 1)*self.template.mut_rate if self.error < 1 else self.error*self.template.mut_rate\n\t\treturn self.error\n\tdef chromatinData(self, ode_calculator):\n\t\tself.getAnnotation()\n\t\tself.expression = []\n\t\tfor cell in range(self.template.ncells):\n\t\t\tself.expression.append(ode_calculator.run(self.ann, self.template.getInitialConcentrations(cell), storeCon = True, fullSeq=True, seqlen = self.template.seq_length))\n\n\n''' This class performs the simulations. The method getNewGeneration contains the core of the genetic algorithm. With parrun a simulation is run with multiprocessing; with basicrun no multiprocessing is used. Each generation, the organisms to reproduce are picked randomly with a probability that is proportional to 1/error^n, with n being the competitive_power variable (intensity of competition)'''\nclass multicellEvolver():\n\tDEFAULT_POPULATION_SIZE=24\n\tDEFAULT_GENERATIONS = 1500\n\tDEFAULT_ERROR = 0.01\n\tDEFAULT_SIM_PARAMS = [0.2, False, 1, 10, 0.001]\n\tDEFAULT_COMPETITIVE_POWER = 5\n\tdef __init__(self, organismclass, template_org, phenotype_eval, competition, sim_params = None, pop_size = None, generations = None, error = None, modelFile = None, sexual = False, predefined_model = 1, instance_name = 'mce0'):\n\t\tif(modelFile is not None and predefined_model != 0):\n\t\t\tself.model = modelReader().readModelFile(modelFile) \n\t\t\tself.template = template_org(4, self.model, phenotype_eval)\n\t\telif(predefined_model == 0):\n\t\t\tself.model = None\n\t\t\tself.template = template_org(0, modelFile, phenotype_eval)\n\t\telse:\n\t\t\tself.model = None\n\t\t\tself.template = template_org(predefined_model, None, phenotype_eval)\n\t\tself.pop_size = pop_size if pop_size is not None else self.DEFAULT_POPULATION_SIZE\n\t\tself.generations = generations if generations is not None else self.DEFAULT_GENERATIONS\n\t\tself.error = error if error is not None else self.DEFAULT_ERROR\n\t\tif (self.model is None):\n\t\t\tself.organismclass = organismclass\n\t\telif('ORGANISM_CLASS' not in self.model.keys()):\n\t\t\tself.organismclass = organismclass\n\t\telse:\n\t\t\tself.organismclass = eval(self.model['ORGANISM_CLASS'])\n\t\tself.population = self.template.generatePopulation(self.organismclass, self.pop_size)\n\t\tself.competitive_power = competition if competition is not None else self.DEFAULT_COMPETITIVE_POWER\n\t\tif(self.model is None):\n\t\t\tsim_params = self.DEFAULT_SIM_PARAMS if sim_params is None else sim_params\n\t\t\tself.gxcalc_class = gx.ODERunner\n\t\t\tself.gxcalc = gx.ODERunner(self.template.tfs, betas = sim_params[0] , with_interactions = sim_params[1], h = sim_params[2], tmax = sim_params[3], min_variation = sim_params[4])\n\t\telse:\n\t\t\tsim_params = self.model['sim_params']\n\t\t\tself.gxcalc_class = eval('gx.' + self.model['ODE_RUNNER_CLASS'])\n\t\t\tself.gxcalc = self.gxcalc_class(self.template.tfs, *sim_params)\n\t\t\t\n\t\tself.sexual = sexual\n\t\tself.last_generation = 0\n\t\tself.error_means_acum = []\n\t\tself.instance_name = instance_name\n\t\tself.param_history = [{'generation':0,'mut_rate':self.template.mut_rate,'competitive_power':self.competitive_power}]\n\tdef parrun(self, save = True, saving_freq = 100):\n\t\timport multiprocessing\n\t\tcompetitivity = np.zeros(self.pop_size)\n\t\tmean_comp = 0\n\t\tif(save):\n\t\t\tself.saveSelf()\n\t\tfor g in range(self.last_generation, self.generations):\n\t\t\t#Heuristics to edit parameters\n\t\t\tself.paramHeuristics(g, mean_comp, np.var(competitivity))\n\t\t\t#True algorithm: calculate expression and error with multiprocessing\n\t\t\tcurrent_pop = multiprocessing.Queue()\n\t\t\tjobs = [multiprocessing.Process(target = self.parstep, args = (i, current_pop)) for i in range(len(self.population))]\n\t\t\tfor j in jobs:\n\t\t\t\tj.start()\n\t\t\tself.population = [current_pop.get() for j in jobs]\n\t\t\tfor i in range(len(self.population)):\n\t\t\t\tcompetitivity[i] = self.population[i].error\t\t\t\t\n\t\t\tmean_comp = np.mean(competitivity)\n\t\t\tself.error_means_acum.append(mean_comp)\n\t\t\t#Save and print\n\t\t\tif(save and (g+1)%saving_freq == 0):\n\t\t\t\tself.saveSelf()\n\t\t\t\tprint('generation ', g, ': mean = ', mean_comp, ', best = ', min(competitivity),', mut_rate = ',self.template.mut_rate, ', k = ',self.k)\n\t\t\tif(mean_comp <= self.error):\n\t\t\t\tbreak\n\t\t\t#Get new generation\n\t\t\tself.population = self.getNewGeneration(competitivity)\n\t\t\tself.last_generation = g\n\t\treturn (g, mean_comp)\t\t\n\tdef parstep(self, i, current_pop):\n\t\to = self.population[i]\n\t\to.equilibriumExpression(self.gxcalc)\n\t\terr = o.getError()\n\t\tcurrent_pop.put(o)\n\t\t#print('org ', i, ' ', err, '\\t')\n\tdef parstepTime(self, i, current_pop):\n\t\to = self.population[i]\n\t\to.timeExpression(self.gxcalc, self.sim_params[3])\n\t\terr = o.getError()\n\t\tcurrent_pop.put(o)\n\tdef basicrun(self, save = True, saving_freq = 100):\n\t\tcompetitivity = np.zeros(self.pop_size)\n\t\tmean_comp = 0\n\t\tfor g in range(self.generations):\n\t\t\tif(save and (g+1)%saving_freq == 0):\n\t\t\t\tself.saveSelf()\n\t\t\tself.paramHeuristics(g, mean_comp, np.var(competitivity))\n\t\t\ti = 0\n\t\t\tfor o in self.population:\n\t\t\t\to.equilibriumExpression(self.gxcalc)\n\t\t\t\tcompetitivity[i] = o.getError()\n\t\t\t\tprint('org ', i, ' ', competitivity[i], '\\t')\n\t\t\t\ti+=1\n\t\t\tmean_comp = np.mean(competitivity)\n\t\t\tprint('generation ', g, ': ', mean_comp)\n\t\t\tif(mean_comp <= self.error):\n\t\t\t\tbreak\n\t\t\tself.population = self.getNewGeneration(competitivity)\n\t\treturn (g, mean_comp)\n\tdef getNewGeneration(self, error):\n\t\tefficacy = 1/np.power(error, self.competitive_power)\n\t\tefficacy = efficacy/np.sum(efficacy)\n\t\tnew_gen = []\n\t\tparents = []\n\t\tfor i in range(self.pop_size):\n\t\t\tif(self.sexual):\n\t\t\t\tparents = np.random.choice(self.population, 2, replace = True, p = efficacy)\n\t\t\t\tmut_rate = 0.5*(parents[0].mut_rate + parents[1].mut_rate)\n\t\t\t\tnew_gen.append(self.template.reproduce(self.organismclass, parents[0], parents[1], mut_rate = mut_rate))\n\t\t\telse:\n\t\t\t\tparent = np.random.choice(self.population, 1, p = efficacy)[0]\n\t\t\t\tnew_gen.append(self.template.reproduce(self.organismclass, parent, None, mut_rate = parent.mut_rate))\n\t\treturn new_gen\n\tdef saveSelf(self):\n\t\tfilename = self.instance_name + 'g' + str(self.last_generation) + '.pck'\n\t\twith open(filename, 'wb') as output: \n \t\t\tpickler = pck.Pickler(output, -1)\n \t\t\tpickler.dump(self)\n\tdef setComp(self, cp):\n\t\tself.competitive_power = cp\n\t\tself.param_history.append({'generation':self.last_generation,'mut_rate':self.template.mut_rate,'k':self.k})\n\tdef setMutRate(self, mut_rate):\n\t\tself.mut_rate = mut_rate\n\t\tself.param_history.append({'generation':self.last_generation,'mut_rate':self.template.mut_rate,'k':self.competitive_power})\n\tdef paramHeuristics(self, g, mean_comp, var_comp):\n\t\tif(g == 100):\n\t\t\tself.setMutRate(0.001)\n\t\tif(g > 300 and (g+1)%20 == 0):\n\t\t\tself.setMutRate(1.5/(self.template.genome_size*self.template.seq_length*mean_comp))\n\t\tif(g > 500 and (g+1)%20 == 0):\n\t\t\tself.setMutRate(1.25/(self.template.genome_size*self.template.seq_length*mean_comp))\n\t\tif(var_comp == 0 and mean_comp > 0 and g > 10):\n\t\t\tself.setMutRate(1.5/(self.template.genome_size*self.template.seq_length*mean_comp))\n\tdef printOrg(self, i = 0):\n\t\to = self.population[i]\n\t\to.equilibriumExpression(self.gxcalc)\n\t\ter = o.getError()\n\t\tprint(\"error of org \", i, \": \", er)\n\t\tprint(\"SITES/EXPRESSION:\")\n\t\tfor i in range(self.template.genome_size):\n\t\t\tif(i >= self.template.tf_num):\n\t\t\t\tif((i - self.template.tf_num)%5 == 0):\n\t\t\t\t\tprint('__________\\n')\n\t\t\tif(o.ann[i] is not None):\n\t\t\t\tprint(i, ': ', o.ann[i].sites.getTFs(), '||||', o.expression[i, :])\n\t\t\telse:\n\t\t\t\tprint(i, ': None', '||||', o.expression[i, :])\n\t\tprint(\"INHIBITORS: \", np.where(self.template.tfs.alphasbtm<1))\n\t\tprint(\"INTERACTIONS: \", self.template.tfs.interactions)\n\t\tprint(\"MEAN BY TYPE:\")\n\t\tfor j in [range(15,20), range(20,25), range(25,30), range(30,35), range(35,40)]:\n\t\t\tprint([np.mean(o.expression[j, i]) for i in range(4)])\n\t\tprint('__________\\n')\n\t@staticmethod\n\tdef readMCE(filenamein, iname=\"default_name\"):\n\t\twith open(filenamein, 'rb') as f:\n\t\t\tprint('opening ' + filenamein)\n\t\t\timport _pickle as pck\n\t\t\tmce = pck.load(f)\n\t\t\tmce.last_generation += 1\n\t\t\tprint('template mut_rate: ', mce.template.mut_rate, ', k:', mce.k, ',tf type: ', mce.template.tfs.direction, '\\n')\n\t\t\tif(iname != mce.instance_name):\n\t\t\t\tmce.instance_name = mce.instance_name + '_' + iname\n\t\treturn mce\n\n\tdef produceDataFrames(self, options, simname):\n\t\timport multiprocessing\n\t\tglobal pd\n\t\timport pandas as pd\n\t\tself.fix()\t## Kmax of TF were not being multiplied. In order to be able to shut them off, they are now multiplied but all should be equal to one \n\t\tcompetitivity = np.zeros(self.pop_size)\n\t\tfor i in range(len(competitivity)):\n\t\t\tcompetitivity[i] = self.population[i].error\n\t\tif(any(competitivity > 10000)):\n\t\t\tprint('recalculating all expression... ', simname)\n\t\t\tcurrent_pop = multiprocessing.Queue()\n\t\t\tjobs = [multiprocessing.Process(target = self.parstep, args = (i, current_pop)) for i in range(len(self.population))]\n\t\t\tfor j in jobs:\n\t\t\t\tj.start()\n\t\t\tself.population = [current_pop.get() for j in jobs]\n\t\t\tfor i in range(len(self.population)):\n\t\t\t\tcompetitivity[i] = self.population[i].error\t\n\t\twinner_ind = np.where(competitivity == np.min(competitivity))[0][0]\t\t\t\n\t\twinner = self.population[winner_ind]\n\t\terror = winner.getError()\n\t\toutname = simname.replace('.pck', '_o'+str(winner_ind)+'err'+str(round(error, 4)))\n\t\tif(options.find('b') >= 0):\n\t\t\td = self.finalExpressionToDF(winner, outname)\n\t\t\td = self.annotationToDF(winner, outname)\n\t\t\td = self.tfsToDF(outname)\n\t\tif(options.find('t') >= 0):\n\t\t\td = self.timeExpressionToDF(winner, outname)\n\t\tif(options.find('m') >= 0):\n\t\t\td = self.mutantAnalysisToDF(winner, outname)\n\t\tif(options.find('s') >= 0):\n\t\t\td = self.mutantSitesByGroupToDF(winner, outname)\n\tdef finalExpressionToDF(self, o, outname = ''):\t\n\t\tdf = pd.DataFrame(o.expression, columns = ['exp' + str(i) for i in range(o.template.ncells)])\n\t\tfor i in range(o.template.ncells):\n\t\t\tdf['opt' + str(i)] = o.template.environment.cells[i].optimal_concentrations\n\t\tfor i in range(o.template.ncells):\n\t\t\tdf['init' + str(i)] = o.template.environment.cells[i].initial_concentrations\n\t\tcols = [col for col in df.columns if 'opt' in col]\n\t\tdf['type'] = df[cols].apply(lambda row: np.array2string(np.where(row>0)[0], separator=''), axis=1)\n\t\tdf['type'][0:self.template.tf_num] = [1 if i == self.template.tfs.ACTIVATOR_TYPE else -1 for i in self.template.tfs.direction]\n\t\tdf['seqs'] = o.seqs\n\t\tif(outname != ''):\n\t\t\tdf.to_csv(outname +'_finalExpression.csv', sep='\\t', header=True,decimal='.', float_format='%.10f')\n\t\treturn df\n\tdef annotationToDF(self, o, outname = ''):\n\t\tdf = pd.DataFrame()\n\t\tfor i in range(o.template.genome_size):\n\t\t\tif(o.ann[i] is not None):\n\t\t\t\taux = pd.DataFrame(o.ann[i].sites.get())\n\t\t\t\taux['gene'] = o.ann[i].ind\n\t\t\t\taux['QonPartial'] = o.ann[i].Qonpartial\n\t\t\t\taux['QoffPartial'] = o.ann[i].Qoffpartial\n\t\t\t\tdf = df.append(aux)\n\t\tif(outname != ''):\n\t\t\tdf.to_csv(outname +'_annotation.csv', sep='\\t', header=True,decimal='.', float_format='%.10f')\n\t\treturn df\n\tdef tfsToDF(self, outname):\n\t\tdf = pd.DataFrame()\n\t\tm = self.template.tfs.getPSSMs()\n\t\tfor i in range(self.template.tf_num):\n\t\t\taux = pd.DataFrame()\n\t\t\tfor l in m[i].pos.alphabet.letters:\n\t\t\t\taux[l] = m[i].pos.get(l)\n\t\t\taux['tf_name'] = self.template.tfs.getSourceIndex(i)\n\t\t\taux['alpha_btm'] = self.template.tfs.alphasbtm[self.template.tfs.getSourceIndex(i)]\n\t\t\taux['k_max'] = self.template.tfs.kmax[self.template.tfs.getSourceIndex(i)]\n\t\t\taux['type'] = self.template.tfs.direction[self.template.tfs.getSourceIndex(i)]\n\t\t\taux['consensus'] = str(m[i].pos.consensus)\n\t\t\tdf = df.append(aux)\n\t\tif(outname != ''):\n\t\t\tdf.to_csv(outname +'_TFset.csv', sep='\\t', header=True,decimal='.', float_format='%.10f')\n\t\tdf2 = self.interactionsToDF(outname)\n\t\treturn df\n\tdef interactionsToDF(self, outname):\n\t\tdf = pd.DataFrame()\n\t\tinteractions = self.template.tfs.interactions\n\t\ttf1 = []\n\t\ttf2 = []\n\t\tweight = []\n\t\tdir_tf1 = []\n\t\tdir_tf2 = []\n\t\tif(len(interactions) > 0):\n\t\t\tfor i in interactions:\n\t\t\t\ttf1.append(i.tf1)\n\t\t\t\ttf2.append(i.tf2)\n\t\t\t\tweight.append(i.weight)\n\t\t\t\tdir_tf1.append(self.template.tfs.direction[i.tf1])\n\t\t\t\tdir_tf2.append(self.template.tfs.direction[i.tf2])\n\t\tdf['tf1'] = tf1\n\t\tdf['tf2'] = tf2\n\t\tdf['weight'] = weight\n\t\tdf['tf1_type'] = dir_tf1\n\t\tdf['tf2_type'] = dir_tf2\n\t\tif(outname != ''):\n\t\t\tdf.to_csv(outname +'_TFinteractions.csv', sep='\\t', header=True,decimal='.', float_format='%.10f')\n\t\treturn df\n\n\t\t\n\tdef timeExpressionToDF(self, o, outname):\n\t\texpr = o.expression\n\t\ttime = int(self.gxcalc.tmax/self.gxcalc.h)\n\t\to.timeExpression(self.gxcalc, time)\n\t\tdf = pd.DataFrame()\n\t\tfor i in range(time):\n\t\t\taux = pd.DataFrame(o.expression[:,i,:], columns = ['cell' + str(n) for n in range(o.template.ncells)])\n\t\t\taux['time'] = i*self.gxcalc.h\n\t\t\taux['gene'] = range(0,self.template.genome_size)\n\t\t\tdf = df.append(aux)\n\t\tif(outname != ''):\n\t\t\tdf.to_csv(outname +'_timeExpression.csv', sep='\\t', header=True,decimal='.', float_format='%.10f')\n\t\to.expression = expr\n\t\treturn df\n\tdef mutantCalculate(self, o, i, mutant_exp):\n\t\ttrue_kmax = o.template.tfs.kmax[i]\n\t\to.template.tfs.kmax[i] = 0\n\t\to.equilibriumExpression(self.gxcalc)\n\t\terr = o.getError()\n\t\taux = self.finalExpressionToDF(o, '')\n\t\taux['tf_mutated'] = i\n\t\taux['overall_error'] = err\n\t\to.template.tfs.kmax[i] = true_kmax\n\t\tmutant_exp.put(aux)\n\tdef mutantAnalysisToDF(self, o, outname):\n\t\tprint('mutant analysis beginning... ', outname)\n\t\tdf = pd.DataFrame()\n\t\timport multiprocessing\n\t\t#for i in range(self.template.tf_num):\n\t\tmutant_exp = multiprocessing.Queue()\n\t\tjobs = [multiprocessing.Process(target = self.mutantCalculate, args = (o, i, mutant_exp)) for i in range(self.template.tf_num)]\n\t\tfor j in jobs:\n\t\t\tj.start()\n\t\tfor j in jobs:\n\t\t\taux = mutant_exp.get()\n\t\t\tdf = df.append(aux)\n\t\tif(outname != ''):\n\t\t\tdf.to_csv(outname +'_mutantTFs.csv', sep='\\t', header=True,decimal='.', float_format='%.10f')\n\t\treturn df\n\tdef mutSitesCalculate(self, o, i, mutant_exp):\n\t\to.mutantExpression(self.gxcalc, i)\n\t\taux = self.finalExpressionToDF(o, '')\n\t\taux['mutated_sites'] = i\n\t\tmutant_exp.put(aux)\t\t\t\n\tdef mutantSitesByGroupToDF(self, o, outname):\n\t\tprint('mutating sites... ', outname)\n\t\tdf = pd.DataFrame()\n\t\timport multiprocessing\n\t\tmutant_exp = multiprocessing.Queue()\n\t\tjobs = [multiprocessing.Process(target = self.mutSitesCalculate, args = (o, i, mutant_exp)) for i in range(self.template.tf_num)]\n\t\tfor j in jobs:\n\t\t\tj.start()\n\t\tfor j in jobs:\n\t\t\taux = mutant_exp.get()\n\t\t\tdf = df.append(aux)\t\n\t\tif(outname != ''):\n\t\t\tdf.to_csv(outname +'_mutantSites.csv', sep='\\t', header=True,decimal='.', float_format='%.10f')\n\t\treturn df\n\tdef fix(self):\n\t\tfor i in range(self.template.tf_num):\n\t\t\tself.template.tfs.kmax[i] = self.template.tfs.DEF_KMAX\n\t\tself.template.tfs.kmax = np.array(self.template.tfs.kmax)\n\t\tfor p in self.population:\n\t\t\tp.template.tfs.kmax = self.template.tfs.kmax\n''' This class performs simulations just like multicellEvolver. The only difference is that it uses tournament algorithm to produce the next generation. It seems to work better'''\nclass multicellTournament(multicellEvolver):\n\tDEFAULT_K = 2\n\tdef __init__(self, organismclass, template_org, phenotype_eval, competition, sim_params = None, pop_size = None, generations = None, error = None, modelFile = None, sexual = False, predefined_model = 1, instance_name = 'mce0', k = None):\n\t\tsuper().__init__( organismclass, template_org, phenotype_eval, competition, sim_params, pop_size, generations, error, modelFile, sexual, predefined_model, instance_name)\n\t\tself.k = self.DEFAULT_K if k is None else k\n\t\tself.changedK = False\n\t\tself.param_history = [{'generation':0,'mut_rate':self.template.mut_rate,'k':self.k}]\n\tdef getNewGeneration(self, error):\n\t\tefficacy = 1/error\n\t\tefficacy = efficacy/np.sum(efficacy)\n\t\tnew_gen = []\n\t\tparents = []\n\t\tfor i in range(self.pop_size):\n\t\t\tif(self.sexual):\n\t\t\t\tparents = [self.population[self.__tournament(efficacy)], self.population[self.__tournament(efficacy)]]\n\t\t\t\tmut_rate = 0.5*(parents[0].mut_rate + parents[1].mut_rate)\n\t\t\t\tnew_gen.append(self.template.reproduce(self.organismclass, parents[0], parents[1], mut_rate = mut_rate))\n\t\t\telse:\n\t\t\t\tparent = self.population[self.__tournament(efficacy)]\n\t\t\t\tnew_gen.append(self.template.reproduce(self.organismclass, parent, None, mut_rate = parent.mut_rate))\n\t\treturn new_gen\n\tdef __tournament(self, efficacy):\n\t\tparticipants = np.random.choice(range(len(efficacy)), self.k, replace = False)\n\t\tm = np.argmax(efficacy[participants])\n\t\treturn participants[m]\n\tdef setK(self, k):\n\t\tself.k = k\n\t\tself.param_history.append({'generation':self.last_generation,'mut_rate':self.template.mut_rate,'k':self.k})\n\tdef setMutRate(self, mut_rate):\n\t\tself.template.mut_rate = mut_rate\n\t\tself.param_history.append({'generation':self.last_generation,'mut_rate':self.template.mut_rate,'k':self.k})\n\tdef paramHeuristics(self, g, mean_comp, var_comp):\n\t\tif(mean_comp < 0.0001):\n\t\t\treturn\n\t\tif(g == 100):\n\t\t\tself.setMutRate(0.001)\n\t\tif(g > 300 and (g+1)%20 == 0):\n\t\t\tself.setMutRate(2/(self.template.genome_size*self.template.seq_length*mean_comp))\n\t\tif(g > 500 and (g+1)%20 == 0):\n\t\t\tself.setMutRate(2/(self.template.genome_size*self.template.seq_length*mean_comp))\n\t\tif(var_comp == 0 and mean_comp > 0 and g > 10):\n\t\t\tself.setMutRate(2/(self.template.genome_size*self.template.seq_length*mean_comp))\n\t\tif(g > 300 and mean_comp < 0.5 and not self.changedK):\n\t\t\tself.setK(min(int(self.k + 0.5*self.k), self.pop_size - 1))\n\t\t\tself.changedK = True\n\n\n\n\n\n\n### Syntax example:\n### Simulation with default name\n#\t python cellEvolver.py \n### Simulation with name, 4cell_mce1.cemod model:\n# \t python cellEvolver.py SimulationName\n### Simulation with name and model file as input:\n# \t python cellEvolver.py SimulationName modelX.cemod\n### Pass a pickle file and make a basic print of it:\n# \tpython cellEvolver.py newName -filename.pck\n### Pass a pickle file and produce data frames (b for basic graph data, t for developmental time analysis. m for mutant TFs and s for TF sites in terminal features mutation):\n# \tpython cellEvolver.py newName -filename.pck btms\n### Pass a pickle and execute another argument\n#\tpython cellEvolver.py newname -filename.pck eval 'import XX;print(xy)'\n### Pass a pickle and keep evolving:\n# \tpython cellEvolver.py newName filename.pck\n\n\ndef main():\n\tiname = sys.argv[1]\n\tif(iname is not None):\n\t\tprint('Simulation name: ', iname, '\\n')\n\telse:\n\t\tiname = 'mceX'\n\t\tprint('Simulation name (default name): ', iname, '\\n')\n\tif(len(sys.argv)>2):\n\t\tfilenamein = sys.argv[2]\n\t\tif(filenamein.endswith('.pck')):\n\t\t\tif(filenamein[0] == '-'):\n\t\t\t\tfilenamein = filenamein[1:]\n\t\t\t\tmce = multicellEvolver.readMCE(filenamein, iname)\n\t\t\t\tif(len(sys.argv)>3):\n\t\t\t\t\tif(sys.argv[3] == 'eval'):\n\t\t\t\t\t\tfor expression in sys.argv[4].split(';'):\n\t\t\t\t\t\t\texec (expression)\n\t\t\t\t\telse:\n\t\t\t\t\t\tmce.produceDataFrames(sys.argv[3], filenamein)\n\t\t\t\telse:\n\t\t\t\t\tmce.printOrg()\n\t\t\t\treturn mce\t\t\t\n\t\t\telse:\n\t\t\t\tmce = multicellEvolver.readMCE(filenamein, iname)\n\t\telif(filenamein.endswith('.cemod')):\n\t\t\tmce = multicellTournament(organismclass = Organism, template_org = OrganismTemplate, phenotype_eval = SimplePhenotypeEvaluator, competition = 6, sim_params = None, pop_size = 24, generations = 10000, error = 0.01, modelFile = filenamein, sexual = True, predefined_model = 4, instance_name = iname, k = 12)\n\telse:\n\t\tmce = multicellTournament(organismclass = Organism, template_org = OrganismTemplate, phenotype_eval = SimplePhenotypeEvaluator, competition = 6, sim_params = None, pop_size = 24, generations = 10000, error = 0.01, modelFile = '4cell_mce1.cemod', sexual = True, predefined_model = 4, instance_name = iname, k = 12)\n\tprint(mce.instance_name, ': Initiating simulations...\\n')\n\tmce.parrun(True, 200)\n\tmce.saveSelf()\n\tmce.produceDataFrames('btms', iname)\n\treturn mce\n\nif __name__ == \"__main__\":\n main()\n\n", "sub_path": "cellEvolver.py", "file_name": "cellEvolver.py", "file_ext": "py", "file_size_in_byte": 36589, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "numpy.random.choice", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 109, "usage_type": "attribute"}, {"api_name": "numpy.floor_divide", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 117, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 120, "usage_type": "call"}, {"api_name": "geneExpression.TFSet", "line_number": 127, "usage_type": "call"}, {"api_name": "geneExpression.interactionTuple", "line_number": 127, "usage_type": "call"}, {"api_name": "geneExpression.ExpressionCalculator", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 144, "usage_type": "call"}, {"api_name": "abc.abstractmethod", "line_number": 162, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 165, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 220, "usage_type": "call"}, {"api_name": "abc.abstractmethod", "line_number": 229, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 232, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 235, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 238, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 241, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 244, "usage_type": "attribute"}, {"api_name": "geneExpression.TFSet", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 353, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 353, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 355, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 359, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 359, "usage_type": "attribute"}, {"api_name": "numpy.floor_divide", "line_number": 361, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 362, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 367, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 367, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 370, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 373, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 373, "usage_type": "attribute"}, {"api_name": "numpy.floor_divide", "line_number": 373, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 374, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 375, "usage_type": "call"}, {"api_name": "numpy.random.poisson", "line_number": 377, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 377, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 378, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 378, "usage_type": "attribute"}, {"api_name": "numpy.floor_divide", "line_number": 381, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 382, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 392, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 405, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 413, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 418, "usage_type": "call"}, {"api_name": "geneExpression.ODERunner", "line_number": 457, "usage_type": "attribute"}, {"api_name": "geneExpression.ODERunner", "line_number": 458, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 471, "usage_type": "call"}, {"api_name": "numpy.var", "line_number": 477, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 479, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 480, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 486, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 510, "usage_type": "call"}, {"api_name": "numpy.var", "line_number": 515, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 522, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 529, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 530, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 535, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 535, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 539, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 539, "usage_type": "attribute"}, {"api_name": "_pickle.Pickler", "line_number": 545, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 576, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 580, "usage_type": "call"}, {"api_name": "_pickle.load", "line_number": 587, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 599, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 604, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 605, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 611, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 611, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 626, "usage_type": "call"}, {"api_name": "numpy.array2string", "line_number": 632, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 632, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 639, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 642, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 651, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 654, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 668, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 696, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 698, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 718, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 721, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 722, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 738, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 740, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 741, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 753, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 766, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 779, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 779, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 780, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 826, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 832, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 833, "usage_type": "attribute"}, {"api_name": "{'multiprocessing': 'multiprocessing', 'pck': '_pickle', 'pd': 'pandas'}.readMCE", "line_number": 837, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 838, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 839, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 840, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 843, "usage_type": "attribute"}, {"api_name": "{'multiprocessing': 'multiprocessing', 'pck': '_pickle', 'pd': 'pandas'}.readMCE", "line_number": 848, "usage_type": "call"}]} +{"seq_id": "220310701", "text": "# 전염병\n# bfs 유형\n# 오전 6:13 2021-04-19\n\n# 이게 왜 bfs인가 했는데, 1개의 노드로 인해 전염병 로직 상 인접하다고 할수있는 노드 2개가 발생\n# 그리고 그 노드 2개는 각자 또다시 밀접한 노드 2개씩 발생\n# 어떻게 보면 갈래의 갯수가 일정한 재귀구조는 큐로도 이해가능함을 알게됨\n\n# 의미적으로는 특정 로직에 의해 인접하다고 판단되는 노드들을 바이러스가 방문해 나가는것\nimport sys\nfrom collections import deque\n\ndef bfs_virus(v):\n global willbevisited,cnt_visitedbyVirus\n\n q=deque()\n willbevisited[v]=1\n q.append(v)\n\n while(q):\n cnt_visitedbyVirus+=1\n\n curV = q.popleft()\n\n adjs=[]\n if(curV*2<=N):\n adjV1= curV*2\n adjs.append(adjV1)\n\n if(curV//3>0):\n adjV2 = curV//3\n adjs.append(adjV2)\n\n for adj in adjs:\n if(willbevisited[adj]==0):\n willbevisited[adj]=1\n q.append(adj)\n\nif __name__==\"__main__\":\n N,K = map(int, sys.stdin.readline().split())\n\n global willbevisited, cnt_visitedbyVirus\n # NOT USING 0 INDEX\n willbevisited=[0]*(N+1)\n cnt_visitedbyVirus=0\n\n bfs_virus(K)\n\n print(N-cnt_visitedbyVirus)", "sub_path": "Algorithm/python/algorithmjobs/L19/L191_08contagion.py", "file_name": "L191_08contagion.py", "file_ext": "py", "file_size_in_byte": 1284, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "collections.deque", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.stdin.readline", "line_number": 40, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 40, "usage_type": "attribute"}]} +{"seq_id": "653254980", "text": "\"\"\"\nMask R-CNN\nTrain on the construction site images dataset and implement color splash effect.\n\nCopyright (c) 2018 Yingge WAN\n\nCopyright (c) 2018 Matterport, Inc.\nLicensed under the MIT License (see LICENSE for details)\nWritten by Waleed Abdulla\n\n------------------------------------------------------------\n\nUsage: import the module (see Jupyter notebooks for examples), or run from\n the command line as such:\n\n # Train a new model starting from pre-trained COCO weights\n python3 cstr.py train --dataset=/path/to/cstr/dataset --weights=coco\n\n # Resume training a model that you had trained earlier\n python3 cstr.py train --dataset=/path/to/cstr/dataset --weights=last\n\n # Train a new model starting from ImageNet weights\n python3 cstr.py train --dataset=/path/to/cstr/dataset --weights=imagenet\n\n # Run detection\n python3 cstr.py detect --dataset=/path/to/dataset --weights=\n\n # Apply color splash to an image\n python3 cstr.py splash --weights=/path/to/weights/file.h5 --image=\n\n # Apply color splash to video using the last weights you trained\n python3 cstr.py splash --weights=last --video=\n\"\"\"\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\nimport sys\nimport json\nimport datetime\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport skimage.draw\n# import labelme\nimport base64\nimport io\nimport PIL.Image\nimport math\n\n\n# Root directory of the project\nROOT_DIR = os.path.abspath(\"../../\")\n\n# Import Mask RCNN\nsys.path.append(ROOT_DIR) # To find local version of the library\nfrom mrcnn.config import Config\nfrom mrcnn import utils\nfrom mrcnn import model as modellib\nfrom mrcnn import visualize\n\n# Path to trained weights file\nCOCO_WEIGHTS_PATH = os.path.join(ROOT_DIR, \"mask_rcnn_coco.h5\")\n\n# Directory to save logs and model checkpoints, if not provided\n# through the command line argument --logs\nDEFAULT_LOGS_DIR = os.path.join(ROOT_DIR, \"logs\")\n\n# Results directory\n# Save submission files here\nRESULTS_DIR = os.path.join(ROOT_DIR, \"results/cstr/\")\n\n# Directory of images to run detection on\nIMAGE_DIR = os.path.join(ROOT_DIR, \"datasets\", \"cstr\")\n\n# Image size info\nIMAGE_HEIGHT = 480\nIMAGE_WIDTH = 640\n\n\n############################################################\n# Configurations\n############################################################\n\n\nclass CstrConfig(Config):\n \"\"\"Configuration for training on the construction site images dataset.\n Derives from the base Config class and overrides some values.\n \"\"\"\n # Give the configuration a recognizable name\n NAME = \"cstr\"\n\n # Train on 2 GPU and 2 images per GPU. Batch size is 4 (GPUs * images/GPU).\n # Adjust down if you use a smaller GPU.\n GPU_COUNT = 2\n IMAGES_PER_GPU = 2\n\n # Number of classes (including background)\n NUM_CLASSES = 1 + 30 # Background + 30 different classes\n\n # Input image resizing\n # Random crops of size 512x512\n # IMAGE_RESIZE_MODE = \"sqaure\"\n IMAGE_MIN_DIM = 512\n IMAGE_MAX_DIM = 512\n IMAGE_MIN_SCALE = 2.0\n\n # Length of square anchor side in pixels\n RPN_ANCHOR_SCALES = (16, 32, 64, 128, 256)\n\n # Number of ROIs per image to feed to classifier/mask heads\n # The Mask RCNN paper uses 512 but often the RPN doesn't generate\n # enough positive proposals to fill this and keep a positive:negative\n # ratio of 1:3. You can increase the number of proposals by adjusting\n # the RPN NMS threshold.\n TRAIN_ROIS_PER_IMAGE = 128\n\n # Number of training steps per epoch.\n # Use a smaller epoch if the data is simple.\n STEPS_PER_EPOCH = 1000\n\n # Use small validation steps since the epoch is small.\n VALIDATION_STEPS = 50\n \n \n############################################################\n# Dataset\n############################################################\n\nclass CstrDataset(utils.Dataset):\n \n def add_image(self, source, image_id, image, **kwargs):\n image_info = {\n \"id\": image_id,\n \"source\": source,\n \"image\": image,\n }\n image_info.update(kwargs)\n self.image_info.append(image_info)\n \n def load_data(self, dataset_dir, subset):\n \"\"\"Load a subset of the cstr dataset. \n dataset_dir: Root directory of the dataset.\n subset: Subset to load: train or val\n \"\"\" \n \n # Class names\n class_names = [\n 'worker-formwork',\n 'worker-concrete',\n 'worker-welding',\n 'worker-rebar',\n 'worker-scaffolding',\n 'worker-dump',\n 'worker-heavy',\n 'worker-aerial',\n 'worker-other',\n 'worker-idle',\n 'rebar-bs',\n 'rebar-wc',\n 'rebar-material',\n 'steel',\n 'concrete-pouring',\n 'concrete-forming',\n 'formwork-bs',\n 'formwork-wc',\n 'formwork-material',\n 'scaffolding',\n 'excavator',\n 'bulldozer',\n 'dump-truck',\n 'concrete-bucket',\n 'concrete-mixer',\n 'concrete-pump',\n 'tower-crane',\n 'crane',\n 'basket',\n 'machine-other'\n ]\n # Add classes\n for i in range(len(class_names)):\n self.add_class(\"cstr\", i + 1, class_names[i])\n \n # Train or validation dataset?\n assert subset in [\"train\", \"val\"]\n dataset_dir = os.path.join(dataset_dir, subset)\n \n # Load annotations \n for annotation_filename in os.listdir(dataset_dir):\n annotation = json.load(open(os.path.join(dataset_dir, annotation_filename)))\n \n # Load image\n f = io.BytesIO()\n f.write(base64.b64decode(annotation['imageData']))\n image = np.array(PIL.Image.open(f))\n \n # image=labelme.utils.img_b64_to_array(annotation['imageData'])\n height, width = image.shape[:2]\n self.add_image(\n \"cstr\",\n image_id=annotation_filename,\n image=image,\n width=width, height=height,\n polygons=annotation['shapes']) \n \n def load_image(self, image_id):\n \"\"\"Load the specified image and return a [H,W,3] Numpy array.\n \"\"\"\n return self.image_info[image_id][\"image\"]\n\n def load_mask(self, image_id):\n \"\"\"Load instance masks for the given image.\n Returns:\n masks: A bool array of shape [height, width, instance count] with\n one binary mask per instance.\n class_ids: a 1D array of class IDs of the instance masks.\n \"\"\" \n # Convert polygons to a bitmap mask of shape\n # [height, width, instance_count]\n info = self.image_info[image_id]\n mask = np.zeros([info[\"height\"], info[\"width\"], len(info[\"polygons\"])],\n dtype=np.uint8)\n class_names = []\n for i, p in enumerate(info[\"polygons\"]):\n # Get indexes of pixels inside the polygon and set them to 1\n rr, cc = skimage.draw.polygon([point[1] for point in p['points']], [point[0] for point in p['points']])\n mask[rr, cc, i] = 1\n class_names.append(p['label'])\n \n # Map class names to class IDs.\n class_ids = np.array([self.class_names.index(class_name) for class_name in class_names], dtype=np.int32)\n\n # Return mask, and array of class IDs of each instance.\n return mask.astype(np.bool), class_ids\n \n# shapes = image_info['shapes']\n# image_data = image_info['image']\n# count = len(shapes)\n# mask = np.zeros([IMAGE_HEIGHT, IMAGE_WIDTH, count], dtype=np.bool)\n# class_names = []\n# for i, region in enumerate(shapes):\n# temp = labelme.utils.polygons_to_mask(image_data.shape, region['points'])\n# mask[:, :, i:i+1] = temp.reshape([IMAGE_HEIGHT, IMAGE_WIDTH, 1])\n# class_names.append(region['label'])\n# return mask, class_ids\n\n\ndef train(model):\n \"\"\"Train the model.\"\"\"\n # Training dataset.\n dataset_train = CstrDataset()\n dataset_train.load_data(args.dataset, \"train\")\n dataset_train.prepare()\n\n # Validation dataset\n dataset_val = CstrDataset()\n dataset_val.load_data(args.dataset, \"val\")\n dataset_val.prepare()\n\n # *** This training schedule is an example. Update to your needs ***\n # Training - Stage 1\n print(\"Training network heads\")\n model.train(dataset_train, dataset_val,\n learning_rate=config.LEARNING_RATE,\n epochs=4, # 40\n layers='heads')\n\n # Training - Stage 2\n # Finetune layers from ResNet stage 4 and up\n print(\"Fine tune Resnet stage 4 and up\")\n model.train(dataset_train, dataset_val,\n learning_rate=config.LEARNING_RATE,\n epochs=12, # 120\n layers='4+')\n\n # Training - Stage 3\n # Fine tune all layers\n print(\"Fine tune all layers\")\n model.train(dataset_train, dataset_val,\n learning_rate=config.LEARNING_RATE / 10,\n epochs=16, # 160\n layers='all')\n \n \ndef color_splash(image, mask):\n \"\"\"Apply color splash effect.\n image: RGB image [height, width, 3]\n mask: instance segmentation mask [height, width, instance count]\n\n Returns result image.\n \"\"\"\n # Make a grayscale copy of the image. The grayscale copy still\n # has 3 RGB channels, though.\n gray = skimage.color.gray2rgb(skimage.color.rgb2gray(image)) * 255\n # We're treating all instances as one, so collapse the mask into one layer\n mask = (np.sum(mask, -1, keepdims=True) >= 1)\n # Copy color pixels from the original color image where mask is set\n if mask.shape[0] > 0:\n splash = np.where(mask, image, gray).astype(np.uint8)\n else:\n splash = gray\n return splash\n\n\ndef detect_and_color_splash(model, image_path=None, video_path=None):\n assert image_path or video_path\n\n # Image or video?\n if image_path:\n # Run model detection and generate the color splash effect\n print(\"Running on {}\".format(args.image))\n # Read image\n image = skimage.io.imread(args.image)\n # Detect objects\n r = model.detect([image], verbose=1)[0]\n # Color splash\n splash = color_splash(image, r['masks'])\n # Save output\n file_name = \"splash_{:%Y%m%dT%H%M%S}.png\".format(datetime.datetime.now())\n skimage.io.imsave(file_name, splash)\n elif video_path:\n import cv2\n # Video capture\n vcapture = cv2.VideoCapture(video_path)\n width = int(vcapture.get(cv2.CAP_PROP_FRAME_WIDTH))\n height = int(vcapture.get(cv2.CAP_PROP_FRAME_HEIGHT))\n fps = vcapture.get(cv2.CAP_PROP_FPS)\n\n # Define codec and create video writer\n file_name = \"splash_{:%Y%m%dT%H%M%S}.avi\".format(datetime.datetime.now())\n vwriter = cv2.VideoWriter(file_name,\n cv2.VideoWriter_fourcc(*'MJPG'),\n fps, (width, height))\n\n count = 0\n success = True\n while success:\n print(\"frame: \", count)\n # Read next image\n success, image = vcapture.read()\n if success:\n # OpenCV returns images as BGR, convert to RGB\n image = image[..., ::-1]\n # Detect objects\n r = model.detect([image], verbose=0)[0]\n # Color splash\n splash = color_splash(image, r['masks'])\n # RGB -> BGR to save image to video\n splash = splash[..., ::-1]\n # Add image to video writer\n vwriter.write(splash)\n count += 1\n vwriter.release()\n print(\"Saved to \", file_name)\n \n \n# Load and display random samples\n\ndef display_samples():\n image_ids = np.random.choice(dataset_train.image_ids, 4)\n for image_id in image_ids:\n image = dataset_train.load_image(image_id)\n mask, class_ids = dataset_train.load_mask(image_id)\n visualize.display_top_masks(image, mask, class_ids, dataset_train.class_names)\n\n \n############################################################\n# Detection\n############################################################\n\ndef detect(model):\n \"\"\"Run detection on images in the given directory.\"\"\"\n print(\"Running on {}\".format(args.dataset))\n\n # Create directory\n if not os.path.exists(RESULTS_DIR):\n os.makedirs(RESULTS_DIR)\n submit_dir = \"submit_{:%Y%m%dT%H%M%S}\".format(datetime.datetime.now())\n submit_dir = os.path.join(RESULTS_DIR, submit_dir)\n os.makedirs(submit_dir)\n\n # Read dataset\n dataset = CstrDataset()\n dataset.load_data(args.dataset, \"val\")\n dataset.prepare()\n # Load over images\n for image_id in dataset.image_ids:\n # Load image and run detection\n image = dataset.load_image(image_id)\n # Detect objects\n r = model.detect([image], verbose=0)[0]\n # Save image with masks\n visualize.display_instances(\n image, r['rois'], r['masks'], r['class_ids'],\n dataset.class_names, r['scores'],\n # show_bbox=False, show_mask=False,\n title=\"Predictions\")\n plt.savefig(\"{}/{}.png\".format(submit_dir, dataset.image_info[image_id][\"id\"])) \n\n\n# There are some problems with this function\ndef evaluate(model, limit=0):\n \"\"\"Runs construction site images dataset evaluation. Compute VOC-Style mAP @ IoU=0.5\n limit: if not 0, it's the number of images to use for evaluation\n \"\"\"\n # Limit to a subset\n if limit:\n image_ids = np.random.choice(args.dataset.image_ids, limit)\n # With no limit, use all images from args.dataset\n else:\n image_ids = args.dataset.image_ids\n\n APs = []\n for image_id in image_ids:\n # Load image and ground truth data\n image, image_meta, gt_class_id, gt_bbox, gt_mask =\\\n modellib.load_image_gt(args.dataset, inference_config,\n image_id, use_mini_mask=False)\n molded_images = np.expand_dims(modellib.mold_image(image, inference_config), 0)\n # Run object detection\n results = model.detect([image], verbose=0)\n r = results[0]\n # Compute AP\n AP, precisions, recalls, overlaps =\\\n utils.compute_ap(gt_bbox, gt_class_id,\n r[\"rois\"], r[\"class_ids\"], r[\"scores\"])\n APs.append(AP)\n\n print(\"mAP: \", np.mean(APs))\n\n \n############################################################\n# Training\n############################################################\n\nif __name__ == '__main__':\n import argparse\n\n # Parse command line arguments\n parser = argparse.ArgumentParser(\n description='Train Mask R-CNN on construction site images dataset.')\n parser.add_argument(\"command\",\n metavar=\"\",\n help=\"'train' or 'detect' or 'splash' on construction site images\")\n parser.add_argument('--dataset', required=False,\n default=IMAGE_DIR,\n metavar=\"/path/to/cstr/dataset/\",\n help='Directory of the construction site images dataset')\n parser.add_argument('--weights', required=False,\n default='coco',\n metavar=\"/path/to/weights.h5\",\n help=\"Path to weights.h5 file or 'coco'\")\n parser.add_argument('--logs', required=False,\n default=DEFAULT_LOGS_DIR,\n metavar=\"/path/to/logs/\",\n help='Logs and checkpoints directory (default=logs/)')\n parser.add_argument('--image', required=False,\n metavar=\"path or URL to image\",\n help='Image to apply the color splash effect on')\n parser.add_argument('--limit', required=False,\n default=50,\n metavar=\"\",\n help='Images to use for evaluation (default=50)')\n args = parser.parse_args()\n # Validate arguments\n if args.command == \"train\":\n assert args.dataset, \"Argument --dataset is required for training\" \n elif args.command == \"detect\":\n assert args.dataset, \"Argument --dataset is required for detection\"\n elif args.command == \"splash\":\n assert args.image, \"Provide --image to apply color splash\"\n \n print(\"Command: \", args.command)\n print(\"Weights: \", args.weights)\n print(\"Dataset: \", args.dataset)\n print(\"Logs: \", args.logs)\n\n # Configurations\n if args.command == \"train\":\n config = CstrConfig()\n else:\n class InferenceConfig(CstrConfig):\n # Set batch size to 1 since we'll be running inference on\n # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU\n GPU_COUNT = 1\n IMAGES_PER_GPU = 1\n config = InferenceConfig()\n config.display()\n\n # Create model\n if args.command == \"train\":\n model = modellib.MaskRCNN(mode=\"training\", config=config,\n model_dir=args.logs)\n else:\n model = modellib.MaskRCNN(mode=\"inference\", config=config,\n model_dir=args.logs)\n\n # Select weights file to load\n if args.weights.lower() == \"coco\":\n weights_path = COCO_WEIGHTS_PATH\n # Download weights file\n if not os.path.exists(weights_path):\n utils.download_trained_weights(weights_path)\n elif args.weights.lower() == \"last\":\n # Find last trained weights\n weights_path = model.find_last()[1]\n elif args.weights.lower() == \"imagenet\":\n # Start from ImageNet trained weights\n weights_path = model.get_imagenet_weights()\n else:\n weights_path = args.weights\n \n # Load weights\n print(\"Loading weights \", weights_path)\n if args.weights.lower() == \"coco\":\n # Exclude the last layers because they require a matching\n # number of classes\n model.load_weights(weights_path, by_name=True, exclude=[\n \"mrcnn_class_logits\", \"mrcnn_bbox_fc\",\n \"mrcnn_bbox\", \"mrcnn_mask\"])\n else:\n model.load_weights(weights_path, by_name=True)\n\n # Train or evaluate or splash\n if args.command == \"train\":\n train(model)\n elif args.command == \"detect\":\n detect(model)\n elif args.command == \"splash\":\n detect_and_color_splash(model, image_path=args.image,\n video_path=args.video)\n else:\n print(\"'{}' is not recognized. \"\n \"Use 'train' or 'detect' or 'splash'\".format(args.command))\n", "sub_path": "samples/cstr/cstr.py", "file_name": "cstr.py", "file_ext": "py", "file_size_in_byte": 18915, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "os.path.abspath", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 55, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "mrcnn.config.Config", "line_number": 85, "usage_type": "name"}, {"api_name": "mrcnn.utils.Dataset", "line_number": 129, "usage_type": "attribute"}, {"api_name": "mrcnn.utils", "line_number": 129, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path", "line_number": 185, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 188, "usage_type": "call"}, {"api_name": "json.load", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path", "line_number": 189, "usage_type": "attribute"}, {"api_name": "io.BytesIO", "line_number": 192, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 194, "usage_type": "call"}, {"api_name": "PIL.Image.Image.open", "line_number": 194, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 194, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 194, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 221, "usage_type": "attribute"}, {"api_name": "skimage.draw.draw.polygon", "line_number": 225, "usage_type": "call"}, {"api_name": "skimage.draw.draw", "line_number": 225, "usage_type": "attribute"}, {"api_name": "skimage.draw", "line_number": 225, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 230, "usage_type": "attribute"}, {"api_name": "numpy.bool", "line_number": 233, "usage_type": "attribute"}, {"api_name": "skimage.draw.color.gray2rgb", "line_number": 293, "usage_type": "call"}, {"api_name": "skimage.draw.color", "line_number": 293, "usage_type": "attribute"}, {"api_name": "skimage.draw", "line_number": 293, "usage_type": "name"}, {"api_name": "skimage.draw.color.rgb2gray", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 298, "usage_type": "attribute"}, {"api_name": "skimage.draw.io.imread", "line_number": 312, "usage_type": "call"}, {"api_name": "skimage.draw.io", "line_number": 312, "usage_type": "attribute"}, {"api_name": "skimage.draw", "line_number": 312, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 318, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 318, "usage_type": "attribute"}, {"api_name": "skimage.draw.io.imsave", "line_number": 319, "usage_type": "call"}, {"api_name": "skimage.draw.io", "line_number": 319, "usage_type": "attribute"}, {"api_name": "skimage.draw", "line_number": 319, "usage_type": "name"}, {"api_name": "cv2.VideoCapture", "line_number": 323, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 324, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 325, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FPS", "line_number": 326, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 329, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 329, "usage_type": "attribute"}, {"api_name": "cv2.VideoWriter", "line_number": 330, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 331, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 359, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 359, "usage_type": "attribute"}, {"api_name": "mrcnn.visualize.display_top_masks", "line_number": 363, "usage_type": "call"}, {"api_name": "mrcnn.visualize", "line_number": 363, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 375, "usage_type": "call"}, {"api_name": "os.path", "line_number": 375, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 376, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 377, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 377, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 378, "usage_type": "call"}, {"api_name": "os.path", "line_number": 378, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 379, "usage_type": "call"}, {"api_name": "mrcnn.visualize.display_instances", "line_number": 392, "usage_type": "call"}, {"api_name": "mrcnn.visualize", "line_number": 392, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 397, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 397, "usage_type": "name"}, {"api_name": "numpy.random.choice", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 407, "usage_type": "attribute"}, {"api_name": "mrcnn.model.load_image_gt", "line_number": 416, "usage_type": "call"}, {"api_name": "mrcnn.model", "line_number": 416, "usage_type": "name"}, {"api_name": "numpy.expand_dims", "line_number": 418, "usage_type": "call"}, {"api_name": "mrcnn.model.mold_image", "line_number": 418, "usage_type": "call"}, {"api_name": "mrcnn.model", "line_number": 418, "usage_type": "name"}, {"api_name": "mrcnn.utils.compute_ap", "line_number": 424, "usage_type": "call"}, {"api_name": "mrcnn.utils", "line_number": 424, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 428, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 439, "usage_type": "call"}, {"api_name": "mrcnn.model.MaskRCNN", "line_number": 491, "usage_type": "call"}, {"api_name": "mrcnn.model", "line_number": 491, "usage_type": "name"}, {"api_name": "mrcnn.model.MaskRCNN", "line_number": 494, "usage_type": "call"}, {"api_name": "mrcnn.model", "line_number": 494, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 501, "usage_type": "call"}, {"api_name": "os.path", "line_number": 501, "usage_type": "attribute"}, {"api_name": "mrcnn.utils.download_trained_weights", "line_number": 502, "usage_type": "call"}, {"api_name": "mrcnn.utils", "line_number": 502, "usage_type": "name"}]} +{"seq_id": "358492687", "text": "#!/usr/bin/python3\n\"\"\" Handles all default RESTFul API actions for Review object \"\"\"\nfrom models import storage\nfrom api.v1.views import app_views\nfrom flask import jsonify, abort, request\nfrom models.review import Review\nfrom models.city import City\nfrom models.place import Place\nfrom models.user import User\n\n\n@app_views.route('/places//reviews',\n methods=['GET'], strict_slashes=False)\ndef get_review_by_placeiD(place_id=None):\n \"\"\"Get the reviews for a place\"\"\"\n reviews = []\n place = storage.get(Place, place_id)\n if place:\n for place in place.reviews:\n reviews.append(place.to_dict())\n return jsonify(reviews)\n else:\n abort(404)\n\n\n@app_views.route('/reviews/',\n methods=['GET'], strict_slashes=False)\ndef get_review_by_reviewiD(review_id=None):\n \"\"\" Retrieves a review with his iD \"\"\"\n rev = storage.get(Review, review_id)\n if rev:\n return jsonify(rev.to_dict())\n else:\n abort(404)\n\n\n@app_views.route('/reviews/',\n methods=['DELETE'], strict_slashes=False)\ndef delete_review(review_id=None):\n \"\"\" Delete a review\"\"\"\n if review_id:\n review = storage.get(Review, review_id)\n if review:\n storage.delete(review)\n storage.save()\n return jsonify({}), 200\n abort(404)\n\n\n@app_views.route('/places//reviews',\n methods=['POST'], strict_slashes=False)\ndef post_review(place_id=None):\n \"\"\" Creates review for a given place \"\"\"\n review_dict = request.get_json()\n\n if not review_dict:\n abort(400, \"Not a JSON\")\n if 'text' not in review_dict:\n abort(400, \"Missing text\")\n if 'user_id' not in review_dict:\n abort(400, \"Missing user_id\")\n\n place = storage.get(Place, place_id)\n user = storage.get(User, review_dict[\"user_id\"])\n if user and place:\n new_review = Review(**review_dict)\n new_review.place_id = place.id\n storage.new(new_review)\n storage.save()\n return jsonify(new_review.to_dict()), 201\n abort(404)\n\n\n@app_views.route('/reviews/',\n methods=['PUT'], strict_slashes=False)\ndef put_review(review_id=None):\n \"\"\"update a review\"\"\"\n review = storage.get(Review, review_id)\n if review is None:\n abort(404)\n requeste = request.get_json()\n if requeste is None:\n abort(400, \"Not a JSON\")\n\n for key, value in requeste.items():\n if key not in ['id', 'user_id', 'place_id',\n 'created_at', 'updated_at']:\n setattr(review, key, value)\n storage.save()\n return jsonify(review.to_dict()), 200\n", "sub_path": "api/v1/views/places_reviews.py", "file_name": "places_reviews.py", "file_ext": "py", "file_size_in_byte": 2700, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "models.storage.get", "line_number": 17, "usage_type": "call"}, {"api_name": "models.place.Place", "line_number": 17, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 23, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 12, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 12, "usage_type": "name"}, {"api_name": "models.storage.get", "line_number": 30, "usage_type": "call"}, {"api_name": "models.review.Review", "line_number": 30, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 34, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 26, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 26, "usage_type": "name"}, {"api_name": "models.storage.get", "line_number": 42, "usage_type": "call"}, {"api_name": "models.review.Review", "line_number": 42, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 42, "usage_type": "name"}, {"api_name": "models.storage.delete", "line_number": 44, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 44, "usage_type": "name"}, {"api_name": "models.storage.save", "line_number": 45, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 45, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 47, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 37, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 61, "usage_type": "call"}, {"api_name": "models.storage.get", "line_number": 63, "usage_type": "call"}, {"api_name": "models.place.Place", "line_number": 63, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 63, "usage_type": "name"}, {"api_name": "models.storage.get", "line_number": 64, "usage_type": "call"}, {"api_name": "models.user.User", "line_number": 64, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 64, "usage_type": "name"}, {"api_name": "models.review.Review", "line_number": 66, "usage_type": "call"}, {"api_name": "models.storage.new", "line_number": 68, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 68, "usage_type": "name"}, {"api_name": "models.storage.save", "line_number": 69, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 69, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 71, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 50, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 50, "usage_type": "name"}, {"api_name": "models.storage.get", "line_number": 78, "usage_type": "call"}, {"api_name": "models.review.Review", "line_number": 78, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 83, "usage_type": "call"}, {"api_name": "models.storage.save", "line_number": 89, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 89, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 90, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 74, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 74, "usage_type": "name"}]} +{"seq_id": "275794272", "text": "import click\nfrom PIL import Image\nfrom steg import steg\nfrom os import path\nfrom pydub import AudioSegment\n\naudio_formats = []\n\n\n@click.group()\n@click.version_option()\ndef cli():\n \"\"\"A utility for concealing or revealing text into/from audio/image files\"\"\"\n\n\n@cli.command()\n@click.argument('message', type=click.File('rb'))\n@click.argument('container', type=click.Path(exists=True, dir_okay=False, readable=True, resolve_path=True))\n@click.argument('output', type=click.Path(dir_okay=False, writable=True, resolve_path=True))\ndef conceal(message, container, output):\n \"\"\"Attempts to hide MESSAGE into CONTAINER, saves to OUTPUT.\n\n MESSAGE: A path to a file, whose contents will be imbedded into OUTPUT.\n If only '-' then text can be read from stdin and terminated with CTRL+D\n\n IMAGE: A path to an image or audio file.\n\n OUTPUT: A path to where the new image or audio should be written.\n\n Supported output formats:\n AUDIO = \"aiff\", \"ast\", \"au\", \"caf\", \"f32be\", \"f32le\", \"f64be\", \"f64le\", \"flac\", \"ircam\", \"s16be\", \"s16le\",\n \"s24be\", \"s24le\", \"s32be\", \"s32le\", \"smjpeg\", \"sox\", \"u16be\", \"u16le\", \"u24be\", \"u24le\", \"u32be\",\n \"u32le\", \"voc\", \"w64\", \"wav\", \"wv\"\n IMAGE = \"bmp\", \"im\",\"j2k\", \"pbm\", \"pcx\", \"pgm\", \"ppm\", \"png\", \"tiff\", \"tif\", \"webp\"\n\n \"\"\"\n input_file = click.format_filename(container)\n input_extension = path.splitext(input_file)[1][1:].lower()\n output_file = click.format_filename(output)\n output_extension = path.splitext(output_file)[1][1:].lower()\n\n message_bytes = message.read()\n\n if output_extension in steg.LOSSLESS_AUDIO:\n conceal_audio(input_file, input_extension, output_file, output_extension, message_bytes)\n elif output_extension in steg.LOSSLESS_IMG:\n conceal_image(input_file, input_extension, output_file, output_extension, message_bytes)\n else:\n raise ValueError(\"Cannot use given output format: \" + output_extension)\n\n\ndef conceal_audio(input_file, input_extension, output_file, output_extension, message_bytes):\n input_audio = AudioSegment.from_file(input_file, format=input_extension)\n\n if not steg.audio_can_fit_message(input_audio, message_bytes):\n raise ValueError('The message is cannot fit in the audio.')\n\n new_audio_data = steg.convert_to_stego_audio(input_audio, message_bytes)\n new_audio_segment = AudioSegment(data=new_audio_data, sample_width=input_audio.sample_width,\n frame_rate=input_audio.frame_rate, channels=input_audio.channels)\n new_audio_segment.export(output_file, format=output_extension)\n\n\ndef conceal_image(input_file, input_extension, output_file, output_extension, message_bytes):\n with Image.open(input_file) as im:\n if output_extension in steg.RGB_ONLY:\n im = im.convert(mode=\"RGB\")\n\n if not (steg.image_can_fit_message(im, message_bytes)):\n raise ValueError('The message is cannot fit in the image.')\n\n new_pixel_data = steg.convert_to_stego_image(im, message_bytes)\n\n # Update and save image contents to file.\n im.putdata(new_pixel_data)\n im.save(output_file, lossless=True)\n\n\n@cli.command()\n@click.argument('input', type=click.Path(exists=True, dir_okay=False, readable=True, resolve_path=True))\n@click.argument('output', type=click.File('wb'))\ndef reveal(input, output):\n \"\"\"Attempts to read from INPUT to OUTPUT.\n\n INPUT: A path to an image or audio file which contains a concealed message.\n\n OUTPUT: A path to where the message content should be written.\n If only '-' then contents are written to stdout\n\n \"\"\"\n input_file = click.format_filename(input)\n input_extension = path.splitext(input_file)[1][1:].lower()\n\n if input_extension in steg.LOSSLESS_AUDIO:\n steg_audio = AudioSegment.from_file(input_file, format=input_extension)\n decoded_message = steg.convert_from_stego_audio(steg_audio)\n elif input_extension in steg.LOSSLESS_IMG:\n with Image.open(input_file) as im:\n decoded_message = steg.convert_from_stego_image(im)\n else:\n raise ValueError(\"Cannot use given input format: \" + input_extension)\n\n output.write(decoded_message)\n output.flush\n\n\nif __name__ == '__main__':\n cli()\n", "sub_path": "steg/scripts/cli.py", "file_name": "cli.py", "file_ext": "py", "file_size_in_byte": 4281, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "click.group", "line_number": 10, "usage_type": "call"}, {"api_name": "click.version_option", "line_number": 11, "usage_type": "call"}, {"api_name": "click.format_filename", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "name"}, {"api_name": "click.format_filename", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "name"}, {"api_name": "steg.steg.LOSSLESS_AUDIO", "line_number": 44, "usage_type": "attribute"}, {"api_name": "steg.steg", "line_number": 44, "usage_type": "name"}, {"api_name": "steg.steg.LOSSLESS_IMG", "line_number": 46, "usage_type": "attribute"}, {"api_name": "steg.steg", "line_number": 46, "usage_type": "name"}, {"api_name": "click.argument", "line_number": 17, "usage_type": "call"}, {"api_name": "click.File", "line_number": 17, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 18, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 18, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 19, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 19, "usage_type": "call"}, {"api_name": "pydub.AudioSegment.from_file", "line_number": 53, "usage_type": "call"}, {"api_name": "pydub.AudioSegment", "line_number": 53, "usage_type": "name"}, {"api_name": "steg.steg.audio_can_fit_message", "line_number": 55, "usage_type": "call"}, {"api_name": "steg.steg", "line_number": 55, "usage_type": "name"}, {"api_name": "steg.steg.convert_to_stego_audio", "line_number": 58, "usage_type": "call"}, {"api_name": "steg.steg", "line_number": 58, "usage_type": "name"}, {"api_name": "pydub.AudioSegment", "line_number": 59, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 65, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 65, "usage_type": "name"}, {"api_name": "steg.steg.RGB_ONLY", "line_number": 66, "usage_type": "attribute"}, {"api_name": "steg.steg", "line_number": 66, "usage_type": "name"}, {"api_name": "steg.steg.image_can_fit_message", "line_number": 69, "usage_type": "call"}, {"api_name": "steg.steg", "line_number": 69, "usage_type": "name"}, {"api_name": "steg.steg.convert_to_stego_image", "line_number": 72, "usage_type": "call"}, {"api_name": "steg.steg", "line_number": 72, "usage_type": "name"}, {"api_name": "click.format_filename", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "name"}, {"api_name": "steg.steg.LOSSLESS_AUDIO", "line_number": 94, "usage_type": "attribute"}, {"api_name": "steg.steg", "line_number": 94, "usage_type": "name"}, {"api_name": "pydub.AudioSegment.from_file", "line_number": 95, "usage_type": "call"}, {"api_name": "pydub.AudioSegment", "line_number": 95, "usage_type": "name"}, {"api_name": "steg.steg.convert_from_stego_audio", "line_number": 96, "usage_type": "call"}, {"api_name": "steg.steg", "line_number": 96, "usage_type": "name"}, {"api_name": "steg.steg.LOSSLESS_IMG", "line_number": 97, "usage_type": "attribute"}, {"api_name": "steg.steg", "line_number": 97, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 98, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 98, "usage_type": "name"}, {"api_name": "steg.steg.convert_from_stego_image", "line_number": 99, "usage_type": "call"}, {"api_name": "steg.steg", "line_number": 99, "usage_type": "name"}, {"api_name": "click.argument", "line_number": 80, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 80, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 81, "usage_type": "call"}, {"api_name": "click.File", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "564743899", "text": "# Homework 4\n# Shweta Narkhede\n# Last edited on 09/19/2020\n\n# %%\n# Import the modules we will use\nimport os\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# %%\n# ** MODIFY **\n# Set the file name and path to where you have stored the data\nfilename = 'streamflow_week4.txt'\nfilepath = os.path.join('/Users/owner/Documents/GitHub/homework-shwetanarkhede/data/', filename)\nprint(os.getcwd())\nprint(filepath)\n\n# %%\n# DON'T change this part -- this creates the lists you \n# should use for the rest of the assignment\n# no need to worry about how this is being done now we will cover\n# this in later sections. \n\n#Read the data into a pandas dataframe\ndata=pd.read_table(filepath, sep = '\\t', skiprows=30,\n names=['agency_cd', 'site_no', 'datetime', 'flow', 'code']\n )\n\n# Expand the dates to year month day\ndata[[\"year\", \"month\", \"day\"]] =data[\"datetime\"].str.split(\"-\", expand=True)\ndata['year'] = data['year'].astype(int)\ndata['month'] = data['month'].astype(int)\ndata['day'] = data['day'].astype(int)\n\n# Make a numpy array of this data\nflow_data = data[['year', 'month','day', 'flow']].to_numpy()\n\n# Getting rid of the pandas dataframe since we wont be using it this week\ndel(data)\n\n# %%\n# Starter Code\n## Answers to assignment questions\nprint(\"Dimension of flow_data = \",flow_data.ndim) # Gives dimension of array\nprint(\"Total size of flow_data = \",flow_data.size) # Gives size of array\n\n# Count the number of values with flow > 48 and month ==9\nflow_count = np.sum((flow_data[:,3] > 48) & (flow_data[:,1]==9))\nprint(\"Number of times daily flow was greater than prediction = \",flow_count)\nflow_count_perc = np.round(flow_count/len(flow_data[:,1]==9)*100,2) # gives percentage value rounded to 2 decimal points\nprint(flow_count_perc,\"% times daily flow was greater than prediction\")\n\n# Count the number of values with flow > 48 and month ==9 and year <= 2000\nflow_count_b2000 = np.sum((flow_data[:,3] > 48) & (flow_data[:,1]==9) & (flow_data[:,0]<= 2000))\nprint(\"Number of times daily flow was greater than prediction in and before year 2000 = \",flow_count_b2000)\nflow_count_perc_b2000 = np.round(flow_count_b2000/len(flow_data[:,1]==9)*100,2) # gives percentage value rounded to 2 decimal points\nprint(flow_count_perc_b2000,\"% times daily flow was greater than prediction in and before year 2000 \")\n\n# Count the number of values with flow > 48 and month ==9 and year >= 2010\nflow_count_a2010 = np.sum((flow_data[:,3] > 48) & (flow_data[:,1]==9) & (flow_data[:,0]>= 2010))\nprint(\"Number of times daily flow was greater than prediction in and after year 2010 = \",flow_count_a2010)\nflow_count_perc_a2010 = np.round(flow_count_a2010/len(flow_data[:,1]==9)*100,2) # gives percentage value rounded to 2 decimal points\nprint(flow_count_perc_a2010,\"% times daily flow was greater than prediction in and after year 2010 \")\n\n# %%\n\n## Quantitative analysis\n\n# Grabbing flow data in september for entire length of record\nsept_flows = flow_data[(flow_data[:,1]==9),3]\n\n# Piecing out mean flows for specific weeks in september\nflow_sept_w = flow_data[(flow_data[:,1] ==9) & (flow_data[:,2]>= 20) & \\\n(flow_data[:,2]<= 26), 3] \n#print('weekly mean =', np.mean(flow_sept_w))\n\n# creating array for storing weekly means \n#allmeans = np.ones((1,len(flow_data[:,0])))\n\n#creating bins for flow data between min an dmax of sept data spaced in 10 bins\n#mybins = np.linspace(min(sept_flows),max(sept_flows),num=20)\n#plt.hist(flow_sept_w, bins = mybins)\n\n# by what percentage streamflow has been changed since past few years\n# Year to year % flow change in this week\n\nweeklymean1 = np.mean(flow_data[(flow_data[:,1] ==9) & (flow_data[:,2]>= 20) & \\\n(flow_data[:,2]<= 26) & (flow_data[:,0] ==2018), 3])\n\nweeklymean2 = np.mean(flow_data[(flow_data[:,1] ==9) & (flow_data[:,2]>= 20) & \\\n(flow_data[:,2]<= 26) & (flow_data[:,0] ==2019), 3])\nperc_change = (weeklymean2-weeklymean1)/weeklymean1*100\nprint('Mean changed by',round(perc_change,2),'% during 2018-2019')\nprint('Mean flow of next week (week1) = ',(weeklymean2*(1+perc_change/100)))\n\n# %% Week 1 forecast\n# but trend in 2020 points towards value close to 70 cfs\nweeklymean3 = np.mean(flow_data[(flow_data[:,1] ==9) & (flow_data[:,2]>=6) & \\\n(flow_data[:,2]<= 12) & (flow_data[:,0] ==2020), 3])\n\nweeklymean4 = np.mean(flow_data[(flow_data[:,1] ==9) & (flow_data[:,2]>=13) & \\\n(flow_data[:,2]<= 19) & (flow_data[:,0] ==2020), 3])\n\nprint('Previous week mean flow =',round(weeklymean3,2),'cfs')\nprint('Current week mean flow =',round(weeklymean4,2),'cfs')\n\nperc1 =((weeklymean4-weeklymean3)/weeklymean3)*100\nprint('Percentage change is',round(perc1,2),'%')\nnext_flow = weeklymean4*(1+perc1/100)\nprint('Week1 mean flow = ',round(next_flow,2),'cfs')\n\n# %% Week 2 forecast\n#Similar approach can be followed for forecasting flow after 2 weeks\n\nprint('Previous week mean flow =',round(weeklymean4,2),'cfs')\nprint('Current week mean flow =',round(next_flow,2),'cfs')\n\nperc2 =(next_flow-weeklymean4)/weeklymean4*100\nprint('Percentage change is',round(perc2,2),'%')\n\nprint('Week2 mean flow = ',next_flow*(1+perc2/100),'cfs') \n\n#%%\n\n# Visual analysis\n# plotting histogram to see which value of forecatsed flow weights more\n# creating 10 linearly spaced bins of flows from 40 cfs to 75 cfs\nmybins1 = np.linspace(40, 75, num=10)\n\n#Plotting the histogram\nHist_1 = plt.hist(sept_flows[(len(sept_flows)-149):len(sept_flows)], bins = mybins1)\nplt.title('Count check for flows close to 40 cfs')\nplt.xlabel('Flow [cfs]')\nplt.ylabel('Count')\n# %%\nmybins2 = np.linspace(70, 200, num=10)\n\n#Plotting the histogram for last 5 years flows in sept\nHist_2 = plt.hist(sept_flows[(len(sept_flows)-149):len(sept_flows)], bins = mybins2)\nplt.title('Count check for flows close to 70 cfs')\nplt.xlabel('Flow [cfs]')\nplt.ylabel('Count')\n\n\n# Get the quantiles of flow\n\nflow_quants1 = np.quantile(sept_flows, q=[0,0.1, 0.5, 0.9])\nprint('Method one flow quantiles:', flow_quants1)\n\n# %%\n\n", "sub_path": "Submissions/Narkhede_HW4.py", "file_name": "Narkhede_HW4.py", "file_ext": "py", "file_size_in_byte": 5919, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.read_table", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "numpy.quantile", "line_number": 149, "usage_type": "call"}]} +{"seq_id": "108708255", "text": "# Copyright 2015 Mirantis, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\n\nimport socket\nimport time\nimport traceback\nimport uuid\n\nfrom concurrent import futures\nfrom oslo_log import log as logging\nfrom oslo_utils import importutils\nfrom oslo_utils import timeutils\nfrom pika import exceptions as pika_exceptions\nfrom pika import spec as pika_spec\nimport pika_pool\nimport six\nimport tenacity\n\n\nimport oslo_messaging\nfrom oslo_messaging._drivers import base\nfrom oslo_messaging._drivers.pika_driver import pika_commons as pika_drv_cmns\nfrom oslo_messaging._drivers.pika_driver import pika_exceptions as pika_drv_exc\nfrom oslo_messaging import _utils as utils\nfrom oslo_messaging import exceptions\n\n\nLOG = logging.getLogger(__name__)\n\n_VERSION_HEADER = \"version\"\n_VERSION = \"1.0\"\n\n\nclass RemoteExceptionMixin(object):\n \"\"\"Used for constructing dynamic exception type during deserialization of\n remote exception. It defines unified '__init__' method signature and\n exception message format\n \"\"\"\n def __init__(self, module, clazz, message, trace):\n \"\"\"Store serialized data\n :param module: String, module name for importing original exception\n class of serialized remote exception\n :param clazz: String, original class name of serialized remote\n exception\n :param message: String, original message of serialized remote\n exception\n :param trace: String, original trace of serialized remote exception\n \"\"\"\n self.module = module\n self.clazz = clazz\n self.message = message\n self.trace = trace\n\n self._str_msgs = message + \"\\n\" + \"\\n\".join(trace)\n\n def __str__(self):\n return self._str_msgs\n\n\nclass PikaIncomingMessage(base.IncomingMessage):\n \"\"\"Driver friendly adapter for received message. Extract message\n information from RabbitMQ message and provide access to it\n \"\"\"\n\n def __init__(self, pika_engine, channel, method, properties, body):\n \"\"\"Parse RabbitMQ message\n\n :param pika_engine: PikaEngine, shared object with configuration and\n shared driver functionality\n :param channel: Channel, RabbitMQ channel which was used for\n this message delivery, used for sending ack back.\n If None - ack is not required\n :param method: Method, RabbitMQ message method\n :param properties: Properties, RabbitMQ message properties\n :param body: Bytes, RabbitMQ message body\n \"\"\"\n headers = getattr(properties, \"headers\", {})\n version = headers.get(_VERSION_HEADER, None)\n if not utils.version_is_compatible(version, _VERSION):\n raise pika_drv_exc.UnsupportedDriverVersion(\n \"Message's version: {} is not compatible with driver version: \"\n \"{}\".format(version, _VERSION))\n\n self._pika_engine = pika_engine\n self._channel = channel\n self._delivery_tag = method.delivery_tag\n\n self._version = version\n\n self._content_type = properties.content_type\n self.unique_id = properties.message_id\n\n self.expiration_time = (\n None if properties.expiration is None else\n time.time() + float(properties.expiration) / 1000\n )\n\n try:\n serializer = pika_drv_cmns.MESSAGE_SERIALIZERS[self._content_type]\n except KeyError:\n raise NotImplementedError(\n \"Content-type['{}'] is not supported.\".format(\n self._content_type\n )\n )\n\n message_dict = serializer.load_from_bytes(body)\n\n context_dict = {}\n\n for key in list(message_dict.keys()):\n key = six.text_type(key)\n if key.startswith('_$_'):\n value = message_dict.pop(key)\n context_dict[key[3:]] = value\n\n super(PikaIncomingMessage, self).__init__(context_dict, message_dict)\n\n def need_ack(self):\n return self._channel is not None\n\n def acknowledge(self):\n \"\"\"Ack the message. Should be called by message processing logic when\n it considered as consumed (means that we don't need redelivery of this\n message anymore)\n \"\"\"\n if self.need_ack():\n self._channel.basic_ack(delivery_tag=self._delivery_tag)\n\n def requeue(self):\n \"\"\"Rollback the message. Should be called by message processing logic\n when it can not process the message right now and should be redelivered\n later if it is possible\n \"\"\"\n if self.need_ack():\n return self._channel.basic_nack(delivery_tag=self._delivery_tag,\n requeue=True)\n\n\nclass RpcPikaIncomingMessage(PikaIncomingMessage, base.RpcIncomingMessage):\n \"\"\"PikaIncomingMessage implementation for RPC messages. It expects\n extra RPC related fields in message body (msg_id and reply_q). Also 'reply'\n method added to allow consumer to send RPC reply back to the RPC client\n \"\"\"\n\n def __init__(self, pika_engine, channel, method, properties, body):\n \"\"\"Defines default values of msg_id and reply_q fields and just call\n super.__init__ method\n\n :param pika_engine: PikaEngine, shared object with configuration and\n shared driver functionality\n :param channel: Channel, RabbitMQ channel which was used for\n this message delivery, used for sending ack back.\n If None - ack is not required\n :param method: Method, RabbitMQ message method\n :param properties: Properties, RabbitMQ message properties\n :param body: Bytes, RabbitMQ message body\n \"\"\"\n super(RpcPikaIncomingMessage, self).__init__(\n pika_engine, channel, method, properties, body\n )\n self.reply_q = properties.reply_to\n self.msg_id = properties.correlation_id\n\n def reply(self, reply=None, failure=None):\n \"\"\"Send back reply to the RPC client\n :param reply: Dictionary, reply. In case of exception should be None\n :param failure: Tuple, should be a sys.exc_info() tuple.\n Should be None if RPC request was successfully processed.\n\n :return RpcReplyPikaIncomingMessage, message with reply\n \"\"\"\n\n if self.reply_q is None:\n return\n\n reply_outgoing_message = RpcReplyPikaOutgoingMessage(\n self._pika_engine, self.msg_id, reply=reply, failure_info=failure,\n content_type=self._content_type,\n )\n\n def on_exception(ex):\n if isinstance(ex, pika_drv_exc.ConnectionException):\n LOG.warning(\n \"Connectivity related problem during reply sending. %s\",\n ex\n )\n return True\n else:\n return False\n\n if self._pika_engine.rpc_reply_retry_attempts:\n retrier = tenacity.retry(\n stop=(\n tenacity.stop_never\n if self._pika_engine.rpc_reply_retry_attempts == -1 else\n tenacity.stop_after_attempt(\n self._pika_engine.rpc_reply_retry_attempts\n )\n ),\n retry=tenacity.retry_if_exception(on_exception),\n wait=tenacity.wait_fixed(\n self._pika_engine.rpc_reply_retry_delay\n )\n )\n else:\n retrier = None\n\n try:\n timeout = (None if self.expiration_time is None else\n max(self.expiration_time - time.time(), 0))\n with timeutils.StopWatch(duration=timeout) as stopwatch:\n reply_outgoing_message.send(\n reply_q=self.reply_q,\n stopwatch=stopwatch,\n retrier=retrier\n )\n LOG.debug(\n \"Message [id:'%s'] replied to '%s'.\", self.msg_id, self.reply_q\n )\n except Exception:\n LOG.exception(\n \"Message [id:'%s'] wasn't replied to : %s\", self.msg_id,\n self.reply_q\n )\n\n\nclass RpcReplyPikaIncomingMessage(PikaIncomingMessage):\n \"\"\"PikaIncomingMessage implementation for RPC reply messages. It expects\n extra RPC reply related fields in message body (result and failure).\n \"\"\"\n def __init__(self, pika_engine, channel, method, properties, body):\n \"\"\"Defines default values of result and failure fields, call\n super.__init__ method and then construct Exception object if failure is\n not None\n\n :param pika_engine: PikaEngine, shared object with configuration and\n shared driver functionality\n :param channel: Channel, RabbitMQ channel which was used for\n this message delivery, used for sending ack back.\n If None - ack is not required\n :param method: Method, RabbitMQ message method\n :param properties: Properties, RabbitMQ message properties\n :param body: Bytes, RabbitMQ message body\n \"\"\"\n super(RpcReplyPikaIncomingMessage, self).__init__(\n pika_engine, channel, method, properties, body\n )\n\n self.msg_id = properties.correlation_id\n\n self.result = self.message.get(\"s\", None)\n self.failure = self.message.get(\"e\", None)\n\n if self.failure is not None:\n trace = self.failure.get('t', [])\n message = self.failure.get('s', \"\")\n class_name = self.failure.get('c')\n module_name = self.failure.get('m')\n\n res_exc = None\n\n if module_name in pika_engine.allowed_remote_exmods:\n try:\n module = importutils.import_module(module_name)\n klass = getattr(module, class_name)\n\n ex_type = type(\n klass.__name__,\n (RemoteExceptionMixin, klass),\n {}\n )\n\n res_exc = ex_type(module_name, class_name, message, trace)\n except ImportError as e:\n LOG.warning(\n \"Can not deserialize remote exception [module:%s, \"\n \"class:%s]. %s\", module_name, class_name, e\n )\n\n # if we have not processed failure yet, use RemoteError class\n if res_exc is None:\n res_exc = oslo_messaging.RemoteError(\n class_name, message, trace\n )\n self.failure = res_exc\n\n\nclass PikaOutgoingMessage(object):\n \"\"\"Driver friendly adapter for sending message. Construct RabbitMQ message\n and send it\n \"\"\"\n\n def __init__(self, pika_engine, message, context, content_type=None):\n \"\"\"Parse RabbitMQ message\n\n :param pika_engine: PikaEngine, shared object with configuration and\n shared driver functionality\n :param message: Dictionary, user's message fields\n :param context: Dictionary, request context's fields\n :param content_type: String, content-type header, defines serialization\n mechanism, if None default content-type from pika_engine is used\n \"\"\"\n\n self._pika_engine = pika_engine\n\n self._content_type = (\n content_type if content_type is not None else\n self._pika_engine.default_content_type\n )\n\n try:\n self._serializer = pika_drv_cmns.MESSAGE_SERIALIZERS[\n self._content_type\n ]\n except KeyError:\n raise NotImplementedError(\n \"Content-type['{}'] is not supported.\".format(\n self._content_type\n )\n )\n\n self.message = message\n self.context = context\n\n self.unique_id = uuid.uuid4().hex\n\n def _prepare_message_to_send(self):\n \"\"\"Combine user's message fields an system fields (_unique_id,\n context's data etc)\n \"\"\"\n msg = self.message.copy()\n\n if self.context:\n for key, value in self.context.items():\n key = six.text_type(key)\n msg['_$_' + key] = value\n\n props = pika_spec.BasicProperties(\n content_type=self._content_type,\n headers={_VERSION_HEADER: _VERSION},\n message_id=self.unique_id,\n )\n return msg, props\n\n @staticmethod\n def _publish(pool, exchange, routing_key, body, properties, mandatory,\n stopwatch):\n \"\"\"Execute pika publish method using connection from connection pool\n Also this message catches all pika related exceptions and raise\n oslo.messaging specific exceptions\n\n :param pool: Pool, pika connection pool for connection choosing\n :param exchange: String, RabbitMQ exchange name for message sending\n :param routing_key: String, RabbitMQ routing key for message routing\n :param body: Bytes, RabbitMQ message payload\n :param properties: Properties, RabbitMQ message properties\n :param mandatory: Boolean, RabbitMQ publish mandatory flag (raise\n exception if it is not possible to deliver message to any queue)\n :param stopwatch: StopWatch, stopwatch object for calculating\n allowed timeouts\n \"\"\"\n if stopwatch.expired():\n raise exceptions.MessagingTimeout(\n \"Timeout for current operation was expired.\"\n )\n try:\n timeout = stopwatch.leftover(return_none=True)\n with pool.acquire(timeout=timeout) as conn:\n if timeout is not None:\n properties.expiration = str(int(timeout * 1000))\n conn.channel.publish(\n exchange=exchange,\n routing_key=routing_key,\n body=body,\n properties=properties,\n mandatory=mandatory\n )\n except pika_exceptions.NackError as e:\n raise pika_drv_exc.MessageRejectedException(\n \"Can not send message: [body: {}], properties: {}] to \"\n \"target [exchange: {}, routing_key: {}]. {}\".format(\n body, properties, exchange, routing_key, str(e)\n )\n )\n except pika_exceptions.UnroutableError as e:\n raise pika_drv_exc.RoutingException(\n \"Can not deliver message:[body:{}, properties: {}] to any \"\n \"queue using target: [exchange:{}, \"\n \"routing_key:{}]. {}\".format(\n body, properties, exchange, routing_key, str(e)\n )\n )\n except pika_pool.Timeout as e:\n raise exceptions.MessagingTimeout(\n \"Timeout for current operation was expired. {}\".format(str(e))\n )\n except pika_pool.Connection.connectivity_errors as e:\n if (isinstance(e, pika_exceptions.ChannelClosed)\n and e.args and e.args[0] == 404):\n raise pika_drv_exc.ExchangeNotFoundException(\n \"Attempt to send message to not existing exchange \"\n \"detected, message: [body:{}, properties: {}], target: \"\n \"[exchange:{}, routing_key:{}]. {}\".format(\n body, properties, exchange, routing_key, str(e)\n )\n )\n\n raise pika_drv_exc.ConnectionException(\n \"Connectivity problem detected during sending the message: \"\n \"[body:{}, properties: {}] to target: [exchange:{}, \"\n \"routing_key:{}]. {}\".format(\n body, properties, exchange, routing_key, str(e)\n )\n )\n except socket.timeout:\n raise pika_drv_exc.TimeoutConnectionException(\n \"Socket timeout exceeded.\"\n )\n\n def _do_send(self, exchange, routing_key, msg_dict, msg_props,\n confirm=True, mandatory=True, persistent=False,\n stopwatch=pika_drv_cmns.INFINITE_STOP_WATCH, retrier=None):\n \"\"\"Send prepared message with configured retrying\n\n :param exchange: String, RabbitMQ exchange name for message sending\n :param routing_key: String, RabbitMQ routing key for message routing\n :param msg_dict: Dictionary, message payload\n :param msg_props: Properties, message properties\n :param confirm: Boolean, enable publisher confirmation if True\n :param mandatory: Boolean, RabbitMQ publish mandatory flag (raise\n exception if it is not possible to deliver message to any queue)\n :param persistent: Boolean, send persistent message if True, works only\n for routing into durable queues\n :param stopwatch: StopWatch, stopwatch object for calculating\n allowed timeouts\n :param retrier: tenacity.Retrying, configured retrier object for\n sending message, if None no retrying is performed\n \"\"\"\n msg_props.delivery_mode = 2 if persistent else 1\n\n pool = (self._pika_engine.connection_with_confirmation_pool\n if confirm else\n self._pika_engine.connection_without_confirmation_pool)\n\n body = self._serializer.dump_as_bytes(msg_dict)\n\n LOG.debug(\n \"Sending message:[body:%s; properties: %s] to target: \"\n \"[exchange:%s; routing_key:%s]\", body, msg_props, exchange,\n routing_key\n )\n\n publish = (self._publish if retrier is None else\n retrier(self._publish))\n\n return publish(pool, exchange, routing_key, body, msg_props,\n mandatory, stopwatch)\n\n def send(self, exchange, routing_key='', confirm=True, mandatory=True,\n persistent=False, stopwatch=pika_drv_cmns.INFINITE_STOP_WATCH,\n retrier=None):\n \"\"\"Send message with configured retrying\n\n :param exchange: String, RabbitMQ exchange name for message sending\n :param routing_key: String, RabbitMQ routing key for message routing\n :param confirm: Boolean, enable publisher confirmation if True\n :param mandatory: Boolean, RabbitMQ publish mandatory flag (raise\n exception if it is not possible to deliver message to any queue)\n :param persistent: Boolean, send persistent message if True, works only\n for routing into durable queues\n :param stopwatch: StopWatch, stopwatch object for calculating\n allowed timeouts\n :param retrier: tenacity.Retrying, configured retrier object for\n sending message, if None no retrying is performed\n \"\"\"\n msg_dict, msg_props = self._prepare_message_to_send()\n\n return self._do_send(exchange, routing_key, msg_dict, msg_props,\n confirm, mandatory, persistent,\n stopwatch, retrier)\n\n\nclass RpcPikaOutgoingMessage(PikaOutgoingMessage):\n \"\"\"PikaOutgoingMessage implementation for RPC messages. It adds\n possibility to wait and receive RPC reply\n \"\"\"\n def __init__(self, pika_engine, message, context, content_type=None):\n super(RpcPikaOutgoingMessage, self).__init__(\n pika_engine, message, context, content_type\n )\n self.msg_id = None\n self.reply_q = None\n\n def send(self, exchange, routing_key, reply_listener=None,\n stopwatch=pika_drv_cmns.INFINITE_STOP_WATCH, retrier=None):\n \"\"\"Send RPC message with configured retrying\n\n :param exchange: String, RabbitMQ exchange name for message sending\n :param routing_key: String, RabbitMQ routing key for message routing\n :param reply_listener: RpcReplyPikaListener, listener for waiting\n reply. If None - return immediately without reply waiting\n :param stopwatch: StopWatch, stopwatch object for calculating\n allowed timeouts\n :param retrier: tenacity.Retrying, configured retrier object for\n sending message, if None no retrying is performed\n \"\"\"\n msg_dict, msg_props = self._prepare_message_to_send()\n\n if reply_listener:\n self.msg_id = uuid.uuid4().hex\n msg_props.correlation_id = self.msg_id\n LOG.debug('MSG_ID is %s', self.msg_id)\n\n self.reply_q = reply_listener.get_reply_qname()\n msg_props.reply_to = self.reply_q\n\n future = reply_listener.register_reply_waiter(msg_id=self.msg_id)\n\n self._do_send(\n exchange=exchange, routing_key=routing_key, msg_dict=msg_dict,\n msg_props=msg_props, confirm=True, mandatory=True,\n persistent=False, stopwatch=stopwatch, retrier=retrier\n )\n\n try:\n return future.result(stopwatch.leftover(return_none=True))\n except BaseException as e:\n reply_listener.unregister_reply_waiter(self.msg_id)\n if isinstance(e, futures.TimeoutError):\n e = exceptions.MessagingTimeout()\n raise e\n else:\n self._do_send(\n exchange=exchange, routing_key=routing_key, msg_dict=msg_dict,\n msg_props=msg_props, confirm=True, mandatory=True,\n persistent=False, stopwatch=stopwatch, retrier=retrier\n )\n\n\nclass RpcReplyPikaOutgoingMessage(PikaOutgoingMessage):\n \"\"\"PikaOutgoingMessage implementation for RPC reply messages. It sets\n correlation_id AMQP property to link this reply with response\n \"\"\"\n def __init__(self, pika_engine, msg_id, reply=None, failure_info=None,\n content_type=None):\n \"\"\"Initialize with reply information for sending\n\n :param pika_engine: PikaEngine, shared object with configuration and\n shared driver functionality\n :param msg_id: String, msg_id of RPC request, which waits for reply\n :param reply: Dictionary, reply. In case of exception should be None\n :param failure_info: Tuple, should be a sys.exc_info() tuple.\n Should be None if RPC request was successfully processed.\n :param content_type: String, content-type header, defines serialization\n mechanism, if None default content-type from pika_engine is used\n \"\"\"\n self.msg_id = msg_id\n\n if failure_info is not None:\n ex_class = failure_info[0]\n ex = failure_info[1]\n tb = traceback.format_exception(*failure_info)\n if issubclass(ex_class, RemoteExceptionMixin):\n failure_data = {\n 'c': ex.clazz,\n 'm': ex.module,\n 's': ex.message,\n 't': tb\n }\n else:\n failure_data = {\n 'c': six.text_type(ex_class.__name__),\n 'm': six.text_type(ex_class.__module__),\n 's': six.text_type(ex),\n 't': tb\n }\n\n msg = {'e': failure_data}\n else:\n msg = {'s': reply}\n\n super(RpcReplyPikaOutgoingMessage, self).__init__(\n pika_engine, msg, None, content_type\n )\n\n def send(self, reply_q, stopwatch=pika_drv_cmns.INFINITE_STOP_WATCH,\n retrier=None):\n \"\"\"Send RPC message with configured retrying\n\n :param reply_q: String, queue name for sending reply\n :param stopwatch: StopWatch, stopwatch object for calculating\n allowed timeouts\n :param retrier: tenacity.Retrying, configured retrier object for\n sending message, if None no retrying is performed\n \"\"\"\n\n msg_dict, msg_props = self._prepare_message_to_send()\n msg_props.correlation_id = self.msg_id\n\n self._do_send(\n exchange=self._pika_engine.rpc_reply_exchange, routing_key=reply_q,\n msg_dict=msg_dict, msg_props=msg_props, confirm=True,\n mandatory=True, persistent=False, stopwatch=stopwatch,\n retrier=retrier\n )\n", "sub_path": "filesystems/vnx_rootfs_lxc_ubuntu64-16.04-v025-openstack-compute/rootfs/usr/lib/python2.7/dist-packages/oslo_messaging/_drivers/pika_driver/pika_message.py", "file_name": "pika_message.py", "file_ext": "py", "file_size_in_byte": 24684, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "oslo_log.log.getLogger", "line_number": 40, "usage_type": "call"}, {"api_name": "oslo_log.log", "line_number": 40, "usage_type": "name"}, {"api_name": "oslo_messaging._drivers.base.IncomingMessage", "line_number": 72, "usage_type": "attribute"}, {"api_name": "oslo_messaging._drivers.base", "line_number": 72, "usage_type": "name"}, {"api_name": "oslo_messaging._utils.version_is_compatible", "line_number": 91, "usage_type": "call"}, {"api_name": "oslo_messaging._utils", "line_number": 91, "usage_type": "name"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_exceptions.UnsupportedDriverVersion", "line_number": 92, "usage_type": "call"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_exceptions", "line_number": 92, "usage_type": "name"}, {"api_name": "time.time", "line_number": 107, "usage_type": "call"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_commons.MESSAGE_SERIALIZERS", "line_number": 111, "usage_type": "attribute"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_commons", "line_number": 111, "usage_type": "name"}, {"api_name": "six.text_type", "line_number": 124, "usage_type": "call"}, {"api_name": "oslo_messaging._drivers.base.RpcIncomingMessage", "line_number": 152, "usage_type": "attribute"}, {"api_name": "oslo_messaging._drivers.base", "line_number": 152, "usage_type": "name"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_exceptions.ConnectionException", "line_number": 195, "usage_type": "attribute"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_exceptions", "line_number": 195, "usage_type": "name"}, {"api_name": "tenacity.retry", "line_number": 205, "usage_type": "call"}, {"api_name": "tenacity.stop_never", "line_number": 207, "usage_type": "attribute"}, {"api_name": "tenacity.stop_after_attempt", "line_number": 209, "usage_type": "call"}, {"api_name": "tenacity.retry_if_exception", "line_number": 213, "usage_type": "call"}, {"api_name": "tenacity.wait_fixed", "line_number": 214, "usage_type": "call"}, {"api_name": "time.time", "line_number": 223, "usage_type": "call"}, {"api_name": "oslo_utils.timeutils.StopWatch", "line_number": 224, "usage_type": "call"}, {"api_name": "oslo_utils.timeutils", "line_number": 224, "usage_type": "name"}, {"api_name": "oslo_utils.importutils.import_module", "line_number": 277, "usage_type": "call"}, {"api_name": "oslo_utils.importutils", "line_number": 277, "usage_type": "name"}, {"api_name": "oslo_messaging.RemoteError", "line_number": 295, "usage_type": "call"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_commons.MESSAGE_SERIALIZERS", "line_number": 325, "usage_type": "attribute"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_commons", "line_number": 325, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 338, "usage_type": "call"}, {"api_name": "six.text_type", "line_number": 348, "usage_type": "call"}, {"api_name": "pika.spec.BasicProperties", "line_number": 351, "usage_type": "call"}, {"api_name": "pika.spec", "line_number": 351, "usage_type": "name"}, {"api_name": "oslo_messaging.exceptions.MessagingTimeout", "line_number": 376, "usage_type": "call"}, {"api_name": "oslo_messaging.exceptions", "line_number": 376, "usage_type": "name"}, {"api_name": "pika.exceptions.NackError", "line_number": 391, "usage_type": "attribute"}, {"api_name": "pika.exceptions", "line_number": 391, "usage_type": "name"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_exceptions.MessageRejectedException", "line_number": 392, "usage_type": "call"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_exceptions", "line_number": 392, "usage_type": "name"}, {"api_name": "pika.exceptions.UnroutableError", "line_number": 398, "usage_type": "attribute"}, {"api_name": "pika.exceptions", "line_number": 398, "usage_type": "name"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_exceptions.RoutingException", "line_number": 399, "usage_type": "call"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_exceptions", "line_number": 399, "usage_type": "name"}, {"api_name": "pika_pool.Timeout", "line_number": 406, "usage_type": "attribute"}, {"api_name": "oslo_messaging.exceptions.MessagingTimeout", "line_number": 407, "usage_type": "call"}, {"api_name": "oslo_messaging.exceptions", "line_number": 407, "usage_type": "name"}, {"api_name": "pika_pool.Connection", "line_number": 410, "usage_type": "attribute"}, {"api_name": "pika.exceptions.ChannelClosed", "line_number": 411, "usage_type": "attribute"}, {"api_name": "pika.exceptions", "line_number": 411, "usage_type": "name"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_exceptions.ExchangeNotFoundException", "line_number": 413, "usage_type": "call"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_exceptions", "line_number": 413, "usage_type": "name"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_exceptions.ConnectionException", "line_number": 421, "usage_type": "call"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_exceptions", "line_number": 421, "usage_type": "name"}, {"api_name": "socket.timeout", "line_number": 428, "usage_type": "attribute"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_exceptions.TimeoutConnectionException", "line_number": 429, "usage_type": "call"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_exceptions", "line_number": 429, "usage_type": "name"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_commons.INFINITE_STOP_WATCH", "line_number": 435, "usage_type": "attribute"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_commons", "line_number": 435, "usage_type": "name"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_commons.INFINITE_STOP_WATCH", "line_number": 473, "usage_type": "attribute"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_commons", "line_number": 473, "usage_type": "name"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_commons.INFINITE_STOP_WATCH", "line_number": 508, "usage_type": "attribute"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_commons", "line_number": 508, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 523, "usage_type": "call"}, {"api_name": "concurrent.futures.TimeoutError", "line_number": 542, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 542, "usage_type": "name"}, {"api_name": "oslo_messaging.exceptions.MessagingTimeout", "line_number": 543, "usage_type": "call"}, {"api_name": "oslo_messaging.exceptions", "line_number": 543, "usage_type": "name"}, {"api_name": "traceback.format_exception", "line_number": 575, "usage_type": "call"}, {"api_name": "six.text_type", "line_number": 585, "usage_type": "call"}, {"api_name": "six.text_type", "line_number": 586, "usage_type": "call"}, {"api_name": "six.text_type", "line_number": 587, "usage_type": "call"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_commons.INFINITE_STOP_WATCH", "line_number": 599, "usage_type": "attribute"}, {"api_name": "oslo_messaging._drivers.pika_driver.pika_commons", "line_number": 599, "usage_type": "name"}]} +{"seq_id": "592212722", "text": "import heapq\nimport json\nimport operator\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport pandas as pd\nimport matplotlib.patches as mpatches\n\n#print(\"fdsfdsf\")\t\t\t\n#df = pd.read_csv('gene_expression.csv')\n#print(\"fdsfdsf\")\t\t\t\n#abc = sns.swarmplot(data=df)\n#plt.xticks(rotation=90)\n#plt.show()\n\ndf = pd.read_csv('gene_expression.csv')\nfor region, df_region in df.groupby('stage'):\n plt.figure(figsize=(14,13))\n sns.stripplot(data=df_region,size=15,marker=\"o\",color=\"red\",linewidth=0.5)\n plt.xticks(rotation=90)\n stage_name=region.replace(\" \",\"_\")\n plt.title(region)\n filename='gene_plots/expressed_genes_%s.png' %(stage_name)\n plt.grid(axis=\"x\")\n plt.savefig(filename)\n plt.close()\n\n\nfor stage, df_stage in df.groupby('stage'):\n for stage2, df_stage2 in df.groupby('stage'):\n plt.figure(figsize=(14,13))\n if stage!=stage2:\n stage_name=stage.replace(\" \",\"_\")\n stage2_name=stage2.replace(\" \",\"_\")\n Green_patch = mpatches.Patch(color='green', label=stage2_name)\n Red_patch = mpatches.Patch(color='red', label=stage_name)\n sns.stripplot(data=df_stage,color=\"red\",linewidth=0.7,marker=\"o\",size=9)\n sns.stripplot(data=df_stage2,color=\"green\",linewidth=0.5,marker=\"x\",alpha=0.5,size=7)\n plt.xticks(rotation=90,fontsize=7)\n plt.title(stage_name+\" and \"+stage2_name)\n filename='gene_plots/compare/expressed_genes_%s.png' %(stage_name+\"+\"+stage2_name)\n plt.legend(handles=[Red_patch,Green_patch])\n plt.grid(axis=\"x\")\n plt.savefig(filename)\n plt.close()\n\nfor stage, df_stage in df.groupby('stage'):\n for stage2, df_stage2 in df.groupby('stage'):\n if stage!=stage2:\n plt.figure(figsize=(14,13))\n stage_name=stage.replace(\" \",\"_\")\n stage2_name=stage2.replace(\" \",\"_\")\n Green_patch = mpatches.Patch(color='black', label=(stage2_name+\" Mean\"))\n Red_patch = mpatches.Patch(color='blue', label=(stage_name+\" Mean\")) \n for i in df_stage.head():\n if i != \"Patient Name\" and i!=\"stage\":\n plt.scatter(i,df_stage[i].mean(),marker=\"o\",color=\"black\",s=15)\n for i in df_stage2.head():\n if i != \"Patient Name\" and i!=\"stage\": \n plt.scatter(i,df_stage2[i].mean(),marker=\"^\",color=\"blue\",s=15)\n filename='gene_plots/mean/expressed_genes_%s.png' %(stage_name+\"+\"+stage2_name+\"_Mean\")\n plt.legend(handles=[Red_patch,Green_patch])\n plt.xticks(rotation=90,fontsize=7)\n plt.grid()\n plt.savefig(filename) \n plt.close()\n ", "sub_path": "test_plot.py", "file_name": "test_plot.py", "file_ext": "py", "file_size_in_byte": 2665, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "seaborn.stripplot", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 36, "usage_type": "name"}, {"api_name": "seaborn.stripplot", "line_number": 37, "usage_type": "call"}, {"api_name": "seaborn.stripplot", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}]} +{"seq_id": "112483586", "text": "# Sets up database\r\nfrom sqlalchemy import *\r\nfrom sqlalchemy.ext.declarative import declarative_base\r\nfrom sqlalchemy.orm import relationship, sessionmaker\r\nfrom json import dumps\r\n\r\nBase = declarative_base()\r\n\r\n#Player table here\r\n\r\nclass Player(Base):\r\n __tablename__ = 'players'\r\n \r\n #Log in info\r\n username = Column(String, nullable = False, primary_key = True)\r\n password = Column(String, nullable = False)\r\n #account info\r\n first_name = Column(String, nullable = False)\r\n last_name = Column(String, nullable = False)\r\n age = Column(Integer, nullable = False)\r\n primary_position = Column(String)\r\n games = relationship('Game', back_populates = 'player')\r\n\r\n def __init__(self, username, password, first_name, last_name, age, primary_position):\r\n self.username = username\r\n self.password = password\r\n self.first_name = first_name\r\n self.last_name = last_name\r\n self.age = age\r\n self.primary_position = primary_position\r\n\r\n def __repr__(self):\r\n #return object type in blue and all variable names in yellow the values held by the variables will be printed in the default white.\r\n return (cyan(\"Player\\n\")\\\r\n + green(\"username\") + white(\": %s\\n\")\\\r\n + green(\"first_name\") + white(\": %s\\n\")\\\r\n + green(\"last_name\") + white(\": %s\\n\")\\\r\n + green(\"age\") + white(\": %d\\n\")\\\r\n + green(\"primary_position\") + white(\": %s\\n\"))\\\r\n %(self.username, self.first_name, self.last_name, self.age, self.primary_position)\r\n\r\n#Game table here\r\n\r\nclass Game(Base):\r\n __tablename__ = 'games'\r\n\r\n id = Column(String, nullable = False, primary_key = True) #The game id is random and will not take a value from the user, if a user provides a game name or id it will be discarded\r\n player_username = Column(String, ForeignKey('players.username', ondelete=\"CASCADE\"), nullable = False, primary_key = True)\r\n player_score = Column(Integer, nullable = False, default = 0)\r\n other_score = Column(Integer, nullable = False, default = 0)\r\n at_bats = Column(Integer, nullable = False, default = 0)\r\n hits = Column(Integer, nullable = False, default = 0)\r\n runs = Column(Integer, nullable = False, default = 0)\r\n runs_batted_in = Column(Integer, nullable = False, default = 0)\r\n walks = Column(Integer, nullable = False, default = 0)\r\n strike_outs = Column(Integer, nullable = False, default = 0)\r\n stolen_bases = Column(Integer, nullable = False, default = 0)\r\n errors = Column(Integer, nullable = False, default = 0)\r\n player = relationship(\"Player\", back_populates = 'games')\r\n\r\n def __init__(self, id, player_score, other_score, at_bat, hits, runs, runs_batted_in, walks, strike_outs, stolen_bases, errors):\r\n self.id = self.id\r\n self.player_score = player_score\r\n self.other_score = other_score\r\n self.at_bat = at_bat\r\n self.hits = hits\r\n self.runs = runs\r\n self.runs_batted_in = runs_batted_in\r\n self.walks = walks\r\n self.strike_outs = strike_outs\r\n self.stolen_bases = stolen_bases\r\n self.errors = errors\r\n\r\n def __repr__(self):\r\n return (cyan(\"Game\\n\")\\\r\n + green(\"id\") + white(\": %s\\n\")\\\r\n + green(\"player_score\") + white(\": %d\\n\")\\\r\n + green(\"other_score\") + white(\": %d\\n\")\\\r\n + green(\"at_bat\") + white(\": %d\\n\")\\\r\n + green(\"hits\") + white(\": %d\\n\")\\\r\n + green(\"runs\") + white(\": %d\\n\")\\\r\n + green(\"runs_batted_in\") + white(\": %d\\n\")\\\r\n + green(\"walks\") + white(\": %d\\n\")\\\r\n + green(\"strike_outs\") + white(\": %d\\n\")\\\r\n + green(\"stolen_bases\") + white(\": %d\\n\")\\\r\n + green(\"errors\") + white(\": %d\\n\"))\\\r\n %(self.id, self.player_score, self.other_score, self.at_bat, self.hits, selif.runs, self.runs_batted_in, self.walks, self.strike_outs, self.stolen_bases, self.errors)\r\n\r\n#Database set up here\r\n\r\nclass Db:\r\n def __init__(self):\r\n engineName = 'sqlite:///test.db'\r\n self.engine = create_engine(engineName)\r\n self.metadata = Base.metadata\r\n self.metadata.bind = self.engine\r\n self.metadata.drop_all(bind=self.engine)\r\n self.metadata.create_all(bind=self.engine)\r\n Session = sessionmaker(bind=self.engine)\r\n self.session = Session()\r\n\r\n def commit(self):\r\n self.session.commit()\r\n\r\n def rollback(self):\r\n self.session.rollback()\r\n\r\n#Database methods here\r\n\r\ndef getPlayers(self):\r\n players = self.session.query(Player).all()\r\n return players\r\n\r\ndef getPlayer(self, username):\r\n players = self.session.query(Player).all()\r\n for user in players:\r\n if user.username == username:\r\n return user\r\n\r\ndef addPlayer(self, username, password, first_name, last_name, age, primary_position = None):\r\n player = Player(username = username, password = password,\\\r\n first_name = first_name, last_name = last_name, age = age, primary_position = primary_position)\r\n self.session.add(player)\r\n\r\ndef deletePlayer(self, player):\r\n self.session.delete(player)\r\n\r\ndef getGame(self, id, player): \r\n games = self.session.query(Game).all()\r\n for game in games:\r\n if game.player == player:\r\n return game\r\n return None\r\n\r\ndef addGame(self, id, player_username, player_score, other_score, at_bat, hits, runs, runs_batted_in, walks, strike_outs, stolen_bases, errors):\r\n game = Game(id = id, player_username = player_username, player_score = player_score, other_score = other_score,\\\r\n at_bat = at_bat, hits = hits, runs = runs, runs_batted_in = runs_batted_in, walks = walks,\\\r\n strike_outs = strike_outs, stolen_bases = stolen_bases, errors = errors)\r\n self.session.add(game)\r\n return(game)\r\n\r\ndef deleteGame(self, game):\r\n self.session.delete(game)\r\n\r\n#Helper Functions\r\n\r\ndef white(text):\r\n white = \"\\033[0m \"\r\n new = white + text\r\n return new\r\n\r\ndef red(text):\r\n red = \"\\033[0;31;40m \"\r\n new = red + text\r\n return new\r\n\r\ndef green(text):\r\n green = \"\\033[0;32;40m \"\r\n new = green + text\r\n return new\r\n\r\ndef blue(text):\r\n blue = \"\\033[0;34;40m \"\r\n new = blue + text\r\n return new\r\n\r\ndef yellow(text):\r\n yellow = \"\\033[0;33;40m \"\r\n new = yellow + text\r\n return \r\n\r\ndef cyan(text):\r\n cyan = \"\\033[0;36;40m\"\r\n new = cyan + text\r\n return new\r\n\r\ndef purple(text):\r\n purple = \"\\033[0;35;40m\"\r\n new = purple + text\r\n return new\r\n\r\n#tests\r\n\r\nplayer = Player(username = \"smithma21\", password = \"password\", first_name = \"Mackenzie\", last_name = \"Smith\", age = 20, primary_position = \"Center Field\")\r\nprint (repr(player))\r\n", "sub_path": "db.py", "file_name": "db.py", "file_ext": "py", "file_size_in_byte": 6644, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 7, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 59, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 99, "usage_type": "call"}]} +{"seq_id": "532689287", "text": "# Copyright (C) 2005 - 2017 Jacques de Hooge, Geatec Engineering\n#\n# This program is free software.\n# You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicence.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY, without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n# See the QQuickLicence for details.\n\nimport sys\nimport logging as lg\n\nfrom .base import *\nfrom .util import *\n\ncurrentEvent = UniqueNumber (1)\ntriggerNode = CallableValue ()\n\nrecursionMessage = 'recursive node evaluation'\n\nlog = lg.debug\n# log = lg.info\n\ndef unequal (a, b):\n try:\n return bool (a != b)\n except: # Needed for Numpy, since != returns a boolean array\n return not a is b\n \nclass Transactor:\n def __init__ (self):\n self.clear ()\n\n def clear (self):\n self.updatedNodes = []\n \n def add (self, node):\n self.updatedNodes.append (node)\n \n def contains (self, node):\n return node in self.updatedNodes # Reference equality\n \n def rollBack (self):\n for node in self.updatedNodes:\n node.rollBack ()\n \n def act (self):\n for node in self.updatedNodes:\n node.act ()\n\ntransactor = Transactor ()\n\nclass Node (object): # Node representing atomary partial state in a state machine\n def __init__ (self, *value, retrigger = False): # Initial value is optional, not needed in case of dependent nodes\n self.sinkNodes = [] # Nodes that depend on this node\n self.links = [] # Zero or more links to bareRead / bareWrite pairs\n self.exceptions = []\n self.actions = []\n self.validator = lambda value: True # Validators are deprecated, use exceptions instead\n \n self.dependsOnSelf = False # y16m12d29\n self.retrigger = retrigger # Set implicitly if it's an event-only node, can also be set explicitly by assigning to this attribute\n self.persistent = False # Assume not worth persisting\n self.evaluating = 0\n if len (value) == 1: # If node is supposed to be freely initialized\n self.currentValue = value [0] # Free initialisation\n self.previousValue = self.currentValue # Make sure previousValue is available in case of free initialisation\n self.event = currentEvent () # Remember up to date\n \n if value [0] is None: # If it is a freely initalized event-only node\n self.retrigger = True # Always retrigger\n else: # Else (it is a freely initialized ordinary node)\n self.persistent = True # Remember it is part of a non-redundant basis for persistence \n\n else: # Else\n self.event = 0 # Should be updated\n self.currentValue = None\n self.previousValue = self.currentValue\n \n version = property (lambda self: self.event)\n \n def trace (self, traceName):\n self.traceName = traceName\n return self\n \n def logTrace (self, message, newLine = False):\n if hasattr (self, 'traceName'):\n splitName = self.traceName.split ('.')\n if len (splitName) == 2:\n log ('{0} {1:<20}{2:>20}: {3}'.format ('\\n' if newLine else '', splitName [0], splitName [1], message))\n else:\n log ('{0} {1:>20}: {2}'.format ('\\n' if newLine else '', splitName [0], message))\n return True\n else:\n return False\n\n def dependsOn (self, sourceNodes, getter = lambda: None): # Lay dependency relations this node and other nodes that it depends on\n if hasattr (self, 'sourceNodes'): # If dependsOn was called before for this node\n self.dependsOnSelf = False\n for sourceNode in self.sourceNodes: # For all nodes that this node depended upon previously\n sourceNode.sinkNodes.remove (self) # Remove the old dependency\n \n for sourceNode in sourceNodes: # For each node that this node depends upon\n sourceNode.sinkNodes.append (self) # Register this node with that other node\n if sourceNode == self:\n self.dependsOnSelf = True # y16m12d29\n \n self.sourceNodes = sourceNodes # Remember sourceNodes\n \n self.getter = getter # Lay down how to construct the value of this node\n \n try:\n self.evaluate () # Dependent initialisation by backward evaluation\n except:\n log ('Can\\'t evaluate')\n pass # Lacks some needed dependency, or getter is incomputable, wait for initialisation by forward propagation\n \n return self\n \n def addException (self, condition, aClass, message):\n self.exceptions.append ((condition, aClass, message))\n return self\n \n def addAction (self, action): # Convenience method, mainly to allow call chaining, added y14m12d10\n self.actions.append (action)\n return self\n \n def invalidate (self): # Invalidation phase, to know where to propagate and prevent cycles\n # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n if hasattr (self, 'currentValue'): # If already initialised\n if not transactor.contains (self): # If currentValue not already saved (prevent saving intermediate from follow)\n self.previousValue = self.currentValue # Remember previousValue early to enable rollBack if getter raises exception\n transactor.add (self) # Register that this node may alter its value as part of the current transaction\n # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\n self.event = 0 # Should be updated ??? Only if it does not have the current event number?\n\n for sinkNode in self.sinkNodes: # For all nodes that depend upon this node\n if sinkNode.event != 0: # If not closing a cycle\n sinkNode.invalidate () # Invalidate that dependent node\n\n def validate (self):\n for exception in self.exceptions:\n try: # Try and except block swapped y14m12d24\n if exception [0] ():\n raise exception [1] (exception [2])\n except TypeError: # Checkfunctions with self.currentValue parameter are deprecated\n if exception [0] (self.currentValue):\n raise exception [1] (exception [2])\n \n if not self.validator (self.currentValue): # Validators are deprecated\n raise Error ('Node value invalid')\n\n def evaluate (self, reevaluate = False): # Evaluation phase, two way propagation\n if self.event == 0 or reevaluate: # So only nodes that lay on the trigger path are REALLY ever computed!\n if self.evaluating > 10:\n self.logTrace ('Value before evaluation is {0}, {1}'.format (self.currentValue, recursionMessage.upper ()))\n raise FatalError (recursionMessage.capitalize ())\n else:\n if self.evaluating:\n message = 'Reentered node evaluation {} times'.format (self.evaluating)\n if not self.logTrace (message):\n log (message)\n \n self.evaluating += 1\n try:\n# if not transactor.contains (self): # If currentValue not already saved (prevent saving intermediate from follow)\n# self.previousValue = self.currentValue # Remember previousValue early to enable rollBack if getter raises exception\n# transactor.add (self) # Register that this node may alter its value as part of the current transaction\n \n self.logTrace ('Value before evaluation is {0}'.format (self.currentValue))\n self.currentValue = self.getter () # Compute currentValue, backpropagate if needed to evaluate getter\n self.logTrace ('Value after evaluation is {0}'.format (self.currentValue))\n \n if self.currentValue is None: # No ==, since NumPy overloads that\n self.retrigger = True\n \n self.event = currentEvent () # Certainly currentValue is up to date at this point\n self.propagate () # Forward propagation\n finally: # Even if getter raises an exception\n self.evaluating -= 1 # Re-enable evaluation\n return self.currentValue # Return possible updated currentValue\n \n def propagate (self): # Forward propagation\n self.validate () # Correct mistakes early, get report on changed node, rather than dependent one\n \n if self.retrigger or unequal (self.currentValue, self.previousValue) or self.dependsOnSelf:\n self.logTrace ('Writing to {0} links'.format (len (self.links)))\n for link in self.links: # For each GUI element associated with this node\n link.write () # Update that GUI element\n \n for sinkNode in self.sinkNodes: # For all sinkNodes\n if not sinkNode.evaluating: # Unless sinkNode is already under evaluation\n sinkNode.evaluate () # Make sure it evaluates, since no other node may ask it to\n else:\n sinkNode.logTrace ('Propagate, blocked') \n \n def act (self): # Called at the end of transaction, to ensure updated values, e.g. on entering an event loop\n if True or not self.new == Pass: #!!!\n if hasattr (self, 'action'): # \"Old style\" single action functionality kept for backward compatibility\n self.action ()\n \n for action in self.actions: # Perform all \"new style\" chainable actions associated with this node\n action ()\n \n new = property (evaluate) # Reading property yields value of node after current event\n\n old = property (lambda self: ifExpr (self.event == currentEvent (), # Reading property yields value of node before current event\n self.previousValue,\n self.currentValue\n ))\n \n touched = property (lambda self: self.event in (currentEvent (), 0) and self.event != 1)\n triggered = property (lambda self: self is triggerNode ())\n changed = property (lambda self: self.new != self.old)\n \n def getModified (self):\n try:\n return self.touched and unequal (self.currentValue, self.previousValue)\n except:\n return False\n \n modified = property (getModified)\n \n def convert (self, convertibleValue):\n return getAsTarget (convertibleValue, self.currentValue.__class__)\n \n def change (self, convertibleValue): # Initiate a change \n if app.handlingNotification:\n return # Forms.Message.Show causes a redundant LostFocus message, that trigger an extra call to Node.change\n \n transactor.clear () # Start new transaction early, to make it work for conversion errors as well\n self.previousValue = self.currentValue # Save previousValue early to enable rollback. No problem if currentValue remains unaltered.\n \n transactor.add (self) # Even if currentValue remains unaltered, the GUI should possibly be rolled back\n \n try:\n convertedValue = self.convert (convertibleValue)\n \n if self.retrigger or unequal (convertedValue, self.currentValue): # If retrigger or value changed\n triggerNode.value = self # Remember that this node started the propagation\n self.invalidate () # Invalidate this node and dependent nodes\n \n self.logTrace ('Event {0}, change from {1} to {2}'.format (self.event, self.currentValue, convertedValue), True)\n \n self.currentValue = convertedValue # Store new, converted value in this node\n self.event = currentEvent.getNext () # Make this node valid\n \n if self.dependsOnSelf: # y16m12d29\n self.evaluate (True) # y16m12d29\n \n self.propagate () # Propagate new value to dependent nodes\n transactor.act () # Late, since actions may need node values and may even enter event loops\n \n except Refusal as refusal:\n log ('Refusal (node)')\n handleNotification (refusal)\n transactor.rollBack ()\n \n except Exception as exception: # This is a barebones Python exception, so convert it to Eden exception\n log ('Exception (node)')\n handleNotification (Objection (exMessage (exception), report = exReport (exception)))\n transactor.rollBack ()\n \n def follow (self, convertibleValue):\n if not transactor.contains (self):\n self.previousValue = self.currentValue\n transactor.add (self)\n \n convertedValue = self.convert (convertibleValue)\n \n if self.retrigger or unequal (convertedValue, self.currentValue): \n self.invalidate ()\n \n self.logTrace ('Follow from {0} to {1}'.format (self.currentValue, convertedValue))\n self.currentValue = convertedValue # Store new, converted value in this node\n self.event = currentEvent () # Make this node valid\n self.propagate () # Propagate new value to dependent nodes\n \n # Don't call transactor.act here, since it would for the second time perform all actions\n # Since the new changed nodes are appended to the nodelist of the transaction, their actions are performed anyhow\n \n state = property (evaluate, lambda self, convertibleValue: self.follow (convertibleValue, True))\n \n def rollBack (self): # Restore previous state after exception in change of evaluate\n self.currentValue = self.previousValue # Restore previous value\n self.event = currentEvent () # State is result of currentEvent, with a rollBack, even in case of a rollBack\n \n for link in self.links: # For each GUI element associated with this node\n link.write () # Restore that GUI element\n \n def tagged (self, tag):\n self.tag = tag\n return self\n\nclass Link: # Link between a node and a particular bareRead / bareWrite pair of the possible multiple bareRead / bareWrite pairs within a view\n # Maintains multiple reading / writing states per view so that e.g. caption can follow content\n # Reads are done from the GUI, writes are done to the GUI\n \n def __init__ (self, node, bareRead, bareWrite, writeBack = True, initialWrite = True): # Tie link to node and to bareRead / bareWrite pair\n self.node = node\n self.node.links.append (self) # Add this link to links of node\n \n self.bareRead = bareRead if bareRead else lambda params: None # Remember bareRead\n self.reading = False # Not busy reading\n \n self.bareWrite = bareWrite if bareWrite else lambda: None # Remember bareWrite\n self.writing = False # Not busy writing\n \n self.writeBack = writeBack # Allow this view to be written back to as result of reading it (auto formatting)\n \n self.firstWrite = True\n \n if bareWrite and initialWrite:\n self.write ()\n \n def read (self, *params): # Read info from this view into associated node\n if not self.writing: # Prevent reading back half-written data, e.g. at re-checking items in a listView\n self.reading = True # Remember reading, to prevent writing while reading if no-writeback mode (see write method)\n self.bareRead (params) # Low level read from widget and / or event params\n self.reading = False # Remember not reading anymore\n \n def write (self): # Write info from associated node to this view\n if self.writeBack or not self.reading: # Prevent bareWrite as consequence of a read on the same view, if no-writeBack mode\n if not self.writing: # Prevent recursive bareWrite as side effect of node () call in bareWrite\n if self.node.retrigger or self.firstWrite or unequal (self.node.currentValue, self.node.previousValue):\n self.firstWrite = False\n self.writing = True # Remember busy writing\n \n try: # If the widget is already instantiated\n self.bareWrite () # Low level write to widget\n except AttributeError as exception: # If widget is not yet instantiated (while passing parameters to execute)\n pass # Do nothing\n except TypeError: # If widget is not yet instantiated (while passing parameters to execute)\n pass # Do nothing\n except NameError as exception: # !!! Tree drag&drop lifetime workaround\n log ('NameError in Link.write:\\n {}'.format (exception))\n \n self.writing = False # Remember not busy writing anymore\n \ndef getNode (valueOrNode, resultIfNone = None):\n if valueOrNode is None: # e.g. valueOrNode == False should lead to condition == True\n return resultIfNone\n else:\n if valueOrNode.__class__ == Node:\n return valueOrNode\n else: \n return Node (valueOrNode)\n ", "sub_path": "rasmus_lib/eden/eden_lib/node.py", "file_name": "node.py", "file_ext": "py", "file_size_in_byte": 20455, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "logging.debug", "line_number": 22, "usage_type": "attribute"}]} +{"seq_id": "255137575", "text": "from __future__ import absolute_import, print_function, unicode_literals\n\nimport copy\nimport csv\nfrom datetime import datetime\nfrom io import open\nimport re\nimport sys\nimport requests\nimport json\nfrom bs4 import BeautifulSoup\nif sys.version_info[0] == 2: # Python 2\n from cStringIO import StringIO\n from urllib import quote\nelse: # Python 3\n from io import StringIO\n from urllib.parse import quote\n\n\nclass pyGTrends(object):\n \"\"\"\n Google Trends API\n \"\"\"\n def __init__(self, username, password, custom_useragent=None):\n \"\"\"\n Initialize hard-coded URLs, HTTP headers, and login parameters\n needed to connect to Google Trends, then connect.\n \"\"\"\n self.username = username\n self.password = password\n self.url_login = \"https://accounts.google.com/ServiceLogin\"\n self.url_auth = \"https://accounts.google.com/ServiceLoginAuth\"\n # custom user agent so users know what \"new account signin for Google\" is\n if custom_useragent is None:\n self.custom_useragent = {'User-Agent': 'Pytrends'}\n else:\n self.custom_useragent = custom_useragent\n self._connect()\n\n def _connect(self):\n \"\"\"\n Connect to Google.\n Go to login page GALX hidden input value and send it back to google + login and password.\n http://stackoverflow.com/questions/6754709/logging-in-to-google-using-python\n \"\"\"\n self.ses = requests.session()\n login_html = self.ses.get(self.url_login, headers=self.custom_useragent)\n soup_login = BeautifulSoup(login_html.content, \"lxml\").find('form').find_all('input')\n dico = {}\n for u in soup_login:\n if u.has_attr('value'):\n dico[u['name']] = u['value']\n # override the inputs with out login and pwd:\n dico['Email'] = self.username\n dico['Passwd'] = self.password\n self.ses.post(self.url_auth, data=dico)\n\n def request_report(self, payload):\n payload['cmpt'] = 'q'\n payload['content'] = 1\n payload['export'] = 1\n if 'hl' not in payload:\n payload['hl'] = 'en-US'\n\n req_url = \"http://www.google.com/trends/trendsReport\"\n req = self.ses.get(req_url, params=payload)\n print(\"Now downloading information for:\")\n print(req.url)\n self.data = req.text\n\n if self.data in [\"You must be signed in to export data from Google Trends\"]:\n print(\"You must be signed in to export data from Google Trends\")\n raise Exception(self.data)\n\n def save_csv(self, path, trend_name):\n file_name = path + trend_name + \".csv\"\n with open(file_name, mode='wb') as f:\n f.write(self.data.encode('utf8'))\n\n def get_data(self):\n return self.data\n\n def get_suggestions(self, keyword):\n kw_param = quote(keyword)\n req = self.ses.get(\"https://www.google.com/trends/api/autocomplete/\" + kw_param)\n print(\"Now requesting keyword suggestions using:\")\n print(req.url)\n # response is invalid json but if you strip off \")]}',\" from the front it is then valid\n json_data = json.loads(req.text[5:])\n return json_data\n\n\ndef parse_data(data):\n \"\"\"\n Parse data in a Google Trends CSV export (as `str`) into JSON format\n with str values coerced into appropriate Python-native objects.\n\n Parameters\n ----------\n data : str\n CSV data as text, output by `pyGTrends.get_data()`\n\n Returns\n -------\n parsed_data : dict of lists\n contents of `data` parsed into JSON form with appropriate Python types;\n sub-tables split into separate dict items, keys are sub-table \"names\",\n and data values parsed according to type, e.g.\n '10' => 10, '10%' => 10, '2015-08-06' => `datetime.datetime(2015, 8, 6, 0, 0)`\n \"\"\"\n parsed_data = {}\n for i, chunk in enumerate(re.split(r'\\n{2,}', data)):\n if i == 0:\n match = re.search(r'^(.*?) interest: (.*)\\n(.*?); (.*?)$', chunk)\n if match:\n source, query, geo, period = match.groups()\n parsed_data['info'] = {'source': source, 'query': query,\n 'geo': geo, 'period': period}\n else:\n chunk = _clean_subtable(chunk)\n rows = [row for row in csv.reader(StringIO(chunk)) if row]\n if not rows:\n continue\n label, parsed_rows = _parse_rows(rows)\n if label in parsed_data:\n parsed_data[label+'_1'] = parsed_data.pop(label)\n parsed_data[label+'_2'] = parsed_rows\n else:\n parsed_data[label] = parsed_rows\n\n return parsed_data\n\n\ndef _clean_subtable(chunk):\n \"\"\"\n The data output by Google Trends is human-friendly, not machine-friendly;\n this function fixes a couple egregious data problems.\n 1. Google replaces rising search percentages with \"Breakout\" if the increase\n is greater than 5000%: https://support.google.com/trends/answer/4355000 .\n For parsing's sake, we set it equal to that high threshold value.\n 2. Rising search percentages between 1000 and 5000 have a comma separating\n the thousands, which is terrible for CSV data. We strip it out.\n \"\"\"\n chunk = re.sub(r',Breakout', ',5000%', chunk)\n chunk = re.sub(r'(,[+-]?[1-4]),(\\d{3}%\\n)', r'\\1\\2', chunk)\n return chunk\n\n\ndef _infer_dtype(val):\n \"\"\"\n Using regex, infer a limited number of dtypes for string `val`\n (only dtypes expected to be found in a Google Trends CSV export).\n \"\"\"\n if re.match(r'\\d{4}-\\d{2}(?:-\\d{2})?', val):\n return 'date'\n elif re.match(r'[+-]?\\d+$', val):\n return 'int'\n elif re.match(r'[+-]?\\d+%$', val):\n return 'pct'\n elif re.match(r'[\\w\\s\\.]+', val):\n return 'text'\n else:\n msg = \"val={0} dtype not recognized\".format(val)\n raise ValueError(msg)\n\n\ndef _convert_val(val, dtype):\n \"\"\"\n Convert string `val` into Python-native object according to its `dtype`:\n '10' => 10, '10%' => 10, '2015-08-06' => `datetime.datetime(2015, 8, 6, 0, 0)`,\n ' ' => None, 'foo' => 'foo'\n \"\"\"\n if not val.strip():\n return None\n elif dtype == 'date':\n match = re.match(r'(\\d{4}-\\d{2}-\\d{2})', val)\n if match:\n return datetime.strptime(match.group(), '%Y-%m-%d')\n else:\n return datetime.strptime(re.match(r'(\\d{4}-\\d{2})', val).group(), '%Y-%m')\n elif dtype == 'int':\n return int(val)\n elif dtype == 'pct':\n return int(val[:-1])\n else:\n return val\n\n\ndef _parse_rows(rows, header='infer'):\n \"\"\"\n Parse sub-table `rows` into JSON form and convert str values into appropriate\n Python types; if `header` == `infer`, will attempt to infer if header row\n in rows, otherwise pass True/False.\n \"\"\"\n if not rows:\n raise ValueError('rows={0} is invalid'.format(rows))\n rows = copy.copy(rows)\n label = rows[0][0].replace(' ', '_').lower()\n\n if header == 'infer':\n if len(rows) >= 3:\n if _infer_dtype(rows[1][-1]) != _infer_dtype(rows[2][-1]):\n header = True\n else:\n header = False\n else:\n header = False\n if header is True:\n colnames = rows[1]\n data_idx = 2\n else:\n colnames = None\n data_idx = 1\n\n data_dtypes = [_infer_dtype(val) for val in rows[data_idx]]\n if any(dd == 'pct' for dd in data_dtypes):\n label += '_pct'\n\n parsed_rows = []\n for row in rows[data_idx:]:\n vals = [_convert_val(val, dtype) for val, dtype in zip(row, data_dtypes)]\n if colnames:\n parsed_rows.append({colname:val for colname, val in zip(colnames, vals)})\n else:\n parsed_rows.append(vals)\n\n return label, parsed_rows\n", "sub_path": "pytrends/pyGTrends.py", "file_name": "pyGTrends.py", "file_ext": "py", "file_size_in_byte": 7873, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "sys.version_info", "line_number": 12, "usage_type": "attribute"}, {"api_name": "requests.session", "line_number": 46, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 48, "usage_type": "call"}, {"api_name": "io.open", "line_number": 77, "usage_type": "call"}, {"api_name": "urllib.parse.quote", "line_number": 84, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 89, "usage_type": "call"}, {"api_name": "re.split", "line_number": 112, "usage_type": "call"}, {"api_name": "re.search", "line_number": 114, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 121, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 121, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 144, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 145, "usage_type": "call"}, {"api_name": "re.match", "line_number": 154, "usage_type": "call"}, {"api_name": "re.match", "line_number": 156, "usage_type": "call"}, {"api_name": "re.match", "line_number": 158, "usage_type": "call"}, {"api_name": "re.match", "line_number": 160, "usage_type": "call"}, {"api_name": "re.match", "line_number": 176, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 178, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 178, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 180, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 180, "usage_type": "name"}, {"api_name": "re.match", "line_number": 180, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 197, "usage_type": "call"}]} +{"seq_id": "152618345", "text": "#-*- coding:utf-8 -*-\r\n\r\nfrom odoo import api, fields, models\r\nfrom itertools import groupby\r\nfrom odoo.tools import DEFAULT_SERVER_DATE_FORMAT\r\nfrom datetime import datetime\r\nimport locale\r\n\r\n\r\ntrimestre_1 = {'janvier':['2018-01-01','2018-01-31'],'fevrier':['2018-02-01','2018-02-28'],'mars':['2018-03-01', '2018-03-31']}\r\ntrimestre_2 = {'avril':['2018-04-01','2018-04-30'], 'mai':['2018-05-01','2018-05-31'],'juin':['2018-06-01','2018-06-30']}\r\ntrimestre_3 = {'juillet':['2018-07-01','2018-07-31'],'aout':['2018-08-01','2018-08-31'],'septembre':['2018-09-01','2018-09-30']}\r\ntrimestre_4 = {'octobre':['2018-10-01','2018-10-31'],'novembre':['2018-11-01','2018-11-30'],'decembre':['2018-12-01','2018-12-31']}\r\n\r\nclass HrCnpsMonthly(models.Model):\r\n _name = 'hr.cnps.trimestriel'\r\n _description = \"Gestion de la CNPS TRIMESTRIEL\"\r\n\r\n\r\n date_from = fields.Date('Date de début')\r\n date_to = fields.Date('Date de fin')\r\n trimestre = fields.Selection([('trimestre_1', 'Premier Trimestre (Janvier-Février-Mars)'),\r\n ('trimestre_2', 'Deuxième Trimestre (Avril-Mai-Juin)'),\r\n ('trimestre_3', 'Troisième Trimestre (Juillet-Août-Septembre)'),\r\n ('trimestre_4', 'Quatrième Trimestre (Octobre-Novembre-Décembre)')],\r\n 'Trimestre', required=True)\r\n company_id= fields.Many2one('res.company', 'Compagnie', required=True, default=1)\r\n assurance_maternite = fields.Integer('Assurance Maternité',compute='compute', store=True)\r\n prestation_familiale = fields.Integer('Prestation Familiale', compute='compute', store=True)\r\n accident_travail = fields.Integer('Accident Travail', compute='compute', store=True)\r\n regime_retraite = fields.Integer('Régime Retraite', compute='compute', store=True)\r\n total_cotisation = fields.Integer('Total Cotisation', compute='compute', store=True)\r\n total_brut = fields.Integer('Total Brut',compute='compute', store=True)\r\n total_regime_retraite = fields.Integer('Total Régime Retraite', compute='_get_totaux', store=True)\r\n total_des_regimes = fields.Integer('Total des Régime', compute='_get_totaux', store=True)\r\n cumul_salaire_retraite = fields.Integer('Cumul retraite')\r\n cumul_salaire_prestafami_acctrav = fields.Integer('Cumul prestation famille et accident de travail')\r\n seq_cnps_trim = fields.Char()\r\n mois_1 = fields.Char()\r\n mois_2 = fields.Char()\r\n mois_3 = fields.Char()\r\n\r\n @api.model\r\n def create(self, vals):\r\n\r\n vals['seq_cnps_trim'] = self.env['ir.sequence'].next_by_code('hr.cnps.trimestriel')\r\n return super(HrCnpsMonthly, self).create(vals)\r\n\r\n def get_amount_by_code(self, slips, code):\r\n result = []\r\n amount = 0\r\n for slip in slips :\r\n tmp= slip.line_ids.filtered(lambda r: r.code==code)\r\n if tmp :\r\n result+= tmp\r\n if result :\r\n amount = sum([line.total for line in result])\r\n return amount\r\n\r\n def computeBrut(self, type, brut):\r\n vals = {}\r\n if type == 'm':\r\n if brut > 1657315:\r\n vals['retraite'] = 1657315\r\n vals['tranche']= 4\r\n elif brut >= 70000 and brut <= 1657315 :\r\n vals['retraite']= brut\r\n vals['tranche']= 3\r\n else :\r\n vals['retraite']= brut\r\n vals['tranche']= 2\r\n vals['autre_cotisation'] = 70000\r\n return vals\r\n\r\n\r\n def computeValues(self, employee, list_slip):\r\n # vals = {}\r\n brut = self.get_amount_by_code(list_slip, 'BRUT')\r\n vals= self.computeBrut(employee.type, brut)\r\n\r\n def compute_data(self, data, tranche):\r\n vals = {\r\n 'tranche': tranche,\r\n 'nombre': 0,\r\n 'retraite': 0,\r\n 'cotisation': 0\r\n }\r\n for item in data:\r\n if item.get('tranche') == tranche:\r\n vals['nombre'] += 1\r\n vals['retraite'] += item.get('retraite')\r\n vals['cotisation'] += item.get('autre_cotisation')\r\n return vals\r\n\r\n def get_taux(self, company):\r\n if company:\r\n vals= {\r\n 'accident': company.taux_accident_travail,\r\n 'cnps': company.taux_cnps_employee_local+ company.taux_cnps_employer,\r\n 'famille': company.taux_prestation_familiale,\r\n 'maternite': company.taux_assurance_mater\r\n }\r\n return vals\r\n return {}\r\n\r\n def _get_totaux(self, data):\r\n nombre = retraite = cotisation = 0\r\n for item in data :\r\n nombre += item.get('nombre')\r\n retraite += item.get('retraite')\r\n cotisation += item.get('cotisation')\r\n # self.total_regime_retraite = retraite\r\n # self.total_des_regimes = cotisation\r\n return {\r\n 'nombre': nombre,\r\n 'retraite': retraite,\r\n 'cotisation': cotisation\r\n }\r\n\r\n def compute(self):\r\n for rec in self:\r\n rec.ensure_one()\r\n periode = {}\r\n res_1 = {}\r\n res_2 = {}\r\n res_3 = {}\r\n periode['ids'] = rec.id\r\n periode['model'] = 'hr.cnps.trimestriel'\r\n lang_code = rec.env.context.get('lang') or 'en_US'\r\n lang = rec.env['res.lang']\r\n lang_id = lang._lang_get(lang_code)\r\n date_format = lang_id.date_format\r\n slip_obj = rec.env['hr.payslip']\r\n\r\n if rec.trimestre == 'trimestre_1':\r\n # Date dédut et fin de Janvier\r\n date_from_j = trimestre_1['janvier'][0]\r\n date_to_j = trimestre_1['janvier'][1]\r\n #date_from_janvier = datetime.strptime(date_from_j, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n #date_to_janvier = datetime.strptime(date_to_j, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n slips_janvier = slip_obj.search([('date_from', '>=', date_to_j), ('date_to', '<=', date_to_j),\r\n ('company_id', '=', rec.company_id.id)])\r\n date = datetime.strptime(date_from_j, '%Y-%m-%d')\r\n res_1['mois'] = date.strftime(\"%B\").upper()\r\n rec.mois_1 = res_1['mois']\r\n data_janvier = []\r\n results_janvier = []\r\n total_brut_janvier = 0\r\n if slips_janvier:\r\n order = 0\r\n\r\n for employee, list_slip in groupby(slips_janvier, lambda l: l.employee_id):\r\n tmp = list(list_slip)\r\n brut = rec.get_amount_by_code(tmp, 'BRUT')\r\n total_brut_janvier += brut\r\n vals = rec.computeBrut(employee.type, brut)\r\n data_janvier.append(vals)\r\n for i in range(5):\r\n result = rec.compute_data(data_janvier, i)\r\n results_janvier.append(result)\r\n res_1['taux'] = rec.get_taux(rec.company_id)\r\n res_1['lines'] = results_janvier\r\n res_1['totaux'] = rec._get_totaux(results_janvier)\r\n res_1['retraite'] = int(res_1['totaux']['retraite'] * (res_1['taux']['cnps'] / 100))\r\n res_1['accident'] = int(res_1['totaux']['cotisation'] * (res_1['taux']['accident'] / 100))\r\n res_1['famille'] = int(res_1['totaux']['cotisation'] * (res_1['taux']['famille'] / 100))\r\n res_1['maternity'] = int(res_1['totaux']['cotisation'] * (res_1['taux']['maternite'] / 100))\r\n res_1['total_brut'] = total_brut_janvier\r\n res_1['total_cotisation'] = int(res_1['maternity'] + res_1['famille'] + res_1['accident'] + res_1['retraite'])\r\n periode['periode_1'] = res_1\r\n\r\n # Date dédut et fin de Février\r\n date_from_f = trimestre_1['fevrier'][0]\r\n date_to_f = trimestre_1['fevrier'][1]\r\n date_from_fevrier = datetime.strptime(date_from_f, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n date_to_fevrier = datetime.strptime(date_to_f, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n slips_fevrier = slip_obj.search([('date_from', '>=', date_from_fevrier), ('date_to', '<=', date_to_fevrier),\r\n ('company_id', '=', rec.company_id.id)])\r\n\r\n res_2['mois'] = 'FEVRIER'\r\n rec.mois_2 = res_2['mois']\r\n data_fevrier = []\r\n results_fevrier = []\r\n total_brut_fevrier = 0\r\n if slips_fevrier:\r\n order = 0\r\n\r\n for employee, list_slip in groupby(slips_fevrier, lambda l: l.employee_id):\r\n tmp = list(list_slip)\r\n brut = rec.get_amount_by_code(tmp, 'BRUT')\r\n total_brut_fevrier += brut\r\n vals = rec.computeBrut(employee.type, brut)\r\n data_fevrier.append(vals)\r\n for i in range(5):\r\n result = rec.compute_data(data_fevrier, i)\r\n results_fevrier.append(result)\r\n res_2['taux'] = rec.get_taux(rec.company_id)\r\n res_2['lines'] = results_fevrier\r\n res_2['totaux'] = rec._get_totaux(results_fevrier)\r\n res_2['retraite'] = int(res_2['totaux']['retraite'] * (res_2['taux']['cnps'] / 100))\r\n res_2['accident'] = int(res_2['totaux']['cotisation'] * (res_2['taux']['accident'] / 100))\r\n res_2['famille'] = int(res_2['totaux']['cotisation'] * (res_2['taux']['famille'] / 100))\r\n res_2['maternity'] = int(res_2['totaux']['cotisation'] * (res_2['taux']['maternite'] / 100))\r\n res_2['total_brut'] = total_brut_fevrier\r\n res_2['total_cotisation'] = int(res_2['maternity'] + res_2['famille'] + res_2['accident'] + res_2['retraite'])\r\n periode['periode_2'] = res_2\r\n\r\n # Date dédut et fin de Mars\r\n date_from_m = trimestre_1['mars'][0]\r\n date_to_m = trimestre_1['mars'][1]\r\n date_from_mars = datetime.strptime(date_from_m, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n date_to_mars = datetime.strptime(date_to_m, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n slips_mars= slip_obj.search([('date_from', '>=', date_from_mars), ('date_to', '<=', date_to_mars),\r\n ('company_id', '=', rec.company_id.id)])\r\n date = datetime.strptime(date_from_m, '%Y-%m-%d')\r\n res_3['mois'] = date.strftime(\"%B\").upper()\r\n rec.mois_3 = res_3['mois']\r\n data_mars = []\r\n results_mars = []\r\n total_brut_mars = 0\r\n if slips_mars :\r\n order = 0\r\n for employee, list_slip in groupby(slips_mars, lambda l: l.employee_id):\r\n tmp = list(list_slip)\r\n brut = rec.get_amount_by_code(tmp, 'BRUT')\r\n total_brut_mars += brut\r\n vals = rec.computeBrut(employee.type, brut)\r\n data_mars.append(vals)\r\n for i in range(5):\r\n result= rec.compute_data(data_mars, i)\r\n results_mars.append(result)\r\n res_3['taux'] = rec.get_taux(rec.company_id)\r\n res_3['lines'] = results_mars\r\n res_3['totaux'] = rec._get_totaux(results_mars)\r\n res_3['retraite'] = int(res_3['totaux']['retraite'] * (res_3['taux']['cnps']/100))\r\n res_3['accident'] = int(res_3['totaux']['cotisation'] * (res_3['taux']['accident']/100))\r\n res_3['famille'] = int(res_3['totaux']['cotisation'] * (res_3['taux']['famille']/100))\r\n res_3['maternity'] = int(res_3['totaux']['cotisation'] * (res_3['taux']['maternite']/100))\r\n res_3['total_brut'] = total_brut_mars\r\n res_3['total_cotisation'] = int(res_3['maternity'] + res_3['famille'] + res_3['accident'] + res_3['retraite'])\r\n periode['periode_3'] = res_3\r\n\r\n periode['cotisation_p'] = periode['periode_1']['totaux']['cotisation'] + periode['periode_2']['totaux']['cotisation'] + periode['periode_3']['totaux']['cotisation']\r\n periode['retraite_p'] = periode['periode_1']['totaux']['retraite'] + periode['periode_2']['totaux']['retraite'] + periode['periode_3']['totaux']['retraite']\r\n rec.assurance_maternite = int(periode['periode_1']['maternity'] + periode['periode_2']['maternity'] + periode['periode_3']['maternity'])\r\n rec.prestation_familiale = int(periode['periode_1']['famille'] + periode['periode_2']['famille'] + periode['periode_3']['famille'])\r\n rec.accident_travail = int(periode['periode_1']['accident'] + periode['periode_2']['accident'] + periode['periode_3']['accident'])\r\n rec.regime_retraite = int(periode['periode_1']['retraite'] + periode['periode_2']['retraite'] + periode['periode_3']['retraite'])\r\n rec.total_brut = int(periode['periode_1']['total_brut'] + periode['periode_2']['total_brut'] + periode['periode_3']['total_brut'])\r\n rec.total_cotisation = int(periode['periode_1']['total_cotisation'] + periode['periode_2']['total_cotisation'] + periode['periode_3']['total_cotisation'])\r\n rec.date_from = trimestre_1['janvier'][0]\r\n rec.date_to = trimestre_1['mars'][1]\r\n rec.cumul_salaire_retraite = rec.regime_retraite\r\n rec.cumul_salaire_prestafami_acctrav = rec.assurance_maternite + rec.prestation_familiale + rec.accident_travail\r\n\r\n if rec.trimestre == 'trimestre_2':\r\n # Date dédut et fin de Avril\r\n date_from_a = trimestre_2['avril'][0]\r\n date_to_a = trimestre_2['avril'][1]\r\n date_from_avril = datetime.strptime(date_from_a, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n date_to_avril = datetime.strptime(date_to_a, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n slips_avril = slip_obj.search([('date_from', '>=', date_from_avril), ('date_to', '<=', date_to_avril),\r\n ('company_id', '=', rec.company_id.id)])\r\n date = datetime.strptime(date_from_a, '%Y-%m-%d')\r\n res_1['mois'] = date.strftime(\"%B\").upper()\r\n rec.mois_1 = res_1['mois']\r\n data_avril = []\r\n results_avril = []\r\n total_brut_avril = 0\r\n if slips_avril:\r\n order = 0\r\n\r\n for employee, list_slip in groupby(slips_avril, lambda l: l.employee_id):\r\n tmp = list(list_slip)\r\n brut = rec.get_amount_by_code(tmp, 'BRUT')\r\n total_brut_avril += brut\r\n vals = rec.computeBrut(employee.type, brut)\r\n data_avril.append(vals)\r\n for i in range(5):\r\n result = rec.compute_data(data_avril, i)\r\n results_avril.append(result)\r\n res_1['taux'] = rec.get_taux(rec.company_id)\r\n res_1['lines'] = results_avril\r\n res_1['totaux'] = rec._get_totaux(results_avril)\r\n res_1['retraite'] = int(res_1['totaux']['retraite'] * (res_1['taux']['cnps'] / 100))\r\n res_1['accident'] = int(res_1['totaux']['cotisation'] * (res_1['taux']['accident'] / 100))\r\n res_1['famille'] = int(res_1['totaux']['cotisation'] * (res_1['taux']['famille'] / 100))\r\n res_1['maternity'] = int(res_1['totaux']['cotisation'] * (res_1['taux']['maternite'] / 100))\r\n res_1['total_brut'] = total_brut_avril\r\n res_1['total_cotisation'] = int(res_1['maternity'] + res_1['famille'] + res_1['accident'] + res_1['retraite'])\r\n periode['periode_1'] = res_1\r\n\r\n # Date dédut et fin de Mai\r\n date_from_m = trimestre_2['mai'][0]\r\n date_to_m = trimestre_2['mai'][1]\r\n date_from_mai = datetime.strptime(date_from_m, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n date_to_mai = datetime.strptime(date_to_m, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n slips_mai = slip_obj.search([('date_from', '>=', date_from_mai), ('date_to', '<=', date_to_mai),\r\n ('company_id', '=', rec.company_id.id)])\r\n date = datetime.strptime(date_from_m, '%Y-%m-%d')\r\n res_2['mois'] = date.strftime(\"%B\").upper()\r\n rec.mois_2 = res_2['mois']\r\n data_mai = []\r\n results_mai = []\r\n total_brut_mai = 0\r\n if slips_mai:\r\n order = 0\r\n\r\n for employee, list_slip in groupby(slips_mai, lambda l: l.employee_id):\r\n tmp = list(list_slip)\r\n brut = rec.get_amount_by_code(tmp, 'BRUT')\r\n total_brut_mai += brut\r\n vals = rec.computeBrut(employee.type, brut)\r\n data_mai.append(vals)\r\n for i in range(5):\r\n result = rec.compute_data(data_mai, i)\r\n results_mai.append(result)\r\n res_2['taux'] = rec.get_taux(rec.company_id)\r\n res_2['lines'] = results_mai\r\n res_2['totaux'] = rec._get_totaux(results_mai)\r\n res_2['retraite'] = int(res_2['totaux']['retraite'] * (res_2['taux']['cnps'] / 100))\r\n res_2['accident'] = int(res_2['totaux']['cotisation'] * (res_2['taux']['accident'] / 100))\r\n res_2['famille'] = int(res_2['totaux']['cotisation'] * (res_2['taux']['famille'] / 100))\r\n res_2['maternity'] = int(res_2['totaux']['cotisation'] * (res_2['taux']['maternite'] / 100))\r\n res_2['total_brut'] = total_brut_mai\r\n res_2['total_cotisation'] = int(res_2['maternity'] + res_2['famille'] + res_2['accident'] + res_2['retraite'])\r\n periode['periode_2'] = res_2\r\n\r\n # Date dédut et fin de Juin\r\n date_from_j = trimestre_2['juin'][0]\r\n date_to_j = trimestre_2['juin'][1]\r\n date_from_juin = datetime.strptime(date_from_j, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n date_to_juin = datetime.strptime(date_to_j, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n slips_juin= slip_obj.search([('date_from', '>=', date_from_juin), ('date_to', '<=', date_to_juin),\r\n ('company_id', '=', rec.company_id.id)])\r\n date = datetime.strptime(date_from_j, '%Y-%m-%d')\r\n res_3['mois'] = date.strftime(\"%B\").upper()\r\n rec.mois_3 = res_3['mois']\r\n data_juin = []\r\n results_juin = []\r\n total_brut_juin = 0\r\n if slips_juin :\r\n order = 0\r\n for employee, list_slip in groupby(slips_juin, lambda l: l.employee_id):\r\n tmp = list(list_slip)\r\n brut = rec.get_amount_by_code(tmp, 'BRUT')\r\n total_brut_juin += brut\r\n vals = rec.computeBrut(employee.type, brut)\r\n data_juin.append(vals)\r\n for i in range(5):\r\n result= rec.compute_data(data_juin, i)\r\n results_juin.append(result)\r\n res_3['taux'] = rec.get_taux(rec.company_id)\r\n res_3['lines'] = results_juin\r\n res_3['totaux'] = rec._get_totaux(results_juin)\r\n res_3['retraite'] = int(res_3['totaux']['retraite'] * (res_3['taux']['cnps']/100))\r\n res_3['accident'] = int(res_3['totaux']['cotisation'] * (res_3['taux']['accident']/100))\r\n res_3['famille'] = int(res_3['totaux']['cotisation'] * (res_3['taux']['famille']/100))\r\n res_3['maternity'] = int(res_3['totaux']['cotisation'] * (res_3['taux']['maternite']/100))\r\n res_3['total_brut'] = total_brut_juin\r\n res_3['total_cotisation'] = int(res_3['maternity'] + res_3['famille'] + res_3['accident'] + res_3['retraite'])\r\n periode['periode_3'] = res_3\r\n\r\n periode['cotisation_p'] = periode['periode_1']['totaux']['cotisation'] + periode['periode_2']['totaux']['cotisation'] + periode['periode_3']['totaux']['cotisation']\r\n periode['retraite_p'] = periode['periode_1']['totaux']['retraite'] + periode['periode_2']['totaux']['retraite'] + periode['periode_3']['totaux']['retraite']\r\n rec.assurance_maternite = int(periode['periode_1']['maternity'] + periode['periode_2']['maternity'] + periode['periode_3']['maternity'])\r\n rec.prestation_familiale = int(periode['periode_1']['famille'] + periode['periode_2']['famille'] + periode['periode_3']['famille'])\r\n rec.accident_travail = int(periode['periode_1']['accident'] + periode['periode_2']['accident'] + periode['periode_3']['accident'])\r\n rec.regime_retraite = int(periode['periode_1']['retraite'] + periode['periode_2']['retraite'] + periode['periode_3']['retraite'])\r\n rec.total_brut = int(periode['periode_1']['total_brut'] + periode['periode_2']['total_brut'] + periode['periode_3']['total_brut'])\r\n rec.total_cotisation = int(periode['periode_1']['total_cotisation'] + periode['periode_2']['total_cotisation'] + periode['periode_3']['total_cotisation'])\r\n rec.date_from = trimestre_2['avril'][0]\r\n rec.date_to = trimestre_2['juin'][1]\r\n rec.cumul_salaire_retraite = rec.regime_retraite\r\n rec.cumul_salaire_prestafami_acctrav = rec.assurance_maternite + rec.prestation_familiale + rec.accident_travail\r\n\r\n if rec.trimestre == 'trimestre_3':\r\n # Date dédut et fin de Juillet\r\n date_from_ju = trimestre_3['juillet'][0]\r\n date_to_ju = trimestre_3['juillet'][1]\r\n date_from_juillet = datetime.strptime(date_from_ju, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n date_to_juillet= datetime.strptime(date_to_ju, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n slips_juillet = slip_obj.search([('date_from', '>=', date_from_juillet), ('date_to', '<=', date_to_juillet),\r\n ('company_id', '=', rec.company_id.id)])\r\n date = datetime.strptime(date_from_ju, '%Y-%m-%d')\r\n res_1['mois'] = date.strftime(\"%B\").upper()\r\n rec.mois_1 = res_1['mois']\r\n data_juillet = []\r\n results_juillet = []\r\n total_brut_juillet = 0\r\n if slips_juillet:\r\n order = 0\r\n\r\n for employee, list_slip in groupby(slips_juillet, lambda l: l.employee_id):\r\n tmp = list(list_slip)\r\n brut = rec.get_amount_by_code(tmp, 'BRUT')\r\n total_brut_juillet += brut\r\n vals = rec.computeBrut(employee.type, brut)\r\n data_juillet.append(vals)\r\n for i in range(5):\r\n result = rec.compute_data(data_juillet, i)\r\n results_juillet.append(result)\r\n res_1['taux'] = rec.get_taux(rec.company_id)\r\n res_1['lines'] = results_juillet\r\n res_1['totaux'] = rec._get_totaux(results_juillet)\r\n res_1['retraite'] = int(res_1['totaux']['retraite'] * (res_1['taux']['cnps'] / 100))\r\n res_1['accident'] = int(res_1['totaux']['cotisation'] * (res_1['taux']['accident'] / 100))\r\n res_1['famille'] = int(res_1['totaux']['cotisation'] * (res_1['taux']['famille'] / 100))\r\n res_1['maternity'] = int(res_1['totaux']['cotisation'] * (res_1['taux']['maternite'] / 100))\r\n res_1['total_brut'] = total_brut_juillet\r\n res_1['total_cotisation'] = int(res_1['maternity'] + res_1['famille'] + res_1['accident'] + res_1['retraite'])\r\n periode['periode_1'] = res_1\r\n\r\n # Date dédut et fin de Aout\r\n date_from_a = trimestre_3['aout'][0]\r\n date_to_a = trimestre_3['aout'][1]\r\n date_from_aout = datetime.strptime(date_from_a, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n date_to_aout = datetime.strptime(date_to_a, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n slips_aout = slip_obj.search([('date_from', '>=', date_from_aout), ('date_to', '<=', date_to_aout),\r\n ('company_id', '=', rec.company_id.id)])\r\n date = datetime.strptime(date_from_a, '%Y-%m-%d')\r\n res_2['mois'] = date.strftime(\"%B\").upper()\r\n rec.mois_2 = res_2['mois']\r\n data_aout = []\r\n results_aout = []\r\n total_brut_aout = 0\r\n if slips_aout:\r\n order = 0\r\n\r\n for employee, list_slip in groupby(slips_aout, lambda l: l.employee_id):\r\n tmp = list(list_slip)\r\n brut = rec.get_amount_by_code(tmp, 'BRUT')\r\n total_brut_aout += brut\r\n vals = rec.computeBrut(employee.type, brut)\r\n data_aout.append(vals)\r\n for i in range(5):\r\n result = rec.compute_data(data_aout, i)\r\n results_aout.append(result)\r\n res_2['taux'] = rec.get_taux(rec.company_id)\r\n res_2['lines'] = results_aout\r\n res_2['totaux'] = rec._get_totaux(results_aout)\r\n res_2['retraite'] = int(res_2['totaux']['retraite'] * (res_2['taux']['cnps'] / 100))\r\n res_2['accident'] = int(res_2['totaux']['cotisation'] * (res_2['taux']['accident'] / 100))\r\n res_2['famille'] = int(res_2['totaux']['cotisation'] * (res_2['taux']['famille'] / 100))\r\n res_2['maternity'] = int(res_2['totaux']['cotisation'] * (res_2['taux']['maternite'] / 100))\r\n res_2['total_brut'] = total_brut_aout\r\n res_2['total_cotisation'] = int(res_2['maternity'] + res_2['famille'] + res_2['accident'] + res_2['retraite'])\r\n periode['periode_2'] = res_2\r\n\r\n # Date dédut et fin de Septembre\r\n date_from_s = trimestre_3['septembre'][0]\r\n date_to_s = trimestre_3['septembre'][1]\r\n date_from_septembre = datetime.strptime(date_from_s, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n date_to_septembre = datetime.strptime(date_to_s, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n slips_septembre = slip_obj.search([('date_from', '>=', date_from_septembre), ('date_to', '<=', date_to_septembre),\r\n ('company_id', '=', rec.company_id.id)])\r\n date = datetime.strptime(date_from_s, '%Y-%m-%d')\r\n res_3['mois'] = date.strftime(\"%B\").upper()\r\n rec.mois_3 = res_3['mois']\r\n data_septembre = []\r\n results_septembre = []\r\n total_brut_septembre = 0\r\n if slips_septembre:\r\n order = 0\r\n for employee, list_slip in groupby(slips_septembre, lambda l: l.employee_id):\r\n tmp = list(list_slip)\r\n brut = rec.get_amount_by_code(tmp, 'BRUT')\r\n total_brut_septembre += brut\r\n vals = rec.computeBrut(employee.type, brut)\r\n data_septembre.append(vals)\r\n for i in range(5):\r\n result = rec.compute_data(data_septembre, i)\r\n results_septembre.append(result)\r\n res_3['taux'] = rec.get_taux(rec.company_id)\r\n res_3['lines'] = results_septembre\r\n res_3['totaux'] = rec._get_totaux(results_septembre)\r\n res_3['retraite'] = int(res_3['totaux']['retraite'] * (res_3['taux']['cnps'] / 100))\r\n res_3['accident'] = int(res_3['totaux']['cotisation'] * (res_3['taux']['accident'] / 100))\r\n res_3['famille'] = int(res_3['totaux']['cotisation'] * (res_3['taux']['famille'] / 100))\r\n res_3['maternity'] = int(res_3['totaux']['cotisation'] * (res_3['taux']['maternite'] / 100))\r\n res_3['total_brut'] = total_brut_septembre\r\n res_3['total_cotisation'] = int(res_3['maternity'] + res_3['famille'] + res_3['accident'] + res_3['retraite'])\r\n periode['periode_3'] = res_3\r\n\r\n periode['cotisation_p'] = periode['periode_1']['totaux']['cotisation'] + periode['periode_2']['totaux']['cotisation'] + periode['periode_3']['totaux']['cotisation']\r\n periode['retraite_p'] = periode['periode_1']['totaux']['retraite'] + periode['periode_2']['totaux']['retraite'] + periode['periode_3']['totaux']['retraite']\r\n rec.assurance_maternite = int(periode['periode_1']['maternity'] + periode['periode_2']['maternity'] + periode['periode_3']['maternity'])\r\n rec.prestation_familiale = int(periode['periode_1']['famille'] + periode['periode_2']['famille'] + periode['periode_3']['famille'])\r\n rec.accident_travail = int(periode['periode_1']['accident'] + periode['periode_2']['accident'] + periode['periode_3']['accident'])\r\n rec.regime_retraite = int(periode['periode_1']['retraite'] + periode['periode_2']['retraite'] + periode['periode_3']['retraite'])\r\n rec.total_brut = int(periode['periode_1']['total_brut'] + periode['periode_2']['total_brut'] + periode['periode_3']['total_brut'])\r\n rec.total_cotisation = int(periode['periode_1']['total_cotisation'] + periode['periode_2']['total_cotisation'] + periode['periode_3']['total_cotisation'])\r\n rec.date_from = trimestre_3['juillet'][0]\r\n rec.date_to = trimestre_3['septembre'][1]\r\n rec.cumul_salaire_retraite = rec.regime_retraite\r\n rec.cumul_salaire_prestafami_acctrav = rec.assurance_maternite + rec.prestation_familiale + rec.accident_travail\r\n\r\n if rec.trimestre == 'trimestre_4':\r\n # Date dédut et fin de Octobre\r\n date_from_o = trimestre_4['octobre'][0]\r\n date_to_o = trimestre_4['octobre'][1]\r\n date_from_octobre = datetime.strptime(date_from_o, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n date_to_octobre= datetime.strptime(date_to_o, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n slips_octobre = slip_obj.search([('date_from', '>=', date_from_octobre), ('date_to', '<=', date_to_octobre),\r\n ('company_id', '=', rec.company_id.id)])\r\n date = datetime.strptime(date_from_o, '%Y-%m-%d')\r\n res_1['mois'] = date.strftime(\"%B\").upper()\r\n rec.mois_1 = res_1['mois']\r\n data_octobre = []\r\n results_octobre = []\r\n total_brut_octobre = 0\r\n if slips_octobre:\r\n order = 0\r\n\r\n for employee, list_slip in groupby(slips_octobre, lambda l: l.employee_id):\r\n tmp = list(list_slip)\r\n brut = rec.get_amount_by_code(tmp, 'BRUT')\r\n total_brut_octobre += brut\r\n vals = rec.computeBrut(employee.type, brut)\r\n data_octobre.append(vals)\r\n for i in range(5):\r\n result = rec.compute_data(data_octobre, i)\r\n results_octobre.append(result)\r\n res_1['taux'] = rec.get_taux(rec.company_id)\r\n res_1['lines'] = results_octobre\r\n res_1['totaux'] = rec._get_totaux(results_octobre)\r\n res_1['retraite'] = int(res_1['totaux']['retraite'] * (res_1['taux']['cnps'] / 100))\r\n res_1['accident'] = int(res_1['totaux']['cotisation'] * (res_1['taux']['accident'] / 100))\r\n res_1['famille'] = int(res_1['totaux']['cotisation'] * (res_1['taux']['famille'] / 100))\r\n res_1['maternity'] = int(res_1['totaux']['cotisation'] * (res_1['taux']['maternite'] / 100))\r\n res_1['total_brut'] = total_brut_octobre\r\n res_1['total_cotisation'] = int(res_1['maternity'] + res_1['famille'] + res_1['accident'] + res_1['retraite'])\r\n periode['periode_1'] = res_1\r\n\r\n # Date dédut et fin de Novembre\r\n date_from_no = trimestre_4['novembre'][0]\r\n date_to_no = trimestre_4['novembre'][1]\r\n date_from_novembre = datetime.strptime(date_from_no, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n date_to_novembre = datetime.strptime(date_to_no, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n slips_novembre = slip_obj.search([('date_from', '>=', date_from_novembre), ('date_to', '<=', date_to_novembre),\r\n ('company_id', '=', rec.company_id.id)])\r\n date = datetime.strptime(date_from_no, '%Y-%m-%d')\r\n res_2['mois'] = date.strftime(\"%B\").upper()\r\n rec.mois_2 = res_2['mois']\r\n data_novembre = []\r\n results_novembre = []\r\n total_brut_novembre = 0\r\n if slips_novembre:\r\n order = 0\r\n\r\n for employee, list_slip in groupby(slips_novembre, lambda l: l.employee_id):\r\n tmp = list(list_slip)\r\n brut = rec.get_amount_by_code(tmp, 'BRUT')\r\n total_brut_novembre += brut\r\n vals = rec.computeBrut(employee.type, brut)\r\n data_novembre.append(vals)\r\n for i in range(5):\r\n result = rec.compute_data(data_novembre, i)\r\n results_novembre.append(result)\r\n res_2['taux'] = rec.get_taux(rec.company_id)\r\n res_2['lines'] = results_novembre\r\n res_2['totaux'] = rec._get_totaux(results_novembre)\r\n res_2['retraite'] = int(res_2['totaux']['retraite'] * (res_2['taux']['cnps'] / 100))\r\n res_2['accident'] = int(res_2['totaux']['cotisation'] * (res_2['taux']['accident'] / 100))\r\n res_2['famille'] = int(res_2['totaux']['cotisation'] * (res_2['taux']['famille'] / 100))\r\n res_2['maternity'] = int(res_2['totaux']['cotisation'] * (res_2['taux']['maternite'] / 100))\r\n res_2['total_brut'] = round(total_brut_novembre)\r\n res_2['total_cotisation'] = int(res_2['maternity'] + res_2['famille'] + res_2['accident'] + res_2['retraite'])\r\n periode['periode_2'] = res_2\r\n\r\n # Date dédut et fin de Decembre\r\n date_from_de = trimestre_4['decembre'][0]\r\n date_to_de = trimestre_4['decembre'][1]\r\n date_from_decembre = datetime.strptime(date_from_de, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n date_to_decembre = datetime.strptime(date_to_de, DEFAULT_SERVER_DATE_FORMAT).strftime(date_format)\r\n slips_decembre = slip_obj.search([('date_from', '>=', date_from_decembre), ('date_to', '<=', date_to_decembre),\r\n ('company_id', '=', rec.company_id.id)])\r\n date = datetime.strptime(date_from_de, '%Y-%m-%d')\r\n res_3['mois'] = 'DECEMBRE'\r\n rec.mois_3 = res_3['mois']\r\n data_decembre = []\r\n results_decembre = []\r\n total_brut_decembre = 0\r\n if slips_decembre:\r\n order = 0\r\n for employee, list_slip in groupby(slips_decembre, lambda l: l.employee_id):\r\n tmp = list(list_slip)\r\n brut = rec.get_amount_by_code(tmp, 'BRUT')\r\n total_brut_decembre += brut\r\n vals = rec.computeBrut(employee.type, brut)\r\n data_decembre.append(vals)\r\n for i in range(5):\r\n result = rec.compute_data(data_decembre, i)\r\n results_decembre.append(result)\r\n res_3['taux'] = rec.get_taux(rec.company_id)\r\n res_3['lines'] = results_decembre\r\n res_3['totaux'] = rec._get_totaux(results_decembre)\r\n res_3['retraite'] = round(int(res_3['totaux']['retraite'] * (res_3['taux']['cnps'] / 100)))\r\n res_3['accident'] = round(int(res_3['totaux']['cotisation'] * (res_3['taux']['accident'] / 100)))\r\n res_3['famille'] = round(int(res_3['totaux']['cotisation'] * (res_3['taux']['famille'] / 100)))\r\n res_3['maternity'] = round(int(res_3['totaux']['cotisation'] * (res_3['taux']['maternite'] / 100)))\r\n res_3['total_brut'] = round(total_brut_decembre)\r\n res_3['total_cotisation'] = round(int(res_3['maternity'] + res_3['famille'] + res_3['accident'] + res_3['retraite']))\r\n periode['periode_3'] = res_3\r\n\r\n periode['cotisation_p'] = periode['periode_1']['totaux']['cotisation'] + periode['periode_2']['totaux']['cotisation'] + periode['periode_3']['totaux']['cotisation']\r\n periode['retraite_p'] = periode['periode_1']['totaux']['retraite'] + periode['periode_2']['totaux']['retraite'] + periode['periode_3']['totaux']['retraite']\r\n rec.assurance_maternite = round(int(periode['periode_1']['maternity'] + periode['periode_2']['maternity'] + periode['periode_3']['maternity']))\r\n rec.prestation_familiale = round(int(periode['periode_1']['famille'] + periode['periode_2']['famille'] + periode['periode_3']['famille']))\r\n rec.accident_travail = round(int(periode['periode_1']['accident'] + periode['periode_2']['accident'] + periode['periode_3']['accident']))\r\n rec.regime_retraite = round(int(periode['periode_1']['retraite'] + periode['periode_2']['retraite'] + periode['periode_3']['retraite']))\r\n rec.total_brut = round(int(periode['periode_1']['total_brut'] + periode['periode_2']['total_brut'] + periode['periode_3']['total_brut']))\r\n rec.total_cotisation = round(int(periode['periode_1']['total_cotisation'] + periode['periode_2']['total_cotisation'] + periode['periode_3']['total_cotisation']))\r\n rec.date_from = trimestre_4['octobre'][0]\r\n rec.date_to = trimestre_4['decembre'][1]\r\n rec.cumul_salaire_retraite = rec.regime_retraite\r\n rec.cumul_salaire_prestafami_acctrav = rec.assurance_maternite + rec.prestation_familiale + rec.accident_travail\r\n\r\n return rec._print_report(periode)\r\n\r\n def _print_report(self, data):\r\n # model = 'hr.cnps.trimestriel'\r\n # records = self.env[model].browse(data.get('ids', []))\r\n records = self.env[data['model']].browse(data.get('ids', []))\r\n # return self.env['report'].with_context(landscape=True).get_action(records, 'hr_payroll_ci_raport.cnps_trimestriel_report', data=data)\r\n print('L626 ',data)\r\n print('L627 ',data['periode_1']['mois'])\r\n return self.env.ref('hr_cnps_trimestriel.hr_cnps_trimestriel_report').report_action(self, data=data, config=False)\r\n", "sub_path": "hr_cnps_trimestriel/wizard/HrCnpsTrimestriel.py", "file_name": "HrCnpsTrimestriel.py", "file_ext": "py", "file_size_in_byte": 40619, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "odoo.models.Model", "line_number": 15, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 15, "usage_type": "name"}, {"api_name": "odoo.fields.Date", "line_number": 20, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 20, "usage_type": "name"}, {"api_name": "odoo.fields.Date", "line_number": 21, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 21, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 22, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 22, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 27, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 27, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 28, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 28, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 29, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 29, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 30, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 30, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 31, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 31, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 32, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 32, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 33, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 33, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 34, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 34, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 35, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 35, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 36, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 36, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 37, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 37, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 38, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 38, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 39, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 39, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 40, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 40, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 41, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 41, "usage_type": "name"}, {"api_name": "odoo.api.model", "line_number": 43, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 43, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 143, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 143, "usage_type": "name"}, {"api_name": "itertools.groupby", "line_number": 152, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 175, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 175, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 175, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 176, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 176, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 176, "usage_type": "name"}, {"api_name": "itertools.groupby", "line_number": 188, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 211, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 211, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 211, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 212, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 212, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 212, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 215, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 215, "usage_type": "name"}, {"api_name": "itertools.groupby", "line_number": 223, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 260, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 260, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 260, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 261, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 261, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 261, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 264, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 264, "usage_type": "name"}, {"api_name": "itertools.groupby", "line_number": 273, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 296, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 296, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 296, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 297, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 297, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 297, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 300, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 300, "usage_type": "name"}, {"api_name": "itertools.groupby", "line_number": 309, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 332, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 332, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 332, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 333, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 333, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 333, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 336, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 336, "usage_type": "name"}, {"api_name": "itertools.groupby", "line_number": 344, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 381, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 381, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 381, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 382, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 382, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 382, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 385, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 385, "usage_type": "name"}, {"api_name": "itertools.groupby", "line_number": 394, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 417, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 417, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 417, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 418, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 418, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 418, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 421, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 421, "usage_type": "name"}, {"api_name": "itertools.groupby", "line_number": 430, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 453, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 453, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 453, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 454, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 454, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 454, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 457, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 457, "usage_type": "name"}, {"api_name": "itertools.groupby", "line_number": 465, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 502, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 502, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 502, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 503, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 503, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 503, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 506, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 506, "usage_type": "name"}, {"api_name": "itertools.groupby", "line_number": 515, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 538, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 538, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 538, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 539, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 539, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 539, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 542, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 542, "usage_type": "name"}, {"api_name": "itertools.groupby", "line_number": 551, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 574, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 574, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 574, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 575, "usage_type": "call"}, {"api_name": "odoo.tools.DEFAULT_SERVER_DATE_FORMAT", "line_number": 575, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 575, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 578, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 578, "usage_type": "name"}, {"api_name": "itertools.groupby", "line_number": 586, "usage_type": "call"}]} +{"seq_id": "334217706", "text": "import argparse\nfrom brkt_cli.gcp import gcp_args\n\n\n# VERY EXPERIMENTAL FEATURE\n# It will not work for you\ndef setup_launch_gcp_image_args(parser, parsed_config):\n parser.add_argument(\n 'image',\n metavar='ID',\n help='The image that will be launched',\n )\n parser.add_argument(\n '--instance-name',\n metavar='NAME',\n dest='instance_name',\n help='Name of the instance'\n )\n parser.add_argument(\n '--instance-type',\n help='Instance type',\n dest='instance_type',\n default='n1-standard-1'\n )\n gcp_args.add_gcp_zone(parser, parsed_config)\n parser.add_argument(\n '--no-delete-boot',\n help='Do not delete boot disk when instance is deleted',\n dest='delete_boot',\n default=True,\n action='store_false'\n )\n gcp_args.add_gcp_project(parser, parsed_config)\n gcp_args.add_gcp_network(parser, parsed_config)\n parser.add_argument(\n '--gcp-tag',\n dest='gcp_tags',\n action='append',\n metavar='VALUE',\n help=(\n 'Set a GCP tag on the encrypted instance being launched. May be '\n 'specified multiple times.'\n )\n )\n\n # Optional startup script. Hidden because it is only used for development\n # and testing. It should be passed as a string containing a multi-line\n # script (bash, python etc.)\n parser.add_argument(\n '--startup-script',\n help=argparse.SUPPRESS,\n dest='startup_script',\n metavar='SCRIPT'\n )\n gcp_args.add_gcp_subnetwork(parser, parsed_config)\n parser.add_argument(\n '--guest-fqdn',\n metavar='FQDN',\n dest='guest_fqdn',\n help=argparse.SUPPRESS\n )\n # Optional (number of) SSD scratch disks because these can only be attached\n # at instance launch time, compared to the other (persistent) disks\n parser.add_argument(\n '--ssd-scratch-disks',\n metavar='N',\n type=int,\n default=0,\n dest='ssd_scratch_disks',\n help='Number of SSD scratch disks to be attached (max. 8)'\n )\n", "sub_path": "brkt_cli/gcp/launch_gcp_image_args.py", "file_name": "launch_gcp_image_args.py", "file_ext": "py", "file_size_in_byte": 2108, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "brkt_cli.gcp.gcp_args.add_gcp_zone", "line_number": 25, "usage_type": "call"}, {"api_name": "brkt_cli.gcp.gcp_args", "line_number": 25, "usage_type": "name"}, {"api_name": "brkt_cli.gcp.gcp_args.add_gcp_project", "line_number": 33, "usage_type": "call"}, {"api_name": "brkt_cli.gcp.gcp_args", "line_number": 33, "usage_type": "name"}, {"api_name": "brkt_cli.gcp.gcp_args.add_gcp_network", "line_number": 34, "usage_type": "call"}, {"api_name": "brkt_cli.gcp.gcp_args", "line_number": 34, "usage_type": "name"}, {"api_name": "argparse.SUPPRESS", "line_number": 51, "usage_type": "attribute"}, {"api_name": "brkt_cli.gcp.gcp_args.add_gcp_subnetwork", "line_number": 55, "usage_type": "call"}, {"api_name": "brkt_cli.gcp.gcp_args", "line_number": 55, "usage_type": "name"}, {"api_name": "argparse.SUPPRESS", "line_number": 60, "usage_type": "attribute"}]} +{"seq_id": "465311752", "text": "# -*- coding:utf-8 -*-\nimport web\nimport time\nimport framework as f\nfrom dao import sqlSession\nfrom dao.goods import Goods\nurls = (\n '/add', 'Add',\n '', 'Goods'\n)\nclass Add:\n def GET(self):\n session = sqlSession()\n goods = Goods(gname=\"nihao\",createtime=time.time())\n session.add(goods)\n session.commit()\n session.close()\n return f.res_success_json();\n\nclass Goods:\n def GET(self):\n obj = {\"goods\":[]}\n return f.res_success_json(obj)\n\n\n\napp = web.application(urls,locals())", "sub_path": "home/goods.py", "file_name": "goods.py", "file_ext": "py", "file_size_in_byte": 541, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "dao.sqlSession", "line_number": 13, "usage_type": "call"}, {"api_name": "dao.goods.Goods", "line_number": 14, "usage_type": "call"}, {"api_name": "time.time", "line_number": 14, "usage_type": "call"}, {"api_name": "framework.res_success_json", "line_number": 18, "usage_type": "call"}, {"api_name": "framework.res_success_json", "line_number": 23, "usage_type": "call"}, {"api_name": "web.application", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "179779159", "text": "import json, sys\n\ndef read_json(path):\n with open(path, encoding=\"utf-8\") as f:\n return json.load(f) # dict, list, etc\n\n# data is a dict, list, etc\ndef write_json(path, data):\n with open(path, 'w', encoding=\"utf-8\") as f:\n json.dump(data, f, indent=2)\n\n# KEY: name, VAL: list of scores for that person\nscore_history_path = \"score_history.json\"\nscores = read_json(score_history_path)\n\nprint(\"sys.argv\", sys.argv)\nname = sys.argv[1]\nscore = int(sys.argv[2])\nif not name in scores:\n scores[name] = []\nscores[name].append(score)\n\nprint(scores)\nprint(\"AVG\", sum(scores[name]) / len(scores[name]))\nwrite_json(score_history_path, scores)\n\n", "sub_path": "tyler/cs301/fall19/materials/code/lec-20/lec2/score_keeper.py", "file_name": "score_keeper.py", "file_ext": "py", "file_size_in_byte": 655, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "json.load", "line_number": 5, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 17, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}]} +{"seq_id": "426815033", "text": "#coding:utf-8\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.io.wavfile as wav\nimport scipy.signal\nimport os.path\nfrom fdicapy import fdica\n\nRATE = 44100\ncomponents=2\nframe_length = 4096\n\n#input voice data\nfilenames = [('mixed' + str(i+1) + '.wav') for i in range(components)]\nlength_array = [(os.path.getsize(filenames[i])-44)/2 for i in range(components)]\ndata_length = min(length_array)\ninput = np.zeros([components,data_length])\n\nfor i in range(components):\n input[i] = wav.read(filenames[i])[1][0:data_length]\n\noutput = fdica(input, frame_length)\n\n\n#volume up = normalization\noutput = output/np.max(np.abs(output[:,0:data_length-RATE]))*32767\noutput = output.astype(np.int16)\n\nfilenames = [('output' + str(i+1) + '.wav') for i in range(components)]\nfor i in range(components):\n wav.write(filenames[i],RATE,output[i][0:data_length])\n\n fs, data = wav.read(filenames[i])\n x = np.frombuffer(data, dtype=\"int16\") / 32768.0\n nyq = fs / 2.0 # ナイキスト周波数\n # フィルタの設計\n # ナイキスト周波数が1になるように正規化\n fe1 = 4000.0 / nyq # カットオフ周波数1\n numtaps = 255 # フィルタ係数（タップ）の数（要奇数）\n b = scipy.signal.firwin(numtaps, fe1)\n y = scipy.signal.lfilter(b, 1, x)\n\n y = np.array(y)\n y = y/(3*np.max(np.abs(y)))*32767\n y = y.astype(np.int16)\n wav.write(\"filtered-\"+filenames[i],RATE,y)\n\nprint(\"\\a\\a\\a\")", "sub_path": "cocktail/FDICA_real3/fdica_test.py", "file_name": "fdica_test.py", "file_ext": "py", "file_size_in_byte": 1460, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "os.path.path.getsize", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 17, "usage_type": "call"}, {"api_name": "scipy.io.wavfile.read", "line_number": 20, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 20, "usage_type": "name"}, {"api_name": "fdicapy.fdica", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 27, "usage_type": "attribute"}, {"api_name": "scipy.io.wavfile.write", "line_number": 31, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 31, "usage_type": "name"}, {"api_name": "scipy.io.wavfile.read", "line_number": 33, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.frombuffer", "line_number": 34, "usage_type": "call"}, {"api_name": "scipy.io.wavfile.signal.firwin", "line_number": 40, "usage_type": "call"}, {"api_name": "scipy.io.wavfile.signal", "line_number": 40, "usage_type": "attribute"}, {"api_name": "scipy.io.wavfile", "line_number": 40, "usage_type": "name"}, {"api_name": "scipy.io.wavfile.signal.lfilter", "line_number": 41, "usage_type": "call"}, {"api_name": "scipy.io.wavfile.signal", "line_number": 41, "usage_type": "attribute"}, {"api_name": "scipy.io.wavfile", "line_number": 41, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 45, "usage_type": "attribute"}, {"api_name": "scipy.io.wavfile.write", "line_number": 46, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 46, "usage_type": "name"}]} +{"seq_id": "568397951", "text": "import time, curses, random\nLOWERCASE = 'abcdefghijklmnopqrstuvwxyz'\n\nWORDSCORES = dict(zip(LOWERCASE,\n\t[1,3,3,2,1,4,2,4,1,8,5,1,3,1,1,3,10,1,1,1,1,4,4,8,4,10]))\n\n\ndef adjacent(coordTuple, width, height):\n\tx,y = coordTuple\n\tadjacentSet = set([(x-1,y-1),(x,y-1),(x+1,y-1),(x-1,y),(x+1,y),(x-1,y+1),(x,y+1),(x+1,y+1)])\n\tif x == 0:\n\t\tadjacentSet.difference_update((x-1,y-1),(x-1,y),(x-1,y+1))\n\telif x == width:\n\t\tadjacentSet.difference_update((x+1,y-1),(x+1,y),(x+1,y+1))\n\tif y == 0:\n\t\tadjacentSet.difference_update((x-1,y-1),(x,y-1),(x+1,y-1))\n\telif y == height:\n\t\tadjacentSet.difference_update((x-1,y+1),(x,y+1),(x+1,y+1))\n\treturn adjacentSet\n\n\nclass Game(object):\n\tdef __init__(self, screen, gameWindow, infoWindow, height, width):\n\t\tself.over = False\n\t\tself.screen = screen\n\t\tself.gameWindow = gameWindow\n\t\tself.infoWindow = infoWindow\n\t\tself.height = height-2\n\t\tself.width = width-2\n\t\tself.newBoard()\n\t\tself.scoredWords = []\n\n\tdef newBoard(self):\n\t\tself.score = 0\n\t\tself.infoWindow.addstr(1,1,str(self.score))\n\t\tself.charLocs = {}\n\t\tfor letter in LOWERCASE:\n\t\t\tself.charLocs[letter] = set()\n\t\n\t\tself.squares = []\t\t\n\t\tfor i in xrange(self.width):\n\t\t\tself.squares.append([])\n\t\t\tfor j in xrange(self.height):\n\t\t\t\trandChar = getRandChar()\n\t\t\t\tself.squares[i].append(randChar)\n\t\t\t\tself.charLocs[randChar].add((i,j))\n\t\t\t\tself.gameWindow.addch(i+1,j+1, ord(randChar))\n\n\t\tself.time = time.time()\n\t\tself.words = []\n\t\tself.selectedWord = 0\n\t\tself.wordBuffer = []\n\t\tself.currentWord = ''\n\n\tdef scoreWord(self):\n\t\tself.scoredWords.append(self.currentWord)\n\t\twordScore = 0\n\t\tfor coords in self.words[self.selectedWord]:\n\t\t\tx,y = coords\n\t\t\twordScore += WORDSCORES[self.squares[x][y]]\n\t\t\tself.replaceChar(x,y)\n\n\t\tself.score += wordScore\n\t\tinfoX, infoY = self.infoWindow.getmaxyx()\n\t\tself.infoWindow.addstr(1,1,str(self.score))\n\t\tpadding = ' '*(infoX-len(self.currentWord)-2)\n\t\tself.infoWindow.addstr(2,1,self.currentWord + padding)\n\t\tself.words = []\n\t\tself.selectedWord = 0\n\t\tself.wordBuffer = []\n\t\tself.currentWord = ''\n\t\tself.highlightWords()\t\n\t\tself.infoWindow.refresh()\n\t\t\n\n\n\tdef replaceChar(self, x, y):\n\t\tnewChar = getRandChar()\n\t\tself.charLocs[self.squares[x][y]].remove((x,y))\n\t\tself.charLocs[newChar].add((x,y))\n\t\tself.squares[x][y] = newChar\n\t\tself.gameWindow.addch(x+1,y+1,ord(newChar))\n\n\tdef restart(self):\n\t\tself.over = False\n\t\tself.newBoard()\n\n\n\tdef extendWord(self, letter):\n\t\tif self.words == []:\n\t\t\tfor firstletter in self.charLocs[letter]:\n\t\t\t\tself.words.append([firstletter,])\n\t\t\tif not self.words == []:\t\n\t\t\t\tself.wordBuffer.append(self.words)\n\t\t\t\tself.currentWord = self.currentWord + letter\n\t\t\t\tself.highlightWords()\n\t\telse:\n\t\t\tnewWords = []\n\t#\t\ttry:\n\t\t\tfor word in self.words:\n\t\t\t\tfor nextLetter in adjacent(word[-1],self.height,self.width).intersection(self.charLocs[letter]).difference(word):\n\t\t\t\t\tnewWords.append(word + [nextLetter,])\n\t#\t\texcept:\n\t#\t\t\traise RuntimeError(word)\n\t\t\tif not newWords == []:\n\t\t\t\tself.words = newWords\n\t\t\t\tself.wordBuffer.append(self.words)\t\n\t\t\t\tself.currentWord = self.currentWord + letter\n\t\t\t\tself.highlightWords()\n\t\tself.selectedWord = 0 # this should be whatever the index of the first returned word is\n\n\n\tdef deleteLetter(self):\n\t\t\n\t\tif not 0 <= len(self.wordBuffer) <= 1:\n\t\t\tself.wordBuffer.pop()\n\t\t\tself.words = self.wordBuffer[-1]\n\n\t\telif len(self.wordBuffer) == 1:\n\t\t\tself.wordBuffer = []\n\t\t\tself.words = []\n\t\t\t\n\t\tif not self.currentWord == '':\n\t\t\tself.currentWord = self.currentWord[:-1]\n\n\t\tself.highlightWords()\n\n\n\tdef highlightWords(self):\n\t\tfor i in xrange(self.width):\n\t\t\tfor j in xrange(self.height):\n\t\t\t\tself.gameWindow.chgat(i+1,j+1, 1,curses.A_NORMAL)\n\t\tif not self.words == []:\n\n\n\t\t\tfor word in self.words:\n\t\t\t\tfor letter in word:\n\t\t\t\t\n\t\t\t\t\tx,y = letter\n\n\t\t\t\t\tself.gameWindow.chgat(x+1,y+1,1,curses.A_BOLD + curses.color_pair(1))\n\t\t\tif self.selectedWord >= len(self.words):\n\t\t\t\tself.selectedWord = 0\n\t\t\tfor letter in self.words[self.selectedWord]:\n\t\t\t\tx,y = letter\n\t\t\t\tself.gameWindow.chgat(x+1,y+1,1,curses.A_BOLD + curses.color_pair(2))\n\n\tdef nextSelection(self):\n\t\tself.selectedWord += 1\n\t\tif self.selectedWord >= len(self.words): #Something about preincrementing?\n\t\t\tself.selectedWord = 0\n\t\tself.highlightWords()\n\ndef getRandChar():\n\treturn LOWERCASE[random.randrange(26)]\n\ndef main(screen, gameWidth = 40, gameHeight = 20):\n\tscreen.clear()\n\tscrWidth, scrHeight = screen.getmaxyx()\n\tfinalHeight, finalWidth = min(gameHeight, scrHeight-2), min(gameWidth,scrWidth-2)\n\tcurses.init_pair(1,curses.COLOR_YELLOW,curses.COLOR_BLACK)\n\tcurses.init_pair(2,curses.COLOR_RED,curses.COLOR_BLACK)\n\tgameWindow = screen.subwin(finalWidth,finalHeight,0,0)\n\n\tgameWindow.border()\n\tinfoWindow = screen.subwin(20,finalHeight,0,finalWidth+2)\n\tinfoWindow.border()\n\n\tgame = Game(screen, gameWindow, infoWindow, finalHeight, finalWidth)\n\tscreen.nodelay(1)\n\tcurses.curs_set(0)\n\n\twhile not game.over:\t\t\n\t\tgame.time = time.time()\n\t\tcurKey = screen.getch()\n#\t\tgame.gameWindow.addch(2,3,ord('w'),curses.color_pair(1))\n\t\tgame.gameWindow.refresh()\n\t\tif 0 < curKey < 256:\n\t\t\tcurKey = chr(curKey)\n\t\t\tif curKey in LOWERCASE:\n\t\t\t\tgame.extendWord(curKey)\n\n\t\t\tif curKey == '0':\n\t\t\t\tgame.over = True\n\n\t\t\tif curKey == ' ':\n\t\t\t\tgame.scoreWord()\n\n\t\t\tif curKey == '\\t':\n\t\t\t\tgame.nextSelection()\n\n\n\t\tif curKey == curses.KEY_BACKSPACE:\n\t\t\tgame.deleteLetter()\nif __name__ == \"__main__\":\n\tcurses.wrapper(main)", "sub_path": "wordsearch.py", "file_name": "wordsearch.py", "file_ext": "py", "file_size_in_byte": 5326, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "time.time", "line_number": 49, "usage_type": "call"}, {"api_name": "curses.A_NORMAL", "line_number": 132, "usage_type": "attribute"}, {"api_name": "curses.A_BOLD", "line_number": 141, "usage_type": "attribute"}, {"api_name": "curses.color_pair", "line_number": 141, "usage_type": "call"}, {"api_name": "curses.A_BOLD", "line_number": 146, "usage_type": "attribute"}, {"api_name": "curses.color_pair", "line_number": 146, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 155, "usage_type": "call"}, {"api_name": "curses.init_pair", "line_number": 161, "usage_type": "call"}, {"api_name": "curses.COLOR_YELLOW", "line_number": 161, "usage_type": "attribute"}, {"api_name": "curses.COLOR_BLACK", "line_number": 161, "usage_type": "attribute"}, {"api_name": "curses.init_pair", "line_number": 162, "usage_type": "call"}, {"api_name": "curses.COLOR_RED", "line_number": 162, "usage_type": "attribute"}, {"api_name": "curses.COLOR_BLACK", "line_number": 162, "usage_type": "attribute"}, {"api_name": "curses.curs_set", "line_number": 171, "usage_type": "call"}, {"api_name": "time.time", "line_number": 174, "usage_type": "call"}, {"api_name": "curses.KEY_BACKSPACE", "line_number": 193, "usage_type": "attribute"}, {"api_name": "curses.wrapper", "line_number": 196, "usage_type": "call"}]} +{"seq_id": "49482138", "text": "# reference\n# https://github.com/awjuliani/DeepRL-Agents/blob/master/A3C-Doom.ipynb\nimport tensorflow as tf\nimport numpy as np\n\nimport gym\n\nimport argparse\n\nfrom collections import deque\nglobal total_rewards\ntotal_rewards = deque(maxlen=100)\ntotal_rewards.append(0)\n\nglobal episode\nepisode = 0\n\nimport threading\n\nenv = gym.make('CartPole-v0')\n\n\nclass ActorCritic(object):\n def __init__(self, input_size, output_size, optimizer, name):\n self.input_size = input_size\n self.output_size = output_size\n self.optimizer = optimizer\n self.name = name\n\n self.hidden1 = 128\n\n self._build_network()\n\n def _build_network(self):\n with tf.variable_scope(self.name):\n self.state = tf.placeholder(dtype=tf.float32, shape=[None, self.input_size], name='state')\n self.dense1 = tf.layers.dense(inputs=self.state, units=self.hidden1, activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer())\n\n self.actor_output = tf.layers.dense(inputs=self.dense1, units=self.output_size, activation=tf.nn.softmax, kernel_initializer=tf.contrib.layers.xavier_initializer())\n self.critic_output = tf.layers.dense(inputs=self.dense1, units=1, activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())\n\n if self.name != 'global_network':\n self.advantages = tf.placeholder(dtype=tf.float32, name='advantages')\n self.targets = tf.placeholder(dtype=tf.float32, name='targets')\n self.actions = tf.placeholder(dtype=tf.int32, name='actions')\n\n self.critic_loss = tf.reduce_mean(tf.squared_difference(self.targets, self.critic_output))\n\n self.actions_one_hot = tf.squeeze(tf.one_hot(self.actions, self.output_size, dtype=tf.float32))\n self.log_probs = tf.log(tf.reduce_sum(self.actor_output * self.actions_one_hot))\n self.actor_loss = -tf.reduce_mean(self.log_probs * self.advantages)\n\n self.entropy = - tf.reduce_sum(self.actor_output * tf.log(self.actor_output))\n\n self.loss = self.actor_loss + self.critic_loss - 0.01 * self.entropy\n\n local_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.name)\n self.gradients = tf.gradients(self.loss, local_variables)\n global_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'global_network')\n self.apply_gradients = self.optimizer.apply_gradients(zip(self.gradients, global_variables))\n\n def get_action(self, sess, state):\n state = np.reshape(state, newshape=[-1, self.input_size])\n prob = sess.run(self.actor_output, feed_dict={self.state: state})\n action = np.random.choice(self.output_size, p=prob[0])\n\n return action\n\n def get_value(self, sess, states):\n states = np.reshape(np.array(states), newshape=[-1, self.input_size])\n return sess.run(self.critic_output, feed_dict={self.state: states})\n\n\nclass Agent(threading.Thread):\n def __init__(self, env_name, global_network, input_size, output_size, n_step, gamma, optimizer, sess, index):\n super(Agent, self).__init__()\n self.env_name = env_name\n self.env = gym.make(env_name)\n self.global_network = global_network\n self.input_size = input_size\n self.output_size = output_size\n self.sess = sess\n self.optimizer = optimizer\n self.n_step = n_step\n self.gamma = gamma\n self.index = str(index)\n\n self.local_network = ActorCritic(self.input_size, self.output_size, self.optimizer, name='local' + self.index)\n\n def run(self):\n global episode\n memory = Memory()\n while np.mean(total_rewards) <= 195:\n state = env.reset()\n episode += 1\n reward_sum = 0\n done = False\n memory.clear()\n step = 0\n\n while not done:\n action = self.local_network.get_action(self.sess, state)\n\n next_state, reward, done, _ = env.step(action)\n reward_sum += reward\n memory.store(state, action, reward)\n\n if ((step + 1) % self.n_step == 0) or done:\n if done:\n running_add = 0.\n else:\n running_add = self.local_network.get_value(self.sess, next_state)\n\n # Get discounted rewards\n discounted_returns = np.zeros_like(memory.rewards)\n for idx in reversed(range(0, len(memory.rewards))): # reverse buffer r\n running_add = memory.rewards[idx] + self.gamma * running_add\n discounted_returns[idx] = running_add\n\n values = self.local_network.get_value(self.sess, memory.states)\n advantage = discounted_returns - values\n\n loss, _ = self.sess.run([self.local_network.loss, self.local_network.apply_gradients],\n feed_dict={self.local_network.state: memory.states,\n self.local_network.advantages: advantage,\n self.local_network.targets: discounted_returns,\n self.local_network.actions: memory.actions})\n\n self.sess.run(update_target_graph('global_network', 'local' + self.index))\n memory.clear()\n\n state = next_state\n step += 1\n\n total_rewards.append(reward_sum)\n print(\"[Episode {0:6d}] Reward Sum: {1:4.2f} Total reward sum mean: {2:4.2f}\".format(episode, reward_sum, np.mean(total_rewards)), end='')\n print(' Loss: ', loss)\n\n\n\ndef update_target_graph(from_scope, to_scope):\n from_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, from_scope)\n to_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, to_scope)\n\n op_holder = []\n for from_var, to_var in zip(from_vars, to_vars):\n op_holder.append(to_var.assign(from_var))\n return op_holder\n\n\n# reference\n# https://medium.com/tensorflow/deep-reinforcement-learning-playing-cartpole-through-asynchronous-advantage-actor-critic-a3c-7eab2eea5296\nclass Memory(object):\n def __init__(self):\n self.states = []\n self.actions = []\n self.rewards = []\n\n def store(self, state, action, reward):\n self.states.append(state)\n self.actions.append(action)\n self.rewards.append(reward)\n\n def clear(self):\n self.states = []\n self.actions = []\n self.rewards = []\n\n\ndef main():\n parser = argparse.ArgumentParser(description='parameter sets')\n\n parser.add_argument('--input-size', type=int, default=env.observation_space.shape[0], help='network input size')\n parser.add_argument('--output-size', type=int, default=env.action_space.n, help='network output size')\n parser.add_argument('--learning-rate', type=float, default=1e-4, help='learning_rate')\n parser.add_argument('--num-workers', type=int, default=1, help='number of workers')\n parser.add_argument('--gamma', type=float, default=0.99, help='discount factor, gamma')\n parser.add_argument('--env-name', type=str, default='CartPole-v0', help='game environment name')\n parser.add_argument('--n-step', type=int, default=4, help='number of transaction with environment')\n\n args = parser.parse_args()\n with tf.device('/cpu:0'):\n optimizer = tf.train.AdamOptimizer(args.learning_rate)\n global_network = ActorCritic(args.input_size, args.output_size, optimizer, name='global_network')\n\n with tf.Session() as sess:\n agents = [Agent(args.env_name, global_network, args.input_size, args.output_size, n_step=args.n_step,\n gamma=args.gamma, optimizer=optimizer, sess=sess, index=_) for _ in range(1, args.num_workers + 1)]\n sess.run(tf.global_variables_initializer())\n import time\n for agent in agents:\n time.sleep(1)\n agent.start()\n\n [agent.join() for agent in agents]\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "A3C/a3c_tensorflow.py", "file_name": "a3c_tensorflow.py", "file_ext": "py", "file_size_in_byte": 8301, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "collections.deque", "line_number": 12, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tensorflow.layers.dense", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.xavier_initializer", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 37, "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.contrib.layers.xavier_initializer", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 39, "usage_type": "attribute"}, {"api_name": "tensorflow.layers.dense", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 40, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.xavier_initializer", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 40, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 43, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 44, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 45, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.squared_difference", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.squeeze", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.one_hot", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tensorflow.log", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.log", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.get_collection", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 57, "usage_type": "attribute"}, {"api_name": "tensorflow.gradients", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.get_collection", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 65, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 74, "usage_type": "attribute"}, {"api_name": "gym.make", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 136, "usage_type": "call"}, {"api_name": "tensorflow.get_collection", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 142, "usage_type": "attribute"}, {"api_name": "tensorflow.get_collection", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 143, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 171, "usage_type": "call"}, {"api_name": "tensorflow.device", "line_number": 182, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 183, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 183, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 186, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 189, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 192, "usage_type": "call"}]} +{"seq_id": "470383394", "text": "import numpy as np\nimport argparse\nfrom model import *\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import TensorDataset\nfrom torch.utils.data import DataLoader\nimport os\nimport logging\n\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument('-p','--path_prefix',default='../')\nparser.add_argument('-y','--output_length',default=1)\nparser.add_argument('-s','--service',default='inflow')\nparser.add_argument('-x','--input_lag_mode',default=1)\nparser.add_argument('-m','--model',default='HA')\nparser.add_argument('-l', '--lr', type = float, default = 0.0002)\nparser.add_argument('-e', '--epochs', type = int, default = 100)\nparser.add_argument('-w', '--weather', type = int, default = 0, help = \"Whether to use weather data. \")\nparser.add_argument('-r', '--regularization', type = float, default = 0, help = \"scale of weight decay\")\nparser.add_argument('-g', '--gpu', type = int, default = 7, help = 'the gpu to use')\nparser.add_argument('-b', '--batch_size', type = int, default = 16, help = \"batch size\")\nparser.add_argument('-a', '--transfer_algorithm', type = str, default = \"finetune\", help = \"what transfer learning algorithm to use\")\nparser.add_argument('--source', type = str, help = \"what source city to use as transfer\")\nparser.add_argument('--target', type = str, help = 'what target city to use as transfer')\nparser.add_argument('--source-path', type = str, help = 'file path that links to the source model')\nparser.add_argument('--loss-w', type = float, default = 0, help = \"the weight w in regiontrans\")\nparser.add_argument('-t', '--transfer_data', type = int, default = 7, help = \"how many days of data to use for tuning. Currently supports 3 and 7\")\nparser.add_argument('--dictpath', type = str, help = \"The matching dict to use for doing regiontrans\")\nargs = parser.parse_args()\nprint(args)\n\nos.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)\ngpu_available = torch.cuda.is_available()\nif gpu_available:\n gpu = torch.device(\"cuda:0\")\n\npref = args.path_prefix\ntarget_cname = args.target\ntarget_shortcname = {\n \"beijing\":\"bj\",\n \"chengdu\":\"cd\",\n \"shanghai\":\"sh\",\n \"shenzhen\":\"sz\",\n \"chongqing\":\"cq\",\n \"xian\":\"xa\",\n}[target_cname]\nsource_cname = args.source\nsource_shortcname = {\n \"beijing\":\"bj\",\n \"chengdu\":\"cd\",\n \"shanghai\":\"sh\",\n \"shenzhen\":\"sz\",\n \"chongqing\":\"cq\",\n \"xian\":\"xa\",\n}[source_cname]\nylength = args.output_length\nservice = args.service\nlag_mode = args.input_lag_mode\nmodel_name = args.model # Currently for transfer, we only support LSTM and ConvLSTM\n\ntransfer_data_range_week = {\n \"beijing\":(0, 336), \n \"shanghai\":(624, 960), \n \"shenzhen\":(576, 912),\n \"chongqing\":(528, 864),\n \"chengdu\":(1632, 1968), \n \"xian\":(1632, 1968)\n}\ntransfer_data_range_3day = {\n \"beijing\":(192, 336), \n \"shanghai\":(816, 960), \n \"shenzhen\":(768, 912),\n \"chongqing\":(720, 864),\n \"chengdu\":(1824, 1968), \n \"xian\":(1824, 1968)\n}\n\ndef min_max_normalize(data,cut_off_percentile=0.99):\n sl = sorted(data.flatten())\n max_val = sl[int(len(sl)*cut_off_percentile)]\n min_val = max(0,sl[0])\n data[data>max_val]=max_val\n data[data0) # True-False array with shape [T] \nspatial_mask_source = (mask_source.mean(0)>0) # True-False array with shape [lng, lat]\nprint('source temporal mask density:',temporal_mask_source.sum()/len(temporal_mask_source))\nprint('source spatial mask density:',spatial_mask_source.sum()/len(spatial_mask_source.flatten()))\n\nmask_target = np.load(pref + \"%s/mask_%s.npy\"%(target_cname, target_shortcname))\ntemporal_mask_target = (mask_target.mean((1,2))>0) # True-False array with shape [T] \nspatial_mask_target = (mask_target.mean(0)>0) # True-False array with shape [lng, lat]\nprint('target temporal mask density:',temporal_mask_target.sum()/len(temporal_mask_target))\nprint('target spatial mask density:',spatial_mask_target.sum()/len(spatial_mask_target.flatten()))\n\n\nsource_data, source_datamax, source_datamin, source_weather, source_weathermax, source_weathermin = load_data(source_cname, source_shortcname, service)\ntarget_data, target_datamax, target_datamin, target_weather, target_weathermax, target_weathermin = load_data(target_cname, target_shortcname, service)\nprint('source data loaded:',service,'-',source_data.shape)\nprint(\"target data loaded:\", service, '-', target_data.shape)\nif args.weather:\n print('source weather data loaded:',source_weather.shape)\n print('target weather data loaded', target_weather.shape)\n\n\nno_lag = [-1]\nhour_lag = [-5,-4,-3,-2,-1]\none_day_lag = [-48,-4,-3,-2,-1]\n\nif(int(lag_mode)==0):\n lag=no_lag\nif(int(lag_mode)==1):\n lag=hour_lag\nif(int(lag_mode)==2):\n lag=one_day_lag\n\ntrain_weather_source, _, val_weather_source, _, test_weather_source, _ = split(source_weather, lag, temporal_mask_source)\ntrain_x_source, train_y_source, val_x_source, val_y_source, test_x_source, test_y_source = split(source_data, lag, temporal_mask_source)\n\ntrain_weather_target, _, val_weather_target, _, test_weather_target, _ = split(target_weather, lag, temporal_mask_target)\ntrain_x_target, train_y_target, val_x_target, val_y_target, test_x_target, test_y_target = split(target_data, lag, temporal_mask_target)\n\n# process transfer data\nif args.transfer_data == 7:\n transfer_data_range = transfer_data_range_week\nelif args.transfer_data == 3:\n transfer_data_range = transfer_data_range_3day\nidx_start_target = transfer_data_range[target_cname][0]\nidx_end_target = transfer_data_range[target_cname][1]\ntrain_data_target = target_data[idx_start_target:idx_end_target]\ntrain_weather_target = target_weather[idx_start_target:idx_end_target]\nidx_start_source = transfer_data_range[source_cname][0]\nidx_end_source = transfer_data_range[source_cname][1]\ntrain_data_source = source_data[idx_start_source:idx_end_source]\ntrain_weather_source = source_weather[idx_start_source:idx_end_source]\n\n\nx_source = []\ny_source = []\nw_source = []\nx_target = []\ny_target = []\nw_target = []\nfor i in range(-lag[0], len(train_data_target) - ylength +1):\n x_idx = list(map(lambda x:x+i, lag))\n y_idx = [i+o for o in range(ylength)]\n biased_idxs = np.array(x_idx + y_idx) + idx_start_source\n if (temporal_mask_source[biased_idxs] == 0).sum() == 0:\n x_source.append(train_data_source[x_idx])\n y_source.append(train_data_source[y_idx])\n w_source.append(train_weather_source[x_idx])\n x_target.append(train_data_target[x_idx])\n y_target.append(train_data_target[y_idx])\n w_target.append(train_weather_target[x_idx])\ntrain_x_target = np.stack(x_target, 0)\ntrain_y_target = np.stack(y_target, 0)\ntrain_weather_target = np.stack(w_target, 0)\ntrain_x_source = np.stack(x_source, 0)\ntrain_y_source = np.stack(y_source, 0)\ntrain_weather_source = np.stack(w_source, 0)\n\n\nprint('Source data split to: train_x-%s, train_y-%s, val_x-%s, val_y-%s, test_x-%s, test_y-%s'%\\\n (train_x_source.shape,train_y_source.shape,val_x_source.shape,val_y_source.shape,test_x_source.shape,test_y_source.shape))\nprint('Target data split to: train_x-%s, train_y-%s, val_x-%s, val_y-%s, test_x-%s, test_y-%s'%\\\n (train_x_target.shape,train_y_target.shape,val_x_target.shape,val_y_target.shape,test_x_target.shape,test_y_target.shape))\n\n\nif args.transfer_algorithm == 'finetune':\n # First load source model\n lag = train_x_target.shape[1]\n lng = train_x_target.shape[2]\n lat = train_x_target.shape[3]\n feat = train_x_target.shape[4]\n if model_name == \"CONVLSTM\":\n model = ConvLSTM(target_cname + \"/\" + service, feat, lag, feat, lng, lat, args.epochs, args.lr, spatial_mask_target, \n target_datamin, target_datamax, use_ext = args.weather, ext_dim = target_weather.shape[1] if args.weather else 0, lstm_hidden = 64\n )\n source_dict = torch.load(args.source_path)\n model.load_state_dict(source_dict)\n \n elif model_name == \"LSTM\":\n model = LSTM(target_cname + \"/\" + service, feat, lag, feat, 64, lng, lat, args.epochs, args.lr, spatial_mask_target,\\\n target_datamin, target_datamax, use_ext = args.weather, ext_dim = target_weather.shape[1] if args.weather else 0)\n source_dict = torch.load(args.source_path)\n target_dict = model.state_dict()\n num_loaded = 0\n for k in target_dict.keys():\n if k in source_dict.keys() and source_dict[k].shape == target_dict[k].shape:\n num_loaded += 1\n target_dict[k] = source_dict[k]\n elif k == 'conv1.weight':\n # convolution kernel channel mismatch\n # mismatch channel is in dimension 1\n if target_dict[k].shape[1] < source_dict[k].shape[1]:\n # target has 3 channels, source has 4\n target_dict[k] = source_dict[k][:, :3, :, :] \n else:\n print(\"Failed to load %s\" % k) \n num_loaded += 1\n elif k == 'linear.weight':\n if target_dict[k].shape[0] < source_dict[k].shape[0]:\n # target has 3 channels, source has 4\n target_dict[k] = source_dict[k][:3, :]\n else:\n print(\"Failed to load %s\" % k)\n num_loaded += 1\n elif k == 'linear.bias':\n if target_dict[k].shape[0] < source_dict[k].shape[0]:\n target_dict[k] = source_dict[k][:3]\n else:\n print(\"Failed to load %s\" % k)\n num_loaded += 1\n else:\n print(\"Failed to load %s\" % k)\n print(\"Loaded %d out of %d parameters\"%(num_loaded, len(target_dict)))\n else:\n raise NotImplementedError(\"Other models not implemented for finetune.\")\n if gpu_available:\n model = model.to(gpu)\n\n trainx = torch.Tensor(train_x_target.transpose((0, 1, 4, 2, 3))).contiguous()\n valx = torch.Tensor(val_x_target.transpose((0, 1, 4, 2, 3))).contiguous()\n testx = torch.Tensor(test_x_target.transpose((0, 1, 4, 2, 3))).contiguous()\n trainy = torch.Tensor(train_y_target.transpose((0, 1, 4, 2, 3)).reshape((-1, feat, lng, lat)))\n valy = torch.Tensor(val_y_target.transpose((0, 1, 4, 2, 3)).reshape((-1, feat, lng, lat)))\n testy = torch.Tensor(test_y_target.transpose((0, 1, 4, 2, 3)).reshape((-1, feat, lng, lat)))\n if model_name == \"LSTM\":\n trainx = trainx.view(-1, lag * feat, lng, lat)\n valx = valx.view(-1, lag * feat, lng, lat)\n testx = testx.view(-1, lag * feat, lng, lat)\n\n if args.weather:\n train_weather_target = torch.Tensor(train_weather_target)\n val_weather_target = torch.Tensor(val_weather_target)\n test_weather_target = torch.Tensor(test_weather_target)\n train_set = TensorDataset(trainx, trainy, train_weather_target)\n valid_set = TensorDataset(valx, valy, val_weather_target)\n test_set = TensorDataset(testx, testy, test_weather_target)\n else:\n train_set = TensorDataset(trainx, trainy)\n valid_set = TensorDataset(valx, valy)\n test_set = TensorDataset(testx, testy)\n trainloader = DataLoader(train_set, batch_size = args.batch_size, shuffle=True)\n validloader = DataLoader(valid_set, batch_size = args.batch_size)\n testloader = DataLoader(test_set, batch_size = args.batch_size)\n model.train_model(trainloader, validloader)\n model.load_model(\"best\")\n val_pred = np.expand_dims(model.predict_loader(validloader).transpose(0, 2, 3, 1), 1)\n test_pred = np.expand_dims(model.predict_loader(testloader).transpose(0, 2, 3, 1), 1)\n\nelif args.transfer_algorithm == 'regiontrans':\n target_lag = train_x_target.shape[1]\n target_lng = train_x_target.shape[2]\n target_lat = train_x_target.shape[3]\n target_feat = train_x_target.shape[4]\n source_lag = train_x_source.shape[1]\n source_lng = train_x_source.shape[2]\n source_lat = train_x_source.shape[3]\n source_feat = train_x_source.shape[4]\n if model_name == \"LSTM\":\n model = RegionTrans_LSTM(source_shortcname, target_shortcname, target_feat, target_lag, target_feat, 64, args.epochs, args.lr, \\\n spatial_mask_source, spatial_mask_target, target_datamin, target_datamax, \n use_ext = args.weather, ext_dim = target_weather.shape[1], loss_w = args.loss_w, matching_dict_path = args.dictpath)\n source_dict = torch.load(args.source_path)\n target_dict = model.state_dict()\n num_loaded = 0\n for k in target_dict.keys():\n if k in source_dict.keys() and source_dict[k].shape == target_dict[k].shape:\n num_loaded += 1\n target_dict[k] = source_dict[k]\n elif k == 'conv1.weight':\n # convolution kernel channel mismatch\n # mismatch channel is in dimension 1\n if target_dict[k].shape[1] < source_dict[k].shape[1]:\n # target has 3 channels, source has 4\n target_dict[k] = source_dict[k][:, :3, :, :] \n else:\n print(\"Failed to load %s\" % k) \n num_loaded += 1\n elif k == 'linear.weight':\n if target_dict[k].shape[0] < source_dict[k].shape[0]:\n # target has 3 channels, source has 4\n target_dict[k] = source_dict[k][:3, :]\n else:\n print(\"Failed to load %s\" % k)\n num_loaded += 1\n elif k == 'linear.bias':\n if target_dict[k].shape[0] < source_dict[k].shape[0]:\n target_dict[k] = source_dict[k][:3]\n else:\n print(\"Failed to load %s\" % k)\n num_loaded += 1\n else:\n print(\"Failed to load %s\" % k)\n print(\"Loaded %d out of %d parameters\"%(num_loaded, len(target_dict)))\n elif model_name == \"CONVLSTM\":\n model = RegionTrans_ConvLSTM(source_shortcname, target_shortcname, target_feat, target_lag, target_feat, args.epochs, args.lr, spatial_mask_source, \\\n spatial_mask_target, target_datamin, target_datamax, use_ext = args.weather, ext_dim = target_weather.shape[1], \n loss_w = args.loss_w, matching_dict_path = args.dictpath)\n source_dict = torch.load(args.source_path)\n model.load_state_dict(source_dict)\n else:\n raise NotImplementedError(\"Models other than LSTM and CONVLSTM are not implemented on RegionTrans.\")\n\n if gpu_available:\n model = model.to(gpu)\n\n # prepare data\n # a pair of temporally aligned data\n if model_name == \"LSTM\":\n rt_trainx_source = torch.Tensor(train_x_source.transpose((0, 1, 4, 2, 3)).reshape((-1, source_lag * source_feat, source_lng, source_lat)))\n rt_trainy_source = torch.Tensor(train_y_source.transpose((0, 1, 4, 2, 3)).reshape((-1, source_feat, source_lng, source_lat)))\n rt_trainx_target = torch.Tensor(train_x_target.transpose((0, 1, 4, 2, 3)).reshape((-1, target_lag * target_feat, target_lng, target_lat)))\n rt_trainy_target = torch.Tensor(train_y_target.transpose((0, 1, 4, 2, 3)).reshape((-1, target_feat, target_lng, target_lat)))\n\n elif model_name == 'CONVLSTM':\n rt_trainx_source = torch.Tensor(train_x_source.transpose((0, 1, 4, 2, 3)).reshape((-1, source_lag, source_feat, source_lng, source_lat)))\n rt_trainy_source = torch.Tensor(train_y_source.transpose((0, 1, 4, 2, 3)).reshape((-1, source_feat, source_lng, source_lat)))\n rt_trainx_target = torch.Tensor(train_x_target.transpose((0, 1, 4, 2, 3)).reshape((-1, target_lag, target_feat, target_lng, target_lat))) \n rt_trainy_target = torch.Tensor(train_y_target.transpose((0, 1, 4, 2, 3)).reshape((-1, target_feat, target_lng, target_lat)))\n\n rt_valx_target = torch.Tensor(val_x_target.transpose((0, 1, 4, 2, 3)).reshape((-1, target_lag * target_feat, target_lng, target_lat))) \n rt_testx_target = torch.Tensor(test_x_target.transpose((0, 1, 4, 2, 3)).reshape((-1, target_lag * target_feat, target_lng, target_lat)))\n rt_valy_target = torch.Tensor(val_y_target.transpose((0, 1, 4, 2, 3)).reshape((-1, target_feat, target_lng, target_lat))) \n rt_testy_target = torch.Tensor(test_y_target.transpose((0, 1, 4, 2, 3)).reshape((-1, target_feat, target_lng, target_lat)))\n\n if args.weather:\n train_weather_source = torch.Tensor(train_weather_source)\n train_weather_target = torch.Tensor(train_weather_target)\n val_weather_source = torch.Tensor(val_weather_source)\n val_weather_target = torch.Tensor(val_weather_target)\n test_weather_source = torch.Tensor(test_weather_source)\n test_weather_target = torch.Tensor(test_weather_target)\n train_set = TensorDataset(rt_trainx_source, train_weather_source, rt_trainx_target, rt_trainy_target, train_weather_target)\n valid_set = TensorDataset(rt_valx_target, rt_valy_target, val_weather_target)\n test_set = TensorDataset(rt_testx_target, rt_testy_target, test_weather_target)\n else:\n train_set = TensorDataset(rt_trainx_source, rt_trainx_target, rt_trainy_target)\n valid_set = TensorDataset(rt_valx_target, rt_valy_target)\n test_set = TensorDataset(rt_testx_target, rt_testy_target)\n\n trainloader = DataLoader(train_set, batch_size = args.batch_size, shuffle=True)\n validloader = DataLoader(valid_set, batch_size = args.batch_size)\n testloader = DataLoader(test_set, batch_size = args.batch_size) \n\n model.train_model(trainloader, validloader)\n model.load_model(\"best\")\n val_pred = np.expand_dims(model.predict_loader(validloader).transpose(0, 2, 3, 1), 1)\n test_pred = np.expand_dims(model.predict_loader(testloader).transpose(0, 2, 3, 1), 1)\n\n \n\nif service =='all':\n name_list = ['inflow','outflow','demand','supply']\n # assert len(name_list) == test_pred.shape[-1] \n scalar = target_datamax - target_datamin\n test_mae = masked_mae(test_y_target*scalar,test_pred*scalar,spatial_mask_target,preserve_f_dim=True)\n test_rmse = masked_rmse(test_y_target*scalar,test_pred*scalar,spatial_mask_target,preserve_f_dim=True)\n val_mae = masked_mae(val_y_target*scalar,val_pred*scalar,spatial_mask_target,preserve_f_dim=True)\n val_rmse = masked_rmse(val_y_target*scalar,val_pred*scalar,spatial_mask_target,preserve_f_dim=True)\n if target_cname not in ['xian', 'chengdu']:\n print('val score (rmse/mae): %s=%.4f/%.4f, %s==%.4f/%.4f, %s==%.4f/%.4f, %s==%.4f/%.4f'%(name_list[0],val_rmse[0],val_mae[0],\\\n name_list[1],val_rmse[1],val_mae[1],\n name_list[2],val_rmse[2],val_mae[2],\n name_list[3],val_rmse[3],val_mae[3]))\n print('test score (rmse/mae): %s=%.4f/%.4f, %s==%.4f/%.4f, %s==%.4f/%.4f, %s==%.4f/%.4f'%(name_list[0],test_rmse[0],test_mae[0],\\\n name_list[1],test_rmse[1],test_mae[1],\n name_list[2],test_rmse[2],test_mae[2],\n name_list[3],test_rmse[3],test_mae[3]))\n logging.info('val score (rmse/mae): %s=%.4f/%.4f, %s==%.4f/%.4f, %s==%.4f/%.4f, %s==%.4f/%.4f'%(name_list[0],val_rmse[0],val_mae[0],\\\n name_list[1],val_rmse[1],val_mae[1],\n name_list[2],val_rmse[2],val_mae[2],\n name_list[3],val_rmse[3],val_mae[3]))\n logging.info('test score (rmse/mae): %s=%.4f/%.4f, %s==%.4f/%.4f, %s==%.4f/%.4f, %s==%.4f/%.4f'%(name_list[0],test_rmse[0],test_mae[0],\\\n name_list[1],test_rmse[1],test_mae[1],\n name_list[2],test_rmse[2],test_mae[2],\n name_list[3],test_rmse[3],test_mae[3]))\n else: \n print('val score (rmse/mae): %s=%.4f/%.4f, %s==%.4f/%.4f, %s==%.4f/%.4f'%(name_list[0],val_rmse[0],val_mae[0],\\\n name_list[1], val_rmse[1], val_mae[1], \n name_list[2], val_rmse[2], val_mae[2]))\n print('test score (rmse/mae): %s=%.4f/%.4f, %s==%.4f/%.4f, %s==%.4f/%.4f'%(name_list[0],test_rmse[0],test_mae[0],\\\n name_list[1], test_rmse[1], test_mae[1], \n name_list[2], test_rmse[2], test_mae[2]))\n logging.info('val score (rmse/mae): %s=%.4f/%.4f, %s==%.4f/%.4f, %s==%.4f/%.4f'%(name_list[0],val_rmse[0],val_mae[0],\\\n name_list[1], val_rmse[1], val_mae[1], \n name_list[2], val_rmse[2], val_mae[2]))\n logging.info('test score (rmse/mae): %s=%.4f/%.4f, %s==%.4f/%.4f, %s==%.4f/%.4f'%(name_list[0],test_rmse[0],test_mae[0],\\\n name_list[1], test_rmse[1], test_mae[1], \n name_list[2], test_rmse[2], test_mae[2]))\n\nelse:\n\n val_rmse = masked_rmse(val_y_target, val_pred, spatial_mask_target)\n val_mae = masked_mae(val_y_target, val_pred, spatial_mask_target)\n test_mae = masked_mae(test_y_target, test_pred, spatial_mask_target)\n test_rmse = masked_rmse(test_y_target, test_pred, spatial_mask_target)\n print(target_datamax - target_datamin)\n print('validation score: rmse = %.4f, mae = %.4f'%(val_rmse*(target_datamax-target_datamin),val_mae*(target_datamax-target_datamin)))\n print('test score: rmse = %.4f, mae = %.4f'%(test_rmse*(target_datamax-target_datamin),test_mae*(target_datamax-target_datamin)))\n logging.info('validation score: rmse = %.4f, mae = %.4f'%(val_rmse*(target_datamax-target_datamin),val_mae*(target_datamax-target_datamin)))\n logging.info('test score: rmse = %.4f, mae = %.4f'%(test_rmse*(target_datamax-target_datamin),test_mae*(target_datamax-target_datamin)))\n", "sub_path": "code1/run_transfer.py", "file_name": "run_transfer.py", "file_ext": "py", "file_size_in_byte": 25841, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "66", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 37, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 149, 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