diff --git "a/2001.jsonl" "b/2001.jsonl" new file mode 100644--- /dev/null +++ "b/2001.jsonl" @@ -0,0 +1,1590 @@ +{"seq_id":"24450985630","text":"#Serhii Maltsev sm5zj\n\ndef st_jeor (mass, height, age, sex):\n if sex == \"male\":\n s = +5\n else:\n s = -161\n\n p = 10.0*mass+6.25*height-5.0*age+s\n return p\n","repo_name":"SerhiiMaltsev/Python-code","sub_path":"Lab 8/bmr.py","file_name":"bmr.py","file_ext":"py","file_size_in_byte":179,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"70867827451","text":"from pywb.utils.statusandheaders import StatusAndHeaders\nfrom pywb.utils.loaders import extract_post_query, append_post_query\n\nimport pprint\n\n\n#=================================================================\nclass WbRequest(object):\n \"\"\"\n Represents the main pywb request object.\n\n Contains various info from wsgi env, add additional info\n about the request, such as coll, relative prefix,\n host prefix, absolute prefix.\n\n If a wburl and url rewriter classes are specified, the class\n also contains the url rewriter.\n\n \"\"\"\n @staticmethod\n def make_host_prefix(env):\n try:\n host = env.get('HTTP_HOST')\n if not host:\n host = env['SERVER_NAME'] + ':' + env['SERVER_PORT']\n\n return env.get('wsgi.url_scheme', 'http') + '://' + host\n except KeyError:\n return ''\n\n def __init__(self, env,\n request_uri=None,\n rel_prefix='',\n wb_url_str='/',\n coll='',\n host_prefix='',\n use_abs_prefix=False,\n wburl_class=None,\n urlrewriter_class=None,\n is_proxy=False,\n cookie_scope=None):\n\n self.env = env\n\n if request_uri:\n self.request_uri = request_uri\n else:\n self.request_uri = env.get('REL_REQUEST_URI')\n\n self.method = self.env.get('REQUEST_METHOD')\n\n self.coll = coll\n\n self.final_mod = ''\n\n if not host_prefix:\n host_prefix = self.make_host_prefix(env)\n\n self.host_prefix = host_prefix\n self.rel_prefix = rel_prefix\n\n if use_abs_prefix:\n self.wb_prefix = host_prefix + rel_prefix\n else:\n self.wb_prefix = rel_prefix\n\n if not wb_url_str:\n wb_url_str = '/'\n\n self.wb_url_str = wb_url_str\n\n # wb_url present and not root page\n if wb_url_str != '/' and wburl_class:\n self.wb_url = wburl_class(wb_url_str)\n self.urlrewriter = urlrewriter_class(self.wb_url,\n self.wb_prefix,\n host_prefix + rel_prefix,\n rel_prefix,\n env.get('SCRIPT_NAME', '/'),\n cookie_scope)\n else:\n # no wb_url, just store blank wb_url\n self.wb_url = None\n self.urlrewriter = None\n\n self.referrer = env.get('HTTP_REFERER')\n\n self.options = dict()\n self.options['is_ajax'] = self._is_ajax()\n self.options['is_proxy'] = is_proxy\n\n self.query_filter = []\n self.custom_params = {}\n\n # PERF\n env['X_PERF'] = {}\n\n self._parse_extra()\n\n def _is_ajax(self):\n value = self.env.get('HTTP_X_REQUESTED_WITH')\n if value and value.lower() == 'xmlhttprequest':\n return True\n\n return False\n\n def __repr__(self):\n varlist = vars(self)\n varstr = pprint.pformat(varlist)\n return varstr\n\n def _parse_extra(self):\n pass\n\n def extract_referrer_wburl_str(self):\n if not self.referrer:\n return None\n\n if not self.referrer.startswith(self.host_prefix + self.rel_prefix):\n return None\n\n wburl_str = self.referrer[len(self.host_prefix + self.rel_prefix):]\n return wburl_str\n\n def normalize_post_query(self):\n if self.method != 'POST':\n return\n\n if not self.wb_url:\n return\n\n mime = self.env.get('CONTENT_TYPE').split(';')[0]\n length = self.env.get('CONTENT_LENGTH')\n stream = self.env['wsgi.input']\n\n post_query = extract_post_query('POST', mime, length, stream)\n\n if post_query:\n self.wb_url.url = append_post_query(self.wb_url.url, post_query)\n\n\n#=================================================================\nclass WbResponse(object):\n \"\"\"\n Represnts a pywb wsgi response object.\n\n Holds a status_headers object and a response iter, to be\n returned to wsgi container.\n \"\"\"\n def __init__(self, status_headers, value=[], **kwargs):\n self.status_headers = status_headers\n self.body = value\n self._init_derived(kwargs)\n\n def _init_derived(self, params):\n pass\n\n @staticmethod\n def text_stream(stream, status='200 OK', content_type='text/plain',\n headers=None):\n def_headers = [('Content-Type', content_type)]\n if headers:\n def_headers += headers\n\n status_headers = StatusAndHeaders(status, def_headers)\n\n return WbResponse(status_headers, value=stream)\n\n @staticmethod\n def text_response(text, status='200 OK', content_type='text/plain'):\n status_headers = StatusAndHeaders(status,\n [('Content-Type', content_type),\n ('Content-Length', str(len(text)))])\n\n return WbResponse(status_headers, value=[text])\n\n @staticmethod\n def redir_response(location, status='302 Redirect', headers=None):\n redir_headers = [('Location', location), ('Content-Length', '0')]\n if headers:\n redir_headers += headers\n\n return WbResponse(StatusAndHeaders(status, redir_headers))\n\n def __call__(self, env, start_response):\n start_response(self.status_headers.statusline,\n self.status_headers.headers)\n\n if env['REQUEST_METHOD'] == 'HEAD':\n if hasattr(self.body, 'close'):\n self.body.close()\n return []\n\n return self.body\n\n def __repr__(self):\n return str(vars(self))\n","repo_name":"jasonliw93/recon","sub_path":"env/lib/python2.7/site-packages/pywb/framework/wbrequestresponse.py","file_name":"wbrequestresponse.py","file_ext":"py","file_size_in_byte":5821,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"} +{"seq_id":"4606939021","text":"import pandas as pd\nimport tensorflow as tf\n\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nfrom tensorflow.keras.utils import to_categorical\n\nfrom tensorflow.keras.layers import Input, LSTM, Embedding, Dense\nfrom tensorflow.keras.models import Model\n\n\ndf = pd.read_csv(f'../data/sample/korean_correct_train_data_100000.csv')\n\ndf = df.iloc[:20000]\ndf['tgt'] = df.tgt.apply(lambda x: '\\t ' + x + ' \\n')\nprint(df['tgt'])\n\nsrc_vocab = set()\n\nfor line in df['src'].tolist():\n for c in line:\n src_vocab.add(c)\n\ntgt_vocab = set()\n\nfor line in df['tgt'].tolist():\n for c in line:\n tgt_vocab.add(c)\n\n\nsrc_vocab_size = len(src_vocab) + 1\ntgt_vocab_size = len(tgt_vocab) + 1\n\nsrc_to_index = dict([(word, i+1) for i , word in enumerate(src_vocab)])\ntgt_to_index = dict([(word, i+1) for i , word in enumerate(tgt_vocab)])\n\nencoder_input = []\n\nfor line in df['src'].tolist():\n encoded_line = []\n for c in line:\n encoded_line.append(src_to_index[c])\n encoder_input.append(encoded_line)\n\ndecoder_input = []\n\nfor line in df['tgt'].tolist():\n encoded_line = []\n for c in line:\n encoded_line.append(tgt_to_index[c])\n decoder_input.append(encoded_line)\n\ndecoder_tgt = []\n\nfor line in df['tgt'].tolist():\n ts = 0\n encoded_line = []\n for c in line:\n if ts > 0:\n encoded_line.append(tgt_to_index[c])\n ts += 1\n decoder_tgt.append(encoded_line)\n\nmax_src_len = max([len(line) for line in df['src'].tolist()])\nmax_tgt_len = max([len(line) for line in df['tgt'].tolist()])\n\nencoder_input = pad_sequences(encoder_input, maxlen=max_src_len, padding='post')\ndecoder_input = pad_sequences(decoder_input, maxlen=max_tgt_len, padding='post')\ndecoder_tgt = pad_sequences(decoder_tgt, maxlen=max_tgt_len, padding='post')\n\nencoder_input = to_categorical(encoder_input)\nprint(1)\ndecoder_input = to_categorical(decoder_input)\nprint(2)\ndecoder_tgt = to_categorical(decoder_tgt)\nprint(3)\nprint(decoder_tgt)","repo_name":"Jeonghoyoung/korean_grammar_corrector","sub_path":"bin/model_test_code/test_seq2seq.py","file_name":"test_seq2seq.py","file_ext":"py","file_size_in_byte":1974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"4344300869","text":"import unittest\nfrom MEDRank.umls.concept import *\n#from MEDRank.umls.concept import _my_concept_pool # Import hidden variable\n # explicitly\nclass my_concrete_concept(object):\n pass\n \nclass conceptTests(unittest.TestCase):\n def setUp(self):\n\n o=my_concrete_concept()\n o.name='a name'\n storage={'c1234': o}\n Concept.init_storage(storage)\n #def tearDown(self):\n # global _my_concept_pool\n # _my_concept_pool={}\n def testInstantiation(self):\n a=Concept('c1234')\n self.assertEquals(a.CUI, 'c1234')\n def testAttributeAccess(self):\n a=Concept('c1234')\n self.assertEquals(a.name, 'a name')\n# def testPool(self):\n# self.assertEquals(_my_concept_pool, {})\n# c=getConcept('c1234')\n# self.assertEquals(c.CUI, 'c1234')\n# d=getConcept('c1234')\n# self.assertEquals(id(c), id(d))\n def testInfoAboutConceptNotInStorageFails(self):\n e=getConcept('c9876')\n # You can create a concept with an unknown ID\n self.assert_(type(e) is Concept)\n # But you can't get info about it...\n self.assertRaises(NoConceptInfoError, e.__getattr__, 'test')\nif __name__ == '__main__':\n unittest.main()","repo_name":"jherskovic/MEDRank","sub_path":"MEDRank/umls/tests/test_concept.py","file_name":"test_concept.py","file_ext":"py","file_size_in_byte":1276,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"78"} +{"seq_id":"10929544678","text":"__author__ = 'iwcrosby'\n\nimport pygame\nfrom random import randrange\nimport var\n\nclass Button:\n '''A clickable button. Coordinate tuple p is the location. w and h are the width and height.'''\n\n def __init__(self, w=1, h=1, p=(0,0), label=\"no label\"):\n\n self.rect = pygame.Rect(p,(w,h))\n self.label = label\n\nclass Customer:\n '''A customer.'''\n\n def __init__(self):\n self.status = 0\n x = randrange(0,100)\n self.size = x * x * 5\n self.price_sens = (x * 3.5) / randrange(1,3)\n var.customer_list.append(self)\n self.signup_date = var.month\n\n # 0 -> prospect\n # 1 -> lost prospect\n # 2 -> trial\n # 3 -> lost trial\n # 4 -> customer\n # 5 -> churn\n\n\n def conv_prospect(self):\n\n size = self.size\n price_sens = self.price_sens\n\n if size <= var.limit3:\n if size <= var.limit2:\n if size <= var.limit1:\n price = var.price1\n else:\n price = var.price2\n else:\n price = var.price3\n else:\n self.status = 1\n self.lost_lead_date = var.month\n\n if price < price_sens:\n self.price = price\n self.status = 2\n self.trial_date = var.month\n\n else:\n self.status = 1\n self.lost_lead_date = var.month\n\n def conv_trial(self):\n\n x = randrange(0,10)\n if x >= 4:\n self.status = 4\n self.cust_date = var.month\n else:\n self.status = 3\n self.lost_trial_date = var.month\n\n def churn(self):\n\n x = randrange(0,100)\n if x >= 98:\n self.status = 5\n self.churn_date = var.month\n\n","repo_name":"iancrosby/SimGame","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":1750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"42230497724","text":"import os\nimport requests\nfrom datetime import datetime, timedelta\n\nFLIGHT_API = os.environ.get('FLIGHT_API')\nFLIGHT_ENDPOINT = os.environ.get('FLIGHT_ENDPOINT')\n\ndate_1 = datetime.now().date() + timedelta(days=1)\nFROM_DATE = date_1.strftime(\"%d/%m/%Y\")\n\ndate_2 = date_1 + timedelta(days=6 * 30)\nTO_DATE = date_2.strftime(\"%d/%m/%Y\")\n\nheaders = {\n \"apikey\": FLIGHT_API,\n}\n\n\nclass FlightSearch:\n\n def __init__(self):\n self.flight_price = []\n self.flight_dates = []\n self.flight_dates_return = []\n self.city_names = []\n\n def get_flight_prices(self, iata_codes):\n for code in iata_codes:\n parameters = {\n \"fly_from\": f\"{code}\",\n \"fly_to\": \"LHR\",\n \"date_from\": f\"{FROM_DATE}\",\n \"date_to\": f\"{TO_DATE}\",\n \"one_for_city\": 1,\n }\n response = requests.get(url=FLIGHT_ENDPOINT, headers=headers, params=parameters)\n response.raise_for_status()\n try:\n self.flight_price.append(response.json()[\"data\"][0][\"price\"])\n self.flight_dates.append(response.json()[\"data\"][0][\"route\"][0][\"local_arrival\"].split(\"T\")[0])\n self.flight_dates_return.append(response.json()[\"data\"][0][\"route\"][1][\"local_arrival\"].split(\"T\")[0])\n self.city_names.append(response.json()[\"data\"][0][\"route\"][0][\"cityFrom\"])\n except IndexError:\n self.flight_price.append(0)\n self.flight_dates.append(0)\n self.flight_dates_return.append(0)\n self.city_names.append(0)\n return self.flight_price\n\n def get_flight_dates(self):\n return self.flight_dates\n\n def get_return_flight_dates(self):\n return self.flight_dates_return\n\n def get_city_name(self):\n return self.city_names\n","repo_name":"bellaryyash23/flight_deals_API","sub_path":"flight_search.py","file_name":"flight_search.py","file_ext":"py","file_size_in_byte":1865,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"72615574333","text":"# -*- coding: utf-8 -*-\n\n# Author : 'hxc'\n\n# Time: 2019/12/17 3:31 PM\n\n# File_name: 'test_demo.py'\n\n\"\"\"\nDescribe: this is a demo!\n\"\"\"\n\nimport requests\nimport json\nimport time\n\nurl = 'http://localhost:5005/consult'\ns = time.time()\ninput = {}\n\nprint(type(input))\nheaders = {\n 'Content-Type': 'application/json'\n}\ndata = json.dumps(input)\nprint(type(data))\n\nres = requests.post(url, data)\nprint(res)\nprint(json.dumps(res.json(), indent=4, ensure_ascii=False))\nprint(time.time() - s)","repo_name":"huangxiancun/nlp_project","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"78"} +{"seq_id":"10464941330","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n # ex: /polls/\n path('', views.DataOwnerHomeView.as_view(), name='data_owner_home'),\n path('files/', views.DataFileListView.as_view(), name='data_file_list'),\n path('files/upload/', views.DataFileUploadView.as_view(), name='data_file_create'),\n path('files/send/',views.SendFileView.as_view(), name='send_file'),\n path('files/download//', views.DownloadView.as_view(), name='download'),\n]","repo_name":"code-with-carlos/centurion","sub_path":"MainDirectories/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"41235081986","text":"INF = int(1e9)\n\nn = int(input()) # 노드의 개수\nm = int(input()) # 간선의 개수\n\narr = [[INF] * (n + 1) for _ in range(n + 1)] # 그래프를 무한으로 초기화\n\n# 자기 자신에게 가는 비용은 0\nfor i in range(1, n + 1):\n arr[i][i] = 0\n\n# 간선을 입력 받을 때 기존에 있던 값과 비교하여 더 작은 값을 넣는다\n# arr[a][b]는 기존의 값, c는 새로운 값\nfor i in range(m):\n a, b, c = map(int, input().split())\n arr[a][b] = min(arr[a][b], c)\n\n# 플리이드 워셜 알고리즘 수행\nfor k in range(1, n + 1):\n for a in range(1, n + 1):\n for b in range(1, n + 1):\n arr[a][b] = min(arr[a][b], arr[a][k] + arr[k][b])\n\n# 결과 출력\nfor i in range(1, n + 1):\n for j in range(1, n + 1):\n if arr[i][j] == INF:\n print(0, end=\" \")\n else:\n print(arr[i][j], end=\" \")\n print()\n","repo_name":"ocxh/std_algorithm","sub_path":"BOJ/11404.py","file_name":"11404.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"23938034980","text":"from fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\n\nfrom src.config.env import *\nfrom src.services import *\n\nfrom src.models.Task import Task\n\nfrom starlette.requests import Request\n\napp = FastAPI()\napp.title = \"Redis App\"\napp.description = \"Create a simple todo using regis and fastapi\"\n\napp.add_middleware(\n CORSMiddleware,\n allow_origins=\"*\",\n allow_credentials=True,\n allow_methods=[\"*\"],\n allow_headers=[\"*\"],\n)\n\n\n@app.get(\"/\")\nasync def say_hello():\n return {\"message\": \"Redis todo with fastapi, developed by António Gabriel\"}\n\n\n@app.get(\"/tasks\")\nasync def get_tasks():\n \"\"\"Get all tasks from redis\"\"\"\n\n return get_tasks_service()\n\n\n@app.post(\"/tasks\")\nasync def create(task: Task):\n \"\"\"Save an new task\"\"\"\n\n return task.save()\n\n\n@app.put(\"/tasks/{pk}\")\nasync def update(pk: str, request: Request):\n \"\"\"Update the task name\"\"\"\n\n task = Task.get(pk)\n parsed_body = await request.json()\n task.name = int(parsed_body[\"name\"])\n\n # return task.save()\n return task.name\n\n\n@app.patch(\"/tasks/{pk}\")\nasync def complete(pk: str, request: Request):\n \"\"\"Mark if the task is done or undone\"\"\"\n\n task = Task.get(pk)\n parsed_body = await request.json()\n task.complete = int(parsed_body[\"complete\"])\n\n return task.save()\n\n\n@app.delete(\"/tasks/{pk}\")\nasync def delete(pk: str):\n \"\"\"Delete the task\"\"\"\n\n return Task.delete(pk)\n","repo_name":"Antonio-Gabriel/redis_fastapi","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"13008723684","text":"import time\nimport transaction\nimport zope.sqlalchemy\n\nfrom pyramid.decorator import reify\nfrom pyramid.testing import (\n DummyRequest,\n setUp,\n tearDown,\n)\nfrom pyramid.util import DottedNameResolver\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nfrom ..config import factory_args_from_settings\nfrom ..session import get_session_factory\n\n\nclass new_context():\n \"\"\" Manager to provide new test context for each test example. \"\"\"\n def __init__(\n self,\n settings,\n set_time=None,\n isolation_level=None\n ):\n import pyramid_sqlalchemy_sessions.session as session_module\n self._sm = session_module\n url = 'sqlite://'\n kwargs = {}\n if isolation_level is not None:\n kwargs['isolation_level'] = isolation_level\n self.engine = create_engine(url, **kwargs)\n self.settings = settings\n self.metadata = settings['model_class'].metadata\n if set_time is None:\n set_time = int(time.time())\n self.time = set_time\n self._cookies = {}\n self.vary = None\n\n def __enter__(self):\n # Configurator expects raw ini settings.\n prefixed = {'session.' + k: v for k, v in self.settings.items()}\n self.config = setUp(settings=prefixed)\n self.metadata.create_all(self.engine)\n self._int_now = self._sm.__dict__['int_now']\n self._sm.__dict__['int_now'] = lambda: self.time\n return self\n\n def __exit__(self, exc_type, exc_value, traceback):\n self._sm.__dict__['int_now'] = self._int_now\n self.metadata.drop_all(self.engine)\n tearDown()\n\n @property\n def cookies(self):\n self._cookies = {\n k: v for k, v in self._cookies.items()\n if v[1] is None or v[1] > self.time\n }\n return {k[0]: v[0] for k, v in self._cookies.items()}\n\n def set_cookie(self, name, path, domain, value, max_age, secure, httponly):\n if max_age is None:\n expire = None\n else:\n expire = self.time + max_age \n self._cookies[(name, path, domain)] = (\n value, expire, secure, httponly\n )\n\n def delete_cookie(self, name, path, domain):\n del self._cookies[(name, path, domain)]\n\n\nclass new_request(DummyRequest):\n def __init__(self, context=None, **kw):\n super().__init__(**kw)\n if context is not None:\n context.vary = None # Cleanup.\n self.context = context\n self.cookies = context.cookies\n self.registry = context.config.registry\n\n def __enter__(self):\n self.tm.begin()\n return self\n\n def __exit__(self, exc_type, exc_value, traceback):\n if exc_value is not None:\n self.tm.abort()\n else:\n self.tm.commit()\n self._process_response_callbacks(self.context)\n\n @property\n def session(self):\n if getattr(self, '_session', None) is None:\n args = factory_args_from_settings(\n self.context.settings,\n DottedNameResolver().maybe_resolve,\n '',\n )\n factory = get_session_factory(**args)\n self._session = factory(self)\n return self._session\n\n @session.setter\n def session(self, v):\n pass\n\n @reify\n def dbsession(self):\n factory = sessionmaker()\n factory.configure(bind=self.context.engine)\n dbsession = factory()\n zope.sqlalchemy.register(dbsession, transaction_manager=self.tm)\n return dbsession\n\n @reify\n def tm(self):\n return transaction.TransactionManager(explicit=True)\n","repo_name":"a3kov/pyramid_sqlalchemy_sessions","sub_path":"pyramid_sqlalchemy_sessions/tests/contexts.py","file_name":"contexts.py","file_ext":"py","file_size_in_byte":3674,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"32959375000","text":"#!/usr/bin/env python3\nfrom tumd_manager import TUMD_Manager\nfrom util.author import author_name\nfrom html.context import Context\nfrom html.default import default_dict\nfrom html.manager import pop, push\n\nroot = 'https://tuacm.com/blog/'\n\ndef get_article_tags(path):\n tag_list = None\n with open(path + '/tags.txt', 'r') as file_reader:\n tag_list = file_reader.read.splitlines()\n return tag_list\n\ndef get_tagged_articles(path):\n global root\n articles = None\n with open(path + '/tagged.txt', 'r') as file_reader:\n articles = file_reader.read().splitlines()\n root = articles[0]\n articles = articles[1:]\n return articles\n\ndef print_tagged_articles(args):\n html = args[0]\n article_box_variables['html'] = html\n path = args[1]\n articles = get_tagged_articles(path)\n for article in articles:\n article = article.split('|')\n article_box_variables['title'] = article[0]\n article_box_variables['author'] = article[1]\n article_box_variables['tagline'] = article[2]\n article_box_variables['directory'] = root + article[3]\n article_box_variables['author-name'] = author_name(article_box_variables['author'])\n with open(\"templates/article-box.tumd\", \"r\") as reader:\n tumd = TUMD_Manager(reader)\n tumd.context_dict = article_box_dict\n tumd.context = ['default', 'article-box' ]\n while (True):\n tumd.find_next_content_line()\n if tumd.line_data[0] == '':\n break\n tumd.handle_context(html)\n return ''\n\narticle_box_variables = {\n 'title' : None,\n 'author' : None,\n 'author-link' : '{{author-name (author)}}',\n 'author-name' : author_name,\n 'tagline' : None,\n 'directory' : None,\n 'pop' : pop,\n 'push' : push,\n 'tupu' : '{{push (html)}}',\n 'tupo' : '{{pop (html)}}'\n}\n\narticle_box_variables = { **default_dict, **article_box_variables }\n\ndef process_article_box(html, line_data):\n html.add(line_data[0])\n\narticle_box_dict = {\n 'article-box': Context(None, process_article_box, None, article_box_variables)\n}\n","repo_name":"TheLandfill/mdxx","sub_path":"tumd/util/tags.py","file_name":"tags.py","file_ext":"py","file_size_in_byte":2146,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"} +{"seq_id":"75232955131","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\n ORCA Open Remote Control Application\r\n Copyright (C) 2013-2020 Carsten Thielepape\r\n Please contact me by : http://www.orca-remote.org/\r\n\r\n This program is free software: you can redistribute it and/or modify\r\n it under the terms of the GNU General Public License as published by\r\n the Free Software Foundation, either version 3 of the License, or\r\n (at your option) any later version.\r\n\r\n This program is distributed in the hope that it will be useful,\r\n but WITHOUT ANY WARRANTY; without even the implied warranty of\r\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\r\n GNU General Public License for more details.\r\n\r\n You should have received a copy of the GNU General Public License\r\n along with this program. If not, see .\r\n\"\"\"\r\n\r\nfrom typing import Dict\r\nfrom typing import List\r\nfrom typing import Callable\r\nfrom typing import Optional\r\n\r\nfrom copy import copy\r\nfrom kivy.logger import Logger\r\nimport ORCA.Globals as Globals\r\n\r\n\r\n__all__ = ['cNotifications']\r\n\r\nclass cNotifications:\r\n \"\"\" Class for in App communication \"\"\"\r\n def __init__(self):\r\n self.dNotifications:Dict[str,List] = {}\r\n self.dNotificationsHash:Dict[int,Dict] = {}\r\n self.dFilterPageNames:Dict[str,int] = {}\r\n\r\n def RegisterNotification(self,*, uNotification:str, fNotifyFunction:Callable, uDescription:str=\"\", bQuiet:bool=False, aValueLinks:Optional[List[Dict]]=None, **kwargs) -> int:\r\n \"\"\"\r\n Registers a notification\r\n\r\n :param uNotification: The notification name\r\n :param fNotifyFunction: The function to be called, if the notification is triggered\r\n :param uDescription: A notification description(for debugging purposes)\r\n :param bQuiet: Flag, if true, no debug message will be triggered on notification\r\n :param aValueLinks: Dict of chained values to passed through chained notification, format \"in:name / out:name\"\r\n :param kwargs: args to pass through the notification\r\n :return:\r\n \"\"\"\r\n if aValueLinks is None:\r\n aValueLinks = []\r\n\r\n dArgs:Dict = copy(kwargs)\r\n dArgs[\"notificationdescription\"] = uDescription\r\n dArgs[\"notification\"] = uNotification\r\n iHash:int = hash(repr(dArgs)+repr(fNotifyFunction))\r\n uFilterPageName:str = kwargs.get(\"filterpagename\",\"\")\r\n\r\n aNotificationFunctions:List = self.dNotifications.get(uNotification, [])\r\n\r\n if iHash in self.dNotificationsHash:\r\n Logger.warning(\"Duplicate notification registration %s:%s, replacing old one\" % (uNotification,dArgs[\"notificationdescription\"]))\r\n\r\n dEntry:Dict = {\"function\":fNotifyFunction,\"args\":dArgs,\"hash\":iHash,\"quiet\":bQuiet,\"valuelinks\": aValueLinks}\r\n aNotificationFunctions.append(dEntry)\r\n self.dNotifications[uNotification] = aNotificationFunctions\r\n self.dNotificationsHash[iHash] = dEntry\r\n if uFilterPageName:\r\n self.dFilterPageNames[uNotification+\"_\"+uFilterPageName] = iHash\r\n\r\n return iHash\r\n\r\n def UnRegisterNotification_ByHash(self,*,iHash:int) -> None:\r\n \"\"\"\r\n Unregisters an registered notification. The notification needs to be identified by the hash, when it has been created\r\n :param int iHash: The hash to identify the notification\r\n :return: Nothing\r\n \"\"\"\r\n dEntry:Optional[Dict] = self.dNotificationsHash.get(iHash,None)\r\n\r\n if dEntry is not None:\r\n uNotification:str = dEntry[\"args\"][\"notification\"]\r\n aNotificationFunctions:List = self.dNotifications.get(uNotification, [])\r\n aNotificationFunctions.remove(dEntry)\r\n del self.dNotificationsHash[iHash]\r\n uFilterPageName:str = dEntry[\"args\"].get(\"filterpagename\",\"\")\r\n uFilterKey:str = uNotification+\"_\"+uFilterPageName\r\n if uFilterKey in self.dFilterPageNames:\r\n del self.dFilterPageNames[uFilterKey]\r\n else:\r\n Logger.error(\"Tried to Degister notification with wrong hash\")\r\n\r\n def SendNotification(self,*,uNotification:str, **kwargs) -> Dict:\r\n \"\"\"\r\n Calls the function of all registered notifications for the notification name\r\n If valuelinks are given, the results of a call are passed to the next called function\r\n :param str uNotification: The notification\r\n :param kwargs: The args to be passed to the called function\r\n :return: A dict of function results\r\n \"\"\"\r\n dRet:Dict = {}\r\n\r\n # Format for dValueLinks\r\n # [{\"out\":\"name\",\"in\":\"name\"}]\r\n\r\n aNotificationFunctions:List = self.dNotifications.get(uNotification, []) + self.dNotifications.get(\"*\", [])\r\n\r\n for dFunction in aNotificationFunctions:\r\n dArgs = copy(kwargs)\r\n aValueLinks=dFunction[\"valuelinks\"]\r\n if dRet is not None:\r\n for dValueLink in aValueLinks:\r\n if dValueLink[\"in\"] in dArgs and dValueLink[\"out\"] in dRet:\r\n dArgs[dValueLink[\"in\"]]=dRet[dValueLink[\"out\"]]\r\n\r\n Globals.oEvents.CopyActionPars(dTarget=dArgs, dSource=dFunction[\"args\"], uReplaceOption=\"donotreplacetarget\")\r\n if not dFunction[\"quiet\"]:\r\n Logger.debug(\"Sending notification %s to %s\" % (uNotification,dArgs[\"notificationdescription\"]))\r\n Ret = dFunction[\"function\"](**dArgs)\r\n if isinstance(Ret,dict):\r\n dRet = Ret\r\n return dRet\r\n","repo_name":"thica/ORCA-Remote","sub_path":"src/ORCA/Notifications.py","file_name":"Notifications.py","file_ext":"py","file_size_in_byte":5734,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"78"} +{"seq_id":"6684334628","text":"# 3.1\r\n# print(\"Welcome to the rollercoaster!\")\r\n# height = int(input(\"What is your height(cm)? \"))\r\n# if height>=120:\r\n# print(\"You can ride the rollercoaster!\")\r\n# else:\r\n# print(\"Sorry, you have to grow taller before you can ride.\")\r\n# 3.2\r\n# number = int(input());\r\n# if number%2==0:\r\n# print(\"Even\")\r\n# else:\r\n# print(\"Odd\")\r\n# 3.3\r\n# print(\"Welcome to the rollercoaster!\")\r\n# height = int(input(\"What is your height(cm)? \"))\r\n# if height>=120:\r\n# print(\"You can ride the rollercoaster!\")\r\n# age = int(input(\"Put you age for price \"))\r\n# if age<12:\r\n# print(\"Pay $5\")\r\n# elif (age>=12) and (age<=18):\r\n# print(\"Pay $7\")\r\n# elif (age>=45) and (age<=55):\r\n# print(\"Have a free ride on us!!\")\r\n# else: \r\n# print(\"Pay $12\")\r\n# else:\r\n# print(\"Sorry, you have to grow taller before you can ride.\")\r\n# 3.4\r\n# weight= float(input(\"Enter Your Weight: \"))\r\n# height= float(input(\"Enter Your Height: \"))\r\n# BMI = round(weight/(height**2))\r\n# if BMI<18.5:\r\n# print(f\"Your bmi is {BMI},you are underweight\")\r\n# elif BMI<25:\r\n# print(f\"Your bmi is {BMI},you have a normal weight\")\r\n# elif BMI<30:\r\n# print(f\"Your bmi is {BMI},you are overweight\")\r\n# elif BMI<35:\r\n# print(f\"Your bmi is {BMI},you are obese\")\r\n# else:\r\n# print(f\"Your bmi is {BMI},you are clinically obese\")\r\n# 3.5\r\n# leapYear = int(input(\"Enter a Year: \"))\r\n# if (leapYear%4==0):\r\n# if leapYear%100==0:\r\n# if leapYear%400==0:\r\n# print(\"This is a leap year\")\r\n# else:\r\n# print(\"This is not a leap year\")\r\n# else:\r\n# print(\"This is a leap year\")\r\n# else:\r\n# print(\"This is not a leap year\")\r\n# 3.6\r\n# print(\"Welcome to the rollercoaster!\")\r\n# height = int(input(\"What is your height(cm)? \"))\r\n# if height>=120:\r\n# print(\"You can ride the rollercoaster!\")\r\n# age = int(input(\"Put your age for price \"))\r\n# if age<12:\r\n# bill1=8\r\n# photos = input(\"Do You want click photo? y/n \")\r\n# if photos=='y':\r\n# print(f\"Your total bill is ${bill1}\")\r\n# if photos == 'n':\r\n# print(\"your total bill is $5\")\r\n# elif (age>=12) and (age<=18):\r\n# bill2=10\r\n# photos = input(\"Do You want click photo? y/n \")\r\n# if photos=='y':\r\n# print(f\"Your total bill is ${bill2}\")\r\n# if photos == 'n':\r\n# print(\"your total bill $7\") \r\n# else: \r\n# bill3=15;\r\n# photos = input(\"Do You want click photo? y/n \")\r\n# if photos=='y':\r\n# print(f\"Your total bill is ${bill3}\")\r\n# if photos == 'n':\r\n# print(\"your total bill is $12\")\r\n \r\n# else:\r\n# print(\"Sorry, you have to grow taller before you can ride.\")\r\n# another method\r\n# bill=0\r\n# print(\"Welcome to the rollercoaster!\")\r\n# height = int(input(\"What is your height(cm)? \"))\r\n# if height>=120:\r\n# print(\"You can ride the rollercoaster!\")\r\n# age = int(input(\"Put your age for price \"))\r\n# if age<12:\r\n# bill=5\r\n \r\n# elif (age>=12) and (age<=18):\r\n# bill=10 \r\n# else: \r\n# bill=15;\r\n# photos = input(\"Do You want click photo? y/n \")\r\n# if photos=='y':\r\n# bill_new=bill+3;\r\n# print(f\"Your total bill is ${bill_new}\")\r\n# if photos == 'n':\r\n# print(f\"your total bill is ${bill}\")\r\n \r\n# else:\r\n# print(\"Sorry, you have to grow taller before you can ride.\")\r\n# 3.7\r\n# s=0\r\n# p=0\r\n# l=0\r\n# m=0\r\n# ch=0\r\n# pizza = input(\"Which type of pizza You want: \")\r\n# pepperoni=input(\"Do you want to add pepperoni: \")\r\n# extra_chesse = input(\"Do you want to add extra_Chesse: \")\r\n# if pizza=='s':\r\n# s=15\r\n# if pepperoni=='y':\r\n# p=2 \r\n# if extra_chesse=='y':\r\n# ch=1\r\n \r\n# print(f\"The total bill is {s+p+ch}\")\r\n# if pizza=='m':\r\n# m=20\r\n# if pepperoni=='y':\r\n# p=3 \r\n# if extra_chesse=='y':\r\n# ch=1\r\n \r\n# print(f\"The total bill is {m+p+ch}\")\r\n# if pizza=='l':\r\n# l=25\r\n# if pepperoni=='y':\r\n# p=3 \r\n# if extra_chesse=='y':\r\n# ch=1\r\n \r\n# print(f\"The total bill is {l+p+ch}\")\r\n# another method\r\n# size = input(\"Which type of pizza You want: \")\r\n# pepperoni=input(\"Do you want to add pepperoni: \")\r\n# extra_chesse = input(\"Do you want to add extra_Chesse: \")\r\n# bill = 0;\r\n# if size ==\"S\":\r\n# bill+=15\r\n# elif size == \"M\":\r\n# bill+=20\r\n# else:\r\n# bill+=25\r\n# if pepperoni == \"Y\":\r\n# if size== \"S\":\r\n# bill+=2\r\n# else:\r\n# bill+=3\r\n# if extra_chesse == \"Y\":\r\n# bill+= 1\r\n# print(f\"Your final bill is ${bill}\")\r\n# a = 9;\r\n# if(a>10) and (a<13):\r\n# if(a>10) or (a<13):\r\n# print(\"true\");\r\n# else:\r\n# print(\"false\")\r\n# 3.8\r\nname1= input(\"Enter Your name? \")\r\nname2= input(\"Enter her name? \")\r\nname = name1 + name2\r\na = name.lower()\r\nb1= a.count(\"t\")\r\nc1= a.count(\"r\")\r\nd1= a.count(\"u\")\r\ne1= a.count(\"e\")\r\nTotal1= int(b1+c1+d1+e1)\r\nb2= a.count(\"l\")\r\nc2= a.count(\"o\")\r\nd2= a.count(\"v\")\r\ne2= a.count(\"e\")\r\nTotal2 = int(b2+c2+d2+e2)\r\nscore=(f\"{Total1}{Total2}\")\r\nif (int(score)<10) or (int(score)>90):\r\n print(\"Your score is \" + score+ \", You go together like coke and mentos\")\r\nelif (int(score)>=40) and (int(score)<=50):\r\n print(\"Your score is \"+ score+\", you are alright together\")\r\nelse:\r\n print(\"Your score is \"+ score)\r\n\r\n\r\n\r\n \r\n\r\n\r\n \r\n","repo_name":"srinjon/Python-Project","sub_path":"Day3.py","file_name":"Day3.py","file_ext":"py","file_size_in_byte":5429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"27825130774","text":"from django.db import models\n# from django.contrib.auth.models import User\nfrom user_app.models import User\n\nclass Post(models.Model):\n author = models.ForeignKey(User, on_delete=models.CASCADE)\n content = models.TextField()\n created_at = models.DateTimeField(auto_now_add=True)\n\nclass Comment(models.Model):\n commenter = models.ForeignKey(User, on_delete=models.CASCADE)\n post = models.ForeignKey(Post, on_delete=models.CASCADE, related_name='comments')\n text = models.TextField()\n created_at = models.DateTimeField(auto_now_add=True)\n","repo_name":"jayaruhbee/RingRelief","sub_path":"backend/post_app/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":557,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"} +{"seq_id":"38693860572","text":"myFile = open(\"text.txt\", \"r\")\nsuma = 0\ni = 0\ncisla = []\nfor i in range(22):\n line = myFile.readline().split()\n if all(x in line for x in (\"$\", \"cd\")) or \"dir\" in line or all(x in line for x in (\"$\", \"ls\")):\n cisla.append(suma)\n suma = 0\n else:\n suma+=int(line[0])\n\ncisla.append(suma)\nfiltered_numbers = [n for n in cisla if n < 100000]\nresult = sum(filtered_numbers)\n \nprint(result)","repo_name":"Joesmejko/Python-Programs","sub_path":"Prog/AOC_Template.py","file_name":"AOC_Template.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"34564745475","text":"#!/usr/bin/python2.7\n# -*- coding: utf-8 -*-\n# vim:ts=4:sw=4:softtabstop=4:smarttab:expandtab\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\"\"\"\nTranslate paths to and from various platforms.\n\nUsing cygwin on Windows presents the small problem of translating NT paths to\ncygwin paths for some applications.\n\n\"\"\"\nfrom __future__ import print_function\n\nimport ntpath, posixpath\n\n\ndef unix2win(path):\n return path.replace(\"/\", \"\\\\\")\n\ndef win2unix(path):\n return path.replace(\"\\\\\", \"/\")\n\ndef nt2cygwin(winpath):\n if winpath.find(\":\") > 0:\n [drive, path] = winpath.split(\":\", 1)\n return \"/cygdrive/%s%s%s\" % (drive.lower(), (\"\" if path.startswith(\"\\\\\") else \"/\"), path.replace(\"\\\\\", \"/\"))\n else:\n return \"/cygdrive/c%s%s\" % ((\"\" if winpath.startswith(\"\\\\\") else \"/\"), winpath.replace(\"\\\\\", \"/\")) # assume C: drive if not given\nwin2cygwin = nt2cygwin # alias\n\n\ndef cygwin2nt(path):\n parts = path.split(\"/\")\n if path.startswith(\"/cygdrive\"):\n return \"%s:\\\\%s\" % (parts[2].upper(), ntpath.join(*tuple(parts[3:])))\n elif not parts[0]: # empty is root\n return ntpath.join(\"C:\\\\cygwin\", *tuple(parts))\n else:\n return ntpath.join(*tuple(parts))\n\n\ndef _test(argv):\n print(cygwin2nt(\"/cygdrive/c/tmp\"), \"C:\\\\tmp\")\n print(cygwin2nt(\"/usr/bin\"), \"C:\\\\cygwin\\\\usr\\\\bin\")\n print(cygwin2nt(\"usr/bin\"), \"usr\\\\bin\")\n print(nt2cygwin(\"C:\\\\share\\\\dir1\"), \"/cygdrive/c/share/dir1\")\n print(nt2cygwin(\"\\\\Program Files\\\\dir1\"), \"/cygdrive/c/Program Files/dir1\")\n print(nt2cygwin(\"\\\\share\\\\dir1\"), \"/cygdrive/c/share/dir1\")\n\nif __name__ == \"__main__\":\n import sys\n _test(sys.argv)\n\n","repo_name":"kdart/pycopia","sub_path":"core/pycopia/anypath.py","file_name":"anypath.py","file_ext":"py","file_size_in_byte":2140,"program_lang":"python","lang":"en","doc_type":"code","stars":83,"dataset":"github-code","pt":"78"} +{"seq_id":"31905761246","text":"#!/usr/bin/env python\nimport rospy\nfrom std_msgs.msg import String\nfrom std_msgs.msg import Int16\n\ndef ticks_callback(msg_ticks):\n global ticks\n global encoder_A\n global encoder_B\n global encoder_C\n global encoder_D\n\n ticks = msg_ticks.data\n encoders = ticks.split('#')\n rospy.loginfo('Encoders: %s', encoders)\n encoder_A = int(encoders[0])\n encoder_B = int(encoders[1])\n encoder_C = int(encoders[2])\n encoder_D = int(encoders[3])\n\n talker()\n\ndef set():\n rospy.Subscriber(\"ticks\", String, ticks_callback)\n rospy.init_node('listenner_ticks', anonymous=True)\n\n\ndef talker():\n #rospy.loginfo('Encoder_A: %d', encoder_A)\n pub_A.publish(encoder_A)\n\n #rospy.loginfo('Encoder_B: %d', encoder_B)\n pub_B.publish(encoder_B)\n\n #rospy.loginfo('Encoder_C: %d', encoder_C)\n pub_C.publish(encoder_C)\n\n #rospy.loginfo('Encoder_D: %d', encoder_D)\n pub_D.publish(encoder_D)\n\n\npub_A = rospy.Publisher('ticks_A', Int16, queue_size=10)\npub_B = rospy.Publisher('ticks_B', Int16, queue_size=10)\npub_C = rospy.Publisher('ticks_C', Int16, queue_size=10)\npub_D = rospy.Publisher('ticks_D', Int16, queue_size=10)\nrospy.Subscriber(\"ticks\", String, ticks_callback)\n\nrospy.init_node('listenner_ticks', anonymous=True)\n\nrospy.spin()\n\n\nif __name__ == '__main__':\n while not rospy.is_shutdown():\n set()\n","repo_name":"GerbersonFelix/speed_control","sub_path":"scripts/listenner_ticks.py","file_name":"listenner_ticks.py","file_ext":"py","file_size_in_byte":1349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"11967892812","text":"#바로 스크린에서 캡쳐해서 그화면을 클릭하는 프로그램\r\n\r\nimport pyautogui\r\n\r\n#한쪽끝을 알아내서 가로세로 네모를 만들므로\r\n#커서가 있는 곳의 좌표를 알아내기(93,819)\r\nprint(pyautogui.position())\r\n\r\n#1이라는 이름으로 스크린샷 하기\r\npyautogui.screenshot('1.png',region=(69,788,97,101))\r\n#같은 폴더에 1이라는 스크린 샷이 생김\r\n\r\n#1.png캡쳐파일의센터값을 알아내서 num1 에 넣음\r\nnum1= pyautogui.locateCenterOnScreen('1.png')\r\n#num1값을 클릭\r\npyautogui.click(num1)","repo_name":"Leeseyoung84/study","sub_path":"macro03.py","file_name":"macro03.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"39560403719","text":"class Horse:\n def __init__(self, name, breed, category, areal, weight, color):\n self.name = name\n self.breed = breed\n self.category = category\n self.areal = areal\n self.weight = weight\n self.color = color\n\n def determine_category_areal(self):\n if self.category == \"верховий\":\n if self.breed in [\"ахалтекінська\", \"арабська\", \"чистокровна верхова\", \"терська\"]:\n return \"верховим\"\n else:\n return \"невідомою\"\n elif self.category == \"упряжний\":\n if self.breed in [\"орловська\", \"російська рисиста\", \"володимирський важковоз\"]:\n return \"важковозами\"\n elif self.breed in [\"російський тяжкий\", \"російський рисисто-гнідий\"]:\n return \"рисистими\"\n else:\n return \"невідомою\"\n\n def determine_areal_category(self):\n\n if self.areal == \"Північна та Південна Америка\":\n return \"американським\"\n elif self.areal == \"Європа та Азія\":\n return \"євроазіатським\"\n elif self.areal == \"Африка\":\n return \"африканським\"\n elif self.areal == \"Австралія\":\n return \"австралійським\"\n else:\n return \"невідомим\"\n\n def generate_message(self):\n category_areal = self.determine_category_areal()\n areal_category = self.determine_areal_category()\n message = f\"Кінь {self.name} породи {self.breed}, що відноситься до {category_areal} {areal_category} порід, важить {self.weight} і має забарвлення {self.color}.\"\n return message\n\n# Запрос данных у пользователя\nname = input(\"Введіть ім'я коня: \")\nbreed = input(\"Введіть породу коня: \")\ncategory = input(\"Введіть категорію коня (верховий/упряжний): \")\nareal = input(\"Введіть ареал коня (американський/євроазіатський/африканський/австралійський): \")\nweight = input(\"Введіть вагу коня: \")\ncolor = input(\"Введіть забарвлення коня: \")\n\n# Створення об'єкту класу Horse\nhorse = Horse(name, breed, category, areal, weight, color)\n\n# Вивід повідомлення про коня\nmessage = horse.generate_message()\nprint(message)","repo_name":"Timalchuk/-DICT_Python_Algorithms_and_data_structures_Timalchuk_Diana","sub_path":"practice1/practice1.py","file_name":"practice1.py","file_ext":"py","file_size_in_byte":2726,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"41815928306","text":"#! /usr/bin/env python\n\nimport argparse\nimport logging\nimport os.path\nimport sys\nfrom collections import defaultdict\n\nfrom conda_build.api import render\n\nimport yaml\n\nfrom conda_recipe_tools.util import get_feedstock_dirs\n\nLOG_FORMAT = '%(asctime)s - %(levelname)s : %(message)s'\n\n\ndef prep_clobber(feedstock_dir):\n \"\"\" create a recipe_clobber.yaml file for a feedstock \"\"\"\n\n recipe_dir = os.path.join(feedstock_dir, 'recipe')\n recipes = render(recipe_dir, finalize=False)\n metadata, download, needs_reparse = recipes[0]\n\n pkg_build_number = metadata.build_number()\n pkg_noarch_python = metadata.noarch_python or metadata.noarch == 'python'\n\n clobber = defaultdict(dict)\n # clobber noarch: python if present\n if pkg_noarch_python:\n clobber['build']['noarch'] = False\n # keep build numbers less than 1000\n if pkg_build_number >= 1000:\n clobber['build']['number'] = int(pkg_build_number - 1000)\n\n # write clobber\n if clobber:\n clobber_file = os.path.join(recipe_dir, 'recipe_clobber.yaml')\n with open(clobber_file, 'w') as f:\n yaml.dump(dict(clobber), f, default_flow_style=False)\n\n\ndef main():\n parser = argparse.ArgumentParser(\n description='Prepare recipe clobber file for a feedstock')\n parser.add_argument(\n 'feedstock_dir', nargs='*',\n help='one or more feedstock directories to prepare clobber file for')\n parser.add_argument(\n '--file', '-f', type=str,\n help='file with feedstock directories to prepare file for')\n parser.add_argument(\n '--base_dir', default='.', type=str,\n help='feedstock base directory, default is current directory')\n parser.add_argument(\n '--log', default='info',\n help='log level; debug, info, warning, error, critical')\n args = parser.parse_args()\n\n # set up logging\n log_numeric_level = getattr(logging, args.log.upper(), None)\n if not isinstance(log_numeric_level, int):\n raise ValueError('Invalid log level: %s' % args.log)\n logging.basicConfig(level=log_numeric_level, format=LOG_FORMAT)\n\n feedstock_dirs = get_feedstock_dirs(args.feedstock_dir, args.file)\n for feedstock_dir in feedstock_dirs:\n if feedstock_dir.endswith('/'):\n feedstock_dir = feedstock_dir[:-1]\n logging.info('preparing files for: ' + feedstock_dir)\n prep_clobber(feedstock_dir)\n return 0\n\n\nif __name__ == \"__main__\":\n sys.exit(main())\n","repo_name":"jjhelmus/conda_recipe_tools","sub_path":"conda_recipe_tools/cli/create_clobber.py","file_name":"create_clobber.py","file_ext":"py","file_size_in_byte":2461,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"78"} +{"seq_id":"72022132091","text":"name = input()\ngame_season = int(input())\n\ncounter_score = 0\nwon = 0\nequal = 0\nlost = 0\npercent_games = 0\n\nfor game in range(0, game_season):\n score = input()\n\n if score == \"W\":\n won += 1\n counter_score += 3\n elif score == \"D\":\n equal += 1\n counter_score += 1\n else:\n lost += 1\n\n percent_games = won / game_season * 100\n\nif game_season == 0:\n print(f'{name} hasn\\'t played any games during this season.')\nelse:\n print(f'{name} has won {counter_score} points during this season.')\n print(f'Total stats:')\n print(f'## W: {won}')\n print(f'## D: {equal}')\n print(f'## L: {lost}')\n print(f'Win rate: {percent_games:.2f}%')\n\n\n","repo_name":"zhyordanova/Python-Basics","sub_path":"Exam-Preparation/football_tournament.py","file_name":"football_tournament.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"23936345658","text":"import util2\r\nimport json\r\nimport util as ut\r\nimport numpy as np\r\nimport torch\r\nimport math\r\nfrom torch.nn.parameter import Parameter\r\nfrom torch.nn.modules.module import Module\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport time\r\nimport torch.optim as optim\r\nfrom tqdm import tnrange\r\nimport pandas as pd\r\n\r\n# 각 그래프를 node 취급하면 될 듯..? 인접 행렬 틀(이미지 규격, rgb)이 동일한데,\r\n# np.array()로 이미지, Label 하나씩 학습시키는 걸 볼 수 있었음\r\n# gnn도 이케 하면 graph classification 아님? 일단 한다...\r\n\r\n\r\n'''\r\n node classification과 동일하게 사용\r\n adj : 이미지 간 동일 클러스터 여부\r\n feature : freObj x freObj 간의 관계가 반영된 matrix를 flatten으로 펼침. 1000x10000\r\n + freObj x freObj 펼치기 전에 fasttext embedding 값을 곱하면 더 좋을 것 같기두\r\n Y : 각 id 별 cluster 값\r\n'''\r\n\r\n\r\n\r\n\r\ntestFile = open('../data/freObj.txt', 'r') # 'r' read의 약자, 'rb' read binary 약자 (그림같은 이미지 파일 읽을때)\r\nreadFile = testFile.readline()\r\nfreObj = (readFile[1:-1].replace(\"'\", '').replace(' ', '')).split(',')\r\nfreObj = freObj[:100]\r\nadjList = []\r\n\r\nwith open('./data/scene_graphs.json') as file:\r\n data1 = json.load(file)\r\nwith open('./data/objects.json') as file: # open json file\r\n data2 = json.load(file)\r\nfor i in range(1000):\r\n adj = ut.createAdj_model2(i, freObj,data1, data2)\r\n adjList.append(adj)\r\n#adjList = np.ndarray(adjList)\r\n\r\n\r\n# gpu 사용\r\ndevice = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\r\n#features = csr_matrix(np.load('./data/idFreFeature.npy'), dtype=np.float32)\r\nfeatures = util2.objNameEmbedding(freObj)\r\n\r\nadj = torch.FloatTensor(np.load('./data/idAdj.npy'))\r\n#features = csr_matrix(features)\r\n\r\n# def normalize(mx):\r\n# rowsum = np.array(mx.sum(1))\r\n# r_inv = (rowsum ** -1).flatten()\r\n# r_inv[np.isinf(r_inv)] = 0.\r\n# r_mat_inv = diags(r_inv)\r\n# mx = r_mat_inv.dot(mx)\r\n# return mx\r\n#features = normalize(features)\r\n#print(\"features after nomalize : \", features)\r\n\r\n\r\ntestFile = open('../data/cluster.txt', 'r') # 'r' read의 약자, 'rb' read binary 약자 (그림같은 이미지 파일 읽을때)\r\nreadFile = testFile.readline()\r\nlabel = (readFile[1:].replace(\"'\", '').replace(' ', '').split(','))\r\nlabels = []\r\nfor i in range(1000):\r\n labels.append(int(label[i]))\r\n\r\nfeatures = torch.FloatTensor(features) # \r\nprint(features)\r\nlabels = torch.LongTensor(labels) # \r\n\r\n# dataset train/test/val로 나눔\r\nnp.random.seed(34)\r\nn_train = 200\r\nn_val = 300\r\nn_test = len(features) - n_train - n_val\r\nidxs = np.random.permutation(len(features))\r\nidx_train = torch.LongTensor(idxs[:n_train])\r\nidx_val = torch.LongTensor(idxs[n_train:n_train + n_val])\r\nidx_test = torch.LongTensor(idxs[n_train + n_val:])\r\n#adj = torch.FloatTensor(adjList)\r\n\r\n# cuda.. gpu로 보냄\r\nadj = torch.FloatTensor(adjList).to(device)\r\nfeatures = features.to(device)\r\nlabels = labels.to(device)\r\nidx_train = idx_train.to(device)\r\nidx_val = idx_val.to(device)\r\nidx_test = idx_test.to(device)\r\n\r\n\r\n# Model\r\nclass GraphConvolution(Module):\r\n def __init__(self, in_features, out_features, bias=True):\r\n super(GraphConvolution, self).__init__()\r\n self.in_features = in_features\r\n self.out_features = out_features\r\n\r\n # weight를 reset해 줌. weight 변화에 따른 정확도 측정을 위해서? 왜하지? 이유 찾아보기\r\n self.weight = Parameter(torch.FloatTensor(in_features, out_features))\r\n if bias:\r\n self.bias = Parameter(torch.FloatTensor(out_features))\r\n else:\r\n self.register_parameter('bias', None)\r\n self.reset_parameters()\r\n\r\n def reset_parameters(self):\r\n stdv = 1. / math.sqrt(self.weight.size(1))\r\n self.weight.data.uniform_(-stdv, stdv)\r\n if self.bias is not None:\r\n self.bias.data.uniform_(-stdv, stdv)\r\n #\r\n\r\n def forward(self, input, adj):\r\n support = torch.mm(input, self.weight)\r\n output = torch.spmm(adj, support)\r\n if self.bias is not None:\r\n return output + self.bias\r\n else:\r\n return output\r\n\r\n def __repr__(self):\r\n return self.__class__.__name__ + ' (' \\\r\n + str(self.in_features) + ' -> ' \\\r\n + str(self.out_features) + ') '\r\n\r\n\r\nclass GCN(nn.Module):\r\n def __init__(self, nfeat, nhid, nclass, dropout):\r\n super(GCN, self).__init__()\r\n\r\n self.gc1 = GraphConvolution(nfeat, nhid)\r\n self.gc2 = GraphConvolution(nhid, nclass)\r\n self.dropout = dropout\r\n\r\n # X : 초기 랜덤값 -> 학습 하면서 변경\r\n def forward(self, x, adj):\r\n x = F.relu(self.gc1(x, adj))\r\n x = F.dropout(x, self.dropout, training=self.training)\r\n x = self.gc2(x, adj)\r\n\r\n return F.log_softmax(x, dim=1)\r\n\r\n\r\nn_labels = labels.max().item() + 1 # 15\r\nn_features = features.shape[1] # 100\r\n\r\n# seed 고정\r\ntorch.manual_seed(34)\r\n\r\n# model\r\nmodel = GCN(nfeat=n_features,\r\n nhid=20, # hidden = 16\r\n nclass=n_labels,\r\n dropout=0.5) # dropout = 0.5\r\noptimizer = optim.Adam(model.parameters(),\r\n lr=0.001, weight_decay=5e-4)\r\n\r\n\r\ndef step():\r\n t = time.time()\r\n model.train() # model 학습모드로\r\n optimizer.zero_grad()\r\n output = model(features, adj) # model에 값 넣음\r\n loss = F.nll_loss(output[idx_train], labels[idx_train]) # loss 함수\r\n acc = accuracy(output[idx_train], labels[idx_train]) # accuracy 파악\r\n loss.backward()\r\n optimizer.step()\r\n return loss.item(), acc\r\n\r\n# 평가\r\ndef evaluate(idx):\r\n model.eval()\r\n output = model(features, adj) # 모델 돌림\r\n loss = F.nll_loss(output[idx], labels[idx]) # 모델이 분류한 값과 label 비교해서 loss 파악\r\n acc = accuracy(output[idx], labels[idx])\r\n\r\n return loss.item(), acc\r\n\r\n\r\n# 정확도\r\ndef accuracy(output, labels):\r\n preds = output.max(1)[1].type_as(labels) #비슷하다고 뽑은 것들중에 제일 비슷한 거... label 예측한 거 전체list\r\n correct = preds.eq(labels).double()\r\n correct = correct.sum()\r\n return correct / len(labels)\r\n\r\ndef alone(output, labels) :\r\n preds = output.max(1)[1].type_as(labels)\r\n print(preds)\r\n return preds\r\n\r\n\r\n# seed 고정\r\ntorch.manual_seed(34)\r\n\r\n# model\r\n # hidden = 16, dropout = 0.5\r\nmodel = GCN(nfeat=n_features,\r\n nhid=20, nclass=n_labels, dropout=0.5)\r\noptimizer = optim.Adam(model.parameters(),\r\n lr=0.001, weight_decay=5e-4)\r\nmodel = model.cuda()\r\nepochs = 100\r\n# epochs = 1000\r\n# print_steps = 100\r\nprint_steps = 10\r\ntrain_loss, train_acc = [], []\r\nval_loss, val_acc = [], []\r\n\r\n\r\n#여기서 행렬 하나씩 넣어주면 됨\r\nfor i in tnrange(epochs):\r\n for a in adj : #self\r\n tl, ta = step(a)\r\n train_loss += [tl]\r\n train_acc += [ta]\r\n\r\n if ((i + 1) % print_steps) == 0 or i == 0:\r\n tl, ta = evaluate(idx_train,a)\r\n vl, va = evaluate(idx_val,a)\r\n val_loss += [vl]\r\n val_acc += [va]\r\n\r\n print('Epochs: {}, Train Loss: {:.3f}, Train Acc: {:.3f}, Validation Loss: {:.3f}, Validation Acc: {:.3f}'.format(i, tl, ta, vl, va))\r\n\r\noutput = model(features, adj[0])\r\n\r\n# test - 범위 지정해서 Y or No 값 확인하기.. 이거 장난질 아닌가 하는 그런..마음.. 원래 의도랑 달라보임..\r\n\r\nidx2lbl = ['0번 그림', '1번 그림', '2번 그림', '3번 그림', '4번 그림', '5번 그림', '6번 그림', '7번 그림'\r\n , '8번 그림', '9번 그림', '10번 그림', '11번 그림', '12번 그림', '13번 그림', '14번 그림']\r\n\r\n#\r\n\r\n\r\n\r\n#각 범위 별 이미지 값으로 동일 여부 확인\r\n#범위 추가하는 변수2개 만들면 될 듯 -> list면 range 쓰겠는데 tensor는 안된다함.\r\n#예제기도 하고, 굳이 쓰려면 a1,b1, a2,b2를 사용하면 됨\r\n\r\n\r\na1 = 1\r\nb1 = 10\r\na2 = 22\r\nb2 = 31\r\n\r\nidx_sample1 = idx_test[torch.randperm(len(idx_test))[1:10]]\r\nrealList1 = [idx2lbl[e] for e in labels[idx_sample1].tolist()]\r\npredList1 = [idx2lbl[e] for e in output[idx_sample1].argmax(1).tolist()]\r\n\r\nidx_sample2 = idx_test[torch.randperm(len(idx_test))[22:31]]\r\nrealList2 = [idx2lbl[e] for e in labels[idx_sample2].tolist()]\r\npredList2 = [idx2lbl[e] for e in output[idx_sample2].argmax(1).tolist()]\r\n\r\nresPred = []\r\nfor i in range(len(predList2)) :\r\n if(predList1[i] == predList2[i]) :\r\n resPred.append('T')\r\n else :\r\n resPred.append('F')\r\n\r\nresReal = []\r\nfor i in range(len(realList1)) :\r\n if(realList1[i] == realList2[i]) :\r\n resReal.append('T')\r\n else :\r\n resReal.append('F')\r\n\r\n#img_id 값으로 그림 확인하고 싶어서 id 찍어봄\r\nprint(\"idx_sample1_Imgid : \",idx_sample1)\r\nprint(\"idx_sample2_Imgid : \", idx_sample2)\r\nidDF1 = pd.DataFrame({'idx_sample1_Imgid': idx_sample1,\r\n 'idx_sample2_Imgid': idx_sample2})\r\nprint(idDF1)\r\n\r\n\r\ndf1 = pd.DataFrame({'Pre1': [idx2lbl[e] for e in output[idx_sample1].argmax(1).tolist()],\r\n 'Pred2': [idx2lbl[e] for e in output[idx_sample2].argmax(1).tolist()],\r\n 'Res(T/F)':resPred})\r\ndf2 = pd.DataFrame({'Real1': [idx2lbl[e] for e in labels[idx_sample1].tolist()],\r\n 'Real2': [idx2lbl[e] for e in labels[idx_sample2].tolist()],\r\n 'Res(T/F)':resReal})\r\n\r\nprint(df1)\r\nprint(df2)\r\n\r\n","repo_name":"KNUT-DBLAB/Image_Retrieval","sub_path":"GraphClassification/GraphClassifi/dumm/GCNNodeClas.py","file_name":"GCNNodeClas.py","file_ext":"py","file_size_in_byte":9555,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"30490183709","text":"\n# QUESTION\n#\n# For this task you will need to train a neural network\n# to predict sunspot activity using the Sunspots.csv dataset.\n# Your neural network must have an MAE\n# of 0.12 or less on the normalized dataset for top marks.\n# Code for normalizing the data is provided and should not be changed.\n# At the bottom of this file, we provide some testing\n# code should you want to check your model.\n\n# Note: Do not use lambda layers in your model, they are not supported\n# on the grading infrastructure.\n\n# =========== 합격 기준 가이드라인 공유 ============= #\n# val_loss 기준에 맞춰 주시는 것이 훨씬 더 중요 #\n# val_loss 보다 조금 높아도 상관없음. (언저리까지 OK) #\n# =================================================== #\n# 문제명: Category 5 - sunspots type B (NO Lambda)\n# val_loss: 상관없음\n# val_mae: 0.1121\n# =================================================== #\n# =================================================== #\n\n\nimport csv\nimport tensorflow as tf\nimport numpy as np\nimport urllib\n\n\n\n\n# DO NOT CHANGE THIS CODE\ndef windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n series = tf.expand_dims(series, axis=-1)\n ds = tf.data.Dataset.from_tensor_slices(series)\n ds = ds.window(window_size + 1, shift=1, drop_remainder=True)\n ds = ds.flat_map(lambda w: w.batch(window_size + 1))\n ds = ds.shuffle(shuffle_buffer)\n ds = ds.map(lambda w: (w[:-1], w[1:]))\n return ds.batch(batch_size).prefetch(1)\n\n\ndef solution_model():\n url = 'https://storage.googleapis.com/download.tensorflow.org/data/Sunspots.csv'\n urllib.request.urlretrieve(url, 'sunspots.csv')\n\n time_step = []\n sunspots = []\n\n with open('sunspots.csv') as csvfile:\n reader = csv.reader(csvfile, delimiter=',')\n next(reader)\n for row in reader:\n sunspots.append(float(row[2]))\n time_step.append(int(row[0]))\n\n series = np.array(sunspots)\n\n # DO NOT CHANGE THIS CODE\n # This is the normalization function\n min = np.min(series)\n max = np.max(series)\n series -= min\n series /= max\n time = np.array(time_step)\n\n # The data should be split into training and validation sets at time step 3000\n # DO NOT CHANGE THIS CODE\n split_time = 3000\n\n time_train = time[:split_time]\n x_train = series[:split_time]\n\n # Get the validation set\n time_valid = time[split_time:]\n x_valid = series[split_time:]\n\n # DO NOT CHANGE THIS CODE\n window_size = 30\n batch_size = 32\n shuffle_buffer_size = 1000\n\n\n train_set = windowed_dataset(x_train, window_size=window_size, batch_size=batch_size, shuffle_buffer=shuffle_buffer_size)\n valid_set = windowed_dataset(x_valid, window_size=window_size, batch_size=batch_size, shuffle_buffer=shuffle_buffer_size)\n\n\n model = tf.keras.models.Sequential([\n tf.keras.layers.Conv1D(filters=64, kernel_size=3,\n strides=1,\n activation=\"relu\",\n padding='causal',\n input_shape=[window_size, 1]),\n tf.keras.layers.LSTM(64, return_sequences=True),\n tf.keras.layers.LSTM(64),\n tf.keras.layers.Dense(30, activation=\"relu\"),\n tf.keras.layers.Dense(10, activation=\"relu\"),\n\n # YOUR CODE HERE. Whatever your first layer is, the input shape will be [None,1] when using the Windowed_dataset above, depending on the layer type chosen\n tf.keras.layers.Dense(1)\n ])\n\n # YOUR CODE HERE TO COMPILE AND TRAIN THE MODEL\n\n learning_rate = 8e-7\n\n # Set the optimizer\n optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate, momentum=0.9)\n\n # Set the training parameters\n model.compile(loss=tf.keras.losses.Huber(),\n optimizer=optimizer,\n metrics=[\"mae\"])\n\n\n checkpointpath='sunspotb.ckpt'\n checkpoint = tf.keras.callbacks.ModelCheckpoint(\n checkpointpath, save_weights_only=True,\n save_best_only=True, monitor='val_mae', verbose=1)\n\n # Train the model\n history = model.fit(train_set, validation_data=(valid_set),\n callbacks=[checkpoint] , epochs=100)\n\n\n return model\n\n\n# Note that you'll need to save your model as a .h5 like this.\n# When you press the Submit and Test button, this .h5 model will be\n# sent to the testing infrastructure for scoring.\n\n# You must use the Submit and Test button to submit your model\n# at least once in each category before you finally submit your exam.\n\nif __name__ == '__main__':\n model = solution_model()\n\n checkpointpath = 'sunspotb.ckpt'\n model.load_weights(checkpointpath)\n\n model.save(\"TF5-sunspots-type-B.h5\")\n\n\n\n# THIS CODE IS USED IN THE TESTER FOR FORECASTING. IF YOU WANT TO TEST YOUR MODEL\n# BEFORE UPLOADING YOU CAN DO IT WITH THIS\n#def model_forecast(model, series, window_size):\n# ds = tf.data.Dataset.from_tensor_slices(series)\n# ds = ds.window(window_size, shift=1, drop_remainder=True)\n# ds = ds.flat_map(lambda w: w.batch(window_size))\n# ds = ds.batch(32).prefetch(1)\n# forecast = model.predict(ds)\n# return forecast\n\n\n#window_size = # YOUR CODE HERE\n#rnn_forecast = model_forecast(model, series[..., np.newaxis], window_size)\n#rnn_forecast = rnn_forecast[split_time - window_size:-1, -1, 0]\n\n#result = tf.keras.metrics.mean_absolute_error(x_valid, rnn_forecast).numpy()\n\n## To get the maximum score, your model must have an MAE OF .12 or less.\n## When you Submit and Test your model, the grading infrastructure\n## converts the MAE of your model to a score from 0 to 5 as follows:\n\n#test_val = 100 * result\n#score = math.ceil(17 - test_val)\n#if score > 5:\n# score = 5\n\n#print(score)\n","repo_name":"yoyoshingu/tfpre","sub_path":"spreTF5-sunspots-type-B.py","file_name":"spreTF5-sunspots-type-B.py","file_ext":"py","file_size_in_byte":5698,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"} +{"seq_id":"41085743880","text":"import pytest\nfrom mock import patch\n\nfrom foolscap.meta_data.tag_history import (\n TagsHistory,\n diff_tags,\n record_tags,\n format_new_history\n)\n\n\n@pytest.fixture(scope='function')\ndef history_object():\n \"\"\"Provide a history object.\n \"\"\"\n patch_load = 'foolscap.meta_data.tag_history.load_tag_history'\n with patch(patch_load) as mock_load:\n mock_load.return_value = ['tag line 1', 'tag line 2']\n return TagsHistory()\n\n\ndef test_TagsHistory_init(history_object):\n assert len(history_object) == 2\n assert history_object == ['tag line 1', 'tag line 2']\n\n\ndef test_TagsHistory_deprecate_less_than(history_object):\n with patch('foolscap.meta_data.tag_history.TAG_HISTORY', 1):\n history_object.check_deprecation(0)\n assert history_object.deprecate_lines == 1\n\n\ndef test_TagsHistory_deprecate_greater_than(history_object):\n with patch('foolscap.meta_data.tag_history.TAG_HISTORY', 1):\n history_object.check_deprecation(2)\n assert history_object.deprecate_lines == 3\n\n\ndef test_TagsHistory_save(history_object):\n patch_save = 'foolscap.meta_data.tag_history.save_tag_history'\n with patch(patch_save) as mock_save:\n history_object.check_deprecation(0)\n history_object.save()\n\n mock_save.assert_called_with(['tag line 1', 'tag line 2'], 0)\n\n\n@pytest.mark.parametrize(\"diff_input, record_input\",\n [\n (\n (\n ['new_tag', 'new_tag_2'],\n ['old_tag'],\n 'note',\n ),\n (\n 'note',\n {'old_tag'},\n {'new_tag', 'new_tag_2'},\n )\n ),\n (\n (\n ['old_tag'],\n ['old_tag'],\n 'note',\n ),\n (\n 'note',\n set(),\n set(),\n )\n )\n ]\n)\ndef test_diff_tags(diff_input, record_input):\n with patch('foolscap.meta_data.tag_history.record_tags') as mock_record:\n diff_tags(*diff_input)\n mock_record.assert_called_with(*record_input)\n\n\ndef test_record_tags(history_object):\n patch_save = 'foolscap.meta_data.tag_history.save_tag_history'\n with patch(patch_save),\\\n patch('foolscap.meta_data.tag_history.TagsHistory') as mock_history,\\\n patch('foolscap.meta_data.tag_history.format_new_history') as style:\n\n style.return_value = ['new line']\n mock_history.return_value = history_object\n record_tags('note', {'deleted_tag'}, {'added_tag'})\n\n expected = [\n 'tag line 1',\n 'tag line 2',\n 'new line',\n 'new line',\n ]\n\n assert history_object.deprecate_lines == 0\n assert history_object == expected\n\n\ndef test_format_new_history():\n from datetime import date\n mock_style = \"{{{tag}}} {note} {date}\"\n note = 'note'\n tags = ['new', 'tags']\n\n with patch('foolscap.meta_data.tag_history.date') as time:\n time.today.return_value = date(2017, 2, 2)\n\n expected = [\n \"{new} note 2017-02-02\",\n \"{tags} note 2017-02-02\",\n ]\n\n result = format_new_history(mock_style, note, tags)\n assert result == expected\n\n","repo_name":"foxyblue/foolscap","sub_path":"tests/test_meta_data/test_tag_history.py","file_name":"test_tag_history.py","file_ext":"py","file_size_in_byte":3247,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"78"} +{"seq_id":"70462837691","text":"import re\n\nimport linstor_client\nimport linstor_client.argparse.argparse as argparse\nfrom linstor import SizeCalc\nfrom linstor.sharedconsts import FLAG_DELETE, FLAG_RESIZE, FLAG_GROSS_SIZE\nfrom linstor_client.commands import Commands, DrbdOptions\nfrom linstor_client.consts import Color\n\n\nclass VolumeDefinitionCommands(Commands):\n OBJECT_NAME = 'volume-definition'\n\n _vlm_dfn_headers = [\n linstor_client.TableHeader(\"ResourceName\"),\n linstor_client.TableHeader(\"VolumeNr\"),\n linstor_client.TableHeader(\"VolumeMinor\"),\n linstor_client.TableHeader(\"Size\"),\n linstor_client.TableHeader(\"Gross\"),\n linstor_client.TableHeader(\"State\", color=Color.DARKGREEN)\n ]\n\n VOLUME_SIZE_HELP = \\\n 'Size of the volume. ' \\\n 'Valid units: ' + SizeCalc.UNITS_LIST_STR + '. ' \\\n 'The default unit is GiB (2 ^ 30 bytes). ' \\\n 'The unit can be specified with a postfix. ' \\\n 'LINSTOR\\'s internal granularity for the capacity of volumes is one ' \\\n 'kibibyte (2 ^ 10 bytes). The actual size used by LINSTOR ' \\\n 'is the smallest natural number of kibibytes that is large enough to ' \\\n 'accommodate a volume of the requested size in the specified size unit.'\n\n def __init__(self):\n super(VolumeDefinitionCommands, self).__init__()\n\n def setup_commands(self, parser):\n # volume definition subcommands\n subcmds = [\n Commands.Subcommands.Create,\n Commands.Subcommands.List,\n Commands.Subcommands.Delete,\n Commands.Subcommands.SetSize,\n Commands.Subcommands.SetProperty,\n Commands.Subcommands.ListProperties,\n Commands.Subcommands.DrbdOptions\n ]\n\n vol_def_parser = parser.add_parser(\n Commands.VOLUME_DEF,\n aliases=[\"vd\"],\n formatter_class=argparse.RawTextHelpFormatter,\n description=\"Volume definition subcommands\")\n\n vol_def_subp = vol_def_parser.add_subparsers(\n title=\"Volume definition commands\",\n metavar=\"\",\n description=Commands.Subcommands.generate_desc(subcmds)\n )\n\n p_new_vol = vol_def_subp.add_parser(\n Commands.Subcommands.Create.LONG,\n aliases=[Commands.Subcommands.Create.SHORT],\n description='Defines a volume with a capacity of `size` for use with '\n 'LINSTOR. If the resource `resource_name` exists already, a new volume is '\n 'added to that resource, otherwise the resource is created automatically '\n 'with default settings. Unless `--minor MINOR` is specified, a minor number '\n \"for the volume's DRBD block device is assigned automatically by the \"\n 'LINSTOR server.')\n p_new_vol.add_argument(\n '--storage-pool', '-s',\n type=str,\n help=\"Storage pool name to use.\").completer = self.storage_pool_dfn_completer\n p_new_vol.add_argument('-n', '--vlmnr', type=int)\n p_new_vol.add_argument('-m', '--minor', type=int)\n p_new_vol.add_argument(\n '--encrypt',\n action=\"store_true\",\n help=\"DEPCRECATED - use --layer-list ...,LUKS,... instead (when creating resource /-definition)\")\n p_new_vol.add_argument('--gross', action=\"store_true\")\n p_new_vol.add_argument('resource_name', type=str,\n help='Name of an existing resource').completer = self.resource_dfn_completer\n p_new_vol.add_argument(\n 'size',\n help=VolumeDefinitionCommands.VOLUME_SIZE_HELP\n ).completer = VolumeDefinitionCommands.size_completer\n p_new_vol.set_defaults(func=self.create)\n\n # remove-volume definition\n p_rm_vol = vol_def_subp.add_parser(\n Commands.Subcommands.Delete.LONG,\n aliases=[Commands.Subcommands.Delete.SHORT],\n description='Removes a volume definition from the LINSTOR cluster, and removes '\n 'the volume definition from the resource definition. The volume is '\n 'undeployed from all nodes and the volume entry is marked for removal '\n \"from the resource definition in LINSTOR's data tables. After all \"\n 'nodes have undeployed the volume, the volume entry is removed from '\n 'the resource definition.')\n p_rm_vol.add_argument(\n '--async',\n action='store_true',\n help='Deprecated, kept for compatibility'\n )\n p_rm_vol.add_argument('resource_name',\n help='Resource name of the volume definition'\n ).completer = self.resource_dfn_completer\n p_rm_vol.add_argument(\n 'volume_nr',\n type=int,\n help=\"Volume number to delete.\")\n p_rm_vol.set_defaults(func=self.delete)\n\n # list volume definitions\n vlm_dfn_groupby = [x.name.lower() for x in self._vlm_dfn_headers]\n vlm_dfn_group_completer = Commands.show_group_completer(vlm_dfn_groupby, \"groupby\")\n\n p_lvols = vol_def_subp.add_parser(\n Commands.Subcommands.List.LONG,\n aliases=[Commands.Subcommands.List.SHORT],\n description=' Prints a list of all volume definitions known to LINSTOR. '\n 'By default, the list is printed as a human readable table.')\n p_lvols.add_argument('-p', '--pastable', action=\"store_true\", help='Generate pastable output')\n p_lvols.add_argument('-g', '--groupby', nargs='+',\n choices=vlm_dfn_groupby,\n type=str.lower).completer = vlm_dfn_group_completer\n p_lvols.add_argument('-r', '--resource-definitions', nargs='+', type=str,\n help='Filter by list of resource definitions').completer = self.resource_dfn_completer\n p_lvols.add_argument('-e', '--external-name', action=\"store_true\", help='Show user specified name.')\n p_lvols.add_argument(\n '-s',\n '--show-props',\n nargs='+',\n type=str,\n default=[],\n help='Show these props in the list. '\n + 'Can be key=value pairs where key is the property name and value column header')\n p_lvols.set_defaults(func=self.list)\n\n # show properties\n p_sp = vol_def_subp.add_parser(\n Commands.Subcommands.ListProperties.LONG,\n aliases=[Commands.Subcommands.ListProperties.SHORT],\n description=\"Prints all properties of the given volume definition.\")\n p_sp.add_argument('-p', '--pastable', action=\"store_true\", help='Generate pastable output')\n p_sp.add_argument(\n 'resource_definition',\n help=\"Resource definition\").completer = self.resource_dfn_completer\n p_sp.add_argument(\n 'volume_nr',\n type=int,\n help=\"Volume number\")\n p_sp.set_defaults(func=self.print_props)\n\n # set properties\n p_setprop = vol_def_subp.add_parser(\n Commands.Subcommands.SetProperty.LONG,\n aliases=[Commands.Subcommands.SetProperty.SHORT],\n formatter_class=argparse.RawTextHelpFormatter,\n description='Sets properties for the given volume definition.')\n p_setprop.add_argument(\n 'resource_name',\n help=\"Resource name\").completer = self.resource_dfn_completer\n p_setprop.add_argument(\n 'volume_nr',\n type=int,\n help=\"Volume number\")\n Commands.add_parser_keyvalue(p_setprop, \"volume-definition\")\n p_setprop.set_defaults(func=self.set_props)\n\n p_drbd_opts = vol_def_subp.add_parser(\n Commands.Subcommands.DrbdOptions.LONG,\n aliases=[Commands.Subcommands.DrbdOptions.SHORT],\n description=DrbdOptions.description(\"volume\")\n )\n p_drbd_opts.add_argument(\n 'resource_name',\n type=str,\n help=\"Resource name\"\n ).completer = self.resource_dfn_completer\n p_drbd_opts.add_argument(\n 'volume_nr',\n type=int,\n help=\"Volume number\"\n )\n DrbdOptions.add_arguments(p_drbd_opts, self.OBJECT_NAME)\n p_drbd_opts.set_defaults(func=self.set_drbd_opts)\n\n # set size\n p_set_size = vol_def_subp.add_parser(\n Commands.Subcommands.SetSize.LONG,\n aliases=[Commands.Subcommands.SetSize.SHORT],\n description='Change the size of a volume. '\n 'Decreasing the size is only supported when the resource definition does not have any '\n 'resources. '\n 'Increasing the size is supported even when the resource definition has resources. '\n 'Filesystems present on the volumes will not be resized.')\n p_set_size.add_argument('resource_name', type=str,\n help='Name of an existing resource').completer = self.resource_dfn_completer\n p_set_size.add_argument(\n 'volume_nr',\n type=int,\n help=\"Volume number\"\n )\n p_set_size.add_argument(\n 'size',\n help=VolumeDefinitionCommands.VOLUME_SIZE_HELP\n ).completer = VolumeDefinitionCommands.size_completer\n p_set_size.add_argument('--gross', action=\"store_true\")\n p_set_size.set_defaults(func=self.set_volume_size)\n\n self.check_subcommands(vol_def_subp, subcmds)\n\n def create(self, args):\n replies = self._linstor.volume_dfn_create(\n args.resource_name,\n Commands.parse_size_str(args.size),\n args.vlmnr,\n args.minor,\n args.encrypt,\n args.storage_pool,\n args.gross\n )\n return self.handle_replies(args, replies)\n\n def delete(self, args):\n async_flag = vars(args)[\"async\"]\n\n replies = self._linstor.volume_dfn_delete(args.resource_name, args.volume_nr, async_flag)\n return self.handle_replies(args, replies)\n\n @classmethod\n def show(cls, args, lstmsg):\n tbl = linstor_client.Table(utf8=not args.no_utf8, colors=not args.no_color, pastable=args.pastable)\n\n vlm_dfn_hdrs = list(cls._vlm_dfn_headers)\n if args.external_name:\n vlm_dfn_hdrs.insert(1, linstor_client.TableHeader(\"External\"))\n for hdr in vlm_dfn_hdrs:\n tbl.add_header(hdr)\n\n show_props = cls._append_show_props_hdr(tbl, args.show_props)\n\n tbl.set_groupby(args.groupby if args.groupby else [tbl.header_name(0)])\n for rsc_dfn in lstmsg.resource_definitions:\n for vlmdfn in rsc_dfn.volume_definitions:\n state = tbl.color_cell(\"ok\", Color.DARKGREEN)\n if FLAG_DELETE in vlmdfn.flags:\n state = tbl.color_cell(\"DELETING\", Color.RED)\n elif FLAG_RESIZE in vlmdfn.flags:\n state = tbl.color_cell(\"resizing\", Color.DARKPINK)\n\n drbd_data = vlmdfn.drbd_data\n row = [\n rsc_dfn.name,\n vlmdfn.number,\n drbd_data.minor if drbd_data else \"\",\n SizeCalc.approximate_size_string(vlmdfn.size),\n \"+\" if FLAG_GROSS_SIZE in vlmdfn.flags else \"\",\n state\n ]\n for sprop in show_props:\n row.append(vlmdfn.properties.get(sprop, ''))\n tbl.add_row(row)\n tbl.show()\n\n def list(self, args):\n lstmsg = self._linstor.resource_dfn_list(\n query_volume_definitions=True,\n filter_by_resource_definitions=args.resource_definitions\n )\n return self.output_list(args, lstmsg, self.show)\n\n @staticmethod\n def size_completer(prefix, **kwargs):\n choices = [unit_str for unit_str, _ in SizeCalc.UNITS_MAP.values()]\n m = re.match(r'(\\d+)(\\D*)', prefix)\n\n digits = m.group(1)\n unit = m.group(2)\n\n if unit and unit != \"\":\n p_units = [x for x in choices if x.startswith(unit)]\n else:\n p_units = choices\n\n return [digits + u for u in p_units]\n\n @classmethod\n def _props_show(cls, args, lstmsg):\n result = []\n if lstmsg and lstmsg.resource_definitions:\n for vlmdfn in lstmsg.resource_definitions[0].volume_definitions:\n if vlmdfn.number == args.volume_nr:\n result.append(vlmdfn.properties)\n break\n return result\n\n def print_props(self, args):\n lstmsg = self._linstor.resource_dfn_list(\n query_volume_definitions=True,\n filter_by_resource_definitions=[args.resource_definition]\n )\n\n return self.output_props_list(args, lstmsg, self._props_show)\n\n def set_props(self, args):\n args = self._attach_aux_prop(args)\n mod_prop_dict = Commands.parse_key_value_pairs([(args.key, args.value)])\n replies = self._linstor.volume_dfn_modify(\n args.resource_name,\n args.volume_nr,\n set_properties=mod_prop_dict['pairs'],\n delete_properties=mod_prop_dict['delete']\n )\n return self.handle_replies(args, replies)\n\n def set_drbd_opts(self, args):\n a = DrbdOptions.filter_new(args)\n del a['resource-name'] # remove resource name key\n del a['volume-nr']\n\n mod_props, del_props = DrbdOptions.parse_opts(a, self.OBJECT_NAME)\n\n replies = self._linstor.volume_dfn_modify(\n args.resource_name,\n args.volume_nr,\n set_properties=mod_props,\n delete_properties=del_props\n )\n return self.handle_replies(args, replies)\n\n def set_volume_size(self, args):\n replies = self._linstor.volume_dfn_modify(\n args.resource_name,\n args.volume_nr,\n size=self.parse_size_str(args.size),\n gross=args.gross\n )\n return self.handle_replies(args, replies)\n","repo_name":"LINBIT/linstor-client","sub_path":"linstor_client/commands/vlm_dfn_cmds.py","file_name":"vlm_dfn_cmds.py","file_ext":"py","file_size_in_byte":14105,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"78"} +{"seq_id":"13700602380","text":"import numpy as np\nimport cv2\nimport os\nimport gzip\nimport pickle\n\n#warping function of numpy version by ASOP\n#flow1 is the forward flow (H, W, 2)\n#flow2 is the backward flow (H, W, 2)\n#img is the input flow (H, W, n)\n#warp_cur = warp(forward_flow, backward_flow, prev)\n#warp_prev = warp(backward_flow, forward_flow, cur)\n\ndef warp(flow1, flow2, img):\n output = np.zeros_like(img, dtype=img.dtype)\n height = flow1.shape[0]\n width = flow1.shape[1]\n flow_t = np.zeros_like(flow1, dtype=flow1.dtype)\n\n grid = np.indices((height, width)).swapaxes(0, 1).swapaxes(1, 2)\n dx = grid[:, :, 0] + flow2[:, :, 1]\n dy = grid[:, :, 1] + flow2[:, :, 0]\n sx = np.floor(dx).astype(int)\n sy = np.floor(dy).astype(int)\n valid = (sx >= 0) & (sx < height - 1) & (sy >= 0) & (sy < width - 1)\n\n sx_mat = np.dstack((sx, sx + 1, sx, sx + 1)).clip(0, height - 1)\n sy_mat = np.dstack((sy, sy, sy + 1, sy + 1)).clip(0, width - 1)\n sxsy_mat = np.abs((1 - np.abs(sx_mat - dx[:, :, np.newaxis])) *\n (1 - np.abs(sy_mat - dy[:, :, np.newaxis])))\n\n for i in range(4):\n flow_t = flow_t + sxsy_mat[:, :, i][:, :, np.\n newaxis] * flow1[sx_mat[:, :, i],\n sy_mat[:, :, i], :]\n\n valid = valid & (np.linalg.norm(\n flow_t[:, :, [1, 0]] + np.dstack((dx, dy)) - grid, axis=2) < 100)\n\n flow_t = (flow2 - flow_t) / 2.0\n dx = grid[:, :, 0] + flow_t[:, :, 1]\n dy = grid[:, :, 1] + flow_t[:, :, 0]\n\n valid = valid & (dx >= 0) & (dx < height - 1) & (dy >= 0) & (dy < width - 1)\n output[valid, :] = img[dx[valid].round().astype(int), dy[valid].round()\n .astype(int), :]\n return output\n\ndef main():\n prev = cv2.imread('00000.jpg')\n cur = cv2.imread('00005.jpg')\n flow1 = pickle.loads(gzip.GzipFile('forward_0_5.pkl', 'rb').read()) #'forward_0_5.pkl'\n flow2 = pickle.loads(gzip.GzipFile('backward_5_0.pkl', 'rb').read()) #'backward_5_0.pkl'\n print(\"read flow and image\")\n #warp(forward, backward, 0th) # 0 -> 1\n #warp(backward, forward, 1th) # 1 -> 0\n\n w0 = warp(flow1,flow2,prev) #0->1\n w1 = warp(flow2,flow1,cur) #1->0\n print(\"finish warp\")\n cv2.imwrite('warp_forward.png', w0)\n cv2.imwrite('warp_backward.png', w1)\n\nif __name__ == '__main__':\n main()\n","repo_name":"ping-sun/temporal-loss-with-optical-flow","sub_path":"utils/warp_numpy.py","file_name":"warp_numpy.py","file_ext":"py","file_size_in_byte":2376,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"78"} +{"seq_id":"27904854306","text":"from PyQt5 import QtWidgets\n\nfrom PyQt5.QtCore import QSettings, QTranslator, qVersion, QCoreApplication, Qt\nfrom PyQt5.QtGui import QIcon, QColor, QBrush\nfrom PyQt5.QtWidgets import QAction, QMessageBox\n\n# Initialize Qt resources from file resources.py\nfrom .resources import *\n# Import the code for the dialog\nfrom .AdminUsers_dialog import AdminUsersDialog\nfrom .usuariosEdicionVer import usuariosEdicionVer\nimport os.path, requests, json\nfrom .cambioClave_Usuario import cambioClave_Usuario\nfrom .asigna_operaciones import operaciones\n\n\n\nclass AdminUsers:\n \"\"\"QGIS Plugin Implementation.\"\"\"\n\n def __init__(self, iface):\n \"\"\"Constructor.\n\n :param iface: An interface instance that will be passed to this class\n which provides the hook by which you can manipulate the QGIS\n application at run time.\n :type iface: QgsInterface\n \"\"\"\n # Save reference to the QGIS interface\n self.iface = iface\n # initialize plugin directory\n self.plugin_dir = os.path.dirname(__file__)\n # initialize locale\n locale = QSettings().value('locale/userLocale')[0:2]\n locale_path = os.path.join(\n self.plugin_dir,\n 'i18n',\n 'AdminUsers_{}.qm'.format(locale))\n\n if os.path.exists(locale_path):\n self.translator = QTranslator()\n self.translator.load(locale_path)\n\n if qVersion() > '4.3.3':\n QCoreApplication.installTranslator(self.translator)\n\n # Create the dialog (after translation) and keep reference\n self.dlg = AdminUsersDialog(parent = iface.mainWindow())\n\n # ------ DECLARACION DE EVENTOS\n\n self.dlg.tabwUsuarios.blockSignals(True)\n self.dlg.tabwUsuarios.currentChanged.connect(self.event_cambioPestania)\n self.dlg.tabwUsuarios.blockSignals(False)\n\n self.dlg.leBusqueda.textChanged.connect(self.event_textChangedLbBusqueda)\n\n self.dlg.btnNuevoUsuario.clicked.connect(self.event_nuevoUsuario)\n self.dlg.btnCambioClave.clicked.connect(self.event_cambioContra)\n self.dlg.btnOperaciones.clicked.connect(self.event_asignaOperaciones)\n\n # ------ CIERRA DECLARACION DE EVENTOS\n\n\n # ------ INICIALIZAR VALORES\n \n # muestra siempre la primer tab\n self.dlg.tabwUsuarios.setCurrentIndex(0)\n\n self.dlg.twUsuarios.setColumnHidden(0, True)\n self.dlg.twUsuarios.setEditTriggers(QtWidgets.QTableWidget.NoEditTriggers)\n\n self.dlg.twUsuarios.setSortingEnabled(True)\n \n header = self.dlg.twUsuarios.horizontalHeader()\n #header.setSectionResizeMode(1, QtWidgets.QHeaderView.ResizeToContents)\n #header.setSectionResizeMode(2, QtWidgets.QHeaderView.ResizeToContents)\n #header.setSectionResizeMode(3, QtWidgets.QHeaderView.ResizeToContents)\n\n\n header.setResizeContentsPrecision(100)\n header.setResizeContentsPrecision(200)\n header.setResizeContentsPrecision(200)\n\n\n self.headers = {'Content-Type': 'application/json'}\n\n self.CFG = None\n self.UTI = None\n\n self.usuarios = []\n\n # ------ CERRAR INICIALIZAR VALORES\n\n # noinspection PyMethodMayBeStatic\n def tr(self, message):\n \"\"\"Get the translation for a string using Qt translation API.\n\n We implement this ourselves since we do not inherit QObject.\n\n :param message: String for translation.\n :type message: str, QString\n\n :returns: Translated version of message.\n :rtype: QString\n \"\"\"\n # noinspection PyTypeChecker,PyArgumentList,PyCallByClass\n return QCoreApplication.translate('AdminUsers', message)\n\n def add_action(\n self,\n icon_path,\n text,\n callback,\n enabled_flag=True,\n add_to_menu=True,\n add_to_toolbar=True,\n status_tip=None,\n whats_this=None,\n parent=None):\n \"\"\"Add a toolbar icon to the toolbar.\n\n :param icon_path: Path to the icon for this action. Can be a resource\n path (e.g. ':/plugins/foo/bar.png') or a normal file system path.\n :type icon_path: str\n\n :param text: Text that should be shown in menu items for this action.\n :type text: str\n\n :param callback: Function to be called when the action is triggered.\n :type callback: function\n\n :param enabled_flag: A flag indicating if the action should be enabled\n by default. Defaults to True.\n :type enabled_flag: bool\n\n :param add_to_menu: Flag indicating whether the action should also\n be added to the menu. Defaults to True.\n :type add_to_menu: bool\n\n :param add_to_toolbar: Flag indicating whether the action should also\n be added to the toolbar. Defaults to True.\n :type add_to_toolbar: bool\n\n :param status_tip: Optional text to show in a popup when mouse pointer\n hovers over the action.\n :type status_tip: str\n\n :param parent: Parent widget for the new action. Defaults None.\n :type parent: QWidget\n\n :param whats_this: Optional text to show in the status bar when the\n mouse pointer hovers over the action.\n\n :returns: The action that was created. Note that the action is also\n added to self.actions list.\n :rtype: QAction\n \"\"\"\n\n icon = QIcon(icon_path)\n action = QAction(icon, text, parent)\n action.triggered.connect(callback)\n action.setEnabled(enabled_flag)\n\n if status_tip is not None:\n action.setStatusTip(status_tip)\n\n if whats_this is not None:\n action.setWhatsThis(whats_this)\n\n if add_to_toolbar:\n self.toolbar.addAction(action)\n\n if add_to_menu:\n self.iface.addPluginToMenu(\n self.menu,\n action)\n\n self.actions.append(action)\n\n return action\n\n def initGui(self):\n \"\"\"Create the menu entries and toolbar icons inside the QGIS GUI.\"\"\"\n\n icon_path = ':/plugins/AdminUsers/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u''),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n def unload(self):\n \"\"\"Removes the plugin menu item and icon from QGIS GUI.\"\"\"\n for action in self.actions:\n self.iface.removePluginMenu(\n self.tr(u'&AdminUsers'),\n action)\n self.iface.removeToolBarIcon(action)\n # remove the toolbar\n del self.toolbar\n\n def run(self):\n \"\"\"Run method that performs all the real work\"\"\"\n # show the dialog\n self.dlg.show()\n \n self.event_cambioPestania(index = 0)\n # Run the dialog event loop\n result = self.dlg.exec_()\n # See if OK was pressed\n if result:\n \n # Do something useful here - delete the line containing pass and\n # substitute with your code.\n pass\n\n\n # --- E V E N T O S Widget ---\n\n # - cambio de pestaña\n def event_cambioPestania(self, index): #changed!\n\n # solo se ejecuta siempre y cuando sea la misma pestaña\n if index == 0:\n\n # remover todos\n self.dlg.twUsuarios.clearContents()\n self.dlg.twUsuarios.setRowCount(0)\n \n for row in range(0, self.dlg.twUsuarios.rowCount()): \n self.dlg.twUsuarios.removeRow(row) \n\n # consumir ws para descargar usuarios\n self.usuarios = self.consumeWSGeneral(url_cons = self.CFG.url_AU_getAllUsers)\n\n if not self.usuarios or len(self.usuarios) == 0:\n return\n\n # mostrar usuarios en tabla\n self.dlg.twUsuarios.setRowCount(len(self.usuarios))\n for i in range(0, len(self.usuarios)):\n\n # auth = \", \".join(self.usuarios[i]['authorities'])\n auth = ''\n if len(self.usuarios[i]['authorities']) > 0:\n auth = \", \".join(d['rol'] for d in self.usuarios[i]['authorities'])\n\n btnVer = QtWidgets.QPushButton('Ver')\n btnEdi = QtWidgets.QPushButton('Editar')\n btnElim = QtWidgets.QPushButton('Eliminar')\n\n\n btnVer.setStyleSheet('''QPushButton{\n background : rgb(174, 116, 0);\n color : rgb(255, 255, 255);\n font-weight: bold;\n }\n QPushButton::disabled {\n background : rgb(187, 129, 13);\n color : rgb(245,245,245);\n border: 1px solid #adb2b5;\n }''')\n\n \n btnEdi.setStyleSheet('''QPushButton{\n background : rgb(174, 116, 0);\n color : rgb(255, 255, 255);\n font-weight: bold;\n }\n QPushButton::disabled {\n background : rgb(187, 129, 13);\n color : rgb(245,245,245);\n border: 1px solid #adb2b5;\n }''')\n\n self.dlg.twUsuarios.setItem(i, 0, QtWidgets.QTableWidgetItem(str(self.usuarios[i]['id'])))\n self.dlg.twUsuarios.setItem(i, 1, QtWidgets.QTableWidgetItem((self.usuarios[i]['firstName'] if self.usuarios[i]['firstName'] else '') + ' ' + (self.usuarios[i]['lastName'] if self.usuarios[i]['lastName'] else '')))\n self.dlg.twUsuarios.setItem(i, 2, QtWidgets.QTableWidgetItem(self.usuarios[i]['login']))\n self.dlg.twUsuarios.setItem(i, 3, QtWidgets.QTableWidgetItem(auth))\n self.dlg.twUsuarios.setCellWidget(i, 4, btnVer)\n self.dlg.twUsuarios.setCellWidget(i, 5, btnEdi)\n #self.dlg.twUsuarios.setCellWidget(i, 6, btnElim)\n \n btnVer.clicked.connect(self.event_currentPositionButtonPressed)\n btnEdi.clicked.connect(self.event_currentPositionButtonPressed)\n btnElim.clicked.connect(self.event_currentPositionButtonPressed)\n\n # - boton presionado dentro de la lista\n def event_currentPositionButtonPressed(self):\n clickme = QtWidgets.qApp.focusWidget()\n # or button = self.sender()\n index = self.dlg.twUsuarios.indexAt(clickme.pos())\n \n if index.isValid():\n item = self.dlg.twUsuarios.item(index.row(), 0)\n usuario = [x for i, x in enumerate(self.usuarios) if str(x['id']) == item.text()]\n\n # ver\n if index.column() == 4:\n\n obj = usuariosEdicionVer(usuario[0] if len(usuario) > 0 else None, False, False, CFG = self.CFG, UTI = self.UTI)\n respuesta = obj.exec()\n pass\n\n # editar\n if index.column() == 5:\n obj = usuariosEdicionVer(usuario[0] if len(usuario) > 0 else None, False, True, CFG = self.CFG, UTI = self.UTI)\n respuesta = obj.exec()\n pass\n\n # eliminar\n if index.column() == 6:\n pass\n\n # regresa un 0 o un 1\n # 0 = RECHAZADO = CANCELAR\n # 1 = ACEPTADO = ACEPTAR\n if respuesta == 0:\n return\n\n # limpiar tabla\n self.limpiaTabla()\n\n self.event_cambioPestania(0)\n\n # - cambio de texto para realizar busquedas\n def event_textChangedLbBusqueda(self, texto):\n \n for row in range(0,self.dlg.twUsuarios.rowCount()):\n self.dlg.twUsuarios.showRow(row)\n\n if texto == '':\n return\n items = self.dlg.twUsuarios.findItems(texto, Qt.MatchContains)\n\n ocultar = True\n rowCount = self.dlg.twUsuarios.rowCount()\n for row in range(0, rowCount):\n ocultar = True\n for item in items:\n\n if row == item.row():\n ocultar = False\n break\n\n if ocultar:\n self.dlg.twUsuarios.hideRow(row)\n\n # - creacion de nuevo usuario\n def event_nuevoUsuario(self):\n obj = usuariosEdicionVer(nuevo = True, edicion = True, CFG = self.CFG, UTI = self.UTI)\n\n respuesta = obj.exec()\n # regresa un 0 o un 1\n # 0 = RECHAZADO = CANCELAR\n # 1 = ACEPTADO = ACEPTAR\n if respuesta == 0:\n return\n\n # limpiar tabla\n self.limpiaTabla()\n\n self.event_cambioPestania(0)\n\n def event_cambioContra(self):\n var = QSettings().value('datoUsuario')\n\n print(var)\n print(type(var))\n obj = cambioClave_Usuario(CFG = self.CFG, UTI = self.UTI, usuario = var, nuevo = True)\n obj.exec()\n\n def event_asignaOperaciones(self):\n #self.createAlert(\"Si conecto\", QMessageBox().Information, \"Si se hizo\")\n \n self.dlg.operaciones = operaciones(self.iface, CFG = self.CFG, UTI = self.UTI, nuevo = True)\n \n self.dlg.operaciones.run()\n\n\n \n\n # --- CERRAR E V E N T O S Widget ---\n\n # --- S E R V I C I O S W E B ---\n\n # - consume ws para informacion de catalogos\n def consumeWSGeneral(self, url_cons = \"\"):\n\n url = url_cons\n data = \"\"\n\n try:\n self.headers['Authorization'] = self.UTI.obtenerToken()\n response = requests.get(url, headers = self.headers)\n except requests.exceptions.RequestException as e:\n self.createAlert(\"Error de servidor, 'consumeWSGeneral()'\" + str(e) + \"'\", QMessageBox().Critical, \"Error de servidor\")\n return\n\n if response.status_code == 200:\n data = response.content\n \n else:\n self.createAlert('Error en peticion \"consumeWSGeneral()\":\\n' + response.text, QMessageBox().Critical, \"Error de servidor\")\n return\n\n return json.loads(data)\n\n # --- S E R V I C I O S W E B CIERRA ---\n\n # --- M E T O D O S ---\n\n def limpiaTabla(self):\n print('entras')\n self.dlg.twUsuarios.clearContents()\n self.dlg.twUsuarios.setRowCount(0)\n \n for row in range(0, self.dlg.twUsuarios.rowCount()): \n self.dlg.twUsuarios.removeRow(row) \n\n # - Crea una alerta para ser mostrada como ventana de advertencia\n def createAlert(self, mensaje, icono = QMessageBox().Critical, titulo = 'Usuarios'):\n #Create QMessageBox\n self.msg = QMessageBox()\n #Add message\n self.msg.setText(mensaje)\n #Add icon of critical error\n self.msg.setIcon(icono)\n #Add tittle\n self.msg.setWindowTitle(titulo)\n #Show of message dialog\n self.msg.show()\n # Run the dialog event loop\n result = self.msg.exec_()\n\n # --- M E T O D O S CERRAR ---\n","repo_name":"gerardoros/CartograficoQgisPlugin","sub_path":"funciones/adminusers/AdminUsers.py","file_name":"AdminUsers.py","file_ext":"py","file_size_in_byte":15156,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"22827877362","text":"from .utils import i18n\n\n\nclass NoEnoughQuotaException(Exception):\n def __init__(self, msg=None):\n if msg is None:\n msg = i18n.t('lang.errorMessages.reservations.noQuota')\n super().__init__(msg)\n\n\nclass NonFinishedUserReservationsException(Exception):\n def __init__(self, msg=None):\n if msg is None:\n msg = i18n.t('lang.errorMessages.reservations.cantCreateTwoReservations')\n super().__init__(msg)\n\n\nclass NonUpdatableReservationException(Exception):\n def __init__(self, msg=None):\n if msg is None:\n msg = i18n.t('lang.errorMessages.reservations.cantUpdateFinishedReservation')\n super().__init__(msg)\n\n\nclass NonReviewableReservation(Exception):\n def __init__(self, msg=None):\n if msg is None:\n msg = i18n.t('lang.errorMessages.reservations.cantReviewNonReviewableReservation')\n super().__init__(msg)\n","repo_name":"coretabs/dorm-portal","sub_path":"api/engine/exceptions.py","file_name":"exceptions.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"it","doc_type":"code","stars":13,"dataset":"github-code","pt":"78"} +{"seq_id":"19991595162","text":"import pandas as pd\n\n# part 1\ndict_data = {\n\t\"Name\" : [\"Rahul\",\"Rahul\",\"Rahul\",\"Zara\",\"Zara\",\"Zara\",\"Akshay\",\"Akshay\",\"Akshay\",\"Misha\",\"Misha\",\"Misha\"],\n\t\"UT\" : [1,2,3,1,2,3,1,2,3,1,2,3],\n\t\"Maths\" : [22,21,14,20,23,22,23,34,12,15,18,17],\n\t\"Science\" : [21,20,19,17,15,18,19,22,25,22,21,18],\n\t\"S.St\" : [18,17,15,22,21,19,20,24,19,25,25,20],\n\t\"Hindi\" : [20,22,24,24,25,23,15,17,21,22,24,25],\n\t\"English\" : [21,24,23,19,15,13,22,21,23,22,23,20]\n}\n\ndict_df = pd.DataFrame(dict_data)\n# print(dict_df)\n\n# Part 2\nminvalue_series = dict_df.min()\n# print(minvalue_series)\n\n# Part 3\ncopy_df = dict_df.set_index(\"Name\")\nextracted_rows = copy_df.loc[\"Misha\"]\nprint(extracted_rows)\n\n# Part 4\nsum_values = dict_df.sum()\n# print(sum_values)\n\n# Part 5\ncount_values = dict_df.count()\n# print(\"Count Value of Column\")\n# print(count_values)\n\ncount_values2 = dict_df.count(axis=1)\n# print(\"Count Value of Row\")\n# print(count_values2)\n\n# Part 6\nmean_values = dict_df.mean(numeric_only=True)\n# print(mean_values)\n\n# Part 7\nmedian_values = dict_df.median(numeric_only=True)\n# print(median_values)\n\n# Part 8\nquantile = dict_df.quantile([0.25,0.5,0.75], numeric_only=True)\n# print(quantile)\n\n# Part 9\ndiscriptive_stat = dict_df.describe(datetime_is_numeric=False)\n# print(discriptive_stat)\n\n# Part 10\nsorted_data = dict_df.sort_values(by=\"Name\")\n# print(sorted_data)","repo_name":"Prakhar0047/Sabudh_Foundation_6_Month_Training-","sub_path":"Pandas/PANDAS_and_Descriptive_Statistics.py","file_name":"PANDAS_and_Descriptive_Statistics.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"71133169851","text":"import rclpy\nfrom threading import Thread\nfrom rclpy.node import Node\nimport time\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge #Converts ros node images to openCV image objects for all other purposes\nimport cv2\nimport numpy as np\nfrom geometry_msgs.msg import Twist, Vector3\nfrom std_msgs.msg import String\n\nimport easyocr\nimport PIL.Image\nif not hasattr(PIL.Image, 'Resampling'): # Pillow<9.0\n PIL.Image.Resampling = PIL.Image\n\nreader = easyocr.Reader(['en']) # this needs to run only once to load the model into memory\n\nclass read_sign(Node):\n \"\"\" The OCR Camera is a Python object that encompasses a ROS node \n that can process images from the camera and run EasyOCR on the image.\n The node will issue motor commands to move forward if and only if the\n # word \"go\" is detected. \"\"\"\n\n def __init__(self, image_topic):\n \"\"\" Initialize the Camera node with OCR builtin \"\"\"\n super().__init__('read_sign')\n self.cv_image = None # the latest image from the camera\n self.bridge = CvBridge() # used to convert ROS messages to OpenCV\n\n self.create_subscription(Image, image_topic, self.process_image, 10)\n self.command_pub = self.create_publisher(String, 'sign_text', 10)\n\n # Toggles the display of the camera feed with output on top of it\n thread = Thread(target=self.loop_wrapper)\n thread.start()\n\n # ocrthread = Thread(target=self.process_text)\n # ocrthread.start()\n\n def process_image(self, msg):\n \"\"\" Process image messages from ROS and stash them in an attribute\n called cv_image for subsequent processing \"\"\"\n self.cv_image = self.bridge.imgmsg_to_cv2(msg, desired_encoding=\"bgr8\")\n\n def process_text(self):\n if not self.cv_image is None:\n result = reader.readtext(self.cv_image) #actual camera output\n # result = [[\"Stop\",\"Go\"],[1,2,3],[4,5,6]] # This is for test\n if (result != []):\n # msg = String(msg) # When testing just go [0][1] for go and [0][0] for stop\n # msg = String(\"Hello World!\")\n result = self.extract_directive(result)\n # print(result) #debug print what the OCR is reading/detecting\n msg = String(data=result) # The piece d'resistance\n self.command_pub.publish(msg)\n\n def extract_directive(self, easyocr_output):\n cmd_options = np.array(['stop','go','back up','turn left','turn right'])\n words = np.array(['null']) #Initialize a array in Numpy so you can search in it\n for entry in easyocr_output:\n words = np.append(words, entry[1].lower()) #add each word from the image\n print(words) #gives the clean output of every word cluster detected\n indices = np.in1d(words, cmd_options)\n directive = words[np.argmax(indices)]\n print(directive)\n return directive\n \n def run_loop(self):\n # TODO: move process_text() to a separate thread somehow and make it asynchronous\n if not self.cv_image is None:\n self.process_text()\n # # self.binary_image = cv2.inRange(self.cv_image, (self.blue_lower_bound,self.green_lower_bound,self.red_lower_bound), (self.blue_upper_bound,self.green_upper_bound,self.red_upper_bound))\n # #print(self.cv_image.shape)\n # cv2.imshow('video_window', self.cv_image)\n # # cv2.imshow('binary_window', self.binary_image)\n # if hasattr(self, 'image_info_window'):\n # cv2.imshow('image_info', self.image_info_window)\n # cv2.waitKey(5) #Prints if a key is pressed at 200hz (5ms sleep)\n\n def loop_wrapper(self):\n \"\"\" This function takes care of calling the run_loop function repeatedly.\n We are using a separate thread to run the loop_wrapper to work around\n issues with single threaded executors in ROS2, aka the openCV window.\"\"\"\n cv2.namedWindow('video_window')\n # cv2.namedWindow('binary_window') #Creates the B&W window \n # cv2.namedWindow('image_info')\n # self.red_lower_bound = 0\n # cv2.createTrackbar('red lower bound', 'binary_window', self.red_lower_bound, 255, self.set_red_lower_bound) # Adds trackbar to the B&W window\n cv2.setMouseCallback('video_window', self.process_mouse_event) #Allows you to click the image and see a pixel BGR value\n while True:\n self.run_loop()\n # time.sleep(0.05) #The openCV windows will update at most 20 hz\n\n # def set_red_lower_bound(self, val):\n # \"\"\" A callback function to handle the OpenCV slider to select the red lower bound with the slider\"\"\"\n # self.red_lower_bound = val\n\n def process_mouse_event(self, event, x,y,flags,param):\n \"\"\" Process mouse events so that you can see the color values\n associated with a particular pixel in the camera images \"\"\"\n self.image_info_window = 255*np.ones((500,500,3))\n cv2.putText(self.image_info_window,\n 'Color (b=%d,g=%d,r=%d)' % (self.cv_image[y,x,0], self.cv_image[y,x,1], self.cv_image[y,x,2]),\n (5,50),\n cv2.FONT_HERSHEY_SIMPLEX,\n 1,\n (0,0,0))\n\ndef main(args=None):\n print(\"GO!\")\n # result = reader.readtext('src/OCR/examples/english.png') #Tests OCR pkg\n # print(result)\n # print(type(result))\n # if (result != None):\n # print(result[0][1])\n rclpy.init()\n node = read_sign(\"/camera/image_raw\")\n rclpy.spin(node)\n rclpy.shutdown()\n\n\nif __name__ == '__main__':\n main()","repo_name":"cjhi/OCR","sub_path":"neatocr/neatocr/read_sign.py","file_name":"read_sign.py","file_ext":"py","file_size_in_byte":5681,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"583436152","text":"#!/usr/bin/python\n\n# Head ends here\nfrom math import sqrt\n\ndef euclidean_dist(x1,y1,x2,y2):\n return sqrt(pow(x1-x2,2)+pow(y1-y2,2))\n\ndef next_move(posr, posc, board):\n\n x,y=(0,0)\n shor_dist=100\n if board[posr][posc]=='d':\n print(\"CLEAN\")\n else:\n for y1 in range(5):\n for x1 in range(5):\n if board[y1][x1]=='d':\n dist=euclidean_dist(x1,y1,posc,posr)\n if dist < shor_dist and not(x1==posc and y1==posr):\n shor_dist=dist\n y=y1\n x=x1\n #same line\n if(posr==y):\n if(posc -1 and 'd' in board[posr] and board[posr].index('d') == posc - 1:\n # print('LEFT')\n # else:\n # if (posr + 1) < 5 and 'd' in board[posr + 1] and board[posr + 1].index('d') == posc:\n # print('DOWN')\n # else:\n # if (posr - 1) > -1 and 'd' in board[posr - 1] and board[posr - 1].index('d') == posc:\n # print('UP')\n # else:\n # if posc+1 < 5:\n # print('RIGHT')\n # else:\n # if posr+1 < 5:\n # print('DOWN')\n # else:\n # if posc-1 > 0:\n # print('LEFT')\n # else:\n # print('UP')\n\n# Tail starts here\n\n\n\nif __name__ == \"__main__\":\n pos = [int(i) for i in input().strip().split()]\n board = [[j for j in input().strip()] for i in range(5)]\n next_move(pos[0], pos[1], board)","repo_name":"renjithmp/machinelearning","sub_path":"clean_bot.py","file_name":"clean_bot.py","file_ext":"py","file_size_in_byte":2126,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"16583050430","text":"import os\nimport time\nfrom traceback import print_exc\nimport tempfile\nfrom .baidu_ai_api import BaiduAudioApi, set_app_info\nfrom unitts.voice import Voice\nfrom unitts.basedriver import BaseDriver\nfrom appPublic.audioplayer import AudioPlayer\nfrom appPublic.background import Background\nfrom text2sentences import text_to_sentences\nfrom .version import __version__\nclass NoAppRegisterInfo(Exception):\n\tpass\n\ndef buildDriver(proxy):\n\treturn BaiduTTSDriver(proxy)\n\n__ALL__ = [\n\tset_app_info,\n\tbuildDriver\n]\nVoices = [\n\tVoice('0', 'duxiaomei', ['zh_CN', 'en_US'], '0', 24),\n\tVoice('1', 'duxiaoyu', ['zh_CN', 'en_US'], '1', 24),\n\tVoice('3', 'duxiaoyao', ['zh_CN', 'en_US'], '1', 24),\n\tVoice('4', 'duyaya', ['zh_CN', 'en_US'], '0', 24),\n]\n\ndef temp_file(suffix='.txt'):\n\tx = tempfile.mkstemp(suffix=suffix)\n\tos.close(x[0])\n\treturn x[1]\n\ndef write_tmp_mp3file(raw):\n\tfn = temp_file(suffix='.mp3')\n\twith open(fn, 'wb') as f:\n\t\tf.write(raw)\n\n\treturn fn\n\nclass BaiduTTSDriver(BaseDriver):\n\tdef __init__(self, proxy):\n\t\tsuper().__init__(proxy)\n\t\tself._tts = BaiduAudioApi()\n\t\tself.player = AudioPlayer(on_stop=self.speak_finish)\n\t\tself.rate = 5\n\t\tself.volume = 1\n\t\tself.sentences = []\n\t\tself.normal_voice = {\n\t\t\t'voice':\"1\",\n\t\t\t\"pitch\":5\n\t\t}\n\t\tself.dialog_voice = {\n\t\t\t\"voice\":\"3\",\n\t\t\t\"pitch\":3\n\t\t}\n\t\tself.pitch = 5\n\t\tself.language = 'zh'\n\t\tself.format = 3\n\t\tself.voice = 0\n\t\tself.cmds = []\n\t\tself._completed = True\n\t\tself.running = True\n\t\tself.task = None\n\t\tprint(f'BaiduTTSDriver version {__version__}')\n\n\tdef destroy(self):\n\t\tself.player.unload()\n\t\tif self.task:\n\t\t\tself.running = False\n\t\t\tself.task.join()\n\t\n\tdef pre_command(self, sentence):\n\t\taufile = self.get_audio_file(sentence)\n\t\treturn sentence.start_pos, aufile\n\n\tdef command(self, pos, aufile):\n\t\tself.player.set_source(aufile)\n\t\tself.player.play()\n\n\tdef set_type_voice(self, attrs, sentence):\n\t\ty = self._tts\n\t\ty.tts_set_rate(attrs.get('rate', self.rate))\n\t\ty.tts_set_pitch(attrs.get('pitch', self.pitch))\n\t\ty.tts_set_voice(attrs.get('voice', self.voice))\n\n\tdef get_audio_file(self, sentence):\n\t\ty = self._tts\n\t\tif sentence.dialog:\n\t\t\tself.set_type_voice(self.dialog_voice, sentence)\n\t\telse:\n\t\t\tself.set_type_voice(self.normal_voice, sentence)\n\t\ty.tts_set_format(self.format)\n\t\t# y.tts_set_language(sentence.lang)\n\t\traw = y.tts(sentence.text)\n\t\tif raw is None:\n\t\t\tprint('baidu api error')\n\t\t\treturn\n\t\tmp3file = write_tmp_mp3file(raw)\n\t\treturn mp3file\n\t\t\n\tdef stop(self):\n\t\tif self._proxy.isBusy():\n\t\t\tself._completed = False\n\t\tself.player.stop()\n\n\tdef getProperty(self, name):\n\t\tif name == 'normal_voice':\n\t\t\treturn self.normal_voice\n\t\tif name == 'dialog_voice':\n\t\t\treturn self.dialog_voice\n\n\t\tif name == 'voices':\n\t\t\treturn Voices\n\n\t\tif name == 'voice':\n\t\t\tfor v in Voices:\n\t\t\t\tif v.id == self.voice:\n\t\t\t\t\treturn v\n\t\t\treturn None\n\t\tif name == 'rate':\n\t\t\treturn self.rate\n\t\tif name == 'volume':\n\t\t\treturn self.volume\n\t\tif name == 'pitch':\n\t\t\treturn self.pitch\n\t\n\tdef setProperty(self, name, value):\n\t\tif name == 'normal_voice':\n\t\t\tself.normal_voice = value\n\t\tif name == 'dialog_voice':\n\t\t\tself.dialog_voice = value\n\t\tif name == 'voice':\n\t\t\tself.voice = value\n\t\tif name == 'rate':\n\t\t\tself.rate = value\n\t\tif name == 'pitch':\n\t\t\tself.rate = value\n\t\tif name == 'language':\n\t\t\tself.language = value\n\t\tif name == 'volume':\n\t\t\tself.volume = value\n\n","repo_name":"yumoqing/baidu_d_tts","sub_path":"baidu_d_tts/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"10417277155","text":"from base import *\n\n\n@algoritmo\ndef falsaposicao(self, f, xi, xf):\n if f(xi) * f(xf) > 0:\n raise ErroIntervalo\n self.tabela('k', 'xi', 'xf', 'f(xi)/f(xf)', 'xm', 'f(xm)')\n for k in xrange(self.nmi):\n p = f(xi) / f(xf)\n xm = xi - (xf - xi) / (1 / p - 1)\n fxm = f(xm)\n self.tabela(k, xi, xf, p, xm, fxm)\n if (abs(xf - xm) < self.tol or abs(xi - xm) < self.tol) and abs(fxm) < self.tol:\n return xm\n (xi, xf) = (xi, xm) if f(xm) * f(xi) < 0 else (xm, xf)\n raise ErroNMI\n\n","repo_name":"jansegre/calcnum","sub_path":"python/trab3/falsaposicao.py","file_name":"falsaposicao.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"13544057895","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport argparse\nimport json\nimport logging\nimport warnings\n\nfrom builtins import str\nfrom typing import Text, Optional, List, Tuple\n\nfrom rasa_core import utils, evaluate\nfrom rasa_core.actions.action import ACTION_LISTEN_NAME\nfrom rasa_core.agent import Agent\nfrom rasa_core.channels import UserMessage\nfrom rasa_core.channels.console import ConsoleInputChannel, ConsoleOutputChannel\nfrom rasa_core.events import UserUttered, ActionExecuted\nfrom rasa_core.trackers import DialogueStateTracker\n\nlogger = logging.getLogger() # get the root logger\n\n\ndef create_argument_parser():\n \"\"\"Parse all the command line arguments for the restore script.\"\"\"\n\n parser = argparse.ArgumentParser(\n description='starts the bot')\n parser.add_argument(\n '-d', '--core',\n required=True,\n type=str,\n help=\"core model to run\")\n parser.add_argument(\n '-u', '--nlu',\n type=str,\n help=\"nlu model to run\")\n parser.add_argument(\n 'tracker_dump',\n type=str,\n help=\"file that contains a dumped tracker state in json format\")\n\n utils.add_logging_option_arguments(parser)\n\n return parser\n\n\ndef _check_prediction_aligns_with_story(last_prediction,\n actions_between_utterances):\n # type: (List[Text], List[Text]) -> None\n \"\"\"Emit a warning if predictions do not align with expected actions.\"\"\"\n\n p, a = evaluate.align_lists(last_prediction, actions_between_utterances)\n if p != a:\n warnings.warn(\"Model predicted different actions than the \"\n \"model used to create the story! Expected: \"\n \"{} but got {}.\".format(p, a))\n\n\ndef replay_events(tracker, agent):\n # type: (DialogueStateTracker, Agent) -> None\n \"\"\"Take a tracker and replay the logged user utterances against an agent.\n\n During replaying of the user utterances, the executed actions and events\n created by the agent are compared to the logged ones of the tracker that\n is getting replayed. If they differ, a warning is logged.\n\n At the end, the tracker stored in the agent's tracker store for the\n same sender id will have quite the same state as the one\n that got replayed.\"\"\"\n\n actions_between_utterances = []\n last_prediction = [ACTION_LISTEN_NAME]\n\n for i, event in enumerate(tracker.events_after_latest_restart()):\n if isinstance(event, UserUttered):\n _check_prediction_aligns_with_story(last_prediction,\n actions_between_utterances)\n\n actions_between_utterances = []\n print(utils.wrap_with_color(event.text, utils.bcolors.OKGREEN))\n agent.handle_message(event.text, sender_id=tracker.sender_id,\n output_channel=ConsoleOutputChannel())\n tracker = agent.tracker_store.retrieve(tracker.sender_id)\n last_prediction = evaluate.actions_since_last_utterance(tracker)\n\n elif isinstance(event, ActionExecuted):\n actions_between_utterances.append(event.action_name)\n\n _check_prediction_aligns_with_story(last_prediction,\n actions_between_utterances)\n\n\ndef load_tracker_from_json(tracker_dump, domain):\n # type: (Text, Agent) -> DialogueStateTracker\n \"\"\"Read the json dump from the file and instantiate a tracker it.\"\"\"\n\n tracker_json = json.loads(utils.read_file(tracker_dump))\n sender_id = tracker_json.get(\"sender_id\", UserMessage.DEFAULT_SENDER_ID)\n return DialogueStateTracker.from_dict(sender_id,\n tracker_json.get(\"events\", []),\n domain)\n\n\ndef recreate_agent(model_directory, # type: Text\n nlu_model=None, # type: Optional[Text]\n tracker_dump=None # type: Optional[Text]\n ):\n # type: (...) -> Tuple[Agent, DialogueStateTracker]\n \"\"\"Recreate an agent instance.\"\"\"\n\n logger.debug(\"Loading Rasa Core Agent\")\n agent = Agent.load(model_directory, nlu_model)\n\n logger.debug(\"Finished loading agent. Loading stories now.\")\n\n tracker = load_tracker_from_json(tracker_dump, agent.domain)\n replay_events(tracker, agent)\n\n return agent, tracker\n\n\nif __name__ == '__main__':\n # Running as standalone python application\n arg_parser = create_argument_parser()\n cmdline_args = arg_parser.parse_args()\n\n utils.configure_colored_logging(cmdline_args.loglevel)\n\n agent, tracker = recreate_agent(cmdline_args.core,\n cmdline_args.nlu,\n cmdline_args.tracker_dump)\n\n print(utils.wrap_with_color(\n \"You can now continue the dialogue. \"\n \"Use '/stop' to exit the conversation.\",\n utils.bcolors.OKGREEN + utils.bcolors.UNDERLINE))\n\n agent.handle_channel(ConsoleInputChannel(tracker.sender_id))\n","repo_name":"Priprocks/Dialogflow_export_to_rasa","sub_path":"rasa_core/restore.py","file_name":"restore.py","file_ext":"py","file_size_in_byte":5139,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"78"} +{"seq_id":"42463413935","text":"from turtle import Screen\nfrom player import Player\nfrom car_manager import CarManager\nfrom score_board import ScoreBoard\nimport time\n\nscreen = Screen()\nscreen.setup(width=600, height=600)\nscreen.colormode(255)\nscreen.bgcolor(\"white\")\nscreen.tracer(0)\n\nplayer = Player()\ncar_manager = CarManager()\nscore_board = ScoreBoard()\n\nscreen.listen()\nscreen.onkey(player.move, \"Up\")\n\ngame_is_on = True\nwhile game_is_on:\n time.sleep(0.1)\n screen.update()\n car_manager.create_car()\n car_manager.move_cars()\n\n #detct turtle's collision with top edge, reset turtle's position\n if player.ycor() > 280:\n player.reset_position()\n car_manager.speed_up_car()\n score_board.level_up()\n\n #detect turtle's collision with car\n for car in car_manager.all_cars:\n if player.distance(car) < 20:\n game_is_on = False\n score_board.print_game_over()\n\nscreen.exitonclick()\n","repo_name":"Woodchucks/Python-Projects-and-Scripts","sub_path":"turtle_crossing_game/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"8722943058","text":"# json和python的转换\n# import json\n#\n# # python转json\n# data = {\"name\": \"张三\", \"age\": 18}\n# print(json.dumps(data, ensure_ascii=False))\n# data = [\n# {\"name\": \"张三\", \"age\": 18},\n# {\"name\": \"张三\", \"age\": 18},\n# {\"name\": \"张三\", \"age\": 18},\n# ]\n# print(json.dumps(data, ensure_ascii=False))\n#\n# # json转python\n# s = '{\"name\": \"张三\", \"age\": 18}'\n# d = json.loads(s)\n# print(type(d))\n# print(d)\n#\n# s = '[{\"name\": \"张三\", \"age\": 18}, {\"name\": \"张三\", \"age\": 18}, {\"name\": \"张三\", \"age\": 18}]'\n# d = json.loads(s)\n# print(type(d))\n# print(d)\nimport json\n\n# ================================折线图===========================================\n# from pyecharts.charts import Line\n# from pyecharts.options import TitleOpts, LegendOpts, ToolboxOpts, VisualMapOpts\n#\n# line = Line()\n# line.add_xaxis([\"中国\", \"美国\", \"英国\"])\n# line.add_yaxis(\"GDP\", [300, 200, 100])\n#\n# # 全局配置项\n# line.set_global_opts(\n# title_opts=TitleOpts(title=\"GDP展示\", pos_left=\"center\", pos_bottom=\"1%\"),\n# legend_opts=LegendOpts(is_show=True),\n# toolbox_opts=ToolboxOpts(is_show=True),\n# visualmap_opts=VisualMapOpts(is_show=True)\n# )\n#\n# line.render()\n\n\"\"\"\nus_f = open(\"E:/fo的python学习/python_learn/a01_python入门语法/美国.txt\", \"r\", encoding=\"UTF-8\")\njp_f = open(\"E:/fo的python学习/python_learn/a01_python入门语法/日本.txt\", \"r\", encoding=\"UTF-8\")\nin_f = open(\"E:/fo的python学习/python_learn/a01_python入门语法/印度.txt\", \"r\", encoding=\"UTF-8\")\nus_data = us_f.read()\njp_data = jp_f.read()\nin_data = in_f.read()\n\n# 关闭文件\nus_f.close()\njp_f.close()\nin_f.close()\n\n# 取出首尾不是json的数据\nus_data = us_data.replace(\"jsonp_1629344292311_69436(\", \"\")\nus_data = us_data[:-2]\njp_data = jp_data.replace(\"jsonp_1629350871167_29498(\", \"\")\njp_data = jp_data[:-2]\nin_data = in_data.replace(\"jsonp_1629350745930_63180(\", \"\")\nin_data = in_data[:-2]\n\n# 转成字典\nus_dict = json.loads(us_data)\njp_dict = json.loads(jp_data)\nin_dict = json.loads(in_data)\n# print(us)\n# 取横纵坐标数据\nus_trend = us_dict['data'][0]['trend']\njp_trend = jp_dict['data'][0]['trend']\nin_trend = in_dict['data'][0]['trend']\n# 取横纵坐标数据\nus_x_data = us_trend['updateDate'][:314]\nus_y_data = us_trend['list'][0]['data'][:314]\njp_x_data = jp_trend['updateDate'][:314]\njp_y_data = jp_trend['list'][0]['data'][:314]\nin_x_data = in_trend['updateDate'][:314]\nin_y_data = in_trend['list'][0]['data'][:314]\n\n# print(x_data)\n# print(y_data)\n\n# 生成图表\nfrom pyecharts.charts import Line\nfrom pyecharts.options import TitleOpts, LabelOpts\n\nline = Line()\nline.add_xaxis(us_x_data)\nline.add_yaxis(\"美国确诊人数\", us_y_data, label_opts=LabelOpts(is_show=False))\nline.add_yaxis(\"日本确诊人数\", jp_y_data, label_opts=LabelOpts(is_show=False))\nline.add_yaxis(\"印度确诊人数\", in_y_data, label_opts=LabelOpts(is_show=False))\n\n# 配置\nline.set_global_opts(\n title_opts=TitleOpts(title=\"2020年美印日三国确诊人数表\", pos_left=\"center\", pos_bottom='1%')\n)\n\nline.render()\n\"\"\"\n\n# ================================地图===========================================\n\"\"\"\n# 读文件\nf = open(\"E:/fo的python学习/python_learn/a01_python入门语法/疫情.txt\", \"r\", encoding=\"UTF-8\")\nprovince_data_list = f.read()\n# 关闭文件\nf.close()\n\n# 对数据进行处理\n# 转为字典\nprovince_dict_list = json.loads(province_data_list)\n# 将省名和确诊数组装成字典封装在列表中\nprovince_list = []\nprovince_dict_list = province_dict_list[\"areaTree\"][0][\"children\"]\n\nfor province_dict in province_dict_list:\n province = province_dict[\"name\"] # 省名\n if len(province_dict[\"name\"]) <= 3:\n province = province + \"省\"\n confirm = province_dict[\"total\"][\"confirm\"] # 确诊人数\n m = (province, confirm) # 封装为字典\n province_list.append(m)\n# print(province_list)\n\n# 生成地图\nfrom pyecharts.charts import Map\nfrom pyecharts.options import *\n\nm = Map()\nm.add(\"全国确诊人数图\", province_list, maptype=\"china\")\n\n# 全局配置\nm.set_global_opts(\n title_opts=TitleOpts(title=\"全国确诊人数图\"),\n visualmap_opts=VisualMapOpts(\n is_show=True,\n is_piecewise=True,\n pieces=[\n {\"min\": 1, \"max\": 99, \"lable\": \"1~99\", \"color\": \"#CCFFFF\"},\n {\"min\": 100, \"max\": 999, \"lable\": \"100~999\", \"color\": \"#FFFF99\"},\n {\"min\": 1000, \"max\": 4999, \"lable\": \"1000~4999\", \"color\": \"#FF9966\"},\n {\"min\": 5000, \"max\": 9999, \"lable\": \"5000~9999\", \"color\": \"#FF6666\"},\n {\"min\": 10000, \"max\": 99999, \"lable\": \"10000~99999\", \"color\": \"#CC3333\"},\n {\"min\": 100000, \"lable\": \"100000+\", \"color\": \"#990033\"},\n ]\n )\n)\n\nm.render(\"全国确诊人数图.html\")\n\"\"\"\n\n# ======================江苏省==========================\n\"\"\"\n# 读文件\nf = open(\"E:/fo的python学习/python_learn/a01_python入门语法/疫情.txt\", \"r\", encoding=\"UTF-8\")\nprovince_data_list = f.read()\n# 关闭文件\nf.close()\n\n# 对数据进行处理\n# 转为字典\nprovince_dict_list = json.loads(province_data_list)\n# 将市名和确诊数组装成字典封装在列表中\njiangsu_list = []\njiangsu_province_dict_list = province_dict_list[\"areaTree\"][0][\"children\"][1][\"children\"]\nfor shi in jiangsu_province_dict_list:\n jiangsu_list.append((shi[\"name\"] + \"市\", shi[\"total\"][\"confirm\"]))\n# print(jiangsu_list)\n\n# 生成地图\nfrom pyecharts.charts import Map\nfrom pyecharts.options import *\n\nm = Map()\nm.add(\"江苏省各市确诊情况\", jiangsu_list, maptype=\"江苏\")\n\n# 全局配置\nm.set_global_opts(\n title_opts=TitleOpts(title=\"江苏省各市确诊情况\", pos_left=\"center\", pos_bottom=\"1%\"),\n visualmap_opts=VisualMapOpts(\n is_show=True,\n is_piecewise=True,\n pieces=[\n {\"min\": 1, \"max\": 9, \"lable\": \"1~9\", \"color\": \"#CCFFFF\"},\n {\"min\": 10, \"max\": 99, \"lable\": \"10~99\", \"color\": \"#FFFF99\"},\n {\"min\": 100, \"max\": 499, \"lable\": \"100~499\", \"color\": \"#FF9966\"},\n {\"min\": 500, \"max\": 999, \"lable\": \"500~999\", \"color\": \"#FF6666\"},\n {\"min\": 1000, \"max\": 9999, \"lable\": \"1000~9999\", \"color\": \"#CC3333\"},\n {\"min\": 10000, \"lable\": \"10000+\", \"color\": \"#990033\"},\n ]\n )\n)\n\nm.render(\"江苏省各市确诊情况.html\")\n\"\"\"\n\n# =========================柱状图==========================\nfrom pyecharts.charts import Bar, Timeline\nfrom pyecharts.options import LabelOpts, TitleOpts\nfrom pyecharts.globals import ThemeType\n\n# 基础柱状图\n\"\"\"\nbar = Bar()\nbar.add_xaxis([\"中国\", \"美国\", \"日本\"])\nbar.add_yaxis(\"GDP\", [30, 20, 10], label_opts=LabelOpts(position=\"right\"))\n\n# 翻转x轴，y轴\nbar.reversal_axis()\n\nbar.render(\"基础柱状图.html\")\n\"\"\"\n# ================基础时间线柱状图==================\n\"\"\"\nbar1 = Bar()\nbar1.add_xaxis([\"中国\", \"美国\", \"日本\"])\nbar1.add_yaxis(\"GDP\", [30, 20, 10], label_opts=LabelOpts(position=\"right\"))\nbar2 = Bar()\nbar2.add_xaxis([\"中国\", \"美国\", \"日本\"])\nbar2.add_yaxis(\"GDP\", [60, 60, 60], label_opts=LabelOpts(position=\"right\"))\nbar3 = Bar()\nbar3.add_xaxis([\"中国\", \"美国\", \"日本\"])\nbar3.add_yaxis(\"GDP\", [80, 50, 10], label_opts=LabelOpts(position=\"right\"))\n\ntimeline = Timeline({\"theme\": ThemeType.LIGHT}) # 设置主题\ntimeline.add(bar1, \"2010年\")\ntimeline.add(bar2, \"2020年\")\ntimeline.add(bar3, \"2030年\")\n\n# 设置自动播放\ntimeline.add_schema(\n is_auto_play=True, # 是否自动播放\n is_loop_play=True, # 是否循环播放\n play_interval=1000 # 跳转间隔时间1s\n)\n\ntimeline.render(\"基础时间线柱状图.html\")\n\"\"\"\n\n# ===================动态柱状图======================\n\"\"\"\n# 对列表进行自定义排序\nmy_list = [['张三', 18], ['里斯', 55], ['王五', 3]]\n\n\n# 1.带名函数\ndef choose_sort(element):\n return element[1]\n\n\n# my_list.sort(key=choose_sort, reverse=True)\n\n# 2.lambda匿名函数\nmy_list.sort(key=lambda element: element[1], reverse=True)\n\nprint(my_list)\n\"\"\"\n\n# ===============GDP动态柱状图=================\n# 读取文件\nf = open(\"E:/fo的python学习/python_learn/a01_python入门语法/1960-2019全球GDP数据.csv\", \"r\", encoding=\"GB2312\")\ndata = f.readlines()\n# 处理内容\n# 去掉第一行\ndata.pop(0)\n# 封装成所需数据\ndata_dict = {}\nfor line in data:\n # 获取年，国家名，GDP\n year = line.split(\",\")[0]\n country = line.split(\",\")[1]\n gdp = float(line.split(\",\")[2].strip()) # 去掉换行符,将科学计数法转成数字\n # 按照年进行分类，放到字典不同元素中\n try:\n data_dict[year].append([country, gdp])\n except KeyError:\n data_dict[year] = []\n data_dict[year].append([country, gdp])\n# 按照时间顺序取前八的国家，字典是无序的，所以要以时间取值\n# 取出所有时间并排序\nkeys = data_dict.keys()\nkeys = sorted(keys)\ntimeline = Timeline({\"theme\": ThemeType.LIGHT})\nfor key in keys:\n value = data_dict[key]\n value.sort(key=lambda e: e[1], reverse=True)\n # 取前八\n value = value[0:8]\n # 封装x,y轴\n x_data = []\n y_data = []\n for v in value:\n x_data.append(v[0])\n y_data.append(v[1])\n bar = Bar()\n # 将数据翻转\n x_data.reverse()\n y_data.reverse()\n bar.add_xaxis(x_data)\n bar.add_yaxis(\"GDP(亿)\", y_data, label_opts=LabelOpts(position=\"right\"))\n # 添加表名\n bar.set_global_opts(\n title_opts=TitleOpts(title=f\"{key}年全球GDP前八国家\")\n )\n # 翻转x,y轴\n bar.reversal_axis()\n # 添加时间线\n timeline.add(bar, key)\n\n# 全局配置\ntimeline.add_schema(\n is_auto_play=True, # 是否自动播放\n is_loop_play=False, # 是否循环播放\n play_interval=1000, # 跳转间隔时间1s\n is_timeline_show=True # 是否显示时间线\n)\n\n# 绘制图像\ntimeline.render(\"GDP动态柱状图.html\")\n","repo_name":"fosss666/python-learn","sub_path":"python_learn/a01_python入门语法/b09_综合案例.py","file_name":"b09_综合案例.py","file_ext":"py","file_size_in_byte":9828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"19053210108","text":"import wx\nfrom gtts.lang import tts_langs\n\nclass ConvertirArchivo(wx.Dialog):\n\tdef __init__(self, *args ,**qwargs):\n\t\tsuper().__init__(*args ,**qwargs)\n\t\tself.speed=['normal','lento']\n\t\tlangs = tts_langs()\n\t\tself.lang=list(langs.values())\n\t\tself.langCode = list(langs.keys())\n\t\tself.audio=['mp3','wav']\n\t\tself.pathDir=\"\"\n\t\tself.SetTitle('convertir a audio')\n\t\t\n\t\tpanel=wx.Panel(self)\n\t\t#speed=['rapida','lenta']\n\t\twx.StaticText(panel,-1,label='nombre del archivo')\n\t\tself.name = wx.TextCtrl(panel)\n\t\tbtnCarpeta = wx.Button(panel , -1 , 'examinar..')\n\t\tself.lbAudio=wx.ListBox(panel , -1 , choices=self.audio)\n\t\tself.lbAudio.SetSelection(0)\n\t\twx.StaticText(panel,-1,label='Velocidad')\n\t\tself.lbSpeed=wx.ListBox(panel , -1 , choices=self.speed)\n\t\tself.lbSpeed.SetSelection(0)\n\t\twx.StaticText(panel,-1,label='Lenguaje')\n\t\tself.lbLang=wx.ListBox(panel , -1 , choices=self.lang)\n\t\tself.lbLang.SetSelection(12)\n\t\tbtnOk= wx.Button(panel, wx.ID_OK,'aceptar')\n\t\tbtnCancel= wx.Button(panel, wx.ID_CANCEL,'Cancelar')\n\t\tbtnCarpeta.Bind(wx.EVT_BUTTON,self.folder)\n\tdef getFilename(self):\n\t\treturn self.name.GetValue()\n\tdef getSpeed(self):\n\t\tid=self.lbSpeed.GetSelection()\n\t\treturn self.speed[id]\n\n\tdef getLang(self):\n\t\tidL=self.lbLang.GetSelection()\n\t\treturn self.langCode[idL]\n\n\tdef getAudio(self):\n\t\tidA=self.lbAudio.GetSelection()\n\t\treturn self.audio[idA]\n\n\tdef folder(self , event):\n\n\t\tdlgFol = wx.DirDialog(self, \"seleccione carpeta\")\n\t\tif dlgFol.ShowModal() == wx.ID_OK:\n\t\t\tself.pathDir= dlgFol.GetPath()\n\t\t\tprint(self.pathDir)\n\t\tdlgFol.Destroy\n\n\n\n\tdef getPath(self):\n\t\treturn self.pathDir\n\n","repo_name":"lopezdavid79/ISA-convert-texto-a-voz","sub_path":"respaldos/dlConvertir.py","file_name":"dlConvertir.py","file_ext":"py","file_size_in_byte":1584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"17293947555","text":"import pandas\nimport matplotlib.pyplot\n\ndf = pandas.read_csv(\"./data/exam.csv\")\nprint(df)\nprint(\"\\n\")\n\n#수학점수와 읽기점수 간의 scatter plot 그리기\ndf.plot(kind=\"scatter\", x=\"math score\", y=\"reading score\")\nmatplotlib.pyplot.show()\n\n#수학점수와 쓰기점수간의 scatter plot 그리기\ndf.plot(kind=\"scatter\", x=\"math score\", y=\"writing score\")\nmatplotlib.pyplot.show()\n\n#읽기점수와 쓰기점수간의 scatter plot 그리기\ndf.plot(kind=\"scatter\", x=\"reading score\", y=\"writing score\")\nmatplotlib.pyplot.show()","repo_name":"JonghunCha/Data_Analysis_Practice","sub_path":"pandas/Graph(Scatterplot).py","file_name":"Graph(Scatterplot).py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"7497879397","text":"## O(N^2) Time, O(1) Space\ndef getMajority_Without_Hash(arr):\n l = list()\n maxVal = 0\n maxCount = 1\n for i in arr:\n count = 0 \n for j in arr:\n if i == j:\n count += 1\n if i not in l:\n if count > maxCount:\n maxVal = i\n maxCount = count\n l = list()\n l.append(i)\n elif count == maxCount:\n l.append(i)\n return l\n\n## O(N) Time, O(N) Space\ndef getMajority_With_Hash(arr):\n countDict = dict()\n for i in arr:\n if i in countDict:\n countDict[i] = countDict[i] + 1\n else:\n countDict.update({i : 1})\n l = list()\n maxVal = 0\n key = 0\n for k, v in countDict.items():\n if v > maxVal:\n key = k\n maxVal = v\n l = list()\n l.append(key)\n elif maxVal == v:\n l.append(k)\n return l\n\n\n## Condition if Majority greater equal to Half of the N \n## O(N) Time, O(1) Space\ndef findMajority(arr, n):\n m_element = 0 # Majority Number\n count = 1 \n for i in range(n):\n if arr[i] == m_element:\n count += 1\n else:\n count -= 1\n if count == 0:\n m_element = arr[i]\n count += 1\n return m_element\n\n\ndef isMajority(arr, n, m_element):\n count = 0\n for i in range(n):\n if arr[i] == m_element:\n count += 1\n if count >= n/2:\n return True, count\n else:\n return False, count\n\n\nif __name__=='__main__':\n arr=[3, 3, 4, 2, 4, 4, 2, 4, 3, 2, 3, 2, 2, 2]\n print(\"Length: \", len(arr))\n # O(N^2) Time, O(1) Space\n print(\"O(N^2) Time, O(1) Space: \", getMajority_Without_Hash(arr))\n \n # O(N) Time, Theta(N) Space\n print(\"O(N) Time, Theta(N) Space: \", getMajority_With_Hash(arr))\n \n # O(N) Time, O(1) Space\n value=findMajority(arr,len(arr))\n val = isMajority(arr,len(arr),value)\n if(val[0]):\n print(\"O(N) Time, O(1) Space: \", value, val[1])\n else:\n print(\"Majority Element Is Less Then N/2, length is: \", val[1])\n","repo_name":"piyush97ps/algoPractice","sub_path":"Problems on Arrays/find_majority_element_in_an_array.py","file_name":"find_majority_element_in_an_array.py","file_ext":"py","file_size_in_byte":2116,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"36817967487","text":"import random\nimport math\n\n'''\nThis is a simulation for a statistics class, which posed the following question.\n\nThe Penguins take a total of 20 shots, each with a 5.5% chance of scoring.\nThe Flyers score exactly 1 goal per game. \nWhich team would you rather be?\n'''\n\nshot_odds = 0.055\nsimulations = 1000\npen_goals = 0\npen_wins = 0\npen_ties = 0\npen_losses = 0\nprob = 0\npen_total = 0\n\nfor sims in range(simulations):\n pen_goals = 0\n for shots in range(20):\n prob = random.random()\n if prob < shot_odds:\n pen_goals += 1\n pen_total += 1\n if pen_goals > 1:\n pen_wins += 1\n elif pen_goals == 1:\n pen_ties += 1\n else:\n pen_losses += 1\n\npen_percentage = (pen_wins/simulations) * 100\nfly_percentage = (pen_losses/simulations) * 100\ntie_percentage = (pen_ties/simulations) * 100\nbetter_team = \"\"\n\nprint(\"\\n\\nAfter simulating\", simulations, \"games...\")\nprint(\"The Penguins won\", pen_wins, \"games with a win percentage of\", round(pen_percentage, 2), \"%\")\nprint(\"The Flyers won\", pen_losses, \"games with a win percentage of\", round(fly_percentage, 2), \"%\")\nprint(\"The two teams tied\", pen_ties, \"games with a tie percentage of\", round(tie_percentage, 2), \"%\")\nprint(\"\")\n\nif pen_wins > pen_losses:\n print(\"In this simulation, the Penguins came out on top.\")\nelif pen_wins < pen_losses:\n print(\"In this simulation, the Flyers came out on top.\")\nelse:\n if pen_total > simulations:\n print(\"In this simulation, the teams had the same number of wins. That said, the Penguins edged out the Flyers\"\n \", scoring\", pen_total, \"goals where the Flyers scored just\", simulations, \".\")\n elif pen_total < simulations:\n print(\"In this simulation, the teams had the same number of wins. That said, the Flyers edged out the Penguins,\"\n \"scoring\", simulations, \"goals where the Penguins scored just\", pen_total, \".\")\n else:\n print(\"AND THEY SCORED THE SAME NUMBER OF GOALS,\", simulations, \"! WILD!\")\n\n\n","repo_name":"sammykagan/ProgrammingII_SP19_Sammy_Kagan","sub_path":"05_hockey_shots.py","file_name":"05_hockey_shots.py","file_ext":"py","file_size_in_byte":2005,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"24466084713","text":"# Given a non-empty array of digits representing a non-negative integer,\n# plus one to the integer.\n\nclass Solution(object):\n def plusOne(self, digits):\n \n \n digits = digits[::-1]\n digits[0] += 1\n \n for index,value in enumerate(digits):\n \n if value != 10:\n break\n \n digits[index] = 0 \n if index == len(digits)-1:\n digits.append(1)\n break\n digits[index + 1] += 1 \n \n return digits[::-1]\n ","repo_name":"orlovska/python","sub_path":"The_start/PlusOne.py","file_name":"PlusOne.py","file_ext":"py","file_size_in_byte":557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"25105634904","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def rightSideView(self, root: TreeNode) -> List[int]:\n if not root: return []\n stem = defaultdict(list)\n def traverse(node, lev):\n if node:\n stem[lev].append(node.val)\n traverse(node.left, lev+1)\n traverse(node.right, lev+1)\n traverse(root, 0)\n arr = []\n for i in stem.keys():\n arr.append(stem[i][-1])\n return arr","repo_name":"rohith788/Leetcode","sub_path":"Others/Trees and Graph/199. Binary Tree Right Side View.py","file_name":"199. Binary Tree Right Side View.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"1641007007","text":"def f(m):\n return m**2-m+1\n\ndef solve(y,x):\n if y List[int]:\n length = len(nums)\n result = [0] * length\n summ = 1\n for i, n in enumerate(nums):\n result[i] = summ\n summ *= n\n\n summ = 1\n for i, n in enumerate(reversed(nums)):\n result[length - i - 1] = result[length - i - 1] * summ\n summ *= n\n return result\n\n def productExceptSelf_2(self, nums: List[int]) -> List[int]:\n # 27.11.2021\n N = len(nums)\n ans = [1] * N\n\n product = 1\n for i in range(1, N):\n product *= nums[i - 1]\n ans[i] = product\n\n product = 1\n for i in reversed(range(0, N - 1)):\n product *= nums[i + 1]\n ans[i] = ans[i] * product\n\n return ans\n","repo_name":"vaiol/leetcode2","sub_path":"src/238/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"39290803831","text":"from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom time import sleep\n\nurl = \"https://courses.letskodeit.com/practice\"\n\nclass SwitchToIframe():\n def test(self):\n driver = webdriver.Firefox()\n driver.maximize_window()\n driver.implicitly_wait(10)\n driver.get(url)\n \n #Switch to frame using ID\n iframe_element = driver.find_element(By.ID, \"courses-iframe\")\n ActionChains(driver).move_to_element(iframe_element).perform()\n driver.switch_to.frame(\"courses-iframe\")\n \n #Switch to frame using name\n #driver.switch_to.frame(\"iframe-name\")\n \n # Switch to frmae using index\n #driver.switch_to.frmae(0)\n \n sleep(5)\n \n search_field = driver.find_element(By.XPATH, \"//form[@name='search']/div/input\")\n search_field.send_keys(\"Python\")\n \n #search_button = driver.find_element(By.XPATH, \"//form[@name=\"search\"]/div/button\")\n #search_button.click()\n \n #sleep(5)\n course_selection = driver.find_element(By.XPATH, \"//div[@id='course-list']/div[1]\")\n print(course_selection.text)\n \n #Switch back to the parent frame\n driver.switch_to.default_content()\n sleep(5)\n \n driver.quit()\n\nff = SwitchToIframe()\nff.test()","repo_name":"rajesh1994/selenium_automation_testing_with_python","sub_path":"switch_to7/switch_to_iframe2.py","file_name":"switch_to_iframe2.py","file_ext":"py","file_size_in_byte":1419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"34987269284","text":"from __future__ import print_function, division\nfrom optparse import OptionParser\nimport os\nimport sys\nfrom scipy.stats import norm\nimport numpy as np\nfrom scipy.optimize import minimize\nimport math \n\n__author__ = \"Louis Dijkstra\"\n\nusage = \"\"\"%prog [options] \n\n\t\tDirectory containing all the observations for the \n\t\t\tnon-null cases. \n\t \tEstimate of the mean \n\t \tEstimate of the standard deviation\n\nOutputs an estimate of the 'error rate', epsilon, for the non-null arm of \nthe model. \n\nSee 'estimate-null-insert-sizes.py' for estimating mu and sigma. \n\"\"\"\n\ndef normalizationFactor(mu, sigma, epsilon, length): \n\t\"\"\"Returns the normalization factor given an estimate for the mean mu and STD sigma,\n\t the error rate epsilon and the length of the indel involved.\"\"\"\n\treturn 1.0 / (1.0 - (1.0 - epsilon) * norm.cdf((-mu - 0.5 - length) / sigma) - epsilon * norm.cdf((-mu - 0.5)/sigma))\n\ndef f(isize, mu, sigma, length):\n\tp = norm.cdf((isize + 0.5 - mu - length)/sigma) - norm.cdf((isize - 0.5 - mu - length)/sigma)\n\tif p < sys.float_info.min:\n\t\treturn sys.float_info.min\n\treturn p\n\ndef negative_loglikelihood(epsilon, mu_est, sigma_est, isizes_del, counts_del, n_del, isizes_ins, counts_ins, n_ins):\n\t\"\"\"returns -1 * loglikelihood\"\"\"\n\tl = 0.0\n\t# work through the deletions \n\tfor length in range(1,1001):\n\t\tl += n_del[length-1] * math.log(normalizationFactor(mu_est, sigma_est, epsilon, length))\n\t\tfor isize, count in zip(isizes_del[length-1], counts_del[length-1]):\n\t\t\tl += count * math.log(epsilon * f(isize, mu_est, sigma_est, 0) + (1.0 - epsilon) * f(isize, mu_est, sigma_est, length))\n\t\t# print('updated log-likelihood for observations induced by deletion of length %d: %lf'%(length, l))\n\t# work through the insertions \n\tfor length in range(1,1001):\n\t\tl += n_ins[length-1] * math.log(normalizationFactor(mu_est, sigma_est, epsilon, -1*length))\n\t\tfor isize, count in zip(isizes_ins[length-1], counts_ins[length-1]):\n\t\t\tl += count * math.log(epsilon * f(isize, mu_est, sigma_est, 0) + (1.0 - epsilon) * f(isize, mu_est, sigma_est, -1*length))\n\t\t# print('updated log-likelihood for observations induced by insertions of length %d: %lf'%(length, l))\n\treturn -1.0 * l \n\n\ndef readInFile (filename):\n\t\"\"\"Reads in a histogram file.\"\"\"\n\tisizes, counts = [], []\n\tfor line in open(filename, 'r'):\n\t\tvalues = map(int, line.split())\n\t\tisizes.append(values[0])\n\t\tcounts.append(values[1])\n\treturn isizes, counts\n\ndef main():\n\tparser = OptionParser(usage=usage)\n\tparser.add_option(\"-f\", action=\"store\", dest=\"maxfun\", default=1000, type=int, \n \t\thelp=\"Maximum number of function evaluations (Default = 1000) \")\n\tparser.add_option(\"-i\", action=\"store\", dest=\"maxiter\", default=100, type=int, \n \t\thelp=\"Maximum number of iterations (Default = 100) \")\n\tparser.add_option(\"-k\", action=\"store\", dest=\"epsilon_min\", default=0.001, type=float, \n \t\thelp=\"Lower bound for epsilon. (Default is 0.001) \")\n\tparser.add_option(\"-l\", action=\"store\", dest=\"epsilon_init\", default=0.05, type=float, \n \t\thelp=\"Initial guess for epsilon. (Default is 0.05) \")\n\tparser.add_option(\"-m\", action=\"store\", dest=\"epsilon_max\", default=0.10, type=float, \n \t\thelp=\"Upper bound for epsilon. (Default is 0.10) \")\n\tparser.add_option(\"-v\", action=\"store_true\", dest=\"verbose\", default=False,\n \t\thelp=\"Verbose. Output of the optimizer is printed. \")\n\t(options, args) = parser.parse_args()\n\n\tif (len(args)!=3):\n\t\tparser.print_help()\n\t\treturn 1\n\n\tresult_dir = args[0]\n\tif result_dir[-1] != '/':\n\t\tresult_dir += '/'\n\t\n\tmu_est \t\t= float(args[1])\n\tsigma_est \t= float(args[2])\n\t\n\t# READ IN THE RAW DATA (BOTH DELETIONS and INSERTIONS)\n\n\t# every variant type and every length will be represented by two lists: \n\t# insert sizes (isizes) and # of observations for that insert size (counts)\n\t# n - # total number of observations \n\tisizes_del, isizes_ins \t= [], [] # insert sizes that were observed\n\tcounts_del, counts_ins \t= [], [] # number of times these insert sizes were observed\n\tn_del, n_ins \t= [], [] # total number of observations \n\n\t# walk through all the files\n\tfor length in range(1,1001): \n\t\tif options.verbose: \n\t\t\tprint(\"Reading data for indels of length %d\"%length) \n\t\tisizes, counts = readInFile(result_dir + 'deletion.length' + str(length) + '.insert-sizes')\n\t\tisizes_del.append(isizes)\n\t\tcounts_del.append(counts)\n\t\tn_del.append(sum(counts))\n\t\tisizes, counts = readInFile(result_dir + 'insertion.length' + str(length) + '.insert-sizes')\n\t\tisizes_ins.append(isizes)\n\t\tcounts_ins.append(counts)\n\t\tn_ins.append(sum(counts))\n\t\t\n\tisizes_del \t= np.array(isizes_del)\n\tcounts_del\t= np.array(counts_del)\n\tn_del\t \t= np.array(n_del)\n\tisizes_ins \t= np.array(isizes_ins)\n\tcounts_ins\t= np.array(counts_ins)\n\tn_ins\t \t= np.array(n_ins)\n\tif options.verbose:\n\t\tprint(\"DONE Reading in data...\")\n\t\n\tres = minimize\t(\tnegative_loglikelihood, \n\t\t\t\toptions.epsilon_init, \n\t\t\t\targs=[mu_est, sigma_est, isizes_del, counts_del, n_del, isizes_ins, counts_ins, n_ins], \n\t\t\t\tmethod=\"L-BFGS-B\", \n\t\t\t\tbounds=[(options.epsilon_min, options.epsilon_max)], \n\t\t\t\toptions={'disp': options.verbose, 'maxfun': options.maxfun, 'maxiter': options.maxiter})\n\t\n\n\tprint(\"\\n*** RESULTS ***\\n\")\n\tprint(\"estimated epsilon \", res.x)\n\tprint(res.message)\n\nif __name__ == '__main__':\n\tsys.exit(main())\n\n","repo_name":"louisdijkstra/error-model-aligner","sub_path":"bin/estimate-non-null-insert-sizes.py","file_name":"estimate-non-null-insert-sizes.py","file_ext":"py","file_size_in_byte":5407,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"25485323106","text":"'''\nThe main program that triggers the application\n'''\n\nimport argparse\nimport os\nimport sys\nimport traceback\nimport time\nimport copy\nimport _thread\nimport util\nimport multiprocessing\nfrom random import randint\nfrom state import AgentState, EnvironmentState\nfrom datetime import datetime\nfrom marlagent.agent.linear.lin_agent import LinearQAgent\nfrom marlagent.agent.dqn.dqn import DQNAgent\nfrom osbrain import run_agent\nfrom osbrain import run_nameserver\nfrom osbrain import NSProxy\nfrom nameserver import NameServer\nfrom cghandler import httpservice\nfrom prediction.energy_generation import EnergyGeneration\n\n\npidfile = \"assets/ns.pid\"\n\ndef exit_check(msg):\n if msg['topic'] == 'exit':\n return True\n\n\ndef energy_request_handler(agent, message):\n\n # Acquire the lock\n lock_count = 0\n while not multiprocessing_lock.acquire(blocking=False):\n try:\n if lock_count <= 2:\n time.sleep(randint(1, 3) / 10)\n lock_count += 1\n else:\n yield {'topic': 'ENERGY_REQUEST_DECLINE'}\n agent.log_info(\"Could not acquire lock! Energy request declined.\")\n return\n except:\n print(traceback.format_exc())\n\n\n agent.log_info(\"Lock Acquired!\")\n\n try:\n print(\"-----------------------Start Transaction-----------------------\")\n agent.log_info('Received: %s' % message)\n\n agent.log_info(\"Deepy copy of global state initiated...\")\n l_g_agent_state = multiprocessing_ns.g_agent_state\n l_curr_state = copy.deepcopy(l_g_agent_state)\n\n # update with new values of energy consumption and generation\n l_curr_state.time = datetime.strptime(message['time'], '%Y/%m/%d %H:%M')\n\n # amount of requested energy\n energy_req = message['energy']\n\n actions = [\n {\n 'action': 'grant',\n 'data': energy_req\n },\n {\n 'action': 'deny_request',\n 'data': energy_req\n }\n ]\n\n # call get action with this new state\n l_rl_agent = multiprocessing_ns.rl_agent\n action = l_rl_agent.get_action(copy.deepcopy(l_curr_state), actions)\n\n agent.log_info('Performing action (%s).' % action)\n\n response = None\n\n # If energy request is declined\n if action['action'] == 'deny_request':\n response = {'topic':'ENERGY_REQUEST_DECLINE'}\n\n # perform action and update global agent state\n next_state, energy_grant = l_rl_agent.do_action(l_curr_state, action, osbrain_ns, agent, args.agentname, allies)\n\n # if energy request is accepted\n if action['action'] == 'grant':\n response = {'topic': 'ENERGY_REQUEST_ACCEPTED', 'energy': energy_grant}\n agent.log_info(\"GRANTING:-----:%s\"%energy_grant)\n next_state.environment_state.update_energy_granted_to_ally(energy_grant)\n print(\"BATTERY AFTER GRANTING-----:%s\"%next_state.battery_curr)\n\n _thread.start_new_thread(cg_http_service.register_transaction, (l_g_agent_state.iter,\n message['time'], message['agentName'],\n energy_grant))\n\n\n l_rl_agent.update(state=l_curr_state, action=action, next_state=next_state, reward=0.0, eoi = False)\n\n \n # update the global state\n l_g_agent_state.energy_consumption = 0.0\n l_g_agent_state.energy_generation = 0.0\n l_g_agent_state.battery_curr = next_state.battery_curr\n l_g_agent_state.environment_state = next_state.environment_state\n\n agent.log_info('Completed update operation. Resting!')\n\n # agent.log_info(next_state)\n agent.log_info(l_g_agent_state.environment_state)\n\n print(\"-----------------------End of Transaction-----------------------\\n\\n\\n\")\n\n # Synchronize Objects\n multiprocessing_ns.g_agent_state = l_g_agent_state\n multiprocessing_ns.rl_agent = l_rl_agent\n agent.log_info(\"Finished synchronizing objects across forked processes.\")\n\n yield response\n except Exception:\n print(traceback.format_exc())\n yield {'topic': 'ENERGY_REQUEST_DECLINE'}\n\n\n finally:\n # Release the lock\n multiprocessing_lock.release()\n agent.log_info(\"Lock Released!\")\n\n\ndef energy_consumption_handler(agent, message):\n yield {'topic': 'Ok'} # immediate reply\n\n # Exit check\n if exit_check(message):\n sys.exit(0)\n\n global osbrain_ns\n\n if message['topic'] == 'ENERGY_CONSUMPTION':\n _thread.start_new_thread(invoke_agent_ec_handle, (agent, osbrain_ns, message))\n\n elif message['topic'] == 'END_OF_ITERATION' or message['topic'] == 'TRAINING_COMPLETE':\n _thread.start_new_thread(eoi_handle, (agent, message))\n\n\ndef invoke_agent_ec_handle(agent, osbrain_ns, message):\n\n try:\n print(\"Trying to acquire lock!\")\n # Acquire the lock\n multiprocessing_lock.acquire()\n except Exception:\n print(traceback.format_exc())\n return\n\n print(\"\\n-----------------------Start Transaction-----------------------\")\n agent.log_info('Received: %s' % message)\n\n try:\n agent.log_info(\"Deepy copy of global state initiated...\")\n l_g_agent_state = multiprocessing_ns.g_agent_state\n l_curr_state = copy.deepcopy(l_g_agent_state)\n\n # update with new values of energy consumption and generation\n l_curr_state.time = datetime.strptime(message['time'], '%Y/%m/%d %H:%M')\n\n # Get energy generation\n energy_generated = energy_generator.get_generation(l_curr_state.time)\n\n l_curr_state.energy_consumption = message['consumption']\n l_curr_state.energy_generation = energy_generated\n\n\n # call get action with this new state\n l_rl_agent = multiprocessing_ns.rl_agent\n action = l_rl_agent.get_action(copy.deepcopy(l_curr_state))\n\n agent.log_info('Performing action (%s).' % action)\n # perform action and update global agent state\n next_state, usable_generated_energy = l_rl_agent.do_action(l_curr_state, action, osbrain_ns, agent, args.agentname, allies)\n\n agent.log_info('Action complete. Registering action effect with the environment.')\n\n # Registering information to CG\n _thread.start_new_thread(cg_http_service.update_energy_status, (message['time'],\n message['iter'],\n float(args.battInit),\n message['consumption'],\n usable_generated_energy,\n next_state.environment_state.get_energy_borrowed_from_CG()\n - l_curr_state.environment_state.get_energy_borrowed_from_CG()))\n\n\n delta_reward = 0.0\n # Get grid status from CG\n # curr_grid_status = cg_http_service.get_energy_status(l_curr_state.iter)\n # net_curr_grid_status = util.calc_net_grid_status(curr_grid_status)\n\n # calculate reward\n # delta_reward = next_state.get_score() + util.reward_transaction(l_curr_state, next_state, action,\n # net_curr_grid_status)\n\n\n agent.log_info('Updating agent with reward %s.' % delta_reward)\n l_rl_agent.update(state=l_curr_state, action=action, next_state=next_state, reward=0.0)\n\n # Update grid status\n # next_state.environment_state.net_grid_status = net_curr_grid_status\n\n # update the global state\n l_g_agent_state.energy_consumption = 0.0\n l_g_agent_state.energy_generation = 0.0\n l_g_agent_state.battery_curr = next_state.battery_curr\n l_g_agent_state.environment_state = next_state.environment_state\n\n # agent.log_info(next_state)\n # agent.log_info(l_g_agent_state.environment_state)\n agent.log_info('Completed update operation. Resting!')\n print(\"-----------------------End of Transaction-----------------------\\n\\n\")\n\n # Synchronize Objects\n multiprocessing_ns.g_agent_state = l_g_agent_state\n multiprocessing_ns.rl_agent = l_rl_agent\n agent.log_info(\"Finished synchronizing objects across forked processes.\")\n\n except Exception:\n print(traceback.format_exc())\n\n finally:\n # Release the lock\n multiprocessing_lock.release()\n agent.log_info(\"Lock Released!\")\n\n\ndef eoi_handle(agent, message):\n '''\n End of iteration handler.\n :return:\n '''\n multiprocessing_lock.acquire()\n global g_env_state\n try:\n print(\"\\n\\n\\-----------------------Iteration (%s) Completed-----------------------\\n\\n\"%message['iter'])\n\n # Fetching Reference\n l_rl_agent = multiprocessing_ns.rl_agent\n l_g_agent_state = multiprocessing_ns.g_agent_state\n g_env_state = l_g_agent_state.environment_state\n\n\n agent.log_info(\"Publishing Stats...\")\n agent.log_info(g_env_state)\n\n nzeb_status = (g_env_state.get_total_generated() + g_env_state.get_energy_borrowed_from_ally()) \\\n - (g_env_state.get_total_consumed() + g_env_state.get_energy_borrowed_from_CG())\n agent.log_info(\"NZEB Status: %s\" % nzeb_status)\n\n\n # Log EOI details to CG\n cg_http_service.log_iteration_status(message['iter'], g_env_state, nzeb_status)\n\n\n # --------------------- Updating reward ---------------------\n agent.log_info('Calculating reward.')\n\n # Get grid status from CG\n curr_grid_status = cg_http_service.get_energy_status(int(message['iter']))\n net_curr_grid_status = util.calc_net_grid_status(curr_grid_status)\n\n # calculate reward\n # delta_reward = util.compare(net_curr_grid_status, multiprocessing_ns.old_grid_status)\n\n\n # If this grid status is better than the previous best grid status\n # if util.compare(net_curr_grid_status, multiprocessing_ns.best_grid_status) > 1 :\n # multiprocessing_ns.best_grid_status = net_curr_grid_status\n # delta_reward += 3\n\n # delta_reward = delta_reward - abs(int(multiprocessing_ns.best_grid_status - net_curr_grid_status)) * 0.1\n\n # multiprocessing_ns.old_grid_status = net_curr_grid_status\n\n delta_reward = util.reward_transaction(state = None, next_state = None, action = None, net_curr_grid_status = net_curr_grid_status)\n l_rl_agent.update(state=None, action=None, next_state=None, reward=delta_reward, eoi = True)\n #---------------------------------------------------------------\n\n\n if int(message['iter']) > 0 and int(message['iter']) % 50 == 0:\n l_rl_agent.epsilon = round(l_rl_agent.epsilon * 0.8, 5)\n agent.log_info(\"Updated Epsilon: %s\"%l_rl_agent.epsilon)\n\n\n # If training phase done then set exploration to 0\n # i.e. complete exploitation\n if message['topic'] == 'TRAINING_COMPLETE':\n l_rl_agent.epsilon = 0.0\n\n\n # reset the agent global state\n print(\".......................RESETTING GLOBAL STATE.......................\")\n l_g_agent_state.reset(float(args.battInit))\n l_g_agent_state.iter = int(message['iter']) + 1\n agent.log_info(l_g_agent_state.environment_state)\n\n\n # Synchronize Objects\n multiprocessing_ns.rl_agent = l_rl_agent\n multiprocessing_ns.g_agent_state = l_g_agent_state\n agent.log_info(\"Finished synchronizing objects across forked processes.\")\n\n except Exception:\n print(traceback.format_exc())\n\n finally:\n # Release the lock\n multiprocessing_lock.release()\n\ndef predict_energy_generation(time):\n print(\"TBD\")\n return 0.0\n\n\ndef get_ref_to_nameserver(ns_socket_addr):\n osbrain_ns = None\n print(\"Fetching reference to existing nameserver...\")\n osbrain_ns = NSProxy(nsaddr=ns_socket_addr)\n return osbrain_ns\n\n\ndef start_server_job(osbrain_ns):\n time.sleep(2)\n ns_agent = NameServer(osbrain_ns)\n\n # Start the scheduled job\n steve = run_agent('Steve', serializer='json')\n ns_agent.schedule_job(steve)\n\n\ndef args_handler():\n parser = argparse.ArgumentParser(description='Agent Module')\n\n parser.add_argument('--agentname', required=True, help='Name of the agent')\n parser.add_argument('--nameserver', required=True, help='Socket address of the nameserver')\n parser.add_argument('--allies', required=False, help='Socket address of the nameserver')\n parser.add_argument('--battInit', required=True, help='Initial battery charge.')\n parser.add_argument('--solarexposure', required=False, help='Path to solar exposure dataset')\n parser.add_argument('--nSolarPanel', required=True, help='Number fo solar panel this house has')\n\n global args\n args = parser.parse_args()\n\n if args.solarexposure is None:\n args.solarexposure = 'assets/toronto_solar_exp_2011.csv'\n\n\nif __name__ == '__main__':\n\n print(\"Started process at (\"+str(datetime.now())+\")\")\n args_handler()\n\n print(\"Hi! I am \"+args.agentname+\". I am taking command of this process.\")\n\n # Initiate name server\n global osbrain_ns\n osbrain_ns = get_ref_to_nameserver(args.nameserver)\n\n global cg_http_service\n cg_http_service = httpservice.CGHTTPHandler(args.agentname)\n\n try:\n from osbrain.logging import pyro_log\n pyro_log()\n\n # instantiate reinforcement learning module and making it globally accessible\n global multiprocessing_ns, multiprocessing_lock\n manager = multiprocessing.Manager()\n multiprocessing_ns = manager.Namespace()\n multiprocessing_lock = manager.RLock()\n\n multiprocessing_ns.rl_agent = DQNAgent()\n # multiprocessing_ns.rl_agent = LinearQAgent()\n\n global energy_generator\n energy_generator = EnergyGeneration(args.solarexposure, float(args.nSolarPanel))\n\n # Declare a agent state and make it global\n environment_state = EnvironmentState(0.0, 0.0, 0.0, 0.0, 0.0, 0.0)\n # global g_agent_state\n multiprocessing_ns.g_agent_state = AgentState(name = args.agentname, iter = 0, energy_consumption = 0.0, energy_generation = 0.0,\n battery_curr = float(args.battInit), time = '2014/01/01 12:00', environment_state = environment_state,\n cg_http_service = cg_http_service)\n\n multiprocessing_ns.old_grid_status = -99999\n multiprocessing_ns.best_grid_status = -99999\n\n global allies\n allies = [ally for ally in args.allies.split(\",\") ]\n # allies = []\n\n # Initialize the agent\n agent = run_agent(name = args.agentname, nsaddr = osbrain_ns.addr(), serializer='json', transport='tcp')\n agent.bind('REP', alias=str('energy_request_'+args.agentname), handler=energy_request_handler)\n agent.bind('REP', alias='consumption', handler=energy_consumption_handler)\n\n\n\n except Exception:\n print(traceback.format_exc())\n\n\n finally:\n\n while(1):\n time.sleep(1)\n\n print(\"Bye!\")\n\n\n","repo_name":"amitection/marl","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":15411,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"81"} +{"seq_id":"32427909226","text":"import os\nimport threading\nimport tuned.logs\nfrom tuned.exceptions import TunedException\nimport tuned.consts as consts\nimport tuned.utils.commands\n\nlog = tuned.logs.get()\n\n\nclass Daemon(object):\n\tdef __init__(self, unit_manager, profile_loader, profile_name=None, config=None):\n\t\tlog.debug(\"initializing daemon\")\n\t\tself._sleep_interval = int(consts.CFG_DEF_SLEEP_INTERVAL)\n\t\tself._update_interval = int(consts.CFG_DEF_UPDATE_INTERVAL)\n\t\tself._dynamic_tuning = consts.CFG_DEF_DYNAMIC_TUNING\n\t\tif config is not None:\n\t\t\tself._sleep_interval = int(config.get(\"sleep_interval\", consts.CFG_DEF_SLEEP_INTERVAL))\n\t\t\tself._update_interval = int(config.get(\"update_interval\", consts.CFG_DEF_UPDATE_INTERVAL))\n\t\t\tself._dynamic_tuning = config.get(\"dynamic_tuning\", consts.CFG_DEF_DYNAMIC_TUNING)\n\t\tif self._sleep_interval <= 0:\n\t\t\tself._sleep_interval = int(consts.CFG_DEF_SLEEP_INTERVAL)\n\t\tif self._update_interval == 0:\n\t\t\tself._dynamic_tuning = False\n\t\telif self._update_interval < self._sleep_interval:\n\t\t\tself._update_interval = self._sleep_interval\n\t\tself._sleep_cycles = self._update_interval // self._sleep_interval\n\t\tlog.info(\"using sleep interval of %d second(s)\" % self._sleep_interval)\n\t\tif self._dynamic_tuning:\n\t\t\tlog.info(\"dynamic tuning is enabled (can be overriden by plugins)\")\n\t\t\tlog.info(\"using update interval of %d second(s) (%d times of the sleep interval)\" % (self._sleep_cycles * self._sleep_interval, self._sleep_cycles))\n\n\t\tself._unit_manager = unit_manager\n\t\tself._profile_loader = profile_loader\n\t\tself._init_threads()\n\t\ttry:\n\t\t\tself._init_profile(profile_name)\n\t\texcept TunedException as e:\n\t\t\tlog.error(\"Cannot set initial profile. No tunings will be enabled!\")\n\n\tdef _init_threads(self):\n\t\tself._thread = None\n\t\tself._terminate = threading.Event()\n\n\tdef _init_profile(self, profile_name):\n\t\tif profile_name is None:\n\t\t\tprofile_name = self._get_active_profile()\n\n\t\tself._profile = None\n\t\tself.set_profile(profile_name)\n\n\tdef set_profile(self, profile_name, save_instantly=False):\n\t\tif self.is_running():\n\t\t\traise TunedException(\"Cannot set profile while the daemon is running.\")\n\n\t\tif profile_name == \"\" or profile_name is None:\n\t\t\tself._profile = None\n\t\telse:\n\t\t\ttry:\n\t\t\t\tself._profile = self._profile_loader.load(profile_name)\n\t\t\texcept:\n\t\t\t\traise TunedException(\"Cannot load profile '%s'.\" % profile_name)\n\n\t\tif save_instantly:\n\t\t\tif profile_name is None:\n\t\t\t\tprofile_name = \"\"\n\t\t\tself._save_active_profile(profile_name)\n\n\t@property\n\tdef profile(self):\n\t\treturn self._profile\n\n\t@property\n\tdef profile_loader(self):\n\t\treturn self._profile_loader\n\n\tdef _thread_code(self):\n\t\tif self._profile is None:\n\t\t\traise TunedException(\"Cannot start the daemon without setting a profile.\")\n\n\t\tself._unit_manager.create(self._profile.units)\n\t\tself._save_active_profile(self._profile.name)\n\t\tself._unit_manager.start_tuning()\n\n\t\t# In python 2 interpreter with applied patch for rhbz#917709 we need to periodically\n\t\t# poll, otherwise the python will not have chance to update events / locks (due to GIL)\n\t\t# and e.g. DBus control will not work. The polling interval of 1 seconds (which is\n\t\t# the default) is still much better than 50 ms polling with unpatched interpreter.\n\t\t# For more details see tuned rhbz#917587.\n\t\t_sleep_cnt = self._sleep_cycles\n\t\twhile not tuned.utils.commands.wait(self._terminate, self._sleep_interval):\n\t\t\tif self._dynamic_tuning:\n\t\t\t\t_sleep_cnt -= 1\n\t\t\t\tif _sleep_cnt <= 0:\n\t\t\t\t\t_sleep_cnt = self._sleep_cycles\n\t\t\t\t\tlog.debug(\"updating monitors\")\n\t\t\t\t\tself._unit_manager.update_monitors()\n\t\t\t\t\tlog.debug(\"performing tunings\")\n\t\t\t\t\tself._unit_manager.update_tuning()\n\n\t\tself._unit_manager.stop_tuning()\n\t\tself._unit_manager.destroy_all()\n\n\tdef _save_active_profile(self, profile_name):\n\t\ttry:\n\t\t\twith open(consts.ACTIVE_PROFILE_FILE, \"w\") as f:\n\t\t\t\tf.write(profile_name)\n\t\texcept (OSError,IOError) as e:\n\t\t\tlog.error(\"Cannot write active profile into %s: %s\" % (consts.ACTIVE_PROFILE_FILE, str(e)))\n\n\tdef _get_active_profile(self):\n\t\ttry:\n\t\t\twith open(consts.ACTIVE_PROFILE_FILE, \"r\") as f:\n\t\t\t\treturn f.read().strip()\n\t\texcept (OSError, IOError, EOFError) as e:\n\t\t\tlog.error(\"Cannot read active profile, setting default.\")\n\t\t\treturn consts.DEFAULT_PROFILE\n\n\tdef is_enabled(self):\n\t\treturn self._profile is not None\n\n\tdef is_running(self):\n\t\treturn self._thread is not None and self._thread.is_alive()\n\n\tdef start(self):\n\t\tif self.is_running():\n\t\t\treturn False\n\n\t\tif self._profile is None:\n\t\t\treturn False\n\n\t\tlog.info(\"starting tuning\")\n\t\tself._thread = threading.Thread(target=self._thread_code)\n\t\tself._terminate.clear()\n\t\tself._thread.start()\n\t\treturn True\n\n\tdef stop(self):\n\t\tif not self.is_running():\n\t\t\treturn False\n\t\tlog.info(\"stopping tunning\")\n\t\tself._terminate.set()\n\t\tself._thread.join()\n\t\tself._thread = None\n\n\t\treturn True\n","repo_name":"mstana/tuned","sub_path":"tuned/daemon/daemon.py","file_name":"daemon.py","file_ext":"py","file_size_in_byte":4772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"43692477904","text":"import sys\n\nsys.path.append('../../')\n\nimport os\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms\n\nimport numpy as np\nimport random\nfrom PIL import Image\n\nfrom models import resnet\nfrom efficientnet_pytorch import EfficientNet as ef_net\nfrom efficientnet_pytorch.utils import efficientnet\n\n\ndef get_model(model_name, channels_num, classes_num=2, image_size=64):\n # resnext mode\n if 'resnext_50' in model_name:\n model = resnet.resnext50_32x4d(in_chs=channels_num, classes_num=classes_num)\n elif 'resnext_101' in model_name:\n model = resnet.resnext101_32x8d(in_chs=channels_num, classes_num=classes_num)\n\n # resnet mode\n elif 'resnet_34' in model_name:\n model = resnet.resnet34(in_chs=channels_num, classes_num=classes_num)\n elif 'resnet_50' in model_name:\n model = resnet.resnet50(in_chs=channels_num, classes_num=classes_num)\n elif 'resnet_101' in model_name:\n model = resnet.resnet101(in_chs=channels_num, classes_num=classes_num)\n\n elif 'wide_resnet50_2' in model_name:\n model = resnet.wide_resnet50_2(in_chs=channels_num, classes_num=classes_num)\n\n # efficientnet mode\n elif 'efficientnet_b4' in model_name:\n model = ef_net.from_name('efficientnet-b4', channels_num, image_size=image_size, num_classes=classes_num)\n elif 'efficientnet_b5' in model_name:\n model = ef_net.from_name('efficientnet-b5', channels_num, image_size=image_size, num_classes=classes_num)\n elif 'efficientnet_b7' in model_name:\n model = ef_net.from_name('efficientnet-b7', channels_num, image_size=image_size, num_classes=classes_num)\n else:\n # _, global_params = efficientnet(image_size=image_size, num_classes=classes_num)\n model = ef_net.from_name('efficientnet-b0', channels_num, image_size=image_size, num_classes=classes_num)\n return model\n\n\ndef open_file(txt_path):\n txt = open(txt_path)\n lines = txt.readlines()\n lines = [i.strip() for i in lines]\n txt.close()\n return lines\n\n\ndef combine_channels(channels_dir):\n image_lst = os.listdir(channels_dir)\n image_lst.sort()\n\n image = []\n for i in image_lst:\n channel = Image.open(os.path.join(channels_dir, i))\n channel = np.asarray(channel, dtype=np.float64)\n image.append(channel)\n return np.asarray(image, dtype=np.float64)\n\n\nclass ClassifiDataset(Dataset):\n def __init__(self, cfg):\n self.cfg = cfg\n self.data_dir = cfg['data_dir']\n self.is_trans = cfg['is_trans']\n self.txt_dir = cfg['txt_dir']\n self.data, self.label = self.read_txt()\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, index):\n image = self.data[index]\n label = self.label[index]\n\n label = np.asarray(label, dtype=np.float32)\n label = torch.LongTensor(label)\n\n if self.is_trans:\n image = self.trans_channel(image)\n\n sample = [image, label]\n return sample\n\n def read_txt(self):\n images_list = open_file(self.txt_dir)\n x = []\n y = []\n for i in images_list:\n image_label = i.split(' ')\n data_path = os.path.join(self.data_dir, image_label[0])\n data = combine_channels(data_path)\n x.append(data)\n y.append(image_label[1])\n return x, y\n\n def trans_channel(self, arr):\n image = arr\n rand = random.randint(0, 1)\n image_new = []\n\n trans = transforms.Compose([\n transforms.Resize((self.cfg['image_size'][0], self.cfg['image_size'][1])),\n # transforms.RandomHorizontalFlip(rand),\n # transforms.RandomVerticalFlip(1 - rand),\n ])\n for i in range(len(image)):\n x = Image.fromarray(np.float64(image[i]))\n trans_x = trans(x)\n trans_x = np.asarray(trans_x, dtype=np.float32)\n image_new.append(trans_x)\n\n image_new = np.asarray(image_new, dtype=np.float32)\n image_new = torch.from_numpy(image_new)\n normalize_trans = transforms.Normalize(self.cfg['mean'], self.cfg['std'])\n image_new = normalize_trans(image_new)\n return image_new\n","repo_name":"freak-junhao/igarss2021_dse","sub_path":"tools/classifi_mode/classifi_datasets.py","file_name":"classifi_datasets.py","file_ext":"py","file_size_in_byte":4211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"22193387102","text":"#!/usr/bin/env python3\nprint('欢迎使用余额宝收益计算器')\njinge = float(input('请输入本金:'))\nlilv = float(input('请输入年化利率:'))\nqishu = float(input('请输入天数:'))\nbenli = 0\nxunhuan = 1\nwhile xunhuan <= qishu:\n\tbenli = jinge + jinge * lilv * (qishu/365) \n\tprint('第{}天, {:.2f}'.format(xunhuan, benli))\n\tjinge = benli\n\txunhuan += 1\n","repo_name":"lsaac128/shiyanlou-code","sub_path":"investment.py","file_name":"investment.py","file_ext":"py","file_size_in_byte":367,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"1807539608","text":"\"\"\"\nCorresponds to Figure 2 in the paper.\n\nYou most likely need to tinker with linear_structure.py first to get the raw\ndata for this experiment.\n\"\"\"\nimport torch, pickle\nimport numpy as np\nimport sklearn.metrics\nimport sklearn.linear_model\nimport sklearn.preprocessing\nimport tqdm\n\ndef yield_items(layer=12):\n\tlayer = layer -1\n\tdef yield_source_target_pairs():\n\t\ttry:\n\t\t\twith open('ff_sole.pkl', 'rb') as istr:\n\t\t\t\twhile True:\n\t\t\t\t\tsrc, tgt = pickle.load(istr), pickle.load(istr)\n\t\t\t\t\tyield src, tgt\n\t\texcept EOFError:\n\t\t\tpass\n\texamples_stream = yield_source_target_pairs()\n\texamples_stream = enumerate(examples_stream)\n\texamples_stream = filter(lambda p: (p[0] % 12) == layer, examples_stream)\n\texamples_stream = map(lambda p: (p[1][0].squeeze(0).numpy(), p[1][1].squeeze(0).numpy()), examples_stream)\n\tyield from examples_stream\n\nfor run in range(1, 2):\n\tfor layer in range(1, 13):\n\t\tall_src, all_tgt = [], []\n\t\tfor a, b in tqdm.tqdm(yield_items(layer=layer), desc=f'read (layer={layer})', total=10_000):\n\t\t\tall_src.append(a)\n\t\t\tall_tgt.append(b)\n\n\t\tall_src = np.vstack(all_src)\n\t\tall_tgt = np.vstack(all_tgt)\n\n\t\tall_src = sklearn.preprocessing.StandardScaler().fit_transform(all_src)\n\t\tall_tgt = sklearn.preprocessing.StandardScaler().fit_transform(all_tgt)\n\n\t\treg = sklearn.linear_model.LinearRegression(n_jobs=-1)\n\t\treg.fit(all_src, all_tgt)\n\n\t\tprint(run, layer, reg.score(all_src, all_tgt))\n","repo_name":"TimotheeMickus/bert-splat","sub_path":"learn_ff_lmap.py","file_name":"learn_ff_lmap.py","file_ext":"py","file_size_in_byte":1398,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"} +{"seq_id":"31019479023","text":"# -*- coding: utf-8 -*-\n# -*- coding: utf-8 -*-\n'''The Possibilistic Policy.\n Reference: [Miguel Martin].'''\n\n__author__ = \"Miguel Martin\"\n__version__ = \"1.0\"\n\n\nfrom math import sqrt, log, exp\nimport random as rand\n\n\nfrom IndexPolicy import IndexPolicy\n\nclass TS_generalized(IndexPolicy):\n \"\"\"Class that implements the UCB-V policy.\n \"\"\"\n\n def __init__(self, nbArms, amplitude=1., lower=0., scale=1):\n self.nbArms = nbArms\n self.factor = amplitude\n self.amplitude = 1\n self.lower = lower\n self.nbDraws = dict()\n self.cumReward = dict()\n self.cumReward2 = dict()\n self.scale = scale\n\n def startGame(self):\n self.t = 1\n for arm in range(self.nbArms):\n self.nbDraws[arm] = 0\n self.cumReward[arm] = 0.0\n self.cumReward2[arm] = 0.0\n\n def fuzzyTransformed(self, x, arm):\n m1 = self.cumReward[arm]/self.nbDraws[arm]\n m = m1/self.factor\n s = self.nbDraws[arm]\n if (x >= self.amplitude) or (x <= 0):\n return 0\n else:\n relative_entropy_a = m*log((m)/x) if m/x > 0 else 0\n relative_entropy_b = (1-m)*log((1-m)/(1-x)) if (1-m)/(1-x) > 0 else 0\n relative_entropy = relative_entropy_a + relative_entropy_b\n # return min([1, 2*exp(-s*self.scale*relative_entropy)])\n try:\n return exp(-s*self.scale*relative_entropy)\n except OverflowError:\n return float('inf')\n\n def fuzzy(self, x, arm):\n m1 = self.cumReward[arm]/self.nbDraws[arm]\n m = m1/self.factor\n s = self.nbDraws[arm]\n return exp(-2*self.scale*s*((m-x)/self.amplitude)**2)\n\n def computeIndex(self, arm):\n if self.nbDraws[arm] < 1:\n return rand.random()*self.amplitude + self.lower\n else:\n mu1 = self.cumReward[arm]/self.nbDraws[arm]\n mu = mu1/self.factor\n s = self.nbDraws[arm]\n a = mu*s\n b = s - a\n bet = rand.betavariate(1+a, 1+b)\n sigma = self.amplitude/sqrt(4*s*self.scale)\n #r1 = mu+sigma*rand.gauss(0,1)\n #r2 = rand.random()\n #while r2 > self.fuzzyTransformed(r1, arm)/self.fuzzy(r1,arm):\n # r1 = mu+sigma*rand.gauss(0,1)\n # r2 = rand.random()\n return bet\n\n\n def getReward(self, arm, reward):\n self.nbDraws[arm] += 1\n self.cumReward[arm] += float(random() < reward)\n self.cumReward2[arm] += self.cumReward[arm]**2\n self.t += 1\n\n","repo_name":"mmartinb75/pyBandits","sub_path":"policy/TS_generalized.py","file_name":"TS_generalized.py","file_ext":"py","file_size_in_byte":2574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"3762755348","text":"from firebase import firebase\n\n\nmyDB = firebase.FirebaseApplication('https://movies-database-95d35-default-rtdb.europe-west1.firebasedatabase.app/', None)\nallRecords= myDB.get('/movies/', None)\nratingVal=0\nfor key in allRecords:\n ratingVal += float(allRecords[key]['Rating'])\n \naverageRating = (ratingVal) / len(allRecords)\nprint(averageRating)\n \n \n \n\ncounter = 0\nfor counter in range(3):\n movieName = input(\"enter movie name: \")\n rating = int(input(\"enter rating 1-5: \"))\n movie = {\"Movie Name\" : movieName,\n \"Rating\" : rating}\n myDBConn.post ('/movies/', movie)\n counter +=1\n\n\n\n \n \n\n\n","repo_name":"mrroberts-mslt/Computer-Science-for-Leaving-Certificate-Solutions","sub_path":"Chapter11/FirebasePython/movieRating.py","file_name":"movieRating.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"81"} +{"seq_id":"40910065471","text":"from django import forms\n\nclass LocationWidget(forms.widgets.Widget):\n \"\"\"Forms widget to represent a location.\n \n Uses Google Maps API to represent a location on a map with a marker.\n \"\"\"\n def __init__(self, *args, **kwargs):\n super(LocationWidget, self).__init__(*args, **kwargs)\n \n def render(self, name, value, attrs):\n if not value:\n lat, lon = (0,0,)\n else:\n lat, lon = value.split(',')\n\n html = []\n if attrs.get('help_text') is not None:\n html.append('

' + attrs['help_text'] + '

')\n html.append(\"\"\"
\n \n
\n \n \n \"\"\" % {'name': name, 'value':value,\n 'height':self.attrs.get('height', '400px'),\n 'width':self.attrs.get('width', '400px'),\n 'lat': lat, 'lon': lon,\n 'marker_text':self.attrs.get('marker_text', 'Drag the marker to the desired location')})\n return ''.join(html)\n\nclass LocationField(forms.Field):\n \"\"\"This form field is used to obtain a latitude and longitude coordinate\n from a Google Map.\n \"\"\"\n widget = LocationWidget\n \n def __init__(self, *args, **kwargs):\n super(LocationField, self).__init__(*args, **kwargs)\n \n def to_python(self, value):\n if not value:\n return None\n else:\n return {'latitude': self.__parse_latitude(value),\n 'longitude': self.__parse_longitude(value)}\n \n def __to_micro_coordinate(self, coord):\n \"\"\"Only works on cleaned data.\"\"\"\n if not coord:\n return None\n return int(float(coord) * 1000000)\n \n def validate(self, value):\n super(LocationField, self).validate(value)\n if type(value) is dict:\n self.__validate_as_dict(value)\n else:\n self.__validate_as_dict({'latitude':self.__parse_latitude(value),\n 'longitude':self.__parse_longitude(value)})\n \n def __validate_as_dict(self, value):\n if not (value['latitude'] and value['longitude']):\n raise forms.ValidationError('Missing at least one coordinate')\n if value['latitude'] > 90.000000 or value['latitude'] < -90.000000:\n raise forms.ValidationError('Latitude out of range')\n if value['longitude'] > 180.000000 or value['longitude'] < -180.000000:\n raise forms.ValidationError('Longitude out of range')\n \n def __parse_latitude(self, value):\n return float(value.split(',')[0])\n \n def __parse_longitude(self, value):\n try:\n return float(value.split(',')[1])\n except IndexError:\n return None\n","repo_name":"dmkelly/Django-Location-Form-Field","sub_path":"fields.py","file_name":"fields.py","file_ext":"py","file_size_in_byte":4316,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"19845286107","text":"import os\nfrom datetime import datetime\n\n\ndef get_path(filename, username, type_='PROFILE_PHOTO'): # throws Exception 'field|message'\n TYPES = ['IMG', 'PROFILE_PHOTO']\n\n if type_ not in TYPES:\n raise Exception('type|unknown type')\n\n file = filename.split('.')\n\n if type_ == 'PROFILE_PHOTO':\n return os.path.join('users', f'{username}', f'{datetime.now()}-{file[0]}.{file[-1]}')\n\n if type_ == 'IMG':\n return os.path.join('images', f'{username}', f'{file[0]}.{file[-1]}')\n","repo_name":"jacksonet00/demo-stack","sub_path":"v2/app/cloud/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"39047845895","text":"import datetime, os\n\ndef logger(path):\n\n file_path = os.path.join(path, 'log.txt')\n info = {}\n \n def decorator(old_func):\n def new_function(*args, **kwargs):\n\n nonlocal info\n date_info = datetime.datetime.today().strftime('%d-%m-%Y, %I:%M')\n result = old_func(*args, **kwargs)\n info['function_name'] = old_func.__name__\n info['args'] = args\n info['date'] = date_info\n info['result'] = result\n with open(file_path, 'a', encoding='utf8') as fo:\n fo.write(str(info)+'\\n')\n print(info)\n return result\n\n return new_function\n\n return decorator\n\n@logger('C:\\\\Homework\\\\log\\\\')\ndef foo(a, b):\n return a * a + b\n\nif __name__ == '__main__':\n print(foo(12, 151))","repo_name":"RTS-1989/adpy-13_hw_4","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"36560774115","text":"from unittest import mock\n\nimport pytest\n\nfrom app import create_app, UserSignUpError, ServiceUnavailableError\nfrom app.extensions import db\nfrom app.schemas import SignUpUserSchema\nfrom app.services import get_country_from_ip, signup_user\nfrom app.models import User\n\n\n@pytest.fixture\ndef client():\n app = create_app(config_object=\"app.config.TestingConfig\")\n with app.test_client() as client:\n with app.app_context():\n db.drop_all()\n db.create_all()\n yield client\n db.session.remove()\n\n\n@mock.patch(\"app.services.subscribe_user\")\ndef test_should_successfully_create_user_if_subscribe_is_successful(subscribe_user, client):\n # when\n mock_response = subscribe_user.return_value\n mock_response.status_code = 201\n\n user_data = SignUpUserSchema().load({\n \"email\": \"test@test.com\",\n \"password\": \"test\"\n })\n user_metadata = {\n \"device\": \"ios\",\n \"country\": \"US\"\n }\n # do\n signup_user(user_data, user_metadata)\n\n # then\n user = User.query.filter_by(email=user_data[\"email\"]).first()\n assert user is not None\n assert user.id is not None\n\n\n@mock.patch(\"app.services.subscribe_user\")\ndef test_should_rise_an_exception_if_subscribe_is_fail(subscribe_user, client):\n # when\n mock_response = subscribe_user.return_value\n mock_response.status_code = 500\n\n user_data = SignUpUserSchema().load({\n \"email\": \"test@test.com\",\n \"password\": \"test\"\n })\n user_metadata = {\n \"device\": \"ios\",\n \"country\": \"US\"\n }\n\n # then\n with pytest.raises(UserSignUpError):\n signup_user(user_data, user_metadata)\n\n user = User.query.filter_by(email=user_data[\"email\"]).first()\n assert user is None\n\n\n@mock.patch(\"app.services.subscribe_user\")\ndef test_should_rollback_user_if_subscription_service_unavailable(subscribe_user, client):\n # when\n subscribe_user.side_effect = ServiceUnavailableError()\n\n user_data = SignUpUserSchema().load({\n \"email\": \"test@test.com\",\n \"password\": \"test\"\n })\n user_metadata = {\n \"device\": \"ios\",\n \"country\": \"US\"\n }\n\n # then\n with pytest.raises(ServiceUnavailableError):\n signup_user(user_data, user_metadata)\n\n user = User.query.filter_by(email=user_data[\"email\"]).first()\n assert user is None\n\n\ndef test_should_successfully_get_country_from_ip():\n # when\n ip = \"8.8.8.8\"\n # do\n country = get_country_from_ip(ip)\n # then\n assert country == \"US\"\n\n\ndef test_should_response_with_unknown_if_ip_not_found():\n # when\n ip = \"8.8.8.888\"\n # do\n country = get_country_from_ip(ip)\n # then\n assert country == \"Unknown\"\n\n\n\n\n","repo_name":"zorhay/news-feed-microservices","sub_path":"client-service/tests/test_services.py","file_name":"test_services.py","file_ext":"py","file_size_in_byte":2697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"5185695326","text":"import requests, json\n\n# def api key here (locally)\nkey = 'DIgcyfb87KQN1S6R72385xkxjNWAPAxWpIZcfoTz'\n\n#endpoint address here\nendpoint = 'https://api.data.gov:443/regulations/v3/document.json?api_key=DIgcyfb87KQN1S6R72385xkxjNWAPAxWpIZcfoTz&documentId=APHIS-2018-0034-'\n\n#build docketCommentList, short list for now\ncommentNum = 6191\n\n# documentId format APHIS-2018-####-####\n # numbers are padded on the left\ndocketCommentList = [str(number).zfill(4) for number in range(1,commentNum+1)]\n\n# \nfor comment in docketCommentList:\n url = ''\n url = endpoint + comment\n #r = requests.get(url = endpoint)\n data = r.json()\n parsed = json.loads(data)\n print(data)\n","repo_name":"daea/regulations_gov_data_requests","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"20880605238","text":"#!/usr/bin/env python\n# @File: genemede/io.py\n# @Author: Niccolo' Bonacchi (@nbonacchi)\n# @Date: Tuesday, April 18th 2023, 9:54:41 am\n\n# Implement generic CRUD functions for *.gnmd files\n\nimport json\nimport shutil\nimport typing as t\nfrom datetime import datetime\nfrom pathlib import Path\n\n# TODO: Consider adding a .gnmd folder to user home directory to store genemede files and backups\n\n\ndef create(fpath: t.Union[str, Path], data: list[dict]) -> None:\n \"\"\"\n Creates a new file with the given file path and writes the given data to it in JSON format.\n\n Args:\n fpath (Union[str, Path]): The path of the file to create.\n data (list[dict]): The data to write to the file in JSON format.\n\n Raises:\n FileExistsError: If the file already exists at the given path.\n\n Returns:\n None\n \"\"\"\n fpath = Path(fpath)\n # If fpath is folder, ask for name\n if fpath.is_dir():\n print(f\"Warning: {fpath} is folder -> creating a new file with same name\")\n fpath.joinpath(fpath.name + \".gnmd\").mkdir(parents=True, exist_ok=True)\n\n # If fpath extension is not 'gnmd', change it to gnmd\n if fpath.suffix != \".gnmd\":\n print(f\"Warning: {fpath} extension is not '.gnmd' -> changing extension to '.gnmd'\")\n fpath = fpath.with_suffix(\".gnmd\")\n\n if fpath.exists():\n raise FileExistsError(f\"{fpath} already exists\")\n return\n\n with open(fpath, \"w\") as f:\n json.dump(data, f, indent=4)\n\n\ndef read(fpath: t.Union[str, Path]) -> ...:\n \"\"\"\n Read a JSON file and return its contents as a Python object.\n\n Args:\n fpath: A string or Path object representing the path of the file to read.\n\n Returns:\n The Python object resulting from loading the JSON file's contents.\n\n Raises:\n FileNotFoundError: If the specified file does not exist.\n JSONDecodeError: If the file does not contain valid JSON data.\n UnicodeDecodeError: If the file is not encoded in UTF-8.\n \"\"\"\n with open(fpath, \"r\") as f:\n return json.load(f)\n\n\ndef backup(fpath: t.Union[str, Path]) -> None:\n \"\"\"Backup fpath by copying it and appending _bak_datetime\n\n This function creates a backup of a file by copying it and appending the\n current date and time to its name. The backup file is saved in the same\n directory as the original file.\n\n Args:\n fpath (str or Path): The path to the file to be backed up.\n\n Returns:\n None\n \"\"\"\n fpath = Path(fpath)\n fpath_bak = fpath.parent.joinpath(\n fpath.stem + \"_bak_\" + datetime.now().strftime(\"%Y-%m-%dT%H_%M_%S.%f\") + fpath.suffix\n )\n fpath_bak.parent.mkdir(parents=True, exist_ok=True)\n shutil.copy(fpath, fpath_bak)\n\n\ndef update(fpath: t.Union[Path, str], data):\n \"\"\"\n Update the file at the given path with the given data, creating a backup\n first.\n\n Args:\n fpath: the path of the file to update\n data: the data to write to the file\n\n Raises:\n FileNotFoundError: if the file at the given path does not exist\n\n Returns:\n None\n \"\"\"\n if not fpath.exists():\n raise FileNotFoundError(f\"{fpath} does not exist\")\n return\n delete(fpath)\n create(fpath, data)\n\n\ndef delete(fpath: t.Union[str, Path]) -> None:\n \"\"\"\n Delete the file at the given path, after making a backup copy.\n\n Args:\n fpath (Union[str, Path]): The path to the file to delete.\n\n Raises:\n FileNotFoundError: If the file does not exist at the given path.\n\n Returns:\n None\n \"\"\"\n fpath = Path(fpath)\n if not fpath.exists():\n raise FileNotFoundError(f\"{fpath} does not exist\")\n return\n backup(fpath)\n fpath.unlink()\n","repo_name":"genemede/genemede","sub_path":"genemede/io.py","file_name":"io.py","file_ext":"py","file_size_in_byte":3710,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"} +{"seq_id":"73024573385","text":"import sublime\nimport sublime_plugin\nimport subprocess\nimport os\nimport time\n\nsublime_version = 2\n\nif not sublime.version() or int(sublime.version()) > 3000:\n sublime_version = 3\n\nclass CaptureSelectionCommand(sublime_plugin.WindowCommand):\n \"\"\"show an input panel to ask for filename, capture image, save it and insert at cursor\"\"\"\n def run(self):\n self.window.show_input_panel(\"file name for capture\", \"\", self.save_file, None, None)\n \n def save_file(self, file_name):\n \"\"\"capture image from screen and save to given filename\"\"\"\n file_extension = 'png'\n \n # use timestamp as filename if no is given\n if not file_name:\n file_name = str(int(time.time()))\n \n # add extension\n full_file_name = file_name + '.' + file_extension\n \n # get currently active view\n active_view = self.window.active_view()\n \n # extract current directory from file in active view\n curr_dir, curr_filename = os.path.split(active_view.file_name())\n \n # contruct image save path\n image_path = os.path.join(curr_dir, full_file_name)\n \n # screen capture and save file\n subprocess.check_call('screencapture -s ' + image_path, shell=True)\n \n # insert filename at cursor\n active_view.run_command(\"capture_selection_second\", { \"argument\" : full_file_name});\n\n\nclass CaptureSelectionSecondCommand(sublime_plugin.TextCommand):\n \"\"\"insert filename at current cursor position\"\"\"\n def run(self, edit, argument):\n sel = self.view.sel()\n self.view.insert(edit, sel[0].begin(), \"\\\"\" + argument + \"\\\"\")\n\ndef plugin_loaded():\n pass\n\n\nif sublime_version == 2:\n plugin_loaded()\n","repo_name":"321hendrik/SikuliTools","sub_path":"sikuli_tools.py","file_name":"sikuli_tools.py","file_ext":"py","file_size_in_byte":1750,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"} +{"seq_id":"21130156707","text":"#!/usr/bin/python3.4\n\n# import\nimport time\nimport subprocess\n\n# from\nfrom sys import exit\n\n\nclass base:\n '''\n A base class with generic methods\n '''\n\n def __init__(self):\n self.tempThreshold = 40.0\n self.plinus = 5.0 # plus or minus tempThreshold\n self.sup_formats = [\"time\", \"localtime\", \"asctime\"]\n self.format = self.sup_formats[0]\n\n def getTempThreshold(self):\n return self.tempThreshold\n\n def getPlinus(self):\n return self.plinus\n\n def getCPUtemperature(self):\n '''\n Return CPU temperature as a float\n '''\n temp = subprocess.Popen(\n [\"vcgencmd\", \"measure_temp\"], stdout=subprocess.PIPE)\n self.res = temp.communicate()[0].decode(encoding='ISO-8859-1')\n return(float(self.res.replace(\"temp=\", \"\").replace(\"'C\\n\", \"\")))\n\n def currentTime(self, format=None):\n '''\n Returns current time in asc format\n Supported formats: time, localtime, asctime\n '''\n if format is None:\n format = self.format\n try:\n assert type(format) is str and (format in self.sup_formats)\n return str(getattr(time, format)())\n except Exception as e:\n print (str(e) + self.timeUsage())\n exit(0)\n\n def timeUsage(self):\n '''\n Prints the usage information to use currentTIme function\n '''\n usage = \"\"\n usage += \"Supported formats: \"\n for item in self.sup_formats:\n usage += item + \", \"\n return usage\n","repo_name":"kamalteja/gpio","sub_path":"common/generic.py","file_name":"generic.py","file_ext":"py","file_size_in_byte":1554,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"13252666558","text":"import random\nfrom element import Cell, Builder\n\n\ndef set_start_point(field, wholeSize):\n while True:\n try:\n numOfUnit = int(input('set number of unit (1 ~ 9) >> ')) + 1\n if 1 <= numOfUnit <= 10:\n break\n else:\n print('please input number in 1 ~ 9')\n except ValueError:\n print('please input number')\n\n while True:\n startPoint = []\n startPointUnique = []\n\n for i in range(numOfUnit):\n startPoint.append([random.randint(1, wholeSize - 2), random.randint(1, wholeSize - 2)])\n\n #check overlap\n for point in startPoint:\n if point not in startPointUnique:\n startPointUnique.append(point)\n\n if len(startPointUnique) == numOfUnit:\n for i in range(numOfUnit - 1):\n field[startPoint[i][1]][startPoint[i][0]] = Cell(startPoint[i][0], startPoint[i][1], i)\n field[startPoint[numOfUnit - 1][1]][startPoint[numOfUnit - 1][0]] = Builder(startPoint[numOfUnit - 1][0], startPoint[numOfUnit - 1][1], 9)\n break\n\n","repo_name":"nishio-kun/cell-automaton","sub_path":"lifegame/initial_value.py","file_name":"initial_value.py","file_ext":"py","file_size_in_byte":1112,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"27205222946","text":"import seaborn as sns\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split, GridSearchCV\nfrom sklearn.preprocessing import OneHotEncoder, LabelEncoder, StandardScaler\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nimport neptune.new as neptune\nimport os\nfrom neptune.new.integrations.tensorflow_keras import NeptuneCallback\n\n\"\"\"\nThis project comes from the book Deep Learning with Python (second edition) by F. Chollet\n\"\"\"\n\nrun = neptune.init(\n project=os.environ['NEPTUNE_PROJECT_KEY'],\n api_token=os.environ['NEPTUNE_API_TOKEN'],\n tags=['deep_learning_iris']\n)\nneptune_cbk = NeptuneCallback(run=run, base_namespace='metrics')\n\n# loading data\niris = sns.load_dataset('iris')\ndf = pd.DataFrame(iris)\n\n# reshaping the data\nlbl_clf = LabelEncoder()\nY = df['species']\nX = df.drop(['species'], axis=1)\nY_encoded = lbl_clf.fit_transform(Y)\nY_final = keras.utils.to_categorical(Y_encoded)\n\n# splitting the data\nx_train, x_test, y_train, y_test = train_test_split(X, Y_final, test_size=0.25,\n random_state=0, stratify=Y_encoded, shuffle=True)\n\n# standarizing the dataset\nstd_clf = StandardScaler()\nx_train_new = std_clf.fit_transform(x_train)\nx_test_new = std_clf.transform(x_test)\n\n# model definition\nmodel = keras.Sequential([\n layers.Dense(512, activation=\"relu\"),\n layers.Dense(3, activation=\"softmax\")\n])\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n\n# model fitting\nparams = {\n 'epochs': 50,\n 'batch_size': 7\n}\niris_model = model.fit(x_train_new, y_train, callbacks=[neptune_cbk], **params)\n\n# logging data\nrun['hyper-parameters'] = params\nrun.stop()","repo_name":"pdabrowskitumanski/Eutopia_ML_excercise","sub_path":"04_deep_learning_iris.py","file_name":"04_deep_learning_iris.py","file_ext":"py","file_size_in_byte":1701,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"72050285065","text":"import pandas\r\n\r\ncount_data={\"Gray\":[0],\"Cinnamon\":[0],\"Black\":[0],\"Other\":[0]}\r\ncsv_data=pandas.DataFrame(count_data)\r\nsquirrel_data=pandas.read_csv(\"2018_Central_Park_Squirrel_Census_-_Squirrel_Data.csv\")\r\nfor i in squirrel_data[\"Primary Fur Color\"]:\r\n if i == \"Gray\":\r\n # count_data[\"Gray\"][0]+=1\r\n csv_data.Gray[0]+=1\r\n elif i == \"Cinnamon\":\r\n # count_data[\"Cinnamon\"][0]+=1\r\n csv_data.Cinnamon[0]+=1\r\n elif i == \"Black\":\r\n # count_data[\"Black\"][0]+=1\r\n csv_data.Black[0]+=1\r\n else :\r\n # count_data[\"Other\"][0]+=1\r\n csv_data.Other[0]+=1\r\n\r\ncsv_data.to_csv(\"squirrel_color.csv\")","repo_name":"Ferdeno/Pythonprojects","sub_path":"Day 25 squirrel us states game/squirrel/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"10280119718","text":"from dateutil.relativedelta import relativedelta\n\nfrom odoo import fields\nfrom odoo.tests import SavepointCase\nfrom odoo.tools.misc import mute_logger\n\nfrom odoo.addons.website.tools import MockRequest\n\nfrom ..controllers.main import WebsiteEvent\n\n\nclass TestFreeText(SavepointCase):\n @classmethod\n def setUpClass(cls):\n super().setUpClass()\n now = fields.Datetime.now()\n cls.user_demo = cls.env.ref(\"base.user_demo\")\n cls.website_event_controller = WebsiteEvent()\n cls.website = cls.env[\"website\"].browse(1)\n cls.event_question_free_text = \"Free Text\"\n cls.event_1 = cls.env[\"event.event\"].create(\n {\n \"name\": \"Event One\",\n \"user_id\": cls.env.ref(\"base.user_admin\").id,\n \"date_begin\": now + relativedelta(days=1),\n \"date_end\": now + relativedelta(days=3),\n \"organizer_id\": cls.env.ref(\"base.res_partner_1\").id,\n \"event_type_id\": cls.env.ref(\"event.event_type_1\").id,\n \"website_published\": True,\n \"description\": \"Test\",\n \"auto_confirm\": True,\n \"website_id\": cls.website.id,\n \"question_ids\": [(0, 0, {\"title\": \"Question One\", \"free_text\": True})],\n }\n )\n\n def test_free_text_answer(self):\n self.confirm(self.event_1)\n self.assertEqual(\n self.event_1.registration_ids.free_answer_ids.answer,\n self.event_question_free_text,\n )\n\n def test_free_text_display_name(self):\n self.confirm(self.event_1)\n display_name = \"{}: {}\".format(\n self.event_1.question_ids.title, self.event_question_free_text\n )\n self.assertEqual(\n self.event_1.registration_ids.free_answer_ids.display_name, display_name\n )\n\n # HACK https://github.com/odoo/odoo/issues/75061\n @mute_logger(\"odoo.http\")\n def confirm(self, event):\n with MockRequest(self.env(user=self.user_demo), website=self.website):\n self.website_event_controller.registration_confirm(\n event, **self._prepare_registration_confirm_values(event)\n )\n\n def _prepare_registration_confirm_values(self, event, counter=1, ticket_id=0):\n return {\n \"{}-name\".format(counter): \"Test\",\n \"{}-email\".format(counter): \"test@test.com\",\n \"{}-ticket_id\".format(counter): ticket_id,\n \"{}-answer_free_text-{}\".format(\n ticket_id, event.question_ids.id\n ): self.event_question_free_text,\n }\n","repo_name":"rtmelektronik/odootest","sub_path":"website_event_questions_free_text/tests/test_free_text.py","file_name":"test_free_text.py","file_ext":"py","file_size_in_byte":2600,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"40455428744","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[3]:\n\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\n\n# Load the dataset\ndata = pd.read_csv(\"Sales.csv\")\n\n# Extract features and target\nX = data[['TV', 'Radio', 'Newspaper']]\ny = data['Sales']\n\n# Split the data into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Linear Regression\nlinear_reg = LinearRegression()\nlinear_reg.fit(X_train, y_train)\nlinear_reg_predictions = linear_reg.predict(X_test)\n\n# Visualize future sales predictions for TV\nplt.figure(figsize=(10, 6))\nplt.scatter(X_test['TV'], y_test, color='blue', label='Actual Sales')\nplt.scatter(X_test['TV'], linear_reg_predictions, color='red', label='Predicted Sales')\nplt.title(\"Future Sales Predictions for TV Advertising\")\nplt.xlabel(\"TV Advertising Budget\")\nplt.ylabel(\"Sales\")\nplt.legend()\nplt.show()\n\n# Visualize future sales predictions for Radio\nplt.figure(figsize=(10, 6))\nplt.scatter(X_test['Radio'], y_test, color='blue', label='Actual Sales')\nplt.scatter(X_test['Radio'], linear_reg_predictions, color='red', label='Predicted Sales')\nplt.title(\"Future Sales Predictions for Radio Advertising\")\nplt.xlabel(\"Radio Advertising Budget\")\nplt.ylabel(\"Sales\")\nplt.legend()\nplt.show()\n\n# Visualize future sales predictions for Newspaper\nplt.figure(figsize=(10, 6))\nplt.scatter(X_test['Newspaper'], y_test, color='blue', label='Actual Sales')\nplt.scatter(X_test['Newspaper'], linear_reg_predictions, color='red', label='Predicted Sales')\nplt.title(\"Future Sales Predictions for Newspaper Advertising\")\nplt.xlabel(\"Newspaper Advertising Budget\")\nplt.ylabel(\"Sales\")\nplt.legend()\nplt.show()\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"TheShhy/OIBSIP","sub_path":"Sales Prediction/Sales Prediction.py","file_name":"Sales Prediction.py","file_ext":"py","file_size_in_byte":1791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"11473250390","text":"from datetime import datetime\r\nimport os,random,glob\r\n\r\nhelloIntent = \"hi\",\"hie\",\"hey\",\"hello\",\"hey bro\",\"wassup\"\r\ntimeIntent = \"time\",\"time please\",\"whats the time\",\"show me time\"\r\ndateIntent = \"date\",\"time date\",\"whats the date\",\"show me date\"\r\nchat=True\r\nwhile chat == True:\r\n msg = input(\"Enter Message\")\r\n if msg in helloIntent:\r\n print(\"Hi\")\r\n elif msg in timeIntent:\r\n dt = datetime.now()\r\n print(dt.strftime(\"%I:%M:%S,%p\"))\r\n elif msg in dateIntent:\r\n dt = datetime.now()\r\n print(dt.strftime(\"%d/%m/%Y,%a\"))\r\n elif msg==\"music\":\r\n x = glob.glob(\"*.mp3\")\r\n os.startfile(random.choice(x))\r\n \r\n elif msg ==\"bye\":\r\n print(\"Good Bye..Tc..\")\r\n chat=False\r\n else:\r\n print(\"Sorry i dont understand\")\r\n","repo_name":"SahilKr4All/PythonRegularApril2022_12to1-","sub_path":"chatbot.py","file_name":"chatbot.py","file_ext":"py","file_size_in_byte":800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"15421460140","text":"from django.contrib.auth.decorators import login_required\nfrom django.core.paginator import Paginator\nfrom django.shortcuts import get_object_or_404, redirect, render\n\nfrom .forms import CommentForm, PostForm\nfrom .models import Follow, Group, Post, User\n\n\ndef index(request):\n post_list = Post.objects.all()\n paginator = Paginator(post_list, 10)\n page_number = request.GET.get(\"page\")\n page = paginator.get_page(page_number)\n return render(request,\n \"index.html\",\n {\"page\": page, \"paginator\": paginator})\n\n\ndef group_posts(request, slug):\n group = get_object_or_404(Group, slug=slug)\n posts = group.posts.all()\n paginator = Paginator(posts, 10)\n page_number = request.GET.get(\"page\")\n page = paginator.get_page(page_number)\n\n return render(\n request,\n \"group.html\",\n {\"group\": group, \"page\": page, \"paginator\": paginator}\n )\n\n\n@login_required\ndef new_post(request):\n if not request.method == \"POST\":\n form = PostForm()\n return render(request, \"new.html\", {\"form\": form})\n form = PostForm(request.POST, files=request.FILES or None)\n if not form.is_valid():\n return render(request, \"new.html\", {\"form\": form})\n post_get = form.save(commit=False)\n post_get.author = request.user\n post_get.save()\n return redirect(\"index\")\n\n\ndef profile(request, username):\n author = get_object_or_404(User, username=username)\n posts = author.posts.all()\n paginator = Paginator(posts, 10)\n page_number = request.GET.get(\"page\")\n page = paginator.get_page(page_number)\n\n context = {\"author\": author, \"page\": page, \"paginator\": paginator,\n \"posts\": posts}\n\n if not request.user.is_anonymous:\n following = Follow.objects.filter(user=request.user,\n author=author).exists()\n context[\"following\"] = following\n\n return render(request, \"profile.html\", context)\n\n\ndef post_view(request, username, post_id):\n author = get_object_or_404(User, username=username)\n post = author.posts.get(author=author, id=post_id)\n count_post = author.posts.count()\n comments = post.comments.all()\n form = CommentForm()\n return render(\n request,\n \"post.html\",\n {\n \"post\": post,\n \"author\": author,\n \"count_post\": count_post,\n \"comments\": comments,\n \"form\": form,\n },\n )\n\n\ndef post_edit(request, username, post_id):\n post = get_object_or_404(Post, pk=post_id, author__username=username)\n if post.author != request.user:\n return redirect(\"post\", username=post.author, post_id=post.pk)\n form = PostForm(request.POST or None, files=request.FILES or None,\n instance=post)\n if form.is_valid():\n post = form.save(commit=False)\n post.author = request.user\n post.save()\n return redirect(\"post\", username=post.author, post_id=post.pk)\n return render(\n request, \"new.html\",\n {\"form\": form, \"post\": post, \"is_form_edit\": True},\n )\n\n\ndef page_not_found(request, exception):\n return render(request, \"misc/404.html\", {\"path\": request.path}, status=404)\n\n\ndef server_error(request):\n return render(request, \"misc/500.html\", status=500)\n\n\n@login_required\ndef add_comment(request, username, post_id):\n post = get_object_or_404(Post, pk=post_id)\n author = get_object_or_404(User, username=username)\n comments = post.comments.all()\n\n form = CommentForm(request.POST or None)\n if request.method == \"POST\" and form.is_valid():\n new_comment = form.save(commit=False)\n new_comment.author = request.user\n new_comment.post = post\n new_comment.save()\n return redirect(\"post\", username=username, post_id=post_id)\n\n return render(\n request,\n \"post.html\",\n {\"post\": post, \"author\": author, \"form\": form, \"comments\": comments},\n )\n\n\n@login_required\ndef follow_index(request):\n posts = Post.objects.filter(author__following__user=request.user)\n paginator = Paginator(posts, 10)\n page_number = request.GET.get(\"page\")\n page = paginator.get_page(page_number)\n return render(request, \"follow.html\",\n {\"page\": page, \"paginator\": paginator})\n\n\n@login_required\ndef profile_follow(request, username):\n follow_author = get_object_or_404(User, username=username)\n if request.user != follow_author:\n Follow.objects.get_or_create(user=request.user, author=follow_author)\n return redirect(\"profile\", username=username)\n\n\n@login_required\ndef profile_unfollow(request, username):\n unfollow_from_author = get_object_or_404(User, username=username)\n Follow.objects.filter(user=request.user).filter(\n author=unfollow_from_author\n ).delete()\n return redirect(\"profile\", username=username)\n","repo_name":"Rybakov-Ilay/hw05_final","sub_path":"posts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4856,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"72234917064","text":"from guitar_scraping.config import Session\n\n\ndef add_df_to_db(df, data_model):\n session = Session()\n for index, row in df.iterrows():\n kwargs = dict(row.dropna())\n dataset = data_model(**kwargs)\n session.merge(dataset)\n session.commit()","repo_name":"DeltiKron/guitar_scraping","sub_path":"guitar_scraping/db_interface/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"13824224136","text":"import unittest\nfrom secure_all.data.revoke_key import RevokeKey\nfrom secure_all import AccessManager, AccessManagementException,\\\n JSON_FILES_PATH, KeysJsonStore, RequestJsonStore\n\n\nclass MyTestCase(unittest.TestCase):\n @classmethod\n def setUpClass(cls) -> None:\n \"\"\"Removing the Stores and creating required AccessRequest for testing\"\"\"\n # pylint: disable=no-member\n requests_store = RequestJsonStore()\n requests_store.empty_store()\n keys_store = KeysJsonStore()\n keys_store.empty_store()\n\n # introduce a key valid and not expired and guest\n my_manager = AccessManager()\n my_manager.request_access_code(\"87654123L\", \"Maria Montero\",\n \"Guest\", \"maria@uc3m.es\", 15)\n my_manager.request_access_code(\"53935158C\", \"Marta Lopez\",\n \"Guest\", \"uc3m@gmail.com\", 5)\n my_manager.request_access_code(\"34753293V\", \"Juan Perez\",\n \"Guest\", \"uc3m@gmail.com\", 2)\n file_name = JSON_FILES_PATH + \"key_ok.json\"\n my_manager.get_access_key(file_name)\n\n file_name = JSON_FILES_PATH + \"key_ok2.json\"\n my_manager.get_access_key(file_name)\n\n file_name = JSON_FILES_PATH + \"test_revoke_expired_access_key.json\"\n my_manager.get_access_key(file_name)\n\n def test_st_rk_rk_iv_1(self):\n test_file = JSON_FILES_PATH + \"test_rev_expired.json\"\n key = RevokeKey.create_key_from_file_for_revoke(test_file)\n with self.assertRaises(AccessManagementException) as c_m:\n key.revoke_key()\n self.assertEqual(\"Key already expired\", c_m.exception.message)\n\n def test_st_rk_rk_v_2(self):\n test_file = JSON_FILES_PATH + \"test_v_1.json\"\n key = RevokeKey.create_key_from_file_for_revoke(test_file)\n result = key.revoke_key()\n self.assertEqual(result, 'mail1@uc3m.es, mail2@uc3m.es')\n\n def test_st_rk_rk_v_4(self):\n test_file = JSON_FILES_PATH + \"test_st_rk_rk_v_4.json\"\n key = RevokeKey.create_key_from_file_for_revoke(test_file)\n result = key.revoke_key()\n self.assertEqual(result, 'mail1@uc3m.es, mail2@uc3m.es')\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"EmmanuelScience/Access-Management-Simulation","sub_path":"src/unittest/python/test_structural_revoke_key_tests.py","file_name":"test_structural_revoke_key_tests.py","file_ext":"py","file_size_in_byte":2261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"15598873713","text":"'''\nCreated on Nov 19, 2013\n\n@author: lwan1@utk.edu\n'''\n\nclass CrushBucket():\n '''\n class for crush bucket\n '''\n\n def __init__(self):\n '''\n Constructor\n '''\n self.id = 0\n self.type = 0\n self.alg = 0\n self.hash = None\n self.weight = 0\n self.size = 0\n self.items = []\n\n self.perm_x = 0\n self.perm_n = 0\n self.perm = []\n\n def get_alg_name(self, alg):\n if alg == 1:\n return 'uniform'\n elif alg == 2:\n return 'list'\n elif alg == 3:\n return 'tree'\n elif alg == 4:\n return 'straw'\n else:\n return 'unknown'\n\n def choose_item_by_rand_perm(self, x, r):\n pr = r % self.size\n if self.perm_x != x or self.perm_n == 0:\n self.perm_x = x\n if pr == 0:\n s = self.hash.hash_32_3(x, self.id, 0) % self.size\n self.perm[0] = s\n self.perm_n = 0xffff\n return self.items[s]\n for i in range(self.size):\n self.perm[i] = i\n self.perm_n = 0\n elif self.perm_n == 0xffff:\n for i in range(1, self.size):\n self.perm[i] = i\n self.perm[self.perm[0]] = 0\n self.perm_n = 1\n while self.perm_n <= pr:\n p = self.perm_n\n if p < self.size-1:\n i = self.hash.hash_32_3(x, self.id, p) % (self.size-p)\n if i:\n t = self.perm[p+i]\n self.perm[p+i] = self.perm[p]\n self.perm[p] = t\n self.perm_n += 1\n s = self.perm[pr]\n return self.items[s]\n\n\nclass UniformCrushBucket(CrushBucket):\n '''\n class for uniform bucket\n '''\n\n def __init__(self):\n self.item_weight = 0\n CrushBucket.__init__(self)\n\n def get_item_weight(self):\n return self.item_weight\n\n def make_bucket(self, hash, type, size, items, item_weight):\n self.alg = 1\n self.hash = hash\n self.type = type\n self.size = size\n self.weight = size*item_weight\n self.item_weight = item_weight\n self.items = items\n self.perm = [None]*size\n\n def add_bucket_item(self, item, weight):\n self.items.append(item)\n self.perm.append(None)\n self.weight += weight\n self.size += 1\n\n def remove_bucket_item(self, item):\n if item in self.items:\n self.items.remove(item)\n else:\n return -1\n self.size -= 1\n self.weight -= self.item_weight\n return 0\n\n def adjust_item_weight(self, item, item_weight):\n diff = (item_weight-self.item_weight)*self.size\n self.item_weight = item_weight\n self.weight = self.item_weight*self.size\n return diff\n\n def choose_item(self, x, r):\n return self.choose_item_by_rand_perm(x, r)\n\n\nclass ListCrushBucket(CrushBucket):\n '''\n class for list bucket\n '''\n\n def __init__(self):\n self.item_weights = []\n self.sum_weights = []\n CrushBucket.__init__(self)\n\n def get_item_weight(self, pos):\n return self.item_weights[pos]\n\n def make_bucket(self, hash, type, size, items, item_weights):\n self.alg = 2\n self.hash = hash\n self.type = type\n self.size = size\n self.items = items\n self.item_weights = item_weights\n self.sum_weights = [0]*size\n self.perm = [None]*size\n\n w = 0\n for i in range(size):\n w += item_weights[i]\n self.sum_weights[i] = w\n\n self.weight = w\n\n def add_bucket_item(self, item, weight):\n self.items.append(item)\n self.item_weights.append(weight)\n self.perm.append(None)\n if self.size > 0:\n self.sum_weights.append(self.sum_weights[self.size-1]+weight)\n else:\n self.sum_weights.append(weight)\n self.weight += weight\n self.size += 1\n\n def remove_bucket_item(self, item):\n if item in self.items:\n item_id = self.items.index(item)\n item_weight = self.item_weights[item_id]\n for i in range(item_id+1, self.size):\n self.sum_weights[i] -= item_weight\n self.sum_weights.pop(item_id)\n\n self.items.remove(item)\n self.item_weights.remove(item_weight)\n else:\n return -1\n self.weight -= item_weight\n self.size -= 1\n return 0\n\n def adjust_item_weight(self, item, item_weight):\n if item in self.items:\n new_item_weight = item_weight\n item_id = self.items.index(item)\n current_item_weight = self.item_weights[item_id]\n diff = new_item_weight-current_item_weight\n self.item_weights[item_id] = new_item_weight\n self.weight += diff\n for i in range(item_id, self.size):\n self.sum_weights[i] += diff\n return diff\n else:\n return -1\n\n def choose_item(self, x, r):\n for i in range(self.size-1, -1, -1):\n w = self.hash.hash_32_4(x, self.items[i], r, self.id)\n w &= 0xffff\n w *= self.sum_weights[i]\n w = w >> 16\n if w < self.item_weights[i]:\n return self.items[i]\n print('bad list sums for bucket '+str(self.id))\n return self.items[0]\n\n\nclass TreeCrushBucket(CrushBucket):\n '''\n class for tree bucket\n '''\n\n def __init__(self):\n self.num_nodes = 0\n self.node_weights = []\n CrushBucket.__init__(self)\n\n def get_node_index(self, pos):\n return ((pos+1) << 1)-1\n\n def get_item_weight(self, pos):\n return self.node_weights[self.get_node_index(pos)]\n\n def get_tree_depth(self, size):\n depth = 1\n t = size-1\n while t:\n t = t >> 1\n depth += 1\n return depth\n\n def get_node_height(self, n):\n h = 0\n while (n & 1) == 0:\n h += 1\n n = n >> 1\n return h\n\n def is_right_child(self, n, h):\n return n & (1 << (h+1))\n\n def get_parent_node(self, n):\n h = self.get_node_height(n)\n if self.is_right_child(n, h):\n return n-(1 << h)\n else:\n return n+(1 << h)\n\n def get_left_child(self, n):\n h = self.get_node_height(n)\n return n-(1 << (h-1))\n\n def get_right_child(self, n):\n h = self.get_node_height(n)\n return n+(1 << (h-1))\n\n def is_leaf(self, n):\n return n & 1\n\n def make_bucket(self, hash, type, size, items, item_weights):\n self.alg = 3\n self.hash = hash\n self.type = type\n self.size = size\n\n depth = self.get_tree_depth(size)\n self.num_nodes = 1 << depth\n self.items = items\n self.node_weights = [0]*self.num_nodes\n self.perm = [None]*size\n\n for i in range(size):\n node_id = self.get_node_index(i)\n self.node_weights[node_id] = item_weights[i]\n self.weight += item_weights[i]\n for j in range(1, depth):\n node_id = self.get_parent_node(node_id)\n self.node_weights[node_id] += item_weights[i]\n\n def add_bucket_item(self, item, weight):\n new_size = self.size+1\n new_depth = self.get_tree_depth(new_size)\n self.num_nodes = 1 << new_depth\n new_node_id = self.get_node_index(new_size-1)\n self.node_weights[new_node_id] = weight\n\n for j in range(1, new_depth):\n new_node_id = self.get_parent_node(new_node_id)\n self.node_weights[new_node_id] += weight\n\n self.perm.append(None)\n self.weight += weight\n self.size += 1\n\n def remove_bucket_item(self, item):\n if item in self.items:\n depth = self.get_tree_depth(self.size)\n item_id = self.items.index(item)\n node_id = self.get_node_index(item_id)\n node_weight = self.node_weights[node_id]\n self.node_weights[node_id] = 0\n for i in range(1, depth):\n node_id = self.get_parent_node(node_id)\n self.node_weights[node_id] -= node_weight\n self.weight -= node_weight\n new_size = self.size\n while new_size > 0:\n tmp_node_id = self.get_node_index(new_size-1)\n if self.node_weights[tmp_node_id]:\n break\n new_size -= 1\n if new_size != self.size:\n for j in range(new_size, self.size):\n self.items.pop()\n old_depth = self.get_tree_depth(self.size)\n new_depth = self.get_tree_depth(new_size)\n if new_depth != old_depth:\n old_num_nodes = self.num_nodes\n new_num_nodes = 1 << new_depth\n for k in range(new_num_nodes, old_num_nodes):\n self.node_weights.pop()\n self.num_nodes = new_num_nodes\n self.size = new_size\n return 0\n else:\n return -1\n\n def adjust_item_weight(self, item, item_weight):\n if item in self.items:\n item_id = self.items.index(item)\n node_id = self.get_node_index(item_id)\n diff = item_weight - self.node_weights[node_id]\n self.node_weights[node_id] = item_weight\n self.weight += diff\n depth = self.get_tree_depth(self.size)\n for i in range(1, depth):\n node_id = self.get_parent_node(node_id)\n self.node_weights[node_id] += diff\n return diff\n else:\n return -1\n\n def choose_item(self, x, r):\n n = self.num_nodes >> 1\n while not self.is_leaf(n):\n w = self.node_weights[n]\n t = self.hash.hash_32_4(x, n, r, self.id)*w\n t = t >> 32\n l = self.get_left_child(n)\n if t < self.node_weights[l]:\n n = l\n else:\n n = self.get_right_child(n)\n return self.items[n >> 1]\n\n\nclass StrawCrushBucket(CrushBucket):\n '''\n class for straw bucket\n '''\n\n def __init__(self):\n self.item_weights = []\n self.straws = []\n CrushBucket.__init__(self)\n\n def get_item_weight(self, pos):\n return self.item_weights[pos]\n\n def set_staw_value(self, size, item_weights):\n sorted_weights_idx = [None]*size\n if size:\n sorted_weights_idx[0] = 0\n for i in range(1, size):\n for j in range(i):\n if item_weights[i] < item_weights[sorted_weights_idx[j]]:\n for k in range(i, j, -1):\n sorted_weights_idx[k] = sorted_weights_idx[k-1]\n sorted_weights_idx[j] = i\n break\n if j+1 == i:\n sorted_weights_idx[i] = i\n\n left_weights_num = size\n straw = 1.0\n weight_below = 0.0\n last_weight = 0.0\n\n i = 0\n while i < size:\n if item_weights[sorted_weights_idx[i]] == 0:\n self.straws[sorted_weights_idx[i]] = 0\n i += 1\n continue\n self.straws[sorted_weights_idx[i]] = straw*0x10000\n\n i += 1\n if i == size:\n break\n\n if item_weights[sorted_weights_idx[i]] == item_weights[sorted_weights_idx[i-1]]:\n continue\n\n weight_below += (float(item_weights[sorted_weights_idx[i-1]])-last_weight)*left_weights_num\n for j in range(i, size):\n if item_weights[sorted_weights_idx[j]] == item_weights[sorted_weights_idx[i]]:\n left_weights_num -= 1\n else:\n break\n weight_next = float(left_weights_num*(item_weights[sorted_weights_idx[i]]-item_weights[sorted_weights_idx[i-1]]))\n prob_below = weight_below/(weight_below+weight_next)\n straw *= pow(1.0/prob_below, 1.0/float(left_weights_num))\n last_weight = item_weights[sorted_weights_idx[i-1]]\n\n def make_bucket(self, hash, type, size, items, item_weights):\n self.alg = 4\n self.hash = hash\n self.type = type\n self.size = size\n self.items = items\n self.item_weights = item_weights\n self.straws = [None]*size\n self.perm = [None]*size\n\n for i in range(size):\n self.weight += item_weights[i]\n\n self.set_staw_value(size, item_weights)\n\n def add_bucket_item(self, item, weight):\n self.items.append(item)\n self.item_weights.append(weight)\n self.perm.append(None)\n self.weight += weight\n self.size += 1\n\n self.set_staw_value(self.size, self.item_weights)\n\n def remove_bucket_item(self, item):\n if item in self.items:\n item_id = self.items.index(item)\n item_weight = self.item_weights[item_id]\n self.size -= 1\n self.weight -= item_weight\n self.items.remove(item)\n self.item_weights.remove(item_weight)\n self.set_staw_value(self.size, self.item_weights)\n return 0\n else:\n return -1\n\n def adjust_item_weight(self, item, item_weight):\n if item in self.items:\n item_id = self.items.index(item)\n diff = item_weight-self.item_weights[item_id]\n self.item_weights[item_id] = item_weight\n self.weight += diff\n self.set_staw_value(self.size, self.item_weights)\n return 0\n else:\n return -1\n\n def choose_item(self, x, r):\n high = 0\n high_draw = 0\n for i in range(self.size):\n draw = self.hash.hash_32_3(x, self.items[i], r)\n draw &= 0xffff\n draw *= self.straws[i]\n if i == 0 or draw > high_draw:\n high = i\n high_draw = draw\n return self.items[high]","repo_name":"lwan86/CRUSHSim","sub_path":"src/crush/crush_bucket.py","file_name":"crush_bucket.py","file_ext":"py","file_size_in_byte":14083,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"} +{"seq_id":"10682739994","text":"from flask import Flask\nfrom datab import db\nfrom flask_login import LoginManager\n\nprint(\"before\")\n\ndef app():\n print(\"after\")\n \n app = Flask(__name__)\n\n app.secret_key = b'_5#y2L\"F4Q8z\\n\\xec]/'\n app.config[\"SQLALCHEMY_DATABASE_URI\"] = \"sqlite:///database.db\"\n db.init_app(app)\n \n from views import views\n \n from auth import auth\n\n app.register_blueprint(views, url_prefix = \"/\")\n app.register_blueprint(auth, url_prefix = \"/\")\n \n from models import User, Note\n\n login_manager = LoginManager()\n login_manager.login_view = \"auth.login\"\n login_manager.init_app(app)\n\n @login_manager.user_loader\n def load_user(id):\n return User.query.get(int(id))\n\n with app.app_context():\n db.create_all()\n\n \n\n return app\n\nif __name__ == \"__main__\":\n app = app()\n \n app.run(debug=True, port=5001)\n\n","repo_name":"Sarthak-Oza/notes_app_flask","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"3148639201","text":"import telebot\nfrom telebot import types\nimport os\nimport requests\nimport time\nfrom . import headlines\nfrom . import query\nimport numpy as np\nfrom pathlib import Path\nimport json\nimport pandas as pd\n\n\ntb = telebot.TeleBot('5623203213:AAH7lof9-02ixyYrc4V4B02JYWjTWw8o6qY')\n\n@tb.message_handler(commands=['start', 'go'])\ndef start_handler(message):\n markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\n items = [types.KeyboardButton(\"Отправить данные\"),\n types.KeyboardButton(\"Часто задаваемые вопросы\"),\n ]\n for item in items:\n markup.add(item)\n msg = tb.send_message(message.chat.id, f'*Добро пожаловать, {message.from_user.first_name}!*\\n\\n'+headlines.start, reply_markup=markup, parse_mode= 'Markdown')\n tb.register_next_step_handler(msg, main_start)\n\ndef main_start(message):\n if message.text.strip() == 'Отправить данные':\n msg = tb.send_message(message.chat.id, f'Пожалуйста, прикрепите и отправьте мне файл с результатми вашего генома.', parse_mode= 'Markdown')\n path = os.path.join(os.path.dirname(__file__), 'guide.png')\n tb.send_photo(message.chat.id, photo=open(path, 'rb'))\n if message.text.strip() == \"Часто задаваемые вопросы\":\n markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\n items = [types.KeyboardButton(\"Отправить данные\"),\n ]\n for item in items:\n markup.add(item)\n msg = tb.send_message(message.chat.id, headlines.question_answer, reply_markup=markup, parse_mode= 'Markdown')\n tb.register_next_step_handler(msg, main_start)\n\n@tb.message_handler(content_types=['document'])\ndef handle_docs_photo(message):\n try:\n file_info = tb.get_file(message.document.file_id)\n downloaded_file = tb.download_file(file_info.file_path) \n ts = time.time()\n path = Path(os.path.dirname(__file__)) / 'load_data'\n path.mkdir(exist_ok=True)\n src = path / f'{message.chat.id}_{ts}.txt'\n with open(src, 'wb') as new_file:\n new_file.write(downloaded_file)\n msg = tb.reply_to(message, \"*Спасибо!*\\n\\n*Мой искусственный интелект начал обрабатывать информацию!*\\n\\n⚙️⚙️⚙️⚙️⚙️⚙️⚙️⚙️\\n\\n_Как только я закончу, я пришлю информацию моего анализа._\", parse_mode= 'Markdown')\n process(message=msg, file_name=src)\n except Exception as e:\n msg = tb.reply_to(message, f'*Упс!*\\n\\nКажется в вашем файле проблемы! Проверьте точно ли вы загружаете нужный файл! {e}\\n\\n_По любым вопросам можно обратиться к моим разработчикам_\\n@voronik1801', parse_mode= 'Markdown')\n tb.register_next_step_handler(msg, handle_docs_photo)\n\ndef process(message, file_name):\n snps = []\n with open(file_name) as f:\n for line in f.readlines():\n if line[0] == '#':\n continue\n tokens = line.split()\n if tokens[1] != '1':\n continue\n try:\n snp = {\n 'rs': int(tokens[0][2:]),\n 'genotype': tokens[3]\n }\n snps.append(snp)\n except ValueError:\n pass\n clinvar_dir = Path(os.path.dirname(__file__)) / '../data/clinvar_filtered.json'\n with open(clinvar_dir, 'r') as file:\n clinvar = json.load(file)\n\n clinvar_df = pd.DataFrame.from_records(clinvar)\n clinvar_df['rs_int'] = clinvar_df['rs_id'].apply(lambda x: x.isnumeric())\n clinvar_df = clinvar_df[clinvar_df['rs_int']].copy().drop('rs_int', axis=1)\n clinvar_df['rs'] = clinvar_df['rs_id'].astype(int)\n\n result = query.check_user_snps(snps, clinvar_df)\n\n df = pd.DataFrame.from_records(result)\n markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\n items = [types.KeyboardButton(\"Отправить данные\"),\n types.KeyboardButton(\"Часто задаваемые вопросы\"),\n ]\n for item in items:\n markup.add(item)\n if len(df) > 0:\n # for rs in response:\n # rs_for_check = rs['_source']['rs_id']\n # tb.send_message(message.chat.id, f\"_Мы обнаружили высокий риск развития болезни, ассоциированной с участком в геноме rs-{rs_for_check}_\", parse_mode= 'Markdown')\n risks = []\n tb.send_message(message.chat.id, f\"Мы обнаружили следующие риски:\", parse_mode= 'Markdown')\n for _, row in df.iterrows():\n try:\n description = row[\"description\"].replace('_', ' ')\n risk = f'RS{row[\"rs\"]} - {description.split(\"|\")[0].split(\",\")[0]}'\n tb.send_message(message.chat.id, risk, parse_mode= 'Markdown')\n \n except Exception:\n pass\n \n\n msg = tb.send_message(message.chat.id, \"Вы можете проверить данные участки в лаборатории *Имя партнера*.\\n\\nПо промокоду *Deeploid* вы получите скидку в 10%\", reply_markup=markup, parse_mode= 'Markdown')\n else:\n msg = tb.send_message(message.chat.id, \"Мы не обнаружили никаких паталогийв вашем геноме.\", reply_markup=markup, parse_mode= 'Markdown')\n tb.register_next_step_handler(msg, main_start)\n return\n\n\n\n result = []\n with open(file_name, 'r') as f:\n lines = [line for line in f if not line.startswith(\"#\")]\n filtered = \"\".join(lines)\n filtered = filtered.split('\\n')\n for line in filtered:\n result.append(line.split('\\t'))\n rs_id = [el[0].replace('rs','') for el in result]\n rs_id = np.random.choice(rs_id, 700, replace=False)\n req_link = f'http://176.9.183.35:4443/v1/items/by/rs/?rs={rs_id}'.replace(\"'\", \"\")\n response = requests.get(req_link, timeout=60)\n response = response.json()['hits']['hits']\n markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\n items = [types.KeyboardButton(\"Отправить данные\"),\n types.KeyboardButton(\"Часто задаваемые вопросы\"),\n ]\n for item in items:\n markup.add(item)\n if len(response) > 0:\n for rs in response:\n rs_for_check = rs['_source']['rs_id']\n tb.send_message(message.chat.id, f\"_Мы обнаружили высокий риск развития болезни, ассоциированной с участком в геноме rs-{rs_for_check}_\", parse_mode= 'Markdown')\n msg = tb.send_message(message.chat.id, \"Вы можете проверить данные участки в лаборатории *Имя партнера*.\\n\\nПо промокоду *Deeploid* вы получите скидку в 10%\", reply_markup=markup, parse_mode= 'Markdown')\n else:\n msg = tb.send_message(message.chat.id, \"Мы не обнаружили никаких паталогийв вашем геноме.\", reply_markup=markup, parse_mode= 'Markdown')\n tb.register_next_step_handler(msg, main_start)","repo_name":"mikgur/fastapi_elastic","sub_path":"bot/telegram_bot/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":7475,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"23173518193","text":"from init import*\n\ndef Message(message,text):\n return True if message.content.startswith(text) else False\n\ndef Admin(author):\n return author.guild_permissions.administrator\n\nasync def get_guilds():\n Putin = ''\n Muffin = ''\n Stock = ''\n async for guild in bot.fetch_guilds():\n if guild.name == \"Poutine lovers\":\n Putin = bot.get_guild(guild.id)\n elif guild.name == \"Muffin Sect\":\n Muffin = bot.get_guild(guild.id)\n elif guild.name == \"Stock Market\":\n Stock = bot.get_guild(guild.id)\n return [Putin,Muffin,Stock]\n\n@bot.event\nasync def on_ready():\n print(\"Hi Elvin i'm here\")\n global talk\n talk = Talk(bot)\n\n\n@bot.event\nasync def on_member_join(member):\n await member.edit(nick = f\"Muffin {member.display_name}\")\n\n@bot.listen('on_message')\nasync def process(message):\n if message.author == bot.user:\n return\n\n if message.author in Muted and not(message.channel.name == \"diplomatie\" or message.channel.name == \"musique\") and not(message.author.id == 281432668196044800):\n await message.delete()\n return\n\n if 'talk' in globals() and len(talk.connected) != 0 and talk.sending(message): #message.channel.id == talk.log_channel.id:\n await talk.send(message)\n return\n\n if 'talk' in globals() and len(talk.connected) != 0 and talk.receiving(message): #message.channel.id == talk.channel.id:\n await talk.log(message)\n\n if isinstance(message.channel, discord.abc.PrivateChannel):\n return\n\n if message.channel.guild.name in Motus.keys():\n await motus(message)\n\n@bot.command()\nasync def connect(ctx):\n await talk.connect(ctx.message)\n\n@bot.command()\nasync def disconnect(ctx):\n await talk.disconnect(ctx.message)\n\n@bot.event\nasync def on_reaction_add(reaction,user):\n if user == bot.user:\n return\n if reaction.emoji in number_list or reaction.emoji == \"✅\":\n for game in Games:\n if reaction.message == game.message[-1]:\n if reaction.emoji == \"✅\":\n await game.addPlayer(user)\n else:\n await tictactoe.PlaceT(reaction,user)\n await reaction.remove(user)\n\n@bot.event\nasync def on_raw_reaction_add(payload):\n if payload.user_id == bot.user.id:\n return\n if payload.emoji.name == '🎉':\n print('yay')\n print(repr(payload.emoji.name))\n\n@bot.command()\nasync def load(ctx,*args):\n if not ctx.author.id == Elvin:\n return\n if len(args) != 0:\n args[0] = name\n else:\n os.system(\"cls\")\n os.system(\"python aelita_mon_amour.py\")\n quit()\n return\n if name == 't':\n tictactoe.reset()\n os.system(\"cls\")\n importlib.reload(tictactoe)\n return\n\n#id hugo = 530726932216807437\n#-------------------- ‼️\nwith open('Id/id.txt','r') as IdFile:\n token = IdFile.read()\n#token = os.getenv(\"BOT_TOKEN\")\nbot.run(token)\n","repo_name":"NovaGamma/Aelita","sub_path":"aelita_mon_amour.py","file_name":"aelita_mon_amour.py","file_ext":"py","file_size_in_byte":2965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"7317100771","text":"# 爬取京东手机数据，并存储到excel或txt文件中\r\n# 测试时间为2017年7月10日\r\n# !C:\\Python\\Python35\r\n#_*_ encoding: utf-8 _*_\r\n\r\nimport requests\r\nfrom bs4 import BeautifulSoup\r\nimport json\r\nimport xlwt\r\n\r\n# 获取商品名称\r\nurlfirst = \"https://list.jd.com/list.html?cat=9987,653,655&page=\"\r\nurllast = \"&sort=sort_dredisprice_asc&trans=1&JL=6_0_0#J_main\"\r\n# 打开文件存放京东手机数据\r\n# with open(\"京东手机信息.txt\", 'w') as jdshouji:\r\nxls = xlwt.Workbook()\r\n# 创建第一个表单\r\nsheet = xls.add_sheet(\"Sheet1\")\r\n# 创建表头\r\nsheet.write(0, 0, '商品名称')\r\nsheet.write(0, 1, '价格')\r\n# 从第一行开始记录产品信息\r\nrow = 1\r\n\r\n# 一页一页获取数据，存入excel文件\r\nfor page in range(1, 147):\r\n\t# 取得每一个网页URL\r\n\tproduct_html = requests.get(urlfirst + str(page) + urllast)\r\n\tsoup = BeautifulSoup(product_html.text, 'lxml')\r\n\tproductname = soup.select(\"ul.gl-warp li.gl-item div.p-name a em\")\r\n\tproductsku = soup.select(\"ul.gl-warp li.gl-item div.gl-i-wrap.j-sku-item\")\r\n\t# for sku in productsku:\r\n\t# print(sku[\"data-sku\"])\r\n\t# 构造json地址，每个json请求30个商品信息\r\n\tcounter = 0\r\n\tplist = []\r\n\tjsonaddr = \"\"\r\n\tskulist = \"\"\r\n\tjsonfirst = \"https://p.3.cn/prices/mgets?callback=jQuery268148&ext=10000000&type=1&area=10_727_728_0&skuIds=\"\r\n\tjsonlast = \"&pdbp=0&pdtk=&pdpin=&pduid=1335704484&source=list_pc_front&_=1499430797963\"\r\n\tfor sku in productsku:\r\n\t\tskulist = skulist + \"J_\" + sku[\"data-sku\"] + \"%2C\"\r\n\t\tcounter += 1\r\n\t\tif counter < 30:\r\n\t\t\tcontinue\r\n\t\t\t# 最后一个skuid要去掉\"%2C\"\r\n\t\tskulist = skulist[0:-3]\r\n\t\t# 得到json地址\r\n\t\tjsonaddr = jsonfirst + skulist + jsonlast\r\n\r\n\t\tjsonstr = requests.get(jsonaddr)\r\n\t\t# 取得json字符串\r\n\t\tjsonstr = jsonstr.text[13:-3]\r\n\t\t# 取得json内对象\r\n\t\tprice_json = json.loads(jsonstr)\r\n\t\tfor price in price_json:\r\n\t\t\tplist.append(price['p'])\r\n\t\t\t# print(price_json['p'])\r\n\t\tjsonaddr = \"\"\r\n\t\tskulist = \"\"\r\n\t\tcounter = 0\r\n\tif skulist:\r\n\t\t# 最后一个skuid要去掉\"%2C\"\r\n\t\tskulist = skulist[0:-3]\r\n\t\t# 得到json地址\r\n\t\tjsonaddr = jsonfirst + skulist + jsonlast\r\n\r\n\t\tjsonstr = requests.get(jsonaddr)\r\n\t\t# 取得json字符串\r\n\t\tjsonstr = jsonstr.text[13:-3]\r\n\t\t# 取得json内对象\r\n\t\tprice_json = json.loads(jsonstr)\r\n\t\tfor price in price_json:\r\n\t\t\tplist.append(price['p'])\r\n\tfor (pname, price) in zip(productname, plist):\r\n# ---------- 商品名称中包含像“™”这样的字符，需要过滤----------\r\n\t\ttry:\r\n\t\t\t# jdshouji.write(pname.text + '\\t' + price + '\\n')\r\n\t\t\tsheet.write(row, 0, pname.text)\r\n\t\t\tsheet.write(row, 1, price)\r\n\t\t\trow = row + 1\r\n\t\texcept:\r\n\t\t\t# jdshouji.write(\"本商品包含特殊字符，请自行查询！商品地址：\" + urlfirst + str(page) + urllast + \"\\n\")\r\n\t\t\terror = \"本商品包含特殊字符，请自行查询！商品地址：\" + urlfirst + str(page) + urllast\r\n\t\t\tsheet.write(row, 0, error)\r\n\t\t\trow = row + 1\r\n\tplist = []\r\n\r\nxls.save('京东手机信息.xls')\r\n\r\n\r\n","repo_name":"maverpig/crawler","sub_path":"crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":2973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"73962902025","text":"import copy\nimport shutil\nimport argparse\nimport json\nimport sys\nimport os\nfrom tqdm import tqdm\n\nsys.path.append('.')\nfrom imaginaire.utils.lmdb import create_metadata, \\\n construct_file_path, check_and_add, build_lmdb # noqa: E402\nfrom imaginaire.config import Config # noqa: E402\n\n\ndef parse_args():\n r\"\"\"Parse user input arguments\"\"\"\n parser = argparse.ArgumentParser(description='Folder -> LMDB conversion')\n parser.add_argument('--data_root', type=str, required=True,\n help='Input data location.')\n parser.add_argument('--config', type=str, required=True,\n help='Config with label info.')\n parser.add_argument('--output_root', type=str, required=True,\n help='Output LMDB location')\n parser.add_argument('--input_list', type=str, default='',\n help='list of images that will be used.')\n parser.add_argument('--metadata_factor', type=float, default=0.75,\n help='Factor of filesize to allocate for metadata?')\n parser.add_argument('--overwrite', default=False, action='store_true',\n help='Overwrite output file if exists')\n parser.add_argument('--paired', default=False, action='store_true',\n help='Is the input data paired?')\n parser.add_argument('--large', default=False, action='store_true',\n help='Is the dataset large?')\n parser.add_argument('--remove_missing', default=False, action='store_true',\n help='Remove missing files from paired datasets?')\n args = parser.parse_args()\n return args\n\n\ndef main():\n r\"\"\" Build lmdb for training/testing.\n Usage:\n python scripts/build_lmdb.py \\\n --config configs/data_image.yaml \\\n --data_root /mnt/bigdata01/datasets/test_image \\\n --output_root /mnt/bigdata01/datasets/test_image/lmdb_0/ \\\n --overwrite\n \"\"\"\n args = parse_args()\n cfg = Config(args.config)\n\n # Check if output file already exists.\n if os.path.exists(args.output_root):\n if args.overwrite:\n print('Deleting existing output LMDB.')\n shutil.rmtree(args.output_root)\n else:\n print('Output root LMDB already exists. Use --overwrite. ' +\n 'Exiting...')\n return\n\n all_filenames, extensions = \\\n create_metadata(data_root=args.data_root,\n cfg=cfg,\n paired=args.paired,\n input_list=args.input_list)\n required_data_types = cfg.data.data_types\n\n # Build LMDB.\n os.makedirs(args.output_root)\n for data_type in required_data_types:\n data_size = 0\n print('Data type:', data_type)\n filepaths, keys = [], []\n print('>> Building file list.')\n\n # Get appropriate list of files.\n if args.paired:\n filenames = all_filenames\n else:\n filenames = all_filenames[data_type]\n\n for sequence in tqdm(filenames):\n for filename in copy.deepcopy(filenames[sequence]):\n filepath = construct_file_path(\n args.data_root, data_type, sequence, filename,\n extensions[data_type])\n key = '%s/%s' % (sequence, filename)\n filesize = check_and_add(filepath, key, filepaths, keys,\n remove_missing=args.remove_missing)\n\n # Remove file from list, if missing.\n if filesize == -1 and args.paired and args.remove_missing:\n print('Removing %s from list' % (filename))\n filenames[sequence].remove(filename)\n data_size += filesize\n\n # Remove empty sequences.\n if args.paired and args.remove_missing:\n for sequence in copy.deepcopy(all_filenames):\n if not all_filenames[sequence]:\n all_filenames.pop(sequence)\n\n # Allocate size.\n data_size = max(int((1 + args.metadata_factor) * data_size), 1e9)\n print('Reserved size: %s, %dGB' % (data_type, data_size // 1e9))\n\n # Write LMDB to file.\n output_filepath = os.path.join(args.output_root, data_type)\n build_lmdb(filepaths, keys, output_filepath, data_size, args.large)\n\n # Output list of all filenames.\n if args.output_root:\n with open(args.output_root + '/all_filenames.json', 'w') as fout:\n json.dump(all_filenames, fout, indent=4)\n\n # Output metadata.\n with open(args.output_root + '/metadata.json', 'w') as fout:\n json.dump(extensions, fout, indent=4)\n else:\n return all_filenames, extensions\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"NVlabs/imaginaire","sub_path":"scripts/build_lmdb.py","file_name":"build_lmdb.py","file_ext":"py","file_size_in_byte":4761,"program_lang":"python","lang":"en","doc_type":"code","stars":3891,"dataset":"github-code","pt":"81"} +{"seq_id":"73190564425","text":"#!/usr/bin/env python3\n# This program implements the game Connect 4 with an AI that runs on Minimax\nimport math\nimport time\n\nclass Board:\n\trows = 6\n\tcols = 7\n\n\tdef __init__(self):\n\t\tself.the_board = [[' ' for i in range(self.cols)] for j in range(self.rows)]\n\t\t\n\t#displays current board configuration\n\tdef display_board(self):\n\t\tnum_cols = [i for i in range(self.cols)]\n\t\tprint(\"\",*num_cols, sep=\" \")\n\n\t\tfor i in range(self.rows):\n\t\t\tprint(\"|\", end=\"\")\n\t\t\tfor j in range(self.cols):\n\t\t\t\tprint(self.the_board[i][j]+\"|\", end=\"\")\n\t\t\tprint('\\n')\n\t\tprint('\\n')\n\n\t# Checks to see if given column is full\n\tdef is_column_full(self, col):\n\t\tfor i in range(self.rows):\n\t\t\tif self.the_board[i][col] == \" \":\n\t\t\t\treturn False\n\t\t\treturn True\n\t\n\t#Checks to see if the board is full\n\tdef is_board_full(self):\n\t\tfor c in range(self.cols):\n\t\t\tif not self.is_column_full(c):\n\t\t\t\treturn False\n\t\treturn True\n\n\t#Adds piece to current board at given column\n\tdef add_piece(self, col, piece):\n\t\ti = 0\n\t\tto_place = i -1\n\t\twhile(i < self.rows and self.the_board[i][col] == ' '):\n\t\t\tto_place = i\n\t\t\ti += 1\n\t\t\t\n\t\tif piece == 0:\n\t\t\tself.the_board[to_place][col] = 'X' \n\t\telse: \n\t\t\tself.the_board[to_place][col] = 'O'\n\n\t#Removes last piece from board at given column\n\tdef remove_piece(self, col):\n\t\ti = 0\n\t\twhile(i < self.rows and self.the_board[i][col] == ' '):\n\t\t\ti += 1\n\n\t\tif i == self.rows:\n\t\t\treturn None\n\t\telse:\n\t\t\tself.the_board[i][col] = ' '\n\n\t#Returns a list of columns with available space\n\tdef empty_spaces(self):\n\t\tempty = []\n\t\t\n\t\tfor j in range(self.cols):\n\t\t\tif not self.is_column_full(j):\n\t\t\t\tempty.append((j))\n\t\treturn empty\n\n\t#checks to see if given player has won the game\n\tdef has_won(self, player):\n\t\t\n\t\tplayer = 'X' if player == 0 else 'O'\n\n\t\t#horizontal win\n\t\tfor c in range(self.cols-3):\n\t\t\tfor r in range(self.rows):\n\t\t\t\tif self.the_board[r][c] == ' ':\n\t\t\t\t\tcontinue\n\t\t\t\telif self.the_board[r][c] == player and self.the_board[r][c+1] == player and self.the_board[r][c+2] == player and self.the_board[r][c+3] == player:\n\t\t\t\t\treturn True\n\n\t\t#vertical win\n\t\tfor c in range(self.cols):\n\t\t\tfor r in range(self.rows-3):\n\t\t\t\tif self.the_board[r][c] == ' ':\n\t\t\t\t\tcontinue\n\t\t\t\telif self.the_board[r][c] == player and self.the_board[r+1][c] == player and self.the_board[r+2][c] == player and self.the_board[r+3][c] == player:\n\t\t\t\t\treturn True\n\n\t\t#pos diaganols win\n\t\tfor c in range(self.cols-3):\n\t\t\tfor r in range(self.rows-3):\n\t\t\t\tif self.the_board[r][c] == ' ':\n\t\t\t\t\tcontinue\n\t\t\t\telif self.the_board[r][c] == player and self.the_board[r+1][c+1] == player and self.the_board[r+2][c+2] == player and self.the_board[r+3][c+3] == player:\n\t\t\t\t\treturn True\n\n\t\t#neg diaganols win\n\t\tfor c in range(self.cols-3):\n\t\t\tfor r in range(3, self.rows):\n\t\t\t\tif self.the_board[r][c] == ' ':\n\t\t\t\t\tcontinue\n\t\t\t\telif self.the_board[r][c] == player and self.the_board[r-1][c+1] == player and self.the_board[r-2][c+2] == player and self.the_board[r-3][c+3] == player:\n\t\t\t\t\treturn True\n\n\t#This method returns the number of pieces in the center of the board for a given player\n\tdef number_middle_pieces(self, player):\n\t\tplayer = 'X' if player == 0 else 'O'\n\t\tnum_pieces = 0\n\n\t\tfor r in range(self.rows):\n\t\t\tfor c in range(2,5):\n\t\t\t\tif self.the_board[r][c] == player:\n\t\t\t\t\tnum_pieces += 1\n\t\treturn num_pieces\n\n\t#Checks if given player has won the game if not checks to see if player has more piece in center of the board\n\tdef evaluate(self, player):\n\t\topponet = 1 if player == 0 else 0\n\t\t \n\t\tif player == 0: \n\t\t\tif self.has_won(player):\n\t\t\t\treturn 100\n\t\t\telif self.number_middle_pieces(player) > self.number_middle_pieces(opponet):\n\t\t\t\treturn 50\n\t\t\telse:\n\t\t\t\treturn 0\n\t\telif player == 1: \n\t\t\tif self.has_won(player):\n\t\t\t\treturn -100\n\t\t\telif self.number_middle_pieces(player) > self.number_middle_pieces(opponet):\n\t\t\t\treturn -50\n\t\t\telse:\n\t\t\t\treturn 0\n\n\t#Finds the best move biased off of evaluation function\n\tdef minimax(self, depth, player):\n\t\t\n\t\tif player == 0:\n\t\t\tbest_move = [None, -math.inf]\n\t\telse:\n\t\t\tbest_move = [None, math.inf]\n\n\t\tif depth == 0 or self.has_won(player) or self.is_board_full():\n\t\t\tscore = self.evaluate(player)\n\t\t\treturn [None,score]\n\n\t\tfor emp_space in self.empty_spaces():\n\t\t\t\n\t\t\tcol = emp_space\n\t\t\tself.add_piece(col, player)\n\t\t\tscore = self.minimax(depth-1, (player+1) % 2)\n\t\t\tself.remove_piece(col)\n\t\t\tscore[0] = col\n\n\t\t\tif player == 0 and score[1] > best_move[1]:\n\t\t\t\t\t best_move = score\n\t\t\telif player != 0 and score[1] < best_move[1]:\n\t\t\t\t\t best_move = score\n\t\t\n\t\treturn best_move\n\ndef main():\n\tnew_board = Board()\n\tnew_board.display_board()\n\t#Player 1 is always the human\n\tcurrent_player = 1\n\tgame_done = False\n\n\twhile(not game_done):\n\n\t\tif current_player == 1:\n\t\t\t# get current player input\n\t\t\twhile True:\n\t\t\t\t\n\t\t\t\ttry:\n\t\t\t\t\tprint(\"Enter Column:\", end=' ')\n\t\t\t\t\tuser_move = int(input())\n\t\t\t\texcept ValueError:\n\t\t\t\t\tprint(\"Not an integer!\")\n\t\t\t\t\tcontinue\n\t\t\t\telse:\n\t\t\t\t\tbreak\n\n\t\t\twhile user_move > new_board.cols or user_move < 0 or new_board.is_column_full(user_move):\n\t\t\t\tprint(\"Enter a valid column\")\n\t\t\t\tprint(\"Enter Column:\", end=' ')\n\t\t\t\tuser_move = int(input())\n\n\t\t\tnew_board.add_piece(user_move, current_player)\n\t\t\tnew_board.display_board()\n\t\t\t\n\t\telse:\n\t\t\t#get cpu move\n\t\t\tcpu_move = new_board.minimax(5, current_player)\n\t\t\tnew_board.add_piece(cpu_move[0], current_player)\n\t\t\tprint(\"The computer played in column {}\".format(cpu_move[0]))\n\t\t\tnew_board.display_board()\n\t\t\t\n\t\t#check for winner\n\t\tif new_board.has_won(current_player):\n\t\t\tgame_done = True\n\t\t\tprint(\"{current_player} has won!\".format(current_player = \"CPU\" if current_player == 0 else \"Player 1\"))\n\t\t#switch player turn\n\t\telse:\n\t\t\tcurrent_player = (current_player + 1) % 2\n\nif __name__ == \"__main__\":\n\tmain()\n\n\t\n\t\n","repo_name":"Jc51aT/ConnectFour","sub_path":"connect_minmax.py","file_name":"connect_minmax.py","file_ext":"py","file_size_in_byte":5677,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"75025258506","text":"#!/bin/python3\n\nimport os\nimport sys\nimport functools\n\n#\n# Complete the getTotalX function below.\ndef gcd(a,b):\n if (b == 0):\n return a\n return gcd(b, a%b)\n\ndef lcm(a,b):\n return (a*b)/gcd(a,b)\n\ndef getTotalX(a, b):\n lcm_a = functools.reduce(lcm, a)\n gcd_b = functools.reduce(gcd, b)\n\n cnt = 0\n multiple = 1\n while True:\n if gcd_b%(lcm_a*multiple)==0:\n cnt+=1\n if lcm_a*multiple >= gcd_b:\n break\n multiple += 1\n return cnt\n\nif __name__ == '__main__':\n f = open(os.environ['OUTPUT_PATH'], 'w')\n\n nm = input().split()\n\n n = int(nm[0])\n\n m = int(nm[1])\n\n a = list(map(int, input().rstrip().split()))\n\n b = list(map(int, input().rstrip().split()))\n\n total = getTotalX(a, b)\n\n f.write(str(total) + '\\n')\n\n f.close()\n\n","repo_name":"fractalis/hackerrank","sub_path":"python-misc/problem-solving/between-two-sets.py","file_name":"between-two-sets.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"36651246884","text":"\"\"\"Preprocess NWP data using geospatial mask\"\"\"\nimport itertools\nimport logging\nimport multiprocessing as mp\nfrom pathlib import Path\nfrom typing import Iterable, Tuple, Union\n\nimport geopandas as gpd\nimport numpy as np\nimport pandas as pd\nimport requests\nimport xarray as xr\nfrom shapely.geometry import MultiPolygon, Point, Polygon\nfrom shapely.ops import unary_union\n\nfrom gradboost_pv.models.utils import DEFAULT_DIRECTORY_TO_PROCESSED_NWP\n\nlogger = logging.getLogger(__name__)\n\nESO_GEO_JSON_URL = (\n \"https://data.nationalgrideso.com/backend/dataset/2810092e-d4b2-472f-b955-d8bea01f9ec0/\"\n \"resource/08534dae-5408-4e31-8639-b579c8f1c50b/download/gsp_regions_20220314.geojson\"\n)\n\n# processing takes quite a long time, so take a subset for now.\nDEFAULT_VARIABLES_FOR_PROCESSING = [\n \"dswrf\",\n # \"hcct\",\n \"lcc\",\n \"t\",\n # \"sde\",\n \"wdir10\",\n]\n\nMAX_FORECAST_HORIZON = 36\n\n\ndef build_local_save_path(\n forecast_horizon_step: int,\n variable: str,\n year: int,\n directory: Path = DEFAULT_DIRECTORY_TO_PROCESSED_NWP,\n) -> Tuple[Path, Path]:\n \"\"\"Paths to inner and outer masked NWP data for specific year/variable/forecast horizon\n\n Args:\n forecast_horizon_step (int): Forecast step index\n variable (str): NWP variable\n year (int): Year of processed data\n directory (Path, optional): Directory to data.\n Defaults to DEFAULT_DIRECTORY_TO_PROCESSED_NWP.\n\n Returns:\n Tuple[Path, Path]: Paths to respective datasets\n \"\"\"\n\n if forecast_horizon_step > MAX_FORECAST_HORIZON:\n logger.debug(\n f\"Forecast horizon step {forecast_horizon_step} exceeds maximum of \"\n f\"{MAX_FORECAST_HORIZON}, so reducing to {MAX_FORECAST_HORIZON}\"\n )\n forecast_horizon_step = MAX_FORECAST_HORIZON\n\n return (\n directory\n / str(year)\n / f\"uk_region_inner_variable_{variable}_step_{forecast_horizon_step}.pickle\",\n directory\n / str(year)\n / f\"uk_region_outer_variable_{variable}_step_{forecast_horizon_step}.pickle\",\n )\n\n\ndef query_eso_geojson() -> gpd.GeoDataFrame:\n \"\"\"Query National grid ESO for spatial structure data for UK GSPs\n\n Returns:\n gpd.GeoDataFrame: GeoDataFrame containing UK-region information\n \"\"\"\n with requests.get(ESO_GEO_JSON_URL) as response:\n shape_gpd = gpd.read_file(response.text)\n return shape_gpd\n\n\ndef process_eso_uk_multipolygon(uk_shape: gpd.GeoDataFrame) -> MultiPolygon:\n \"\"\"Processes the response from `query_eso_geojson` into a MultiPolygon object\n\n Args:\n uk_shape (gpd.GeoDataFrame): Input from National Grid ESO\n\n Returns:\n MultiPolygon: Object representing the UK-region.\n \"\"\"\n concat_poly = unary_union(uk_shape[\"geometry\"].values)\n\n return MultiPolygon(Polygon(p.exterior) for p in concat_poly.geoms)\n\n\ndef generate_polygon_mask(\n coordinates_x: Iterable[int], coordinates_y: Iterable[int], polygon: MultiPolygon\n) -> np.ndarray:\n \"\"\"Multiprocessed wrapper function to check if lists of coordinates lie within a polygon.\n\n Args:\n coordinates_x (Iterable[int]): x-coordinates of points of interest in OSGB\n coordinates_y (Iterable[int]): y-coordinates of points of interest in OSGB\n polygon (MultiPolygon): polygon to infer if points of interest lie within.\n\n Returns:\n np.ndarray: 2-D array where each (x_i, y_i) value signifies if the point (x_i, y_i) belong\n to the polygon.\n \"\"\"\n coords = list(map(lambda x: Point(x[0], x[1]), itertools.product(coordinates_x, coordinates_y)))\n\n # create a mask for belonging to UK region or not\n mask = check_points_in_multipolygon_multiprocessed(coords, polygon)\n mask = mask.reshape(len(coordinates_x), len(coordinates_y)).T\n mask = mask.astype(float)\n mask[mask == 0] = np.nan\n\n return mask\n\n\ndef check_point_in_multipolygon(point: Point, polygon: Union[MultiPolygon, Polygon]) -> bool:\n \"\"\"Check if a point exists in a polygon\n\n Args:\n point (Point): Point of interest\n polygon (Union[MultiPolygon, Polygon]): Polygon to check\n\n Returns:\n bool: True if Point is in the Polygon\n \"\"\"\n return polygon.contains(point)\n\n\ndef check_points_in_multipolygon_multiprocessed(\n points: Iterable[Point],\n polygon: Union[MultiPolygon, Polygon],\n num_processes: int = 3,\n) -> np.ndarray:\n \"\"\"Multiprocessed wrapper for checking points within a polygon\n\n Args:\n points (Iterable[Point]): collection of Points\n polygon (Union[MultiPolygon, Polygon]): polygon to check\n num_processes (int, optional): Defaults to 3.\n\n Returns:\n np.ndarray: _description_\n \"\"\"\n items = [(point, polygon) for point in points]\n results = list()\n with mp.Pool(num_processes) as pool:\n for result in pool.starmap(check_point_in_multipolygon, items):\n results.append(result)\n return np.asarray(results)\n\n\ndef _process_nwp(\n nwp_slice: xr.Dataset, mask: xr.DataArray, x_coord: str = \"x\", y_coord: str = \"y\"\n) -> Tuple[xr.Dataset, xr.Dataset]:\n \"\"\"Processing logic for region masked downsampling\n\n Args:\n nwp_slice (xr.Dataset): slice of NWP data\n mask (xr.DataArray): geospatial mask of nan/non-nan values\n x_coord (str, optional): coordinate name of x dimension in NWP dataset. Defaults to \"x\".\n y_coord (str, optional): coordinate name of y dimension in NWP dataset. Defaults to \"y\".\n\n Returns:\n Tuple[xr.Dataset, xr.Dataset]: _description_\n \"\"\"\n uk_region = xr.where(~mask.isnull(), nwp_slice, np.nan).mean(dim=[x_coord, y_coord])\n outer_region = xr.where(mask.isnull(), nwp_slice, np.nan).mean(dim=[x_coord, y_coord])\n\n return uk_region, outer_region\n\n\nclass NWPUKRegionMaskedDatasetBuilder:\n \"\"\"Class for iteratively processing NWP data.\"\"\"\n\n def __init__(self, nwp: xr.Dataset, evaluation_timepoints: pd.DatetimeIndex) -> None:\n \"\"\"Initalise dataset builder.\n\n Args:\n nwp (xr.Dataset): NWP xarray dataset [variable, step, init_time, x, y]\n evaluation_timepoints (pd.DatetimeIndex): datetime points to interpolate onto\n \"\"\"\n self.nwp = nwp\n self.eval_timepoints = evaluation_timepoints\n self.mask = self.load_mask()\n\n def load_mask(self) -> xr.DataArray:\n \"\"\"Loads UK region mask from National Grid ESO\n\n Returns:\n xr.DataArray: UK-region mask, on NWP (x,y) coords\n \"\"\"\n uk_polygon = query_eso_geojson()\n uk_polygon = process_eso_uk_multipolygon(uk_polygon)\n mask = generate_polygon_mask(self.nwp.coords[\"x\"], self.nwp.coords[\"y\"], uk_polygon)\n\n # convert numpy array to xarray mask for a 1 variable, 1 step times series of (x,y) coords\n mask = xr.DataArray(\n np.tile(mask.T, (len(self.nwp.coords[\"init_time\"]), 1, 1)),\n dims=[\"init_time\", \"x\", \"y\"],\n )\n return mask\n\n def build_region_masked_covariates(\n self,\n variable: str,\n step: int,\n ) -> Tuple[pd.DataFrame, pd.DataFrame]:\n \"\"\"Process NWP sliced by variable and forecast step\n\n Args:\n variable (str): variable to slice\n step (int): forecast horizon index to slice\n\n Returns:\n Tuple[pd.DataFrame, pd.DataFrame]: inner and outer downsampled data\n \"\"\"\n _nwp = self.nwp.isel(step=step).sel(variable=variable)\n\n uk_region, outer_region = _process_nwp(_nwp, self.mask)\n\n # interpolate both to the common GSP time points\n uk_region = (\n uk_region.interp(init_time=self.eval_timepoints, method=\"linear\").to_array().as_numpy()\n )\n outer_region = (\n outer_region.interp(init_time=self.eval_timepoints, method=\"linear\")\n .to_array()\n .as_numpy()\n )\n\n # cast to dataframe\n uk_region = pd.DataFrame(\n index=self.eval_timepoints, columns=[f\"{variable}_within\"], data=uk_region\n )\n outer_region = pd.DataFrame(\n index=self.eval_timepoints, columns=[f\"{variable}_outer\"], data=outer_region\n )\n\n return (uk_region, outer_region)\n","repo_name":"openclimatefix/uk-pv-national-xg","sub_path":"gradboost_pv/preprocessing/region_filtered.py","file_name":"region_filtered.py","file_ext":"py","file_size_in_byte":8187,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"} +{"seq_id":"30405050659","text":"from random import randint as rnd\n\n# На выбор:\n\ndef game1():\n \"\"\"\n 1. Создайте программу для игры с конфетами.\n Условие игры: На столе лежит 117 конфета. Играют два игрока делая ход друг после друга. \n Первый ход определяется жеребьёвкой. За один ход можно забрать не более чем 28 конфет. \n Все конфеты оппонента достаются сделавшему последний ход.\n a) человек против человека.\n b) добавьте игру против бота\n \"\"\"\n def move_people(player, candys, max_take):\n \"\"\"Ход человека\"\"\"\n while True:\n move = int(input(f'{player}, Ваш ход... '))\n if move > 0 and move <= max_take and move <= candys:\n print(f'Ты забрал(а) {move} конфет')\n candys -= move\n print(f'Осталось {candys} конфет')\n break\n else:\n print(f'Столько взять нельзя. Можно взять до {max_take} или не больше оставшегося количества конфет')\n return candys\n\n def move_bot(candys, max_take):\n \"\"\"Ход бота\"\"\"\n print('Ход бота')\n move = candys % (max_take + 1)\n if move == 0:\n move = rnd(1, max_take) if candys >= max_take else candys\n print(f'Бот забрал {move} конфет')\n candys -= move\n print(f'Осталось {candys} конфет')\n return candys\n\n def win(candys, move, player1, player2):\n \"\"\"Чей выйгрыш\"\"\"\n if candys <= 28:\n return player1 if move % 2 == 1 else player2\n else:\n return False\n\n def people(candys, max_take):\n \"\"\"Игра с человеком\"\"\"\n print('Итак, начнём!')\n player1 = input('Введите имя первого игрока: ')\n player2 = input('Введите имя второго игрока: ')\n \n move = rnd(1, 2)\n \n while True:\n if move % 2 == 0:\n candys = move_people(player1, candys, max_take)\n else:\n candys = move_people(player2, candys, max_take)\n \n if move >= (candys // max_take) - 1:\n temp = win(candys, move, player1, player2)\n if temp:\n print(f'{temp} выиграл')\n break\n move += 1\n\n def bot(candys, max_take):\n \"\"\"Игра с ботом\"\"\"\n print('Итак, начнём!')\n player = input('Введите имя игрока: ')\n \n move = rnd(1, 2) \n\n while True:\n if move % 2 == 0:\n candys = move_people(player, candys, max_take)\n else:\n candys = move_bot(candys, max_take)\n\n if move >= (candys // max_take) - 1:\n temp = win(candys, move, player, 'Бот')\n if temp:\n print(f'{temp} выиграл')\n break\n move += 1\n\n candys = 117 \n max_take = 28 \n print('\"Привет! Тебя приветствует игра \"Забери все конфеты!\"')\n print(f'Основные правила игры: Дано {candys} конфет, за один ход можно взять не более {max_take} конфет')\n print('С кем хочешь играть? Введи: 1 - человек, 2 - бот')\n game = int(input()) \n if game == 1:\n people(candys, max_take) \n else:\n bot(candys, max_take)\n \ndef game2():\n \"\"\"\n 2.Создайте программу для игры в \"\"Крестики-нолики\"\".\n (в консоли происходит выбор позиции)\n \"\"\" \n def show_field(field):\n \"\"\"Поле\"\"\"\n for i in range(0, len(field), 3):\n print(field[i], field[i+1], field[i+2])\n return field\n \n def input_pos(field):\n \"\"\"Позиция на поле\"\"\"\n while True:\n position = int(input('Введите позицию: '))\n if type(field[position-1]) == int and 1 <= position <= 9:\n field[position-1] = 'X'\n break\n else:\n print('Позиция занята')\n return field\n \n print('Привет! Тебя приветствует игра \"Крестики-нолики!\"')\n \n field = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n \n print('-'*10)\n show_field(field)\n print('-'*10)\n #цикл \n input_pos(field)\n show_field(field)\n print('-'*10)\n\n\n\ngame1()\n\n","repo_name":"MaryVecherik/Python-seminars-and-homework","sub_path":"seminar5/homework5.py","file_name":"homework5.py","file_ext":"py","file_size_in_byte":4924,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"4405468898","text":"def validate_subsequence(array: list, sequence: list) -> bool:\n seq_idx, array_idx = 0, 0\n while seq_idx < len(sequence) and array_idx < len(array):\n if sequence[seq_idx] == array[array_idx]:\n seq_idx += 1\n array_idx += 1\n return seq_idx == len(sequence)\n\n\nif __name__ == '__main__':\n array = [5, 1, 22, 25, 6, -1, 8, 10]\n sequence = [1, 6, -1, 10]\n expected = True\n print(f\"Actual result: {validate_subsequence(array, sequence)}. Expected: {expected}\")","repo_name":"tung491/algo-expert-2021","sub_path":"array/validate_subsequence/validate_subsequence.py","file_name":"validate_subsequence.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"701349741","text":"import httpx as requests\nfrom asyncio import run, gather\n\nfrom functools import wraps\nimport time\n\n\ndef stop_watch(func) :\n @wraps(func)\n def wrapper(*args, **kargs) :\n start = time.time()\n result = func(*args,**kargs)\n elapsed_time = time.time() - start\n print(f\"{func.__name__} is {elapsed_time} sec\")\n return result\n return wrapper\n\nn=10\n \nurls1 = [\"http://fastapi-uvicorn:8000/sync\"] * n\nurls2 = [\"http://fastapi-uvicorn:8000/async\"] * n\nurls3 = [\"http://fastapi-gunicorn:8000/sync\"] * n\nurls4 = [\"http://fastapi-gunicorn:8000/async\"] * n\nurls5 = [\"http://flask:8000/wait\"] * n\n\n@stop_watch\ndef req(url):\n return requests.get(url).json()[\"wait\"]\n\ndef sync_func(urls):\n res=sorted([float(req(u)) for u in urls])\n\ndef main(urls):\n start = time.time()\n sync_func(urls)\n elapsed_time = time.time() - start\n print(f\"tolal time: {elapsed_time} sec.\")\n\n# print(\"sync\")\n# main(urls1)\n# main(urls2)\n# main(urls3)\n# main(urls4)\n\nasync def async_request(client,url):\n start = time.time()\n r = await client.get(url)\n j = r.json()\n elapsed_time = time.time() - start\n #return float(j[\"wait\"])\n return elapsed_time\n\nasync def async_func(urls):\n async with requests.AsyncClient(timeout=requests.Timeout(50.0, read=100.0)) as client:\n tasks = [async_request(client,u) for u in urls]\n res=await gather(*tasks, return_exceptions=True)\n print(sorted(res))\n\n\ndef main2(urls):\n print(urls[0])\n start = time.time()\n run(async_func(urls))\n elapsed_time = time.time() - start\n print(f\"total time: {elapsed_time} sec.\")\n print(\"\")\n\nprint(\"\")\nprint(\"async\")\nmain2(urls1)\nmain2(urls2)\nmain2(urls3)\nmain2(urls4)\nmain2(urls5)\n","repo_name":"kameyama/practices","sub_path":"gunicorn_vs_uvicorn/client/src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"3139595194","text":"# BOJ 2632\nimport sys\n\nsi = sys.stdin.readline\n\n\ndef add(arr, t):\n psum = [0] * 2000001\n psum[0] = 1 # 아무것도 더하지 않는 경우의 수\n for i in range(len(arr)):\n s = 0\n for j in range(len(arr)):\n s += arr[(i + j) % len(arr)]\n if s > t:\n break\n else:\n psum[s] += 1\n psum[sum(arr)] = 1\n return psum\n\n\ndef solve(left, right, t):\n res = 0\n for i in range(t + 1):\n res += left[i] * right[t - i]\n return res\n\n\nt = int(si())\nn, m = map(int, si().split())\nleft = [int(si()) for _ in range(n)]\nright = [int(si()) for _ in range(m)]\n\nlsum = add(left, t)\nrsum = add(right, t)\n\nprint(solve(lsum, rsum, t))","repo_name":"mrbartrns/algorithm-and-structure","sub_path":"BOJ/exaustive_search_boj/boj_2632.py","file_name":"boj_2632.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"22605481609","text":"import unittest\n\nfrom ash_aed.db import DB\nfrom ash_aed.errors import ServiceError\nfrom ash_aed.models import (\n AEDInstallationLocation,\n AEDInstallationLocationFactory,\n CurrentLocation\n)\nfrom ash_aed.services import AEDInstallationLocationService\n\ntest_data = [\n {\n \"area\": \"一条通〜十条通\",\n \"location_id\": 1,\n \"location_name\": \"旭川市教育委員会\",\n \"postal_code\": \"070-0036\",\n \"address\": \"北海道旭川市6条通8丁目セントラル旭川ビル6階\",\n \"phone_number\": \"0166-25-7534\",\n \"available_time\": \"\",\n \"installation_floor\": \"6階教育政策課\",\n \"latitude\": 43.7703945,\n \"longitude\": 142.3631408,\n },\n {\n \"area\": \"一条通〜十条通\",\n \"location_id\": 9,\n \"location_name\": \"フィール旭川\",\n \"postal_code\": \"070-0031\",\n \"address\": \"北海道旭川市1条通8丁目\",\n \"phone_number\": \"0166-25-5443\",\n \"available_time\": \"※平日午前8時45分から午後7時30分まで土日祝午前10時から午後7時30分まで\",\n \"installation_floor\": \"7階国際交流スペース内\",\n \"latitude\": 43.76572279,\n \"longitude\": 142.3597048,\n },\n {\n \"area\": \"一条通〜十条通\",\n \"location_id\": 34,\n \"location_name\": \"旭川市ときわ市民ホール\",\n \"postal_code\": \"078-8215\",\n \"address\": \"北海道旭川市5条通4丁目\",\n \"phone_number\": \"0166-23-5577\",\n \"available_time\": \"施設休館日を除く， 午前9時〜午後10時\",\n \"installation_floor\": \"1階事務室\",\n \"latitude\": 43.77216158,\n \"longitude\": 142.356329,\n },\n {\n \"area\": \"末広\",\n \"location_id\": 187,\n \"location_name\": \"旭川市立春光小学校\",\n \"postal_code\": \"071-8131\",\n \"address\": \"北海道旭川市末広1条1丁目\",\n \"phone_number\": \"0166-51-5288\",\n \"available_time\": \"\",\n \"installation_floor\": \"1階(体育教官室前)廊下\",\n \"latitude\": 43.80256755,\n \"longitude\": 142.3819691,\n },\n {\n \"area\": \"末広\",\n \"location_id\": 195,\n \"location_name\": \"旭川市立六合中学校\",\n \"postal_code\": \"071-8133\",\n \"address\": \"北海道旭川市末広3条2丁目\",\n \"phone_number\": \"0166-51-5388\",\n \"available_time\": \"\",\n \"installation_floor\": \"2階職員室\",\n \"latitude\": 43.80730293,\n \"longitude\": 142.3777754,\n },\n {\n \"area\": \"花咲\",\n \"location_id\": 357,\n \"location_name\": \"旭川市花咲スポーツ公園　球技場\",\n \"postal_code\": \"070-0901\",\n \"address\": \"北海道旭川市花咲町3丁目\",\n \"phone_number\": \"0166-51-5288\",\n \"available_time\": \"4月20日〜10月20日(延長の可能性あり)専用使用時のみ\",\n \"installation_floor\": \"1階事務室内\",\n \"latitude\": 43.78868943,\n \"longitude\": 142.3701686,\n },\n {\n \"area\": \"宮前\",\n \"location_id\": 447,\n \"location_name\": \"旭川地方法務局\",\n \"postal_code\": \"\",\n \"address\": \"旭川市宮前1条3丁目3番15号\",\n \"phone_number\": \"\",\n \"available_time\": \"\",\n \"installation_floor\": \"\",\n \"latitude\": 43.75798757,\n \"longitude\": 142.3723008,\n },\n {\n \"area\": \"宮前\",\n \"location_id\": 448,\n \"location_name\": \"旭川中税務署(旭川合同庁舎)\",\n \"postal_code\": \"\",\n \"address\": \"旭川市宮前1条3丁目3番15号 旭川合同庁舎\",\n \"phone_number\": \"\",\n \"available_time\": \"\",\n \"installation_floor\": \"\",\n \"latitude\": 43.7577086,\n \"longitude\": 142.3730304,\n },\n {\n \"area\": \"宮前\",\n \"location_id\": 449,\n \"location_name\": \"旭川市民活動交流センター　CoCoDe\",\n \"postal_code\": \"\",\n \"address\": \"旭川市宮前1条3丁目3番30号\",\n \"phone_number\": \"\",\n \"available_time\": \"\",\n \"installation_floor\": \"\",\n \"latitude\": 43.7566658,\n \"longitude\": 142.3717082,\n },\n {\n \"area\": \"宮前\",\n \"location_id\": 450,\n \"location_name\": \"旭川市科学館　サイパル\",\n \"postal_code\": \"\",\n \"address\": \"旭川市宮前1条3丁目3番32号\",\n \"phone_number\": \"\",\n \"available_time\": \"\",\n \"installation_floor\": \"\",\n \"latitude\": 43.7563103,\n \"longitude\": 142.3705926,\n },\n {\n \"area\": \"宮前\",\n \"location_id\": 451,\n \"location_name\": \"旭川市障害者福祉センター「おぴった」\",\n \"postal_code\": \"\",\n \"address\": \"旭川市宮前1条3丁目3番7号\",\n \"phone_number\": \"\",\n \"available_time\": \"\",\n \"installation_floor\": \"\",\n \"latitude\": 43.7583754,\n \"longitude\": 142.370498,\n },\n]\n\n\nclass TestAEDInstallationLocationService(unittest.TestCase):\n @classmethod\n def setUpClass(self):\n self.factory = AEDInstallationLocationFactory()\n for row in test_data:\n self.factory.create(**row)\n self.db = DB()\n self.service = AEDInstallationLocationService(self.db)\n self.current_location = CurrentLocation(\n latitude=43.77082378, longitude=142.3650193\n )\n\n @classmethod\n def tearDownClass(self):\n self.db.close()\n\n def test_create(self):\n self.service.truncate()\n for item in self.factory.items:\n self.assertTrue(self.service.create(item))\n self.db.commit()\n\n def test_get_all(self):\n for item in self.service.get_all():\n self.assertTrue(isinstance(item, AEDInstallationLocation))\n\n def test_find_by_location_id(self):\n location = self.service.find_by_location_id(9)\n self.assertEqual(location[0].location_name, \"フィール旭川\")\n\n def test_find_by_location_name(self):\n # 検索結果が10件以上ある場合、先頭10件が表示される\n results = self.service.find_by_location_name(\"旭川\")\n results_body = results[\"pagenated_results_body\"]\n results_number = results[\"all_results_number\"]\n max_page = results[\"max_page\"]\n self.assertEqual(len(results_body), 10)\n self.assertEqual(results_number, 11)\n self.assertEqual(max_page, 2)\n\n # 2ページ目は検索結果が11件目以降が表示される\n results = self.service.find_by_location_name(location_name=\"旭川\", page=2)\n results_body = results[\"pagenated_results_body\"]\n results_number = results[\"all_results_number\"]\n max_page = results[\"max_page\"]\n self.assertEqual(len(results_body), 1)\n self.assertEqual(results_body[0].location_name, \"旭川市障害者福祉センター「おぴった」\")\n self.assertEqual(results_number, 11)\n self.assertEqual(max_page, 2)\n\n # 検索結果が10件未満の場合\n results = self.service.find_by_location_name(\"学校\")\n results_body = results[\"pagenated_results_body\"]\n results_number = results[\"all_results_number\"]\n max_page = results[\"max_page\"]\n self.assertEqual(len(results_body), 2)\n self.assertEqual(results_body[0].location_name, \"旭川市立春光小学校\")\n self.assertEqual(results_number, 2)\n self.assertEqual(max_page, 1)\n\n # 指定したページ数が上限を超えている場合\n with self.assertRaises(ServiceError):\n self.service.find_by_location_name(location_name=\"旭川\", page=3)\n\n def test_get_area_names(self):\n expect = [\"一条通〜十条通\", \"花咲\", \"宮前\", \"末広\"]\n self.assertEqual(self.service.get_area_names(), expect)\n\n def test_find_by_area_name(self):\n area_locations = self.service.find_by_area_name(\"一条通〜十条通\")\n self.assertEqual(area_locations[0].location_name, \"旭川市教育委員会\")\n\n def test_get_near_locations(self):\n near_locations = self.service.get_near_locations(self.current_location)\n # 一番近い避難場所\n self.assertEqual(near_locations[0][\"order\"], 1)\n self.assertEqual(near_locations[0][\"location\"].location_name, \"旭川市教育委員会\")\n self.assertEqual(near_locations[0][\"distance\"], 0.16)\n # 二番目に近い避難場所\n self.assertEqual(near_locations[1][\"order\"], 2)\n self.assertEqual(near_locations[1][\"location\"].location_name, \"フィール旭川\")\n self.assertEqual(near_locations[1][\"distance\"], 0.71)\n # 五番目に近い避難場所\n self.assertEqual(near_locations[-1][\"order\"], 5)\n self.assertEqual(near_locations[-1][\"location\"].location_name, \"旭川地方法務局\")\n self.assertEqual(near_locations[-1][\"distance\"], 1.54)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"takedah/ash_aed","sub_path":"tests/test_services.py","file_name":"test_services.py","file_ext":"py","file_size_in_byte":8913,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"37021700080","text":"import re\n\n\ndef get_shape_category(token):\n if re.match('^[\\n]+$', token): # IS LINE BREAK\n return 'NL'\n if any(char.isdigit() for char in token) and re.match('^[0-9.,]+$', token): # IS NUMBER (E.G., 2, 2.000)\n return 'NUMBER'\n if re.fullmatch('[^A-Za-z0-9\\t\\n ]+', token): # IS SPECIAL CHARS (E.G., $, #, ., *)\n return 'SPECIAL'\n if re.fullmatch('^[A-Z\\-.]+$', token): # IS UPPERCASE (E.G., AGREEMENT, INC.)\n return 'ALL-CAPS'\n if re.fullmatch('^[A-Z][a-z\\-.]+$', token): # FIRST LETTER UPPERCASE (E.G. This, Agreement)\n return '1ST-CAP'\n if re.fullmatch('^[a-z\\-.]+$', token): # IS LOWERCASE (E.G., may, third-party)\n return 'LOWER'\n if not token.isupper() and not token.islower(): # WEIRD CASE (E.G., 3RD, E2, iPhone)\n return 'MISC'\n return 'MISC'\n\n\ndef get_shape_category_simple(token):\n if token.islower():\n return 'ALL-LOWER'\n elif token.isupper():\n return 'ALL-UPPER'\n elif re.fullmatch('[A-Z][a-z]+', token):\n return 'FIRST-UPPER'\n else:\n return 'MISC'\n","repo_name":"achilleas-michos/esgi.nlp","sub_path":"projects/document_model/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"7843035990","text":"from spack import *\n\n\nclass Ffmpeg(AutotoolsPackage):\n \"\"\"FFmpeg is a complete, cross-platform solution to record,\n convert and stream audio and video.\"\"\"\n\n homepage = \"https://ffmpeg.org\"\n url = \"http://ffmpeg.org/releases/ffmpeg-3.2.4.tar.bz2\"\n\n version('3.2.4', 'd3ebaacfa36c6e8145373785824265b4')\n version('4.2', '41b4ade83439fafe635001127f1056d4')\n\n variant('shared', default=True,\n description='build shared libraries')\n variant('x264', default=True,\n description='enable x264 encoder')\n\n depends_on('yasm@1.2.0:')\n depends_on('x264', when='@4.2: +x264')\n\n\n def configure_args(self):\n spec = self.spec\n config_args = ['--enable-pic']\n\n if '+shared' in spec:\n config_args.append('--enable-shared')\n\n if '+x264' in spec and self.version >= Version('4.2'):\n config_args.append('--enable-libx264')\n config_args.append('--enable-gpl')\n return config_args\n","repo_name":"MatMaul/spack","sub_path":"var/spack/repos/builtin/packages/ffmpeg/package.py","file_name":"package.py","file_ext":"py","file_size_in_byte":984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"78"} +{"seq_id":"72632277372","text":"import sys\nfrom PIL import Image\nimport numpy as np\n\nfrom image_utils import *\n\nclass PieceEdge:\n def __init__(self, piece, path, out_score, index):\n self.piece = piece\n self.path = path\n self.index = index\n if abs(out_score) < EDGE_STICK_OUT_SCORE_CUTOFF:\n self.piece_type = 0\n elif out_score > 0:\n self.piece_type = 1\n else:\n self.piece_type = -1\n\n def isStickingOut(self):\n return self.piece_type == 1\n\n def isStickingIn(self):\n return self.piece_type == -1\n\n def isFlat(self):\n return self.piece_type == 0\n\nclass PuzzlePiece:\n def __init__(self, image_mat):\n if areAllNotPiecePixels(image_mat):\n sys.exit(\"Got a puzzle piece with no pixels\")\n\n image_mat = defrag(image_mat)\n image_mat = crop(image_mat)\n image_mat = straighten(image_mat)\n image_mat = crop(image_mat)\n self.image_mat = image_mat\n\n edge_paths, edge_out_scores = getEdgeTypes(image_mat)\n self.edges = [PieceEdge(self, edge_paths[i], edge_out_scores[i], i) for i in xrange(len(edge_paths))]\n\n def saveImage(self, filename):\n save_mat = np.copy(self.image_mat)\n\n for x in xrange(save_mat.shape[0]):\n for y in xrange(save_mat.shape[1]):\n if not areAllPiecePixels(save_mat[x, y]):\n save_mat[x, y, :] = np.array([255, 0, 0])\n\n Image.fromarray(save_mat.astype('uint8')).save(filename)\n\n def countPix(self):\n count = 0\n for x in xrange(self.image_mat.shape[0]):\n for y in xrange(self.image_mat.shape[1]):\n if not areAllPiecePixels(save_mat[x,y]):\n continue\n count += 1\n return count","repo_name":"jandress94/puzzle_solver","sub_path":"code/piece.py","file_name":"piece.py","file_ext":"py","file_size_in_byte":1774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"10042966340","text":"num = int(input(\"Enter a number: \"))\r\nsum = 0\r\ntemp = num\r\ndigits_in_num = len(str(num))\r\nwhile temp > 0:\r\n n = temp % 10\r\n sum += n ** digits_in_num\r\n temp = temp//10\r\n\r\nif num == sum:\r\n print(num,\"is an Armstrong number\")\r\nelse:\r\n print(num,\"is not an Armstrong number\")","repo_name":"piyush-mk/MIT_CCE_Lab_Sem5","sub_path":"Advanced_Programming_Lab/Lab01/Lab_add2.py","file_name":"Lab_add2.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"73985500410","text":"from socket import AF_INET, SOCK_STREAM, socket\nimport threading\n\n\nclass Client:\n def __init__(self):\n self.nickname = None\n self.listening = True\n self.host = '127.0.0.1'\n self.port = 3333\n self.__start_client()\n\n def close(self):\n self.server_instance.close()\n\n def list_all_users(self):\n self.server_instance.send(f'{self.nickname}: /list'.encode('ascii'))\n\n def __start_client(self):\n try:\n self.nickname = input(\"Choose your nickname: \")\n self.server_instance = socket(AF_INET, SOCK_STREAM)\n self.server_instance.connect((self.host, self.port))\n self.server_instance.send(self.nickname.encode('ascii'))\n except ConnectionRefusedError:\n print('Server is not running!')\n\n def receive(self):\n while self.listening:\n try:\n message = self.server_instance.recv(1024).decode('ascii')\n print(message)\n except ConnectionError:\n print(\"An error occured!\")\n self.server_instance.close()\n break\n\n def write(self):\n while True:\n content = input('')\n message = '{}: {}'.format(self.nickname, content)\n self.server_instance.send(message.encode('ascii'))\n if content == '/quit':\n self.server_instance.close()\n self.listening = False\n break\n\n def run(self):\n receive_thread = threading.Thread(target=self.receive)\n receive_thread.start()\n write_thread = threading.Thread(target=self.write)\n write_thread.start()\n\n\nif __name__ == '__main__':\n client = Client()\n client.run()\n","repo_name":"guilhermesam/online-chat","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1735,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"30580223395","text":"from PIL import Image\nimport numpy as np\nimport os\nfrom keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img\nfrom keras.models import Sequential\nfrom keras.layers import Dropout, Flatten, Dense\nfrom keras import applications\nfrom keras.utils.np_utils import to_categorical\nimport cv2\n\n\ndef predict_classes(image_paths):\n\tclass_dict = np.load('categories_dict.npy').item()\n\n\tstr_arr = []\n\n\tfor image in image_paths:\n\n\t\timage_path = image\n\t\tprint(image_path)\n\n\t\timage = load_img(image_path, target_size=(224, 224))\n\t\timage_arr = img_to_array(image)\n\n\t\timage_arr = image_arr / 255\n\n\t\timage_expanded = np.expand_dims(image_arr, axis=0)\n\n\t\tmodel = applications.VGG16(include_top=False, weights='imagenet')\n\n\t\textract_features = model.predict(image_expanded)\n\n\t\tfeatures_shape = extract_features.shape\n\t\tfeatures_shape_input = features_shape[1:]\n\n\t\tmodel = Sequential()\n\t\tmodel.add(Flatten(input_shape=features_shape_input))\n\t\tmodel.add(Dense(256, activation='relu'))\n\t\tmodel.add(Dropout(0.5))\n\t\tmodel.add(Dense(8, activation='softmax'))\n\n\t\tmodel.load_weights('weights.h5')\n\n\t\tclass_prediction = model.predict_classes(extract_features)\n\n\t\tclass_index = class_prediction[0]\n\n\t\tfor food_item, dict_index in class_dict.items():\n\t\t\tif dict_index == class_index:\n\t\t\t\tclass_label = food_item\n\n\t\tstr_arr.append(class_label)\n\n\treturn(str_arr)\n\n\n","repo_name":"nehalrawat/AI-Nutrition-Project","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"23791023849","text":"# -*- mode: python -*-\r\na = Analysis([os.path.join(HOMEPATH,'support\\\\_mountzlib.py'), os.path.join(HOMEPATH,'support\\\\useUnicode.py'), '../src/main.py'],\r\n pathex=['C:\\\\Users\\\\jeff\\\\Desktop\\\\martus-summarizer\\\\package-win'])\r\npyz = PYZ(a.pure)\r\nexe = EXE( pyz,\r\n a.scripts,\r\n a.binaries,\r\n a.zipfiles,\r\n a.datas,\r\n name=os.path.join('dist', 'main.exe'),\r\n debug=False,\r\n strip=False,\r\n upx=True,\r\n console=False , icon='martus.ico')\r\napp = BUNDLE(exe,\r\n name=os.path.join('dist', 'main.exe.app'))\r\n","repo_name":"benetech/Martus-Summarizer","sub_path":"package-win/main.spec","file_name":"main.spec","file_ext":"spec","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"5433939242","text":"\nfrom question_model import Question\nfrom data import question_data\nfrom quiz_brain import QuizBrain\n\n\nquestion_bank = []\nfor i in question_data:\n q = i['question']\n a = i['correct_answer']\n question_bank.append(Question(q, a))\n\n# print(f'question_bank : {question_bank}')\n# print(question_bank[0].text)\n# print(f'how much questions : {len(question_bank)}')\n\nuser_quiz = QuizBrain(question_bank)\n\nwhile user_quiz.still_has_guestions():\n user_quiz.next_question()\n\n\nprint(f'your final scoue {user_quiz.user_points} out of questions {user_quiz.question_number}')\n\n# https://opentdb.com/api_config.php","repo_name":"Andrzej-007/quize_game_start","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"31568106930","text":"import pygame\nimport random\n\nWIDTH = 800\nHEIGHT = 600\nFPS = 60\n\n# Определяем цвета\nWHITE = (255, 255, 255)\nBLACK = (0, 0, 0)\nRED = (255, 0, 0)\n\npygame.init()\npygame.mixer.init()\nscreen = pygame.display.set_mode((WIDTH, HEIGHT))\npygame.display.set_caption(\"Neural Network Ball Game\")\nclock = pygame.time.Clock()\n\nclass Ball(pygame.sprite.Sprite):\n def __init__(self, x, y):\n pygame.sprite.Sprite.__init__(self)\n self.image = pygame.Surface((50, 50))\n self.image.fill(RED)\n self.rect = self.image.get_rect()\n self.rect.center = (x, y)\n self.vel = [random.randint(-5, 5), random.randint(-5, 5)]\n self.acceleration = [0, 0]\n\n def update(self, balls):\n for ball in balls:\n if ball != self:\n dx = ball.rect.centerx - self.rect.centerx\n dy = ball.rect.centery - self.rect.centery\n distance = (dx ** 2 + dy ** 2) ** 0.5\n if distance <= 50:\n self.acceleration[0] -= dx / 50\n self.acceleration[1] -= dy / 50\n self.vel[0] += self.acceleration[0]\n self.vel[1] += self.acceleration[1]\n self.rect.x += self.vel[0]\n self.rect.y += self.vel[1]\n self.acceleration = [0, 0]\n if self.rect.right > WIDTH or self.rect.left < 0:\n self.vel[0] *= -1\n if self.rect.bottom > HEIGHT or self.rect.top < 0:\n self.vel[1] *= -1\n\nall_sprites = pygame.sprite.Group()\nballs = []\nfor i in range(10):\n x = random.randint(0, WIDTH)\n y = random.randint(0, HEIGHT)\n ball = Ball(x, y)\n all_sprites.add(ball)\n balls.append(ball)\n\n# running = True\n# while running:\n# clock.tick(FPS)\n# for event in pygame.event.get():\n# if event.type == pygame.QUIT:\n# running = False\n\n# screen.fill(BLACK)\n# all_sprites.update(balls)\n# all_sprites.draw(screen)\n# pygame.display.flip()\n\npygame.quit()\n\nimport tensorflow as tf\nfrom tensorflow import keras\nimport numpy as np\n\n# Создаем модель нейронной сети\nmodel = keras.Sequential([\n keras.layers.Dense(32, input_shape=(3,), activation='relu'),\n keras.layers.Dense(16, activation='relu'),\n keras.layers.Dense(1, activation='sigmoid')\n])\n\n# Компилируем модель и задаем функцию потерь и оптимизатор\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n# Генерируем случайные данные для обучения\ntrain_data = []\nfor i in range(10000):\n x = random.randint(0, WIDTH)\n y = random.randint(0, HEIGHT)\n dx = random.randint(-5, 5)\n dy = random.randint(-5, 5)\n label = 1 if any(ball.rect.collidepoint(x, y) for ball in balls) else 0\n train_data.append([x, y, label])\n\ntrain_data = np.array(train_data)\n\n# Обучаем модель на сгенерированных данных\nmodel.fit(train_data[:, :2], train_data[:, 2], epochs=10, batch_size=32)\n","repo_name":"lilianzzz/neural_games","sub_path":"ff.py","file_name":"ff.py","file_ext":"py","file_size_in_byte":3020,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"11477567684","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\nimport math\n\ndef apoklisi(emb, perim, char):\n\n seven_perc = round(emb*0.07, 2)\n apoklisi = round((pow(math.sqrt(emb) + (2*char), 2) - emb)*(perim/(4*math.sqrt(109.3742))),2)\n \n if apoklisi > seven_perc:\n apod_apokl = seven_perc\n else:\n apod_apokl = apoklisi\n \n return apod_apokl\n\ndef main():\n emb = float(input('Εμβαδόν: '))\n perim = float(input('Περίμετρος: '))\n inp = input(\"a/Αστικό ή g/Αγροτικό: \")\n \n if inp == 'a':\n char = 0.5\n elif inp == 'g':\n char = 2.0\n \n print(\"Η αποδεκτή απόκλιση του γεωτεμαχίου είναι \" + str(apoklisi(emb, perim, char)) + \" τ.μ.\")\n \nif __name__ == \"__main__\":\n main()","repo_name":"DLampr/Miscellaneous","sub_path":"apodekth_apoklish.py","file_name":"apodekth_apoklish.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"el","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"15421616030","text":"'''\n구현, 문자열 문제\n\n우선 문제에서 종료 조건이 따로 없다.\n그래서 while문 안에 try, expect(catch) 입력이 안들어오면 종료할 수 있게 했다.\n\n문제는 문자열을 잘 다루면 된다. 조건은 어렵지 않다.\n문자열에서 23:00 이런식으로 들어올때 ':'만 잘 제거하면 된다.\n ㄴ> 방법은 여러가지가 있는데, 본인이 편한걸로 사용하면 될거 같다.\n ㄴ> list로 입력받아서 remove도 사용해봤고, 슬라이싱을 이용해서도 해봤다.\n\n조건은 학생이 입장하는 시간이 s보다 작거나 같으면 되고,\n퇴장하는 시간은 종료 시간보다 크거나 같고 스트리밍(q) 시간보다 작거나 같으면 된다.\n이때, ck에 있는 학생인지도 체크해야 된다.\n\nin\n 22:00 23:00 23:30\n 21:30 malkoring\n 21:33 tolelom\n 21:34 minjae705\n 21:35 hhan14\n 21:36 dicohy27\n 21:40 906bc\n 23:00 906bc\n 23:01 tolelom\n 23:10 minjae705\n 23:11 hhan14\n 23:20 dicohy27\nout\n 5\n\nin\n 06:00 12:00 18:00\n 06:00 shinyo17\n 06:00 kimchist\n 06:00 swoon\n 06:00 kheee512\n 06:00 Green55\n 09:00 kimchist\n 11:59 shinyo17\n 12:00 kimchist\n 17:59 swoon\n 17:59 swoon\n 18:00 kheee512\n 18:01 swoon\n 18:01 Green55\n 18:01 kheee512\n 18:01 swoon\n 18:21 jinius36\n 18:40 jeongyun1206\nout\n 3\n'''\nimport sys; input = sys.stdin.readline\n\ns, e, q = map(list, input().split())\ns.remove(':'); e.remove(':'); q.remove(':')\ns, e, q = int(''.join(s)), int(''.join(e)), int(''.join(q))\nres = 0\nck = set()\n\nwhile 1:\n try:\n time, student = input().split()\n time = int(time[:2] + time[3:])\n\n if s >= time:\n ck.add(student)\n elif e <= time <= q and student in ck:\n res += 1\n ck.remove(student)\n \n except:\n print(res)\n break\n","repo_name":"rkdalsdn94/algoalgo","sub_path":"solved_ac/Silver_2/싸이버개강총회_19583.py","file_name":"싸이버개강총회_19583.py","file_ext":"py","file_size_in_byte":1873,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"42379367637","text":"# Problem: https://www.hackerrank.com/challenges/re-sub-regex-substitution/problem\n\nimport re\n\nn = int(input())\nfor i in range(n):\n line = input()\n remove_and = re.sub(r\"(?<= )(&&)(?= )\", \"and\", line)\n remove_or = re.sub(r\"(?<= )(\\|\\|)(?= )\", \"or\", remove_and)\n print(remove_or)\n","repo_name":"mohdzubairshafi/HackerRank_Python_Practice","sub_path":"007. Regex and Parsing/006. Regex Substitution.py","file_name":"006. Regex Substitution.py","file_ext":"py","file_size_in_byte":291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"24203051154","text":"#!/usr/bin/env python3\n\n# Question:\n# Write a funciton that works like a \"Rock-Paper-Scissors\" game, remember the\n# rules:\n# - Rock beats scissors\n# - Scissors beats paper\n# - Paper beats rock\n#\n# The result should be:\n# - It's a tie!\n# - Rock wins!\n# - Scissors win!\n# - Paper wins!\n# - Invalid input!\n#\n# Suppose the following parameter is supplied to the program:\n# rock scissors\n# Then, the output should be:\n# Rock wins!\n\n\ndef play_game(option1, option2):\n\n if option1 == option2:\n return(\"It's a tie!\")\n elif option1 == 'rock':\n if option2 == 'scissors':\n return(\"Rock wins!\")\n else:\n return(\"Paper wins!\")\n elif option1 == 'scissors':\n if option2 == 'paper':\n return(\"Scissors win!\")\n else:\n return(\"Rock wins!\")\n elif option1 == 'paper':\n if option2 == 'rock':\n return(\"Paper wins!\")\n else:\n return(\"Scissors win!\")\n else:\n return(\"Invalid input!\")\n\n\n# ------- START TDD TESTS DEFINITION -----------\ndef test_play_game_rock_scissors():\n assert play_game(\"rock\", \"scissors\") == \"Rock wins!\"\n\n\ndef test_play_game_scissors_scissors():\n assert play_game(\"scissors\", \"scissors\") == \"It's a tie!\"\n\n\ndef test_play_game_paper_rock():\n assert play_game(\"paper\", \"rock\") == \"Paper wins!\"\n\n\ndef test_play_game_scissors_paper():\n assert play_game(\"scissors\", \"paper\") == \"Scissors win!\"\n\n\ndef test_play_game_invalid_paper():\n assert play_game(\"invalid\", \"paper\") == \"Invalid input!\"\n# ------- END TDD TESTS DEFINITION -------------\n\n\n# Program entrypoint\nif __name__ == \"__main__\":\n\n if len(sys.argv) == 3:\n\n option1 = sys.argv[1]\n option2 = sys.argv[2]\n result = play_game(option1, option2)\n print(result)\n\n else:\n print(\"Usage: %s \" % sys.argv[0])\n","repo_name":"erseco/ugr_tfm_maes_sample_exercises","sub_path":"homework_01_python/exercise_5.py","file_name":"exercise_5.py","file_ext":"py","file_size_in_byte":1858,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"34536500539","text":"# 나무 재테크\n# 입력: 1) 땅 크기 N, 나무 갯수 M, 지난 햇수 K 2~) 겨울에 양분을 추가해주는 배열 A 3~) 나무 위치(x, y), 나이 z\n# 출력: K년이 지난 후 살아남은 나무의 수\n\nimport sys\nfrom collections import deque\n\nN, M, K = map(int, input().split())\nnutrient_map = [[5] * N for _ in range(N)] # 양분\nTrees = deque() # x, y, 나이\nTree_index = set()\nA = [list(map(int, sys.stdin.readline().rstrip().split())) for _ in range(N)]\nfor i in range(M):\n x, y, age = list(map(int, sys.stdin.readline().rstrip().split()))\n Trees.append([x - 1, y - 1, age])\n Tree_index.add((x - 1, y - 1))\n\nsteps = [[-1, -1], [-1, 0], [-1, 1], [0, -1], [0, 1], [1, -1], [1, 0], [1, 1]]\n\nfor year in range(K):\n DieTrees = [] # x, y, 나이\n AppendTrees = [] # x, y\n # spring: 나무가 나이만큼 양분 먹고, 나이 1 증가, 나이 어린 나무부터 먹음, 양분 못먹으면 죽음\n for i in range(len(Trees)):\n x, y, age = Trees.popleft()\n if (A[x][y] * year) + nutrient_map[x][y] < age:\n DieTrees.append([x, y, age])\n else:\n nutrient_map[x][y] -= age\n Trees.append([x, y, age + 1])\n if (age + 1) % 5 == 0: # autumn: 나무의 번식, 번식하는 나무는 나이가 5의 배수, 인접한 8개의 칸에는 나이가 1인 나무 생성\n for x_step, y_step in steps:\n if 0 <= (x + x_step) < N and 0 <= (y + y_step) < N:\n AppendTrees.append([x + x_step, y + y_step])\n\n # summer: 죽은 나무가 양분으로 변함 (죽은 나무의 나이 / 2)\n for x, y, age in DieTrees:\n nutrient_map[x][y] += age // 2\n for x, y in AppendTrees:\n Trees.appendleft([x, y, 1])\n\nprint(len(Trees))","repo_name":"PeopleAndService/AlgorithmStudy","sub_path":"yoongyeong/by_python/week1/B16235.py","file_name":"B16235.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"ko","doc_type":"code","stars":8,"dataset":"github-code","pt":"78"} +{"seq_id":"39605520271","text":"# flake8: noqa\nimport json\nfrom tests.base import BaseTestCase\n\n\nclass TestEmailsBlueprint(BaseTestCase):\n \"\"\"Tests for the Emails Endpoints\"\"\"\n\n def test_check_verification_status(self):\n\n res = self.client.get(\n \"/api/email/verify/foobar@email.com/\",\n headers=[(\"Accept\", \"application/json\")]\n )\n\n self.assertEqual(res.status_code, 410)\n\n\n def test_update_registration_status(self):\n\n res = self.client.get(\n \"/api/email/verify/?token=foobar\",\n headers=[(\"Accept\", \"application/json\")]\n )\n\n self.assertEqual(res.status_code, 410)\n\n def test_send_verification_email(self):\n\n\n res = self.client.post(\n \"/api/email/verify/foobar@email.com/\",\n headers=[\n (\"Accept\", \"application/json\")\n ]\n )\n\n self.assertEqual(res.status_code, 410)\n","repo_name":"KnightHacks/hackathon-2021-backend","sub_path":"tests/routes/test_emails.py","file_name":"test_emails.py","file_ext":"py","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"78"} +{"seq_id":"33600068367","text":"\"\"\"empty message\n\nRevision ID: 85d9dcae1755\nRevises: 3ca87d1613fe\nCreate Date: 2023-08-02 22:50:17.577016\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '85d9dcae1755'\ndown_revision = '3ca87d1613fe'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('task',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('title', sa.String(), nullable=True),\n sa.Column('description', sa.String(), nullable=True),\n sa.Column('completed', sa.Boolean(), nullable=True),\n sa.Column('due_date', sa.DateTime(), nullable=True),\n sa.PrimaryKeyConstraint('id')\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('task')\n # ### end Alembic commands ###\n","repo_name":"christineelia/Flask-App---Task-Management","sub_path":"migrations/versions/85d9dcae1755_.py","file_name":"85d9dcae1755_.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"13963853851","text":"import argparse\nimport dataclasses\nimport logging\nimport random\nimport re\nimport signal\nimport time\nimport asyncio\nimport typing\n\nfrom paho.mqtt import client as mqtt\n\nfrom .ElcobusMessage import ElcobusFrame\n\n\nparser = argparse.ArgumentParser(description='Elcobus communication daemon')\nparser.add_argument('--mqtt-topic-prefix', help=\"output topic prefix\", type=str, default=\"elcobus\")\nparser.add_argument('--logfile', help=\"Log to the given file\", type=str)\nparser.add_argument('--debug', help=\"Enable debug mode\", action='store_true')\nparser.add_argument('mqtt_uri', help=\"mqtt://host/topic/prefix url to communicate on\")\n\nargs = parser.parse_args()\n\nlogging.getLogger(None).setLevel(logging.INFO)\nlogging.Formatter.converter = time.gmtime\n\nif args.debug:\n # load all decoders\n __import__('ElcobusMessage', globals(), level=1, fromlist=['*'])\n\n logging.getLogger(None).setLevel(logging.DEBUG)\n\nif args.logfile:\n log_file_handler = logging.FileHandler(args.logfile)\nelse:\n log_file_handler = logging.StreamHandler()\nlog_file_handler.setFormatter(logging.Formatter(\n fmt=\"%(asctime)sZ [%(name)s %(levelname)s] %(message)s\"\n))\nlogging.getLogger(None).addHandler(log_file_handler)\n\n\nlogger = logging.getLogger(__name__)\n\n\n# Print loaded modules\nlogger.info(\"Loaded ElcobusMessage decoder for:\")\nfor field in ElcobusFrame.Field:\n logger.info(f\" - 0x{field.value:04x} {field.name} (data: {field.data_type.__name__})\")\n\nloop = asyncio.get_event_loop()\n\ndef handle_sighup():\n logger.info(\"Received SIGHUP, reopening log file\")\n log_file_handler.close()\n logger.info(\"Received SIGHUP, log file reopened\")\n\nloop.add_signal_handler(signal.SIGHUP, handle_sighup)\n\n\n# MQTT stuff\n@dataclasses.dataclass()\nclass MqttConnectionDetails:\n protocol: str\n username: typing.Optional[str]\n password: typing.Optional[str]\n host: str\n port: int\n topic: str\n\n @classmethod\n def from_uri(cls, uri: str) -> \"MqttConnectionDetails\":\n mqtt_component_match = re.fullmatch(r'(?Pmqtt)://'\n r'((?P[^:@]+)(:(?P[^@]*)?@))?'\n r'(?P[^/:]+)'\n r'(:(?P\\d+))?'\n r'(/(?P.*))?', uri)\n mqtt_component = mqtt_component_match.groupdict()\n\n if mqtt_component['port'] is None:\n mqtt_component['port'] = 1883\n else:\n mqtt_component['port'] = int(mqtt_component['port'])\n\n ret = cls(**mqtt_component)\n logger.debug(\"Parsed MQTT URI as: \" + repr(ret))\n return ret\n\n\nmqtt_connection_details = MqttConnectionDetails.from_uri(args.mqtt_uri)\n\n\nclass PahoMqttAsyncioHelper:\n def __init__(self, client, loop):\n self.client = client\n self.client.on_socket_open = self.on_socket_open\n self.client.on_socket_close = self.on_socket_close\n self.client.on_socket_register_write = self.on_socket_register_write\n self.client.on_socket_unregister_write = self.on_socket_unregister_write\n self.loop = loop\n\n def on_socket_open(self, client, userdata, sock):\n logger.info(\"MQTT Socket opened\")\n\n def cb():\n # print(\"Socket is readable, calling loop_read\")\n client.loop_read()\n\n self.loop.add_reader(sock, cb)\n self.misc = self.loop.create_task(self.misc_loop())\n\n def on_socket_close(self, client, userdata, sock):\n logger.info(\"MQTT Socket closed\")\n self.loop.remove_reader(sock)\n self.misc.cancel()\n\n def on_socket_register_write(self, client, userdata, sock):\n # print(\"Watching socket for writability.\")\n\n def cb():\n # print(\"Socket is writable, calling loop_write\")\n client.loop_write()\n\n self.loop.add_writer(sock, cb)\n\n def on_socket_unregister_write(self, client, userdata, sock):\n # print(\"Stop watching socket for writability.\")\n self.loop.remove_writer(sock)\n\n async def misc_loop(self):\n # print(\"misc_loop started\")\n while self.client.loop_misc() == mqtt.MQTT_ERR_SUCCESS:\n try:\n await asyncio.sleep(1)\n except asyncio.CancelledError:\n break\n # print(\"misc_loop finished\")\n\n\nclass MqttClient:\n def __init__(\n self,\n mqtt_connection_details: MqttConnectionDetails,\n loop: asyncio.AbstractEventLoop = None,\n ):\n self.connection_details = mqtt_connection_details\n\n if loop is None:\n loop = asyncio.get_event_loop()\n self.loop = loop\n\n self.client = None\n self.aio_helper = None\n\n def _connect(self) -> None:\n if self.client is not None and self.client.is_connected():\n self.client.disconnect()\n\n self.client = mqtt.Client()\n self.aio_helper = PahoMqttAsyncioHelper(self.client, self.loop)\n\n self.client.on_connect = self.on_connect\n self.client.on_message = self.on_message\n self.client.on_disconnect = self.on_disconnect\n\n if self.connection_details.username is not None:\n self.client.username_pw_set(\n username=self.connection_details.username,\n password=self.connection_details.password,\n )\n self.client.connect(\n host=self.connection_details.host,\n port=self.connection_details.port\n )\n\n def get_mqtt_client(self):\n return self.client\n\n async def main(self):\n self._connect()\n\n while True:\n try:\n await asyncio.sleep(1)\n except asyncio.CancelledError:\n break\n\n def on_connect(self, client: mqtt.Client, user_data, flags, rc):\n rx_topic = self.connection_details.topic + '/bus_rx'\n client.subscribe(rx_topic, 2)\n\n def on_disconnect(self, client: mqtt.Client, user_data, rc):\n self.loop.call_soon(self.attempt_reconnect) # Will deadlock if called from here\n\n def attempt_reconnect(self):\n try:\n logger.info(\"Attempting reconnect...\")\n self._connect()\n except (ConnectionRefusedError, OSError):\n timeout = 1\n logger.warning(f\"Reconnect failed, retrying in {timeout} seconds\")\n self.loop.call_later(delay=timeout, callback=self.attempt_reconnect)\n\n def on_message(self, client: mqtt.Client, user_data, msg: mqtt.MQTTMessage):\n try:\n ebm = ElcobusFrame.ElcobusFrame.from_bytes(msg.payload)\n logger.info(f\"Rx: [{' '.join(['{:02x}'.format(b) for b in ebm.to_bytes()])}]\")\n logger.debug(\"Rx: %r\", ebm)\n # ^^ don't use ''.format()\n # This allows the repr(ebm) call to be omitted if the message is discarded\n\n except BufferError:\n logger.warning(\"Invalid message: too short?\")\n return\n\n except ValueError as e:\n logger.warning(\"Invalid message: {e}\".format(\n e=e,\n ))\n return\n\n process_frame(ebm)\n\n\nmy_source = 0x01\n\n\ndef process_frame(ebm: ElcobusFrame.ElcobusFrame):\n if not isinstance(ebm, ElcobusFrame.ElcobusMessage):\n return\n if ebm.message_type not in {\n ElcobusFrame.ElcobusMessage.MessageType.Info,\n ElcobusFrame.ElcobusMessage.MessageType.Ret,\n }:\n return\n\n if ebm.field in {ElcobusFrame.Field.BoilerTemperature, ElcobusFrame.Field.BoilerSetTemperature,\n ElcobusFrame.Field.BoilerReturnTemperature,\n ElcobusFrame.Field.OutdoorTemperature,\n ElcobusFrame.Field.TapWaterTemperature, ElcobusFrame.Field.TapWaterSetTemperature,\n }:\n mqtt_client.client.publish(args.mqtt_topic_prefix + '/' + ebm.field.name, ebm.data.temperature, qos=1)\n elif ebm.field in {ElcobusFrame.Field.HeatingCircuitTemperature, ElcobusFrame.Field.HeatingCircuitSetTemperature}:\n circuit = ebm.logical_source - 32 # 33 => 1, 34 => 2\n mqtt_client.client.publish(args.mqtt_topic_prefix + '/' + ebm.field.name + f\" {circuit}\", ebm.data.temperature,\n qos=1)\n elif ebm.field in {ElcobusFrame.Field.PumpModulation, ElcobusFrame.Field.BurnerMoludation}:\n mqtt_client.client.publish(args.mqtt_topic_prefix + '/' + ebm.field.name, ebm.data.percent, qos=1)\n elif ebm.field == ElcobusFrame.Field.Pressure:\n mqtt_client.client.publish(args.mqtt_topic_prefix + '/' + ebm.field.name, ebm.data.pressure, qos=1)\n elif ebm.field == ElcobusFrame.Field.Status:\n mqtt_client.client.publish(args.mqtt_topic_prefix + '/' + ebm.field.name, ebm.data.status, qos=1)\n\n\nasync def poll_every(interval_secs: int, ebm: ElcobusFrame.ElcobusMessage):\n await asyncio.sleep(random.randint(0, interval_secs)) # stagger the calls\n while True:\n await asyncio.sleep(interval_secs + random.uniform(-interval_secs/10, interval_secs/10))\n mqtt_client.client.publish(mqtt_connection_details.topic + '/bus_tx', ebm.to_bytes(), qos=2)\n # Reply is automatically processed in process_frame, even if it is unsollicited\n\n# Do not poll boiler temperature: it is polled by the display of the boiler itself\nloop.create_task(poll_every(\n 60,\n ElcobusFrame.ElcobusMessage(\n source_address=my_source, destination_address=0x00,\n message_type=ElcobusFrame.ElcobusMessage.MessageType.Get,\n logical_source=0x3d, logical_destination=0x0d,\n field=ElcobusFrame.Field.BoilerSetTemperature,\n )\n))\nloop.create_task(poll_every(\n 60,\n ElcobusFrame.ElcobusMessage(\n source_address=my_source, destination_address=0x00,\n message_type=ElcobusFrame.ElcobusMessage.MessageType.Get,\n logical_source=0x3d, logical_destination=0x11,\n field=ElcobusFrame.Field.BoilerReturnTemperature,\n )\n))\nloop.create_task(poll_every(\n 60,\n ElcobusFrame.ElcobusMessage(\n source_address=my_source, destination_address=0x00,\n message_type=ElcobusFrame.ElcobusMessage.MessageType.Get,\n logical_source=0x3d, logical_destination=0x05,\n field=ElcobusFrame.Field.OutdoorTemperature,\n )\n))\nloop.create_task(poll_every(\n 60,\n ElcobusFrame.ElcobusMessage(\n source_address=my_source, destination_address=0x00,\n message_type=ElcobusFrame.ElcobusMessage.MessageType.Get,\n logical_source=0x3d, logical_destination=0x31,\n field=ElcobusFrame.Field.TapWaterTemperature,\n )\n))\nloop.create_task(poll_every(\n 60,\n ElcobusFrame.ElcobusMessage(\n source_address=my_source, destination_address=0x00,\n message_type=ElcobusFrame.ElcobusMessage.MessageType.Get,\n logical_source=0x3d, logical_destination=0x31,\n field=ElcobusFrame.Field.TapWaterSetTemperature,\n )\n))\nloop.create_task(poll_every(\n 60,\n ElcobusFrame.ElcobusMessage(\n source_address=my_source, destination_address=0x00,\n message_type=ElcobusFrame.ElcobusMessage.MessageType.Get,\n logical_source=0x3d, logical_destination=0x11,\n field=ElcobusFrame.Field.BurnerModulation,\n )\n))\nloop.create_task(poll_every(\n 60,\n ElcobusFrame.ElcobusMessage(\n source_address=my_source, destination_address=0x00,\n message_type=ElcobusFrame.ElcobusMessage.MessageType.Get,\n logical_source=0x3d, logical_destination=0x05,\n field=ElcobusFrame.Field.PumpModulation,\n )\n))\nloop.create_task(poll_every(\n 250, # pressure changes slowly\n ElcobusFrame.ElcobusMessage(\n source_address=my_source, destination_address=0x00,\n message_type=ElcobusFrame.ElcobusMessage.MessageType.Get,\n logical_source=0x3d, logical_destination=0x11,\n field=ElcobusFrame.Field.Pressure,\n )\n))\nloop.create_task(poll_every(\n 60,\n ElcobusFrame.ElcobusMessage(\n source_address=my_source, destination_address=0x00,\n message_type=ElcobusFrame.ElcobusMessage.MessageType.Get,\n logical_source=0x3d, logical_destination=0x09,\n field=ElcobusFrame.Field.Status,\n )\n))\n\nfor circuit in (1, 2):\n loop.create_task(poll_every(\n 60,\n ElcobusFrame.ElcobusMessage(\n source_address=my_source, destination_address=0x00,\n message_type=ElcobusFrame.ElcobusMessage.MessageType.Get,\n logical_source=0x3d, logical_destination=0x20 + circuit,\n field=ElcobusFrame.Field.HeatingCircuitTemperature,\n )\n ))\n loop.create_task(poll_every(\n 60,\n ElcobusFrame.ElcobusMessage(\n source_address=my_source, destination_address=0x00,\n message_type=ElcobusFrame.ElcobusMessage.MessageType.Get,\n logical_source=0x3d, logical_destination=0x20 + circuit,\n field=ElcobusFrame.Field.HeatingCircuitSetTemperature,\n )\n ))\n\n\nmqtt_client = MqttClient(mqtt_connection_details)\nloop.run_until_complete(mqtt_client.main())\n","repo_name":"niobos/elcobuspy","sub_path":"src/elcobus/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":12974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"30330155609","text":"import pandas as pd\nimport os\n\npath = os.getcwd()\nfile_name = \"/data/crawling_Corona.xlsx\"\nfile_directory = path + file_name\n\ndf = pd.read_excel(file_directory, engine='openpyxl')\n\ntags_total = []\nfor tags in df['tags']:\n tags_list = tags[2:-2].split(\"', '\")\n for tag in tags_list:\n tags_total.append(tag)\n\nfrom collections import Counter\n\nstopwords = ['', '#맞팔', '#좋아요', '#일상', '#선팔', '#좋반', '#소통', '#좋아요반사', '#follow', '#데일리',\n '#instagoo', '#팔로우', '#선팔하면맞팔', '#선팔맞팔', '#인친', '#첫줄', '#팔로우늘리기', '#일상스타그램', '#얼스타그램',\n '#팔로워', '#셀피', '#첫줄반사', '#협찬', '#selfie', '#맛스타그램', '#좋아요테러', '#좋테', '#팔로워판매', '#좋아요판매',\n '#팔로워구매', '#소개계정맞팔', '#셀스타그램']\n\ntag_selection = []\nfor tag in tags_total:\n if tag not in stopwords:\n tag_selection.append(tag)\n\ntag_counts = Counter(tag_selection)\n\nimport matplotlib.pyplot as plt\nfrom wordcloud import WordCloud\nimport platform\nfrom matplotlib import font_manager, rc\n\nif platform.system() == 'Windows':\n font_path = \"c:/Windows/Fonts/gulim.ttc\"\nelif platform.system() == \"Darwin\": #Mac 의 경우\n font_path = \"c:/Users/$USER/Library/Fonts/AppleGothic.ttf\"\n\nfont_name = font_manager.FontProperties(fname=font_path).get_name()\nrc('font', family=font_name)\n\nwordcloud = WordCloud(font_path=font_path,\n background_color=\"white\",\n max_words=100,\n relative_scaling= 0.2,\n width = 1500,\n height = 1000)\nwordcloud.generate_from_frequencies(tag_counts)\nplt.figure(figsize=(10, 5))\n\nplt_word = plt.subplot(1, 2, 1)\nplt_word.imshow(wordcloud)\nplt_word.axis('off')\nplt.savefig(path+\"/data/hashtag_wordcloud\")\n\ntag_common = tag_counts.most_common(10)\n\npie_label = []\npie_value = []\nfor i in range(10):\n pie_label.append(tag_common[i][0])\nfor i in range(10):\n pie_value.append(tag_common[i][1])\n\nexplode = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0)\nplt_pie = plt.subplot(1, 2, 2)\n\ntextprops = dict(horizontalalignment=\"center\",\n verticalalignment=\"top\",\n rotation=0,\n rotation_mode=\"anchor\",\n size=7, color=\"black\")\n\nplt_pie.pie(pie_value, explode=explode, labels=pie_label, autopct=\"%.0f%%\", shadow=True, textprops=textprops)\n# plt_pie.setp(plt_pie, fontproperties=fontprop)\nplt_pie.set_title(\"코로나 해쉬태그(#) 순위 10 비율\")\nplt.show()\n","repo_name":"zoai-bot/BigData","sub_path":"Insta_Corona.py","file_name":"Insta_Corona.py","file_ext":"py","file_size_in_byte":2608,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"69942837692","text":"from setuptools import setup, find_packages\nfrom codecs import open\nfrom os import path\n\nhere = path.abspath(path.dirname(__file__))\n\nwith open(path.join(here, 'README.md'), encoding='utf-8') as f:\n long_description = f.read()\n\nsetup(\n name='MTheory',\n version='0.0.1',\n description='Music Theory Boilerplate',\n long_description=long_description,\n url='https://github.com/athuras/MTheory',\n author='Alexander Huras',\n author_email='athuras@gmail.com',\n license='MIT',\n classifiers=[\n 'Development Status :: 3 - Alpha',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3'\n ],\n packages=['MTheory'],\n package_dir={'MTheory': 'MTheory'},\n)\n","repo_name":"athuras/MTheory","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"73538871931","text":"from django.shortcuts import render, get_object_or_404, reverse\nfrom django.views import generic, View\nfrom django.views.generic import ListView, DetailView, CreateView\nfrom django.views.generic import UpdateView, DeleteView\nfrom django.http import HttpResponseRedirect\nfrom .models import Listing, Question, Answer\nfrom .forms import ListingForm, QuestionForm\nfrom django.urls import reverse_lazy\nfrom django.contrib.auth.mixins import LoginRequiredMixin, UserPassesTestMixin\n\n\nclass ListingList(generic.ListView):\n\n model = Listing\n queryset = Listing.objects.filter(status=0).order_by(\"-created_on\")\n template_name = \"index.html\"\n paginate_by = 6\n\n\nclass ListingDetail(DetailView):\n\n def get(self, request, slug, *args, **kwargs):\n queryset = Listing.objects.filter(status=0)\n listing = get_object_or_404(queryset, slug=slug)\n questions = listing.questions.filter(\n approved=True).order_by(\"-created_on\")\n liked = False\n asked = False\n if listing.likes.filter(id=self.request.user.id).exists():\n liked = True\n\n return render(\n request,\n \"listing_detail.html\",\n {\n \"listing\": listing,\n \"questions\": questions,\n \"asked\": asked,\n \"liked\": liked,\n \"question_form\": QuestionForm(),\n },\n )\n\n def post(self, request, slug, *args, **kwargs):\n\n if not request.user.is_authenticated:\n return redirect(\n reverse('/listing-detail')\n )\n\n queryset = Listing.objects.filter(status=0)\n listing = get_object_or_404(queryset, slug=slug)\n questions = listing.questions.filter(\n approved=True).order_by(\"-created_on\")\n liked = False\n asked = False\n if listing.likes.filter(id=self.request.user.id).exists():\n liked = True\n if listing.questions.filter(id=self.request.user.id).exists():\n asked = True\n\n question_form = QuestionForm(data=request.POST)\n\n if question_form.is_valid():\n question_form.instance.email = request.user.email\n question_form.instance.name = request.user.username\n question = question_form.save(commit=False)\n question.listing = listing\n question.save()\n else:\n question_form = QuestionForm()\n\n return render(\n request,\n \"listing_detail.html\",\n {\n \"listing\": listing,\n \"questions\": questions,\n \"asked\": asked,\n \"liked\": liked,\n \"question_form\": QuestionForm(),\n },\n )\n\n\nclass ListingLike(View):\n\n def post(self, request, slug):\n listing = get_object_or_404(Listing, slug=slug)\n\n if listing.likes.filter(id=self.request.user.id).exists():\n listing.likes.remove(request.user)\n else:\n listing.likes.add(request.user)\n\n return HttpResponseRedirect(reverse('listing_detail', args=[slug]))\n\n\nclass AddListingView(LoginRequiredMixin, UserPassesTestMixin, CreateView):\n model = Listing\n form_class = ListingForm\n template_name = 'add_listing.html'\n\n def test_func(self):\n return self.request.user.is_staff\n\n\nclass UpdateListingView(LoginRequiredMixin, UserPassesTestMixin, UpdateView):\n model = Listing\n template_name = 'update_listing.html'\n fields = ['content', 'status', 'price']\n\n def test_func(self):\n return self.request.user.is_staff\n\n\nclass DeleteListingView(LoginRequiredMixin, UserPassesTestMixin, DeleteView):\n model = Listing\n template_name = 'delete_listing.html'\n success_url = reverse_lazy('home')\n\n def test_func(self):\n return self.request.user.is_staff\n","repo_name":"ValeP314/pp4-future-home","sub_path":"futurehomeapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3838,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"23244496417","text":"from PIL import Image\nimport os \nimport assets.feature.filesystem as fs\nimport assets.message.MessageManage as Msg\n\nclass Switch:\n\timageaddress = [\"\"]\n\timagetype = [\"default\",\"copy\",\"normal\"]\n\tdef __init__(self,ftype=\"JPEG\"):\n\t\tself.ftype = ftype \n\t\tself.image = None\n\t\tself.fname = None\n\n\tdef imageChangesize(self,fsize,fname):\n\t\timg = Image.open(fname)\n\t\tself.fname = fname\n\t\tself.image = img.resize(fsize,Image.ANTIALIAS) #resize image with high-quality\n\t\treturn self\n\n\tdef imageSavetype(self):\n\t\tself.image.save(self.fname,self.ftype)\n\t\treturn self\n\n\tdef imageCopy(self,fname,savelocate):\n\t\tself.fname = os.getcwd()+savelocate+fname\n\t\treturn self\n\n\t\n\tdef tryTest(self,locate,fsize,type,savelocate=\"\"):\n\t\tif self.modelCheck(locate,type) == \"tt\":\n\t\t\tfor addr,dire,fi in os.walk(locate):\n\t\t\t\tfor i in range(len(fi)):\n\t\t\t\t\tif fi[i].endswith(\".jpg\") or fi[i].endswith(\".jpeg\") or fi[i].endswith(\".png\"):\n\t\t\t\t\t\tself.imageChangesize(fsize,addr+fi[i])\n\t\t\t\t\t\tself.imageCopy(fi[i],\"/\"+savelocate)\n\t\t\t\t\t\tif type.upper().strip()==\"COPY\":\n\t\t\t\t\t\t\tself.imageCopy(fi[i],\"/Backup/\"+savelocate)\n\t\t\t\t\t\tself.imageSavetype()\n\t\t\tMsg.Message.sucMessage(2,\" Finished work!\")\n\n\t\telif self.modelCheck(locate,type) == \"ft\":\n\t\t\tMsg.Message.errMessage(3)\n\n\n\tdef modelCheck(self,locate,type):\n\t\tcheck = [False,False]\n\t\t# check image type\n\t\tfor i in self.imagetype:\n\t\t\tif type.upper().strip() == i.upper():\n\t\t\t\tcheck[0] = True\t\t\t\n\t\t# check address \n\t\t# pass\n\t\t# return \n\t\tif check[0] == True:\n\t\t\treturn \"tt\"\n\t\telif check[0] == False:\n\t\t\treturn \"ft\"","repo_name":"Onetail/ImageMethod","sub_path":"assets/feature/feature.py","file_name":"feature.py","file_ext":"py","file_size_in_byte":1521,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"} +{"seq_id":"41413646878","text":"import torch.nn as nn\nfrom collections import OrderedDict\nimport torch\nfrom torchsummary import torchsummary\nimport torch.nn.functional as F\nfrom cbam import MS_CAM\n\n\nclass Unet(nn.Module):\n def __init__(self, in_channels=3, out_channels_s=2, out_channels_c=5, init_features=16):\n super(Unet, self).__init__()\n\n features = init_features\n # 原图片256 * 256 * 3 -----》256 * 256 * 16\n self.encoder1 = Unet._block(in_channels, features, name='enc1')\n # 128 * 128 * 16\n self.pool1 = nn.MaxPool2d(2, 2)\n # 128 * 128 * 32\n self.encoder2 = Unet._block(features, features * 2, name='enc2')\n # 64 * 64 * 32\n self.pool2 = nn.MaxPool2d(2, 2)\n # 64 * 64 * 64\n self.encoder3 = Unet._block(features * 2, features * 4, name='enc2')\n # 32 * 32 * 64\n self.pool3 = nn.MaxPool2d(2, 2)\n # 32 * 32 * 128\n self.encoder4 = Unet._block(features * 4, features * 8, name='enc2')\n # 16 * 16 * 128\n self.pool4 = nn.MaxPool2d(2, 2)\n # bottleneck表示瓶底的意思， 16 * 16 * 256\n self.bottleneck = Unet._block(features * 8, features * 16, name='bottleneck')\n\n self.upconv4 = nn.ConvTranspose2d(features * 16, features * 8, kernel_size=2, stride=2)\n # 32 * 32 * 128\n self.decoder4 = Unet._block((features * 8) * 2, features * 8, name=\"dec4\")\n self.upconv3 = nn.ConvTranspose2d(features * 8, features * 4, kernel_size=2, stride=2)\n # 64 * 64 * 64\n self.decoder3 = Unet._block((features * 4) * 2, features * 4, name=\"dec3\")\n self.upconv2 = nn.ConvTranspose2d(features * 4, features * 2, kernel_size=2, stride=2)\n # 128 * 128 * 32\n self.decoder2 = Unet._block((features * 2) * 2, features * 2, name=\"dec2\")\n self.upconv1 = nn.ConvTranspose2d(features * 2, features, kernel_size=2, stride=2)\n # 256 * 256 * 16\n self.decoder1 = Unet._block(features * 2, features, name=\"dec1\")\n\n # 最后使用1x1的卷积核压缩通道数\n self.conv_s = nn.Conv2d(features, out_channels_s, kernel_size=1)\n\n # Siamese classifier layers\n self.upconv4_c = nn.ConvTranspose2d(features * 16, features * 8, kernel_size=2, stride=2)\n self.conv4_c = Unet._block(features * 16, features * 16, name=\"conv4\")\n\n self.upconv3_c = nn.ConvTranspose2d(features * 16, features * 4, kernel_size=2, stride=2)\n self.conv3_c = Unet._block(features * 8, features * 8, name=\"conv3\")\n\n self.upconv2_c = nn.ConvTranspose2d(features * 8, features * 2, kernel_size=2, stride=2)\n self.conv2_c = Unet._block(features * 4, features * 4, name=\"conv2\")\n\n self.upconv1_c = nn.ConvTranspose2d(features * 4, features, kernel_size=2, stride=2)\n self.conv1_c = Unet._block(features * 2, features * 2, name=\"conv1\")\n\n self.conv_c = nn.Conv2d(in_channels=features * 2, out_channels=out_channels_c, kernel_size=1)\n\n\n # 注意力机制\n self.feat4_attention = MS_CAM(channel=features * 8)\n self.bottle_attention = MS_CAM(channel=features * 16)\n\n\n # def forward(self, x1):\n # # 编码阶段\n # enc1 = self.encoder1(x1)\n # enc2 = self.encoder2(self.pool1(enc1))\n # enc3 = self.encoder3(self.pool2(enc2))\n # enc4 = self.encoder4(self.pool3(enc3))\n # bottleneck = self.bottleneck(self.pool4(self.feat4_attention(enc4)))\n # bottleneck = self.bottle_attention(bottleneck)\n #\n # # 解码阶段\n # dec4 = self.upconv4(bottleneck)\n # dec4 = torch.cat((dec4, enc4), dim=1)\n # dec4 = self.decoder4(dec4)\n #\n # dec3 = self.upconv3(dec4)\n # dec3 = torch.cat((dec3, enc3), dim=1)\n # dec3 = self.decoder3(dec3)\n #\n # dec2 = self.upconv2(dec3)\n # dec2 = torch.cat((dec2, enc2), dim=1)\n # dec2 = self.decoder2(dec2)\n #\n # dec1 = self.upconv1(dec2)\n # dec1 = torch.cat((dec1, enc1), dim=1)\n # dec1 = self.decoder1(dec1)\n #\n # out = self.out(dec1)\n # return out\n def forward(self, x1, x2):\n # UNet on x1\n enc1_1 = self.encoder1(x1)\n enc2_1 = self.encoder2(self.pool1(enc1_1))\n enc3_1 = self.encoder3(self.pool2(enc2_1))\n enc4_1 = self.encoder4(self.pool3(enc3_1))\n\n bottleneck_1 = self.bottleneck(self.pool4(self.feat4_attention(enc4_1)))\n bottleneck_1 = self.bottle_attention(bottleneck_1)\n\n dec4_1 = self.upconv4(bottleneck_1)\n dec4_1 = torch.cat((dec4_1, enc4_1), dim=1)\n dec4_1 = self.decoder4(dec4_1)\n\n dec3_1 = self.upconv3(dec4_1)\n dec3_1 = torch.cat((dec3_1, enc3_1), dim=1)\n dec3_1 = self.decoder3(dec3_1)\n\n dec2_1 = self.upconv2(dec3_1)\n dec2_1 = torch.cat((dec2_1, enc2_1), dim=1)\n dec2_1 = self.decoder2(dec2_1)\n\n dec1_1 = self.upconv1(dec2_1)\n dec1_1 = torch.cat((dec1_1, enc1_1), dim=1)\n dec1_1 = self.decoder1(dec1_1)\n\n # 添加注意力机制\n\n # UNet on x2\n enc1_2 = self.encoder1(x2)\n enc2_2 = self.encoder2(self.pool1(enc1_2))\n enc3_2 = self.encoder3(self.pool2(enc2_2))\n enc4_2 = self.encoder4(self.pool3(enc3_2))\n\n bottleneck_2 = self.bottleneck(self.pool4(self.feat4_attention(enc4_2)))\n bottleneck_2 = self.bottle_attention(bottleneck_2)\n\n dec4_2 = self.upconv4(bottleneck_2)\n dec4_2 = torch.cat((dec4_2, enc4_2), dim=1)\n dec4_2 = self.decoder4(dec4_2)\n\n dec3_2 = self.upconv3(dec4_2)\n dec3_2 = torch.cat((dec3_2, enc3_2), dim=1)\n dec3_2 = self.decoder3(dec3_2)\n\n dec2_2 = self.upconv2(dec3_2)\n dec2_2 = torch.cat((dec2_2, enc2_2), dim=1)\n dec2_2 = self.decoder2(dec2_2)\n\n dec1_2 = self.upconv1(dec2_2)\n dec1_2 = torch.cat((dec1_2, enc1_2), dim=1)\n dec1_2 = self.decoder1(dec1_2)\n\n # Siamese\n dec1_c = bottleneck_2 - bottleneck_1\n\n dec1_c = self.upconv4_c(dec1_c) # features * 16 -> features * 8\n diff_2 = enc4_2 - enc4_1 # features * 16 -> features * 8\n dec2_c = torch.cat((diff_2, dec1_c), dim=1) # 512\n dec2_c = self.conv4_c(dec2_c)\n\n dec2_c = self.upconv3_c(dec2_c) # 512->256\n diff_3 = enc3_2 - enc3_1\n dec3_c = torch.cat((diff_3, dec2_c), dim=1) # ->512\n dec3_c = self.conv3_c(dec3_c)\n\n dec3_c = self.upconv2_c(dec3_c) # 512->256\n diff_4 = enc2_2 - enc2_1\n dec4_c = torch.cat((diff_4, dec3_c), dim=1) #\n dec4_c = self.conv2_c(dec4_c)\n\n dec4_c = self.upconv1_c(dec4_c)\n diff_5 = enc1_2 - enc1_1\n dec5_c = torch.cat((diff_5, dec4_c), dim=1)\n dec5_c = self.conv1_c(dec5_c)\n\n # return self.conv_s(dec1_1), self.conv_s(dec1_2), self.conv_c(dec5_c)\n return self.conv_c(dec5_c)\n # 两个卷积块\n # OrderDict里面放的list\n @staticmethod\n def _block(in_channels, features, name):\n return nn.Sequential(\n OrderedDict([\n\n (name+'conv1', nn.Conv2d(in_channels, features, kernel_size=3, padding=1, bias=False)),\n (name+'norm1', nn.BatchNorm2d(features)),\n (name+'relu1', nn.ReLU(inplace=True)),\n\n\n # 注意这里输入和输出通道要相同，因为第二次卷积不包含通道数\n (name+'conv2', nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False)),\n (name+'norm2', nn.BatchNorm2d(features)),\n (name+'relu2', nn.ReLU(inplace=True)),\n ])\n )\n\n\nclass ChangeUnet(nn.Module):\n def __init__(self, in_channels=3, out_channels=2, init_features=16):\n super(ChangeUnet, self).__init__()\n\n features = init_features\n # 原图片256 * 256 * 3 -----》256 * 256 * 16\n self.encoder1 = Unet._block(in_channels, features, name='enc1')\n # 128 * 128 * 16\n self.pool1 = nn.MaxPool2d(2, 2)\n # 128 * 128 * 32\n self.encoder2 = Unet._block(features, features * 2, name='enc2')\n # 64 * 64 * 32\n self.pool2 = nn.MaxPool2d(2, 2)\n # 64 * 64 * 64\n self.encoder3 = Unet._block(features * 2, features * 4, name='enc2')\n # 32 * 32 * 64\n self.pool3 = nn.MaxPool2d(2, 2)\n # 32 * 32 * 128\n self.encoder4 = Unet._block(features * 4, features * 8, name='enc2')\n # 16 * 16 * 128\n self.pool4 = nn.MaxPool2d(2, 2)\n # bottleneck表示瓶底的意思， 16 * 16 * 256\n self.bottleneck = Unet._block(features * 8, features * 16, name='bottleneck')\n\n self.upconv4 = nn.ConvTranspose2d(features * 16, features * 8, kernel_size=2, stride=2)\n # 32 * 32 * 128\n self.decoder4 = Unet._block((features * 8) * 2, features * 8, name=\"dec4\")\n self.upconv3 = nn.ConvTranspose2d(features * 8, features * 4, kernel_size=2, stride=2)\n # 64 * 64 * 64\n self.decoder3 = Unet._block((features * 4) * 2, features * 4, name=\"dec3\")\n self.upconv2 = nn.ConvTranspose2d(features * 4, features * 2, kernel_size=2, stride=2)\n # 128 * 128 * 32\n self.decoder2 = Unet._block((features * 2) * 2, features * 2, name=\"dec2\")\n self.upconv1 = nn.ConvTranspose2d(features * 2, features, kernel_size=2, stride=2)\n # 256 * 256 * 16\n self.decoder1 = Unet._block(features * 2, features, name=\"dec1\")\n\n # 最后使用1x1的卷积核压缩通道数\n self.out = nn.Conv2d(features * 2, out_channels, kernel_size=1)\n\n def forward(self, x1, x2):\n # 编码阶段 x1\n enc1_1 = self.encoder1(x1)\n enc2_1 = self.encoder2(self.pool1(enc1_1))\n enc3_1 = self.encoder3(self.pool2(enc2_1))\n enc4_1 = self.encoder4(self.pool3(enc3_1))\n bottleneck = self.bottleneck(self.pool4(enc4_1))\n\n # 解码阶段 x1\n dec4_1 = self.upconv4(bottleneck)\n dec4_1 = torch.cat((dec4_1, enc4_1), dim=1)\n dec4_1 = self.decoder4(dec4_1)\n\n dec3_1 = self.upconv3(dec4_1)\n dec3_1 = torch.cat((dec3_1, enc3_1), dim=1)\n dec3_1 = self.decoder3(dec3_1)\n\n dec2_1 = self.upconv2(dec3_1)\n dec2_1 = torch.cat((dec2_1, enc2_1), dim=1)\n dec2_1 = self.decoder2(dec2_1)\n\n dec1_1 = self.upconv1(dec2_1)\n dec1_1 = torch.cat((dec1_1, enc1_1), dim=1)\n dec1_1 = self.decoder1(dec1_1)\n\n\n # 编码阶段 x2\n enc1_2 = self.encoder1(x2)\n enc2_2 = self.encoder2(self.pool1(enc1_2))\n enc3_2 = self.encoder3(self.pool2(enc2_2))\n enc4_2 = self.encoder4(self.pool3(enc3_2))\n bottleneck = self.bottleneck(self.pool4(enc4_2))\n\n # 解码阶段 x2\n dec4_2 = self.upconv4(bottleneck)\n dec4_2 = torch.cat((dec4_2, enc4_2), dim=1)\n dec4_2 = self.decoder4(dec4_2)\n\n dec3_2 = self.upconv3(dec4_2)\n dec3_2 = torch.cat((dec3_2, enc3_2), dim=1)\n dec3_2 = self.decoder3(dec3_2)\n\n dec2_2 = self.upconv2(dec3_2)\n dec2_2 = torch.cat((dec2_2, enc2_2), dim=1)\n dec2_2 = self.decoder2(dec2_2)\n\n dec1_2 = self.upconv1(dec2_2)\n dec1_2 = torch.cat((dec1_2, enc1_2), dim=1)\n dec1_2 = self.decoder1(dec1_2)\n\n\n # 将特征的通道进行融合\n output = torch.cat((dec1_1, dec1_2), dim=1)\n return self.out(output)\n\n # 两个卷积块\n # OrderDict里面放的list\n @staticmethod\n def _block(in_channels, features, name):\n return nn.Sequential(\n OrderedDict([\n\n (name+'conv1', nn.Conv2d(in_channels, features, kernel_size=3, padding=1, bias=False)),\n (name+'norm1', nn.BatchNorm2d(features)),\n (name+'relu1', nn.ReLU(inplace=True)),\n\n\n # 注意这里输入和输出通道要相同，因为第二次卷积不包含通道数\n (name+'conv2', nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False)),\n (name+'norm2', nn.BatchNorm2d(features)),\n (name+'relu2', nn.ReLU(inplace=True)),\n ])\n )\n\n\nclass PSPModule(nn.Module):\n def __init__(self, in_channels, pool_sizes, norm_layer): # pool_sizes 可以有 6 3 2 1 等组合，即池化的大小\n super(PSPModule, self).__init__()\n out_channels = in_channels // len(pool_sizes)\n self.stages = nn.ModuleList(\n [self._make_stages(in_channels, out_channels, pool_size, norm_layer) for pool_size in pool_sizes ]\n )\n self.bottleneck = nn.Sequential(\n nn.Conv2d(in_channels+(out_channels * len(pool_sizes)), out_channels, kernel_size=3, padding=1, bias=False),\n norm_layer(out_channels),\n nn.ReLU(inplace=True),\n nn.Dropout(0.1)\n ) # 将堆叠的特征层进行通道数的调整\n def _make_stages(self, in_channels, out_channels, bin_sz, norm_layer):\n prior = nn.AdaptiveAvgPool2d(output_size=bin_sz) # 可以指定任意大小的输出形状\n conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False) # 首先利用1x1的卷积进行通道数的调整\n bn = norm_layer(out_channels)\n relu = nn.ReLU(inplace=True)\n return nn.Sequential(prior, conv, bn, relu)\n\n def forward(self, features):\n h, w = features.size()[2], features.size()[3]\n pyramids = [features]\n pyramids.extend([F.interpolate(stage(features), size=(h, w), mode='bilinear', align_corners=True) for stage in self.stages])\n output = self.bottleneck(torch.cat(pyramids, dim=1))\n return output\n\n\n\nif __name__ == '__main__':\n i1 = torch.randn(1, 3, 256, 256)\n i2 = torch.randn(1, 3, 256, 256)\n model = Unet(init_features=64, out_channels_c=2)\n out = model(i1, i2)\n # output = model(i1, i2)\n print('sss')\n # model = ChangeUnet()\n torchsummary.summary(model, [(3, 128, 128), (3, 128, 128)])\n image = torch.randn(32, 3, 256, 256)\n # ppm = PSPModule(3, [1, 2, 3, 6], nn.BatchNorm2d())\n","repo_name":"zzzwind/unet","sub_path":"models/unet.py","file_name":"unet.py","file_ext":"py","file_size_in_byte":13944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"5326764324","text":"import tensorflow as tf\r\n#tf.logging.set_verbosity(tf.logging.ERROR)\r\nimport numpy as np\r\nfrom tensorflow.keras.preprocessing import image\r\n\r\nmodel = tf.keras.models.load_model('mymodel.h5')\r\n\r\npath = 'test image paths'\r\nimg = image.load_img(path, target_size=(128, 128))\r\nx = image.img_to_array(img)\r\nx = np.expand_dims(x, axis=0)\r\n\r\nimages = np.vstack([x])\r\nclasses = model.predict(images)\r\nprint(classes[0])\r\nif classes[0]<0.5:\r\n print(\"0\")\r\nelse:\r\n print(\"1\")","repo_name":"MITHUN2626/Tire-Fitness","sub_path":"Tyre.py","file_name":"Tyre.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"8908122724","text":"# -*- coding: utf-8 -*-\n\"\"\"File implements a simple doubly linked list in python.\"\"\"\nfrom __future__ import unicode_literals\n\n\nclass Node(object):\n \"\"\"Class implements Nodes for use in our dll.\"\"\"\n def __init__(self, data=None, nxt=None, prev=None):\n \"\"\"Init node with attributes.\"\"\"\n self.data = data\n self._next = nxt\n self._prev = prev\n\n\nclass DoublyLinkedList(object):\n \"\"\"Class implements a simple doubly linked list.\"\"\"\n def __init__(self, iterable=None):\n \"\"\"Init doubly linked list, iterate through iterable data\n if provided as an argument.\"\"\"\n self.head = None\n self.tail = None\n try:\n if iterable:\n for item in iterable:\n self.push(item)\n except TypeError:\n raise TypeError(\"Please enter an object that is iterable.\")\n\n def push(self, data):\n \"\"\"Insert data at the head of the list.\"\"\"\n new_node = Node(data)\n new_node._prev = None\n new_node._next = self.head\n if self.head:\n self.head._prev = new_node\n self.head = new_node\n if self.tail is None:\n self.tail = new_node\n\n def append(self, data):\n \"\"\"Inserts data at the tail of the list.\"\"\"\n new_node = Node(data)\n new_node._next = None\n new_node._prev = self.tail\n if self.tail:\n self.tail._next = new_node\n self.tail = new_node\n if self.head is None:\n self.head = new_node\n\n def pop(self):\n \"\"\"Remove data from the head of the list and return it.\"\"\"\n pop_node = self.head\n try:\n self.head._next._prev = None\n self.head = self.head._next\n except AttributeError:\n self.head = None\n self.tail = None\n return pop_node.data\n\n def shift(self):\n \"\"\"Remove data from the tail of the list and return it.\"\"\"\n shift_node = self.tail\n try:\n self.tail = self.tail._prev\n self.tail._next = None\n except AttributeError:\n self.tail = None\n self.head = None\n return shift_node.data\n\n def remove(self, rem_val):\n \"\"\"Remove first instance of data from anywhere in the list,\n starting at the head.\"\"\"\n current = self.head\n try:\n while current.data != rem_val:\n current = current._next\n except AttributeError:\n raise IndexError(\"That value is not in the list.\")\n if current._next is None and current._prev is None:\n self.head = None\n return current.data\n if current._prev is not None:\n current._prev._next = current._next\n if current._next is not None:\n current._next._prev = current._prev\n return current.data\n\n def size(self):\n \"\"\"Finds the size of our dll.\"\"\"\n count = 0\n current = self.head\n while current is not None:\n count += 1\n current = current._next\n return count\n\n def __len__(self):\n \"\"\"Binds .size to __len__ builtin so len() works.\"\"\"\n return self.size()\n","repo_name":"vbenavente/data-structures","sub_path":"src/dll.py","file_name":"dll.py","file_ext":"py","file_size_in_byte":3186,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"} +{"seq_id":"74724097212","text":"import time\r\n\r\ndef for_loop(n):\r\n result=[]\r\n for i in range(n):\r\n result.append(i**2)\r\n\r\ndef list_compression(n):\r\n return [i**2 for i in range(n)]\r\n\r\nbegin = time.time()\r\nfor_loop(10**6)\r\nend=time.time()\r\nprint(\"time taken by for loop:\",round(end-begin,2))\r\n\r\nbegin = time.time()\r\nlist_compression(10**6)\r\nend=time.time()\r\nprint('Time taken for list_comprehension:', round(end-begin, 2))\r\n\r\n","repo_name":"deepanshu136/Python-Tutorials","sub_path":"python tutorial/listcomprehension/timeAnalysis.py","file_name":"timeAnalysis.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"15422466070","text":"'''\n단순 구현 문제\n\n문제에 주어진 식? 대로 코드로 옮기면 된다.\n아스키 코드 상에서 소문자 a 는 97부터 시작한다.\n그래서, a-z까지 입력 받았을 때 1-26 까지 사용하려고 입력받은 문자열에서 96을 뺏다.\n\n출력할 때 % 1234567891를 하지 않으면 절반만 성공한다.\n% 1234567891를 잘 해야된다.\n\n---- 옛날에 '백준 - 01타일'이란 문제를 풀었을 때, 수가 커질수록 나머지 연산의 비용(시간)이 많이 발생한다고 한다.\n ㄴ-> 현재는 메모리 초과가 나오는거 같다.\n현재 문제에선 최종 결과값에서만 나머지 연산해도 통과가 가능하다.\n그래도, 안전하게 코딩하려면 반복문안에서 res += (ord(word[i]) - 96) * (31 ** i) % 1234567891 해주는 것이 좋다.\n\n(python에선 정수 오버플로가 없다 ex. java는 int값이 -21억 ~ +21억의 수의 범위를 갖는다.)\n'''\n\nn, word = int(input()), input()\n\n# 테스트\n# n = 5\n# word = 'abcde' # 4739715\n# n = 3\n# word = 'zzz' # 25818\n# n = 1\n# word = 'i' # 9\n\nres = 0\n\nfor i in range(n):\n # res += (ord(word[i]) - 96) * (31 ** i)\n res += (ord(word[i]) - 96) * (31 ** i) % 1234567891\n\nprint(res % 1234567891)\n","repo_name":"rkdalsdn94/algoalgo","sub_path":"solved_ac/bronze_2/Hashing_15829.py","file_name":"Hashing_15829.py","file_ext":"py","file_size_in_byte":1231,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"2982273168","text":"# used to feed data while training models\n\nimport numpy as np\nimport os\nimport pickle\nimport inspect\nimport time\n\nfrom spear.Implyloss import *\n\n# from .my_data_types import *\n# from .my_data_feeder_utils import *\nfrom .data_types import *\nfrom .data_feeder_utils import *\n\nclass DataFeeder():\n def __init__(self, d_pickle, U_pickle, validation_pickle, map_json, \n shuffle_batches, num_load_d, num_load_U, num_classes, \n f_d_class_sampling, min_rule_coverage, rule_classes, num_load_validation, \n f_d_batch_size, f_d_U_batch_size, test_w_batch_size, out_dir='./'):\n '''\n Func Desc:\n Initialize the object with the given parameter files\n\n Input: \n self\n d_pickle - labelled data file\n U_pickle - unlabelled data file\n validation_pickle - validation data file\n out_dir (Default = './') - output directory\n\n Output:\n Void\n '''\n self.f_d_U_start = 0\n self.shuffle_batches=shuffle_batches\n self.out_dir = out_dir\n \n self.raw_d = load_data(d_pickle, map_json, num_load_d)\n self.raw_U = load_data(U_pickle, map_json, num_load_U)\n\n if num_classes is not None:\n self.num_classes = num_classes\n assert self.num_classes >= np.max(self.raw_d.L) + 1\n else:\n self.num_classes = np.max(self.raw_d.L) + 1\n\n self.f_d_class_sampling = f_d_class_sampling\n if self.f_d_class_sampling:\n assert len(self.f_d_class_sampling) == self.num_classes\n else:\n self.f_d_class_sampling = [1] * self.num_classes\n # Set f_d_U class sampling dist to be the same as f_d class sampling dist\n self.f_d_U_class_sampling = self.f_d_class_sampling\n\n self.phi = self.num_classes\n self.num_features = self.raw_d.x.shape[1]\n self.num_rules = self.raw_d.l.shape[1]\n\n # If min coverage threshold is specified for rules then apply it\n self.min_rule_coverage = min_rule_coverage\n self.num_rules_to_train = self.num_rules\n if self.min_rule_coverage > 0:\n self.satisfying_rules, self.not_satisfying_rules, \\\n self.rule_map_new_to_old, self.rule_map_old_to_new = \\\n utils.extract_rules_satisfying_min_coverage(self.raw_U.m,\n self.min_rule_coverage)\n self.num_rules_to_train = len(self.satisfying_rules)\n assert np.all(self.satisfying_rules ==\n self.rule_map_new_to_old[0:self.num_rules_to_train])\n assert np.all(self.not_satisfying_rules ==\n self.rule_map_new_to_old[self.num_rules_to_train:])\n print('Originally %d rules. To train on %d rules' %\n (self.num_rules, self.num_rules_to_train))\n print('Rule map new to old: ', self.rule_map_new_to_old)\n if self.num_rules != self.num_rules_to_train:\n utils.modify_d_or_U_using_rule_map(self.raw_U,\n self.rule_map_old_to_new)\n utils.modify_d_or_U_using_rule_map(self.raw_d,\n self.rule_map_old_to_new)\n\n # Determine rule classes from the truncated rule list\n self.rule_classes = rule_classes \n if not self.rule_classes:\n self.rule_classes = get_rule_classes(self.raw_d.l, self.num_classes)\n\n print('Rule classes: ', self.rule_classes)\n\n # Remove data from U for which no rule makes any predictions\n self.covered_U = self.remove_instances_labeled_by_no_rules(self.raw_U)\n print(\"length of covered U: {}\".format(len(self.covered_U.x)))\n\n # Now combine d and U data\n self.f_d_U = self.combine_f_d_U(self.raw_d, self.covered_U, self.f_d_U_class_sampling)\n\n self.f_d = self.convert_raw_d_to_f_d(self.raw_d, num_load=0)\n\n raw_test_data = load_data(validation_pickle, map_json,\n num_load_validation)\n\n self.test_f_x, self.test_f_labels, self.test_f_labels_one_hot, \\\n self.test_f_l, self.test_f_m, self.test_f_d, self.test_f_r = \\\n self.convert_raw_test_data_to_f(raw_test_data)\n\n self.test_w = self.convert_raw_test_data_to_w(raw_test_data)\n print('test_w len: ', len(self.test_w.x))\n\n self.batch_counter = {\n f_d:0,\n f_d_U:0,\n test_w:0,\n }\n\n self.data_lens = {\n f_d: len(self.f_d.x),\n f_d_U: len(self.f_d_U.x),\n test_w:len(self.test_w.x),\n }\n\n print('test_w len: ', self.data_lens[test_w])\n\n self.batch_size = {\n f_d: f_d_batch_size ,\n f_d_U: f_d_U_batch_size,\n test_w: test_w_batch_size,\n }\n\n self.data_store = {\n test_w: self.test_w,\n }\n\n self.shuf_indices = {}\n for data_type in [f_d, f_d_U]:\n self.shuf_indices[data_type] = np.arange(self.data_lens[data_type])\n self.reset_batch(data_type)\n\n \n def convert_raw_test_data_to_f(self, raw_test_data):\n '''\n Func Desc:\n to convert raw test data to f (classification network)\n\n Input:\n self\n raw_test_data - \n\n Output:\n f data with the required parameters\n '''\n x = raw_test_data.x\n labels = np.squeeze(raw_test_data.L)\n assert max(labels) <= self.num_classes - 1 or np.all(labels == self.num_classes)\n labels_one_hot = np.eye(self.num_classes + 1)[labels][:, : -1]\n return x, labels, labels_one_hot, raw_test_data.l, raw_test_data.m, raw_test_data.d, raw_test_data.r\n\n def convert_raw_test_data_to_w(self, raw_test_data):\n '''\n Func Desc:\n to convert raw test data to w (rule network)\n\n Input:\n self\n raw_test_data - \n\n Output:\n F_d_U_data\n '''\n test_w = self.remove_instances_labeled_by_no_rules(raw_test_data)\n print('Setting value of d to 0 for test data')\n d_new = np.zeros_like(test_w.d)\n \n return F_d_U_Data(test_w.x,\n test_w.l,\n test_w.m,\n test_w.L,\n d_new,\n test_w.r)\n\n\n def remove_instances_labeled_by_no_rules(self, raw_U):\n '''\n Func Desc:\n Removes those instances that are labelled by no rules\n\n Input:\n self\n raw_U - raw Unlabelled Data\n\n Output:\n F_d_U_data\n '''\n xx = []\n ll = []\n mm = []\n LL = []\n dd = []\n rr = []\n for x, l, m, L, d, r in zip(raw_U.x, raw_U.l, raw_U.m, raw_U.L, raw_U.d, raw_U.r):\n if np.all(l == self.phi):\n continue\n xx.append(x)\n ll.append(l)\n mm.append(m)\n LL.append(L)\n dd.append(d)\n rr.append(r)\n\n assert len(xx) == len(ll)\n assert len(xx) == len(mm)\n assert len(xx) == len(LL)\n assert len(xx) == len(dd)\n assert len(xx) == len(rr)\n return F_d_U_Data(np.array(xx),\n np.array(ll),\n np.array(mm),\n np.array(LL),\n np.array(dd),\n np.array(rr))\n\n\n def combine_f_d_U(self, raw_d, raw_U, d_class_sampling):\n '''\n Func Desc:\n combines the labelled (raw_d) and Unlabelled (raw_U) data\n\n Input:\n self\n raw_d - labelled data\n raw_U - unlabelled data\n d_class_sampling - sampling distribution\n \n '''\n print('Size of d before oversampling: ', len(raw_d.x))\n print('Size of U (covered) : ', len(raw_U.x))\n # Oversample d according to its class\n #\n # Note that we cannot oversample U according to their true labels since these should not be available\n # during training.\n raw_d = oversample_d(raw_d, d_class_sampling)\n print('Size of d after oversampling: ', len(raw_d.x))\n\n new_d_d = np.ones_like(raw_d.d)\n new_U_d = np.zeros_like(raw_U.d)\n\n #num_classes = np.max(raw_d.L) + 1\n #new_U_L = np.zeros_like(raw_U.L) + num_classes\n # We let the true U labels flow through for observation during f_d_U training\n new_U_L = raw_U.L\n\n\n xx = np.concatenate((raw_d.x, raw_U.x))\n ll = np.concatenate((raw_d.l, raw_U.l))\n mm = np.concatenate((raw_d.m, raw_U.m))\n LL = np.concatenate((raw_d.L, new_U_L))\n dd = np.concatenate((new_d_d, new_U_d))\n rr = np.concatenate((raw_d.r, raw_U.r))\n\n print('Size of d_U after combining: ', len(xx))\n f_d_U = F_d_U_Data(xx, ll, mm, LL, dd, rr)\n return f_d_U\n\n # Need x and true labels only (x, L)\n def convert_raw_d_to_f_d(self, raw_d, num_load=30):\n '''\n Func Desc:\n converts raw d to f\n\n Input:\n self\n raw_d - \n num_load (default = 30)\n\n Output:\n F_d_Data\n '''\n if num_load <= 0:\n num_load = len(raw_d.x)\n\n print('Loading %d elements from d' % num_load)\n\n x = raw_d.x[0:num_load]\n label = np.squeeze(raw_d.L[0:num_load])\n x, label = oversample_f_d(x, label, self.f_d_class_sampling)\n print('num instances in d: ', len(x))\n return F_d_Data(x, label) \n\n def reset_batch(self, data_type):\n '''\n Func Desc:\n restes the batch\n\n Input:\n self\n data_type\n\n Output:\n\n '''\n self.batch_counter[data_type] = 0\n if not self.shuffle_batches or data_type == test_w:\n print('Not shuffling batch for data type: ', data_type)\n return\n\n #print('Shuffling batch for data type: ', data_type)\n np.random.shuffle(self.shuf_indices[data_type])\n\n idx = self.shuf_indices[data_type]\n # We need to actually shuffle each array\n if data_type == f_d:\n np.take(self.f_d.x, idx, axis=0, out=self.f_d.x)\n np.take(self.f_d.labels, idx, axis=0, out=self.f_d.labels)\n\n elif data_type == f_d_U:\n np.take(self.f_d_U.x, idx, axis=0, out=self.f_d_U.x)\n np.take(self.f_d_U.l, idx, axis=0, out=self.f_d_U.l)\n np.take(self.f_d_U.m, idx, axis=0, out=self.f_d_U.m)\n np.take(self.f_d_U.L, idx, axis=0, out=self.f_d_U.L)\n np.take(self.f_d_U.d, idx, axis=0, out=self.f_d_U.d)\n np.take(self.f_d_U.r, idx, axis=0, out=self.f_d_U.r)\n else:\n raise ValueError('Data type not recognized: ', data_type)\n\n\n # Get next batch indices. Shuffle if necessary\n #\n # NOTE: We DO NOT skip the last (incomplete) batch\n def next_batch(self, data_type):\n '''\n Func Desc:\n get the next batch for computation\n\n Input:\n self\n data_type\n\n Output:\n start - start of the next batch\n end - end of the next batch\n\n '''\n batch_size = self.batch_size[data_type]\n\n num_instances = self.data_lens[data_type]\n total_batch = num_instances // batch_size\n remaining = num_instances % batch_size\n if remaining > 0 and (total_batch == 0 or 'test' in data_type):\n #print('Should not skip last batch')\n skip_last_batch = False\n else:\n #print('Should skip last batch')\n skip_last_batch = True\n\n if skip_last_batch:\n check = self.batch_counter[data_type] * batch_size + batch_size > self.data_lens[data_type]\n else:\n check = self.batch_counter[data_type] * batch_size >= self.data_lens[data_type]\n\n #print('check is: ', check)\n if check:\n #print('Resetting batch')\n self.reset_batch(data_type)\n\n start = self.batch_counter[data_type] * batch_size\n end = min(start + batch_size, self.data_lens[data_type])\n self.batch_counter[data_type] += 1\n return start, end\n\n\n # x: [batch_size, num_features]\n # y: [batch_size, num_classes] --> one-hot\n #\n # from d data\n def get_f_d_next_batch(self):\n '''\n Func Desc:\n get the next batch in f_d (labelled data)\n\n Input:\n self\n\n Output:\n x ([batch_size, num_features]) - the data\n labels_one_hot\n '''\n if True:\n start, end = self.next_batch(f_d)\n else:\n if np.random.rand() < 0.17:\n self.reset_batch(f_d)\n self.f_d_U_start = (self.f_d_U_start + 1) % 6\n start = self.f_d_U_start\n end = np.random.geometric(0.5)\n end = end + start\n end = min(end, 6)\n\n x = self.f_d.x[start:end]\n labels = self.f_d.labels[start:end]\n\n labels_one_hot = np.eye(self.num_classes)[labels]\n return x, labels_one_hot\n\n\n def get_f_d_U_next_batch(self):\n '''\n Func Desc:\n get the next batch in f_d_U (labelled + unlabelled data)\n\n Input:\n self\n\n Output:\n x ([batch_size, num_features]) - the data\n l ([batch_size, num_rules]) - the data labels\n m ([batch_size, num_rules]) - rule association matrix\n L ([batch_size, 1]) - labelling check vector\n d ([batch_size, 1]) - labelled data check vector\n r ([batch_size, num_rules]) - rule coverage matrix\n '''\n start, end = self.next_batch(f_d_U)\n\n x = self.f_d_U.x[start:end]\n l = self.f_d_U.l[start:end]\n m = self.f_d_U.m[start:end]\n L = self.f_d_U.L[start:end]\n d = self.f_d_U.d[start:end]\n r = self.f_d_U.r[start:end]\n return x, l, m, L, d, r\n\n # Number of instances\n def get_f_d_num_instances(self):\n '''\n Func Desc:\n gives the number of data instances in f_d\n\n Input:\n self\n\n Output:\n the required count\n\n '''\n return len(self.f_d.x)\n\n def get_f_d_U_num_instances(self):\n '''\n Func Desc:\n gives the number of data instances in f_d_U\n\n Input:\n self\n\n Output:\n the required count\n\n '''\n return len(self.f_d_U.x)\n\n # Batch Sizes\n def get_f_d_batch_size(self):\n '''\n Func Desc:\n gives the batch_size in f_d\n\n Input:\n self\n\n Output:\n the required size\n\n '''\n return self.batch_size[f_d]\n\n def get_f_d_U_batch_size(self):\n '''\n Func Desc:\n gives the batch_size in f_d_U\n\n Input:\n self\n\n Output:\n the required size\n\n '''\n return self.batch_size[f_d_U]\n\n def get_batch_size(self, data_type):\n '''\n Func Desc:\n gives the batch_size of the required data type\n\n Input:\n self\n dat_type\n\n Output:\n the required size\n\n '''\n return self.batch_size[data_type]\n\n def get_batches_per_epoch(self, data_type):\n '''\n Func Desc:\n gives the total number of batches in the required data type\n\n Input:\n self\n dat_type\n\n Output:\n the required count\n\n '''\n num_instances = self.data_lens[data_type]\n batch_size = self.batch_size[data_type]\n total_batch = num_instances // batch_size\n remaining = num_instances % batch_size\n print('num_instances: ', num_instances )\n print('batch_size: ', batch_size )\n print('total_batch: ', total_batch )\n print('remaining: ', remaining )\n # Add last batch if it is the only batch\n # Else last batch is discarded\n #\n # Unless we are in test mode and the entire dataset needs to be tested\n if remaining > 0 and (total_batch == 0 or 'test' in data_type):\n total_batch += 1\n\n print('total_batch: ', total_batch )\n #print('instances\\t batch_size\\t total_batch\\t remaining')\n #print('%d\\t %d\\t %d\\t %d' % (num_instances, batch_size, total_batch,\n # remaining))\n return total_batch\n\n def get_features_classes_rules(self):\n '''\n Func Desc:\n get the features, classes and rules of the object\n\n Input:\n self\n\n Output:\n num_features\n num_classes\n num_rules\n num_rules_to_train\n\n '''\n return self.num_features, self.num_classes, self.num_rules, \\\n self.num_rules_to_train\n\n def get_f_test_data(self, data_type):\n '''\n Func Desc:\n get the test data for f_network\n\n Input:\n self\n data_type\n\n Output:\n test_f_x\n test_f_labels_one_hot\n test_f_L\n test_f_m\n test_f_d\n\n '''\n return self.test_f_x, self.test_f_labels_one_hot, \\\n self.test_f_l, self.test_f_m, self.test_f_d\n\n def get_w_test_data(self, data_type='test_w'):\n '''\n Func Desc:\n get the test data for w_network\n\n Input:\n self\n data_type (fixed to test_w)\n\n Output:\n x ([batch_size, num_features]) - the data\n l ([batch_size, num_rules]) - the data labels\n m ([batch_size, num_rules]) - rule association matrix\n L ([batch_size, 1]) - labelling check vector\n d ([batch_size, 1]) - labelled data check vector\n \n '''\n assert data_type == test_w\n start, end = self.next_batch(data_type)\n\n x = self.data_store[data_type].x[start:end]\n l = self.data_store[data_type].l[start:end]\n m = self.data_store[data_type].m[start:end]\n L = self.data_store[data_type].L[start:end]\n d = self.data_store[data_type].d[start:end]\n\n return x, l, m, L, d\n\n","repo_name":"decile-team/spear","sub_path":"spear/Implyloss/data_feeders.py","file_name":"data_feeders.py","file_ext":"py","file_size_in_byte":17821,"program_lang":"python","lang":"en","doc_type":"code","stars":96,"dataset":"github-code","pt":"78"} +{"seq_id":"41930078459","text":"__author__ = 'hujie'\n\nimport pandas as pd\nimport numpy as np\n\n# Returns train_x,valid_x in 2d, train_y, valid_y in 1d and start with '0'\ndef loadData():\n # Preprocess\n class2num={\"Class_1\":1,\"Class_2\":2,\"Class_3\":3,\"Class_4\":4,\"Class_5\":5,\"Class_6\":6,\"Class_7\":7,\"Class_8\":8,\"Class_9\":9}\n\n features=[]\n for i in range(1,94):\n features.append(\"feat_\"+str(i))\n\n # Load data\n train = pd.read_csv(\"../data/train_data.csv\")\n valid = pd.read_csv(\"../data/valid_data.csv\")\n test = pd.read_csv(\"../data/test_data.csv\")\n\n train_labels = train['target']\n valid_labels = valid['target']\n\n train_x=train[features].values\n valid_x=valid[features].values\n test_x=test[features].values\n\n train_y=[]\n valid_y=[]\n\n for i in range(0,len(train_labels.values)):\n train_y.append(class2num[train_labels.values[i]]-1)\n train_y=np.array(train_y)\n\n for i in range(0,len(valid_labels.values)):\n valid_y.append(class2num[valid_labels.values[i]]-1)\n valid_y=np.array(valid_y)\n\n train_x = train_x.astype(np.float64)\n valid_x = valid_x.astype(np.float64)\n train_y = train_y.astype(np.float64)\n valid_y = valid_y.astype(np.float64)\n test_x = test_x.astype(np.float64)\n print('Data has been loaded!')\n return train_x,train_y,valid_x,valid_y,test_x\n\n# Saves predictions(2-d numpy array) into './results/results.csv'\ndef saveData(predictions,fpath):\n df = pd.DataFrame(predictions) #predictions is a numpy 2d array\n df.index+=1\n headers = [\"Class_1\",\"Class_2\",\"Class_3\",\"Class_4\",\"Class_5\",\"Class_6\",\"Class_7\",\"Class_8\",\"Class_9\"]\n df.to_csv(fpath, header=headers,index=True, index_label = 'id')\n print('Predictions has been saved!')\n\n","repo_name":"timothywangdev/otto","sub_path":"python/models/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":1716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"3556892277","text":"import tarfile\nimport zipfile\nfrom pathlib import Path\n\nfrom . import ifcb, logger\n\nlog = logger.get_logger(\"files\")\n\n\ndef create_archive(src, dest, compression):\n src = Path(src)\n if not src.is_dir():\n raise ValueError(f\"{src} does not exist\")\n if compression in (\"tar\", \"gzip\", \"tar.gz\", \"gz\"):\n mode = \"w\" if compression == \"tar\" else \"w:gz\"\n with tarfile.open(dest, mode) as tar:\n for src_file in src.iterdir():\n tar.add(src_file, arcname=src_file.name)\n elif compression == \"zip\":\n with zipfile.ZipFile(dest, \"w\", zipfile.ZIP_DEFLATED) as tar:\n for src_file in src.iterdir():\n tar.write(src_file, arcname=src_file.name)\n else:\n raise ValueError(f\"Unknown compression {compression}\")\n\n\ndef sample_csv_path(sample_path, out_dir, suffix=None):\n sample_path = Path(sample_path)\n sample = sample_path.name\n if suffix:\n out_name = sample + suffix + \".csv\"\n else:\n out_name = sample + \".csv\"\n csv_path = (\n Path(out_dir) / ifcb.sample_to_datetime(sample).strftime(\"%Y/%m/%d\") / out_name\n )\n return csv_path\n\n\ndef list_sample_paths(root_dir, filter=None):\n path_gen = (roi.with_suffix(\"\") for roi in Path(root_dir).glob(\"**/*.roi\"))\n if filter is not None:\n path_gen = (path for path in path_gen if path.name in filter)\n return list(path_gen)\n\n\ndef list_sample_csvs(root_dir, filter=None):\n return [\n path\n for path in Path(root_dir).glob(\"**/*.csv\")\n if path.with_suffix(\"\").stem in filter or not filter\n ]\n","repo_name":"sykefi/syke-pic","sub_path":"sykepic/utils/files.py","file_name":"files.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"78"} +{"seq_id":"32194145317","text":"import pygame\nfrom start_screen import Start_screen, WIDTH, HEIGHT\nfrom db import Statistic\nfrom main_screen import Main_screen\n\nif __name__ == '__main__':\n pygame.mixer.init()\n pygame.mixer.music.load('music_fone.mp3')\n pygame.mixer.music.play(-1)\n pygame.mixer.music.set_volume(0.8)\n pygame.init()\n size = WIDTH, HEIGHT\n screen = pygame.display.set_mode(size)\n clock = pygame.time.Clock()\n\n background_color = 'white'\n fps = 60\n\n stat = Statistic()\n start_name, start_board, start_score = stat.load_session()\n start_screen = Start_screen(screen, start_name)\n\n running = True\n start_screen_flag = True\n\n while start_screen_flag: # цикл стартового экрана\n # обработка событий\n for event in pygame.event.get():\n if start_screen.name.handle_event(event) or start_screen.start_button.handle_event(event):\n start_screen_flag = False\n if event.type == pygame.QUIT:\n pygame.quit()\n\n start_screen.name.update()\n start_screen.hello()\n start_screen.name.draw(screen)\n start_screen.start_button.draw(screen)\n start_screen.show_leaders(stat.get_leaders())\n\n pygame.display.flip()\n clock.tick(fps)\n\n # start_board = [0,0,0,0,1024,1024,0,0,0,0,0,0,0,0,0,0,0,0,0,0]\n while True:\n game = Main_screen(screen,\n name=start_screen.name.text,\n data=start_board,\n score=start_score,\n max_score=stat.get_record())\n running = True\n while running: # Основной игровой цикл\n # обработка событий\n for event in pygame.event.get():\n state = game.handle_event(event)\n # новая игра\n if state == 2:\n if not game.game_over:\n stat.save_leader(game.name, game.score)\n start_score = 0\n start_board = []\n running = False\n if state == 1:\n stat.save_leader(game.name, game.score)\n if event.type == pygame.QUIT:\n stat.save_leader(game.name, game.score)\n stat.save_session(*game.export_state())\n pygame.quit()\n\n # обновление экрана\n screen.fill(pygame.Color(\"#faf8ef\"))\n game.content()\n pygame.display.flip()\n clock.tick(fps)\n","repo_name":"varvara-ali/2048","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"78"} +{"seq_id":"6839031080","text":"from featureUtils import FeatureUtils\n\nclass Predictor:\n def __init__(self, numericFeaturePath, cateFeaturePath, textFeaturePath, modelPath):\n self.utils = FeatureUtils()\n self.numericFeature = self.utils.loadBinary(numericFeaturePath)\n self.cateFeature = self.utils.loadBinary(cateFeaturePath)\n self.textFeature = self.utils.loadBinary(textFeaturePath)\n self.modelPath = modelPath\n\n def predict(self):\n learner = self.utils.loadBinary(self.modelPath)\n preds = learner.predict(self.textFeature, self.cateFeature, self.numericFeature)\n return preds\n","repo_name":"whitepaper/easyML","sub_path":"predictor.py","file_name":"predictor.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"78"} +{"seq_id":"34564522205","text":"#!/usr/bin/python2.7\n# -*- coding: utf-8 -*-\n# vim:ts=4:sw=4:softtabstop=4:smarttab:expandtab\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 __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport unittest\n\nfrom pycopia import aid\nfrom pycopia import dictlib\nfrom pycopia import UserFile\nfrom pycopia import getopt\nfrom pycopia import gzip\nfrom pycopia import socket\nfrom pycopia import timelib\nfrom pycopia import tty\nfrom pycopia import urlparse\nfrom pycopia import textutils\nfrom pycopia import timespec\n\nclass MyBaseClass(object):\n pass\n\nclass AidTests(unittest.TestCase):\n def setUp(self):\n pass\n\n def test_mapstr(self):\n TEST = aid.mapstr(\"some%(one)s one\\nsome%(two)s three\\nsome%(three)s four\")\n print(TEST.attributes)\n try:\n print(TEST)\n except ValueError:\n print(\"got correct error from %r\" % TEST)\n TEST.one = \"one\"\n TEST.two = \"thing\"\n TEST.three = \"where\"\n print(TEST)\n s = str(TEST) # makes new, substituted, string\n assert s == \"someone one\\nsomething three\\nsomewhere four\"\n print(TEST.three)\n\n def test_formatstr(self):\n src = \"one {one} {{notaone}} two {two}\"\n fmt = aid.formatstr(src)\n assert src.format(one=\"ONE\", two=\"TWO\") == fmt(one=\"ONE\", two=\"TWO\")\n assert fmt.attributes == [\"two\", \"one\"]\n\n def test_newclass(self):\n New = aid.newclass(\"New\", MyBaseClass)\n print(New())\n\n def test_AttrDictWrapper(self):\n ld = {\"one\":1, \"two\":2, \"three\":3}\n gd = {\"gbone\":1, \"gbtwo\":2, \"gbthree\":3}\n lw = dictlib.AttrDictWrapper(ld)\n lw.four = gd\n print(lw.one)\n print(lw.two)\n print(lw.four.gbone)\n print(lw.four[\"gbtwo\"])\n\n def test_AttrDict(self):\n d = dictlib.AttrDict()\n d.one = \"one\"\n print(d)\n print(d.get)\n print(d.one)\n print(d[\"one\"])\n d[\"two\"] = 2\n print(d.two)\n print(d[\"two\"])\n\n def test_UserFile(self):\n fd = UserFile.UserFile(\"/etc/hosts\", \"rb\")\n while 1:\n d = fd.read(1024)\n if not d:\n break\n fd.close()\n\n def test_timelib(self):\n mt = timelib.localtime_mutable()\n print(mt)\n mt.add_seconds(3600)\n print(mt)\n print(timelib.strftime(\"%Y-%m-%d\", timelib.weekof(timelib.time())))\n\n t = timelib.now()\n for d in range(1, 60):\n week = timelib.weekof(t+(d*60*60*24))\n print(timelib.MutableTime(week))\n\n print(\"Local time:\")\n print(timelib.localtimestamp())\n\n p = timespec.TimespecParser()\n for spec, secs in [\n (\"0s\", 0.0),\n (\"3m\", 180.0),\n (\"3.0m\", 180.0),\n (\"3minute+2secs\", 182.0),\n (\"2h 3minute+2.2secs\", 7382.2),\n (\"-3m\", -180.0),\n (\"-3.0m\", -180.0),\n (\"1h3m\", 3780.0),\n (\"1h-3m\", 3420.0),\n (\"1d 3m\", 86580.0)]:\n p.parse(spec)\n self.assert_(p.seconds == secs)\n\n self.assertRaises(ValueError, p.parse, \"12m -m\")\n\n def XXXtest_tty_SerialPort(self):\n # just call some setup methods. This really needs some serial\n # loopback to fully test.\n sp = tty.SerialPort(\"/dev/ttyS0\")\n sp.set_serial(\"9600 8N1\")\n sp.stty(\"-parenb\", \"-parodd\", \"cs8\", \"hupcl\", \"-cstopb\", \"cread\",\n \"clocal\", \"-crtscts\", \"ignbrk\", \"-brkint\", \"ignpar\", \"-parmrk\",\n \"-inpck\", \"-istrip\", \"-inlcr\", \"-igncr\", \"-icrnl\", \"-ixon\",\n \"-ixoff\", \"-iuclc\", \"-ixany\", \"-imaxbel\", \"-opost\", \"-olcuc\",\n \"-ocrnl\", \"onlcr\", \"-onocr\", \"-onlret\", \"-ofill\", \"-ofdel\", \"nl0\",\n \"cr0\", \"tab0\", \"bs0\", \"vt0\", \"ff0\", \"-isig\", \"-icanon\", \"-iexten\",\n \"-echo\", \"echoe\", \"echok\", \"-echonl\", \"-noflsh\", \"-xcase\",\n \"-tostop\", \"-echoprt\", \"echoctl\", \"echoke\")\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"kdart/pycopia","sub_path":"aid/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":4530,"program_lang":"python","lang":"en","doc_type":"code","stars":83,"dataset":"github-code","pt":"78"} +{"seq_id":"37598505962","text":"import hashlib\nimport hmac\nimport re\nimport time\n\nfrom .common import InfoExtractor\nfrom ..compat import compat_str\nfrom ..utils import (\n dict_get,\n ExtractorError,\n js_to_json,\n int_or_none,\n parse_iso8601,\n str_or_none,\n traverse_obj,\n try_get,\n unescapeHTML,\n update_url_query,\n)\n\n\nclass ABCIE(InfoExtractor):\n IE_NAME = 'abc.net.au'\n _VALID_URL = r'https?://(?:www\\.)?abc\\.net\\.au/(?:news|btn)/(?:[^/]+/){1,4}(?P\\d{5,})'\n\n _TESTS = [{\n 'url': 'http://www.abc.net.au/news/2014-11-05/australia-to-staff-ebola-treatment-centre-in-sierra-leone/5868334',\n 'md5': 'cb3dd03b18455a661071ee1e28344d9f',\n 'info_dict': {\n 'id': '5868334',\n 'ext': 'mp4',\n 'title': 'Australia to help staff Ebola treatment centre in Sierra Leone',\n 'description': 'md5:809ad29c67a05f54eb41f2a105693a67',\n },\n 'skip': 'this video has expired',\n }, {\n 'url': 'http://www.abc.net.au/news/2015-08-17/warren-entsch-introduces-same-sex-marriage-bill/6702326',\n 'md5': '4ebd61bdc82d9a8b722f64f1f4b4d121',\n 'info_dict': {\n 'id': 'NvqvPeNZsHU',\n 'ext': 'mp4',\n 'upload_date': '20150816',\n 'uploader': 'ABC News (Australia)',\n 'description': 'Government backbencher Warren Entsch introduces a cross-party sponsored bill to legalise same-sex marriage, saying the bill is designed to promote \"an inclusive Australia, not a divided one.\". Read more here: http://ab.co/1Mwc6ef',\n 'uploader_id': 'NewsOnABC',\n 'title': 'Marriage Equality: Warren Entsch introduces same sex marriage bill',\n },\n 'add_ie': ['Youtube'],\n 'skip': 'Not accessible from Travis CI server',\n }, {\n 'url': 'http://www.abc.net.au/news/2015-10-23/nab-lifts-interest-rates-following-westpac-and-cba/6880080',\n 'md5': 'b96eee7c9edf4fc5a358a0252881cc1f',\n 'info_dict': {\n 'id': '6880080',\n 'ext': 'mp3',\n 'title': 'NAB lifts interest rates, following Westpac and CBA',\n 'description': 'md5:f13d8edc81e462fce4a0437c7dc04728',\n },\n }, {\n 'url': 'http://www.abc.net.au/news/2015-10-19/6866214',\n 'only_matching': True,\n }, {\n 'url': 'https://www.abc.net.au/btn/classroom/wwi-centenary/10527914',\n 'info_dict': {\n 'id': '10527914',\n 'ext': 'mp4',\n 'title': 'WWI Centenary',\n 'description': 'md5:c2379ec0ca84072e86b446e536954546',\n }\n }, {\n 'url': 'https://www.abc.net.au/news/programs/the-world/2020-06-10/black-lives-matter-protests-spawn-support-for/12342074',\n 'info_dict': {\n 'id': '12342074',\n 'ext': 'mp4',\n 'title': 'Black Lives Matter protests spawn support for Papuans in Indonesia',\n 'description': 'md5:2961a17dc53abc558589ccd0fb8edd6f',\n }\n }, {\n 'url': 'https://www.abc.net.au/btn/newsbreak/btn-newsbreak-20200814/12560476',\n 'info_dict': {\n 'id': 'tDL8Ld4dK_8',\n 'ext': 'mp4',\n 'title': 'Fortnite Banned From Apple and Google App Stores',\n 'description': 'md5:a6df3f36ce8f816b74af4bd6462f5651',\n 'upload_date': '20200813',\n 'uploader': 'Behind the News',\n 'uploader_id': 'behindthenews',\n }\n }, {\n 'url': 'https://www.abc.net.au/news/2023-06-25/wagner-boss-orders-troops-back-to-bases-to-avoid-bloodshed/102520540',\n 'info_dict': {\n 'id': '102520540',\n 'title': 'Wagner Group retreating from Russia, leader Prigozhin to move to Belarus',\n 'ext': 'mp4',\n 'description': 'Wagner troops leave Rostov-on-Don and\\xa0Yevgeny Prigozhin will move to Belarus under a deal brokered by Belarusian President Alexander Lukashenko to end the mutiny.',\n 'thumbnail': 'https://live-production.wcms.abc-cdn.net.au/0c170f5b57f0105c432f366c0e8e267b?impolicy=wcms_crop_resize&cropH=2813&cropW=5000&xPos=0&yPos=249&width=862&height=485',\n }\n }]\n\n def _real_extract(self, url):\n video_id = self._match_id(url)\n webpage = self._download_webpage(url, video_id)\n\n mobj = re.search(r'[^\"]+)\"\\s+data-duration=\"\\d+\"\\s+title=\"Download audio directly\">', webpage)\n if mobj:\n urls_info = mobj.groupdict()\n youtube = False\n video = False\n else:\n mobj = re.search(r'http://www\\.youtube\\.com/watch\\?v=[^\"]+)\">External Link:',\n webpage)\n if mobj is None:\n mobj = re.search(r'