string\n nyt_df['Article Title'] = nyt_df['Article Title'].apply(lambda x: re.search(r'(?<=\\>).+?(?=\\<)', x)[0])\n nyt_df['Article Title'] = nyt_df['Article Title'].apply(lambda x: re.search(r'(?<=\\>).+', x)[0])\n\n # using Regex to extract the links\n nyt_df['Article Link'] = nyt_link_str\n nyt_df['Article Link'] = nyt_df['Article Link'].apply(lambda x: re.search(r'(?<=\\\").+?(?=\\\")', x)[0])\n nyt_df['Article Link'] = 'nytimes.com' + nyt_df['Article Link']\n\n #using regex to extract the data and convert to Pandas datetime obejct\n nyt_df['Article Date'] = nyt_df['Article Link'].apply(lambda x: re.search(r'\\d+/\\d+/\\d+', x))\n nyt_df['Article Date'] = nyt_df['Article Date'].apply(date_extract)\n nyt_df['Article Date'] = pd.to_datetime(nyt_df['Article Date'], errors='coerce', utc=True)\n\n\n #----------------------------------------------------------------------------------------------\n # YAHOO w/ CUSTOM QUERY\n\n\n url = 'https://news.search.yahoo.com/search?p=' + final_query + '&fr=uh3_news_vert_gs&fr2=p%3Anews%2Cm%3Asb'\n response = requests.get(url)\n soup = BeautifulSoup(response.text, 'html.parser')\n\n y_links = soup.find_all('a')\n\n y_links_str = []\n for link in y_links:\n if '16px' in str(link):\n y_links_str.append(str(link))\n\n y_links_str = y_links_str[0:10]\n\n y_titles = []\n for link in y_links_str:\n url = re.search(r'(?<=\\\").+?(?=\\\")', link)[0]\n response = requests.get(url)\n soup = BeautifulSoup(response.text, 'html.parser')\n y_titles.append(soup.find_all('h1'))\n \n y_titles_str = [str(title) for title in y_titles]\n\n y_df = pd.DataFrame()\n \n y_name = ['Yahoo News' for item in range(len(y_titles_str))]\n\n y_df['News Site'] = y_name\n\n y_df['Article Title'] = y_titles_str\n y_df['Article Title'] = y_df['Article Title'].apply(lambda x: re.findall(r'(?<=\\>).+?(?=\\<)', x)[0])\n\n y_df['Article Link'] = y_links_str\n y_df['Article Link'] = y_df['Article Link'].apply(lambda x: re.search(r'(?<=\\\").+?(?=\\\")', x)[0])\n\n y_dates = []\n for link in y_links_str:\n url = re.search(r'(?<=\\\").+?(?=\\\")', link)[0]\n response = requests.get(url)\n soup = BeautifulSoup(response.text, 'html.parser')\n y_dates.append(soup.find_all('time'))\n\n y_dates_str = []\n for item in y_dates:\n if item == None:\n y_dates_str.append(None)\n else:\n y_dates_str.append(str(item))\n\n y_df['Article Date'] = y_dates_str\n y_df['Article Date'] = y_df['Article Date'].apply(lambda x: re.search(r'(?<=>).+(?=<)',x))\n y_df['Article Date'] = y_df['Article Date'].apply(date_extract)\n y_df['Article Date'] = pd.to_datetime(y_df['Article Date'], errors='coerce', utc=True)\n y_df = y_df.sort_values('Article Date', ascending=False)[0:3].reset_index(drop=True)\n\n\n #----------------------------------------------------------------------------------------------\n # GOOGLE NEWS w/ Query option\n\n\n url = 'https://news.google.com/search?q=' + final_query + '%20when%3A7d&hl=en-US&gl=US&ceid=US%3Aen'\n response = requests.get(url)\n soup = BeautifulSoup(response.text, 'html.parser')\n\n g_titles = soup.find_all(['h3','h4'], class_='ipQwMb ekueJc gEATFF RD0gLb')[0:10]\n \n g_titles_str = [str(title) for title in g_titles]\n \n g_name = ['Google News' for title in g_titles_str]\n\n g_df = pd.DataFrame()\n g_df['News Site'] = g_name\n\n g_df['Article Title'] = g_titles_str\n g_df['Article Title'] = g_df['Article Title'].apply(lambda x: re.findall(r'(?<=\\>).+?(?=\\<)', x)[0])\n g_df['Article Title'] = g_df['Article Title'].apply(lambda x: re.search(r'(?<=\\>).+', x)[0])\n\n g_links = []\n for h3 in g_titles:\n for a in h3:\n g_links.append(a['href'])\n\n g_df['Article Link'] = g_links\n g_df['Article Link'] = 'https://www.news.google.com' + g_df['Article Link']\n\n g_dates = soup.find_all('time', class_='WW6dff uQIVzc Sksgp')[0:10]\n \n #g_dates_final = []\n #for time in g_dates:\n #g_dates_final.append(time['datetime'])\n \n g_dates_final = [time['datetime'] for time in g_dates]\n\n g_df['Article Date'] = g_dates_final\n g_df['Article Date'] = g_df['Article Date'].apply(lambda x: x[0:10])\n g_df['Article Date'] = pd.to_datetime(g_df['Article Date'], errors='coerce', utc=True)\n\n #----------------------------------------------------------------------------------------------\n # Final DF Concatenation\n\n final_df = pd.concat([nyt_df, y_df, g_df]).reset_index(drop=True)\n final_df = final_df.sort_values('Article Date', ascending=False).reset_index(drop=True)\n final_df['Article Date'] = final_df['Article Date'].apply(lambda x: x.strftime('%m-%d-%Y'))\n\n\n #----------------------------------------------------------------------------------------------\n # Pop-up Window Output\n\n class PandasModel(QAbstractTableModel):\n\n def __init__(self, data):\n QAbstractTableModel.__init__(self)\n self._data = data\n \n def rowCount(self, parent=None):\n return self._data.shape[0]\n\n def columnCount(self, parent=None):\n return self._data.shape[1]\n\n def data(self, index, role=Qt.DisplayRole):\n if index.isValid():\n if role == Qt.DisplayRole:\n return str(self._data.iloc[index.row(), index.column()])\n else:\n return None\n\n def headerData(self, col, orientation, role):\n if orientation == Qt.Horizontal and role == Qt.DisplayRole:\n return self._data.columns[col]\n else:\n return None\n\n app = QApplication(sys.argv)\n model = PandasModel(final_df)\n view = QTableView()\n view.setModel(model)\n view.resizeColumnToContents(0)\n view.resizeColumnToContents(1)\n view.resizeColumnToContents(3)\n view.resize(1200, 1600)\n view.show()\n app.exec_()\n\nif __name__ == '__main__':\n popup()\n","repo_name":"kevin-anderson1/NewsFetcher","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"30466123332","text":"#!/usr/bin/env python3\n\nimport binascii\nimport ipaddress\nimport os\nimport sys\nimport warnings\n\nfrom itertools import cycle\n\nfrom scapy.all import IP, UDP, Ether, Raw, bytes_encode, PcapWriter, DLT_EN10MB\n\n\n# Bypassing scapy's awfully slow wrpcap, have to use raw packets as input\n# To get a raw packet from a scapy packet use `bytes_encode(pkt)`.\ndef wrpcap(pcap_name, raw_packets):\n with PcapWriter(pcap_name, linktype=DLT_EN10MB) as pkt_wr:\n for raw_pkt in raw_packets:\n if not pkt_wr.header_present:\n pkt_wr._write_header(raw_pkt)\n pkt_wr._write_packet(raw_pkt)\n\n\ndef generate_pcap(nb_pkts, out_pcap, pkt_size, nb_src, nb_dest, batch_size):\n sample_pkts = []\n ipv4_len = pkt_size - 14 - 4\n for i in range(nb_dest):\n dst_ip = ipaddress.ip_address(\"192.168.0.0\") + i\n src_offset = int(i / (nb_dest / nb_src))\n src_ip = ipaddress.ip_address(\"192.168.0.0\") + src_offset\n pkt = (\n Ether()\n / IP(dst=str(dst_ip), src=str(src_ip), len=ipv4_len)\n / UDP(dport=80, sport=8080)\n )\n\n missing_bytes = pkt_size - len(pkt) - 4 # no CRC\n payload = binascii.unhexlify(\"00\" * missing_bytes)\n pkt = pkt / Raw(load=payload)\n pkt = bytes_encode(pkt)\n sample_pkts.append(pkt)\n\n def cycle_batches():\n for pkt in cycle(sample_pkts):\n for _ in range(batch_size):\n yield pkt\n\n def pkt_gen():\n for _, pkt in zip(range(nb_pkts), cycle_batches()):\n yield pkt\n\n wrpcap(out_pcap, pkt_gen())\n\n\ndef main():\n if (len(sys.argv) < 6) or (len(sys.argv) > 7):\n print(\n \"Usage:\",\n sys.argv[0],\n \"nb_pkts pkt_size nb_src nb_dest output_pcap [batch_size]\",\n )\n sys.exit(1)\n\n nb_pkts = int(sys.argv[1])\n pkt_size = int(sys.argv[2])\n nb_src = int(sys.argv[3])\n nb_dest = int(sys.argv[4])\n out_pcap = sys.argv[5]\n if len(sys.argv) > 6:\n batch_size = int(sys.argv[6])\n else:\n batch_size = 1\n\n if os.path.exists(out_pcap):\n warnings.warn(\"Pcap with the same name already exists. Skipping.\")\n sys.exit(0)\n\n generate_pcap(nb_pkts, out_pcap, pkt_size, nb_src, nb_dest, batch_size)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"crossroadsfpga/enso","sub_path":"hardware/input_gen/generate_synthetic_trace.py","file_name":"generate_synthetic_trace.py","file_ext":"py","file_size_in_byte":2307,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"47"}
+{"seq_id":"31177676809","text":"from django.urls import path\n\nfrom .views import (\n CartView,\n CartUpdateView,\n OrderItemDeleteView,\n FinishOrderView,\n OrderStateView,\n OrderListView,\n update_order_state,\n ChefOrderListView,\n ConfirmDeliveryView,\n)\n\n\napp_name = \"orders\"\n\nurlpatterns = [\n path(\"update/\", CartUpdateView.as_view(), name=\"cart_update\"),\n path(\"delete//\", OrderItemDeleteView.as_view(), name=\"order_item_delete\"),\n path(\"cart/\", CartView.as_view(), name=\"cart_view\"),\n path(\"finish/\", FinishOrderView.as_view(), name=\"finish_order\"),\n path(\"state//\", OrderStateView.as_view(), name=\"order_status\"),\n path(\"\", OrderListView.as_view(), name=\"orders_list\"),\n path(\"chef/update//\", update_order_state, name=\"chef_update_order_state\"),\n path(\"chef/list/\", ChefOrderListView.as_view(), name=\"chef_list_orders\"),\n path(\"confirm_delivery//\", ConfirmDeliveryView().as_view(), name=\"confirm_delivery\"),\n]\n","repo_name":"mohamad-zahiry/foodhub_api","sub_path":"apps/orders/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":981,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"3970185605","text":"from utils import globo , xdv\n\nimport cv2\nimport numpy as np , os \n\nRESOLUTION = 224\nchannels = 3\nframe_count = 16\nfeatures_per_bag = 32\n\n\nfn, labels, tframes = xdv.load_test_npy()\n\n\ndef view_clips(clips, delay_between_frames=int(1000/24), pause_between_clips=1000):\n for clip_idx, clip in enumerate(clips):\n print(f\"Displaying clip {clip_idx + 1}/{len(clips)}\")\n for frame in clip:\n cv2.imshow(\"Clip Frame\", frame)\n key = cv2.waitKey(delay_between_frames)\n if key == ord(\"q\"): # Quit\n break\n\n #print(f\"SPACE to view next clip || Q to quit\")\n #while True:\n # key = cv2.waitKey(pause_between_clips)\n # if key == ord(\" \"): # Next clip\n # break\n # if key == ord(\"q\"): # Quit\n # return\n cv2.destroyAllWindows()\n\n## how data is feed to extract features of UCF-Crime\n## from tfm-anomaly-detection/proposal/utils/video_util.py\ndef sliding_window(arr, size, stride):\n num_chunks = int((len(arr) - size) / stride) + 2\n result = []\n print(\"num_chuncks\",num_chunks,len(arr))\n for i in range(0, num_chunks * stride, stride):\n if len(arr[i:i + size]) > 0:\n result.append(arr[i:i + size])\n return result #np.array(result) \n\ndef get_video_frames(video_path):\n cap = cv2.VideoCapture(video_path)\n frames = []\n while (cap.isOpened()):\n sucess, frame = cap.read()\n if not sucess: break\n frame = cv2.resize(frame, (RESOLUTION,RESOLUTION))\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n frames.append(frame)\n cap.release()\n return frames \n\ndef get_video_clips(video_path):\n frames = get_video_frames(video_path)\n clips = sliding_window(frames, frame_count, frame_count)\n return clips, len(frames)\n\n\nfor i in range(len(fn)):\n #if \"label_A\" not in f:\n clips , frames = get_video_clips(fn[i+100])\n view_clips(clips)\n ","repo_name":"zuble/vigia","sub_path":"zurgb22/x.py","file_name":"x.py","file_ext":"py","file_size_in_byte":1951,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"20435777821","text":"#################################################################\r\n########### Inicializar base de salario e experiencia ###########\r\n#################################################################\r\n\r\n# Base de dados inicial\r\n\r\nsalaries_and_tenures = [(83000, 8.7), (88000, 8.1), (48000, 0.7), (76000, 6), (69000, 6.5),\r\n\t\t\t\t\t\t(76000, 7.5), (60000, 2.5), (83000, 10), (48000, 1.9), (63000, 4.2)]\r\n\t\t\t\t\t\t\r\nprint(\"Base de dados inicial:\")\r\nprint(salaries_and_tenures, \"\\n\")\r\n\r\nfrom collections import defaultdict\r\n\r\n# Média salarial por ano de experiencia (utilizando intervalos de tempo)\r\n\r\ndef tenure_bucket(tenure):\r\n\tif tenure < 2:\r\n\t\treturn \"Nivel Trainee\"\r\n\telif tenure < 5:\r\n\t\treturn \"Nivel Junior\"\r\n\telse :\r\n\t\treturn \"Nivel Pleno\"\r\n\r\nsalary_by_tenure = defaultdict(list)\r\n\r\nfor salary, tenure in salaries_and_tenures:\r\n\tsalary_by_tenure[tenure_bucket(tenure)].append(salary)\r\n\r\naverage_salary_by_tenure = {\r\n\ttenure : sum(salaries) / len(salaries)\r\n\tfor tenure, salaries in salary_by_tenure.items()\r\n}\r\n\r\nprint(\"Media salarial por nivel de experiencia\")\r\nprint(average_salary_by_tenure, \"\\n\")","repo_name":"erickfecruz/Data_Science_from_Scratch-Book_OReilly","sub_path":"Chapter 01 - Introduction/Exemplo 02 - Salario e Experiencia.py","file_name":"Exemplo 02 - Salario e Experiencia.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"32175301057","text":"from vosk import Model, KaldiRecognizer\nimport pyaudio\n\nsample_rate = 44100\nchunk = 8000\nmodel = Model(\"model\")\nrec = KaldiRecognizer(model, sample_rate)\n\np = pyaudio.PyAudio()\nstream = p.open(format=pyaudio.paInt16, channels=2,\n rate=sample_rate, input=True, frames_per_buffer=chunk)\nstream.start_stream()\n\nwhile True:\n data = stream.read(chunk, exception_on_overflow=False)\n if len(data) == 0:\n break\n if rec.AcceptWaveform(data):\n print(rec.Result())\n else:\n print(rec.PartialResult())\n\nprint(rec.FinalResult())\n","repo_name":"nandordevai/vau","sub_path":"example/test_microphone.py","file_name":"test_microphone.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"40244507506","text":"from argparse import ArgumentParser, Namespace\nfrom copy import deepcopy\n\nfrom fedavg import FedAvgServer, get_fedavg_argparser\nfrom src.client.knnper import kNNPerClient\n\n\ndef get_knnper_argparser() -> ArgumentParser:\n parser = get_fedavg_argparser()\n parser.add_argument(\"--capacity\", type=int, default=500)\n parser.add_argument(\"--weight\", type=float, default=0.5)\n parser.add_argument(\"--scale\", type=float, default=1)\n parser.add_argument(\"--k\", type=int, default=5)\n return parser\n\n\nclass kNNPerServer(FedAvgServer):\n def __init__(\n self,\n algo: str = \"kNN-Per\",\n args: Namespace = None,\n unique_model=False,\n default_trainer=False,\n ):\n if args is None:\n args = get_knnper_argparser().parse_args()\n super().__init__(algo, args, unique_model, default_trainer)\n self.trainer = kNNPerClient(\n deepcopy(self.model), self.args, self.logger, self.device\n )\n\n\nif __name__ == \"__main__\":\n server = kNNPerServer()\n server.run()\n","repo_name":"KarhouTam/FL-bench","sub_path":"src/server/knnper.py","file_name":"knnper.py","file_ext":"py","file_size_in_byte":1041,"program_lang":"python","lang":"en","doc_type":"code","stars":262,"dataset":"github-code","pt":"47"}
+{"seq_id":"29101265747","text":"# -*- coding: utf-8 -*-\n\"\"\"\n\n@author: timpr\n\"\"\"\n\nimport yfinance as yf\nimport pandas as pd\nfrom datetime import datetime\n\ndef convert_df_to_list(dividend_df):\n \"\"\"\n Converts dataframe entries into a list of lists, to maintain consistency with the format of other data\n used in the analysis\n\n Parameters\n ----------\n dividend_df (pandas.Dataframe) : A dataframe with a row index of datetimes in '%Y-%m-%d' format, and one column (Dividends) of floats\n\n Returns\n -------\n dividend_list (List>): A list containing entries for each dividend for a particulare ETF.\n Embedded lists are of the form [Date, Unit Dividend Price]\n\n \"\"\"\n dividend_list = []\n date_list = dividend_df.head(len(dividend_df)).index.strftime('%Y-%m-%d').tolist()\n dividend_val_list = dividend_df.tolist()\n for i in range (len(dividend_val_list)):\n dividend_list.append([date_list[i], dividend_val_list[i]]) \n \n return(dividend_list)\n\ndef lookup_dividends(ticker):\n \"\"\"\n Looks up the dividend history for a stock or ETF ticker\n \n Parameters\n ----------\n ticker (yfinance Ticker object): The ticker for the stock of interest\n \n Returns\n -------\n dividend_list (List>): A list containing a list for each stock/etf dividend payment.\n Embedded lists are of the form [Date, Unit Dividend Price]\n \"\"\"\n dividend_df = ticker.dividends\n return(convert_df_to_list(dividend_df))\n\ndef build_dividend_lists(portfolio_dict):\n \"\"\"\n Creates lists of the histoical dividends for the following ETFs \n DOW JONES: State Street Global Advisors SPDR Dow Jones Industrial Average ETF (DIA)\n S&P500: State Street Global Advisors SPDR S&P 500 ETF Trust (SPY)\n NASDAQ: Invesco QQQ ETF (QQQ)\n TOTAL MARKET: Vanguard Total Stock Market ETF (VTI)\n \n Parameters\n ----------\n portfolio_dict (Dict>): A list containing a list for each SPY dividend payment.\n Embedded lists are of the form [Date, Unit Dividend Price]\n dow_dividends (List>): A list containing a list for each DIA dividend payment.\n Embedded lists are of the form [Date, Unit Dividend Price]\n nasdaq_dividends (List>): A list containing a list for each QQQ dividend payment.\n Embedded lists are of the form [Date, Unit Dividend Price]\n totalmarket_dividends (List>): A list containing a list for each VTI dividend payment.\n Embedded lists are of the form [Date, Unit Dividend Price]\n portfolio_dividend_dict (Dict>>): A dictionary with dividend details for each holding\n in portfolio, same format as for etfs\n \n \"\"\"\n # ETF dividend list\n dow_dividends = lookup_dividends(yf.Ticker(\"DIA\")) \n sp500_dividends = lookup_dividends(yf.Ticker(\"SPY\")) \n nasdaq_dividends = lookup_dividends(yf.Ticker(\"QQQ\")) \n totalmarket_dividends = lookup_dividends(yf.Ticker(\"VTI\")) \n \n # Portfolio dividends\n portfolio_dividend_dict = {}\n for key in portfolio_dict:\n portfolio_dividend_dict[key] = lookup_dividends(yf.Ticker(key))\n \n return (dow_dividends, sp500_dividends, nasdaq_dividends, totalmarket_dividends, portfolio_dividend_dict)\n","repo_name":"Timpryor91/robinhood_portfolio_check","sub_path":"modules/build_dividend_lists.py","file_name":"build_dividend_lists.py","file_ext":"py","file_size_in_byte":3836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"16159075881","text":"def findMedian(nums1, nums2):\n totalLen = len(nums1) + len(nums2)\n if totalLen % 2 == 0:\n return (findKth(totalLen // 2, nums1, 0, len(nums1), nums2, 0,len(nums2)) + findKth((totalLen // 2) - 1, nums1,0,len(nums1), nums2,0,len(nums2)) )/ 2\n else:\n return findKth(totalLen // 2, nums1, 0, len(nums1), nums2, 0, len(nums2))\n\ndef findKth(k, nums1, start1, end1, nums2, start2, end2):\n if end1 <= start1:\n return nums2[start2 + k]\n if end2 <= start2:\n return nums1[start1 + k]\n\n \n mid1 = start1 + (end1 - start1) // 2\n mid2 = start2 + (end2 - start2) // 2\n\n if k > (mid1 - start1) + (mid2 - start2): #k on the right half\n if nums1[mid1] > nums2[mid2]: #we eliminate the left half of num2\n return findKth(k - (mid2 - start2) - 1, nums1, start1, end1, nums2, mid2 + 1, end2)\n else:\n return findKth(k - (mid1 - start1) - 1, nums1,mid1 + 1, end1, nums2, start2, end2)\n else: #k on the left half\n if nums1[mid1] > nums2[mid2]:\n return findKth(k, nums1,start1, mid1, nums2, start2, end2)\n else:\n return findKth(k, nums1,start1, end1, nums2, start2, mid2)\nclass Solution:\n def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:\n totalLen = len(nums1) + len(nums2)\n if totalLen % 2 == 0:\n return (self.findKth(totalLen // 2, nums1, nums2) + self.findKth((totalLen // 2) - 1, nums1, nums2)) / 2\n else:\n return self.findKth(totalLen // 2, nums1, nums2)\n def findKth(self,k, nums1, nums2):\n if len(nums1) == 0:\n return nums2[k]\n if len(nums2) == 0:\n return nums1[k]\n\n mid1 = len(nums1) // 2\n mid2 = len(nums2) // 2\n\n if k > mid1 + mid2: #k on the right half\n if nums1[mid1] > nums2[mid2]: #we eliminate the left half of num2\n return self.findKth(k - mid2 - 1, nums1, nums2[mid2 + 1:])\n else:\n return self.findKth(k - mid1 - 1, nums1[mid1 + 1:], nums2)\n else: #k on the left half\n if nums1[mid1] > nums2[mid2]:\n return self.findKth(k, nums1[:mid1], nums2)\n else:\n return self.findKth(k, nums1, nums2[:mid2])","repo_name":"snail15/AlgorithmPractice","sub_path":"LeetCode/Python/medianOfTwoSortedArrays.py","file_name":"medianOfTwoSortedArrays.py","file_ext":"py","file_size_in_byte":2262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"72080332302","text":"import time\n\nimport telebot\nfrom config import *\nimport database\nfrom parse_anekdot import *\n\nbot = telebot.TeleBot(token) # access token to bot\nCURRENT_ANEKDOT_ID = 1\nCURRENT_ANEKDOT_TYPE = 0\n\n\ndef register_user(message):\n with database.db:\n user = database.User(cnt_likes_smatom=0, cnt_likes_bezmata=0, user_id=message.chat.id, likes=\"\",\n cnt_anekdots_smatom=0,\n cnt_anekdots_bezmata=0, page_smatom=1,\n page_bezmata=1).save()\n photo = open(\"images/hello.jpg\", \"rb\")\n bot.send_photo(message.chat.id, photo)\n\n\n@bot.message_handler(commands=['start'])\ndef start(message):\n register_user(message)\n markup = types.ReplyKeyboardMarkup(\n resize_keyboard=True) # создание интрфейса кнопок для взаимодействия с пользователем\n markup.add(bez_mata, s_matom, like, likes)\n bot.send_message(message.chat.id, f\"{message.chat.first_name}, добро пожаловать! Угощаю вас порцией анекдотов))\",\n reply_markup=markup)\n\n\ndef update_db(message, isMat):\n with database.db:\n if isMat:\n parse_anekdot_smatom(message)\n else:\n parse_anekdot_bezmata(message)\n\n\ndef send_anekdot_smatom(message):\n global CURRENT_ANEKDOT_ID, CURRENT_ANEKDOT_TYPE\n with database.db:\n user = database.User.get(user_id=message.chat.id)\n anekdot = database.AnekdotMat\n cnt_anekdots = user.cnt_anekdots_smatom\n cnt_likes = user.cnt_likes_smatom\n if cnt_anekdots == cnt_likes:\n if cnt_anekdots == 0:\n cnt_anekdots += 1\n user.save()\n flag = 1\n update_db(message, 1)\n if flag:\n user.cnt_anekdots_smatom = cnt_anekdots + 1\n user.save()\n CURRENT_ANEKDOT_ID = anekdot.get(anekdot.id == user.cnt_anekdots_smatom).id\n CURRENT_ANEKDOT_TYPE = 1\n bot.send_message(message.chat.id, f'{anekdot.get(anekdot.id == user.cnt_anekdots_smatom).text}')\n flag = 0\n else:\n user.cnt_anekdots_smatom = cnt_anekdots + 1\n user.save()\n CURRENT_ANEKDOT_ID = anekdot.get(anekdot.id == user.cnt_anekdots_smatom).id\n CURRENT_ANEKDOT_TYPE = 1\n bot.send_message(message.chat.id, f'{anekdot.get(anekdot.id == user.cnt_anekdots_smatom).text}')\n\n\ndef send_anekdot_bezmata(message):\n global CURRENT_ANEKDOT_ID, CURRENT_ANEKDOT_TYPE\n with database.db:\n user = database.User.get(user_id=message.chat.id)\n anekdot = database.AnekdotBezMata\n cnt_anekdots = user.cnt_anekdots_bezmata\n cnt_likes = user.cnt_likes_bezmata\n if cnt_anekdots == cnt_likes:\n if cnt_anekdots == 0:\n cnt_anekdots += 1\n user.save()\n flag = 1\n update_db(message, 0)\n if flag:\n user.cnt_anekdots_bezmata = cnt_anekdots + 1\n user.save()\n bot.send_message(message.chat.id, f'{anekdot.get(anekdot.id == user.cnt_anekdots_bezmata).text}')\n CURRENT_ANEKDOT_ID = anekdot.get(anekdot.id == user.cnt_anekdots_bezmata).id\n CURRENT_ANEKDOT_TYPE = 0\n flag = 0\n else:\n user.cnt_anekdots_bezmata = cnt_anekdots + 1\n user.save()\n CURRENT_ANEKDOT_ID = anekdot.get(anekdot.id == user.cnt_anekdots_bezmata).id\n CURRENT_ANEKDOT_TYPE = 0\n bot.send_message(message.chat.id, f'{anekdot.get(anekdot.id == user.cnt_anekdots_bezmata).text}')\n\n\ndef like_anekdot(message):\n if CURRENT_ANEKDOT_TYPE:\n user = database.User.get(user_id=message.chat.id)\n user.likes_id += f'1:{CURRENT_ANEKDOT_ID} '\n user.save()\n else:\n user = database.User.get(user_id=message.chat.id)\n user.likes_id += f'0:{CURRENT_ANEKDOT_ID} '\n user.save()\n\n\ndef send_likes(message):\n likes_anekodts = database.User.get(user_id=message.chat.id).likes_id\n for i in likes_anekodts.split():\n if int(i[0]) == 0:\n bot.send_message(message.chat.id,\n f'{database.AnekdotBezMata.get(database.AnekdotBezMata.id == i[2:]).text}')\n else:\n bot.send_message(message.chat.id, f'{database.AnekdotMat.get(database.AnekdotMat.id == i[2:]).text}')\n\n\n@bot.message_handler(content_types=['text'])\ndef bot_message(message):\n if message.chat.type == 'private':\n if message.text == bez_mata.text:\n send_anekdot_bezmata(message)\n elif message.text == s_matom.text:\n send_anekdot_smatom(message)\n elif message.text == likes.text:\n send_likes(message)\n elif message.text == like.text:\n like_anekdot(message)\n\n\nwhile True: # for all time polling bot without exit from exceptions\n try:\n bot.polling(none_stop=False)\n time.sleep(0.3)\n except Exception as e:\n print(e)\n time.sleep(15)\n","repo_name":"Yurkkka/PolniyAnektodBot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5127,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"40438903043","text":"import time\nfrom typing import Union\n\nimport numpy as np\nfrom tenseal.tensors.ckksvector import CKKSVector\n\nfrom nvflare.apis.dxo import DXO, DataKind, MetaKey\nfrom nvflare.apis.dxo_filter import DXOFilter\nfrom nvflare.apis.event_type import EventType\nfrom nvflare.apis.fl_context import FLContext\nfrom nvflare.apis.shareable import Shareable\nfrom nvflare.app_opt.he import decomposers\nfrom nvflare.app_opt.he.constant import HE_ALGORITHM_CKKS\nfrom nvflare.app_opt.he.homomorphic_encrypt import (\n count_encrypted_layers,\n deserialize_nested_dict,\n load_tenseal_context_from_workspace,\n)\n\n\nclass HEModelDecryptor(DXOFilter):\n def __init__(self, tenseal_context_file=\"client_context.tenseal\", data_kinds: [str] = None):\n \"\"\"Filter to decrypt Shareable object using homomorphic encryption (HE) with TenSEAL\n https://github.com/OpenMined/TenSEAL.\n\n Args:\n tenseal_context_file: tenseal context files containing decryption keys and parameters\n data_kinds: kinds of DXOs to filter\n\n \"\"\"\n if not data_kinds:\n data_kinds = [DataKind.WEIGHT_DIFF, DataKind.WEIGHTS]\n\n super().__init__(supported_data_kinds=[DataKind.WEIGHTS, DataKind.WEIGHT_DIFF], data_kinds_to_filter=data_kinds)\n\n self.logger.info(\"Using HE model decryptor.\")\n self.tenseal_context = None\n self.tenseal_context_file = tenseal_context_file\n\n decomposers.register()\n\n def handle_event(self, event_type: str, fl_ctx: FLContext):\n if event_type == EventType.START_RUN:\n self.tenseal_context = load_tenseal_context_from_workspace(self.tenseal_context_file, fl_ctx)\n elif event_type == EventType.END_RUN:\n self.tenseal_context = None\n\n def decryption(self, params: dict, encrypted_layers: dict, fl_ctx: FLContext):\n\n n_params = len(params.keys())\n self.log_info(fl_ctx, f\"Running HE Decryption algorithm {n_params} variables\")\n if encrypted_layers is None:\n raise ValueError(\"encrypted_layers is None!\")\n deserialize_nested_dict(params, context=self.tenseal_context)\n\n start_time = time.time()\n n_decrypted, n_total = 0, 0\n for i, param_name in enumerate(params.keys()):\n values = params[param_name]\n if encrypted_layers[param_name]:\n _n = values.size()\n n_total += _n\n if isinstance(values, CKKSVector):\n self.log_info(fl_ctx, f\"Decrypting vars {i+1} of {n_params}: {param_name} with {_n} values\")\n params[param_name] = values.decrypt(secret_key=self.tenseal_context.secret_key())\n n_decrypted += _n\n else:\n self.log_info(\n fl_ctx,\n f\"{i} of {n_params}: {param_name} = {np.shape(params[param_name])} already decrypted (RAW)!\",\n )\n raise ValueError(\"Should be encrypted at this point!\")\n else:\n params[param_name] = values\n end_time = time.time()\n self.log_info(fl_ctx, f\"Decryption time for {n_decrypted} of {n_total} params {end_time - start_time} seconds.\")\n\n return params\n\n def process_dxo(self, dxo: DXO, shareable: Shareable, fl_ctx: FLContext) -> Union[None, DXO]:\n \"\"\"Filter process apply to the Shareable object.\n\n Args:\n dxo: Data Exchange Object\n shareable: shareable\n fl_ctx: FLContext\n\n Returns: DXO object with decrypted weights\n\n \"\"\"\n # TODO: could be removed later\n if self.tenseal_context is None:\n self.tenseal_context = load_tenseal_context_from_workspace(self.tenseal_context_file, fl_ctx)\n\n self.log_info(fl_ctx, \"Running decryption...\")\n encrypted_layers = dxo.get_meta_prop(key=MetaKey.PROCESSED_KEYS, default=None)\n if not encrypted_layers:\n self.log_warning(\n fl_ctx,\n \"DXO does not contain PROCESSED_KEYS (do nothing). \"\n \"Note, this is normal in the first round of training, as the initial global model is not encrypted.\",\n )\n return None\n\n encrypted_algo = dxo.get_meta_prop(key=MetaKey.PROCESSED_ALGORITHM, default=None)\n if encrypted_algo != HE_ALGORITHM_CKKS:\n self.log_error(fl_ctx, \"shareable is not HE CKKS encrypted\")\n return None\n\n n_encrypted, n_total = count_encrypted_layers(encrypted_layers)\n self.log_info(fl_ctx, f\"{n_encrypted} of {n_total} layers encrypted\")\n decrypted_params = self.decryption(\n params=dxo.data,\n encrypted_layers=encrypted_layers,\n fl_ctx=fl_ctx,\n )\n\n dxo.data = decrypted_params\n dxo.remove_meta_props([MetaKey.PROCESSED_ALGORITHM, MetaKey.PROCESSED_KEYS])\n dxo.update_shareable(shareable)\n\n return dxo\n","repo_name":"NVIDIA/NVFlare","sub_path":"nvflare/app_opt/he/model_decryptor.py","file_name":"model_decryptor.py","file_ext":"py","file_size_in_byte":4941,"program_lang":"python","lang":"en","doc_type":"code","stars":455,"dataset":"github-code","pt":"47"}
+{"seq_id":"29568343960","text":"import torch\nfrom torch.fft import fft,ifft\nimport torch.nn as nn\nfrom torch.autograd import grad\n\n\nclass CirConvNet(nn.Module):\n def __init__(self):\n super().__init__()\n num_channels = 5\n kernel_size = 5\n self.layer1 = torch.nn.Conv1d(1, num_channels, kernel_size)\n self.layer2 = torch.nn.Conv1d(num_channels, num_channels, kernel_size)\n self.layer3 = torch.nn.Linear(305, 1)\n def forward(self, x):\n x = torch.unsqueeze(x, 1)\n x = torch.sin(self.layer1(x))\n x = torch.sin(self.layer2(x))\n x = torch.flatten(x, 1)\n y = self.layer3(x)\n return y\n\n\nclass ConvNet2D(nn.Module):\n def __init__(self):\n super().__init__()\n num_channels = 5\n kernel_size = 5\n self.layer1 = torch.nn.Conv2d(1, num_channels, (5,1))\n self.layer2 = torch.nn.Conv2d(num_channels, num_channels, (5,1))\n self.layer3 = torch.nn.Linear(305, 1)\n def forward(self, x):\n x = torch.unsqueeze(x, 1)\n x = torch.unsqueeze(x, -1)\n x = torch.sin(self.layer1(x))\n x = torch.sin(self.layer2(x))\n x = torch.flatten(x, 1)\n y = self.layer3(x)\n return y\n\n\nclass HRNet(nn.Module):\n def __init__(self,nLayers,hiddenN,nIn,nOut):\n super(HRNet, self).__init__()\n layers = []\n layers.append(torch.nn.Linear(nIn, hiddenN))\n layers.append(torch.nn.ReLU())\n for i in range(nLayers):\n layers.append(torch.nn.Linear(hiddenN, hiddenN))\n layers.append(torch.nn.ReLU())\n layers.append(torch.nn.Linear(hiddenN, nOut))\n \n self.net = nn.Sequential(*layers)\n \n def forward(self,x):\n return self.net(x)","repo_name":"tataganesh/HRV-edgedevice","sub_path":"networks/regressor_circular.py","file_name":"regressor_circular.py","file_ext":"py","file_size_in_byte":1731,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"34446554049","text":"#! /usr/bin/env python\n\nimport math\nimport random\nimport sys\nfrom timeit import default_timer as timer\n\nimport rospy\n\nimport detection\nimport movement\nimport score\nimport aftergettingshot\n\nclass Main:\n def __init__(self):\n rospy.on_shutdown(self.shutdown)\n rospy.init_node('robocops')\n\n self.mover = movement.Movement(self)\n self.detector = detection.Detection(self)\n self.scorer = score.Score()\n self.after = aftergettingshot.AfterGettingShot(self)\n\n self.rate = rospy.Rate(50)\n self.TO_SHOOT_OR_NOT_TO_SHOOT = 15\n self.cool_down = timer() - 15\n self.prev_disabled = False\n\n def run(self):\n if self.mover.disabled:\n # Shot!\n if not self.prev_disabled:\n print(\"===Shot...===\")\n self.prev_disabled = True\n return\n elif self.prev_disabled:\n self.prev_disabled = False\n # After getting shot!\n print(\"===AfterGettingShot===\")\n self.mover.rotate_to(self.after.target, self.after.direction)\n return\n\n if not self.detector.detected:\n # Explore!\n print(\"===Explore===\")\n self.mover.explore()\n else:\n if timer() - self.cool_down < 10:\n # After shooting!\n print(\"===CoolDown(Follow)===\")\n self.mover.follow(self.detector.best_position)\n # print(\"===CoolDown(Explore)===\")\n # self.mover.explore()\n else:\n print(\"Estimate score: \" + str(self.detector.best_score))\n if self.detector.best_score > self.TO_SHOOT_OR_NOT_TO_SHOOT:\n # Shoot!\n print(\"===Shoot===\")\n score = self.scorer.send(self.detector.captured_image,\n self.detector.camera_info)\n if score > 0:\n # Shot successfully.\n self.cool_down = timer()\n else:\n # Follow!\n print(\"===Follow===\")\n self.mover.follow(self.detector.best_position)\n\n self.rate.sleep()\n\n def shutdown(self):\n self.mover.abort()\n print(\"Shutdown!\")\n\nif __name__ == '__main__':\n try:\n main = Main()\n while not rospy.is_shutdown():\n main.run()\n except rospy.ROSInterruptException:\n print(\"Program interrupted before completion.\")\n","repo_name":"yilinjuang/RoboCops","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2508,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"13676789753","text":"# -*- coding: utf-8 -*-w\r\nimport json\r\n\r\n'''\r\n## 핵심 정리 \r\n\r\n* try/finally 복합문을 이용하면 try 블록에서 예외 발생 여부와 상관없이 정리 코드를 실행할 수 있다.\r\n* else 블록은 try 블록에 있는 코드의 양을 최소로 줄이는 데 도움을 주며 try/except 블록과 성공한 경우에 실행할 코드를 시각적으로 구분해준다.\r\n* else 블록은 try 블록의 코드가 성공적으로 실행된 후 finally 블록에서 공통 정리 코드를 실행하기 전에 추가 작업을 하는 데 사용할 수 있다.\r\n'''\r\n\r\n\r\nhandle = open(\"./random_data.txt\")\r\n# IOError가 일어날 수 있음.\r\n\r\ntry:\r\n data = handle.read()\r\n # UnicodeDecodeError 가 일어날 수 있음.\r\nfinally:\r\n handle.close()\r\n\r\n\r\ndef load_json_key(data, key):\r\n try:\r\n result_dict = json.loads(data)\r\n # ValueError 가 일어날 수 있음\r\n except ValueError as e:\r\n raise KeyError from e\r\n else:\r\n return result_dict[key]\r\n # KeyError 가 일어날 수 있음\r\n\r\n\r\nUNDEFINED = object()\r\n\r\ndef divide_json(path):\r\n handle = open(\"path\", \"r+\")\r\n # IOError 가 일어날 수 있음\r\n try:\r\n data = handle.read()\r\n # UnicodeDecodeError 가 일어날 수 있음\r\n op = json.loads(data)\r\n # ValueError 가 일어날 수 있음\r\n value = (\r\n op[\"numerator\"] /\r\n op[\"denominator\"]\r\n # ZeroDivisionError 가 일어날 수 있음\r\n )\r\n except ZeroDivisionError as e:\r\n return UNDEFINED\r\n else:\r\n op[\"result\"] = value\r\n result = json.dumps(op)\r\n handle.seek(0)\r\n handle.write(result)\r\n # IOError 가 일어날 수 있음\r\n return value\r\n finally:\r\n handle.close()\r\n # 항상 실행함","repo_name":"sunghun7511/Effective-Python","sub_path":"Better-way-13-try, except, else, finally 에서 각 블록의 장점을 이용하자/try-except-else-finally.py","file_name":"try-except-else-finally.py","file_ext":"py","file_size_in_byte":1825,"program_lang":"python","lang":"ko","doc_type":"code","stars":6,"dataset":"github-code","pt":"47"}
+{"seq_id":"22451241425","text":"import pandas as pd\nimport numpy as np\n\ndef yelp_category(df):\n '''\n Categorize each restaurant in yelp_data.csv by its first category to 12 country regions\n '''\n Chinese = ['Shanghainese','Taiwanese','HotPot','Chinese','Szechuan','DimSum','Cantonese']\n Japanese = ['Japanese','SushiBars','Izakaya','Ramen']\n Asian = ['Filipino','Indian','Thai','Cambodian','AsianFusion','Korean',\n 'Vietnamese','Thai','Himalayan/Nepalese','Malaysian','SriLankan','Pakistani'\n ,'Bangladeshi']\n Italian = ['Italian','Pizza']\n French = ['French','Creperies']\n US = ['American','Steakhouses','American(New)','Sandwiches',\n 'American(Traditional)','Breakfast&Brunch','Salad','Burgers','Southern',\n 'HotDogs','Breakfast&Brunch','Barbeque','FastFood',\n 'ChickenWings','Cajun/Creole','Hawaiian'] \n European = ['Greek','Mediterranean','British','ModernEuropean','Russian','German','Basque'\n ,'Tuscan', 'Polish','Belgian','Ukrainian','Irish','Austrian','Australian']\n LatinAmerican = ['Mexican','LatinAmerican','Peruvian','Brazilian','Venezuelan', 'Colombian','Cuban'\n ,'Argentine','Tapas/SmallPlates','Salvadoran','Tacos','Empanadas','Dominican',\n 'Caribbean','PuertoRican']\n Cafe_bar = ['Cafes','Bars','Coffee&Tea','WineBars', 'CocktailBars','BeerBar','TeaRooms'\n ,'Gastropubs','Jazz&Blues','Pubs','DiveBars','SportsBars','Nightlife']\n African = ['African','Moroccan','Egyptian','Kosher','Ethiopian']\n MiddleEastern = ['MiddleEastern','Lebanese','Turkish','Afghan','Halal','Falafel','Persian/Iranian']\n Other = ['Delis','SeafoodMarkets','BoatCharters', 'Venues&EventSpaces','Bookstores',\n 'Bakeries','FoodStands','Bagels','MusicVenues','Desserts','Caterers', \n 'Lounges','Restaurants','SpecialtyFood', 'MeatShops', 'JuiceBars&Smoothies',\n 'Fruits&Veggies', 'StreetVendors', 'FoodCourt','ComfortFood',\n 'CheeseShops', 'Brasseries','FarmersMarket', 'Soup', 'Poutineries',\n 'IceCream&FrozenYogurt', 'PerformingArts','OrganicStores', \n 'Fondue', 'Gluten-Free','Grocery', 'Poke', 'Butcher', 'Noodles', 'SoulFood', 'Buffets',\n 'Cheesesteaks', 'ConvenienceStores','Tex-Mex', 'ChickenShop', 'Donuts',\n 'CulturalCenter', 'ToyStores', 'LocalFlavor','Seafood',\n 'CookingSchools','Food','Vegan','Vegetarian']\n\n category = {'Chinese':Chinese,'Japanese':Japanese,'Asian':Asian,'Italian':Italian,\n 'French':French, 'US':US, 'European':European, 'LatinAmerican':LatinAmerican,\n 'Cafe_bar':Cafe_bar, 'African':African, 'MiddleEastern':MiddleEastern,\n 'Other':Other}\n\n def categorize(x):\n \"\"\"\n For a given category \"x\", search in the category dictionary and return which region it belongs to.\n \"\"\"\n for region,region_list in category.items():\n if x in region_list:\n return region\n\n # for the original categories of each restaurant, get the first one\n df['first_category'] = df['category'].apply(lambda x: x.split(',')[0])\n # add a column to record the region category each restaurant is in. \n df['ctg'] = df['first_category'].apply(categorize)\n df.drop(['first_category'], axis = 1)\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"HezhiWang/Trip-Helper","sub_path":"Sort/yelp_sort.py","file_name":"yelp_sort.py","file_ext":"py","file_size_in_byte":3252,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
+{"seq_id":"30387327849","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Apr 24 11:16:53 2020\r\n\r\n@author: kanno\r\n\"\"\"\r\n\r\nimport cv2\r\nimport numpy as np\r\nfrom matplotlib import pyplot as plt\r\n\r\npath = '131128.JPG'\r\ni = cv2.imread(path,1)\r\n\r\n#変換前後の対応点を設定\r\n#p_original = np.float32([[292*4,325*4], [350*4,265*4], [373*4,364*4], [422*4,289*4]])\r\np_original = np.float32([[8*4,270*4], [400*4,68*4], [268*4,551*4], [536*4,69*4]])\r\n#変更後のピクセル\r\nheight = 500\r\nwidth = 1000\r\np_trans = np.float32([[0,0],[width,0],[0,height],[width,height]])\r\n \r\n# 変換マトリクスと射影変換\r\nM = cv2.getPerspectiveTransform(p_original, p_trans)\r\ni_trans = cv2.warpPerspective(i, M, (width,height))\r\n\r\n# 画像保存\r\ncv2.imwrite('trans'+str(path), i_trans)\r\n\r\n# ここからグラフ設定\r\nfig = plt.figure()\r\nax1 = fig.add_subplot(111)\r\n\r\n# 画像をプロット\r\nshow = cv2.cvtColor(i_trans, cv2.COLOR_BGR2RGB)\r\nax1.imshow(show)\r\n\r\nfig.tight_layout()\r\nplt.show()\r\nplt.close()\r\n","repo_name":"kanno0725/Projective-transformation","sub_path":"Projective transformation.py","file_name":"Projective transformation.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"1359664341","text":"x = 1\ny = 1\n\nNORTH = \"n\"\nSOUTH = \"s\"\nWEST = \"w\"\nEAST =\"e\"\n\n\npossible_directions = \"(N)orth.\"\nisValid = True\n\n\nwhile x != 3 or y != 1: \n\n if isValid == True:\n print(\"You can travel:\", possible_directions)\n move = input(\"Direction: \")\n isValid = True\n\n if (x is 1 and y is 1 ) or (x is 2 and y is 1):\n if move.lower() != NORTH:\n isValid = False\n\n elif x is 1 and y is 2:\n if move.lower() != EAST and move.lower() != SOUTH and move.lower() != NORTH:\n isValid = False\n\n elif x is 1 and y is 3: \n if move.lower() != EAST and move.lower() != SOUTH:\n isValid = False\n\n elif (x is 2 and y is 2) or (x is 3 and y is 3):\n if move.lower() != SOUTH and move.lower() != WEST:\n isValid = False\n\n elif x is 2 and y is 3: \n if move.lower() != EAST and move.lower() != WEST:\n isValid = False\n\n elif x is 3 and y is 2:\n if move.lower() != NORTH and move.lower() != SOUTH:\n isValid = False\n\n if isValid == True:\n if move.lower()== NORTH and y < 3:\n y +=1\n elif move.lower()== SOUTH and y > 1:\n y -=1 \n elif move.lower()== WEST and x > 1:\n x -=1\n elif move.lower() == EAST and x < 3:\n x +=1\n else:\n isValid = False\n\n if (x is 1 and y is 1 ) or (x is 2 and y is 1):\n possible_directions = \"(N)orth.\"\n\n elif x is 1 and y is 2:\n possible_directions = \"(N)orth or (E)ast or (S)outh.\"\n\n elif x is 1 and y is 3: \n possible_directions = \"(E)ast or (S)outh.\"\n\n elif (x is 2 and y is 2) or (x is 3 and y is 3):\n possible_directions = \"(S)outh or (W)est.\"\n\n elif x is 2 and y is 3:\n possible_directions = \"(E)ast or (W)est.\" \n\n elif x is 3 and y is 2:\n possible_directions =\"(N)orth or (S)outh.\"\n\n\n if isValid == False:\n print(\"Not a valid direction!\")\n\n\nelse:\n print(\"Victory!\")\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"svavaosk/tileTraveller","sub_path":"tileTraveller_imp1.py","file_name":"tileTraveller_imp1.py","file_ext":"py","file_size_in_byte":2008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"22710570881","text":"# --------------------------------------------------------\n# SiamMask\n# Licensed under The MIT License\n# Written by Qiang Wang (wangqiang2015 at ia.ac.cn)\n# Modified by Inomjon for auto labeling images\n# --------------------------------------------------------\n\nfrom PIL import Image\nimport os\nimport random\nimport cv2\nfrom utils.consts import *\nimport glob, os\nfrom tools.test import *\nfrom utils.label import data_generate, auto_label, stylize\nfrom stylize import net\nimport torch.nn as nn\nfrom stylize.stylize import input_transform\nfrom stylize.stylize import style_transfer\nparser = argparse.ArgumentParser(description='PyTorch Tracking Demo')\n\nparser.add_argument('--resume', default='experiments/siammask_sharp/SiamMask_DAVIS.pth', type=str,\n metavar='PATH',help='path to latest checkpoint (default: SiamMask_DAVIS)')\nparser.add_argument('--config', dest='config', default='experiments/siammask_sharp/config_davis.json',\n help='hyper-parameter of SiamMask in json format')\nparser.add_argument('--base_path', default='data/Images_xmls/images/', help='datasets')\nparser.add_argument('--cpu', action='store_true', help='cpu mode')\nargs = parser.parse_args()\n\n\nprogress = 0\n\ndef tracking(label):\n\n # Setup device\n device = torch.device('cpu' if torch.cuda.is_available() else 'cpu')\n #device = torch.device('cuda')\n torch.backends.cudnn.benchmark = True\n # ---------stylize data ----\n decoder = net.decoder\n vgg = net.vgg\n decoder.eval()\n vgg.eval()\n\n decoder.load_state_dict(torch.load('stylize/models/decoder.pth'))\n vgg.load_state_dict(torch.load('stylize/models/vgg_normalised.pth'))\n vgg = nn.Sequential(*list(vgg.children())[:31])\n\n vgg.to(device)\n decoder.to(device)\n content_tf = input_transform(0, False)\n style_tf = input_transform(0, False)\n # --------------------------\n # Setup Model\n cfg = load_config(args)\n from experiments.siammask_sharp.custom import Custom\n siammask = Custom(anchors=cfg['anchors'])\n if args.resume:\n assert isfile(args.resume), 'Please download {} first.'.format(args.resume)\n siammask = load_pretrain(siammask, args.resume)\n\n #siammask, device, cfg = init_tracking_model()\n siammask.eval().to(device)\n\n # Parse Image file\n img_files = sorted(glob.glob(join(args.base_path, '*.jp*')))\n ims = []\n img_names = []\n for imf in img_files:\n ims.append(cv2.imread(imf))\n imf = imf.split(\"/\")[-1]\n img_names.append(imf)\n # Select ROI\n cv2.namedWindow(\"AutoLabel\", cv2.WINDOW_NORMAL)\n try:\n cv2.putText(ims[0], \"Please select an object and press 'Enter' to continue\", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1)\n init_rect = cv2.selectROI('AutoLabel', ims[0], True, False)\n x, y, w, h = init_rect\n #cv2.destroyAllWindows()\n except Exception:\n exit()\n noObject=0\n conf = 0.99\n toc = 0\n for f, im in enumerate(ims):\n tic = cv2.getTickCount()\n if f == 0 : # init\n target_pos = np.array([x + w / 2, y + h / 2])\n target_sz = np.array([w, h])\n #labelWin.showWin(labelWin)\n state = siamese_init(im, target_pos, target_sz, siammask, cfg['hp'], device=device) # init tracker\n init_state = state\n\n elif f > 0: # tracking\n state = siamese_track(state, im, mask_enable=True, refine_enable=True, device=device) # track\n location = state['ploygon'].flatten()\n score = state[\"score\"]\n #mask = state['mask'] > state['p'].seg_thr\n #print(location)\n\n if score > conf:\n conf = 0.99\n\n if len(location)==8:\n xlist = []\n ylist = []\n for jj in range(0,8, 2):\n xlist.append(location[jj])\n ylist.append(location[jj +1])\n\n xlist = sorted(xlist)\n ylist = sorted(ylist)\n #print(\"xlist is: \", xlist)\n #print(\"ylist is:\", ylist)\n xmin = xlist[0]\n xmax = xlist[-1]\n ymin = ylist[0]\n ymax = ylist[-1]\n box = [int(xmin), int(ymin), int(xmax), int(ymax)]\n name = img_names[f]\n if not os.path.exists(os.path.join(jpg_path, name)):\n os.system(copy_command.format(os.path.join(src_path, name), jpg_path))\n height = im.shape[0]\n width = im.shape[1]\n depth=im.shape[2]\n auto_label(im, box, name, label, height, width, depth)\n\n data_generate(im, box, name, label)\n #stylize(name, device,vgg, decoder, content_tf, style_tf)\n cv2.polylines(im, [np.int0(location).reshape((-1, 1, 2))], True, (0, 255, 0), 3)\n #getProgress(f)\n\n # print(\"conf\", conf)\n # print(\"score\", score)\n cv2.imshow('AutoLabel', im)\n key = cv2.waitKey(1)\n if key > 0:\n break\n\n toc += cv2.getTickCount() - tic\n toc /= cv2.getTickFrequency()\n fps = f / toc\n print('SiamMask Time: {:02.1f}s Speed: {:3.1f}fps (with visulization!)'.format(toc, fps))\n cv2.destroyAllWindows()\n\n# def getProgress(value):\n# global progress\n# progress = value\n\n# def setProgress():\n# global progress\n# if progress is None:\n# progress = 0\n# return progress\n","repo_name":"simshineaicamera/magic","sub_path":"tools/tracking.py","file_name":"tracking.py","file_ext":"py","file_size_in_byte":5612,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"47"}
+{"seq_id":"69803218384","text":"class Solution:\n def countBinarySubstrings(self, s: str) -> int:\n if len(s) == 1:\n return 0\n cB = s[0]\n currL = 1\n bufferL = 0\n count = 0\n for i in range(1, len(s)):\n if s[i] == cB:\n currL += 1\n else:\n count += min(currL, bufferL)\n bufferL = currL\n cB = s[i]\n currL = 1\n count += min(bufferL, currL)\n return count\ninS = input()\nprint(Solution().countBinarySubstrings(inS))","repo_name":"KartikJha/code-quiver","sub_path":"python/comp-coding/count-binary-substrings.py","file_name":"count-binary-substrings.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"13927580997","text":"from fastapi import FastAPI\n\nfrom .schemas.config import ClientConfig, ModelConfig, SearchConfig\nfrom .schemas.request import Query\nfrom .schemas.response import SearchResults\nfrom .search import VectorSearch\n\n\napp = FastAPI()\n\n\nclient_config = ClientConfig.load()\nmodel_config = ModelConfig.load()\nsearch_config = SearchConfig.load()\n\nvector_search = VectorSearch(\n client_config,\n model_config,\n search_config\n)\n\n@app.get(\"/ping\")\ndef ping() -> str:\n return \"ping\"\n\n\n@app.post(\"/search\", response_model=SearchResults)\ndef search(query: Query) -> SearchResults:\n results = vector_search.search(query.context)\n return SearchResults(results=results)\n","repo_name":"wararaki718/news_search_with_opensearch","sub_path":"api/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"10992282587","text":"#\n# htt6_2.py\n#\n# Author: Muntaser Khan\n#\n\nimport turtle\nwn = turtle.Screen()\nwn.bgcolor(\"lightgreen\")\ntess = turtle.Turtle()\n\nN = int(input(\"Enter the number of nested squares to draw\"))\n\ndef draw_square(t,size):\n print(tess.pos())\n for i in range(4):\n tess.forward(size)\n tess.left(90)\n\nsize = 20\nfor j in range(N):\n tess.pensize(3)\n draw_square(tess,size)\n size = size + 20\n tess.penup()\n tess.goto(tess.pos() + (-10, -10))\n tess.pendown()\n\nwn.mainloop()","repo_name":"Muntaser/PythonExploration","sub_path":"HTT_6/htt6_2.py","file_name":"htt6_2.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"41169001245","text":"import pyodbc\nimport scrapy\nimport requests\nfrom lxml import html \nimport datetime\nimport requests\nimport lxml\nfrom io import StringIO, BytesIO\nfrom lxml import html, etree\nfrom selenium import webdriver\nfrom selenium.webdriver import Chrome\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom bs4 import BeautifulSoup\nimport time\nfrom requests_html import HTML\nfrom requests.exceptions import RequestException\n\nclass MySpider(scrapy.Spider): \n name = \"crawl_news\" \n \n def connectDB(self): \n conn = pyodbc.connect('Driver={SQL Server};'\n 'Server=ANISE-TR\\SQLEXPRESS;'\n 'Database=WebDB;'\n 'Trusted_Connection=yes;') \n cursor = conn.cursor()\n return cursor\n \n \n def covertStringToResponse(self,url):\n response = requests.get(url) \n doc = html.fromstring(response.text)\n return doc\n \n \n def getUrl(self):\n category_page_info = self.connectDB().execute('select ministry_id,category_link_root, category_id from category_info where ministry_id = 21')\n for row in category_page_info: \n page_param_info = self.connectDB().execute('select page_rule,article_param_xpath,article_url_xpath from ministry_category_configuration where ministry_id = $'+str(row[0])+' and category_id = $'+str(row[2]) ) \n for page_info in page_param_info: \n #if there's no get param link\n if (page_info[1]==\"\"):\n try:\n list_baiviet = self.crawlBySelenium(row[1],page_info[2], row[0])\n except RequestException as e:\n print(e)\n \n try:\n for baiviet in list_baiviet:\n self.parseArticleResponse(baiviet, row[0])\n except RequestException as e:\n print(e)\n else: \n #get url with param \n url = self.covertStringToResponse(row[1]).xpath(page_info[1])\n \n if (row[0]==7): \n param = int(url[len(url)-1])\n elif (row[0]==14):\n param = self.getMicParam(str(url[len(url)-1])) \n elif (row[0]==24):\n param = self.getVassParam(str(url[len(url)-1])) \n else:\n param = self.getParam(str(url[len(url)-1]))\n for i in range (1,1+1): \n ##ministry 6, 11 doesn't use param\n if (row[0]==6 or row[0]==11 or row[0]==16 or row[0]==19 or row[0] == 21):\n articleUrl = row[1] \n else:\n ##ministry 8, 14 need to be removed default last param before crawl by param\n if (row[0]==8 or row[0]==24):\n row[1] = row[1][:-1] \n if (row[0]==14):\n startPoint = self.getMicStartpoint(str(url))\n endPoint = self.getMicEndpoint(str(url))\n articleUrl = startPoint + str(i) + endPoint\n articleUrl = \"https://www.mic.gov.vn\"+articleUrl[2:-2] \n else: \n articleUrl = row[1]+str(i) \n self.parseCategoryResponse(self.covertStringToResponse(articleUrl), row[0]) \n i += page_info[0]\n\n \n def parseCategoryResponse(self, response, ministryId): \n category_detail = self.connectDB().execute(' select ministry_id,article_url_xpath,article_thumbnail_xpath from ministry_category_configuration where ministry_id = $'+str(ministryId))\n for row in category_detail: \n for i in range (len(row)):\n ## i = 1 for article url xpath to query the article url \n if (i == 1): \n ## response is category url\n article_url_xpaths = response.xpath(row[i])\n for url_index in range (len(article_url_xpaths)): \n ##bo cong an\n if (ministryId == 1):\n article_url_xpaths[url_index] = \"http://bocongan.gov.vn\"+str(article_url_xpaths[url_index])\n ##bo gddt\n elif (ministryId == 4):\n article_url_xpaths[url_index] = \"https://moet.gov.vn/tintuc/Pages/Thongbao.aspx\"+str(article_url_xpaths[url_index])\n ##bo ldtbxh\n elif (ministryId == 6): \n article_url_xpaths[url_index] = \"http://www.mpi.gov.vn/Pages/\"+str(article_url_xpaths[url_index])\n ##bo ldtbxh\n elif (ministryId == 8): \n article_url_xpaths[url_index] = \"http://www.molisa.gov.vn\"+str(article_url_xpaths[url_index])\n ##bo nong nghiep\n elif (ministryId == 10): \n article_url_xpaths[url_index] = \"http://www.mard.gov.vn\"+str(article_url_xpaths[url_index])\n ##bo quoc phong\n elif (ministryId == 11): \n article_url_xpaths[url_index] = \"http://www.mod.gov.vn/wps/portal/!ut/p/b1/04_Sj9CPykssy0xPLMnMz0vMAfGjzOLdHP2CLJwMHQ38zT0sDDyNnZ1NjcOMDQ2CzIEKIoEKDHAARwNC-sP1o8BKnN0dPUzMfQwMLHzcTQ08HT1CgywDjY0NHI2hCvBY4eeRn5uqX5AbYZBl4qgIANgfRb4!/dl4/d5/L2dBISEvZ0FBIS9nQSEh/\"+str(article_url_xpaths[url_index])\n ##bo \n elif (ministryId == 12): \n article_url_xpaths[url_index] = \"ttps://www.mof.gov.vn\"+str(article_url_xpaths[url_index])\n ##bo thong tin truyen thong\n elif (ministryId == 14):\n article_url_xpaths[url_index] = \"https://www.mic.gov.vn\"+str(article_url_xpaths[url_index])\n ##bo tu phap\n elif (ministryId == 15):\n article_url_xpaths[url_index] = \"https://moj.gov.vn\"+str(article_url_xpaths[url_index])\n ##bo vh, tt & dl\n elif (ministryId == 16):\n article_url_xpaths[url_index] = \"https://bvhttdl.gov.vn\"+str(article_url_xpaths[url_index])\n ##uy ban dan toc\n elif (ministryId == 20):\n article_url_xpaths[url_index] = \"http://cema.gov.vn\"+str(article_url_xpaths[url_index])\n ##ngan hang nnvn\n elif (ministryId == 21):\n article_url_xpaths[url_index] = \"https://www.sbv.gov.vn\"+str(article_url_xpaths[url_index])\n ##bo y te\n elif (ministryId == 22):\n article_url_xpaths[url_index] = \"https://baohiemxahoi.gov.vn/tintuc/Pages/linh-vuc-bao-hiem-y-te.aspx\"+str(article_url_xpaths[url_index])\n ##vien han lam khcn\n elif (ministryId == 23):\n article_url_xpaths[url_index] = \"https://vast.gov.vn\"+str(article_url_xpaths[url_index])\n\n ##article_url_xpaths[url_index] is detail article url\n self.parseArticleResponse(article_url_xpaths[url_index], ministryId)\n ## i = 2 for article thumbnail \n elif (i == 2 and row[i] and not row[i].isspace()):\n article_thumbnail_xpath = response.xpath(row[i])\n \n \n \n def parseArticleResponse(self, article_url, ministryId): \n article_response = self.covertStringToResponse(article_url)\n article_detail = self.connectDB().execute('select article_title_xpath,article_description_xpath,article_time_xpath,article_author_xpath,article_content_xpath from ministry_articles_configuration where ministry_id = $'+str(ministryId))\n for row in article_detail:\n article_title = \"\"\n article_description = \"\"\n article_time = \"\"\n article_author = \"\"\n article_content = \"\"\n for i in range (len(row)):\n if row[i] and not row[i].isspace():\n if (i == 0):\n article_title = article_response.xpath(row[i])\n print(\"Title: \"+str(article_title))\n elif (i == 1):\n article_description = article_response.xpath(row[i])\n print(\"Des: \"+str(article_description))\n elif (i == 2):\n article_time = article_response.xpath(row[i])\n print(\"Time: \"+str(article_time))\n elif (i == 3): \n article_author = article_response.xpath(row[i])\n print(\"Author: \"+str(article_author))\n elif (i == 4):\n article_content = self.clearSpace(article_response.xpath(row[i]))\n if (ministryId==11):\n article_content = article_content[2:]\n print(\"Content: \"+str(article_content))\n # self.saveArticleToDB(ministryId,article_url, article_title,article_description,article_time,article_author,article_content)\n print(\"\\n -----------------\")\n # self.select()\n # print(\"\\n -----------------\")\n \n \n def getParam(self, param_url):\n param = \"\"\n for i in range(len(param_url),0,-1):\n if (param_url[i-1] != \"=\"):\n param += param_url[i-1]\n else:\n return int(param[::-1])\n \n def getMicParam(self, param_url):\n param = \"\"\n checkFirstDash = False\n for i in range(len(param_url),0,-1):\n if (param_url[i-1] != \"/\"):\n param += param_url[i-1] \n else:\n if (checkFirstDash == False):\n checkFirstDash = True\n param = \"\"\n else: \n return param[::-1]\n \n def getMicEndpoint(self, param_url):\n param = \"\"\n for i in range(len(param_url),0,-1):\n if (param_url[i-1] != \"/\"):\n param += param_url[i-1]\n else:\n return '/'+str(param[::-1])\n \n def getMicStartpoint(self, param_url):\n param = \"\"\n count = 0\n for i in range(len(param_url),0,-1):\n if (param_url[i-1] != \"/\"):\n if (count == 2):\n param = str(param_url[:i+1])\n return param\n else: \n count += 1 \n \n def getVassParam(self, param_url):\n param = \"\"\n for i in range(len(param_url),0,-1):\n if (param_url[i-1] != \".\"):\n param += param_url[i-1]\n else:\n return int(param[::-1])\n \n \n def clearSpace(self, listString):\n return [string for string in listString if string != ' ']\n \n \n def covertStringFromArticleToSqlFormat (self, dateString):\n #from 28/02/2021 to 2021/02/28\n dt = datetime.datetime.strptime(dateString, '%d/%m/%Y')\n return '{2}/{1:02}/{0:02}'.format(dt.day, dt.month, dt.year)\n \n\n def covertStringFromSqlToArticleFormat (self, dateString):\n #from 2021/02/28 to 28/02/2021\n dt = datetime.datetime.strptime(dateString, '%Y/%m/%d')\n return '{0:02}/{1:02}/{2}'.format(dt.day, dt.month, dt.year)\n \n \n def saveArticleToDB(self, ministry_id, article_url, article_title,article_description,article_time,article_author, article_content): \n try:\n conn = pyodbc.connect('Driver={SQL Server};'\n 'Server=ANISE-TR\\SQLEXPRESS;'\n 'Database=WebDB;'\n 'Trusted_Connection=yes;') \n value = [(ministry_id, article_url, article_title[0],article_description[0],article_time[0],article_author[0], \"\")]\n print (\"hu: \"+str(value[0]))\n conn.cursor().execute(\"\"\" \n INSERT INTO WebDB.dbo.article_info \n (ministry_id, article_url , article_title,article_description,article_time,article_author, article_content) \n VALUES (?, ?, ?, ?, ?, ?, ?)\"\"\", value[0])\n conn.commit()\n\n except Exception as e:\n print(e) \n finally:\n conn.cursor().close()\n conn.close()\n \n def select(self):\n article = self.connectDB().execute('select * from article_info')\n for row in article:\n print(\"Row: \"+str(row))\n \n \n def read_config(self):\n \tchrome_options = webdriver.ChromeOptions()\n \tchrome_options.add_argument('--no-sandbox')\n \tchrome_options.add_argument('--headless')\n \tdriver = webdriver.Chrome('chromedriver', options=chrome_options)\n \treturn driver\n \n def crawlBySelenium(self, categoryUrl, detailUrlXpath, ministryId):\n driver = self.read_config()\n driver.get(categoryUrl)#link tin chứa tức\n WebDriverWait(driver,5)\n \n list_baiviet = []#danh sách bài viết\n count = 1\n html = HTML(html=driver.page_source)\n list_baiviet = html.xpath(detailUrlXpath)#crawl đầu tiên\n print(\"ur: \"+str(list_baiviet))\n\n while True:\n try:\n try:\n \n nextBtnXpath = \"\"\n if (ministryId == 3):\n nextBtnXpath = '/html/body/form/div[3]/main/div/div/div/div[1]/div[2]/div[2]/div/div/div/div[2]/div/ul/li[3]'\n if (ministryId == 5): \n nextBtnXpath = '//*[@id=\"ctl00_SPWebPartManager1_g_0623dffd_eff8_4f9c_bf6d_2cdf2561adec_ctl00_lkNext2\"]'\n if (ministryId == 6):\n nextBtnXpath = '/html/body/form/div[8]/div/div[3]/div[2]/div[2]/div/div/div[2]/div[2]/div[1]/div/table/tbody/tr/td/table/tbody/tr/td/div/div/div/table/tbody/tr/td[1]/div/div/span[2]/a[1]' \n if (ministryId == 7):\n nextBtnXpath = '//*[@id=\"ctl00_SPWebPartManager1_g_0623dffd_eff8_4f9c_bf6d_2cdf2561adec_ctl00_lkNext2\"]'\n if (ministryId == 11): \n nextBtnXpath = '//*[@id=\"pc1623250410559_nextPage\"]'\n if (ministryId == 12):\n nextBtnXpath = '//*[@id=\"T:oc_1601563139region1:listTmplt:cil5\"]'\n if (ministryId == 16):\n nextBtnXpath = '//*[@id=\"p_p_id_101_INSTANCE_TW6LTp1ZtwaN_\"]/div/div/div[22]/ul/li[5]/a'\n if (ministryId == 18):\n nextBtnXpath = '//*[@id=\"p_p_id_101_INSTANCE_k206Q9qkZOqn_\"]/div/div/div[23]/ul/li[4]/a'\n if (ministryId == 19):\n nextBtnXpath = '//*[@id=\"ctl00_SPWebPartManager1_g_0623dffd_eff8_4f9c_bf6d_2cdf2561adec_ctl00_lkNext2\"]'\n if (ministryId == 20):\n nextBtnXpath = '//*[@id=\"ctl00_SPWebPartManager1_g_0623dffd_eff8_4f9c_bf6d_2cdf2561adec_ctl00_lkNext2\"]'\n if (ministryId == 21):\n nextBtnXpath = '//*[@class=\"x28y\"]/div[4]/a'\n if (ministryId == 23):\n nextBtnXpath = '//*[@id=\"ctl00_SPWebPartManager1_g_0623dffd_eff8_4f9c_bf6d_2cdf2561adec_ctl00_lkNext2\"]'\n\n element = driver.find_element_by_xpath(nextBtnXpath)#tìm nút next\n element.click()# thực hiện click để chuyển trang\n time.sleep(2)# ngủ 2s để load bài mới\n except Exception as e:#nếu crawl hết dừng\n print(e)\n break\n count += 1\n if count == 3:#crawl 2 lần, tắt đi để crawl hết\n break\n \n html = HTML(html=driver.page_source)#page thành HTML để xpath\n tmp = html.xpath(detailUrlXpath)#lấy bài mới\n for url in tmp:\n \n if (ministryId==11):\n url = \"http://www.mod.gov.vn/wps/portal/!ut/p/b1/04_Sj9CPykssy0xPLMnMz0vMAfGjzOLdHP2CLJwMHQ38zT0sDDyNnZ1NjcOMDQ2CzIEKIoEKDHAARwNC-sP1o8BKnN0dPUzMfQwMLHzcTQ08HT1CgywDjY0NHI2hCvBY4eeRn5uqX5AbYZBl4qgIANgfRb4!/dl4/d5/L2dBISEvZ0FBIS9nQSEh/\"+str(url)\n elif (ministryId == 12): \n url = \"https://www.mof.gov.vn\"+str(url)\n elif (ministryId == 16):\n url = \"https://bvhttdl.gov.vn\"+str(url)\n elif (ministryId == 21):\n url = \"https://www.sbv.gov.vn\"+str(url)\n elif (ministryId == 23):\n url = \"https://www.sbv.gov.vn\"+str(url)\n\n self.parseArticleResponse(url, ministryId) \n \n list_baiviet.extend(tmp)#thêm vào tập link \n except Exception as e:#gặp sự cố dừng\n print(e)\n break \n return list_baiviet \n \n\np = MySpider()\np.getUrl()","repo_name":"tpnanh/DACNTT2_N26","sub_path":"DA-26_Web/Lib/site-packages/django/db/backends/sqlite3/Scrapy.py","file_name":"Scrapy.py","file_ext":"py","file_size_in_byte":18356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"15374671878","text":"from collections import Counter\n\n\nclass FirstUnique:\n '''\n Implement a FirstUnique class that will allow you to\n retrieve the first unique integer in the queue\n '''\n\n def __init__(self, nums):\n self.q = Counter(nums)\n\n def showFirstUnique(self) -> int:\n for item in self.q.items():\n if item[1] == 1:\n return item[0]\n else:\n continue\n return -1\n\n def add(self, value: int) -> None:\n if value in self.q:\n self.q[value] += 1\n else:\n self.q.update({value: 1})\n\n","repo_name":"sp00ks-L/LeetCode-Problems","sub_path":"firstUniqueNumber.py","file_name":"firstUniqueNumber.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"3145472319","text":"# '도'음 출력 (262Hz)\nimport RPi.GPIO as GPIO\nimport time\n\nBUZZER_PIN = 4\n\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(BUZZER_PIN, GPIO.OUT)\n\n# 주파수 (262)\npwm = GPIO.PWM(BUZZER_PIN, 262)\npwm.start(10) # duty cycle (0~100) . 소리 크기\n\ntime.sleep(2)\npwm.ChangeDutyCycle(0) # 부저음 끄기\n\npwm.stop()\nGPIO.cleanup()\nprint('cleanup and exit')\n\n","repo_name":"Dongkyun24/Raspberrypi","sub_path":"02_gpio_pwm/piezo_buzzer.py","file_name":"piezo_buzzer.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"5589213626","text":"import numpy as np\nimport cv2\nimport easygui\n\n\n\n\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n\n\n# Opening an image from a file\nf = easygui.fileopenbox()\nimg = cv2.imread(f)\n\nwindow=\"Output\"\n#define the screen resulation\nscreen_res = 1280, 720\nscale_width = screen_res[0] / img.shape[1]\nscale_height = screen_res[1] / img.shape[0]\nscale = min(scale_width, scale_height)\n#resized window width and height\nwindow_width = int(img.shape[1])/3\nwindow_height = int(img.shape[0])\n \n#cv2.WINDOW_NORMAL makes the output window resizealbe\ncv2.namedWindow(window, cv2.WINDOW_NORMAL)\n \n#resize the window according to the screen resolution\ncv2.resizeWindow(window, window_width, window_height)\n\n\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\nfaces = face_cascade.detectMultiScale(gray, 1.3, 5)\n(x,y,w,h) = faces[0]\nphoto_crop = img[y-35:y+h+80,x-40:x+w+50]\n# img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\nroi_gray = gray[y:y+h, x:x+w]\nroi_color = img[y:y+h, x:x+w]\n \n# sign = cv2.rectangle(img,(x-w-170,y+h+160),(x+w+50,y+h+320),(255,0,0),2)\nsign_crop = img[y+h+160:y+h+320,x-w-170:x+w+50]\n\ncv2.imshow(window,img)\ncv2.imshow(\"Cropped Photo\", photo_crop)\ncv2.imshow(\"Cropped Sign\", sign_crop)\ncv2.imwrite('cropped_photo.jpg',photo_crop)\ncv2.imwrite('cropped_sign.jpg',sign_crop)\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()","repo_name":"lapmid/ImageProcesing","sub_path":"detectFaceFromImage.py","file_name":"detectFaceFromImage.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"30273135090","text":"from django.contrib.auth.middleware import RemoteUserMiddleware\nfrom django.contrib.auth.backends import RemoteUserBackend\nfrom django.contrib import auth\nfrom django.core.exceptions import ImproperlyConfigured\nfrom django.contrib.auth import load_backend\nfrom django.conf import settings\n\nclass GroupEnabledRemoteUserMiddleware(RemoteUserMiddleware):\n header = 'HTTP_REMOTE_USER'\n groups_header = 'HTTP_REMOTE_GROUPS'\n group_separator = '|'\n\n def __init__(self):\n backends = getattr(settings, 'AUTHENTICATION_BACKENDS', [])\n if 'django.contrib.auth.backends.RemoteUserBackend' not in backends:\n raise ImproperlyConfigured(\n \"The 'cumulus.backends.GroupEnabledRemoteUserBackend' backend requires \"\n \"the 'django.contrib.auth.backends.RemoteUserBackend' be loaded. Please \"\n \"alter your AUTHENTICATION_BACKENDS setting to suit.\")\n\n def process_request(self, request):\n super(GroupEnabledRemoteUserMiddleware, self).process_request(request)\n if request.user:\n request.user = auth.authenticate(\n user=request.user, remote_groups=self._get_groups(request))\n\n def _get_groups(self, request):\n groups_string = request.META.get(self.groups_header, None)\n if groups_string:\n groups = groups_string.split(self.group_separator)\n else:\n groups = []\n return groups\n","repo_name":"jcmcken/cloudcover-cumulus","sub_path":"cumulus/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":1431,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"70806804349","text":"\n\nimport matplotlib.pyplot as plt\nx=[\"python\",'c','c++','java',\"c#\",\"html\",'css']\ny=[85,70,65,82,10,20,30]\nplt.bar(x,y,width=0.6,color=\"b\")\n\nplt.title(\"Bar Graph\",fontsize=36,color='g')\nplt.xlabel(\"Programming language\",fontsize=15,color='g')\nplt.ylabel(\"NO\")\nplt.show()","repo_name":"belaletech/Python","sub_path":"DtataVisualization/01Bar_Plot.py","file_name":"01Bar_Plot.py","file_ext":"py","file_size_in_byte":270,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"6"}
+{"seq_id":"25926428692","text":"'''\nCreated on 08 May 2014\n\n@author: Deon\n'''\nfrom PyQt4.QtGui import QGroupBox as QGroupBox\n#from EntryCurveGUI import Ui_EntryCurve\nimport PyQt4.QtGui as qt\nimport PyQt4.QtCore as qtCore\nfrom pylab import np\nimport sys\nfrom matplotlib.lines import Line2D\nfrom scanlib import gauss\n#from pyspec import fit, fitfuncs\nfrom thirdparty.pyspec import fitfuncs\nfrom thirdparty.pyspec import fit #This is the exact one from pyspec, but modified in the way that the stdev is calculated for mpfit\n\n\nfrom Fit.FitCommon import *\n\n\ndef voigt(x, p, mode='eval'):\n \"\"\"Pearson Type VII\n From Hutchings et al. 2005. Introduction to the characterisation of residual stress by neutron diffraction.2005. Page 160\n\n Function:\n :math:`f(x) = k + m*x + p_2 \\exp\\left(\\\\frac{-(x - p_0)^2}{2p_1^2}\\\\right)`\n\n \"\"\"\n try:\n if mode == 'eval':\n #cent=p[0];wid=p[1];amp=p[2];const=p[3];slope=p[4]\n x0=p[0]; ux=p[1]; H0=p[2]; const=p[3];slope=p[4]; m=p[5]\n \n out = H0*(1+(x-x0)**2.0/((1.0/m)*(0.5*ux)**2))**-(1.0/m) + const+slope*x\n elif mode == 'params':\n out = ['cent', 'sigma', 'amp', 'const', 'slope', 'shape']\n elif mode == 'name':\n out = \"Voigt\"\n elif mode == 'guess':\n g = fitfuncs.peakguess(x, p)\n out = [g[0], g[1], g[3],g[5], g[4], 0.5]\n else:\n out = []\n except:\n out = [0,0,0,0,0,0]\n\n return np.array(out)\n\n#**************************************************************************************\n#**************************************************************************************\nclass VoigtDEF(FitCommon,QGroupBox):\n def __init__(self, ScanmanMain):\n FitCommon.__init__(self, ScanmanMain)\n \n #self.ui.moreOptionsGroupBox.hide()\n #self.ui.moreOptionsLayout.removeWidget(self.ui.moreOptionsGroupBox)\n #self.ui.moreOptionsGroupBox=EntryCurveOptionsDEF.EntryCurveOptionsDEF(self)\n #self.ui.moreOptionsLayout.addWidget(self.ui.moreOptionsGroupBox)\n #self.ui.moreOptionsLayout.update()\n #self.ui.moreOptionsGroupBox.show()\n \n self.name = \"Voigt\"\n #self.paramxy.update({'Channel':\"x\", 'Intensity':\"del_y\", 'Slit':\"min=0.0 max=10.0\", 'Theta':\"min=0.0 max=180.0\", 'Background':\"y\", \n # 'Thickness':\"del_x\", 'Absorption':\"min=0.0 max=2.0\"})\n #self.iterparams = ['Channel', 'Intensity', 'Slit', 'Theta', 'Background', 'Thickness', 'Absorption']\n #self.iterparams_values_format = {'Channel':'{0:.2f}', 'Position':'{0:.2f}', 'Angle':'{0:.2f}', 'd-spacing':'{0:.5f}', \n # 'Intensity':\"{0:.2f}\", 'Slit':\"{0:.2f}\", 'Theta':\"{0:.2f}\", \n # 'Background':\"{0:.2f}\", 'Thickness':\"{0:.3f}\", 'Absorption':\"{0:.4f}\"}\n #self.iterparams_stdev_format = {'Channel':'{0:.2f}', 'Position':'{0:.2f}', 'Angle':'{0:.2f}', 'd-spacing':'{0:.5f}', \n # 'Intensity':\"{0:.2f}\", 'Slit':\"{0:.2f}\", 'Theta':\"{0:.2f}\", \n # 'Background':\"{0:.2f}\", 'Thickness':\"{0:.2f}\", 'Absorption':\"{0:.4f}\"}\n self.paramxy.update({'Channel':\"x\", 'FWHM':\"del_x\", 'Intensity':\"del_y\", 'Background':\"y\", 'Slope':\"min=-10.0 max=10.0\",'Shape':\"min=0.0001 max=1.0\"})\n self.iterparams = ['Channel', 'FWHM', 'Intensity', 'Background', 'Slope', 'Shape']\n self.iterparams_values_format = {'Channel':'{0:.4f}', 'Position':'{0:.4f}', 'Angle':'{0:.4f}', 'd-spacing':'{0:.4f}', \n 'FWHM':\"{0:0.4f}\", 'Intensity':\"{0:.4f}\", 'Background':\"{0:.4f}\",'Slope':\"{0:.4f}\", 'Shape':\"{0:.4f}\"}\n self.iterparams_stdev_format = {'Channel':'{0:.3e}', 'Position':'{0:.3e}', 'Angle':'{0:.3e}', 'd-spacing':'{0:.3e}', \n 'FWHM':\"{0:.3e}\", 'Intensity':\"{0:.3e}\", 'Background':\"{0:.3e}\",'Slope':\"{0:.3e}\", 'Shape':\"{0:.3e}\"}\n \n \n self.miscparams = ['Intensity_sum','Intensity_area','Counts']\n\n self.SetVLables(self.rangeparams, self.iterparams, self.miscparams, self.fitparams) #Also creates the rownumbers list\n self.axislinked[str(self.rownumbers[\"Channel\"])] = \"Channel\"\n True\n \n \n #**************************************************************************************\n \n def FitRange(self, rngnum=-1):\n #FitCommon.FitRange(self, rngnum=rngnum)\n rngtbl=self.ui.range_tbl\n start = rngnum\n end = rngnum +1\n if (rngnum == -1):\n start = 0\n end = len(self.rangeList)\n\n #self.CalcBG()\n \n #wedisconnected = False\n #try:\n # rngtbl.cellChanged.disconnect() #otherwise this function might be called recursively\n # wedisconnected = True\n #except:\n # True\n \n for i in range(start, end): \n rng = self.rangeList[i]\n #xdata = rng.line.get_xdata(True)\n #ydata = rng.line.get_ydata(True)\n xdata = np.asfarray(self.scanman.datasrc.x[rng.rangeparams[\"Range_start\"].value:rng.rangeparams[\"Range_end\"].value])\n ydata = np.asfarray(self.scanman.datasrc.y[rng.rangeparams[\"Range_start\"].value:rng.rangeparams[\"Range_end\"].value])\n xwidth = xdata[1:]-xdata[:-1]\n xmin, xmax, ymin, ymax = np.asfarray([xdata.min(), xdata.max(), ydata.min(), ydata.max()])\n \n if ((len(xdata) < 4) or (len(ydata) < 4)): continue #break out if the range was chosen wrong\n #if (min(ydata) == max(ydata)): continue #break out is there is no difference in values\n \n fitfunct = voigt\n \n gparams = fitfunct(xdata, ydata, 'guess')\n gparamsfix = [0]*len(gparams)\n\n #if rng.iterparams.has_key(\"Channel\"): xkey = \"Channel\"\n #elif rng.iterparams.has_key(\"Position\"): xkey = \"Position\"\n #elif rng.iterparams.has_key(\"Angle\"): xkey = \"Angle\"\n #elif rng.iterparams.has_key(\"d-spacing\"): xkey = \"d-spacing\"\n if \"Channel\" in rng.iterparams.keys(): xkey = \"Channel\"\n elif \"Position\" in rng.iterparams.keys(): xkey = \"Position\"\n elif \"Angle\" in rng.iterparams.keys(): xkey = \"Angle\"\n elif \"d-spacing\" in rng.iterparams.keys(): xkey = \"d-spacing\"\n\n #iterkeys = [xkey, 'Intensity', 'Slit', 'Theta', 'Background', 'Thickness', 'Absorption']\n iterkeys = [xkey]+self.iterparams[1:]\n for i in range(len(iterkeys)):\n if rng.iterparams[iterkeys[i]].fix == True or rng.iterparams[iterkeys[i]].enabled == False:\n gparams[i] = rng.iterparams[iterkeys[i]].value\n gparamsfix[i] = 1\n \n \n fitob = fit.fit(x=xdata, y=ydata, guess=gparams, ifix=gparamsfix ,quiet=True, \n funcs=[fitfunct], optimizer = \"mpfit\", r2min=-1000000)\n minbgnd = -100.0\n if gparamsfix[4] == 1 and gparams[4] == 0.0:\n minbgnd = 0.0\n \n limits = ([xmin,xmax],[0.0,xmax-xmin],[0.0,ymax],[minbgnd,ymax],[-100.0,100.0],[0.0001, 0.9999])\n fitob.ilimits = np.array(limits)\n limited = np.array([[1,1]]*len(limits))\n fitob.ilimited = limited\n#mpfit\n if 1:#else:\n fitob.go(interactive=False)\n gparams=fitob.result\n stdev=fitob.stdev\n #rng.bgndfitparms[1] = gparams[3]\n #rng.bgndfitparms[0] = gparams[4]\n xexpanded = np.linspace(xdata.min(), xdata.max(), 50)\n fittedexpanded = fitfunct(xexpanded,gparams)\n fitted = fitfunct(xdata,gparams)\n\n rng.fittedline.set_data(xexpanded, fittedexpanded)\n rng.diffline.set_data(xdata, ydata - fitted)\n \n for i in range(len(iterkeys)):\n rng.iterparams[iterkeys[i]].value = gparams[i]\n rng.iterparams[iterkeys[i]].stdev = stdev[i]\n \n self.asignfitparamvalues(rng.fitparams,fitob)\n \n ybgnd = fitfuncs.linear(xdata, [gparams[4],gparams[3]],\"eval\") \n rng.miscparams['Intensity_sum'].value = np.sum(fitted-ybgnd) \n rng.miscparams['Intensity_area'].value = np.sum(abs((fitted-ybgnd)[:-1]*xwidth))\n rng.miscparams['Counts'].value = np.sum(ydata)\n \n \n #Determine if the calculated fits are valid based on some 'obvious' rules\n rng.iterparams[\"Intensity\"].valid = False if rng.iterparams[\"Intensity\"].value < 0 else True\n for key in iterkeys:\n curkey = rng.iterparams[key]\n curkey.valid = True\n if curkey.stdev / curkey.value > 1 or (curkey.stdev == 0.0 and curkey.fix == False): curkey.valid = False\n \n \n self.ReflectInTable(rng.iterparams,self.iterparams_values_format,self.iterparams_stdev_format)\n self.ReflectInTable(rng.miscparams)\n self.ReflectInTable(rng.fitparams)\n \n #self.scanman.ui.graph.draw()\n \n ymin = y = 0.0\n ymax = x = 0.0\n for rng in self.rangeList:\n if (len(rng.diffline._y) > 0):\n y = min(rng.diffline.get_ydata(True))\n if (y < ymin): ymin = y\n y = max(rng.diffline.get_ydata(True))\n if (y > ymax): ymax = y\n ybuf = (ymax - ymin) * 0.1\n self.scanman.ui.diffgraph.figure.axes[0].set_ylim(ymin - ybuf, ymax + ybuf)\n \n \n #self.scanman.ui.diffgraph.draw()\n \n \n #if (wedisconnected): rngtbl.cellChanged.connect(self.CellValueChanged) #Reconnect the signal\n #self.fittedsignal.emit() #To call any listeners\n True\n \n \n \n #**************************************************************************************\n def Test(self):\n print (\"yeye\")\n self.FitRange(-1)\n \n \n #**************************************************************************************\nif __name__ == '__main__':\n \n app = qt.QApplication(sys.argv)\n window=EntryCurveDEF()\n window.show()\n sys.exit(app.exec_())\n\n ","repo_name":"Deon-Marais/ScanManipulator","sub_path":"Fit/VoigtDEF.py","file_name":"VoigtDEF.py","file_ext":"py","file_size_in_byte":10431,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"50145741","text":"class Solution:\n def nextGreaterElement(self, n: int) -> int:\n def getDigits(n): \n digits = []\n while n > 0: \n d = n % 10 \n digits.append(d)\n n //= 10 \n digits = digits[::-1]\n return digits\n\n def getN(digits): \n res = 0\n for d in digits:\n res += d \n res *= 10 \n res //= 10 \n return res \n\n digits = getDigits(n)\n if len(digits) < 2: \n return -1 \n \n j = 0 \n for i in range(len(digits)-2, -1, -1): \n if digits[i] < digits[i+1]:\n j = i \n break \n swap = False \n for i in range(len(digits)-1, j, -1):\n if digits[i] > digits[j]:\n digits[i], digits[j] = digits[j], digits[i]\n swap = True \n break \n if not swap: \n i = len(digits)-1\n digits[i], digits[j] = digits[j], digits[i]\n digits[j+1:] = digits[j+1:][::-1]\n res = getN(digits)\n if res <= n: \n return -1 \n if res > 2 ** 31 - 1: \n return -1 \n\n return res \n\nif __name__ == \"__main__\": \n s = Solution()\n\n assert s.nextGreaterElement(12) == 21 \n assert s.nextGreaterElement(21) == -1 \n assert s.nextGreaterElement(101) == 110 \n assert s.nextGreaterElement(230241) == 230412 \n assert s.nextGreaterElement(2147483476) == 2147483647 ","repo_name":"code-cp/leetcode","sub_path":"solutions/556/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1514,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"21253626892","text":"# ----------------------------------------------------------------------\nimport os, django\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"wanwenyc.settings\")\ndjango.setup()\n# ----------------------------------------------------------------------\n#独运行某一个py文件时会出现如下错误：django.core.exceptions.AppRegistryNotReady: Apps aren't loaded yet.，以上内容可以解决此问题,加载django中的App\n\n\nclass RecriminatDataOrderDepend(object):\n\n def makeRecriminatDataOrderList(self,depend_id):\n recriminat_data_order_list = []\n\n from shucaiyidate.modelsorder import RecriminatDataOrder\n RecriminatDataOrder_list = RecriminatDataOrder.objects.filter(xieyitestcase_id=depend_id)\n RecriminatDataOrder_list_count = RecriminatDataOrder_list.count()\n if RecriminatDataOrder_list_count==0:\n pass\n else:\n for RecriminatDataOrder_one in RecriminatDataOrder_list:\n recriminat_data_order_one_list = []\n recriminat_data_order_one_list.append(RecriminatDataOrder_one.send_wait_time)\n recriminat_data_order_one_list.append(RecriminatDataOrder_one.com_send_date)\n # recriminat_data_order_one_list.append(RecriminatDataOrder_one.is_need_expect)\n expect_data_str= RecriminatDataOrder_one.com_expect_date\n print(\"原数据：%s\" % expect_data_str)\n # expect_data_bytes =bytes(expect_data_str,'utf-8')\n # print(\"原数据字节：\")\n # print(expect_data_bytes)\n # expect_data_bytes_to_str = str(expect_data_bytes, encoding=\"utf-8\")\n # print(\"原数据字节转为字符串：\")\n # print(expect_data_bytes_to_str)\n # expect_data_bytes_to_str_with_huiche = expect_data_bytes_to_str+r\"\\r\\n\" #字符串组合回车换行\n # print(\"原数据字节转为字符串带回车换行：\")\n # print(expect_data_bytes_to_str_with_huiche)\n\n if expect_data_str == None:\n recriminat_data_order_one_list.append(expect_data_str)\n else:\n #有双斜杠转发单斜杠\n expect_data_str_new = expect_data_str.encode(\"gbk\").decode(\"unicode_escape\") #将字符串先编码后解码，解决单斜杠，变为双斜杠问题\n print(\"双斜杠变为单斜杠的数据：%s\" % expect_data_str_new)\n recriminat_data_order_one_list.append(expect_data_str_new)\n\n recriminat_data_order_list.append(recriminat_data_order_one_list)\n\n print(\"recriminat_data_order_list:\")\n print(recriminat_data_order_list)\n return recriminat_data_order_list\n\n\nrecriminatdataorderdepend = RecriminatDataOrderDepend()\n\nif __name__ == '__main__':\n my_list = recriminatdataorderdepend.makeRecriminatDataOrderList(\"15\")\n print(my_list[0][1])\n\n\n\n\n","repo_name":"wawj901124/shangbaogongju","sub_path":"depend/shucaiyi/modelorderdepend/RecriminatDataOrderDependClass.py","file_name":"RecriminatDataOrderDependClass.py","file_ext":"py","file_size_in_byte":2961,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"13338194361","text":"# Emotion detector for Raspberry Pi 4\r\n# Author: Elliot Blanford\r\n# Date: 1/18/2021\r\n# Description:\r\n\r\n# Original code/inspiration by Evan Juras\r\n# https://github.com/EdjeElectronics/TensorFlow-Object-Detection-on-the-Raspberry-Pi/blob/master/Object_detection_picamera.py\r\n# I updated it to work with tensorflow v2, changed it to an emotion detection model, and made it run on my intel\r\n# neural compute stick 2\r\n\r\n## Some of the code is copied from Google's example at\r\n## https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb\r\n\r\n## and some is copied from Dat Tran's example at\r\n## https://github.com/datitran/object_detector_app/blob/master/object_detection_app.py\r\n\r\n\r\n# Import packages\r\nimport os\r\nimport cv2\r\nimport numpy as np\r\nfrom picamera.array import PiRGBArray\r\nfrom picamera import PiCamera\r\nimport tensorflow.compat.v1 as tf\r\nimport argparse\r\nimport sys\r\nfrom PIL import Image\r\n\r\nimport tflite_runtime.interpreter as tflite\r\n\r\n\r\n# tf.disable_v2_behavior()\r\n\r\n# Set up camera constants\r\nIM_WIDTH = 1280\r\nIM_HEIGHT = 720\r\n#IM_WIDTH = 640 # Use smaller resolution for\r\n#IM_HEIGHT = 480 # slightly faster framerate\r\n\r\n# Select camera type (if user enters --usbcam when calling this script,\r\n# a USB webcam will be used)\r\ncamera_type = 'picamera'\r\n\r\n# This is needed since the working directory is the object_detection folder.\r\nsys.path.append('..')\r\n\r\n# Import utilites\r\nfrom utils import label_map_util\r\nfrom utils import visualization_utils as vis_util\r\n\r\n# Name of the directory containing the object detection module we're using\r\n# MODEL_NAME = 'ssdlite_mobilenet_v2_coco_2018_05_09'\r\n\r\n# Grab path to current working directory\r\nCWD_PATH = os.getcwd()\r\n\r\n# Path to frozen detection graph .pb file, which contains the model that is used\r\n# for object detection.\r\n# PATH_TO_CKPT = os.path.join(CWD_PATH, MODEL_NAME, 'frozen_inference_graph.pb')\r\n\r\n# Path to label map file\r\n# PATH_TO_LABELS = os.path.join(CWD_PATH, 'data', 'mscoco_label_map.pbtxt')\r\n\r\n# Number of classes the object detector can identify\r\n# NUM_CLASSES = 90\r\n\r\n## Load the label map.\r\n# Label maps map indices to category names, so that when the convolution\r\n# network predicts `5`, we know that this corresponds to `airplane`.\r\n# Here we use internal utility functions, but anything that returns a\r\n# dictionary mapping integers to appropriate string labels would be fine\r\n# label_map = label_map_util.load_labelmap(PATH_TO_LABELS)\r\n# categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES,\r\n# use_display_name=True)\r\n# category_index = label_map_util.create_category_index(categories)\r\n\r\n# Load the Tensorflow model into memory.\r\n# detection_graph = tf.Graph()\r\n# with detection_graph.as_default():\r\n# od_graph_def = tf.GraphDef()\r\n# with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\r\n# serialized_graph = fid.read()\r\n# od_graph_def.ParseFromString(serialized_graph)\r\n# tf.import_graph_def(od_graph_def, name='')\r\n#\r\n# sess = tf.Session(graph=detection_graph)\r\n\r\n# Define input and output tensors (i.e. data) for the object detection classifier\r\n\r\n# Input tensor is the image\r\n# image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\r\n\r\n# Output tensors are the detection boxes, scores, and classes\r\n# Each box represents a part of the image where a particular object was detected\r\n# detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\r\n\r\n# Each score represents level of confidence for each of the objects.\r\n# The score is shown on the result image, together with the class label.\r\n# detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')\r\n# detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')\r\n\r\n# Number of objects detected\r\n# num_detections = detection_graph.get_tensor_by_name('num_detections:0')\r\n\r\n# Initialize frame rate calculation\r\nframe_rate_calc = 1\r\nfreq = cv2.getTickFrequency()\r\nfont = cv2.FONT_HERSHEY_SIMPLEX\r\n\r\n# Initialize camera and perform object detection.\r\n# The camera has to be set up and used differently depending on if it's a\r\n# Picamera or USB webcam.\r\n\r\n# I know this is ugly, but I basically copy+pasted the code for the object\r\n# detection loop twice, and made one work for Picamera and the other work\r\n# for USB.\r\nfrs = np.array([])\r\nnum_frames = 0\r\naverage_fr = 0\r\nmapper = {0:'anger', 1:'disgust', 2:'fear', 3:'happiness', 4: 'sadness', 5: 'surprise', 6: 'neutral'}\r\n### Picamera ###\r\nif camera_type == 'picamera':\r\n # Initialize Picamera and grab reference to the raw capture\r\n camera = PiCamera()\r\n camera.resolution = (IM_WIDTH, IM_HEIGHT)\r\n camera.framerate = 10\r\n rawCapture = PiRGBArray(camera, size=(IM_WIDTH, IM_HEIGHT))\r\n rawCapture.truncate(0)\r\n\r\n for frame1 in camera.capture_continuous(rawCapture, format=\"bgr\", use_video_port=True):\r\n\r\n t1 = cv2.getTickCount()\r\n\r\n # Acquire frame and expand frame dimensions to have shape: [1, None, None, 3]\r\n # i.e. a single-column array, where each item in the column has the pixel RGB value\r\n frame = np.copy(frame1.array)\r\n frame.setflags(write=1)\r\n frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\r\n # print('frame rgb = ', type(frame_rgb), np.shape(frame_rgb))\r\n # frame_rgb = vis_util._resize_original_image([frame_rgb], [48,48])\r\n frame_rgb = cv2.resize(frame_rgb, (48,48))\r\n # print('frame rgb = ', type(frame_rgb), np.shape(frame_rgb))\r\n frame_expanded = np.expand_dims(frame_rgb/255, axis=2).astype('float32')\r\n # print('frame expanded = ', type(frame_expanded[0][0][0]), np.shape([frame_expanded]))\r\n # Load the TFLite model and allocate tensors.\r\n interpreter = tflite.Interpreter(model_path=\"emotions.tflite\")\r\n interpreter.allocate_tensors()\r\n\r\n # Get input and output tensors.\r\n input_details = interpreter.get_input_details()\r\n output_details = interpreter.get_output_details()\r\n\r\n # Test the model on random input data.\r\n # input_shape = input_details[0]['shape']\r\n # input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)\r\n\r\n interpreter.set_tensor(input_details[0]['index'], [frame_expanded])\r\n\r\n interpreter.invoke()\r\n\r\n # cv2_imshow(image * 255)\r\n\r\n # The function `get_tensor()` returns a copy of the tensor data.\r\n # Use `tensor()` in order to get a pointer to the tensor.\r\n output_data = interpreter.get_tensor(output_details[0]['index'])\r\n\r\n # need to show predition on the screen, if it's a 'confident' prediction, i'll show the %\r\n if(np.max(output_data[0] * 100) > 50):\r\n print(\"Guess: \", mapper[np.where(output_data[0] == np.max(output_data[0]))[0][0]],\r\n \"(%.02f%%)\" % np.max(output_data[0] * 100))\r\n else:\r\n print(\"Guess: \", mapper[np.where(output_data[0] == np.max(output_data[0]))[0][0]])\r\n\r\n # Draw the results of the detection (aka 'visulaize the results')\r\n # vis_util.visualize_boxes_and_labels_on_image_array(\r\n # frame,\r\n # np.squeeze(boxes),\r\n # np.squeeze(classes).astype(np.int32),\r\n # np.squeeze(scores),\r\n # category_index,\r\n # use_normalized_coordinates=True,\r\n # line_thickness=8,\r\n # min_score_thresh=0.40)\r\n #\r\n # cv2.putText(frame, \"FPS: {0:.2f}\".format(frame_rate_calc), (30, 50), font, 1, (255, 255, 0), 2, cv2.LINE_AA)\r\n\r\n # All the results have been drawn on the frame, so it's time to display it.\r\n #cv2.imshow('Emotion detector', frame)\r\n\r\n t2 = cv2.getTickCount()\r\n time1 = (t2 - t1) / freq\r\n frame_rate_calc = 1 / time1\r\n \r\n #frs = np.append(frs, frame_rate_calc)\r\n #average_fr = np.mean(frs)\r\n #print(\"Average frame rate = \", average_fr)\r\n # Press 'q' to quit\r\n \r\n if cv2.waitKey(1) == ord('q'):\r\n break\r\n\r\n rawCapture.truncate(0)\r\n\r\n camera.close()\r\n\r\ncv2.destroyAllWindows()\r\n","repo_name":"Elliot-Bl/rp_test","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"21544184554","text":"#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\n@author: HuRuiFeng\n@file: docx_file_rw.py\n@time: 2023/9/27 17:34\n@project: tiny-python-tools\n@desc: docx文件读取与保存\n\"\"\"\nimport os\nimport random\nfrom pathlib import Path, PurePath\n\nfrom docx import Document\n\n\ndef batch_process(file_dir):\n \"\"\"\n 批量修改文件名\n :param file_dir: 文件目录\n \"\"\"\n p = Path(file_dir)\n files = [x for x in p.iterdir() if PurePath(x).match('*.docx')]\n # 循环处理每个文件\n for file in sorted(files):\n text_data = read_docx(file)\n res_data = file_handle(text_data)\n new_file_name = \"[res]\" + file.name\n save_docx(res_data, new_file_name, file_dir)\n print(\"success file[{}] done\".format(file.name))\n\n\ndef read_docx(file):\n text_data = \"\"\n doc = Document(file)\n for para in doc.paragraphs:\n text_data += para.text + \"\\n\"\n\n return text_data\n\n\ndef save_docx(res_data, file_name, file_dir):\n doc = Document()\n doc.add_paragraph(res_data)\n doc.save(Path(file_dir, file_name))\n\n\ndef file_handle(text_data):\n return \"aaaa\" + str(random.randint(1, 10))\n\n\nif __name__ == '__main__':\n file_dir = \"E:\\\\files\"\n batch_process(file_dir)\n","repo_name":"Relph1119/tiny-python-tools","sub_path":"src/fileop/docx_file_rw.py","file_name":"docx_file_rw.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"6"}
+{"seq_id":"73936916987","text":"from fractions import Fraction\nfrom hashlib import md5\nimport math\n\n\nclass ArithmeticEncoder:\n def __init__(self, content):\n self.content = content\n\n # вероятность символа\n def get_symbol_probability(self, symbol):\n content_len = len(self.content)\n symbol_count = self.content.count(symbol)\n symbol_probability = Fraction(symbol_count, content_len)\n return symbol_probability\n\n # вероятность для каждого символа потока, вер-ти отсортированы в порядке убывания\n # для ускорения работы\n def get_symbols_probabilities(self):\n symbols = [*set(self.content)]\n pairs = [[s, self.get_symbol_probability(s)] for s in symbols]\n pairs = sorted(pairs, key=lambda x: x[1], reverse=True)\n return pairs\n\n # вер-ть преобразуется в двоичную дробь\n @staticmethod\n def get_floor_quantized_probability(probability):\n floor_exponent = abs(math.floor(math.log2(probability)))\n return Fraction(1, 2**floor_exponent)\n\n # преобразование вер-тей в двоичные дроби\n def get_floor_quantized_probabilities(self, symbol_probabilities):\n pairs = [[s, self.get_floor_quantized_probability(p)] for s, p in symbol_probabilities]\n return pairs\n\n @staticmethod\n def get_probabilities_sum(symbol_probabilities):\n return sum([x[1] for x in symbol_probabilities])\n\n # корректировка вер-тей домножением на 2\n @staticmethod\n def optimize_symbols_probabilities(symbol_probabilities, stock_floor):\n for i in range(len(symbol_probabilities)):\n probability = symbol_probabilities[i][1]\n if probability <= stock_floor:\n symbol_probabilities[i][1] *= 2\n break\n\n # пока сумма вер-тей меньше 1, вер-ти коррекируются\n def get_optimized_probabilities(self, symbol_probabilities):\n probabilities_sum = self.get_probabilities_sum(symbol_probabilities)\n\n while probabilities_sum < 1.0:\n stock = Fraction(1, 1) - probabilities_sum\n stock_floor = self.get_floor_quantized_probability(stock)\n\n self.optimize_symbols_probabilities(symbol_probabilities, stock_floor)\n probabilities_sum = self.get_probabilities_sum(symbol_probabilities)\n\n return symbol_probabilities\n\n def get_optimal_quantized_probabilities(self):\n symbol_probabilities = self.get_symbols_probabilities()\n symbol_probabilities = self.get_floor_quantized_probabilities(symbol_probabilities)\n symbol_probabilities = self.get_optimized_probabilities(symbol_probabilities)\n\n return symbol_probabilities\n\n def get_symbols_dict(self):\n pairs = self.get_optimal_quantized_probabilities()\n\n recent_range_stop = Fraction(0, 1)\n symbols_dict = {}\n\n for symbol, probability in pairs:\n symbol_range_start = recent_range_stop\n symbol_range_delta = probability\n\n symbols_dict[symbol] = (symbol_range_start, symbol_range_delta)\n recent_range_stop = symbol_range_start + symbol_range_delta\n\n return symbols_dict\n\n def encode(self):\n #content_md5 = md5(self.content).hexdigest()\n symbols_dict = self.get_symbols_dict()\n\n current_range_start = Fraction(0, 1)\n current_range_delta = Fraction(1, 1)\n\n for c in self.content:\n symbol_range_start, symbol_range_delta = symbols_dict[c]\n\n current_range_start += (current_range_delta * symbol_range_start)\n current_range_delta *= symbol_range_delta\n\n return current_range_start, len(self.content), symbols_dict\n","repo_name":"AnaNek/Information_security","sub_path":"lab_06/coding/encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":3852,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"6"}
+{"seq_id":"29217170256","text":"from selenium import webdriver\r\nimport time\r\nfrom selenium.webdriver.common.keys import Keys\r\nimport requests\r\nimport wget\r\nimport os\r\nimport pandas as pd\r\nfrom bs4 import BeautifulSoup\r\nbrowser=webdriver.Chrome()\r\nbrowser.get(\"https://fr.global.nba.com/statistics/teamstats/\")\r\ntime.sleep(5)\r\n\"\"\" accept cookies \"\"\"\r\nlink = browser.find_element_by_css_selector(\"#onetrust-accept-btn-handler\")\r\nlink.click()\r\ntime.sleep(2)\r\n\"\"\"get all the information on all teams\"\"\"\r\nrows = []\r\n\r\nfor i in range(30):\r\n rows.append(browser.find_element_by_css_selector('#main-container > div > div.col-xl-8.col-lg-12.content-container > div.content > div > div > div > div:nth-child(2) > div.ng-scope > nba-stat-table > div > div.nba-stat-table__overflow > table > tbody > tr:nth-child('+str(i+1)+')'))\r\n\r\ncolumns = [\"Rank\",\"Team\",\"M\",\"FG%\",\"3P%\",\"%LF\",\"REBO\",\"REBD\",\"PPM\",\"RPM\",\"PDPM\",\"BP\",\"IPM\",\"CPM\",\"FP\"]\r\nlines = [str(row.text).split(' ') for row in rows]\r\nprint(lines)\r\n\r\nfor i,line in enumerate(lines):\r\n name = line[1:-13]\r\n print(name)\r\n del lines[i][1:-13]\r\n full_name=\"\"\r\n for j in range(len(name)):\r\n full_name +=name[j]\r\n if j+1!=len(name):\r\n full_name+=\" \"\r\n lines[i].insert(1,full_name)\r\n print(lines[i])\r\n print(len(lines[i]))\r\n #print(len(lines[i]))\r\nprint(lines)\r\n \r\n\r\n \r\ndf=pd.DataFrame(lines, columns=columns)\r\ndf.index=df['Rank']\r\ndf.drop(\"Rank\",axis=1,inplace=True)\r\ndf.to_excel('Teams.xlsx')\r\n\r\n\r\n\r\n\r\n","repo_name":"dest50/NBA_Visualization","sub_path":"Teams/NBA_Teams_WebScrapping.py","file_name":"NBA_Teams_WebScrapping.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"73727704188","text":"import numpy as np\r\nimport os\r\nimport torch\r\nfrom dataloader import Dataset, collate_fn, make_train_loader\r\nfrom torch.utils.data import DataLoader\r\nimport random\r\n\r\nfrom dataloader import Dataset, collate_fn, make_train_loader\r\n\r\ntrain_path = '/home/osvald/Projects/Diagnostics/github/srtr_data/multi_label/n_train_tensors/'\r\nvalid_path = '/home/osvald/Projects/Diagnostics/github/srtr_data/multi_label/n_valid_tensors/'\r\n\r\nt_neg_weights = torch.Tensor([0.147, 0.344, 0.287, 0.249, 0.240, 0.531, 0.251, 0.188, 0.181, 0.165]) # for balance between pos & neg\r\nt_class_weights = torch.Tensor([3.94, 1.95, 2.24, 2.50, 2.58, 1.44, 2.49, 3.16, 3.26, 3.53]) # for balance between classes\r\n\r\nv_neg_weights = torch.Tensor([2.69, 0.0445, 0.0411, 0.0875, 0.121, 3.97, 0.0338, 0.0286, 0.0645, 0.0873])\r\nv_class_weights = torch.Tensor([ 0.686, 11.7, 12.7, 6.21, 4.63, 0.626, 15.3, 18.0, 8.25, 6.23])\r\n\r\n\r\ndef get_train_weights():\r\n\r\n pos_l = np.zeros((10))\r\n neg_l = np.zeros((10))\r\n weights = np.zeros((10))\r\n\r\n for epoch in range(5):\r\n train_loader = make_train_loader(train_path, batch_size=1024, shuffle=True, collate_fn=collate_fn)\r\n for batch, labels, seq_len in train_loader:\r\n neg_l += torch.sum(torch.sum((labels == 0), dim=0),dim=0).cpu().numpy()\r\n pos_l += torch.sum(torch.sum((labels == 1), dim=0),dim=0).cpu().numpy()\r\n t_weights = torch.sum(torch.sum((labels == 0), dim=0),dim=0).cpu().numpy() * t_neg_weights.cpu().numpy()\r\n t_weights += torch.sum(torch.sum((labels == 1), dim=0),dim=0).cpu().numpy()\r\n weights += t_weights / ((torch.sum(labels == 1).cpu().numpy()+ torch.sum(labels == 0).cpu().numpy())/10)\r\n\r\n neg_weights = pos_l/neg_l \r\n weights /= len(train_loader)\r\n multiplier = (1/(weights / max(weights)))\r\n multiplier = (1/np.mean(weights * multiplier)) * multiplier\r\n return neg_weights, multiplier\r\n \r\ndef get_valid_weights():\r\n \r\n val_data = Dataset(valid_indices, valid_path)\r\n val_loader = DataLoader(val_data, batch_size=1024, shuffle=True, collate_fn=collate_fn)\r\n\r\n pos_l = np.zeros((10))\r\n neg_l = np.zeros((10))\r\n weights = np.zeros((10))\r\n\r\n for batch, labels, seq_len in val_loader:\r\n neg_l += torch.sum(torch.sum((labels == 0), dim=0),dim=0).cpu().numpy()\r\n pos_l += torch.sum(torch.sum((labels == 1), dim=0),dim=0).cpu().numpy()\r\n t_weights = torch.sum(torch.sum((labels == 0), dim=0),dim=0).cpu().numpy() * v_neg_weights.cpu().numpy()\r\n t_weights += torch.sum(torch.sum((labels == 1), dim=0),dim=0).cpu().numpy()\r\n weights += t_weights / ((torch.sum(labels == 1).cpu().numpy()+ torch.sum(labels == 0).cpu().numpy())/10)\r\n\r\n neg_weights = pos_l/neg_l\r\n weights /= len(val_loader)\r\n multiplier = (1/(weights / max(weights)))\r\n multiplier = (1/np.mean(weights * multiplier)) * multiplier\r\n return neg_weights, multiplier\r\n\r\nif __name__ == '__main__':\r\n \r\n valid_indices = list(range(4214))\r\n\r\n\r\n # val data same every epoch\r\n val_data = Dataset(valid_indices, valid_path)\r\n val_loader = DataLoader(val_data, batch_size=1024, shuffle=True, collate_fn=collate_fn)\r\n\r\n v_neg_weights, v_class_weights = get_valid_weights()\r\n t_neg_weights, t_class_weights = get_train_weights()\r\n\r\n print('v_neg_weights', v_neg_weights)\r\n print('v_class_weights', v_class_weights) \r\n print('t_neg_weights', t_neg_weights)\r\n print('t_class_weights', t_class_weights)","repo_name":"bowang-lab/Transplant_Time_Series","sub_path":"Models/common/balance.py","file_name":"balance.py","file_ext":"py","file_size_in_byte":3478,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"6"}
+{"seq_id":"3401202546","text":"# -*- coding: utf-8 -*-\n\nfrom flask import current_app as app\n\nimport smtplib\nfrom email.mime.multipart import MIMEMultipart\nfrom email.utils import formatdate\n\n\nclass MailSendingError(Exception):\n msg = 'Ошибка отправки письма'\n\n def __init__(self, exc):\n print(exc)\n self.exc = exc\n\n\ndef create_message(to, subject, alternative=False):\n msg = MIMEMultipart()\n from_add = f'\"{app.config[\"SMTP_FROM_SYS_NAME\"]}\" <{app.config[\"SMTP_FROM\"]}>'\n msg['Subject'] = subject\n msg['Date'] = formatdate(localtime=True)\n msg['From'] = from_add\n msg['Reply-To'] = from_add\n msg['To'] = to\n return msg\n\n\ndef send_message(to, msg):\n\n try:\n smtp = smtplib.SMTP(app.config['SMTP_SERVER'], app.config['SMTP_PORT'])\n smtp.sendmail(app.config['SMTP_FROM'], to, msg.encode('utf-8'))\n except Exception as exc:\n if isinstance(exc, smtplib.SMTPRecipientsRefused):\n for r in exc.recipients:\n app.logger.info('mail address refused: %s, %s' % (r, exc.recipients[r]))\n else:\n app.logger.info('send message error: %s', to)\n raise MailSendingError(exc=exc)\n else:\n smtp.quit()\n","repo_name":"tigal/mooc","sub_path":"utils/mail.py","file_name":"mail.py","file_ext":"py","file_size_in_byte":1200,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"2573091429","text":"import sys\nfrom os import remove\nfrom unittest import TestCase\nfrom tests.ai.globals import ai, server\nfrom src.ai.commands import Objects, ElevationException\nfrom src.ai.logic._evolve import has_stones\nfrom src.ai.utils import set_verbose\nfrom time import time\n\n\nclass InitTester(TestCase):\n\n def setUp(self):\n global ai\n self.ai = ai\n sys.stdout = open(\"init_output\", \"w+\", buffering=1)\n self.output = open(\"init_output\", \"r+\")\n set_verbose(True)\n\n def tearDown(self):\n global server\n server.reset()\n sys.stdout.close()\n sys.stdout = sys.__stdout__\n self.output.close()\n remove(\"init_output\")\n\n def test_take_food(self):\n inventory = {\"food\": 10}\n self.ai.take_food(inventory)\n output = self.output.read()\n self.assertIn(\"Looting food\", output)\n self.assertTrue(output.count(\"Take food\") == 10)\n\n self.ai.take_food({})\n output = self.output.read()\n self.assertIn(\"Looting food\", output)\n self.assertTrue(output.count(\"Take food\") == 20)\n\n def test_take_stones(self):\n global server\n self.ai.take_stones({})\n output = self.output.read()\n self.assertIn(\"Looting stones\", output)\n self.assertTrue(output.count(\"Take linemate\") == 1)\n self.assertTrue(output.count(\"Set linemate\") == 1)\n self.assertTrue(output.count(\"Dropped all stones needed to evolve.\") == 1)\n\n self.ai.level = 2\n self.ai.take_stones({})\n output = self.output.read()\n self.assertIn(\"Looting stones\", output)\n self.assertTrue(output.count(\"Take linemate\") == 1)\n self.assertNotIn(\"Dropped all stones needed to evolve.\", output)\n\n server.set_response(\"Look\", \"[ player, food, , ]\")\n self.ai.take_stones({})\n output = self.output.read()\n self.assertIn(\"Looting stones\", output)\n self.assertNotIn(\"Take linemate\", output)\n self.assertNotIn(\"Set linemate\", output)\n self.assertNotIn(\"Dropped all stones needed to evolve.\", output)\n\n def test_handle_broadcast(self):\n self.ai.handle_broadcast([\"incantation\", time() + 1], {})\n output = self.output.read()\n self.assertIn(\"Ignoring broadcast incantation\", output)\n\n self.ai.messages_uuids = []\n self.ai.handle_broadcast([\"message 4, uuid1|~uuid2|~looted~|team1~|linemate~|1\", time()], {})\n output = self.output.read()\n self.assertEqual(self.ai.shared_inventory[\"linemate\"], 1)\n\n def test_handle_evolve(self):\n global server\n self.ai.handle_evolve({})\n output = self.output.read()\n self.assertIn(\"incantation~|\", output)\n\n self.ai.level = 1\n self.ai.fast_mode = False\n try:\n self.ai.handle_evolve({\"linemte\": 1})\n self.fail(\"Should have raised an ElevationException\") # pragma: no cover\n except ElevationException:\n pass\n\n server.set_response(\"Look\", \"[ player player player player player player, food, , ]\")\n self.ai.handle_evolve({\"linemte\": 1})\n output = self.output.read()\n self.assertIn(\"Trying to evolve to level 2\", output)\n self.assertIn(\"Not enough stones to evolve\", output)\n self.assertIn(\"incantation~|\", output)\n\n def test_can_survive(self):\n self.assertFalse(self.ai.can_survive({\"food\": 7}))\n\n self.ai.leader = self.ai.id\n self.assertTrue(self.ai.can_survive({\"food\": 7}))\n self.ai.leader = None\n\n self.assertTrue(self.ai.can_survive({\"food\": 12}))\n\n","repo_name":"Quentin-Desmettre/Epitech-Zappy","sub_path":"tests/ai/test_init.py","file_name":"test_init.py","file_ext":"py","file_size_in_byte":3591,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"1692603317","text":"#!/usr/bin/env python3\nfrom setuptools import setup\n\nPLUGIN_ENTRY_POINT = 'ovos-phal-mk1=ovos_PHAL_plugin_mk1:MycroftMark1'\nsetup(\n name='ovos-PHAL-plugin-mk1',\n version='0.0.1a3',\n description='A PHAL plugin for mycroft',\n url='https://github.com/OpenVoiceOS/ovos-PHAL-plugin-mk1',\n author='JarbasAi',\n author_email='jarbasai@mailfence.com',\n license='Apache-2.0',\n packages=['ovos_PHAL_plugin_mk1'],\n install_requires=[\"ovos-plugin-manager>=0.0.24a2\",\n \"ovos-bus-client\",\n \"pyserial~=3.0\"],\n zip_safe=True,\n classifiers=[\n 'Development Status :: 3 - Alpha',\n 'Intended Audience :: Developers',\n 'Topic :: Text Processing :: Linguistic',\n 'License :: OSI Approved :: Apache Software License',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: 3.6',\n ],\n entry_points={'ovos.plugin.phal': PLUGIN_ENTRY_POINT}\n)\n","repo_name":"OpenVoiceOS/ovos-PHAL-plugin-mk1","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":962,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"74160870588","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n[chapra_06_036.py]\n[Kush Gulati]\n[4/14/2020]\n\nI understand and have adhered to all the tenets of the Duke Community Standard \nin creating this code.\nSigned: [kg227]\n\"\"\"\nimport numpy as np\nimport scipy.optimize as opt\nimport matplotlib.pyplot as plt \nk = 1.4\nc = np.sqrt(k*287*277.15)\nv = 625\nM = v/c\n\n\ndef fun(B):\n\tfirst = (2*(M**2*(np.sin(B)**2)-1))\n\tsecond = (np.tan(B) * M**2*(k+np.cos(2*B)+2))\n\treturn (first/second) - np.tan(4*np.pi/180)\n\nB = np.linspace(2*np.pi/180,88*np.pi/180,100)\n\nf = fun(B)\n\nfig = plt.figure(num=1, clear=True)\nax = fig.add_subplot(1, 1, 1)\nax.grid(True)\nax.plot(B,f, 'k-')\nax.set(ylabel = r'$f(\\beta_u)$', xlabel = r'$\\beta_u$', title = r\"$f(\\beta_u)$ vs. $\\beta_u$\")\nfig.tight_layout()\nfig.savefig('chapra_6_036_plot.png')\n\nbeta = opt.brentq(fun, .4, .8)\nprint(beta)\n\npressure = 110*((2*k/(k+1)) * (M*np.sin(beta))**2 - ((k-1)/(k+1)))\nprint(pressure)\n","repo_name":"kg227-dev/Memorable-Code","sub_path":"EGR 103 Labs/Lab 9 EGR 103/chapra_6_036.py","file_name":"chapra_6_036.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"33826896776","text":"from bs4 import BeautifulSoup\r\nimport requests\r\nimport csv\r\nimport os\r\n\r\nMAX_PAGES = 100\r\nurl = \"https://etherscan.io/txs?a=0x74381D4533cc43121abFef7566010dD9FB7c9F7a&p=\"\r\npage = 1\r\nfull_url = url + str(page)\r\n\r\nheaders = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'}\r\nresponse = requests.get(full_url, headers=headers)\r\nsoup = BeautifulSoup(response.content, \"html.parser\")\r\n\r\ntable = soup.find(\"table\", {\"class\": \"table table-hover\"})\r\n\r\ntable_headers = table.findAll(\"th\")\r\ncsv_headers = []\r\nfor header in table_headers:\r\n csv_headers.append(header)\r\n\r\nprocessed_table = []\r\nfor page in range(1, MAX_PAGES + 1):\r\n\r\n full_url = url + str(page)\r\n headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'}\r\n response = requests.get(full_url, headers=headers)\r\n soup = BeautifulSoup(response.content, \"html.parser\")\r\n table = soup.find(\"table\", {\"class\": \"table table-hover\"})\r\n\r\n table_body = table.find(\"tbody\")\r\n table_rows = table_body.findAll('tr')\r\n\r\n for row in table_rows:\r\n processed_row = []\r\n table_columns = row.findAll('td')\r\n for column in table_columns:\r\n if not column.text == '':\r\n processed_row.append(column.text)\r\n processed_table.append(processed_row)\r\n\r\n if page % 20 == 0:\r\n processed = page/MAX_PAGES*100\r\n print(\"{}% of content processed\".format(processed))\r\n\r\nwith open('output.csv', 'w', newline='\\n') as csvfile:\r\n writer = csv.writer(csvfile, delimiter=',')\r\n writer.writerows(processed_table)\r\n\r\nsizefile = os.stat('output.csv').st_size\r\nprint(\"File generated with {0} rows and size {1} Bytes\".format(len(processed_table), sizefile))\r\n\r\n\r\n","repo_name":"matrueba/Others","sub_path":"SimpleWebScrapping.py","file_name":"SimpleWebScrapping.py","file_ext":"py","file_size_in_byte":1859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"33388892076","text":"import torch\nimport torch.nn as nn\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\nclass EncoderRNN(nn.Module):\n def __init__(self, input_size, hidden_size):\n super(EncoderRNN, self).__init__()\n self.hidden_size = hidden_size\n\n self.embedding = nn.Embedding(input_size, hidden_size)\n self.gru = nn.GRU(hidden_size, hidden_size)\n\n def forward(self, input, hidden):\n embedded = self.embedding(input).view(1, 1, -1)\n output = embedded\n output, hidden = self.gru(output, hidden)\n return output, hidden\n\n def initHidden(self):\n return torch.zeros(1, 1, self.hidden_size, device=device)","repo_name":"tiwarikajal/Seq2SQL--Natural-Language-sentences-to-SQL-Queries","sub_path":"baseline/model/encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"6"}
+{"seq_id":"39955808964","text":"class Graph:\r\n #A Graph is:\r\n # A dictionary of Nodes\r\n #A Node\r\n # A int/string &\r\n # A list\r\n\r\n def __init__(self):\r\n self._graph = {}\r\n self._nodes = self._graph.keys()\r\n\r\n def plot(self,node,edge):\r\n self._graph[node] = edge\r\n\r\n def remove(self,node):\r\n self._graph.pop(node)\r\n\r\n def change_node(self,node,newnode):\r\n temp_edge = self._graph[node]\r\n self.remove(node)\r\n self._graph[newnode] = temp_edge\r\n\r\n def change_edge(self,node,newedge):\r\n self._graph[node] = newedge\r\n\r\n def print(self):\r\n print(self._graph)\r\n\r\n def findpath1(self,start,end):\r\n path = []\r\n visited = []\r\n if start == end:\r\n path.append(start)\r\n print(path)\r\n else:\r\n return self.find_path(start,end,path,visited)\r\n\r\n def find_path(self,start,end,path,visited):\r\n path.append(start)\r\n if start in self._nodes:\r\n visited.append(start)\r\n children = self._graph[start]\r\n if end in children:\r\n path.append(end)\r\n print(path)\r\n else:\r\n for i in children:\r\n if i not in visited:\r\n return self.find_path(i,end,path,visited)\r\n\r\n \r\ngraph = Graph()\r\ngraph.plot('A',['B','C'])\r\ngraph.plot('B',['C','D'])\r\ngraph.plot('C',['D'])\r\ngraph.plot('D',['E'])\r\ngraph.plot('E',['F'])\r\ngraph.plot('F',['A'])\r\n#graph.remove('D')\r\n#graph.change_node('C','G')\r\n#graph.change_edge('A',['B','D'])\r\n#graph.change_edge('A',['B','D'])\r\n#graph.print()\r\n#print(graph.getnodes())\r\ngraph.findpath('A','D')\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"Emanoid/Graphs","sub_path":"Graph.py","file_name":"Graph.py","file_ext":"py","file_size_in_byte":1696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"12791287810","text":"from diagrams import Cluster, Diagram\nfrom diagrams.aws.compute import ECS, EKS, Lambda\nfrom diagrams.aws.integration import Eventbridge\nfrom diagrams.aws.management import Cloudwatch\n\nwith Diagram(\"Event Processing\", show=False):\n \n with Cluster(\"Event Flows\"):\n CloudWatch = Eventbridge(\"Cloudwatch Event\")\n workers = Lambda(\"Get Mail\")\n \n\n trading = Lambda(\"Trading Desk\")\n with Cluster(\"Event Workers\"):\n \tsticker = [\n \t\tLambda(\"Sticker\"),\n \t\tLambda(\"Sticker\"),\n \t\tLambda(\"Sticker\")\n \t]\n \t\n\n CloudWatch >> workers >> trading >> sticker\n ","repo_name":"EddieKuo723/ARK-Invest-Trading-Desk","sub_path":"Diagram/diagram.py","file_name":"diagram.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"6"}
+{"seq_id":"7209685651","text":"\"\"\"\n\n\"\"\"\n\n\nclass Express: # A - express 를 기다리는 줄을 관리하는 클래스 생성\n\n # 현재 오렌지의 위치 초기값\n oi = 0\n oj = 0\n\n # 현재 멜론의 위치 초기값\n mi = 0\n mj = 0\n\n # 규칙4에서 활용할 변수 초기화\n xx = 0\n yy = 0\n\n def __init__(self, n, m, s):\n self.max_p = 0 # 1부터 max_P까지 사람들이 이동하는 순서를 나타냄\n self.p = [] # 격자 내 사람들이 기다리는 줄을 나타낸 리스트\n self.n = n # 격자의 행 크기\n self.m = m # 격자의 열 크기\n self.s = s # 멜론의 위치 설정\n self.t = 1 # 오렌지의 위치 설정(초기값 1로 시작)\n self.C = [] # 오렌지가 멜론을 볼 수 있는 위치를 입력 받는 리스트\n\n def add_grid(self): # 격자 내 사람들이 기다리는 줄을 입력받는 함수\n print(N, \"X\", M, \"크기 격자의 이동순서를 입력하세요.\")\n for i in range(N):\n for j in range(M):\n self.p[i][j] = int(input()) # 첫 칸에서 부터 순서대로 숫자를 입력 받음\n\n def find_max_grid(self): # 사람들이 이동하는 순서를 구하는 함수(1부터 max_P까지 이동)\n for i in range(N):\n for j in range(M):\n if self.p[i][j] > self.p[i - 1][j - 1]: # 이전 값보다 지금의 값이 더 크다면\n self.max_p = self.p[i][j] # 큰 값을 max_p에 집어 넣도록 함\n\n \"\"\"\n 아래의 rule 함수는 오렌지가 멜론을 볼 수 있는 규칙을 확인하여 리스트에 저장하는 함수이다.\n 규칙은 총 4가지이다.\n \n 주의사항 : 격자는 (1, 1)부터 시작하지만 리스트 인덱스는 (0, 0)부터 시작한다.\n \"\"\"\n\n def rule(self): # 오렌지가 멜론을 볼 수 있는 규칙을 확인하여 리스트에 저장하는 함수\n while self.s <= self.max_p: # 멜론의 위치(s)가 마지막 이동경로(max_p)에 도달할 때까지\n for a in range(self.n):\n for b in range(self.m):\n if self.p[a][b] == self.s: # p 리스트에 입력된 숫자와 멜론의 위치 숫자와 같으면\n mi = a + 1 # 멜론의 행(mi)과 열(mj)을 설정함\n mj = b + 1 # 격자는 (1, 1)부터 시작하므로 행과 열에 각 1씩 더함\n elif self.p[a][b] == self.t: # p 리스트에 입력된 숫자와 오렌지의 위치 숫자와 같으면\n oi = a + 1 # 오렌지의 행(oi)과 열(oj)을 설정함\n oj = b + 1 # 격자는 (1, 1)부터 시작하므로 행과 열에 각 1씩 더함\n\n temp_array = [] # 현재 오렌지와 멜론의 위치에서 지나치는 모든 위치값의 임시 리스트 생성 및 초기화\n\n # 규칙1) 행 또는 열의 차이가 1일 경우\n if abs(mi - oi) == 1 or 1 == abs(mj - oj): # 리스트 인덱스에 맞게 각 1씩 차감하여 C 리스트에 저장\n self.C.append(self.p[oi - 1][oj - 1])\n\n # 규칙2) 오렌지와 멜론의 현재 위치에서 행만 같을 경우\n elif oi == mi:\n if oj < mj: # 오렌지의 열보다 멜론의 열이 더 클 경우\n for a in range(1, mj - oj): # 오렌지와 멜론 사이의 위치변수 값을 temp_array 리스트에 저장\n temp_array.append(self.p[oi - 1][oj + a - 1])\n else: # 오렌지의 열보다 멜론의 열이 더 작을 경우\n for a in range(1, oj - mj): # 오렌지와 멜론 사이의 위치변수 값을 temp_array 리스트에 저장\n temp_array.append(self.p[oi - 1][oj - a - 1])\n temp_array.sort(reverse=True) # temp_array 리스트 내림차순 정렬\n if temp_array[0] == 0: # temp_array 리스트의 첫 번째 값이 0일 경우 C 리스트에 저장\n self.C.append(self.p[oi - 1][oj - 1])\n\n # 규칙3) 오렌지와 멜론의 현재 위치에서 열만 같을 경우\n elif oj == mj:\n if oi < mi: # 오렌지의 행보다 멜론의 행이 더 클 경우\n for a in range(1, mi - oi): # 오렌지와 멜론 사이의 위치변수 값을 temp_array 리스트에 저장\n temp_array.append(self.p[oi + a - 1][oj - 1])\n else: # 오렌지의 행보다 멜론의 행이 더 작을 경우\n for a in range(1, oi - mi): # 오렌지와 멜론 사이의 위치변수 값을 temp_array 리스트에 저장\n temp_array.append(self.p[oi - a - 1][oj - 1])\n temp_array.sort(reverse=True) # temp_array 리스트 내림차순 정렬\n if temp_array[0] == 0: # temp_array 리스트의 첫 번째 값이 0일 경우 C 리스트에 저장\n self.C.append(self.p[oi - 1][oj - 1])\n\n # 규칙4) 오렌지와 멜론의 현재 위치가 행과 열 모두 다를 경우\n else:\n x: int = abs(mi - oi) # 오렌지 행과 멜론 행의 차이(절댓값)\n y: int = abs(mj - oj) # 오렌지 열과 멜론 열의 차이(절댓값)\n if max(x, y) % min(x, y) > 0: # x, y 중 큰 수에서 작은 수를 나눈 나머지가 0이 아닐 경우 C 리스트에 저장\n self.C.append(self.p[oi - 1][oj - 1])\n else: # x, y 중 큰 수에서 작은 수를 나눈 나머지가 0일 경우\n if x == y: # x와 y값이 같을 경우\n xx = (mi - oi) // x # 오렌지 행과 멜론 행 사이의 증가 비율(최솟값)\n yy = (mj - oj) // x # 오렌지 열과 멜론 열 사이의 증가 비율(최솟값)\n for a in range(1, x): # 오렌지와 멜론 사이의 위치변수 값을 temp_array 리스트에 저장\n temp_array.append(self.p[oi + (xx * a) - 1][oj + (yy * a) - 1])\n temp_array.sort(reverse=True) # temp_array 리스트 내림차순 정렬\n if temp_array[0] == 0: # temp_array 리스트의 첫 번째 값이 0일 경우 C 리스트에 저장\n self.C.append(self.p[oi - 1][oj - 1])\n elif x < y: # x보다 y값이 클 경우\n xx = (mi - oi) // x # 오렌지 행과 멜론 행 사이의 증가 비율(최솟값)\n yy = (mj - oj) // y # 오렌지 열과 멜론 열 사이의 증가 비율(최솟값)\n for a in range(1, y // x): # 오렌지와 멜론 사이의 위치변수 값을 temp_array 리스트에 저장\n temp_array.append(self.p[oi + (xx * a) - 1][oj + (yy * a) - 1])\n temp_array.sort(reverse=True) # temp_array 리스트 내림차순 정렬\n if temp_array[0] == 0: # temp_array 리스트의 첫 번째 값이 0일 경우 C 리스트에 저장\n self.C.append(self.p[oi - 1][oj - 1])\n else: # x보다 y값이 작을 경우\n xx = (mi - oi) // y # 오렌지 행과 멜론 행 사이의 증가 비율(최솟값)\n yy = (mj - oj) // x # 오렌지 열과 멜론 열 사이의 증가 비율(최솟값)\n for a in range(1, x // y): # 오렌지와 멜론 사이의 위치변수 값을 temp_array 리스트에 저장\n temp_array.append(self.p[oi + (xx * a) - 1][oj + (yy * a) - 1])\n temp_array.sort(reverse=True) # temp_array 리스트 내림차순 정렬\n if temp_array[0] == 0: # temp_array 리스트의 첫 번째 값이 0일 경우 C 리스트에 저장\n self.C.append(self.p[oi - 1][oj - 1])\n self.s += 1 # 현재 멜론의 위치에 1을 증가시켜 다음 값을 계산하게 함\n self.t += 1 # 현재 오렌지의 위치에 1을 증가시켜 다음 값을 계산하게 함\n\n\nif __name__ == \"__main__\": # 메인문\n\n print(\"행(N)과 열(M)과 멜론의 위치(S)를 차례로 입력하세요.\")\n N = int(input()) # 행 크기 설정\n M = int(input()) # 열 크기 설정\n S = int(input()) # 멜론의 위치 설정\n\n om = Express(N, M, S) # Express 클래스를 사용하는 om 객체 생성\n\n om.p = [[0 for i in range(M)] for j in range(N)] # om 객체의 p 리스트를 NxM 크기의 리스트로 생성\n om.add_grid() # 격자 이동순서 설정 함수 실행\n om.find_max_grid() # 줄의 크기(max_p)를 구하는 함수 실행\n\n print(\"총 이동 수는\", om.max_p, \"입니다.\") # om 객체의 줄의 크기를 나타냄\n print(om.p) # 격자 출력\n om.rule() # 오렌지가 멜론을 볼 수 있는 규칙 함수 실행\n\n # 출력\n om.C.sort() # C 리스트 오름차순 정렬\n\n print(\"오렌지가 멜론을 본 횟수는\", len(om.C), \"번 입니다.\") # 오렌지가 멜론을 본 횟수\n print(om.C) # 멜론을 본 오렌지의 위치를 나타낸 리스트 출력\n","repo_name":"poetbin/2017052242","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9313,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"36808473992","text":"from __future__ import print_function, division\n\nimport pandas as pd\nfrom eeglibrary.src import train\nfrom eeglibrary.src import Metric\nfrom eeglibrary.utils import train_args, add_adda_args\nfrom utils import class_names, subject_dir_names, arrange_paths\n\n\ndef voting(args, pred_list, path_list):\n def ensemble_preds(pred_list, path_list, sub_df, thresh):\n # もともとのmatファイルごとに振り分け直す\n patient_name = path_list[0][0].split('/')[-3]\n orig_mat_list = sub_df[sub_df['clip'].apply(lambda x: '_'.join(x.split('_')[:2])) == patient_name]\n ensembled_pred_list = []\n for orig_mat_name in orig_mat_list['clip']:\n seg_number = int(orig_mat_name[-8:-4])\n one_segment_preds = [pred for path, pred in zip(path_list[0], pred_list) if\n int(path.split('/')[-2].split('_')[-1]) == seg_number]\n ensembled_pred = int(sum(one_segment_preds) >= len(one_segment_preds) * thresh)\n ensembled_pred_list.append(ensembled_pred)\n orig_mat_list['preictal'] = ensembled_pred_list\n return orig_mat_list\n\n # preds to csv\n sub_df = pd.read_csv(args.sub_path, engine='python')\n thresh = args.thresh # 1の割合がthreshを超えたら1と判断\n pred_df = ensemble_preds(pred_list, path_list, sub_df, thresh)\n sub_df.loc[pred_df.index, 'preictal'] = pred_df['preictal']\n sub_df.to_csv(args.sub_path, index=False)\n\n\ndef label_func(path):\n return path.split('/')[-2].split('_')[2]\n\n\nif __name__ == '__main__':\n args = add_adda_args(train_args()).parse_args()\n\n metrics = [\n Metric('loss', initial_value=10000, inequality='less', save_model=True),\n Metric('accuracy', initial_value=0, inequality='more'),\n Metric('far', initial_value=1000, inequality='less')]\n\n if args.train_manifest == 'all':\n for sub_name in subject_dir_names:\n args = arrange_paths(args, sub_name)\n train(args, class_names, label_func, metrics)\n elif args.inference:\n pred_list, path_list = train(args, class_names, label_func, metrics)\n voting(args, pred_list, path_list)\n else:\n train(args, class_names, label_func, metrics)\n\n","repo_name":"koike-ya/seizure-prediction","sub_path":"src/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2231,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"29667320124","text":"# coding: utf-8\n\nimport MySQLdb\n\n\n\n\ndef db_set_connect(sql_ip, sql_login, sql_pass, sql_db):\n\ttry:\n\t\tconn = MySQLdb.connect(host=sql_ip, user=sql_login, passwd=sql_pass, db=sql_db, use_unicode=True, charset=\"utf8\")\n\t\tprint(\"Переподключение к BD MySQL - ОК\") # отладочное, удалить\n\t\treturn conn\n\texcept MySQLdb.Error as err:\n\t\terr_handle(1, err)\n\t\t# print(\"Connection error: {}\".format(err))\n\t\tconn.close()\n\n\n\ndef save_quote(sql_conn, quote):\n\tif (quote['bid'] > 0) and (quote['offer'] > 0):\n\t\ttry:\n\t\t\tcursor = sql_conn.cursor()\n\t\t\tmid = (quote['bid'] + quote['offer']) / 2\n\t\t\tsql = \"INSERT INTO quotes (instrument, date_time, date_time_str, band, bid, offer, mid) VALUES ('\" + quote['instrument'] + \"','\" + format_date_time(quote['datetime']) + \"', '\" + quote['datetime'] + \"', \" + str(quote['band']) + \", \" + str(quote['bid']) + \", \" + str(quote['offer']) + \", \" + str(mid) + \")\"\n\t\t\t# print(sql)\n\t\t\tcursor.execute(sql)\n\t\t\tsql_conn.commit()\n\t\t\treturn True\n\t\texcept MySQLdb.Error as err:\n\t\t\terr_handle(2, err)\n\t\t\treturn False\n\n\n\ndef clear_old_quotes(sql_conn):\n\t# удалить из quotes старые котировки\n\ttry:\n\t\tcursor = sql_conn.cursor()\n\t\tsql = \"DELETE FROM quotes WHERE date_time < ADDDATE(NOW(), INTERVAL -4 HOUR)\"\n\t\tcursor.execute(sql)\n\t\tsql_conn.commit()\n\t\tprint('DB Quotes cleaning completed!')\n\t\treturn True\n\texcept MySQLdb.Error as err:\n\t\terr_handle(2, err)\n\t\treturn False\t\n\n\n\ndef err_handle(err_num, param):\n\tif err_num == 1:\n\t\t# Ошибка подключения к базе данных! Проверьте настройки подключения!\n\t\tprint(\"Ошибка подключения к базе данных! Проверьте настройки подключения!\")\n\t\tprint(\"Connection error: {}\".format(param))\n\telif err_num == 2:\n\t\t# Ошибка обращения к базе данных, проверьте соединение\n\t\tprint(\"Ошибка обращения к базе данных, проверьте соединение\")\n\t\tprint(\"Query error: {}\".format(param))\n\n\n\ndef format_date_time(dt_str):\n\tdtparts = dt_str.split('-')\n\tdy = dtparts[0][:4]\n\tdm = dtparts[0][4:6]\n\tdd = dtparts[0][6:]\n\tfdt= dy + '-' + dm + '-' + dd + ' ' + dtparts[1]\n\treturn fdt","repo_name":"StroevPavel/WebSocketAPI","sub_path":"fix/db_oper.py","file_name":"db_oper.py","file_ext":"py","file_size_in_byte":2257,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"42017941441","text":"from django.db import models\n\nfrom collab_app.mixins.models import BaseModel\n\n\nclass Invite(BaseModel):\n class InviteState(models.IntegerChoices):\n CREATED = 1\n ACCEPTED = 2\n DENIED = 3\n CANCELED = 4\n\n inviter = models.ForeignKey(\n 'collab_app.User',\n related_name='invites_sent',\n on_delete=models.PROTECT\n )\n organization = models.ForeignKey(\n 'collab_app.Organization',\n related_name='invites',\n on_delete=models.PROTECT\n )\n\n email = models.EmailField()\n state = models.PositiveSmallIntegerField(choices=InviteState.choices, default=InviteState.CREATED)\n key = models.CharField(max_length=64, unique=True, default='')\n\n class Meta:\n constraints = [\n models.UniqueConstraint(fields=['organization', 'email', 'state'], name='unique_invite')\n ]\n","repo_name":"CollabSauce/collab-backend","sub_path":"collab_app/models/invite.py","file_name":"invite.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"22347631132","text":"from django.urls import path \n\nfrom .views import EstadoCreate, CidadeCreate, EmpresaCreate, PropriedadeCreate, ServicoCreate, ContratoCreate, ConfiguracaoSistemaCreate\nfrom .views import EmpresaUpdate, PropriedadeUpdate, ServicoUpdate, EstadoUpdate, CidadeUpdate, ContratoUpdate\nfrom .views import EstadoList, CidadeList, EmpresaList, PropriedadeList, ServicoList, ContratoList\nfrom .views import EstadoDelete, CidadeDelete, EmpresaDelete, PropriedadeDelete, ServicoDelete\nfrom .views import ContratoDetailView, EmpresaDetailView\n\nurlpatterns = [\n path('cadastros/contrato', ContratoCreate.as_view(), name = \"cadastro-contrato\"),\n path('cadastros/estado', EstadoCreate.as_view(), name = \"cadastro-estado\"),\n path('cadastros/cidade', CidadeCreate.as_view(), name = \"cadastro-cidade\"), \n path('cadastros/empresa/', EmpresaCreate.as_view(), name =\"cadastro-empresa\"),\n path('cadastros/propriedade/', PropriedadeCreate.as_view(), name =\"cadastro-propriedade\"),\n path('cadastros/servico/', ServicoCreate.as_view(), name =\"cadastro-servico\"),\n path('cadastros/sistema/', ConfiguracaoSistemaCreate.as_view(), name =\"cadastro-sistema\"),\n\n\n path('editar/estado/', EstadoUpdate.as_view(), name =\"editar-estado\"),\n path('editar/cidade/', CidadeUpdate.as_view(), name =\"editar-cidade\"),\n path('editar/empresa/', EmpresaUpdate.as_view(), name =\"editar-empresa\"), \n path('editar/propriedade/', PropriedadeUpdate.as_view(), name =\"editar-propriedade\"),\n path('editar/servico/', ServicoUpdate.as_view(), name =\"editar-servico\"),\n path('editar/contrato/', ContratoUpdate.as_view(), name =\"editar-contrato\"),\n \n\n\n\n path('excluir/estado/', EstadoDelete.as_view(), name=\"excluir-estado\"),\n path('excluir/cidade/', CidadeDelete.as_view(), name=\"excluir-cidade\"),\n path('excluir/propriedade/', PropriedadeDelete.as_view(), name=\"excluir-propriedade\"),\n path('excluir/Empresa/', EmpresaDelete.as_view(), name=\"excluir-empresa\"),\n path('excluir/servico/', ServicoDelete.as_view(), name=\"excluir-servico\"),\n\n\n\n\n path('listar/estado/', EstadoList.as_view(), name=\"listar-estado\"),\n path('listar/cidade/', CidadeList.as_view(), name=\"listar-cidade\"),\n path('listar/empresa/', EmpresaList.as_view(), name=\"listar-empresa\"),\n path('listar/propriedade/', PropriedadeList.as_view(), name=\"listar-propriedade\"),\n path('listar/servico/', ServicoList.as_view(), name=\"listar-servico\"),\n path('listar/contrato/', ContratoList.as_view(), name=\"listar-contrato\"),\n\n path('detail/contrato/', ContratoDetailView.as_view(), name=\"detail-contrato\"),\n path('detail/empresa/', EmpresaDetailView.as_view(), name=\"detail-empresa\"),\n]","repo_name":"YuriApolinario/GerContTcc","sub_path":"cadastros/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2787,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"6605861416","text":"def solution(skill, skill_trees):\n _skill = {val : idx for idx, val in enumerate(skill)}\n answer = 0\n \n def check(s):\n learned_idx = 0\n for user_skill in s:\n # 현재 배운 스킬이 주어진 스킬트리에 존재하는지\n if user_skill not in _skill:\n continue\n # 해당 스킬이 주어진 스킬트리의 구조에 맞게 되고 있는지\n need_idx = _skill[user_skill]\n\n if learned_idx < need_idx:\n return False\n elif learned_idx == need_idx:\n learned_idx += 1\n return True\n for skill_tree in skill_trees:\n if check(skill_tree):\n answer += 1\n return answer","repo_name":"JeongGod/Algo-study","sub_path":"JeongGod/2week/programmers/49993.py","file_name":"49993.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"ko","doc_type":"code","stars":7,"dataset":"github-code","pt":"6"}
+{"seq_id":"74518691388","text":"from git import Git, Repo\nimport os\n\nrepo_folder = '/Users/administrator/MyProject'\n\nrepo = Repo(repo_folder)\n\nos.chdir(repo_folder)\n\ng = Git(repo)\n\nfile_name = 'dict_comps.py'\ng.add(file_name)\ng.commit(file_name, message=\"first commit\")\nprint(g.log())\n\n# create new branch\ng.checkout('-b', 'dev001')\n\n# work on existing branch\ng.checkout('dev003')","repo_name":"jstrickler/20230710USGS","sub_path":"git_add_commit.py","file_name":"git_add_commit.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"6"}
+{"seq_id":"72532248509","text":"from fastapi import Depends, FastAPI, Request\n\nfrom ...core.settings import ComputationalBackendSettings\nfrom ...modules.comp_scheduler.base_scheduler import BaseCompScheduler\nfrom . import get_app\n\n\ndef get_scheduler(request: Request) -> BaseCompScheduler:\n scheduler: BaseCompScheduler = request.app.state.scheduler\n return scheduler\n\n\ndef get_scheduler_settings(\n app: FastAPI = Depends(get_app),\n) -> ComputationalBackendSettings:\n settings: ComputationalBackendSettings = (\n app.state.settings.DIRECTOR_V2_COMPUTATIONAL_BACKEND\n )\n return settings\n","repo_name":"ITISFoundation/osparc-simcore","sub_path":"services/director-v2/src/simcore_service_director_v2/api/dependencies/scheduler.py","file_name":"scheduler.py","file_ext":"py","file_size_in_byte":578,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"6"}
+{"seq_id":"39001117088","text":"from ChainExecutor.chain_executor import ChainExecutor\n\n\ndef test_update_node_args():\n def func1(arg1):\n print(\n f'Func 1 running with `{arg1}`')\n return f'Func 1 result is {arg1}'\n\n def func2(peer_dep):\n return f\"Func 2 running with `{peer_dep}` as peer dependency\"\n\n exe = ChainExecutor()\n\n exe.add_node(func1) \\\n .add_node(func2)\n\n exe.add_edge_from_node_order()\n\n exe.update_node_args({'node_name': func1.__name__,\n 'args': {'arg1': 'hello world'}})\n\n exe.execute()\n\n result = exe.get_node_result('func2')\n assert result == \"\"\"Func 2 running with `Func 1 result is hello world` as peer dependency\"\"\"\n\n\ndef test_update_multiple_nodes():\n def func1(arg_1):\n return f\"hello {arg_1} from func 1\"\n\n def func2(arg_2):\n return f\"hello {arg_2} from func 2\"\n\n def func3(arg_1, arg_2):\n return f\"func 3 is returning {arg_1} and {arg_2}\"\n\n g = ChainExecutor()\n g.add_node(func1, 'node_1')\n g.add_node(func2, 'node_2')\n g.add_node(func3, 'node_3')\n\n g.add_edge('node_1', 'node_3')\n g.add_edge('node_2', 'node_3')\n\n g.update_node_args([{'node_name': 'node_1', 'args': {'arg_1': 'Mập'}},\n {'node_name': 'node_2', 'args': {'arg_2': 'Rex'}}])\n\n g.execute()\n\n result_1 = g.get_node_result()\n\n\n g.update_node_args({'node_name': 'node_1', 'args': {'arg_1': 'Shipapa'}})\n g.execute()\n\n result_2 = g.get_node_result()\n\n assert result_1 == \"func 3 is returning hello Mập from func 1 and hello Rex from func 2\"\n assert result_2 == 'func 3 is returning hello Shipapa from func 1 and hello Rex from func 2'\n\n\ndef test_execute_node():\n def func1(arg1):\n print(\n f'Func 1 running with `{arg1}`')\n return f'Func 1 result is {arg1}'\n\n def func2(peer_dep):\n return f\"Func 2 running with `{peer_dep}` as peer dependency\"\n\n exe = ChainExecutor()\n\n exe.add_node(func1) \\\n .add_node(func2)\n\n exe.add_edge_from_node_order()\n\n exe.update_node_args({'node_name': func1.__name__,\n 'args': {'arg1': 'hello world'}})\n\n exe.execute_node('func1')\n\n result = exe.get_node_result('func1')\n print(result)\n\n\nif __name__ == '__main__':\n test_update_node_args()\n test_execute_node()\n test_update_multiple_nodes()\n","repo_name":"highlander-spirou/chain_execute","sub_path":"tests/test_methods.py","file_name":"test_methods.py","file_ext":"py","file_size_in_byte":2357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"41551670184","text":"import unittest\nfrom unittest import mock\n\nfrom . make import make\n\n\nPROJECT_FAILURE1 = \"\"\"\n{\n \"driver\": {\n \"typename\": \"test.bibliopixel.failure.Failure\",\n \"num\": 12\n },\n\n \"layout\": {\n \"typename\": \"bibliopixel.layout.strip.Strip\"\n },\n\n \"animation\": {\n \"typename\": \"bibliopixel.animation.tests.StripChannelTest\"\n }\n}\n\"\"\"\n\n\nPROJECT_FAILURE2 = \"\"\"\n{\n \"driver\": {\n \"typename\": \"test.bibliopixel.failure2.NON_EXISTENT\",\n \"num\": 12\n },\n\n \"layout\": {\n \"typename\": \"bibliopixel.layout.strip.Strip\"\n },\n\n \"animation\": {\n \"typename\": \"bibliopixel.animation.tests.StripChannelTest\"\n }\n}\n\"\"\"\n\n\nPROJECT_FAILURE3 = \"\"\"\n{\n \"driver\": {\n \"typename\": \"test.NON_EXISTENT.Failure\",\n \"num\": 12\n },\n\n \"layout\": {\n \"typename\": \"bibliopixel.layout.strip.Strip\"\n },\n\n \"animation\": {\n \"typename\": \"bibliopixel.animation.tests.StripChannelTest\"\n }\n}\n\"\"\"\n\nBAD_JSON_ERROR = \"\"\"\nwhile parsing a flow node\nexpected the node content, but found ']'\n in \"\", line 1, column 2:\n {]\n ^\n\"\"\"\n\n\nclass ImportFailureTest(unittest.TestCase):\n @mock.patch('bibliopixel.util.data_file.ALWAYS_LOAD_YAML', False)\n def test_bad_import_json(self):\n with self.assertRaises(Exception):\n make('{]')\n\n @mock.patch('bibliopixel.util.data_file.ALWAYS_LOAD_YAML', True)\n def test_bad_import_yaml(self):\n with self.assertRaises(Exception) as e:\n make('{]')\n\n self.assertEqual(str(e.exception).strip(), BAD_JSON_ERROR.strip())\n\n def test_failure1(self):\n with self.assertRaises(ImportError) as e:\n make(PROJECT_FAILURE1)\n self.assertEqual(e.exception.name, 'test.bibliopixel.failure.Failure')\n\n def test_failure2(self):\n with self.assertRaises(ImportError) as e:\n make(PROJECT_FAILURE2)\n self.assertEqual(e.exception.name,\n 'test.bibliopixel.failure2.NON_EXISTENT')\n\n def test_failure3(self):\n with self.assertRaises(ImportError) as e:\n make(PROJECT_FAILURE3)\n self.assertEqual(e.exception.name, 'test.NON_EXISTENT.Failure')\n","repo_name":"ManiacalLabs/BiblioPixel","sub_path":"test/bibliopixel/project/import_failure_test.py","file_name":"import_failure_test.py","file_ext":"py","file_size_in_byte":2193,"program_lang":"python","lang":"en","doc_type":"code","stars":263,"dataset":"github-code","pt":"6"}
+{"seq_id":"28711969308","text":"from math import *\r\nimport tkinter as tk\r\nexpression = \"\" # EXPRESSION\r\nπ = 3.141592654 # Pi number\r\n################################### functions ######################################\r\n# Function to add in the entry of text display\r\ndef btn_click(item, equation):\r\n \r\n global expression\r\n expression = expression + str(item)\r\n equation.set(expression)\r\n# Function to calculate the percentage of a number\r\ndef percentage( equation):\r\n \r\n global expression\r\n expression = expression + \"/100\"\r\n equation.set(expression)\r\n# Function to delete one by one from the last in the entry of text display\r\ndef delete(equation):\r\n global expression\r\n text = expression[:-1]\r\n expression = text\r\n equation.set(text)\r\n# Function to calculate the factorial of a number \r\ndef btn_fact( equation):\r\n \r\n global expression\r\n expression = expression + \"factorial(\"\r\n equation.set(expression)\r\n# Function to calculate ln \r\ndef btn_ln( equation):\r\n \r\n global expression\r\n expression = expression + \"log1p(\"\r\n equation.set(expression)\r\n# Function to calculate the power\r\ndef btn_power(equation):\r\n \r\n global expression\r\n expression = expression + \"**\"\r\n equation.set(expression)\r\n# Function to calculate log\r\ndef btn_log( equation):\r\n \r\n global expression\r\n expression = expression + \"log10(\"\r\n equation.set(expression)\r\n# Function to find the square root of a number\r\ndef btn_sqrt( equation):\r\n \r\n global expression\r\n expression = expression + \"sqrt(\"\r\n equation.set(expression) \r\ndef btn_pi( equation):\r\n \r\n global expression\r\n expression = expression + str(\"π\")\r\n equation.set(expression)\r\n# Function to clear the whole entry of text display\r\ndef btn_clear(equation):\r\n \r\n global expression\r\n expression = \"\"\r\n equation.set(\"\")\r\n# uses whatever is stored in memory_recall\r\ndef answer(equation):\r\n global expression\r\n global result\r\n answer= str(result)\r\n expression = expression + answer\r\n equation.set(expression)\r\n# Funtion to find the result of an operation \r\ndef btn_equal(equation):\r\n \r\n global expression\r\n global result\r\n result = str(eval(expression)) # 'eval' function evalutes the string expression directly\r\n equation.set(result)\r\n expression = \"\"\r\nexpression = \"\"\r\ndef main():\r\n # creating basic window\r\n f = tk.Tk()\r\n f.title(\"Scientific Calculator\")\r\n # 'StringVar()' is used to get the instance of input field\r\n equation = tk.StringVar()\r\n # creating a frame for the input field\r\n input_field = tk.Entry(f, textvariable=equation).grid(row=0, ipadx=150, ipady=10, columnspan=9)\r\n equation.set(\"0\")\r\n # first row\r\n exp = tk.Button(f, text=' e ', fg='black', bg='#87927e', \r\n command=lambda: btn_click(\"exp(\", equation), height=2, width=7).grid(row=1, column=0)\r\n pi = tk.Button(f, text=' π ', fg='black', bg='#87927e', \r\n command=lambda: btn_pi( equation), height=2, width=7).grid(row=1, column=1)\r\n fac = tk.Button(f, text=' x! ', fg='black', bg='#87927e', \r\n command=lambda: btn_fact( equation), height=2, width=7).grid(row=1, column=2)\r\n left_brack = tk.Button(f, text=' ( ', fg='black', bg='#87927e', \r\n command=lambda: btn_click(\"(\", equation), height=2, width=7).grid(row=1, column=3)\r\n right_brack = tk.Button(f, text=' ) ', fg='black', bg='#87927e', \r\n command=lambda: btn_click(\")\", equation), height=2, width=7).grid(row=1, column=4)\r\n pour = tk.Button(f, text=' % ', fg='black', bg='#87927e', \r\n command=lambda: percentage( equation), height=2, width=7).grid(row=1, column=5)\r\n AC = tk.Button(f, text=' AC ', fg='black', bg='#87927e', \r\n command=lambda: btn_clear(equation), height=2, width=7).grid(row=1, column=6)\r\n # second row\r\n arcsin = tk.Button(f, text=' arcsin ', fg='black', bg='#87927e', \r\n command=lambda: btn_click('asin(', equation), height=2, width=7).grid(row=2, column=0)\r\n \r\n sin = tk.Button(f, text=' sin ', fg='black', bg='#87927e', \r\n command=lambda: btn_click(\"sin(\", equation), height=2, width=7).grid(row=2, column=1)\r\n ln = tk.Button(f, text=' ln ', fg='black', bg='#87927e', \r\n command=lambda: btn_ln( equation), height=2, width=7).grid(row=2, column=2)\r\n seven = tk.Button(f, text=' 7 ', fg='black', bg='#e6f6df', \r\n command=lambda: btn_click(7, equation), height=2, width=7).grid(row=2, column=3) \r\n \r\n eight = tk.Button(f, text=' 8 ', fg='black', bg='#e6f6df', \r\n command=lambda:btn_click(8, equation), height=2, width=7).grid(row=2, column=4) \r\n \r\n nine = tk.Button(f, text=' 9 ', fg='black', bg='#e6f6df', \r\n command=lambda: btn_click(9, equation), height=2, width=7).grid(row=2, column=5)\r\n divide = tk.Button(f, text=' / ', fg='black', bg='#87927e', \r\n command=lambda: btn_click(\"/\", equation), height=2, width=7).grid(row=2, column=6)\r\n \r\n # third row\r\n arccos = tk.Button(f, text=' arccos ',fg='black', bg='#87927e', \r\n command=lambda: btn_click('acos(', equation),height=2, width=7).grid(row=3, column=0)\r\n cos = tk.Button(f, text=' cos ', fg='black', bg='#87927e', \r\n command=lambda: btn_click(\"cos(\", equation), height=2, width=7).grid(row=3, column=1)\r\n log = tk.Button(f, text=' log ', fg='black', bg='#87927e', \r\n command=lambda: btn_log( equation), height=2, width=7).grid(row=3, column=2)\r\n four = tk.Button(f, text=' 4 ', fg='black', bg='#e6f6df', \r\n command=lambda: btn_click(4, equation), height=2, width=7).grid(row=3, column=3)\r\n five = tk.Button(f, text=' 5 ', fg='black', bg='#e6f6df', \r\n command=lambda: btn_click(5, equation), height=2, width=7).grid(row=3, column=4) \r\n \r\n six = tk.Button(f, text=' 6 ', fg='black', bg='#e6f6df', \r\n command=lambda: btn_click(6, equation), height=2, width=7).grid(row=3, column=5)\r\n multiply = tk.Button(f, text=' * ', fg='black', bg='#87927e', \r\n command=lambda: btn_click(\"*\", equation), height=2, width=7).grid(row=3, column=6)\r\n # fourth row\r\n arctan = tk.Button(f, text=' arctan ', fg='black', bg='#87927e', \r\n command=lambda: btn_click(\"atan(\", equation), height=2, width=7).grid(row=4, column=0)\r\n tang = tk.Button(f, text=' tan ', fg='black', bg='#87927e', \r\n command=lambda: btn_click(\"tan(\", equation), height=2, width=7).grid(row=4, column=1)\r\n rac = tk.Button(f, text=' √ ', fg='black', bg='#87927e', \r\n command=lambda: btn_sqrt( equation), height=2, width=7).grid(row=4, column=2)\r\n one = tk.Button(f, text=' 1 ', fg='black', bg='#e6f6df', cursor =\"hand2\",\r\n command=lambda: btn_click(1, equation), height=2, width=7).grid(row=4, column=3) \r\n two = tk.Button(f, text=' 2 ', fg='black', bg='#e6f6df', \r\n command=lambda: btn_click(2, equation), height=2, width=7).grid(row=4, column=4) \r\n \r\n three = tk.Button(f, text=' 3 ', fg='black', bg='#e6f6df', \r\n command=lambda: btn_click(3, equation), height=2, width=7).grid(row=4, column=5) \r\n minus = tk.Button(f, text=' - ', fg='black', bg='#87927e', \r\n command=lambda: btn_click(\"-\", equation), height=2, width=7).grid(row=4, column=6)\r\n \r\n # fourth row \r\n Ans = tk.Button(f, text=' Ans ', fg='black', bg='#87927e', \r\n command=lambda: answer(equation), height=2, width=7).grid(row=5, column=0)\r\n Del = tk.Button(f, text=' Del ', fg='black', bg='#87927e', \r\n command=lambda: delete(equation), height=2, width=7).grid(row=5, column=2)\r\n power = tk.Button(f, text=' power ', fg='black', bg='#87927e', \r\n command=lambda: btn_power(equation), height=2, width=7).grid(row=5, column=1)\r\n zero = tk.Button(f, text=' 0 ', fg='black', bg='#e6f6df', \r\n command=lambda: btn_click(0, equation), height=2, width=7).grid(row=5, column=3)\r\n point = tk.Button(f, text=' . ', fg='black', bg='#e6f6df', \r\n command=lambda: btn_click(\".\", equation), height=2, width=7).grid(row=5, column=4)\r\n equals = tk.Button(f, text=' = ', fg='black', bg='#00c0ff', \r\n command=lambda: btn_equal(equation), height=2, width=7).grid(row=5, column=5)\r\n plus = tk.Button(f, text=' + ', fg='black', bg='#87927e', \r\n command=lambda: btn_click(\"+\", equation), height=2, width=7).grid(row=5, column=6)\r\n f.mainloop()\r\nif __name__ == '__main__':\r\n main()\r\n","repo_name":"mohamedprojects/Scientific-Calculator","sub_path":"scientific calculator.py","file_name":"scientific calculator.py","file_ext":"py","file_size_in_byte":8765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"3445630634","text":"# \n# Nesse modulo constam as funcoes responsaveis pelas operacoes nas tabelas dos jogadores\n# \n# \n\nimport tableinterface as ti\n__all__ = ['beginTables', 'insertValue', 'calcTable','getUserTable','getSampleTable','loadTable','prepareTableToSave','canYahtbonus']\n\n\ntables = []\n\n# \n# Inicializa a tabela de acordo com o numero de jogadores\n# \ndef beginTables(nPlayers):\n if tables != [] or not isinstance(nPlayers,int) or nPlayers < 1:\n return False\n for i in range(0,nPlayers):\n tables[:] += [['0','0','0','0','0','0','0','0','0','0','0','0','0','0']]\n return True\n# \n# Prepara a tabela de um usuario para string para ser salva no txt\n# \ndef prepareTableToSave(nPlayer):\n tableLst = \"\"\n for i in tables[nPlayer]:\n tableLst += str(i) + \" \"\n return tableLst\n\n# \n# Atualiza a tabela com a de um jogo anterior\n# \ndef loadTable(loadedTable):\n tables[:] = loadedTable\n\n# \n# Insere valor na tabela\n# \ndef insertValue(nPlayer,cel,value):\n if not value:\n return False\n if cel == 13:\n if(tables[nPlayer][11] == '0'):\n return False\n else:\n tables[nPlayer][cel] = str(int(tables[nPlayer][cel]) + value)\n return True\n elif(tables[nPlayer][cel] != '0'):\n return False\n else:\n tables[nPlayer][cel] = str(value)\n print('Insercao concluida!')\n return True\n\n# \n# Calcula valor total da tabela de um usuario\n# \ndef calcTable(nPlayer):\n totalUpper = 0\n for cel in range(0,6):\n totalUpper += int(tables[nPlayer][cel])\n totalLower = 0\n for cel in range(6,14):\n totalLower += int(tables[nPlayer][cel])\n totalUpper += int(tables[nPlayer][13])*int(tables[nPlayer][11])\n if totalLower > 62:\n totalLower += 35\n\n return (totalUpper+totalLower)\n\ndef getSampleTable(dCelulas):\n it.displayTable(dCelulas)\n return\n\n# \n# Retorna a tabela de um usuario\n# \ndef getUserTable(nPlayer):\n t = tables[nPlayer]\n ti.displayTable(t, nPlayer)\n return\n\n# \n# Retorna se pode fazer bonus\n# \ndef canYahtbonus(nPlayer):\n if(tables[nPlayer][11] != '0'):\n return True\n\n\n\n","repo_name":"pedroripper/yahtzee","sub_path":"tablemanager.py","file_name":"tablemanager.py","file_ext":"py","file_size_in_byte":2366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"29544150396","text":"from survey import get_google_sheet_client, column_mapping\nimport time # For display statistics timeout\nfrom termcolor import colored # Adds colored text and emoji support\n\nSHEET = get_google_sheet_client(\n creds_file=\"creds.json\", sheet_name=\"career_analyzer\"\n)\n\n\"\"\"\nDictionary list that assigns colors for bar charts\n\"\"\"\nanswer_color_mapping = {\n \"Healthcare\": \"🟥\",\n \"Technology\": \"🟧\",\n \"Finance\": \"🟨\",\n \"Education\": \"🟩\",\n \"Unsatisfied\": \"🟥\",\n \"Unhappy\": \"🟧\",\n \"Somewhat Satisfied\": \"🟨\",\n \"Happy\": \"🟩\",\n \"Dreamjob\": \"🟦\",\n \"Company culture\": \"🟥\",\n \"Salary\": \"🟧\",\n \"Work-life balance\": \"🟨\",\n \"Other\": \"🟩\",\n \"Location\": \"🟦\",\n \"Opportunities for growth\": \"🟪\",\n \"yes\": \"🟩\",\n \"no\": \"🟥\",\n}\n\n\nclass DataAnalyzer:\n \"\"\"Class that contains statistics logic creates bar charts\"\"\"\n\n def __init__(self, column_mapping):\n self.column_mapping = column_mapping\n\n def view_survey_statistics(self):\n \"\"\"Calculate and display statistics based on survey data.\"\"\"\n try:\n worksheet = SHEET.get_worksheet(0)\n data = worksheet.get_all_records()\n\n \"\"\"\n Count timestamps and survey responses\n Reset variable\n Loop through Timestamp data\n Map to spreadsheet row\n Check if entry is timestamp\n Add data\n Count timestamps\n Print survey count\n Print empty line\n \"\"\"\n unique_timestamps = set()\n for row in data:\n timestamp = row[\"Timestamp\"]\n if timestamp.startswith(\"20\"):\n unique_timestamps.add(timestamp)\n survey_count = len(unique_timestamps)\n survey_count_text = colored(\n f\"Count of Surveys: {survey_count}\", \"cyan\"\n )\n print(survey_count_text)\n print()\n time.sleep(1)\n\n \"\"\"\n Average age calculation\n Can age be converted to integer\n All of the ages added together\n Number of entries into the age column\n Total sum of ages divided by total entries\n Print avg age\n Print empty line\n Delay by 1 sec\n \"\"\"\n total_age = 0\n age_count = 0\n for row in data:\n age = row[column_mapping[\"How old are you?\"]]\n try:\n age = int(age)\n total_age += age\n age_count += 1\n except ValueError:\n pass\n\n if age_count > 0:\n average_age = total_age / age_count\n avg_age_text = colored(\n f\"Average Age of Survey Participant: {average_age:.2f}\",\n \"green\",\n )\n print(avg_age_text)\n print()\n time.sleep(1)\n\n \"\"\"\n Extract data from the Google Spreadsheet using column_mapping\n Ensure it's a string\n Remove extra spaces\n Get the index of the current question_column\n Print header text \n Print empty line\n \"\"\"\n question_columns = list(self.column_mapping.values())[2:]\n for question_column in question_columns:\n answer_counts = {}\n total_responses = len(data)\n for row in data:\n answer = row[question_column]\n if isinstance(answer, str):\n answer = answer.strip()\n if answer in answer_counts:\n answer_counts[answer] += 1\n else:\n answer_counts[answer] = 1\n\n column_index = list(self.column_mapping.values()).index(\n question_column\n )\n\n \"\"\"Get the corresponding question title from column_mapping\"\"\"\n question_title = list(self.column_mapping.keys())[\n column_index\n ]\n\n \"\"\"Print the question before creating the bar chart\"\"\"\n header_text = colored(\n f\"Statistics for Question: {question_title}\", \"cyan\"\n )\n print(header_text)\n print()\n\n \"\"\"Prepare data for the bar chart\"\"\"\n chart_data = []\n for answer, count in answer_counts.items():\n percentage = (count / total_responses) * 100\n chart_data.append(\n (f\"{answer} ({percentage:.2f}%)\", count)\n )\n\n \"\"\"Find the longest label length for alignment\"\"\"\n longest_label_length = max(\n len(label) for label, _ in chart_data\n )\n\n \"\"\" \n Idea for this taken from:\n # https://alexwlchan.net/2018/ascii-bar-charts/\n \"\"\"\n\n for label, count in chart_data:\n \"\"\"Calculate the percentage and prepare the label\"\"\"\n percentage = (count / total_responses) * 100\n formatted_label = f\"{label:<{longest_label_length}}\"\n\n \"\"\"Calculate the number of blocks and spaces needed\"\"\"\n bar_blocks = int((percentage / 100) * 40)\n space_blocks = 10 - bar_blocks\n\n \"\"\"\n Get the appropriate colored block or emoji for the answer\n Get the first word of the label (answer)\n Default to blue block\n Draw the bar with colored blocks or emojis and spaces\n Delay for 2 seconds\n \"\"\"\n answer = label.split()[0]\n answer_color = answer_color_mapping.get(answer, \"🟦\")\n\n bar = answer_color * bar_blocks\n space = \" \" * space_blocks\n\n formatted_label = f\"{label:<{longest_label_length}}\"\n print(\n f\"{formatted_label} � {count:#4d} {bar}{space}\"\n )\n print()\n time.sleep(2)\n\n except Exception as e:\n error_message = colored(\n f\"Error analyzing survey data: {str(e)}\", \"red\"\n )\n print(error_message)\n","repo_name":"emidombek/career-analyzer","sub_path":"data_analysis.py","file_name":"data_analysis.py","file_ext":"py","file_size_in_byte":6569,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"6"}
+{"seq_id":"31126723133","text":"from sshkeyboard import listen_keyboard_manual\nfrom Motors.motors import motors\nimport asyncio\nfrom Encoders.encoders import left_encoder\nimport time\n\n\n\n\n\n\n\ntime.sleep(1)\n\n\n\n\n\n\n\nasync def start_listening():\n #do i need to put a sleep in here, how often does it poll input may use up resources?\n await listen_keyboard_manual(\n on_press=press,\n )\n\nasync def main():\n listen = asyncio.create_task(start_listening())\n left_encoder_task = asyncio.create_task(left_encoder.start())\n await listen\n\nasync def press(key):\n print(f\"'{key}' pressed\")\n if(key=='up'):\n motors.forward(40)\n elif(key=='down'):\n motors.backward(40)\n elif(key=='left'):\n motors.pivot_left(30)\n elif(key=='right'):\n motors.pivot_right(30)\n elif(key=='space'):\n motors.stop()\n\ntry:\n asyncio.run(main())\nexcept KeyboardInterrupt:\n motors.stop()\nfinally:\n motors.stop()\n","repo_name":"gregorianrants/technic-bot","sub_path":"technic/drive_motors.py","file_name":"drive_motors.py","file_ext":"py","file_size_in_byte":914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"22218675266","text":"import PythonQt\nfrom PythonQt import QtCore, QtGui\nimport director.visualization as vis\nimport director.objectmodel as om\nfrom director.debugVis import DebugData\nimport director.vtkAll as vtk\nfrom director import callbacks\nimport numpy as np\nfrom director.affordanceitems import AffordanceItem\n\n\nclass PointPicker(object):\n\n def __init__(self, view, obj=None, callback=None, numberOfPoints=2, drawLines=True, abortCallback=None):\n\n self.view = view\n self.obj = obj\n self.pickType = 'points'\n self.tolerance = 0.01\n self.numberOfPoints = numberOfPoints\n self.drawLines = drawLines\n self.drawClosedLoop = False\n self.annotationObj = None\n self.annotationFunc = callback\n self.abortFunc = abortCallback\n self.annotationName = 'annotation'\n self.annotationFolder = 'segmentation'\n self.eventFilter = None\n self.clear()\n\n def start(self):\n self.installEventFilter()\n self.clear()\n\n def stop(self):\n self.removeEventFilter()\n\n def installEventFilter(self):\n\n self.eventFilter = PythonQt.dd.ddPythonEventFilter()\n self.view.vtkWidget().installEventFilter(self.eventFilter)\n\n self.eventFilter.addFilteredEventType(QtCore.QEvent.MouseMove)\n self.eventFilter.addFilteredEventType(QtCore.QEvent.MouseButtonPress)\n self.eventFilter.addFilteredEventType(QtCore.QEvent.KeyPress)\n self.eventFilter.addFilteredEventType(QtCore.QEvent.KeyRelease)\n self.eventFilter.connect('handleEvent(QObject*, QEvent*)', self.onEvent)\n\n def removeEventFilter(self):\n if self.eventFilter:\n self.view.vtkWidget().removeEventFilter(self.eventFilter)\n self.eventFilter = None\n\n def onEvent(self, obj, event):\n\n if event.type() == QtCore.QEvent.KeyPress and event.key() == QtCore.Qt.Key_Escape:\n self.stop()\n self.clear()\n if self.abortFunc is not None:\n self.abortFunc()\n return\n\n if event.modifiers() != QtCore.Qt.ShiftModifier:\n if self.annotationObj:\n self.annotationObj.setProperty('Visible', False)\n return\n\n if self.annotationObj:\n self.annotationObj.setProperty('Visible', True)\n\n if event.type() == QtCore.QEvent.MouseMove:\n self.onMouseMove(vis.mapMousePosition(obj, event), event.modifiers())\n elif event.type() == QtCore.QEvent.MouseButtonPress:\n self.onMousePress(vis.mapMousePosition(obj, event), event.modifiers())\n\n def clear(self):\n if self.annotationObj:\n self.annotationObj.setProperty('Visible', False)\n self.annotationObj = None\n self.points = [None for i in range(self.numberOfPoints)]\n self.hoverPos = None\n self.lastMovePos = [0, 0]\n\n def onMouseMove(self, displayPoint, modifiers=None):\n self.lastMovePos = displayPoint\n self.tick()\n\n def onMousePress(self, displayPoint, modifiers=None):\n\n for i in range(self.numberOfPoints):\n if self.points[i] is None:\n self.points[i] = self.hoverPos\n break\n\n if self.points[-1] is not None:\n self.finish()\n\n def finish(self):\n\n points = [p.copy() for p in self.points]\n if self.annotationFunc is not None:\n self.annotationFunc(*points)\n\n self.clear()\n\n\n def draw(self):\n\n d = DebugData()\n\n points = [p if p is not None else self.hoverPos for p in self.points]\n\n # draw points\n for p in points:\n if p is not None:\n d.addSphere(p, radius=0.008)\n\n if self.drawLines:\n # draw lines\n for a, b in zip(points, points[1:]):\n if b is not None:\n d.addLine(a, b)\n\n # connect end points\n if points[-1] is not None and self.drawClosedLoop:\n d.addLine(points[0], points[-1])\n\n self.annotationObj = vis.updatePolyData(d.getPolyData(),\n self.annotationName,\n parent=self.annotationFolder,\n view=self.view)\n self.annotationObj.setProperty('Color', [1,0,0])\n self.annotationObj.actor.SetPickable(False)\n\n\n def tick(self):\n\n if self.obj is None:\n pickedPointFields = vis.pickPoint(self.lastMovePos, self.view, pickType=self.pickType,\n tolerance=self.tolerance)\n self.hoverPos = pickedPointFields.pickedPoint\n prop = pickedPointFields.pickedProp\n if prop is None:\n self.hoverPos = None\n else:\n pickedPointFields = vis.pickPoint(self.lastMovePos, self.view, obj=self.obj, pickType=self.pickType, tolerance=self.tolerance)\n self.hoverPos = pickedPointFields.pickedPoint\n\n self.draw()\n\n\n\nclass ImagePointPicker(object):\n\n\n DOUBLE_CLICK_EVENT = 'DOUBLE_CLICK_EVENT'\n\n\n def __init__(self, imageView, obj=None, callback=None, numberOfPoints=1, drawLines=True):\n\n self.imageView = imageView\n self.view = imageView.view\n self.obj = obj\n self.drawLines = drawLines\n self.annotationObj = None\n self.annotationFunc = callback\n self.eventFilter = None\n self.numberOfPoints = numberOfPoints\n self.showCursor = False\n self.cursorObj = None\n self.callbacks = callbacks.CallbackRegistry([self.DOUBLE_CLICK_EVENT])\n self.clear()\n\n def start(self):\n self.installEventFilter()\n self.clear()\n\n def stop(self):\n self.removeEventFilter()\n\n def installEventFilter(self):\n\n self.eventFilter = PythonQt.dd.ddPythonEventFilter()\n self.view.vtkWidget().installEventFilter(self.eventFilter)\n\n self.eventFilter.addFilteredEventType(QtCore.QEvent.MouseMove)\n self.eventFilter.addFilteredEventType(QtCore.QEvent.MouseButtonPress)\n self.eventFilter.addFilteredEventType(QtCore.QEvent.MouseButtonDblClick)\n self.eventFilter.addFilteredEventType(QtCore.QEvent.KeyPress)\n self.eventFilter.addFilteredEventType(QtCore.QEvent.KeyRelease)\n self.eventFilter.connect('handleEvent(QObject*, QEvent*)', self.onEvent)\n\n def removeEventFilter(self):\n if self.eventFilter:\n self.view.vtkWidget().removeEventFilter(self.eventFilter)\n self.eventFilter = None\n\n def connectDoubleClickEvent(self, func):\n return self.callbacks.connect(self.DOUBLE_CLICK_EVENT, func)\n\n def disconnectDoubleClickEvent(self, callbackId):\n self.callbacks.disconnect(callbackId)\n\n def onEvent(self, obj, event):\n\n if event.type() == QtCore.QEvent.MouseButtonDblClick and event.button() == QtCore.Qt.LeftButton:\n\n self.callbacks.process(self.DOUBLE_CLICK_EVENT, vis.mapMousePosition(obj, event), event.modifiers(), self.imageView)\n\n if event.type() in (QtCore.QEvent.MouseMove, QtCore.QEvent.MouseButtonPress, QtCore.QEvent.Wheel):\n if self.showCursor:\n self.updateCursor(vis.mapMousePosition(obj, event))\n elif event.type() == QtCore.QEvent.KeyPress:\n if event.key() == QtCore.Qt.Key_Shift:\n self.showCursor = True\n\n elif event.type() == QtCore.QEvent.KeyRelease:\n if event.key() == QtCore.Qt.Key_Shift:\n self.showCursor = False\n self.hideCursor()\n\n if event.modifiers() != QtCore.Qt.ShiftModifier:\n self.showCursor = False\n if self.annotationObj:\n self.hoverPos = None\n self.draw()\n self.annotationObj.setProperty('Color', [1, 0, 0])\n self.clear()\n return\n\n if self.annotationObj:\n self.annotationObj.setProperty('Visible', True)\n\n if event.type() == QtCore.QEvent.MouseMove:\n self.onMouseMove(vis.mapMousePosition(obj, event), event.modifiers())\n\n elif event.type() == QtCore.QEvent.MouseButtonPress:\n self.onMousePress(vis.mapMousePosition(obj, event), event.modifiers())\n\n\n def clear(self):\n if self.annotationObj:\n self.annotationObj.setProperty('Visible', False)\n self.annotationObj = None\n self.points = []\n self.hoverPos = None\n self.lastMovePos = [0, 0]\n\n def onMouseMove(self, displayPoint, modifiers=None):\n self.lastMovePos = displayPoint\n self.hoverPos = self.displayPointToImagePoint(self.lastMovePos)\n self.draw()\n\n def onMousePress(self, displayPoint, modifiers=None):\n\n point = self.displayPointToImagePoint(displayPoint)\n if point is None:\n return\n\n self.points.append(point)\n\n if len(self.points) == self.numberOfPoints:\n self.finish()\n\n\n def finish(self):\n points = [np.array(p) for p in self.points]\n self.clear()\n if self.annotationFunc is not None:\n self.annotationFunc(*points)\n\n def draw(self):\n\n d = DebugData()\n\n points = list(self.points)\n if self.hoverPos is not None:\n points.append(self.hoverPos)\n\n # draw points\n radius = 5\n scale = (2*self.view.camera().GetParallelScale())/(self.view.renderer().GetSize()[1])\n for p in points:\n d.addSphere(p, radius=radius*scale)\n\n if self.drawLines and len(points) > 1:\n for a, b in zip(points, points[1:]):\n d.addLine(a, b)\n\n # connect end points\n # d.addLine(points[0], points[-1])\n\n if self.annotationObj:\n self.annotationObj.setPolyData(d.getPolyData())\n else:\n self.annotationObj = vis.updatePolyData(d.getPolyData(), 'annotation', parent='segmentation', color=[1,0,0], view=self.view)\n self.annotationObj.addToView(self.view)\n self.annotationObj.actor.SetPickable(False)\n self.annotationObj.actor.GetProperty().SetLineWidth(2)\n\n def hideCursor(self):\n if self.cursorObj:\n om.removeFromObjectModel(self.cursorObj)\n\n def updateCursor(self, displayPoint):\n\n center = self.displayPointToImagePoint(displayPoint, restrictToImageDimensions=False)\n center = np.array(center)\n\n d = DebugData()\n d.addLine(center + [0, -3000, 0], center + [0, 3000, 0])\n d.addLine(center + [-3000, 0, 0], center + [3000, 0, 0])\n self.cursorObj = vis.updatePolyData(d.getPolyData(), 'cursor', alpha=0.5, view=self.view)\n self.cursorObj.addToView(self.view)\n self.cursorObj.actor.SetUseBounds(False)\n self.cursorObj.actor.SetPickable(False)\n self.view.render()\n\n def displayPointToImagePoint(self, displayPoint, restrictToImageDimensions=True):\n point = self.imageView.getImagePixel(displayPoint, restrictToImageDimensions)\n if point is not None:\n point[2] = np.sign(self.view.camera().GetPosition()[2])\n return point\n\n\nclass PlacerWidget(object):\n\n def __init__(self, view, handle, points):\n\n assert handle.actor\n assert handle.actor.GetUserTransform()\n\n self.view = view\n self.handle = handle\n self.points = points\n self.moving = False\n self.eventFilter = None\n\n def start(self):\n self.installEventFilter()\n\n def stop(self):\n self.removeEventFilter()\n\n def installEventFilter(self):\n\n self.eventFilter = PythonQt.dd.ddPythonEventFilter()\n self.view.vtkWidget().installEventFilter(self.eventFilter)\n\n self.eventFilter.addFilteredEventType(QtCore.QEvent.MouseMove)\n self.eventFilter.addFilteredEventType(QtCore.QEvent.MouseButtonPress)\n self.eventFilter.addFilteredEventType(QtCore.QEvent.MouseButtonRelease)\n self.eventFilter.connect('handleEvent(QObject*, QEvent*)', self.onEvent)\n\n def removeEventFilter(self):\n if self.eventFilter:\n self.view.vtkWidget().removeEventFilter(self.eventFilter)\n self.eventFilter = None\n\n def onEvent(self, obj, event):\n\n if event.type() == QtCore.QEvent.MouseMove:\n self.onMouseMove(vis.mapMousePosition(obj, event), event.modifiers())\n elif event.type() == QtCore.QEvent.MouseButtonPress and event.button() == QtCore.Qt.LeftButton:\n self.onMousePress(vis.mapMousePosition(obj, event), event.modifiers())\n elif event.type() == QtCore.QEvent.MouseButtonRelease and event.button() == QtCore.Qt.LeftButton:\n self.onMouseRelease(vis.mapMousePosition(obj, event), event.modifiers())\n\n def onMouseMove(self, displayPoint, modifiers=None):\n\n self.updateHighlight(displayPoint)\n\n if not self.moving:\n return\n\n\n self.eventFilter.setEventHandlerResult(True)\n\n pickPoint = self.getPointPick(displayPoint)\n # print displayPoint, pickPoint\n\n if pickPoint is not None:\n t = self.handle.actor.GetUserTransform()\n assert t\n currentPos = np.array(t.GetPosition())\n t.Translate(np.array(pickPoint - currentPos))\n t.Modified()\n self.handle._renderAllViews()\n\n def onMousePress(self, displayPoint, modifiers=None):\n\n picked = self.getHandlePick(displayPoint)\n if picked is not None:\n self.moving = True\n self.eventFilter.setEventHandlerResult(True)\n\n def onMouseRelease(self, displayPoint, modifiers=None):\n\n if self.moving:\n self.eventFilter.setEventHandlerResult(True)\n self.moving = False\n\n def updateHighlight(self, displayPoint):\n if self.getHandlePick(displayPoint) is not None:\n self.handle.actor.GetProperty().SetAmbient(0.5)\n self.handle._renderAllViews()\n else:\n self.handle.actor.GetProperty().SetAmbient(0.0)\n self.handle._renderAllViews()\n\n def getHandlePick(self, displayPoint):\n pickData = vis.pickPoint(displayPoint, self.view, obj=self.handle, pickType='cells', tolerance=0.01)\n return pickData.pickedPoint\n\n def getPointPick(self, displayPoint):\n pickData = vis.pickPoint(displayPoint, self.view, obj=self.points, pickType='cells', tolerance=0.01)\n return pickData.pickedPoint\n\n\nclass ObjectPicker(object):\n\n def __init__(self, view, callback=None, abortCallback=None, numberOfObjects=1, getObjectsFunction=None, hoverColor=[1.0, 0.8, 0.8, 1.0]):\n\n self.view = view\n self.tolerance = 0.01\n self.numberOfObjects = numberOfObjects\n self.getObjectsFunction = getObjectsFunction\n self.callbackFunc = callback\n self.abortFunc = abortCallback\n self.hoverColor = hoverColor[0:3]\n self.hoverAlpha = hoverColor[3]\n self.modifier = 0\n self.mouseSelectionEventType = QtCore.QEvent.MouseButtonPress\n self.eventFilter = None\n self.pickedObj = None\n self.storedProps = {}\n self.repeat = False\n self.clear()\n\n def start(self):\n self.installEventFilter()\n self.clear()\n\n def stop(self):\n self.removeEventFilter()\n\n def installEventFilter(self):\n\n self.eventFilter = PythonQt.dd.ddPythonEventFilter()\n self.view.vtkWidget().installEventFilter(self.eventFilter)\n\n self.eventFilter.addFilteredEventType(QtCore.QEvent.MouseMove)\n self.eventFilter.addFilteredEventType(QtCore.QEvent.MouseButtonPress)\n self.eventFilter.addFilteredEventType(QtCore.QEvent.MouseButtonDblClick)\n self.eventFilter.addFilteredEventType(QtCore.QEvent.KeyPress)\n self.eventFilter.addFilteredEventType(QtCore.QEvent.KeyRelease)\n self.eventFilter.connect('handleEvent(QObject*, QEvent*)', self.onEvent)\n\n def removeEventFilter(self):\n if self.eventFilter:\n self.view.vtkWidget().removeEventFilter(self.eventFilter)\n self.eventFilter = None\n\n def onEvent(self, obj, event):\n\n if event.type() == QtCore.QEvent.KeyPress and event.key() == QtCore.Qt.Key_Escape:\n self.stop()\n self.clear()\n if self.abortFunc is not None:\n self.abortFunc()\n return\n\n if event.type() == QtCore.QEvent.MouseMove:\n self.onMouseMove(vis.mapMousePosition(obj, event), event.modifiers())\n elif event.type() == self.mouseSelectionEventType and event.button() == QtCore.Qt.LeftButton:\n self.onMousePress(vis.mapMousePosition(obj, event), event.modifiers())\n\n def clear(self):\n self.objects = [None for i in range(self.numberOfObjects)]\n self.hoverPos = None\n self.lastMovePos = [0, 0]\n self.unsetHoverProperties(self.pickedObj)\n self.pickedObj = None\n\n\n def onMouseMove(self, displayPoint, modifiers=None):\n self.lastMovePos = displayPoint\n if modifiers == self.modifier:\n self.tick()\n else:\n self.unsetHoverProperties(self.pickedObj)\n\n def onMousePress(self, displayPoint, modifiers=None):\n if modifiers != self.modifier:\n return\n\n for i in range(self.numberOfObjects):\n\n if self.objects[i] is None:\n self.objects[i] = self.pickedObj\n break\n\n if self.objects[-1] is not None:\n self.finish()\n\n def finish(self):\n if self.callbackFunc is not None:\n try:\n self.callbackFunc(self.objects)\n finally:\n self.clear()\n if not self.repeat:\n self.stop()\n\n def unsetHoverProperties(self, obj):\n if obj is None:\n return\n\n for propName, value in list(self.storedProps.items()):\n if obj.hasProperty(propName):\n obj.setProperty(propName, value)\n self.storedProps = {}\n\n def setHoverProperties(self, obj):\n if obj is None:\n return\n\n for propName, value in [['Color', self.hoverColor],\n ['Color By', 'Solid Color'],\n ['Alpha', self.hoverAlpha]]:\n\n if obj.hasProperty(propName):\n self.storedProps[propName] = obj.getProperty(propName)\n obj.setProperty(propName, value)\n\n def tick(self):\n\n objs = self.getObjectsFunction() if self.getObjectsFunction else None\n\n pickedPointFields = vis.pickPoint(self.lastMovePos, self.view, pickType='cells', tolerance=self.tolerance,\n obj=objs)\n self.hoverPos = pickedPointFields.pickedPoint\n prop = pickedPointFields.pickedProp\n\n prevPickedObj = self.pickedObj\n curPickedObj = vis.getObjectByProp(prop)\n\n if curPickedObj is not prevPickedObj:\n self.unsetHoverProperties(prevPickedObj)\n self.setHoverProperties(curPickedObj)\n self.pickedObj = curPickedObj\n\n\nclass AffordancePicker(ObjectPicker):\n\n def __init__(self, view, affordanceManager, filterFunc=None):\n AffordancePicker.__init__(self, view, getObjectsFunction=self.getObjects)\n self.affordanceManager = affordanceManager\n self.filterFunc = filterFunc\n\n def getObjects(self):\n affs = self.affordanceManager.getAffordances()\n affs = [a for a in affs if a.getProperty('Visible')]\n if self.filterFunc is not None:\n affs = [a for a in affs if self.filterFunc(a)]\n return affs\n","repo_name":"RobotLocomotion/director","sub_path":"src/python/director/pointpicker.py","file_name":"pointpicker.py","file_ext":"py","file_size_in_byte":19595,"program_lang":"python","lang":"en","doc_type":"code","stars":176,"dataset":"github-code","pt":"6"}
+{"seq_id":"30529798796","text":"from pathlib import Path\nfrom typing import Union\n\nimport pandas as pd\n\nfrom freesurfer_statistics.cortical_stats.format import SpecialHeaders\nfrom freesurfer_statistics.freesurfer_stats import FreesurferStats\n\n\nclass CorticalStats(FreesurferStats):\n #: Headers structure\n HEADERS_END = \"Measure\"\n\n #: Special headers\n SPECIAL_HEADERS = SpecialHeaders\n\n def __init__(self, stats_file: Union[Path, str]) -> None:\n super().__init__(stats_file)\n\n def parse_whole_brain_measurements(\n self,\n ) -> pd.DataFrame:\n \"\"\"\n Parse whole brain measurements from Freesurfer's .stats file.\n\n Returns\n -------\n pd.DataFrame\n Whole brain measurements.\n \"\"\"\n data = pd.DataFrame(columns=[\"index\", \"description\", \"unit\", \"value\"])\n for i, line in enumerate(self._get_wholebrain_measures()):\n _, col, description, val, unit = [j.strip() for j in line.split(\",\")]\n data.loc[i, [\"index\", \"description\", \"unit\"]] = [\n col,\n description,\n unit,\n ]\n data.loc[i, \"value\"] = float(val)\n return data\n\n def _get_wholebrain_measures(self) -> list:\n \"\"\"\n Read stats file's measures\n\n Returns\n -------\n list\n A list of the measures from the stats file.\n \"\"\"\n measures = []\n for line in self.lines:\n line = self._read_header_line(line)\n if line.startswith(self.HEADERS_END):\n measures.append(line.replace(self.HEADERS_END, \"\").strip())\n return measures\n\n @property\n def whole_brain_measurements(self) -> pd.DataFrame:\n \"\"\"\n Get whole brain measurements.\n\n Returns\n -------\n pd.DataFrame\n Whole brain measurements.\n \"\"\"\n return self.parse_whole_brain_measurements()\n","repo_name":"GalKepler/freesurfer-statistics","sub_path":"src/freesurfer_statistics/cortical_stats/cortical_stats.py","file_name":"cortical_stats.py","file_ext":"py","file_size_in_byte":1910,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"6"}
+{"seq_id":"19875983852","text":"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport time\n\nimport numpy as np\nimport pandas as pd\nimport time\nimport gc\nimport random\nimport sklearn\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import cross_val_score, GridSearchCV, cross_validate, train_test_split\nfrom sklearn.metrics import accuracy_score, classification_report\nfrom sklearn.svm import SVC\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.preprocessing import StandardScaler, normalize\nfrom sklearn.decomposition import PCA\nfrom sklearn.impute import SimpleImputer\nfrom sklearn import tree\nfrom sklearn.metrics import plot_roc_curve\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom sklearn.cluster import KMeans\nfrom kneed import KneeLocator\nimport seaborn as sb\nfrom sklearn.metrics import silhouette_score\nfrom sklearn import manifold\nfrom sklearn.mixture import GaussianMixture\nfrom sklearn.decomposition import FastICA\nfrom sklearn.mixture import GaussianMixture\nfrom sklearn.random_projection import GaussianRandomProjection\nfrom sklearn.decomposition import KernelPCA\nfrom sklearn.model_selection import ShuffleSplit\nfrom sklearn.model_selection import learning_curve\n\n\nclass Data():\n\n def dataAllocation(self, path):\n # Separate out the x_data and y_data and return each\n # args: string path for .csv file\n # return: pandas dataframe, pandas series\n # -------------------------------\n # ADD CODE HERE\n df = pd.read_csv(path)\n xcols = ['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8']\n ycol = ['y']\n x_data = df[xcols]\n y_data = df[ycol]\n# print(y_data[y_data.y == 1].shape[0])\n # print(df.shape[0])\n # -------------------------------\n return x_data, y_data.values.ravel()\n\n def processed_data_Allocation(self, path):\n # Read the processed dataset\n # -------------------------------\n df = pd.read_csv(path)\n xcols = [\"age\",\"education\",\"default\",\"housing\",\"loan\",\"contact\",\"month\",\"day_of_week\",\"campaign\",\"previous\",\"poutcome\",\"emp.var.rate\",\"cons.price.idx\",\"cons.conf.idx\",\"euribor3m\",\"nr.employed\",\"job_blue-collar\",\"job_entrepreneur\",\"job_housemaid\",\"job_management\",\"job_retired\",\"job_self-employed\",\"job_services\",\"job_student\",\"job_technician\",\"job_unemployed\",\"marital_married\",\"marital_single\"]\n ycol = ['y']\n x_data = df[xcols]\n y_data = df[ycol]\n\n return x_data, y_data.values.ravel()\n\n def trainSets(self, x_data, y_data):\n # Split 70% of the data into training and 30% into test sets. Call them x_train, x_test, y_train and y_test.\n # Use the train_test_split method in sklearn with the parameter 'shuffle' set to true and the 'random_state' set to 614.\n # args: pandas dataframe, pandas dataframe\n # return: pandas dataframe, pandas dataframe, pandas series, pandas series\n # -------------------------------\n # ADD CODE HERE\n x_train, x_test, y_train, y_test = train_test_split(\n x_data, y_data, test_size=0.2, shuffle=True, random_state=614)\n # -------------------------------\n return x_train, x_test, y_train, y_test\n\n def dataPreProcess(self, x_train, x_test):\n # Pre-process the data to standardize it, otherwise the grid search will take much longer.\n # args: pandas dataframe, pandas dataframe\n # return: pandas dataframe, pandas dataframe\n # -------------------------------\n # ADD CODE HERE\n scaler = StandardScaler()\n scaler.fit(x_train)\n scaled_x_train = scaler.transform(x_train)\n scaled_x_test = scaler.transform(x_test)\n # -------------------------------\n return scaled_x_train, scaled_x_test\n\n\n_, axes = plt.subplots(1, 5)\n##################### Bank marketing data #############################\ndataset = Data()\n#data = 'data/bank_marketing.csv'\n#x_data, y_data = dataset.processed_data_Allocation(data)\ndata = 'data/pima-indians-diabetes.csv'\nx_data, y_data = dataset.dataAllocation(data)\n#x_train, x_test, y_train, y_test = dataset.trainSets(x_data, y_data)\n#x_train_scaled, x_test_scaled = dataset.dataPreProcess(x_train, x_test)\nscaler = StandardScaler()\nscaler.fit(x_data)\nx_data = scaler.transform(x_data)\n\ncv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)\nestimator = MLPClassifier(\n hidden_layer_sizes=(3), activation='logistic', solver='sgd', learning_rate_init=0.1, max_iter=10000, random_state=0)\n\n# Original data\nstart = time.time()\ntrain_sizes, train_scores, test_scores, fit_times, _ = \\\n learning_curve(estimator, x_data, y_data, cv=cv, n_jobs=4,\n train_sizes=np.linspace(.1, 1.0, 5),return_times=True)\nend = time.time()\nprint(\"Original time\", end-start)\ntrain_scores_mean = np.mean(train_scores, axis=1)\ntrain_scores_std = np.std(train_scores, axis=1)\ntest_scores_mean = np.mean(test_scores, axis=1)\ntest_scores_std = np.std(test_scores, axis=1)\nfit_times_mean = np.mean(fit_times, axis=1)\nfit_times_std = np.std(fit_times, axis=1)\n\naxes[0].set_title(\"NN - original data\")\naxes[0].set_xlabel(\"Training examples\")\naxes[0].set_ylabel(\"Score\")\n\naxes[0].set_xlabel(\"Numer of training samples\")\naxes[0].set_ylabel(\"Score\")\naxes[0].grid()\naxes[0].fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\naxes[0].fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1,\n color=\"g\")\naxes[0].plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label=\"Training score\")\naxes[0].plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation score\")\naxes[0].legend(loc=\"best\")\naxes[0].set_ylim(0.6,1.0)\naxes[0].set_title(\"Original data\")\n\n# PCA\npca = PCA(n_components=6)\npca.fit(x_data)\nx_data_reduced = pca.transform(x_data)\nstart = time.time()\ntrain_sizes, train_scores, test_scores, fit_times, _ = \\\n learning_curve(estimator, x_data_reduced, y_data, cv=cv, n_jobs=4,\n train_sizes=np.linspace(.1, 1.0, 5),return_times=True)\nend = time.time()\nprint(\"PCA time\", end-start)\ntrain_scores_mean = np.mean(train_scores, axis=1)\ntrain_scores_std = np.std(train_scores, axis=1)\ntest_scores_mean = np.mean(test_scores, axis=1)\ntest_scores_std = np.std(test_scores, axis=1)\nfit_times_mean = np.mean(fit_times, axis=1)\nfit_times_std = np.std(fit_times, axis=1)\n\naxes[1].set_title(\"NN - PCA data\")\naxes[1].set_xlabel(\"Training examples\")\naxes[1].set_ylabel(\"Score\")\n\naxes[1].set_xlabel(\"Numer of training samples\")\naxes[1].set_ylabel(\"Score\")\naxes[1].grid()\naxes[1].fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\naxes[1].fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1,\n color=\"g\")\naxes[1].plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label=\"Training score\")\naxes[1].plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation score\")\naxes[1].legend(loc=\"best\")\naxes[1].set_ylim(0.6,1.0)\naxes[1].set_title(\"PCA\")\n\n# ICA\nica = FastICA(n_components=6)\nx_data_reduced = ica.fit_transform(x_data)\nstart = time.time()\ntrain_sizes, train_scores, test_scores, fit_times, _ = \\\n learning_curve(estimator, x_data_reduced, y_data, cv=cv, n_jobs=4,\n train_sizes=np.linspace(.1, 1.0, 5),return_times=True)\nend = time.time()\nprint(\"ICA time\", end-start)\ntrain_scores_mean = np.mean(train_scores, axis=1)\ntrain_scores_std = np.std(train_scores, axis=1)\ntest_scores_mean = np.mean(test_scores, axis=1)\ntest_scores_std = np.std(test_scores, axis=1)\nfit_times_mean = np.mean(fit_times, axis=1)\nfit_times_std = np.std(fit_times, axis=1)\n\naxes[2].set_title(\"NN ICA data\")\naxes[2].set_xlabel(\"Training examples\")\naxes[2].set_ylabel(\"Score\")\n\naxes[2].set_xlabel(\"Numer of training samples\")\naxes[2].set_ylabel(\"Score\")\naxes[2].grid()\naxes[2].fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\naxes[2].fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1,\n color=\"g\")\naxes[2].plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label=\"Training score\")\naxes[2].plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation score\")\naxes[2].legend(loc=\"best\")\naxes[2].set_ylim(0.6,1.0)\naxes[2].set_title(\"ICA\")\n\n# RP\nrng = np.random.RandomState(42)\nrp = GaussianRandomProjection(n_components=6, random_state=rng)\nx_data_reduced = rp.fit_transform(x_data)\nstart = time.time()\ntrain_sizes, train_scores, test_scores, fit_times, _ = \\\n learning_curve(estimator, x_data_reduced, y_data, cv=cv, n_jobs=4,\n train_sizes=np.linspace(.1, 1.0, 5),return_times=True)\nend = time.time()\nprint(\"RP time\", end-start)\ntrain_scores_mean = np.mean(train_scores, axis=1)\ntrain_scores_std = np.std(train_scores, axis=1)\ntest_scores_mean = np.mean(test_scores, axis=1)\ntest_scores_std = np.std(test_scores, axis=1)\nfit_times_mean = np.mean(fit_times, axis=1)\nfit_times_std = np.std(fit_times, axis=1)\n\naxes[3].set_title(\"NN RP data\")\naxes[3].set_xlabel(\"Training examples\")\naxes[3].set_ylabel(\"Score\")\n\naxes[3].set_xlabel(\"Numer of training samples\")\naxes[3].set_ylabel(\"Score\")\naxes[3].grid()\naxes[3].fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\naxes[3].fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1,\n color=\"g\")\naxes[3].plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label=\"Training score\")\naxes[3].plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation score\")\naxes[3].legend(loc=\"best\")\naxes[3].set_ylim(0.6,1.0)\naxes[3].set_title(\"RP\")\n\n# KPCA\nkpca = KernelPCA(n_components = 6, kernel='poly', fit_inverse_transform=True)\nkpca.fit(x_data)\nx_data_reduced = kpca.transform(x_data)\nstart = time.time()\ntrain_sizes, train_scores, test_scores, fit_times, _ = \\\n learning_curve(estimator, x_data_reduced, y_data, cv=cv, n_jobs=4,\n train_sizes=np.linspace(.1, 1.0, 5),return_times=True)\nend = time.time()\nprint(\"KPCA time\", end-start)\ntrain_scores_mean = np.mean(train_scores, axis=1)\ntrain_scores_std = np.std(train_scores, axis=1)\ntest_scores_mean = np.mean(test_scores, axis=1)\ntest_scores_std = np.std(test_scores, axis=1)\nfit_times_mean = np.mean(fit_times, axis=1)\nfit_times_std = np.std(fit_times, axis=1)\n\naxes[4].set_xlabel(\"Training examples\")\naxes[4].set_ylabel(\"Score\")\n\naxes[4].set_xlabel(\"Numer of training samples\")\naxes[4].set_ylabel(\"Score\")\naxes[4].grid()\naxes[4].fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\naxes[4].fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1,\n color=\"g\")\naxes[4].plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n label=\"Training score\")\naxes[4].plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation score\")\naxes[4].legend(loc=\"best\")\naxes[4].set_ylim(0.6,1.0)\naxes[4].set_title(\"KPCA\")\n\n\"\"\" \n# EM with DS1 - original data\nem_kwargs = {'covariance_type': 'full', 'n_init':10, 'max_iter':100, 'random_state':42, 'init_params':'random'}\nem = GaussianMixture(n_components=5, **em_kwargs)\nlabel = em.fit(x_train_scaled).predict(x_train_scaled)\n\ntsne_transform = manifold.TSNE(n_components=2, perplexity=100, init='pca', random_state=42)\nfeature2D_DS1 = tsne_transform.fit_transform(x_train_scaled)\naxs[1, 0].scatter(feature2D_DS1[:,0], feature2D_DS1[:,1], c=label, cmap=plt.cm.Spectral, s=5)\naxs[1, 0].set_title('EM - original - DS2')\n\n# EM with DS1 - PCA reduced data\nem_kwargs = {'covariance_type': 'full', 'n_init':10, 'max_iter':100, 'random_state':42, 'init_params':'random'}\npca = PCA(n_components=6)\npca.fit(x_train_scaled)\nx_train_scaled_reduced = pca.transform(x_train_scaled)\nem = GaussianMixture(n_components=6, **em_kwargs)\nlabel = em.fit(x_train_scaled_reduced).predict(x_train_scaled_reduced)\n\ntsne_transform = manifold.TSNE(n_components=2, perplexity=100, init='pca', random_state=42)\nfeature2D_DS1 = tsne_transform.fit_transform(x_train_scaled)\naxs[1, 1].scatter(feature2D_DS1[:,0], feature2D_DS1[:,1], c=label, cmap=plt.cm.Spectral, s=5)\naxs[1, 1].set_title('EM - PCA - DS2')\n\n# EM with DS1 - ICA reduced data\nem_kwargs = {'covariance_type': 'full', 'n_init':10, 'max_iter':100, 'random_state':42, 'init_params':'random'}\nica = FastICA(n_components=6)\nx_train_scaled_reduced = ica.fit_transform(x_train_scaled)\nem = GaussianMixture(n_components=6, **em_kwargs)\nlabel = em.fit(x_train_scaled_reduced).predict(x_train_scaled_reduced)\n\ntsne_transform = manifold.TSNE(n_components=2, perplexity=100, init='pca', random_state=42)\nfeature2D_DS1 = tsne_transform.fit_transform(x_train_scaled)\naxs[1, 2].scatter(feature2D_DS1[:,0], feature2D_DS1[:,1], c=label, cmap=plt.cm.Spectral, s=5)\naxs[1, 2].set_title('EM - ICA - DS2')\n\n# EM with DS1 - RP reduced data\nem_kwargs = {'covariance_type': 'full', 'n_init':10, 'max_iter':100, 'random_state':42, 'init_params':'random'}\nrng = np.random.RandomState(42)\nrp = GaussianRandomProjection(n_components=6, random_state=rng)\nx_train_scaled_reduced = rp.fit_transform(x_train_scaled)\nem = GaussianMixture(n_components=9, **em_kwargs)\nlabel = em.fit(x_train_scaled_reduced).predict(x_train_scaled_reduced)\n\ntsne_transform = manifold.TSNE(n_components=2, perplexity=100, init='pca', random_state=42)\nfeature2D_DS1 = tsne_transform.fit_transform(x_train_scaled)\naxs[1, 3].scatter(feature2D_DS1[:,0], feature2D_DS1[:,1], c=label, cmap=plt.cm.Spectral, s=5)\naxs[1, 3].set_title('EM - RP - DS2')\n\n# EM with DS1 - KPCA reduced data\nkmeans_kwargs = {'init': 'random', 'n_init':10, 'max_iter':100, 'random_state':42, 'algorithm':'full',}\nkpca = KernelPCA(n_components = 6, kernel='poly', fit_inverse_transform=True)\nkpca.fit(x_train_scaled)\nx_train_scaled_reduced = kpca.transform(x_train_scaled)\nem = GaussianMixture(n_components=9, **em_kwargs)\nlabel = em.fit(x_train_scaled_reduced).predict(x_train_scaled_reduced)\n\ntsne_transform = manifold.TSNE(n_components=2, perplexity=100, init='pca', random_state=42)\nfeature2D_DS1 = tsne_transform.fit_transform(x_train_scaled)\naxs[1, 4].scatter(feature2D_DS1[:,0], feature2D_DS1[:,1], c=label, cmap=plt.cm.Spectral, s=5)\naxs[1, 4].set_title('EM - KPCA - DS2')\n\ntsne_transform = manifold.TSNE(n_components=2, perplexity=100, init='pca', random_state=42)\nfeature2D_DS1 = tsne_transform.fit_transform(x_train_scaled)\naxs[1, 5].scatter(feature2D_DS1[:,0], feature2D_DS1[:,1], c=y_train, cmap=plt.cm.Spectral, s=5)\naxs[1, 5].set_title('True Label - DS2') \"\"\"\n\nplt.show()","repo_name":"RuizeHu/Gatech_CS_7641_UnsupervisedLearning","sub_path":"code/NN_Reduced.py","file_name":"NN_Reduced.py","file_ext":"py","file_size_in_byte":15629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"5001055837","text":"from django import template\nfrom accounts.models import UserProfile\nregister = template.Library()\n@register.inclusion_tag('list_users.html',takes_context=True)\ndef list_users(context, profiles):\n request = context['request']\n media_url=context['MEDIA_URL']\n user=request.user\n args={}\n args['profiles']=profiles\n args['user']=user\n args['MEDIA_URL']=media_url\n return args\n\n@register.inclusion_tag('top_users.html')\ndef top_users():\n profiles=UserProfile.objects.all().order_by('-rank')[:10]\n args={}\n args['profiles']=profiles\n return args\n","repo_name":"duonghau/hoidap","sub_path":"accounts/templatetags/user_template.py","file_name":"user_template.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"137570743","text":"# Escribir una función mas_larga() que tome una lista de palabras y devuelva la más larga.\n\ndef mas_larga(lista):\n mayor = 0\n for i in range(0, len(lista)):\n if len(lista[i]) > mayor:\n mayor = len(lista[i])\n palabra = lista[i]\n return palabra\n\npalabras = ['Azul', 'Verde', 'Blanco', 'Amarillo']\n\nassert(mas_larga(palabras) == 'Amarillo')\n","repo_name":"solchusalin/frro-utn-soporte2019-05","sub_path":"practico_01/ejercicio-10.py","file_name":"ejercicio-10.py","file_ext":"py","file_size_in_byte":378,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"3951920799","text":"from django.shortcuts import render,HttpResponse,redirect\r\nfrom .forms import BookingForm\r\nfrom django.http import HttpResponseRedirect\r\nfrom .models import Booking\r\nfrom django.contrib import messages\r\n\r\n# Create your views here.\r\ndef carbooking(request):\r\n form = BookingForm()\r\n return render(request, 'car/form.html',{'form': form})\r\n\r\n\r\ndef car_booking_submission(request):\r\n print(\"Hello form is submitted\")\r\n sapno = request.POST[\"sapno\"]\r\n your_name = request.POST[\"your_name\"]\r\n #mobileNumber = request.POST[\"mobileNumber\"]\r\n guestsapno=request.POST[\"guestsapno\"]\r\n email=request.POST[\"email\"]\r\n time=request.POST[\"time\"]\r\n #mobilenumber=request.POST[\"mobilenumber\"]\r\n booking = Booking(sapno=sapno, your_name=your_name,guestsapno=guestsapno,email=email,time=time)\r\n booking.save()\r\n messages.success(request, 'Form submission successful')\r\n return render(request, 'car/form.html')\r\n\r\n\r\n\r\n\r\n'''def get_name(request):\r\n # if this is a POST request we need to process the form data\r\n if request.method == 'POST':\r\n # create a form instance and populate it with data from the request:\r\n form = BookingForm(request.POST)\r\n # check whether it's valid:\r\n if form.is_valid():\r\n requestedBy = form.save(commit=False)\r\n requestedBy.user = request.user\r\n requestedBy.save()\r\n # process the data in form.cleaned_data as required\r\n # ...\r\n # redirect to a new URL:\r\n return redirect('form:form')\r\n #return HttpResponseRedirect('/thanks/')\r\n\r\n # if a GET (or any other method) we'll create a blank form\r\n else:\r\n form = BookingForm()\r\n\r\n return render(request, 'car/form.html', {'form': form})'''\r\n\r\n\r\n","repo_name":"rachnasoundatti/Rental-Car-Booking","sub_path":"car/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"9785874511","text":"from pathlib import Path\nimport os\nimport cv2\nimport argparse\nimport albumentations.augmentations.functional as F\n\n\nINPUT_DIM = 668\nIMG_DIM = 2000 + 134 * 2\nSLICE_LEN = 400\n# N = 5 * 5\nPADDING = 134\n\n\ndef crop_images(img_list, seg_list, imag_path, seg_path):\n \"\"\"\n Function that crops the image into N = 2000 / SLICE_LEN tiles\n\n :param img_list: list of the images to crop\n :param seg_list: list of the segmentations to crop\n :param imag_path: path where to save the cropped images\n :param seg_path: path where to save the cropped segmentations\n :return:\n \"\"\"\n for idx, (img_path, mask_path) in enumerate(zip(img_list, seg_list)):\n image = cv2.imread(str(img_path))\n image = F.pad(image, min_height=IMG_DIM, min_width=IMG_DIM)\n\n mask = cv2.imread(str(mask_path))\n mask = F.pad(mask, min_height=IMG_DIM, min_width=IMG_DIM)\n\n tile = 0\n for i in range(0, 2000, SLICE_LEN):\n for j in range(0, 2000, SLICE_LEN):\n tile += 1\n cv2.imwrite(os.path.join(imag_path, f\"{idx:04}_{tile:02}.png\"),\n image[i:i + INPUT_DIM, j:j + INPUT_DIM])\n cv2.imwrite(os.path.join(seg_path, f\"{idx:04}_{tile:02}.png\"),\n mask[i:i + INPUT_DIM, j:j + INPUT_DIM])\n\n\ndef make_dataset(in_path, out_path):\n \"\"\"\n Take the original dataset and creates the cropped version of the original dataset\n\n :param in_path: path of the original dataset\n :param out_path: path for the cropped dataset\n :return:\n \"\"\"\n # Training Set\n train_mask_path = os.path.join(out_path, \"Train/masks\")\n train_imag_path = os.path.join(out_path, \"Train/images\")\n\n Path(train_mask_path).mkdir(parents=True, exist_ok=True)\n Path(train_imag_path).mkdir(parents=True, exist_ok=True)\n\n patients = sorted(Path(in_path).glob(\"Train/[!.]*/[!.]*/\"))\n dataset_descr = \"patient_id, patient, cancer_type, num_imgs\\n\"\n print(\"Processing Train Set\")\n for patient_id, patient in enumerate(patients):\n imgs = sorted(Path(patient).glob(\"*/[!*seg*]*.png\"))\n segs = sorted(Path(patient).glob(\"*/*seg*.png\"))\n\n print(str(patient).split(\"/\")[-2], \"\\t\", str(patient).split(\"/\")[-1])\n Path(os.path.join(train_imag_path, f\"{patient_id}\"))\\\n .mkdir(parents=True, exist_ok=True)\n Path(os.path.join(train_mask_path, f\"{patient_id}\"))\\\n .mkdir(parents=True, exist_ok=True)\n\n dataset_descr += f'{patient_id}, {str(patient).split(\"/\")[-1]}, {str(patient).split(\"/\")[-2]}, {str(len(imgs))}\\n'\n crop_images(imgs, segs,\n os.path.join(train_imag_path, f\"{patient_id}\"),\n os.path.join(train_mask_path, f\"{patient_id}\"))\n\n print(dataset_descr)\n with open(os.path.join(out_path, \"train_descr.csv\"), \"w+\") as f:\n f.write(dataset_descr)\n\n # Test Set\n test_mask_path = os.path.join(out_path, \"Test/masks\")\n test_imag_path = os.path.join(out_path, \"Test/images\")\n\n Path(test_mask_path).mkdir(parents=True, exist_ok=True)\n Path(test_imag_path).mkdir(parents=True, exist_ok=True)\n\n cancer_list = sorted(Path(in_path).glob(\"Test/[!.]*/\"))\n dataset_descr = \"patient_id, patient, cancer_type, num_imgs\\n\"\n print(\"Proessing Test Set\")\n for cancer_id, cancer in enumerate(cancer_list):\n imgs = sorted(Path(cancer).glob(\"[!*seg*]*.png\"))\n segs = sorted(Path(cancer).glob(\"*seg*.png\"))\n Path(os.path.join(test_imag_path, f\"{cancer_id}\")) \\\n .mkdir(parents=True, exist_ok=True)\n Path(os.path.join(test_mask_path, f\"{cancer_id}\")) \\\n .mkdir(parents=True, exist_ok=True)\n\n print(str(cancer).split(\"/\")[-1])\n dataset_descr += f'{cancer_id}, ,{str(cancer).split(\"/\")[-1]}, {str(len(imgs))}\\n'\n crop_images(imgs, segs,\n os.path.join(test_imag_path, f\"{cancer_id}\"),\n os.path.join(test_mask_path, f\"{cancer_id}\"))\n\n print(dataset_descr)\n with open(os.path.join(out_path, \"test_descr.csv\"), \"w+\") as f:\n f.write(dataset_descr)\n\n\n# in_path = './drive/MyDrive/Bioinformatics/vascular_segmentation/'\n# out_path = './drive/MyDrive/Bioinformatics/dataset'\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Organize the vascular_segmentation\\\n dataset into image/mask directories')\n parser.add_argument('in_path', type=str, help='input path')\n parser.add_argument('out_path', type=str, help='output path')\n\n args = parser.parse_args()\n make_dataset(args.in_path, args.out_path)\n","repo_name":"rm-wu/bioinfo_project","sub_path":"dataset/makeDataset.py","file_name":"makeDataset.py","file_ext":"py","file_size_in_byte":4586,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"3663891743","text":"# To plot the bar graph of detected log events\nimport matplotlib.pyplot as plt; plt.rcdefaults()\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom random import randint as rnd\nimport pandas\n\nclass barplotter:\n \"\"\"\n barplotter internally uses the matplotlib.pyplot to plot\n the bar graph\n \"\"\"\n\n def __init__(self,obj,performance1,performance2,title,label1,label2):\n \"\"\"\n ..obj : labels on x axis\n ..perfomance : y axis co-ordinates\n ..title : title for graph\n \"\"\"\n self.obj=obj\n self.performance1=performance1\n self.performance2=performance2\n self.label1=label1\n self.label2=label2\n self.title=title\n\n def plot(self):\n \"\"\"\n ..plots the data stored in barplotter object\n \"\"\"\n index=np.arange(len(self.obj))\n fig,ax=plt.subplots()\n bar_wid=0.35\n opacity=0.5\n\n rects1 = plt.bar(index, self.performance1, bar_wid,\n alpha=opacity,\n color='g',\n label=self.label1)\n\n rects2 = plt.bar(index + bar_wid, self.performance2, bar_wid,\n alpha=opacity,\n color='r',\n label=self.label2)\n\n plt.xticks(index + bar_wid,self.obj)\n plt.title(self.title)\n plt.legend()\n plt.show()\n\nif __name__==\"__main__\":\n obj=('Level 1','Level 2','Level 3','Level 4','Level 5')\n performance0=[]\n performance1=[]\n for i in range(5):\n performance0.append(rnd(1,1000))\n performance1.append(rnd(1,1000))\n bar=barplotter(obj,performance0,performance1,'event hits plot','Distinct','Duplicates')\n bar.plot()\n\n","repo_name":"saurprg/Multi-source-event-coalescing","sub_path":"barplt.py","file_name":"barplt.py","file_ext":"py","file_size_in_byte":1657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"34118040858","text":"from __future__ import print_function\n\nimport os.path\n\nimport json\nfrom typing import get_type_hints\n\nfrom google.auth.transport.requests import Request\nfrom google.oauth2.credentials import Credentials\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom googleapiclient.discovery import build\n\nfrom datetime import *\n\n# If modifying these scopes, delete the file token.json.\nSCOPES = ['https://www.googleapis.com/auth/admin.directory.user', 'https://www.googleapis.com/auth/admin.directory.group', 'https://www.googleapis.com/auth/admin.directory.group.member', 'https://www.googleapis.com/auth/apps.licensing']\n\n\ncreds = None\n# The file token.json stores the user's access and refresh tokens, and is\n# created automatically when the authorization flow completes for the first\n# time.\nif os.path.exists('token.json'):\n creds = Credentials.from_authorized_user_file('token.json', SCOPES)\n# If there are no (valid) credentials available, let the user log in.\nif not creds or not creds.valid:\n if creds and creds.expired and creds.refresh_token:\n creds.refresh(Request())\n else:\n flow = InstalledAppFlow.from_client_secrets_file(\n 'credentials.json', SCOPES)\n creds = flow.run_local_server(port=0)\n # Save the credentials for the next run\n with open('token.json', 'w') as token:\n token.write(creds.to_json())\n\nservice = build('admin', 'directory_v1', credentials=creds)\nlicenseService = build('licensing', 'v1', credentials=creds)\n\n\nproductId = '101031' # https://developers.google.com/admin-sdk/licensing/v1/how-tos/products\nskus = ['1010310002', '1010310003'] # 1010310002 is teacher, 1010310003 is student\ncustomer = 'd118.org'\n\n# get a list of all license assignments for given product and sku, go through each user with that license and check if they are suspended, if so remove the license\ndef removeLicenses(product, sku):\n newToken = ''\n while newToken is not None: # do a while loop while we still have the next page token to get more results with\n licenseResults = licenseService.licenseAssignments().listForProductAndSku(productId=product, skuId=sku, customerId= customer, pageToken=newToken).execute() # get the licenses for the specified product and sku IDs\n newToken = licenseResults.get('nextPageToken')\n # print(licenseResults)\n userLicenses = licenseResults.get('items', []) # get the actual license assignments block out of the overall results\n for user in userLicenses: # go through each user in the license assignments\n try:\n email = user.get('userId') # get the email from result\n userResults = service.users().get(userKey=email).execute() # do a query for their email to get their Google profile info\n if userResults.get('suspended'): # if the suspended flag is true on their account, they should have a license removed\n print(f'ACTION: {email} is suspended and should not have a license, removing!')\n print(f'ACTION: {email} is suspended and should not have a license, removing!', file=log)\n foo = licenseService.licenseAssignments().delete(productId=product, skuId=sku, userId=email).execute() # does the actual removal of the license\n # print(foo) # debug\n else: # debug\n print(f'INFO: {email} is enabled, no changes needed')\n print(f'INFO: {email} is enabled, no changes needed', file=log)\n except Exception as er:\n print(f'ERROR on {user}: {er}')\n print(f'ERROR on {user}: {er}', file=log)\n\n\n\n# main program\nwith open('suspendedLicensesLog.txt', 'w') as log:\n startTime = datetime.now()\n startTime = startTime.strftime('%H:%M:%S')\n print(f'Execution started at {startTime}')\n print(f'Execution started at {startTime}', file=log)\n \n for entry in skus:\n removeLicenses(productId, entry)\n\n endTime = datetime.now()\n endTime = endTime.strftime('%H:%M:%S')\n print(f'Execution ended at {endTime}')\n print(f'Execution ended at {endTime}', file=log)","repo_name":"Philip-Greyson/D118-Google-Groups-Licensing","sub_path":"removeSuspendedLicenses.pyw","file_name":"removeSuspendedLicenses.pyw","file_ext":"pyw","file_size_in_byte":4217,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"41963251054","text":"from django.urls import path\n\nfrom . import views\n\napp_name = 'core'\n\nurlpatterns = [\n path('', views.home, name='home'),\n path('menu/', views.oldMenu, name='menu'),\n path('helper/menu/', views.helperMenu, name='helperMenu'),\n path('help-requests/', views.helpRequests, name='helpRequests'),\n path('help-requests/all/', views.allHelpRequests, name='allHelpRequests'),\n path('help-request/create/', views.createHelpRequest, name='createHelpRequest'),\n path('help-request/edit/', views.editHelpRequest, name='editHelpRequest'),\n path('help-request/delete/', views.deleteHelpRequest, name='deleteHelpRequest'),\n path('help-offer/create/', views.createHelpOffer, name='createHelpOffer'),\n path('help-request/candidates/', views.getCandidates, name='getCandidates'),\n path('help-request/candidate/reject/', views.rejectHelpOffer, name='rejectHelpOffer'),\n path('help-request/candidate/acept/', views.acceptHelpOffer, name='acceptHelpOffer'),\n path('my-offer/delete/', views.deleteHelpOffer, name='deleteHelpOffer'),\n path('my-offer/edit/', views.editHelpOffer, name='editHelpOffer'),\n path('my-offer-help/', views.seeOffer, name='seeOffer'),\n path('my-offers', views.myOffers, name='myOffers'),\n\n]","repo_name":"anapaolacw/virtual-grandparent","sub_path":"core/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1297,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"6"}
+{"seq_id":"17592563791","text":"# Examples and Exercises From 'Computer Graphics using Open GL\" by Hill 2nd Ed\n# all code by George A. Merrill (except where otherwise noted)\n#################################################################################################\n# Example 3_5_2 a hook motif(Turtle Graphics)\n\nfrom Canvas import Canvas\nfrom OpenGL.GLUT import *\n\ncvs = Canvas(640, 480, 'a hook motif(Turtle Graphics)')\n\n\ndef hook(side):\n global cvs\n cvs.forward(3 * side, True)\n cvs.turn(90)\n cvs.forward(side, True)\n cvs.turn(90)\n cvs.forward(side, True)\n cvs.turn(90)\n\n\ndef my_display():\n global cvs\n cvs.set_bc(1.0, 1.0, 1.0)\n cvs.clear_screen()\n cvs.set_color(0, 0, 0)\n cvs.cp = [250 , 350]\n for i in range(4):\n hook(70)\n\n\n# register the callback functions\nglutDisplayFunc(my_display)\nglutMainLoop()\n","repo_name":"arttype1/Computer_graphics_using_openGL","sub_path":"Chapter_03/example_3_5_2.py","file_name":"example_3_5_2.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"20803401653","text":"# The yes/no function. If user responds with \"yes\" the program continues. If they say \"no\", the program will\n# display the instructions.\ndef yes_no(question):\n valid = False\n while not valid:\n response = input(question).lower()\n if response == \"yes\" or response == \"y\":\n response = \"yes\"\n return response\n elif response == \"no\" or response ==\"n\":\n response = \"no\"\n return response\n else:\n print(\"Please answer with either yes or no.\")\n\n# The instructions function. This is the function that explains what the instructions are and when to show them.\ndef instructions():\n print(\"***** How to Play *****\")\n print(\"\")\n return instructions\n\nplayed_before = yes_no(\"Have you played the game before? \")\n\nif played_before == \"no\":\n instructions()\nelse:\n print(\"Continue program.\")\n","repo_name":"gaskinaimee/Assessment_Quiz","sub_path":"yes_no_v2.py","file_name":"yes_no_v2.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"36414956538","text":"\nclass Solution:\n def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool:\n numparent = [0]*numCourses\n next1 = defaultdict(set)\n \n for course,pre in prerequisites:\n numparent[course] += 1\n next1[pre].add(course)\n \n que = deque()\n for i in range(numCourses):\n if numparent[i] == 0:\n que.append(i)\n \n count = 0\n \n while que:\n for i in range(len(que)):\n temp = que.popleft()\n for course in next1[temp]:\n numparent[course] -= 1\n if numparent[course] == 0:\n que.append(course)\n count += 1\n \n return count == numCourses\n \n ","repo_name":"eyosiasbitsu/Competitive-programming-A2SV","sub_path":"After BootCamp/week1/207-course-schedule/207-course-schedule.py","file_name":"207-course-schedule.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"6"}
+{"seq_id":"12933290178","text":"from datetime import datetime\n\nfrom hypothesis.extra.dateutil import timezones\nfrom hypothesis.strategies import composite, datetimes, lists, text\n\nfrom swh.model.hypothesis_strategies import origins as new_origin_strategy\nfrom swh.model.hypothesis_strategies import persons as new_person_strategy\nfrom swh.model.hypothesis_strategies import sha1_git\nfrom swh.model.hypothesis_strategies import snapshots as new_snapshot\nfrom swh.model.model import Revision, RevisionType, TimestampWithTimezone\n\n# Module dedicated to the generation of input data for tests through\n# the use of hypothesis.\n\n\ndef new_origin():\n \"\"\"Hypothesis strategy returning a random origin not ingested\n into the test archive.\n \"\"\"\n return new_origin_strategy()\n\n\ndef visit_dates(nb_dates=None):\n \"\"\"Hypothesis strategy returning a list of visit dates.\"\"\"\n min_size = nb_dates if nb_dates else 2\n max_size = nb_dates if nb_dates else 8\n return lists(\n datetimes(\n min_value=datetime(2015, 1, 1, 0, 0),\n max_value=datetime.now(),\n timezones=timezones(),\n ),\n min_size=min_size,\n max_size=max_size,\n unique=True,\n ).map(sorted)\n\n\ndef new_person():\n \"\"\"Hypothesis strategy returning random raw swh person data.\"\"\"\n return new_person_strategy()\n\n\n@composite\ndef new_swh_date(draw):\n \"\"\"Hypothesis strategy returning random raw swh date data.\"\"\"\n timestamp = draw(\n datetimes(min_value=datetime(2015, 1, 1, 0, 0), max_value=datetime.now()).map(\n lambda d: int(d.timestamp())\n )\n )\n return {\n \"timestamp\": timestamp,\n \"offset\": 0,\n \"negative_utc\": False,\n }\n\n\n@composite\ndef new_revision(draw):\n \"\"\"Hypothesis strategy returning random raw swh revision data\n not ingested into the test archive.\n \"\"\"\n return Revision(\n directory=draw(sha1_git()),\n author=draw(new_person()),\n committer=draw(new_person()),\n message=draw(text(min_size=20, max_size=100).map(lambda t: t.encode())),\n date=TimestampWithTimezone.from_datetime(draw(new_swh_date())),\n committer_date=TimestampWithTimezone.from_datetime(draw(new_swh_date())),\n synthetic=False,\n type=RevisionType.GIT,\n )\n\n\ndef new_snapshots(nb_snapshots=None):\n min_size = nb_snapshots if nb_snapshots else 2\n max_size = nb_snapshots if nb_snapshots else 8\n return lists(\n new_snapshot(min_size=2, max_size=10, only_objects=True),\n min_size=min_size,\n max_size=max_size,\n )\n","repo_name":"SoftwareHeritage/swh-web","sub_path":"swh/web/tests/strategies.py","file_name":"strategies.py","file_ext":"py","file_size_in_byte":2548,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"6"}
+{"seq_id":"21691835598","text":"from django.shortcuts import render,redirect\r\nfrom .models import Product,Order_detail\r\nfrom .forms import ProductForm,UserRegistrationForm\r\nfrom django.contrib.auth.decorators import login_required\r\n\r\n# Create your views here.\r\n@login_required(login_url='login')\r\ndef index(request):\r\n products = Product.objects.all()\r\n return render(request,'myapp/index.html',{'products':products})\r\n\r\n\r\ndef detail(request,id):\r\n product = Product.objects.get(id=id)\r\n return render(request,'myapp/detail.html',{'product':product})\r\n\r\n\r\ndef checkout(request,id):\r\n product = Product.objects.get(id=id)\r\n order = Order_detail()\r\n order.product = product\r\n order.amount = int(product.price)\r\n product = Product.objects.get(id=order.product.id)\r\n product.total_sales_amount = product.total_sales_amount + int(product.price)\r\n product.total_sales = product.total_sales + 1\r\n order.save()\r\n\r\n return render(request,'myapp/checkout.html',{'checkout':checkout})\r\n\r\n\r\ndef create_product(request):\r\n if request.method == 'POST':\r\n product_form = ProductForm(request.POST,request.FILES)\r\n if product_form.is_valid():\r\n new_form = product_form.save(commit=False)\r\n new_form.seller = request.user\r\n new_form.save()\r\n return redirect('index')\r\n\r\n product_form = ProductForm()\r\n return render(request,'myapp/create_product.html',{'product_form':product_form})\r\n\r\n\r\n\r\ndef edit_product(request,id):\r\n product = Product.objects.get(id=id)\r\n if product.seller != request.user:\r\n return redirect('invalid')\r\n\r\n product_form = ProductForm(request.POST or None,request.FILES or None,instance=product)\r\n if request.method == 'POST':\r\n if product_form.is_valid():\r\n product_form.save()\r\n return redirect('index')\r\n \r\n return render(request,'myapp/edit_product.html',{'product_form':product_form,'product':product})\r\n\r\n\r\ndef delete_product(request,id):\r\n product = Product.objects.get(id=id)\r\n if product.seller != request.user:\r\n return redirect('invalid')\r\n \r\n if request.method == 'POST':\r\n product.delete()\r\n return redirect('index')\r\n \r\n return render(request,'myapp/delete_product.html',{'product':product})\r\n\r\n\r\ndef dashboard(request):\r\n products = Product.objects.filter(seller=request.user)\r\n return render(request,'myapp/dashboard.html',{'products':products})\r\n\r\n\r\n\r\ndef register(request):\r\n if request.method == 'POST':\r\n user_form = UserRegistrationForm(request.POST)\r\n new_user = user_form.save(commit=False)\r\n new_user.set_password(user_form.cleaned_data['password'])\r\n new_user.save()\r\n return redirect('index')\r\n user_form = UserRegistrationForm()\r\n return render(request,'myapp/register.html',{'user_form':user_form})\r\n\r\n\r\ndef invalid(request):\r\n return render(request,'myapp/invalid.html')\r\n\r\n\r\n\r\ndef my_purchases(request):\r\n orders = Order_detail.objects.all()\r\n return render(request,'myapp/purchases.html',{'orders':orders})\r\n\r\n\r\n\r\ndef sales(request):\r\n return render(request,'myapp/sales.html')","repo_name":"subhankar333/digital_marketplace","sub_path":"myapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3144,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"72046935823","text":"# Classification (U)\n\n\"\"\"Program: run_program.py\n\n Description: Unit testing of run_program in mysql_rep_change.py.\n\n Usage:\n test/unit/mysql_rep_change/run_program.py\n\n Arguments:\n\n\"\"\"\n\n# Libraries and Global Variables\n\n# Standard\nimport sys\nimport os\nimport unittest\nimport mock\n\n# Local\nsys.path.append(os.getcwd())\nimport lib.gen_libs as gen_libs\nimport mysql_rep_change\nimport version\n\n__version__ = version.__version__\n\n\nclass ArgParser(object):\n\n \"\"\"Class: ArgParser\n\n Description: Class stub holder for gen_class.ArgParser class.\n\n Methods:\n __init__\n get_args_keys\n get_val\n\n \"\"\"\n\n def __init__(self):\n\n \"\"\"Method: __init__\n\n Description: Class initialization.\n\n Arguments:\n\n \"\"\"\n\n self.cmdline = None\n self.args_array = dict()\n\n def get_args_keys(self):\n\n \"\"\"Method: get_args_keys\n\n Description: Method stub holder for gen_class.ArgParser.get_args_keys.\n\n Arguments:\n\n \"\"\"\n\n return list(self.args_array.keys())\n\n def get_val(self, skey, def_val=None):\n\n \"\"\"Method: get_val\n\n Description: Method stub holder for gen_class.ArgParser.get_val.\n\n Arguments:\n\n \"\"\"\n\n return self.args_array.get(skey, def_val)\n\n\nclass MasterRep(object):\n\n \"\"\"Class: MasterRep\n\n Description: Class stub holder for mysql_class.MasterRep class.\n\n Methods:\n __init__\n connect\n\n \"\"\"\n\n def __init__(self):\n\n \"\"\"Method: __init__\n\n Description: Class initialization.\n\n Arguments:\n\n \"\"\"\n\n self.name = \"Master_Server_Name\"\n self.read_only = \"OFF\"\n self.server_id = 10\n self.sql_user = \"User\"\n self.sql_pass = None\n self.machine = \"Linux\"\n self.host = \"HostName\"\n self.port = 3306\n self.defaults_file = None\n self.rep_user = \"RepUser\"\n self.rep_japd = None\n self.extra_def_file = \"FileName\"\n self.conn = True\n self.conn_msg = None\n\n\nclass SlaveRep(object):\n\n \"\"\"Class: SlaveRep\n\n Description: Class stub holder for mysql_class.SlaveRep class.\n\n Methods:\n __init__\n connect\n\n \"\"\"\n\n def __init__(self):\n\n \"\"\"Method: __init__\n\n Description: Class initialization.\n\n Arguments:\n\n \"\"\"\n\n self.name = \"Server_Name\"\n self.read_only = \"OFF\"\n self.server_id = 10\n self.sql_user = \"User\"\n self.sql_pass = None\n self.machine = \"Linux\"\n self.host = \"HostName\"\n self.port = 3306\n self.defaults_file = None\n self.rep_user = \"RepUser\"\n self.rep_japd = None\n self.extra_def_file = \"FileName\"\n self.conn = True\n self.conn_msg = None\n\n\ndef move_slave(master, slave, **kwargs):\n\n \"\"\"Function: move_slave\n\n Description: move_slave function.\n\n Arguments:\n (input) master -> Master instance.\n (input) slave -> Slave instance.\n\n \"\"\"\n\n status = False\n msg = None\n\n if master and slave and kwargs.get(\"args\"):\n status = False\n\n return status, msg\n\n\ndef move_slave_up(master, slave, **kwargs):\n\n \"\"\"Function: move_slave_up\n\n Description: move_slave_up function.\n\n Arguments:\n (input) master -> Master instance.\n (input) slave -> Slave instance.\n\n \"\"\"\n\n status = True\n msg = \"Error Message\"\n\n if master and slave and kwargs.get(\"args\"):\n status = True\n\n return status, msg\n\n\nclass UnitTest(unittest.TestCase):\n\n \"\"\"Class: UnitTest\n\n Description: Class which is a representation of a unit testing.\n\n Methods:\n setUp\n test_no_master_slave_conn\n test_two_no_slave_conn\n test_one_no_slave_conn\n test_no_master_conn\n test_with_option_fails\n test_with_multiple_options\n test_with_option\n test_no_option\n\n \"\"\"\n\n def setUp(self):\n\n \"\"\"Function: setUp\n\n Description: Initialization for unit testing.\n\n Arguments:\n\n \"\"\"\n\n self.err_msg = \"Connection Error\"\n self.args = ArgParser()\n self.args2 = ArgParser()\n self.args3 = ArgParser()\n self.args4 = ArgParser()\n self.args.args_array = {\"-m\": \"master\", \"-n\": \"slaves\"}\n self.args2.args_array = {\"-m\": \"master\", \"-n\": \"slaves\", \"-M\": True}\n self.args3.args_array = {\n \"-m\": \"master\", \"-n\": \"slaves\", \"-M\": True, \"-R\": True}\n self.args4.args_array = {\"-m\": \"master\", \"-n\": \"slaves\", \"-S\": True}\n self.func_names = {\"-M\": move_slave, \"-R\": move_slave,\n \"-S\": move_slave_up}\n self.master = MasterRep()\n self.master2 = MasterRep()\n self.master2.conn = False\n self.master2.conn_msg = self.err_msg\n self.slave = SlaveRep()\n self.slave2 = SlaveRep()\n self.slave3 = SlaveRep()\n self.slave3.conn = False\n self.slave3.conn_msg = self.err_msg\n self.slave4 = SlaveRep()\n self.slave4.conn = False\n self.slave4.conn_msg = self.err_msg\n self.slave_list = [self.slave, self.slave2]\n self.slave_list2 = [self.slave, self.slave2, self.slave3]\n self.slave_list3 = [self.slave, self.slave2, self.slave3, self.slave4]\n\n @mock.patch(\"mysql_rep_change.mysql_libs.disconnect\",\n mock.Mock(return_value=True))\n @mock.patch(\"mysql_rep_change.create_instances\")\n def test_no_master_slave_conn(self, mock_create):\n\n \"\"\"Function: test_no_master_slave_conn\n\n Description: Test with no master and slave connection.\n\n Arguments:\n\n \"\"\"\n\n mock_create.return_value = (self.master2, self.slave_list2)\n\n with gen_libs.no_std_out():\n self.assertFalse(\n mysql_rep_change.run_program(self.args2, self.func_names))\n\n @mock.patch(\"mysql_rep_change.mysql_libs.disconnect\",\n mock.Mock(return_value=True))\n @mock.patch(\"mysql_rep_change.create_instances\")\n def test_two_no_slave_conn(self, mock_create):\n\n \"\"\"Function: test_two_no_slave_conn\n\n Description: Test with two no slave connections.\n\n Arguments:\n\n \"\"\"\n\n mock_create.return_value = (self.master, self.slave_list3)\n\n with gen_libs.no_std_out():\n self.assertFalse(\n mysql_rep_change.run_program(self.args2, self.func_names))\n\n @mock.patch(\"mysql_rep_change.mysql_libs.disconnect\",\n mock.Mock(return_value=True))\n @mock.patch(\"mysql_rep_change.create_instances\")\n def test_one_no_slave_conn(self, mock_create):\n\n \"\"\"Function: test_one_no_slave_conn\n\n Description: Test with one no slave connection.\n\n Arguments:\n\n \"\"\"\n\n mock_create.return_value = (self.master, self.slave_list2)\n\n with gen_libs.no_std_out():\n self.assertFalse(\n mysql_rep_change.run_program(self.args2, self.func_names))\n\n @mock.patch(\"mysql_rep_change.mysql_libs.disconnect\",\n mock.Mock(return_value=True))\n @mock.patch(\"mysql_rep_change.create_instances\")\n def test_no_master_conn(self, mock_create):\n\n \"\"\"Function: test_no_master_conn\n\n Description: Test with no master connection.\n\n Arguments:\n\n \"\"\"\n\n mock_create.return_value = (self.master2, self.slave_list)\n\n with gen_libs.no_std_out():\n self.assertFalse(\n mysql_rep_change.run_program(self.args2, self.func_names))\n\n @mock.patch(\"mysql_rep_change.mysql_libs.disconnect\",\n mock.Mock(return_value=True))\n @mock.patch(\"mysql_rep_change.create_instances\")\n def test_with_option_fails(self, mock_create):\n\n \"\"\"Function: test_with_option_fails\n\n Description: Test with option failing.\n\n Arguments:\n\n \"\"\"\n\n mock_create.return_value = (self.master, self.slave_list)\n\n with gen_libs.no_std_out():\n self.assertFalse(\n mysql_rep_change.run_program(self.args4, self.func_names))\n\n @mock.patch(\"mysql_rep_change.mysql_libs.disconnect\",\n mock.Mock(return_value=True))\n @mock.patch(\"mysql_rep_change.create_instances\")\n def test_with_multiple_options(self, mock_create):\n\n \"\"\"Function: test_with_multiple_options\n\n Description: Test with multiple options selected.\n\n Arguments:\n\n \"\"\"\n\n mock_create.return_value = (self.master, self.slave_list)\n\n self.assertFalse(\n mysql_rep_change.run_program(self.args3, self.func_names))\n\n @mock.patch(\"mysql_rep_change.mysql_libs.disconnect\",\n mock.Mock(return_value=True))\n @mock.patch(\"mysql_rep_change.create_instances\")\n def test_with_option(self, mock_create):\n\n \"\"\"Function: test_with_option\n\n Description: Test with option selected.\n\n Arguments:\n\n \"\"\"\n\n mock_create.return_value = (self.master, self.slave_list)\n\n self.assertFalse(\n mysql_rep_change.run_program(self.args2, self.func_names))\n\n @mock.patch(\"mysql_rep_change.mysql_libs.disconnect\",\n mock.Mock(return_value=True))\n @mock.patch(\"mysql_rep_change.create_instances\")\n def test_no_option(self, mock_create):\n\n \"\"\"Function: test_no_option\n\n Description: Test with no option selected.\n\n Arguments:\n\n \"\"\"\n\n mock_create.return_value = (self.master, self.slave_list)\n\n self.assertFalse(\n mysql_rep_change.run_program(self.args, self.func_names))\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"mjpernot/mysql-change","sub_path":"test/unit/mysql_rep_change/run_program.py","file_name":"run_program.py","file_ext":"py","file_size_in_byte":9619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"13685376149","text":"import json\nimport imp\nimport os\nimport shutil\nimport subprocess\n\nfrom google.protobuf import text_format\n\n########################################################################\n\n# Should be more robust criterion, may be lib should provide some env var\ndef model_img_w(model_path): return 300 if '-300' in model_path else 512\ndef model_img_h(model_path): return 300 if '-300' in model_path else 512\n\n########################################################################\n\ndef run_command(args_list, log_file=None):\n cmd = ' '.join(args_list)\n print(cmd)\n if log_file:\n if os.path.isfile(log_file):\n os.remove(log_file)\n cmd = cmd + ' 2>&1|tee ' + log_file\n subprocess.call(cmd, shell=True)\n\n########################################################################\n\n# Despite of CK_ENV_LIB_CAFFE_PYTHON is in PYTHONPATH, we can't import caffe_pb2\n# because of caffe.proto is not a package (at least in package:lib-caffe-ssd-cpu)\ndef import_caffe_pb2():\n caffe_python = os.getenv('CK_ENV_LIB_CAFFE_PYTHON')\n module_path = os.path.join(caffe_python, 'caffe', 'proto', 'caffe_pb2.py')\n return imp.load_source('caffe_pb2', module_path)\n\ncaffe_pb2 = import_caffe_pb2()\n\n########################################################################\n\ndef read_json(file_name):\n with open(file_name, 'r') as f:\n return json.load(f)\n\ndef write_json(file_name, obj):\n with open(file_name, 'w') as f:\n json.dump(obj, f, indent=2, sort_keys=True)\n\n########################################################################\n\ndef read_text(file_name):\n with open(file_name, 'r') as f:\n return f.read()\n\ndef write_text(file_name, txt):\n with open(file_name, 'w') as f:\n f.write(txt)\n\n########################################################################\n\ndef read_prototxt(file_name):\n proto = caffe_pb2.NetParameter()\n txt = read_text(file_name) \n text_format.Merge(txt, proto)\n return proto\n\ndef write_prototxt(file_name, proto):\n txt = text_format.MessageToString(proto)\n write_text(file_name, txt)\n\n########################################################################\n\ndef rmdir(dir_name):\n if os.path.isdir(dir_name):\n shutil.rmtree(dir_name)\n\ndef mkdir(dir_name):\n if os.path.isdir(dir_name):\n shutil.rmtree(dir_name)\n os.mkdir(dir_name)\n\n########################################################################\n\ndef prepare_test_prototxt(src_file, dst_file, lmdb_dir, batch_size,\n label_map_file, name_size_file, image_count):\n '''\n Prepares test.prototxt file replacing ck-variables to their real values\n and substituting real paths to lmdb, label map, etc.\n '''\n net = caffe_pb2.NetParameter()\n txt = read_text(src_file)\n txt = txt.replace('$#val_batch_size#$', str(batch_size))\n txt = txt.replace('$#num_test_image#$', str(image_count))\n text_format.Merge(txt, net)\n for layer in net.layer:\n if layer.name == 'data':\n layer.data_param.source = lmdb_dir\n layer.data_param.batch_size = batch_size\n layer.annotated_data_param.label_map_file = label_map_file\n elif layer.name == 'detection_out':\n p = layer.detection_output_param.save_output_param\n p.label_map_file = label_map_file\n p.name_size_file = name_size_file\n p.num_test_image = image_count\n elif layer.name == 'detection_eval':\n layer.detection_evaluate_param.name_size_file = name_size_file\n write_prototxt(dst_file, net)\n","repo_name":"ctuning/ck-request-asplos18-caffe-intel","sub_path":"program/convert-ssd-to-i8/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3412,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"47"}
+{"seq_id":"72241236302","text":"class Node:\n def __init__(self,value) -> None:\n self.val = value\n self.left = None\n self.right = None\n\nclass Solution:\n # use mergesort technique \n def minHeightBST(self, array):\n low = 0\n high = len(array)-1\n\n def generateTree(array,low,high):\n if(high 0:\n #CROSSOVER POPULATION \n print(\"Crossing population: \",counter) \n self.next_population.clear()\n list(map(crossover_function,self.population))\n self.population.clear()\n self.population = self.next_population\n crossing_time = datetime.now()\n print('Duration: {}'.format(crossing_time - start_time))\n #FITNESS FUNCTION\n print(\"Applying Fitness Function: \",counter) \n array_fitness = self.fitness_function()\n #SORT BY BEST WINNER\n sorted_fitness = sorted(array_fitness,key=lambda x: x[0],reverse=True)\n #CLEAR SORTED ARRAY\n sorted_fitness = list(map(clear_function,sorted_fitness))\n end_time = datetime.now()\n print('Duration: {}'.format(end_time - crossing_time))\n #Limit next generation\n print(\"Limiting population: \",counter) \n self.population =sorted_fitness[:limiter]\n counter-=1\n end_time = datetime.now()\n print('Total Time: {}'.format(end_time - start_time))\n return self.get_winner_from_generation()\n\n\n \n \n","repo_name":"ProyectoII-IA/Connect4","sub_path":"model/genetics/genetics_algorithm_by_agent.py","file_name":"genetics_algorithm_by_agent.py","file_ext":"py","file_size_in_byte":4348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"24376113972","text":"#! /usr/local/bin python3\n# -*- coding: utf-8 -*-\n\"\"\"\n\tDescription:\n\t\tConsider a sequence u where u is defined as follows:\n\n\t\tThe number u(0) = 1 is the first one in u.\n\t\tFor each x in u, then y = 2 * x + 1 and z = 3 * x + 1 must be in u too.\n\t\tThere are no other numbers in u.\n\t\tEx: u = [1, 3, 4, 7, 9, 10, 13, 15, 19, 21, 22, 27, ...]\n\n\t\t1 gives 3 and 4, then 3 gives 7 and 10, 4 gives 9 and 13, \n\t\tthen 7 gives 15 and 22 and so on...\n\n\t\tTask:\n\n\t\tGiven parameter n the function dbl_linear (or dblLinear...) \n\t\treturns the element u(n) of the ordered (with <) sequence u.\n\n\tExample:\n\n\t\tdbl_linear(10) should return 22\n\n Args:\n n (int)\n\n Author:\n \tyangxw163@gmail.com\n\n Created:\n \t13 March 2017\n\"\"\"\t\nfrom collections import deque\ndef dbl_linear(n):\n\tqueue = [1]\n\tx , y = 0, 0\n\twhile len(queue) < n + 1:\n\t\txvalue = 2 * queue[x] + 1\n\t\tyvalue = 3 * queue[y] + 1\n\t\tif xvalue < yvalue:\n\t\t\tif queue[-1] == xvalue:\n\t\t\t\tx += 1\n\t\t\t\tcontinue\n\t\t\tqueue.append(xvalue)\n\t\t\tx += 1\n\t\telse:\n\t\t\tif queue[-1] == yvalue:\n\t\t\t\ty += 1\n\t\t\t\tcontinue\n\t\t\tqueue.append(yvalue)\n\t\t\ty += 1\n\n\treturn queue[n]\n\nif __name__ == '__main__':\n\tu = [1, 3, 4, 7, 9, 10, 13, 15, 19, 21, 22, 27]\n\t# print(dbl_linear(10))\n\t# print(next(gen_linear()))\n\tprint(dbl_linear(10))\n","repo_name":"lam268/python-coding-everyday","sub_path":"[4 kyu] Twice linear.py","file_name":"[4 kyu] Twice linear.py","file_ext":"py","file_size_in_byte":1253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"30689990574","text":"import sys\r\ninput = sys.stdin.readline\r\nfrom collections import defaultdict, deque\r\nimport math\r\n\r\nn=int(input())\r\nqueue = deque(enumerate(map(int,input().split()), start=1))\r\nans=[]\r\nwhile queue:\r\n hola= queue.popleft()\r\n ans.append(hola[0])\r\n val = hola[1]\r\n if val>0:\r\n queue.rotate(-(val-1))\r\n else:\r\n queue.rotate(-val)\r\nprint(*ans)","repo_name":"brian6484/AlgorithmGrind","sub_path":"백준/Silver/2346. 풍선 터뜨리기/풍선 터뜨리기.py","file_name":"풍선 터뜨리기.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"28493075722","text":"# -*- coding: utf-8 -*-\r\nfrom google.cloud import speech_v1\r\nfrom google.cloud.speech_v1 import enums\r\nimport io\r\nimport os\r\n\r\nos.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"file.json\"\r\n\r\n\r\ndef sample_long_running_recognize(local_file_path):\r\n client = speech_v1.SpeechClient()\r\n\r\n language_code = \"uk-UA\"\r\n\r\n sample_rate_hertz = 16000\r\n\r\n encoding = enums.RecognitionConfig.AudioEncoding.OGG_OPUS\r\n config = {\r\n \"language_code\": language_code,\r\n \"sample_rate_hertz\": sample_rate_hertz,\r\n \"encoding\": encoding,\r\n }\r\n with io.open(local_file_path, \"rb\") as f:\r\n content = f.read()\r\n audio = {\"content\": content}\r\n\r\n operation = client.long_running_recognize(config, audio)\r\n\r\n print(u\"Waiting for operation to complete...\")\r\n response = operation.result()\r\n my_array = []\r\n for result in response.results:\r\n alternative = result.alternatives[0]\r\n my_array.append(u\"{}\".format(alternative.transcript))\r\n return my_array\r\n\r\n","repo_name":"Korzhak/VoiceITStepBot","sub_path":"voice_recognition.py","file_name":"voice_recognition.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"23912344663","text":"from flask import request, jsonify\nfrom flask_restful import Resource\nfrom flask_jwt_extended import jwt_required\nfrom app import flask_bcrypt\n\nfrom ..database import get_db_connection, close_db_connection, commit_and_close_db_connection\nfrom ..database.user_db import get_users, create_users, get_user_details_from_email, delete_users\n\nfrom ..models.user import User\nfrom ..schemas.user_schema import UserSchema\n\nfrom ..exceptions import InvalidUserPayload, UserExistsException\n\nuser_schema = UserSchema()\n\nfrom ..decorators.security import admin_required\n\nclass UsersApi(Resource):\n decorators = [jwt_required(), admin_required()]\n\n def get(self):\n conn = get_db_connection()\n users = get_users(conn)\n close_db_connection(conn)\n return users\n \n def post(self):\n errors = user_schema.validate(request.json)\n print('errors', str(errors))\n\n if errors:\n raise InvalidUserPayload(errors, 400)\n \n conn = get_db_connection()\n\n existing_user = get_user_details_from_email(conn, request.json.get('email'))\n\n if existing_user is not None:\n raise UserExistsException(f\"User with email id: [{request.json.get('email')}] already exists\", 400)\n \n print('existing_user',existing_user)\n\n user = User.from_json(request.json)\n user.password = flask_bcrypt.generate_password_hash(user.password).decode('utf-8')\n create_users(conn, user)\n users = get_users(conn)\n commit_and_close_db_connection(conn)\n return users, 201\n\n\n def delete(self):\n conn= get_db_connection()\n delete_users(conn)\n\n return {'message': \"All users deleted...\"}\n\n ","repo_name":"Once31/User-Management-System","sub_path":"app/api/users_api.py","file_name":"users_api.py","file_ext":"py","file_size_in_byte":1715,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"28295637716","text":"from modulos import *\n\n# ------- Función de aplicación -------\ndef main():\n \n while True: \n # Visualizaciones iniciales \n figura()\n visConfig()\n\n # Validación Número de Jugadores\n while True:\n try:\n global numJugadores # se define globalmente para luego ser llamado\n numJugadores = numeroJugadores()\n if 2 < numJugadores < 9:\n break\n else:\n print(Fore.RED+'\\n MENSAJE: Debe ingresar 3 jugadores mínimo, máximo 8\\n')\n except ValueError:\n print(Fore.RED+'\\n ERROR: Debe ingresar un número válido\\n')\n \n # Lista de Jugadores\n global listaJugadores\n listaJugadores = nombreJugadores(numJugadores)\n \n\n # Validación Pista \n while True:\n try:\n pistas()\n global pista \n pista = int(input('\\n Ingrese la opción: '))\n if 0 < pista < 5:\n break\n else:\n print('MENSAJE: Debe ingresar un número mayor a 0 y menor a 5')\n except ValueError:\n print(Fore.RED+'\\n ERROR: Debe ingresar un número válido\\n')\n \n \n # Iniciación del Juego\n while True:\n try:\n datos(numJugadores,pista,listaJugadores)\n enter = input(\"\\n Presione\"+Fore.MAGENTA+\" ENTER \"+Fore.WHITE+\"para continuar\\n\")\n if True:\n break\n except ValueError:\n print('ERROR')\n \n # Movimiento de la Carrera \n while True:\n try:\n pistasKm = [1000,5000,10000,21000]\n global listaPodio\n listaPodio = carrera(numJugadores,listaJugadores,pistasKm,pista)\n if True:\n break\n except ValueError:\n print('ERROR')\n\n # Podio de la Carrera\n while True:\n try:\n calificacion(listaPodio)\n #enter = input(\"\\n Presione\"+Fore.RED+\" ENTER \"+Fore.WHITE+\"para Finalizar el Juego\")\n if True:\n break\n except ValueError:\n print(\"ERROR\")\n\n # Persistencia de Resultados\n while True:\n try:\n guardarJuego(numJugadores,listaPodio)\n #enter = input(\"\\n Presione\"+Fore.RED+\" ENTER \"+Fore.WHITE+\"para Finalizar el Juego\")\n if True:\n break\n except ValueError:\n print(\"ERROR\")\n \n # Consulta al usuario para jugar nuevamente o no\n otroJuego = input('\\n ¿Desea jugar nuevamente? S/N: ')\n if otroJuego =='S' or otroJuego =='s':\n True\n if otroJuego =='N' or otroJuego =='n':\n break \n\n# ------- Ejecución de aplicación -------\nmain()\n","repo_name":"sergio9610/Cars","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2946,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"8676171218","text":"num1 = int(input(\"Enter the first value: \"))\r\nif ((num1 < 1) or (num1 > 6)):\r\n print(\"Invalid value\")\r\n exit()\r\n\r\nnum2 = int(input(\"Enter the second value: \"))\r\nif ((num2 < 1) or (num2 > 6)):\r\n print(\"Invalid value\")\r\n exit()\r\n\r\nsum = num1 + num2\r\n\r\nif(sum == 7 or sum == 11):\r\n print(\"Congratulations! You win!\")\r\nelif(sum == 2 or sum == 3 or sum == 12):\r\n print(\"Better luck next time.\")\r\nelse:\r\n print(f\"You have {sum} points.\")\r\n\r\n","repo_name":"mgbaybay/TCS-Data-Analytics","sub_path":"scripts/craps_Baybay.py","file_name":"craps_Baybay.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"3168839983","text":"\"\"\"In this Kata, you will be given an array of strings and your task is to remove all consecutive duplicate letters from each string in the array.\n\nFor example:\n\ndup([\"abracadabra\",\"allottee\",\"assessee\"]) = [\"abracadabra\",\"alote\",\"asese\"].\ndup([\"kelless\",\"keenness\"]) = [\"keles\",\"kenes\"].\nStrings will be alphabet characters only. Don't worry about lower and upper case. See test cases for more examples.\n\n\"\"\"\n\n\ndef dup(arry):\n result = []\n word = ''\n\n for item in arry:\n word = item[0]\n for i in range(1, len(item)):\n\n if item[i - 1] != item[i]:\n word += item[i]\n\n result.append(word)\n return result\n\n\ndup([\"ccooddddddewwwaaaaarrrrsssss\",\"piccaninny\",\"hubbubbubboo\"]) # ==> ['codewars','picaniny','hubububo']\ndup([\"abracadabra\",\"allottee\",\"assessee\"]) # ['abracadabra','alote','asese']\n","repo_name":"melissa3000/Code-Challenges","sub_path":"dup.py","file_name":"dup.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"37193431409","text":"import logging\nimport inspect\nimport traceback\nimport json\n\ndef get_function_parameters_and_values():\n func_name = traceback.extract_stack(None, 2)[0][2]\n frame = inspect.currentframe().f_back\n args, _, _, values = inspect.getargvalues(frame)\n func_args_str = \", \".join([f\"{i}={str(values[i])}\" for i in args])\n args_as_dict = values\n return func_name, args_as_dict, f\"{func_name}({func_args_str})\"\n\n # args = locals()\n # keys = list(args.keys())\n # keys.reverse()\n # func_name = inspect.currentframe().f_code.co_name\n # args_str = \", \".join([f\"{key}={args[key]}\" for key in keys])\n # logging.info(f\"{func_name}({args_str})\")\n\ndef calc_n(x, y, counter):\n func_name, args_as_dict, args_as_str = get_function_parameters_and_values()\n logging.info(f\"{args_as_str}\")\n with open(f'{func_name}_input.json', 'w') as fp:\n json.dump(args_as_dict, fp)\n\n if counter==0:\n logging.warn(f\"counter == {counter}\")\n r = 0\n for index in range(0, counter):\n r = r + (index+1)*(x+y)\n logging.debug(f\"#{index}, r={r}\")\n logging.info(f\"calc_o({x}, {y}, {counter})={r}\")\n return r\n\n\nif __name__ == \"__main__\":\n logging.basicConfig(level=logging.DEBUG)\n calc_n(1, 2, 3)\n calc_n(1, 2, 0)","repo_name":"constructor-igor/PythonDev","sub_path":"04_func_args/04_func_args_n.py","file_name":"04_func_args_n.py","file_ext":"py","file_size_in_byte":1259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"70022569742","text":"from typing import List\nclass Solution:\n def maxAbsValExpr(self, arr1: List[int], arr2: List[int]) -> int:\n arr = [[] for _ in range(4)]\n for i in range(4):\n if i == 0:\n for j in range(len(arr1)):\n arr[i].append(arr1[j] + arr2[j] + j)\n elif i == 1:\n for j in range(len(arr1)):\n arr[i].append(arr1[j] - arr2[j] + j)\n elif i == 2:\n for j in range(len(arr1)):\n arr[i].append(-arr1[j] + arr2[j] + j)\n else:\n for j in range(len(arr1)):\n arr[i].append(-arr1[j] - arr2[j] + j)\n _maxv = 0\n for i in range(4):\n _maxv = max(_maxv, max(arr[i]) - min(arr[i]))\n return _maxv","repo_name":"chuzhumin98/PythonForMillions","sub_path":"LeetCode/simple-problems/1131.py","file_name":"1131.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"47"}
+{"seq_id":"18333104356","text":"import cv2\r\nfrom new_motion_interp import predict_frame_uni\r\nimport numpy as np\r\n\r\ninput_video = '../videos/stefan.mp4'\r\ncap = cv2.VideoCapture(input_video)\r\nfourcc = cv2.VideoWriter_fourcc(*'DIVX')\r\nret, frame = cap.read()\r\nout = cv2.VideoWriter('ballet.avi', fourcc, 30, (frame.shape[1], frame.shape[0]))\r\nstarted = False\r\ni = 0\r\nout.write(frame)\r\n\r\nwhile(ret):\r\n print(\"Frame: \", i)\r\n i += 1\r\n frame1 = frame\r\n ret, frame = cap.read()\r\n frame2 = frame\r\n if ret==True:\r\n interp = predict_frame_uni(frame1, frame2, 3, 5)\r\n out.write(interp.astype(np.uint8))\r\n\r\n out.write(frame2)\r\n\r\ncap.release()\r\nout.release()\r\n\r\n\r\n\r\n\r\n","repo_name":"kelleherdes/mcfi","sub_path":"code/fruc.py","file_name":"fruc.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"1744032995","text":"import os\nimport random\nfrom jigsaw_generator_info import Widgets, Core, Gui, Svg, PYSIDE_VERSION\n\nfrom ui_jigsaw_generator_main_window import Ui_JigsawGenerator\nfrom jigsaw_generator_core import JigsawGeneratorCore\nfrom smoothed_path import smoothed_path\n\nQMainWindow, QFileDialog, QInputDialog = Widgets.QMainWindow, Widgets.QFileDialog, Widgets.QInputDialog\nQColorDialog, QApplication, QStyleFactory = Widgets.QColorDialog, Widgets.QApplication, Widgets.QStyleFactory\nQShortcut = Gui.QShortcut if int(PYSIDE_VERSION) >= 6 else Widgets.QShortcut\nQPixmap, QPainter, QPainterPath = Gui.QPixmap, Gui.QPainter, Gui.QPainterPath\nQColor, QPalette, QKeySequence = Gui.QColor, Gui.QPalette, Gui.QKeySequence\nQt, QPointF, QSize, QRect = Core.Qt, Core.QPointF, Core.QSize, Core.QRect\nQSvgGenerator = Svg.QSvgGenerator\n# from PySide2.QtWidgets import QMainWindow, QFileDialog, QInputDialog\n# from PySide2.QtWidgets import QColorDialog, QApplication, QStyleFactory\n# from PySide2.QtWidgets import QShortcut\n# from PySide2.QtGui import QPixmap, QPainter, QPainterPath, QColor, QPalette\n# from PySide2.QtGui import QKeySequence\n# from PySide2.QtCore import Qt, QPointF, QSize, QRect\n# from PySide2.QtSvg import QSvgGenerator\n\n\nclass JigsawGenerator(QMainWindow):\n \"\"\"\n Summary.\n\n More details\n\n Attributes\n ----------\n ui: Ui_JigsawGenerator\n Instance of the class Ui_JigsawGenerator.\n\n x: int\n Number of rows of the Jigsaw.\n Input from ui.spinBoxX.value()\n\n y: int\n Number of rows of the Jigsaw.\n Input from ui.spinBoxY.value()\n\n core: JigsawGeneratorCore\n Instance of the JigsawGeneratorCore class.\n\n pen_color: QColor\n Color of the pen used to draw the image and the SVG\n\n image_path: str\n Absolute path to the image where the jigsaw will be built upon.\n\n cell_width: float\n Float variable that indicates the width of each cell of the jigsaw on the image.\n\n cell_height: float\n Float variable that indicates the height of each cell of the jigsaw on the image.\n \"\"\"\n\n @staticmethod\n def draw_borders(width: int, height: int, painter: QPainter):\n \"\"\"\n Draw a rectangular using the given painter with the dimensions passed as arguments.\n\n When drawing on a QPixmap, pass the dimensions with one decreasing\n one unit, for example:\n ```\n JigsawGenerator.draw_borders(w-1, h-1, painter)\n ```\n\n Parameters\n ----------\n width: int\n The width of the draw.\n\n height: int\n The height of the draw\n\n painter: QPainter\n Element of the QPainter class used to paint the borders.\n \"\"\"\n painter.drawLine(0, 0, 0, height)\n painter.drawLine(0, 0, width, 0)\n painter.drawLine(0, height, width, height)\n painter.drawLine(width, 0, width, height)\n return painter\n\n @staticmethod\n def paint_masculine_border(\n cell_coordinates, where, cell_width, cell_height, patterns, painter, smooth_factor\n ):\n \"\"\"\n Draw the masculine border of one cell.\n\n Parameters\n ----------\n cell_coordinates: List[int]\n Coordinates [x, y] of the cell.\n\n where: JigsawGeneratorCore.WhichBorder\n Indicates which border will be painted.\n\n cell_width: float\n Indicates the width of each cell.\n\n cell_height: float\n Indicates the height of each cell.\n\n patterns: List[str]\n The patterns considered to paint the border.\n Supported \"Triangle\" and \"Square\"\n\n painter: QPainter\n The QPainter element used to paint the borders\n\n smooth_factor: float\n \"\"\"\n\n pattern = random.choice(patterns)\n\n if \"Triangle\" in pattern:\n # C ---*\n # / \\\n # / \\\n # / \\\n # ___________/ \\___________\n # | | | |\n # A B D E\n\n A, B, C, D, E = None, None, None, None, None\n\n # parameters necessary to make the borders different between\n # each other\n b0, b1 = .3, .45\n c0, c1 = .4, .6\n c2, c3 = .15, .25\n d0, d1 = .55, .7\n x0, x1 = -.05, .05\n\n if where == JigsawGeneratorCore.WhichBorder.DOWN:\n y = (cell_coordinates[1] + 1)*cell_height\n x_begin = cell_coordinates[0]*cell_width\n A = QPointF(x_begin, y)\n B = QPointF(x_begin + cell_width*random.uniform(b0, b1),\n y + cell_height*random.uniform(x0, x1))\n C = QPointF(x_begin + cell_width*random.uniform(c0, c1),\n y + cell_height*random.uniform(c2, c3))\n D = QPointF(x_begin + cell_width*random.uniform(d0, d1),\n y + cell_height*random.uniform(x0, x1))\n E = QPointF(x_begin + cell_width, y)\n\n elif where == JigsawGeneratorCore.WhichBorder.UP:\n y = cell_coordinates[1]*cell_height\n x_begin = cell_coordinates[0]*cell_width\n A = QPointF(x_begin, y)\n B = QPointF(x_begin + cell_width*random.uniform(b0, b1),\n y + cell_height*random.uniform(x0, x1))\n C = QPointF(x_begin + cell_width*random.uniform(c0, c1),\n y - cell_height*random.uniform(c2, c3))\n D = QPointF(x_begin + cell_width*random.uniform(d0, d1),\n y + cell_height*random.uniform(x0, x1))\n E = QPointF(x_begin + cell_width, y)\n\n elif where == JigsawGeneratorCore.WhichBorder.LEFT:\n x = cell_coordinates[0]*cell_width\n y_begin = cell_coordinates[1]*cell_height\n A = QPointF(x, y_begin)\n B = QPointF(x + cell_width*random.uniform(x0, x1),\n y_begin + cell_height*random.uniform(b0, b1))\n C = QPointF(x - cell_width*random.uniform(c2, c3),\n y_begin + cell_height*random.uniform(c0, c1))\n D = QPointF(x + cell_width*random.uniform(x0, x1),\n y_begin + cell_height*random.uniform(d0, d1))\n E = QPointF(x, y_begin + cell_height)\n\n elif where == JigsawGeneratorCore.WhichBorder.RIGHT:\n x = (cell_coordinates[0] + 1)*cell_width\n y_begin = cell_coordinates[1]*cell_height\n A = QPointF(x, y_begin)\n B = QPointF(x + cell_width*random.uniform(x0, x1),\n y_begin + cell_height*random.uniform(b0, b1))\n C = QPointF(x + cell_width*random.uniform(c2, c3),\n y_begin + cell_height*random.uniform(c0, c1))\n D = QPointF(x + cell_width*random.uniform(x0, x1),\n y_begin + cell_height*random.uniform(d0, d1))\n E = QPointF(x, y_begin + cell_height)\n\n path = QPainterPath(A)\n\n if \"Rounded\" in pattern:\n smoothed_path(smooth_factor, [A, B, C, D, E], path)\n else:\n path.lineTo(B)\n path.lineTo(C)\n path.lineTo(D)\n path.lineTo(E)\n\n painter.drawPath(path)\n\n elif \"Square\" in pattern:\n # C * ________ * D\n # | |\n # | |\n # | |\n # ___________| |___________\n # | | | |\n # A B E F\n\n A, B, C, D, E, F = None, None, None, None, None, None\n\n # parameters necessary to make the borders different between\n # each other\n b0, b1 = .30, .40\n c0, c1 = .25, .35\n c2, c3 = .15, .25\n d0, d1 = .65, .75\n d2, d3 = c2, c3\n e0, e1 = .60, .70\n x0, x1 = -.05, .05\n\n if where == JigsawGeneratorCore.WhichBorder.DOWN:\n y = (cell_coordinates[1] + 1)*cell_height\n x_begin = cell_coordinates[0]*cell_width\n A = QPointF(x_begin, y)\n B = QPointF(x_begin + cell_width*random.uniform(b0, b1),\n y + cell_height*random.uniform(x0, x1))\n C = QPointF(x_begin + cell_width*random.uniform(c0, c1),\n y + cell_height*random.uniform(c2, c3))\n D = QPointF(x_begin + cell_width*random.uniform(d0, d1),\n y + cell_height*random.uniform(d2, d3))\n E = QPointF(x_begin + cell_width*random.uniform(e0, e1),\n y + cell_height*random.uniform(x0, x1))\n F = QPointF(x_begin + cell_width, y)\n\n elif where == JigsawGeneratorCore.WhichBorder.UP:\n y = cell_coordinates[1]*cell_height\n x_begin = cell_coordinates[0]*cell_width\n A = QPointF(x_begin, y)\n B = QPointF(x_begin + cell_width*random.uniform(b0, b1),\n y + cell_height*random.uniform(x0, x1))\n C = QPointF(x_begin + cell_width*random.uniform(c0, c1),\n y - cell_height*random.uniform(c2, c3))\n D = QPointF(x_begin + cell_width*random.uniform(d0, d1),\n y - cell_height*random.uniform(d2, d3))\n E = QPointF(x_begin + cell_width*random.uniform(e0, e1),\n y + cell_height*random.uniform(x0, x1))\n F = QPointF(x_begin + cell_width, y)\n\n elif where == JigsawGeneratorCore.WhichBorder.LEFT:\n x = cell_coordinates[0]*cell_width\n y_begin = cell_coordinates[1]*cell_height\n A = QPointF(x, y_begin)\n B = QPointF(x + cell_width*random.uniform(x0, x1),\n y_begin + cell_height*random.uniform(b0, b1))\n C = QPointF(x - cell_width*random.uniform(c2, c3),\n y_begin + cell_height*random.uniform(c0, c1))\n D = QPointF(x - cell_width*random.uniform(d2, d3),\n y_begin + cell_height*random.uniform(d0, d1))\n E = QPointF(x + cell_width*random.uniform(x0, x1),\n y_begin + cell_height*random.uniform(e0, e1))\n F = QPointF(x, y_begin + cell_height)\n\n elif where == JigsawGeneratorCore.WhichBorder.RIGHT:\n x = (cell_coordinates[0] + 1)*cell_width\n y_begin = cell_coordinates[1]*cell_height\n A = QPointF(x, y_begin)\n B = QPointF(x + cell_width*random.uniform(x0, x1),\n y_begin + cell_height*random.uniform(b0, b1))\n C = QPointF(x + cell_width*random.uniform(c2, c3),\n y_begin + cell_height*random.uniform(c0, c1))\n D = QPointF(x + cell_width*random.uniform(d2, d3),\n y_begin + cell_height*random.uniform(d0, d1))\n E = QPointF(x + cell_width*random.uniform(x0, x1),\n y_begin + cell_height*random.uniform(e0, e1))\n F = QPointF(x, y_begin + cell_height)\n\n path = QPainterPath(A)\n\n if \"Rounded\" in pattern:\n smoothed_path(smooth_factor, [A, B, C, D, E, F], path)\n else:\n path.lineTo(B)\n path.lineTo(C)\n path.lineTo(D)\n path.lineTo(E)\n path.lineTo(F)\n\n painter.drawPath(path)\n\n # elif \"Circle\" in pattern:\n # A, B, C, D, E, F = None, None, None, None, None, None\n #\n # # parameters necessary to make the borders different between\n # # each other\n # b0, b1 = .3, .45\n # c0, c1 = .15, .25\n # e0, e1 = .55, .7\n # x0, x1 = -.05, .05\n # r0, r1 = (e1 - b0)/2, .5/2\n #\n # if where == JigsawGeneratorCore.WhichBorder.DOWN:\n # y = (cell_coordinates[1] + 1)*cell_height\n # x_begin = cell_coordinates[0]*cell_width\n # A = QPointF(x_begin, y)\n # B = QPointF(x_begin + cell_width*random.uniform(b0, b1),\n # y + cell_height*random.uniform(x0, x1))\n # C = QPointF(x_begin + cell_width*random.uniform(b0, b1),\n # y + cell_height*random.uniform(c0, c1))\n # D = QPointF(x_begin + cell_width*random.uniform(e0, e1),\n # y + cell_height*random.uniform(c0, c1))\n # E = QPointF(x_begin + cell_width*random.uniform(e0, e1),\n # y + cell_height*random.uniform(x0, x1))\n # F = QPointF(x_begin + cell_width, y)\n #\n # elif where == JigsawGeneratorCore.WhichBorder.UP:\n # y = cell_coordinates[1]*cell_height\n # x_begin = cell_coordinates[0]*cell_width\n # A = QPointF(x_begin, y)\n # B = QPointF(x_begin + cell_width*random.uniform(b0, b1),\n # y + cell_height*random.uniform(x0, x1))\n # C = QPointF(x_begin + cell_width*random.uniform(b0, b1),\n # y - cell_height*random.uniform(c0, c1))\n # D = QPointF(x_begin + cell_width*random.uniform(e0, e1),\n # y - cell_height*random.uniform(c0, c1))\n # E = QPointF(x_begin + cell_width*random.uniform(e0, e1),\n # y + cell_height*random.uniform(x0, x1))\n # F = QPointF(x_begin + cell_width, y)\n #\n # elif where == JigsawGeneratorCore.WhichBorder.LEFT:\n # x = cell_coordinates[0]*cell_width\n # y_begin = cell_coordinates[1]*cell_height\n # A = QPointF(x, y_begin)\n # B = QPointF(x + cell_width*random.uniform(x0, x1),\n # y_begin + cell_height*random.uniform(b0, b1))\n # C = QPointF(x - cell_width*random.uniform(c0, c1),\n # y_begin + cell_height*random.uniform(b0, b1))\n # D = QPointF(x - cell_width*random.uniform(c0, c1),\n # y_begin + cell_height*random.uniform(e0, e1))\n # E = QPointF(x + cell_width*random.uniform(x0, x1),\n # y_begin + cell_height*random.uniform(e0, e1))\n # F = QPointF(x, y_begin + cell_height)\n #\n # elif where == JigsawGeneratorCore.WhichBorder.RIGHT:\n # x = (cell_coordinates[0] + 1)*cell_width\n # y_begin = cell_coordinates[1]*cell_height\n # A = QPointF(x, y_begin)\n # B = QPointF(x + cell_width*random.uniform(x0, x1),\n # y_begin + cell_height*random.uniform(b0, b1))\n # C = QPointF(x + cell_width*random.uniform(c0, c1),\n # y_begin + cell_height*random.uniform(b0, b1))\n # D = QPointF(x + cell_width*random.uniform(c0, c1),\n # y_begin + cell_height*random.uniform(e0, e1))\n # E = QPointF(x + cell_width*random.uniform(x0, x1),\n # y_begin + cell_height*random.uniform(e0, e1))\n # F = QPointF(x, y_begin + cell_height)\n #\n # path = QPainterPath(A)\n # path.lineTo(B)\n # path.lineTo(C)\n # # Connect point `C` with point `D` with an arc that has radius between\n # # `r0` and `r1`\n # center = QPointF((C + D)/2)\n # radiusX = random.uniform(r0, r1)*cell_width\n # radiusY = random.uniform(r0, r1)*cell_height\n # rec = QRectF(center.x() - radiusX, center.y() - radiusY, 2*radiusX, 2*radiusY)\n #\n # path.arcTo(rec, 60, 240)\n #\n # path.lineTo(E)\n # path.lineTo(F)\n # painter.drawPath(path)\n\n return painter\n\n def draw_on_pixmap(self):\n \"\"\"\n Draw the jigsaw on the pixmap located on `image_path`.\n\n The pixmap which the jigsaw will be drawn upon uses a QPen of the color\n `pen_color`, with number of rows `x` and number of lines `y`. The output\n is saved on the label `ui.labelImage` when the process is finished.\n\n Parameters\n ----------\n self: JigsawGenerator\n Instance of the own class.\n \"\"\"\n self.load_image(self.image_path)\n pixmap = self.ui.labelImage.pixmap()\n painter = QPainter(pixmap)\n painter.setPen(self.pen_color)\n painter.setRenderHint(QPainter.SmoothPixmapTransform, True)\n painter.setRenderHint(QPainter.Antialiasing, True)\n\n JigsawGenerator.draw_borders(pixmap.width() - 1, pixmap.height() - 1, painter)\n\n patterns = list()\n\n if self.ui.checkBoxTriangleBorders.isChecked():\n patterns.append(\"Triangle\")\n if self.ui.checkBoxTriangleRounded.isChecked():\n patterns.append(\"Triangle Rounded\")\n if self.ui.checkBoxSquaredBorders.isChecked():\n patterns.append(\"Square\")\n if self.ui.checkBoxSquaredRounded.isChecked():\n patterns.append(\"Square Rounded\")\n\n if not list:\n print(\"Select at least one border pattern\")\n painter.end()\n self.ui.labelImage.setPixmap(pixmap)\n return\n\n rounded_factor = self.ui.doubleSpinBoxSmoothFactor.value()\n\n for i in range(self.x):\n for j in range(self.y):\n cell = self.core.get_cell([i, j])\n\n if cell.up == JigsawGeneratorCore.BorderType.MASCULINE:\n JigsawGenerator.paint_masculine_border(\n [i, j], JigsawGeneratorCore.WhichBorder.UP,\n self.cell_width, self.cell_height, patterns, painter, rounded_factor\n )\n if cell.down == JigsawGeneratorCore.BorderType.MASCULINE:\n JigsawGenerator.paint_masculine_border(\n [i, j], JigsawGeneratorCore.WhichBorder.DOWN,\n self.cell_width, self.cell_height, patterns, painter, rounded_factor\n )\n if cell.left == JigsawGeneratorCore.BorderType.MASCULINE:\n JigsawGenerator.paint_masculine_border(\n [i, j], JigsawGeneratorCore.WhichBorder.LEFT,\n self.cell_width, self.cell_height, patterns, painter, rounded_factor\n )\n if cell.right == JigsawGeneratorCore.BorderType.MASCULINE:\n JigsawGenerator.paint_masculine_border(\n [i, j], JigsawGeneratorCore.WhichBorder.RIGHT,\n self.cell_width, self.cell_height, patterns, painter, rounded_factor\n )\n\n painter.end()\n self.ui.labelImage.setPixmap(pixmap)\n\n def draw_on_svg(self, width, height):\n \"\"\"\n Generate a SVG jigsaw of the given width and height.\n\n The SVG which the jigsaw will be drawn upon uses a QPen of the color\n `pen_color`, with number of rows `x` and number of lines `y`.\n\n Parameters\n ----------\n self: JigsawGenerator\n Instance of the own class.\n\n width: int\n Width of the SVG.\n\n height: int\n Hieght of the SVG.\n \"\"\"\n filename, filters = QFileDialog.getSaveFileName(\n parent=self, caption=\"Save Image\", filter=\"SVG (*.svg)\",\n selected_filter=\"output.svg\"\n )\n\n generator = QSvgGenerator()\n generator.setFileName(filename)\n generator.setSize(QSize(width, height))\n generator.setViewBox(QRect(0, 0, width, height))\n\n painter = QPainter(generator)\n painter.setPen(self.pen_color)\n painter.setRenderHint(QPainter.Antialiasing, True)\n\n JigsawGenerator.draw_borders(width, height, painter)\n\n patterns = list()\n\n if self.ui.checkBoxTriangleBorders.isChecked():\n patterns.append(\"Triangle\")\n if self.ui.checkBoxTriangleRounded.isChecked():\n patterns.append(\"Triangle Rounded\")\n if self.ui.checkBoxSquaredBorders.isChecked():\n patterns.append(\"Square\")\n if self.ui.checkBoxSquaredRounded.isChecked():\n patterns.append(\"Square Rounded\")\n\n if not list:\n print(\"Select at least one border pattern\")\n return\n\n rounded_factor = self.ui.doubleSpinBoxSmoothFactor.value()\n\n cell_width = float(width)/self.x\n cell_height = float(height)/self.y\n\n for i in range(self.x):\n for j in range(self.y):\n cell = self.core.get_cell([i, j])\n\n if cell.up == JigsawGeneratorCore.BorderType.MASCULINE:\n JigsawGenerator.paint_masculine_border(\n [i, j], JigsawGeneratorCore.WhichBorder.UP,\n cell_width, cell_height, patterns, painter, rounded_factor\n )\n if cell.down == JigsawGeneratorCore.BorderType.MASCULINE:\n JigsawGenerator.paint_masculine_border(\n [i, j], JigsawGeneratorCore.WhichBorder.DOWN,\n cell_width, cell_height, patterns, painter, rounded_factor\n )\n if cell.left == JigsawGeneratorCore.BorderType.MASCULINE:\n JigsawGenerator.paint_masculine_border(\n [i, j], JigsawGeneratorCore.WhichBorder.LEFT,\n cell_width, cell_height, patterns, painter, rounded_factor\n )\n if cell.right == JigsawGeneratorCore.BorderType.MASCULINE:\n JigsawGenerator.paint_masculine_border(\n [i, j], JigsawGeneratorCore.WhichBorder.RIGHT,\n cell_width, cell_height, patterns, painter, rounded_factor\n )\n\n painter.end()\n\n def SLOT_generate_image(self):\n \"\"\"\n Function called when `ui.pushButtonGenerateImage` is released.\n\n It generates the jigsaw and update the pixmap of `ui.labelImage`\n\n Parameters\n ----------\n self: JigsawGenerator\n Instance of the class\n \"\"\"\n self.x = self.ui.spinBoxX.value()\n self.y = self.ui.spinBoxY.value()\n self.core.set_shape([self.x, self.y])\n self.core.generate_random()\n self.draw_on_pixmap()\n\n def SLOT_generate_svg(self):\n \"\"\"\n Function called when `ui.pushButtonGenerateSvg` is released.\n\n It generates the jigsaw on a SVG file and export it to the\n desired path.\n\n Parameters\n ----------\n self: JigsawGenerator\n Instance of the class\n \"\"\"\n width, ok = QInputDialog.getInt(\n self, \"Width\", \"Set width:\",\n self.ui.labelImage.pixmap().width()\n )\n height, ok = QInputDialog.getInt(\n self, \"Hieght\", \"Set height:\",\n self.ui.labelImage.pixmap().height()\n )\n\n self.draw_on_svg(width, height)\n\n def SLOT_load_image_dialog(self):\n \"\"\"\n Function called when `ui.pushButtonLoadImage` is released.\n\n Create a `QFileDialog` and try to load a new file.\n\n Parameters\n ----------\n self: JigsawGenerator\n Instance of the class\n \"\"\"\n file_path, filter = QFileDialog.getOpenFileName(\n parent=self,\n caption=\"Load Image\",\n filter=\"Images (*.png *.jpg *.jpeg *.gif *.bmp *.svg)\"\n )\n\n if file_path:\n self.load_image(file_path)\n\n def SLOT_save_image_dialog(self):\n \"\"\"\n Function called when `ui.pushButtonSaveImage` is released.\n\n Create a QFileDialog and try to save the generated pixmap\n on the selected file.\n\n Parameters\n ----------\n self: JigsawGenerator\n Instance of the class\n \"\"\"\n\n file_path, filter = QFileDialog.getSaveFileName(\n parent=self,\n caption=\"Save Image as...\",\n filter=\"Images (*.png *.jpg *.jpeg *.gif *.bmp *.svg)\"\n )\n\n if file_path:\n self.save_image(file_path)\n\n def SLOT_select_pen_color_dialog(self):\n \"\"\"\n Function called when ui.pushButtonPenColor is released.\n\n Create a QColorDialog to select the color of the pen and update\n the background of `ui.pushButtonPenColor` when finished.\n\n Parameters\n ----------\n self: JigsawGenerator\n Instance of the class\n \"\"\"\n\n self.pen_color = QColorDialog.getColor(\n self.pen_color, self, \"Select pen color\"\n )\n self.ui.pushButtonPenColor.setStyleSheet(\n \"QPushButton {{ background-color: {} }}\".format(str(self.pen_color.name()))\n )\n\n def load_image(self, image_path):\n \"\"\"\n Try to load the image on the given path.\n\n Returns `True` if succeeded.\n\n Parameters\n ----------\n self: JigsawGenerator\n Instance of the class\n\n image_path: str\n Path to the file\n \"\"\"\n\n pixmap = QPixmap(image_path)\n\n if pixmap.isNull():\n print(\"It was not possible to load the file {}\".format(image_path))\n return False\n\n self.image_path = image_path\n self.ui.labelImage.resize(pixmap.size())\n self.ui.labelImage.setPixmap(pixmap)\n self.cell_width = float(pixmap.width())/self.x\n self.cell_height = float(pixmap.height())/self.y\n return True\n\n def save_image(self, image_path):\n \"\"\"\n Try to save the image on the given path.\n\n Returns `True` if succeeded.\n\n Parameters\n ----------\n self: JigsawGenerator\n Instance of the class\n\n image_path: str\n Path to the file\n \"\"\"\n pixmap = self.ui.labelImage.pixmap()\n\n return pixmap.save(image_path)\n\n def set_application_theme(self, theme_name):\n \"\"\"\n Set the GUI theme with the theme passed\n\n Parameters\n ----------\n self: JigsawGenerator\n Instance of the class\n\n theme_name: str\n Which theme (can be \"Fusion Dark\" and \"Fusion Light\")\n \"\"\"\n if theme_name == \"Fusion Dark\":\n QApplication.instance().setStyle(QStyleFactory.create(\"Fusion\"))\n darkPalette = QPalette()\n darkPalette.setColor(QPalette.BrightText, Qt.red)\n darkPalette.setColor(QPalette.WindowText, Qt.white)\n darkPalette.setColor(QPalette.ToolTipBase, Qt.white)\n darkPalette.setColor(QPalette.ToolTipText, Qt.white)\n darkPalette.setColor(QPalette.Text, Qt.white)\n darkPalette.setColor(QPalette.ButtonText, Qt.white)\n darkPalette.setColor(QPalette.HighlightedText, Qt.black)\n darkPalette.setColor(QPalette.Window, QColor(53, 53, 53))\n darkPalette.setColor(QPalette.Base, QColor(25, 25, 25))\n darkPalette.setColor(QPalette.AlternateBase, QColor(53, 53, 53))\n darkPalette.setColor(QPalette.Button, QColor(53, 53, 53))\n darkPalette.setColor(QPalette.Link, QColor(42, 130, 218))\n darkPalette.setColor(QPalette.Highlight, QColor(42, 130, 218))\n QApplication.instance().setPalette(darkPalette)\n\n elif theme_name == \"Fusion Light\":\n QApplication.instance().setStyle(QStyleFactory.create(\"Fusion\"))\n lightPalette = QPalette()\n lightPalette.setColor(QPalette.BrightText, Qt.cyan)\n lightPalette.setColor(QPalette.WindowText, Qt.black)\n lightPalette.setColor(QPalette.ToolTipBase, Qt.black)\n lightPalette.setColor(QPalette.ToolTipText, Qt.black)\n lightPalette.setColor(QPalette.Text, Qt.black)\n lightPalette.setColor(QPalette.ButtonText, Qt.black)\n lightPalette.setColor(QPalette.HighlightedText, Qt.white)\n lightPalette.setColor(QPalette.Window, QColor(202, 202, 202))\n lightPalette.setColor(QPalette.Base, QColor(228, 228, 228))\n lightPalette.setColor(QPalette.AlternateBase, QColor(202, 202, 202))\n lightPalette.setColor(QPalette.Button, QColor(202, 202, 202))\n lightPalette.setColor(QPalette.Link, QColor(213, 125, 37))\n lightPalette.setColor(QPalette.Highlight, QColor(42, 130, 218))\n\n QApplication.instance().setPalette(lightPalette)\n\n def __init__(self):\n super(JigsawGenerator, self).__init__()\n\n self.ui = Ui_JigsawGenerator()\n self.ui.setupUi(self)\n\n self.ui.pushButtonGenerateImage.released.connect(self.SLOT_generate_image)\n self.ui.pushButtonLoadImage.released.connect(self.SLOT_load_image_dialog)\n self.ui.pushButtonSaveImage.released.connect(self.SLOT_save_image_dialog)\n self.ui.pushButtonGenerateSvg.released.connect(self.SLOT_generate_svg)\n self.ui.pushButtonPenColor.released.connect(self.SLOT_select_pen_color_dialog)\n\n shortcut_close = QShortcut(QKeySequence(Qt.CTRL + Qt.Key_Q), self)\n shortcut_close.activated.connect(self.close)\n\n self.x = self.ui.spinBoxX.value()\n self.y = self.ui.spinBoxY.value()\n self.core = JigsawGeneratorCore([self.x, self.y])\n\n self.pen_color = QColor(Qt.white)\n\n self.load_image(os.path.dirname(os.path.realpath(__file__)) + \"/image_template.png\")\n\n self.SLOT_generate_image()\n\n self.set_application_theme(\"Fusion Dark\")\n","repo_name":"Bollos00/JigsawGenerator","sub_path":"jigsaw_generator/jigsaw_generator.py","file_name":"jigsaw_generator.py","file_ext":"py","file_size_in_byte":30250,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"9191252268","text":"\"\"\"\nRoutes and views for the flask application.\n\"\"\"\n\nfrom datetime import datetime\nfrom flask import render_template, redirect, request, jsonify\nfrom Python_toDoList import app\nfrom Python_toDoList.models import toDoNotFound\nfrom Python_toDoList.models.factory import create_repository\nfrom Python_toDoList.settings import REPOSITORY_NAME, REPOSITORY_SETTINGS\n\nimport json\nrepository = create_repository(REPOSITORY_NAME, REPOSITORY_SETTINGS)\nprint('REPOSITORY_SETTINGS='+str(REPOSITORY_SETTINGS))\n\n\n@app.route('/')\n@app.route('/home')\ndef home():\n \"\"\"Renders the home page.\"\"\"\n return render_template(\n 'index.html',\n title='Home Page',\n year=datetime.now().year,\n )\n\n@app.route('/contact')\ndef contact():\n \"\"\"Renders the contact page.\"\"\"\n return render_template(\n 'contact.html',\n title='Contact',\n year=datetime.now().year,\n message='Your contact page.'\n )\n\n@app.route('/about')\ndef about():\n \"\"\"Renders the about page.\"\"\"\n return render_template(\n 'about.html',\n title='About',\n year=datetime.now().year,\n message='Your application description page.'\n )\n\n@app.route('/seed', methods=['POST'])\ndef seed():\n \"\"\"Seeds the database with sample polls.\"\"\"\n repository.add_sample_todolist()\n return redirect('/todolist')\n\n@app.route('/todolists/all', methods=['GET'])\ndef todolists_all():\n return render_template(\n 'todolists.html',\n title='To Do List',\n year=datetime.now().year,\n todolists = repository.get_todolists(),\n message='test'\n )\n\n\n@app.route('/todolist', methods=['GET'])\ndef todolist_name():\n print('Here is def todolist_name(): function')\n if 'todolist_name' in request.args:\n todolist_name = request.args['todolist_name']\n print('todolist_name='+todolist_name)\n else:\n return 'Error:No ToDoList provided. Please specify a ToDoList name.'\n \n list = repository.get_todolist(todolist_name)\n print('list='+str(list))\n #return jsonify(list)\n #return list\n return render_template(\n 'todolist.html',\n title=list.text,\n year=datetime.now().year,\n todolist = list,\n message = str(list),\n \n )\n\n\nclass result:\n def __init__(self):\n self.status=\"\"\n self.msg=''\n\n\n@app.route('/todolist',methods=['POST'])\ndef addList():\n \n data = request.get_json()#from body\n print('view data = '+str(data))\n res=result()\n \n res = repository.add_list(data['name'])\n jsondata=json.dumps(res)\n print('jsondata= '+str(jsondata))\n return jsondata\n\n@app.route('/todolist/', methods=['PUT'])\ndef editList(key):\n print('ok')\n data = request.get_json()#from body\n print('name='+str(data))\n res=result()\n print('key={key},name={name}'.format(key=str(key),name=str(data['name'])))\n print('PUT')\n res = repository.edit_list(key,data['name']) \n jsondata=json.dumps(res)\n return jsondata\n\n@app.route('/todolist/', methods=['DELETE'])\ndef delList(key):\n res=result()\n print('key='+str(key))\n print('delete')\n res = repository.del_list(key) \n jsondata=json.dumps(res)\n print('jsondata= '+jsondata)\n return jsondata\n \n#######ToDo Item#######\n@app.route('/todoitem',methods=['POST'])\ndef addItem():\n todolist_name = request.args['todolist_name']\n\n print('todolist_name='+todolist_name)\n \n data = request.get_json()\n print(' str(data[name]).strip()='+ str(data['name']).strip()+\"*\")\n if str(data['name']).strip()=='':#後端防空白字串\n print('empty')\n jsondata=['error','ItemName is empty.']\n jsondata=json.dumps(jsondata)\n else:\n print('data='+ str(data))\n res = result()\n res=repository.add_item(todolist_name, data['name'])\n jsondata=json.dumps(res)\n print('jsondata= '+jsondata)\n return jsondata\n\n \n@app.route('/todoitem',methods=['PUT'])\ndef editItem():\n \n data = request.get_json()\n print('data='+ str(data))\n res = result()\n res=repository.edit_item(data['todolist_name'], data['id'],data['newName'])\n jsondata=json.dumps(res)\n print('jsondata= '+jsondata)\n return jsondata\n\n\n@app.route('/todoitem/', methods=['DELETE'])\ndef delItem():\n todolist_name = request.args['todolist_name']\n \n todoitem_name = request.args['todoitem_name']\n print('todolist_name={list},todoitem_name={item}'.format(list=str(todolist_name),item=str(todoitem_name)))\n \n res=result()\n \n print('delete')\n res = repository.del_item(todolist_name,todoitem_name) \n jsondata=json.dumps(res)\n print('jsondata= '+jsondata)\n return jsondata\n","repo_name":"jason0100/Python-ToDoList","sub_path":"Python-toToList/Python_toDoList/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"73098740622","text":"import pandas as pd\r\nimport math\r\nimport matplotlib\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport re\r\n\r\n\r\n\r\ntrace_file_path = \"datarate.tr\"\r\n\r\nattacker_id_list = [12,13,14]\r\n\r\nattacker_num = len(attacker_id_list)\r\nreceiver_id_list = [10]\r\nreceiver_num = len(receiver_id_list)\r\n# sender_id_list = [3, 4, 5, 6]\r\nsender_id_list = [5, 6,7,8]\r\nsender_num = len(sender_id_list)\r\n#switch_id_list = [0, 1, 2]\r\nswitch_id_list = [0, 1, 2,3,4]\r\n# switch_id_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]\r\nswitch_num = len(switch_id_list)\r\nbase_rate_list = [50, 50, 50, 50] # case 2\r\n\r\nsender_index_dict = {}\r\nfor i in range(sender_num):\r\n sender_id = str(sender_id_list[i])\r\n sender_index_dict[sender_id] = i\r\n\r\nattacker_index_dict = {}\r\nfor i in range(attacker_num):\r\n attacker_id = str(attacker_id_list[i])\r\n attacker_index_dict[attacker_id] = i\r\n\r\ndatarate_list = [[] for _ in range(sender_num)]\r\ntime_list = [[] for _ in range(sender_num)]\r\n\r\ndata = pd.read_table(trace_file_path, header=None, sep='\\s+', names=['time', 'node', 'rate'], low_memory=False).dropna()\r\n\r\nfor index, row in data.iterrows():\r\n node = row[1]\r\n if node not in sender_id_list:\r\n continue\r\n else:\r\n current_time = row[0]\r\n current_rate = float(re.sub(\"\\D\", \"\", row[2])) / 1e+9\r\n node_index = sender_index_dict[str(node)]\r\n if current_time not in time_list[node_index]:\r\n datarate_list[node_index].append(current_rate)\r\n time_list[node_index].append(current_time)\r\n\r\nfor i in range(sender_num):\r\n label_str = 'Sender' + str(sender_id_list[i])\r\n time_list[i], datarate_list[i] = (list(t) for t in zip(*sorted(zip(time_list[i], datarate_list[i]))))\r\n plt.plot(time_list[i], datarate_list[i], label=label_str)\r\n plt.legend(loc='upper right')\r\n\r\n\r\n\r\nplt.xlim(2.0008, 2.11)\r\nplt.xlabel('Time (s)')\r\nplt.ylabel('Sending Rate (Gbps)')\r\n# plt.show()\r\nplt.savefig(\"flowrate.png\")\r\n\r\n\r\n\r\n","repo_name":"wangshicheng1225/LoRDMA","sub_path":"simulation/windows/ns-3-dev/x64/Release/mix/rate_plot.py","file_name":"rate_plot.py","file_ext":"py","file_size_in_byte":1982,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"12560810825","text":"import matplotlib.pyplot as plt\r\nimport numpy as np\r\n#input values\r\nx = np.array([10,9,2,15,10,16,11,16])\r\ny = np.array([95,80,10,50,45,98,38,93])\r\n\r\n\r\nplt.plot(x,y,\"ro\")\r\n\r\nslope , intercept = np.polyfit(x,y,1)\r\ny = slope*x + intercept\r\n\r\nplt.plot(x,y,\"bo\")\r\n\r\n\r\nplt.plot(x,y,'-b',label = \"y=mx+c\")\r\nplt.ylabel(\"Risk Score on a scale of o-100\")\r\nplt.xlabel(\"No. of hours spent on Driving\")\r\n\r\nplt.show()","repo_name":"durgeshrb8374/monster","sub_path":"linear_regression.py","file_name":"linear_regression.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"26697148333","text":"import random\n\nuser = input('Choose X or O: \\n')\nif user == 'X' or user == 'x':\n bot = 'O'\n if user == 'x':\n user = 'X'\nelse:\n bot = 'X'\n\nspaces = [' 1 ',' 2 ',' 3 ',' 4 ',' 5 ',' 6 ',' 7 ',' 8 ',' 9 ']\nexclude = [0,1,2,3,4,5,6,7,8]\n\nwhile True:\n print('\\n', spaces[0], '|', spaces[1], '|', spaces[2], '\\n----------------\\n', spaces[3], '|', spaces[4], '|', spaces[5], '\\n----------------\\n', spaces[6], '|', spaces[7], '|', spaces[8],'\\n')\n\n option = int(input('Choose a space: ')) -1 \n spaces[option] = ' '+user+' '\n exclude.remove(option)\n print(spaces,'\\n',exclude)\n \n option2 = random.choice(list(exclude))\n print (\"Your opponent chooses \" + str(option2+1))\n spaces[option2] = ' '+bot+' '\n exclude.remove(option2)\n print(spaces,'\\n',exclude)\n\n\n if spaces[0:3] == f' {user} | {user} | {user} ' or spaces[3:6] == f' {user} | {user} | {user} ' or spaces[6:] == f' {user} | {user} | {user} ':\n print('You win!')\n break\n elif spaces[0] == f' {user} ' and spaces[4] == f' {user} ' and spaces[8] == f' {user} ':\n print('You win!')\n break\n elif spaces[2] == f' {user} ' and spaces[4] == f' {user} ' and spaces[6] == f' {user} ':\n print('You win!')\n break\n elif spaces[0:3] == f' {bot} | {bot} | {bot} ' or spaces[3:6] == f' {bot} | {bot} | {bot} ' or spaces[6:] == f' {bot} | {bot} | {bot} ':\n print('I win!')\n break\n elif spaces[0] == f' {bot} ' and spaces[4] == f' {bot} ' and spaces[8] == f' {bot} ':\n print('I win!')\n break\n elif spaces[2] == f' {bot} ' and spaces[4] == f' {bot} ' and spaces[6] == f' {bot} ':\n print('I win!')\n break\n ","repo_name":"Korvag/Random","sub_path":"test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":1692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"1530296506","text":"# open and read file line by line\ninput = open('input.txt')\ncuts = input.readlines()\n\n# parse input (##### @ colstart,rowstart: collen\\x\\rowlen)\ncolstart = []\nrowstart = []\ncollen = []\nrowlen = []\n# keep track of canvas dimension requirements\nheight = 0\nwidth = 0\nfor cut in cuts:\n cut = cut.rstrip()\n\n at = cut.split('@ ')\n cut = at[1]\n\n comma = cut.split(',')\n colstart.append(int(comma[0]))\n cut = comma[1]\n\n colon = cut.split(': ')\n rowstart.append(int(colon[0]))\n cut = colon[1]\n\n x = cut.split('x')\n collen.append(int(x[0]))\n rowlen.append(int(x[1]))\n\n if (int(comma[0]) + int(x[0])) > width:\n width = int(comma[0]) + int(x[0])\n if (int(colon[0]) + int(x[1])) > height:\n height = int(colon[0]) + int(x[1])\n\n# initialize fabric sheet; height and width reqs based on max cut coords in respective dimension\ngrid = [[0 for i in range(width)] for j in range(height)]\n\n# count duplicate insertions\ndupes = 0\n\n# function to insert an item into array\ndef insert_cut(row, col):\n grid[row][col] += 1\n if grid[row][col] == 2:\n return 1\n else:\n return 0\n\n# insert data into grid\nfor i in range(len(cuts)):\n for x in range(rowstart[i], rowstart[i] + rowlen[i]):\n for y in range(colstart[i], colstart[i] + collen[i]):\n dupes += insert_cut(x, y)\n\nprint(dupes)\n","repo_name":"a-g-green/adventofcode","sub_path":"day3/day3.py","file_name":"day3.py","file_ext":"py","file_size_in_byte":1351,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"26633233937","text":"count = int(input())\n\n\nprice = 0\nmoney=[]\n\nfor i in range(count):\n a,b,c = map(int, input().split())\n\n if a == b == c :\n money.append(10000 + a*1000)\n\n elif (a == b and a != c):\n money.append(1000 + a*100)\n elif (a == c and a != b):\n money.append(1000 + a*100)\n elif (b == c and a != b):\n money.append(1000 + b*100)\n else:\n m=max(a,b,c)\n money.append(m*100)\n\nprint(max(money))","repo_name":"soochangoforit/Algorithm","sub_path":"BackJoon Online Judge/LearnPython/2476.py","file_name":"2476.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"10815724504","text":"import re\nfrom datetime import datetime, time\n\nfrom city_scrapers_core.constants import ADVISORY_COMMITTEE, CANCELLED\nfrom city_scrapers_core.items import Meeting\nfrom city_scrapers_core.spiders import CityScrapersSpider\n\n\nclass CookMedicalExaminerSpider(CityScrapersSpider):\n name = \"cook_medical_examiner\"\n agency = \"Cook County Medical Examiner's Advisory Committee\"\n timezone = \"America/Chicago\"\n start_urls = [\n \"https://www.cookcountyil.gov/service/medical-examiners-advisory-committee\"\n ]\n location = {\n \"name\": \"Office of the Medical Examiner\",\n \"address\": \"2121 W Harrison St, Chicago, IL 60612\",\n }\n\n def parse(self, response):\n \"\"\"\n `parse` should always `yield` Meeting items.\n\n Change the `_parse_title`, `_parse_start`, etc methods to fit your scraping\n needs.\n \"\"\"\n self._validate_location(response)\n cancel_date = None\n for header in response.css(\".field-items h2::text, .field-items h3::text\"):\n header_text = header.extract()\n if header_text and \"cancel\" in header_text.lower():\n cancel_date = self._parse_date(header_text)\n\n # Parse default start and end time from description text\n default_start_time, default_end_time = self._parse_times(\n \" \".join(response.css(\".field-items p:not([align])::text\").extract())\n )\n for date_item in response.css(\".field-items p[align='center']\"):\n date_str = \" \".join(date_item.css(\"*::text\").extract())\n date_obj = self._parse_date(date_str)\n if not date_obj:\n continue\n start_time, end_time = self._parse_times(date_str)\n meeting = Meeting(\n title=\"Medical Examiner's Advisory Committee\",\n description=\"\",\n classification=ADVISORY_COMMITTEE,\n start=self._parse_start(date_obj, start_time or default_start_time),\n end=self._parse_end(date_obj, end_time or default_end_time),\n time_notes=\"\",\n all_day=False,\n location=self.location,\n links=[],\n source=response.url,\n )\n meeting[\"status\"] = self._parse_status(\n meeting, date_str, date_obj, cancel_date\n )\n meeting[\"id\"] = self._get_id(meeting)\n yield meeting\n\n def _parse_date(self, date_str):\n \"\"\"Parse date string from common format\"\"\"\n date_match = re.search(r\"[a-zA-Z]{3,10} \\d{1,2},? \\d{4}\", date_str)\n if not date_match:\n return\n return datetime.strptime(date_match.group().replace(\",\", \"\"), \"%B %d %Y\").date()\n\n def _parse_times(self, text):\n time_strs = re.findall(r\"(\\d{1,2}(\\:\\d{2})? ?[apm\\.]{2,4})\", text, flags=re.I)\n start_time = None\n end_time = None\n if len(time_strs) > 0:\n start_time_str = re.sub(r\"[\\s\\.]\", \"\", time_strs[0][0])\n if \":\" not in start_time_str:\n start_time_str = re.sub(\n r\"(\\d+)([apm\\.])\", r\"\\1:00\\2\", start_time_str, flags=re.I\n )\n start_time = datetime.strptime(start_time_str, \"%I:%M%p\").time()\n if len(time_strs) > 1:\n end_time_str = re.sub(r\"[\\s\\.]\", \"\", time_strs[1][0])\n if \":\" not in end_time_str:\n end_time_str = re.sub(\n r\"(\\d+)([apm\\.])\", r\"\\1:00\\2\", end_time_str, flags=re.I\n )\n end_time = datetime.strptime(end_time_str, \"%I:%M%p\").time()\n return start_time or time(11), end_time\n\n def _parse_start(self, date_obj, start_time):\n \"\"\"Parse start datetime as a naive datetime object.\"\"\"\n return datetime.combine(date_obj, start_time)\n\n def _parse_end(self, date_obj, end_time):\n \"\"\"Parse end datetime as a naive datetime object. Added by pipeline if None\"\"\"\n # Dont return an end time if the end time object is None\n if end_time is None:\n return\n return datetime.combine(date_obj, end_time)\n\n def _parse_status(self, item, date_str, date_obj, cancel_date):\n if date_obj == cancel_date:\n return CANCELLED\n return self._get_status(item, text=date_str)\n\n def _validate_location(self, response):\n response_text = \" \".join(response.css(\"*::text\").extract())\n if \"2121 W\" not in response_text:\n raise ValueError(\"Meeting location has changed\")\n","repo_name":"City-Bureau/city-scrapers","sub_path":"city_scrapers/spiders/cook_medical_examiner.py","file_name":"cook_medical_examiner.py","file_ext":"py","file_size_in_byte":4517,"program_lang":"python","lang":"en","doc_type":"code","stars":309,"dataset":"github-code","pt":"47"}
+{"seq_id":"30900275943","text":"import datetime\nimport hmac\nimport base64\nimport aiohttp\nimport config\nimport time\nimport asyncio\nasync def get(url):\n timestamp=datetime.datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%S.%f')[:-3] + 'Z'\n method=\"GET\"\n requestpath=url\n sign=base64.b64encode(hmac.new(config.secret.encode(),(timestamp + 'GET' +url).encode(),digestmod='sha256').digest())\n headers = {\"OK-ACCESS-KEY\": config.apikey, 'OK-ACCESS-TIMESTAMP': timestamp,\n \"OK-ACCESS-PASSPHRASE\": \"Xiaochuan1021@\", \"OK-ACCESS-SIGN\": sign.decode(),\n \"Content-Type\": \"application/json\",\n \"accept\": 'application/json'\n }\n print('sign',sign.decode())\n if config.DEBUG:\n headers['x-simulated-trading'] = '1'\n async with aiohttp.ClientSession() as session:\n async with session.get(config.restserver+url,headers=headers) as r:\n data=await r.json()\n print(data)\n return data['data']\n\nasync def getallswapproducts():\n url = '/api/v5/public/instruments?instType=SWAP'\n data=await get(url)\n return [item for item in data if item['instId'].endswith('USDT-SWAP')]\nasync def getdelttime():\n url=\"/api/v5/public/time\"\n try:\n data=await get(url)\n #print('servertime:',int(data[0]['ts']))\n config.delttime=int(time.time())-int(int(data[0]['ts'])/1000)\n print('delttime',config.delttime)\n except Exception as e:\n print(e,37)\n pass\n\nasync def gethistoryorder():\n url='/api/v5/trade/orders-history?instType=SWAP'\n data=await get(url)\n print(\"len(data)\",data)\n return data\n\nasync def getbalance():\n url='/api/v5/account/balance'\n data=await get(url)\n print(data)\n return data\nasync def getpositions():\n url='/api/v5/account/positions'\n data=await get(url)\n return data\nasync def tmain():\n url='/api/v5/account/balance?instType=SWAP'\n data=await get(url)\n print(data)\n\nif __name__ == '__main__':\n asyncio.run(getbalance())","repo_name":"fengchuan1021/okrobot","sub_path":"request.py","file_name":"request.py","file_ext":"py","file_size_in_byte":2008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"266916401","text":"# Nattawut Klinsawas from Clicknext Camp Online Workshop @Kmutt\n\n# Create a string from list of integer. If values stick together (such as 1 2 3), group it together (such as 1 - 3). If not, separate with comma\ndef tvSchedule(arr):\n result = ''\n \n temp = 0\n for i in range(len(arr) - 1):\n if(arr[i] + 1 == arr[i + 1]):\n temp += 1\n elif(temp == 0):\n result += str(arr[i]) + ', '\n else:\n result += str(arr[i - temp]) + ' - ' + str(arr[i]) + ', '\n temp = 0\n if (temp == 0):\n result += str(arr[-1])\n else:\n result += str(arr[-1-temp]) + ' - ' + str(arr[-1])\n\n return result\n\n# Check if 'char' is a digit (0 - 9)\ndef isDigit(char):\n # Check using ASCII code where 48 = 0 and 57 = 9\n if ord(char) >= 48 and ord(char) <= 57:\n return True\n else:\n return False\n\n# Check if 'char' is a letter (a - z or A - Z)\ndef isLetter(char):\n # Check using ASCII code where 65 = A and 90 = Z and 97 = a and 122 = z\n if (ord(char) >= 65 and ord(char) <= 90) or (ord(char) >= 97 and ord(char) <= 122):\n return True\n else:\n return False\n# Convert string into list of Int\ndef listOfInt(string):\n listInt = []\n digitAmount = 0 # Count the amount of digit before go to\n\n for i in range(len(string)):\n if isDigit(string[i]):\n digitAmount += 1\n # If current char is not a digit and amount of digit is not 0. that's mean there is an integer, convert it and add to the list\n elif digitAmount != 0: \n num = ''\n # Work with integer that isn't a digit e.g. 152, 34\n for j in range(i - digitAmount, i):\n num += string[j]\n listInt.append(int(num))\n digitAmount = 0\n # If there is a letter or '.', that's mean it is not a string of integer and raise an error\n elif isLetter(string[i]) or string[i] == '.':\n raise ValueError(\"'arr' contain letter or float\")\n else:\n continue\n # If amount of digit is not 0, that's mean the last integer is left. Add that integer to the list\n if digitAmount != 0:\n num = ''\n for j in range(-digitAmount, 0):\n num += string[j]\n listInt.append(int(num))\n digitAmount = 0\n\n return listInt\n\ntry:\n arr = listOfInt(input(\"arr: \"))\n\n result = tvSchedule(arr)\n print(result)\nexcept:\n print(\"Error: Please make sure that arr is an array of integers\")\n\n","repo_name":"flarious/Clicknext_Internship","sub_path":"3. tvSchedule.py","file_name":"3. tvSchedule.py","file_ext":"py","file_size_in_byte":2495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"28172607872","text":"# Question Link\n# https://leetcode.com/problems/implement-trie-prefix-tree/\n\n# Description\n'''\nA trie (pronounced as \"try\") or prefix tree is a tree data structure used to efficiently store and retrieve keys in a dataset of strings. There are various applications of this data structure, such as autocomplete and spellchecker.\n\nImplement the Trie class:\n\n- Trie() Initializes the trie object.\n- void insert(String word) Inserts the string word into the trie.\n- boolean search(String word) Returns true if the string word is in the trie (i.e., was inserted before), and false otherwise.\n- boolean startsWith(String prefix) Returns true if there is a previously inserted string word that has the prefix prefix, and false otherwise.\n \n'''\n\nclass Node:\n def __init__(self):\n self.children = {}\n self.endWord = False\nclass Trie:\n\n def __init__(self):\n self.root = Node()\n\n def insert(self, word: str) -> None:\n curr = self.root\n for i in word:\n if i not in curr.children.keys():\n curr.children[i] = Node()\n curr = curr.children[i]\n curr.endWord = True\n def search(self, word: str) -> bool:\n curr = self.root\n i = 0\n while i < len(word):\n if word[i] not in curr.children.keys():\n return False\n else:\n curr = curr.children[word[i]]\n i+=1\n #print(curr.children)\n if curr.endWord :\n return True\n return False\n\n def startsWith(self, prefix: str) -> bool:\n curr = self.root\n i = 0\n while i < len(prefix):\n if prefix[i] not in curr.children.keys():\n return False\n else:\n curr = curr.children[prefix[i]]\n i+=1\n \n return True\n\n\n# Your Trie object will be instantiated and called as such:\n# obj = Trie()\n# obj.insert(word)\n# param_2 = obj.search(word)\n# param_3 = obj.startsWith(prefix)","repo_name":"Nawarrr/leetcode","sub_path":"Problems/(208) Implement Trie (Prefix Tree).py","file_name":"(208) Implement Trie (Prefix Tree).py","file_ext":"py","file_size_in_byte":1996,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"7732250065","text":"import datetime\nimport json\n\nwith open('./config.json', 'r') as config_file:\n config = json.load(config_file)\n\n\ndef add_log(text):\n today = datetime.datetime.now() # Raw date\n log = open(config['log_file'], 'a')\n log.write('*** ' + str(today) + ' *** ' + text + '\\n')\n log.close()\n\n\ndef time_to_sleep(wake_time):\n \"\"\"\n :param: wake_time format HH:MM\n :return: returns total seconds until wake_time\n \"\"\"\n\n current_time = datetime.datetime.today()\n current_hour = (int(current_time.hour))\n wake_hour = (int(wake_time[0:2]))\n current_min = (int(current_time.minute))\n wake_min = (int(wake_time[3:5]))\n sleep_time = 0\n\n # Handle where wake_time is between 23-24 hours away\n if wake_hour == current_hour and wake_min < current_min:\n sleep_time = 23 * 60\n\n elif wake_hour == current_hour and wake_min > current_min:\n sleep_time = wake_min - (current_min + 1)\n\n # Calc Hours\n if wake_hour < current_hour and wake_min > current_min:\n sleep_time += (24 - current_hour) * 60\n sleep_time += wake_hour * 60\n\n elif wake_hour < current_hour:\n sleep_time += (24 - current_hour) * 60\n sleep_time += (wake_hour - 1) * 60\n\n elif wake_hour >= current_hour + 1 and wake_min > current_min:\n sleep_time += (wake_hour - current_hour) * 60\n\n elif wake_hour > current_hour + 1:\n sleep_time += (wake_hour - (current_hour + 1)) * 60\n\n elif wake_hour > current_hour and wake_min > current_min:\n sleep_time += 60\n\n else:\n pass\n\n # Calc minutes\n if wake_min < current_min:\n sleep_time += 60 - (current_min + 1) + wake_min\n\n elif wake_min > current_min:\n sleep_time += wake_min - (current_min + 1)\n\n # Testing outputs\n # sleep_hours = sleep_time // 60\n # sleep_mins = sleep_time - (sleep_hours * 60)\n # if sleep_hours < 10:\n # sleep_hours = f'0{sleep_hours}'\n # if sleep_mins < 10:\n # sleep_mins = f'0{sleep_mins}'\n # if current_hour < 10:\n # current_hour = f'0{current_hour}'\n # if current_min < 10:\n # current_min = f'0{current_min}'\n # if wake_hour < 10:\n # wake_hour = f'0{wake_hour}'\n # if wake_min < 10:\n # wake_min = f'0{wake_min}'\n #\n # print(f'Current time - {current_hour}:{current_min}')\n # print(f'Wake time - {wake_hour}:{wake_min}')\n # print(f'Time to sleep - {sleep_hours}:{sleep_mins}')\n\n return sleep_time * 60\n\n\ndef date_today(): # YYYY-MM-DD Format\n today = datetime.datetime.now() # Raw date\n date_today = today.strftime('%Y') + '-' + today.strftime('%m') + '-' + today.strftime('%d')\n return date_today\n\n\ndef check_date(): # DD-MMM-YYYY Format\n today = datetime.datetime.now() # Raw date\n check_date = today.strftime('%d') + '-' + today.strftime('%B') + '-' + today.strftime('%Y')\n return check_date\n\n\ndef prettify_json(raw_json):\n with open(raw_json) as json_file:\n parsed = json.load(json_file)\n pretty_json = (json.dumps(parsed, indent=4, sort_keys=True))\n return pretty_json\n","repo_name":"RonninP/sportmonksapp","sub_path":"src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"12368446239","text":"# coding: utf-8\nimport os\nimport urllib2\nfrom datetime import date, datetime\nfrom bs4 import BeautifulSoup\nfrom PyRSS2Gen import RSS2, RSSItem, Guid\n\nfrom flask import Flask, render_template\napp = Flask(__name__)\n\n@app.route('/ru')\ndef menu():\n\tresource = urllib2.urlopen(\"http://www.ufrgs.br/ufrgs/ru\")\n\tpage = BeautifulSoup(resource)\n\titems = []\n\tfor ru in page.find_all(\"div\", \"ru\"):\n\t\tru_name = ru.h3.contents[0]\n\t\tdesc = ', '.join([(item or '').strip() for item in ru.div.contents if not hasattr(item, 'contents')])\n\t\titems.append(RSSItem(\n\t\t\ttitle = '%s - %s' % (ru_name, date.today().strftime('%d/%m/%Y')),\n\t\t\tlink='http://www.ufrgs.br/ufrgs/ru',\n\t\t\tdescription=desc,\n\t\t\tguid=Guid(ru_name+date.today().isoformat()),\n\t\t))\n\tfeed = RSS2(\n\t\ttitle=u\"Cardápio do RU-UFRGS - diário\",\n\t\tlink='http://www.ufrgs.br/ufrgs/ru',\n\t\tdescription=u\"Cardápio do dia no Restaurante Universitário da UFRGS\",\n\t\tpubDate=datetime.today(),\n\t\titems=items,\n\t)\n\treturn feed.to_xml()\n\nif __name__ == '__main__':\n\tport = int(os.environ.get('PORT', 5000))\n\tapp.run(host='0.0.0.0', port=port)\n","repo_name":"ElSaico/ru-ufrgs-rss","sub_path":"ru_ufrgs.py","file_name":"ru_ufrgs.py","file_ext":"py","file_size_in_byte":1073,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"75181404943","text":"# coding :utf-8\n# 对目标目录下的图片进行扩展\n# 通过将图片上下翻转、左右翻转、\n# 逆时针旋转90度、180度、270度\n# 形成6倍的图片数据文件\n\nfrom PIL import Image\nimport os\n\ndef ensure_dir(dirPath, flag=0):\n '''\n 检查目录路径\n Args:\n dirPath: 目录路径\n flag: 是否创建，1：创建；其他：退出\n\n Returns:\n None\n '''\n isExists = os.path.exists(dirPath)\n if not isExists:\n print(\"不存在目录:\" + str(dirPath), end=\"\")\n if flag == 1:\n print(\" 正在生成...\")\n os.makedirs(dirPath)\n return\n else :\n print(\" 退出！\")\n return\n\n\ndef augment_pics(srcDir, dstDir):\n '''\n 扩充图片数量\n 将源图片目录中的所有图片通过翻转和选装，扩展成6倍的图片至目标目录\n Args:\n srcDir: 图片源目录\n dstDir: 图片目标目录\n\n Returns:\n None\n '''\n ensure_dir(srcDir)\n ensure_dir(dstDir, flag = 1)\n\n files = os.listdir(srcDir)\n i = 0\n print(\"目录下有文件\" + str(len(files)) + \"个\")\n for file in files:\n if str(file).startswith('.'):\n # 跳过隐藏文件\n continue\n\n with Image.open(srcDir + file) as im:\n i += 1\n # 左右翻转，上下翻转，逆时针旋转90， 180， 270度\n im1 = im.transpose(Image.FLIP_LEFT_RIGHT)\n im2 = im.transpose(Image.FLIP_TOP_BOTTOM)\n im3 = im.transpose(Image.ROTATE_90)\n im4 = im.transpose(Image.ROTATE_180)\n im5 = im.transpose(Image.ROTATE_270)\n\n # 提取文件名，分隔文件名和后缀\n name1, suffix = file.split('.')\n # print(name1, suffix)\n\n # 返回工作目录 防止相对路径问题\n # os.chdir(workingPath)\n\n # 保存6种图片至指定目录\n im.save(dstDir + str(name1) + '_ORI.' + str(suffix))\n im1.save(dstDir + str(name1) + '_LR.' + str(suffix))\n im2.save(dstDir + str(name1) + '_TB.' + str(suffix))\n im3.save(dstDir + str(name1) + '_LEFT.' + str(suffix))\n im4.save(dstDir + str(name1) + '_DOWN.' + str(suffix))\n im5.save(dstDir + str(name1) + '_RIGHT.' + str(suffix))\n\n if (i % 100 == 1):\n print(str(i) + \"->\", end='')\n\n print(\"finish!\")\n print(\"FROM \" + str(srcDir))\n print(\"TO \" + str(dstDir))\n print(str(i) + \"pics completed!\")\n return\n\n\nif __name__ == '__main__':\n picSrcDir = \"./herb_pic_ori/\"\n picDstDir = \"./newPicDir/\"\n\n srcDirs = os.listdir(picSrcDir)\n\n labels = {\"白花蛇舌草\": 0, \"白芍\": 1, \"白术\": 2, \"苍术\": 3, \"柴胡\": 4, \\\n \"川芎\": 5, \"丹参\": 6, \"党参\": 7, \"甘草\": 8, \"红花\": 9, \"黄连\": 10, \\\n \"黄芪\": 11, \"菊花\": 12, \"山药\": 13, \"生地黄\": 14, \"太子参\": 15, \"天麻\": 16, \\\n \"仙鹤草\": 17, \"续断\": 18}\n\n i = 0\n for item in srcDirs:\n if os.path.isdir(picSrcDir + item) and (item in labels):\n dstD = str(picDstDir) + str(labels[item]) + '/'\n srcD = str(picSrcDir) + str(item) + '/'\n # print(\"src: \" + str(srcD))\n # print(\"dst: \" + str(dstD))\n augment_pics(srcD, dstD)\n i += 1\n\n print(\"legal dir nums:\" + str(i))\n","repo_name":"xiaohaizhe/herbal_medicine_recognition","sub_path":"train/pics_augmetation.py","file_name":"pics_augmetation.py","file_ext":"py","file_size_in_byte":3396,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"37251360646","text":"\"\"\"\nInstructions --\n\n- Import the functions urlopen and Request from the subpackage urllib.request.\n- Package the request to the url \"http://www.datacamp.com/teach/documentation\" using the function Request() and assign it to request.\n- Send the request and catch the response in the variable response with the function urlopen().\n- Run the rest of the code to see the datatype of response and to close the connection!\n\"\"\"\n\n# Import packages\nfrom urllib.request import urlopen, Request\n\n# Specify the url\nurl = \"http://www.datacamp.com/teach/documentation\"\n\n# This packages the request: request\nrequest = Request(url)\n\n# Sends the request and catches the response: response\nresponse = urlopen(request)\n\n# Print the datatype of response\nprint(type(response))\n\n# Be polite and close the response!\nresponse.close()\n","repo_name":"jabhij/PY-DataImporting_PART2","sub_path":"1-ImportingData_from_Internet/4-HTTP_Requests_Python.py","file_name":"4-HTTP_Requests_Python.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"23151615359","text":"def BinarySearch(lst,n):\r\n low = 0\r\n high = len(lst) - 1\r\n mid = 0\r\n while low <= high:\r\n mid = (high+low)//2\r\n if lst[mid] < n:\r\n low = mid + 1\r\n elif lst[mid] > n:\r\n high = mid - 1\r\n else:\r\n return mid\r\n return -1\r\n\r\ntup_val = input('Enter a value (enter exit to stop!): ')\r\ntup_x = ()\r\nif tup_val != 'exit':\r\n tup_x = tuple(tup_val)\r\nwhile True:\r\n if tup_val != 'exit':\r\n tup_val = input('Enter a value (enter exit to stop!): ')\r\n if tup_val != 'exit':\r\n tup_x = tup_x + (tup_val,)\r\n else:\r\n break\r\nprint(tup_x)\r\nsearch = input(\"Enter the element of the tuple that you want to search: \")\r\nval = BinarySearch(tup_x,search)\r\nif val == -1:\r\n print(search + \" is not present in the tuple\")\r\nelse:\r\n print(search + \" is present in the tuple\")","repo_name":"fromjyce/PythonPrograms","sub_path":"LabRecords/TupleIterativeBinarySearch.py","file_name":"TupleIterativeBinarySearch.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"1859009044","text":"import pygame\nfrom settings import *\n\nfont = pygame.font.Font(FONT, 30)\n\nclass Board(pygame.sprite.Sprite):\n def __init__(self,group,width,height):\n super().__init__(group)\n self.width = width\n self.height = height\n\n self.image = pygame.Surface((width*50,height*50))\n self.rect = self.image.get_rect()\n self.draw_board()\n\n def draw_board(self):\n \"\"\" 绘制棋盘 \"\"\"\n\n self.image.fill('white')\n\n #画格子\n for x in range(self.width):\n for y in range(self.height):\n pygame.draw.rect(self.image,\"black\", (x*50, y*50, 50, 50), 1)\n\n #河流\n for pos in RIVER:\n pygame.draw.rect(self.image,\"deepskyblue\",(pos[0]*50, pos[1]*50,50,50),0)\n #陷阱\n for t in TRAP:\n for pos in t: \n trap_surface=font.render(\"陷\",True,\"blue\",\"white\")\n self.image.blit(trap_surface,(pos[0]*50+DETA_X, pos[1]*50+ DETA_Y))\n\n #兽穴\n for pos in HOME:\n home_surface = font.render(\"穴\",True,\"blue\",\"white\")\n self.image.blit(home_surface,(pos[0]*50+ DETA_X, pos[1]*50+ DETA_Y))\n \n def update(self):\n pass","repo_name":"Al-Qt/DouShouQi","sub_path":"board.py","file_name":"board.py","file_ext":"py","file_size_in_byte":1212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"19003016658","text":"class Solution:\n def longestArithSeqLength(self, A: List[int]) -> int:\n dps = [{}]\n l = len(A)\n if l == 0:\n return 0\n out = 1\n for i in range(1, l):\n cdp = {}\n for j in range(i):\n d = A[i] - A[j]\n cdp[d] = max(dps[j].get(d, 1) + 1, cdp.get(d, 2))\n out = max(cdp[d], out)\n dps.append(cdp)\n return out\n","repo_name":"devilhtc/leetcode-solutions","sub_path":"0x0403_1027.Longest_Arithmetic_Sequence/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"13604286036","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision\nimport numpy as np\n\nimport math\nfrom .attention_blocks import DualAttBlock\nfrom .resnet import BasicBlock as ResBlock\nfrom . import GSConv as gsc\nimport cv2\nfrom .norm import Norm2d\n\n\ndef conv3x3(in_planes, out_planes, stride=1, has_bias=False):\n \"3x3 convolution with padding\"\n return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n padding=1, bias=has_bias)\n\n\ndef conv3x3_bn_relu(in_planes, out_planes, stride=1):\n return nn.Sequential(\n nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1),\n nn.BatchNorm2d(out_planes),\n nn.ReLU(inplace=True)\n )\n\n\ndef conv1x1_bn_relu(in_planes, out_planes, stride=1):\n return nn.Sequential(\n nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0),\n nn.BatchNorm2d(out_planes),\n nn.ReLU(inplace=True)\n )\n\n\nclass ConvRelu(nn.Module):\n def __init__(self, in_, out):\n super(ConvRelu, self).__init__()\n self.conv = conv3x3(in_, out)\n self.activation = nn.ReLU(inplace=True)\n\n def forward(self, x):\n x = self.conv(x)\n x = self.activation(x)\n return x\n\n\nclass CenterBlock(nn.Module):\n def __init__(self, in_channels, middle_channels, out_channels, is_deconv=True):\n super(CenterBlock, self).__init__()\n self.in_channels = in_channels\n self.is_deconv=is_deconv\n\n if self.is_deconv: #Dense Block\n self.conv1 = conv3x3_bn_relu(in_channels, middle_channels)\n self.conv2 = conv3x3_bn_relu(in_channels+middle_channels, middle_channels)\n self.convUp = nn.Sequential(\n nn.ConvTranspose2d(in_channels+2*middle_channels, out_channels,\n kernel_size=4, stride=2, padding=1),\n nn.ReLU(inplace=True)\n )\n\n else:\n self.convUp = nn.Unsample(scale_factor=2, mode='bilinear', align_corners=True),\n self.conv1 = conv3x3_bn_relu(in_channels, middle_channels),\n self.conv2 = conv3x3_bn_relu(in_channels+middle_channels, out_channels)\n\n def forward(self, x):\n tmp = []\n if self.is_deconv == False:\n convUp = self.convUp(x); tmp.append(convUp)\n conv1 = self.conv1(convUp); tmp.append(conv1)\n conv2 = self.conv2(torch.cat(tmp, 1))\n return conv2\n\n else:\n tmp.append(x)\n conv1 = self.conv1(x); tmp.append(conv1)\n conv2 = self.conv2(torch.cat(tmp, 1)); tmp.append(conv2)\n convUp = self.convUp(torch.cat(tmp, 1))\n return convUp\n\n\nclass DecoderBlock(nn.Module):\n def __init__(self, in_channels, middle_channels, out_channels, is_deconv=True):\n super(DecoderBlock, self).__init__()\n self.in_channels = in_channels\n\n if is_deconv:\n self.block = nn.Sequential(\n conv3x3_bn_relu(in_channels, middle_channels),\n nn.ConvTranspose2d(middle_channels, out_channels, kernel_size=4, stride=2, padding=1),\n nn.BatchNorm2d(out_channels),\n nn.ReLU(inplace=True)\n )\n else:\n self.block = nn.Sequential(\n nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),\n conv3x3_bn_relu(in_channels, middle_channels),\n conv3x3_bn_relu(middle_channels, out_channels),\n )\n\n ### initialize\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n m.weight.data.normal_(0, math.sqrt(2. / n))\n elif isinstance(m, nn.BatchNorm2d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n elif isinstance(m, nn.Linear):\n m.bias.data.zero_()\n\n def forward(self, x):\n return self.block(x)\n\n\nclass SkipConv(nn.Module):\n def __init__(self, in_channels, out_channels):\n self.block = nn.Sequential(\n nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0),\n nn.BatchNorm2d(out_channels),\n nn.ReLU(inplace=True))\n\n ## initialize\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n m.weight.data.normal_(0, math.sqrt(2. / n))\n elif isinstance(m, nn.BatchNorm2d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n elif isinstance(m, nn.Linear):\n m.bias.data.zero_()\n\n def forward(self, x):\n return self.block(x)\n\n\nclass SAUNet(nn.Module): #SAUNet\n def __init__(self, num_classes=4, num_filters=32, pretrained=True, is_deconv=True):\n super(SAUNet, self).__init__()\n\n self.num_classes = num_classes\n print(\"SAUNet w/ Shape Stream\")\n self.pool = nn.MaxPool2d(2,2)\n self.encoder = torchvision.models.densenet121(pretrained=pretrained)\n\n self.shape_inp = nn.Conv2d(1, 3, kernel_size=1, padding=0)\n \n #for n, p in self.encoder.named_parameters():\n # print(n)\n\n self.relu = nn.ReLU(inplace=True)\n self.sigmoid = nn.Sigmoid()\n\n #Shape Stream\n self.c3 = nn.Conv2d(256, 1, kernel_size=1)\n self.c4 = nn.Conv2d(512, 1, kernel_size=1)\n self.c5 = nn.Conv2d(1024, 1, kernel_size=1)\n\n self.d0 = nn.Conv2d(128, 64, kernel_size=1)\n self.res1 = ResBlock(64, 64)\n self.d1 = nn.Conv2d(64, 32, kernel_size=1)\n self.res2 = ResBlock(32, 32)\n self.d2 = nn.Conv2d(32, 16, kernel_size=1)\n self.res3 = ResBlock(16, 16)\n self.d3 = nn.Conv2d(16, 8, kernel_size=1)\n self.fuse = nn.Conv2d(8, 1, kernel_size=1, padding=0, bias=False)\n\n self.cw = nn.Conv2d(2, 1, kernel_size=1, padding=0, bias=False)\n\n self.gate1 = gsc.GatedSpatialConv2d(32, 32)\n self.gate2 = gsc.GatedSpatialConv2d(16, 16)\n self.gate3 = gsc.GatedSpatialConv2d(8, 8)\n\n self.expand = nn.Sequential(nn.Conv2d(1, num_filters, kernel_size=1),\n Norm2d(num_filters),\n nn.ReLU(inplace=True))\n\n #Encoder\n self.conv1 = nn.Sequential(self.encoder.features.conv0,\n self.encoder.features.norm0)\n self.conv2 = self.encoder.features.denseblock1\n self.conv2t = self.encoder.features.transition1\n self.conv3 = self.encoder.features.denseblock2\n self.conv3t = self.encoder.features.transition2\n self.conv4 = self.encoder.features.denseblock3\n self.conv4t = self.encoder.features.transition3\n self.conv5 = nn.Sequential(self.encoder.features.denseblock4,\n self.encoder.features.norm5)\n\n #Decoder\n self.center = conv3x3_bn_relu(1024, num_filters * 8 * 2)\n self.dec5 = DualAttBlock(inchannels=[512, 1024], outchannels=512)\n self.dec4 = DualAttBlock(inchannels=[512, 512], outchannels=256)\n self.dec3 = DualAttBlock(inchannels=[256, 256], outchannels=128)\n self.dec2 = DualAttBlock(inchannels=[128, 128], outchannels=64)\n self.dec1 = DecoderBlock(64, 48, num_filters, is_deconv)\n self.dec0 = conv3x3_bn_relu(num_filters*2, num_filters)\n\n self.final = nn.Conv2d(num_filters, self.num_classes, kernel_size=1)\n\n def forward(self, x):\n \n x = self.shape_inp(x)\n x_size = x.size()\n \n #Encoder\n conv1 = self.conv1(x)\n conv2 = self.conv2t(self.conv2(conv1))\n conv3 = self.conv3t(self.conv3(conv2))\n conv4 = self.conv4t(self.conv4(conv3))\n conv5 = self.conv5(conv4)\n\n #Shape Stream\n ss = F.interpolate(self.d0(conv2), x_size[2:],\n mode='bilinear', align_corners=True)\n ss = self.res1(ss)\n c3 = F.interpolate(self.c3(conv3), x_size[2:],\n mode='bilinear', align_corners=True)\n ss = self.d1(ss)\n ss = self.gate1(ss, c3)\n ss = self.res2(ss)\n ss = self.d2(ss)\n c4 = F.interpolate(self.c4(conv4), x_size[2:],\n mode='bilinear', align_corners=True)\n ss = self.gate2(ss, c4)\n ss = self.res3(ss)\n ss = self.d3(ss)\n c5 = F.interpolate(self.c5(conv5), x_size[2:],\n mode='bilinear', align_corners=True)\n ss = self.gate3(ss, c5)\n ss = self.fuse(ss)\n ss = F.interpolate(ss, x_size[2:], mode='bilinear', align_corners=True)\n edge_out = self.sigmoid(ss)\n\n ### Canny Edge\n im_arr = np.mean(x.cpu().detach().numpy(), axis=1).astype(np.uint8)\n canny = np.zeros((x_size[0], 1, x_size[2], x_size[3]))\n for i in range(x_size[0]):\n canny[i] = cv2.Canny(im_arr[i], 10, 100)\n canny = torch.from_numpy(canny).cuda().float()\n ### End Canny Edge\n\n cat = torch.cat([edge_out, canny], dim=1)\n acts = self.cw(cat)\n acts = self.sigmoid(acts)\n edge = self.expand(acts)\n\n #Decoder\n conv2 = F.interpolate(conv2, scale_factor=2, mode='bilinear', align_corners=True)\n conv3 = F.interpolate(conv3, scale_factor=2, mode='bilinear', align_corners=True)\n conv4 = F.interpolate(conv4, scale_factor=2, mode='bilinear', align_corners=True)\n\n center = self.center(self.pool(conv5))\n dec5, _ = self.dec5([center, conv5])\n dec4, _ = self.dec4([dec5, conv4])\n dec3, att = self.dec3([dec4, conv3])\n dec2, _ = self.dec2([dec3, conv2])\n dec1 = self.dec1(dec2)\n dec0 = self.dec0(torch.cat([dec1, edge], dim=1))\n\n x_out = self.final(dec0)\n\n att = F.interpolate(att, scale_factor=4, mode='bilinear', align_corners=True)\n\n return nn.functional.softmax(x_out, dim=1) #, edge_out, att\n\n def pad(self, x, y):\n diffX = y.shape[3] - x.shape[3]\n diffY = y.shape[2] - x.shape[2]\n\n return nn.functional.pad(x, (diffX // 2, diffX - diffX//2,\n diffY // 2, diffY - diffY //2))\n","repo_name":"saturnMars/TDT4265_Project","sub_path":"src/saunet/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":10370,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"9393118084","text":"# encoding = utf-8\nfrom datetime import datetime\nimport time\n\ndef clock(func):\n def inner(*args):\n begin = time.time()\n res = func(*args)\n end = time.time()\n elapsed = end - begin\n args_str = ','.join([str(arg) for arg in args])\n print('[%.8fs] %s(%s)->%r' % (elapsed, func.__name__, args_str, res))\n return res\n\n return inner\n\n\n@clock\ndef factorial(n: int) -> int:\n time.sleep(0.0001)\n res = 1\n if n <= 1:\n return res\n else:\n for i in range(1, n + 1):\n res *= i\n return res\n\n\ndef main():\n print('6!=', factorial(6))\n print('10!=', factorial(10))\n\n\nif __name__ == '__main__':\n main()\n '''\n [15625.00000000ms] factorial(6)->720\n 6!= 720\n [15625.00000000ms] factorial(10)->3628800\n 10!= 3628800\n '''\n","repo_name":"czhnju161220026/LearnPython","sub_path":"chapter7/DecoratorDemo.py","file_name":"DecoratorDemo.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"23924530364","text":"# https://gongybable.medium.com/meta-interview-question-leetcode-1136-a421f0651584 https://leetcode.ca/all/1136.html\n# This is exactly the same as the Intro.py\nfrom typing import List\nfrom collections import deque\n\nclass Solution:\n def minimumSemesters(self, n: int, relations: List[List[int]]) -> int:\n adjList = [set() for _ in range(n+1)]\n inDegree = [0] * (n+1)\n\n for (fromm, to) in relations:\n adjList[fromm].add(to)\n inDegree[to] += 1 # for every (to, fromm), the in-degree of 'to' increases\n \n q = deque([course for course, indegree in enumerate(inDegree) if indegree == 0]) # Traversing starts from subject with 0 in degree\n semester = 0\n\n while q:\n for _ in range(len(q)):\n cur_course = q.popleft()\n\n for next_course in adjList[cur_course]:\n inDegree[next_course] -= 1 # in-degree of next_course decreases\n if inDegree[next_course] == 0: # if a subject's all pre-requisite is finished \n q.append(next_course)\n\n semester += 1\n\n return -1 if any(inDegree) == True else semester\n \n\ndef main():\n n = 3\n relations = [[1,3], [2,3]]\n s = Solution()\n print(s.minimumSemesters(n, relations))\n\nif __name__ == '__main__':\n main()","repo_name":"speedy1601/Graph","sub_path":"TOPOLOGICAL SORTING/LEETCODE_PREMIUM_1136.py","file_name":"LEETCODE_PREMIUM_1136.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"38006371117","text":"import requests\n\n__author__ = \"Liang\"\n__Date__ = 2017 / 8 / 13\n\n\nclass HtmlDownloader(object):\n def download(self, url):\n if url is None:\n return None\n headers={\"User-Agent\":\"Mozilla/5.0 (Windows NT 6.1; WOW64; rv:53.0) Gecko/20100101 Firefox/53.0\",\n \"Host\":\"baike.baidu.com\",\"Upgrade-Insecure-Requests\":1}\n resp=requests.get(url)\n resp.encoding=\"utf-8\"\n if resp.status_code!=200:\n return None\n return resp.text","repo_name":"fallenleaveslikewearyButterfly/Baike_Spider","sub_path":"baike_spider/html_downloader.py","file_name":"html_downloader.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"30475624727","text":"import numpy as np\nimport matplotlib.pyplot as plt\n \n \nWINDOW_OPEN = False\n \n \ndef plot(data, classes, centers):\n global WINDOW_OPEN\n if classes.max():\n colors = classes / classes.max()\n else:\n colors = classes\n plt.clf()\n plt.scatter(data[:, 0], data[:, 1], c=colors, s=100)\n plt.scatter(centers[:, 0], centers[:, 1], marker='x', s=1000)\n if WINDOW_OPEN:\n plt.draw()\n else:\n WINDOW_OPEN = True\n plt.show(block=False)\n \n \ndef get_distances(points, centers):\n return np.sum(np.absolute(points[:, :, None] - centers.T[None, :, :]),\n axis=1)\n \n \ndef get_centers(points, assignment, k):\n centers = np.zeros((k, 2))\n for index, _ in enumerate(centers):\n mask = (assignment == index)\n centers[index] = np.mean(points[mask, :], axis=0)\n return centers\n \n \ndef kmeans(data, k):\n centers = np.random.rand(k, 2)\n previous_assignment = np.zeros(len(data))\n while True:\n distances = get_distances(data, centers)\n assignment = np.argmin(distances, axis=1)\n plot(data, assignment, centers)\n input('Press any key to continue')\n if np.array_equal(previous_assignment, assignment):\n break\n centers = get_centers(data, assignment, k)\n previous_assignment = assignment\n return assignment\n \n \nif __name__ == '__main__':\n # data = np.array([\n # [3, 8], [4, 7], [3, 6], [3, 4],\n # [4, 5], [5, 5], [5, 2], [8, 4], [9, 4], [9, 1]])\n data = np.random.rand(100, 2)\n \n classes = kmeans(data, k=3)\n input('Press any key to exit')\n","repo_name":"rshkv/bda","sub_path":"2.3.py","file_name":"2.3.py","file_ext":"py","file_size_in_byte":1602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"42226628729","text":"from copy import deepcopy\nfrom PKG import *\nimport pandas as pd\nfrom rich import print\nimport os\n\n# open data\nfileCenter = FileCenter()\n# get data\ndataBase, dataName, dataType, dpi = (\n fileCenter.get_data_basie(),\n fileCenter.get_data_name(),\n fileCenter.get_file_type(),\n get_ppi(),\n)\n\n\ndef to_rr_interval(dataHR: list) -> list:\n \"\"\"_summary_\n let the HR data to be a RR interval\n Args:\n dataHR (list):input the HR list data \n\n Returns:\n list: about the rr interval (type of float)\n \"\"\"\n\n lstSize = len(dataHR)\n\n return [\n float(dataHR[index + 1]) - float(var) if index + 1 < lstSize\n and float(dataHR[index + 1]) - float(var) > 0 else 0\n for index, var in enumerate(dataHR)\n ]\n\n\n# about the path\nmainFolder, branchFolder = 'src', 'RR Data Method 2'\n\nsaveMainFolder, saveRrCSVFolder = (\n os.path.join(mainFolder, branchFolder),\n os.path.join(mainFolder, \"RRM2_csv\"),\n)\n\n# save folder\nif not os.path.exists(saveMainFolder):\n os.mkdir(saveMainFolder)\n\nif not os.path.exists(saveRrCSVFolder):\n os.mkdir(saveRrCSVFolder)\n\ntaData = (dataBase[dataName[0]][\"index\"], dataBase[dataName[1]][\"index\"])\ntaDataDict = dict(zip(dataName, taData))\ndataStruct = []\n\n# make the dataChange for save the tuple data (file state , DataFrame and RR data)\ndataChangeDict = {\n dataName[0]: deepcopy(dataStruct),\n dataName[1]: deepcopy(dataStruct)\n}\n\nfor taName, valDataList in taDataDict.items():\n for fileObj in valDataList:\n # get data from the file\n tmpFileList = fileObj.get_file_data()\n #tmpFileList = sorted(tmpFileList) # sort data\n\n _, fileState, _ = fileObj.get_file_type_detail()\n tranToRR = to_rr_interval(tmpFileList)\n '''\n tmpDf = pd.DataFrame({\n \"Heart Beats\": tmpFileList,\n \"RRI Value\": tranToRR\n })#\n '''\n RRSeries = pd.Series(tranToRR)\n\n # save data\n dataChangeDict[taName].append((\n fileState, # the state of the file\n # tmpDf, # Data Frame of the RR and Heart Beats\n RRSeries,\n )) # RR Series (for other code )\n\n# check the data\nprint(dataChangeDict)\n\n# to file\nfor taName, valTuples in dataChangeDict.items():\n\n # open save file path\n savePath = os.path.join(saveMainFolder, taName)\n if not os.path.exists(savePath):\n os.mkdir(savePath)\n\n # save for rr csv file\n rrCSVPath = os.path.join(saveRrCSVFolder, taName)\n if not os.path.exists(rrCSVPath):\n os.mkdir(rrCSVPath)\n\n for state, RRdata in valTuples:\n saveBranchFolder = os.path.join(savePath, state)\n\n if not os.path.exists(saveBranchFolder):\n os.mkdir(saveBranchFolder)\n\n title = f\"RR-interval {state}\" # about the file name\n\n # Data Frame to png\n save_to_png(\n folderPath=saveBranchFolder,\n titleStr=title,\n x_ticks=False,\n data=RRdata,\n labelName=f'rr interval {state}',\n dpi=dpi,\n )\n\n # save file name\n fileCSVSavePath = os.path.join(saveBranchFolder, f\"{title}.csv\")\n RRdata.to_csv(fileCSVSavePath, index=False)\n\n rrCSVFilePath = os.path.join(rrCSVPath, f\"RRM2_{state}.csv\")\n RRdata.to_csv(rrCSVFilePath, index=False)\n","repo_name":"KeithLin724/Signal-and-system-final-project-2022","sub_path":"src/code/5 Find RR Waveform/Find_RR_Waveform_method2.py","file_name":"Find_RR_Waveform_method2.py","file_ext":"py","file_size_in_byte":3310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"9857909841","text":"\"\"\"\nweb server\n\n提供一个服务端使用类,通过这个类可以快速的搭建一个web server服务,用以展示自己的简单网页\n\"\"\"\nfrom socket import *\nfrom select import *\nimport re\n\n\nclass HTTPServer:\n def __init__(self, host='0.0.0.0', port=80, html=None):\n self.host = host\n self.port = port\n self.html = html\n self.create_socket()\n self.bind()\n self.dict_fd = {self.sockfd.fileno(): self.sockfd}\n self.ep = epoll()\n\n def create_socket(self):\n self.sockfd = socket()\n self.sockfd.setblocking(False)\n\n def bind(self):\n self.address = (self.host, self.port)\n self.sockfd.bind(self.address)\n\n def start(self):\n self.sockfd.listen(3)\n print(\"Listen the port %s\" % self.port)\n self.ep.register(self.sockfd, EPOLLIN)\n while True:\n # 对IO进行监控\n events = self.ep.poll()\n # 遍历列表分情况讨论\n for fd, event in events:\n if fd == self.sockfd.fileno():\n # 监听套接字就绪\n connect, addr = self.dict_fd[fd].accept()\n print(\"Connect from\", addr)\n # 添加客户端链接套接字作为监控对象\n connect.setblocking(False)\n self.ep.register(connect, EPOLLIN | EPOLLET)\n self.dict_fd[connect.fileno()] = connect\n else:\n # 客户端链接套接字就绪\n self.handle(self.dict_fd[fd])\n\n # 对每一个客户端请求的具体处理\n def handle(self, connect):\n request = connect.recv(1024).decode()\n # print(request)\n pattern = r\"[A-Z]+\\s+(/\\S*)\"\n try:\n info = re.match(pattern, request).group(1)\n # print(\"info-->\",info)\n except:\n del self.dict_fd[connect.fileno()]\n connect.close()\n return\n else:\n self.get_http(connect,info)\n\n def get_http(self,connect,info):\n if info == \"/\":\n filename = self.html + \"/index.html\"\n else:\n filename = self.html + info\n try:\n file = open(filename,\"rb\")\n except:\n response_heads = \"HTTP/1.1 404 NO\\r\\n\"\n response_heads += \"Content-type: text-html\\r\\n\"\n response_heads += \"\\r\\n\"\n response_content = \"Not Found\"\n response = response_heads.encode() + response_content.encode()\n else:\n response_heads = \"HTTP/1.1 200 YES\\r\\n\"\n response_heads += \"Content-type: text-html\\r\\n\"\n response_heads += \"\\r\\n\"\n response_content= file.read()\n response = response_heads.encode() + response_content\n file.close()\n finally:\n connect.send(response)\n\n\nif __name__ == '__main__':\n host = \"0.0.0.0\"\n port = 9696\n path = \"./static\"\n\n # 实例化对象[\n http = HTTPServer(host=host, port=port, html=path)\n\n # 调用方法启动服务\n http.start()\n","repo_name":"MOYSS-LCX/AID2002-stage02","sub_path":"web_server.py","file_name":"web_server.py","file_ext":"py","file_size_in_byte":3095,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"425182868","text":"from typing import List \n\ndef sortedSquares( nums: List[int]) -> List[int]:\n res = [0 ] * len(nums)\n # print(res)\n l = 0\n r = len(nums)-1 \n for i in range(len(nums)-1, -1,-1):\n if abs(nums[l])>abs(nums[r]):\n res[i] = nums[l]**2\n l+=1\n else:\n res[i] = nums[r]**2\n r-=1\n return res\n\nif __name__ == '__main__':\n print(sortedSquares([-7,-3,2,3,11]))\n print(sortedSquares([-4,-1,0,3,10]))","repo_name":"karanssh/code-practice","sub_path":"python/lc977.py","file_name":"lc977.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"69815643053","text":"#this class implements trainining using the agent class\nfrom multienvironment import Action, Reward, Goal\nfrom agent import Agent, Parameters, Decay, Modes, Env, Optimizer\nfrom networks import Network\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib\nmatplotlib.interactive(True)\n#dictionary containing arguments to initialize the environment\nenv_dict = {'width': 640, 'height': 480, 'mineral_scale': .5, \n 'camera_height': 3.5,'camera_tilt':0, \n 'actions':[Action.FORWARDS,Action.CW,Action.CCW,Action.STAY], \n 'reward': Reward.RELATIVE_PROPORTIONAL,\n 'decorations':True,'resize_scale':16, 'silver': (.8,.8,.8), 'random_colors':True,\n 'random_lighting':True, 'silver_mineral_num':3, 'point_distance':9, 'stationary_scale':6,\n 'normal_scale':2, 'stationary_win_count':5, 'shift_offset': 2,\n 'goal': Goal.ALIGN, 'walls_terminal': True,'frame_stacking': True, 'stack_size': 3}\n#dictionary containing arguments for training settings\n\ntraining_dict = {Parameters.START_EPSILON:1,Parameters.GAMMA:.95, Parameters.ALPHA:.001,\n Parameters.EPOCHS: 10, Parameters.MAX_MOVES:20, Parameters.WIN_REWARD: 100,\n Parameters.LOSS_REWARD: -100, Parameters.MAX_MEMORY_SIZE: 10 * 150,\n Parameters.BATCH_SIZE: 16, Parameters.OPTIMIZER: Optimizer.ADAM,\n Parameters.MIN_EPSILON: .1, Parameters.TEST_EPOCHS: 3, \n Parameters.TEST_MAX_MOVES:10, Parameters.EPSILON_DECAY: Decay.LINEAR, Parameters.CONTINUOUS: False}\n\n#create agent using dictionaries\nagent = Agent(env_type = Env.MULTI, env_dict = env_dict, \n training_dict = training_dict, network_type = Network.SA_TO_Q, training_name = 'test_training')\n \n#Or load from a previously stored \n#agent = Agent(env_type = Env.MULTI, env_file_name = \"test_training\", \n# training_file_name = \"test_training\", load_model = True, model_load_name = \"test_training\", \n# network_type = Network.SA_TO_Q, training_name = \"test_training\")\n\n\n#train, test, and save the agent. If you want to do alll of this, just run agent.train_test_save()\n\ntraining_wins, training_losses = agent.train()\n\nreward_list = agent.plot_rewards()\n\nwins, losses, reached_max = agent.test()\n\n#saves the environment and training arguments as well as the model\nagent.save_all()","repo_name":"daniellawson9999/Capstone_Python","sub_path":"Robot/Rotation/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2371,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"55"}
+{"seq_id":"25920373404","text":"import numpy as np\n\n\ndef diag_indices(n, k=0):\n rows, cols = np.diag_indices(n)\n if k < 0:\n return rows[-k:], cols[:k]\n elif k > 0:\n return rows[:-k], cols[k:]\n else:\n return rows, cols\n\n\ndef unband_matrix(banded_matrix, symmetric=True, lower=True):\n \"\"\"\n Assumes banded_matrix.shape=(n_diags, lent). banded_matrix=[diag0, diag1, diag2, ....]. See scipy format\n :param banded_matrix:\n :return:\n \"\"\"\n N = banded_matrix.shape[1]\n unbanded_matrix = np.zeros((N, N))\n for diag in range(banded_matrix.shape[0]):\n indices = diag_indices(N, k=diag)\n unbanded_matrix[indices] = banded_matrix[diag, :N - diag]\n if symmetric:\n indices = np.tril_indices(N)\n unbanded_matrix[indices] = unbanded_matrix.T[indices]\n if not (symmetric) and lower:\n unbanded_matrix = unbanded_matrix.T\n return unbanded_matrix\n\n\ndef band_matrix(unbanded_matrix, max_band=None):\n N = unbanded_matrix.shape[1]\n max_band = max_band if max_band is not None else N\n banded_matrix = np.zeros((max_band, N))\n for diag in range(max_band):\n indices = diag_indices(N, k=diag)\n banded_matrix[diag, :N - diag] = unbanded_matrix[indices]\n return banded_matrix","repo_name":"diegoarri91/iclamp-glm","sub_path":"icglm/utils/linalg.py","file_name":"linalg.py","file_ext":"py","file_size_in_byte":1258,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"55"}
+{"seq_id":"20056014630","text":"import abc\nimport copy\nfrom typing import Any, Dict, List, Optional, Tuple\n\nfrom flsim.common.timeline import Timeline\nfrom flsim.interfaces.batch_metrics import IFLBatchMetrics\nfrom flsim.interfaces.metrics_reporter import (\n Channel,\n IFLMetricsReporter,\n Metric,\n TrainingStage,\n)\nfrom torch.utils.tensorboard import SummaryWriter\n\n\nclass FLMetricsReporter(IFLMetricsReporter, abc.ABC):\n \"\"\"MetricsReporter with Tensorboard support.\"\"\"\n\n def __init__(self, channels: List[Channel], log_dir: Optional[str] = None):\n self.channels = channels\n self.log_dir = log_dir\n if Channel.TENSORBOARD in channels:\n self.set_summary_writer(log_dir)\n if Channel.STDOUT in channels:\n self.print = print\n self.losses = []\n self.num_examples_list = []\n self.predictions_list = []\n self.targets_list = []\n self.model_inputs_list = []\n self.latest_scores = {}\n self.best_eval_metrics = None\n\n def set_summary_writer(self, log_dir: Optional[str]):\n self.writer = SummaryWriter(log_dir=log_dir)\n\n def add_batch_metrics(self, metrics: IFLBatchMetrics) -> None:\n self.losses.append(metrics.loss.item())\n self.num_examples_list.append(metrics.num_examples)\n self.predictions_list.append(metrics.predictions)\n self.targets_list.append(metrics.targets)\n self.model_inputs_list.append(metrics.model_inputs)\n\n def aggregate(self, one_user_metrics):\n pass\n\n def report_metrics(\n self,\n reset: bool,\n stage: TrainingStage,\n extra_metrics: Optional[List[Metric]] = None,\n **kwargs,\n ) -> Tuple[Any, bool]:\n metrics = self._report_metrics(\n reset=reset, stage=stage, extra_metrics=extra_metrics, **kwargs\n )\n if stage != TrainingStage.EVAL:\n return (metrics, False)\n\n if self.best_eval_metrics is None or self.compare_metrics(\n metrics, self.best_eval_metrics\n ):\n self.best_eval_metrics = copy.deepcopy(metrics)\n return (metrics, True)\n else:\n return (metrics, False)\n\n def _report_metrics(\n self,\n reset: bool,\n stage: TrainingStage,\n extra_metrics: Optional[List[Metric]] = None,\n **kwargs,\n ) -> Any:\n timeline: Timeline = kwargs.get(\"timeline\", Timeline(global_round=1))\n # handle legacy case when epoch was provided\n epoch = kwargs.get(\"epoch\", 0)\n if epoch > 0 and timeline.global_round == 1:\n timeline = Timeline(epoch=epoch, round=1)\n eval_metrics = None\n\n training_stage_in_str = TrainingStage(stage).name.title()\n if len(self.losses) > 0:\n mean_loss = sum(self.losses) / len(self.losses)\n\n if Channel.STDOUT in self.channels:\n self.print(f\"{timeline}, Loss/{training_stage_in_str}: {mean_loss}\")\n if Channel.TENSORBOARD in self.channels:\n self.writer.add_scalar(\n f\"Loss/{training_stage_in_str}\",\n mean_loss,\n timeline.global_round_num(),\n )\n\n # Score is usually a more interpretable metric than loss and higher is better\n # For classification tasks, accuracy is a typical score\n scores = self.compute_scores()\n self.latest_scores = scores\n\n for score_name, score in scores.items():\n if Channel.STDOUT in self.channels:\n self.print(\n f\"{timeline}, {score_name}/{training_stage_in_str}: {score}\"\n )\n if Channel.TENSORBOARD in self.channels:\n self.writer.add_scalar(\n f\"{score_name}/{training_stage_in_str}\",\n score,\n timeline.global_round_num(),\n )\n\n # Construct evaluation metric object\n eval_metrics = self.create_eval_metrics(\n scores, mean_loss, timeline=timeline, stage=stage\n )\n\n # Miscellaneous metrics beyond loss and score\n metrics = extra_metrics or []\n for metric in metrics:\n value = Metric.to_dict(metric.value) if metric.is_compund else metric.value\n if Channel.STDOUT in self.channels:\n self.print(\n f\"{timeline}, {metric.name}/{training_stage_in_str}: {value}\"\n )\n if Channel.TENSORBOARD in self.channels:\n self.writer.add_scalars(\n f\"{metric.name}/{training_stage_in_str}\",\n value,\n timeline.global_round_num(),\n ) if metric.is_compund else self.writer.add_scalar(\n f\"{metric.name}/{training_stage_in_str}\",\n value,\n timeline.global_round_num(),\n )\n\n if reset:\n self.reset()\n\n return eval_metrics\n\n def reset(self):\n self.losses = []\n self.num_examples_list = []\n self.predictions_list = []\n self.targets_list = []\n self.model_inputs_list = []\n\n def get_latest_scores(self) -> Dict[str, Any]:\n return self.latest_scores\n\n @abc.abstractmethod\n def compare_metrics(self, eval_metrics, best_metrics) -> bool:\n \"\"\"One should provide concrete implementation of how to compare\n eval_metrics and best_metrics.\n Return True if eval_metrics is better than best_metrics\n \"\"\"\n pass\n\n @abc.abstractmethod\n def compute_scores(self) -> Dict[str, Any]:\n \"\"\"One should override this method to specify how to compute scores\n (e.g. accuracy) of the model based on metrics.\n Return dictionary where key is name of the scores and value is\n score.\n \"\"\"\n pass\n\n @abc.abstractmethod\n def create_eval_metrics(\n self, scores: Dict[str, Any], total_loss: float, **kwargs\n ) -> Any:\n \"\"\"One should provide a concrete implementation of how to construct\n an object that represents evaluation metrics based on scores and\n total loss. Usually, one would just pick one of the scores or\n total loss as the evaluation metric to pick the better model, but\n this interface also allows one to make evaluation metrics more\n complex and use them in conjunction with the compare_metrics()\n function to determine which metrics and corresponding model are\n better.\n \"\"\"\n pass\n","repo_name":"facebookresearch/FLSim","sub_path":"flsim/metrics_reporter/tensorboard_metrics_reporter.py","file_name":"tensorboard_metrics_reporter.py","file_ext":"py","file_size_in_byte":6602,"program_lang":"python","lang":"en","doc_type":"code","stars":215,"dataset":"github-code","pt":"55"}
+{"seq_id":"73133437930","text":"import torch\nimport sys\nimport os\nimport warnings\n\nimport sys\nsys.path.append('../../..')\nfrom tools.audio_representation import AudioRepresentation\n\n# List of representations to test\nrepresentations_to_test = [\n {\"model_name\": \"wav2vec2\", \"model_checkpoint\": \"facebook/wav2vec2-base-960h\", \"extra_params\": {\"layer_number\": 0}},\n {\"model_name\": \"wav2vec2\", \"model_checkpoint\": \"jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn\",\n \"extra_params\": {\"layer_number\": 0}},\n {\"model_name\": \"wavLM\", \"model_checkpoint\": \"microsoft/wavlm-base\", \"extra_params\": {\"layer_number\": 0}},\n {\"model_name\": \"HuBERT\", \"model_checkpoint\": \"facebook/hubert-base-ls960\", \"extra_params\": {\"layer_number\": 0}},\n {\"model_name\": \"data2vec\", \"model_checkpoint\": \"facebook/data2vec-audio-base\", \"extra_params\": {\"layer_number\": 0}},\n {\"model_name\": \"Whisper\", \"model_checkpoint\": \"openai/whisper-base\", \"extra_params\": {\"layer_number\": 0}},\n {\"model_name\": \"pyannote_audio\", \"model_checkpoint\": None, \"extra_params\": None},\n {\"model_name\": \"pyannote_audio\", \"model_checkpoint\": None,\n \"extra_params\": {'hf_token': 'KEY'}}, # TODO: REPLACE HERE\n {\"model_name\": \"pyannote_audio\", \"model_checkpoint\": None, \"extra_params\": {'window': 'sliding'}},\n {\"model_name\": \"apc\", \"model_checkpoint\": None, \"extra_params\": {\"layer_number\": 0}},\n {\"model_name\": \"apc\", \"model_checkpoint\": 'tera_fbankBase_T_F_AdamW_b32_200k_100hr', \"extra_params\": None},\n {\"model_name\": \"tera\", \"model_checkpoint\": None, \"extra_params\": {\"layer_number\": 0}},\n {\"model_name\": \"tera\", \"model_checkpoint\": 'apc_960hr', \"extra_params\": None},\n {\"model_name\": \"Byol\", \"model_checkpoint\": None, \"extra_params\": None},\n {\"model_name\": \"EcapaTDNN\", \"model_checkpoint\": None, \"extra_params\": None},\n {\"model_name\": \"EcapaTDNN\", \"model_checkpoint\": \"speechbrain/spkrec-xvect-voxceleb\", \"extra_params\": None},\n {\"model_name\": \"EcapaTDNN\", \"model_checkpoint\": \"Ubenwa/ecapa-voxceleb-ft2-cryceleb\", \"extra_params\": None},\n {\"model_name\": \"HumanCochleagram\", \"model_checkpoint\": None, \"extra_params\": None},\n {\"model_name\": \"LogMelSpectrogram\", \"model_checkpoint\": None, \"extra_params\": None},\n {\"model_name\": \"FAKE\", \"model_checkpoint\": None, \"extra_params\": None},\n]\n\n#representations_to_test = representations_to_test[:4]\nprint(\"representations_to_test\")\nprint(representations_to_test)\n\n# Ignore warnings\nwith warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n\n # Create a random input waveform\n input_waveform = torch.rand(4, 1, 32000)\n\n if input_waveform.size(1) == 1:\n input_waveform = input_waveform.squeeze(1)\n else:\n input_waveform = input_waveform[:, :1].squeeze(1)\n\n print('MONO DATA BATCH')\n print(input_waveform.shape)\n\n # Iterate over representations to test\n for representation_to_test in representations_to_test:\n print(representation_to_test)\n\n try:\n # Create an instance of the AudioRepresentation class\n audio_repr = AudioRepresentation(\n model_name=representation_to_test['model_name'],\n model_checkpoint=representation_to_test['model_checkpoint'],\n extra_params=representation_to_test['extra_params']\n )\n\n try:\n if audio_repr.contextual_encoding_exists:\n print('CONTEXTUAL AUDIO REPRESENTATION')\n raw_encoder_response, filtered_encoder_response = audio_repr.contextual_encoding(input_waveform)\n print(filtered_encoder_response.shape)\n print(audio_repr.contextual_encoding_size())\n else:\n print('NO CONTEXTUAL AUDIO REPRESENTATION')\n except Exception as e:\n print(\"An error has occurred : \", e)\n\n try:\n if audio_repr.temporal_encoding_exists:\n print('TEMPORAL AUDIO REPRESENTATION')\n raw_encoder_response, filtered_encoder_response = audio_repr.temporal_encoding(input_waveform)\n print(filtered_encoder_response.shape)\n print(audio_repr.temporal_encoding_size())\n else:\n print('NO TEMPORAL AUDIO REPRESENTATION')\n except Exception as e:\n print(\"An error has occurred : \", e)\n\n try:\n if audio_repr.pooled_temporal_encoding_exists:\n print('POOLED TEMPORAL AUDIO REPRESENTATION')\n raw_encoder_response, filtered_encoder_response = audio_repr.pooled_temporal_encoding(input_waveform)\n print(len(filtered_encoder_response))\n print(filtered_encoder_response[0]['global_min_pooling'].shape)\n print(audio_repr.pooled_temporal_encoding_size())\n else:\n print('NO POOLED TEMPORAL AUDIO REPRESENTATION')\n except Exception as e:\n print(\"An error has occurred : \", e)\n except Exception as e:\n print(\"An error has occurred : \", e)\n\n raw_encoder_response = None\n filtered_encoder_response = None","repo_name":"fabiocat93/fab","sub_path":"tools/audio_representation/test/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":5180,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"8725419838","text":"from Block import Block\n\n\n\ngenesis_block = Block(\"Chancellor on the brink...\", [\"Satoshi sent 1 BTC to Sam\",\n \"Maria sent 5 BTC to Jen\", \n \"Satoshi sent 10 BTC to Fin\"])\n\nsecond_block = Block(genesis_block.block_hash, [\"Manansh sent 5 BTC to Max\",\n \"John sent 10 BTC to Arnold\"])\n\nthird_block = Block(second_block.block_hash, [\"Alex sent 2 BTC to Siakam\",\n \"Arthur sent 10 BTC to Merlin\",\n \"Mac sent 0.22 BTC to Alice\"])\n\n\nprint(\"Block Hash: Genesis Block\\n\")\nprint(genesis_block.block_hash + \"\\n\")\n\n\nprint(\"Block hash: Second Block\\n\")\nprint(second_block.block_hash + \"\\n\")\n\n\nprint(\"Block hash: Third Block\\n\")\nprint(third_block.block_hash + \"\\n\")","repo_name":"manansh11/Python","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"2220838505","text":"from dataclasses import dataclass\nfrom netex.parking_bay_status_ref_structure import ParkingBayStatusRefStructure\n\n__NAMESPACE__ = \"http://www.netex.org.uk/netex\"\n\n\n@dataclass(unsafe_hash=True, kw_only=True)\nclass RentalAvailabilityRef(ParkingBayStatusRefStructure):\n \"\"\"Reference to a RENTAL AVAILABILITY.\n\n +v1.2.2\n \"\"\"\n class Meta:\n namespace = \"http://www.netex.org.uk/netex\"\n","repo_name":"skinkie/reference","sub_path":"gtfs-netex-test/netex/rental_availability_ref.py","file_name":"rental_availability_ref.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"20260443871","text":"import pandas as pd\nimport re\nimport jieba\nimport json\nimport os\n\nfrom numpy import *\nfrom sklearn.utils import shuffle\ndef set_result_to_file(result):\n train_num = result['train_spam_in_rows']+result['train_ham_in_rows']\n test_num = result['test_spam_in_rows']+result['test_ham_in_rows']\n result_path = os.path.join('./result','{train}_{test}'.format(train=train_num,test=test_num))\n with open(result_path, 'a') as f:\n f.write('\\n')\n json.dump(result, f)\n\n\ndef clean_str(string):\n string = re.sub(r\"[^\\u4e00-\\u9fff]\", \" \", string)\n string = re.sub(r\"\\s{2,}\", \" \", string)\n return string.strip()\n\n\ndef get_index(skiprows,nrows):\n index = pd.read_csv('./email/full/index', sep=' ', header=None, names=['type', 'path'], skiprows=skiprows, nrows=nrows)\n index = shuffle(index)\n print(\"data shape:\", index.shape)\n spams_in_rows = index.loc[index['type'] == \"spam\"].shape[0]\n ham_in_rows = index.loc[index['type'] == \"ham\"].shape[0]\n spam_path = index[index[\"type\"] == \"spam\"][\"path\"]\n ham_path = index[index[\"type\"] == \"ham\"][\"path\"]\n path = index[\"path\"]\n return index\n\n\ndef createVocabList(dataSet):\n '''\n 创建一个包含在所有文档中出现的不重复词的列表\n :param dataSet: 词\n :return: vocabset: 词汇表\n '''\n vocabSet = set([])\n for document in dataSet:\n vocabSet = vocabSet | set(document) # 并集\n return list(vocabSet)\n\n\n\n\n# 通过index文件获取spam和ham对应的文件的path,并且处理数据\ndef get_emailframe(path):\n with open(os.path.join('.', 'email', 'data', path), 'r', encoding='gbk', errors='ignore') as f:\n lines = f.readlines()\n email = ''\n for line in lines:\n line = clean_str(line)\n email += line\n f.close()\n email_word = [word for word in jieba.cut(email) if word.strip() != '']\n return email_word\n\n\n\ndef setOfWords2Vec(vocabList, inputSet):\n '''\n 词集模型\n 输入邮件的分词与词汇表对照,出现的标为1\n :param vocabList: 词汇表\n :param inputSet: 某个文档\n :return: returnVec: 文档向量，向量的每一元素为1或0\n '''\n returnVec = [0] * len(vocabList)\n for word in inputSet:\n returnVec[vocabList.index(word)] = 1\n else:\n print('词: {word} 不在字典中'.format(word=word))\n return returnVec\n\n\ndef bagOfWords2VecMN(vocabList, inputSet):\n '''\n 词袋模型\n 输入邮件的分词与词汇表对照,出现的标为出现次数\n :param vocabList: 词汇表\n :param inputSet: 某个文档\n :return: returnVec: 文档向量\n '''\n returnVec = [0] * len(vocabList)\n for word in inputSet:\n if word in vocabList:\n returnVec[vocabList.index(word)] += 1\n return returnVec\n\n\ndef trainNB0(trainMatrix, trainCategory):\n '''\n\n :param trainMatrix:\n :param trainCategory:\n :return:\n\n '''\n # numtraindocs 邮件总数\n numTrainDocs = len(trainMatrix)\n # 词袋中词汇总数\n numWords = len(trainMatrix[0])\n # pSpam 垃圾邮件百分比\n pSpam = sum(trainCategory) / float(numTrainDocs)\n #拉普拉斯平滑 由于2分类 每个词汇数量+1，词汇总数+2 防止0/0\n p0Num = ones(numWords)\n p1Num = ones(numWords)\n p0Denom = 2.0\n p1Denom = 2.0\n for i in range(numTrainDocs):\n if trainCategory[i]==1:\n p1Num+= trainMatrix[i]\n p1Denom+=sum(trainMatrix[i])\n else:\n p0Num+=trainMatrix[i]\n p0Denom+=sum(trainMatrix[i])\n # 某词语在所有词语中的比例 加上log防止过小\n p1Vect = log(p1Num/p1Denom)\n p0Vect = log(p0Num/p0Denom)\n return p0Vect,p1Vect,pSpam\n\ndef classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):\n '''\n\n :param vec2Classify:\n :param p0Vec:\n :param p1Vec:\n :param pClass1:\n :return:\n '''\n p1=sum(vec2Classify*p1Vec)+log(pClass1)\n p0=sum(vec2Classify*p0Vec)+log(1.0-pClass1)\n if p1>p0:\n return 1\n else:\n return 0\n\n\ndef spamTest(trainNum, testNum):\n docList = []\n classList = []\n index = get_index(0,trainNum+testNum)\n spams_in_rows = index.loc[index['type'] == \"spam\"].shape[0]\n ham_in_rows = index.loc[index['type'] == \"ham\"].shape[0]\n print(spams_in_rows,ham_in_rows)\n for type, path in zip(index['type'], index['path']):\n wordList = get_emailframe(path)\n docList.append(wordList) # 用来创建字典\n if type == 'spam':\n classList.append(1) # 1代表垃圾邮件\n else:\n classList.append(0)\n vocabList = createVocabList(docList) # 创建词典\n trainingSet = range(trainNum)\n testSet = range(trainNum, trainNum + testNum)\n trainMat = [] # 训练向量\n trainClasses = [] # 训练类\n for docIndex in trainingSet:\n trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))\n trainClasses.append(classList[docIndex])\n\n p0V, p1V, pSpam = trainNB0(array(trainMat), array(trainClasses))\n TP = 0\n FP = 0\n TN = 0\n FN = 0\n # accuracy=0\n # precision=0\n # recall=0\n for docIndex in testSet: # classify the remaining items\n wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])\n if classifyNB(array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:\n if(classifyNB(array(wordVector), p0V, p1V, pSpam)):\n FP+=1\n print(\"分类错误\", docList[docIndex],'\\n',classList[docIndex])\n else:\n FN+=1\n else:\n if (classifyNB(array(wordVector), p0V, p1V, pSpam)):\n TP+=1\n else:\n TN+=1\n accuracy = (TP + TN )/( TP + FP + TN + FN)\n precision = TP / (TP + FP)\n recall = TP / (TP + FN)\n train_type = get_index(0,trainNum)\n test_type = get_index(trainNum,testNum)\n\n train_spam_in_rows = train_type.loc[train_type['type'] == \"spam\"].shape[0]\n train_ham_in_rows = train_type.loc[train_type['type'] == \"ham\"].shape[0]\n test_spam_in_rows = test_type.loc[test_type['type'] == \"spam\"].shape[0]\n test_ham_in_rows = test_type.loc[test_type['type'] == \"ham\"].shape[0]\n result = {}\n result['accuracy']=accuracy\n result['precision'] = precision\n result['recall'] = recall\n result['train_spam_in_rows'] = train_spam_in_rows\n result['train_ham_in_rows'] = train_ham_in_rows\n result['test_spam_in_rows'] = test_spam_in_rows\n result['test_ham_in_rows'] = test_ham_in_rows\n set_result_to_file(result)\n print(result)\n\n # return vocabList,fullText\n\n\nif __name__ == '__main__':\n # spamTest(800,300)\n # spamTest(800, 300)\n # spamTest(800, 300)\n # spamTest(800, 300)\n # spamTest(1000,300)\n # spamTest(1000, 300)\n # spamTest(1000, 300)\n # spamTest(1500,400)\n # spamTest(1500, 400)\n # spamTest(1500, 400)\n spamTest(3000,500)\n spamTest(3000, 500)\n\n#统计正确率（accuracy）、准确率(Precision)、召回率（recall）；","repo_name":"nichoushad/bayesian","sub_path":"new.py","file_name":"new.py","file_ext":"py","file_size_in_byte":6971,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"55"}
+{"seq_id":"37411999483","text":"import pandas\nimport sys\nsys.path.insert(1, '/scripts')\nfrom automation_utils import AutomationUtils\nfrom atr_sdk import ATRConsul, ATRApi\nimport datetime\nimport csv\nimport os\nimport requests\nimport json\n\nutils = AutomationUtils()\nconfiguration = utils.load_configuration()\nuniqueTaskIDs = sys.argv[1]\nprint(uniqueTaskIDs)\nDiff_Task_IDs = uniqueTaskIDs.split(', ')\nprint(Diff_Task_IDs)\n\n\n\n# for fields in configuration['Consider ticket fields']:\nprint(configuration['Consider ticket fields']['Caller and Affected contact details'])\n\nfile_path = \"WorkflowMonitoringWithoutAgent/TaskNumber_AssignmentGrp.csv\"\nfile_path_shortName = \"WorkflowMonitoringWithoutAgent/ShortNameOfResolverGroups.csv\"\nTaskIncidentFilePath = \"WorkflowMonitoringWithoutAgent/testappend.csv\"\n\ndf = pandas.read_csv(file_path)\ndf_shortName = pandas.read_csv(file_path_shortName)\n\ndicts = df.to_dict('records')\ndicts_shortName = df_shortName.to_dict('records')\n\n# print(\"Weekday: \", datetime.datetime.now().weekday())\n\n\n#defining all field values for the ticket\ndef main():\n \n # Diff_Task_IDs = [\"TS99000692\", \"TS99000691\", \"TS99000926\", \"TS00008316\", \"TS99001106\", \"TS99001360\", \"TS99000783\", \"TS99000241\", \"TS99000231\", \"TS99001030\"]\n\n\n\n dict_list = {}\n \n i = 0 \n \n try:\n \n for key in dicts:\n csvdata = dicts[i]\n TaskID = csvdata.get('Task ID')\n Assignment_group = csvdata.get('Assignment')\n dict_var = {\n TaskID : Assignment_group\n }\n i += 1\n \n print(dict_var)\n dict_list.update(dict_var)\n \n print(dict_list)\n print(\"$$$$$$$$$$$$$$$$$$$$$$\")\n \n ticket_assignment_dict = {}\n \n weekday_int = datetime.datetime.now().weekday() #Get the current day of the week as an integer (0 = Monday, 6 = Sunday)\n print(weekday_int)\n \n if weekday_int == 0:\n print(\"Only Monday...\")\n for item in Diff_Task_IDs:\n if item in dict_list:\n \n print(dict_list[item])\n print(item)\n ticket_assignment_dict.update({item: dict_list[item]})\n # payload(item, dict_list[item])\n # Incident_Number = Create_Ticket(item, dict_list[item])\n # print(Incident_Number)\n if not is_value_present_in_csv(TaskIncidentFilePath, item):\n Incident_Number = Create_Ticket(item, dict_list[item])\n print(Incident_Number)\n append_to_csv(TaskIncidentFilePath, item, Incident_Number)\n else:\n print(\"Value already present in the CSV file.\")\n \n \n else:\n print(dict_list[\"Other\"])\n ticket_assignment_dict.update({item: dict_list[\"Other\"]})\n # payload(item, dict_list[\"Other\"])\n # print(ticket_assignment_dict)\n # Create_Ticket(item, dict_list[\"Other\"])\n print(\"Testing\")\n # Incident_Number = Create_Ticket(item, dict_list[\"Other\"])\n # print(Incident_Number)\n if not is_value_present_in_csv(TaskIncidentFilePath, item):\n Incident_Number = Create_Ticket(item, dict_list[\"Other\"])\n print(Incident_Number)\n append_to_csv(TaskIncidentFilePath, item, Incident_Number)\n else:\n print(\"Value already present in the CSV file.\")\n \n else:\n print(\"except Monday...\")\n for item in Diff_Task_IDs:\n if item in dict_list:\n \n print(dict_list[item])\n ticket_assignment_dict.update({item: dict_list[item]})\n \n # Incident_Number = Create_Ticket(item, dict_list[item])\n # print(Incident_Number)\n if not is_value_present_in_csv(TaskIncidentFilePath, item):\n Incident_Number = Create_Ticket(item, dict_list[item])\n print(Incident_Number)\n append_to_csv(TaskIncidentFilePath, item, Incident_Number)\n else:\n print(\"Value already present in the CSV file.\")\n \n \n else:\n print(dict_list[\"Other\"])\n ticket_assignment_dict.update({item: dict_list[\"Other\"]})\n # Incident_Number = Create_Ticket(item, dict_list[\"Other\"])\n # print(Incident_Number)\n if not is_value_present_in_csv(TaskIncidentFilePath, item):\n Incident_Number = Create_Ticket(item, dict_list[\"Other\"])\n print(Incident_Number)\n append_to_csv(TaskIncidentFilePath, item, Incident_Number)\n else:\n print(\"Value already present in the CSV file.\")\n \n print(\"ticket_assignment_dict: \", ticket_assignment_dict)\n except Exception as e:\n print(e) \n\n\n# Ticket_Type, Assignment_Group, Short_Description, Description, Business_Service, Configuration_item, Caller, Affected_Contact, Priority_level, Urgency_level, Diff_TaskID\ndef Create_Ticket(Task_number, Assignment_Group):\n \n # def payload(Task_number, Assignment_Group):\n current_date = datetime.datetime.now().strftime(\"%d.%m.%Y\")\n print(current_date)\n \n # Diff_TASKID = Task_number\n tickettype = \"Incident\"\n print(tickettype)\n assignmentgroup = Assignment_Group\n print(assignmentgroup)\n \n if assignmentgroup == \"IST RTBS Functional Support MP PY\":\n prioritylevel = \"3- Medium\"\n urgencylevel = \"3 - Medium\"\n \n else:\n prioritylevel = \"4- Low\"\n urgencylevel = \"4 - Low\"\n print(prioritylevel)\n print(urgencylevel)\n S_name = \"\"\n j = 0\n for key_shortName in dicts_shortName:\n \n csvData_shortName = key_shortName\n resolverGroup = csvData_shortName.get('Resolver Group')\n short_resolverGroup = csvData_shortName.get('Short Name')\n \n if resolverGroup == assignmentgroup:\n S_name = short_resolverGroup\n # shortdescription = \"Workflow without agent- \" + S_name + \" - \"+ current_date + \" - \" +Diff_TASKID\n \n j += 1\n shortdescription = \"Workflow without agent- \" + S_name + \" - \"+ current_date + \" - \" +Task_number \n description = \"Workflow without agent- \" + S_name + \" - \"+ current_date + \" - \" +Task_number \n print(shortdescription)\n print(description)\n \n businessservice = \"RTBS/Workflow\"\n print(businessservice)\n configurationitem = \"RTBS/Workflow\"\n print(configurationitem)\n callername = \"Agarwal, Anshul (IST-ACCENTURE)\"\n print(callername)\n affected = \"Agarwal, Anshul (IST-ACCENTURE)\"\n print(affected)\n \n # return current_date, tickettype, Assignment_Group, shortdescription, description, businessservice, configurationitem, callername, affected\n\n atr_consul = ATRConsul()\n \n admin_password = atr_consul.get('configuration/aaam-atr-v3-identity-management/admin.password')\n atr = ATRApi('admin', admin_password)\n token = atr.token\n # print(token)\n # print(atr.im_url)\n # print(atr.atr_user)\n # print(atr.atr_password)\n postPayload = {\n \"type\": tickettype,\n \"assignmentGroupATR\": assignmentgroup,\n \"priority\": prioritylevel,\n \"urgency\": urgencylevel,\n \"shortDescription\":shortdescription,\n \"description\":description,\n \"business_service\": businessservice,\n \"cmdb_ci\":configurationitem,\n \"caller_id\": callername,\n \"contact_type\": affected,\n \n }\n\n\n\n apiBaseUrl = \"http://ticket-management:8080/api/v1/tickets\"\n try:\n \n res = requests.request(\"POST\", apiBaseUrl, headers=atr.headers, data=json.dumps(postPayload), verify = False)\n Ticket_Incident = res.json()['coreData']['number']\n print(Ticket_Incident)\n return Ticket_Incident\n code = res.status_code\n print(code)\n # try: \n \n # if res.status_code == 201:\n \n # Ticket_Incident = res.json()['coreData']['number']\n # print(Ticket_Incident)\n # return Ticket_Incident\n # else:\n # return \"Ticket not created...\"\n except requests.exceptions.RequestException as e:\n print(\"Error making POST request:\", e)\n return None, None\n except requests.exceptions.HTTPError as e:\n print(\"HTTP error occurred:\", e)\n return None, res.status_code\n # print(res.text['coreData']['number'])\n\n # print(res.std_err)\n\n\n\n# Create_Ticket(tickettype, assignmentgroup, shortdescription, description, businessservice, configurationitem, callername, affected, prioritylevel, urgencylevel)\ndef is_value_present_in_csv(TaskIncidentFilePath, search_value):\n with open(TaskIncidentFilePath, 'r', newline='') as csvfile:\n csv_reader = csv.reader(csvfile)\n for row in csv_reader:\n # Assuming the value to be checked is in the first column (index 0)\n if row and row[0] == search_value:\n return True\n return False\n \ndef append_to_csv(TaskIncidentFilePath, task_id, incident):\n fieldnames = ['Task ID', 'Incidents']\n \n if not os.path.isfile(TaskIncidentFilePath):\n with open(TaskIncidentFilePath, 'w', newline='') as TaskIncidentCSVFile:\n # fieldnames = ['Task ID', 'Incidents']\n writer = csv.DictWriter(TaskIncidentCSVFile, fieldnames=fieldnames)\n # if csvfile.tell() == 0:\n writer.writeheader()\n \n \n with open(TaskIncidentFilePath, 'a', newline='') as appendTaskIncidentCSVFile:\n writer = csv.DictWriter(appendTaskIncidentCSVFile, fieldnames=fieldnames)\n writer.writerow({'Task ID': task_id, 'Incidents': incident})\n \n return \"Successfully appended\"\n\n# append_to_csv(path, 'task...ID', \"incidentnumber\")\n \n \nif __name__ == \"__main__\":\n try:\n sys.exit(main())\n except Exception as e:\n print(str.join(\" \", str(e).splitlines())) \n \n","repo_name":"anshul2701/WorkflowMonitoringCreateTicketOnBasicChecks","sub_path":"WFMWACreateTicketsViaBasicChecks.py","file_name":"WFMWACreateTicketsViaBasicChecks.py","file_ext":"py","file_size_in_byte":10470,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"22051322134","text":"from camera import HDIntegratedCamera\nfrom observer_pattern.observer import Observer\nimport numpy\nfrom widefind import WideFind\nimport widefind as wf\nimport pymysql\n\nclass Controller(Observer):\n def __init__(self):\n #Instantiates all relevant tools the controller needs to operate\n \n self.getAllLogs()\n\n self.src = \"http://130.240.105.144/cgi-bin/mjpeg?resolution=1920x1080&framerate=5&quality=1\"\n\n self.camera_bedroom_pos = numpy.array([619, 3935, 2600])\n self.camera_bedroom_zero = numpy.array([-765, 4112, 2878])\n self.camera_bedroom_floor = numpy.array([531, 3377, 331])\n\n self.camera_kitchen_pos = numpy.array([2873, -2602, 2186])\n self.camera_kitchen_zero = numpy.array([3413, -2722, 2284])\n self.camera_kitchen_floor = numpy.array([2694, -2722, 193])\n\n self.cam = HDIntegratedCamera(\"http://130.240.105.144/cgi-bin/aw_ptz?cmd=%23\")\n self.cam_trans = wf.Transform(self.camera_kitchen_pos, self.camera_kitchen_zero, self.camera_kitchen_floor)\n\n self.rotate(210, 140)\n\n self.trackers = []\n self.trackersDict = {}\n\n self.rot_amount = 6\n \n self.followTarget = \"\"\n self.is_follow = False\n\n def createWideFindNameDict(self):\n #A function for creating a dictionary to link sensor id to a name\n oldNamesDict = {\"Kitchen counter\":\"543D85B1B2D91E29\",\n \"Kitchen corner 1\":\"9691FE799F371A4C\",\n \"Kitchen corner 2\":\"D4984282E2E4D10B\",\n \"Bedroom computer\": \"4B2A8EE2B9BAAAC0\",\n \"Door\":\"03FF5C0A2BFA3A9B\",\n \"person1\": \"F1587D88122BE247\",\n \"Bed\": \"6881445FDC01E3F2\"\n }\n self.WideFindNameDict = {}\n for key, value in self.trackersDict.items():\n for name, old_value in oldNamesDict.items():\n if key == old_value and key not in self.WideFindNameDict.values():\n self.WideFindNameDict[name] = key \n\n def rotate(self, i, j):\n #A function handling a rotate command\n self.cam.rotate(i, j)\n\n def lookAtWideFind(self, val):\n #A function handling a look at sensor command by calling rotate command with specific cordinates \n if val in self.trackers:\n tracker_pos = self.trackersDict[val]\n new_yaw = self.cam_trans.get_yaw_from_zero(tracker_pos)\n new_pitch = self.cam_trans.get_pitch_from_zero(tracker_pos)\n if new_pitch > 70:\n new_pitch = 70\n self.cam.rotate(new_yaw, new_pitch + 80)\n\n def followWideFind(self, val):\n #A function turning on follow so camera follows a specific sensor\n self.followTarget = val\n\n def switchCam(self, cam):\n #A function that switches camera by changing url to camera and changing its transform to have the right room values\n if(cam == \"Kitchen\"):\n self.cam = HDIntegratedCamera(\"http://130.240.105.144/cgi-bin/aw_ptz?cmd=%23\")\n self.cam_trans = wf.Transform(self.camera_kitchen_pos, self.camera_kitchen_zero, self.camera_kitchen_floor)\n if(cam == \"Bedroom\"):\n self.cam = HDIntegratedCamera(\"http://130.240.105.145/cgi-bin/aw_ptz?cmd=%23\")\n self.cam_trans = wf.Transform(self.camera_bedroom_pos, self.camera_bedroom_zero, self.camera_bedroom_floor)\n\n #Handling of manual input with arrow keys\n def up(self):\n self.is_follow = False\n self.cam.rotate(self.cam.get_current_yaw(), self.cam.get_current_pitch() + self.rot_amount)\n\n def down(self):\n self.is_follow = False\n self.cam.rotate(self.cam.get_current_yaw(), self.cam.get_current_pitch() - self.rot_amount)\n\n def left(self):\n self.is_follow = False\n self.cam.rotate(self.cam.get_current_yaw() - self.rot_amount, self.cam.get_current_pitch())\n\n def right(self):\n self.is_follow = False\n self.cam.rotate(self.cam.get_current_yaw() + self.rot_amount, self.cam.get_current_pitch())\n\n #Handling of zoom in and zoom out on interface\n def zoomIn(self):\n self.cam.zoom(50)\n\n def zoomOut(self):\n self.cam.zoom(0)\n\n def databaseConn(self):\n #localhost xampp phpmyadmin database\n self.connection = pymysql.connect(host=\"localhost\", user=\"root\", passwd=\"\", database=\"log\")\n self.cursor = self.connection.cursor()\n\n #logtable( log_id(int), entry(text), created_at(timestamp))\n\n def getAllLogs(self):\n #A function that connects to database and gets the 10 most relevant actions done on the interface\n self.databaseConn()\n\n sql = \"SELECT * FROM log_table ORDER BY log_id DESC LIMIT 10\"\n self.cursor.execute(sql)\n self.log_rows = self.cursor.fetchall()\n self.connection.close()\n \n def databaseActions(self, action):\n #A function that adds a action, done through the interface, to the database\n self.databaseConn()\n sql = \"INSERT INTO log_table(entry) VALUES('\" + str(action) + \"');\"\n print(sql)\n self.cursor.execute(sql)\n self.connection.commit()\n self.connection.close()\n self.getAllLogs()\n return self.log_rows\n\n \n\n def update(self, subject: WideFind):\n #Gets notifications from widefind and updates all relevant data aswell as handling following a sensor\n self.trackersDict = subject.trackers\n self.createWideFindNameDict()\n self.trackers = subject.trackers.keys()\n if(self.is_follow == True):\n if(self.followTarget in self.trackers):\n self.lookAtWideFind(self.followTarget)\n \n","repo_name":"ItzCornflakez/D0020E_Project","sub_path":"controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":5776,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"55"}
+{"seq_id":"21755255356","text":"#!/usr/bin/env python\nfrom __future__ import print_function\n\nimport roslib\nroslib.load_manifest('camera1')\nimport sys\nimport rospy\nimport cv2\nimport numpy as np\nfrom std_msgs.msg import String,Int32,Int32MultiArray,MultiArrayLayout,MultiArrayDimension\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge, CvBridgeError\n\nclass image_converter:\n\n def __init__(self):\n self.image_pub = rospy.Publisher(\"image_topic_2\",Image)\n self.pub = rospy.Publisher('pixpos', Int32MultiArray, queue_size=1)\n self.bridge = CvBridge()\n self.image_sub = rospy.Subscriber(\"/camera/color/image_raw\",Image,self.callback)\n\n def callback(self,data):\n try:\n cv_image = self.bridge.imgmsg_to_cv2(data, \"bgr8\")\n cv2.imshow(\"Image window r\", cv_image)\n except CvBridgeError as e:\n print(e)\n (rows,cols,channels) = cv_image.shape\n if cols > 60 and rows > 60 :\n cv2.circle(cv_image, (50,50), 10, 255)\n cv_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB)\n cv_image = cv2.cvtColor(cv_image, cv2.COLOR_RGB2HSV)\n dark_green_bgr = np.uint8([[[100,10,10 ]]])\n dark_green = cv2.cvtColor(dark_green_bgr, cv2.COLOR_BGR2HSV)\n dark_green = (100,0,0)\n light_green = (300,255,255)\n lower_red = np.array([100,150,10]) \n upper_red = np.array([180,255,200])\n mask = cv2.inRange(cv_image,lower_red, upper_red)\n result = cv2.bitwise_and(cv_image, cv_image, mask=mask)\n result = cv2.cvtColor(result, cv2.COLOR_HSV2BGR)\n cv_image = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)\n ret,thresh = cv2.threshold(cv_image, 0, 255, 0)\n cv_image, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n c = max(contours, key = cv2.contourArea)\n M = cv2.moments(c)\n cX = int(M[\"m10\"] / M[\"m00\"])\n cY = int(M[\"m01\"] / M[\"m00\"])\n pix_coord = [cX,cY]\n msg = Int32MultiArray()\n msg.data = pix_coord\n self.pub.publish(msg)\n cv2.circle(cv_image, (cX,cY), 7, (0,0,0),-1)\n cv2.putText(cv_image, \"center\", (cX -20, cY - 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 2)\n #print(pix_coord)\n cv2.drawContours(cv_image, [c], 0, (80,0,0), 3)\n cv2.imshow(\"Image window\", cv_image)\n cv2.waitKey(3)\n\n try:\n self.image_pub.publish(self.bridge.cv2_to_imgmsg(cv_image, \"mono8\"))\n except CvBridgeError as e:\n print(e)\n\ndef main(args):\n \n rospy.init_node('image_converter', anonymous=True)\n ic = image_converter()\n \n try:\n rospy.spin()\n except KeyboardInterrupt:\n print(\"Shutting down\")\n cv2.destroyAllWindows()\n\nif __name__ == '__main__':\n main(sys.argv)\n","repo_name":"NicolaiMatukhno/test","sub_path":"camera1/scripts/opencv_viewer_example.py","file_name":"opencv_viewer_example.py","file_ext":"py","file_size_in_byte":2576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"14206276012","text":"import os\nimport speech_recognition as sr\nimport ffmpeg\n\nimport openai\nimport re\n\ncommand2mp3 = \"ffmpeg -i /YourFolder/FileName.mp4 /YourFolder/FileName.mp3\"\ncommand2wav = \"ffmpeg -i /YourFolder/FileName.mp3 /YourFolder/FileName.wav\"\n\nos.system(command2mp3)\nos.system(command2wav)\n\n# Set the chunk size and the audio file path\nchunk_size = 1024\naudio_file = \"/YourFolder/FileName.wav\"\n\nfull_text = \"\"\n\n# Initialize the recognizer\nr = sr.Recognizer()\n\n# Open the audio file\nwith sr.AudioFile(audio_file) as source:\n # Iterate over the audio chunks\n while True:\n # Read the chunk from the audio file\n chunk = source.stream.read(chunk_size)\n \n # Exit the loop if there is no more data to read\n if len(chunk) == 0:\n break\n \n # Feed the chunk to the recognizer\n r.adjust_for_ambient_noise(source)\n r.operation_timeout = 1000\n r.pause_threshold = 5.0\n audio = r.record(source, duration=120)\n\n # Print the recognized text\n full_text += r.recognize_google(audio)\n \n# Set up OpenAI API key and model\nopenai.api_key = \"YourKey\"\nmodel_engine = \"text-davinci-002\"\n\n# Define function to punctuate a text string\ndef punctuate_text(text):\n # Split the text into chunks of 2048 characters or less\n text_chunks = re.findall(r\".{1,2048}(?:\\s|$)\", text)\n\n # Initialize the punctuated text string\n punctuated_text = \"\"\n\n # Punctuate each chunk of text and append it to the punctuated text string\n for chunk in text_chunks:\n prompt = f\"Punctuate and break it into readable paragraphs the following text:\\n{chunk}\"\n response = openai.Completion.create(\n engine=model_engine,\n prompt=prompt,\n max_tokens=1024,\n n=1,\n stop=None,\n temperature=0.5,\n )\n punctuated_chunk = response.choices[0].text.strip()\n punctuated_text += punctuated_chunk\n\n # Return the punctuated text string\n return punctuated_text\n\n# usage\npunctuated_article = punctuate_text(full_text)\nprint(punctuated_article)\n","repo_name":"Owl132580/Video2Article","sub_path":"Code.py","file_name":"Code.py","file_ext":"py","file_size_in_byte":2100,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"55"}
+{"seq_id":"15705628272","text":"# -*- coding: utf-8 -*- \n\"\"\"\n--- Day 4: Security Through Obscurity ---\nFinally, you come across an information kiosk with a list of rooms. Of course, \nthe list is encrypted and full of decoy data, but the instructions to decode the \nlist are barely hidden nearby. Better remove the decoy data first.\n\nEach room consists of an encrypted name (lowercase letters separated by dashes) \nfollowed by a dash, a sector ID, and a checksum in square brackets.\n\nA room is real (not a decoy) if the checksum is the five most common letters in \nthe encrypted name, in order, with ties broken by alphabetization. For example:\n\n- aaaaa-bbb-z-y-x-123[abxyz] is a real room because the most common letters are \n a (5), b (3), and then a tie between x, y, and z, which are \n listed alphabetically.\n- a-b-c-d-e-f-g-h-987[abcde] is a real room because although the letters are all \n tied (1 of each), the first five are listed alphabetically.\n- not-a-real-room-404[oarel] is a real room.\n- totally-real-room-200[decoy] is not.\n\nOf the real rooms from the list above, the sum of their sector IDs is 1514.\n\nWhat is the sum of the sector IDs of the real rooms?\n\nPERSONAL NOTES:\n* Not awfully complex. Iterate over each line in the input\n* Assume everything after the last - is the ID and the checksum. \n* Iterate over everything before that to tally up the usage of the letters.\n* Knowing regex would be useful, but not necessary.\n\"\"\"\n\nimport os\n\n\ndef _make_letter_usage_map( room_name ):\n\t\"\"\"\n\tEnter a description of the function here.\n\t\n\t**Arguments:**\n\t\n\t\t:``Argument``:\t`arg_type` Enter a description for the argument here.\n\t\n\t**Keyword Arguments:**\n\t\n\t\t:``Argument``:\t`arg_type` Enter a description for the keyword argument here.\n\t\n\t**Returns:**\n\t\n\t\t`arg_type` If any, enter a description for the return value here.\n\t\"\"\"\n\n\tletter_usage_map = { }\n\n\tfor c in room_name:\n\t\tletter_usage_map[ c ] = letter_usage_map.get( c, 0 ) + 1\n\n\treturn letter_usage_map\n\n\ndef _verify_real_room( letter_usage_map, checksum ):\n\t\"\"\"\n\tEnter a description of the function here.\n\t\n\t**Arguments:**\n\t\n\t\t:``Argument``:\t`arg_type` Enter a description for the argument here.\n\t\n\t**Keyword Arguments:**\n\t\n\t\t:``Argument``:\t`arg_type` Enter a description for the keyword argument here.\n\t\n\t**Returns:**\n\t\n\t\t`arg_type` If any, enter a description for the return value here.\n\t\"\"\"\n\t\"\"\"\n\t* Idea: Iterate over checksum. If any char in it is\n\t not in letter_usage_map, early return False.\n\t* Otherwise use weighting values in letter_usage_map to verify the char is\n\t in the correct location.\n\n\t\"\"\"\n\n\t# Characters in checksum are in order of highest usage to lowest.\n\t# Usage ties are won by alphabetical order.\n\t# For instance in the first line of the data (which is a valid room) the\n\t# checksum is 'qhiwf'. i and w are both used 4 times in the room name.\n\t# Since i comes before w in the checksum they are in the correct locations.\n\n\t# Checksums must be exactly five characters.\n\tif len( checksum ) != 5:\n\t return False \n\t\n\tvalue = 9999 # ridiculously large to start with.\n\tchar = ''\n\tfor c in checksum:\n\n\t\tif c not in letter_usage_map.keys( ):\n\t\t\treturn False\n\t\telse:\n\t\t\tv = letter_usage_map.get( c )\n\t\t\tif v < value:\n\t\t\t\tvalue = v\n\t\t\t\tchar = c\n\t\t\telif v == value:\n\t\t\t\t# use ASCII codes to determine if items are in the correct order.\n\t\t\t\tif ord( char ) < ord( c ):\n\t\t\t\t\tvalue = v\n\t\t\t\t\tchar = c\n\t\t\t\telse:\n\t\t\t\t\treturn False\n\t\t\telse:\n\t\t\t\treturn False\n\n\treturn True\n\n\n\ndef find_real_rooms( ):\n\t\"\"\"\n\tEnter a description of the function here.\n\t\n\t**Arguments:**\n\t\n\t\t:``Argument``:\t`arg_type` Enter a description for the argument here.\n\t\n\t**Keyword Arguments:**\n\t\n\t\t:``Argument``:\t`arg_type` Enter a description for the keyword argument here.\n\t\n\t**Returns:**\n\t\n\t\t`arg_type` If any, enter a description for the return value here.\n\t\"\"\"\n\n\tid_sum = 0\n\tpuzzle_input_filepath = os.path.abspath( \n\t\t\t\t\t\t\t\t\tos.path.join( os.getcwd( ),'04_puzzle_input.txt' ) )\n\n\twith open( puzzle_input_filepath ) as file:\n\t\tfor line in file:\n\t\t\tparts = line.rstrip( ']\\n' ).split( '-' )\n\n\t\t\t# Last element in parts contains the ID and the checksum.\n\t\t\tid, checksum = parts[ -1 ].split( '[' )\n\t\t\tid = int( id )\n\n\t\t\troom_name = ''\n\t\t\tfor i in range( len( parts ) - 1 ):\n\t\t\t\troom_name += parts[ i ] + '-'\n\n\t\t\troom_name = room_name.rstrip( '-' )\n\t\t\tletter_usage_map = _make_letter_usage_map( room_name )\n\t\t\t\n\t\t\tif _verify_real_room( letter_usage_map, checksum ):\n\t\t\t\tid_sum += id\n\n\treturn id_sum\n\n\n\t\t\t\t\t\t\t\t\t\t\t\t \nif __name__ == '__main__':\n\tid_sum = find_real_rooms( )\n\tprint( 'The sum of the sector IDs of the real rooms is {0}.'.format( id_sum ) )","repo_name":"JeffHanna/Advent_of_Code_2016","sub_path":"Day_04/04_01.py","file_name":"04_01.py","file_ext":"py","file_size_in_byte":4565,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"4131779537","text":"\n# Simulate random walks\nnp.random.seed(2020) # set random seed\nsim = random_walk_simulator(5000, 1000, mu=0, sigma=1)\n\n# Compute mean\nmu = np.mean(sim, axis=0)\n\n# Compute variance\nvar = np.var(sim, axis=0)\n\n# Visualize\nwith plt.xkcd():\n plot_mean_var_by_timestep(mu, var)","repo_name":"NeuromatchAcademy/course-content","sub_path":"tutorials/W2D2_LinearSystems/solutions/W2D2_Tutorial3_Solution_796a6346.py","file_name":"W2D2_Tutorial3_Solution_796a6346.py","file_ext":"py","file_size_in_byte":273,"program_lang":"python","lang":"en","doc_type":"code","stars":2510,"dataset":"github-code","pt":"55"}
+{"seq_id":"14071552738","text":"\"\"\"\n\npypovlib/pypovrayqueue.py\n\nwritten by: Oliver Cordes 2019-03-04\nchanged by: Oliver Cordes 2020-04-04\n\n\"\"\"\n\nimport sys, os\n\nimport configparser\nimport tarfile\nimport uuid\nimport time\n\ntry:\n from rq_client.api import Session\n from rq_client.projects import Project, PROJECT_TYPE_IMAGE, PROJECT_TYPE_ANIMATION\n from rq_client.images import Image\n from rq_client.files import File\nexcept:\n print('rayqueue client modules not found!')\n sys.exit(-1)\n\n\nfrom pypovlib.pypovobjects import *\nfrom pypovlib.pypovanimation import *\n\n\n# helper functions\n\"\"\"\n\n\"\"\"\ndef tarinfo_reset(tarinfo):\n tarinfo.uid = tarinfo.gid = 0\n tarinfo.uname = tarinfo.gname = 'root'\n return tarinfo\n\n\n\nclass RQPovObj(object):\n def __init__(self, config=None,\n rq_project_name=None,\n timeout=3600,\n sleep=5,\n width=640,\n height=480):\n\n # RQ specific information\n self._session = Session(config=config, verbose=True)\n\n self._rq_project_name = rq_project_name\n self._rq_projects = None\n self._rq_project = None\n\n self._timeout = timeout\n self._sleep = sleep\n\n self._width = width\n self._height = height\n\n self._add_args = None\n\n\n def set_project(self, new_project):\n self._rq_project_name = new_project\n\n\n def set_geometry(self, width, height):\n if width is not None:\n self._width = width\n if height is not None:\n self._height = height\n\n\n def set_add_args(self, args):\n self._add_args = args\n\n\n def _rq_login(self):\n if not self._session.login():\n print('Cannot login into the RQ service!')\n return False\n\n self._session.send_host_info()\n\n print('Successfully logged in to the RQ service!')\n\n return True\n\n\n def _select_rq_project(self, project_type):\n self._rq_projects = Project.queryall(self._session)\n if (self._rq_project_name is not None) and (self._rq_project_name != ''):\n for p in self._rq_projects:\n if p.name == self._rq_project_name:\n return p\n\n # ask for selecting a project\n retry = True\n while retry:\n print('All projects:')\n for p in self._rq_projects:\n print('<%4i> %s' % (p.id, p.name))\n\n print('-'*80)\n user_input = input('Enter the project id: ')\n print()\n\n user_input = int(user_input)\n\n # check for user abort\n if user_input == 0:\n return None\n\n id = user_input-1 # count from zero!\n\n #checks\n if id >= len(self._rq_projects):\n print('Index outside of list. Please retry!')\n else:\n p = self._rq_projects[id]\n if p.project_type != project_type:\n print('Wrong project type! Select a new one!')\n else:\n return self._rq_projects[id]\n\n print()\n\n\n\n def _create_image_archive(self, filename, listoffiles):\n with tarfile.open(filename, 'w:gz') as tar:\n for f in listoffiles:\n tar.add(f, filter=tarinfo_reset)\n\n\n def _create_master_ini(self, filename):\n\n pre, ext = os.path.splitext(filename)\n outname = pre + '.png'\n logfile = pre + '.log'\n\n data = { 'scene': filename,\n 'width': self._width,\n 'height': self._height,\n 'outfile': outname,\n 'logfile': logfile }\n\n if self._add_args is not None:\n data['args'] = self._add_args\n\n config = configparser.ConfigParser()\n config['DEFAULT'] = data\n filename = 'scene.ini'\n with open(filename, 'w') as configfile:\n config.write(configfile)\n\n return filename\n\n\n def _create_image(self, filename):\n # generates a tempfile name\n\n tempfile = os.path.join('/tmp','image_%s.tar.gz' %('simple'))\n\n listoffiles = []\n listoffiles.append(filename)\n listoffiles.append(self._create_master_ini(filename))\n\n # extra_files are not intrinsic for this class\n if hasattr(self, 'extra_files'):\n listoffiles += self.extra_files\n\n print('Creating image files:')\n for i in listoffiles:\n print(' {} ...'.format(i))\n\n self._create_image_archive(tempfile, listoffiles)\n\n # upload the image description\n\n do_trying = True\n while do_trying:\n image_id = Image.create(self._session, self._rq_project.id, tempfile)\n\n if image_id != -1:\n do_trying = False\n else:\n print('Image creation failed! Possible solutions:')\n print('------------------------------------------')\n print(' <1> Reset project')\n user_input = int(input('Your choice: '))\n\n if user_input != 1:\n do_trying = False\n else:\n self._rq_project.reset(self._session)\n # end while\n\n # remove temporary file\n os.remove(tempfile)\n\n\n if image_id == -1:\n print('Image couldn\\'t be created!')\n image = None\n else:\n print('New image with id=%i created' % image_id)\n\n # this is the code for testing the loop\n # waiting for the image to be ready!\n image = Image.query(self._session, image_id)\n return image\n\n\n def _create_images_from_filenames(self, filenames):\n images = []\n missed = 0\n # compile all data and create a rq image\n for filename in filenames:\n print('Submitting %s ...' % filename)\n image = self._create_image(filename)\n if image is not None:\n images.append(image)\n else:\n missed += 1\n if missed == 3:\n print('Too many errors, submitting aborted!')\n return None\n\n if len(images) == 0:\n return None\n\n return images\n\n\n def _download_file(self, fileid, directory='.'):\n if fileid != -1:\n dbfile = File.get_db_by_id(self._session, fileid)\n md5sum = dbfile.md5sum\n\n status, filename = File.get_by_id(self._session, fileid,\n directory, md5sum=md5sum)\n print('Downloaded \\'%s\\'' % filename )\n\n\n def _download_files(self, list_of_images, nr_images, directory='.', verbose=False):\n new_list = []\n im_queued = 0\n im_rendering = 0\n im_inlist = 0\n for image in list_of_images:\n image.update(self._session)\n #if verbose:\n # print('Image status: %s' % image.status())\n if image.status() == 'Finished':\n if hasattr(image, 'render_image_id'):\n self._download_file(image.render_image_id, directory=directory)\n if hasattr(image, 'log_file_id'):\n self._download_file(image.log_file_id, directory=directory)\n print('error code of rendering process: %i' % image.error_code)\n else:\n new_list.append(image)\n if image.status() == 'Queued':\n im_queued += 1\n if image.status() == 'Rendering':\n im_rendering += 1\n im_inlist += 1\n\n if verbose:\n print('Queued : %5i' % im_queued)\n print('Rendering : %5i' % im_rendering)\n print('Finished : %5i' % (nr_images - im_inlist))\n\n return new_list\n\n\n \"\"\"\n _wait_download_files\n\n takes a python list of images and waits until all images are processed!\n Images which are finished will be downloaded as soon as possible\n\n :param list_of_images : python-list of submitted images\n :param directory : directory for the results\n \"\"\"\n def _wait_download_files(self, list_of_images, directory='.'):\n running_time = 0\n is_running = True\n nr_images = len(list_of_images)\n while is_running:\n # update project data\n self._rq_project.update(self._session)\n list_of_images = self._download_files(list_of_images, nr_images, directory=directory, verbose=True)\n is_running = self._rq_project.status() != 'Finished'\n\n if is_running != False:\n if len(list_of_images) == 0:\n # all images downloaded?\n print('Images all downloaded but project is still not finished!')\n\n else:\n if running_time >= self._timeout:\n print('Running into timeout!')\n return False\n else:\n print('Waiting ... %i/%i' % (running_time, self._timeout))\n time.sleep(self._sleep)\n running_time += self._sleep\n return True\n\n\n \"\"\"\n _prepare_submit\n\n does the preperations, login, project selection, project clearing\n\n :param project_type : image or animation\n \"\"\"\n def _prepare_submit(self, project_type):\n # now connects to the RQ service\n if not self._rq_login():\n return False\n\n self._rq_project = self._select_rq_project(project_type)\n\n if self._rq_project is None:\n print('User abort!')\n return False\n\n # clear old files...\n ret = self._rq_project.clear_images(self._session)\n if ret:\n print('All old files cleared!')\n else:\n print('Something went wrong while clearing old files!')\n return False\n\n return True\n\n\n \"\"\"\n _render_download\n\n takes a python list of submitted images, starts the queuing and waits\n until all images are processed and download the files\n\n :param images : python-list of submitted images\n :param diretory : directory for all results\n \"\"\"\n def _render_download(self, images, directory='.'):\n # switch the project into rendering mode\n started = self._rq_project.start_rendering(self._session)\n\n if started:\n print('Project switched to rendering mode, waiting for worker ...')\n\n if self._wait_download_files(images, directory=directory):\n print('Rendering was successful!')\n return True\n else:\n print('File is not rendered within the time frame of %i seconds' %self._timeout)\n return False\n else:\n print('Project cannot be switched to rendering mode!')\n\n return False\n\n\n \"\"\"\n rq_execute\n\n the summary which is necessary to render a single image or an\n image set for an animation. Flexible and easy\n\n :param project_type: type of project necessary, image or animation\n :param filenames : python-list of filenames to render\n :param directory : optional the directory to store the results\n \"\"\"\n def rq_execute(self, project_type, filenames, directory='.'):\n\n print('Submitting image(s) to RQ for rendering ...')\n\n if self._prepare_submit(project_type) == False:\n return False\n\n images = self._create_images_from_filenames(filenames)\n\n if images is not None:\n return self._render_download(images, directory=directory)\n\n return False\n\n\n\nclass RQPovFile(PovFile, RQPovObj):\n def __init__(self, filename=None,\n verbose=False,\n camera_optimize=False,\n config=None,\n rq_project_name=None,\n width=640,\n height=480,\n timeout=3600,\n sleep=5):\n PovFile.__init__(self,filename=filename,\n verbose=verbose,\n camera_optimize=camera_optimize)\n\n RQPovObj.__init__(self, config=config,\n rq_project_name=rq_project_name,\n timeout=timeout,\n sleep=sleep,\n width=width,\n height=height)\n\n\n def write_povfile(self, filename=None, submit=True):\n # first save the standard file\n PovFile.write_povfile(self, filename=filename)\n\n if submit:\n self.rq_execute(PROJECT_TYPE_IMAGE, [self._filename], directory='.')\n\n return\n\n\n\nclass RQPovAnimation(PovAnimation, RQPovObj):\n def __init__(self, directory=None,\n verbose=False,\n camera_optimize=False,\n config=None,\n rq_project_name=None,\n width=640,\n height=480,\n timeout=3600,\n sleep=5):\n PovAnimation.__init__(self, directory=directory,\n verbose=verbose,\n camera_optimize=camera_optimize)\n\n RQPovObj.__init__(self, config=config,\n rq_project_name=rq_project_name,\n timeout=timeout,\n sleep=sleep,\n width=width,\n height=height)\n\n self._animation_files = []\n\n\n def write_povfile(self, filename=None):\n # first save the standard file\n PovFile.write_povfile(self, filename=filename)\n\n self._animation_files.append(self._filename)\n\n\n def animate(self, frames = None, duration = None, fps = None, submit=True):\n PovAnimation.animate(self, frames=frames, duration=duration, fps=fps)\n\n if submit:\n self.rq_execute(PROJECT_TYPE_ANIMATION, self._animation_files, directory=self._directory)\n\n return\n","repo_name":"ocordes/pypovray","sub_path":"pypovlib/pypovrayqueue.py","file_name":"pypovrayqueue.py","file_ext":"py","file_size_in_byte":14102,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"2289726655","text":"import sqlite3\nimport requests as req\n\nconn = sqlite3.connect('gen4dex_db.sqlite3')\nconn.row_factory = sqlite3.Row\nc = conn.cursor()\nc2 = conn.cursor()\n\n\ntrad = []\nregiones = ['Kanto', 'Johto', 'Sinnoh']\nconta = -1\nregionactiva = False\nf = open(\"traducciones_raw.txt\", \"r\", encoding='utf8')\nfor x in f:\n if x.startswith('==='):\n regionactiva = x.replace('===', '').replace('\\n', '') in regiones\n if regionactiva:\n if x.startswith('| [['):\n eng = x.replace('| ', '').replace('\\n', '').replace('[[', '').replace(']]', '')\n conta = 0\n if conta == 4:\n esp = x.replace('| ', '').replace('\\n', '').replace('[[', '').replace(']]', '')\n trad.append({'eng': eng, 'esp': esp})\n conta = -1\n if conta >= 0:\n conta += 1\n\nesp = {}\nfor t in trad:\n esp[t['eng']] = t['esp']\n\nfosiles = {\n'Armor Fossil': 'Fósil Coraza',\n'Claw Fossil': 'Fósil Garra',\n'Dome Fossil': 'Fósil Domo',\n'Helix Fossil': 'Fósil Helix',\n'Old Amber': 'Ámbar viejo',\n'Root Fossil': 'Fósil Raíz',\n'Skull Fossil': 'Fósil Cráneo'\n}\n\nesp[''] = ''\nesp['Victory Road (Sinnoh)'] = 'Calle Victoria (Sinnoh)'\nesp['Event exclusive'] = 'Exclusivo de evento'\nesp['Roaming Johto'] = 'Pokémon errante, rutas de Johto'\nesp['Roaming Kanto'] = 'Pokémon errante, rutas de Kanto'\nesp['Roaming Sinnoh'] = 'Pokémon errante, rutas de Sinnoh'\nesp['Safari Zone'] = 'Zona Safari'\nesp['Event or via Pokémon Ranger'] = 'Evento o vía Pokémon Ranger'\nesp['Olivine Gym'] = 'Gimnasio de Ciudad Olivo'\nesp['Valor Lakefront'] = 'Orilla Valor'\nesp['Trophy Garden (Check Mr. Backlot)'] = 'Jardín Trofeo (Consultar al Sr. Fortuny)'\nesp['Solaceon Ruins'] = 'Ruinas Sosiego'\nesp['Snowpoint Temple'] = 'Templo Puntaneva'\nesp['Rock Peak Ruins'] = 'Ruinas Pico Roca'\nesp['Pokémon League (Sinnoh)'] = 'Liga Pokémon (Sinnoh)'\nesp['Oreburgh Mine'] = 'Mina Pirita'\nesp['Oreburgh Gate'] = 'Puerta Pirita'\nesp['Maniac Tunnel'] = 'Túnel Maníaco'\nesp['Lost Tower'] = 'Torre Perdida'\nesp['Iron Ruins'] = 'Ruinas Hierro'\nesp['Iceberg Ruins'] = 'Ruinas Iceberg'\nesp['Eterna Condominiums'] = 'Centro Lúdico de Ciudad Vetusta'\nesp['Acuity Lakefront'] = 'Orilla Agudeza'\nesp['Victory Road'] = 'Calle Victoria'\nesp['Team Rocket HQ (Trap floor)'] = 'Escondite Team Rocket (Sótano 1)'\nesp['Team Rocket HQ (Transmitter room Poké Balls)'] = 'Escondite Team Rocket (Poké Balls del transmisor)'\nesp['Silph Company'] = 'Silph S.A.'\nesp['Saffron City Magnet Train station'] = 'Estación del Magnetotrén de Ciudad Azafrán'\nesp['Safari Zone Gate'] = 'Entrada Safari'\nesp[\"Professor Oak's Laboratory\"] = 'Laboratorio del Profesor Oak'\nesp['Lake of Rage (Center of lake, shiny)'] = 'Lago de la Furia (Centro del lago, variocolor)'\nesp['Goldenrod Department Store'] = 'Centro Comercial de Ciudad Trigal'\nesp['Bell Tower'] = 'Torre Campana'\n\ninciensos = {\n 'Sea Incense': 'Incienso Marino',\n 'Lax Incense': 'Incienso Suave',\n 'Odd Incense': 'Incienso Raro',\n 'Rock Incense': 'Incienso Roca',\n 'Full Incense': 'Incienso Lento',\n 'Wave Incense': 'Incienso Aqua',\n 'Rose Incense': 'Incienso Floral',\n 'Luck Incense': 'Incienso Duplo',\n 'Pure Incense': 'Incienso Puro'\n}\n\nitemsevo = {\n \"Metal Coat\": 'Rev. Metálico',\n \"Electirizer\": 'Electrizador',\n \"Magmarizer\": 'Magmatizador',\n \"King's Rock\": 'Roca del Rey',\n \"Dragon Scale\": 'Escamadragón',\n \"Upgrade\": 'Mejora',\n \"Deep Sea Tooth\": 'Diente Marino',\n \"Deep Sea Scale\": 'Escama Marina',\n \"Protector\": 'Protector',\n \"Dubious Disc\": 'Discoxtraño',\n \"Reaper Cloth\": 'Telaterrible',\n 'Razor Claw': 'Garrafilada',\n 'Razor Fang': 'Colmillagudo',\n 'Oval Stone': 'Piedra Oval'\n}\n\npiedras = {\n 'Moon Stone': 'Piedra Lunar',\n 'Fire Stone': 'Piedra Fuego',\n 'Thunder Stone': 'Piedratrueno',\n 'Water Stone': 'Piedra Agua',\n 'Leaf Stone': 'Piedra Hoja',\n 'Sun Stone': 'Piedra Solar',\n 'Shiny Stone': 'Piedra Día',\n 'Dusk Stone': 'Piedra Noche',\n 'Dawn Stone': 'Piedra Alba'\n}\n\nmoves = {\n 'Mimic': 'Mimético',\n 'Double Hit': 'Doble golpe',\n 'Rollout': 'Desenrollar',\n 'AncientPower': 'Poder pasado'\n}\n\ndef quitar_paja(p):\n data = p\n paja = ''\n if '(Revive' in p:\n data = p.split(' (Revive ')[0]\n paja = ' (Revivir ' + fosiles[p.split(' (Revive ')[1].replace(')','')] + ')'\n if 'F)' in p:\n data = p.split(' (')[0]\n paja = ' (P' + p.split(' (')[1].replace('F','')\n if '(Gift egg' in p:\n data = p.split(' (Gift')[0]\n if 'Primo' in p:\n paja = ' (Huevo regalo de Cástor)'\n else:\n paja = ' (Huevo regalo)'\n if p.endswith('Incense'):\n data = p.split(' while')[0]\n inc = p.split(' holding ')[1]\n paja = ' con ' + inciensos[inc] + ' equipado'\n return({'data':data, 'paja':paja})\n\ndef trad_evo(p):\n t = p\n if p.startswith('Train'):\n t = 'Sube a ' + p.split(' ')[2] + ' al nv.' + p.split('lv.')[1]\n if t.endswith('party)'):\n t = t.split(' (')[0] + ' (teniendo una Poké Ball y un espacio en el equipo)'\n if t.endswith('female)'):\n t = t.split(' (')[0] + ' (sólo hembra)'\n elif p.startswith('Trade'):\n t = 'Intercambia un ' + t.split(' ')[2]\n if 'holding' in p:\n t += ' con ' + itemsevo[p.split('holding a ')[1]] + ' equipado'\n elif p.startswith('Use a'):\n t = 'Usa una ' + piedras[p.split(' on')[0].replace('Use a ', '').split(' (')[0]] + \" en un \" + p.split(' ')[-1]\n if '(male only' in p:\n t += ' (sólo macho)'\n if '(female only' in p:\n t += ' (sólo hembra)'\n elif p.startswith('Level up'):\n t = 'Sube un nivel a un ' + p.split(' ')[3]\n if 'high friendship' in p:\n t += \" con alta amistad\"\n if p.endswith('day'):\n t += \" de día\"\n if p.endswith('night'):\n t += \" de noche\"\n elif 'knowing' in p:\n if 'Mime Jr.' in p:\n t = 'Sube un nivel a un Mime Jr.'\n t += ' con ' + moves[p.split(' (')[0].split('knowing ')[1]] + ' aprendido (nv.' + p.split('lv.')[1][:-1] + ')'\n elif 'holding' in p:\n t += \" equipado con \" + itemsevo[p.split('holding ')[1].split(' in')[0]] + \" de \"\n if p.endswith('night'):\n t += \"noche\"\n if p.endswith('day'):\n t += \"día\"\n elif 'Coronet' in p:\n t += ' en el Monte Corona (sólo DPPt)'\n elif 'in the party' in p:\n t += ' con un Remoraid en el equipo'\n elif 'Beauty' in p:\n t += ' con el stat de Belleza alto'\n elif 'Moss' in p:\n t += ' cerca de la Roca Musgo del Bosque Vetusta (sólo DPPt)'\n elif 'Ice Rock' in p:\n t += ' cerca de la Roca Hielo de la Ruta 217 (sólo DPPt)'\n\n return t\n\ntabla_trad = {}\nfor row in c.execute(\"select * from alldata\"):\n id = str(row['id'])\n p = row['place']\n dp = quitar_paja(p)\n p = dp['data']\n paja = dp['paja']\n if p in esp:\n tabla_trad[id] = esp[p] + paja\n else:\n if p.startswith('Sinnoh') or p.startswith('Route'):\n tabla_trad[id] = p.replace('Sinnoh ', '').replace('Route', 'Ruta')\n else:\n if p.startswith('Breed'):\n tabla_trad[id] = p.replace('Breed', 'Cría un') + paja\n else:\n tabla_trad[id] = trad_evo(p)\n\nfor row in c.execute(\"select * from alldata\"):\n c2.execute('insert into esp (id,place_esp) values (?,?)', (str(row['id']),tabla_trad[str(row['id'])]))\nconn.commit()\nconn.close()\n\n ","repo_name":"jaime0907/gen4dex_node","sub_path":"traducir_esp.py","file_name":"traducir_esp.py","file_ext":"py","file_size_in_byte":7640,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"8130833126","text":"from bs4 import BeautifulSoup\nwith open('courses.html','r') as courses_file:\n content=courses_file.read()\n\nout=BeautifulSoup(content,\"lxml\")\nprint(out.prettify())\ntags=out.find_all('h5')\nfor txt in tags:\n print(txt.text)\ncards=out.find_all('div',class_='card')\nfor card in cards:\n course=card.h5.text\n price=card.a.text.split()[-1]\n print(f\"course:{course}{' '}price:{price}\")\n \n","repo_name":"AbhishekVarma11/Web-Scrapping","sub_path":"web2.py","file_name":"web2.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"71220009131","text":"import argparse\n\ndef parameter_parser():\n \"\"\"\n A method to parse up command line parameters. By default it trains on a synthetic dataset.\n The default hyperparameters give a good quality representation without grid search.\n \"\"\"\n parser = argparse.ArgumentParser(description = \"Run .\")\n\n parser.add_argument(\"--edges-path\",\n nargs = \"?\",\n default = \"./input/edges.npy\",\n\t help = \"Edges array.\")\n\n parser.add_argument(\"--node-features-path\",\n nargs = \"?\",\n default = \"./input/node_features.npy\",\n\t help = \"Node features array.\")\n\t \n parser.add_argument(\"--edge-features-path\",\n nargs = \"?\",\n default = \"./input/edge_features.npy\",\n\t help = \"Edge features array.\")\t \n\n parser.add_argument(\"--target-path\",\n nargs = \"?\",\n default = \"./input/target.npy\",\n\t help = \"Target classes array.\")\n\t \n parser.add_argument(\"--seed\",\n type = int,\n default = 42,\n\t help = \"Random seed. Default is 42.\")\n\n parser.add_argument(\"--epochs\",\n type = int,\n default = 200,\n\t help = \"Number of training epochs. Default is 200.\")\n\n parser.add_argument(\"--edge-filters\",\n type = int,\n default = 32,\n\t help = \"PDN layer filters. Default is 32.\")\n\n parser.add_argument(\"--node-filters\",\n type = int,\n default = 32,\n\t help = \"GCN layer filters. Default is 32.\")\t \n\n parser.add_argument(\"--learning-rate\",\n type = float,\n default = 0.01,\n\t help = \"Learning rate. Default is 0.01.\")\n\n parser.add_argument(\"--test-size\",\n type = float,\n default = 0.9,\n\t help = \"Test data ratio. Default is 0.9.\")\n \n return parser.parse_args()\n","repo_name":"benedekrozemberczki/PDN","sub_path":"src/param_parser.py","file_name":"param_parser.py","file_ext":"py","file_size_in_byte":2218,"program_lang":"python","lang":"en","doc_type":"code","stars":56,"dataset":"github-code","pt":"55"}
+{"seq_id":"43151649916","text":"from django import forms\nfrom django.conf import settings\nfrom django.core.exceptions import ValidationError\nfrom django.core.validators import MinValueValidator, MaxValueValidator\nfrom django.forms import (\n CharField,\n IntegerField,\n BooleanField,\n NullBooleanField,\n)\nfrom django.urls import reverse_lazy\n\nfrom netbox.forms import (\n NetBoxModelBulkEditForm,\n NetBoxModelFilterSetForm,\n NetBoxModelImportForm,\n NetBoxModelForm,\n)\nfrom utilities.forms import (\n BulkEditNullBooleanSelect,\n DynamicModelMultipleChoiceField,\n TagFilterField,\n StaticSelect,\n CSVChoiceField,\n CSVModelChoiceField,\n DynamicModelChoiceField,\n APISelect,\n add_blank_choice,\n)\n\nfrom netbox_dns.models import View, Zone, ZoneStatusChoices, NameServer\nfrom netbox_dns.utilities import name_to_unicode\n\n\nclass ZoneForm(NetBoxModelForm):\n \"\"\"Form for creating a new Zone object.\"\"\"\n\n nameservers = DynamicModelMultipleChoiceField(\n queryset=NameServer.objects.all(),\n required=False,\n )\n default_ttl = IntegerField(\n required=False,\n label=\"Default TTL\",\n help_text=\"Default TTL for new records in this zone\",\n validators=[MinValueValidator(1)],\n )\n soa_ttl = IntegerField(\n required=True,\n label=\"SOA TTL\",\n help_text=\"TTL for the SOA record of the zone\",\n validators=[MinValueValidator(1)],\n )\n soa_rname = CharField(\n required=True,\n label=\"SOA Responsible\",\n help_text=\"Mailbox of the zone's administrator\",\n )\n soa_serial_auto = BooleanField(\n required=False,\n label=\"Generate SOA Serial\",\n help_text=\"Automatically generate the SOA Serial\",\n )\n soa_serial = IntegerField(\n required=False,\n label=\"SOA Serial\",\n help_text=\"Serial number of the current zone data version\",\n validators=[MinValueValidator(1)],\n )\n soa_refresh = IntegerField(\n required=True,\n label=\"SOA Refresh\",\n help_text=\"Refresh interval for secondary name servers\",\n validators=[MinValueValidator(1)],\n )\n soa_retry = IntegerField(\n required=True,\n label=\"SOA Retry\",\n help_text=\"Retry interval for secondary name servers\",\n validators=[MinValueValidator(1)],\n )\n soa_expire = IntegerField(\n required=True,\n label=\"SOA Expire\",\n help_text=\"Expire time after which the zone is considered unavailable\",\n validators=[MinValueValidator(1)],\n )\n soa_minimum = IntegerField(\n required=True,\n label=\"SOA Minimum TTL\",\n help_text=\"Minimum TTL for negative results, e.g. NXRRSET\",\n validators=[MinValueValidator(1)],\n )\n fieldsets = (\n (\n \"Zone\",\n (\n \"view\",\n \"name\",\n \"status\",\n \"nameservers\",\n \"default_ttl\",\n \"description\",\n ),\n ),\n (\n \"SOA\",\n (\n \"soa_ttl\",\n \"soa_mname\",\n \"soa_rname\",\n \"soa_refresh\",\n \"soa_retry\",\n \"soa_expire\",\n \"soa_minimum\",\n \"soa_serial_auto\",\n \"soa_serial\",\n ),\n ),\n (\"Tags\", (\"tags\",)),\n )\n\n def __init__(self, *args, **kwargs):\n \"\"\"Override the __init__ method in order to provide the initial value for the default fields\"\"\"\n super().__init__(*args, **kwargs)\n\n initial_name = self.initial.get(\"name\")\n if initial_name:\n self.initial[\"name\"] = name_to_unicode(initial_name)\n\n defaults = settings.PLUGINS_CONFIG.get(\"netbox_dns\")\n\n def _initialize(initial, setting):\n if initial.get(setting, None) in (None, \"\"):\n initial[setting] = defaults.get(f\"zone_{setting}\", None)\n\n for setting in (\n \"default_ttl\",\n \"soa_ttl\",\n \"soa_rname\",\n \"soa_serial_auto\",\n \"soa_refresh\",\n \"soa_retry\",\n \"soa_expire\",\n \"soa_minimum\",\n ):\n _initialize(self.initial, setting)\n\n if self.initial.get(\"soa_ttl\", None) is None:\n self.initial[\"soa_ttl\"] = self.initial.get(\"default_ttl\", None)\n\n if self.initial.get(\"soa_serial_auto\"):\n self.initial[\"soa_serial\"] = None\n\n if self.initial.get(\"soa_mname\", None) in (None, \"\"):\n default_soa_mname = defaults.get(\"zone_soa_mname\", None)\n if default_soa_mname is not None:\n try:\n self.initial[\"soa_mname\"] = NameServer.objects.get(\n name=default_soa_mname\n )\n except NameServer.DoesNotExist:\n pass\n\n if not self.initial.get(\"nameservers\", []):\n default_nameservers = defaults.get(\"zone_nameservers\", [])\n if default_nameservers:\n self.initial[\"nameservers\"] = NameServer.objects.filter(\n name__in=default_nameservers\n )\n\n def clean_default_ttl(self):\n return (\n self.cleaned_data[\"default_ttl\"]\n if self.cleaned_data[\"default_ttl\"]\n else self.initial[\"default_ttl\"]\n )\n\n class Meta:\n model = Zone\n\n fields = (\n \"name\",\n \"view\",\n \"status\",\n \"nameservers\",\n \"default_ttl\",\n \"description\",\n \"tags\",\n \"soa_ttl\",\n \"soa_mname\",\n \"soa_rname\",\n \"soa_serial_auto\",\n \"soa_serial\",\n \"soa_refresh\",\n \"soa_retry\",\n \"soa_expire\",\n \"soa_minimum\",\n )\n widgets = {\n \"view\": StaticSelect(),\n \"status\": StaticSelect(),\n \"soa_mname\": StaticSelect(),\n }\n help_texts = {\n \"view\": \"View the zone belongs to\",\n \"soa_mname\": \"Primary name server for the zone\",\n }\n\n\nclass ZoneFilterForm(NetBoxModelFilterSetForm):\n \"\"\"Form for filtering Zone instances.\"\"\"\n\n model = Zone\n\n view_id = DynamicModelMultipleChoiceField(\n queryset=View.objects.all(),\n required=False,\n label=\"View\",\n )\n status = forms.ChoiceField(\n choices=add_blank_choice(ZoneStatusChoices),\n required=False,\n widget=StaticSelect(),\n )\n name = CharField(\n required=False,\n label=\"Name\",\n )\n nameservers = DynamicModelMultipleChoiceField(\n queryset=NameServer.objects.all(),\n required=False,\n )\n tag = TagFilterField(Zone)\n\n\nclass ZoneImportForm(NetBoxModelImportForm):\n view = CSVModelChoiceField(\n queryset=View.objects.all(),\n required=False,\n to_field_name=\"name\",\n help_text=\"View the zone belongs to\",\n error_messages={\n \"invalid_choice\": \"View not found.\",\n },\n )\n status = CSVChoiceField(\n choices=ZoneStatusChoices,\n required=False,\n help_text=\"Zone status\",\n )\n default_ttl = IntegerField(\n required=False,\n help_text=\"Default TTL\",\n )\n soa_ttl = IntegerField(\n required=False,\n help_text=\"TTL for the SOA record of the zone\",\n )\n soa_mname = CSVModelChoiceField(\n queryset=NameServer.objects.all(),\n required=False,\n to_field_name=\"name\",\n help_text=\"Primary name server for the zone\",\n error_messages={\n \"invalid_choice\": \"Nameserver not found.\",\n },\n )\n soa_rname = CharField(\n required=False,\n help_text=\"Mailbox of the zone's administrator\",\n )\n soa_serial_auto = BooleanField(\n required=False,\n help_text=\"Generate the SOA serial\",\n )\n soa_serial = IntegerField(\n required=False,\n help_text=\"Serial number of the current zone data version\",\n )\n soa_refresh = IntegerField(\n required=False,\n help_text=\"Refresh interval for secondary name servers\",\n )\n soa_retry = IntegerField(\n required=False,\n help_text=\"Retry interval for secondary name servers\",\n )\n soa_expire = IntegerField(\n required=False,\n help_text=\"Expire time after which the zone is considered unavailable\",\n )\n soa_minimum = IntegerField(\n required=False,\n help_text=\"Minimum TTL for negative results, e.g. NXRRSET\",\n )\n\n def _get_default_value(self, field):\n _default_values = settings.PLUGINS_CONFIG.get(\"netbox_dns\", {})\n if _default_values.get(\"zone_soa_ttl\", None) is None:\n _default_values[\"zone_soa_ttl\"] = _default_values.get(\n \"zone_default_ttl\", None\n )\n\n return _default_values.get(f\"zone_{field}\", None)\n\n def _clean_field_with_defaults(self, field):\n if self.cleaned_data[field]:\n value = self.cleaned_data[field]\n else:\n value = self._get_default_value(field)\n\n if value is None:\n raise ValidationError(f\"{field} not set and no default value available\")\n\n return value\n\n def clean_default_ttl(self):\n return self._clean_field_with_defaults(\"default_ttl\")\n\n def clean_soa_ttl(self):\n return self._clean_field_with_defaults(\"soa_ttl\")\n\n def clean_soa_mname(self):\n soa_mname = self._clean_field_with_defaults(\"soa_mname\")\n if type(soa_mname) == str:\n try:\n soa_mname = NameServer.objects.get(name=soa_mname)\n except NameServer.DoesNotExist:\n raise ValidationError(f\"Default name server {soa_mname} does not exist\")\n\n return soa_mname\n\n def clean_soa_rname(self):\n return self._clean_field_with_defaults(\"soa_rname\")\n\n def clean_soa_serial_auto(self):\n try:\n return self._clean_field_with_defaults(\"soa_serial_auto\")\n except ValidationError:\n if self.cleaned_data[\"soa_serial\"] or self._get_default_value(\"soa_serial\"):\n return None\n\n raise\n\n def clean_soa_serial(self):\n try:\n return self._clean_field_with_defaults(\"soa_serial\")\n except ValidationError:\n if self.cleaned_data[\"soa_serial_auto\"] or self._get_default_value(\n \"soa_serial_auto\"\n ):\n return None\n\n raise\n\n def clean_soa_refresh(self):\n return self._clean_field_with_defaults(\"soa_refresh\")\n\n def clean_soa_retry(self):\n return self._clean_field_with_defaults(\"soa_retry\")\n\n def clean_soa_expire(self):\n return self._clean_field_with_defaults(\"soa_expire\")\n\n def clean_soa_minimum(self):\n return self._clean_field_with_defaults(\"soa_minimum\")\n\n class Meta:\n model = Zone\n\n fields = (\n \"view\",\n \"name\",\n \"status\",\n \"default_ttl\",\n \"description\",\n \"soa_ttl\",\n \"soa_mname\",\n \"soa_rname\",\n \"soa_serial_auto\",\n \"soa_serial\",\n \"soa_refresh\",\n \"soa_retry\",\n \"soa_expire\",\n \"soa_minimum\",\n )\n\n\nclass ZoneBulkEditForm(NetBoxModelBulkEditForm):\n view = DynamicModelChoiceField(\n queryset=View.objects.all(),\n required=False,\n label=\"View\",\n widget=APISelect(\n attrs={\"data-url\": reverse_lazy(\"plugins-api:netbox_dns-api:view-list\")}\n ),\n )\n status = forms.ChoiceField(\n choices=add_blank_choice(ZoneStatusChoices),\n required=False,\n widget=StaticSelect(),\n )\n nameservers = DynamicModelMultipleChoiceField(\n queryset=NameServer.objects.all(),\n required=False,\n )\n default_ttl = IntegerField(\n required=False,\n label=\"Default TTL\",\n validators=[MinValueValidator(1)],\n )\n description = CharField(max_length=200, required=False)\n soa_ttl = IntegerField(\n required=False,\n label=\"SOA TTL\",\n validators=[MinValueValidator(1)],\n )\n soa_mname = DynamicModelChoiceField(\n queryset=NameServer.objects.all(),\n required=False,\n label=\"SOA Primary Nameserver\",\n widget=APISelect(\n attrs={\n \"data-url\": reverse_lazy(\"plugins-api:netbox_dns-api:nameserver-list\")\n }\n ),\n )\n soa_rname = CharField(\n required=False,\n label=\"SOA Responsible\",\n )\n soa_serial_auto = NullBooleanField(\n required=False,\n widget=BulkEditNullBooleanSelect(),\n label=\"Generate SOA Serial\",\n )\n soa_serial = IntegerField(\n required=False,\n label=\"SOA Serial\",\n validators=[MinValueValidator(1), MaxValueValidator(4294967295)],\n )\n soa_refresh = IntegerField(\n required=False,\n label=\"SOA Refresh\",\n validators=[MinValueValidator(1)],\n )\n soa_retry = IntegerField(\n required=False,\n label=\"SOA Retry\",\n validators=[MinValueValidator(1)],\n )\n soa_expire = IntegerField(\n required=False,\n label=\"SOA Expire\",\n validators=[MinValueValidator(1)],\n )\n soa_minimum = IntegerField(\n required=False,\n label=\"SOA Minimum TTL\",\n validators=[MinValueValidator(1)],\n )\n\n model = Zone\n\n fieldsets = (\n (\n None,\n (\n \"view\",\n \"status\",\n \"nameservers\",\n \"default_ttl\",\n \"description\",\n ),\n ),\n (\n \"SOA\",\n (\n \"soa_ttl\",\n \"soa_mname\",\n \"soa_rname\",\n \"soa_serial_auto\",\n \"soa_serial\",\n \"soa_refresh\",\n \"soa_retry\",\n \"soa_expire\",\n \"soa_minimum\",\n ),\n ),\n )\n nullable_fields = (\"view\", \"description\")\n\n def clean(self):\n \"\"\"\n If soa_serial_auto is True, set soa_serial to None.\n \"\"\"\n cleaned_data = super().clean()\n if cleaned_data.get(\"soa_serial_auto\"):\n cleaned_data[\"soa_serial\"] = None\n","repo_name":"auroraresearchlab/netbox-dns","sub_path":"netbox_dns/forms/zone.py","file_name":"zone.py","file_ext":"py","file_size_in_byte":14294,"program_lang":"python","lang":"en","doc_type":"code","stars":208,"dataset":"github-code","pt":"55"}
+{"seq_id":"7002945649","text":"import ConfigParser\n\nconfig_level_option_key = 'config_level'\n\n\nclass MConfig(ConfigParser):\n def __init__(self, config_file_path):\n if not os.path.exists(config_file_path):\n raise ValueError('ConfigFileNotExists')\n config = ConfigParser.ConfigParser()\n config.read(config_file_path)\n section_list = []\n total_options = []\n for section in config.options():\n if config.has_option(section):\n config.get(section, config_level_option_key)\n for option in config.options(section):\n total_options.append(option)\n","repo_name":"oldsuper/dialog_service_api_test","sub_path":"src/utils/m_config.py","file_name":"m_config.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"25175283693","text":"from turtle import Turtle, Screen\nimport random\n\nrace_on = False\n\nscreen = Screen()\nscreen.setup(width=1000, height=800)\n\nuser_bet = screen.textinput(title=\"Make your bet\", prompt=\"Which turtle will win the race? Enter the color: \")\nprint(user_bet)\n\ncolor = [\"red\", \"orange\", \"yellow\", \"green\", \"blue\", \"purple\"]\ny_pos = [-200, -100, 0, 100, 200, 300]\nturtles = []\n\nfor i in range(0, 6):\n new_turtle = Turtle(shape=\"turtle\")\n new_turtle.color(color[i])\n new_turtle.penup()\n new_turtle.goto(x=-470, y=y_pos[i])\n turtles.append(new_turtle)\n\nif user_bet:\n race_on = True\n\nwhile race_on:\n for turtle in turtles:\n if turtle.xcor() > 480:\n winner = turtle.pencolor()\n race_on = False\n if winner == user_bet:\n print(f\"You've won! The {winner} turtle is the winner!\")\n else:\n print(f\"You've lost! The {winner} turtle is the winner!\")\n random_distance = random.randint(0, 10)\n turtle.forward(random_distance)\n","repo_name":"kristoftverdota/turtle-race-python","sub_path":"turtle_race.py","file_name":"turtle_race.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"7086074658","text":"# Licensed under a 3-clause BSD style license - see LICENSE.rst\nfrom astropy import config as _config\n\n\nclass Conf(_config.ConfigNamespace):\n \"\"\"\n Configuration parameters for `astroquery.xmatch`.\n \"\"\"\n url = _config.ConfigItem(\n 'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync',\n 'xMatch URL')\n\n timeout = _config.ConfigItem(\n 300,\n 'time limit for connecting to xMatch server')\n\n\nconf = Conf()\n\n\nfrom .core import XMatch, XMatchClass\n\n__all__ = ['XMatch', 'XMatchClass',\n 'Conf', 'conf',\n ]\n","repo_name":"astropy/astroquery","sub_path":"astroquery/xmatch/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","stars":665,"dataset":"github-code","pt":"55"}
+{"seq_id":"27303878001","text":"import pickle\nimport unittest\nfrom decimal import Decimal\n\nfrom agate import Table\nfrom agate.data_types import Number, Text\n\n\nclass TestColumn(unittest.TestCase):\n def setUp(self):\n self.rows = (\n (1, 2, 'a'),\n (2, 3, 'b'),\n (None, 4, 'c')\n )\n\n self.number_type = Number()\n self.text_type = Text()\n\n self.column_names = ['one', 'two', 'three']\n self.column_types = [self.number_type, self.number_type, self.text_type]\n\n self.table = Table(self.rows, self.column_names, self.column_types)\n\n def test_index(self):\n self.assertEqual(self.table.columns['one'].index, 0)\n self.assertEqual(self.table.columns['two'].index, 1)\n self.assertEqual(self.table.columns['three'].index, 2)\n\n def test_name(self):\n self.assertEqual(self.table.columns['one'].name, 'one')\n\n def test_data_type(self):\n self.assertIs(self.table.columns['one'].data_type, self.number_type)\n\n def test_pickleable(self):\n pickle.dumps(self.table.columns['one'])\n\n def test_row_names(self):\n table = Table(self.rows, self.column_names, self.column_types, row_names='three')\n column = table.columns['one']\n\n self.assertSequenceEqual(column._keys, ['a', 'b', 'c'])\n self.assertEqual(column['b'], 2)\n\n def test_keys(self):\n table = Table(self.rows, self.column_names, self.column_types, row_names='three')\n\n self.assertIs(self.table.columns['one'].keys(), None)\n self.assertSequenceEqual(table.columns['one'].keys(), ['a', 'b', 'c'])\n\n def test_values(self):\n self.assertSequenceEqual(\n self.table.columns['one'].values(),\n [Decimal('1'), Decimal('2'), None]\n )\n\n def test_values_distinct(self):\n rows = (\n (1, 2),\n (2, 3),\n (None, 3)\n )\n\n table = Table(rows, ('one', 'two'), [self.number_type, self.number_type])\n self.assertSequenceEqual(\n table.columns['two'].values_distinct(),\n [Decimal('2'), Decimal('3')]\n )\n\n def test_items(self):\n table = Table(self.rows, self.column_names, self.column_types, row_names='three')\n\n self.assertSequenceEqual(table.columns['one'].items(), [\n ('a', Decimal('1')),\n ('b', Decimal('2')),\n ('c', None)\n ])\n\n def test_dict(self):\n table = Table(self.rows, self.column_names, self.column_types, row_names='three')\n\n self.assertDictEqual(table.columns['one'].dict(), {\n 'a': Decimal('1'),\n 'b': Decimal('2'),\n 'c': None\n })\n\n def test_values_without_nulls(self):\n self.assertSequenceEqual(\n self.table.columns['one'].values_without_nulls(),\n [Decimal('1'), Decimal('2')]\n )\n\n def test_values_sorted(self):\n rows = (\n (2, 2, 'a'),\n (None, 3, 'b'),\n (1, 4, 'c')\n )\n\n table = Table(rows, self.column_names, self.column_types)\n\n self.assertSequenceEqual(\n table.columns['one'].values_sorted(),\n [Decimal('1'), Decimal('2'), None]\n )\n\n def test_values_without_nulls_sorted(self):\n rows = (\n (2, 2, 'a'),\n (None, 3, 'b'),\n (1, 4, 'c')\n )\n\n table = Table(rows, self.column_names, self.column_types)\n\n self.assertSequenceEqual(\n table.columns['one'].values_without_nulls_sorted(),\n [Decimal('1'), Decimal('2')]\n )\n","repo_name":"wireservice/agate","sub_path":"tests/test_columns.py","file_name":"test_columns.py","file_ext":"py","file_size_in_byte":3563,"program_lang":"python","lang":"en","doc_type":"code","stars":1149,"dataset":"github-code","pt":"55"}
+{"seq_id":"40447825940","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 isValidBST(self, root: Optional[TreeNode]) -> bool:\n \n def inorder(node):\n nonlocal last_value\n \n if node is None:\n return True\n \n valid = inorder(node.left)\n if valid is False:\n return False\n \n if node.val <= last_value:\n return False\n \n last_value = node.val\n \n return inorder(node.right)\n \n last_value = -1 * pow(2, 32)\n \n valid = inorder(root)\n return valid\n \n ","repo_name":"Jaforbubt63/Valid-BST.1","sub_path":"Valid BST1.py","file_name":"Valid BST1.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"1670693215","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Aug 30 23:26:38 2021\r\n\r\n@author: rmorn\r\n\"\"\"\r\n\r\n\"Open csv files\"\r\nimport csv\r\n\r\n# csv file name\r\nstate_abb = \"State_Abbreviations.csv\"\r\n# initializing the titles and rows list\r\nfieldsstate = []\r\nrowsstate = []\r\n\r\nstateinput = input('Enter the state data you want? e.g: New York or Alabama: ')\r\n\r\nfile = open(state_abb, 'r')\r\ncount = 0\r\nfor rows in csv.reader(file):\r\n #print(rows[count])\r\n if rows[count] == stateinput:\r\n count += 1\r\n print(count)\r\n\r\n\r\n\r\n","repo_name":"RobertUAGreatDanes/GraduateSchool_Data_Analytics","sub_path":"A.py","file_name":"A.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"25792388588","text":"import os\nfrom random import sample\nimport csv\nfrom common.paths import ruta_csv\n\ndef opciones_random(nombre:str,cant_caracteristicas:int):\n \"\"\"\n funcion opciones_random\n \n Def:\n Esta funcion retorna las 5 opciones a escribir en la tarjeta, entre las que esta la correcta\n y sus características\n\n Args:\n nombre(str): nombre del csv del cual se leen las opciones\n cant_caracteristicas(int): cantidad de características de la opción correcta a mostrar\n Ret:\n retorna una lista de las opciones elegidas de manera aleatoria, la opción correcta\n y la lista de sus características\n \"\"\"\n \n ruta_csv_actual = os.path.join(ruta_csv,'data_set_'+nombre.lower()+'.csv')\n try:\n with open(ruta_csv_actual,'r',encoding='UTF-8') as archivo:\n reader = csv.reader(archivo,delimiter =',')\n columnas=next(reader)\n data = list(map(lambda x: x,reader))\n \n exito = False\n while (not exito):\n opciones = sample(data, k=5)\n nombres_opciones = list(map(lambda x:x[5],opciones))\n nombres_sin_rep = set(nombres_opciones)\n exito = len(nombres_sin_rep) == len(nombres_opciones)\n \n descartadas = opciones[1:5]\n elegida = opciones[0]\n caracteristicas = elegida[0:cant_caracteristicas]\n opcion_correcta = elegida[5]\n opciones_incorrectas = list(map(lambda x: x[5],descartadas))\n\n return columnas,caracteristicas,opcion_correcta,opciones_incorrectas\n \n except FileNotFoundError:\n return False\n","repo_name":"fvenegasn/FiguRace","sub_path":"helpers/elegir_opciones.py","file_name":"elegir_opciones.py","file_ext":"py","file_size_in_byte":1608,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"55"}
+{"seq_id":"10063503854","text":"\"\"\"test GridAxis class\"\"\"\n\nimport pytest\nimport numpy as np\n\nfrom py3dinterpolations.core.grid3d import GridAxis\n\nAXIS_PARAMETERS = [\n (\"X\", 0, 1, 10),\n (\"Y\", 0.5, 12.7, 0.33),\n]\n\n\n@pytest.mark.parametrize(\"axis_name, min, max, res\", AXIS_PARAMETERS)\ndef test_GridAxis(axis_name, min, max, res):\n \"\"\"test gridaxis initialization\"\"\"\n\n axis = GridAxis(\n axis_name,\n min,\n max,\n res,\n )\n\n assert axis.name == axis_name\n assert axis.min == min\n assert axis.max == max\n assert axis.res == res\n assert isinstance(axis.grid, np.ndarray)\n","repo_name":"giocaizzi/py3dinterpolations","sub_path":"tests/core/test_gridaxis.py","file_name":"test_gridaxis.py","file_ext":"py","file_size_in_byte":588,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"19376821973","text":"from flask import Flask, request, jsonify\nfrom flask_sqlalchemy import SQLAlchemy\nfrom ..models.Team import Team\nfrom ..models.Match import Match\nfrom flask_cors import CORS\nfrom sqlalchemy import not_, func\nfrom src.football_competition import db\nfrom datetime import datetime\nimport json\nimport functools\n\nclass MatchController():\n def addTeams():\n try:\n data = request.get_json()\n teams = data['teams']\n for team in teams:\n if team == '':\n continue\n team = team.split()\n team_name = team[0]\n registration_date = datetime.strptime(team[1], '%d/%m')\n registration_date = registration_date.replace(year=datetime.today().year)\n group = team[2]\n team_obj = Team(team_name = team_name, registration_date = registration_date, group = group) \n db.session.add(team_obj)\n db.session.flush()\n \n db.session.commit()\n return jsonify({\n \"data\": {\n \"teams\": [team for team in teams]\n }\n }), 201\n except Exception as e:\n return jsonify({\n \"message\": str(e)\n }), 400\n\n def addMatches():\n try:\n data = request.get_json()\n matches = data['matches']\n for match in matches:\n if match == '':\n continue\n match = match.split()\n sorted_team_names = [match[0], match[1]]\n sorted_team_names.sort()\n match_obj = Match(team = sorted_team_names[0], opponent = sorted_team_names[1], team_goals = match[2], opponent_goals = match[3]) \n team = Team.query.filter(Team.team_name == match_obj.team).first()\n opponent = Team.query.filter(Team.team_name == match_obj.opponent).first()\n if team == None or opponent == None:\n raise Exception(\"Either one or both teams do not exist\")\n \n team.total_goals += int(match[2])\n opponent.total_goals += int(match[3])\n \n if team.group != opponent.group:\n raise Exception(\"both teams are not in the same group\")\n \n if int(match[2]) > int(match[3]):\n team.current_points += 3\n team.wins += 1\n opponent.losses += 1\n elif int(match[2]) == int(match[3]):\n team.current_points += 1\n team.draws += 1\n opponent.current_points += 1\n opponent.draws += 1\n else:\n opponent.current_points += 3\n opponent.wins += 1\n team.losses += 1\n \n db.session.add(match_obj)\n db.session.flush()\n db.session.commit()\n return jsonify({\n \"data\": {\n \"matches\": [match for match in matches]\n }\n }), 201\n except Exception as e:\n db.session.rollback()\n return jsonify({\n \"message\": str(e)\n }), 400\n\n \n def getTeamRankings():\n def compare(teamA, teamB):\n if teamA.current_points != teamB.current_points:\n return teamB.current_points - teamA.current_points\n \n if teamA.total_goals != teamB.total_goals:\n return teamB.total_goals - teamA.total_goals\n \n teamA_points = teamA.wins * 5 + teamA.draws * 3 + teamA.losses\n teamB_points = teamB.wins * 5 + teamB.draws * 3 + teamB.losses\n if teamA_points != teamB_points:\n return teamB_points - teamA_points\n \n if teamA.registration_date.date() > teamB.registration_date.date():\n return 1\n elif teamA.registration_date.date() < teamB.registration_date.date():\n return -1\n \n return 0\n \n try:\n teams = Team.query.order_by(Team.group.asc()).all()\n group_1 = teams[:6]\n group_1 = sorted(group_1, key=functools.cmp_to_key(compare))\n group_2 = teams[6:]\n group_2 = sorted(group_2, key=functools.cmp_to_key(compare))\n return jsonify({\n \"data\": {\n \"group_1\": [team.to_dict() for team in group_1],\n \"group_2\": [team.to_dict() for team in group_2]\n }\n }), 200\n except Exception as e:\n return jsonify({\n \"message\": str(e)\n }), 400\n \n def deleteCompetitionData():\n try:\n match_rows_deleted = Match.query.delete()\n team_rows_deleted = Team.query.delete()\n db.session.commit()\n return jsonify({\n \"data\": {\n \"match_rows_deleted\": match_rows_deleted,\n \"team_rows_deleted\": team_rows_deleted\n }\n }), 200\n except Exception as e:\n db.session.rollback()\n return jsonify({\n \"message\": str(e)\n }), 400","repo_name":"danteliew6/tap-gds-assessment","sub_path":"src/football_competition/controllers/MatchController.py","file_name":"MatchController.py","file_ext":"py","file_size_in_byte":5381,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"37927024252","text":"salaires = []\nsalaire_min = 0\nsalaire_max = 0\nsalaire_moyen = 0\n\n# Entrées\nNBRE_EMPLOYÉS = int(input())\nfor i in range (NBRE_EMPLOYÉS):\n salaires += [float(input())]\n\n# Calcul et sortie du salaire min, max, et moyen. À faire\nif len(salaires) != 0:\n salaire_min = salaires[0]\n salaire_max = salaires[0]\n for salaire in salaires:\n if salaire > salaire_max:\n salaire_max = salaire\n elif salaire < salaire_min:\n salaire_min = salaire\n salaire_moyen += salaire\n # end if\n # end for\n print(salaire_min)\n print(salaire_max)\n print(salaire_moyen / len(salaires))\nelse:\n print('aucun')\n# end if\n","repo_name":"CupidonSauce173/school-stuff","sub_path":"session_1/progression/1.8.2_stats_salaires.py","file_name":"1.8.2_stats_salaires.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"2081265167","text":"#The purpose of this analysis with python is to determine from an available dataset of 20 restaurant, what cuisine and category/package was bought more by customers\n\nimport numpy as np\n\nimport pandas as pd\n\n#import the dataset\ndf = pd.read_excel(r'/Users/great ness/Desktop/Restraurant/Restaurants.xlsx')\n\n#this prints the first 5 rows in the dataset\nprint(df.head())\n\n#this gets the total values of data in the category and cuisine column\ne = df['Category'].value_counts()\nprint(e)\n\n\nf= df['Cuisine'].value_counts()\nprint(f)\n\n#begin to set the parameters for the data visualiztion\nimport matplotlib.pyplot as plt\nplt_1 = plt.figure(figsize=(16, 9))\nplt.style.use('fivethirtyeight')\n\n\n#this plots a horizontal bar chart based on data gotten from the cuisine column\nf.plot(kind= 'barh', x = 'Pro', y='Ordinary', alpha=0.6)\n\n#plt.pie(list,explode = [0,0.1],autopct='%.2f%%', colors= colors,shadow = True)\nplt.title('Restaurants order',fontsize=14)\nplt.ylabel('')\nplt.xticks(rotation=90)\n\nplt.xlabel('')\n\n#plt.legend(labels1, loc='upper left')\nlabels = ['Cuisine']\nplt.legend(labels, loc = 'upper right')\nplt.savefig('restr.png')\nplt.show()\n\n\n#now create a pie chat that gives a visual representation of what category customers ordered\n\na = df['Category'].value_counts()\n\nimport matplotlib.pyplot as plt\n\nplt_1 = plt.figure(figsize=(16, 9))\nplt.style.use('ggplot')\nlist = a\nlabels1 = ['Percentage of those that ate Ordinary cuisine category', 'Percentage of those that ate Pro cuisine category']\n\ncolors = ['#7590a3','lightblue']\n\nplt.pie(list,explode = [0,0.1],autopct='%.2f%%', colors= colors,shadow = True)\n\nplt.title('Restaurants Analysis',fontsize=15)\nplt.ylabel('')\nplt.xticks(rotation=90)\n\nplt.xlabel('')\n\nplt.legend(labels1, loc='lower left')\n\n\nplt.savefig('restpie.png')\nplt.show()\n","repo_name":"Chukwuebuka-2003/Restraurant-Data-Analytics","sub_path":"restaurant.py","file_name":"restaurant.py","file_ext":"py","file_size_in_byte":1788,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"14076217267","text":"\"\"\"\nEntrypoint\n\"\"\"\nimport argparse\nfrom pathlib import Path\nimport logging\n\nfrom bioformatsXML import BioformatsXML\n\n\ndef get_args() -> argparse.Namespace:\n \"\"\"\n Get the arguments from the commandline\n :return:\n \"\"\"\n myparser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n myparser.add_argument(\n \"-d\",\n type=str,\n help=\"Directory: Runs on all bioformats compatible files.\",\n default=Path.cwd(),\n )\n myparser.add_argument(\n \"-f\",\n type=str,\n help=\"File: Run on single file.\",\n default='',\n )\n myparser.add_argument(\n \"-l\",\n type=str,\n help=\"LogLevel: 0 error (default), 1 warning, 2 info\",\n default=0,\n )\n return myparser.parse_args()\n\n\ndef load_files(args: argparse.Namespace) -> None:\n pth = Path(args.d)\n files = [x for x in pth.glob('*') if x.is_file()]\n bfx = BioformatsXML()\n for file in files:\n _load_file(bfx, file)\n\n\ndef load_file(args: argparse.Namespace) -> None:\n file = Path(args.f)\n bfx = BioformatsXML()\n _load_file(bfx, file)\n\n\ndef _load_file(bfx: BioformatsXML, file) -> None:\n xml = bfx.get_xml(file)\n if xml:\n out = Path(file.parent, file.stem + '.xml')\n xml.write(out)\n logging.info(f'Wrote {out}')\n if not bfx.verify_schema(out):\n logging.error(f'Invalid xml: {out}')\n else:\n logging.info(f'Validated {out}')\n\n\nif __name__ == \"__main__\":\n args = get_args()\n loglevels = [logging.ERROR, logging.WARNING, logging.INFO]\n loglevel = loglevels[int(args.l)]\n logging.basicConfig(level=loglevel)\n logging.info('Arguments parsed')\n logging.info(f\"Loglevel: {logging.getLevelName(loglevel)}\")\n if args.f:\n load_file(args)\n else:\n load_files(args)\n","repo_name":"rharkes/bioformatsXML","sub_path":"bioformatsXML.py","file_name":"bioformatsXML.py","file_ext":"py","file_size_in_byte":1844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"69871260013","text":"import unittest\nfrom unittest.mock import Mock\n\nfrom dpl.libs.shift_reg_buffered import ShiftRegBuffered\n\nfrom dpl.libs.abs_shift_reg import AbsShiftRegister\n\nsr_base = Mock(spec_set=AbsShiftRegister)\nsr_base.get_capacity.return_value = 8\n\n\nclass TestSRBuffer(unittest.TestCase):\n def test_init_state(self):\n sr = ShiftRegBuffered(sr_base)\n\n self.assertEqual(sr.get_buffer(), 0b0)\n\n def test_set_invalid_value(self):\n sr = ShiftRegBuffered(sr_base)\n\n with self.assertRaisesRegex(ValueError, 'Value must be 1 or zero, True or False'):\n sr.set_buf_bit(0, 'str')\n\n with self.assertRaisesRegex(ValueError, 'Value must be 1 or zero, True or False'):\n sr.set_buf_bit(0, 2)\n\n with self.assertRaisesRegex(ValueError, 'Value must be 1 or zero, True or False'):\n sr.set_buf_bit(0, -1)\n\n def test_set_invalid_position(self):\n sr = ShiftRegBuffered(sr_base)\n\n with self.assertRaisesRegex(ValueError, 'Bit number must be an integer'):\n sr.set_buf_bit('str', 1)\n\n with self.assertRaisesRegex(ValueError, 'Bit number must be positive or zero'):\n sr.set_buf_bit(-1, 1)\n\n with self.assertRaisesRegex(ValueError, 'Bit position can\\'t be bigger than '\n 'register capacity \\({0}\\)'.format(sr.get_capacity())):\n sr.set_buf_bit(sr.get_capacity(), 1)\n\n with self.assertRaisesRegex(ValueError, 'Bit position can\\'t be bigger than '\n 'register capacity \\({0}\\)'.format(sr.get_capacity())):\n sr.set_buf_bit(sr.get_capacity() + 1, 1)\n\n def test_set_first_buf_bit(self):\n sr = ShiftRegBuffered(sr_base)\n\n sr.set_buf_bit(0, True)\n\n self.assertEqual(sr.get_buffer(), 0b1)\n\n def test_set_last_buf_bit(self):\n sr = ShiftRegBuffered(sr_base)\n\n max_bit_pos = 7\n expected = 1 << max_bit_pos # 0b10000000\n\n sr.set_buf_bit(max_bit_pos, True)\n\n self.assertEqual(sr.get_buffer(), expected)\n\n def test_set_bits_successively(self):\n sr = ShiftRegBuffered(sr_base)\n\n max_bit_pos = 7\n expected = (1 << max_bit_pos) | 1 # 0b10000001\n\n sr.set_buf_bit(0, True)\n sr.set_buf_bit(max_bit_pos, True)\n\n self.assertEqual(sr.get_buffer(), expected)\n\n def test_set_same_bit_successively(self):\n sr = ShiftRegBuffered(sr_base)\n\n sr.set_buf_bit(0, True)\n sr.set_buf_bit(0, False)\n\n self.assertEqual(sr.get_buffer(), 0b0)\n\n sr.set_buf_bit(0, True)\n sr.set_buf_bit(0, True)\n\n self.assertEqual(sr.get_buffer(), 0b1)\n\n def test_get_bit_value(self):\n sr = ShiftRegBuffered(sr_base)\n\n bit_pos = 1\n\n sr.set_buf_bit(bit_pos, True)\n\n self.assertEqual(sr.get_buf_bit(bit_pos), True)\n\n sr.set_buf_bit(bit_pos, False)\n\n self.assertEqual(sr.get_buf_bit(bit_pos), False)\n\n\nclass TestSRWrite(unittest.TestCase):\n def test_buffer_state_after_write(self):\n sr = ShiftRegBuffered(sr_base)\n\n test_data = 0b1100\n\n sr.write_data(test_data)\n\n self.assertEqual(sr.get_buffer(), test_data)\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"dot-cat/dotcat_platform","sub_path":"tests/unittests/shift_reg/test_shift_reg_buffered.py","file_name":"test_shift_reg_buffered.py","file_ext":"py","file_size_in_byte":3253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"29499515123","text":"# Project: advent_of_code\n# File: day_1\n# Author: Joinemm\n# Date created: 04/12/18\n# Python Version: 3.6.6\n\nchanges = []\nwith open(\"input_1.txt\", \"r\") as input:\n for line in input:\n change = int(line.rstrip())\n changes.append(change)\n\nresults = [0]\nfound = False\nwhile not found:\n for x in changes:\n result = results[-1]\n result += x\n if result in results:\n print(result)\n found = True\n break\n else:\n results.append(result)\n","repo_name":"joinemm/advent-of-code-2018","sub_path":"day_1.py","file_name":"day_1.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"23459668879","text":"from scrapy.contrib.spiders import CrawlSpider, Rule\nfrom scrapy.contrib.linkextractors.sgml import SgmlLinkExtractor\nfrom scrapy.selector import HtmlXPathSelector\nfrom scrapy.http import Request\nimport re\nimport urlparse\nfrom scraper.items import MyImageItem\nfrom scrapy.log import log\n\nclass GlaSpider(CrawlSpider):\n \"\"\"A simple spider for gla.ac.uk\"\"\"\n name = \"anonspider\"\n allowed_domains = ['www.gla.ac.uk']\n start_urls = [\"http://dcs.gla.ac.uk/\"]\n rules = (\n #Rule(SgmlLinkExtractor(allow=(\"/\", )),callback='parse_data'),\n Rule(SgmlLinkExtractor(allow=('(.*)\\.html$', )), callback='parse_data'),\n )\n\n\n def parse_data(self, response):\n self.log('Entering the data parser!')\n hxs = HtmlXPathSelector(response)\n items = []\n images = hxs.select('//img/@src').extract()\n for image in images:\n item = MyImageItem()\n item['image_urls']=[]\n if re.match('^http+', image):\n imgurl = image\n #If we have a relative url, append the site name to make it\n #absolute\n else:\n imgurl = urlparse.urljoin(response.url,image)\n if re.match('^.*(jpg|jpeg)$', imgurl, re.IGNORECASE):\n item['image_urls'].append(imgurl)\n yield item\n for url in hxs.select('//a/@href').extract():\n yield Request(url, callback=self.parse_data)\n","repo_name":"ritesh/sc","sub_path":"scraper/spiders/anonspider.py","file_name":"anonspider.py","file_ext":"py","file_size_in_byte":1447,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"55"}
+{"seq_id":"26803968403","text":"\"\"\"Risk and Controls Assesment Dashboard\n### Author - Tom Welsh twelsh37@gmail.com\n\n## Description\nThis program is used to read ina standard set of Risk and Control assesment forms and display various metrics that can\nbe gleaned from the data.\n\nThe program can also be used as a tool to help clense/sanataise your data. Those annoying humands that substitute '&'\nfor 'and' or as a spurious 's's at the end of some standard term from our lexicon\n\n## Deconstruction\n\n\"\"\"\n\nimport dash\nimport dash_table\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport dash_bootstrap_components as dbc\nfrom dash.dependencies import Input, Output\nimport pandas as pd\nimport re\nimport plotly.express as px\n\n# -------------------- Data Import and Cleansing---------------------------------#\n# Read in our RACA raw data files and append them to the initial df dataframe\ndf = pd.read_excel('c:/Users/twelsh/data/racas/master/master1.xlsx')\n\n# Rename all column headings to human readable and remove spaces, brackets in description names\n# We will use raca_df and the new column headings from here forward.\nraca_df = df.rename(columns={'Process (Title)': 'process_title',\n 'Process description': 'process_description',\n 'Risk ID': 'risk_id',\n 'Risk Owner': 'risk_owner',\n 'Risk(Title)': 'risk_title',\n 'Risk Description': 'risk_description',\n 'Risk Category 1': 'risk_types',\n 'Risk Category 2': 'risk',\n 'Risk Category 3': 'level3',\n 'Associated KRIs': 'associated_kris',\n 'I': 'gross_impact',\n 'L': 'gross_likelihood',\n 'Control ID': 'control_id',\n 'Control Owner': 'control_owner',\n 'Control (Title)': 'control_title',\n 'Control Description': 'control_description',\n 'Control Activity': 'control_activity',\n 'Control Type': 'control_type',\n 'Control Frequency': 'control_frequency',\n 'DE & OE?': 'de_oe',\n 'Commentary on DE & OE assessment': 'de_oe_commentary',\n 'I.1': 'net_impact',\n 'L.1': 'net_likelihood',\n 'Commentary on Net Risk Assessment': 'net_risk_assesment_commentary',\n 'Risk Decision': 'risk_decision',\n 'Issue Description (if applicable)': 'issue_description',\n 'Action Description': 'action_description',\n 'Action Owner': 'action_owner',\n 'Action Due Date': 'action_due_date',\n 'Completion Date': 'completion_date',\n 'Action ID': 'action_id'\n }\n )\n\n# create our function to work through df['risk_id'] and just extract\n# the alpha prefix from the risk_id. E.g 'GMBH-P01-R01' becomes 'GMBH'\n# 'GMBH' is then appended to the list prefix[]\n\nprefix = []\ndef business_unit():\n prefix_search=re.compile(r'^[a-zA-Z]+')\n\n for value in raca_df['risk_id']:\n zz = prefix_search.findall(str(value))\n prefix.append(zz)\n return prefix\n\nbusiness_unit()\n\n# This takes our list of lists, 'prefix', from the function above and pulls out all its members into one list 'extract'\nextract = [item[0] for item in prefix]\n\n# Insert a new column to hold our business unit and populate it with Business Unit Names\n# We get the byusiness unit names from the 'extract[]' list in the step above\nresult = []\nfor value in extract:\n print(value)\n if value == 'DP':\n result.append('Data Privacy')\n elif value == 'COSECG':\n result.append('Company Secretariat - London')\n elif value == 'BI':\n result.append('Business Inteligence')\n elif value == 'ITDEV':\n result.append('IT Development')\n elif value == 'GMBH':\n result.append('GmbH Subsiduary')\n elif value == 'SEC':\n result.append('Information Security')\n elif value == 'FR':\n result.append('Financial Risk')\n elif value == 'CASS':\n result.append('Client Money')\n elif value == 'PROD':\n result.append('Market Data')\n elif value == 'CSA':\n result.append('Client Services APAC')\n elif value == 'SDBO':\n result.append('Stockbroking Dealing & Business Operations')\n elif value == 'SBDC':\n result.append('Stockbroking Business Change')\n elif value == 'SBBC':\n result.append('Stockbroking Operations')\n elif value == 'SCM':\n result.append('Stockbroking Client Money')\n elif value == 'SOS':\n result.append('Stockbroking Operations - APAC')\n elif value == 'COSECA':\n result.append('Company Secretariat - Aus')\n elif value == 'SP':\n result.append('Stockbroking Partners - APAC')\n elif value == 'WD':\n result.append('Stockbroking Web Development')\n elif value == 'HR':\n result.append('Human Resources')\n elif value == 'BCG':\n result.append('Business Continuity')\n elif value == 'ISP':\n result.append('Institutional Sales & Partners')\n elif value == 'CSG':\n result.append('Client Services - London')\n elif value == 'ST':\n result.append('Sales Trading - London')\n elif value == 'TAX':\n result.append('Tax')\n elif value == 'FIN':\n result.append('Finance')\n elif value == 'FACL':\n result.append('Facilities')\n elif value == 'ITPROD':\n result.append('IT Production')\n else:\n print(f\"Business Unit {value} has not been added to the function yet\")\n #print(f'DEBUG1: Results just in {result}')\n\n# Apply reuslts to 'business_unit' to create the column in the dataframe\nraca_df['business_unit'] = result\n\n# Create a new dataframe to hold our risk id's risk owners, action id, action owners and action due date.\naction_df = raca_df[['action_id',\n 'risk_id',\n 'risk_title',\n 'risk_owner',\n 'business_unit',\n 'action_owner',\n 'action_due_date']]\n\n# Setup some display options if we want to look at our Actions output\n# pd.set_option('display.max_columns', None)\n# pd.set_option('display.max_rows', None)\n\n# Drop all rows in the action_df dataframe who do not have data in all 7 fields\n# Write thgis back out to actiuon_id so we have a dataframe soley for tracking actions\naction_df = action_df.dropna(thresh=7)\n\n# calculate our gross and net risk scores\n# it does this by multiplying the impact and likelihood columns\n# the results are appended to teh df dataframe under columns\n# gross_risk and net_risk respectivly\nraca_df['gross_risk'] = raca_df['gross_impact'] * raca_df['gross_likelihood']\n\nraca_df['net_risk'] = raca_df['net_impact'] * raca_df['net_likelihood']\n\n# Reset our index colum so it is contiguous\nraca_df.reset_index(drop=True,\n inplace=True,\n col_level=0)\n\n# Start the row index at 1 just to make it easier for mortals\nraca_df.index = raca_df.index + 1\n\n# -------------------- Data Import and Cleansing Completed -------------------------#\n\n# -----------------------------------------------------#\n\n# Global variable to hold our data frame olutput from teh dropdown listboxes\noutput_dataframe = None\n\n# DEBUGGING ##\n# -----------------------------------------------------#\n# Inform user that data loaded sucsessfully\n#print('Data loaded successfully')\n#print(raca_df.head())\n#print(raca_df['risk_types'].unique())\n# def which_operations():\n# if raca_df['risk'] == 'Operations':\n# print(raca_df['risk_id'].bool())\n#\n# which_operations()\n\n# -----------------------------------------------------#\n\n# ----------------Dash Page Layout---------------------#\napp = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])\napp.title = 'OpRiskRACA '\n# app = dash.Dash(__name__,external_stylesheets=[dbc.themes.BOOTSTRAP]),\\\n# meta_tags=[{'name': 'viewport', 'content': 'width=device-width, initial-scale=1.0'}]\n\n# Dash layout - max 12 columns per page.\n# 3 columns on Row 1 and two of width 4.\n# 2 columns on row 3 with width 9 and 3.\n\napp.layout = dbc.Container([\n dbc.Row([\n dbc.Col([\n html.H1(\"Risk and Controls Assessment Dashboard\", className='text-center text-primary mb-4'),\n ], width=12),\n ], align='center'),\n html.Br(),\n dbc.Row([\n dbc.Col([\n html.H3(\"Risk Type\", className='text-center text-primary')\n ], width=2),\n dbc.Col([\n html.H3(\"Risk\", className='text-center text-primary')\n ], width=2),\n dbc.Col([\n html.H3(\"Level 3\", className='text-center text-primary')\n ], width=2),\n ], align='center'),\n html.Br(),\n dbc.Row([\n dbc.Col([\n # Taxonomy Level 1 Dropdown List box. # '+[{'label': 'All', 'value': 'All'}] adds alkl option\n dcc.Dropdown(id='risk_types', multi=False, value = 'All', clearable=False,\n options=[{'label': k, 'value': k} for k in sorted(raca_df['risk_types'].astype(str).unique())]\n + [{'label': 'All', 'value': 'All'}],\n placeholder='Select...'),\n ], width=2),\n\n dbc.Col([\n dcc.Dropdown(id='risk', multi=False,\n options=[{'label': k, 'value': k} for k in sorted(raca_df['risk'].astype(str).unique())]\n + [{'label': 'All', 'value': 'All'}],\n placeholder='Select...'),\n ], width=2),\n\n dbc.Col([\n dcc.Dropdown(id='level3', multi=False,\n options=[],\n placeholder='Select...'),\n ], width=2),\n ], align='top'),\n html.Br(),\n dbc.Row([\n dbc.Col([\n html.Div(id='answer', className='text-center text-primary')\n ], width=6),\n ], align='start'),\n # html.Br(),\n # dbc.Row([\n # dbc.Col([\n # html.Div(id='answer1', className='text-center text-primary')\n # ], width=6),\n # ], align='center'),\n# This styles the container\n],style = {'padding': '50px',\n 'backgroundColor': '#EBEFF0',\n 'textPrimary': '#071633'},\nfluid=True)\n\n# ----------------Dash Page Layout Complete---------------------#\n\n# ----------------Dash Page Callbacks---------------------#\n@app.callback(\n Output('risk', 'options'),\n Input('risk_types', 'value'))\ndef set_tl2_options(tl1_options):\n if tl1_options != 'All':\n raca_options = raca_df[raca_df['risk_types'] == tl1_options]\n print(f'DEBUG1: TL 1 Not equal to all: {raca_options}')\n else:\n raca_options = raca_df\n print(f'DEBUG2: TL1 equal to all: {raca_options}')\n\n return [{'label': i, 'value': i} for i in sorted(raca_options['risk'].astype(str).unique())]\n\n@app.callback(\n Output('level3', 'options'),\n Input('risk', 'value'))\ndef set_tl3_options(tl2_options):\n if tl2_options != 'All':\n raca_options = raca_df[raca_df['risk'] == tl2_options]\n print(f'DEBUG3: TL2 Not equal to all: {raca_options}')\n else:\n raca_options = raca_df\n print(f'DEBUG4: TL2 equal to all: {raca_options}')\n return [{'label': i, 'value': i} for i in sorted(raca_options['level3'].astype(str).unique())]\n\n# # Get all the inputs and output them to a sentence\n# # This proves we can get values from the dropdowns\n# # so we should now be able to pull values to sort\n# # dataframes\n#\n#\n@app.callback(\n Output('answer', 'children'),\n Input('risk_types', 'value'),\n Input('risk', 'value'),\n Input('level3', 'value')\n)\ndef return_dropdown_selections(risk_types, risk, level3):\n return 'Taxonomy Level 1 is {}, Taxonomy Level 2 is {} and level 3 is {}'.format(\n risk_types, risk, level3,\n )\n\n# @app.callback(\n# Output('answer1', 'children'),\n# )\n# def return_dropdown_selections(answer1):\n# return 'bitches be like weeeee!!!!'\n\n# # Total Number of Risks in whole raca\n# @app.callback(\n# Output('tnro', 'value'),\n# Input('tnro','value'))\n# def tnro():\n# tnro = raca_df['risk_id'].nunique()\n# print('DEBUG: total number of Risks in Raca {}'.format(tnro,))\n# return 'Total Number of Risks is {}'.format(\n# tnro,\n# )\n\n# ----------------Dash Page Callbacks Completed---------------------#\n\nif __name__ == '__main__':\n app.run_server(debug=True)\n","repo_name":"twelsh37/wip","sub_path":"wip.py","file_name":"wip.py","file_ext":"py","file_size_in_byte":12810,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"39745184425","text":"#!/usr/bin/python3\n\"\"\"\npascal triangle module\n\"\"\"\n\n\ndef pascal_triangle(n):\n \"\"\" return ist that represent pascal's triangle\"\"\"\n my_list = []\n if n <= 0:\n return my_list\n for i in range(1, n + 1):\n value = 1\n tmp_list = []\n for j in range(1, i + 1):\n tmp_list.append(str(value))\n value = value * (i - j) // j\n my_list.append(tmp_list)\n return my_list\n","repo_name":"luiscode92/holbertonschool-higher_level_programming","sub_path":"0x0B-python-input_output/14-pascal_triangle.py","file_name":"14-pascal_triangle.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"30602773015","text":"from brokerstream import BrokerStreams\nfrom config.config import Settings\n\ntry:\n brokerStr = None\n consumer_jobs = None\n producer_job = None\n\n brokerStr = BrokerStreams('consumer_jobs', Settings().producer_topic_name, Settings().broker_url)\n producer_job = Settings().producer_topic_name \n consumer_jobs = Settings().consume_topic_name.split(\",\") \n\nexcept:\n print('=============================================\\n')\n print('[Error] : No broker avaialble on the cluster\\n')\n print('=============================================\\n')\n","repo_name":"Tristanchrt/profil-hunt-osint-tool","sub_path":"transform/broker/broker.py","file_name":"broker.py","file_ext":"py","file_size_in_byte":557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"11557131789","text":"import sys,os, subprocess\nimport argparse\n\ndef parse_args():\n\tparser = argparse.ArgumentParser(prog=\"aggregate_blastn\")\n\tparser = argparse.ArgumentParser(description='Given a txt file with the species names for every line, it reads the information and retrieves the sequences of interest')\n\tparser.add_argument('-blast_directory', '--Dir', help='Give this command a directory containing all blastn file outputs')\n\tparser.add_argument('-number_seqs', '--N_seqs', help='Give this command a file with all the species names')\n\targs = parser.parse_args()\n\treturn args\n\ndef main(args):\n\toriginal_path = os.getcwd()\n\tos.chdir(args.Dir)\n\tfinal_file = open(os.path.join(original_path,'blastn.xlsx'),'a+')\n\tfinal_file.write('Sample\\tContig\\tSpecies Name\\tKingdom\\tTitle,Score\\n')\n\tfinal_file.close()\n\tfor file in sorted(os.listdir()):\n\t\tcurrent_blastn = open(file,'r')\n\t\ti = 0\n\t\tj = 1\n\t\tfinal_file = open(os.path.join(original_path,'blastn.xlsx'),'a+')\n\t\tfinal_file.write(file[:-7])\n\t\tfor lines in current_blastn:\n\t\t\tlines = lines.replace(',',';')\n\t\t\ti += 1\n\t\t\tif i == 1:\n\t\t\t\tfinal_file.write('\\tContig ' + str(j) + '\\t' + lines)\n\t\t\tif i == int(args.N_seqs):\n\t\t\t\tj += 1\n\t\t\t\ti = 0\n\t\t\t\tfinal_file.write('' + '\\t ' + '\\t' + lines)\n\t\t\telif i != args.N_seqs and i != 1: \n\t\t\t\tfinal_file.write('' + '\\t ' + '\\t' + lines)\n\t\tcurrent_blastn.close()\n\t\tfinal_file.close()\n\nif __name__ == \"__main__\":\n\targs = parse_args()\n\tmain(args)\n","repo_name":"JoaoEduardo12/Bioinformatic-Scripts","sub_path":"blastn_aggregate.py","file_name":"blastn_aggregate.py","file_ext":"py","file_size_in_byte":1411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"72718354411","text":"from django.contrib import admin\nfrom .models import Team\nfrom django.utils.html import format_html\n\n# Register your models here.\nclass TeamAdmin(admin.ModelAdmin):\n def show_photo(self, object):\n return format_html(f\"
\")\n\n show_photo.short_description = \"Photo\" \n list_display = (\"id\",\"show_photo\",\"first_name\",\"designation\", \"created_date\")\n list_display_links = (\"first_name\",\"id\",\"show_photo\")\n search_fields = (\"first_name\",\"designation\")\n list_filter = (\"designation\",)\n\nadmin.site.register(Team, TeamAdmin)","repo_name":"Abduqayyum/carzone-abduqayum","sub_path":"pages/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"16299722781","text":"class PID():\n #first, we have to initialize the controller\n\tdef __init__(self,SP,MV,KP,KI,KD,dt,Umax,Umin):\n\t\tself.kp = KP # Proportional Gain\n\t\tself.ki = KI # Integral Gain\n\t\tself.kd = KD # Derivative Gain\n\t\tself.sp = SP # Setpoint Value\n\t\tself.mv = MV # Measured Variable\n\t\tself.dt = dt # integral element\n\t\tself.error_last = 0 # Previous error\n\t\tself.integral_error = 0 # Integral of error\n\t\tself.saturation_max = Umax #max speed of the robot\n\t\tself.saturation_min = Umin #min speed of the robot\n \n #using the controller\n\tdef compute(self):\n\t\terror = self.mv - self.sp #compute the error\n\t\tderivative_error = (error - self.error_last) / self.dt #find the derivative of the error (how the error changes with time) Backward Difference\n\t\toutput = self.kp*error + self.ki*self.integral_error + self.kd*derivative_error \n\t\t\n\t\t# Update State\n\t\tself.integral_error += error * self.dt #error build up over time\n\t\tself.error_last = error\n\t\t# Saturation\n\t\tif output > self.saturation_max:\n\t\t\toutput = self.saturation_max\n\t\telif output < self.saturation_min:\n\t\t\toutput = self.saturation_min\n\t\treturn output\n","repo_name":"MahdiShahrajabian/DigitalControlLab","sub_path":"Project/PID.py","file_name":"PID.py","file_ext":"py","file_size_in_byte":1122,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"38615010519","text":"import pytest\nimport pandas as pd\n\nfrom pybitbay import BitBayAPI\n\n\nTICKER = 'btcpln'\nJSON_DATA = [\n {\"date\": 1396094988, \"price\": 4500.0, \"type\": \"buy\", \"amount\": 0.0129, \"tid\": \"0\"},\n {\"date\": 1396096603, \"price\": 4400.0, \"type\": \"sell\", \"amount\": 0.011364, \"tid\": \"1\"},\n]\nCOLUMNS = JSON_DATA[0].keys()\nEXPECTED_DF = pd.DataFrame(\n data=JSON_DATA,\n columns=COLUMNS\n)\n\n\nclass MockResponse:\n def json(self):\n return JSON_DATA\n\n\nclass EmptyMockResponse:\n def json(self):\n return {}\n\n\ndef test_BitBayAPI(mocker):\n mocker.patch('requests.Session.get', side_effect=[MockResponse(), EmptyMockResponse()])\n trades = BitBayAPI().get_all_trades(ticker=TICKER)\n assert next(trades).equals(EXPECTED_DF)\n with pytest.raises(StopIteration):\n next(trades)\n\n\ndef test_bitbay_public_api():\n df = BitBayAPI().get_trades(ticker=TICKER, since=-1)\n assert list(COLUMNS) == df.columns.tolist()\n assert df[0:2].equals(EXPECTED_DF)\n","repo_name":"dominikheinisch/pybitbay","sub_path":"test/test_bitbay.py","file_name":"test_bitbay.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"5938036520","text":"from sequencescape import WellJSONDecoder, WellJSONEncoder\nfrom sequencescape.json_converters import JSON_INTERNAL_ID_PROPERTY, MultiplexedLibraryJSONDecoder, \\\n MultiplexedLibraryJSONEncoder, LibraryJSONDecoder, LibraryJSONEncoder, JSON_LIBRARY_TYPE, StudyJSONDecoder, \\\n StudyJSONEncoder, JSON_FACULTY_SPONSER, JSON_STUDY_VISIBILITY, JSON_STUDY_TITLE, JSON_DESCRIPTION, JSON_STUDY_TYPE, \\\n JSON_ACCESSION_NUMBER_PROPERTY, SampleJSONDecoder, SampleJSONEncoder, JSON_GEOGRAPHICAL_REGION_PROPERTY, \\\n JSON_COUNTRY_OF_ORIGIN_PROPERTY, JSON_COHORT_PROPERTY, JSON_ETHNICITY_PROPERTY, JSON_GENDER_PROPERTY, \\\n JSON_TAXON_ID_PROPERTY, JSON_COMMON_NAME_PROPERTY, JSON_ORGANISM_PROPERTY\nfrom sequencescape.json_converters import JSON_NAME_PROPERTY\nfrom sequencescape.tests._helpers import create_stub_well, create_stub_multiplexed_library, create_stub_library, \\\n create_stub_study, create_stub_sample\nfrom sequencescape.tests._json_converters_test_factory import create_json_converter_test\n\n\n_setups = [(\n create_stub_sample,\n [JSON_NAME_PROPERTY, JSON_INTERNAL_ID_PROPERTY, JSON_ACCESSION_NUMBER_PROPERTY, JSON_ORGANISM_PROPERTY,\n JSON_COMMON_NAME_PROPERTY, JSON_TAXON_ID_PROPERTY, JSON_GENDER_PROPERTY, JSON_ETHNICITY_PROPERTY,\n JSON_COHORT_PROPERTY, JSON_COUNTRY_OF_ORIGIN_PROPERTY, JSON_GEOGRAPHICAL_REGION_PROPERTY],\n SampleJSONEncoder,\n SampleJSONDecoder\n ), (\n create_stub_study,\n [JSON_NAME_PROPERTY, JSON_INTERNAL_ID_PROPERTY, JSON_ACCESSION_NUMBER_PROPERTY, JSON_STUDY_TYPE, JSON_DESCRIPTION,\n JSON_STUDY_TITLE, JSON_STUDY_VISIBILITY, JSON_FACULTY_SPONSER],\n StudyJSONEncoder,\n StudyJSONDecoder\n ), (\n create_stub_library,\n [JSON_NAME_PROPERTY, JSON_INTERNAL_ID_PROPERTY, JSON_LIBRARY_TYPE],\n LibraryJSONEncoder,\n LibraryJSONDecoder\n ), (\n create_stub_multiplexed_library,\n [JSON_NAME_PROPERTY, JSON_INTERNAL_ID_PROPERTY],\n MultiplexedLibraryJSONEncoder,\n MultiplexedLibraryJSONDecoder\n ), (\n create_stub_well,\n [JSON_NAME_PROPERTY, JSON_INTERNAL_ID_PROPERTY],\n WellJSONEncoder,\n WellJSONDecoder\n )\n]\n\nfor _setup in _setups:\n _encoder_test_class, _decoder_test_class = create_json_converter_test(*_setup)\n globals()[_encoder_test_class.__name__] = _encoder_test_class\n globals()[_decoder_test_class.__name__] = _decoder_test_class\n del _encoder_test_class, _decoder_test_class\n","repo_name":"wtsi-hgi/python-sequencescape-db","sub_path":"sequencescape/tests/test_json_converts.py","file_name":"test_json_converts.py","file_ext":"py","file_size_in_byte":2489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"7054822370","text":"import asyncio\nimport enum\nimport json\nimport socket\nfrom typing import List\n\nfrom aiohttp.abc import AbstractResolver\nfrom aiohttp.client import ClientSession as CS\nfrom aiohttp.connector import TCPConnector\nfrom aiohttp.resolver import DefaultResolver\n\n__all__ = 'ClientSession', 'DNSOverHTTPSResolver', 'RecordType'\n\n\nclass RecordType(enum.Enum):\n \"\"\"Record Type\"\"\"\n\n A = 1\n AAAA = 28\n\n\nclass DNSOverHTTPSResolver(AbstractResolver):\n \"\"\"DNS over HTTPS Resolver\"\"\"\n\n def __init__(\n self,\n *,\n endpoints: List[str],\n json_loads=json.loads,\n resolver_class=None,\n ) -> None:\n self.endpoints = endpoints\n self.json_loads = json_loads\n if resolver_class is None:\n resolver_class = DefaultResolver\n self.resolveer_class = resolver_class\n \n async def _resolve(self, endpoint: str, host, port, family):\n if family == socket.AF_INET6:\n record_type = RecordType.AAAA\n else:\n record_type = RecordType.A\n\n params = {\n 'ct': 'application/dns-json',\n 'name': host,\n 'type': record_type.name,\n }\n\n resolver = self.resolveer_class()\n connector = TCPConnector(resolver=resolver)\n \n async with CS(connector=connector) as session:\n async with session.get(endpoint, params=params) as resp:\n data = self.json_loads(await resp.text())\n\n await connector.close()\n\n if data['Status'] != 0:\n raise OSError(\"DNS lookup failed\")\n\n return [\n {\n 'hostname': host,\n 'host': r['data'],\n 'port': port,\n 'family': family,\n 'proto': 0,\n 'flags': socket.AI_NUMERICHOST\n } for r in data['Answer']\n if r['type'] in (\n record_type.name,\n record_type.value,\n ) and r['data']\n ]\n\n async def resolve(self, host, port=0, family=socket.AF_INET):\n tasks = [\n self._resolve(endpoint, host, port, family)\n for endpoint in self.endpoints\n ]\n done, pending = await asyncio.wait(\n tasks,\n return_when=asyncio.FIRST_COMPLETED,\n )\n for p in pending:\n p.cancel()\n return list(done)[0].result()\n\n async def close(self):\n pass\n\n\ndef ClientSession(*args, **kwargs) -> CS: # noqa\n \"\"\"Shortcut of aiohttp.ClientSession and DNSOverHTTPSResolver\"\"\"\n\n endpoints = kwargs.pop(\n 'endpoints',\n [\n 'https://dns.google.com/resolve',\n 'https://cloudflare-dns.com/dns-query',\n ],\n )\n json_loads = kwargs.pop('json_loads', json.loads)\n resolver_class = kwargs.pop('resolver_class', None)\n resolver = DNSOverHTTPSResolver(\n endpoints=endpoints,\n json_loads=json_loads,\n resolver_class=resolver_class,\n )\n connector = TCPConnector(resolver=resolver)\n\n return CS(*args, **kwargs, connector=connector)\n","repo_name":"item4/aiohttp-doh","sub_path":"aiohttp_doh.py","file_name":"aiohttp_doh.py","file_ext":"py","file_size_in_byte":3068,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"55"}
+{"seq_id":"69871665453","text":"from __future__ import annotations\n\nfrom datetime import datetime\nfrom typing import List, Dict, Optional, Union\nfrom pydantic import BaseModel, Field, validator\n\n\nclass DataRecordingModel(BaseModel):\n time: datetime\n value: float\n\n\nclass NodeMetricsModel(BaseModel):\n node_name: str\n arrow_metrics: List[float]\n memory_used: List[DataRecordingModel] = Field(default=[])\n cpu_busy: List[DataRecordingModel] = Field(default=[])\n disk_io_util: List[DataRecordingModel] = Field(default=[])\n network_util: List[DataRecordingModel] = Field(default=[])\n\n @staticmethod\n def cap(value: Union[int, float], replace_zero_with: Optional[float] = None):\n new_value = min(100, max(0, value))\n if new_value == 0 and replace_zero_with is not None:\n new_value = replace_zero_with\n return new_value\n\n @validator(\"memory_used\")\n def normalize_memory_used(cls, memory_used):\n return [DataRecordingModel(time=el.time, value=cls.cap(el.value)) for el in memory_used]\n\n @validator(\"cpu_busy\")\n def normalize_cpu_busy(cls, cpu_busy):\n return [DataRecordingModel(time=el.time, value=cls.cap(el.value)) for el in cpu_busy]\n\n @validator(\"disk_io_util\")\n def normalize_disk_io_util(cls, disk_io_util):\n return [DataRecordingModel(time=el.time, value=cls.cap(el.value)) for el in disk_io_util]\n\n @validator(\"network_util\")\n def normalize_network_util(cls, network_util):\n return [DataRecordingModel(time=el.time, value=cls.cap(el.value)) for el in network_util]\n\n\nclass BaseInformationModel(BaseModel):\n framework_name: str\n algorithm_name: str\n dataset_name: str\n\n\nclass GenericWorkloadModel(BaseInformationModel):\n workload_name: str\n\n node_count: int\n machine_name: str\n runtime: float\n cost: Optional[float]\n completed: bool\n timeout: bool\n abandon: bool\n\n\nclass RawWorkloadModel(GenericWorkloadModel):\n node_metrics: Dict[str, NodeMetricsModel]\n configuration: Dict[str, str]\n result: Dict[str, str]\n","repo_name":"dos-group/perona-infrastructure-fingerprinting","sub_path":"usecase_dataflows/classes/raw_workload.py","file_name":"raw_workload.py","file_ext":"py","file_size_in_byte":2033,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"55"}
+{"seq_id":"38821796262","text":"import os\nimport math\n\n\ndef good_proc(nproc, ncore):\n if ncore == 24:\n if nproc == 5:\n nproc = 4\n elif nproc == 7:\n nproc = 6\n elif 8 < nproc < 12:\n nproc = 8\n elif 12 < nproc < 24:\n nproc = 12\n else: # ncore == 40\n if nproc == 3:\n nproc = 2\n elif 5 < nproc < 8:\n nproc = 5\n elif nproc == 9:\n nproc = 8\n elif 10 < nproc < 20:\n nproc = 10\n elif 20 < nproc < 40:\n nproc = 20\n\n return nproc\n\n\ndef write_sh(nks, nkd, nk_path, atom, prefix, atomwfc_dict, queue):\n pw = \"~/program/QE/qe-6.2.1/bin/pw.x\"\n proj = \"~/program/QE/qe-6.2.1/bin/projwfc.x\"\n vf = \"~/program/QE/qe-6.2.1/bin/fermi_velocity.x\"\n bands = \"~/program/QE/qe-6.2.1/bin/bands.x\"\n sumpdos = \"~/program/QE/qe-6.2.1/bin/sumpdos.x\"\n fproj = \"~/program/QE/qe-6.2.1/bin/fermi_proj.x\"\n typ = set(atom)\n #\n if queue == \"F4cpus\":\n maxnode = 4\n ncore = 24\n elif queue == \"F4cpue\":\n maxnode = 4\n ncore = 40\n elif queue == \"F36cpus\":\n maxnode = 36\n ncore = 24\n elif queue == \"F9cpue\":\n maxnode = 9\n ncore = 40\n elif queue == \"F36cpue\":\n maxnode = 36\n ncore = 40\n else: # queue == \"F144cpus\":\n maxnode = 144\n ncore = 24\n #\n # Structure optimization\n #\n nk = min(ncore*maxnode, nks)\n ntg = good_proc(int(ncore*maxnode / nk), ncore)\n nproc = nk*ntg\n node = math.ceil(nproc / ncore)\n if not os.path.isfile(\"rx.sh\"):\n with open(\"rx.sh\", 'w') as f:\n print(\"#!/bin/sh\", file=f)\n print(\"#QSUB -queue\", queue[0:len(queue) - 1], file=f)\n print(\"#QSUB -node\", node, file=f)\n print(\"#PBS -l walltime=8:00:00\", file=f)\n print(\"source ~/.bashrc\", file=f)\n print(\"cd $PBS_O_WORKDIR\", file=f)\n print(\"mpijob -n %d %s -nk %d -ntg %d -in rx.in > rx_s.out\"\n % (nproc, pw, nk, ntg), file=f)\n print(\"sed -n -e '/occupations/c occupations=\\\"tetrahedra_opt\\\"' -e '1,/CELL_PARAMETERS/p' rx.in > rx_t.in\",\n file=f)\n print(\"grep -A 3 CELL_PARAMETERS rx_s.out | tail -n 3 >> rx_t.in\", file=f)\n print(\"awk '/ATOMIC_SPECIES/,/ATOMIC_POSITIONS/' rx.in >> rx_t.in\", file=f)\n print(\"grep -A %d ATOMIC_POSITIONS rx_s.out |tail -n %d >> rx_t.in\" % (len(atom), len(atom)), file=f)\n print(\"sed -n -e '/K_POINTS/,$p' rx.in >> rx_t.in\", file=f)\n print(\"sed -i -e '/occupations/c occupations=\\\"tetrahedra_opt\\\"' rx_t.in\", file=f)\n print(\"mpijob -n %d %s -nk %d -ntg %d -in rx_t.in > rx_t.out\"\n % (nproc, pw, nk, ntg), file=f)\n #\n # Charge optimization\n #\n if not os.path.isfile(\"scf.sh\"):\n with open(\"scf.sh\", 'w') as f:\n print(\"#!/bin/sh\", file=f)\n print(\"#QSUB -queue\", queue[0:len(queue) - 1], file=f)\n print(\"#QSUB -node\", node, file=f)\n print(\"#PBS -l walltime=8:00:00\", file=f)\n print(\"source ~/.bashrc\", file=f)\n print(\"cd $PBS_O_WORKDIR\", file=f)\n print(\"mpijob -n %d %s -nk %d -ntg %d -in scf.in > scf.out\"\n % (nproc, pw, nk, ntg), file=f)\n #\n # Projected DOS\n #\n nk = min(ncore*maxnode, nkd)\n ntg = good_proc(int(ncore*maxnode / nk), ncore)\n nproc = nk * ntg\n node = math.ceil(nproc / ncore)\n #\n # Atomwfc dictionary for fermi_proj.x\n #\n pfermi = {ityp: [[] for il in range(len(atomwfc_dict[ityp][0]))] for ityp in typ}\n ii = 0\n for iat in atom:\n for il in range(len(atomwfc_dict[iat][0])):\n for im in range(atomwfc_dict[iat][0][il]):\n ii += 1\n pfermi[iat][il].append(ii)\n #\n if not os.path.isfile(\"proj.sh\"):\n with open(\"proj.sh\", 'w') as f:\n print(\"#!/bin/sh\", file=f)\n print(\"#QSUB -queue\", queue[0:len(queue) - 1], file=f)\n print(\"#QSUB -node\", node, file=f)\n print(\"#PBS -l walltime=8:00:00\", file=f)\n print(\"source ~/.bashrc\", file=f)\n print(\"cd $PBS_O_WORKDIR\", file=f)\n print(\"mpijob -n %d %s -nk %d -ntg %d -in nscf.in > nscf.out\"\n % (nproc, pw, nk, ntg), file=f)\n print(\"mpijob -n 1 %s -in nscf.in > vfermi.out\" % vf, file=f)\n print(\"ef=`grep Fermi nscf.out| awk '{print $5}'`\", file=f)\n print(\"sed -i -e '/emin/c emin = '${ef}'' -e '/emax/c emax = '${ef}'' proj.in\", file=f)\n print(\"mpijob -n %d %s -nk %d -ntg %d -in proj.in > proj.out\"\n % (nproc, proj, nk, ntg), file=f)\n #\n # Sum PDOS at each Atom and L\n #\n for ityp in typ:\n for il in range(len(atomwfc_dict[ityp][1])):\n print(\"%s %s.pdos_atm*\\\\(%s\\\\)_wfc#%d* > %s.pdos_%s%s\"\n % (sumpdos, prefix, ityp, il+1, prefix, ityp, atomwfc_dict[ityp][1][il]), file=f)\n #\n # Fermi surface with atomic projection\n #\n for ityp in typ:\n for il in range(len(atomwfc_dict[ityp][1])):\n print(\"sed -e '$a %d\\\\n\" % len(pfermi[ityp][il]), end=\"\", file=f)\n for ii in pfermi[ityp][il]:\n print(\" %d\" % ii, end=\"\", file=f)\n print(\"' proj.in > proj_f.in\", file=f)\n print(\"mpijob -n 1 %s -in proj_f.in\" % fproj, file=f)\n print(\"mv proj.frmsf %s%s.frmsf\" % (ityp, atomwfc_dict[ityp][1][il]), file=f)\n #\n # Band\n #\n nk = min(ncore*maxnode, nk_path)\n ntg = good_proc(int(ncore*maxnode / nk), ncore)\n nproc = nk * ntg\n node = math.ceil(nproc / ncore)\n if not os.path.isfile(\"band.sh\"):\n with open(\"band.sh\", 'w') as f:\n print(\"#!/bin/sh\", file=f)\n print(\"#QSUB -queue\", queue[0:len(queue) - 1], file=f)\n print(\"#QSUB -node\", node, file=f)\n print(\"#PBS -l walltime=8:00:00\", file=f)\n print(\"source ~/.bashrc\", file=f)\n print(\"cd $PBS_O_WORKDIR\", file=f)\n print(\"mpijob -n %d %s -nk %d -ntg %d -in band.in > band.out\"\n % (nproc, pw, nk, ntg), file=f)\n print(\"mpijob -n %d %s -nk %d -ntg %d -in bands.in > bands.out\"\n % (nproc, bands, nk_path, ntg), file=f)\n","repo_name":"Huaguiyuan/cif2input","sub_path":"write_sh.py","file_name":"write_sh.py","file_ext":"py","file_size_in_byte":6446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"39384928767","text":"# from math import exp, log10\r\nimport numpy as np\r\n\r\n\"\"\"\r\nQuiz Question\r\n\r\nThe sigmoid function is defined as sigmoid(x) = 1/(1+e^-x). \r\nIf the score is defined by (4x1 + 5x2 - 9 = score), \r\nthen which of the following points has exactly a 50% probability \r\nof being blue or red? (Choose all that are correct.)\r\n\r\nsigmoid(x) = 1 / (1 + exp(-x))\r\n\r\npoints:\r\n(1, 1)\r\n(2, 4)\r\n(5, -5)\r\n(-4, 5)\r\n\"\"\"\r\ndef sigmoid(X, W, b):\r\n linear_value = (np.matmul(X, W) + b)[0]\r\n return (1 / (1 + np.exp(-linear_value)))\r\n\r\n\r\npoints = [(1, 1), (2, 4), (5, -5), (-4, 5)]\r\nW = np.array([4, 5]).reshape(2, 1)\r\nb = -9\r\n\r\nfor point in points:\r\n point_prop = sigmoid(point, W, b)\r\n print(f'{point} has probability with: {point_prop}')\r\n\r\nprint(np.exp(-500))\r\n# sigmoid(x) = 1 / 1 + exp(-x)\r\n","repo_name":"KaisChebata/AI-Programming-with-Python-Nanodegree-Program","sub_path":"Core_Curriculum/6_Neural_Networks/Lesson_1_Introduction_to_Neural_Networks/perceptrons/discrete_vs_continuous.py","file_name":"discrete_vs_continuous.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"36059389365","text":"from django.urls import path\nfrom . import views\n\n\nurlpatterns = [\n path('Shome', views.adminhome),\n path('', views.admin),\n path('create_user', views.create_user),\n path('display_user', views.display_user),\n path('update_user', views.update_user),\n path('delete_user', views.delete_user),\n path('add_category', views.add_category),\n path('display_category', views.display_category),\n path('update_category', views.update_category),\n path('delete_category', views.delete_category),\n path('add_product', views.add_product),\n path('display_product', views.display_product),\n path('update_product', views.update_product),\n path('delete_product', views.delete_product),\n]","repo_name":"ShamnaK46/ecommerce","sub_path":"admin/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"71573158542","text":"#!/usr/bin/env python\n__author__ = 'Sergei F. Kliver'\n\nimport argparse\nfrom RouToolPa.Routines import AnnotationsRoutines\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument(\"-g\", \"--gtf_file\", action=\"store\", dest=\"input\", required=True,\n help=\"Input gtf file\")\nparser.add_argument(\"-o\", \"--output\", action=\"store\", dest=\"output\", required=True,\n help=\"Output accordance file\")\n\nargs = parser.parse_args()\n\nAnnotationsRoutines.get_transcript_to_pep_accordance_from_gtf(args.input, args.output, comment_symbol=\"#\")\n","repo_name":"mahajrod/MAVR","sub_path":"scripts/annotation/get_transcript_to_pep_accordance_from_gtf.py","file_name":"get_transcript_to_pep_accordance_from_gtf.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"}
+{"seq_id":"23869231891","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Oct 15 13:58:02 2023\n\n@author: ubuntu\n\"\"\"\nimport pandas as pd\nimport requests\n\nimport apicall as api\nfrom settings import FPL_ID, BASE_URL\n\n\ndef load_bootstrap():\n response = api.load('bootstrap-static', 'bootstrap.json')\n\n events = pd.json_normalize(response['events'])\n game_settings = pd.json_normalize(response['game_settings'])\n phases = pd.json_normalize(response['phases'])\n teams = pd.json_normalize(response['teams'])\n total_players = response['total_players']\n elements = pd.json_normalize(response['elements'])\n element_stats = pd.json_normalize(response['element_stats'])\n element_types = pd.json_normalize(response['element_types'])\n\n return events, game_settings, phases, teams, total_players, elements, element_stats, element_types\n\n\ndef load_entry():\n response = api.load(\"entry/\" + str(FPL_ID), 'entry.json')\n entry = pd.json_normalize(response, max_level=0)\n entry = entry.drop(columns=\"leagues\")\n\n classic = pd.json_normalize(response['leagues']['classic'])\n h2h = pd.json_normalize(response['leagues']['h2h'])\n cup = pd.json_normalize(response['leagues']['cup'])\n cup_matches = pd.json_normalize(response['leagues']['cup_matches'])\n return response, entry, classic, h2h, cup, cup_matches\n\n\ndef load_league_standings(league_id):\n url = \"leagues-classic/\" + str(league_id) + \"/standings\"\n return api.load(url, \"league_\" + str(league_id) + \".json\")\n\n\ndef load_picks(entry, event):\n url = \"entry/\" + str(entry) + \"/event/\" + str(event) + \"/picks/\"\n return api.load(url, str(entry) + \"-\" + str(event) + \".json\")\n\n\ndef load_event_live(current_event):\n i = 1\n all_stats = []\n\n while i <= current_event:\n url = \"event/\" + str(i) + \"/live/\"\n file = \"event-live-\" + str(i) + \".json\"\n event = api.load(url, file)\n for element in event['elements']:\n element_id = element['id']\n if len(element['explain']):\n fixture_id = (element['explain'][0]['fixture'])\n else:\n fixture_id = 999\n stats = element['stats']\n stats['id'] = element_id\n stats['fixtureId'] = fixture_id\n all_stats.append(stats)\n i += 1\n\n df = pd.DataFrame(all_stats)\n\n df['expected_goals'] = pd.to_numeric(df['expected_goals'])\n df['expected_assists'] = pd.to_numeric(df['expected_assists'])\n df['expected_goal_involvements'] = pd.to_numeric(\n df['expected_goal_involvements'])\n df['expected_goals_conceded'] = pd.to_numeric(\n df['expected_goals_conceded'])\n\n return df\n\n\ndef load_fixtures():\n response = api.load('fixtures', 'fixtures.json')\n fixtures = pd.json_normalize(response, max_level=0)\n # stats not decoded. Useless as is.\n stats = fixtures['stats']\n\n fixtures = fixtures.drop(columns='stats')\n return fixtures, stats\n\n\ndef load_future_fixtures():\n fixtures = requests.get(BASE_URL + 'fixtures/?future=1').json()\n\n return pd.DataFrame(fixtures)\n\n\ndef build_players(elements, teams, element_types):\n # join players to teams\n df = pd.merge(\n left=elements,\n right=teams,\n left_on='team',\n right_on='id'\n )\n\n # join player positions\n df = df.merge(\n element_types,\n left_on='element_type',\n right_on='id'\n )\n\n # rename columns\n df = df.rename(\n columns={'name': 'team_name', 'singular_name': 'position_name', 'minutes_x': 'minutes',\n 'short_name': 'Team', 'id': 'element_type',\n 'id_x': 'player_id'}\n )\n\n df = df.drop(columns=['minutes', 'goals_scored', 'assists', 'clean_sheets',\n 'goals_conceded', 'own_goals', 'penalties_saved', 'penalties_missed',\n 'yellow_cards', 'red_cards', 'saves', 'bonus', 'bps', 'influence', 'creativity',\n 'threat', 'ict_index', 'expected_goals', 'expected_assists', 'expected_goal_involvements',\n 'expected_goals_conceded', 'starts', 'in_dreamteam', 'pulse_id'\n ])\n # Maybe trouble. Why are there duplicates???\n df = df.loc[:, ~df.columns.duplicated()].copy()\n df['now_cost'] = df['now_cost'] / 10\n\n # Drop players who are not available\n df = df[~df['status'].isin(['u'])]\n\n return df\n","repo_name":"doublep563/fantasyfootball","sub_path":"load/load_data.py","file_name":"load_data.py","file_ext":"py","file_size_in_byte":4392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"72846773263","text":"import tensorflow as tf\nimport numpy as np\n\ntf.random.set_seed(42)\n\n# create a tensor of size 50 with random values between 0 and 100 and add 2 extra dimensions at the start\ntensor_random1 = tf.constant(tf.random.uniform(shape=[50], minval=0, maxval=100), shape=(1, 1, 50))\nprint(tensor_random1)\n\n# create a tensor of size 50 with random values between 0 and 100 and add 2 extra dimensions at the start\ntensor_random2 = tf.constant(np.random.random(50))\nprint(tensor_random2)\ntensor_random2 = tf.expand_dims(tensor_random2, 0)\ntensor_random2 = tf.expand_dims(tensor_random2, 0)\nprint(tensor_random2)\n\n","repo_name":"ionutnoje/tutoriale","sub_path":"ZTM TensorFlow Course/1. Deep Learning and TensorFlow Fundamentals/Tests/Tensor_Creation.py","file_name":"Tensor_Creation.py","file_ext":"py","file_size_in_byte":601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"8373336248","text":"# ДОПОЛНИТЕЛЬНО, НО НЕОБЯЗАТЕЛЬНО:\r\n# Написать программу, которая состоит 4 из этапов:\r\n# - создает список из рандомных четырех значных чисел\r\n# - принимает с консоли цифру и удаляет ее из всех элементов списка\r\n# - цифры каждого элемента суммирует пока результат не станет однозначным числом\r\n# - из финального списка убирает все дублирующиеся элементы\r\n# - после каждого этапа выводить результат в консоль\r\n# Пример:\r\n# - 1 этап: [2634, 6934, 7286, 3353, 4602, 3176, 3796]\r\n# - 2 этап: Введ��те цифру: 3\r\n# - 2 этап: [264, 694, 7286, 5, 4602, 176, 796]\r\n# - 3 этап: 264 -> 2+6+4 -> 12 -> 1+2 -> 3\r\n# - 3 этап: [3, 1, 5, 5, 3, 5, 4]\r\n# - 4 этап: [3, 1, 5, 4]\r\n\r\nfrom random import randint\r\n\r\n# - создает список из рандомных четырех значных чисел\r\ndef create_list (number_of_elem):\r\n result_list = []\r\n for _ in range (number_of_elem):\r\n result_list.append(randint(1000, 10000))\r\n return result_list\r\n\r\n# - принимает с консоли цифру и удаляет ее из всех элементов списка\r\ndef delete_digit(list, number_to_delete):\r\n for i in range(len(list)):\r\n list[i] = str(list[i])\r\n if number_to_delete in list[i]:\r\n list[i] = list[i].replace(number_to_delete, '')\r\n list[i] = int(list[i])\r\n return list\r\n\r\n# - цифры каждого элемента суммирует пока результат не станет однозначным числом\r\ndef replace_element(list):\r\n for i in range(len(list)):\r\n while list[i] > 9:\r\n list[i] = sum_digits(list[i])\r\n return list \r\n\r\ndef sum_digits(number):\r\n if number < 10:\r\n return number\r\n else:\r\n return number % 10 + sum_digits(number//10)\r\n\r\n# - из финального списка убирает все дублирующиеся элементы\r\n\r\ndef delete_duplecates(list):\r\n new_list = []\r\n for elem in list:\r\n if elem not in new_list:\r\n new_list.append(elem)\r\n return new_list\r\n\r\nnumber_to_delete = input(\"Введите число: \")\r\nmy_list = delete_digit([2634, 6934, 7286, 3353, 4602, 3176, 3796], number_to_delete)\r\nprint(my_list)\r\nmy_list = replace_element(my_list)\r\nprint(my_list)\r\nmy_list = delete_duplecates(my_list)\r\nprint(my_list)","repo_name":"AlinaYun/Python-Homework2","sub_path":"Task4.py","file_name":"Task4.py","file_ext":"py","file_size_in_byte":2680,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"34437216997","text":"import json\nfrom difflib import get_close_matches as gcm\n\ndata = json.load(open(\"data.json\"))\n\ndef find_meaning(word):\n word = word.lower()\n if word in data:\n return data[word]\n elif word.title() in data:\n return data[word.title()]\n elif word.upper() in data:\n return data[word.upper()] \n else:\n matches = gcm(word,data.keys())\n if len(matches) > 0:\n return \"Did you mean \" + str(matches[0]) + \" instead\"\n else:\n return \"Word Not Found\"\n\nsearch_word = input(\"Enter the word to search: \")\n\nmeaning = find_meaning(search_word)\nif type(meaning) == list:\n for m in meaning:\n print(m + \"\\n\")\nelse:\n print(meaning)\n","repo_name":"AdmiralStone/Py_dictionary","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"10907590625","text":"import csv\nimport numpy\nimport math\nimport pandas as pd\n#분산도 구하고 중앙값도 구하고, 클러스터별로 정렬도 한파일\n\nwith open(\"./20K_클러스터파일/20K_notcos.csv\", 'r', encoding=\"utf8\") as f:\n reader = csv.reader(f)\n twolist = []\n for row in reader:\n twolist.append(row)\n\nplist = sorted(twolist, key=lambda row: row[1])\n#그냥 정렬이 하고싶었음 클러스터별로 정렬됨(그럼 연산좀더 빠를꺼같아서)\nlist0 = []\nlist1 = []\nlist2 = []\nlist3 = []\nlist4 = []\nlist5 = []\nlist6 = []\nlist7 = []\nlist8 = []\nlist9 = []\nlist10 = []\nlist11 = []\nlist12 = []\nlist13 = []\nlist14 = []\nlist15 = []\nlist16 = []\nlist17 = []\nlist18 = []\nlist19 = []\n\n#각클러스터별 분리\nfor i in range(len(plist)):\n if plist[i][1] == \"0\":\n list0.append(plist[i])\n elif plist[i][1] == \"1\":\n list1.append(plist[i])\n elif plist[i][1] == \"2\":\n list2.append(plist[i])\n elif plist[i][1] == \"3\":\n list3.append(plist[i])\n elif plist[i][1] == \"4\":\n list4.append(plist[i])\n elif plist[i][1] == \"5\":\n list5.append(plist[i])\n elif plist[i][1] == \"6\":\n list6.append(plist[i])\n elif plist[i][1] == \"7\":\n list7.append(plist[i])\n elif plist[i][1] == \"8\":\n list8.append(plist[i])\n elif plist[i][1] == \"9\":\n list9.append(plist[i])\n elif plist[i][1] == \"10\":\n list10.append(plist[i])\n elif plist[i][1] == \"11\":\n list11.append(plist[i])\n elif plist[i][1] == \"12\":\n list12.append(plist[i])\n elif plist[i][1] == \"13\":\n list13.append(plist[i])\n elif plist[i][1] == \"14\":\n list14.append(plist[i])\n elif plist[i][1] == \"15\":\n list15.append(plist[i])\n elif plist[i][1] == \"16\":\n list16.append(plist[i])\n elif plist[i][1] == \"17\":\n list17.append(plist[i])\n elif plist[i][1] == \"18\":\n list18.append(plist[i])\n elif plist[i][1] == \"19\":\n list19.append(plist[i])\n\nnodeamount = []#각 클러스터별 갯수 확인 리스트\nnodeamount.append(len(list0))\nnodeamount.append(len(list1))\nnodeamount.append(len(list2))\nnodeamount.append(len(list3))\nnodeamount.append(len(list4))\nnodeamount.append(len(list5))\nnodeamount.append(len(list6))\nnodeamount.append(len(list7))\nnodeamount.append(len(list8))\nnodeamount.append(len(list9))\nnodeamount.append(len(list10))\nnodeamount.append(len(list11))\nnodeamount.append(len(list12))\nnodeamount.append(len(list13))\nnodeamount.append(len(list14))\nnodeamount.append(len(list15))\nnodeamount.append(len(list16))\nnodeamount.append(len(list17))\nnodeamount.append(len(list18))\nnodeamount.append(len(list19))\n#각 클러스터별 중점을 찾기위한 리스트들\nsum = [0]*100\nsum1 = [0]*100\nsum2 = [0]*100\nsum3 = [0]*100\nsum4 = [0]*100\nsum5 = [0]*100\nsum6 = [0]*100\nsum7 = [0]*100\nsum8 = [0]*100\nsum9 = [0]*100\nsum10 = [0]*100\nsum11 = [0]*100\nsum12 = [0]*100\nsum13 = [0]*100\nsum14 = [0]*100\nsum15 = [0]*100\nsum16 = [0]*100\nsum17 = [0]*100\nsum18 = [0]*100\nsum19 = [0]*100\n\n#클러스터별 중앙값계산과정\nfor i in range(len(list0)):\n for j in range(2, len(list0[i])):\n sum[j-2] = float(list0[i][j])\nfor i in range(len(list1)):\n for j in range(2, len(list1[i])):\n sum1[j-2] = float(list1[i][j])\nfor i in range(len(list2)):\n for j in range(2, len(list2[i])):\n sum2[j-2] = float(list2[i][j])\nfor i in range(len(list3)):\n for j in range(2, len(list3[i])):\n sum3[j-2] = float(list3[i][j])\nfor i in range(len(list4)):\n for j in range(2, len(list4[i])):\n sum4[j-2] = float(list4[i][j])\nfor i in range(len(list5)):\n for j in range(2, len(list5[i])):\n sum5[j-2] = float(list5[i][j])\nfor i in range(len(list6)):\n for j in range(2, len(list6[i])):\n sum6[j-2] = float(list6[i][j])\nfor i in range(len(list7)):\n for j in range(2, len(list7[i])):\n sum7[j-2] = float(list7[i][j])\nfor i in range(len(list8)):\n for j in range(2, len(list8[i])):\n sum8[j-2] = float(list8[i][j])\nfor i in range(len(list9)):\n for j in range(2, len(list9[i])):\n sum9[j-2] = float(list9[i][j])\nfor i in range(len(list10)):\n for j in range(2, len(list10[i])):\n sum10[j-2] = float(list10[i][j])\nfor i in range(len(list11)):\n for j in range(2, len(list11[i])):\n sum11[j-2] = float(list11[i][j])\nfor i in range(len(list12)):\n for j in range(2, len(list12[i])):\n sum12[j-2] = float(list12[i][j])\nfor i in range(len(list13)):\n for j in range(2, len(list13[i])):\n sum13[j-2] = float(list13[i][j])\nfor i in range(len(list14)):\n for j in range(2, len(list14[i])):\n sum14[j-2] = float(list14[i][j])\nfor i in range(len(list15)):\n for j in range(2, len(list15[i])):\n sum15[j-2] = float(list15[i][j])\nfor i in range(len(list16)):\n for j in range(2, len(list16[i])):\n sum16[j-2] = float(list16[i][j])\nfor i in range(len(list17)):\n for j in range(2, len(list17[i])):\n sum17[j-2] = float(list17[i][j])\nfor i in range(len(list18)):\n for j in range(2, len(list18[i])):\n sum18[j-2] = float(list18[i][j])\nfor i in range(len(list19)):\n for j in range(2, len(list19[i])):\n sum19[j-2] = float(list19[i][j])\n#클러스터별 중앙값 계산\nfor i in range(len(sum)):\n sum[i] = sum[i]/nodeamount[0]\n\nfor i in range(len(sum1)):\n sum1[i] = sum1[i]/nodeamount[1]\n\nfor i in range(len(sum2)):\n sum2[i] = sum2[i]/nodeamount[2]\nfor i in range(len(sum3)):\n sum3[i] = sum3[i]/nodeamount[3]\nfor i in range(len(sum4)):\n sum4[i] = sum4[i]/nodeamount[4]\nfor i in range(len(sum5)):\n sum5[i] = sum5[i]/nodeamount[5]\nfor i in range(len(sum6)):\n sum6[i] = sum6[i]/nodeamount[6]\nfor i in range(len(sum7)):\n sum7[i] = sum7[i]/nodeamount[7]\nfor i in range(len(sum8)):\n sum8[i] = sum8[i]/nodeamount[8]\nfor i in range(len(sum9)):\n sum9[i] = sum9[i]/nodeamount[9]\nfor i in range(len(sum10)):\n sum10[i] = sum10[i]/nodeamount[10]\nfor i in range(len(sum11)):\n sum11[i] = sum11[i]/nodeamount[11]\nfor i in range(len(sum12)):\n sum12[i] = sum12[i]/nodeamount[12]\nfor i in range(len(sum13)):\n sum13[i] = sum13[i]/nodeamount[13]\nfor i in range(len(sum14)):\n sum14[i] = sum14[i]/nodeamount[14]\nfor i in range(len(sum15)):\n sum15[i] = sum15[i]/nodeamount[15]\nfor i in range(len(sum16)):\n sum16[i] = sum16[i]/nodeamount[16]\nfor i in range(len(sum17)):\n sum17[i] = sum17[i]/nodeamount[17]\nfor i in range(len(sum18)):\n sum18[i] = sum18[i]/nodeamount[18]\nfor i in range(len(sum19)):\n sum19[i] = sum19[i]/nodeamount[19]\n\ndef euclidean_distance(pt1, pt2):#각 노드별 중앙값기준의 거리 계산 함수\n puty = []\n for i in range(len(pt2)):\n distance = 0\n for j in range(2, len(pt2[i])):\n distance += (pt1[j-2] - float(pt2[i][j])) ** 2\n distance = distance ** 0.5\n puty.append(distance)\n return puty\n\n\ndistance_list = []#각 클러스터 중점으로부터의 노드 거리 리스트\ndistance_list.append(euclidean_distance(sum, list0))\ndistance_list.append(euclidean_distance(sum, list1))\ndistance_list.append(euclidean_distance(sum, list2))\ndistance_list.append(euclidean_distance(sum, list3))\ndistance_list.append(euclidean_distance(sum, list4))\ndistance_list.append(euclidean_distance(sum, list5))\ndistance_list.append(euclidean_distance(sum, list6))\ndistance_list.append(euclidean_distance(sum, list7))\ndistance_list.append(euclidean_distance(sum, list8))\ndistance_list.append(euclidean_distance(sum, list9))\ndistance_list.append(euclidean_distance(sum, list10))\ndistance_list.append(euclidean_distance(sum, list11))\ndistance_list.append(euclidean_distance(sum, list12))\ndistance_list.append(euclidean_distance(sum, list13))\ndistance_list.append(euclidean_distance(sum, list14))\ndistance_list.append(euclidean_distance(sum, list15))\ndistance_list.append(euclidean_distance(sum, list16))\ndistance_list.append(euclidean_distance(sum, list17))\ndistance_list.append(euclidean_distance(sum, list18))\ndistance_list.append(euclidean_distance(sum, list19))\n\nid_dis0 = {}\nid_dis1 = {}\nid_dis2 = {}\nid_dis3 = {}\nid_dis4 = {}\nid_dis5 = {}\nid_dis6 = {}\nid_dis7 = {}\nid_dis8 = {}\nid_dis9 = {}\nid_dis10 = {}\nid_dis11 = {}\nid_dis12 = {}\nid_dis13 = {}\nid_dis14 = {}\nid_dis15 = {}\nid_dis16 = {}\nid_dis17 = {}\nid_dis18 = {}\nid_dis19 = {}\n\n#각 클러스터에대한 노드의 거리 가지고 있으므로 (노드 거리별로 제곱합)/노드의수 = 분산\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[0])):\n id_dis0[list0[j][0]] = float(distance_list[0][j])\nid_dis_li0 = sorted(id_dis0.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[1])):\n id_dis1[list1[j][0]] = float(distance_list[1][j])\nid_dis_li1 = sorted(id_dis1.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[2])):\n id_dis2[list2[j][0]] = float(distance_list[2][j])\nid_dis_li2 = sorted(id_dis2.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[3])):\n id_dis3[list3[j][0]] = float(distance_list[3][j])\nid_dis_li3 = sorted(id_dis3.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[4])):\n id_dis4[list4[j][0]] = float(distance_list[4][j])\nid_dis_li4 = sorted(id_dis4.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[5])):\n id_dis5[list5[j][0]] = float(distance_list[5][j])\nid_dis_li5 = sorted(id_dis5.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[6])):\n id_dis6[list6[j][0]] = float(distance_list[6][j])\nid_dis_li6 = sorted(id_dis6.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[7])):\n id_dis7[list7[j][0]] = float(distance_list[7][j])\nid_dis_li7 = sorted(id_dis7.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[8])):\n id_dis8[list8[j][0]] = float(distance_list[8][j])\nid_dis_li8 = sorted(id_dis8.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[9])):\n id_dis9[list9[j][0]] = float(distance_list[9][j])\nid_dis_li9 = sorted(id_dis9.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[10])):\n id_dis10[list10[j][0]] = float(distance_list[10][j])\nid_dis_li10 = sorted(id_dis10.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[11])):\n id_dis11[list11[j][0]] = float(distance_list[11][j])\nid_dis_li11 = sorted(id_dis11.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[12])):\n id_dis12[list12[j][0]] = float(distance_list[12][j])\nid_dis_li12 = sorted(id_dis12.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[13])):\n id_dis13[list13[j][0]] = float(distance_list[13][j])\nid_dis_li13 = sorted(id_dis13.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[14])):\n id_dis14[list14[j][0]] = float(distance_list[14][j])\nid_dis_li14 = sorted(id_dis14.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[15])):\n id_dis15[list15[j][0]] = float(distance_list[15][j])\nid_dis_li15 = sorted(id_dis15.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[16])):\n id_dis16[list16[j][0]] = float(distance_list[16][j])\nid_dis_li16 = sorted(id_dis16.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[17])):\n id_dis17[list17[j][0]] = float(distance_list[17][j])\nid_dis_li17 = sorted(id_dis17.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[18])):\n id_dis18[list18[j][0]] = float(distance_list[18][j])\nid_dis_li18 = sorted(id_dis18.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[19])):\n id_dis19[list19[j][0]] = float(distance_list[19][j])\nid_dis_li19 = sorted(id_dis19.items(), key=lambda t : t[1])\ntotal = []#다 합침.\ntotal.append(id_dis_li0)\ntotal.append(id_dis_li1)\ntotal.append(id_dis_li2)\ntotal.append(id_dis_li3)\ntotal.append(id_dis_li4)\ntotal.append(id_dis_li5)\ntotal.append(id_dis_li6)\ntotal.append(id_dis_li7)\ntotal.append(id_dis_li8)\ntotal.append(id_dis_li9)\ntotal.append(id_dis_li10)\ntotal.append(id_dis_li11)\ntotal.append(id_dis_li12)\ntotal.append(id_dis_li13)\ntotal.append(id_dis_li14)\ntotal.append(id_dis_li15)\ntotal.append(id_dis_li16)\ntotal.append(id_dis_li17)\ntotal.append(id_dis_li18)\ntotal.append(id_dis_li19)\nfor i in range(len(total)):#각 노드의 클러스터위치를 연산하기위해 썻는데 auto파일에 자동화시켰음.\n for j in range(len(total[i])):\n if 'cve-2017-18403' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2019-4439' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2015-4812' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2018-7034' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2017-6553' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2017-10915' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2015-5791' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2019-3833' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2017-18383' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2019-10997' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2016-9393' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2018-7738' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2017-16197' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2017-11536' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2017-6381' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2019-2756' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2015-0536' in total[i][j]:\n print(i)\n\n\n\n\n#클러스터넘버 추가\nfor i in range(len(total)):\n total[i].insert(0, i)\n\nfor i in range(len(nodeamount)):\n total[i].insert(1, int(nodeamount[i]))\n\nSSum = []\nSSum.append(sum)\nSSum.append(sum1)\nSSum.append(sum2)\nSSum.append(sum3)\nSSum.append(sum4)\nSSum.append(sum5)\nSSum.append(sum6)\nSSum.append(sum7)\nSSum.append(sum8)\nSSum.append(sum9)\nSSum.append(sum10)\nSSum.append(sum11)\nSSum.append(sum12)\nSSum.append(sum13)\nSSum.append(sum14)\nSSum.append(sum15)\nSSum.append(sum16)\nSSum.append(sum17)\nSSum.append(sum18)\nSSum.append(sum19)\n\nbbunsan = []#클러스터별 분산계산 리스트\nbsum = 0\nfor i in range(len(distance_list[0])):\n bsum += (distance_list[0][i])**2\nbunsan = bsum/len(distance_list[0])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[1])):\n bsum += (distance_list[1][i])**2\nbunsan = bsum/len(distance_list[1])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[2])):\n bsum += (distance_list[2][i])**2\nbunsan = bsum/len(distance_list[2])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[3])):\n bsum += (distance_list[3][i])**2\nbunsan = bsum/len(distance_list[3])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[4])):\n bsum += (distance_list[4][i])**2\nbunsan = bsum/len(distance_list[4])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[5])):\n bsum += (distance_list[5][i])**2\nbunsan = bsum/len(distance_list[5])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[6])):\n bsum += (distance_list[6][i])**2\nbunsan = bsum/len(distance_list[6])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[7])):\n bsum += (distance_list[7][i])**2\nbunsan = bsum/len(distance_list[7])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[8])):\n bsum += (distance_list[8][i])**2\nbunsan = bsum/len(distance_list[8])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[9])):\n bsum += (distance_list[9][i])**2\nbunsan = bsum/len(distance_list[9])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[10])):\n bsum += (distance_list[10][i])**2\nbunsan = bsum/len(distance_list[10])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[11])):\n bsum += (distance_list[11][i])**2\nbunsan = bsum/len(distance_list[11])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[12])):\n bsum += (distance_list[12][i])**2\nbunsan = bsum/len(distance_list[12])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[13])):\n bsum += (distance_list[13][i])**2\nbunsan = bsum/len(distance_list[13])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[14])):\n bsum += (distance_list[14][i])**2\nbunsan = bsum/len(distance_list[14])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[15])):\n bsum += (distance_list[15][i])**2\nbunsan = bsum/len(distance_list[15])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[16])):\n bsum += (distance_list[16][i])**2\nbunsan = bsum/len(distance_list[17])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[17])):\n bsum += (distance_list[17][i])**2\nbunsan = bsum/len(distance_list[17])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[18])):\n bsum += (distance_list[18][i])**2\nbunsan = bsum/len(distance_list[18])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[19])):\n bsum += (distance_list[19][i])**2\nbunsan = bsum/len(distance_list[19])\nbbunsan.append(bunsan)\n#분산구하기 (평균값-현재값=구해놈)^2을 더하면 됩니다. /\n# print(bbunsan)\n\ndavg = pd.DataFrame(SSum)\ndp = pd.DataFrame(bbunsan)\n\n#df.to_csv('20K_notcos_notword_sort.csv')\n\n","repo_name":"ksw7564/node2vec_CVE","sub_path":"2kavg.py","file_name":"2kavg.py","file_ext":"py","file_size_in_byte":18896,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"30522653681","text":"#“how far is a value from the mean” \n#“how likely is a value this far from the mean to be from the same group of observations?”\n\nimport numpy as np\ntokaji_avg = np.average(tokaji)\nlambrusco_avg = np.average(lambrusco)\ntokaji_std = np.std(tokaji)\nlambrusco = np.std(lambrusco)\n\n# Let's see what the results are\nprint(\"Tokaji: \", tokaji_avg, tokaji_std)\nprint(\"Lambrusco: \", lambrusco_avg, lambrusco_std)\n# Tokaji: 90.9 2.65015722804\n# Lambrusco: 84.4047619048 1.61922267961\n\nz = (tokaji_avg - lambrusco_avg) / lambrusco_std\n#4.0113309781438229\n# We'll bring in scipy to do the calculation of probability from the Z-table\n\nimport scipy.stats as st\nst.norm.cdf(z)\n# 0.99996981130231266\n# We need the probability from the right side, so we'll flip it!\n1 - st.norm.cdf(z)\n# 3.0188697687338895e-05\n","repo_name":"chunxu/learning","sub_path":"probility.py","file_name":"probility.py","file_ext":"py","file_size_in_byte":800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"38381276170","text":"import typing\nfrom functools import partial\n\nimport PySide2\nfrom PySide2.QtCore import Qt, QItemSelection, QModelIndex, QItemSelectionModel, Signal\nfrom PySide2.QtGui import QIcon, QPixmap, QColor, QBrush\nfrom PySide2.QtWidgets import QDialog, QTreeWidgetItem, QTreeWidget, QAbstractItemView, QDialogButtonBox, QVBoxLayout, \\\n QWidget, QGroupBox\n\nfrom arthropod_describer.common.common import Info\nfrom arthropod_describer.common.label_hierarchy import Node\nfrom arthropod_describer.common.label_tree_model import LabelTreeModel, LabelTreeMode\nfrom arthropod_describer.common.plugin import PropertyComputation\nfrom arthropod_describer.common.state import State\nfrom arthropod_describer.common.user_params import UserParam, UserParamWidgetBinding, create_params_widget\nfrom arthropod_describer.measurements_viewer.ui_measurement_assign_dialog import Ui_MeasurementAssignDialog\nfrom arthropod_describer.plugin_manager import RegionCompsListModel\nfrom arthropod_describer.color_tolerance_dialog import ColorToleranceDialog\n\nABS_KEY_ROLE = Qt.UserRole\nPARENT_KEY_ROLE = Qt.UserRole + 1\nPROP_KEY_ROLE = Qt.UserRole + 2\nLABEL_ROLE = Qt.UserRole + 3\nLEAF_ITEM_ROLE = Qt.UserRole + 4\n\n\nclass MeasurementAssignDialog(QDialog):\n compute_measurements = Signal(dict)\n\n def __init__(self, state: State, comp_model: RegionCompsListModel, parent: typing.Optional[PySide2.QtWidgets.QWidget] = None,\n f: Qt.WindowFlags = Qt.WindowFlags()):\n super().__init__(parent, f)\n self.ui = Ui_MeasurementAssignDialog()\n self.ui.setupUi(self)\n self.ui.labelTree.setSelectionMode(QAbstractItemView.MultiSelection)\n self.ui.btnAssign.clicked.connect(self.assign_measurements)\n\n self.ui.assignmentTree.itemSelectionChanged.connect(self.assignment_selection_changed)\n self.ui.assignmentTree.clicked.connect(tree_item_double_click_handler(self.ui.assignmentTree))\n self.ui.assignmentTree.selectionModel().selectionChanged.connect(self.deselect_ancestors_of_leaves)\n\n self.ui.measurementTree.itemSelectionChanged.connect(self.measurement_selection_changed)\n\n self.ui.btnLabelSelectAll.clicked.connect(self.ui.labelTree.selectAll)\n self.ui.btnLabelDeselectAll.clicked.connect(self.ui.labelTree.clearSelection)\n\n self.ui.btnMeasSelectAll.clicked.connect(self.ui.measurementTree.selectAll)\n self.ui.btnMeasDeselectAll.clicked.connect(self.ui.measurementTree.clearSelection)\n\n self.ui.btnAssignmentSelectAll.clicked.connect(self.ui.assignmentTree.selectAll)\n self.ui.btnAssignmentDeselectAll.clicked.connect(self.ui.assignmentTree.clearSelection)\n\n self.ui.btnAssignmentRemove.clicked.connect(self.remove_assignments)\n\n self.ui.btnDemoSelectColorAndTolerance.clicked.connect(self.demo_select_color_and_tolerance)\n\n self.ui.buttonBox.button(QDialogButtonBox.Apply).setEnabled(False)\n\n self.ui.buttonBox.button(QDialogButtonBox.Apply).clicked.connect(self.accept)\n\n self.state = state\n self._label_tree_model = LabelTreeModel(self.state, LabelTreeMode.Choosing)\n self.ui.labelTree.setModel(self._label_tree_model)\n self.ui.labelTree.selectionModel().selectionChanged.connect(self.label_selection_changed)\n self.ui.labelTree.setStyleSheet('QTreeView::item:disabled {color: #c0c0c0;}')\n\n self.comps_model = comp_model\n\n self.measurement_items: typing.List[QTreeWidgetItem] = []\n\n self.label_items: typing.Dict[int, QTreeWidgetItem] = {}\n\n # self.assignments: typing.Dict[str, typing.Set[int]] = {}\n self.assignments: typing.Dict[str, typing.Set[int]] = {}\n self.assignment_items: typing.List[QTreeWidgetItem] = []\n self.assignment_prop_items: typing.Dict[str, QTreeWidgetItem] = {}\n self.assignment_label_items: typing.Dict[int, QTreeWidgetItem] = {}\n\n self._setup_demo_color_tolerance_dialog()\n\n self.settings_layout = QVBoxLayout()\n self.ui.scrollAreaWidgetContents.setLayout(self.settings_layout)\n self.param_settings_for_props: typing.Dict[str, typing.List[UserParam]] = {}\n self.param_bindings_for_props: typing.Dict[str, UserParamWidgetBinding] = {}\n self.param_widgets_for_props: typing.Dict[str, QWidget] = {}\n\n def _populate_label_tree(self):\n self.ui.labelTree.clear()\n hierarchy = self.state.storage.get_label_hierarchy2('Labels')\n colormap = hierarchy.colormap\n codes = [hierarchy.code(label) for label in hierarchy.labels]\n codes.sort()\n parent = self.ui.labelTree\n sibling = None\n stack = []\n depth = -1\n used_labels = self.state.storage.used_regions('Labels')\n print(used_labels)\n for code in codes[1:]:\n label = hierarchy.label(code)\n code_depth = hierarchy.get_level(hierarchy.label(code))\n if code_depth > depth:\n if depth >= 0:\n stack.append(parent)\n parent = parent if sibling is None else sibling\n depth = code_depth\n sibling = None\n elif code_depth < depth:\n pop_count = depth - code_depth\n if pop_count > 1:\n for _ in range(pop_count - 1):\n sibling = stack.pop()\n else:\n sibling = parent\n parent = stack.pop()\n depth = code_depth\n twidget = QTreeWidgetItem(parent, after=sibling)\n twidget.setExpanded(True)\n label = hierarchy.label(code)\n label_node = hierarchy.nodes[label]\n # TODO REMOVE\n #twidget.setText(0, self.state.colormap.label_names[label])\n twidget.setText(0, label_node.name)\n twidget.setData(0, Qt.UserRole, label)\n pixmap = QPixmap(24, 24)\n pixmap.fill(QColor.fromRgb(*colormap[label]))\n icon = QIcon(pixmap)\n twidget.setIcon(0, icon)\n self.label_items[label] = twidget\n if label in used_labels or any(map(partial(hierarchy.is_ancestor_of, label), used_labels)):\n twidget.setFlags(Qt.ItemIsEnabled | Qt.ItemIsSelectable)\n else:\n twidget.setFlags(Qt.ItemIsEnabled)\n twidget.setForeground(0, QBrush(QColor.fromRgb(120, 120, 120)))\n sibling = twidget\n self.ui.labelTree.addTopLevelItem(stack[2])\n self.ui.labelTree.doubleClicked.connect(tree_item_double_click_handler(self.ui.labelTree))\n\n def _populate_measurement_comps_tree(self):\n prop_comps: typing.List[PropertyComputation] = self.comps_model.region_comps\n self.ui.measurementTree.clear()\n group_items: typing.Dict[str, QTreeWidgetItem] = {}\n\n for prop_comp in prop_comps:\n if prop_comp.group not in group_items:\n _item = QTreeWidgetItem(self.ui.measurementTree)\n _item.setText(0, prop_comp.group)\n _item.setFlags(Qt.ItemIsEnabled)\n group_items[prop_comp.group] = _item\n parent = group_items[prop_comp.group]\n # parent = QTreeWidgetItem(self.ui.measurementTree)\n # parent.setText(0, prop_comp.info.name)\n # parent.setData(0, Qt.UserRole, prop_comp.info.key)\n # parent.setFlags(Qt.ItemIsEnabled)\n for prop in prop_comp.computes.values():\n kid = QTreeWidgetItem(parent)\n kid.setText(0, prop.name)\n kid.setToolTip(0, prop.description)\n kid.setData(0, Qt.UserRole, prop_comp.info.key)\n parent.addChild(kid)\n self.ui.measurementTree.expandAll()\n self.ui.measurementTree.doubleClicked.connect(tree_item_double_click_handler(self.ui.measurementTree))\n\n def _populate_assignment_tree(self):\n self.ui.assignmentTree.clear()\n top_level = []\n prop_comps: typing.List[PropertyComputation] = self.comps_model.region_comps\n for prop_comp in prop_comps:\n twidget = QTreeWidgetItem(self.ui.assignmentTree)\n twidget.setText(0, prop_comp.info.name)\n twidget.setData(0, ABS_KEY_ROLE, prop_comp.info.key)\n twidget.setData(0, LEAF_ITEM_ROLE, False)\n twidget.setFlags(Qt.ItemIsEnabled)\n top_level.append(twidget)\n twidget.setHidden(True)\n twidget.setExpanded(True)\n props: typing.List[Info] = list(prop_comp.computes.values())\n for prop in props:\n absolute_key = f'{prop_comp.info.key}.{prop.key}'\n self.assignments[absolute_key] = set()\n kid = QTreeWidgetItem()\n kid.setText(0, prop.name)\n kid.setToolTip(0, prop.description)\n kid.setData(0, ABS_KEY_ROLE, absolute_key)\n kid.setData(0, LEAF_ITEM_ROLE, False)\n kid.setExpanded(True)\n kid.setHidden(True)\n kid.setFlags(Qt.ItemIsEnabled)\n twidget.addChild(kid)\n self.assignment_prop_items[absolute_key] = kid\n self.ui.assignmentTree.addTopLevelItems(top_level)\n self.assignment_items = top_level\n\n def show_dialog(self) -> typing.Dict[str, typing.Set[int]]:\n # self._populate_label_tree()\n self._populate_measurement_comps_tree()\n # self._populate_assignment_tree()\n self._label_tree_model = LabelTreeModel(self.state, LabelTreeMode.Choosing)\n self.ui.labelTree.setModel(self._label_tree_model)\n self.ui.labelTree.expandAll()\n self.ui.labelTree.selectionModel().selectionChanged.connect(self.label_selection_changed)\n if self.exec_() == QDialog.Accepted:\n return self.assignments\n #self.compute_measurements.emit(self.assignments)\n #computations = {}\n #for prop_path, labels in self.assignments.items():\n # dot_splits = prop_path.split('.')\n # prop_name = dot_splits.pop()\n # comp_key = '.'.join(dot_splits)\n # prop_labels = computations.setdefault(comp_key, {})\n # prop_labels[prop_name] = labels\n #for computation_key, prop_labels in computations.items():\n # print(f'{computation_key} will compute {prop_labels}')\n #for i in range(self.state.storage.image_count):\n # photo = self.state.storage.get_photo_by_idx(i)\n # for computation_key, prop_labels in computations.items():\n # computation: PropertyComputation = self.comps_model.computations_dict[computation_key]\n # reg_props = computation(photo, prop_labels)\n # for prop in reg_props:\n # prop.info.key = f'{computation_key}.{prop.info.key}'\n # photo['Labels'].set_region_prop(prop.label, prop)\n else:\n self.assignment_label_items.clear()\n self.assignments.clear()\n self.ui.assignmentTree.clear()\n self.ui.buttonBox.button(QDialogButtonBox.Apply).setEnabled(False)\n return {}\n\n # def assign_measurements_(self):\n # # label_selection: typing.List[QTreeWidgetItem] = self.ui.labelTree.selectedItems()\n # label_selection: typing.List[QModelIndex] = self.ui.labelTree.selectedIndexes()\n # prop_selection: typing.List[QModelIndex] = self.ui.measurementTree.selectedIndexes()\n # # TODO replace hardcoded `Labels`\n # hierarchy = self.state.storage.get_label_hierarchy2('Labels')\n # for prop_idx in prop_selection:\n # if not prop_idx.parent().isValid():\n # continue\n # parent = prop_idx.parent()\n # parent_key = self.ui.measurementTree.model().data(parent, Qt.UserRole)\n # prop_key = self.ui.measurementTree.model().data(prop_idx, Qt.UserRole)\n # absolute_key = f'{parent_key}.{prop_key}'\n # prop_item = self.assignment_prop_items[absolute_key]\n # for lab_index in label_selection:\n # lab_node: Node = lab_index.internalPointer()\n # if (label := lab_node.label) not in self.assignments.setdefault(absolute_key, set()):\n # self.assignments[absolute_key].add(label)\n # twidget = QTreeWidgetItem()\n # #label_node = self.state.label_hierarchy.nodes[label]\n # label_node = hierarchy.nodes[label]\n # # TODO REMOVE\n # #twidget.setText(0, self.state.colormap.label_names[label])\n # twidget.setText(0, label_node.name)\n # twidget.setIcon(0, self.label_items[label].icon(0))\n # twidget.setData(0, ABS_KEY_ROLE, f'{absolute_key}')\n # twidget.setData(0, PARENT_KEY_ROLE, parent_key)\n # twidget.setData(0, PROP_KEY_ROLE, prop_key)\n # twidget.setData(0, LABEL_ROLE, label)\n # twidget.setData(0, LEAF_ITEM_ROLE, True)\n # prop_item.addChild(twidget)\n # prop_item.setExpanded(True)\n # parent_item = self.assignment_items[parent.row()]\n # parent_item.setHidden(False)\n # prop_item.setHidden(False)\n # for prop_item in self.assignment_prop_items.values():\n # prop_item.setHidden(prop_item.childCount() == 0)\n #\n # self.ui.buttonBox.button(QDialogButtonBox.Apply).setEnabled(len(self.assignments) > 0)\n\n def assign_measurements(self):\n # to each label in `label_selection` assigns all props in `prop_selection` and stores the assignments\n # in `self.assignments` and also in the QTreeView self.ui.measurementTree\n\n label_selection: typing.List[QModelIndex] = self.ui.labelTree.selectedIndexes()\n prop_selection: typing.List[QModelIndex] = self.ui.measurementTree.selectedIndexes()\n # TODO replace hardcoded `Labels`\n hierarchy = self.state.storage.get_label_hierarchy2('Labels')\n\n for label_index in label_selection:\n if not label_index.isValid():\n continue\n label_node: Node = label_index.internalPointer()\n if label_node.label not in self.assignment_label_items:\n label_item = QTreeWidgetItem()\n label_item.setText(0, label_node.name)\n pixmap = QPixmap(32, 32)\n pixmap.fill(QColor(*label_node.color))\n label_item.setIcon(0, QIcon(pixmap))\n label_item.setData(0, LABEL_ROLE, label_node.label)\n label_item.setData(0, LEAF_ITEM_ROLE, False)\n self.ui.assignmentTree.addTopLevelItem(label_item)\n self.assignment_label_items[label_node.label] = label_item\n label_tree_item: QTreeWidgetItem = self.assignment_label_items[label_node.label]\n for prop_idx in prop_selection:\n prop_parent = prop_idx.parent() # The Plugin index that is the parent of `prop_idx`\n parent_key = self.ui.measurementTree.model().data(prop_parent, Qt.UserRole) # unique key of the parent\n prop_key = self.ui.measurementTree.model().data(prop_idx, Qt.UserRole)\n # absolute_key = f'{parent_key}.{prop_key}'\n absolute_key = prop_key\n # if absolute_key not in self.assignments.setdefault(label_node.label, set()):\n computation = self.comps_model.computations_dict[prop_key]\n if prop_key not in self.param_widgets_for_props and len(computation.user_params) > 0:\n widget = create_params_widget(computation.user_params, self.state)\n binding = UserParamWidgetBinding(self.state)\n binding.bind(computation.user_params, widget)\n group = QGroupBox()\n group.setTitle(computation.info.name)\n layout = QVBoxLayout()\n layout.addWidget(widget)\n group.setLayout(layout)\n self.param_widgets_for_props[prop_key] = group\n self.param_bindings_for_props[prop_key] = binding\n self.settings_layout.addWidget(group)\n if label_node.label not in self.assignments.setdefault(absolute_key, set()):\n # self.assignments[label_node.label].add(absolute_key)\n self.assignments[absolute_key].add(label_node.label)\n twidget = QTreeWidgetItem()\n # twidget.setText(0, self.comps_model.computations_dict[parent_key].computes[prop_key].name)\n twidget.setText(0, self.comps_model.computations_dict[prop_key].info.name)\n twidget.setData(0, ABS_KEY_ROLE, f'{absolute_key}')\n # twidget.setData(0, PARENT_KEY_ROLE, parent_key)\n # twidget.setData(0, PROP_KEY_ROLE, prop_key)\n twidget.setData(0, LABEL_ROLE, label_node.label)\n twidget.setData(0, LEAF_ITEM_ROLE, True)\n label_tree_item.addChild(twidget)\n label_tree_item.setExpanded(True)\n label_tree_item.setHidden(label_tree_item.childCount() == 0)\n\n self.ui.buttonBox.button(QDialogButtonBox.Apply).setEnabled(len(self.assignments) > 0)\n\n def label_selection_changed(self, sel, des):\n self.enable_assign_button()\n\n def measurement_selection_changed(self):\n self.enable_assign_button()\n\n def assignment_selection_changed(self):\n self.ui.btnAssignmentRemove.setEnabled(len(self.ui.assignmentTree.selectedIndexes()) > 0)\n\n def enable_assign_button(self):\n self.ui.btnAssign.setEnabled(len(self.ui.labelTree.selectedIndexes()) > 0 and\n len(self.ui.measurementTree.selectedIndexes()) > 0)\n\n def remove_assignments(self):\n items: typing.List[QTreeWidgetItem] = self.ui.assignmentTree.selectedItems()\n deleted: typing.Set[str] = set()\n\n leaves: typing.List[QTreeWidgetItem] = [item for item in items if item.data(0, LEAF_ITEM_ROLE)]\n nodes: typing.List[QTreeWidgetItem] = [item for item in items if not item.data(0, LEAF_ITEM_ROLE)]\n\n for leaf in leaves:\n key = leaf.data(0, ABS_KEY_ROLE)\n leaf.parent().removeChild(leaf)\n self.assignments[key].remove(leaf.data(0, LABEL_ROLE))\n if len(self.assignments[key]) == 0:\n del self.assignments[key]\n if key in self.param_widgets_for_props:\n widget = self.param_widgets_for_props[key]\n self.settings_layout.removeWidget(widget)\n widget.deleteLater()\n del self.param_widgets_for_props[key]\n del self.param_bindings_for_props[key]\n # del self.param_settings_for_props[key]\n self.ui.assignmentTree.removeItemWidget(leaf, 0)\n\n for node in self.assignment_label_items.values():\n if node.childCount() == 0:\n node.setHidden(True)\n for node in self.assignment_items:\n node.setHidden(all([node.child(i).isHidden() for i in range(node.childCount())]))\n\n print(self.assignments)\n self.ui.buttonBox.button(QDialogButtonBox.Apply).setEnabled(len(self.assignments) > 0)\n self.ui.assignmentTree.update()\n\n def deselect_ancestors_of_leaves(self, selected: QItemSelection, deselected: QItemSelection):\n for index in deselected.indexes():\n if index.child(0, 0).isValid():\n continue\n parent = index.parent()\n while parent.isValid():\n self.ui.assignmentTree.selectionModel().select(parent, QItemSelectionModel.Deselect)\n parent = parent.parent()\n\n def _setup_demo_color_tolerance_dialog(self):\n self._color_tolerance_dialog = ColorToleranceDialog(self.state)\n self._color_tolerance_dialog.hide()\n\n def demo_select_color_and_tolerance(self):\n self._color_tolerance_dialog.get_color_and_tolerances()\n\n\ndef tree_item_double_click_handler(tree_widget: QTreeWidget):\n def select_subtree(index: QModelIndex):\n print(id(tree_widget))\n if not index.child(0, 0).isValid():\n return\n stack = [index]\n while len(stack) > 0:\n idx = stack.pop()\n if idx.isValid():\n curr_idx = idx.child(0, 0)\n child_idx = 0\n indexes_to_select = []\n while curr_idx.isValid():\n indexes_to_select.append(curr_idx)\n stack.append(curr_idx)\n child_idx += 1\n curr_idx = idx.child(child_idx, 0)\n if len(indexes_to_select) > 0:\n tree_widget.selectionModel().select(QItemSelection(indexes_to_select[0], indexes_to_select[-1]),\n QItemSelectionModel.Select)\n return select_subtree\n\n\ndef visit_subtree(root: QTreeWidgetItem):\n stack = [root]\n while len(stack) > 0:\n item = stack.pop()\n for i in range(len(item.childCount())):\n stack.append(item.child(i))\n yield item\n yield None\n","repo_name":"mrazr/maphis","sub_path":"arthropod_describer/measurements_viewer/measurement_assign_dialog.py","file_name":"measurement_assign_dialog.py","file_ext":"py","file_size_in_byte":21359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"74354085581","text":"import os\nimport uuid\nfrom flask import Flask\n\n\napp = Flask(__name__)\nmy_uuid = str(uuid.uuid1())\nBLUE = \"#0099FF\"\nGREEN = \"#33CC33\"\n\nCOLOR = BLUE\ncounter = 0 \n\n\n@app.route('/')\ndef hello():\n global counter\n counter += 1\n if counter %2 == 0:\n COLOR = GREEN\n else:\n COLOR = BLUE\n \n return \"\"\"\n \n \n\n Hi, I'm GUID:
\n {}\n \n
\n \n Page Hit Count {}\n\n
\n\n \n \n \"\"\".format(COLOR,my_uuid,counter)\n\nif __name__ == \"__main__\":\n\tapp.run(debug=True,host='0.0.0.0', port=int(os.getenv('PORT', '5000')))\n","repo_name":"davlloyd/devops-bootcamp-anz-python","sub_path":"hello-python/hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"35873265426","text":"import csv\n\nemployees = [\n ['employee', 'salary'],\n ['John Smith', '2500'],\n ['Jenny Scoot', '7500'],\n ['Kate Noris', '10000'],\n]\n\nwith open('employees.csv', 'w', newline='') as csv_file:\n csv_writer = csv.writer(csv_file)\n csv_writer.writerow(employees[0])\n for emp in employees[1:]:\n # if int(emp[1]) > 3000:\n csv_writer.writerow(emp)\n # csv_writer.writerows(employees)\n","repo_name":"Dggz/pybook","sub_path":"sda/intermediate/csving.py","file_name":"csving.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"18378823596","text":"\nclass User:\n # class attributes get defined in the class \n bank_name = \"First National Dojo\"\n # now our method has 2 parameters!\n def __init__(self, name, email_address):\n # we assign them accordingly\n self.name = name\n self.email = email_address\n # the account balance is set to $0\n self.account_balance = 0\n\n def make_deposit(self, amount):\t# takes an argument that is the amount of the deposit\n self.account_balance += amount\t# the specific user's account increases by the amount of the value received\n\n def make_withdrawal(self, amount):\n self.account_balance -= amount\n \n def make_transfer(self, amount, name):\n self.account_balance -= amount\n name.account_balance += amount\n\n def display_user_balance(self):\n print(self.name, self.account_balance)\n\nDevin = User(\"Devin Dale\", \"Devin@DDale.com\")\nJane = User(\"Jane Hylton\", \"Jane@python.com\")\nBrian = User(\"Brian Dale\", \"Brian@python.com\")\n\n\n\nDevin.make_deposit(100)\nDevin.make_deposit(200)\nDevin.make_deposit(50)\nDevin.make_withdrawal(100)\nDevin.make_transfer(20, Jane) # Makes transfer if amount to selected user.\nDevin.display_user_balance()\n\nJane.make_deposit(500)\nJane.make_deposit(200)\nJane.make_withdrawal(100)\nJane.make_withdrawal(25)\nJane.display_user_balance()\n\nBrian.make_deposit(600)\nBrian.make_withdrawal(200)\nBrian.make_withdrawal(50)\nBrian.make_withdrawal(100)\nBrian.display_user_balance()\n\nclass BankAccount:\n def __init__(self, balance = 0, int_rate = .01): \n # default_int_rate = .01\n # default_balance = 0\n # self.int_rate = .01\n # self.balance = balance if balance is None else default_balance\n self.int_rate = int_rate\n self.balance = balance\n def deposit(self, amount):\n self.balance += amount\n return self\n def withdraw(self, amount):\n if self.balance > amount:\n self.balance -= amount\n return self\n else:\n print(\"Insufficient funds: Charging a $5 fee\")\n self.balance -= 5\n return self\n def display_account_info(self):\n print(\"Balance: $\" + str(self.balance) + \" Interest Rate: %\" + str(self.int_rate*100))\n return self\n def yield_interest(self):\n self.balance = (self.balance * self.int_rate) + self.balance\n return self\n def print_all_account_info(self, balance, int_rate):\n print(self.name, self.account_balance, self.balance, self.int_rate)\n\naccount1 = BankAccount(100)\naccount2 = BankAccount()\n\naccount1.display_account_info()\naccount2.display_account_info()\n\naccount1.deposit(100).deposit(200).deposit(600).withdraw(400).yield_interest().display_account_info()\naccount2.deposit(200).deposit(400).withdraw(200).withdraw(300).withdraw(100).withdraw(100).yield_interest().display_account_info()\naccount1.print_all_account_info()","repo_name":"brianpdale/Bootcamp---Public","sub_path":"Python/User.py","file_name":"User.py","file_ext":"py","file_size_in_byte":2881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"3847188129","text":"import random\nimport time\nimport os\n\nrandom.seed()\n\nheight = int(input(\"Height = \"))\nwidth = int(input(\"Width = \"))\nstatus = []\n\nfor i in range(0, height):\n status.append(-1)\nwhile(1):\n status[0] = random.randrange(width)\n for k in status:\n if k != -1:\n print(\" \" * k + \"x\")\n time.sleep(0.3)\n os.system('cls')\n for k in range(height - 1, 0, -1):\n status[k] = status[k - 1]","repo_name":"Varil426/AGH-Introduction-to-CS","sub_path":"RainV2.py","file_name":"RainV2.py","file_ext":"py","file_size_in_byte":415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"10638074294","text":"# Задайте последовательность чисел. Напишите программу,\n# которая выведет список неповторяющихся элементов исходной последовательности.\n\nimport random\n\ndef fill_number_list(n=10, min=1, max=10) -> list:\n number_list = [random.randint(min, max)]\n for i in range (1, n):\n number_list.append(random.randint(min, max)) \n return number_list\n\ndef main():\n source_list = fill_number_list()\n print(source_list)\n unique_numbers = []\n for i in source_list:\n if source_list.count(i) == 1:\n unique_numbers.append(i)\n print(unique_numbers)\n\nif __name__ == '__main__':\n main()\n#s=input()\n#print(s.isdigit.count())\n\n# Второй вариант\n# def row_without_repeatitions(row: list) -> list:\n# sieve = {}\n# clean_row = []\n# for i in row:\n# if i in sieve:\n# sieve[i] += 1\n# else:\n# sieve.setdefault(i, 0)\n# for key, value in sieve.items():\n# if not value:\n# clean_row.append(key)\n\n# return clean_row\n\n\n# if __name__ == '__main__':\n# row = [random.randint(1, 8) for _ in range(10)]\n# print(row)\n# print(row_without_repeatitions(row))\n","repo_name":"FShurik73/Homework-python","sub_path":"Homework4/Task3.py","file_name":"Task3.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"71946763664","text":"from django.db import models\nfrom league.models import Stadium, League\n#reusable apps\nfrom reusable.constants import *\n\n\nclass Category(models.Model):\n name = models.CharField(max_length=50, **NULL)\n\n class Meta:\n verbose_name = 'Categoria'\n verbose_name_plural = 'Categorias'\n \n def __str__(self):\n return self.name.title()\n\n\nclass Team(models.Model):\n name = models.CharField(max_length=100, **REQUIRED)\n fb_id = models.CharField(max_length=50, **NULL)\n picture = models.ImageField(\n upload_to='teams/picture/',\n max_length=1000,\n blank=True, \n null=True\n )\n stadium_team = models.ForeignKey(\n Stadium,\n related_name='equipos_stadium',\n **NULL,\n on_delete=models.CASCADE\n )\n league_team = models.ForeignKey(\n League,\n related_name='equipos_league',\n **NULL,\n on_delete=models.CASCADE\n )\n category_team = models.ForeignKey(\n Category,\n related_name='equipos_categories',\n **NULL,\n on_delete=models.CASCADE\n )\n\n class Meta:\n verbose_name = 'Equipo'\n verbose_name_plural = 'Equipos'\n \n def __str__(self):\n return self.name.title()\n\n","repo_name":"IvanMSP/soccer-league","sub_path":"apps/league/models/teams.py","file_name":"teams.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"1656981973","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nimport sys\n\n# OpenMM Imports\nimport simtk.openmm as mm\nimport simtk.unit as su\nimport simtk.openmm.app as app\n\nimport simtk.openmm.app.charmmpsffile as om_psf\nimport simtk.openmm.app.charmmparameterset as om_paramset\n\n# ParmEd Imports\nfrom parmed.charmm import CharmmPsfFile, CharmmCrdFile, CharmmParameterSet\nfrom parmed.openmm.reporters import StateDataReporter\nfrom parmed import unit as u\n\n\n# Load CHARMM parameter, psf and pdb files from the Solution Builder\ndrive_path = ''\nom_params = om_paramset.CharmmParameterSet(drive_path + 'top_all36_prot.rtf', drive_path + 'par_all36m_prot.prm',\n drive_path + 'top_all36_cgenff.rtf', drive_path + 'par_all36_cgenff.prm',\n drive_path + 'ben.rtf', drive_path + 'ben.prm',\n drive_path + 'toppar_water_ions.str')\nom_solv = om_psf.CharmmPsfFile(drive_path + 'step3_pbcsetup.psf')\nom_crds = app.PDBFile(drive_path + 'step3_pbcsetup.pdb')\n\n\n# Fetching the corners of our solvant box for the PBCs.\ncoords = om_crds.positions\nmin_crds = [coords[0][0], coords[0][1], coords[0][2]]\nmax_crds = [coords[0][0], coords[0][1], coords[0][2]]\n\nfor coord in coords:\n min_crds[0] = min(min_crds[0], coord[0])\n min_crds[1] = min(min_crds[1], coord[1])\n min_crds[2] = min(min_crds[2], coord[2])\n max_crds[0] = max(max_crds[0], coord[0])\n max_crds[1] = max(max_crds[1], coord[1])\n max_crds[2] = max(max_crds[2], coord[2])\n\nom_solv.setBox(max_crds[0]-min_crds[0], max_crds[1]-min_crds[1], max_crds[2]-min_crds[2])\nprint(\"Sidelengths of solvant box: \", om_solv.boxLengths)\n\n\n# Simulation parameters\nstep_size = 2.0* su.femtosecond\nsim_len = 50.0 * su.nanosecond\nsteps = round(sim_len / step_size)\n\nreport_time = 25.0*su.picosecond\nreport_steps = round(report_time / step_size)\nprint(f\"Initialized| step_size = {step_size}, sim_len = {sim_len}, Num steps = {steps}, retport_time = {report_time}\")\n\n\n# Create our OpenMM system with the CHARMM parameters\nsystem = om_solv.createSystem(om_params, nonbondedMethod=app.PME,\n nonbondedCutoff=1 * su.nanometer,\n constraints=app.HBonds)\n\n\n# Create the integrator for the NVT ensemble\nheat_bath = 303.15*su.kelvin\nintegrator = mm.LangevinIntegrator(heat_bath, # Temperature of heat bath\n 1.0/su.picosecond, # Friction coefficient\n step_size) # Time step\n\n\n# Change to CUDA platform\nplatform = mm.openmm.Platform.getPlatformByName('CUDA')\nproperties = {'CudaDeviceIndex': '1'} # you can add other things like the precision here\n\n# Create our Simulation object with our system and chosen integrator\nsim = app.Simulation(om_solv.topology, system, integrator, platform, properties)\n\n\n# Set the positions from our PDB file\nsim.context.setPositions(om_crds.positions)\n\n\n# Minimize Energy\nprint(\"Minimizing Energy\")\nsim.minimizeEnergy()\n\n\n# Reporters\nprint(\"Appending Reporters\")\nsim.reporters.append(StateDataReporter(sys.stdout, round(steps/10), step=True,\n potentialEnergy=False, kineticEnergy=False,\n temperature=True, volume=True, density=False))\n\n# Write out to DCD file\nsim.reporters.append(app.DCDReporter('tryps_ben_solv.dcd', report_steps))\n\n\n# Run (100ns)\nsim.step(steps)\n","repo_name":"SimonParschat/final_project","sub_path":"finalP_test.py","file_name":"finalP_test.py","file_ext":"py","file_size_in_byte":3432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"31719337982","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n'''A bot for my personal use.'''\nfrom __future__ import annotations\n\nimport contextlib\nfrom datetime import datetime\nimport functools\nfrom typing import Any, Callable, Coroutine\n\nimport aiohttp\nimport asqlite\nimport discord\nfrom discord.ext import commands\n\nimport config\n\nclass Ctx(commands.Context):\n '''A custom command context.'''\n bot: Bot\n\n def __init__(self, **kwargs) -> None:\n super().__init__(**kwargs)\n\n async def react(self, emoji):\n '''Adds a reaction to this message.'''\n with contextlib.suppress(discord.HTTPException):\n await self.message.add_reaction(emoji)\n\n async def rocket(self) -> None:\n '''Reacts with a rocket emoji.'''\n await self.react(\"\\N{ROCKET}\")\n\n async def boom(self, message = None, **kwargs) -> None:\n '''Reacts with a boom emoji, and sends an optional error message.'''\n await self.react(\"\\N{COLLISION SYMBOL}\")\n await self.send(message, **kwargs)\n \n async def log(self, **kwargs):\n await self.bot.log(self, **kwargs)\n\n @property\n def session(self):\n return self.bot.session\n \n @property\n def conn(self):\n return self.bot.db\n\n def cursor(self):\n return self.bot.cursor()\n\n @property\n def color(self):\n return self.bot.color\n\nclass Bot(commands.Bot):\n '''Custom bot class with convenience methods and attributes.'''\n def __init__(self, prefixes: list[str], *, color: discord.Color=discord.Color.default(), db: str, webhook_id: int, secret_password: str, **kwargs):\n self.exit_code = 0\n self.color = color\n self.webhook_id = webhook_id\n self.secret_password = secret_password\n self.cog_names = config.cogs\n # late initialization, forced type-ignore\n self.start_time: datetime = None # type: ignore\n self.log: Callable[..., Coroutine[Any]] = None # type: ignore\n self.log_raw: Callable[..., Coroutine[Any]] = None # type: ignore\n self.db: asqlite.Connection = None # type: ignore\n self.session: aiohttp.ClientSession = None # type: ignore\n\n super().__init__(command_prefix=commands.when_mentioned_or(*prefixes), **kwargs)\n for cog in config.cogs:\n try:\n self.load_extension(cog)\n except Exception as exc:\n print(f\"Could not load extension {cog} due to {exc.__class__.__name__}: {exc}\")\n\n # Connection acquisition must be asynchronous\n self.loop.create_task(self.connect_sessions(db=db))\n \n async def close(self):\n print(\"Shutting down...\")\n await self.session.close()\n await self.db.close()\n await super().close()\n\n async def connect_sessions(self, *, db: str):\n self.db = await asqlite.connect(db) # type: ignore\n self.session = aiohttp.ClientSession()\n \n await self.wait_until_ready()\n self.start_time = datetime.utcnow()\n \n self.dispatch(\"initialized\")\n print(f\"Logged in as {self.user} (ID: {self.user.id})\")\n print(\"Invite:\", discord.utils.oauth_url(str(self.user.id)))\n\n def cursor(self):\n '''Obtains a cursor once awaited.'''\n return self.db.cursor()\n\n # Hook for custom context, as well as reply invokes\n async def get_context(self, message: discord.Message, *, cls=commands.Context):\n if message.reference is not None:\n resolved = message.reference.resolved\n if isinstance(resolved, discord.Message):\n resolved.content = f\"{message.content} {resolved.content}\"\n return await super().get_context(resolved, cls=Ctx)\n return await super().get_context(message, cls=Ctx)\n\nbot = Bot(\n [\"rocket \", \"Rocket \"], # auto-capitalization aware\n color=discord.Color(0xe0e0f0),\n db=config.db,\n webhook_id=config.webhook_id,\n secret_password = config.secret_password,\n allowed_mentions=discord.AllowedMentions(everyone=False),\n intents=discord.Intents(\n guilds=True,\n messages=True,\n reactions=True,\n )\n)\n\nbot.run(config.token)\n","repo_name":"RocketRace/rocketbot","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":4146,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"18469914998","text":"################################################################################\n# Vehicle Assignments\n################################################################################\n# This is still a pretty terrible solution, but I haven't been able to figure\n# out a better system, so here we are...\n\ndef assign(emp_num, eno, zone, w_date, config):\n\n # Supervisors\n S1 = config['Supervisors']['S1']\n S2 = config['Supervisors']['S2']\n\n # Police Vehicles\n V1 = config['PO_Vehicles']['V1']\n V2 = config['PO_Vehicles']['V2']\n V3 = config['PO_Vehicles']['V3']\n V4 = config['PO_Vehicles']['V4']\n V5 = config['PO_Vehicles']['V5']\n V6 = config['PO_Vehicles']['V6']\n V7 = config['PO_Vehicles']['V7']\n V8 = config['PO_Vehicles']['V8']\n\n # Bicycles\n B1 = config['Bicycles']['B1']\n\n # CSO Vehicles\n\n C1 = config['CSO_Vehicles']['C1']\n C2 = config['CSO_Vehicles']['C2']\n C3 = config['CSO_Vehicles']['C3']\n C4 = config['CSO_Vehicles']['C4']\n C5 = config['CSO_Vehicles']['C5']\n C6 = config['CSO_Vehicles']['C6']\n C7 = config['CSO_Vehicles']['C7']\n C8 = config['CSO_Vehicles']['C8']\n\n XX = C7\n\n # This 'array' assigns a vehicle (above) to an officer (emp_num), which is\n # according to the order in which an employee appears in the po_num and\n # cso_num lists. i.e. if '507' is the fisrt ENO in the po_num list, then\n # then emp_num[0] is '507'.\n\n patrol = {emp_num[0]: ['', '', '', B1, B1, B1, B1], # Greeks\n emp_num[1]: ['', '', '', B1, B1, B1, B1],\n\n emp_num[2]: ['', '', '', S1, S1, S1, S1], # Lieutenant\n\n emp_num[3]: ['', '', S1, S2, S2, S2, ''], # Sergeants\n emp_num[4]: [S1, S1, S2, '', '', '', S2],\n\n emp_num[5]: ['', '', '', V4, V1, V1, V1], # Officers\n emp_num[6]: [V2, '', '', '', V4, V2, V2],\n emp_num[7]: [V3, V3, '', '', '', V4, V3],\n emp_num[8]: [V5, V5, '', '', '', V5, V5],\n emp_num[9]: [V4, V4, V4, '', '', '', V4],\n emp_num[10]: [V1, V1, V1, V1, '', '', ''],\n emp_num[11]: ['', V2, V2, V2, V2, '', ''],\n emp_num[12]: ['', '', V3, V3, V3, V3, ''],\n\n emp_num[13]: ['', '', '', C1, C1, C1, C1], # CSOs\n emp_num[14]: [C2, '', '', '', C2, C2, C2],\n emp_num[15]: [C3, C3, '', '', '', C3, C3],\n emp_num[16]: [C4, C4, C4, '', '', '', C4],\n emp_num[17]: [C5, C5, C5, '', '', '', C5], #Stewart\n emp_num[18]: [C1, C1, C1, XX, '', '', ''], #Dixon\n emp_num[19]: ['', C2, C2, C2, XX, '', ''],\n emp_num[20]: ['', '', C3, C3, C3, XX, ''],\n emp_num[21]: ['', '', XX, C4, C4, C4, ''],\n emp_num[22]: ['', '', '', C5, C5, C5, XX]\n }\n\n v = patrol[eno_lookup(emp_num, eno)][weekday_num(w_date)]\n\n if zone == 20:\n v = S1\n elif zone == 66:\n v = C6\n\n# The try:except block catches entries on the schedule that are not 'zones'\n# e.g. \"X\" is not a number so it will throw an error.\n try:\n if v == '' and int(str(zone)[:2]) < 30:\n v = V6\n elif v == '' and int(str(zone)[:2]) > 30:\n v = C7\n except ValueError:\n v = ''\n\n return v\n\n# Fix for isoweekday, which returns Sunday as '7'\n# This will now return Sunday = 0, Saturday = 6\ndef weekday_num(day_num):\n if day_num.isoweekday() == 7:\n return 0\n else:\n return day_num.isoweekday()\n\n# Finds the order for employee numbers in the emp_num list\n# In other words if '507' is the first employee number on the list\n# '507' is emp_num[0]\ndef eno_lookup(emp_num, eno):\n return emp_num[emp_num.index(str(eno))]\n","repo_name":"pconwell/excel-sharepoint-schedule","sub_path":"vehicles.py","file_name":"vehicles.py","file_ext":"py","file_size_in_byte":3699,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"17244482810","text":"# %%\nimport re\nfrom collections import defaultdict, Counter\nfrom operator import itemgetter\n\n# %%\n\n\ndef read_input(FileName):\n result = []\n\n with open('6/' + FileName) as fp:\n for _, line in enumerate(fp):\n t = re.findall(r'(\\d+), (\\d+)', line)[0]\n result.append((int(t[0]), int(t[1])))\n\n return result\n\n\ndef Manhattan_Distance(a, b):\n return abs(a[0] - b[0]) + abs(a[1] - b[1])\n\n\n# %%\ninput = read_input('input.txt')\n\nmin_x = min([i[0] for i in input])\nmax_x = max([i[0] for i in input])\nmin_y = min([i[1] for i in input])\nmax_y = max([i[1] for i in input])\n\nmax_dist = 10000\n\ngrid = defaultdict(int)\n\nfor x in range(min_x, max_x + 1):\n for y in range(min_y, max_y + 1):\n key = (x, y)\n total_dist = 0\n\n for coord in input:\n total_dist += Manhattan_Distance(coord, (x, y))\n\n if total_dist < max_dist:\n grid[key] = total_dist\n\n\n# print(*grid.items(), sep='\\n')\n\nsolution = len(grid)\nprint('solution: ', solution)\n","repo_name":"chhenning/python_tutorial","sub_path":"Advent Of Code/2018/6/solution_2.py","file_name":"solution_2.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"39160141607","text":"import matplotlib.pyplot as plt\nimport tensorflow as tf\nfrom src.utils.preprocessing import get_imagenet_label\nfrom src.models.pre_trained import Model\nfrom skimage.transform import resize\n\nclass ImageHandler():\n def __init__(self):\n self.image_list=[]\n self.description_list=[]\n self.label_list=[]\n self.confidence_list = []\n self.pertubation_list = []\n\n def display_images(self,model_name, adv_x, image, eps, perturbations, description, decode_predictions,quantisation=False):\n\n \"\"\"\n Helper function to display the images along with their corresponding adversarial attack pertubations\n\n :param get_imagenet_label: get the imagenet label of the prediction made\n :param interpreter: loading the quantised model\n\n return: plot showing the pertubation as well as the image\n \"\"\"\n initialize_model=Model(model_name)\n pre_trained_model = initialize_model.model\n if quantisation == '32 bit':\n _, label, confidence = get_imagenet_label(pre_trained_model.predict(adv_x), decode_predictions)\n elif quantisation == '16 bit':\n _, label, confidence = initialize_model.predict(adv_x, True,quantisation)\n elif quantisation == '8 bit':\n _, label, confidence = initialize_model.predict(adv_x, True, quantisation)\n self.image_list.append(adv_x[0])\n self.description_list.append(description)\n self.label_list.append(label)\n self.confidence_list.append(confidence)\n self.pertubation_list.append(perturbations)\n return self.image_list, self.description_list, self.label_list,self.confidence_list,self.pertubation_list\n\n def visualize(self, image, image_class, class_confidence):\n\n for img, lbl, conf in zip(image, image_class, class_confidence):\n img = tf.image.flip_up_down(img)\n img=tf.reshape(img, (224,224,3))\n fig = plt.figure(figsize=(15, 16))\n ax2 = fig.add_subplot(4, 4, 2)\n ax2.imshow(img * 0.5 + 0.5, origin='lower',\n extent=[-4, 4, -1, 1], aspect=4)\n plt.title('32 bit -> {} : {:.2f}% Confidence'.format(lbl, conf*100,))\n\n \"\"\"#image,image_class,class_confidence= self.model.predict(r_image,False)\n #image1,image_class1,class_confidence1= self.model.predict(r_image,True)\n \n \n f = plt.figure(figsize=(20,21))\n f.add_subplot(1,2, 1)\n plt.imshow(image[0] * 0.5 + 0.5)\n plt.title('32 Bit-> {} : {:.2f}% Confidence'.format(image_class, class_confidence*100))\n #f.add_subplot(1,2, 2)\n #plt.title('{} -> {} : {:.2f}% Confidence'.format(self.quant_type,image_class1, class_confidence1*100))\n #plt.imshow(image1[0] * 0.5 + 0.5)\n\n # set the spacing between subplots\n #plt.subplots_adjust(left=0.1,\n #bottom=0.1, \n #right=0.9, \n #top=0.9, \n #wspace=0.4, \n #hspace=0.4)\"\"\"\n\n #plt.show(astype('uint8'))\n\n\n def image_drawer(self,image_32,description_32,label,confidence_32,pertubations_32,\n image_16, description_16, label_16, confidence_16,\n image_8, description_8, label_8, confidence_8): \n\n zipped_results = zip(image_32, description_32, label, confidence_32, pertubations_32,\n image_16, description_16, label_16, confidence_16,\n image_8, description_8, label_8, confidence_8)\n for im_32, des_32, lbl_32, conf_32, pertbs_32, im_16, des_16, lbl_16, conf_16, im_8, des_8, lbl_8, conf_8 in zipped_results:\n im_32 = tf.image.flip_up_down(im_32)\n im_16 = tf.image.flip_up_down(im_16)\n im_8 = tf.image.flip_up_down(im_8)\n \n fig =plt.figure(figsize=(15,16))\n ax1 = fig.add_subplot(4,4,1)\n ax1.imshow(tf.reshape(pertbs_32, (224,224,3), name=None), aspect='auto')\n ax2 = fig.add_subplot(4,4,2)\n ax2.imshow(im_32* 0.5 + 0.5,origin='lower', extent=[-4, 4, -1, 1], aspect=4)\n plt.title('32 bit -> {} \\n {} : {:.2f}% Confidence'.format(des_32,\n lbl_32, conf_32*100,))\n \n ax3 = fig.add_subplot(4,4,3)\n ax3.imshow(im_16* 0.5 + 0.5,origin='lower', extent=[-4, 4, -1, 1], aspect=4)\n plt.title('16 bit ->->{} \\n {} : {:.2f}% Confidence'.format(des_16,\n lbl_16, conf_16*100,))\n \n ax4 = fig.add_subplot(4, 4, 4)\n ax4.imshow(im_8 * 0.5 + 0.5, origin='lower',\n extent=[-4, 4, -1, 1], aspect=4)\n plt.title('8 bit ->->{} \\n {} : {:.2f}% Confidence'.format(des_8,\n lbl_8, conf_8*100,))\n \n plt.subplots_adjust(left=0.1,\n bottom=0.1, \n right=0.9, \n top=0.9, \n wspace=0.4, )\n \n\n\n\n \n","repo_name":"YMuskrat/Adversarial-Attacks","sub_path":"src/utils/Plotter.py","file_name":"Plotter.py","file_ext":"py","file_size_in_byte":5181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"18522475058","text":"import requests\nfrom bs4 import BeautifulSoup\n\n\ndependents_list = []\n\n\ndef get_dependents(repo):\n page_num = 3000\n url = 'https://github.com/{}/network/dependents'.format(repo)\n\n for i in range(page_num):\n print(\"GET \" + url)\n r = requests.get(url)\n print(r)\n soup = BeautifulSoup(r.content, \"html.parser\")\n\n dependents_exist = soup.find('h3', {\"data-view-component\": \"true\"})\n if(dependents_exist and dependents_exist.text == \"We haven’t found any dependents for this repository yet.\"):\n return {}\n\n data = [\n \"{}/{}\".format(\n t.find('a', {\"data-repository-hovercards-enabled\": \"\"}).text,\n t.find('a', {\"data-hovercard-type\": \"repository\"}).text\n )\n for t in soup.findAll(\"div\", {\"class\": \"Box-row\"})\n ]\n dependents_list.extend(data)\n\n next_url = soup.find(\n \"div\", {\"class\": \"paginate-container\"})\n next_disabled = soup.find(\n \"button\", {\"disabled\": \"disabled\"})\n if(not next_url or next_disabled):\n return dependents_list\n\n url = next_url.find('a')[\"href\"]\n return dependents_list\n\n\ndef main():\n dependents = get_dependents(\"memgraph/pymgclient\")\n print(len(dependents))\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"memgraph/data-streams","sub_path":"datasets/github/scraper/dependency_graph.py","file_name":"dependency_graph.py","file_ext":"py","file_size_in_byte":1332,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"47"}
+{"seq_id":"17289274066","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Nov 7 16:45:06 2019\n\n@author: matthew-bailey\n\"\"\"\n\nfrom typing import Dict, Sequence, NewType, Tuple, Any\n\nimport networkx as nx\nimport numpy as np\nfrom scipy.spatial import Delaunay\nfrom matplotlib.patches import Polygon\nfrom matplotlib.collections import PatchCollection\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as anm\n\nNode = NewType('Node', Any)\nGraph = NewType('Graph', Any)\nCoord = NewType('Coord', np.array)\n\n\ndef calculate_polygon_area(node_list: Sequence[Node],\n coords_dict: Dict[Node, Coord]) -> float:\n \"\"\"\n Calculates the signed area of this polygon,\n using the Shoelace algorithm.\n :param node_list: an ordered list of connected nodes,\n can be clockwise or anticlockwise.\n :param coords_dict: a dictionary, keyed by nodes,\n that has [x, y] coordinates as values.\n :return signed_area: The area of the polygon,\n which is negative if the points are ordered clockwise\n and positive if the points are ordered anticlockwise.\n \"\"\"\n signed_area = 0.0\n for i, node in enumerate(node_list):\n this_coord = coords_dict[node]\n next_node = node_list[(i + 1) % len(node_list)]\n next_coord = coords_dict[next_node]\n signed_area += (this_coord[0] * next_coord[1]\n - this_coord[1] * next_coord[0])\n return 0.5 * signed_area\n\ndef node_list_to_edges(node_list: Sequence[Node],\n is_ring: bool = True):\n \"\"\"\n Takes a list of connected nodes, such that node[i] is connected\n to node[i - 1] and node[i + 1] and turn it into a set of edges.\n This is the opposite function to Shape.to_node_list\n :param node_list: a list of nodes which\n must be a hashable type.\n :param is_ring: is this a linked ring, i.e.\n is node[-1] connected to node[0]\n :return edges: a set of frozensets, each frozenset\n containing two edges.\n \"\"\"\n list_size = len(node_list)\n edges = set()\n\n # If this is a ring, iterate one over the size\n # of the list. If not, make sure to stop\n # before the end.\n if is_ring:\n offset = 0\n else:\n offset = -1\n for i in range(list_size + offset):\n next_index = (i + 1) % list_size\n edges.add(frozenset([node_list[i],\n node_list[next_index]]))\n return frozenset(edges)\n\nclass Shape:\n def __init__(self,\n edges: Sequence[Tuple[Node, Node]],\n coords_dict: Dict[Node, Coord] = None):\n self.edges = edges\n self.coords_dict = coords_dict\n\n def merge(self, other) -> None:\n \"\"\"\n Merges two shapes together, removing their common edges.\n Sets both self and other to the new shape.\n :param other: the shape to merge in to this one.\n \"\"\"\n if self.coords_dict != other.coords_dict:\n raise ValueError(\"Two shapes are not using the same coordinates.\")\n unique_edges = self.edges.symmetric_difference(other.edges)\n new_shape = Shape(unique_edges, coords_dict=self.coords_dict)\n other = new_shape\n self = new_shape\n return new_shape\n\n @property\n def nodes(self):\n nodes = set([node for edge in self.edges\n for node in edge])\n return nodes\n\n def to_node_list(self):\n node_list = [min(self.nodes)]\n seen_nodes = set(node_list)\n while len(node_list) < len(self.edges):\n last_node = node_list[-1]\n # Find the two nodes this is connected to.\n connected_nodes = set()\n for edge in self.edges:\n if last_node in edge:\n connected_nodes = connected_nodes.union(edge)\n connected_nodes = connected_nodes.difference(seen_nodes)\n # Pick the smallest node to move to next, arbitrarily.\n # We'll sort out winding later.\n next_node = min(connected_nodes)\n node_list.append(next_node)\n seen_nodes = set(node_list)\n\n if self.coords_dict is not None:\n signed_area = calculate_polygon_area(node_list, self.coords_dict)\n if signed_area < 0:\n # If the signed area is negative, then the ordering\n # is wrong. That's easily fixed by reversing the list,\n # and then putting the smallest element at the front.\n node_list = list(reversed(node_list))\n node_list = node_list[-1:] + node_list[:-1]\n return node_list\n\n def to_polygon(self):\n \"\"\"\n Turns this shape into a matplotlib polygon object.\n \"\"\"\n node_list = self.to_node_list()\n coord_array = np.empty([len(node_list), 2], dtype=float)\n for i, node in enumerate(node_list):\n coord_array[i, :] = self.coords_dict[node]\n return Polygon(coord_array, closed=True)\n\n def __contains__(self, obj) -> bool:\n \"\"\"\n Override the in / not in magic method, because this shape is\n solely defined by its edges. If an edge is in this shape,\n return True.\n \"\"\"\n return obj in self.edges\n\n def __hash__(self) -> int:\n \"\"\"\n Override the hash magic method, because this shape is\n solely defined by its edges. This means that shapes\n in any rotation or order of edges hash the same.\n \"\"\"\n return hash(self.edges)\n\n def __eq__(self, other) -> bool:\n \"\"\"\n Override the equals magic method, because this shape is\n solely defined by its edges. This means that shapes\n in any rotation or order of edges hash the same.\n \"\"\"\n return self.edges == other.edges\n\n def __str__(self) -> str:\n \"\"\"\n Override the string magic method to make a pretty\n output.\n \"\"\"\n return str(self.to_node_list())\n\n def __len__(self) -> int:\n \"\"\"\n Override the length magic method to return the\n size of the shape\n \"\"\"\n return len(self.edges)\n\n\nclass RingFinder:\n \"\"\"\n A group of subroutines to find rings in a combination\n of a networkx graph and a set of coordinates. The rings\n it identifies correspond to the faces on the polyhedron\n that this graph represents, according to Euler's formula.\n Proceeds by using a Delaunay triangulation which has\n rings well-defined by simplicies and then removes\n edges one-by-one.\n \"\"\"\n def __init__(self,\n graph: Graph,\n coords_dict: Dict[Node, Coord],\n cutoffs=None):\n self.graph: Graph = graph\n self.coords_dict: Dict[Node, Coord] = coords_dict\n \n # Tidying up stage -- remove the long edges,\n # and remove the single coordinate sites.\n if cutoffs is not None:\n self.remove_long_edges(cutoffs)\n self.remove_single_coordinate_sites()\n self.removable_edges = None\n # Now triangulate the graph and do the real heavy lifting.\n self.tri_graph, self.simplices = self.triangulate_graph()\n self.current_shapes = set([Shape(node_list_to_edges(simplex), \n self.coords_dict)\n for simplex in self.simplices]) \n self.identify_rings(max_to_remove=0) \n\n def remove_long_edges(self,\n cutoffs: Sequence[float]):\n \"\"\"\n Remove any edges that are longer than\n a set of cutoffs, useful to make a periodic cell\n aperiodic.\n :param graph: the networkx graph to detect single-coordinate\n nodes in\n :param coords_dict: a dictionary, keyed by nodes,\n with values being the [x, y] coordinates of the nodes, which\n we use to remove long bonds.\n :param cutoffs: an [max_x, max_y] sequence, removing any edges\n with a component longer than max_x or max_y. For the minimum\n image convention, we want these to be half the both length.\n :return graph: a graph minus the edges that are too long. Note\n that this mutates the original graph, so the return value can\n be ignored.\n \"\"\"\n to_remove = set()\n for edge in self.graph.edges():\n pos_a = self.coords_dict[edge[0]]\n pos_b = self.coords_dict[edge[1]]\n distance = np.abs(pos_b - pos_a)\n if distance[0] > cutoffs[0]:\n to_remove.add(edge)\n elif distance[1] > cutoffs[1]:\n to_remove.add(edge)\n self.graph.remove_edges_from(to_remove)\n\n def triangulate_graph(self):\n \"\"\"\n Constructs a Delauney triangulation\n of a set of coordinates, and returns\n it as a networkx graph.\n :param coordinates_dict: a dictionary, with key\n being a node and the value being an [x, y]\n numpy array.\n :return tri_graph: a Delaunay triangulation\n of the original graph.\n :return mapped_simplices: a list of all the\n edges making up triangular simplicies\n \"\"\"\n\n # Turn the coordinate dictionary into\n # an array. The index of a given key\n # corresponds to its position in the\n # sorted list of keys, which is stored\n # in the index_to_key dict.\n coords_array = np.empty([len(self.coords_dict), 2])\n index_to_key = {}\n for i, key in enumerate(sorted(self.coords_dict.keys())):\n if self.coords_dict[key].shape[0] != 2:\n raise RuntimeError(\"Coordinates in the dictionary must be 2D.\")\n index_to_key[i] = key\n coords_array[i, :] = self.coords_dict[key]\n\n tri_graph = nx.Graph()\n delaunay_res = Delaunay(coords_array)\n mapped_simplices = []\n for simplex in delaunay_res.simplices:\n # Convert these indicies to the same ones\n # the master graph uses, to avoid horrors.\n mapped_simplex = [index_to_key[node] for node in simplex]\n mapped_simplices.append(mapped_simplex)\n # Iterate over all the simplex edges and add them to\n # a graph.\n edges = node_list_to_edges(mapped_simplex)\n tri_graph.add_edges_from(edges)\n return tri_graph, mapped_simplices\n\n\n def remove_single_coordinate_sites(self) -> Graph:\n \"\"\"\n Recursively finds all the single coordinate sites,\n and all the sites that would be single coordinate\n if that one were removed, and so on.\n Mutates the input data by deleting entries.\n :param main_graph: the networkx graph to detect single-coordinate\n nodes in\n :param coords_dict: the coordinates of the nodes, which we\n remove to make sure they don't get misused in the Delauney\n triangulation.\n :return graph: a graph minus the single coordinate notes. Note\n that this mutates the original graph, so the return value can\n be ignored.\n \"\"\"\n while True:\n # Find the 0 or 1 coordinate nodes and make a list of them,\n # then remove both their entry in the graph and their\n # coordinate.\n nodes_to_remove = [item[0] for item in self.graph.degree()\n if item[1] < 2]\n if not nodes_to_remove:\n break\n self.graph.remove_nodes_from(nodes_to_remove)\n for node in nodes_to_remove:\n del self.coords_dict[node]\n\n def identify_rings(self,\n max_to_remove:int = None):\n \"\"\"\n Removes the edges from a triangulated graph that do not exist\n in the original graph, identifying rings in the process.\n Start off with a set of simplices as the building blocks\n of rings.\n :param main_graph: the networkx graph to detect cycles in\n :param tri_graph: the Delauney triangulation of main_graph,\n as the same graph type.\n :param simplices: a list of tuples, each of which is three\n node ids representing a triangle.\n :param max_to_remove: the maximum number of edges to remove.\n Useful for making animations, but is None by default.\n \"\"\"\n\n # First we need to check if there are any edges\n # that exist in the main graph that do not exist\n # in the triangulated graph, usually an indication\n # of unphysicality. However, networkx doesn't have\n # consistent ordering of edges, so we need to make it\n # insensitive to (a, b) <-> (b, a) swaps.\n main_edge_set = set([frozenset(edge) for edge in self.graph.edges()])\n tri_edge_set = set([frozenset(edge) for edge in self.tri_graph.edges()])\n\n if not main_edge_set.issubset(tri_edge_set):\n missing_edges = main_edge_set.difference(tri_edge_set)\n raise RuntimeError(\"There are edges in the main graph that do not \" +\n \"exist in the Delauney triangulation: \" +\n f\"{missing_edges}.\")\n\n self.removable_edges = tri_edge_set.difference(main_edge_set)\n if max_to_remove is None:\n max_to_remove = len(self.removable_edges)\n # Each edge that we wish to remove belongs to one or two\n # shapes. Find the shapes it is in, and merge them.\n edges_removed = 1\n edge = self.removable_edges.pop()\n while self.removable_edges:\n edges_removed += 1\n self.remove_one_edge(edge)\n print(edges_removed)\n if edges_removed >= max_to_remove:\n break\n edge = self.removable_edges.pop()\n \n def remove_one_edge(self, edge):\n shapes_with_edge = []\n for shape in self.current_shapes:\n if edge in shape:\n shapes_with_edge.append(shape)\n if len(shapes_with_edge) == 2:\n break\n\n if len(shapes_with_edge) == 1:\n # It's only part of one shape.\n # Scrap it.\n # TODO: this might have to change for periodic.\n self.current_shapes.remove(shapes_with_edge[0])\n return\n \n elif len(shapes_with_edge) == 0:\n # This is a stranded edge. This means\n # something has gone horribly wrong\n # and we should bail out.\n print([str(shape) for shape in self.current_shapes], edge)\n raise ValueError(\"Found an edge associated with no shapes. \" +\n \"Did you remove all single coordinate nodes?\")\n\n new_shape = shapes_with_edge[0].merge(shapes_with_edge[1])\n\n for shape in shapes_with_edge:\n self.current_shapes.remove(shape)\n self.current_shapes.add(new_shape)\n \n @property\n def ring_sizes(self):\n return [len(shape) for shape in self.current_shapes]\n \n def as_polygons(self):\n return [shape.to_polygon() for shape in self.current_shapes]\n\nif __name__ == \"__main__\":\n G: Graph = nx.Graph()\n with open(\"./edges.dat\", \"r\") as fi:\n fi.readline() # Skip header\n for line in fi.readlines():\n x, y = [int(item) for item in line.split(\",\")]\n G.add_edge(x, y)\n\n COORDS_DICT: Dict[Node, Coord] = {}\n with open(\"./coords.dat\", \"r\") as fi:\n fi.readline() # Skip header\n for line in fi.readlines():\n line = line.split(\",\")\n node_id, x, y = int(line[0]), float(line[1]), float(line[2])\n COORDS_DICT[node_id] = np.array([x, y])\n \n ring_finder = RingFinder(G, COORDS_DICT, np.array([20.0, 20.0]))\n\n FIG, AX = plt.subplots()\n FIG.patch.set_visible(False)\n AX.axis('off')\n POLYS = ring_finder.as_polygons()\n SIZES = ring_finder.ring_sizes\n # SIZE_RANGE = max(SIZES) + 1 - min(SIZES)\n SIZE_RANGE = 30 - 4\n THIS_CMAP = plt.cm.get_cmap(\"viridis\")(np.linspace(0, 1, SIZE_RANGE))\n COLOURS = [THIS_CMAP[SIZE - 4] for SIZE in SIZES]\n \n p = PatchCollection(POLYS, alpha=1, linewidth=5,\n linestyle=\"dotted\")\n p.set_color(COLOURS)\n p.set_edgecolor(\"black\")\n AX.add_collection(p)\n AX.set_xlim(0, 155)\n AX.set_ylim(0, 155)\n nx.draw_networkx_edges(ring_finder.graph, ax=AX, pos=COORDS_DICT,\n edge_color=\"black\", zorder=1000, width=5)\n LASTFRAME = 0\n def animate(frame):\n global LASTFRAME\n for i in range(frame - LASTFRAME):\n try:\n edge = ring_finder.removable_edges.pop()\n print(edge, len(ring_finder.removable_edges), i, frame - LASTFRAME)\n ring_finder.remove_one_edge(edge)\n except KeyError:\n break\n LASTFRAME = frame\n patches = ring_finder.as_polygons()\n SIZES = ring_finder.ring_sizes\n COLOURS = [THIS_CMAP[SIZE - 4] for SIZE in SIZES]\n p.set_color(COLOURS)\n p.set_paths(patches)\n p.set_edgecolor(\"red\")\n\n anim = anm.FuncAnimation(FIG, animate,\n frames=range(430),\n interval=1,\n repeat=False)\n Writer = anm.writers['ffmpeg']\n FIG.set_size_inches(8, 8, True)\n writer = Writer(fps=30, metadata=dict(artist='Matt Bailey'), bitrate=-1)\n anim.save(\"animation.mp4\", writer=writer, dpi=100)\n plt.show()\n # nx.draw(G, pos=COORDS_DICT, ax=AX, edge_color=\"red\", width=5)\n\n\n\n","repo_name":"dormrod/rings","sub_path":"matt_ring_finder.py","file_name":"matt_ring_finder.py","file_ext":"py","file_size_in_byte":17462,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"72833025424","text":"import click\nfrom click import echo\n\nfrom npc.util import ParseError\nfrom npc_cli.presenters import tabularize, type_list, wrapped_paragraphs, tag_table_data\nfrom npc_cli.helpers import cwd_campaign\nfrom npc_cli.errors import BadCharacterTypeException, CampaignRequiredException\n\nfrom .main_group import cli, arg_settings, pass_settings\n\n##########################\n# Config description group\n##########################\n\n@cli.group()\ndef describe():\n \"\"\"Show info about systems, types, or tags\"\"\"\n\n###################\n# Describe systems\n###################\n\n@describe.command()\n@pass_settings\ndef systems(settings):\n \"\"\"Show all configured game systems\"\"\"\n systems = [settings.get_system(key) for key in settings.get_system_keys()]\n system_headers = [\"Name\", \"Key\", \"Description\"]\n system_data = [[system.name, system.key, system.desc] for system in systems]\n echo(tabularize(system_data, headers = system_headers, title = \"Configured Systems\"))\n\n campaign = cwd_campaign(settings)\n if campaign:\n echo(f\"\\nCurrently using {campaign.system.name}\")\n\n##########################\n# Describe system details\n##########################\n\n@describe.command()\n@click.option(\"-s\", \"--system\",\n type=click.Choice(arg_settings.get_system_keys(), case_sensitive=False),\n help=\"ID of the game system to show\")\n@pass_settings\ndef system(settings, system):\n \"\"\"Show details about a single system\"\"\"\n campaign = cwd_campaign(settings)\n try:\n if system:\n game_system = settings.get_system(system)\n elif campaign:\n game_system = campaign.system\n else:\n raise CampaignRequiredException\n except ParseError as err:\n raise click.FileError(err.path, hint=err.strerror)\n\n echo(f\"=== {game_system.name} ===\")\n echo(game_system.desc)\n for line in wrapped_paragraphs(game_system.doc):\n echo(\"\")\n echo(line)\n if game_system.links:\n echo(f\"\\nRelevant Links:\")\n for link in game_system.links:\n echo(f'* {link[\"label\"]}: {link[\"url\"]}')\n\n###################\n# Describe types\n###################\n\n@describe.command()\n@click.option(\"-s\", \"--system\",\n type=click.Choice(arg_settings.get_system_keys(), case_sensitive=False),\n help=\"ID of the game system to use\")\n@pass_settings\ndef types(settings, system):\n \"\"\"Show all configured character types\"\"\"\n campaign = cwd_campaign(settings)\n try:\n if system:\n game_system = settings.get_system(system)\n chartypes = game_system.types\n title = f\"Character Types for {game_system.name}\"\n elif campaign:\n chartypes = campaign.types\n title = f\"Character Types in {campaign.name}\"\n else:\n raise CampaignRequiredException(['-s', '--system'])\n except ParseError as err:\n raise click.FileError(err.path, hint=err.strerror)\n\n chartype_headers = [\"Name\", \"Key\", \"Description\"]\n chartype_data = [[chartype.name, chartype.key, chartype.desc] for chartype in chartypes.values()]\n echo(tabularize(chartype_data, headers = chartype_headers, title = title))\n\n########################\n# Describe type details\n########################\n\n@describe.command()\n@click.option(\"-s\", \"--system\",\n type=click.Choice(arg_settings.get_system_keys(), case_sensitive=False),\n help=\"ID of the game system to use\")\n@click.option(\"-t\", \"--type\", \"type_\",\n required=True,\n help=\"Character type to show\")\n@pass_settings\ndef type(settings, system, type_):\n \"\"\"Show details about a single character type\"\"\"\n campaign = cwd_campaign(settings)\n try:\n if system:\n target = settings.get_system(system)\n elif campaign:\n target = campaign\n else:\n raise CampaignRequiredException\n except ParseError as err:\n raise click.FileError(err.path, hint=err.strerror)\n\n if type_ not in target.types:\n raise BadCharacterTypeException(type_, type_list(target.types), ['-t', '--type'])\n chartype = target.get_type(type_)\n\n echo(f\"Character Type: {chartype.name}\")\n echo(f\"ID: {chartype.key}\")\n echo(f\"File suffix: {chartype.default_sheet_suffix}\")\n echo(f\"Sheet template: {chartype.sheet_path}\")\n echo(\"\")\n echo(chartype.desc)\n\n###################\n# Describe tags\n###################\n\n@describe.command()\n@click.option(\"-s\", \"--system\", \"system_key\",\n type=click.Choice(arg_settings.get_system_keys(), case_sensitive=False),\n help=\"ID of the game system to use\")\n@click.option(\"-t\", \"--type\", \"type_\", help=\"Only show valid tags for this character type\")\n@pass_settings\ndef tags(settings, system_key, type_):\n \"\"\"Show all configured tags for this campaign\n\n Can show the tags available to all character types, or just the ones for a specific type.\n \"\"\"\n campaign = cwd_campaign(settings)\n try:\n if system_key:\n target = settings.get_system(system_key)\n system = target\n elif campaign:\n target = campaign\n system = campaign.system\n else:\n raise CampaignRequiredException\n except ParseError as err:\n raise click.FileError(err.path, hint=err.strerror)\n\n headers = [\"Name\", \"Description\"]\n if type_:\n if type_ not in target.types:\n raise BadCharacterTypeException(type_, type_list(target.types), ['-t', '--type'])\n\n title = f\"Tags for {target.get_type(type_).name} in {target.name}\"\n tags = target.type_tags(type_)\n else:\n title = f\"Tags in {target.name}\"\n tags = target.tags\n\n data = tag_table_data(tags)\n echo(tabularize(data, headers = headers, title = title))\n\n#######################\n# Describe tag details\n#######################\n\n@describe.command()\n@click.option(\"-s\", \"--system\", \"system_key\",\n type=click.Choice(arg_settings.get_system_keys(), case_sensitive=False),\n help=\"ID of the game system to use\")\n@click.option(\"-t\", \"--type\", \"type_\",\n help=\"ID of the character type to use for finding the tag\")\n@click.option(\"-a\", \"--tag\", \"tag_name\",\n required=True,\n help=\"Name of the tag to show\")\n@click.option(\"-c\", \"--context\",\n help=\"Name of the tag's parent, if looking for a subtag\")\n@pass_settings\ndef tag(settings, system_key, type_, tag_name, context):\n \"\"\"Show details for the named tag\"\"\"\n campaign = cwd_campaign(settings)\n try:\n if system_key:\n target = settings.get_system(system_key)\n system = target\n elif campaign:\n target = campaign\n system = campaign.system\n else:\n raise CampaignRequiredException\n except ParseError as err:\n raise click.FileError(err.path, hint=err.strerror)\n\n headers = [\"Name\", \"Description\"]\n if type_:\n if type_ not in target.types:\n raise BadCharacterTypeException(type_, type_list(target.types), ['-t', '--type'])\n spec = target.get_type_tag(tag_name, type_)\n else:\n spec = target.get_tag(tag_name)\n\n if spec.needs_context:\n if not context:\n raise click.UsageError(f\"Tag '{tag_name}' is a subtag, so the --context option must be provided\")\n spec = spec.in_context(context)\n title = f\"Tag: @{spec.name} (subtag of @{context})\"\n else:\n title = f\"Tag: @{spec.name}\"\n\n echo(title)\n echo(spec.desc)\n for line in wrapped_paragraphs(spec.doc):\n echo(\"\")\n echo(line)\n","repo_name":"aurule/npc","sub_path":"src/npc_cli/commands/describe_group.py","file_name":"describe_group.py","file_ext":"py","file_size_in_byte":7441,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"47"}
+{"seq_id":"3912288738","text":"''' Selection utilities\n\nThis module provides a set of functions to handle selections.\n\n.. Created on Nov 4, 2013\n.. codeauthor:: Robert Langlois \n'''\n\nimport spider_utility\nimport relion_utility\nimport format_utility\nimport numpy\n\ndef select_file_subset(files, select, id_len=0, fill=False):\n ''' Create a list of files based on the given selection\n \n This function serves as an interface for the different\n file naming conventions.\n \n .. seealso:: spider_utility.select_file_subset\n \n :Parameters:\n \n files : list\n List of filenames\n select : array\n Array of file ids\n id_len : int\n Maximum length of SPIDER id\n fill : bool\n Fill missing filenames missing from files with those in the selection file\n \n :Returns:\n \n out : list\n List of selected filenames\n '''\n \n return spider_utility.select_file_subset(files, select, id_len, fill)\n\ndef select_stack_subset(vals, sel_by_stack):\n ''' Create a list of objects based on the given selection\n \n .. seealso:: \n \n relion_utility.select_subset\n spider_utility.select_subset\n \n :Parameters:\n \n vals : list\n List of objects\n sel_by_stack : dict\n Selections arranged by stack\n \n :Returns:\n \n out : list\n List of selected objects\n '''\n \n if len(vals) > 0 and hasattr(vals[0], 'rlnImageName'):\n return relion_utility.select_subset(vals, sel_by_stack)\n return spider_utility.select_subset(vals, sel_by_stack)\n\ndef select_subset(vals, select):\n ''' Create a list of objects based on the given selection\n \n >>> from arachnid.core.metadata.selection_utility import *\n >>> select_subset([(1,2), (2,3), (3,4) (5,2), (6,3)], [1,2])\n [(1,2), (2,3)]\n \n >>> select_subset([(1,2), (2,3), (3,4) (5,2), (6,3)], [Select(id=1),Select(id=2)])\n [(1,2), (2,3)]\n \n >>> select_subset([(1,2), (2,3), (3,4) (5,2), (6,3)], [Select(id=1,select=1),Select(id=2,select=1),Select(id=2,select=0)])\n [(1,2), (2,3)]\n \n :Parameters:\n \n vals : list\n List of objects\n select : array\n Array of selected indices where first element is 1 not 0\n \n :Returns:\n \n out : list\n List of selected objects\n '''\n \n if len(select) == 0 or len(vals) == 0: return []\n if hasattr(select[0], 'select'):\n return [vals[s.id-1] for s in select if s.select > 0]\n elif hasattr(select[0], 'id'):\n return [vals[s.id-1] for s in select]\n else:\n return [vals[s[0]-1] for s in select]\n\ndef create_selection_doc(n, start=1, micrograph_id=None):\n ''' Create a selection document from a range and optional micrograph id\n \n The default header for the output namedtuple is id,select. If micrograph_id is\n specified, then new header becomes micrograph,particle.\n \n >>> from arachnid.core.metadata.selection_utility import *\n >>> create_selection_doc(3)\n [Selection(id=1, select=1),Selection(id=2, select=1),Selection(id=3, select=1)]\n \n >>> create_selection_doc(3,1,10)\n [Selection(micrograph=10, particle=1),Selection(micrograph=10, particle=2),Selection(micrograph=10, particle=3)]\n \n :Parameters:\n \n n : int\n Length of the range\n start : int\n Starting value for the range, if 0, then all the values are incremented\n by 1.\n micrograph_id : int, optional\n Micrograph id\n \n :Returns:\n \n vals : array\n List of namedtuples\n '''\n \n values = numpy.ones((n, 2))\n pid = 0\n header=\"id,select\"\n if micrograph_id is not None:\n pid = 1\n values[:, 0] = micrograph_id\n header = \"micrograph,particle\"\n values[:, pid] = range(start, start+n)\n if start == 0: values[:, pid] += 1\n return format_utility.create_namedtuple_list(values, \"Selection\", header=header)\n\ndef create_selection_map(offset, n, micrograph_id):\n ''' Create a selection document that maps a global ID\n to micrograph, stack slice IDs.\n \n >>> from arachnid.core.metadata.selection_utility import *\n >>> create_selection_map(50, 2, 10)\n [Selection(id=50, micrograph=10,slice_id=1),Selection(id=51, micrograph=10,slice_id=2)]\n \n >>> create_selection_map(50, [3,9], 10)\n [Selection(id=50, micrograph=10,slice_id=3),Selection(id=51, micrograph=10,slice_id=9)]\n\n :Parameters:\n \n offset : int\n Offset for global range of ids\n n : int or array\n List of ids or length (1...n+1)\n micrograph_id : int\n Micrograph id\n \n :Returns:\n \n vals : array\n List of namedtuples\n '''\n \n if hasattr(n, '__iter__'):\n ids = numpy.asarray(n)\n n = len(ids)\n else:\n ids = numpy.arange(1, 1+n, dtype=numpy.int)\n values = numpy.ones((n, 3), dtype=numpy.int)\n values[:, 0] = numpy.arange(offset+1, offset+1+n, dtype=numpy.int)\n values[:, 1] = micrograph_id\n values[:, 2] = ids\n header = \"id,micrograph,slice_id\"\n return format_utility.create_namedtuple_list(values, \"Selection\", header=header)\n\n\n","repo_name":"ezralanglois/arachnid","sub_path":"arachnid/core/metadata/selection_utility.py","file_name":"selection_utility.py","file_ext":"py","file_size_in_byte":5443,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"47"}
+{"seq_id":"24597226591","text":"#!/usr/bin/python3\ndef safe_print_list_integers(my_list=[], x=0):\n \"\"\"\n Prints the first x elements of a list and only integers.\n\n my_list: the list\n x: the number of elements to access in my_list.\n\n Return: the number of elements printed\n Description:\n x can be bigger than the length of my_list - if it's the case, an\n exception is expected to occur.\n All integers have to printed on the same line followed by a new line,\n - other type of value in the list must be skipped(in silence).\n You are not allowed to import any module.\n You are not allowed to use len().\n \"\"\"\n count = 0\n for i in range(x):\n try:\n print(\"{:d}\".format(my_list[i]), end=\"\")\n count += 1\n except (TypeError, ValueError):\n continue\n print(\"\")\n return (count)\n","repo_name":"GEMMEE/alx-higher_level_programming","sub_path":"0x05-python-exceptions/2-safe_print_list_integers.py","file_name":"2-safe_print_list_integers.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"29213250515","text":"import re\nfrom pyquery import PyQuery as pq\nfrom lxml import etree\nfrom bs4 import BeautifulSoup\nimport json\nfrom Function.getHtml import get_html\nfrom Function.getHtml import post_html\n\n\ndef getActorPhoto(htmlcode):\n soup = BeautifulSoup(htmlcode, 'lxml')\n a = soup.find_all(attrs={'class': 'star-name'})\n d = {}\n for i in a:\n l = i.a['href']\n t = i.get_text()\n html = etree.fromstring(get_html(l), etree.HTMLParser())\n p = 'https://javbus.com' + str(html.xpath('//*[@id=\"waterfall\"]/div[1]/div/div[1]/img/@src')).strip(\" ['']\")\n p2 = {t: p}\n d.update(p2)\n return d\n\n\ndef getTitle(htmlcode): # 获取标题\n doc = pq(htmlcode)\n title = str(doc('div.container h3').text())\n try:\n title2 = re.sub('n\\d+-', '', title)\n return title2\n except:\n return title\n\n\ndef getStudio(htmlcode): # 获取厂商\n html = etree.fromstring(htmlcode, etree.HTMLParser())\n result = str(html.xpath('//span[contains(text(),\"製作商\")]/following-sibling::a/text()')).strip(\" ['']\")\n return result\n\n\ndef getPublisher(htmlcode): # 获取发行商\n html = etree.fromstring(htmlcode, etree.HTMLParser())\n result = str(html.xpath('//span[contains(text(),\"發行商\")]/following-sibling::a/text()')).strip(\" ['']\")\n return result\n\n\ndef getYear(getRelease): # 获取年份\n try:\n result = str(re.search('\\d{4}', getRelease).group())\n return result\n except:\n return getRelease\n\n\ndef getCover(htmlcode): # 获取封面链接\n doc = pq(htmlcode)\n image = doc('a.bigImage')\n return 'https://javbus.com' + image.attr('href')\n\n\ndef getExtraFanart(htmlcode): # 获取封面链接\n html = etree.fromstring(htmlcode, etree.HTMLParser())\n extrafanart_list = html.xpath(\"//div[@id='sample-waterfall']/a/@href\")\n return extrafanart_list\n\n\ndef getRelease(htmlcode): # 获取出版日期\n html = etree.fromstring(htmlcode, etree.HTMLParser())\n result = str(html.xpath('//span[contains(text(),\"發行日期\")]/../text()')).strip(\" ['']\")\n return result\n\n\ndef getRuntime(htmlcode): # 获取分钟\n html = etree.fromstring(htmlcode, etree.HTMLParser())\n result = str(html.xpath('//span[contains(text(),\"長度\")]/../text()')).strip(\" ['']\")\n return result\n\n\ndef getActor(htmlcode): # 获取女优\n b = []\n soup = BeautifulSoup(htmlcode, 'lxml')\n a = soup.find_all(attrs={'class': 'star-name'})\n for i in a:\n b.append(i.get_text())\n return b\n\n\ndef getNum(htmlcode): # 获取番号\n html = etree.fromstring(htmlcode, etree.HTMLParser())\n result = str(html.xpath('//span[contains(text(),\"識別碼\")]/following-sibling::span/text()')).strip(\" ['']\")\n return result\n\n\ndef getDirector(htmlcode): # 获取导演\n html = etree.fromstring(htmlcode, etree.HTMLParser())\n result = str(html.xpath('//span[contains(text(),\"導演\")]/following-sibling::a/text()')).strip(\" ['']\")\n return result\n\n\ndef getOutlineScore(number): # 获取简介\n outline = ''\n score = ''\n try:\n response = post_html(\"https://www.jav321.com/search\", query={\"sn\": number})\n detail_page = etree.fromstring(response, etree.HTMLParser())\n outline = str(detail_page.xpath('/html/body/div[2]/div[1]/div[1]/div[2]/div[3]/div/text()')).strip(\" ['']\")\n if re.search(r'平均評価: ![]()
', response):\n score = re.findall(r'平均評価: ![]()
', response)[0]\n score = str(float(score) / 10.0)\n else:\n score = str(re.findall(r'平均評価: ([^<]+)
', response)).strip(\" [',']\").replace('\\'', '')\n if outline == '':\n dmm_htmlcode = get_html(\n \"https://www.dmm.co.jp/search/=/searchstr=\" + number.replace('-', '') + \"/sort=ranking/\")\n if 'に一致する商品は見つかりませんでした' not in dmm_htmlcode:\n dmm_page = etree.fromstring(dmm_htmlcode, etree.HTMLParser())\n url_detail = str(dmm_page.xpath('//*[@id=\"list\"]/li[1]/div/p[2]/a/@href')).split(',', 1)[0].strip(\n \" ['']\")\n if url_detail != '':\n dmm_detail = get_html(url_detail)\n html = etree.fromstring(dmm_detail, etree.HTMLParser())\n outline = str(html.xpath('//*[@class=\"mg-t0 mg-b20\"]/text()')).strip(\" ['']\").replace('\\\\n',\n '').replace(\n '\\n', '')\n except Exception as error_info:\n print('Error in javbus.getOutlineScore : ' + str(error_info))\n return outline, score\n\n\ndef getSeries(htmlcode):\n html = etree.fromstring(htmlcode, etree.HTMLParser())\n result = str(html.xpath('//span[contains(text(),\"系列\")]/following-sibling::a/text()')).strip(\" ['']\")\n return result\n\n\ndef getCover_small(number): # 从avsox获取封面图\n try:\n htmlcode = get_html('https://avsox.website/cn/search/' + number)\n html = etree.fromstring(htmlcode, etree.HTMLParser())\n counts = len(html.xpath(\"//div[@id='waterfall']/div/a/div\"))\n if counts == 0:\n return ''\n for count in range(1, counts + 1): # 遍历搜索结果，找到需要的番号\n number_get = html.xpath(\n \"//div[@id='waterfall']/div[\" + str(count) + \"]/a/div[@class='photo-info']/span/date[1]/text()\")\n if len(number_get) > 0 and number_get[0].upper() == number.upper():\n cover_small = \\\n html.xpath(\"//div[@id='waterfall']/div[\" + str(count) + \"]/a/div[@class='photo-frame']/img/@src\")[0]\n return cover_small\n except Exception as error_info:\n print('Error in javbus.getCover_small : ' + str(error_info))\n return ''\n\n\ndef getTag(htmlcode): # 获取标签\n tag = []\n soup = BeautifulSoup(htmlcode, 'lxml')\n a = soup.find_all(attrs={'class': 'genre'})\n for i in a:\n if 'onmouseout' in str(i):\n continue\n tag.append(i.get_text())\n return tag\n\n\ndef find_number(number):\n # =======================================================================有码搜索\n if not (re.match('^\\d{4,}', number) or re.match('n\\d{4}', number) or 'HEYZO' in number.upper()):\n htmlcode = get_html('https://www.javbus.com/search/' + number + '&type=1')\n html = etree.fromstring(htmlcode, etree.HTMLParser()) # //table/tr[1]/td[1]/text()\n counts = len(html.xpath(\"//div[@id='waterfall']/div[@id='waterfall']/div\"))\n if counts != 0:\n for count in range(1, counts + 1): # 遍历搜索结果，找到需要的番号\n number_get = html.xpath(\"//div[@id='waterfall']/div[@id='waterfall']/div[\" + str(\n count) + \"]/a[@class='movie-box']/div[@class='photo-info']/span/date[1]/text()\")[0]\n number_get = number_get.upper()\n number = number.upper()\n if number_get == number or number_get == number.replace('-', '') or number_get == number.replace('_',\n ''):\n result_url = html.xpath(\n \"//div[@id='waterfall']/div[@id='waterfall']/div[\" + str(\n count) + \"]/a[@class='movie-box']/@href\")[0]\n return result_url\n # =======================================================================无码搜索\n htmlcode = get_html('https://www.javbus.com/uncensored/search/' + number + '&type=1')\n html = etree.fromstring(htmlcode, etree.HTMLParser()) # //table/tr[1]/td[1]/text()\n counts = len(html.xpath(\"//div[@id='waterfall']/div[@id='waterfall']/div\"))\n if counts == 0:\n return 'not found'\n for count in range(1, counts + 1): # 遍历搜索结果，找到需要的番号\n number_get = html.xpath(\"//div[@id='waterfall']/div[@id='waterfall']/div[\" + str(\n count) + \"]/a[@class='movie-box']/div[@class='photo-info']/span/date[1]/text()\")[0]\n number_get = number_get.upper()\n number = number.upper()\n if number_get == number or number_get == number.replace('-', '') or number_get == number.replace('_', ''):\n result_url = html.xpath(\n \"//div[@id='waterfall']/div[@id='waterfall']/div[\" + str(count) + \"]/a[@class='movie-box']/@href\")[0]\n return result_url\n elif number_get == number.replace('-', '_') or number_get == number.replace('_', '-'):\n result_url = html.xpath(\n \"//div[@id='waterfall']/div[@id='waterfall']/div[\" + str(count) + \"]/a[@class='movie-box']/@href\")[0]\n return result_url\n return 'not found'\n\n\ndef main(number, appoint_url):\n try:\n if appoint_url:\n result_url = appoint_url\n else:\n result_url = find_number(number)\n if result_url == 'not found':\n raise Exception('Movie Data not found in javbus.main!')\n htmlcode = get_html(result_url)\n if str(htmlcode) == 'ProxyError':\n raise TimeoutError\n outline, score = getOutlineScore(number)\n number = getNum(htmlcode)\n dic = {\n 'title': str(getTitle(htmlcode)).replace(number, '').strip().replace(' ', '-'),\n 'studio': getStudio(htmlcode),\n 'publisher': getPublisher(htmlcode),\n 'year': getYear(getRelease(htmlcode)),\n 'outline': outline,\n 'score': score,\n 'runtime': getRuntime(htmlcode).replace('分鐘', '').strip(),\n 'director': getDirector(htmlcode),\n 'actor': getActor(htmlcode),\n 'release': getRelease(htmlcode),\n 'number': number,\n 'cover': getCover(htmlcode),\n 'extrafanart': getExtraFanart(htmlcode),\n 'imagecut': 1,\n 'tag': getTag(htmlcode),\n 'series': getSeries(htmlcode),\n 'actor_photo': getActorPhoto(htmlcode),\n 'website': result_url,\n 'source': 'javbus.py',\n }\n except TimeoutError:\n dic = {\n 'title': '',\n 'website': 'timeout',\n }\n except Exception as error_info:\n print('Error in javbus.main : ' + str(error_info))\n dic = {\n 'title': '',\n 'website': '',\n }\n js = json.dumps(dic, ensure_ascii=False, sort_keys=True, indent=4, separators=(',', ':'), ) # .encode('UTF-8')\n return js\n\n\ndef main_uncensored(number, appoint_url):\n try:\n result_url = ''\n if appoint_url == '':\n result_url = find_number(number)\n else:\n result_url = appoint_url\n if result_url == 'not found':\n raise Exception('Movie Data not found in javbus.main_uncensored!')\n htmlcode = get_html(result_url)\n if str(htmlcode) == 'ProxyError':\n raise TimeoutError\n number = getNum(htmlcode)\n outline = ''\n score = ''\n if 'HEYZO' in number.upper():\n outline, score = getOutlineScore(number)\n dic = {\n 'title': getTitle(htmlcode).replace(number, '').strip().replace(' ', '-'),\n 'studio': getStudio(htmlcode),\n 'publisher': '',\n 'year': getYear(getRelease(htmlcode)),\n 'outline': outline,\n 'score': score,\n 'runtime': getRuntime(htmlcode).replace('分鐘', '').strip(),\n 'director': getDirector(htmlcode),\n 'actor': getActor(htmlcode),\n 'release': getRelease(htmlcode),\n 'number': getNum(htmlcode),\n 'cover': getCover(htmlcode),\n 'extrafanart': getExtraFanart(htmlcode),\n 'tag': getTag(htmlcode),\n 'series': getSeries(htmlcode),\n 'imagecut': 3,\n 'cover_small': getCover_small(number), # 从avsox获取封面图\n 'actor_photo': getActorPhoto(htmlcode),\n 'website': result_url,\n 'source': 'javbus.py',\n }\n if dic['cover_small'] == '':\n dic['imagecut'] = 0\n except TimeoutError:\n dic = {\n 'title': '',\n 'website': 'timeout',\n }\n except Exception as error_info:\n print('Error in javbus.main_uncensored : ' + str(error_info))\n dic = {\n 'title': '',\n 'website': '',\n }\n js = json.dumps(dic, ensure_ascii=False, sort_keys=True, indent=4, separators=(',', ':'), ) # .encode('UTF-8')\n return js\n\n\ndef main_us(number, appoint_url=''):\n try:\n if appoint_url:\n result_url = appoint_url\n else:\n htmlcode = get_html('https://www.javbus.one/search/' + number)\n if str(htmlcode) == 'ProxyError':\n raise TimeoutError\n html = etree.fromstring(htmlcode, etree.HTMLParser()) # //table/tr[1]/td[1]/text()\n counts = len(html.xpath(\"//div[@class='row']/div[@id='waterfall']/div\"))\n if counts == 0:\n raise Exception('Movie Data not found in javbus.main_us!')\n result_url = ''\n cover_small = ''\n for count in range(1, counts + 1): # 遍历搜索结果，找到需要的番号\n number_get = html.xpath(\"//div[@id='waterfall']/div[\" + str(\n count) + \"]/a[@class='movie-box']/div[@class='photo-info']/span/date[1]/text()\")[0]\n if number_get.upper() == number.upper() or number_get.replace('-', '').upper() == number.upper():\n result_url = html.xpath(\n \"//div[@id='waterfall']/div[\" + str(count) + \"]/a[@class='movie-box']/@href\")[0]\n cover_small = html.xpath(\n \"//div[@id='waterfall']/div[\" + str(\n count) + \"]/a[@class='movie-box']/div[@class='photo-frame']/img[@class='img']/@src\")[0]\n break\n if result_url == '':\n raise Exception('Movie Data not found in javbus.main_us!')\n htmlcode = get_html(result_url)\n if str(htmlcode) == 'ProxyError':\n raise TimeoutError\n number = getNum(htmlcode)\n dic = {\n 'title': getTitle(htmlcode).replace(number, '').strip(),\n 'studio': getStudio(htmlcode),\n 'year': getYear(getRelease(htmlcode)),\n 'runtime': getRuntime(htmlcode).replace('分鐘', '').strip(),\n 'director': getDirector(htmlcode),\n 'actor': getActor(htmlcode),\n 'release': getRelease(htmlcode),\n 'number': getNum(htmlcode),\n 'tag': getTag(htmlcode),\n 'series': getSeries(htmlcode),\n 'cover': getCover(htmlcode),\n 'extrafanart': getExtraFanart(htmlcode),\n 'cover_small': '',\n 'imagecut': 0,\n 'actor_photo': getActorPhoto(htmlcode),\n 'publisher': '',\n 'outline': '',\n 'score': '',\n 'website': result_url,\n 'source': 'javbus.py',\n }\n except TimeoutError:\n dic = {\n 'title': '',\n 'website': 'timeout',\n }\n except Exception as error_info:\n print('Error in javbus.main_us : ' + str(error_info))\n dic = {\n 'title': '',\n 'website': '',\n }\n js = json.dumps(dic, ensure_ascii=False, sort_keys=True, indent=4, separators=(',', ':'), ) # .encode('UTF-8')\n return js\n\n\n'''\nprint(find_number('KA-001'))\nprint(main_uncensored('010115-001'))\nprint(main('ssni-644'))\nprint(main_uncensored('012715-793'))\nprint(main_us('sexart.15.06.10'))\nprint(main_uncensored('heyzo-1031'))\n'''\n\n# print(main('ssni-644', \"https://www.javbus.com/SSNI-644\"))\n# print(main('ssni-802', \"\"))\n# print(main_us('DirtyMasseur.20.07.26', \"https://www.javbus.one/DirtyMasseur-20-07-26\"))\n","repo_name":"DevilMayCry4/AVDC","sub_path":"Getter/javbus.py","file_name":"javbus.py","file_ext":"py","file_size_in_byte":15992,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"47"}
+{"seq_id":"31248921545","text":"# Import module\nimport xlrd\n\n\ndef main1():\n # Open excel\n wb = xlrd.open_workbook('BD1.xls')\n\n # Select table\n sh = wb.sheet_by_name('Sheet1')\n\n # Min and max row/column\n coluns = sh.ncols\n rows = sh.nrows\n\n # Search word\n word = input('Search: ')\n\n # Load all table\n for i in range(0, rows):\n for j in range(0, coluns):\n c = sh.cell(i, j)\n # Find the search\n if str(c.value).lower().find(word.lower()) != -1:\n for x in range(0, coluns):\n bd2 = sh.cell(i, x).value\n\n return bd2\n","repo_name":"marvinbraescher/Excelfile","sub_path":"Excel2.py","file_name":"Excel2.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"43747697113","text":"from datetime import datetime\nimport os \nfrom dotenv import load_dotenv\n \nclass Scraper:\n \"\"\" Scraper class for pulling data from API endpoint.\"\"\"\n \n def __init__(self, *args):\n \n self.AAPL = args[0]\n self.MSFT = args[1]\n self.IBM = args[2]\n load_dotenv()\n self._api_key = os.getenv('api_key')\n \n def get_api_key(self) -> str:\n \"\"\" u87786returns api key which is stored as a private variable from this class for accessor classes\"\"\"\n \n api_key = self._api_key\n \n return api_key\n \n def pull_stock_data(self, tick, multiplier, timespan, date_to, date_from):\n \"\"\" 5 minute data from 01-30 jan 2022.\n Two extra variables passed are firstly tick which refers to stock name, and duration which refers to the allocated duration of data aggregation.\"\"\"\n \n import requests as r \n \n # user and secret initialisation\n user = 'Bearer'\n api_key = self.get_api_key()\n \n # parsing request url\n url = \"https://api.polygon.io/v2/aggs/ticker/{0}/range/{1}/{2}/{3}/{4}?adjusted=true&sort=asc&limit=5000\".format(tick, multiplier, timespan, date_from, date_to)\n \n # setting session object python3 main.py\n session = r.session()\n\n agg_stock_bars = session.get(url, headers={'Authorization': '{0} {1}'.format(user, api_key), 'Accept': 'text/csv'})\n \n return agg_stock_bars\n \n \n ","repo_name":"nbdevs/WS","sub_path":"collect_data.py","file_name":"collect_data.py","file_ext":"py","file_size_in_byte":1478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"32394619368","text":"import tensorflow as tf \nfrom tensorflow.examples.tutorials.mnist import input_data\n\n# описание обычного слоя {грубо говоря записываем функция активации в каждом нейроне, что бы потом оптимизировать}\ndef fullyconnected_layer(tensor, input_size, out_size):\n W = tf.Variable(tf.truncated_normal([input_size, out_size], stddev = 0.1))\n b = tf.Variable(tf.truncated_normal([out_size], stddev = 0.1))\n return tf.nn.tanh(tf.matmul(tensor,W) + b)\n\n#слой нормализации по минибатчам\ndef batch_norm(tensor, size):\n batch_mean, batch_var = tf.nn.moments(tensor, [0])\n beta = tf.Variable(tf.zeros(size))\n scale = tf.Variable(tf.ones(size))\n return tf.nn.batch_normalization(\n tensor, batch_mean, batch_var, beta,scale, 0.001 )\n\ndef _main():\n mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot = True)\n x = tf.placeholder(tf.float32, [None, 784])\n y = tf.placeholder(tf.float32, [None, 10])\n # layers\n h1 = fullyconnected_layer(x, 784, 100)\n h1_batchnorm = batch_norm(h1, 100)\n h2 = fullyconnected_layer(h1_batchnorm, 100, 100)\n y_logit = fullyconnected_layer(h2, 100, 10)\n # loss function\n loss = tf.nn.sigmoid_cross_entropy_with_logits(logits = y_logit, labels = y)\n train_optimizer = tf.train.GradientDescentOptimizer(0.01).minimize(loss)\n\n correct_prediction = tf.equal(tf.argmax(y_logit,1), tf.argmax(y,1))\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n\n init = tf.initialize_all_variables()\n sess = tf.Session()\n sess.run(init)\n for i in range(1000):\n batch_x, batch_y = mnist.train.next_batch(1000)\n sess.run(train_optimizer, feed_dict={x:batch_x, y:batch_y})\n\n print(\"Accuracy score - %s\" %sess.run(accuracy,\n feed_dict = {x: mnist.test.images, y: mnist.test.labels}))\nif __name__ == \"__main__\":\n _main()","repo_name":"sergey-byk0v/Examples_from_book","sub_path":"minibatch_normalization.py","file_name":"minibatch_normalization.py","file_ext":"py","file_size_in_byte":1954,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"1474306845","text":"# encoding: utf-8\n\nfrom django.test import TestCase\nfrom eventex.subscriptions.forms import SubscriptionForm\n\nclass SubscriptionFormTest(TestCase):\n def test_has_fields(self):\n 'o form tem que ter os campos'\n form = SubscriptionForm()\n self.assertItemsEqual(['name', 'email', 'cpf', 'phone'], form.fields)\n\n def test_cpf_is_digit(self):\n 'CPF somente digitos'\n form = self.make_validated_form(cpf='abc00000000')\n self.assertItemsEqual(['cpf'], form.errors)\n \n def test_cpf_has_11_digits(self):\n 'CPF deve ter 11 digitos'\n form = self.make_validated_form(cpf='123')\n self.assertItemsEqual(['cpf'], form.errors)\n \n def test_email_is_optional(self):\n 'email opcional'\n form = self.make_validated_form(email='')\n self.assertFalse(form.errors)\n \n def make_validated_form(self, **kwargs):\n data = dict(name='Diego Tolentino', email='diegotolentino@gmail.com',\n cpf='12345678901', phone='62-39201997')\n data.update(kwargs)\n form = SubscriptionForm(data)\n form.is_valid()\n return form\n \n","repo_name":"diegotolentino/eventex","sub_path":"eventex/subscriptions/tests/test_forms.py","file_name":"test_forms.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"35946403984","text":"import unittest\r\n\r\nfrom project_exam.student import Student\r\n\r\nclass StudentTest(unittest.TestCase):\r\n STUDENT_NAME = 'PESHO'\r\n\r\n def setUp(self) -> None:\r\n self.student = Student(self.STUDENT_NAME)\r\n\r\n def test_student_init_without_courses(self):\r\n self.assertEqual(self.STUDENT_NAME, self.student.name)\r\n self.assertEqual({}, self.student.courses)\r\n\r\n def test_student_init_with_courses(self):\r\n courses = {\"Python Advanced\": ['note 1', 'note 2']}\r\n\r\n student = Student(self.STUDENT_NAME, courses)\r\n\r\n self.assertEqual(self.STUDENT_NAME, student.name)\r\n self.assertEqual(courses, student.courses)\r\n\r\n def test_enroll_student_updates_cours_notes_when_course_is_already_enrolled(self):\r\n course_name = \"Python Advanced\"\r\n courses = {course_name: ['note 1', 'note 2']}\r\n\r\n student = Student(self.STUDENT_NAME, courses)\r\n\r\n result = student.enroll(course_name, ['note 3', 'note 4'])\r\n\r\n self.assertEqual('Course already added. Notes have been updated.', result)\r\n self.assertEqual(['note 1', 'note 2', 'note 3', 'note 4'], student.courses[course_name])\r\n\r\n def test_enroll_student_extends_courses_with_course_when_add_course_notes_is_not_passed(self):\r\n course_name = \"Python Advanced\"\r\n course_notes = ['note 1', 'note 2']\r\n\r\n result = self.student.enroll(course_name, course_notes)\r\n\r\n self.assertEqual('Course and course notes have been added.', result)\r\n self.assertTrue(course_name in self.student.courses)\r\n self.assertEqual(course_notes, self.student.courses[course_name])\r\n\r\n def test_enroll_student_extends_courses_with_course_when_add_course_notes_is_y(self):\r\n course_name = \"Python Advanced\"\r\n course_notes = ['note 1', 'note 2']\r\n\r\n result = self.student.enroll(course_name, course_notes, 'Y')\r\n\r\n self.assertEqual('Course and course notes have been added.', result)\r\n self.assertTrue(course_name in self.student.courses)\r\n self.assertEqual(course_notes, self.student.courses[course_name])\r\n\r\n def test_enroll_student_extends_courses_with_course_without_notes_when_invalid_add_course_notes_arg_is_passed(self):\r\n course_name = \"Python Advanced\"\r\n course_notes = ['note 1', 'note 2']\r\n\r\n result = self.student.enroll(course_name, course_notes, 'N')\r\n\r\n self.assertEqual('Course has been added.', result)\r\n self.assertTrue(course_name in self.student.courses)\r\n self.assertEqual([], self.student.courses[course_name])\r\n\r\n def test_add_notes_raises_error_when_no_such_course_name(self):\r\n with self.assertRaises(Exception) as error:\r\n self.student.add_notes('Python Advanced', 'Note 3')\r\n self.assertEqual(\"Cannot add notes. Course not found.\", str(error.exception))\r\n\r\n def test_add_notes_raises_error_when_course_exists(self):\r\n course_name = \"Python Advanced\"\r\n courses = {course_name: ['A', 'B']}\r\n student = Student(self.STUDENT_NAME, courses)\r\n\r\n result = student.add_notes(course_name, 'C')\r\n\r\n self.assertEqual(\"Notes have been updated\", result)\r\n self.assertEqual(['A', 'B', 'C'], student.courses[course_name])\r\n\r\n def test_leave_course_raises_error_when_course_not_existing(self):\r\n self.student.enroll('Python Basics', [])\r\n\r\n with self.assertRaises(Exception) as error:\r\n self.student.leave_course('Python Advanced')\r\n self.assertEqual(\"Cannot remove course. Course not found.\", str(error.exception))\r\n\r\n def test_leave_course_when_course_exists(self):\r\n course_name = \"Python Advanced\"\r\n courses = {course_name: []}\r\n student = Student(self.STUDENT_NAME, courses)\r\n\r\n result = student.leave_course(course_name)\r\n\r\n self.assertEqual(\"Course has been removed\", result)\r\n self.assertTrue(course_name not in student.courses)\r\n\r\n","repo_name":"PhilipKolarov/student-test-project","sub_path":"tests/test_student.py","file_name":"test_student.py","file_ext":"py","file_size_in_byte":3939,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"25524835432","text":"from pathlib import Path\nfrom typing import Optional\nfrom tqdm import tqdm\n\nNUM_PROCS=4\nINKSCAPE=\"inkscape\"\nICON_CLASSES=[ \"icons\", \"icons-dark\" ]\nICON_SIZES=[ 16, 22, 32, 48, 64, 96]\n\nPATH_SRC=Path(\"./breeze-icons\")\nPATH_PNG=Path(\"./png\")\n\n\ndef get_png_name(p: Path) -> Path:\n *_, cls, cat, size, name = p.with_suffix(\"\").parts\n return PATH_PNG / cls / f\"{cat}-{name}_{size}.png\"\n\ndef parallel_execute(commands, np: Optional[int] = None):\n if np == 1:\n for command in tqdm(commands):\n run_command(command)\n else:\n from multiprocessing.pool import ThreadPool\n import subprocess\n\n def run_command(cmd):\n try:\n res = subprocess.run(cmd)\n return res.returncode\n except Exception as e:\n print(e)\n return 0\n\n tp = ThreadPool(np)\n gen = tp.imap_unordered(run_command, commands, chunksize=1)\n for r in tqdm(gen, total=len(commands), smoothing=0.1):\n pass\n tp.close()\n tp.join()\n\n\ndef update_conversions():\n svg_files = []\n for cls in ICON_CLASSES:\n pclass = PATH_PNG / cls\n pclass.mkdir(parents=True, exist_ok=True)\n sclass = PATH_SRC / cls\n for size in ICON_SIZES:\n svg_files += sclass.glob(f\"*/{size}/*.svg\")\n png_files = [get_png_name(f) for f in svg_files]\n # detect removed files\n print(\"Checking removed files...\")\n existing_png_files = PATH_PNG.glob(\"**/*.png\")\n extras = list(set(existing_png_files) - set(png_files))\n print(len(extras), \" removed files\")\n for p in extras:\n p.unlink()\n # detect new/modified files\n print(\"Checking new/modified files...\")\n commands = []\n for svg, png in zip(svg_files, png_files):\n sstat = svg.stat()\n if sstat.st_size <= 50:\n continue\n with svg.open(\"rt\") as t:\n if \"= b)or(a >= 0)or(n < 1): #проверка на то, что начало должно быть меньше конца и наличие отрицательных элементов\n print('! A < B ! A < 0 ! N >= 1 !')\nelse:\n s = [] #пустой список\n i = 1\n while i <= n: #добавляем элементы в список\n s.append(random.randint(a,b))\n i = i + 1\n print('Исходный список') #исходный список\n print(s)\n i = 0\n while i < len(s): # удаление отрицательных элементов, расположенных между положительными\n if (i-1 >= 0):\n if (s[i-1] > 0)and(i+1 < len(s)):\n if (s[i+1] > 0):\n if (s[i] < 0):\n del s[i]\n i = i + 1\n print('Преобразованный список') #преобразованный список\n print(s)\n","repo_name":"IlshatKutdusov/Python_Labs","sub_path":"лаба3/Задание3.py","file_name":"Задание3.py","file_ext":"py","file_size_in_byte":1205,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"3983552151","text":"\"\"\"\r\nCreated on Tue Sep 13 13:45:28 2022\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport math\r\nimport matplotlib.pyplot as plt\r\nimport time\r\n\r\nplt.close(\"all\")\r\n\r\n\r\ndef gauss(x, mu, sigma):\r\n val = ( 1/( np.sqrt(2*np.pi) *sigma ) ) * np.exp( ((-1/2)*( (x-mu)**2.0 )/ sigma**2.0 ) )\r\n return val\r\n\r\ndef uniform():\r\n return 1/15\r\n\r\ndef p(x):\r\n p1 = gauss(x,2.0,1.0)\r\n p2 = gauss(x,5.0,2.0)\r\n p3 = gauss(x,9.0,1.0)\r\n pose = 0.3*p1 + 0.4*p2 + 0.3*p3\r\n return pose\r\n\r\ndef initproposal(k):\r\n poses = np.random.uniform(0,15, k)\r\n return np.array(poses)\r\n\r\ndef calcWeight(poses):\r\n weights = p(poses)/uniform()\r\n weights = weights/np.sum(weights)\r\n return np.array(weights)\r\n\r\ndef resample(weights, poses,k):\r\n new_samples = np.random.choice(poses,k, p = weights)\r\n return np.array(new_samples)\r\n\r\ndef particle():\r\n poses = initproposal(k)\r\n weights = calcWeight(poses)\r\n new_samples = resample(weights,poses,k)\r\n return new_samples\r\n\r\n\r\nk = 10000\r\nstart_time = time.perf_counter()\r\nresult = particle()\r\nend_time = time.perf_counter()\r\nprint(f'{(end_time-start_time):.5f}')\r\n\r\n\r\n#print(len(result))\r\n\r\n\r\nnej = initproposal(k)\r\nplt.close(\"all\")\r\nplt.figure()\r\nplt.xlabel(\"Pose\")\r\nplt.ylabel(\"Counts\")\r\nplt.title(f\"K = {k}\")\r\nplt.hist(result, bins =int(math.sqrt(k)))\r\nplt.plot(nej,p(nej)*0.1*k, \".\")\r\nplt.show()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"YaronLa/Robotics","sub_path":"Sampling-Importance-Resampling.py","file_name":"Sampling-Importance-Resampling.py","file_ext":"py","file_size_in_byte":1410,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"11454933145","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import classification_report, accuracy_score, confusion_matrix, recall_score, f1_score\n\n# data = pd.read_csv('openSmile_resampled/overall.csv')\n\n# data = pd.read_csv('openSmile_raw/f.csv')\n\ndata = pd.read_csv('openSmile_balanced/f.csv')\n\n# Split the data into features (X) and labels (y)\nX = data.drop('y', axis=1)\ny = data['y']\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\nn_estimators_custom = 100\nmax_depth_custom = 10\nmin_samples_split_custom = 5\ncriterion_custom = 'gini'\n\nclf = RandomForestClassifier(n_estimators=n_estimators_custom,\n max_depth=max_depth_custom,\n min_samples_split=min_samples_split_custom,\n criterion=criterion_custom,\n random_state=42)\nclf.fit(X_train, y_train)\ny_pred = clf.predict(X_test)\n\naccuracy = accuracy_score(y_test, y_pred)\nconf_matrix = confusion_matrix(y_test, y_pred)\ntn, fp, fn, tp = conf_matrix.ravel()\nsensitivity = tp / (tp + fn)\nspecificity = tn / (tn + fp)\nuf1 = (2 * tp) / (2 * tp + fp + fn)\nuar = (sensitivity + specificity) / 2\n\n# Calculate UAR and F1\n# uar_macro = recall_score(y_test, y_pred, average='macro')\n# uf1_macro = f1_score(y_test, y_pred, average='macro')\n\n# Calculate Classification Report\nclassification_rep = classification_report(y_test, y_pred, target_names=['Class 0', 'Class 1'])\nnormalized_conf_matrix = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n\n# Set up the plot\nplt.rcParams['font.family'] = 'Times New Roman'\nplt.rcParams['font.size'] = '46'\nsns.set(font_scale=1.2)\nsns.heatmap(normalized_conf_matrix, annot=True, cmap=\"Blues\", fmt=\".2f\", xticklabels=['Normal', 'Abnormal'], yticklabels=['Normal', 'Abnormal'])\n\nplt.xlabel('Predicted label')\nplt.ylabel('True label')\nplt.title('Normalised Confusion Matrix')\n\nplt.show()\n\n\nprint(\"Accuracy:\", accuracy)\nprint(\"Sensitivity:\", sensitivity)\nprint(\"Specificity:\", specificity)\nprint(\"Unweighted F1-Score (UF1):\", uf1)\nprint(\"Unweighted Average Recall (UAR):\", uar)\nprint(\"Classification Report:\\n\", classification_rep)\n","repo_name":"jomaron/Fed-MStacking","sub_path":"RF3.py","file_name":"RF3.py","file_ext":"py","file_size_in_byte":2364,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
+{"seq_id":"42197891353","text":"\"\"\"\n54. Faça uma função que recebe, por parâmetro, uma matriz A[4][4] e retorna a soma dos\nseus elementos.\n\n\"\"\"\n\nmatriz = [[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]]\ndef calculo(matriz):\n soma = 0\n for l in range(4):\n soma += sum(matriz[l])\n \n return soma\n\nprint(calculo(matriz))\n","repo_name":"pand-oly/curso_python","sub_path":"secao-08/ex54.py","file_name":"ex54.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"33799501455","text":"#!/usr/bin/env python3\nimport sys, termios, tty, os, time\nimport cv2\nimport numpy as np\nfrom scipy import interpolate\n\nmin_distance = 230 # out of 255\ndmin = 1000\ndmax = 3000\nnum_points = 150\nsmoothing_interval = 61\nmeasurements = 10\n\ndx = -5\ndy = 35\ndpx = 0.95\ndpy = 0.9\n\nfake = False\nif len(sys.argv) > 1:\n if sys.argv[1] == 'fake':\n num_im = 1\n fake = True\n fake_depth = np.load(\"kinect_data.npy\")\n fake_video = np.load(\"color_kinect_data.npy\")\n else:\n from freenect import sync_get_depth, sync_get_video\n from freenect import DEPTH_MM\n num_im = int(sys.argv[1])\nelse:\n from freenect import sync_get_depth, sync_get_video\n from freenect import DEPTH_MM\n num_im = 1\n\n\nfake_d_ind = 0\nfake_v_ind = 0\ndef get_depth():\n global fake_d_ind\n if fake:\n depth = fake_depth[fake_d_ind%500]\n fake_d_ind+=1\n time.sleep(0.05)\n else:\n (depth,_) = sync_get_depth(format=DEPTH_MM)\n return depth\n\ndef get_video():\n global fake_v_ind\n if fake:\n rgb = fake_video[fake_v_ind%500]\n fake_v_ind+=1\n time.sleep(0.05)\n else:\n (rgb,_) = sync_get_video()\n return rgb\n\n\ndef threshold(d):\n t = d*(d>=1) + dmax*(d<1)\n t = (t-dmin)*(t>dmin)\n t = t*(d=dmax)\n t = (t.astype(np.float32)*255/(dmax-dmin))\n return t\n\ndef remove_background(im,bg):\n diff = im - bg\n return im*( diff<-3 ) + 254*( diff>=-3 )\n\ndef measure_depth( n = measurements ):\n depth = get_depth()\n depth = threshold(depth)\n depth = depth.astype(np.float32)/n\n for m in range(1,n):\n d = get_depth()\n d = threshold(d)\n d = d.astype(np.float32)/n\n depth += d\n return depth\n\ndef measure_background():\n print(\"Measuring background\")\n depth = measure_depth( 20 )\n np.save(\"floor.npy\", depth)\n\ndef doloop():\n global min_distance, num_im, dx, dy, dpx, dpy\n while True:\n # Get a fresh frame\n rgb = get_video()\n depth = measure_depth()\n depthimage = np.dstack((depth,depth,depth)).astype(np.uint8)\n\n # remove the background\n if not os.path.exists(\"floor.npy\"):\n measure_background()\n floor = np.load(\"floor.npy\")\n depth = remove_background(depth,floor)\n\n cutimage = np.dstack((depth,depth,depth)).astype(np.uint8)\n\n #Find contour\n gray = cv2.cvtColor(cutimage,cv2.COLOR_BGR2GRAY)\n _, thresholded = cv2.threshold(gray,min_distance,255,cv2.THRESH_BINARY_INV)\n \n try:\n contours, hierarchies = cv2.findContours(thresholded,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\n contour = max(contours, key=cv2.contourArea)\n cv2.drawContours(depthimage, contour, -1, (0,0,255), 2)\n\n outline = np.copy(contour[:,0,:])\n tck, u = interpolate.splprep(outline.transpose(), s=0)\n du = 1/(num_points-1) \n unew = np.arange(0, 1.0, du)\n out = interpolate.splev(unew, tck)\n \n outline[1,:] = outline[1,:]*dpy\n outline[1,:] = outline[1,:]+dy\n outline[0,:] = outline[0,:]*dpx\n outline[0,:] = outline[0,:]+dx\n \n outline = outline.transpose().reshape((-1,1,2))\n \n cv2.drawContours(rgb, [outline.astype(int)], -1, (0,0,255), 2)\n \n mask = np.zeros_like(rgb)\n cv2.drawContours(mask, [outline.astype(int)], -1, (255,255,255), -1)\n cropped = np.zeros_like(rgb)\n cropped[mask==255] = rgb[mask==255]\n \n da = np.hstack((depthimage,rgb))\n db = np.hstack((mask,cropped))\n da = np.vstack((da,db))\n except Exception as error:\n #raise error\n da = np.hstack((depthimage,rgb))\n\n # Simple Downsample\n cv2.imshow('both',np.array(da))\n #cv2.imshow('both',np.array(da[::2,::2,::-1]))\n\n key = cv2.waitKey(5)\n if chr( key & 255) == ' ': #space\n print('Writing contour')\n outline = outline.reshape((-1,2))\n outline[:,1:] = 480-outline[:,1:]\n #outline[:,0] = 640-outline[:,0]\n np.savetxt(\"scf{}-outline-coords.dat\".format(num_im), outline, fmt='%i %i')\n cv2.imwrite(\"scf{}-depthimage.png\".format(num_im),depthimage)\n cv2.imwrite(\"scf{}-colorimage.png\".format(num_im), cv2.cvtColor(cropped, cv2.COLOR_RGB2BGR) )\n cv2.imwrite(\"scf{}-fullcolorimage.png\".format(num_im), cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR))\n cv2.imwrite(\"scf{}-blackandwhiteimage.png\".format(num_im), cv2.cvtColor(rgb, cv2.COLOR_RGB2GRAY))\n num_im += 1\n elif chr( key & 255) == 'b' : \n measure_background()\n elif chr( key & 255) == 'w' :\n dy-=5\n elif chr( key & 255) == 's' :\n dy+=5\n elif chr( key & 255) == 'a' : \n dx-=5\n elif chr( key & 255) == 'd' : \n dx+=5\n elif chr( key & 255) == 'r' :\n dpy*=1.02\n elif chr( key & 255) == 'f' :\n dpy/=1.02\n elif chr( key & 255) == 't' :\n dpx*=1.02\n elif chr( key & 255) == 'g' :\n dpx/=1.02\n elif key == 65362: #up on linux\n min_distance += 10\n print(\"threshold \",min_distance)\n elif key == 65364: #down on linux\n min_distance -= 10\n print(\"threshold \",min_distance)\n elif key == -1: #none\n continue\n else:\n print(key)\n print(dx,dy,dpx,dpy) \n\ndoloop()\n\n","repo_name":"markgdawson/pi-cluster-server","sub_path":"client/kinectlib/kinect_run.py","file_name":"kinect_run.py","file_ext":"py","file_size_in_byte":5546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"31213292353","text":"#!/usr/bin/env python3.5\n# Plots the distribution of contig sizes from a fasta file\n# and analyzes percentages\n# JP Tomkins July 6, 2018\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\ndef get_fastadata(fastafile):\n '''Makes a list of tuples, each with a list containing fasta\n header data and a string containing DNA seq ([info], 'seq')'''\n with open(fastafile, 'r') as fi:\n return [(part[0],\n part[2].replace('\\n', ''))\n for part in\n [entry.partition('\\n')\n for entry in fi.read().split('>')[1:]]]\n\nseqdata = get_fastadata(\"contigs_all.fa\")\n\n# Array of seq lengths for seqdata\nseq_lens = [len(x[1]) for x in seqdata]\n\nseqs_under_50k = [x for x in seq_lens if x < 50000]\nseqs_under_250k = [x for x in seq_lens if x < 250000]\nseqs_over_250k = [x for x in seq_lens if x > 250000]\nseqs_over_300k = [x for x in seq_lens if x > 300000]\nseqs_over_400k = [x for x in seq_lens if x > 400000]\n\nprint(\"seqs_under_50k: \", round(len(seqs_under_50k)/18000*100, 2),\"%\")\nprint(\"seqs_under_250k: \", round(len(seqs_under_250k)/18000*100, 2),\"%\")\nprint(\"seqs_over_250k: \", round(len(seqs_over_250k)/18000*100, 2),\"%\")\nprint(\"seqs_over_300k: \", round(len(seqs_over_300k)/18000*100, 2),\"%\")\nprint(\"seqs_over_300k: \", len(seqs_over_300k))\nprint(\"seqs_over_400k: \", len(seqs_over_400k))\n\n# Make plot\nsns.kdeplot(data=seq_lens, shade=True, legend=False)\nplt.show()\n\n","repo_name":"jt-icr/chimp_contigs","sub_path":"contig_analysis.py","file_name":"contig_analysis.py","file_ext":"py","file_size_in_byte":1397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"43262458774","text":"import tensorflow as tf\nimport numpy as np\nimport cifar10\n# from google.colab import files\n\n\ndef squash(s, axis=-1, epsilon=1e-7, name=None):\n with tf.name_scope(name, default_name=\"squash\"):\n squared_norm = tf.reduce_sum(tf.square(s), axis=axis,\n keep_dims=True)\n safe_norm = tf.sqrt(squared_norm + epsilon)\n squash_factor = squared_norm / (1. + squared_norm)\n unit_vector = s / safe_norm\n return squash_factor * unit_vector\n\n\ndef safe_norm(s, axis=-1, epsilon=1e-7, keep_dims=False, name=None):\n with tf.name_scope(name, default_name=\"safe_norm\"):\n squared_norm = tf.reduce_sum(tf.square(s), axis=axis,\n keep_dims=keep_dims)\n return tf.sqrt(squared_norm + epsilon)\n\n\ndef batch_getter(data, labels, batch_s):\n assert data.shape[0] == labels.shape[0]\n train_data_size = data.shape[0]\n data_per_batch = train_data_size // batch_s\n training_batches = np.split(data, data_per_batch)\n labels_batches = np.split(labels, data_per_batch)\n return np.array(training_batches), np.array(labels_batches)\n\n\nclass QinCapsNet:\n def __init__(self, caps1_n_maps, caps1_n_dims, caps2_n_caps, caps2_n_dims, n_epochs, batch_size, restore_checkpoint,\n init_sigma=0.1):\n print(\"ATTENTION: \", \" caps1_n_maps: \", caps1_n_maps, \" caps1_n_dims: \", caps1_n_dims, \" caps2_n_dims: \",\n caps2_n_dims)\n tf.reset_default_graph()\n\n self.caps1_n_maps = caps1_n_maps\n self.caps1_n_caps = caps1_n_maps * 8 * 8\n self.caps1_n_dims = caps1_n_dims\n\n self.caps2_n_caps = caps2_n_caps\n self.caps2_n_dims = caps2_n_dims\n\n self.init_sigma = init_sigma\n\n self.n_epochs = n_epochs\n self.batch_size = batch_size\n self.restore_checkpoint = restore_checkpoint\n\n tf.reset_default_graph()\n\n np.random.seed(42)\n tf.set_random_seed(42)\n\n self.load_data()\n\n def load_data(self):\n cifar10.maybe_download_and_extract()\n self.images_train, self.cls_train, self.labels_train = cifar10.load_training_data()\n self.images_test, self.cls_test, self.labels_test = cifar10.load_test_data()\n\n def create_a_net(self):\n conv1_params = {\n \"filters\": 256,\n \"kernel_size\": 9,\n \"strides\": 1,\n \"padding\": \"valid\",\n \"activation\": tf.nn.relu,\n }\n\n conv2_params = {\n \"filters\": self.caps1_n_maps * self.caps1_n_dims, # 256 convolutional filters\n \"kernel_size\": 9,\n \"strides\": 2,\n \"padding\": \"valid\",\n \"activation\": tf.nn.relu\n }\n\n X = tf.placeholder(shape=[None, 32, 32, 3], dtype=tf.float32, name=\"X\")\n\n conv1 = tf.layers.conv2d(X, name=\"conv1\", **conv1_params)\n conv2 = tf.layers.conv2d(conv1, name=\"conv2\", **conv2_params)\n\n caps1_raw = tf.reshape(conv2, [-1, self.caps1_n_caps, self.caps1_n_dims], name=\"caps1_raw\")\n\n caps1_output = squash(caps1_raw, name=\"caps1_output\")\n\n W_init = tf.random_normal(\n shape=(1, self.caps1_n_caps, self.caps2_n_caps, self.caps2_n_dims, self.caps1_n_dims),\n stddev=self.init_sigma, dtype=tf.float32, name=\"W_init\")\n W = tf.Variable(W_init, name=\"W\")\n\n batch_size = tf.shape(X)[0]\n W_tiled = tf.tile(W, [batch_size, 1, 1, 1, 1], name=\"W_tiled\")\n\n caps1_output_expanded = tf.expand_dims(caps1_output, -1,\n name=\"caps1_output_expanded\")\n caps1_output_tile = tf.expand_dims(caps1_output_expanded, 2,\n name=\"caps1_output_tile\")\n caps1_output_tiled = tf.tile(caps1_output_tile, [1, 1, self.caps2_n_caps, 1, 1],\n name=\"caps1_output_tiled\")\n\n caps2_predicted = tf.matmul(W_tiled, caps1_output_tiled,\n name=\"caps2_predicted\")\n\n raw_weights = tf.zeros([batch_size, self.caps1_n_caps, self.caps2_n_caps, 1, 1],\n dtype=np.float32, name=\"raw_weights\")\n\n routing_weights = tf.nn.softmax(raw_weights, dim=2, name=\"routing_weights\")\n\n weighted_predictions = tf.multiply(routing_weights, caps2_predicted,\n name=\"weighted_predictions\")\n\n weighted_sum = tf.reduce_sum(weighted_predictions, axis=1, keep_dims=True,\n name=\"weighted_sum\")\n\n caps2_output_round_1 = squash(weighted_sum, axis=-2, name=\"caps2_output_round_1\")\n\n caps2_output_round_1_tiled = tf.tile(\n caps2_output_round_1, [1, self.caps1_n_caps, 1, 1, 1],\n name=\"caps2_output_round_1_tiled\")\n\n agreement = tf.matmul(caps2_predicted, caps2_output_round_1_tiled,\n transpose_a=True, name=\"agreement\")\n\n raw_weights_round_2 = tf.add(raw_weights, agreement,\n name=\"raw_weights_round_2\")\n\n routing_weights_round_2 = tf.nn.softmax(raw_weights_round_2,\n dim=2,\n name=\"routing_weights_round_2\")\n weighted_predictions_round_2 = tf.multiply(routing_weights_round_2,\n caps2_predicted,\n name=\"weighted_predictions_round_2\")\n weighted_sum_round_2 = tf.reduce_sum(weighted_predictions_round_2,\n axis=1, keep_dims=True,\n name=\"weighted_sum_round_2\")\n caps2_output_round_2 = squash(weighted_sum_round_2,\n axis=-2,\n name=\"caps2_output_round_2\")\n\n caps2_output = caps2_output_round_2\n\n y_proba = safe_norm(caps2_output, axis=-2, name=\"y_proba\")\n\n y_proba_argmax = tf.argmax(y_proba, axis=2, name=\"y_proba\")\n\n y_pred = tf.squeeze(y_proba_argmax, axis=[1, 2], name=\"y_pred\")\n\n y = tf.placeholder(shape=[None], dtype=tf.int64, name=\"y\")\n\n m_plus = 0.9\n m_minus = 0.1\n lambda_ = 0.5\n\n T = tf.one_hot(y, depth=self.caps2_n_caps, name=\"T\")\n\n caps2_output_norm = safe_norm(caps2_output, axis=-2, keep_dims=True,\n name=\"caps2_output_norm\")\n\n present_error_raw = tf.square(tf.maximum(0., m_plus - caps2_output_norm),\n name=\"present_error_raw\")\n present_error = tf.reshape(present_error_raw, shape=(-1, 10),\n name=\"present_error\")\n\n absent_error_raw = tf.square(tf.maximum(0., caps2_output_norm - m_minus),\n name=\"absent_error_raw\")\n absent_error = tf.reshape(absent_error_raw, shape=(-1, 10),\n name=\"absent_error\")\n\n L = tf.add(T * present_error, lambda_ * (1.0 - T) * absent_error,\n name=\"L\")\n\n margin_loss = tf.reduce_mean(tf.reduce_sum(L, axis=1), name=\"margin_loss\")\n\n mask_with_labels = tf.placeholder_with_default(False, shape=(), name=\"mask_with_labels\")\n\n reconstruction_targets = tf.cond(mask_with_labels, # condition\n lambda: y, # if True\n lambda: y_pred, # if False\n name=\"reconstruction_targets\")\n\n reconstruction_mask = tf.one_hot(reconstruction_targets,\n depth=self.caps2_n_caps,\n name=\"reconstruction_mask\")\n\n # reconstruction_mask_reshaped: (?, 1, 10, 1, 1)\n reconstruction_mask_reshaped = tf.reshape(\n reconstruction_mask, [-1, 1, self.caps2_n_caps, 1, 1],\n name=\"reconstruction_mask_reshaped\")\n\n caps2_output_masked = tf.multiply(\n caps2_output, reconstruction_mask_reshaped,\n name=\"caps2_output_masked\")\n\n decoder_input = tf.reshape(caps2_output_masked,\n [-1, self.caps2_n_caps * self.caps2_n_dims],\n name=\"decoder_input\")\n\n\n n_hidden1 = 512\n n_hidden2 = 1024\n n_output = 32 * 32 * 3\n\n with tf.name_scope(\"decoder\"):\n hidden1 = tf.layers.dense(decoder_input, n_hidden1,\n activation=tf.nn.relu,\n name=\"hidden1\")\n hidden2 = tf.layers.dense(hidden1, n_hidden2,\n activation=tf.nn.relu,\n name=\"hidden2\")\n decoder_output = tf.layers.dense(hidden2, n_output,\n activation=tf.nn.sigmoid,\n name=\"decoder_output\")\n X_flat = tf.reshape(X, [-1, n_output], name=\"X_flat\")\n squared_difference = tf.square(X_flat - decoder_output,\n name=\"squared_difference\")\n reconstruction_loss = tf.reduce_mean(squared_difference,\n name=\"reconstruction_loss\")\n\n alpha = 0.0005\n loss = tf.add(margin_loss, alpha * reconstruction_loss, name=\"loss\")\n\n correct = tf.equal(y, y_pred, name=\"correct\")\n accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name=\"accuracy\")\n\n optimizer = tf.train.AdamOptimizer()\n training_op = optimizer.minimize(loss, name=\"training_op\")\n\n init = tf.global_variables_initializer()\n saver = tf.train.Saver()\n\n training_batched_data, training_batched_labels = batch_getter(self.images_train, self.cls_train, self.batch_size)\n testing_batched_data, testing_batched_labels = batch_getter(self.images_test, self.cls_test, self.batch_size)\n\n best_loss_val = np.infty\n\n save_suffix = '_' + str(self.caps1_n_maps) + '_' + str(self.caps1_n_dims) + '_' + str(self.caps2_n_dims)\n\n checkpoint_path = \"./generated_files/qin_capsule_network.ckpt\" + save_suffix\n\n with tf.Session() as sess:\n if self.restore_checkpoint and tf.train.checkpoint_exists(checkpoint_path):\n print(\"Restored\")\n saver.restore(sess, checkpoint_path)\n else:\n init.run()\n\n # for epoch in range(1):\n for epoch in range(self.n_epochs):\n print(\"epoch: \", epoch)\n\n for batch_i in range(len(training_batched_data)):\n # for batch_i in range(1):\n if batch_i % 50 == 0:\n print(\"batch: \", batch_i)\n _, loss_train = sess.run(\n [training_op, loss],\n feed_dict={X: training_batched_data[batch_i].reshape([-1, 32, 32, 3]),\n y: training_batched_labels[batch_i],\n mask_with_labels: True})\n\n # At the end of each epoch,\n # measure the validation loss and accuracy:\n loss_vals = []\n acc_vals = []\n\n for batch_i in range(len(testing_batched_data)):\n # for batch_i in range(1):\n loss_val, acc_val = sess.run(\n [loss, accuracy],\n feed_dict={X: testing_batched_data[batch_i].reshape([-1, 32, 32, 3]),\n y: testing_batched_labels[batch_i]})\n loss_vals.append(loss_val)\n acc_vals.append(acc_val)\n if batch_i % 20 == 0:\n print(\"Current loss: \", loss_val)\n print(\"Current acc: \", acc_val)\n loss_val = np.mean(loss_vals)\n acc_val = np.mean(acc_vals)\n print(\"\\rEpoch: {} Val accuracy: {:.4f}% Loss: {:.6f}{}\".format(\n epoch + 1, acc_val * 100, loss_val,\n \" (improved)\" if loss_val < best_loss_val else \"\"))\n\n # And save the model if it improved:\n if loss_val < best_loss_val:\n save_path = saver.save(sess, checkpoint_path)\n best_loss_val = loss_val\n print(\"Model saved in path: %s\" % save_path)\n\n tmp_file_name = './generated_files/' + str(self.caps1_n_maps) + '_' + str(self.caps1_n_dims) + '_' + str(self.caps2_n_dims) + \\\n '.txt'\n with open(tmp_file_name, 'a') as out:\n out.write(str(acc_val))\n return acc_val\n\n # files.download(checkpoint_path + '.data-00000-of-00001')\n # files.download(checkpoint_path + '.index')\n # files.download(checkpoint_path + '.meta')\n","repo_name":"ZhenyueQin/Qin-Capsule-Network","sub_path":"QinCapsNet.py","file_name":"QinCapsNet.py","file_ext":"py","file_size_in_byte":12992,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"5349367437","text":"import os\n\nimport testbase\nfrom rpath_storage import api1 as storage\n\n\nclass StorageConfig(object):\n __slots__ = [ 'storagePath' ]\n\nclass StorageTest(testbase.TestCase):\n def setUp(self):\n testbase.TestCase.setUp(self)\n self.storageCfg = StorageConfig\n self.storageCfg.storagePath = os.path.join(self.workDir, 'storageTest')\n self.diskStorage = storage.DiskStorage(self.storageCfg)\n\n def testDiskBasedStorage(self):\n stg = self.diskStorage\n\n nk = stg.store(42)\n expPath = os.path.join(self.storageCfg.storagePath, nk)\n self.failUnless(os.path.exists(expPath))\n self.failUnlessEqual(file(expPath).read(), \"42\")\n self.failUnlessEqual(stg.get(nk), \"42\")\n self.failUnless(stg.exists(nk))\n self.failUnlessEqual(stg.getFileFromKey(nk), expPath)\n\n nk = stg.store(42, keyPrefix = '11')\n expPath = os.path.join(self.storageCfg.storagePath, nk)\n self.failUnless(nk.startswith('11/'))\n self.failUnless(os.path.exists(expPath))\n self.failUnlessEqual(file(expPath).read(), \"42\")\n self.failUnlessEqual(stg.get(nk), \"42\")\n self.failUnlessEqual(stg[nk], \"42\")\n self.failUnless(stg.exists(nk))\n\n stg.set(nk, 43)\n self.failUnlessEqual(stg[nk], \"43\")\n\n stg[nk] = 44\n self.failUnlessEqual(stg[nk], \"44\")\n\n self.failUnlessEqual(stg.get('adfadfadf'), None)\n self.failUnlessEqual(stg.get('adfadfadf', '123'), '123')\n self.failIf(stg.exists('adfadfadf'))\n\n self.failUnlessRaises(storage.InvalidKeyError,\n stg.exists, 'a/../b')\n self.failUnlessRaises(storage.InvalidKeyError,\n stg.exists, '/a/b')\n self.failUnlessRaises(storage.InvalidKeyError,\n stg.exists, 'a//b')\n\n self.failUnlessRaises(storage.KeyNotFoundError,\n stg.__getitem__, 'nosuchkey')\n\n # Test enumerating the keys\n keys = [ stg.store(str(x), keyPrefix = 'enum') for x in range(3) ]\n keys.sort()\n self.failUnlessEqual(keys, stg.enumerate(keyPrefix = 'enum'))\n\n self.failUnlessEqual(stg.isCollection('enum'), True)\n self.failUnlessEqual(stg.isCollection('abc'), False)\n self.failUnlessEqual(stg.isCollection(nk), False)\n\n # Create an empty collection\n stg.newCollection('bleep')\n self.failUnlessEqual(stg.isCollection('bleep'), True)\n self.failUnlessEqual(stg.enumerate(keyPrefix = 'bleep'), [])\n\n # Create an empty collection, with a random name\n ncoll = stg.newCollection(keyPrefix = \"collections\")\n self.failUnless(ncoll.startswith(\"collections/\"), ncoll)\n self.failUnlessEqual(stg.isCollection(ncoll), True)\n self.failUnlessEqual(stg.enumerate(keyPrefix = ncoll), [])\n\n # Delete a collection\n stg.delete('enum')\n self.failUnlessEqual(stg.enumerate(keyPrefix = 'enum'), [])\n self.failUnlessEqual(stg.get('enum'), None)\n\n # Mock exists to always return true, to test the key failure exception\n self.mock(stg, \"exists\", lambda x: True)\n self.failUnless(stg.exists('adfadfadf'))\n\n e = self.failUnlessRaises(storage.StorageError,\n stg.store, '123')\n self.failUnlessEqual(str(e), 'Failed to generate a new key')\n\n def testEnumerateAll(self):\n stg = self.diskStorage\n stg.set(\"a/a0/a00\", \"a00\")\n stg.set(\"a/a0/a01\", \"a01\")\n stg.set(\"a/a1/a10\", \"a10\")\n stg.set(\"a/a1/a11\", \"a11\")\n stg.set(\"a/a2\", \"a2\")\n stg.set(\"b\", \"b\")\n self.failUnlessEqual(\n [ x for x in stg.enumerateAll() ],\n [ 'a/a2', 'a/a1/a11', 'a/a1/a10', 'a/a0/a01', 'a/a0/a00', 'b' ])\n\n def testSetFields(self):\n stg = self.diskStorage\n stg.setFields(\n [ ((\"a/a0\", \"a00\"), \"a00\"), (\"a/a0/a01\", \"a01\"),\n (\"a/a1/a10\", \"a10\"), (\"a/a1/a11\", \"a11\"),\n (\"a/a2\", \"a2\"), (\"b\", \"b\") ])\n self.failUnlessEqual(\n [ x for x in stg.enumerateAll() ],\n [ 'a/a2', 'a/a1/a11', 'a/a1/a10', 'a/a0/a01', 'a/a0/a00', 'b' ])\n # Remove a01\n stg.setFields(\n [ ((\"a/a0\", \"a00\"), \"b00\"), (\"a/a0/a01\", None) ])\n self.failUnlessEqual(\n [ x for x in stg.enumerateAll() ],\n [ 'a/a2', 'a/a1/a11', 'a/a1/a10', 'a/a0/a00', 'b' ])\n\n def testDelete(self):\n stg = self.diskStorage\n stg.setFields( [(('a', 'a1'), 'a1'), (('a', 'a2'), 'a2') ] )\n self.failUnlessEqual(\n sorted([ x for x in stg.enumerateAll() ]),\n [ 'a/a1', 'a/a2'])\n stg.delete(('a', 'a2'))\n self.failUnlessEqual(\n [ x for x in stg.enumerateAll() ],\n [ 'a/a1'])\n\n def testNewKey(self):\n stg = self.diskStorage\n key = stg.newKey(('a0', 'a1'))\n stg.set(key, 'blah')\n self.failUnlessEqual(\n [ x for x in stg.enumerateAll() ],\n [ key ])\n self.failUnlessEqual(stg.get(key), 'blah')\n # Dummy\n stg.commit()\n\n def testBaseStorage(self):\n stg = storage.BaseStorage()\n self.failUnlessRaises(NotImplementedError, stg._real_exists, 'a')\n self.failUnlessRaises(NotImplementedError, stg._real_get, 'a')\n self.failUnlessRaises(NotImplementedError, stg._real_set, 'a', 'a')\n self.failUnlessRaises(NotImplementedError, stg._real_enumerate, 'a')\n self.failUnlessRaises(NotImplementedError, stg._real_is_collection, 'a')\n","repo_name":"sassoftware/rpath-storage","sub_path":"storage_test/storagetest.py","file_name":"storagetest.py","file_ext":"py","file_size_in_byte":5596,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
+{"seq_id":"38052829855","text":"import math \n\nclass bm25Scorer(object):\n\n def __init__(self, docs):\n\n self.docs = docs\n self.D = len(self.docs)\n self.avgdl = sum([len(doc) for doc in self.docs]) / self.D # average doc length\n\n self.df = {}\n self.idf = {}\n self.k1 = 1.5\n self.b = 0.75\n \n self.initialize()\n\n def initialize(self, ngram=1):\n for doc in self.docs:\n word_set = set(doc)\n for word in word_set:\n if word not in self.df:\n self.df[word] = 1\n self.df[word] += 1\n for k, v in self.df.items():\n self.idf[k] = math.log(self.D - v + 0.5) - math.log(v + 0.5)\n\n def sim(self, doc1, doc2):\n score = 0\n d = len(doc2)\n loc = self.build_local_df(doc2)\n for word in doc1:\n if word not in loc:\n continue\n score += (self.idf[word] * loc[word] * (self.k1 + 1)\n / (loc[word] + self.k1 * (1 - self.b + self.b * d\n / self.avgdl)))\n return score\n\n def build_local_df(self, doc):\n tmp = {}\n for word in doc:\n if not word in tmp:\n tmp[word] = 0\n tmp[word] += 1\n return tmp\n\n def add_ngram(self,n):\n idx = 0\n for doc in self.docs:\n ngram = self.generate_ngram(n, self.titles[idx])\n seg_list = seg_list + ngram\n idx += 1\n\n def generate_ngram(self, n, sentence):\n return [sentence[i:i+n] for i in range(0, len(sentence) - 1)]","repo_name":"zake7749/CIKM-AnalytiCup-2018","sub_path":"closer/data_utils/bm25.py","file_name":"bm25.py","file_ext":"py","file_size_in_byte":1613,"program_lang":"python","lang":"en","doc_type":"code","stars":77,"dataset":"github-code","pt":"47"}
+{"seq_id":"9004819132","text":"# -*- coding: utf-8 -*-\n# @Time:2021/4/20 9:40\n# @Author:WangHQ\n# @File:add_department.py\nfrom select import select\nfrom time import sleep\n\nfrom selenium.webdriver.common.by import By\n\nfrom page.base_page import BasePage\n\n\nclass AddDepartmentPage(BasePage):\n # 设定为元组\n # 业务用例不需要了解页面元素，所以要加私有\n\n __ele_departname = (By.XPATH, \"//*[@id='__dialog__MNDialog__']/div/div[2]/div/form/div[1]/input\")\n\n __ele_supdepart = (By.CSS_SELECTOR, \".js_toggle_party_list\")\n\n def add_department(self, departname):\n \"\"\"\n 页面的return 分成两个部分\n 1.其他页面的实例\n 2.用例所需要的断言\n 快捷导入 alt + 回车\n :return:\n \"\"\"\n # * 的作用是 解元组 self.driver.find_element(*departname) 等同于\n # self.drvier.find_element(By.ID,\"departname\")\n from page.contact import ContactPage\n self.find(self.__ele_departname).send_keys(departname)\n sleep(5)\n self.find(self.__ele_supdepart).click()\n sleep(20)\n # 组合定位\n self.find(By.CSS_SELECTOR, \".qui_dialog_body.ww_dialog_body [id='1688850917233905_anchor']\").click()\n sleep(5)\n self.find(By.LINK_TEXT, \"确定\").click()\n sleep(5)\n return ContactPage(self.driver)\n\n def add_department_fail(self, departname):\n self.find(self.__ele_departname).send_keys(departname)\n self.find(self.__ele_supdepart).click()\n # 组合定位\n self.find(By.CSS_SELECTOR, \".qui_dialog_body.ww_dialog_body [id='1688850917233905_anchor']\").click()\n sleep(5)\n self.find(By.LINK_TEXT, \"确定\").click()\n\n element = self.finds(By.ID, \"js_tips\")\n error_list = []\n for ele in element:\n error_list.append(ele.text)\n print(error_list)\n return error_list\n","repo_name":"whqhrh/test_web_wechat","sub_path":"page/add_department.py","file_name":"add_department.py","file_ext":"py","file_size_in_byte":1879,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"1498045953","text":"# coins = [1,3,4, 5] amount = 7, Find least number of coins that make up the amount\n# if we do greedily, [5,1,1] - > 3 coins\n# but [3,4] is the correct answer\n# can be figured out by backtracking + DFS (because we need to explore all possible combinations)\n# But many subproblems are involved while drawing the choice map.\n# so we memoize it with a DP array\n# recursively it will take forever to finish\n\n# Q1: Find least number of coins that can make up the amount.\n# Q2: Find number of ways of making up the amount.\n\n\ndef num_ways_of_coins(denominations, n, way, ways):\n if n < 0:\n return\n if n == 0:\n ways.append(way)\n for i in range(len(denominations)):\n num_ways_of_coins(denominations[i:], n - denominations[i], way + [denominations[i]], ways)\n\n\ndef num_ways_of_coins_dp(dens, amount):\n coins = [0] * (amount + 1)\n coins[0] = 1\n for c in dens:\n for amt in range(1, amount + 1):\n if amt - c >= 0:\n coins[amt] += coins[amt - c]\n print(coins)\n return coins[amount]\n\n\ndef coin_change_ii(nums, target):\n ways = []\n num_ways_of_coins(nums, target, [], ways)\n num_ways_of_coins_dp(nums, target)\n print(ways)\n print(len(ways))\n\n\ncoin_change_ii([1, 2, 5], 7)\n\n\ndef least_num_coins_for_amt(coins, amount):\n dp = [amount + 1] * (amount + 1)\n dp[0] = 0 # number of coins required for making up amount=0\n\n for a in range(1, amount + 1):\n for c in coins:\n if a - c >= 0:\n dp[a] = min(dp[a], dp[a - c] + 1)\n return dp[amount] if dp[amount] != amount + 1 else -1\n\n\n# print(least_num_coins_for_amt([186, 419, 83, 408], 6249))","repo_name":"amrithajayadev/misc","sub_path":"dp/coin_change_problem.py","file_name":"coin_change_problem.py","file_ext":"py","file_size_in_byte":1652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"11885981286","text":"\"\"\"\nsyntax:\nif conditions:\ndo something\nwhen above conditions are true\nelse:\nwe'll do something \nif above conditions are false\n\nQ - divisibility by 4\n\nQ - if weather is sunny and temperature is less than 32, then go to office, else stay at home.\n\nQ - if a student gets more than 90% marks or has won the maths olympiad, then give him admission, else reject the student.\n\nQ - if a number is div by 4, or div by 2 but not by 4.\n\nQ - FizzBuzz, div by 3 - fizz, div by 5 - Fuzz, div by both - fizz-buzz\n\"\"\"\n\n# a = int(input(\"Enter value of a: \"))\n# if a%4==0:\n# print(\"number is divisible by 4\")\n# else:\n# print(\"number not divisible by 4\")\n\n# weather = \"sunny\"\n# temperature = 9\n\n# if weather == \"sunny\" and temperature<32 and temperature>10: \n# print(\"go to office\") #this runs when condition is true\n# else : \n# print(\"stay at home\") #this runs when condition is false\n\n# marks = 82\n# olympiad = \"lost\"\n\n# if marks>=90 or olympiad==\"won\":\n# print(\"Take admission\")\n# else:\n# print(\"reject the student\")\n\n\na = int(input(\"Enter value of a: \"))\n\nif a%2==0:\n n = a%100\n if n%4==0:\n print(\"div by 4 and div by 2\")\n else:\n print(\"div by 2 but not by 4\")\nelse:\n print(\"a is not divisible by 2, and not divisible by 4 also\")\n\n# a = int(input(\"Enter value of a: \"))\n\n# if a%3==0 or a%5==0:\n# print(\"FIZZ_BUZZ\")\n# elif a%3==0 :\n# print(\"FIZZ\")\n# elif a%5==0:\n# print(\"FUZZ\")\n# else:\n# print(\"not div by 3 or 5\")\n \n# n = 5 \n# if(n>3):\n# print(\"greater than 5\")\n# elif(n>4):\n# print(\"greater than 4\")","repo_name":"jazib-mahmood-attainu/Ambedkar_Batch","sub_path":"W3D1 - python4/Conditional.py","file_name":"Conditional.py","file_ext":"py","file_size_in_byte":1584,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"47"}
+{"seq_id":"8567266422","text":"import sys\nimport pandas as pd\nimport numpy as np\nimport feather\nimport datetime\nimport time\n\nimport cbpro\n\nstart_date = datetime.date(2018, 2, 1)\nend_date = datetime.date.today() + datetime.timedelta(days=1)\n\ndate_list = [start_date + datetime.timedelta(days=x) for x in range(0, (end_date-start_date).days, 10)]\n\npublic_client = cbpro.PublicClient()\n\noutput = pd.DataFrame()\n\nfor test_date in date_list:\n date1 = str(test_date)\n date2 = str(test_date + datetime.timedelta(days=10))\n\n df = pd.DataFrame(public_client.get_product_historic_rates('LTC-EUR',\n granularity=3600,\n start=date1,\n end=date2),\n columns=['time', 'low', 'high', 'open', 'close', 'volume'])\n df['time'] = pd.to_datetime(df['time'], unit='s')\n df.sort_values(by=['time'], ascending=True, inplace=True)\n df['returns'] = df['close'] / df['close'].shift(1) - 1\n output = pd.concat([output, df])\n time.sleep(2)\n\nfeather.write_dataframe(output, dest='./ltc_eur_data.feather')","repo_name":"jorisvanson9/crypto_project","sub_path":"create_data.py","file_name":"create_data.py","file_ext":"py","file_size_in_byte":1172,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"20588852275","text":"\nimport os\n\nfrom shutil import chown\nfrom configparser import SafeConfigParser\n\nfrom charmhelpers.core.host import service_running\nfrom charmhelpers.core.host import service_restart\nfrom charmhelpers.core.host import service_reload\nfrom charmhelpers.core.host import chownr\n\n\nAPP_DIR = '/etc/uwsgi/apps-enabled'\nHEADER = '[uwsgi]\\n'\n\n\ndef configure(site, dir, uid='www-data', gid='www-data', plugins=None,\n cfg=None):\n \"\"\" create or update a UWSGI config\n\n :param site: name of configuration\n :param chdir: directory of UWSGI app, same as chdir in UWSGI\n :param uid: user id/name to run with\n :param gid: group id/name to run with\n :param cfg: additional UWSGI configuration params\n :param plugins: plugin to use\n \"\"\"\n\n if not cfg:\n cfg = {}\n\n cfg['uid'] = uid\n cfg['gid'] = gid\n cfg['chdir'] = dir\n\n if plugins:\n cfg['plugins'] = plugins\n\n if 'socket' not in cfg and not cfg.get('socket'):\n socket_dir = '/srv/run/uwsgi'\n if not os.path.exists(socket_dir):\n os.makedirs(socket_dir)\n\n chown(socket_dir, uid, gid)\n chownr(socket_dir, uid, gid)\n\n cfg['socket'] = os.path.join(socket_dir, '%s.socket' % site)\n\n if 'master' not in cfg:\n cfg['master'] = 'true'\n\n reload = True\n if not os.path.exists(cfg_path(site)):\n reload = False # need to restart for new configuration\n\n with open(cfg_path(site), 'w') as f:\n wsgi_txt = '\\n'.join(['%s = %s' % (k, d) for k, d in cfg.items()])\n f.write(HEADER + wsgi_txt)\n\n restart(reload)\n\n\ndef config(site):\n \"\"\" retrieve configuration for UWSGI site\n\n :param site: name of configuration to retrieve\n \"\"\"\n\n c = SafeConfigParser()\n c.read(cfg_path(site))\n return {s[0]: s[1] for s in c.items('uwsgi')}\n\n\ndef remove(site):\n \"\"\" remove a wsgi configured site\n\n :param site: name of configuration to delete\n \"\"\"\n\n os.unlink(cfg_path(site))\n\n\ndef running():\n \"\"\" check if uwsgi is running \"\"\"\n return service_running('uwsgi')\n\n\ndef restart(reload=False):\n \"\"\" restart or reload uwsgi\n\n :param reload: boolean to reload instead of restart\n \"\"\"\n if reload:\n return service_reload('uwsgi', True)\n\n return service_restart('uwsgi')\n\n\ndef cfg_path(site):\n return os.path.join(APP_DIR, '%s.ini' % site)\n","repo_name":"marcoceppi/layer-uwsgi","sub_path":"lib/charms/layer/uwsgi.py","file_name":"uwsgi.py","file_ext":"py","file_size_in_byte":2344,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"33013948763","text":"#!/usr/bin/env python\n\n\"\"\"The setup script.\"\"\"\nfrom setuptools import find_packages, setup\n\nwith open(\"README.rst\") as readme_file:\n readme = readme_file.read()\n\nwith open(\"HISTORY.rst\") as history_file:\n history = history_file.read()\n\nrequirements = [\"Click>=7.0\", \"kombu\", \"kafka-python\"]\n\nsetup_requirements = []\n\ntest_requirements = []\n\nsetup(\n author=\"Yiannis Giannelos\",\n author_email=\"jgiannelos@wikimedia.org\",\n python_requires=\">=3.5\",\n classifiers=[\n \"Development Status :: 5 - Production/Stable\",\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: GNU General Public License v3 (GPLv3)\",\n \"Natural Language :: English\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: 3.9\",\n ],\n description=\"Poppy is a simple message queue CLI tool\",\n entry_points={\n \"console_scripts\": [\n \"poppy=poppy.cli:main\",\n ],\n },\n install_requires=requirements,\n license=\"GNU General Public License v3\",\n long_description=readme + \"\\n\\n\" + history,\n include_package_data=True,\n keywords=\"poppy\",\n name=\"poppy\",\n packages=find_packages(include=[\"poppy\", \"poppy.*\"]),\n setup_requires=setup_requirements,\n test_suite=\"tests\",\n tests_require=test_requirements,\n version=\"0.3.0\",\n zip_safe=False,\n)\n","repo_name":"wikimedia/poppy-cli","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1503,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"47"}
+{"seq_id":"9730255642","text":"''' Duckworth liews method claculator for target score estimation and par score table production'''\r\nimport resource_demo as rd\r\nimport test_im_rs as pst\r\nscore=S=0\r\novers=O=50\r\nR1=R2=100\r\n'''-------------------------------------------------------------------------------------------'''\r\ndef get_team1_score():\r\n global S\r\n S=int(input(\"innings end team1 score: \"))\r\ndef put_team1_score():\r\n return S\r\ndef get_overs_at_start():\r\n global O\r\n O=int(input(\"innings start team1 overs: \"))\r\ndef put_overs_at_start():\r\n return O\r\ndef get_wicket_lost():\r\n return int(input())\r\n'''-------------------------------------------------------------------------------------------'''\r\ndef interuption_1(s1,ovr,wlst):\r\n global R1,R2\r\n wl=10-wlst\r\n print(\"s1: \",s1)\r\n print(\"Enter over played yet: \")\r\n ovr_ply=int(input())\r\n print(\"Enter overs lost : \")\r\n ovr_lst=int(input())\r\n ovr_rst=ovr-ovr_lst\r\n ovr_lft=ovr-ovr_ply\r\n R1=rd.resource(ovr,0)\r\n print(\"R1: \",R1)\r\n ovr=ovr-ovr_lst\r\n if ovr_ply>=45:#resource lost at end of innings(cut short)\r\n r1=rd.resource(ovr_lft,wl)#resources func call\r\n r2=0\r\n print(\"ovr_lft: \",ovr_lft)\r\n print(\"r1: \",r1)\r\n R1=R1-r1+r2\r\n print(\"R1: \",R1)\r\n #ovr=ovr_ply\r\n else: #resourse lost in middle of innings(interupted)\r\n wl=10-wl\r\n r1=rd.resource(ovr_lft,wl)\r\n print(\"r1: \",r1)\r\n ovr_lft_rst=ovr_rst-ovr_ply\r\n r2=rd.resource(ovr_lft_rst,wl)#resources func call\r\n print(\"r2: \",r2)\r\n R1=R1-r1+r2\r\n print(\"R1: \",R1)\r\n print(\"\\n\\t once again interupted?(y/n): \")\r\n ans=input()\r\n while(ans is \"y\" or ans==\"Y\"):\r\n print(\"Enter over played yet: \")\r\n ovr_ply=int(input())\r\n print(\"Enter overs lost : \")\r\n ovr_lst=int(input()) \r\n ovr=ovr_rst-ovr_lst\r\n ovr_lft=ovr_lft_rst-ovr_ply\r\n ovr_lft_rst=ovr_lft-ovr_lst\r\n print(\"ovr\",ovr)\r\n r1=rd.resource(ovr_lft,wl)#resources func call\r\n print(\"r1: \",r1)\r\n r2=rd.resource(ovr_lft_rst,wl)#resources func call\r\n print(\"r2: \",r2)\r\n R1=R1-r1+r2\r\n print(\"\\n\\t once again interupted?(y/n): \")\r\n ans=input() \r\n print(\"R1: \",R1) \r\n R2=rd.resource(ovr,0)\r\n print(\"R2: \",R2)\r\n S2=target_score(R1,R2)\r\n print(\"\\n\\tSCORE FOR TEAM 2: \",S2)\r\n pst.parscore(s1,0,0,ovr_lst,ovr,R1,R2)#parscore for future overs(always for team 2)\r\n'''-------------------------------------------------------------------------------------------'''\r\ndef target_score(R1,R2):\r\n s1=put_team1_score()\r\n print(\"s1: \",s1)\r\n if R1>R2:\r\n return int(s1*R2/R1)\r\n elif R1==R2:\r\n return s1\r\n else:\r\n G50=245\r\n return int(s1+G50*(R2-R1)/100) \r\n'''-------------------------------------------------------------------------------------------'''\r\ndef interuption_2(ovr,wl):\r\n global R2\r\n s1=put_team1_score()\r\n if s1==0:\r\n return\r\n print(\"Enter over played yet: \")\r\n ovr_ply=int(input())\r\n print(\"Enter overs lost : \")\r\n ovr_lst=int(input())\r\n ovr_rst=ovr-ovr_lst\r\n ovr_lft=ovr-ovr_ply\r\n R2=rd.resource(ovr,0)\r\n ovr=ovr-ovr_lst\r\n print(\"R2: \",R2)\r\n if ovr_ply==0:#resource lost at start of innings(delayed)\r\n print(\"ovr_rst: \",ovr_rst)\r\n print(\"wl: \",wl)\r\n r1=rd.resource(ovr_rst,wl)#resource func call\r\n R2=r1\r\n elif ovr_ply>=45:#resource lost at end of innings(cut short)\r\n r1=rd.resource(ovr_lft,wl)#resources func call\r\n print(\"ovr_lft: \",ovr_lft)\r\n print(\"wl: \",wl)\r\n print(\"r1: \",r1)\r\n R2=R2-r1\r\n else: #resource lost in middle of innings(interupted)\r\n r1=rd.resource(ovr_lft,wl)\r\n print(\"r1: \",r1)\r\n ovr_lft_rst=ovr_rst-ovr_ply\r\n r2=rd.resource(ovr_lft_rst,wl)#resources func call\r\n print(\"r2: \",r2)\r\n R2=R2-r1+r2\r\n print(\"R2: \",R2)\r\n print(\"\\n\\t once again interupted?(y/n): \")\r\n ans=input()\r\n while(ans is \"y\" or ans==\"Y\"):\r\n ovr=ovr_lft_rst\r\n print(\"Enter over played yet: \")\r\n ovr_ply=int(input())\r\n print(\"Enter overs lost : \")\r\n ovr_lst=int(input())\r\n print(\"Enter Team2 wickets lost: \")\r\n wl=get_wicket_lost()\r\n ovr_lft=ovr-ovr_ply\r\n ovr_lft_rst=ovr_lft-ovr_lst\r\n if ovr_lft==ovr_lft_rst:\r\n break\r\n r1=rd.resource(ovr_lft,wl)#resources func call\r\n print(\"r1: \",r1)\r\n r2=rd.resource(ovr_lft_rst,wl)#resources func call\r\n print(\"r2: \",r2)\r\n R2=R2-r1+r2\r\n print(\"\\n\\t once again interupted?(y/n): \")\r\n ans=input()\r\n print(\"R2: \",R2)\r\n S2=target_score(R1,R2) \r\n print(\"\\n\\tSCORE FOR TEAM 2: \",S2)\r\n pst.parscore(s1,ovr_ply,wl,ovr_lst,ovr,R1,R2)#parscore for future overs(always for team 2) \r\n'''-------------------------------------------------------------------------------------------''' \r\ndef innings2(o):\r\n global R2\r\n print(\"\\n\\tAny innterruption(Y,N) in inings 2: \")\r\n ans=input()\r\n if ans==\"Y\" or ans==\"y\":\r\n print(\"Enter Team2 wickets lost: \")\r\n w=get_wicket_lost()\r\n interuption_2(o,w) #cal. updated resources for interrupted innings\r\n else:\r\n R2=rd.resource(O,0)#cal. resources for team 1 uninterupted innings\r\n s2=target_score(100,R2)\r\n print(\"\\n\\tSCORE FOR TEAM 2: \",s2)\r\n'''------------------------------------------------------------------------------------------'''\r\ndef innings1():\r\n global R1\r\n get_team1_score()\r\n s=put_team1_score()\r\n get_overs_at_start()\r\n o=put_overs_at_start()\r\n print(\"\\n\\tAny innterruption(Y,N) in inings1(if any): \")\r\n ans=input()\r\n if ans==\"Y\" or ans==\"y\":\r\n print(\"Enter Team1 wickets lost: \")\r\n w=get_wicket_lost()\r\n print(\"s: \",s)\r\n interuption_1(s,o,w) #cal. updated resources for interrupted innings \r\n else:\r\n print(\"o:\",o)\r\n R1=rd.resource(o,0)#cal. resources for team 1 uninterupted innings\r\n print(\"R1: \",R1)\r\n innings2(o)\r\n'''------------------------------------------------------------------------------------------''' \r\ninnings1() \r\n \r\n\r\n \r\n \r\n \r\n","repo_name":"Boggart97/Duckworth-lewis-Method","sub_path":"dls.py","file_name":"dls.py","file_ext":"py","file_size_in_byte":6447,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"7634436765","text":"\nfrom settings import *\nfrom mount import inode_table,addr_space,part_info,free_blocks,data\n\ndef hardWrite(fname):\n inode_string_table = \"\"\n\n for i in range(len(inode_table)):\n inode_table[i][-1] = '_'.join(str(x) for x in inode_table[i][-1]);\n inode_string_table = inode_string_table + ' '.join(str(x) for x in inode_table[i]) + \"\\n\"\n with open(fname, 'w+') as f:\n f.write(str(addr_space) + \"\\n\")\n f.write(str(part_info) + \"\\n\");\n blocks4free_blocks = math.ceil(len(free_blocks) / ints_per_block)\n for i in range(blocks4free_blocks):\n if i == blocks4free_blocks:\n f.write(' '.join(str(x) for x in free_blocks[i * ints_per_block:-1]) + \"\\n\")\n else:\n f.write(' '.join(str(x) for x in free_blocks[i * ints_per_block:(i + 1) * ints_per_block]) + \"\\n\")\n f.write(inode_string_table + '\\n'.join(str(x) for x in data))\n\n\n\n\n","repo_name":"s-mrb/FileSystem","sub_path":"hard_write.py","file_name":"hard_write.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"42791386383","text":"import awkward as ak\nimport numpy as np\nimport pyjet\n\ndef makeJets(vectors,R):\n\n # cluster jets for a single event from track vectors \n sequence = pyjet.cluster(vectors, R=R, p=-1)\n psuedojets = sequence.inclusive_jets()\n #print(psuedojets)\n\n jets = []\n for jet in psuedojets: \n # save a dict\n jet_dict = { \"pt\":jet.pt, \n \"eta\":jet.eta, \n \"phi\":jet.phi,\n \"mass\":jet.mass,\n \"ntracks\":len(jet) }\n jets.append(jet_dict)\n\n return jets \n\ndef convertToJagged(jets):\n # takes a list of list of dicts\n # converts to JaggedArrayCandidates\n\n # make awkward array\n jagged_jets = ak.JaggedArray.fromiter(jets)\n\n # make jagged candidate array\n fatjets = JaggedCandidateArray.candidatesfromcounts(jagged_jets.counts,\n pt=jagged_jets.pt.flatten(),\n eta=jagged_jets.eta.flatten(),\n phi=jagged_jets.phi.flatten(),\n mass=jagged_jets.mass.flatten(),\n ntracks=jagged_jets.ntracks.flatten())\n return fatjets \n \ndef makeFatJets(tracks):\n\n print(\"making fat jets\")\n # clusters fat jets from tracks\n # makes jets with three different radii\n # \"anti-pattern\"\n\n # list of list of dicts\n jets_20 = []\n jets_15 = []\n jets_10 = []\n\n for ievt in range(tracks.size):\n \n # for debug\n #if ievt > 10: continue\n if ievt % 1000 == 0: print(\"Event {}\".format(ievt))\n\n # make a structured array from tracks\n\n vectors = np.zeros(tracks.pt[ievt].size, np.dtype([('pT', 'f8'), ('eta', 'f8'),\n ('phi', 'f8'), ('mass', 'f8')]) )\n vectors['pT' ] = tracks.pt[ievt]\n vectors['eta' ] = tracks.eta[ievt]\n vectors['phi' ] = tracks.phi[ievt]\n vectors['mass'] = tracks.mass[ievt]\n #print(vectors)\n\n # make events jets \n jets_20.append(makeJets(vectors, 2.0))\n jets_15.append(makeJets(vectors, 1.5))\n jets_10.append(makeJets(vectors, 1.0))\n\n fatjets_20 = convertToJagged(jets_20) \n fatjets_15 = convertToJagged(jets_15) \n fatjets_10 = convertToJagged(jets_10) \n \n return (fatjets_20, fatjets_15, fatjets_10)\n","repo_name":"kdipetri/SUEP_coffea","sub_path":"utils/fatjets.py","file_name":"fatjets.py","file_ext":"py","file_size_in_byte":2419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"12170543301","text":"# Created on 23 September 2019\n\nimport pygame\nfrom math import log2\n\n\nclass Square:\n def __init__(self, val, w):\n self.w = w\n self.val = val\n self.surface = pygame.Surface((w, w))\n self.drawSurface()\n\n def upgrade(self):\n self.val *= 2\n self.drawSurface()\n\n def changeVal(self, new_val):\n self.val = new_val\n self.drawSurface()\n\n def drawSurface(self):\n COLORS = ((255, 0, 0), (200, 80, 80), (255, 255, 0), (0, 255, 0), (64, 64, 255), (0, 0, 128), (200, 0, 200))\n color = COLORS[int(log2(self.val) - 1) % len(COLORS)]\n self.surface.fill((0, 0, 0))\n img = pygame.image.load(\"back.png\")\n img = pygame.transform.scale(img, (self.w, self.w))\n self.surface.blit(img, (0, 0))\n text_dim = (int(self.w * 3 / 5), int(self.w * 3 / 5))\n font = getScaledFont(\"Times New Roman\", text_dim, str(self.val))\n text = font.render(str(self.val), 1, color)\n text_rect = text.get_rect(center=(int(self.w / 2), int(self.w / 2)))\n self.surface.blit(text, text_rect)\n\n\ndef getScaledFont(font_type, dim, text):\n font_size = 0\n font = pygame.font.SysFont(font_type, font_size)\n w, h = font.size(text)\n while w < dim[0] and h < dim[1]:\n font_size += 1\n font = pygame.font.SysFont(font_type, font_size)\n w, h = font.size(text)\n return pygame.font.SysFont(font_type, font_size - 1)\n","repo_name":"AaronOrenstein210/2048","sub_path":"square.py","file_name":"square.py","file_ext":"py","file_size_in_byte":1433,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"6"}
+{"seq_id":"30381310743","text":"# Stars in a night sky\nfrom turtle import *\nimport random\nbgcolor('DeepSkyBlue4')\ndef star(s, c):\n x, y = random.randint(-100, 100), random.randint(-100, 100)\n penup()\n goto(x, y) # goto() creates black line at the start so we have to lift pen up\n begin_fill()\n color(c)\n pendown()\n for i in range(5):\n forward(s)\n right(144)\n end_fill()\n \n\nfor s in range(20):\n star(10, 'white')\n\nmainloop()\n","repo_name":"JeffDanh/python-exercises","sub_path":"turtle-exercises/stars_sky.py","file_name":"stars_sky.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"10423025263","text":"from __future__ import annotations\n\nfrom randovania.game_description import data_reader, data_writer, game_migration\n\n\ndef test_round_trip_small(test_files_dir):\n # Setup\n original_data = test_files_dir.read_json(\"prime2_small_v1.json\")\n migrated_data = game_migration.migrate_to_current(original_data)\n\n game = data_reader.decode_data(migrated_data)\n\n encoded_data = data_writer.write_game_description(game)\n encoded_data[\"used_trick_levels\"] = None\n assert encoded_data == migrated_data\n","repo_name":"randovania/randovania","sub_path":"test/game_description/test_schema_migration.py","file_name":"test_schema_migration.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"en","doc_type":"code","stars":165,"dataset":"github-code","pt":"6"}
+{"seq_id":"9293771331","text":"#!/usr/bin/env python\n\nclass Solution:\n def anagrams(self, strs):\n if not strs:\n return []\n table = {}\n for elem in strs:\n key = ''.join(sorted(elem))\n table[key] = table.get(key, []) + [elem]\n result = []\n for val in table.values():\n if len(val) > 1:\n result += val\n return result\n","repo_name":"rioshen/Problems","sub_path":"leetcode/python/anagrams.py","file_name":"anagrams.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"6"}
+{"seq_id":"1651088548","text":"# FOR PRINTING OUT SIDE-BY-SIDE IMAGE TRANSFORMATION COMPARISONS FROM DATA LOADER\n\n\n\n# Run command:\n# cd slowfast\n# python3 evaluation/print_loader.py --cfg configs/MVITv2_B_32x3_inf.yaml\n\n\n\n\nfrom __future__ import print_function\n\nimport argparse\nimport os\nimport random\n\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom torchvision.utils import save_image\n\nfrom slowfast.datasets import loader\nfrom slowfast.config.defaults import assert_and_infer_cfg\nfrom slowfast.utils.parser import load_config\nfrom slowfast.datasets import transform\n\nfrom visual_prompting.utils import launch_job\n\n\ndef parse_option():\n\n parser = argparse.ArgumentParser('Visual Prompting for Vision Models')\n\n # pyslowfast cfg\n parser.add_argument(\n \"--shard_id\",\n help=\"The shard id of current node, Starts from 0 to num_shards - 1\",\n default=0,\n type=int,\n )\n parser.add_argument(\n \"--num_shards\",\n help=\"Number of shards using by the job\",\n default=1,\n type=int,\n )\n parser.add_argument(\n \"--init_method\",\n help=\"Initialization method, includes TCP or shared file-system\",\n default=\"tcp://localhost:9999\",\n type=str,\n )\n parser.add_argument(\n \"--cfg\",\n dest=\"cfg_files\",\n help=\"Path to the config files\",\n default=[\"configs/Kinetics/SLOWFAST_4x16_R50.yaml\"],\n nargs=\"+\",\n )\n parser.add_argument(\n \"--opts\",\n help=\"See slowfast/config/defaults.py for all options\",\n default=None,\n nargs=argparse.REMAINDER,\n )\n\n args = parser.parse_args()\n\n return args\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\ndef main(args, cfg):\n global device\n\n # create dataloaders\n # train_loader = loader.construct_loader(cfg, \"train\")\n val_loader = loader.construct_loader(cfg, \"val\")\n # test_loader = loader.construct_loader(cfg, \"test\")\n\n lder = val_loader\n\n for batch_iter, data in enumerate(lder):\n if(cfg.DATA.CROP_PROMPT == True and cfg.DATA.RETURN_CROPPING_PARAMS == True):\n inputs, labels, index, times, meta, crop_params_dict = data\n\n aspect_ratio_0_batch = crop_params_dict[\"aspect_ratio\"][0].tolist()\n aspect_ratio_1_batch = crop_params_dict[\"aspect_ratio\"][1].tolist()\n\n scale_0_batch = crop_params_dict[\"scale\"][0].tolist()\n scale_1_batch = crop_params_dict[\"scale\"][1].tolist()\n else:\n inputs, labels, index, times, meta = data\n \n images = inputs[0]\n \n if(batch_iter <= 5):\n for idx in range(images.shape[0]):\n if(cfg.DATA.CROP_PROMPT == True and cfg.DATA.RETURN_CROPPING_PARAMS == True):\n frames_crop_train = transform.random_resized_crop(\n images=images[idx],\n target_height=crop_params_dict[\"crop_size\"][idx].item(),\n target_width=crop_params_dict[\"crop_size\"][idx].item(),\n scale=(scale_0_batch[idx], scale_1_batch[idx]),\n ratio=(aspect_ratio_0_batch[idx], aspect_ratio_1_batch[idx]),\n )\n\n frames_crop_jit, _ = transform.random_short_side_scale_jitter(\n images=images[idx],\n min_size=crop_params_dict[\"min_scale\"][idx].item(),\n max_size=crop_params_dict[\"max_scale\"][idx].item(),\n inverse_uniform_sampling=crop_params_dict[\"inverse_uniform_sampling\"][idx].item(),\n )\n\n frames_crop_val, _ = transform.random_crop(frames_crop_jit, crop_params_dict[\"crop_size\"][idx].item())\n frames_crop_test, _ = transform.uniform_crop(frames_crop_jit, crop_params_dict[\"crop_size\"][idx].item(), spatial_idx=1)\n frames_orig = images[idx]\n \n clip_crop_train = frames_crop_train.permute(1, 0, 2, 3)\n clip_crop_val = frames_crop_val.permute(1, 0, 2, 3)\n clip_crop_test = frames_crop_test.permute(1, 0, 2, 3)\n\n clip_orig = torch.nn.functional.interpolate(\n frames_orig.permute(1, 0, 2, 3),\n size=(crop_params_dict[\"crop_size\"][idx].item(), crop_params_dict[\"crop_size\"][idx].item()),\n mode=\"bilinear\",\n align_corners=False,\n )\n\n clip = torch.cat([clip_orig, clip_crop_train, clip_crop_val, clip_crop_test], dim=3)\n\n else:\n frames = images[idx]\n clip = frames.permute(1, 0, 2, 3)\n \n for jdx in range(clip.shape[0]):\n if(jdx == 0):\n save_image(clip_orig[jdx], os.getcwd() + f\"/visual_prompting/images/originals/{batch_iter}_{idx}_{jdx}.png\")\n\n\nif __name__ == '__main__':\n\n # parse config and params\n args = parse_option()\n for path_to_config in args.cfg_files:\n cfg = load_config(args, path_to_config)\n cfg = assert_and_infer_cfg(cfg)\n\n args.image_size = cfg.DATA.TRAIN_CROP_SIZE\n cfg.DATA.CROP_PROMPT = True\n cfg.DATA.RETURN_CROPPING_PARAMS = True\n\n # gather preds and targets from validation dataset\n launch_job(cfg=cfg, args=args, init_method=args.init_method, func=main)\n\n\n","repo_name":"CarrotPeeler/WPI-Naturalistic-Driving-Action-Recognition-MQP","sub_path":"slowfast/evaluation/print_loader.py","file_name":"print_loader.py","file_ext":"py","file_size_in_byte":5411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"10775216159","text":"import re\nimport os\nfrom collections import Counter, namedtuple\nfrom itertools import combinations\nfrom pprint import pprint\nfrom graph import Graph\nfrom parse import parse, findall\nfrom math import prod\nfrom bisect import bisect_left, insort\ndirname = os.path.dirname(__file__)\ndata = open(f'{dirname}/15-input.txt').read().splitlines()\ndata = [[int(e) for e in d] for d in data]\n\nH = len(data)\nW = len(data[0])\nassert W == H\n\nfor row in data:\n for j in range(1, 5):\n row += [((d + j - 1) % 9) + 1 for d in row[:W]]\n\nfor i in range(1, 5):\n data += [[((d + i - 1) % 9) + 1 for d in row] for row in data[:H]]\n\nH = len(data)\nW = len(data[0])\nassert W == H\n\ndef gnns(a, b):\n return [\n (i, j)\n for (i, j) in [(a + 1, b), (a, b + 1), (a - 1, b), (a, b - 1)]\n if (i, j) != (a, b) and 0 <= i < H and 0 <= j < W\n ]\n\nclass node:\n pass\n\nseen_and_unvisited = [(0, 0, 0)]\nunvisited = set((i, j) for i in range(H) for j in range(W))\nnode_score = {(0, 0): 0}\nwhile unvisited:\n curr, i, j = seen_and_unvisited.pop(0)\n\n unvisited.remove((i, j))\n for nn in gnns(i, j):\n nn_score = node_score.get(nn)\n new_nn_score = min(nn_score, curr + data[nn[0]][nn[1]]) if nn_score else curr + data[nn[0]][nn[1]]\n node_score[nn] = new_nn_score\n if nn in unvisited:\n if nn_score:\n seen_and_unvisited.pop(bisect_left(seen_and_unvisited, (nn_score, nn[0], nn[1])))\n insort(seen_and_unvisited, (new_nn_score, nn[0], nn[1]))\n\nprint(node_score[(W-1, W-1)])\n","repo_name":"knjmooney/Advent-Of-Code","sub_path":"2021/15-chitons.py","file_name":"15-chitons.py","file_ext":"py","file_size_in_byte":1538,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"42719237847","text":"\"\"\"\nWSGI config for antmaps_dataserver project.\n\nIt exposes the WSGI callable as a module-level variable named ``application``.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/1.7/howto/deployment/wsgi/\n\"\"\"\n\nimport os\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"antmaps_dataserver.settings\")\n\nfrom django.core.wsgi import get_wsgi_application\n\ndef application(environ, start_response):\n \n # Pass Apache environment variables starting with ANTMAPS_ to django via os.environ\n for key in environ:\n if key.startswith('ANTMAPS_'):\n os.environ[key] = environ[key]\n \n \n _application = get_wsgi_application()\n return _application(environ, start_response)\n","repo_name":"mziegler/antmaps-backend","sub_path":"antmaps_dataserver/antmaps_dataserver/wsgi.py","file_name":"wsgi.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"6"}
+{"seq_id":"22195293673","text":"import sys\n\nfrom PySide2 import QtCore, QtWidgets\n\nclass Test(QtWidgets.QGraphicsView):\n def __init__(self, parent=None):\n super(Test, self).__init__(parent)\n self.s = QtWidgets.QGraphicsScene()\n self.setScene(self.s)\n\na = QtWidgets.QApplication(sys.argv)\nt = Test()\nt.show()\nQtCore.QTimer.singleShot(0, t.close)\nsys.exit(a.exec_())\n","repo_name":"RiggestOu/pyside2","sub_path":"tests/QtWidgets/bug_433.py","file_name":"bug_433.py","file_ext":"py","file_size_in_byte":357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"6"}
+{"seq_id":"41443903395","text":"# Project 1: ILLUMINATION AND DELICATE...\r\ndef gettime():\r\n import datetime\r\n return datetime.datetime.now()\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n c = int(input(\"Enter: \\n 1.Bus Incoming \\n 2.Bus Outgoing\\n\"))\r\n if c == 1:\r\n In = open(\"Bus Incoming.txt\", \"a\")\r\n BusIncomingdata = input(\"Type the Bus Number!!\\n\").upper()\r\n # I.write(str([str(gettime())]) + \": \" + BusIncomingdata + \"\\n\")\r\n In.write(str(\"ENTERED:\" + BusIncomingdata +\r\n \": \" + str([str(gettime())])) + \"\\n\")\r\n In.close()\r\n # Case 1:Example of KA32EF1884..\r\n if BusIncomingdata == \"KA32EF1884\":\r\n In1 = open(\"KA32EF1884.txt\", \"a\")\r\n In1.write(str(\"ENTERED:\" + BusIncomingdata +\r\n \": \" + str([str(gettime())])) + \"\\n\")\r\n In1.close()\r\n\r\n # Case 2:Example of KA12ER5858..\r\n if BusIncomingdata == \"KA12ER5858\":\r\n In1 = open(\"KA12ER5858.txt\", \"a\")\r\n In1.write(str(\"ENTERED:\" + BusIncomingdata +\r\n \": \" + str([str(gettime())])) + \"\\n\")\r\n In1.close()\r\n # More cases can be inserted with our choice or as per the number of buses\r\n\r\n elif c == 2:\r\n Out = open(\"Bus Outgoing.txt\", \"a\")\r\n BusOutgoingdata = (input(\"Type the Bus Number!!\\n\")).upper()\r\n Out.write(str(\"EXITED:\" + BusOutgoingdata +\r\n \": \" + str([str(gettime())])) + \"\\n\")\r\n Out.close()\r\n # Case 1:Example of KA32EF1884..\r\n if BusOutgoingdata == \"KA32EF1884\":\r\n Out1 = open(\"KA32EF1884.txt\", \"a\")\r\n Out1.write(str(\"EXITED:\" + BusOutgoingdata +\r\n \": \" + str([str(gettime())])) + \"\\n\")\r\n Out1.close()\r\n\r\n # Case 2:Example of KA12ER5858..\r\n if BusOutgoingdata == \"KA12ER5858\":\r\n Out1 = open(\"KA12ER5858.txt\", \"a\")\r\n Out1.write(str(\"EXITED:\" + BusOutgoingdata +\r\n \": \" + str([str(gettime())])) + \"\\n\")\r\n Out1.close()\r\n # More cases can be inserted with our choice or as per the number of buses\r\n else:\r\n print(\"Invalid Choice!!Please try again...\")\r\n print(\"And Enter the bus number correctly\\n\")\r\n","repo_name":"CurlySuraj/NumPlateReader","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2221,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"23067598911","text":"import os\nimport sys\n\nimport runner\nimport network.terraform as tf\nimport network.config\nimport sim.timeline\n\nfrom exceptions import LrException\n\nPYTHON = sys.executable\nHELPER = os.path.join(\n os.path.dirname(os.path.realpath(__file__)), \n \"helpers\", \"load.py\")\nFAMILY = {\n \"intkey\": \"txnintegration.integer_key_workload.IntegerKeyWorkload\",\n \"bond\": \"sawtooth_bond.bond_workload.BondWorkload\",\n}\n\n\ndef run(args, network_config):\n print(\"got {}, {}\".format(args, network_config))\n procs = []\n\n if 'family' in args:\n if args['family'] not in FAMILY:\n raise LrException(\"Unkown load family: {}\".format(family))\n family = FAMILY[args['family']]\n\n else:\n family = FAMILY['intkey']\n\n if 'sawtooth_path' in network_config:\n src_dir = network_config['sawtooth_path']\n if not os.path.exists(src_dir):\n raise LrException(\"Sawtooth not found at: {}\", src_dir)\n else:\n raise LrException(\n \"Network config does not contain `sawtooth_path`.\"\n \" Cannot load without access to sawtooth-core.\"\n )\n\n if 'duration' in args:\n try:\n duration = sim.timeline.parse_time(args['duration'])\n except:\n raise LrException(\n \"Duration must be integral, {} is invalid\".format(duration)\n )\n else:\n duration = -1\n\n for node in args['nodes']:\n node_url = \"http://{}:8800\".format(\n tf.get_node_ip(node, network_config))\n\n rate = args['nodes'][node]\n\n print(\"Starting {} workload: {} TPM => {} for {} seconds...\".format(\n family, rate, node_url, duration))\n\n procs.append(\n runner.spawn([\n PYTHON, HELPER, node_url, str(rate), src_dir, family, str(duration)\n ])\n )\n\n return procs\n","repo_name":"lntdev/raft_22Oct","sub_path":"sawtooth-ci/demonet/demomgr/sim/modules/load.py","file_name":"load.py","file_ext":"py","file_size_in_byte":1844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"36079674938","text":"import unittest\nfrom log.constant import ACTION\nfrom log.logdb import LogDb\nfrom log.qvalue import OrderPrices\nfrom log.qvalue import QValue\n\n\nclass MyTestCase(unittest.TestCase):\n\n def test_list_price_info(self):\n db = LogDb(db_name='/bitlog/bitlog.db')\n db.connect()\n\n time = None # 0\n market_order_sell = None # 1\n market_order_buy = None # 2\n fix_order_sell = None # 3\n fix_order_sell_time = None # 4\n fix_order_buy = None # 5\n fix_order_buy_time = None # 6\n\n org_price = OrderPrices()\n\n line_count = 0\n block_count = 0\n\n for line in db.list_price():\n price = OrderPrices()\n price.set_price_record(line)\n\n if (\n (price.market_order_sell != org_price.market_order_sell)\n or (price.market_order_buy != org_price.market_order_buy)\n or (price.fix_order_sell != org_price.fix_order_sell)\n or (price.fix_order_buy != org_price.fix_order_buy)):\n\n print(price, ',')\n block_count += 1\n\n line_count += 1\n org_price.set_price_record(line)\n\n print('line', line_count, 'block_count', block_count)\n\n\n def test_crate_db(self):\n db = LogDb() # create on memory\n db.connect()\n db.create_cursor()\n db.create()\n db.commit()\n\n def test_crate_db_insert_q(self):\n db = LogDb() # create on memory\n db.connect()\n db.create_cursor()\n db.create()\n db.commit()\n\n q = QValue()\n q.q[ACTION.NOP] = 1\n q.q[ACTION.BUY] = 1\n q.q[ACTION.SELL] = 1\n q.q[ACTION.SELL_NOW] = 1\n q.q[ACTION.BUY_NOW] = 1\n\n db.insert_q(100, 101, ACTION.BUY, q)\n\n def test_select_db_q(self):\n db = LogDb() # create on memory\n db.connect()\n db.create_cursor()\n db.create()\n db.commit()\n\n q = QValue()\n q.q[ACTION.NOP] = 1\n q.q[ACTION.BUY] = 2\n q.q[ACTION.SELL] = 3\n q.q[ACTION.SELL_NOW] = 4\n q.q[ACTION.BUY_NOW] = 5\n\n db.insert_q(100, 101, ACTION.BUY, q)\n\n r = db.select_q(100, 101, ACTION.BUY)\n print(r)\n\n def test_list_db_q(self):\n db = LogDb() # create on memory\n db.connect()\n db.create_cursor()\n db.create()\n db.commit()\n\n q = QValue()\n q.q[ACTION.NOP] = 1\n q.q[ACTION.BUY] = 2\n q.q[ACTION.SELL] = 3\n q.q[ACTION.SELL_NOW] = 4\n q.q[ACTION.BUY_NOW] = 5\n\n db.insert_q(100, 0, ACTION.BUY, q)\n db.insert_q(101, 100, ACTION.BUY, q)\n db.insert_q(102, 100, ACTION.BUY, q)\n db.insert_q(103, 100, ACTION.BUY, q)\n db.insert_q(101, 101, ACTION.BUY, q)\n db.commit()\n\n r = db.select_q(102, 100, ACTION.BUY)\n print(r)\n\n r = db.list_q(100, ACTION.BUY)\n print(r)\n\n for q in r:\n print(q)\n\n def test_list_update_q(self):\n db = LogDb('/bitlog/bitlog.db') # create on memory\n db.connect()\n db.create_cursor()\n db.create()\n db.commit()\n\n q = QValue()\n q.q[ACTION.NOP] = 1\n q.q[ACTION.BUY] = 2\n q.q[ACTION.SELL] = 3\n q.q[ACTION.SELL_NOW] = 4\n q.q[ACTION.BUY_NOW] = 5\n\n db.insert_q(100, 0, ACTION.BUY, q)\n db.insert_q(101, 100, ACTION.BUY, q)\n db.insert_q(102, 100, ACTION.BUY, q)\n db.insert_q(103, 100, ACTION.BUY, q)\n db.insert_q(101, 101, ACTION.BUY, q)\n db.commit()\n\n db.insert_updated_q()\n\n\n def test_create_q_values(self):\n db = LogDb('/bitlog/bitlog.db')\n db.connect()\n db.create_cursor()\n db.create()\n\n start, end = db.get_db_info()\n print('start/end', start, end)\n\n price = db.list_price(start_time=start, end_time=start + 150)\n print(len(price))\n\n q_seq = db.create_q_sequence(start_time=start, action=ACTION.SELL, start_price=7245)\n q_seq.dump_q()\n\n\n def test_create_q_and_insert(self):\n db = LogDb('/bitlog/bitlog.db')\n db.connect()\n db.create_cursor()\n db.create()\n\n start, end = db.get_db_info()\n print('start/end', start, end)\n\n db.insert_updated_q()\n\n def test_select_q(self):\n db = LogDb('/bitlog/bitlog.db')\n db.connect()\n db.create_cursor()\n db.create()\n\n start, end = db.get_db_info()\n\n #q = db.select_q_values(start, ACTION.NOP)\n #q = db.select_q_values(start, ACTION.BUY)\n #q = db.select_q_values(start, ACTION.BUY_NOW)\n q = db.select_q_values(start, ACTION.SELL)\n print(q)\n q = db.select_q_values(start, ACTION.SELL_NOW)\n print(q)\n\n\n def test_update_q_on_nop(self):\n db = LogDb('/bitlog/bitlog.db')\n db.connect()\n db.create_cursor()\n\n db.update_q_on_nop()\n\n\n def test_update_all_q_values(self):\n db = LogDb('/bitlog/bitlog.db')\n db.connect()\n db.create_cursor()\n\n db.update_all_q()\n\n db.commit()\n\n def test_select_hi_price(self):\n db = LogDb('/bitlog/bitlog.db')\n db.connect()\n db.create_cursor()\n\n start, _ = db.get_db_info()\n p = db.select_center_price(start)\n r = db.select_highest_price_time(start)\n print(start, p, r)\n\n start += 500\n p = db.select_center_price(start)\n r = db.select_highest_price_time(start)\n print(start, p, r)\n\n start += 500\n p = db.select_center_price(start)\n r = db.select_highest_price_time(start)\n print(start, p, r)\n\n start += 500\n p = db.select_center_price(start)\n r = db.select_highest_price_time(start)\n print(start, p, r)\n\n start += 5000\n p = db.select_center_price(start)\n r = db.select_highest_price_time(start)\n print(start, p, r)\n\n\n def test_create_high_q_sequcen(self):\n db = LogDb('/bitlog/bitlog.db')\n db.connect()\n db.create_cursor()\n\n start, _ = db.get_db_info()\n\n start_price = 7000\n action = ACTION.BUY\n\n db.create_q_sequence(start_time=start, action=action, start_price=start_price)\n\n\n def test_create_low_q_sequcen(self):\n db = LogDb('/bitlog/bitlog.db')\n db.connect()\n db.create_cursor()\n\n start, _ = db.get_db_info()\n\n start_price = 7220\n action = ACTION.SELL\n\n db.create_q_sequence(start_time=start, action=action, start_price=start_price)\n\n\n\n\n def test_select_low_price(self):\n db = LogDb('/bitlog/bitlog.db')\n db.connect()\n db.create_cursor()\n\n start, _ = db.get_db_info()\n r = db.select_lowest_price_time(start)\n\n print(start, r)\n\n\n def test_select_q(self):\n db = LogDb('/bitlog/bitlog.db')\n db.connect()\n db.create_cursor()\n\n q = db.select_q(0, 0, 0)\n print(q)\n\n q = db.select_q(0, 0, 1)\n print(q)\n\n q = db.select_q(1575501187, 1575501187, 2)\n print(q)\n\n q = db.select_q(1575542888, 1575542888, 1)\n print(q)\n q = db.select_q(1575542888, 1575542888, 2)\n print(q)\n q = db.select_q(1575542888, 1575542888, 3)\n print(q)\n q = db.select_q(1575542888, 1575542888, 4)\n print(q)\n\n\n\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"yasstake/mmf","sub_path":"test/log/logdb_test.py","file_name":"logdb_test.py","file_ext":"py","file_size_in_byte":7525,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"6"}
+{"seq_id":"10835238171","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*- #\nfrom __future__ import unicode_literals\n\nAUTHOR = u'Mitch Lindgren'\nSITENAME = u'mlindgren.ca'\nSITEURL = 'http://blog.mlindgren.ca'\n\nPATH = 'content'\n\nTIMEZONE = 'America/Los_Angeles'\n\nDEFAULT_LANG = u'en'\n\n# Feed generation is usually not desired when developing\nFEED_ALL_ATOM = None\nCATEGORY_FEED_ATOM = None\nTRANSLATION_FEED_ATOM = None\nAUTHOR_FEED_ATOM = None\nAUTHOR_FEED_RSS = None\n\nDISPLAY_PAGES_ON_MENU = True\n\nDISQUS_SITENAME = 'mlindgrenca'\nGOOGLE_ANALYTICS = 'UA-56108181-1'\n\n# Blogroll\nLINKS = ()\n\n# Social widget\nSOCIAL = (\n('![](\"http://files.mlindgren.ca/images/github.png\"/)
Github', 'https://github.com/mlindgren'),\n('![](\"http://files.mlindgren.ca/images/last.fm.png\"/)
last.fm', 'http://last.fm/user/lindgrenM'),\n('![](\"http://files.mlindgren.ca/images/stackoverflow.png\")
Stack Overflow', 'http://stackoverflow.com/users/108340/mitch-lindgren'),\n('![](\"http://files.mlindgren.ca/images/yelp.png\"/)
Yelp', 'http://mlindgren.yelp.com'),\n('![](\"http://files.mlindgren.ca/images/yt.png\"/)
YouTube', 'http://www.youtube.com/user/lindgrenMitch'),\n)\n\nGITHUB_USER = 'mlindgren'\nGITHUB_REPO_COUNT = 5\nGITHUB_SKIP_FORK = False\nGITHUB_SHOW_USER_LINK = False\n\nX_MIN_READ = True\n\nDEFAULT_PAGINATION = 10\n\nARTICLE_URL = 'entry/{date:%Y}/{date:%m}/{date:%d}/{slug}/'\nARTICLE_SAVE_AS = 'entry/{date:%Y}/{date:%m}/{date:%d}/{slug}/index.html'\nARTICLE_LANG_URL = 'entry/{date:%Y}/{date:%m}/{date:%d}/{slug}-{lang}/'\nARTICLE_LANG_SAVE_AS = 'entry/{date:%Y}/{date:%m}/{date:%d}/{slug}-{lang}/index.html'\nPAGE_URL = 'blog/{slug}/'\nPAGE_SAVE_AS = 'blog/{slug}/index.html'\nARCHIVES_SAVE_AS = 'blog/archives/index.html'\nTAG_URL = 'tag/{slug}/'\nTAG_SAVE_AS = 'tag/{slug}/index.html'\n\nTHEME = '../mlindgren-pelican-theme'\n\nSUMMARY_MAX_LENGTH = None\n\nPLUGIN_PATHS = [\"../pelican-plugins\"]\nPLUGINS = [\"liquid_tags.img\", \"liquid_tags.video\", \"liquid_tags.youtube\",\n \"liquid_tags.include_code\", \"summary\", \"post_stats\", \"render_math\"]\n\nSTATIC_PATHS = ['extra/favicon.ico', 'extra/favicon.png']\nEXTRA_PATH_METADATA = {\n 'extra/favicon.ico': {'path': 'favicon.ico'},\n 'extra/favicon.png': {'path': 'favicon.png'}\n}\n\n# Uncomment following line if you want document-relative URLs when developing\n#RELATIVE_URLS = True\n","repo_name":"mlindgren/blog","sub_path":"pelicanconf.py","file_name":"pelicanconf.py","file_ext":"py","file_size_in_byte":2292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"42729406050","text":"# Title : Caecar Cipher\n# Author : Lance R. Bernal\n# Date: 10-07-2022\n# Description: A simple program to encrypt and decrypt text using caecar cipher. This program can cipher ascii values from 32 upto 122\n\nimport os\n\ndef clrscr(): # function for clearing the termninal\n if os.name == 'posix': \n _= os.system('clear')\n else:\n _= os.system('cls')\n\ndef encrypt(): # function for encrypting the texts\n clrscr()\n divider()\n print(\" Encryption \")\n text = input(\"Enter word/text to be encrypted: \") # stores the string entered by user\n pin = int(input(\"Enter PIN to encrypt [ex. 1234]: \")) # stores the pin entered by user\n\n pin = pin % ord('z') # modulo the pin entered to the maximum ascii value which is ord('z') or 122\n textEncrypt = \"\" # stores the converted string\n #check = [] # stores ascii values of each character for checking\n\n for index in text: # loop for assigning ascii with shifted value for each character of string \n asciiVal = ord(index) \n asciiVal = asciiVal + pin \n if asciiVal <= ord('z'):\n asciiVal = asciiVal\n else: # if the ascii value of a character exceeds ord('z'), it will be modulo-ed, 31 is added to start the shift at the minimum value of ord(' ') or 32\n asciiVal = (asciiVal % ord('z')) + 31\n\n #check.append(asciiVal) # write each encrypted ascii value for checking\n textEncrypt += chr(asciiVal)\n\n clrscr()\n divider()\n\n #print(check) # display encrypted ascii values for checking\n print(\" The encrypted text/word is:\", textEncrypt) # display the encrypted string\n again()\n\ndef decrypt(): # function for decrypting the encrypted string\n clrscr()\n divider()\n print(\" Decryption \")\n text = input(\"Enter word/text to be decrypted: \") # stores the encrypted string\n pin = int(input(\"Enter PIN to decrypt [ex. 1234]: \")) # stores the pin entered by the user\n\n pin = pin % ord('z') # modulo the pin entered to the maximum ascii value which is ord('z') or 122\n textDecrypt = \"\" # stores the converted string\n #check = [] # stores ascii values of each character for checking\n\n for index in text: # loop for decrypting the string using the pin\n asciiVal = ord(index)\n asciiVal = asciiVal - pin\n if asciiVal >= ord(' '):\n asciiVal = asciiVal\n else: # if the ascii value of a character is less than ord(' ') or 32, 31 will be deducted and the ascii value will be modulo-ed to start the shift at ord(' ') or 32\n asciiVal = (asciiVal - 31) % ord('z')\n\n # check.append(asciiVal) # write each decrypted ascii value for checking\n textDecrypt += chr(asciiVal)\n\n clrscr()\n divider()\n # print(check) # display encrypted ascii values for checking\n print(\"The decrypted text/word is:\", textDecrypt) # display the decrypted string\n again()\n\ndef divider(): # function to divide contents in terminal\n print(\" \")\n print(\"*******************************************\")\n print(\" \")\n\ndef again(): # function for going back to main menu/page\n divider()\n choice = input(\" Do you want to go to menu? [y/n]: \")\n if choice == 'y': # if user choose 'y', the program will go to menu, if not it will exit\n menu()\n else:\n exit()\n\ndef menu(): # main menu/page of program \n clrscr()\n divider()\n print(\" Menu \")\n print(\" 1. Encrypt Text\")\n print(\" 2. Decrypt Text\")\n print(\" 3. Exit\")\n print(\" \")\n choice = int(input(\"Enter you choice [1-3]: \"))\n if choice == 1: # the user's choice corresponds a condtion, and the program will proceed accordingly\n encrypt()\n elif choice == 2:\n decrypt()\n else:\n exit()\n\nmenu() # calling of function main","repo_name":"lancewrt/Python","sub_path":"CaecarCipherFInal.py","file_name":"CaecarCipherFInal.py","file_ext":"py","file_size_in_byte":3832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"7964714918","text":"import logging\nimport os\nimport shutil\n\nfrom ops.charm import CharmBase\nfrom ops.framework import StoredState\nfrom ops.main import main\nfrom ops.model import ActiveStatus, WaitingStatus, MaintenanceStatus\nimport subprocess\nimport sys\n\nimport utils\n\nlogger = logging.getLogger(__name__)\n\nEMOJI_CORE_HOOK_EVENT = \"\\U0001F4CC\"\nEMOJI_MESSAGE = \"\\U0001F4AC\"\nEMOJI_GREEN_DOT = \"\\U0001F7E2\"\nEMOJI_RED_DOT = \"\\U0001F534\"\nEMOJI_PACKAGE = \"\\U0001F4E6\"\n\n\nclass IPFSCharm(CharmBase):\n \"\"\"Charm the hello service with all core hooks.\"\"\"\n\n _stored = StoredState()\n\n def __init__(self, *args):\n super().__init__(*args)\n self.framework.observe(self.on.install, self._on_install)\n self.framework.observe(self.on.config_changed, self._on_config_changed)\n self.framework.observe(self.on.start, self._on_start)\n self.framework.observe(self.on.leader_elected, self._on_leader_elected)\n self.framework.observe(self.on.leader_settings_changed, self._on_leader_settings_changed)\n self.framework.observe(self.on.stop, self._on_stop)\n self.framework.observe(self.on.remove, self._on_remove)\n self.framework.observe(self.on.stop, self._on_stop)\n self.framework.observe(self.on.update_status, self._on_update_status)\n self.framework.observe(self.on.collect_metrics, self._on_collect_metrics)\n self.framework.observe(self.on.upgrade_charm, self._on_upgrade_charm)\n\n self._stored.set_default(snap_channel=self.config[\"snap-channel\"],\n restart_on_reconfig=self.config[\"restart-on-reconfig\"])\n\n\n def _on_install(self, event):\n \"\"\"\n Install your software here plus any dependencies, utilities and everything you need to run\n your service over time.\n\n Optionally take care of installing resources attached with the charm deployment.\n\n In this charm, we install a package and a service unit file which will use hello.\n\n This hook is ran after the storage-filesystem-attached hook - only once in the entire lifetime of the unit.\n \"\"\"\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n\n logger.info(f\"Installing from snap ipfs {EMOJI_PACKAGE}\")\n os.system(f\"snap install ipfs --channel={self._stored.snap_channel}\")\n\n # Install unit file for hello (one-shot service)\n shutil.copyfile('templates/etc/systemd/system/ipfs-daemon.service', '/etc/systemd/system/ipfs-daemon.service')\n\n # (re)config hello.\n self._reconfig_ipfs(restart=False)\n\n\n def _on_config_changed(self, event):\n \"\"\"\n Deal with charm configuration changes here.\n\n Detect changes to individual config items, by storing and comparing values in self._stored\n\n This hook is run after the start hook.\n This hook run after the upgrade-charm hook.\n This hook is run after the leader-elected hook.\n \"\"\"\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n\n if self.config[\"snap-channel\"] != self._stored.snap_channel:\n self._stored.snap_channel = self.config[\"snap-channel\"]\n self._reconfig_ipfs(restart=self.config[\"restart-on-reconfig\"])\n\n self._on_update_status(event)\n\n def _on_start(self, event):\n \"\"\"\n Start your service here, possibly defer (wait) until conditions are OK.\n \"\"\"\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n logger.debug(f\"ipfs init\")\n os.system('sudo -u ubuntu ipfs init')\n logger.info(f\"{EMOJI_GREEN_DOT} Starting the ipfs-daemon service...\")\n os.system('systemctl start ipfs-daemon.service')\n\n # Calling update_status gives quick feedback when deploying starts up.\n self._on_update_status(event)\n\n def _on_leader_elected(self, event):\n \"\"\"\n This is only run on the unit which is selected by juju as leader.\n We are not implementing anything here. See the \"leadership\" charm for an example.\n \"\"\"\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n\n def _on_leader_settings_changed(self, event):\n \"\"\"\n This is only run on the unit which is selected by juju as leader.\n We are not implementing anything here. See the \"leadership\" charm for an example.\n \"\"\"\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n\n def _on_update_status(self, event):\n \"\"\"\n This runs every 5 minutes.\n\n Have one place to figure out status for the charm is a good strategy for a beginner charmer.\n \"\"\"\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n\n if not os.system('systemctl is-active ipfs-daemon.service') == 0:\n logger.info(\"ipfs-daemon service is not running.\")\n self.unit.status = MaintenanceStatus(\"Inactive.\")\n else:\n logger.info(f\"ipfs-daemon service is running.\")\n self.unit.status = ActiveStatus(\"Running.\")\n\n if self.model.unit.is_leader():\n self.unit.set_workload_version(utils.getIpfsVersion())\n \n def _on_upgrade_charm(self, event):\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n\n # Re-install systemd unit\n shutil.copyfile('templates/etc/systemd/system/ipfs-daemon.service', '/etc/systemd/system/ipfs-daemon.service')\n\n def _on_stop(self, event):\n \"\"\"\n Bring down your service, possibly defer until all systems are good to go similar to start hook.\n \"\"\"\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n\n logger.info(f\"{EMOJI_RED_DOT} Stopping the ipfs-daemon service...\")\n os.system('systemctl stop ipfs-daemon.service')\n\n\n def _on_remove(self, event):\n \"\"\"\n Remove stuff you might want to clean up.\n\n This hook is run after the stop hook.\n \"\"\"\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n\n logger.info(f\"Removing ipfs {EMOJI_PACKAGE}\")\n os.system('snap remove ipfs')\n\n\n def _on_collect_metrics(self, event):\n \"\"\"\n This runs every 5 minutes - if metrics are defined in metrics.yaml.\n\n We don't implement any metrics this in this charm. See the metrics charm for a working example.\n \n ipfs stats might be useful\n\n \"\"\"\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n\n\n def _reconfig_ipfs(self, restart=False):\n \"\"\"\n Reconfigures the startup parameters of hello.service by modifying the /etc/default/hello file.\n Reloads systemd daemons.\n\n Optionally, restart the service.\n \"\"\"\n logger.info(f\"{EMOJI_MESSAGE} Configuring ipfs-daemon snap-channel: {self._stored.snap_channel}\")\n self._stored.snap_channel = self.config[\"snap-channel\"]\n os.system(f\"snap refresh ipfs --channel={self._stored.snap_channel}\")\n\n with open('/etc/default/ipfs', 'w') as f:\n f.write(f\"CUSTOM_ARGS=\\\\'daemon\\\\'\")\n os.system('systemctl daemon-reload')\n\n if restart:\n logger.info(f\"{EMOJI_GREEN_DOT} Restarting hello.\")\n os.system('systemctl restart ipfs-daemon.service')\n\n def _get_ipfs_peerid(self):\n import json\n f = open('/home/ubuntu/snap/ipfs/common/config')\n data = json.load(f)\n p = data['Identity']['PeerID']\n print(p)\n f.close()\n\n \nif __name__ == \"__main__\":\n main(IPFSCharm)\n","repo_name":"erik78se/ipfs-charms","sub_path":"ipfs-daemon/src/charm.py","file_name":"charm.py","file_ext":"py","file_size_in_byte":7591,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"6"}
+{"seq_id":"38929054676","text":"# Import the required modules\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\r\nfrom selenium.webdriver.chrome.service import Service\r\nimport time\r\nfrom bs4 import BeautifulSoup\r\n\r\n# Main Function\r\nif __name__ == \"__main__\":\r\n\r\n # Enable Performance Logging of Chrome.\r\n desired_capabilities = DesiredCapabilities.CHROME\r\n desired_capabilities[\"goog:loggingPrefs\"] = {\"performance\": \"ALL\"}\r\n\r\n # Create the webdriver object and pass the arguments\r\n options = webdriver.ChromeOptions()\r\n\r\n # Chrome will start in Headless mode\r\n options.add_argument('headless')\r\n\r\n # Ignores any certificate errors if there is any\r\n options.add_argument(\"--ignore-certificate-errors\")\r\n\r\n # Startup the chrome webdriver with executable path and\r\n # pass the chrome options and desired capabilities as\r\n # parameters.\r\n service = Service(executable_path='C:/Users/milov/Downloads/chromedriver_win32/chromedriver.exe',\r\n chrome_options=options,\r\n desired_capabilities=desired_capabilities)\r\n driver = webdriver.Chrome(service=service)\r\n\r\n # Send a request to the website and let it load\r\n driver.get(\"https://soundcloud.com/discover\")\r\n\r\n # Sleeps for 10 seconds\r\n time.sleep(10)\r\n\r\n # Gets content of page\r\n content = driver.page_source\r\n\r\n print(\"Quitting Selenium WebDriver\")\r\n driver.quit()\r\n\r\n # Parse the HTML content using BeautifulSoup\r\n soup = BeautifulSoup(content, 'html.parser')\r\n\r\n # Extract the title and artist of the track\r\n playlists = soup.find_all(\"a\", class_=\"playableTile__artworkLink\")\r\n print(len(playlists))\r\n for playlist in playlists:\r\n print(playlist['href'])\r\n","repo_name":"milovdpas/SoundZam","sub_path":"test-python-scripts/scrape_soundcloud_website.py","file_name":"scrape_soundcloud_website.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"34752272848","text":"import toga\nfrom toga.style.pack import COLUMN, Pack\n\n\ndef action_top_travel_icon(widget):\n print(\"amr\")\n\n\ndef action_top_browser_icon(widget):\n print(\"browser\")\n\n\ndef build(app):\n browser_icon = \"icons/browser.png\"\n plane_icon = \"icons/plane.png\"\n\n\n data = [\n # ('root%s' % i, 'value %s' % i)\n # for i in range(1, 100)\n ]\n\n left_container = toga.Table(headings=['Oracle Open', 'World'], data=data)\n\n right_content = toga.Box(\n style=Pack(direction=COLUMN, padding_top=50)\n )\n\n # for b in range(0, 10):\n # right_content.add(\n # toga.Button(\n # 'Oracle World - %s' % b,\n # on_press=button_handler,\n # style=Pack(width=200, padding=20)\n # )\n # )\n\n right_content.add (\n toga.Button(\n \"Log In / Sign In\",\n style=Pack(width=500,height=100, padding=20)\n )\n )\n\n right_content.add (\n toga.Button(\n \"Oracle World Sessions\",\n style=Pack(width=500,height=100, padding=20)\n )\n )\n\n \n right_container = toga.ScrollContainer(horizontal=False)\n\n right_container.content = right_content\n\n split = toga.SplitContainer()\n\n # The content of the split container can be specified as a simple list:\n # split.content = [left_container, right_container]\n # but you can also specify \"weight\" with each content item, which will\n # set an initial size of the columns to make a \"heavy\" column wider than\n # a narrower one. In this example, the right container will be twice\n # as wide as the left one.\n split.content = [\n (left_container, 1),\n (right_container, 2)\n ]\n\n # Create a \"Things\" menu group to contain some of the commands.\n # No explicit ordering is provided on the group, so it will appear\n # after application-level menus, but *before* the Command group.\n # Items in the Things group are not explicitly ordered either, so they\n # will default to alphabetical ordering within the group.\n things = toga.Group('Things')\n top_travel_icon = toga.Command(\n action_top_travel_icon,\n label='Planner',\n tooltip='Plan Your Trip',\n icon=plane_icon,\n group=things\n )\n\n top_browser_icon = toga.Command(\n action_top_browser_icon,\n label='Browser',\n tooltip='Browser View',\n icon=browser_icon,\n group=things\n )\n \n\n # The order in which commands are added to the app or the toolbar won't\n # alter anything. Ordering is defined by the command definitions.\n app.commands.add(top_travel_icon, top_browser_icon)\n app.main_window.toolbar.add(top_travel_icon, top_browser_icon)\n\n return split\n\n\ndef main():\n return toga.App('OracleWorld2022', 'org.beeware.helloworld', startup=build)\n\n\nif __name__ == '__main__':\n main().main_loop()","repo_name":"RahulMR42/oci-devop-grapql-sample-mobapp-1","sub_path":"src/oracleworld_graphql/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2882,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"7366612293","text":"\ndef fatorial(num, show=False):\n \"\"\"\n -> Calcula o fatorial de um número\n :param num: Número a ser calculado o fatorial\n :param show: (Opcional) Mostra o passo a passo do fatorial\n :return: O valor do fatorial do número num\n \"\"\"\n print(f'_'*40)\n f = 1\n for c in range(num, 0, -1):\n if show:\n print(f'{c}', end=' x ' if c != 1 else ' = ')\n f *= c\n return f\n\n\nprint(fatorial(5, show=True))\n\n","repo_name":"mateuzh/Python","sub_path":"desafio102.py","file_name":"desafio102.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"39688724244","text":"# 77. Combinations\n# Time: k*nCk (Review: nCk combinations each of length k)\n# Space: k*nCk\nclass Solution:\n def combine(self, n: int, k: int) -> List[List[int]]:\n\n if n==0:\n return []\n\n if k==0:\n return [[]]\n\n if k==1:\n return [[i] for i in range(1,n+1)]\n\n without_n = self.combine(n-1, k)\n with_n = self.combine(n-1, k-1)\n\n for index in range(len(with_n)):\n with_n[index] = [n]+with_n[index]\n\n return without_n + with_n\n","repo_name":"cmattey/leetcode_problems","sub_path":"Python/lc_77_combinations.py","file_name":"lc_77_combinations.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"6"}
+{"seq_id":"21568997174","text":"'''\nAuthor: ‘puzhiyuan’ ‘puzhiyuan185489643@gmail.com’\nDate: 2023-11-19 16:59:44\nLastEditors: ‘puzhiyuan’ ‘puzhiyuan185489643@gmail.com’\nLastEditTime: 2023-11-19 21:59:04\nFilePath: \\AutonomousDriving\\PyTorch\\src\\VGG.py\nDescription: VGG16 and VGG18\n'''\n\nfrom turtle import Turtle\nfrom getDataloader import getFashionMNIST\nfrom getDevice import get_device\nfrom model_test import model_test\nfrom model_train import model_train\nfrom sympy import ode_order\nimport torch\nimport torch.nn as nn\nfrom torchvision import datasets, transforms\n\n\nclass VGG_16(nn.Module):\n def __init__(self, in_channels=1, num_classes=1000, *args, **kwargs) -> None:\n super().__init__(*args, **kwargs)\n self.layers = nn.Sequential(\n nn.Conv2d(in_channels, 64, 3, padding=1), nn.ReLU(),\n nn.Conv2d(64, 64, 3, padding=1), nn.ReLU(),\n nn.MaxPool2d(2, 2),\n nn.Conv2d(64, 128, 3, padding=1), nn.ReLU(),\n nn.Conv2d(128, 128, 3, padding=1), nn.ReLU(),\n nn.MaxPool2d(2, 2),\n nn.Conv2d(128, 256, 3, padding=1), nn.ReLU(),\n nn.Conv2d(256, 256, 3, padding=1), nn.ReLU(),\n nn.Conv2d(256, 256, 3, padding=1), nn.ReLU(),\n nn.MaxPool2d(2, 2),\n nn.Conv2d(256, 512, 3, padding=1), nn.ReLU(),\n nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(),\n nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(),\n nn.MaxPool2d(2, 2),\n nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(),\n nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(),\n nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(),\n nn.MaxPool2d(2, 2),\n nn.Flatten(),\n nn.Linear(7*7*512, 4096), nn.ReLU(),\n nn.Linear(4096, 4096), nn.ReLU(),\n nn.Linear(4096, 4096), nn.ReLU(),\n nn.Linear(4096, num_classes)\n )\n\n def forward(self, X):\n return self.layers(X)\n\n\n\nclass VGG_19(nn.Module):\n def __init__(self, num_classes, *args, **kwargs) -> None:\n super().__init__(*args, **kwargs)\n self.features = self._make_layers([64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M',\n 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'])\n self.classifier = nn.Sequential(\n nn.Linear(7*7*512, 4096),nn.ReLU(), nn.Dropout(),\n nn.Linear(4096, 4096), nn.ReLU(), nn.Dropout(),\n nn.Linear(4096, num_classes)\n )\n def forward(self, X):\n return self.classifier(nn.Flatten(self.features(X)))\n \n\n def _make_layers(self, cfg):\n layers = []\n in_channels = 1\n for i in cfg:\n if i == \"M\":\n layers.append(nn.MaxPool2d(2,2))\n else:\n layers.append(nn.Conv2d(in_channels, i, 3, padding=1))\n layers.append(nn.ReLU())\n in_channels = i\n return nn.Sequential(*layers)\n\n\nif __name__ == \"__main__\":\n batch_size, lr, epochs = 64, 0.01, 30\n device = get_device()\n print(f\"device: {device}\")\n root = \"PyTorch/data\"\n transform = transforms.Compose([transforms.Resize(224), transforms.ToTensor()])\n train_dataloader, test_dataloader = getFashionMNIST(root, transform, batch_size)\n\n model = VGG_16().to(device)\n\n loss = nn.CrossEntropyLoss()\n optimizer = torch.optim.SGD(model.parameters(), lr=lr)\n\n for i in range(epochs):\n train_loss = model_train(model, train_dataloader, loss, optimizer, device)\n test_accuracy = model_test(model, test_dataloader, device)\n print(f\"eopch: {i+1}/{epochs} train_loss: {train_loss} test_accuracy: {test_accuracy}\")\n","repo_name":"puzhiyuan/AutonomousDriving","sub_path":"PyTorch/src/VGG.py","file_name":"VGG.py","file_ext":"py","file_size_in_byte":3656,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"6"}
+{"seq_id":"7346864958","text":"import random\nimport numpy as np\n\ndef data_fmt(file_name):\n \"\"\"\n data format\n\n :arg file_name: string, name of data file\n\n :return: 2Darray including features and label(the last column)\n \"\"\"\n with open(file_name, \"r\") as file:\n content = file.readlines()\n ## dataset.shape(m, n),m=number of samples, n=number of features + 1(label)\n dataset = []\n for line in content:\n dataset.append(list(map(float, line.strip().split(\"\\t\"))))\n return np.array(dataset)\n\ndef selectJrand(i, n):\n \"\"\"\n Function to select the index of another alpha.\n\n :args i: int, index of the selected alpha\n :args n: int, number of all the alpha\n\n :return: int, index of the another alpha\n \"\"\"\n j = i\n while(j == i):\n j = int(random.uniform(0, n))\n return j\n\ndef limit_alpha(alpha, H, L):\n \"\"\"\n Function to limit the range of some alpha.\n\n :args alpha: int\n :args H: int, upper limit\n :args L: int, lower limit\n \n :return: int\n \"\"\"\n if alpha > H:\n alpha = H\n if alpha < L:\n alpha = L\n return alpha\n\ndef SMO_simple(dataset, C, toler, maxIter):\n \"\"\"\n 简易版SMO优化算法\n\n :args dataset: 2Darray including features and label(the last column)\n :args C: int, slack variable(松弛变量)\n :args toler: int, fault tolerance\n :args maxInter: int, maximun number of iterations\n\n :returns b: int, bias\n :returns alphas: vector \n \"\"\"\n features = dataset[:, :-1]\n labels = dataset[:, -1]\n m, n = features.shape\n ## 初始化\n b = 0\n alphas = np.zeros(m)\n iters = 0\n ## 外循环\n while (iters < maxIter):\n ## 改变的alpha对数\n alphaPairsChanged = 0\n ## 遍历样本\n for i in range(m):\n ## 第一个alpha\n fxi = float((alphas*labels).dot(features.dot(features[i, :].T))) + b # 计算预测值\n Ei = fxi - float(labels[i]) # 计算误差\n ## 满足KKT条件，则选取第二个alpha进行计算\n if (((labels[i] * Ei < -toler) and (alphas[i] < C)) or ((labels[i] * Ei > toler) and (alphas[i] > 0))):\n j = selectJrand(i, m) # 随机选取第二个alpha的索引\n fxj = float((alphas*labels).dot((features.dot(features[j, :].T)))) + b # 计算预测值\n Ej = fxj - float(labels[j]) # 计算误差\n ## 记录两个alpha的原始值，便于后续比较\n alphaIold = alphas[i].copy()\n alphaJold = alphas[j].copy()\n ## 如果两个alpha对应的样本标签不同\n if (labels[i] != labels[j]):\n # 求出相应的上下边界\n L = max(0, alphas[j] - alphas[i])\n H = min(C, C+alphas[j] - alphas[i])\n else:\n L = max(0, alphas[j] + alphas[i] - C)\n H = min(C, alphas[j] + alphas[i])\n if L == H: print(\"L == H\");continue\n ## 根据公式计算未经剪辑的alphaj\n #--------------------------\n eta = 2 * features[i, :].dot(features[j, :].T) - \\\n features[i, :].dot(features[i, :].T) - \\\n features[j, :].dot(features[j, :].T)\n # 如果eta>=0，跳出循环\n if eta >= 0:print(\"eta >= 0\");continue\n alphas[j] -= labels[j] * (Ei - Ej) / eta\n alphas[j] = limit_alpha(alphas[j], H, L)\n #--------------------------\n ## 如果改变后的alphaj的值变化不大，跳出本次循环\n if (abs(alphas[j] - alphaJold) < 0.00001):\n print(\"j not moving enough\")\n continue\n ## 否则计算相应的alphai的值\n alphas[i] += labels[j] * labels[i] * (alphaJold - alphas[j])\n ## 分别计算两个alpha情况下对应的b值\n b1 = b - Ei - labels[i] * (alphas[i] - alphaIold) * \\\n features[i, :].dot(features[i, :].T) - labels[j] * \\\n (alphas[j] - alphaJold) * features[i, :].dot(features[j, :].T)\n b2 = b - Ej - labels[i] * (alphas[i] - alphaIold) * \\\n features[i, :].dot(features[j, :].T) - \\\n labels[j] * (alphas[j] - alphaJold) * \\\n features[j, :].dot(features[j, :].T)\n ## 如果0 alphas[j]):\n b = b1\n elif (0 < alphas[j]) and (C > alphas[j]):\n b = b2\n else:\n b = (b1 + b2) / 2\n ## 如果走到此步， 说明改变了一对alpha的值\n alphaPairsChanged += 1\n print(\"iter: {:d} i: {:d}, paird changed {:d}\".format(iters, i, alphaPairsChanged))\n ## 最后判断是否有改变的alpha对，没有就进行下一次迭代\n if (alphaPairsChanged == 0):\n iters += 1\n ## 否则， 迭代次数置为０,继续循环\n else:\n iters = 0\n print(\"iteration number: {:d}\".format(iters))\n return b, alphas\n\n\nif __name__ == \"__main__\":\n file_name = \"./data/testSet.txt\"\n dataset = data_fmt(file_name)\n b, alphas = SMO_simple(dataset, 0.6, 0.001, 40)\n import pdb;pdb.set_trace()","repo_name":"LQY318/MachineLearning","sub_path":"C06/optimizer_simple.py","file_name":"optimizer_simple.py","file_ext":"py","file_size_in_byte":5378,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"6"}
+{"seq_id":"10929579052","text":"from pymongo import MongoClient\n\n\ndef associate_product_category(product_id: int, category_id: int):\n client = MongoClient('mongodb://localhost:27017/')\n db = client['store']\n collection_products = db['products']\n collection_categories = db['categories']\n product_category = db['product_category']\n\n product = collection_products.find_one({'_id': product_id})\n category = collection_categories.find_one({'_id': category_id})\n\n if not product:\n print('This product does not exist.')\n return\n\n if not category:\n print('This category does not exist')\n return\n\n document = {'productId': product_id, 'categoryId': category_id}\n product_category.insert_one(document)\n\n\nassociate_product_category(2, 1)\n","repo_name":"ThallesCansi/Programacao-para-Web","sub_path":"2º Bimestre/Capítulo 10 - Bancos de Dados/Exercise 10.20.py","file_name":"Exercise 10.20.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"30816897113","text":"from ogb.graphproppred import GraphPropPredDataset\nimport tqdm as tq\nimport shutil\nimport os\n\ndef ogb2tu(dataname, out_dir='.', base = 1, attr_as_label=None, task=0, iszipped=False):\n dataset = GraphPropPredDataset(name = dataname)\n dirpath = os.path.join(out_dir,dataname)\n print(\"Working directory \",dirpath)\n if not os.path.exists(dirpath):\n os.mkdir(dirpath)\n f1 = open(\"%s/%s_A.txt\"%(dirpath,dataname),'w')\n has_vertex_attr = True if 'node_feat' in dataset.graphs[0].keys() else False\n has_edge_attr = True if 'edge_feat' in dataset.graphs[0].keys() else False\n if has_vertex_attr: f2 = open(\"%s/%s_node_attributes.txt\"%(dirpath,dataname),'w')\n if has_edge_attr: f3 = open(\"%s/%s_edge_attributes.txt\"%(dirpath,dataname),'w')\n f4 = open(\"%s/%s_graph_labels.txt\"%(dirpath,dataname),'w')\n f5 = open(\"%s/%s_graph_indicator.txt\"%(dirpath,dataname),'w')\n has_vertex_label = True if (attr_as_label is not None and len(dataset.graphs[0]['node_feat'][0:]) > attr_as_label) else False\n if has_vertex_label: f6 = open(\"%s/%s_node_labels.txt\"%(dirpath,dataname),'w')\n nonodes = 0\n y = dataset.labels\n for i,g in enumerate(tq.tqdm(dataset.graphs)):\n f4.write(\"%d\\n\"%(y[i][task]))\n j = 0\n for source,target in list(zip(g['edge_index'][0], g['edge_index'][1])):\n f1.write(\"%d, %d\\n\"%(source+base+(nonodes), target+base+(nonodes)))\n if has_edge_attr:\n edge_features = [str(e) for e in g['edge_feat'][j]]\n f3.write(\"%s\\n\"%(','.join(edge_features)))\n j += 1\n for node in range(g['num_nodes']):\n f5.write(\"%d\\n\"%(i+base))\n if has_vertex_attr:\n node_features = [str(n) for n in g['node_feat'][node,:]]\n f2.write(\"%s\\n\"%(','.join(node_features)))\n if has_vertex_label:\n try:\n f6.write(\"%d\\n\"%(g['node_feat'][node,attr_as_label]))\n except:\n raise Exception(\"Wrong attribute index as label\")\n else: # otherwise, canculate degree and use it as 'label'\n print(\"Warning: TUD format requires node labels!\")\n\n nonodes += g['num_nodes']\n f1.close()\n if has_vertex_attr: f2.close()\n if has_edge_attr: f3.close()\n if has_vertex_label: f6.close()\n f4.close()\n f5.close()\n if iszipped:\n shutil.make_archive(os.path.join(out_dir,dataname), 'zip', dirpath)","repo_name":"giordamaug/BIONETdatasets","sub_path":"TUD/converters/ogb2tud.py","file_name":"ogb2tud.py","file_ext":"py","file_size_in_byte":2465,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"6"}
+{"seq_id":"765266172","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\ndef load_data():\n\turl = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n\tnames = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\n\tdataset = pd.read_csv(url, names=names)\n\ttraining_data, validation_data = train_test_split(dataset, test_size=0.2)\n\ttest_data = dataset\n\treturn (training_data, validation_data, dataset)\n\ndef array_conv(a, col1, col2):\n\tarr = [pd.DataFrame(a, columns=col1).values, pd.DataFrame(a, columns=col2).values]\n\treturn arr\n\ndef load_data_wrapper():\n\ttr_d, va_d, te_d = load_data()\n\tclass_data = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width']\n\ttrain_d = array_conv(tr_d, class_data, ['class'])\n\tvalid_d = array_conv(va_d, class_data, ['class'])\n\ttest_d = array_conv(te_d, class_data, ['class'])\n\ttraining_inputs = [np.reshape(x, (4, 1)) for x in train_d[0]]\n\ttraining_results = [vectorized_result(y) for y in train_d[1]]\n\ttraining_data = zip(training_inputs, training_results)\n\tvalidation_inputs = [np.reshape(x, (4, 1)) for x in valid_d[0]]\n\tvalidation_data = zip(validation_inputs, valid_d[1])\n\ttest_inputs = [np.reshape(x, (4, 1)) for x in test_d[0]]\n\ttest_data = zip(test_inputs, test_d[1])\n\treturn (training_data, validation_data, test_data)\n\ndef vectorized_result(j):\n\te = np.zeros((10, 1))\n\tif j == 'Iris-sentosa':\n\t\te[0] = 1.0\n\telif j == 'Iris-versicolor':\n\t\te[1] = 1.0\n\telse:\n\t\te[2] = 1.0\n\treturn e\n\nload_data_wrapper()","repo_name":"psa1302/iris","sub_path":"src/iris_data_loader.py","file_name":"iris_data_loader.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"19757841629","text":"# shop.product.py\nimport datetime as dtt\nimport urllib.parse as urlparse\nfrom typing import Optional, Union, ClassVar, List\n\nimport pytz\n\nimport debug.logger as clog\nfrom scraper.base import Scrapable\nfrom storage.base import Identifiable\nfrom story.baseConverter import BaseConverter\n\nlogger = clog.getLogger(__name__)\n\n\nclass Product(Identifiable, Scrapable):\n \"\"\" Product class.\n\n Note: All date/datetime data in this class are stored as UTC and UNIX timestamp (float) format.\n \"\"\"\n def __init__(self, **kwargs):\n self.__uid: str = kwargs.get(\"uid\", self.generateUID())\n self.__name: str = kwargs.get(\"name\", \"\")\n self.__url: str = kwargs.get(\"url\", \"\")\n self.basePrice: Optional[float] = kwargs.get(\"basePrice\", None)\n self.currency: Optional[str] = kwargs.get(\"currency\", None)\n self.__sizes: [Size] = kwargs.get(\"sizes\", list())\n self.urlThumb: Optional[str] = kwargs.get(\"urlThumb\", None)\n self.__releaseDateStamp: Optional[float] = kwargs.get(\"releaseDateStamp\", None) # **\n self.__lastScanStamp: float = kwargs.get(\"lastScanStamp\", 0.0) # **\n\n # ** is of format UTC UNIX epoch\n\n def __repr__(self):\n info = f\"<{self.__class__.__name__} uid: {self.uid}, name: {self.name}, \" \\\n f\"url: {self.url}, price: {self.basePrice}, currency: {self.currency}, \" \\\n f\"sizes: {self.__sizes}, urlThumb: {self.urlThumb}, \" \\\n f\"releaseDateStamp: {self.__releaseDateStamp}, \" \\\n f\"lastScanStamp: {self.lastScanStamp}\" \\\n \">\"\n\n return info\n\n @property\n def uid(self) -> str:\n return self.__uid\n\n @uid.setter\n def uid(self, val: str) -> None:\n self.__uid = val\n\n @property\n def url(self) -> str:\n return self.__url\n\n @url.setter\n def url(self, val: str) -> None:\n self.__url = val\n\n @property\n def name(self) -> str:\n return self.__name\n\n @name.setter\n def name(self, val: str) -> None:\n self.__name = val\n\n @property\n def lastScanStamp(self) -> float:\n return self.__lastScanStamp\n\n @lastScanStamp.setter\n def lastScanStamp(self, val: float) -> None:\n self.__lastScanStamp = val\n\n @property\n def sizes(self) -> List['Size']:\n return self.__sizes\n\n @sizes.setter\n def sizes(self, sizeList: List['Size']) -> None:\n if not sizeList: return\n # Type checking\n if all(isinstance(i, Size) for i in sizeList):\n self.__sizes = sizeList\n else:\n raise TypeError(\"Could not set sizes list. All elements must be of type 'Size'.\")\n\n def addSize(self, size: 'Size') -> None:\n \"\"\" Adds a size to the product's list of sizes.\n\n :param size: An instance of type Size.\n :type size: Size\n :return: None\n \"\"\"\n if not isinstance(size, Size):\n raise TypeError(\"Could not add size. Expected an instance of type 'Size' \"\n f\"but got {type(size)}\")\n\n if size:\n self.sizes.append(size)\n logger.debug(\"Added new size %s for %s\", size.sizeEU, self.url)\n\n def findSize(self, sizeStr: str) -> Optional['Size']:\n \"\"\" Find and return a Size with the given size string.\n\n :param sizeStr: Complete size string to search for.\n :return: Size object if found, otherwise None\n \"\"\"\n for size in self.sizes:\n if size.sizeEU == sizeStr:\n logger.debug(\"Found existing size %s for %s\", sizeStr, self.url)\n # Size exists. Return reference(!) of product.size\n return size\n return None\n\n @property\n def releaseDateStamp(self) -> Optional[float]:\n \"\"\"\n :return: The internally stored UNIX epoch timestamp as float OR None if not set.\n \"\"\"\n return self.__releaseDateStamp\n\n def invalidateReleaseDate(self) -> None:\n \"\"\" Sets release date back to None.\n :return: None\n \"\"\"\n self.__releaseDateStamp = None\n\n def getReleaseDate(self, forTimezone: str, forType: type) -> \\\n Optional[Union[str, dtt.datetime]]:\n \"\"\"Returns the release date and time for the product when there is one - else returns None.\n\n :param forType: Pass type 'str' for human readable return; pass type 'datetime' for\n python datetime. Unknown types will raise a NotImplementedError.\n :param forTimezone: Timezone string for the returned release date. Raises\n error on invalid time zone. For a list of valid strings,\n see 'pytz.all_timezones'\n :return: None when no release date is set.\n Else a timezone aware datetime or a readable string.\n \"\"\"\n if not self.__releaseDateStamp:\n return None\n\n allowedTypes = (str, dtt.datetime)\n if forType not in allowedTypes:\n raise NotImplementedError(f\"Given type '{forType}' is not allowed as an output type.\")\n\n # Return formatted local date/time string\n if forType is str:\n try:\n localDatetime = self.__releaseStampToLocalDatetime(forTimezone)\n result = dtt.datetime.strftime(localDatetime, \"%d.%m.%Y, %H:%M:%S\")\n return result\n except pytz.UnknownTimeZoneError:\n raise\n\n # Return local datetime\n elif forType is dtt.datetime:\n try:\n return self.__releaseStampToLocalDatetime(forTimezone)\n except pytz.UnknownTimeZoneError:\n raise\n\n def setReleaseDate(self, datetime: dtt.datetime, timezone: str) -> None:\n \"\"\" Set the release date for the product providing a Python datetime.\n\n :param datetime: Python datetime (without any timezone info).\n You must define the timezone in param 'timezone'!\n :param timezone: Timezone string, see also 'pytz.all_timezones'\n :return: None\n \"\"\"\n try:\n if not timezone:\n raise pytz.UnknownTimeZoneError(\"No timezone given.\")\n\n givenZone = pytz.timezone(timezone) # set the source's timezone\n localizedDatetime = givenZone.localize(datetime) # make source timezone-aware\n datetimeUTC: dtt.datetime = localizedDatetime.astimezone(pytz.utc) # convert to UTC\n\n except pytz.UnknownTimeZoneError as e:\n logger.error(\"Could not set product release date. Invalid time zone string. %s\", e,\n exc_info=True)\n\n except Exception as e:\n logger.error(\"Could not set product release date. %s\", e, exc_info=True)\n\n else:\n self.__releaseDateStamp = datetimeUTC.timestamp()\n\n def getPriceWithCurrency(self) -> str:\n if self.basePrice and self.currency:\n return \"{:.2f} {:s}\".format(self.basePrice, self.currency)\n elif self.basePrice:\n return \"{:.2f} [UNKNOWN CURRENCY]\".format(self.basePrice)\n else:\n return \"unknown\"\n\n def __releaseStampToLocalDatetime(self, forTimezone: str) -> Optional[dtt.datetime]:\n \"\"\" Result is a so called AWARE datetime with the given timezone - which means that\n the offset to the UTC is included in the result.\n Ex.: A result 2035-07-15 12:55:00+02:00 says:\n 1) Local time is 12:55:00 and\n 2) the offset to the UTC is +2 hours, so UTC for this local is 10:55:00.\n\n Raises on invalid timezone string!\n\n :param forTimezone: Timezone string for the returned release date. Raises\n error on invalid time zone. For a list of valid strings,\n see 'pytz.all_timezones'\n :return: None when no release date is set. Else a timezone aware datetime.\n \"\"\"\n pytzGivenZone = pytz.timezone(forTimezone) # raises on invalid timezone string!\n utcDatetime = dtt.datetime.utcfromtimestamp(self.__releaseDateStamp) # get as datetime\n utcAwareDatetime = pytz.utc.localize(utcDatetime) # make timezone-aware\n localAwareDatetime = utcAwareDatetime.astimezone(pytzGivenZone) # convert to given zone\n\n return localAwareDatetime\n\n\nclass Size(Identifiable):\n\n def __init__(self, **kwargs):\n self.__uid: str = kwargs.get(\"uid\", self.generateUID())\n # Size itself must be str for patterns like '42 2/3'. None for unknown.\n self.sizeEU: Optional[str] = kwargs.get(\"sizeEU\", None)\n # Price None for unknown.\n self.price: Optional[float] = kwargs.get(\"price\", None)\n # URL None for unknown.\n self.url: Optional[str] = kwargs.get(\"url\", None)\n # addToCart url: None for unknown.\n self.urlAddToCart: Optional[str] = kwargs.get(\"urlAddToCart\", None)\n # In stock: None for unknown. Optionals won't not work for bool (as of Py 3.7.7)\n self.isInStock: Optional[bool] = kwargs.get(\"isInStock\", None)\n\n def __repr__(self):\n info = f\"<{self.__class__.__name__} uid: {self.uid}, sizeEU: {self.sizeEU}, \" \\\n f\"price: {self.price}, url: {self.url}, urlAddToCart: {self.urlAddToCart}, \" \\\n f\"isInStock: {self.isInStock}\" \\\n \">\"\n\n return info\n\n @property\n def uid(self) -> str:\n return self.__uid\n\n @uid.setter\n def uid(self, val: str) -> None:\n self.__uid = val\n\n @property\n def inStockReadable(self) -> str:\n # Explicitly ask for None. This is no glitch!\n if self.isInStock is None:\n return \"Unknown\"\n\n # Explicitly ask for False. This is no glitch!\n elif self.isInStock is False:\n return \"Out of stock\"\n\n # Explicitly ask for True. This is no glitch!\n elif self.isInStock is True:\n return \"In stock\"\n\n else:\n msg = \"Variable 'isInStock' neither seems to be a boolean, nor a None. \" \\\n f\"Its type is: {type(self.isInStock)}\"\n\n logger.critical(msg)\n raise TypeError(msg)\n\n\nclass StringProductUrlConverter(BaseConverter):\n\n def __init__(self, source, target):\n super().__init__(source, target, allowedTypes=(Product, str))\n\n def getConverted(self):\n if self._target == Product:\n return self.__urlStringToProduct()\n return self.__productToUrlString()\n\n def __urlStringToProduct(self) -> Optional[Product]:\n urlStr: str = self._source\n if not urlStr: return None\n\n # We'll a valid netloc to assign a product to a shop later in the app flow,\n # let's check if we get one - else raise because we consider it\n # to be an invalid product URL.\n netloc = urlparse.urlsplit(urlStr).netloc\n if not netloc:\n raise ValueError(f\"URL not splittable into a valid netloc part: {urlStr}\")\n\n newProduct = Product()\n newProduct.url = urlStr\n\n logger.debug(\"New Product created from URL string. Product URL: %s\",\n newProduct.url)\n\n return newProduct\n\n def __productToUrlString(self) -> Optional[str]:\n product: Product = self._source\n if not product or not product.url: return None\n\n return product.url\n\n\nclass DictProductsUrlsConverter(BaseConverter):\n URL_RECORDS_KEY: ClassVar = \"data\" # See also TextFileDao._recordArrayKey\n\n def __init__(self, source, target):\n super().__init__(source, target, allowedTypes=(List[Product], dict))\n\n def getConverted(self) -> Union[List[Product], dict]:\n if self._target == dict:\n return self.__productsToUrlDict()\n return self.__urlDictToProducts()\n\n def __urlDictToProducts(self) -> [Product]:\n urlDict: dict = self._source\n\n if self.URL_RECORDS_KEY not in urlDict.keys():\n raise KeyError(f\"Missing dictionary key '{self.URL_RECORDS_KEY}' in urlDict.\")\n\n products: [Product] = []\n urls = urlDict.get(self.URL_RECORDS_KEY)\n\n for url in urls:\n if not url: continue\n converter = StringProductUrlConverter(source=url, target=Product)\n product: Product = converter.getConverted()\n if product:\n products.append(product)\n\n return products\n\n def __productsToUrlDict(self) -> dict:\n products: [Product] = self._source\n\n urlDict = {self.URL_RECORDS_KEY: []}\n\n for product in products:\n if not product: continue\n if product.url:\n urlDict[self.URL_RECORDS_KEY].append(product.url)\n\n return urlDict\n","repo_name":"dbyte/WebtomatorPublicEdition","sub_path":"webtomator/shop/product.py","file_name":"product.py","file_ext":"py","file_size_in_byte":12643,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"42162636493","text":"#! /usr/bin/python3.6\nimport boto3\nimport boto3.ec2\nimport sys\nimport json\nimport time\nimport yaml\nimport subprocess\nimport os\nfrom EKS_config import k8s_admin_role_name,region\n\n# Connect to AWS, EC2, cloudformation, IAM and EKS\ns = boto3.Session(region_name=region)\niam = s.client(\"iam\")\n\n\n############################################################################\n# IAM role for K8s\n############################################################################\n\nprint(\"*** IAM role***********\")\n\ntry:\n\n # See if role exists.\n role = iam.get_role(RoleName=k8s_admin_role_name)\n print(\"IAM role exists.\")\n\nexcept:\n print(\"IAM role does not exist. Creating...\")\n\n # This is an AWS role policy document. Allows access for EKS.\n policy_doc = json.dumps({\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Action\": \"sts:AssumeRole\",\n \"Effect\": \"Allow\",\n \"Principal\": {\n \"Service\": \"eks.amazonaws.com\"\n }\n }\n ]\n })\n\n # Create role.\n iam.create_role(\n RoleName=k8s_admin_role_name,\n AssumeRolePolicyDocument=policy_doc,\n Description=\"Role providing access to EKS resources from EKS\"\n )\n\n print(\"Role created.\")\n print(\"Attaching policies...\")\n\n # Add policies allowing access to EKS API.\n\n iam.attach_role_policy(\n RoleName=k8s_admin_role_name,\n PolicyArn=\"arn:aws:iam::aws:policy/AmazonEKSClusterPolicy\"\n )\n\n iam.attach_role_policy(\n RoleName=k8s_admin_role_name,\n PolicyArn=\"arn:aws:iam::aws:policy/AmazonEKSServicePolicy\"\n )\n\n print(\"Attached.\")\n\n# Get role ARN for later.\nrole = iam.get_role(RoleName=k8s_admin_role_name)\nrole_arn = role[\"Role\"][\"Arn\"]\n\nprint(role_arn)","repo_name":"Shirley-xjh/aws-eks-try","sub_path":"venv/eks/EKS_create_IAMrole.py","file_name":"EKS_create_IAMrole.py","file_ext":"py","file_size_in_byte":1792,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"26839051425","text":"from django.http import request\nfrom django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('', views.scoreboard, name='index'),\n path('coach/', views.coach, name='coach'),\n # ex: /bms/player/1\n path('player//', views.player, name='player'),\n path('player/', views.player, name='player'),\n path('team/', views.team, name='team'),\n path('team//', views.team, name='team'),\n path('scoreboard/', views.scoreboard, name='home'),\n path('stats/', views.stats, name='stats'),\n]\n","repo_name":"rajhiren/django-bms","sub_path":"mysite/bms/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"6"}
+{"seq_id":"41968413050","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.shortcuts import render\n\nfrom django.http import HttpResponse\n\n#Views for Journal\n\ndef journal(request):\n\tjournal = (\n\t{'id': 1,\n\t'name': u'Подоба Віталій',},\n\t{'id': 2,\n\t'name': u'Корост Андрій',},\n\t{'id': 3,\n\t'name': u'Притула Тарас',},\n\t)\n\theader_groups = (\n\t{'group': u'МтМ - 21',\n\t'leader': u'Подоба Віталій',\n\t'leader_ticket': 235},\n\t{'group': u'МтМ - 22',\n\t'leader': u'Корост Андрій',\n\t'leader_ticket': 2123},\n\t)\n\treturn render(request, 'students/journal.html', {'journal': journal, 'GROUPS': header_groups})","repo_name":"emilbullet69/test","sub_path":"students/views/journal.py","file_name":"journal.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"39476136115","text":"\nimport requests\nimport json\nfrom lxml import etree\nimport random\n\n\nheaders = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.110 Safari/537.36'}\n\nurl = \"https://www.bohe.cn/ask/\"\nr = requests.get(url, headers=headers)\nr = r.text\nhtml = etree.HTML(r)\n\ntitle_href_list = html.xpath('//*[@class=\"si-list\"]/div/a/@href')\nprint(title_href_list)\n# 93\n# print(len(title_list))\n\nfor url2 in title_href_list:\n print(url2)\n\n r2 = requests.get(url2, headers=headers)\n r2 = r2.text\n # print(r2)\n html2 = etree.HTML(r2)\n try:\n num = html2.xpath('//*[@class=\"iask-pages-p\"]/p/a[5]/text()')\n # print(num)\n if len(num) > 0:\n n = int(num[0])\n print(n, type(n))\n if n > 100:\n n = 100\n for i in range(1, n+1):\n u = url2[0:-5]\n url3 = (u + \"-{}\" + \".html\").format(i)\n print(url3)\n with open('./bhys_href.txt', 'a+', encoding='utf-8') as f:\n f.writelines(url3 + '\\n')\n else:\n with open('./bhys_href.txt', 'a+', encoding='utf-8') as f:\n f.writelines(url2 + '\\n')\n except:\n pass\n\n\n","repo_name":"zhang8929/zhangyuguang","sub_path":"gaoyuan/scrapy_request/bhys_ask/href_list.py","file_name":"href_list.py","file_ext":"py","file_size_in_byte":1239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"35260411164","text":"import tweepy\n\nfrom debunkbot.models import Impact, Tweet\nfrom debunkbot.twitter.api import create_connection, get_tweet_status\n\n\ndef check_reply_impact():\n api = create_connection()\n tweets = Tweet.objects.filter(responded=True)\n for tweet in tweets:\n retweet_count = 0\n likes_count = 0\n replies = []\n response = dict()\n if not hasattr(tweet, \"reply\"):\n continue\n tweet_reply_author = tweet.reply.reply_author\n reply_id = tweet.reply.reply_id\n\n tweet_status = get_tweet_status(api, tweet.tweet.get(\"id\"))\n if not tweet_status:\n # The tweet has been deleted thus we should update the database\n tweet.deleted = True\n tweet.save()\n\n reply_impact = get_tweet_status(api, reply_id)\n\n if reply_impact:\n retweet_count = reply_impact._json.get(\"retweet_count\")\n likes_count = reply_impact._json.get(\"favorite_count\")\n interractions = tweepy.Cursor(\n api.search, q=f\"to:{tweet_reply_author}\", since_id=reply_id, max_id=None\n ).items()\n for interraction in interractions:\n response = interraction._json\n usr_who_responded_to_our_response = response.get(\"user\").get(\n \"screen_name\"\n )\n message = response.get(\"text\")\n replies.append((usr_who_responded_to_our_response, message))\n\n try:\n impact = Impact.objects.get(reply=tweet.reply)\n except Exception:\n impact = Impact(reply=tweet.reply)\n impact.likes_count = likes_count\n impact.replies_count = len(replies)\n impact.retweet_count = retweet_count\n impact.replies = replies\n impact.data = response\n impact.tweet_deleted = tweet.deleted\n impact.save()\n","repo_name":"CodeForAfrica/DebunkBot","sub_path":"debunkbot/twitter/check_reply_impact.py","file_name":"check_reply_impact.py","file_ext":"py","file_size_in_byte":1914,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"6"}
+{"seq_id":"19380515440","text":"import pytest\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\n\n\nclass Test_pagetitle():\n @pytest.mark.skip\n def test_pageTitle_001(self):\n\n driver=webdriver.Edge()\n driver.maximize_window()\n driver.implicitly_wait(5)\n self.driver.get(\"https://www.amazon.in/\")\n if self.driver.find_element(By.ID,\"nav-logo-sprites\").is_displayed():\n assert True\n else:\n assert False\n","repo_name":"Jawanjalajay/demoamazon","sub_path":"testcases/test_pagetitle_amazon.py","file_name":"test_pagetitle_amazon.py","file_ext":"py","file_size_in_byte":462,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"20615004860","text":"'''\r\nCreated on 28-Nov-2018\r\n\r\n@author: Bhujay K Bhatta\r\n\r\nData Encapsulation is seen as the bundling of data with the\r\nmethods that operate on that data. Information hiding on the\r\nother hand is the principle that some internal information or\r\ndata is \"hidden\", so that it can't be accidentally changed\r\n\r\nAbstraction = Data Encapsulation + Data Hiding\r\n\r\nEncapsulation is often accomplished by providing two kinds of\r\nmethods for attributes: The methods for retrieving or accessing the\r\nvalues of attributes are called getter methods. Getter methods do not\r\nchange the values of attributes, they just return the values.\r\nThe methods used for changing the values of attributes are called setter\r\nmethods.\r\n'''\r\n\r\n\r\nclass Robot(object):\r\n '''\r\n classdocs\r\n '''\r\n\r\n def __init__(self, name=None, color=None):\r\n # Private attributes __name can only be accessed from within the class\r\n self.__name = name\r\n # restricted attributes for internal usage\r\n self._life = \"my life is eternal\"\r\n # public attributes for usage outside the class\r\n self.color = color\r\n\r\n def get_name(self):\r\n return self.__name\r\n\r\n def set_name(self, name):\r\n self.__name = name\r\n\r\n\r\ny = Robot()\r\nprint(\"Using encapsulated setter method to access\"\r\n \" data of private attribute __name\")\r\ny.set_name('Mavin')\r\nprint(\"Using encapsulated getter method to access data\"\r\n \" data of private attribute __name = %s \" % y.get_name())\r\n\r\nprint(\"Using restricted attributes _life %s\" % y._life)\r\nx = Robot('Calvin')\r\n# Direct aceess of private attributes are not allowed here\r\nprint(\"Using direct access to private name attribute __name is\"\r\n \" not allowed and will fail\")\r\nprint (x.__name)\r\n","repo_name":"BhujayKumarBhatta/OOPLearning","sub_path":"pOOP/pOOp/encap_abstrat_private_attrib.py","file_name":"encap_abstrat_private_attrib.py","file_ext":"py","file_size_in_byte":1743,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"6"}
+{"seq_id":"7176577129","text":"from .builders import ( # noqa: F401\n at_block_number,\n build,\n chain_id,\n chain_split,\n copy,\n dao_fork_at,\n disable_dao_fork,\n disable_pow_check,\n enable_pow_mining,\n fork_at,\n genesis,\n import_block,\n import_blocks,\n mine_block,\n mine_blocks,\n name,\n)\nfrom .builders import ( # noqa: F401\n byzantium_at,\n frontier_at,\n homestead_at,\n spurious_dragon_at,\n tangerine_whistle_at,\n constantinople_at,\n petersburg_at,\n istanbul_at,\n muir_glacier_at,\n berlin_at,\n london_at,\n arrow_glacier_at,\n gray_glacier_at,\n paris_at,\n shanghai_at,\n latest_mainnet_at,\n)\n\n\nmining_mainnet_fork_at_fns = (\n byzantium_at,\n frontier_at,\n homestead_at,\n spurious_dragon_at,\n tangerine_whistle_at,\n petersburg_at,\n istanbul_at,\n muir_glacier_at,\n berlin_at,\n london_at,\n arrow_glacier_at,\n gray_glacier_at,\n)\npos_mainnet_fork_at_fns = (\n paris_at,\n shanghai_at,\n)\nmainnet_fork_at_fns = mining_mainnet_fork_at_fns + pos_mainnet_fork_at_fns\n\n\nclass API:\n #\n # Chain Class Construction\n #\n\n # Primary wrapper function\n build = staticmethod(build)\n\n # Configure chain vm_configuration\n fork_at = staticmethod(fork_at)\n\n # Configure chain name\n name = staticmethod(name)\n\n # Configure chain chain_id\n chain_id = staticmethod(chain_id)\n\n # Mainnet Forks\n frontier_at = staticmethod(frontier_at)\n homestead_at = staticmethod(homestead_at)\n tangerine_whistle_at = staticmethod(tangerine_whistle_at)\n spurious_dragon_at = staticmethod(spurious_dragon_at)\n byzantium_at = staticmethod(byzantium_at)\n constantinople_at = staticmethod(constantinople_at)\n istanbul_at = staticmethod(istanbul_at)\n muir_glacier_at = staticmethod(muir_glacier_at)\n berlin_at = staticmethod(berlin_at)\n london_at = staticmethod(london_at)\n arrow_glacier_at = staticmethod(arrow_glacier_at)\n gray_glacier_at = staticmethod(gray_glacier_at)\n paris_at = staticmethod(paris_at)\n shanghai_at = staticmethod(shanghai_at)\n\n # iterable of the fork specific functions\n mainnet_fork_at_fns = mainnet_fork_at_fns\n mining_mainnet_fork_at_fns = mining_mainnet_fork_at_fns\n\n # DAO Fork specific\n dao_fork_at = staticmethod(dao_fork_at)\n disable_dao_fork = staticmethod(disable_dao_fork)\n\n # Chain Mining config\n enable_pow_mining = staticmethod(enable_pow_mining)\n disable_pow_check = staticmethod(disable_pow_check)\n\n #\n # Chain Instance Initialization\n #\n genesis = staticmethod(genesis)\n\n #\n # Chain Building\n #\n copy = staticmethod(copy)\n\n import_block = staticmethod(import_block)\n import_blocks = staticmethod(import_blocks)\n\n mine_block = staticmethod(mine_block)\n mine_blocks = staticmethod(mine_blocks)\n\n chain_split = staticmethod(chain_split)\n at_block_number = staticmethod(at_block_number)\n\n\napi = API()\n","repo_name":"ethereum/py-evm","sub_path":"eth/tools/builder/chain/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2936,"program_lang":"python","lang":"en","doc_type":"code","stars":2109,"dataset":"github-code","pt":"6"}
+{"seq_id":"32471728582","text":"# [21년 재직자 대회 예선] 비밀 메뉴\n\nimport sys\ninput = sys.stdin.readline\n\n# test\nM, N, K = map(int, input().split())\nmenu = ''.join(input().rstrip())\nbutton = ''.join(input().rstrip())\n\nif menu in button:\n print(\"secret\")\nelse:\n print(\"normal\")\n\n# test 1\n'''\nM, N, K = map(int, input().split())\nmenu = list(map(str, input().split()))\nbutton = list(map(str, input().split()))\n\nm = ''.join(menu)\nb = ''.join(button)\n\nif m in b:\n print(\"secret\")\nelse:\n print(\"normal\")\n'''","repo_name":"JinDDung2/python-pratice","sub_path":"softeer/lv2/secret_menu.py","file_name":"secret_menu.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"40159544133","text":"import os\nimport sys\n\nsys.path.insert(0, os.path.abspath(\"../..\"))\n\n\n# -- Project information -----------------------------------------------------\n\nproject = \"orinoco\"\ncopyright = \"Paysure Solutions Ltd.\"\nauthor = \"Martin Vo\"\n\n# The full version, including alpha/beta/rc tags\nrelease = \"1.0.0\"\n\n\n# -- General configuration ---------------------------------------------------\n\nextensions = [\"sphinx.ext.autodoc\", \"m2r2\", \"sphinx.ext.intersphinx\", \"sphinx.ext.viewcode\"]\n\ntemplates_path = [\"_templates\"]\n\nexclude_patterns = []\n\n\n# -- Options for HTML output -------------------------------------------------\n\nhtml_theme = \"furo\"\n\n# html_theme_options = {\"sidebarwidth\": 350, \"body_max_width\": 1000}\n\nhtml_static_path = [\"_static\"]\n\nsource_suffix = [\".rst\", \".md\"]\n","repo_name":"paysure/orinoco","sub_path":"docs/source/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"6"}
+{"seq_id":"31642253264","text":"from django.template.loader import render_to_string\n\nfrom .api import ChatworkApiClient\n\n\nclient = ChatworkApiClient()\napi_account_info = client.get_my_profile()\napi_account_id = getattr(api_account_info, 'account_id', '0')\napi_room_id = getattr(api_account_info, 'room_id', '0')\n\n\ndef get_rooms(room_type='group'):\n \"\"\" 所属するルームを取得する \"\"\"\n rooms = client.get_rooms()\n return [room for room in rooms if room['type'] == room_type]\n\n\ndef send_chatwork(text, room, title=None, to_all=None):\n \"\"\" 一つのルームにメッセージを送信する \"\"\"\n context = {\n 'body': text,\n 'title': title,\n 'to_all': to_all,\n }\n message = render_to_string('chatwork/message.txt', context)\n return client.add_messages(room, message.strip())\n\n\ndef send_chatwork_many(text, rooms, title=None, to_all=None):\n \"\"\" 複数のルームにメッセージを送信する \"\"\"\n results = []\n for room in rooms:\n result = send_chatwork(text, room, title=title, to_all=to_all)\n results.append(result)\n return results\n\n\ndef delete_message(room_id, message_id):\n \"\"\" 指定したメッセージを削除する \"\"\"\n return client.delete_message(room_id, message_id)\n\n\ndef create_task(text, room, assigned_to, limit=None, **kwargs):\n \"\"\" タスクを依頼する \"\"\"\n data = {\n 'body': text,\n 'to_ids': ','.join(list(map(str, assigned_to))),\n }\n if limit is not None:\n data['limit'] = int(limit.timestamp())\n return client.add_tasks(room, **data)\n","repo_name":"kacchan822/django-chatwork","sub_path":"chatwork/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1552,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"}
+{"seq_id":"30465372885","text":"#coding=utf8\n\n########################################################################\n### ###\n### Created by Martin Genet, 2012-2016 ###\n### ###\n### University of California at San Francisco (UCSF), USA ###\n### Swiss Federal Institute of Technology (ETH), Zurich, Switzerland ###\n### École Polytechnique, Palaiseau, France ###\n### ###\n########################################################################\n\nimport math\nimport numpy\n\nimport myVTKPythonLibrary as myVTK\n\n########################################################################\n\ndef computeHelixTransverseSheetAngles(\n farray_eRR,\n farray_eCC,\n farray_eLL,\n farray_eF,\n farray_eS,\n farray_eN,\n use_new_definition=False,\n verbose=1):\n\n myVTK.myPrint(verbose, \"*** computeHelixTransverseSheetAngles ***\")\n\n n_tuples = farray_eRR.GetNumberOfTuples()\n assert (farray_eCC.GetNumberOfTuples() == n_tuples)\n assert (farray_eLL.GetNumberOfTuples() == n_tuples)\n assert (farray_eF.GetNumberOfTuples() == n_tuples)\n assert (farray_eS.GetNumberOfTuples() == n_tuples)\n assert (farray_eN.GetNumberOfTuples() == n_tuples)\n\n farray_angle_helix = myVTK.createFloatArray(\"angle_helix\", 1, n_tuples)\n farray_angle_trans = myVTK.createFloatArray(\"angle_trans\", 1, n_tuples)\n farray_angle_sheet = myVTK.createFloatArray(\"angle_sheet\", 1, n_tuples)\n\n for k_tuple in xrange(n_tuples):\n eRR = numpy.array(farray_eRR.GetTuple(k_tuple))\n eCC = numpy.array(farray_eCC.GetTuple(k_tuple))\n eLL = numpy.array(farray_eLL.GetTuple(k_tuple))\n\n eF = numpy.array(farray_eF.GetTuple(k_tuple))\n eF -= numpy.dot(eF, eRR) * eRR\n eF /= numpy.linalg.norm(eF)\n angle_helix = math.copysign(1., numpy.dot(eF, eCC)) * math.asin(min(1., max(-1., numpy.dot(eF, eLL)))) * (180./math.pi)\n farray_angle_helix.InsertTuple(k_tuple, [angle_helix])\n\n eF = numpy.array(farray_eF.GetTuple(k_tuple))\n eF -= numpy.dot(eF, eLL) * eLL\n eF /= numpy.linalg.norm(eF)\n angle_trans = math.copysign(-1., numpy.dot(eF, eCC)) * math.asin(min(1., max(-1., numpy.dot(eF, eRR)))) * (180./math.pi)\n farray_angle_trans.InsertTuple(k_tuple, [angle_trans])\n\n #if (use_new_definition):\n #assert 0, \"TODO\"\n #else:\n #assert 0, \"TODO\"\n\n return (farray_angle_helix,\n farray_angle_trans,\n farray_angle_sheet)\n\n########################################################################\n\ndef addHelixTransverseSheetAngles(\n ugrid,\n type_of_support=\"cell\",\n use_new_definition=False,\n verbose=1):\n\n myVTK.myPrint(verbose, \"*** addHelixTransverseSheetAngles ***\")\n\n if (type_of_support == \"cell\"):\n ugrid_data = ugrid.GetCellData()\n elif (type_of_support == \"point\"):\n ugrid_data = ugrid.GetPointData()\n\n farray_eRR = ugrid_data.GetArray(\"eRR\")\n farray_eCC = ugrid_data.GetArray(\"eCC\")\n farray_eLL = ugrid_data.GetArray(\"eLL\")\n\n farray_eF = ugrid_data.GetArray(\"eF\")\n farray_eS = ugrid_data.GetArray(\"eS\")\n farray_eN = ugrid_data.GetArray(\"eN\")\n\n (farray_angle_helix,\n farray_angle_trans,\n farray_angle_sheet) = computeHelixTransverseSheetAngles(\n farray_eRR=farray_eRR,\n farray_eCC=farray_eCC,\n farray_eLL=farray_eLL,\n farray_eF=farray_eF,\n farray_eS=farray_eS,\n farray_eN=farray_eN,\n use_new_definition=use_new_definition,\n verbose=verbose-1)\n\n ugrid_data.AddArray(farray_angle_helix)\n ugrid_data.AddArray(farray_angle_trans)\n ugrid_data.AddArray(farray_angle_sheet)\n\n return (farray_angle_helix,\n farray_angle_trans,\n farray_angle_sheet)\n","repo_name":"gacevedobolton/myVTKPythonLibrary","sub_path":"computeHelixTransverseSheetAngles.py","file_name":"computeHelixTransverseSheetAngles.py","file_ext":"py","file_size_in_byte":3999,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
+{"seq_id":"1142683503","text":"from samples import *\r\nimport json\r\n\r\nclass configuracoes(Conexao):\r\n def __init__(self):\r\n Conexao.__init__(self)\r\n\r\n def db_insert(self, idIntegracao, id_cloud, ano, natureza, nroDocumento):\r\n try:\r\n sql = \"\"\"\r\n INSERT INTO configuracoes ( \r\n idIntegracao, \r\n id_cloud, \r\n ano, \r\n natureza,\r\n nroDocumento \r\n ) VALUES (\r\n %(idIntegracao)s, \r\n %(id_cloud)s, \r\n %(ano)s,\r\n %(natureza)s,\r\n %(nroDocumento)s\r\n )\r\n \"\"\"\r\n data = dict (\r\n idIntegracao = idIntegracao,\r\n id_cloud = id_cloud, \r\n ano = ano, \r\n natureza = natureza,\r\n nroDocumento = nroDocumento \r\n )\r\n self.execute(sql, data)\r\n self.commit()\r\n send_log_info(f\"Agrupamentos {configuracoes} (id_cloud: {id_cloud}) inserido com sucesso.\")\r\n except Exception as error:\r\n send_log_error(f\"Erro ao inserir o anistias {configuracoes}. {error}\")\r\n\r\n def db_delete(self):\r\n try:\r\n sql_s = f\"SELECT * FROM configuracoes\"\r\n if not self.query(sql_s):\r\n send_log_warning(f\"configuracoes não encontrado para excluir.\")\r\n return\r\n sql_d = f\"DELETE FROM configuracoes WHERE id is not null\"\r\n self.execute(sql_d)\r\n self.commit()\r\n send_log_info(f\"anistias excluídos com sucesso.\")\r\n except Exception as error:\r\n send_log_error(f\"Erro ao executar a operação de exclusão do atividades econômicas. {error}\")\r\n\r\n def db_update(self, id, id_cloud, json, descricao):\r\n try:\r\n sql_s = f\"SELECT * FROM configuracoes WHERE id = {id}\"\r\n if not self.query(sql_s):\r\n send_log_warning(f\"atividades Economicas {id} não encontrado para atualizar.\")\r\n return\r\n sql = \"\"\"\r\n UPDATE \r\n configuracoes \r\n SET \r\n id_cloud = %(id_cloud)s,\r\n json_post = %(json)s,\r\n resposta_post = %(descricao)s\r\n WHERE\r\n id = %(id)s\r\n \"\"\"\r\n data = dict (\r\n id = id,\r\n id_cloud = id_cloud,\r\n json = json,\r\n descricao = descricao\r\n )\r\n self.execute(sql, data)\r\n self.commit()\r\n send_log_info(f\"atividades Economicas {id} atualizado com sucesso.\")\r\n except Exception as error:\r\n send_log_error(f\"Erro ao executar a operação de atualização da atividades Economicas. {error}\")\r\n\r\n def db_search(self, id):\r\n try:\r\n sql = f\"SELECT * FROM configuracoes WHERE id = {id}\"\r\n data = self.query(sql)\r\n if data:\r\n return data\r\n send_log_info(f\"atividades Economicas {id} não encontrado.\")\r\n except Exception as error:\r\n send_log_error(f\"Erro ao executar a operação de busca. {error}\")\r\n\r\n def db_list(self):\r\n try:\r\n sql = \"SELECT * FROM configuracoes WHERE id_cloud is null\"\r\n data = self.query(sql)\r\n if data:\r\n send_log_info(\"Consulta de todos os atividades Economicas realizada com sucesso.\")\r\n return data\r\n return None\r\n except Exception as error:\r\n send_log_error(f\"Erro ao executar a operação de busca. {error}\")\r\n\r\n def get_id_cloud(self, id):\r\n if (id == None):\r\n return None\r\n try:\r\n sql = f\"SELECT id_cloud FROM configuracoes WHERE id_origem = {id}\"\r\n data = self.query(sql)\r\n if data:\r\n return data[0][0]\r\n send_log_info(f\"atosFontesDivulgacoes {id} não encontrado.\")\r\n except Exception as error:\r\n send_log_error(f\"Erro ao executar a operação de busca. {error}\")\r\n\r\n def send_post(self, id, ano, natureza, nroDocumento):\r\n objeto = {\r\n \"idIntegracao\": f\"configuracoes{id}\",\r\n \"content\": {} \r\n }\r\n\r\n if natureza != None:\r\n objeto[0][\"content\"][\"CalculosTributariosAvancado\"] = f\"{natureza}\" \r\n \r\n if ano != None:\r\n objeto[0][\"camposadicionais\"][\"ano\"] = { \"id\": int(ano)}\r\n\r\n if nroDocumento != None:\r\n objeto[0][\"camposadicionais\"][\"nroDocumento\"] = { \"id\": int(nroDocumento)}\r\n\r\n envio = api_post(\"configuracoes\", objeto)\r\n\r\n if (envio[\"code\"] == 200 or envio[\"code\"] == 201):\r\n self.db_update(id, envio[\"descricao\"], json.dumps(objeto, ensure_ascii=False), None)\r\n else:\r\n self.db_update(id, None, json.dumps(objeto), json.dumps(envio[\"descricao\"], ensure_ascii=False))\r\n\r\nconfiguracoes = configuracoes()","repo_name":"MarcosRBasso/TributosExes","sub_path":"records/configuracoes.py","file_name":"configuracoes.py","file_ext":"py","file_size_in_byte":5430,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"20408420603","text":"import json\nimport re\n\nfile = \"test_v2.json\"\n# file = \"verification set.json\"\n\ntitle_list = []\nabstract_list = []\nlevel1_list = []\nlevel2_list = []\nlevel3_list = []\nlevels_list = []\ndata_new = []\n\nwith open(file, 'r') as f:\n data = json.load(f)\n\nprint(\"Load file successfully.\")\n\nfor d in data:\n title_list.append(d.get(\"title\"))\n abstract_list.append(d.get(\"abstract\"))\n level1_list.append(d.get(\"level1\"))\n level2_list.append(d.get(\"level2\"))\n level3_list.append(d.get(\"level3\"))\n levels_list.append(d.get(\"levels\"))\n\nfor i in range(0, len(data)):\n title_list[i] = re.sub(\n \"[αβγδεζηθικλμνξοπρστυφχψω†=→@*‘+]+\", \"\", title_list[i]) # 希腊字母与特殊符号\n title_list[i] = re.sub(\"\\\\<[^>]*\\\\>\", \"\", title_list[i]) # 尖括号及其内容\n title_list[i] = re.sub(\"\\\\([^)]*\\\\)\", \"\", title_list[i]) # 圆括号及其内容\n title_list[i] = re.sub(\"\\\\[[^]]*\\\\]\", \"\", title_list[i]) # 方括号及其内容\n title_list[i] = re.sub(\"\\\\\\\\u\\d{4}\", \"\", title_list[i]) # 特殊unicode\n\n abstract_list[i] = re.sub(\n \"[αβγδεζηθικλμνξοπρστυφχψω†=→@*‘+]+\", \"\", abstract_list[i])\n abstract_list[i] = re.sub(\"\\\\<[^>]*\\\\>\", \"\", abstract_list[i])\n abstract_list[i] = re.sub(\"\\\\([^)]*\\\\)\", \"\", abstract_list[i])\n abstract_list[i] = re.sub(\"\\\\[[^]]*\\\\]\", \"\", abstract_list[i])\n abstract_list[i] = re.sub(\"\\\\\\\\u\\d{4}\", \"\", abstract_list[i])\n\n data_new.append(dict(title=title_list[i], abstract=abstract_list[i], level1=level1_list[i],\n level2=level2_list[i], level3=level3_list[i], levels=levels_list[i]))\n\n\nwith open(file[:-5] + \"_modified.json\", 'w') as f:\n json.dump(data_new, f)\n\nprint(\"Finished.\")\n\n'''\n化学式（有bug，会把大写字母开头的单词也匹配到，暂时去除）\n[A-Z][a-z]?\\d*|\\((?:[^()]*(?:\\(.*\\))?[^()]*)+\\)\\d+\n\n'''\n","repo_name":"DUT-lujunyu/MHCAN","sub_path":"data/data_preprocess.py","file_name":"data_preprocess.py","file_ext":"py","file_size_in_byte":1890,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"}
+{"seq_id":"73080612623","text":"# https://leetcode.com/problems/maximum-product-subarray/\n\nfrom typing import List\n\n\nclass Solution:\n def maxProduct(self, nums: List[int]) -> int:\n dp_max = [0] * len(nums)\n dp_min = [0] * len(nums)\n dp_max[0] = dp_min[0] = nums[0]\n\n for i in range(1, len(nums)):\n # 有三种情况：\n # 1. 前面的积或当前的数有负数\n # 2. 前面的积或当前的数有 0~1 的小数\n # 3. 乘完之后变大了（包含都是负数的可能）\n dp_max[i] = max(dp_max[i - 1] * nums[i], nums[i],\n dp_min[i - 1] * nums[i])\n dp_min[i] = min(dp_max[i - 1] * nums[i], nums[i],\n dp_min[i - 1] * nums[i])\n\n return max(dp_max)\n\n\ns = Solution()\nprint(s.maxProduct([2, 3, -2, 4])) # 6\nprint(s.maxProduct([-2, 0, -1])) # 0\nprint(s.maxProduct([-2, 3, 1, -5, -1])) # 30\n","repo_name":"BYJRK/LeetCode-Solutions","sub_path":"Problems/Dynamic Programming/152. Maximum Product Subarray.py","file_name":"152. Maximum Product Subarray.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"17004389550","text":"import sys\n\ninput = sys.stdin.readline\n\ns = list(input().strip())\nresult = set() # set을 통해서 중복된 값들을 제거해준다.\n\nfor i in range(len(s)):\n for j in range(i, len(s)):\n result.add(''.join(s[i : j + 1])) # i번째 문자부터 부분문자열을 구해서 result에 추가해준다.\n \nprint(len(result))\n","repo_name":"GeonHui2/Baekjoon","sub_path":"data structure/11478.py","file_name":"11478.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"746104872","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.neural_network import MLPRegressor\nfrom sklearn.preprocessing import MaxAbsScaler\nfrom statistics import mean, stdev\n\nprint('Carregando Arquivo de teste')\narquivo = np.load('teste5.npy')\nx = arquivo[0]\nscale = MaxAbsScaler().fit(arquivo[1])\ny = np.ravel(scale.transform(arquivo[1]))\nerroMaior = 100000\n\nErros = []\nfor vez in range(0,10):\n iter = 30000\n\n regr = MLPRegressor(hidden_layer_sizes=(15,15,15,15,15,15,15,15,15,15,15,15,15,15,15),\n max_iter=iter,\n activation='relu', #{'identity', 'logistic', 'tanh', 'relu'},\n solver='adam',\n learning_rate = 'adaptive',\n n_iter_no_change=30000)\n print('Treinando RNA')\n regr = regr.fit(x,y)\n\n print('Preditor')\n y_est = regr.predict(x)\n\n if(regr.loss_curve_[-1] < erroMaior):\n erroMaior = regr.loss_curve_[-1]\n plt.figure(figsize=[14,7])\n #plot curso original\n plt.subplot(1,3,1)\n plt.plot(x,y)\n\n #plot aprendizagem\n plt.subplot(1,3,2)\n plt.plot(regr.loss_curve_)\n\n #plot regressor\n plt.subplot(1,3,3)\n plt.plot(x,y,linewidth=1,color='yellow')\n plt.plot(x,y_est,linewidth=2)\n\n plt.show()\n Erros.append(regr.loss_curve_[-1])\n\nprint(Erros)\nprint(np.average(Erros))\nprint(np.std(Erros)) ","repo_name":"LeoMarinaro/IA","sub_path":"DestroTarefa/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"42641520773","text":"import os\n\nos.environ.setdefault('DJANGO_SETTINGS_MODULE',\n 'wad2project.settings')\n\nimport django\ndjango.setup()\nfrom stack_underflow.models import Category,Thread, Reply\nfrom django.contrib.auth.models import User\nfrom django.db import IntegrityError\n\ndef populate():\n user = User.objects.create_user('username', 'user@gmail.com', 'password1234')\n\n replies = [\n {'reply': 'Example reply 1?', 'user': user},\n {'reply': 'Example reply 2?', 'user': user},\n {'reply': 'Example reply 3?', 'user': user},\n {'reply': 'Example reply 4?', 'user': user},\n {'reply': 'Example reply 5?', 'user': user},\n {'reply': 'Example reply 6?', 'user': user},\n {'reply': 'Example reply 7?', 'user': user},\n {'reply': 'Example reply 8?', 'user': user},\n {'reply': 'Example reply 9?', 'user': user} ]\n\n\n programming_threads = [\n {'question': 'How to implement a linked list in Java?',\n 'replies_no': 13, 'user': user},\n {'question': 'Should I learn C++ or C# next?',\n 'replies_no': 25, 'user': user},\n {'question': 'What is a null pointer exception?',\n 'replies_no': 53, 'user': user} ]\n\n technology_threads = [\n {'question': 'Best gaming laptop 2020?',\n 'replies_no': 27, 'user': user},\n {'question': 'How to beat the Ender Dragon?',\n 'replies_no': 1200, 'user': user},\n {'question': 'AI vs Machine learning?',\n 'replies_no': 121, 'user': user} ]\n\n politics_threads = [\n {'question': 'How do we elect supreme court judges in the UK?',\n 'replies_no': 5, 'user': user},\n {'question': 'What are WTO rules?',\n 'replies_no': 76, 'user': user},\n {'question': 'Why are some cabinet members not MPs?',\n 'replies_no': 14, 'user': user} ]\n\n cats = {'Programming': {'threads': programming_threads, 'threads_no': 3600, 'user': user},\n 'Technology': {'threads': technology_threads, 'threads_no': 2800, 'user': user},\n 'Politics': {'threads': politics_threads, 'threads_no': 2100, 'user': user} }\n\n threads_list =[]\n\n for cat, cat_data in cats.items():\n c = add_cat(cat_data['user'], cat, cat_data['threads_no'])\n for t in cat_data['threads']:\n thread = add_thread(t['user'], c, t['question'], t['replies_no'])\n threads_list.append(thread)\n\n for i in range(len(threads_list)):\n add_reply(replies[i]['user'], threads_list[i], replies[i]['reply'])\n\n\n for c in Category.objects.all():\n for t in Thread.objects.filter(category=c):\n print(f'-{c}: {t}')\n\n\ndef add_thread(user, cat,question,replies=0):\n t = Thread.objects.get_or_create(category=cat, question=question, user=user)[0]\n t.replies = replies\n t.save()\n return t\n\ndef add_cat(user, name,threads=0):\n c=Category.objects.get_or_create(name=name, threads=threads, user=user)[0]\n c.save()\n return c\n\ndef add_reply(user, thread, text):\n r=Reply.objects.get_or_create(thread=thread, text=text, user=user)[0]\n r.save()\n return r\n\n\nif __name__=='__main__':\n print('Starting StackUnderflow population script...')\n populate()","repo_name":"wad2Project/group_project","sub_path":"population_script.py","file_name":"population_script.py","file_ext":"py","file_size_in_byte":3202,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"4126897018","text":"import urllib\n\n\ndef find_dict_in_list(list_, key, value, not_found=None):\n \"\"\"\n A helper function to find a dict based on a given key and value pair\n inside a list of dicts. Only the first occurence is returned if\n there are multiple dicts with the key and value.\n\n Args:\n ``list_`` (list): A list of dicts to search in.\n\n ``key`` (str): The key of the dict to look at.\n\n ``value`` (str): The value of the key the dict has to match.\n\n Kwargs:\n ``not_found`` (dict): The return value if no dict was found.\n Defaults to ``{}``.\n\n Returns:\n ``dict``. The dict if found. If not found, the return value as\n provided is returned or an empty dict by default.\n \"\"\"\n if not_found is None:\n not_found = {}\n if value is not None:\n for el in list_:\n if el.get(key) == value:\n return el\n return not_found\n\n\ndef is_empty_list_of_dicts(list_):\n \"\"\"\n A helper function to find out if a list of dicts contains values or\n not. The following values are considered as empty values:\n\n * ``[{\"key\": \"\"}]``\n * ``[{\"key\": None}]``\n * ``[{\"key\": []}]``\n\n Args:\n ``list_`` (list): A list of dicts.\n\n Returns:\n ``bool``. ``True`` if the list contains only empty dicts,\n ``False`` if not.\n \"\"\"\n for d in list_:\n for key, value in d.items():\n if isinstance(value, dict):\n for k, v in value.items():\n if v is not None and v != '':\n return False\n elif value is not None and value != '' and value != []:\n return False\n return True\n\n\ndef url_with_querystring(path, **kwargs):\n \"\"\"\n Build a URL with query strings.\n\n Args:\n ``path`` (str): The base path of the URL before the query\n strings.\n\n Kwargs:\n ``**(key=value)``: One or more parameters as keys and values to\n be added as query strings.\n\n Returns:\n ``str``. A URL with query strings.\n \"\"\"\n return path + '?' + urllib.parse.urlencode(kwargs)\n","repo_name":"sotkonstantinidis/testcircle","sub_path":"apps/qcat/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"33503189969","text":"import numpy as np \nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport os\nimport sys\nimport re\nimport pandas as pd \nimport glob\nimport scipy\nimport scipy.io\nimport math\nfrom collections import Counter\nimport scanpy as sc\nimport h5py\nimport gseapy as gp\nimport time\nfrom matplotlib import rcParams\nimport harmonypy as hm\nimport scrublet as scr\n\n\n ####################\n #\n # NEW FUNCTIONS ANALYSIS 2\n #\n ####################\n\n\ndef adata_preproc(files_list, samples_names, batch_corr = False, max_genes = 3000, \n min_genes = 250, min_cells=10,\n perc_mito = 0.15, plots = False, samples_file = 'samples.csv',\n verbose = False, dir_for_figs = '', convert_ids = False,\n scrublet = False, file_h5 = False, flex_filt = False, del_genes = False):\n samples = samples_names\n raw_adata = {}\n samples_info = pd.read_csv(samples_file, sep = '\\t')\n if verbose:\n print(\"reading samples ...\")\n if convert_ids:\n converted_once = False\n annot = sc.queries.biomart_annotations(\"hsapiens\",[\"ensembl_gene_id\", \"start_position\", \"end_position\", \"chromosome_name\", \"hgnc_symbol\"]).set_index(\"ensembl_gene_id\")\n for sample in samples:\n if file_h5:\n raw_adata[sample] = sc.read_10x_h5(files_list[sample])\n else:\n raw_adata[sample] = sc.read_10x_mtx(files_list[sample], cache = True)\n raw_adata[sample].obs.index = [\"cell_\" + str(sample) + '_' + str(i) for i in range(len(raw_adata[sample].obs.index))]\n if convert_ids:\n print(raw_adata[sample])\n raw_adata[sample].var['old_index'] = raw_adata[sample].var.index\n if converted_once:\n raw_adata[sample].var.index = new_genes_index\n else:\n new_index = [annot.loc[x, 'hgnc_symbol'] if (x in annot.index.to_list()) else x for x in raw_adata[sample].var['old_index']]\n new_genes_index = [raw_adata[sample].var.index.to_list()[i] if (not isinstance(new_index[i], str)) else new_index[i] for i in range(len(new_index))]\n raw_adata[sample].var.index = new_genes_index\n converted_once = True\n raw_adata[sample].var_names_make_unique()\n print(raw_adata[sample].var.index[:20])\n if verbose:\n print(raw_adata[sample])\n if del_genes:\n print('after...')\n raw_adata[sample] = raw_adata[sample][:,~(raw_adata[sample].var.index.isin(del_genes))]\n print(raw_adata[sample])\n raw_adata[sample].var_names_make_unique()\n if scrublet != False:\n if verbose:\n print('pre-scrublet filtering ...')\n for sample in samples:\n print(sample)\n sc.pp.filter_cells(raw_adata[sample], min_genes=min_genes)\n if verbose:\n print('after filt genes')\n print(raw_adata[sample])\n mito_genes = raw_adata[sample].var_names.str.startswith('MT-')\n raw_adata[sample].obs['percent_mito'] = np.sum(\n raw_adata[sample][:, mito_genes].X, axis=1).A1 / np.sum(raw_adata[sample].X, axis=1).A1\n raw_adata[sample].obs['n_counts'] = raw_adata[sample].X.sum(axis=1).A1\n if plots:\n sc.pl.violin(raw_adata[sample], ['n_genes'], jitter=0.2)\n sc.pl.violin(raw_adata[sample], ['n_counts'], jitter=0.2)\n sc.pl.violin(raw_adata[sample], ['percent_mito'], jitter=0.2)\n sc.pl.scatter(raw_adata[sample], x='n_counts', y='percent_mito')\n sc.pl.scatter(raw_adata[sample], x='n_counts', y='n_genes')\n sc.pl.highest_expr_genes(raw_adata[sample], n_top=15)\n raw_adata[sample] = raw_adata[sample][raw_adata[sample].obs.n_genes <= max_genes]\n if verbose:\n print('after filt max')\n print(raw_adata[sample])\n if flex_filt:\n print(raw_adata[sample].obs.shape[0])\n print((1/math.sqrt(raw_adata[sample].obs.shape[0])) * perc_mito)\n if ((1/math.sqrt(raw_adata[sample].obs.shape[0])) * perc_mito) < 0.90:\n new_perc_mito = raw_adata[sample].obs.percent_mito.quantile((1/math.sqrt(raw_adata[sample].obs.shape[0])) * perc_mito)\n else:\n new_perc_mito = raw_adata[sample].obs.percent_mito.quantile(0.90)\n print(\"New perc_mito: \" + str(new_perc_mito))\n else:\n new_perc_mito = perc_mito\n raw_adata[sample] = raw_adata[sample][raw_adata[sample].obs['percent_mito'] < new_perc_mito]\n if verbose:\n print('after filt mito')\n print(raw_adata[sample])\n if verbose:\n print('scrublet ...')\n for sample in samples:\n if verbose:\n print(sample)\n scrub = scr.Scrublet(raw_adata[sample].X, expected_doublet_rate=0.06)\n doublet_scores, predicted_doublets = scrub.scrub_doublets(min_counts=2, \n min_cells=3, \n min_gene_variability_pctl=85, \n n_prin_comps=30)\n scrub.call_doublets(threshold=scrublet)\n if plots:\n scrub.plot_histogram()\n #scrub.set_embedding('UMAP', scr.get_umap(scrub.manifold_obs_, 10, min_dist=0.3))\n #scrub.plot_embedding('UMAP', order_points=True)\n if verbose:\n print('before filtering ..')\n print(raw_adata[sample])\n raw_adata[sample].obs['doublet_score'] = scrub.doublet_scores_obs_\n raw_adata[sample] = raw_adata[sample][scrub.predicted_doublets_ == False]\n if verbose: \n print('after filtering...')\n print(raw_adata[sample])\n if verbose:\n print(\"concatenation...\")\n for sample in samples:\n if not raw_adata[sample].var.index.is_unique:\n idx = pd.Series(raw_adata[sample].var.index.to_list())\n idx.loc[raw_adata[sample].var.index.duplicated()] = raw_adata[sample].var[raw_adata[sample].var.index.duplicated()].index + '-1'\n raw_adata[sample].var.index = idx\n adata = list(raw_adata.values())[0].concatenate(list(raw_adata.values())[1:])\n samples_dict = { i : samples[i] for i in range(0, len(samples) ) }\n adata.obs['batch'] = adata.obs['batch'].astype(int).replace(samples_dict)\n adata.obs['batch'] = pd.Categorical(adata.obs.batch)\n adata.obs['sample_id'] = adata.obs['batch']\n for i, column in enumerate(samples_info.columns):\n if (i > 0):\n adata.obs[column] = [ samples_info[samples_info.Sample_id == sample_id][column].values[0] for sample_id in list(adata.obs.sample_id)]\n adata.obs[column] = pd.Categorical(adata.obs[column])\n if verbose:\n print(\"filtering...\")\n if scrublet == False:\n sc.pp.filter_cells(adata, min_genes=min_genes)\n if plots:\n sc.pl.highest_expr_genes(adata, n_top=15, save = dir_for_figs + \"highest_expr1.png\")\n mito_genes = adata.var_names.str.startswith('MT-')\n adata.obs['percent_mito'] = np.sum(\n adata[:, mito_genes].X, axis=1).A1 / np.sum(adata.X, axis=1).A1\n adata.obs['n_counts'] = adata.X.sum(axis=1).A1\n if plots:\n sc.pl.violin(adata, ['n_genes'],\n jitter=0.2, save= dir_for_figs + \"quality_metrics_aft_filt1_genes.png\")\n sc.pl.violin(adata, ['n_counts'],\n jitter=0.2, save= dir_for_figs + \"quality_metrics_aft_filt1_counts.png\")\n sc.pl.violin(adata, ['percent_mito'],\n jitter=0.2, save= dir_for_figs + \"quality_metrics_aft_filt1_pers_mito.png\")\n sc.pl.scatter(adata, x='n_counts', y='percent_mito', save= dir_for_figs + \"count_vs_mito1.png\")\n sc.pl.scatter(adata, x='n_counts', y='n_genes', save= dir_for_figs + \"count_vs_gene1.png\")\n if verbose:\n print(adata)\n print(adata.obs[\"sample_id\"].value_counts())\n if scrublet == False:\n if plots:\n plt.hist(adata.obs.n_counts, bins = 50)\n plt.show()\n adata = adata[adata.obs.n_genes <= max_genes,:]\n if plots:\n plt.hist(adata.obs.n_counts, bins = 500)\n plt.show()\n for sample in samples:\n print(sample)\n plt.hist(adata[adata.obs.sample_id == sample].obs.n_counts, bins = 500)\n plt.show()\n adata = adata[adata.obs['percent_mito'] < perc_mito, :]\n if verbose:\n print(adata)\n print(adata.obs[\"sample_id\"].value_counts())\n print(\"mean_ncounts\")\n print(adata.obs['n_counts'].mean())\n for sample in samples:\n print(sample)\n print(adata[adata.obs.sample_id == sample].obs['n_counts'].mean())\n print(\"mean ngenes\")\n print(adata.obs['n_genes'].mean())\n for sample in samples:\n print(sample)\n print(adata[adata.obs.sample_id == sample].obs['n_genes'].mean())\n if plots:\n sc.pl.violin(adata, ['n_genes'],\n jitter=0.2, save= dir_for_figs + \"quality_metrics_aft_filt2_genes.png\")\n sc.pl.violin(adata, ['n_counts'],\n jitter=0.2, save= dir_for_figs + \"quality_metrics_aft_filt2_counts.png\")\n sc.pl.violin(adata, ['percent_mito'],\n jitter=0.2, save= dir_for_figs + \"quality_metrics_aft_filt2_pers_mito.png\")\n for sample in samples:\n print(sample)\n sc.pl.violin(adata[adata.obs.sample_id == sample], ['n_genes'], jitter=0.2)\n sc.pl.violin(adata[adata.obs.sample_id == sample], ['n_counts'], jitter=0.2)\n sc.pl.violin(adata[adata.obs.sample_id == sample], ['percent_mito'], jitter=0.2)\n sc.pl.scatter(adata, x='n_counts', y='percent_mito', save= dir_for_figs + \"count_vs_mito2.png\")\n sc.pl.scatter(adata, x='n_counts', y='n_genes', save= dir_for_figs + \"count_vs_gene2.png\")\n sc.pl.highest_expr_genes(adata, n_top=15, save = dir_for_figs + \"highest_expr2.png\")\n if verbose:\n print(\"Normalisation ...\")\n sc.pp.normalize_total(adata, target_sum=1e4)\n sc.pp.log1p(adata)\n adata.raw = adata\n if verbose:\n print(\"Highly var genes\")\n sc.pp.filter_genes(adata, min_cells=min_cells)\n sc.pp.highly_variable_genes(adata, min_mean=0.005, max_mean=5, min_disp=-0.25)\n if plots:\n sc.pl.highly_variable_genes(adata, save=dir_for_figs + \"var_genes.png\")\n adata = adata[:, adata.var.highly_variable]\n if verbose:\n print(adata)\n if verbose:\n print(\"regression ...\")\n sc.pp.regress_out(adata, ['n_counts', 'percent_mito'])\n sc.pp.scale(adata, max_value=10)\n if plots:\n print(\"without batch correction:\")\n sc.tl.pca(adata, svd_solver='arpack')\n sc.pl.pca(adata, color=['batch', 'sample_id', 'phenotype'], save = dir_for_figs + \"PCA_uncorr.png\")\n sc.pp.neighbors(adata, n_neighbors=10, n_pcs=40)\n sc.tl.umap(adata)\n sc.tl.leiden(adata)\n sc.pl.umap(adata, color=['batch', 'sample_id', 'phenotype', 'leiden'], save = dir_for_figs + \"UMAP_uncorr.png\")\n if batch_corr:\n if verbose:\n print(\"Batch correction per sample...\")\n ho = hm.run_harmony(adata.obsm['X_pca'], adata.obs, \"sample_id\")\n #adata.obsm['X_pca'] = ho.Z_corr.T\n adata.obsm['X_pca'] = ho.Z_cos.T\n adata.uns['PCA_corr'] = ho.Z_corr.T\n adata.uns['PCA_cos'] = ho.Z_cos.T\n if plots:\n print(\"with batch correction:\")\n #sc.tl.pca(adata, svd_solver='arpack')\n print('cos')\n sc.pl.pca(adata, color=['batch', 'sample_id', 'phenotype'], save = dir_for_figs + \"PCA_corr.png\")\n sc.pp.neighbors(adata, n_neighbors=10, n_pcs=40)\n sc.tl.umap(adata)\n sc.tl.leiden(adata)\n sc.pl.umap(adata, color=['batch', 'sample_id', 'phenotype', 'leiden'], save = dir_for_figs + \"UMAP_corr.png\")\n print('corr')\n adata.obsm['X_pca'] = ho.Z_corr.T\n sc.pl.pca(adata, color=['batch', 'sample_id', 'phenotype'], save = dir_for_figs + \"PCA_corr.png\")\n sc.pp.neighbors(adata, n_neighbors=10, n_pcs=40)\n sc.tl.umap(adata)\n sc.tl.leiden(adata)\n sc.pl.umap(adata, color=['batch', 'sample_id', 'phenotype', 'leiden'], save = dir_for_figs + \"UMAP_corr.png\")\n return adata\n\ndef plots_markers(adata, markers_dict = \"default\", save_plots = False, dim_type = 'UMAP'):\n if markers_dict == \"default\":\n markers_dict = {\n 'CM': ['leiden', 'sample_id', 'TTN', 'MYH7', 'MYH6', 'TNNT2'],\n 'VEC': ['leiden', 'sample_id', 'VWF', 'ANO2', 'PECAM1'],\n 'EEC': ['leiden', 'MYRIP', 'LEPR', 'EMCN', 'HMCN1', 'PECAM1'],\n 'FB': ['leiden', 'sample_id', 'DCN', 'LUM', 'FBLN1', 'COL1A2'],\n 'MESO' : ['WT1', 'UPK3B', 'HAS1', 'MSLN'],\n 'MC': ['leiden', 'sample_id', 'CD163', 'CCL4', 'MRC1', 'SLC9A9'],\n 'SMC': ['leiden', 'ACTA2', 'MYH11'],\n 'PER': ['leiden', \"RGS5\", \"PDGFRB\", \"ABCC9\"],\n 'AD': ['leiden', 'PLIN4', 'PLIN1', 'LIPE', 'ADIPOQ', 'CIDEA', 'LPL'],\n 'N' : ['SYT1', 'SNAP25', 'GRIN1'],\n 'SC' : ['PLP1', 'MPZ', 'PMP22'],\n 'L' : ['CD79A', 'CD79B', 'CD3E', 'CD247'],\n 'Others': ['leiden', 'RHOA', 'JCAD', 'PHACTR1', 'KLF2']\n }\n for markers in markers_dict:\n print(markers)\n if save_plots:\n save_file = save_plots + markers + '.png'\n else:\n save_file = False\n if dim_type == 'UMAP':\n sc.pl.umap(adata, color=markers_dict[markers], save= save_file)\n else:\n sc.pl.tsne(adata, color=markers_dict[markers], save= save_file)\n\ndef plot_heatmap_markers(adata, dir_for_figs, groupby = 'leiden', use_raw = False):\n markers_all = [\"TTN\", \"MYH6\", \"MYH7\", \"TNNT2\", \n \"VWF\", \"ANO2\", \"PECAM1\", \n \"DCN\", \"C7\", \"LUM\", \"FBLN1\", \"COL1A2\",\n \"CD163\", \"SLC9A9\", \"MRC1\", \n \"MYH11\", \"ACTA2\",\n \"RGS5\", \"PDGFRB\", \"ABCC9\",\n \"PLIN4\", \"PLIN1\",\n 'SYT1', 'SNAP25', 'GRIN1',\n 'PLP1', 'MPZ', 'PMP22',\n 'CD79A', 'CD79B', 'CD3E', 'CD247',\n 'WT1', 'UPK3B', 'HAS1', 'MSLN',\n 'MYRIP', 'LEPR', 'NPR3', 'NFATC1'\n ]\n markers_groups = [(0,3), (4,6), (7,11), (12,14), (15,16), (17,19), (20, 21), (22, 24), \n (25, 27), (28, 31), (32, 35), (36, 39)]\n markers_groups_names = [\"CM\", \"VEC\" , \"FB\", \"MP\", \"SMC\", \"PER\", \"AD\", 'N', 'SC', 'L', 'MESO', 'EEC']\n sc.tl.dendrogram(adata, groupby = groupby)\n sc.pl.dotplot(adata, markers_all, groupby = groupby, use_raw = use_raw, dendrogram= True,\n var_group_positions = markers_groups,\n var_group_labels = markers_groups_names, save = dir_for_figs + \"dot_unscaled.png\")\n sc.pl.dotplot(adata, markers_all, groupby = groupby, use_raw = use_raw, dendrogram= True,\n standard_scale = \"var\", var_group_positions = markers_groups, \n var_group_labels = markers_groups_names, save = dir_for_figs + \"dot_scaled.png\")\n sc.pl.matrixplot(adata, markers_all, groupby = groupby, use_raw = use_raw, dendrogram= True,\n standard_scale = \"var\", var_group_positions = markers_groups, \n var_group_labels = markers_groups_names,\n cmap = \"Blues\", save = dir_for_figs + \"heatmap.png\")\n\n\ndef run_umap_leiden(adata, plot = False):\n n_pcs = 50\n if adata.shape[0] < 50:\n n_pcs = adata.shape[0] - 1\n sc.tl.pca(adata, svd_solver='arpack', n_comps = n_pcs)\n sc.pp.neighbors(adata, n_neighbors=10, n_pcs= n_pcs)\n sc.tl.umap(adata)\n adata.obs[\"old_leiden\"] = adata.obs[\"leiden\"].copy()\n sc.tl.leiden(adata)\n if plot:\n sc.pl.umap(adata, color=['batch', 'leiden'])\n return adata\n\n######\n##\n## Preprocessing functions:\n##\n######\n\ndef partition_adata_obs(adata, categories_name):\n categories = list(set(adata.obs[categories_name].to_list()))\n subadata = {}\n for category in categories:\n subadata[category] = adata[adata.obs[categories_name] == category]\n sc.pl.umap(subadata[category], color=['leiden', 'cell_types'])\n return subadata\n\ndef rerun_umap_leiden(subadata_1, scale = False):\n if scale:\n sc.pp.scale(subadata_1, max_value=10)\n n_pcs = 50\n if subadata_1.shape[0] < 50:\n n_pcs = subadata_1.shape[0] - 1\n sc.tl.pca(subadata_1, svd_solver='arpack', n_comps = n_pcs)\n sc.pp.neighbors(subadata_1, n_neighbors=10, n_pcs= n_pcs)\n sc.tl.umap(subadata_1)\n subadata_1.obs[\"old_leiden\"] = subadata_1.obs[\"leiden\"].copy()\n sc.tl.leiden(subadata_1)\n sc.pl.umap(subadata_1, color=['leiden', 'cell_types'])\n return subadata_1\n\ndef rerun_all_umap_leiden(subadata, scale = False):\n for category in subadata:\n print('\\n\\n' + category + '\\n')\n subadata[category] = rerun_umap_leiden(subadata[category], scale)\n return subadata\n\ndef filter_markers(markers, adata):\n new_markers = {}\n for cell_type in markers:\n new_markers[cell_type] = []\n for marker in markers[cell_type]:\n marker = marker.upper()\n if marker in set(adata.var.index.to_list()):\n new_markers[cell_type].append(marker)\n return new_markers\n\ndef umap_fig(adata, markers, save_file):\n sc.pl.umap(adata, color=markers, save =save_file)\n \ndef multiple_umap_leiden(adata, markers, file_beg, add_markers = [], ext = '.pdf'):\n for subtype in markers:\n umap_fig(adata, [\"leiden\"] + add_markers + markers[subtype], file_beg + subtype + ext)\n\ndef display_markers_subtypes(subadata, markers, file_name, ext = '.pdf'):\n for category in subadata:\n multiple_umap_leiden(subadata[category], markers[category], category + \"/\" + file_name, ext = '.pdf')\n\ndef rank_plot_all(subadata, n_genes, folder, file_name, ext = '.pdf'):\n for category in subadata:\n dirName = folder + \"/\" + category\n try:\n os.makedirs(dirName) \n print(\"Directory \" , dirName , \" Created \")\n except FileExistsError:\n print(\"Directory \" , dirName , \" already exists\") \n sc.settings.figdir = dirName + '/'\n print(category)\n sc.tl.rank_genes_groups(subadata[category], 'leiden', method='t-test', n_genes = 1000)\n sc.pl.rank_genes_groups_dotplot(subadata[category], groupby = 'leiden', n_genes = n_genes, save = file_name + ext)\n\n\ndef enrichment_all(subadata, gene_sets, folder, cutoff = 0.05, verbose = False):\n subadata_enr = {}\n for category in subadata:\n dirName = folder + \"/\" + category\n try:\n os.makedirs(dirName) \n print(\"Directory \" , dirName , \" Created \")\n except FileExistsError:\n print(\"Directory \" , dirName , \" already exists\") \n sc.settings.figdir = dirName + '/'\n print(category + \"7\\n\\n\")\n sc.tl.rank_genes_groups(subadata[category], 'leiden', method='t-test', n_genes = 1000)\n enr = {}\n groups = set(subadata[category].obs['leiden'].to_list())\n i = 0\n while i < len(groups):\n #enr[group] = {}\n group = list(groups)[i]\n num_sign = (subadata[category].uns['rank_genes_groups']['pvals'][group] < cutoff).sum()\n print(\"i: {}, group: {}, num_genes: {} \\n\".format(i, group, num_sign))\n list_genes = pd.DataFrame(subadata[category].uns['rank_genes_groups']['names'])[group].head(num_sign)\n list_genes = [x for x in list_genes if not x.startswith(\"MT-\")]\n #for gene_set in gene_sets:\n time.sleep(0.5)\n try:\n enr[group] = gp.enrichr(gene_list = list_genes,\n description = group, # + gene_set, \n gene_sets = gene_sets,\n outdir = dirName + '/' + group + '/', \n cutoff = 0.5,\n verbose = verbose, format='png')\n i += 1\n except:\n time.sleep(3)\n print(\"connection error \\n\")\n subadata_enr[category] = enr\n return subadata_enr\n\ndef enrichment_once(adata, group, gene_sets, folder, cutoff = 0.05, verbose = False, n_genes = 1000, method = 'wilcoxon', to_remove = False, n_max = False):\n dirName = folder + \"/\"\n try:\n os.makedirs(dirName) \n print(\"Directory \" , dirName , \" Created \")\n except FileExistsError:\n print(\"Directory \" , dirName , \" already exists\") \n sc.settings.figdir = dirName + '/'\n sc.tl.rank_genes_groups(adata, group, method= method, n_genes = n_genes)\n enr = {}\n groups = set(adata.obs[group].to_list())\n i = 0\n while i < len(groups):\n #enr[group] = {}\n group = list(groups)[i]\n num_sign = (adata.uns['rank_genes_groups']['pvals'][group] < cutoff).sum()\n #print(\"i: {}, group: {}, num_genes: {} \\n\".format(i, group, num_sign))\n list_genes = pd.DataFrame(adata.uns['rank_genes_groups']['names'])[group].head(num_sign)\n list_genes = [x for x in list_genes if not x.startswith(\"MT-\")]\n list_genes = [x for x in list_genes if not x.startswith(\"RPL\")]\n list_genes = [x for x in list_genes if not x.startswith(\"RPS\")]\n if not (to_remove == False):\n list_genes = [x for x in list_genes if not x in to_remove]\n if n_max :\n list_genes = list_genes[:n_max]\n #for gene_set in gene_sets:\n print(\"i: {}, group: {}, num_genes: {} \\n\".format(i, group, len(list_genes)))\n time.sleep(0.5)\n try:\n enr[group] = gp.enrichr(gene_list = list_genes,\n description = group, # + gene_set, \n gene_sets = gene_sets,\n outdir = dirName + '/' + group + '/', \n cutoff = 0.5,\n verbose = verbose, format='png')\n i += 1\n except:\n time.sleep(3)\n print(\"connection error \\n\")\n return enr","repo_name":"eschmauch/Heart_single_cell","sub_path":"Python/methods.py","file_name":"methods.py","file_ext":"py","file_size_in_byte":22711,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"31518731234","text":"from abc import ABCMeta, abstractmethod\nfrom typing import List, Optional, Tuple\n\nimport numpy as np\nimport pandas as pd\nfrom Fun.data.source import (\n DAILY,\n FREQUENCY,\n INTRADAY_15MINUTES,\n INTRADAY_30MINUTES,\n INTRADAY_60MINUTES,\n MONTHLY,\n WEEKLY,\n)\nfrom matplotlib import ticker\n\n\nclass Ticker(metaclass=ABCMeta):\n @abstractmethod\n def ticks(self) -> Tuple[List[float], List[str]]:\n raise NotImplementedError\n\n\nclass TimeTicker(Ticker):\n def __init__(self, quotes: pd.DataFrame) -> None:\n self._quotes = quotes\n\n def ticks(self) -> Tuple[List[float], List[str]]:\n frequency: FREQUENCY\n\n start = self._quotes.index[0]\n end = self._quotes.index[-1]\n\n period = end - start\n if period.days < 3:\n frequency = INTRADAY_15MINUTES\n elif period.days < 6:\n frequency = INTRADAY_30MINUTES\n elif period.days < 16:\n frequency = INTRADAY_60MINUTES\n elif period.days < 365:\n frequency = DAILY\n elif period.days < 365 * 5:\n frequency = WEEKLY\n else:\n frequency = MONTHLY\n\n assert frequency is not None\n\n loc: List[float] = []\n labels: List[str] = []\n\n if frequency == DAILY:\n dates = self._quotes.index.strftime(\"%Y-%b\")\n labels = np.unique(dates)\n loc = [np.argwhere(dates == l).min() for l in labels]\n\n elif frequency == WEEKLY:\n dates = self._quotes.index.strftime(\"%Y-%b\")\n labels = np.unique(dates)\n loc = [np.argwhere(dates == l).min() for l in labels]\n\n func = np.vectorize(\n lambda x: x.split(\"-\")[0] if \"Jan\" in x else x.split(\"-\")[1]\n )\n labels = func(labels)\n\n elif frequency == MONTHLY:\n dates = self._quotes.index.strftime(\"%Y\")\n labels = np.unique(dates)\n loc = [np.argwhere(dates == l).min() for l in labels]\n\n elif frequency == INTRADAY_60MINUTES:\n pattern = \"%y-%m-%d\\n%H:%M\"\n dates = self._quotes.index.strftime(pattern)\n\n labels = self._quotes.index[\n (self._quotes.index.hour == 17)\n | (self._quotes.index.hour == 6)\n | (self._quotes.index.hour == 0)\n | (self._quotes.index.hour == 11)\n ].strftime(pattern)\n loc = [np.argwhere(dates == l).min() for l in labels]\n\n elif frequency == INTRADAY_15MINUTES or frequency == INTRADAY_30MINUTES:\n pattern = \"%y-%m-%d\\n%H:%M\"\n dates = self._quotes.index.strftime(pattern)\n\n labels = self._quotes.index[\n (\n (self._quotes.index.hour == 17)\n | (self._quotes.index.hour == 6)\n | (self._quotes.index.hour == 0)\n | (self._quotes.index.hour == 11)\n )\n & (self._quotes.index.minute == 0)\n ].strftime(pattern)\n loc = [np.argwhere(dates == l).min() for l in labels]\n\n else:\n raise NotImplementedError\n\n aloc = np.array(loc)\n condition = aloc >= 4\n\n return np.extract(condition, aloc), np.extract(condition, labels)\n\n\nclass StepTicker(Ticker):\n def __init__(\n self,\n mn: float,\n mx: float,\n nbins: int = 25,\n steps: Optional[List[int]] = [1, 2, 5, 10],\n decimals: int = 3,\n ) -> None:\n self._nbins = nbins\n self._steps = steps\n self._min = mn\n self._max = mx\n\n self._decimals = decimals\n\n def ticks(self) -> Tuple[List[float], List[str]]:\n locator = ticker.MaxNLocator(nbins=self._nbins, steps=self._steps)\n values = locator.tick_values(self._min, self._max)\n\n return values, [f\"{v:.{self._decimals}f}\" for v in values]\n","repo_name":"KushamiNeko/PythonFun","sub_path":"Fun/chart/ticker.py","file_name":"ticker.py","file_ext":"py","file_size_in_byte":3883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"40439841853","text":"import threading\n\nfrom nvflare.apis.event_type import EventType\nfrom nvflare.apis.fl_constant import SystemComponents\nfrom nvflare.apis.workspace import Workspace\nfrom nvflare.private.fed.server.fed_server import FederatedServer\nfrom nvflare.private.fed.server.job_runner import JobRunner\nfrom nvflare.private.fed.server.run_manager import RunManager\nfrom nvflare.private.fed.server.server_cmd_modules import ServerCommandModules\n\n\nclass ServerDeployer:\n \"\"\"FL Server deployer.\"\"\"\n\n def __init__(self):\n \"\"\"Init the ServerDeployer.\"\"\"\n self.cmd_modules = ServerCommandModules.cmd_modules\n self.server_config = None\n self.secure_train = None\n self.app_validator = None\n self.host = None\n self.snapshot_persistor = None\n self.overseer_agent = None\n self.components = None\n self.handlers = None\n\n def build(self, build_ctx):\n \"\"\"To build the ServerDeployer.\n\n Args:\n build_ctx: build context\n\n \"\"\"\n self.server_config = build_ctx[\"server_config\"]\n self.secure_train = build_ctx[\"secure_train\"]\n self.app_validator = build_ctx[\"app_validator\"]\n self.host = build_ctx[\"server_host\"]\n self.snapshot_persistor = build_ctx[\"snapshot_persistor\"]\n self.overseer_agent = build_ctx[\"overseer_agent\"]\n self.components = build_ctx[\"server_components\"]\n self.handlers = build_ctx[\"server_handlers\"]\n\n def create_fl_server(self, args, secure_train=False):\n \"\"\"To create the FL Server.\n\n Args:\n args: command args\n secure_train: True/False\n\n Returns: FL Server\n\n \"\"\"\n # We only deploy the first server right now .....\n first_server = sorted(self.server_config)[0]\n heart_beat_timeout = first_server.get(\"heart_beat_timeout\", 600)\n\n if self.host:\n target = first_server[\"service\"].get(\"target\", None)\n first_server[\"service\"][\"target\"] = self.host + \":\" + target.split(\":\")[1]\n\n services = FederatedServer(\n project_name=first_server.get(\"name\", \"\"),\n min_num_clients=first_server.get(\"min_num_clients\", 1),\n max_num_clients=first_server.get(\"max_num_clients\", 100),\n cmd_modules=self.cmd_modules,\n heart_beat_timeout=heart_beat_timeout,\n args=args,\n secure_train=secure_train,\n snapshot_persistor=self.snapshot_persistor,\n overseer_agent=self.overseer_agent,\n shutdown_period=first_server.get(\"shutdown_period\", 30.0),\n check_engine_frequency=first_server.get(\"check_engine_frequency\", 3.0),\n )\n return first_server, services\n\n def deploy(self, args):\n \"\"\"To deploy the FL server services.\n\n Args:\n args: command args.\n\n Returns: FL Server\n\n \"\"\"\n first_server, services = self.create_fl_server(args, secure_train=self.secure_train)\n services.deploy(args, grpc_args=first_server, secure_train=self.secure_train)\n\n job_runner = JobRunner(workspace_root=args.workspace)\n workspace = Workspace(args.workspace, \"server\", args.config_folder)\n run_manager = RunManager(\n server_name=services.project_name,\n engine=services.engine,\n job_id=\"\",\n workspace=workspace,\n components=self.components,\n handlers=self.handlers,\n )\n job_manager = self.components.get(SystemComponents.JOB_MANAGER)\n services.engine.set_run_manager(run_manager)\n services.engine.set_job_runner(job_runner, job_manager)\n\n run_manager.add_handler(job_runner)\n run_manager.add_component(SystemComponents.JOB_RUNNER, job_runner)\n\n with services.engine.new_context() as fl_ctx:\n services.engine.fire_event(EventType.SYSTEM_BOOTSTRAP, fl_ctx)\n\n threading.Thread(target=self._start_job_runner, args=[job_runner, fl_ctx]).start()\n\n services.engine.fire_event(EventType.SYSTEM_START, fl_ctx)\n print(\"deployed FL server trainer.\")\n\n return services\n\n def _start_job_runner(self, job_runner, fl_ctx):\n job_runner.run(fl_ctx)\n\n def close(self):\n \"\"\"To close the services.\"\"\"\n pass\n","repo_name":"NVIDIA/NVFlare","sub_path":"nvflare/private/fed/app/deployer/server_deployer.py","file_name":"server_deployer.py","file_ext":"py","file_size_in_byte":4298,"program_lang":"python","lang":"en","doc_type":"code","stars":455,"dataset":"github-code","pt":"47"}
+{"seq_id":"11874029619","text":"from app.core import Result\nfrom app.core.exceptions import AppException\nfrom app.repositories.faq_repository import FaqRepository\n\nASSERT_OBJ = \"missing object data {}\"\nASSERT_DICT_OBJ = \"object {} is not a dict\"\nASSERT_LIST_OBJ = \"object {} is not a list\"\nASSERT_OBJECT_IS_DICT = \"object data not a dict\"\n\n\nclass FaqController:\n def __init__(\n self,\n faq_repository: FaqRepository,\n ):\n self.faq_repository = faq_repository\n\n def all_faq(self):\n result = self.faq_repository.index()\n return Result(result, 200)\n\n def register_faq(self, obj_data):\n assert obj_data, ASSERT_OBJECT_IS_DICT\n\n question = self.faq_repository.create(obj_data)\n return Result(question, 201)\n\n def update_faq(self, obj_id: str, obj_data: dict):\n\n assert obj_id, ASSERT_OBJ.format(\"obj_id\")\n assert obj_data, ASSERT_OBJ.format(\"obj_data\")\n assert isinstance(obj_data, dict), ASSERT_DICT_OBJ.format(\"obj_data\")\n\n try:\n result = self.faq_repository.find({\"id\": obj_id})\n except AppException.NotFoundException:\n raise AppException.NotFoundException(context=\"faq id does not exist \")\n self.faq_repository.update_by_id(obj_id=result.id, obj_in=obj_data)\n return Result(result, 200)\n\n def delete_faq(self, obj_id: str):\n assert obj_id, ASSERT_OBJ.format(\"obj_id\")\n\n try:\n result = self.faq_repository.find({\"id\": obj_id})\n except AppException.NotFoundException:\n raise AppException.NotFoundException(\n error_message=\"promotion id does not exist\"\n )\n\n self.faq_repository.delete_by_id(obj_id=result.id)\n\n return Result(None, 204)\n","repo_name":"Amankashyap4/nova-be-customer","sub_path":"app/controllers/faq_controller.py","file_name":"faq_controller.py","file_ext":"py","file_size_in_byte":1727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"15351271632","text":"import preprocess\nimport symbol_read\nimport string\nfrom termcolor import colored\n# a procedural program for parsing\n# based on the EBNF rules in https://golang.org/ref/spec\n\n#state variables\nfile = None\nsymbol = None\nline_no = None\ncolumn_no = None\nprev_isspace = False\n\n#helper\ndef open_symbolic_file(filename):\n\tglobal file\n\tfile = open(filename)\n\tfile = preprocess.preprocess(file)\n\tfile = symbol_read.read_symbols(file)\n\ndef read_one_symbol():\n\tglobal symbol, line_no, column_no, prev_isspace\n\tsymbol, line_no, column_no, prev_isspace = next(file)\n\ndef output_error_and_halt():\n\t#implement ouput error here\n\tprint(\"error\")\n\tprint(\"symbol\", symbol, \"line_no\", line_no, \"column_no\", column_no)\n\traise SystemExit\n\ndef main(filename):\n\ttry:\n\t\t#main program\n\t\topen_symbolic_file(filename)\n\t\tread_one_symbol()\n\t\t#call start symbol\n\t\tnt_SourceFile()\n\n\t\t#output if no error\n\t\tprint(\"=========================================\")\n\t\twith open(filename) as file:\n\t\t\tfor line in file:\n\t\t\t\tprint(line,end='')\n\t\tprint()\n\t\tprint(\"=========================================\")\n\n\t\tprint(\"no error was found. file syntax is valid\")\n\texcept SystemExit as e:\n\t\tprint(\"specifically in the file please see below\")\n\t\tprint(\"=========================================\")\n\t\tif line_no!=\"last\":\n\t\t\t#output final error\n\t\t\twith open(filename) as file:\n\t\t\t\tl=1\n\t\t\t\tfor line in file:\n\t\t\t\t\tc_no=1\n\t\t\t\t\tfor c in line:\n\t\t\t\t\t\tif l==line_no and c_no >= column_no and c_no column_no) or (l>line_no):\n\t\t\t\t\t\t\tprint(colored(c,'grey'),end='')\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprint(c,end='')\n\t\t\t\t\t\tc_no=c_no+1\n\t\t\t\t\tl=l+1\n\t\t\tprint('\\033[0m')\n\t\telse:\n\t\t\twith open(filename) as file:\n\t\t\t\tfor line in file:\n\t\t\t\t\tprint(line,end='')\n\t\t\t\tprint()\n\t\t\t\tprint(colored('_','red'))\n\t\tprint(\"=========================================\")\n\t\tprint(\"error! see above\")\n\n#GRAMMAR IMPLEMENTATION HERE\n# | | | |\n# v v v v\ndef accept(T):\n\tglobal symbol\n\tif T == symbol:\n\t\tread_one_symbol()\n\telse:\n\t\toutput_error_and_halt()\n\ndef acceptset(Ts):\n\tglobal symbol\n\tif symbol in Ts:\n\t\tread_one_symbol()\n\telse:\n\t\toutput_error_and_halt()\n\ndef acceptsemicolon():\n\t#this is to abide to the specification's semicolon ignorance rule no. 2:\n\t#\tTo allow complex statements to occupy a single line, a semicolon may be omitted before a closing \")\" or \"}\".\n\tglobal symbol\n\tif symbol==\";\":\n\t\tread_one_symbol()\n\telif symbol!=\"}\" and symbol != \")\":\n\t\toutput_error_and_halt()\n\n\n\n#all nonterminal symbols\n#format: nt_\n\n# ::= /* the Unicode code point U+000A */ \ndef nt_newline():\n\taccept('\\n')\n\n# ::= /* an arbitrary Unicode code point except newline */ \ndef nt_unicode_char():\n\taccept(symbol)\n\n# ::= /* a Unicode code point classified as \"Letter\" */\ndef nt_unicode_letter():\n\tif symbol in set(string.ascii_letters):\n\t\taccept(symbol)\n\n# ::= /* a Unicode code point classified as \"Number, decimal digit\" */ \ndef nt_unicode_digit():\n\tif symbol in set(string.digits):\n\t\taccept(symbol)\n\n# ::= unicode_letter | \"_\"\ndef nt_letter(): \n\tif symbol == \"_\":\n\t\taccept(\"_\")\n\telif symbol in set(string.ascii_letters):\n\t\taccept(symbol)\n\telse:\n\t\toutput_error_and_halt()\n\t\n# ::= \"0\" … \"9\"\ndef nt_decimal_digit():\n\tif symbol in set(string.digits):\n\t\taccept(symbol)\n\n# ::= \"0\" … \"7\"\ndef nt_octal_digit():\n\tif symbol in set(string.digits):\n\t\taccept(symbol)\n\n# ::= \"0\" … \"9\" | \"A\" … \"F\" | \"a\" … \"f\"\ndef nt_hex_digit():\n\tif symbol in set(string.digits):\n\t\taccept(symbol)\n\telif symbol in set({\"A\",\"B\",\"C\",\"D\",\"E\",\"F\",\"a\",\"b\",\"c\",\"d\",\"e\",\"f\"}):\n\t\taccept(symbol)\n\nimport traceback\n# ::= letter { letter | unicode_digit }\ndef nt_identifier():\n\tnt_letter()\n\twhile symbol in set(string.ascii_letters+string.digits+\"_\") and not prev_isspace:\n\t\tif symbol in set(string.ascii_letters+\"_\"):\n\t\t\tnt_letter()\n\t\telif symbol in set(string.digits):\n\t\t\tnt_unicode_digit()\n\n# ::= decimal_lit | octal_lit | hex_lit\ndef nt_int_lit():\n\tif symbol in set(string.digits):\n\t\tnt_decimal_lit()\n\tif symbol in set(string.octdigits):\n\t\tnt_octal_lit()\n\tif symbol in set(string.hexdigits):\n\t\tnt_hex_lit()\n\n# ::= ( \"1\" … \"9\" ) { decimal_digit }\ndef nt_decimal_lit():\n\tif symbol == '1':\n\t\taccept('1')\n\telif symbol == '2':\n\t\taccept('2')\n\telif symbol == '3':\n\t\taccept('3')\n\telif symbol == '4':\n\t\taccept('4')\n\telif symbol == '5':\n\t\taccept('5')\n\telif symbol == '6':\n\t\taccept('6')\n\telif symbol == '7':\n\t\taccept('7')\n\telif symbol == '8':\n\t\taccept('8')\n\telif symbol == '9':\n\t\taccept('9')\n\telse:\n\t\toutput_error_and_halt()\n\n\twhile symbol in set(string.digits):\n\t\tnt_decimal_digit()\n\t\n# ::= \"0\" { octal_digit }\ndef nt_octal_lit():\n\taccept(\"0\")\n\twhile symbol in set(string.octdigits):\n\t\tnt_octal_digit()\n\n# ::= \"0\" ( \"x\" | \"X\" ) hex_digit { hex_digit }\ndef nt_hex_lit():\n\taccept(\"0\")\n\tif symbol == \"x\":\n\t\taccept(\"x\")\n\telif symbol == \"X\":\n\t\taccept(\"X\")\n\telse:\n\t\toutput_error_and_halt()\n\tnt_hex_digit()\n\twhile symbol in set(string.hexdigits):\n\t\tnt_hex_digit()\n\n# ::= decimals \".\" [ decimals ] [ exponent ] | decimals exponent | \".\" decimals [ exponent ]\ndef nt_float_lit():\n\tif symbol == \".\":\n\t\taccept(\".\")\n\t\tnt_decimals()\n\t\tif symbol in set({\"e\",\"E\"}):\n\t\t\tnt_exponent()\n\telse:\n\t\tnt_decimals()\n\t\taccept(\".\")\n\t\tif symbol in set(string.digits):\n\t\t\tnt_decimals()\n\t\tif symbol in set({\"e\",\"E\"}):\n\t\t\tnt_exponent()\n\t\n# ::= decimal_digit { decimal_digit }\ndef nt_decimals():\n\tnt_decimal_digit()\n\twhile symbol in set(string.digits):\n\t\tnt_decimal_digit()\n\n# ::= ( \"e\" | \"E\" ) [ \"+\" | \"-\" ] decimals\ndef nt_exponent():\n\tif symbol == \"e\":\n\t\taccept(\"e\")\n\telif symbol == \"E\":\n\t\taccept(\"E\")\n\telse:\n\t\toutput_error_and_halt()\n\n\tif symbol == \"+\":\n\t\taccept(\"+\")\n\telif symbol == \"-\":\n\t\taccept(\"-\")\n\n\tnt_decimals()\n\n# ::= (decimals | float_lit) \"i\"\ndef nt_imaginary_lit():\n\tif symbol in set(string.digits):\n\t\tnt_decimals()\n\telif symbol == \".\":\n\t\tfloat_lit()\n\taccept(\"i\")\n\n# ::= \" ' \" ( unicode_value | byte_value ) \" ' \"\n# ::= \" ' \" ( unicode_value_or_byte_value ) \" ' \"\ndef nt_rune_lit():\n\taccept(\"'\")\n\tnt_unicode_or_byte_value()\n\taccept(\"'\")\n\n# ::= unicode_char | little_u_value | big_u_value | escaped_char\ndef nt_unicode_value():\n\tif symbol != \"\\\\\":\n\t\tnt_unicode_char()\n\telse:\n\t\taccept(\"\\\\\")\n\t\tif symbol == \"u\":\n\t\t\tnt_little_u_value()\n\t\telif symbol == \"U\":\n\t\t\tnt_big_u_value()\n\t\telse:\n\t\t\tnt_escaped_char()\n\ndef nt_unicode_value_or_byte_value():\n\tif symbol!='\\\\':\n\t\taccept(symbol)\n\telse:\n\t\taccept(\"\\\\\")\n\t\tif symbol in {\"a\",\"b\",\"f\",\"n\",\"r\",\"t\",\"v\",'\\\\', \"'\", '\"'}:\n\t\t\taccept(symbol)\n\t\telif symbol==\"x\":\n\t\t\taccept(\"x\")\n\t\t\tnt_hex_digit()\n\t\t\tnt_hex_digit()\n\t\telif symbol==\"u\":\n\t\t\tnt_hex_digit()\n\t\t\tnt_hex_digit()\n\t\t\tnt_hex_digit()\n\t\t\tnt_hex_digit()\n\t\telif symbol==\"U\":\n\t\t\tnt_hex_digit()\n\t\t\tnt_hex_digit()\n\t\t\tnt_hex_digit()\n\t\t\tnt_hex_digit()\n\t\telse:\n\t\t\tnt_octal_digit()\n\t\t\tnt_octal_digit()\n\t\t\tnt_octal_digit()\n\ndef is_unicode_value(symbol):\n\treturn len(symbol)==1 and symbol!=\"\\n\"\n\n# ::= octal_byte_value | hex_byte_value\ndef nt_byte_value():\n\tif symbol == \"x\":\n\t\tnt_hex_byte_value()\n\telse:\n\t\tnt_octal_byte_value()\n\t\t\n# ::= `\\` octal_digit octal_digit octal_digit\ndef nt_octal_byte_value():\n\tnt_octal_digit()\n\tnt_octal_digit()\n\tnt_octal_digit()\n\t\n# ::= `\\` \"x\" hex_digit hex_digit\ndef nt_hex_byte_value():\n\taccept(\"x\")\n\tnt_hex_digit()\n\tnt_hex_digit()\n\n\n# ::= `\\` \"u\" hex_digit hex_digit hex_digit hex_digit\ndef nt_little_u_value():\n\t# accept('\\\\')\n\taccept(\"u\")\n\tnt_hex_digit()\n\tnt_hex_digit()\n\tnt_hex_digit()\n\tnt_hex_digit()\n\t\n# ::= `\\` \"U\" hex_digit hex_digit hex_digit hex_digit hex_digit hex_digit hex_digit hex_digit\ndef nt_big_u_value():\n\t# accept('\\\\')\n\taccept(\"U\")\n\tnt_hex_digit()\n\tnt_hex_digit()\n\tnt_hex_digit()\n\tnt_hex_digit()\n\tnt_hex_digit()\n\tnt_hex_digit()\n\tnt_hex_digit()\n\tnt_hex_digit()\n\t\n\n# ::= `\\` ( \"a\" | \"b\" | \"f\" | \"n\" | \"r\" | \"t\" | \"v\" | `\\` | \"'\" | `\"` )\ndef nt_escaped_char():\n\t# accept('\\\\')\n\tif symbol == \"a\":\n\t\taccept(\"a\")\n\telif symbol == \"b\":\n\t\taccept(\"b\")\n\telif symbol == \"f\":\n\t\taccept(\"f\")\n\telif symbol == \"n\":\n\t\taccept(\"n\")\n\telif symbol == \"r\":\n\t\taccept(\"r\")\n\telif symbol == \"t\":\n\t\taccept(\"t\")\n\telif symbol == \"v\":\n\t\taccept(\"v\")\n\telif symbol == \"\\\\\":\n\t\taccept(\"\\\\\")\n\telif symbol == \"'\":\n\t\taccept(\"'\")\n\telif symbol == '\"':\n\t\taccept('\"')\n\t\n# ::= raw_string_lit | interpreted_string_lit\ndef nt_string_lit():\n\tif symbol == \"`\":\n\t\tnt_raw_string_lit()\n\telif symbol == '\"':\n\t\tnt_interpreted_string_lit()\n# ::= \"`\" { unicode_char | newline } \"`\"\ndef nt_raw_string_lit():\n\taccept(\"`\")\n\twhile symbol!=\"`\":\n\t\tif symbol == \"\\n\":\n\t\t\tnt_newline()\n\t\telse:\n\t\t\tnt_unicode_char()\n\taccept(\"`\")\n\n# ::= `\"` { unicode_value | byte_value } `\"`\n#\tTHIS IS NOT LL(1)\n#\tchange to: ::= `\"` { unicode_or_byte_value } `\"`\ndef nt_interpreted_string_lit():\n\taccept('\"')\n\twhile symbol != '\"':\n\t\tnt_unicode_or_byte_value()\n\taccept('\"')\n\n# added a new rule:\n#\t ::= unicode_char | \"\\\\\" ( little_u_value | big_u_value | escaped_char | byte_value )\ndef nt_unicode_or_byte_value():\n\tif symbol!=\"\\\\\":\n\t\tnt_unicode_char()\n\telse:\n\t\taccept(\"\\\\\")\n\t\tif symbol==\"u\":\n\t\t\tnt_little_u_value()\n\t\telif symbol==\"U\":\n\t\t\tnt_big_u_value()\n\t\telif symbol in {\"a\", \"b\", \"f\", \"n\", \"r\",\"t\", \"v\", \"\\\\\", \"'\", '\"' }:\n\t\t\tnt_escaped_char()\n\t\telse:\n\t\t\tnt_byte_value()\n\n# ::= TypeName | TypeLit | \"(\" Type \")\"\ndef nt_Type():\n\tif symbol == \"(\":\n\t\taccept(\"(\")\n\t\tnt_Type()\n\t\taccept(\")\")\n\telif symbol in set({\"[\",\"struct\",\"*\",\"func\",\"interface\",\"map\",\"chan\",\"<-\"}):\n\t\tnt_TypeLit()\n\telif symbol in set(string.ascii_letters).union({\"_\"}):\n\t\tnt_TypeName()\n\n# ::= identifier | QualifiedIdent\n# change to: ::= identifier [ \".\" identifier ]\ndef nt_TypeName():\n\tif symbol in set(string.ascii_letters).union({\"_\"}):\n\t\tnt_identifier()\n\tif symbol==\".\":\n\t\taccept(\".\")\n\t\tnt_identifier()\n\n# ::= ArrayType | StructType | PointerType | FunctionType | InterfaceType | SliceType | MapType | ChannelType\ndef nt_TypeLit():\n\tif symbol == \"[\":\n\t\tnt_ArrayOrSliceType()\n\telif symbol == \"struct\":\n\t\tnt_StructType()\n\telif symbol == \"*\":\n\t\tnt_PointerType()\n\telif symbol == \"func\":\n\t\tnt_FunctionType()\n\telif symbol == \"interface\":\n\t\tnt_InterfaceType()\n\telif symbol == \"map\":\n\t\tnt_MapType()\n\telse:\n\t\tnt_ChannelType()\n\n# ::= \"[\" [ ArrayLength ] \"]\" ElementType\ndef nt_ArrayOrSliceType():\n\taccept(\"[\")\n\tif symbol!=\"]\":\n\t\tnt_ArrayLength()\n\taccept(\"]\")\n\tnt_ElementType()\n\n# ::= \"[\" ArrayLength \"]\" ElementType\ndef nt_ArrayType():\n\taccept(\"[\")\n\tnt_ArrayLength()\n\taccept(\"]\")\n\tnt_ElementType()\n\n# ::= Expression\ndef nt_ArrayLength():\n\tnt_Expression()\n\n# ::= Type\ndef nt_ElementType():\n\tnt_Type()\n\n# ::= \"[\" \"]\" ElementType\ndef nt_SliceType():\n\taccept(\"[]\")\n\tnt_ElementType()\n\n# ::= \"struct\" \"{\" { FieldDecl \";\" } \"}\"\ndef nt_StructType():\n\taccept(\"struct\")\n\taccept(\"{\")\n\twhile symbol in set(string.ascii_letters).union({\"_\",\"*\"}):\n\t\tnt_FieldDecl()\n\t\tacceptsemicolon()\n\taccept(\"}\")\n\n# ::= (IdentifierList Type | EmbeddedField) [ Tag ]\ndef nt_FieldDecl():\n\tif symbol in set(string.ascii_letters).union({\"_\"}):\n\t\tnt_IdentifierList()\n\t\tnt_Type()\n\telif symbol in set(string.ascii_letters).union({\"_\",\"*\"}):\n\t\tnt_EmbeddedField()\n\telse:\n\t\toutput_error_and_halt()\n\t\n\tif symbol in {\"'\",'\"',\"`\"}:\n\t\tnt_Tag()\n\n# ::= [ \"*\" ] TypeName\ndef nt_EmbeddedField():\n\tif symbol == \"*\":\n\t\taccept(\"*\")\n\tnt_TypeName()\n\n# ::= string_lit\ndef nt_Tag():\n\tnt_string_lit()\n\n# ::= \"*\" BaseType\ndef nt_PointerType():\n\taccept(\"*\")\n\tnt_BaseType()\n\n# ::= Type\ndef nt_BaseType():\n\tnt_Type()\n\n# ::= \"func\" Signature\ndef nt_FunctionType():\n\taccept(\"func\")\n\tnt_Signature()\n\n# ::= Parameters [ Result ]\ndef nt_Signature():\n\tnt_Parameters()\n\tif symbol in set(string.ascii_letters).union({\"(\",\"_\"}).union({\"[\",\"struct\",\"*\",\"func\",\"interface\",\"map\",\"chan\",\"<-\"}):\n\t\tnt_Result()\n\n# ::= Parameters | Type\ndef nt_Result():\n\tif symbol == \"(\":\n\t\tnt_Parameters()\n\telif symbol in set(string.ascii_letters).union({\"(\",\"_\"}).union({\"[\",\"struct\",\"*\",\"func\",\"interface\",\"map\",\"chan\",\"<-\"}):\n\t\tnt_Type()\n\n# ::= \"(\" [ ParameterList [ \",\" ] ] \")\"\ndef nt_Parameters():\n\taccept(\"(\")\n\tif symbol in set(string.ascii_letters).union({\"(\",\"_\"}).union({\"[\",\"struct\",\"*\",\"func\",\"interface\",\"map\",\"chan\",\"<-\"}):\n\t\tnt_ParameterList()\n\t\tif symbol == \",\":\n\t\t\taccept(\",\")\n\taccept(\")\")\n\n# ::= ParameterDecl { \",\" ParameterDecl }\ndef nt_ParameterList():\n\tnt_ParameterDecl()\n\twhile symbol == \",\":\n\t\taccept(\",\")\n\t\tnt_ParameterDecl()\n\n# ::= [ IdentifierList ] [ \"...\" ] Type\ndef nt_ParameterDecl():\n\tif symbol in set(string.ascii_letters).union({\"_\"}):\n\t\tnt_IdentifierList()\n\tif symbol == \"...\":\n\t\taccept(\"...\")\n\tnt_Type()\n\n# ::= \"interface\" \"{\" { MethodSpec \";\" } \"}\"\ndef nt_InterfaceType():\n\taccept(\"interface\")\n\taccept(\"{\")\n\twhile symbol in set(string.ascii_letters).union({\"_\"}):\n\t\tnt_MethodSpec()\n\t\tacceptsemicolon()\n\taccept(\"}\")\n\n# ::= MethodName Signature | InterfaceTypeName\ndef nt_MethodSpec():\n\tif symbol in set(string.ascii_letters).union({\"_\"}):\n\t\tnt_MethodName()\n\t\tnt_Signature()\n\telif symbol in set(string.ascii_letters).union({\"_\"}):\n\t\tnt_InterfaceTypeName()\n\n# ::= identifier\ndef nt_MethodName():\n\tnt_identifier()\n\n# ::= TypeName\ndef nt_InterfaceTypeName():\n\tnt_TypeName()\n\n# ::= \"map\" \"[\" KeyType \"]\" ElementType\ndef nt_MapType():\n\taccept(\"map\")\n\taccept(\"[\")\n\tnt_KeyType()\n\taccept(\"]\")\n\tnt_ElementType()\n\n# ::= Type\ndef nt_KeyType():\n\tnt_Type()\n\n# ::= ( \"chan\" | \"chan\" \"<-\" | \"<-\" \"chan\" ) ElementType\ndef nt_ChannelType():\n\tif symbol == \"chan\":\n\t\taccept(\"chan\")\n\t\tif symbol == \"<-\":\n\t\t\taccept(\"<-\")\n\telif symbol == \"<-\":\n\t\taccept(\"<-\")\n\t\taccept(\"chan\")\n\telse:\n\t\toutput_error_and_halt()\n\tnt_ElementType()\n\n# ::= \"{\" StatementList \"}\"\ndef nt_Block():\n\taccept(\"{\")\n\tnt_StatementList()\n\taccept(\"}\")\n\n# ::= { Statement \";\" }\ndef nt_StatementList():\n\twhile symbol in set(string.ascii_letters+'.').union({\"_\"}).union({\"const\", \"type\", \"var\",\"go\",\"return\",\"break\",\"continue\",\"goto\",\"fallthrough\",\"{\",\"if\",\"switch\",\"select\",\"for\",\"defer\"}).union(nt_unary_op_set).union(nt_literal_first_set).union({\"(\"}):\n\t\tnt_Statement()\n\t\tacceptsemicolon()\n\n# ::= ConstDecl | TypeDecl | VarDecl\ndef nt_Declaration():\n\tif symbol == \"const\":\n\t\tnt_ConstDecl()\n\telif symbol == \"type\":\n\t\tnt_TypeDecl()\n\telif symbol == \"var\":\n\t\tnt_VarDecl()\n\telse:\n\t\toutput_error_and_halt()\n\n# ::= Declaration | FunctionDecl | MethodDecl\n# change to:\n#\t ::= Declaration | \"func\" ( MethodDecl | FunctionDecl )\n#\tMethodDecl and FunctionDecl is also changed\ndef nt_TopLevelDecl():\n\tif symbol in set({\"const\", \"type\", \"var\"}):\n\t\tnt_Declaration()\n\telif symbol == \"func\":\n\t\taccept(\"func\")\n\t\tif symbol == \"(\":\n\t\t\tnt_MethodDecl()\n\t\telif symbol in set(string.ascii_letters).union({\"_\",\"(\"}):\n\t\t\tnt_FunctionDecl()\n\n# ::= \"const\" ( ConstSpec | \"(\" { ConstSpec \";\" } \")\" )\ndef nt_ConstDecl():\n\taccept(\"const\")\n\tif symbol in set(string.ascii_letters+\"_\").union({\"_\",\"(\"}):\n\t\tif symbol == \"(\":\n\t\t\taccept(\"(\")\n\t\t\twhile symbol in set(string.ascii_letters+\"_\"):\n\t\t\t\tnt_ConstSpec()\n\t\t\t\tacceptsemicolon()\n\t\t\taccept(\")\")\n\t\telse:\n\t\t\tnt_ConstSpec()\n\n# ::= IdentifierList [ [ Type ] \"=\" ExpressionList ]\ndef nt_ConstSpec():\n\tnt_IdentifierList()\n\tif symbol in set(string.ascii_letters+\"_=\").union({\"(\",\"[\",\"struct\",\"*\",\"func\",\"interface\",\"map\",\"chan\",\"<-\"}):\n\t\tif symbol in set(string.ascii_letters+\"_\").union({\"(\",\"[\",\"struct\",\"*\",\"func\",\"interface\",\"map\",\"chan\",\"<-\"}):\n\t\t\tnt_Type()\n\t\taccept(\"=\")\n\t\tnt_ExpressionList()\n\t\n# ::= identifier { \",\" identifier }\ndef nt_IdentifierList():\n\tnt_identifier()\n\twhile symbol == \",\":\n\t\taccept(\",\")\n\t\tnt_identifier()\n\n# ::= Expression { \",\" Expression }\ndef nt_ExpressionList(mightFollowBlock=False):\n\tnt_Expression(mightFollowBlock)\n\twhile symbol == \",\":\n\t\taccept(\",\")\n\t\tnt_Expression(mightFollowBlock)\n\t\t\n# ::= \"type\" ( TypeSpec | \"(\" { TypeSpec \";\" } \")\" )\ndef nt_TypeDecl():\n\taccept(\"type\")\n\tif symbol in set(string.ascii_letters).union({\"_\"}):\n\t\tnt_TypeSpec()\n\telif symbol == \"(\":\n\t\taccept(\"(\")\n\t\twhile symbol in set(string.ascii_letters).union({\"_\"}):\n\t\t\tnt_TypeSpec()\n\t\t\tacceptsemicolon()\n\t\taccept(\")\")\n\telse:\n\t\toutput_error_and_halt()\n\n# ::= AliasDecl | TypeDef\n# change to:\n#\t ::= identifier [ \"=\" ] Type\ndef nt_TypeSpec():\n\tnt_identifier()\n\tif symbol== \"=\":\n\t\taccept(\"=\")\n\tnt_Type()\n\t\n# ::= identifier \"=\" Type\ndef nt_AliasDecl():\n\tnt_identifier()\n\taccept(\"=\")\n\tnt_Type()\n\n# ::= identifier Type\ndef nt_TypeDef():\n\tnt_identifier()\n\tnt_Type()\n\n# ::= \"var\" ( VarSpec | \"(\" { VarSpec \";\" } \")\" )\ndef nt_VarDecl():\n\taccept(\"var\")\n\tif symbol == \"(\":\n\t\taccept(\"(\")\n\t\twhile symbol in set(string.ascii_letters).union({\"_\"}):\n\t\t\tnt_VarSpec()\n\t\t\tacceptsemicolon()\n\t\taccept(\")\")\n\telif symbol in set(string.ascii_letters).union({\"_\"}):\n\t\tnt_VarSpec()\n\telse:\n\t\toutput_error_and_halt()\n\n# ::= IdentifierList ( Type [ \"=\" ExpressionList ] | \"=\" ExpressionList )\ndef nt_VarSpec():\n\tnt_IdentifierList()\n\tif symbol == \"=\":\n\t\taccept(\"=\")\n\t\tnt_ExpressionList()\n\telif symbol in set(string.ascii_letters).union({\"_\",\"(\",\"[\",\"struct\",\"*\",\"func\",\"interface\",\"map\",\"chan\",\"<-\"}):\n\t\tnt_Type()\n\t\tif symbol == \"=\":\n\t\t\taccept(\"=\")\n\t\t\tnt_ExpressionList()\n\telse:\n\t\toutput_error_and_halt()\n\n# ::= IdentifierList \":=\" ExpressionList\ndef nt_ShortVarDecl():\n\tnt_IdentifierList()\n\taccept(\":=\")\n\tnt_ExpressionList()\n\n# ::= \"func\" FunctionName Signature [ FunctionBody ]\n# change to: ::= \"func\" FunctionName Signature [ FunctionBody ]\ndef nt_FunctionDecl():\n\tnt_FunctionName()\n\tnt_Signature()\n\tif symbol == \"{\":\n\t\tnt_FunctionBody()\n\n# ::= identifier\ndef nt_FunctionName():\n\tnt_identifier()\n\n# ::= Block\ndef nt_FunctionBody():\n\tnt_Block()\n\n# ::= \"func\" Receiver MethodName Signature [ FunctionBody ]\n# change to: ::= Receiver MethodName Signature [ FunctionBody ]\ndef nt_MethodDecl():\n\tnt_Receiver()\n\tnt_MethodName()\n\tnt_Signature()\n\tif symbol == \"{\":\n\t\tnt_FunctionBody()\n\n# ::= Parameters\ndef nt_Receiver():\n\tnt_Parameters()\n\n# ::= Literal | OperandName | \"(\" Expression \")\"\ndef Operand():\n\tif symbol in set(string.ascii_letters+\"_\"):\n\t\tnt_OperandName()\n\telif symbol == \"(\":\n\t\taccept(\"(\")\n\t\tnt_Expression()\n\t\taccept(\")\")\n\telse:\n\t\tnt_Literal()\n\nnt_literal_first_set = set(string.digits+\".'`\"+'\"').union({\"struct\",\"[\",\"map\",\"func\"})\n# ::= BasicLit | CompositeLit | FunctionLit\ndef nt_Literal():\n\tif symbol in set(string.digits+\".'`\"+'\"'):\n\t\tnt_BasicLit()\n\telif symbol in {\"struct\",\"[\",\"map\"}.union(set(string.ascii_letters+'_')):\n\t\tnt_CompositeLit()\n\telif symbol == \"func\":\n\t\tnt_FunctionLit()\n\telse:\n\t\toutput_error_and_halt()\n# ::= int_lit | float_lit | imaginary_lit | rune_lit | string_lit\n# change to: ::= numeric_lit | rune_lit | string_lit\ndef nt_BasicLit():\n\tif symbol in set(string.digits+\".\"):\n\t\tnt_numeric_lit()\n\telif symbol==\"'\":\n\t\tnt_rune_lit()\n\telif symbol in set('\"`'):\n\t\tnt_string_lit()\n\telse:\n\t\toutput_error_and_halt()\n#\tadded rule: ::= \tdecimal_lit [ \".\" [ decimals ] ] [ exponent ] |\n#\t\t\t\t\t\t\t\t\t\"0\" ( ( \"x\" | \"X\" ) hex_digit { hex_digit } |\n#\t\t\t\t\t\t\t\t\t\t{ decimal_digit } [ \".\" [ decimals ] ] [ exponent ] |\n#\t\t\t\t\t\t\t\t\t\".\" decimals [ exponent ]\n#\t\t\t\t(we assume that octal_digit is included in decimal_digit, although it might output \"malformed integer\")\ndef nt_numeric_lit():\n\tif symbol in set(\"123456789\"):\n\t\tnt_decimal_lit()\n\t\tif symbol==\".\":\n\t\t\taccept(\".\")\n\t\t\tif symbol in set(string.digits):\n\t\t\t\tnt_decimals()\n\t\tif symbol in {\"e\",\"E\"}:\n\t\t\tnt_exponent()\n\telif symbol==\"0\":\n\t\taccept(\"0\")\n\t\tif symbol in {\"x\",\"X\"}:\n\t\t\tacceptset({\"x\",\"X\"})\n\t\t\tnt_hex_digit()\n\t\t\twhile symbol in set(string.hexdigits):\n\t\t\t\tnt_hex_digit()\n\t\telif symbol in set(string.digits+\".eE\"):\n\t\t\twhile symbol in set(string.digits):\n\t\t\t\tnt_decimal_digit()\n\t\t\tif symbol==\".\":\n\t\t\t\taccept(\".\")\n\t\t\t\tif symbol in set(string.digits):\n\t\t\t\t\tnt_decimals()\n\t\t\tif symbol in {\"e\",\"E\"}:\n\t\t\t\tnt_exponent()\n\telif symbol==\".\":\n\t\taccept(\".\")\n\t\tnt_decimals()\n\t\tif symbol in {\"e\",\"E\"}:\n\t\t\tnt_exponent()\t\n\telse:\n\t\toutput_error_and_halt()\n\tif symbol==\"i\":\n\t\taccept(\"i\")\n# ::= identifier | QualifiedIdent.\n# NOT LL(1)\n# change to: ::= identifier [ \".\" identifier] \ndef nt_OperandName():\n\tnt_identifier()\n\tif symbol==\".\":\n\t\taccept(\".\")\n\t\tnt_identifier()\n\n# ::= PackageName \".\" identifier\ndef nt_QualifiedIdent():\n\tnt_PackageName()\n\taccept(\".\")\n\tnt_identifier()\n\n# ::= LiteralType LiteralValue\ndef nt_CompositeLit():\n\tnt_LiteralType()\n\tnt_LiteralValue()\n\n# ::= StructType | ArrayType | \"[\" \"...\" \"]\" ElementType | SliceType | MapType | TypeName\ndef nt_LiteralType():\n\tif symbol==\"struct\":\n\t\tnt_StructType()\n\telif symbol==\"[\": #ArrayType or \"[\" \"...\" \"]\" ElementType or SliceType\n\t\taccept(\"[\")\n\t\tif symbol==\"...\":\n\t\t\taccept(\"...\")\n\t\t\taccept(\"]\")\n\t\t\tnt_ElementType()\n\t\telif symbol==\"]\":\n\t\t\taccept(\"]\")\n\t\t\tnt_ElementType()\n\t\telse:\n\t\t\tnt_ArrayLength()\n\t\t\taccept(\"]\")\n\t\t\tnt_ElementType()\n\telif symbol==\"map\":\n\t\tnt_MapType()\n\telif symbol in set(string.ascii_letters+'_'):\n\t\tnt_TypeName()\n\n# ::= \"{\" [ ElementList [ \",\" ] ] \"}\"\n# ::= KeyedElement { \",\" KeyedElement }\n#\tthis one is implementation for both LiteralValue and ElementList\n#\t\t ::= \"{\" [ KeyedElement { \",\" KeyedElement } \",\" ] \"}\"\ndef nt_LiteralValue():\n\taccept(\"{\")\n\tif symbol != \"}\":\n\t\tnt_KeyedElement()\n\t\twhile symbol ==\",\":\n\t\t\taccept(\",\")\n\t\t\tif symbol != \"}\":\n\t\t\t\tnt_KeyedElement()\n\taccept(\"}\")\n\n# ::= [ Key \":\" ] Element\n#\tKey and element is the same, so:\ndef nt_KeyedElement():\n\tnt_Element() #also works with key\n\tif symbol==\":\":\n\t\taccept(\":\")\n\t\tnt_Element()\n\n# ::= FieldName | Expression | LiteralValue\n# not LL(1)\n# FieldName is also covered in Expression, so:\ndef nt_Key():\n\tif symbol != \"{\":\n\t\tnt_Expression()\n\telse:\n\t\tnt_LiteralValue()\n\n# ::= identifier\ndef nt_FieldName():\n\tnt_identifier()\n\n# ::= Expression | LiteralValue\ndef nt_Element():\n\tif symbol!=\"{\":\n\t\tnt_Expression()\n\telse:\n\t\tnt_LiteralValue()\n\n# ::= \"func\" Signature FunctionBody\ndef nt_FunctionLit():\n\taccept(\"func\")\n\tnt_Signature()\n\tnt_FunctionBody()\n\n# ::= Operand | Conversion | MethodExpr | PrimaryExpr Selector | PrimaryExpr Index | PrimaryExpr Slice | PrimaryExpr TypeAssertion | PrimaryExpr Arguments\n#\tFOLLOW(Selector) contains \".\" which is in FIRST(Selector)\n#\t ::= ( identifier | Literal | \"(\" Expression \")\" ) { SelectorOrTypeAssertion | IndexOrSlice }\n#\tLiteral can be CompositeLit, which can be LiteralType LiteralValue\n#\tLiteralType can be TypeName, which is identifier | QualifiedIdent\n#\t\t\t\t\tCompositeLit is skipped for now\ndef nt_PrimaryExprFront(mightFollowBlock=False):\n\tif symbol in set(string.ascii_letters + \"_\"):\n\t\tnt_identifier()\n\t\tif symbol==\"{\" and not mightFollowBlock:\n\t\t\tnt_LiteralValue()\n\telif symbol in {\"(\",\"[\",\"struct\",\"*\",\"interface\",\"map\",\"chan\"}:\n\t\tcount_openparentheses = 0\n\t\twhile symbol==\"(\":\n\t\t\taccept(\"(\")\n\t\t\tcount_openparentheses += 1\n\n\t\tif symbol in {\"[\",\"struct\",\"*\",\"interface\",\"map\",\"chan\"}: #obviously Type or LiteralType\n\t\t\tif symbol==\"[\":\n\t\t\t\tnt_LiteralType()\n\t\t\telse:\n\t\t\t\tnt_Type()\n\t\t\tif symbol==\"{\": #CompositeLit\n\t\t\t\tisConversion=False\n\t\t\t\tnt_LiteralValue()\n\t\t\telse:\n\t\t\t\tisConversion=True\n\n\t\telif symbol==\"<-\": #might be ChannelType, might be not\n\t\t\taccept(\"<-\")\n\t\t\tif symbol==\"chan\":\n\t\t\t\taccept(\"chan\")\n\t\t\t\tnt_ElementType()\n\t\t\t\tisConversion = True\n\t\t\telse:\n\t\t\t\tisConversion = False\n\t\t\t\tnt_Expression()\n\t\telse:\n\t\t\tisConversion = False\n\t\t\tnt_Expression()\n\n\t\twhile count_openparentheses > 0:\n\t\t\taccept(\")\")\n\t\t\tcount_openparentheses -= 1\n\n\t\tif isConversion: # \"(\" Expression [ \",\" ] \")\"\n\t\t\taccept(\"(\")\n\t\t\tnt_Expression()\n\t\t\tif symbol==\",\":\n\t\t\t\taccept(\",\")\n\t\t\taccept(\")\")\n\telif symbol==\"func\":\n\t\tnt_FunctionLitOrFunctionTypeConversionOrFunctionTypeCompositeLit()\n\telse:\n\t\tnt_Literal()\n\ndef nt_FunctionLitOrFunctionTypeConversionOrFunctionTypeCompositeLit():\n\taccept(\"func\")\n\tnt_Signature()\n\tif symbol==\"{\":\n\t\tnt_FunctionBody()\n\telif symbol==\"(\":#conversion\n\t\taccept(\"(\")\n\t\tnt_Expression()\n\t\tif symbol==\",\":\n\t\t\taccept(\",\")\n\t\taccept(\")\")\n\ndef nt_PrimaryExprRepeat():\n\twhile symbol in {\".\", \"[\", \"(\"}:\n\t\tif symbol==\".\":\n\t\t\tnt_SelectorOrTypeAssertion()\n\t\telif symbol==\"[\":\n\t\t\tnt_IndexOrSlice()\n\t\telse: #symbol==\"(\"\n\t\t\t# Arguments, also includes conversion\n\t\t\tnt_Arguments()\n\ndef nt_PrimaryExpr(mightFollowBlock=False):\n\tnt_PrimaryExprFront(mightFollowBlock)\n\tnt_PrimaryExprRepeat()\n# also FOLLOW(TypeAssertion) is the same\n# ::= \".\" identifier\n# ::= \"[\" Expression \"]\"\n# ::= \"[\" [ Expression ] \":\" [ Expression ] \"]\" | \"[\" [ Expression ] \":\" Expression \":\" Expression \"]\"\n# ::= \".\" \"(\" Type \")\"\ndef nt_SelectorOrTypeAssertion():\n\taccept(\".\")\n\tif symbol==\"(\":\n\t\taccept(\"(\")\n\t\tnt_Type()\n\t\taccept(\")\")\n\telse:\n\t\tnt_identifier()\ndef nt_IndexOrSlice():\n\taccept(\"[\")\n\tif symbol != \":\":\n\t\tnt_Expression()\n\tif symbol==\"]\":\n\t\taccept(\"]\")\n\telse:\n\t\taccept(\":\")\n\t\tif symbol !=\"]\":\n\t\t\tnt_Expression()\n\t\tif symbol == \"]\":\n\t\t\taccept(\"]\")\n\t\telse:\n\t\t\taccept(\":\")\n\t\t\tnt_Expression()\n\t\t\taccept(\"]\")\n\n# ::= \"(\" [ ( ExpressionList | Type [ \",\" ExpressionList ] ) [ \"...\" ] [ \",\" ] ] \")\"\ndef nt_Arguments():\n\taccept(\"(\")\n\tif symbol != \")\":\n\t\tcount_openparentheses=0\n\t\twhile symbol==\"(\": #possible Type\n\t\t\taccept(\"(\")\n\t\t\tcount_openparentheses+=1\n\t\tif symbol in set({\"[\",\"struct\",\"*\",\"func\",\"interface\",\"map\",\"chan\"}): #definitely Type\n\t\t\tnt_Type()\n\t\t\tif symbol==\"(\": #conversion\n\t\t\t\taccept(\"(\")\n\t\t\t\tnt_Expression()\n\t\t\t\tif symbol==\",\":\n\t\t\t\t\taccept(\",\")\n\t\t\t\taccept(\")\")\n\t\t\t\tnt_PrimaryExprRepeat()\n\t\t\telif symbol==\"{\": #CompositeLit\n\t\t\t\tnt_LiteralValue()\n\t\t\twhile count_openparentheses>0:\n\t\t\t\taccept(\")\")\n\t\t\t\tcount_openparentheses-=1\n\t\telif symbol==\"<-\": #might be ChannelType, might be not\n\t\t\taccept(\"<-\")\n\t\t\tif symbol==\"chan\":\n\t\t\t\taccept(\"chan\")\n\t\t\t\tnt_ElementType()\n\t\t\t\twhile count_openparentheses>0:\n\t\t\t\t\taccept(\")\")\n\t\t\t\t\tcount_openparentheses-=1\n\t\t\telse:\n\t\t\t\tnt_Expression() #TypeName is included\n\t\t\t\twhile count_openparentheses>0:\n\t\t\t\t\taccept(\")\")\n\t\t\t\t\tcount_openparentheses-=1\n\t\t\t\t\t#remember:\n\t\t\t\t\t#\t ::= Operand | Conversion | MethodExpr | PrimaryExpr Selector | PrimaryExpr Index | PrimaryExpr Slice | PrimaryExpr TypeAssertion | PrimaryExpr Arguments\n\t\t\t\t\twhile symbol in {\".\", \"[\", \"(\"}:\n\t\t\t\t\t\t\tif symbol==\".\":\n\t\t\t\t\t\t\t\tnt_SelectorOrTypeAssertion()\n\t\t\t\t\t\t\telif symbol==\"[\":\n\t\t\t\t\t\t\t\tnt_IndexOrSlice()\n\t\t\t\t\t\t\telse: #symbol==\"(\"\n\t\t\t\t\t\t\t\t# Arguments, also includes conversion\n\t\t\t\t\t\t\t\tnt_Arguments()\n\t\t\t\t\t#\t ::= UnaryExpr | Expression binary_op Expression\n\t\t\t\t\tif symbol != \")\":\n\t\t\t\t\t\tnt_binary_op()\n\t\t\t\t\t\tnt_Expression()\n\t\telse:\n\t\t\tnt_Expression() #TypeName is included\n\t\t\twhile count_openparentheses>0:\n\t\t\t\taccept(\")\")\n\t\t\t\tcount_openparentheses-=1\n\t\t\t\t#remember:\n\t\t\t\t#\t ::= Operand | Conversion | MethodExpr | PrimaryExpr Selector | PrimaryExpr Index | PrimaryExpr Slice | PrimaryExpr TypeAssertion | PrimaryExpr Arguments\n\t\t\t\twhile symbol in {\".\", \"[\", \"(\"}:\n\t\t\t\t\t\tif symbol==\".\":\n\t\t\t\t\t\t\tnt_SelectorOrTypeAssertion()\n\t\t\t\t\t\telif symbol==\"[\":\n\t\t\t\t\t\t\tnt_IndexOrSlice()\n\t\t\t\t\t\telse: #symbol==\"(\"\n\t\t\t\t\t\t\t# Arguments, also includes conversion\n\t\t\t\t\t\t\tnt_Arguments()\n\t\t\t\t#\t ::= UnaryExpr | Expression binary_op Expression\n\t\t\t\tif symbol != \")\":\n\t\t\t\t\tnt_binary_op()\n\t\t\t\t\tnt_Expression()\n\n\t\twhile symbol==\",\":\n\t\t\t# cannot use ExpressionList because of possible final \",\"\n\t\t\taccept(\",\")\n\t\t\tif symbol != \")\":\n\t\t\t\tnt_Expression()\n\t\tif symbol==\"...\":\n\t\t\taccept(\"...\")\n\t\tif symbol==\",\":\n\t\t\taccept(\",\")\n\taccept(\")\")\n\n# ::= ReceiverType \".\" MethodName\ndef nt_MethodExpr():\n\tnt_ReceiverType()\n\taccept(\".\")\n\tnt_MethodName()\n\n# ::= Type\ndef nt_ReceiverType():\n\tnt_Type()\n\n# ::= UnaryExpr | Expression binary_op Expression\n#\tNOT LL(1)\n#\tconvert to: UnaryExpr { binary_op UnaryExpr }\n#\tFOLLOW(Expression): ,;]):\ndef nt_Expression(mightFollowBlock=False):\n\tnt_UnaryExpr(mightFollowBlock)\n\twhile symbol in nt_binary_op_set:\n\t\tnt_binary_op()\n\t\tnt_UnaryExpr(mightFollowBlock)\n\n# ::= PrimaryExpr | unary_op UnaryExpr\ndef nt_UnaryExpr(mightFollowBlock=False):\n\tif symbol in nt_unary_op_set:\n\t\tnt_unary_op()\n\t\tnt_UnaryExpr(mightFollowBlock)\n\telse:\n\t\tnt_PrimaryExpr(mightFollowBlock)\n\n# ::= \"||\" | \"&&\" | rel_op | add_op | mul_op\ndef nt_binary_op():\n\tif symbol==\"||\":\n\t\taccept(\"||\")\n\telif symbol==\"&&\":\n\t\taccept(\"&&\")\n\telif symbol in nt_rel_op_set:\n\t\tnt_rel_op()\n\telif symbol in nt_add_op_set:\n\t\tnt_add_op()\n\telif symbol in nt_mul_op_set:\n\t\tnt_mul_op()\n\telse:\n\t\toutput_error_and_halt()\n\n# ::= \"==\" | \"!=\" | \"<\" | \"<=\" | \">\" | \">=\"\nnt_rel_op_set = {\"==\" , \"!=\" , \"<\" , \"<=\" , \">\" , \">=\"}\ndef nt_rel_op():\n\tacceptset(nt_rel_op_set)\n\n# ::= \"+\" | \"-\" | \"|\" | \"^\"\nnt_add_op_set = {\"+\" , \"-\" , \"|\" , \"^\"}\ndef nt_add_op():\n\tacceptset(nt_add_op_set)\n\n# ::= \"*\" | \"/\" | \"%\" | \"<<\" | \">>\" | \"&\" | \"&^\"\nnt_mul_op_set = {\"*\" , \"/\" , \"%\" , \"<<\" , \">>\" , \"&\" , \"&^\"}\ndef nt_mul_op():\n\tacceptset(nt_mul_op_set)\n\n# ::= \"+\" | \"-\" | \"!\" | \"^\" | \"*\" | \"&\" | \"<-\"\nnt_unary_op_set = {\"+\",\"-\",\"!\",\"^\",\"*\",\"&\",\"<-\"}\ndef nt_unary_op():\n\tacceptset(nt_unary_op_set)\n\nnt_binary_op_set = nt_rel_op_set.union(nt_add_op_set).union(nt_mul_op_set).union({\"||\",\"&&\"})\n# ::= Type \"(\" Expression [ \",\" ] \")\"\ndef nt_Conversion():\n\tnt_Type()\n\taccept(\"(\")\n\tnt_Expression()\n\tif symbol==\",\":\n\t\taccept(\",\")\n\taccept(\")\")\n\n# ::= Declaration | LabeledStmt | SimpleStmt | GoStmt | ReturnStmt | BreakStmt | ContinueStmt | GotoStmt | FallthroughStmt | Block | IfStmt | SwitchStmt | SelectStmt | ForStmt | DeferStmt\n# change to: ::= Declaration | LabeledStmtOrSimpleStmt | GoStmt | ReturnStmt | BreakStmt | ContinueStmt | GotoStmt | FallthroughStmt | Block | IfStmt | SwitchStmt | SelectStmt | ForStmt | DeferStmt\ndef nt_Statement():\n\tif symbol in set({\"const\", \"type\", \"var\"}):\n\t\tnt_Declaration()\n\telif symbol==\"go\":\n\t\tnt_GoStmt()\n\telif symbol==\"return\":\n\t\tnt_ReturnStmt()\n\telif symbol==\"break\":\n\t\tnt_BreakStmt()\n\telif symbol==\"continue\":\n\t\tnt_ContinueStmt()\n\telif symbol==\"goto\":\n\t\tnt_GotoStmt()\n\telif symbol==\"fallthrough\":\n\t\tnt_FallthroughStmt()\n\telif symbol==\"{\":\n\t\tnt_Block()\n\telif symbol==\"if\":\n\t\tnt_IfStmt()\n\telif symbol==\"switch\":\n\t\tnt_SwitchStmt()\n\telif symbol==\"select\":\n\t\tnt_SelectStmt()\n\telif symbol==\"for\":\n\t\tnt_ForStmt()\n\telif symbol==\"defer\":\n\t\tnt_DeferStmt()\n\telse:\n\t\tnt_LabeledStmtOrSimpleStmt()\n\n# add new rule: \ndef nt_LabeledStmtOrSimpleStmt():\n\tif symbol in {\";\",\"{\",\"}\"}:\n\t\tnt_EmptyStmt()\n\telif symbol in set(string.ascii_letters+\"_\"): #begins with identifier\n\t\tnt_identifier()\n\t\tif symbol==\":\": #LabeledStmt\n\t\t\taccept(\":\")\n\t\t\tnt_Statement()\n\t\telse:\n\t\t\tif symbol==\",\":\n\t\t\t\taccept(\",\")\n\t\t\t\tnt_IdentifierList()\n\t\t\tif symbol == \":=\":\n\t\t\t\taccept(\":=\")\n\t\t\t\tnt_ExpressionList()\n\t\t\telse:\n\t\t\t\tstatementFinished = False\n\t\t\t\t#finish the expression\n\t\t\t\twhile symbol in {\".\", \"[\", \"(\"}:\n\t\t\t\t\tif symbol==\".\":\n\t\t\t\t\t\tnt_SelectorOrTypeAssertion()\n\t\t\t\t\telif symbol==\"[\":\n\t\t\t\t\t\tnt_IndexOrSlice()\n\t\t\t\t\telse: #symbol==\"(\"\n\t\t\t\t\t\t# Arguments, also includes conversion\n\t\t\t\t\t\tnt_Arguments()\n\n\t\t\t\tif symbol in {\",\",\"+\", \"-\", \"|\", \"^\",\"*\", \"/\", \"%\", \"<<\", \">>\", \"&\", \"&^\"}:\n\t\t\t\t\tacceptset({\",\",\"+\", \"-\", \"|\", \"^\",\"*\", \"/\", \"%\", \"<<\", \">>\", \"&\", \"&^\"})\n\t\t\t\t\tif symbol==\"=\":\n\t\t\t\t\t\taccept(\"=\")\n\t\t\t\t\t\tnt_ExpressionList()\n\t\t\t\t\t\tstatementFinished=True\n\t\t\t\t\tnt_ExpressionList()\n\t\t\t\telif symbol not in {\",\",\"++\",\"--\",\"<-\",\"=\",\";\",\"}\"}:\n\t\t\t\t\tif symbol in nt_assign_op_set:\n\t\t\t\t\t\tnt_assign_op()\n\t\t\t\t\telse:\n\t\t\t\t\t\tnt_binary_op()\n\t\t\t\t\tnt_ExpressionList()\n\n\t\t\t\tif symbol==\",\" and not statementFinished:\n\t\t\t\t\taccept(\",\")\n\t\t\t\t\tnt_ExpressionList()\n\n\t\t\t\t#after that:\n\t\t\t\tnt_SimpleStmtRhs(statementFinished)\n\telse: #begins with expression\n\t\t# ExpressionStmt | SendStmt | IncDecStmt | Assignment\n\t\t# Expression [ \"<-\" Expression | \"++\" | \"--\" | [ \",\" ExpressionList ] assign_op ExpressionList]\n\t\tnt_Expression()\n\t\tnt_SimpleStmtRhs()\n\n# ::= EmptyStmt | ExpressionStmt | SendStmt | IncDecStmt | Assignment | ShortVarDecl\n#\tNOT LL(1)\ndef nt_SimpleStmt(mightFollowBlock=False):\n\tif symbol in {\";\",\"{\",\"}\"}:\n\t\tnt_EmptyStmt()\n\telif symbol in set(string.ascii_letters+\"_\"): #begins with identifier\n\t\tnt_IdentifierList()\n\t\tif symbol == \":=\":\n\t\t\taccept(\":=\")\n\t\t\tnt_ExpressionList(mightFollowBlock)\n\t\telse:\n\t\t\tstatementFinished = False\n\t\t\t#finish the expression\n\t\t\tif symbol==\"(\": #Conversion, identifier is type, then follows \"(\" Expression [ \",\" ] \")\"\n\t\t\t\taccept(\"(\")\n\t\t\t\tnt_Expression()\n\t\t\t\tif symbol==\",\":\n\t\t\t\t\tnt_Expression()\n\t\t\t\taccept(\")\")\n\t\t\twhile symbol in {\".\", \"[\", \"(\"}:\n\t\t\t\tif symbol==\".\":\n\t\t\t\t\tnt_SelectorOrTypeAssertion()\n\t\t\t\telif symbol==\"[\":\n\t\t\t\t\tnt_IndexOrSlice()\n\t\t\t\telse: #symbol==\"(\"\n\t\t\t\t\t# Arguments, also includes conversion\n\t\t\t\t\tnt_Arguments()\n\t\t\tif symbol in {\",\",\"+\", \"-\", \"|\", \"^\",\"*\", \"/\", \"%\", \"<<\", \">>\", \"&\", \"&^\"}:\n\t\t\t\tacceptset({\",\",\"+\", \"-\", \"|\", \"^\",\"*\", \"/\", \"%\", \"<<\", \">>\", \"&\", \"&^\"})\n\t\t\t\tif symbol==\"=\":\n\t\t\t\t\taccept(\"=\")\n\t\t\t\t\tnt_ExpressionList()\n\t\t\t\t\tstatementFinished=True\n\t\t\t\tnt_ExpressionList(mightFollowBlock=True)\n\t\t\telif symbol not in {\",\",\"++\",\"--\",\"<-\",\"=\",\";\",\"}\"}:\n\t\t\t\tnt_binary_op()\n\t\t\t\tnt_ExpressionList(mightFollowBlock=True)\n\n\t\t\tif symbol==\",\" and not statementFinished:\n\t\t\t\taccept(\",\")\n\t\t\t\tnt_ExpressionList(mightFollowBlock=True)\n\n\t\t\t#after that:\n\t\t\tnt_SimpleStmtRhs(statementFinished,mightFollowBlock=True)\n\telse: #begins with expression\n\t\t# ExpressionStmt | SendStmt | IncDecStmt | Assignment\n\t\t# Expression [ \"<-\" Expression | \"++\" | \"--\" | [ \",\" ExpressionList ] assign_op ExpressionList]\n\t\tnt_Expression()\n\t\tnt_SimpleStmtRhs()\n\n# ::=\ndef nt_EmptyStmt():\n\tpass\n\n# ::= Label \":\" Statement\ndef nt_LabeledStmt():\n\tnt_Label()\n\taccept(\":\")\n\tnt_Statement()\n\n#
\")\n\n show_photo.short_description = \"Photo\" \n list_display = (\"id\",\"show_photo\",\"first_name\",\"designation\", \"created_date\")\n list_display_links = (\"first_name\",\"id\",\"show_photo\")\n search_fields = (\"first_name\",\"designation\")\n list_filter = (\"designation\",)\n\nadmin.site.register(Team, TeamAdmin)","repo_name":"Abduqayyum/carzone-abduqayum","sub_path":"pages/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"16299722781","text":"class PID():\n #first, we have to initialize the controller\n\tdef __init__(self,SP,MV,KP,KI,KD,dt,Umax,Umin):\n\t\tself.kp = KP # Proportional Gain\n\t\tself.ki = KI # Integral Gain\n\t\tself.kd = KD # Derivative Gain\n\t\tself.sp = SP # Setpoint Value\n\t\tself.mv = MV # Measured Variable\n\t\tself.dt = dt # integral element\n\t\tself.error_last = 0 # Previous error\n\t\tself.integral_error = 0 # Integral of error\n\t\tself.saturation_max = Umax #max speed of the robot\n\t\tself.saturation_min = Umin #min speed of the robot\n \n #using the controller\n\tdef compute(self):\n\t\terror = self.mv - self.sp #compute the error\n\t\tderivative_error = (error - self.error_last) / self.dt #find the derivative of the error (how the error changes with time) Backward Difference\n\t\toutput = self.kp*error + self.ki*self.integral_error + self.kd*derivative_error \n\t\t\n\t\t# Update State\n\t\tself.integral_error += error * self.dt #error build up over time\n\t\tself.error_last = error\n\t\t# Saturation\n\t\tif output > self.saturation_max:\n\t\t\toutput = self.saturation_max\n\t\telif output < self.saturation_min:\n\t\t\toutput = self.saturation_min\n\t\treturn output\n","repo_name":"MahdiShahrajabian/DigitalControlLab","sub_path":"Project/PID.py","file_name":"PID.py","file_ext":"py","file_size_in_byte":1122,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"38615010519","text":"import pytest\nimport pandas as pd\n\nfrom pybitbay import BitBayAPI\n\n\nTICKER = 'btcpln'\nJSON_DATA = [\n {\"date\": 1396094988, \"price\": 4500.0, \"type\": \"buy\", \"amount\": 0.0129, \"tid\": \"0\"},\n {\"date\": 1396096603, \"price\": 4400.0, \"type\": \"sell\", \"amount\": 0.011364, \"tid\": \"1\"},\n]\nCOLUMNS = JSON_DATA[0].keys()\nEXPECTED_DF = pd.DataFrame(\n data=JSON_DATA,\n columns=COLUMNS\n)\n\n\nclass MockResponse:\n def json(self):\n return JSON_DATA\n\n\nclass EmptyMockResponse:\n def json(self):\n return {}\n\n\ndef test_BitBayAPI(mocker):\n mocker.patch('requests.Session.get', side_effect=[MockResponse(), EmptyMockResponse()])\n trades = BitBayAPI().get_all_trades(ticker=TICKER)\n assert next(trades).equals(EXPECTED_DF)\n with pytest.raises(StopIteration):\n next(trades)\n\n\ndef test_bitbay_public_api():\n df = BitBayAPI().get_trades(ticker=TICKER, since=-1)\n assert list(COLUMNS) == df.columns.tolist()\n assert df[0:2].equals(EXPECTED_DF)\n","repo_name":"dominikheinisch/pybitbay","sub_path":"test/test_bitbay.py","file_name":"test_bitbay.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"5938036520","text":"from sequencescape import WellJSONDecoder, WellJSONEncoder\nfrom sequencescape.json_converters import JSON_INTERNAL_ID_PROPERTY, MultiplexedLibraryJSONDecoder, \\\n MultiplexedLibraryJSONEncoder, LibraryJSONDecoder, LibraryJSONEncoder, JSON_LIBRARY_TYPE, StudyJSONDecoder, \\\n StudyJSONEncoder, JSON_FACULTY_SPONSER, JSON_STUDY_VISIBILITY, JSON_STUDY_TITLE, JSON_DESCRIPTION, JSON_STUDY_TYPE, \\\n JSON_ACCESSION_NUMBER_PROPERTY, SampleJSONDecoder, SampleJSONEncoder, JSON_GEOGRAPHICAL_REGION_PROPERTY, \\\n JSON_COUNTRY_OF_ORIGIN_PROPERTY, JSON_COHORT_PROPERTY, JSON_ETHNICITY_PROPERTY, JSON_GENDER_PROPERTY, \\\n JSON_TAXON_ID_PROPERTY, JSON_COMMON_NAME_PROPERTY, JSON_ORGANISM_PROPERTY\nfrom sequencescape.json_converters import JSON_NAME_PROPERTY\nfrom sequencescape.tests._helpers import create_stub_well, create_stub_multiplexed_library, create_stub_library, \\\n create_stub_study, create_stub_sample\nfrom sequencescape.tests._json_converters_test_factory import create_json_converter_test\n\n\n_setups = [(\n create_stub_sample,\n [JSON_NAME_PROPERTY, JSON_INTERNAL_ID_PROPERTY, JSON_ACCESSION_NUMBER_PROPERTY, JSON_ORGANISM_PROPERTY,\n JSON_COMMON_NAME_PROPERTY, JSON_TAXON_ID_PROPERTY, JSON_GENDER_PROPERTY, JSON_ETHNICITY_PROPERTY,\n JSON_COHORT_PROPERTY, JSON_COUNTRY_OF_ORIGIN_PROPERTY, JSON_GEOGRAPHICAL_REGION_PROPERTY],\n SampleJSONEncoder,\n SampleJSONDecoder\n ), (\n create_stub_study,\n [JSON_NAME_PROPERTY, JSON_INTERNAL_ID_PROPERTY, JSON_ACCESSION_NUMBER_PROPERTY, JSON_STUDY_TYPE, JSON_DESCRIPTION,\n JSON_STUDY_TITLE, JSON_STUDY_VISIBILITY, JSON_FACULTY_SPONSER],\n StudyJSONEncoder,\n StudyJSONDecoder\n ), (\n create_stub_library,\n [JSON_NAME_PROPERTY, JSON_INTERNAL_ID_PROPERTY, JSON_LIBRARY_TYPE],\n LibraryJSONEncoder,\n LibraryJSONDecoder\n ), (\n create_stub_multiplexed_library,\n [JSON_NAME_PROPERTY, JSON_INTERNAL_ID_PROPERTY],\n MultiplexedLibraryJSONEncoder,\n MultiplexedLibraryJSONDecoder\n ), (\n create_stub_well,\n [JSON_NAME_PROPERTY, JSON_INTERNAL_ID_PROPERTY],\n WellJSONEncoder,\n WellJSONDecoder\n )\n]\n\nfor _setup in _setups:\n _encoder_test_class, _decoder_test_class = create_json_converter_test(*_setup)\n globals()[_encoder_test_class.__name__] = _encoder_test_class\n globals()[_decoder_test_class.__name__] = _decoder_test_class\n del _encoder_test_class, _decoder_test_class\n","repo_name":"wtsi-hgi/python-sequencescape-db","sub_path":"sequencescape/tests/test_json_converts.py","file_name":"test_json_converts.py","file_ext":"py","file_size_in_byte":2489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"7054822370","text":"import asyncio\nimport enum\nimport json\nimport socket\nfrom typing import List\n\nfrom aiohttp.abc import AbstractResolver\nfrom aiohttp.client import ClientSession as CS\nfrom aiohttp.connector import TCPConnector\nfrom aiohttp.resolver import DefaultResolver\n\n__all__ = 'ClientSession', 'DNSOverHTTPSResolver', 'RecordType'\n\n\nclass RecordType(enum.Enum):\n \"\"\"Record Type\"\"\"\n\n A = 1\n AAAA = 28\n\n\nclass DNSOverHTTPSResolver(AbstractResolver):\n \"\"\"DNS over HTTPS Resolver\"\"\"\n\n def __init__(\n self,\n *,\n endpoints: List[str],\n json_loads=json.loads,\n resolver_class=None,\n ) -> None:\n self.endpoints = endpoints\n self.json_loads = json_loads\n if resolver_class is None:\n resolver_class = DefaultResolver\n self.resolveer_class = resolver_class\n \n async def _resolve(self, endpoint: str, host, port, family):\n if family == socket.AF_INET6:\n record_type = RecordType.AAAA\n else:\n record_type = RecordType.A\n\n params = {\n 'ct': 'application/dns-json',\n 'name': host,\n 'type': record_type.name,\n }\n\n resolver = self.resolveer_class()\n connector = TCPConnector(resolver=resolver)\n \n async with CS(connector=connector) as session:\n async with session.get(endpoint, params=params) as resp:\n data = self.json_loads(await resp.text())\n\n await connector.close()\n\n if data['Status'] != 0:\n raise OSError(\"DNS lookup failed\")\n\n return [\n {\n 'hostname': host,\n 'host': r['data'],\n 'port': port,\n 'family': family,\n 'proto': 0,\n 'flags': socket.AI_NUMERICHOST\n } for r in data['Answer']\n if r['type'] in (\n record_type.name,\n record_type.value,\n ) and r['data']\n ]\n\n async def resolve(self, host, port=0, family=socket.AF_INET):\n tasks = [\n self._resolve(endpoint, host, port, family)\n for endpoint in self.endpoints\n ]\n done, pending = await asyncio.wait(\n tasks,\n return_when=asyncio.FIRST_COMPLETED,\n )\n for p in pending:\n p.cancel()\n return list(done)[0].result()\n\n async def close(self):\n pass\n\n\ndef ClientSession(*args, **kwargs) -> CS: # noqa\n \"\"\"Shortcut of aiohttp.ClientSession and DNSOverHTTPSResolver\"\"\"\n\n endpoints = kwargs.pop(\n 'endpoints',\n [\n 'https://dns.google.com/resolve',\n 'https://cloudflare-dns.com/dns-query',\n ],\n )\n json_loads = kwargs.pop('json_loads', json.loads)\n resolver_class = kwargs.pop('resolver_class', None)\n resolver = DNSOverHTTPSResolver(\n endpoints=endpoints,\n json_loads=json_loads,\n resolver_class=resolver_class,\n )\n connector = TCPConnector(resolver=resolver)\n\n return CS(*args, **kwargs, connector=connector)\n","repo_name":"item4/aiohttp-doh","sub_path":"aiohttp_doh.py","file_name":"aiohttp_doh.py","file_ext":"py","file_size_in_byte":3068,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"55"}
+{"seq_id":"69871665453","text":"from __future__ import annotations\n\nfrom datetime import datetime\nfrom typing import List, Dict, Optional, Union\nfrom pydantic import BaseModel, Field, validator\n\n\nclass DataRecordingModel(BaseModel):\n time: datetime\n value: float\n\n\nclass NodeMetricsModel(BaseModel):\n node_name: str\n arrow_metrics: List[float]\n memory_used: List[DataRecordingModel] = Field(default=[])\n cpu_busy: List[DataRecordingModel] = Field(default=[])\n disk_io_util: List[DataRecordingModel] = Field(default=[])\n network_util: List[DataRecordingModel] = Field(default=[])\n\n @staticmethod\n def cap(value: Union[int, float], replace_zero_with: Optional[float] = None):\n new_value = min(100, max(0, value))\n if new_value == 0 and replace_zero_with is not None:\n new_value = replace_zero_with\n return new_value\n\n @validator(\"memory_used\")\n def normalize_memory_used(cls, memory_used):\n return [DataRecordingModel(time=el.time, value=cls.cap(el.value)) for el in memory_used]\n\n @validator(\"cpu_busy\")\n def normalize_cpu_busy(cls, cpu_busy):\n return [DataRecordingModel(time=el.time, value=cls.cap(el.value)) for el in cpu_busy]\n\n @validator(\"disk_io_util\")\n def normalize_disk_io_util(cls, disk_io_util):\n return [DataRecordingModel(time=el.time, value=cls.cap(el.value)) for el in disk_io_util]\n\n @validator(\"network_util\")\n def normalize_network_util(cls, network_util):\n return [DataRecordingModel(time=el.time, value=cls.cap(el.value)) for el in network_util]\n\n\nclass BaseInformationModel(BaseModel):\n framework_name: str\n algorithm_name: str\n dataset_name: str\n\n\nclass GenericWorkloadModel(BaseInformationModel):\n workload_name: str\n\n node_count: int\n machine_name: str\n runtime: float\n cost: Optional[float]\n completed: bool\n timeout: bool\n abandon: bool\n\n\nclass RawWorkloadModel(GenericWorkloadModel):\n node_metrics: Dict[str, NodeMetricsModel]\n configuration: Dict[str, str]\n result: Dict[str, str]\n","repo_name":"dos-group/perona-infrastructure-fingerprinting","sub_path":"usecase_dataflows/classes/raw_workload.py","file_name":"raw_workload.py","file_ext":"py","file_size_in_byte":2033,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"55"}
+{"seq_id":"38821796262","text":"import os\nimport math\n\n\ndef good_proc(nproc, ncore):\n if ncore == 24:\n if nproc == 5:\n nproc = 4\n elif nproc == 7:\n nproc = 6\n elif 8 < nproc < 12:\n nproc = 8\n elif 12 < nproc < 24:\n nproc = 12\n else: # ncore == 40\n if nproc == 3:\n nproc = 2\n elif 5 < nproc < 8:\n nproc = 5\n elif nproc == 9:\n nproc = 8\n elif 10 < nproc < 20:\n nproc = 10\n elif 20 < nproc < 40:\n nproc = 20\n\n return nproc\n\n\ndef write_sh(nks, nkd, nk_path, atom, prefix, atomwfc_dict, queue):\n pw = \"~/program/QE/qe-6.2.1/bin/pw.x\"\n proj = \"~/program/QE/qe-6.2.1/bin/projwfc.x\"\n vf = \"~/program/QE/qe-6.2.1/bin/fermi_velocity.x\"\n bands = \"~/program/QE/qe-6.2.1/bin/bands.x\"\n sumpdos = \"~/program/QE/qe-6.2.1/bin/sumpdos.x\"\n fproj = \"~/program/QE/qe-6.2.1/bin/fermi_proj.x\"\n typ = set(atom)\n #\n if queue == \"F4cpus\":\n maxnode = 4\n ncore = 24\n elif queue == \"F4cpue\":\n maxnode = 4\n ncore = 40\n elif queue == \"F36cpus\":\n maxnode = 36\n ncore = 24\n elif queue == \"F9cpue\":\n maxnode = 9\n ncore = 40\n elif queue == \"F36cpue\":\n maxnode = 36\n ncore = 40\n else: # queue == \"F144cpus\":\n maxnode = 144\n ncore = 24\n #\n # Structure optimization\n #\n nk = min(ncore*maxnode, nks)\n ntg = good_proc(int(ncore*maxnode / nk), ncore)\n nproc = nk*ntg\n node = math.ceil(nproc / ncore)\n if not os.path.isfile(\"rx.sh\"):\n with open(\"rx.sh\", 'w') as f:\n print(\"#!/bin/sh\", file=f)\n print(\"#QSUB -queue\", queue[0:len(queue) - 1], file=f)\n print(\"#QSUB -node\", node, file=f)\n print(\"#PBS -l walltime=8:00:00\", file=f)\n print(\"source ~/.bashrc\", file=f)\n print(\"cd $PBS_O_WORKDIR\", file=f)\n print(\"mpijob -n %d %s -nk %d -ntg %d -in rx.in > rx_s.out\"\n % (nproc, pw, nk, ntg), file=f)\n print(\"sed -n -e '/occupations/c occupations=\\\"tetrahedra_opt\\\"' -e '1,/CELL_PARAMETERS/p' rx.in > rx_t.in\",\n file=f)\n print(\"grep -A 3 CELL_PARAMETERS rx_s.out | tail -n 3 >> rx_t.in\", file=f)\n print(\"awk '/ATOMIC_SPECIES/,/ATOMIC_POSITIONS/' rx.in >> rx_t.in\", file=f)\n print(\"grep -A %d ATOMIC_POSITIONS rx_s.out |tail -n %d >> rx_t.in\" % (len(atom), len(atom)), file=f)\n print(\"sed -n -e '/K_POINTS/,$p' rx.in >> rx_t.in\", file=f)\n print(\"sed -i -e '/occupations/c occupations=\\\"tetrahedra_opt\\\"' rx_t.in\", file=f)\n print(\"mpijob -n %d %s -nk %d -ntg %d -in rx_t.in > rx_t.out\"\n % (nproc, pw, nk, ntg), file=f)\n #\n # Charge optimization\n #\n if not os.path.isfile(\"scf.sh\"):\n with open(\"scf.sh\", 'w') as f:\n print(\"#!/bin/sh\", file=f)\n print(\"#QSUB -queue\", queue[0:len(queue) - 1], file=f)\n print(\"#QSUB -node\", node, file=f)\n print(\"#PBS -l walltime=8:00:00\", file=f)\n print(\"source ~/.bashrc\", file=f)\n print(\"cd $PBS_O_WORKDIR\", file=f)\n print(\"mpijob -n %d %s -nk %d -ntg %d -in scf.in > scf.out\"\n % (nproc, pw, nk, ntg), file=f)\n #\n # Projected DOS\n #\n nk = min(ncore*maxnode, nkd)\n ntg = good_proc(int(ncore*maxnode / nk), ncore)\n nproc = nk * ntg\n node = math.ceil(nproc / ncore)\n #\n # Atomwfc dictionary for fermi_proj.x\n #\n pfermi = {ityp: [[] for il in range(len(atomwfc_dict[ityp][0]))] for ityp in typ}\n ii = 0\n for iat in atom:\n for il in range(len(atomwfc_dict[iat][0])):\n for im in range(atomwfc_dict[iat][0][il]):\n ii += 1\n pfermi[iat][il].append(ii)\n #\n if not os.path.isfile(\"proj.sh\"):\n with open(\"proj.sh\", 'w') as f:\n print(\"#!/bin/sh\", file=f)\n print(\"#QSUB -queue\", queue[0:len(queue) - 1], file=f)\n print(\"#QSUB -node\", node, file=f)\n print(\"#PBS -l walltime=8:00:00\", file=f)\n print(\"source ~/.bashrc\", file=f)\n print(\"cd $PBS_O_WORKDIR\", file=f)\n print(\"mpijob -n %d %s -nk %d -ntg %d -in nscf.in > nscf.out\"\n % (nproc, pw, nk, ntg), file=f)\n print(\"mpijob -n 1 %s -in nscf.in > vfermi.out\" % vf, file=f)\n print(\"ef=`grep Fermi nscf.out| awk '{print $5}'`\", file=f)\n print(\"sed -i -e '/emin/c emin = '${ef}'' -e '/emax/c emax = '${ef}'' proj.in\", file=f)\n print(\"mpijob -n %d %s -nk %d -ntg %d -in proj.in > proj.out\"\n % (nproc, proj, nk, ntg), file=f)\n #\n # Sum PDOS at each Atom and L\n #\n for ityp in typ:\n for il in range(len(atomwfc_dict[ityp][1])):\n print(\"%s %s.pdos_atm*\\\\(%s\\\\)_wfc#%d* > %s.pdos_%s%s\"\n % (sumpdos, prefix, ityp, il+1, prefix, ityp, atomwfc_dict[ityp][1][il]), file=f)\n #\n # Fermi surface with atomic projection\n #\n for ityp in typ:\n for il in range(len(atomwfc_dict[ityp][1])):\n print(\"sed -e '$a %d\\\\n\" % len(pfermi[ityp][il]), end=\"\", file=f)\n for ii in pfermi[ityp][il]:\n print(\" %d\" % ii, end=\"\", file=f)\n print(\"' proj.in > proj_f.in\", file=f)\n print(\"mpijob -n 1 %s -in proj_f.in\" % fproj, file=f)\n print(\"mv proj.frmsf %s%s.frmsf\" % (ityp, atomwfc_dict[ityp][1][il]), file=f)\n #\n # Band\n #\n nk = min(ncore*maxnode, nk_path)\n ntg = good_proc(int(ncore*maxnode / nk), ncore)\n nproc = nk * ntg\n node = math.ceil(nproc / ncore)\n if not os.path.isfile(\"band.sh\"):\n with open(\"band.sh\", 'w') as f:\n print(\"#!/bin/sh\", file=f)\n print(\"#QSUB -queue\", queue[0:len(queue) - 1], file=f)\n print(\"#QSUB -node\", node, file=f)\n print(\"#PBS -l walltime=8:00:00\", file=f)\n print(\"source ~/.bashrc\", file=f)\n print(\"cd $PBS_O_WORKDIR\", file=f)\n print(\"mpijob -n %d %s -nk %d -ntg %d -in band.in > band.out\"\n % (nproc, pw, nk, ntg), file=f)\n print(\"mpijob -n %d %s -nk %d -ntg %d -in bands.in > bands.out\"\n % (nproc, bands, nk_path, ntg), file=f)\n","repo_name":"Huaguiyuan/cif2input","sub_path":"write_sh.py","file_name":"write_sh.py","file_ext":"py","file_size_in_byte":6446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"55"}
+{"seq_id":"39384928767","text":"# from math import exp, log10\r\nimport numpy as np\r\n\r\n\"\"\"\r\nQuiz Question\r\n\r\nThe sigmoid function is defined as sigmoid(x) = 1/(1+e^-x). \r\nIf the score is defined by (4x1 + 5x2 - 9 = score), \r\nthen which of the following points has exactly a 50% probability \r\nof being blue or red? (Choose all that are correct.)\r\n\r\nsigmoid(x) = 1 / (1 + exp(-x))\r\n\r\npoints:\r\n(1, 1)\r\n(2, 4)\r\n(5, -5)\r\n(-4, 5)\r\n\"\"\"\r\ndef sigmoid(X, W, b):\r\n linear_value = (np.matmul(X, W) + b)[0]\r\n return (1 / (1 + np.exp(-linear_value)))\r\n\r\n\r\npoints = [(1, 1), (2, 4), (5, -5), (-4, 5)]\r\nW = np.array([4, 5]).reshape(2, 1)\r\nb = -9\r\n\r\nfor point in points:\r\n point_prop = sigmoid(point, W, b)\r\n print(f'{point} has probability with: {point_prop}')\r\n\r\nprint(np.exp(-500))\r\n# sigmoid(x) = 1 / 1 + exp(-x)\r\n","repo_name":"KaisChebata/AI-Programming-with-Python-Nanodegree-Program","sub_path":"Core_Curriculum/6_Neural_Networks/Lesson_1_Introduction_to_Neural_Networks/perceptrons/discrete_vs_continuous.py","file_name":"discrete_vs_continuous.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"}
+{"seq_id":"36059389365","text":"from django.urls import path\nfrom . import views\n\n\nurlpatterns = [\n path('Shome', views.adminhome),\n path('', views.admin),\n path('create_user', views.create_user),\n path('display_user', views.display_user),\n path('update_user', views.update_user),\n path('delete_user', views.delete_user),\n path('add_category', views.add_category),\n path('display_category', views.display_category),\n path('update_category', views.update_category),\n path('delete_category', views.delete_category),\n path('add_product', views.add_product),\n path('display_product', views.display_product),\n path('update_product', views.update_product),\n path('delete_product', views.delete_product),\n]","repo_name":"ShamnaK46/ecommerce","sub_path":"admin/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"71573158542","text":"#!/usr/bin/env python\n__author__ = 'Sergei F. Kliver'\n\nimport argparse\nfrom RouToolPa.Routines import AnnotationsRoutines\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument(\"-g\", \"--gtf_file\", action=\"store\", dest=\"input\", required=True,\n help=\"Input gtf file\")\nparser.add_argument(\"-o\", \"--output\", action=\"store\", dest=\"output\", required=True,\n help=\"Output accordance file\")\n\nargs = parser.parse_args()\n\nAnnotationsRoutines.get_transcript_to_pep_accordance_from_gtf(args.input, args.output, comment_symbol=\"#\")\n","repo_name":"mahajrod/MAVR","sub_path":"scripts/annotation/get_transcript_to_pep_accordance_from_gtf.py","file_name":"get_transcript_to_pep_accordance_from_gtf.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"}
+{"seq_id":"23869231891","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Oct 15 13:58:02 2023\n\n@author: ubuntu\n\"\"\"\nimport pandas as pd\nimport requests\n\nimport apicall as api\nfrom settings import FPL_ID, BASE_URL\n\n\ndef load_bootstrap():\n response = api.load('bootstrap-static', 'bootstrap.json')\n\n events = pd.json_normalize(response['events'])\n game_settings = pd.json_normalize(response['game_settings'])\n phases = pd.json_normalize(response['phases'])\n teams = pd.json_normalize(response['teams'])\n total_players = response['total_players']\n elements = pd.json_normalize(response['elements'])\n element_stats = pd.json_normalize(response['element_stats'])\n element_types = pd.json_normalize(response['element_types'])\n\n return events, game_settings, phases, teams, total_players, elements, element_stats, element_types\n\n\ndef load_entry():\n response = api.load(\"entry/\" + str(FPL_ID), 'entry.json')\n entry = pd.json_normalize(response, max_level=0)\n entry = entry.drop(columns=\"leagues\")\n\n classic = pd.json_normalize(response['leagues']['classic'])\n h2h = pd.json_normalize(response['leagues']['h2h'])\n cup = pd.json_normalize(response['leagues']['cup'])\n cup_matches = pd.json_normalize(response['leagues']['cup_matches'])\n return response, entry, classic, h2h, cup, cup_matches\n\n\ndef load_league_standings(league_id):\n url = \"leagues-classic/\" + str(league_id) + \"/standings\"\n return api.load(url, \"league_\" + str(league_id) + \".json\")\n\n\ndef load_picks(entry, event):\n url = \"entry/\" + str(entry) + \"/event/\" + str(event) + \"/picks/\"\n return api.load(url, str(entry) + \"-\" + str(event) + \".json\")\n\n\ndef load_event_live(current_event):\n i = 1\n all_stats = []\n\n while i <= current_event:\n url = \"event/\" + str(i) + \"/live/\"\n file = \"event-live-\" + str(i) + \".json\"\n event = api.load(url, file)\n for element in event['elements']:\n element_id = element['id']\n if len(element['explain']):\n fixture_id = (element['explain'][0]['fixture'])\n else:\n fixture_id = 999\n stats = element['stats']\n stats['id'] = element_id\n stats['fixtureId'] = fixture_id\n all_stats.append(stats)\n i += 1\n\n df = pd.DataFrame(all_stats)\n\n df['expected_goals'] = pd.to_numeric(df['expected_goals'])\n df['expected_assists'] = pd.to_numeric(df['expected_assists'])\n df['expected_goal_involvements'] = pd.to_numeric(\n df['expected_goal_involvements'])\n df['expected_goals_conceded'] = pd.to_numeric(\n df['expected_goals_conceded'])\n\n return df\n\n\ndef load_fixtures():\n response = api.load('fixtures', 'fixtures.json')\n fixtures = pd.json_normalize(response, max_level=0)\n # stats not decoded. Useless as is.\n stats = fixtures['stats']\n\n fixtures = fixtures.drop(columns='stats')\n return fixtures, stats\n\n\ndef load_future_fixtures():\n fixtures = requests.get(BASE_URL + 'fixtures/?future=1').json()\n\n return pd.DataFrame(fixtures)\n\n\ndef build_players(elements, teams, element_types):\n # join players to teams\n df = pd.merge(\n left=elements,\n right=teams,\n left_on='team',\n right_on='id'\n )\n\n # join player positions\n df = df.merge(\n element_types,\n left_on='element_type',\n right_on='id'\n )\n\n # rename columns\n df = df.rename(\n columns={'name': 'team_name', 'singular_name': 'position_name', 'minutes_x': 'minutes',\n 'short_name': 'Team', 'id': 'element_type',\n 'id_x': 'player_id'}\n )\n\n df = df.drop(columns=['minutes', 'goals_scored', 'assists', 'clean_sheets',\n 'goals_conceded', 'own_goals', 'penalties_saved', 'penalties_missed',\n 'yellow_cards', 'red_cards', 'saves', 'bonus', 'bps', 'influence', 'creativity',\n 'threat', 'ict_index', 'expected_goals', 'expected_assists', 'expected_goal_involvements',\n 'expected_goals_conceded', 'starts', 'in_dreamteam', 'pulse_id'\n ])\n # Maybe trouble. Why are there duplicates???\n df = df.loc[:, ~df.columns.duplicated()].copy()\n df['now_cost'] = df['now_cost'] / 10\n\n # Drop players who are not available\n df = df[~df['status'].isin(['u'])]\n\n return df\n","repo_name":"doublep563/fantasyfootball","sub_path":"load/load_data.py","file_name":"load_data.py","file_ext":"py","file_size_in_byte":4392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"72846773263","text":"import tensorflow as tf\nimport numpy as np\n\ntf.random.set_seed(42)\n\n# create a tensor of size 50 with random values between 0 and 100 and add 2 extra dimensions at the start\ntensor_random1 = tf.constant(tf.random.uniform(shape=[50], minval=0, maxval=100), shape=(1, 1, 50))\nprint(tensor_random1)\n\n# create a tensor of size 50 with random values between 0 and 100 and add 2 extra dimensions at the start\ntensor_random2 = tf.constant(np.random.random(50))\nprint(tensor_random2)\ntensor_random2 = tf.expand_dims(tensor_random2, 0)\ntensor_random2 = tf.expand_dims(tensor_random2, 0)\nprint(tensor_random2)\n\n","repo_name":"ionutnoje/tutoriale","sub_path":"ZTM TensorFlow Course/1. Deep Learning and TensorFlow Fundamentals/Tests/Tensor_Creation.py","file_name":"Tensor_Creation.py","file_ext":"py","file_size_in_byte":601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"8373336248","text":"# ДОПОЛНИТЕЛЬНО, НО НЕОБЯЗАТЕЛЬНО:\r\n# Написать программу, которая состоит 4 из этапов:\r\n# - создает список из рандомных четырех значных чисел\r\n# - принимает с консоли цифру и удаляет ее из всех элементов списка\r\n# - цифры каждого элемента суммирует пока результат не станет однозначным числом\r\n# - из финального списка убирает все дублирующиеся элементы\r\n# - после каждого этапа выводить результат в консоль\r\n# Пример:\r\n# - 1 этап: [2634, 6934, 7286, 3353, 4602, 3176, 3796]\r\n# - 2 этап: Введ��те цифру: 3\r\n# - 2 этап: [264, 694, 7286, 5, 4602, 176, 796]\r\n# - 3 этап: 264 -> 2+6+4 -> 12 -> 1+2 -> 3\r\n# - 3 этап: [3, 1, 5, 5, 3, 5, 4]\r\n# - 4 этап: [3, 1, 5, 4]\r\n\r\nfrom random import randint\r\n\r\n# - создает список из рандомных четырех значных чисел\r\ndef create_list (number_of_elem):\r\n result_list = []\r\n for _ in range (number_of_elem):\r\n result_list.append(randint(1000, 10000))\r\n return result_list\r\n\r\n# - принимает с консоли цифру и удаляет ее из всех элементов списка\r\ndef delete_digit(list, number_to_delete):\r\n for i in range(len(list)):\r\n list[i] = str(list[i])\r\n if number_to_delete in list[i]:\r\n list[i] = list[i].replace(number_to_delete, '')\r\n list[i] = int(list[i])\r\n return list\r\n\r\n# - цифры каждого элемента суммирует пока результат не станет однозначным числом\r\ndef replace_element(list):\r\n for i in range(len(list)):\r\n while list[i] > 9:\r\n list[i] = sum_digits(list[i])\r\n return list \r\n\r\ndef sum_digits(number):\r\n if number < 10:\r\n return number\r\n else:\r\n return number % 10 + sum_digits(number//10)\r\n\r\n# - из финального списка убирает все дублирующиеся элементы\r\n\r\ndef delete_duplecates(list):\r\n new_list = []\r\n for elem in list:\r\n if elem not in new_list:\r\n new_list.append(elem)\r\n return new_list\r\n\r\nnumber_to_delete = input(\"Введите число: \")\r\nmy_list = delete_digit([2634, 6934, 7286, 3353, 4602, 3176, 3796], number_to_delete)\r\nprint(my_list)\r\nmy_list = replace_element(my_list)\r\nprint(my_list)\r\nmy_list = delete_duplecates(my_list)\r\nprint(my_list)","repo_name":"AlinaYun/Python-Homework2","sub_path":"Task4.py","file_name":"Task4.py","file_ext":"py","file_size_in_byte":2680,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"34437216997","text":"import json\nfrom difflib import get_close_matches as gcm\n\ndata = json.load(open(\"data.json\"))\n\ndef find_meaning(word):\n word = word.lower()\n if word in data:\n return data[word]\n elif word.title() in data:\n return data[word.title()]\n elif word.upper() in data:\n return data[word.upper()] \n else:\n matches = gcm(word,data.keys())\n if len(matches) > 0:\n return \"Did you mean \" + str(matches[0]) + \" instead\"\n else:\n return \"Word Not Found\"\n\nsearch_word = input(\"Enter the word to search: \")\n\nmeaning = find_meaning(search_word)\nif type(meaning) == list:\n for m in meaning:\n print(m + \"\\n\")\nelse:\n print(meaning)\n","repo_name":"AdmiralStone/Py_dictionary","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"10907590625","text":"import csv\nimport numpy\nimport math\nimport pandas as pd\n#분산도 구하고 중앙값도 구하고, 클러스터별로 정렬도 한파일\n\nwith open(\"./20K_클러스터파일/20K_notcos.csv\", 'r', encoding=\"utf8\") as f:\n reader = csv.reader(f)\n twolist = []\n for row in reader:\n twolist.append(row)\n\nplist = sorted(twolist, key=lambda row: row[1])\n#그냥 정렬이 하고싶었음 클러스터별로 정렬됨(그럼 연산좀더 빠를꺼같아서)\nlist0 = []\nlist1 = []\nlist2 = []\nlist3 = []\nlist4 = []\nlist5 = []\nlist6 = []\nlist7 = []\nlist8 = []\nlist9 = []\nlist10 = []\nlist11 = []\nlist12 = []\nlist13 = []\nlist14 = []\nlist15 = []\nlist16 = []\nlist17 = []\nlist18 = []\nlist19 = []\n\n#각클러스터별 분리\nfor i in range(len(plist)):\n if plist[i][1] == \"0\":\n list0.append(plist[i])\n elif plist[i][1] == \"1\":\n list1.append(plist[i])\n elif plist[i][1] == \"2\":\n list2.append(plist[i])\n elif plist[i][1] == \"3\":\n list3.append(plist[i])\n elif plist[i][1] == \"4\":\n list4.append(plist[i])\n elif plist[i][1] == \"5\":\n list5.append(plist[i])\n elif plist[i][1] == \"6\":\n list6.append(plist[i])\n elif plist[i][1] == \"7\":\n list7.append(plist[i])\n elif plist[i][1] == \"8\":\n list8.append(plist[i])\n elif plist[i][1] == \"9\":\n list9.append(plist[i])\n elif plist[i][1] == \"10\":\n list10.append(plist[i])\n elif plist[i][1] == \"11\":\n list11.append(plist[i])\n elif plist[i][1] == \"12\":\n list12.append(plist[i])\n elif plist[i][1] == \"13\":\n list13.append(plist[i])\n elif plist[i][1] == \"14\":\n list14.append(plist[i])\n elif plist[i][1] == \"15\":\n list15.append(plist[i])\n elif plist[i][1] == \"16\":\n list16.append(plist[i])\n elif plist[i][1] == \"17\":\n list17.append(plist[i])\n elif plist[i][1] == \"18\":\n list18.append(plist[i])\n elif plist[i][1] == \"19\":\n list19.append(plist[i])\n\nnodeamount = []#각 클러스터별 갯수 확인 리스트\nnodeamount.append(len(list0))\nnodeamount.append(len(list1))\nnodeamount.append(len(list2))\nnodeamount.append(len(list3))\nnodeamount.append(len(list4))\nnodeamount.append(len(list5))\nnodeamount.append(len(list6))\nnodeamount.append(len(list7))\nnodeamount.append(len(list8))\nnodeamount.append(len(list9))\nnodeamount.append(len(list10))\nnodeamount.append(len(list11))\nnodeamount.append(len(list12))\nnodeamount.append(len(list13))\nnodeamount.append(len(list14))\nnodeamount.append(len(list15))\nnodeamount.append(len(list16))\nnodeamount.append(len(list17))\nnodeamount.append(len(list18))\nnodeamount.append(len(list19))\n#각 클러스터별 중점을 찾기위한 리스트들\nsum = [0]*100\nsum1 = [0]*100\nsum2 = [0]*100\nsum3 = [0]*100\nsum4 = [0]*100\nsum5 = [0]*100\nsum6 = [0]*100\nsum7 = [0]*100\nsum8 = [0]*100\nsum9 = [0]*100\nsum10 = [0]*100\nsum11 = [0]*100\nsum12 = [0]*100\nsum13 = [0]*100\nsum14 = [0]*100\nsum15 = [0]*100\nsum16 = [0]*100\nsum17 = [0]*100\nsum18 = [0]*100\nsum19 = [0]*100\n\n#클러스터별 중앙값계산과정\nfor i in range(len(list0)):\n for j in range(2, len(list0[i])):\n sum[j-2] = float(list0[i][j])\nfor i in range(len(list1)):\n for j in range(2, len(list1[i])):\n sum1[j-2] = float(list1[i][j])\nfor i in range(len(list2)):\n for j in range(2, len(list2[i])):\n sum2[j-2] = float(list2[i][j])\nfor i in range(len(list3)):\n for j in range(2, len(list3[i])):\n sum3[j-2] = float(list3[i][j])\nfor i in range(len(list4)):\n for j in range(2, len(list4[i])):\n sum4[j-2] = float(list4[i][j])\nfor i in range(len(list5)):\n for j in range(2, len(list5[i])):\n sum5[j-2] = float(list5[i][j])\nfor i in range(len(list6)):\n for j in range(2, len(list6[i])):\n sum6[j-2] = float(list6[i][j])\nfor i in range(len(list7)):\n for j in range(2, len(list7[i])):\n sum7[j-2] = float(list7[i][j])\nfor i in range(len(list8)):\n for j in range(2, len(list8[i])):\n sum8[j-2] = float(list8[i][j])\nfor i in range(len(list9)):\n for j in range(2, len(list9[i])):\n sum9[j-2] = float(list9[i][j])\nfor i in range(len(list10)):\n for j in range(2, len(list10[i])):\n sum10[j-2] = float(list10[i][j])\nfor i in range(len(list11)):\n for j in range(2, len(list11[i])):\n sum11[j-2] = float(list11[i][j])\nfor i in range(len(list12)):\n for j in range(2, len(list12[i])):\n sum12[j-2] = float(list12[i][j])\nfor i in range(len(list13)):\n for j in range(2, len(list13[i])):\n sum13[j-2] = float(list13[i][j])\nfor i in range(len(list14)):\n for j in range(2, len(list14[i])):\n sum14[j-2] = float(list14[i][j])\nfor i in range(len(list15)):\n for j in range(2, len(list15[i])):\n sum15[j-2] = float(list15[i][j])\nfor i in range(len(list16)):\n for j in range(2, len(list16[i])):\n sum16[j-2] = float(list16[i][j])\nfor i in range(len(list17)):\n for j in range(2, len(list17[i])):\n sum17[j-2] = float(list17[i][j])\nfor i in range(len(list18)):\n for j in range(2, len(list18[i])):\n sum18[j-2] = float(list18[i][j])\nfor i in range(len(list19)):\n for j in range(2, len(list19[i])):\n sum19[j-2] = float(list19[i][j])\n#클러스터별 중앙값 계산\nfor i in range(len(sum)):\n sum[i] = sum[i]/nodeamount[0]\n\nfor i in range(len(sum1)):\n sum1[i] = sum1[i]/nodeamount[1]\n\nfor i in range(len(sum2)):\n sum2[i] = sum2[i]/nodeamount[2]\nfor i in range(len(sum3)):\n sum3[i] = sum3[i]/nodeamount[3]\nfor i in range(len(sum4)):\n sum4[i] = sum4[i]/nodeamount[4]\nfor i in range(len(sum5)):\n sum5[i] = sum5[i]/nodeamount[5]\nfor i in range(len(sum6)):\n sum6[i] = sum6[i]/nodeamount[6]\nfor i in range(len(sum7)):\n sum7[i] = sum7[i]/nodeamount[7]\nfor i in range(len(sum8)):\n sum8[i] = sum8[i]/nodeamount[8]\nfor i in range(len(sum9)):\n sum9[i] = sum9[i]/nodeamount[9]\nfor i in range(len(sum10)):\n sum10[i] = sum10[i]/nodeamount[10]\nfor i in range(len(sum11)):\n sum11[i] = sum11[i]/nodeamount[11]\nfor i in range(len(sum12)):\n sum12[i] = sum12[i]/nodeamount[12]\nfor i in range(len(sum13)):\n sum13[i] = sum13[i]/nodeamount[13]\nfor i in range(len(sum14)):\n sum14[i] = sum14[i]/nodeamount[14]\nfor i in range(len(sum15)):\n sum15[i] = sum15[i]/nodeamount[15]\nfor i in range(len(sum16)):\n sum16[i] = sum16[i]/nodeamount[16]\nfor i in range(len(sum17)):\n sum17[i] = sum17[i]/nodeamount[17]\nfor i in range(len(sum18)):\n sum18[i] = sum18[i]/nodeamount[18]\nfor i in range(len(sum19)):\n sum19[i] = sum19[i]/nodeamount[19]\n\ndef euclidean_distance(pt1, pt2):#각 노드별 중앙값기준의 거리 계산 함수\n puty = []\n for i in range(len(pt2)):\n distance = 0\n for j in range(2, len(pt2[i])):\n distance += (pt1[j-2] - float(pt2[i][j])) ** 2\n distance = distance ** 0.5\n puty.append(distance)\n return puty\n\n\ndistance_list = []#각 클러스터 중점으로부터의 노드 거리 리스트\ndistance_list.append(euclidean_distance(sum, list0))\ndistance_list.append(euclidean_distance(sum, list1))\ndistance_list.append(euclidean_distance(sum, list2))\ndistance_list.append(euclidean_distance(sum, list3))\ndistance_list.append(euclidean_distance(sum, list4))\ndistance_list.append(euclidean_distance(sum, list5))\ndistance_list.append(euclidean_distance(sum, list6))\ndistance_list.append(euclidean_distance(sum, list7))\ndistance_list.append(euclidean_distance(sum, list8))\ndistance_list.append(euclidean_distance(sum, list9))\ndistance_list.append(euclidean_distance(sum, list10))\ndistance_list.append(euclidean_distance(sum, list11))\ndistance_list.append(euclidean_distance(sum, list12))\ndistance_list.append(euclidean_distance(sum, list13))\ndistance_list.append(euclidean_distance(sum, list14))\ndistance_list.append(euclidean_distance(sum, list15))\ndistance_list.append(euclidean_distance(sum, list16))\ndistance_list.append(euclidean_distance(sum, list17))\ndistance_list.append(euclidean_distance(sum, list18))\ndistance_list.append(euclidean_distance(sum, list19))\n\nid_dis0 = {}\nid_dis1 = {}\nid_dis2 = {}\nid_dis3 = {}\nid_dis4 = {}\nid_dis5 = {}\nid_dis6 = {}\nid_dis7 = {}\nid_dis8 = {}\nid_dis9 = {}\nid_dis10 = {}\nid_dis11 = {}\nid_dis12 = {}\nid_dis13 = {}\nid_dis14 = {}\nid_dis15 = {}\nid_dis16 = {}\nid_dis17 = {}\nid_dis18 = {}\nid_dis19 = {}\n\n#각 클러스터에대한 노드의 거리 가지고 있으므로 (노드 거리별로 제곱합)/노드의수 = 분산\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[0])):\n id_dis0[list0[j][0]] = float(distance_list[0][j])\nid_dis_li0 = sorted(id_dis0.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[1])):\n id_dis1[list1[j][0]] = float(distance_list[1][j])\nid_dis_li1 = sorted(id_dis1.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[2])):\n id_dis2[list2[j][0]] = float(distance_list[2][j])\nid_dis_li2 = sorted(id_dis2.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[3])):\n id_dis3[list3[j][0]] = float(distance_list[3][j])\nid_dis_li3 = sorted(id_dis3.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[4])):\n id_dis4[list4[j][0]] = float(distance_list[4][j])\nid_dis_li4 = sorted(id_dis4.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[5])):\n id_dis5[list5[j][0]] = float(distance_list[5][j])\nid_dis_li5 = sorted(id_dis5.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[6])):\n id_dis6[list6[j][0]] = float(distance_list[6][j])\nid_dis_li6 = sorted(id_dis6.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[7])):\n id_dis7[list7[j][0]] = float(distance_list[7][j])\nid_dis_li7 = sorted(id_dis7.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[8])):\n id_dis8[list8[j][0]] = float(distance_list[8][j])\nid_dis_li8 = sorted(id_dis8.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[9])):\n id_dis9[list9[j][0]] = float(distance_list[9][j])\nid_dis_li9 = sorted(id_dis9.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[10])):\n id_dis10[list10[j][0]] = float(distance_list[10][j])\nid_dis_li10 = sorted(id_dis10.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[11])):\n id_dis11[list11[j][0]] = float(distance_list[11][j])\nid_dis_li11 = sorted(id_dis11.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[12])):\n id_dis12[list12[j][0]] = float(distance_list[12][j])\nid_dis_li12 = sorted(id_dis12.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[13])):\n id_dis13[list13[j][0]] = float(distance_list[13][j])\nid_dis_li13 = sorted(id_dis13.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[14])):\n id_dis14[list14[j][0]] = float(distance_list[14][j])\nid_dis_li14 = sorted(id_dis14.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[15])):\n id_dis15[list15[j][0]] = float(distance_list[15][j])\nid_dis_li15 = sorted(id_dis15.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[16])):\n id_dis16[list16[j][0]] = float(distance_list[16][j])\nid_dis_li16 = sorted(id_dis16.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[17])):\n id_dis17[list17[j][0]] = float(distance_list[17][j])\nid_dis_li17 = sorted(id_dis17.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[18])):\n id_dis18[list18[j][0]] = float(distance_list[18][j])\nid_dis_li18 = sorted(id_dis18.items(), key=lambda t : t[1])\n\nfor i in range(len(distance_list)):\n for j in range(len(distance_list[19])):\n id_dis19[list19[j][0]] = float(distance_list[19][j])\nid_dis_li19 = sorted(id_dis19.items(), key=lambda t : t[1])\ntotal = []#다 합침.\ntotal.append(id_dis_li0)\ntotal.append(id_dis_li1)\ntotal.append(id_dis_li2)\ntotal.append(id_dis_li3)\ntotal.append(id_dis_li4)\ntotal.append(id_dis_li5)\ntotal.append(id_dis_li6)\ntotal.append(id_dis_li7)\ntotal.append(id_dis_li8)\ntotal.append(id_dis_li9)\ntotal.append(id_dis_li10)\ntotal.append(id_dis_li11)\ntotal.append(id_dis_li12)\ntotal.append(id_dis_li13)\ntotal.append(id_dis_li14)\ntotal.append(id_dis_li15)\ntotal.append(id_dis_li16)\ntotal.append(id_dis_li17)\ntotal.append(id_dis_li18)\ntotal.append(id_dis_li19)\nfor i in range(len(total)):#각 노드의 클러스터위치를 연산하기위해 썻는데 auto파일에 자동화시켰음.\n for j in range(len(total[i])):\n if 'cve-2017-18403' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2019-4439' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2015-4812' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2018-7034' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2017-6553' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2017-10915' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2015-5791' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2019-3833' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2017-18383' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2019-10997' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2016-9393' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2018-7738' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2017-16197' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2017-11536' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2017-6381' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2019-2756' in total[i][j]:\n print(i)\nfor i in range(len(total)):\n for j in range(len(total[i])):\n if 'cve-2015-0536' in total[i][j]:\n print(i)\n\n\n\n\n#클러스터넘버 추가\nfor i in range(len(total)):\n total[i].insert(0, i)\n\nfor i in range(len(nodeamount)):\n total[i].insert(1, int(nodeamount[i]))\n\nSSum = []\nSSum.append(sum)\nSSum.append(sum1)\nSSum.append(sum2)\nSSum.append(sum3)\nSSum.append(sum4)\nSSum.append(sum5)\nSSum.append(sum6)\nSSum.append(sum7)\nSSum.append(sum8)\nSSum.append(sum9)\nSSum.append(sum10)\nSSum.append(sum11)\nSSum.append(sum12)\nSSum.append(sum13)\nSSum.append(sum14)\nSSum.append(sum15)\nSSum.append(sum16)\nSSum.append(sum17)\nSSum.append(sum18)\nSSum.append(sum19)\n\nbbunsan = []#클러스터별 분산계산 리스트\nbsum = 0\nfor i in range(len(distance_list[0])):\n bsum += (distance_list[0][i])**2\nbunsan = bsum/len(distance_list[0])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[1])):\n bsum += (distance_list[1][i])**2\nbunsan = bsum/len(distance_list[1])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[2])):\n bsum += (distance_list[2][i])**2\nbunsan = bsum/len(distance_list[2])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[3])):\n bsum += (distance_list[3][i])**2\nbunsan = bsum/len(distance_list[3])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[4])):\n bsum += (distance_list[4][i])**2\nbunsan = bsum/len(distance_list[4])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[5])):\n bsum += (distance_list[5][i])**2\nbunsan = bsum/len(distance_list[5])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[6])):\n bsum += (distance_list[6][i])**2\nbunsan = bsum/len(distance_list[6])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[7])):\n bsum += (distance_list[7][i])**2\nbunsan = bsum/len(distance_list[7])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[8])):\n bsum += (distance_list[8][i])**2\nbunsan = bsum/len(distance_list[8])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[9])):\n bsum += (distance_list[9][i])**2\nbunsan = bsum/len(distance_list[9])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[10])):\n bsum += (distance_list[10][i])**2\nbunsan = bsum/len(distance_list[10])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[11])):\n bsum += (distance_list[11][i])**2\nbunsan = bsum/len(distance_list[11])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[12])):\n bsum += (distance_list[12][i])**2\nbunsan = bsum/len(distance_list[12])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[13])):\n bsum += (distance_list[13][i])**2\nbunsan = bsum/len(distance_list[13])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[14])):\n bsum += (distance_list[14][i])**2\nbunsan = bsum/len(distance_list[14])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[15])):\n bsum += (distance_list[15][i])**2\nbunsan = bsum/len(distance_list[15])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[16])):\n bsum += (distance_list[16][i])**2\nbunsan = bsum/len(distance_list[17])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[17])):\n bsum += (distance_list[17][i])**2\nbunsan = bsum/len(distance_list[17])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[18])):\n bsum += (distance_list[18][i])**2\nbunsan = bsum/len(distance_list[18])\nbbunsan.append(bunsan)\n\nbsum = 0\nfor i in range(len(distance_list[19])):\n bsum += (distance_list[19][i])**2\nbunsan = bsum/len(distance_list[19])\nbbunsan.append(bunsan)\n#분산구하기 (평균값-현재값=구해놈)^2을 더하면 됩니다. /\n# print(bbunsan)\n\ndavg = pd.DataFrame(SSum)\ndp = pd.DataFrame(bbunsan)\n\n#df.to_csv('20K_notcos_notword_sort.csv')\n\n","repo_name":"ksw7564/node2vec_CVE","sub_path":"2kavg.py","file_name":"2kavg.py","file_ext":"py","file_size_in_byte":18896,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"30522653681","text":"#“how far is a value from the mean” \n#“how likely is a value this far from the mean to be from the same group of observations?”\n\nimport numpy as np\ntokaji_avg = np.average(tokaji)\nlambrusco_avg = np.average(lambrusco)\ntokaji_std = np.std(tokaji)\nlambrusco = np.std(lambrusco)\n\n# Let's see what the results are\nprint(\"Tokaji: \", tokaji_avg, tokaji_std)\nprint(\"Lambrusco: \", lambrusco_avg, lambrusco_std)\n# Tokaji: 90.9 2.65015722804\n# Lambrusco: 84.4047619048 1.61922267961\n\nz = (tokaji_avg - lambrusco_avg) / lambrusco_std\n#4.0113309781438229\n# We'll bring in scipy to do the calculation of probability from the Z-table\n\nimport scipy.stats as st\nst.norm.cdf(z)\n# 0.99996981130231266\n# We need the probability from the right side, so we'll flip it!\n1 - st.norm.cdf(z)\n# 3.0188697687338895e-05\n","repo_name":"chunxu/learning","sub_path":"probility.py","file_name":"probility.py","file_ext":"py","file_size_in_byte":800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"38381276170","text":"import typing\nfrom functools import partial\n\nimport PySide2\nfrom PySide2.QtCore import Qt, QItemSelection, QModelIndex, QItemSelectionModel, Signal\nfrom PySide2.QtGui import QIcon, QPixmap, QColor, QBrush\nfrom PySide2.QtWidgets import QDialog, QTreeWidgetItem, QTreeWidget, QAbstractItemView, QDialogButtonBox, QVBoxLayout, \\\n QWidget, QGroupBox\n\nfrom arthropod_describer.common.common import Info\nfrom arthropod_describer.common.label_hierarchy import Node\nfrom arthropod_describer.common.label_tree_model import LabelTreeModel, LabelTreeMode\nfrom arthropod_describer.common.plugin import PropertyComputation\nfrom arthropod_describer.common.state import State\nfrom arthropod_describer.common.user_params import UserParam, UserParamWidgetBinding, create_params_widget\nfrom arthropod_describer.measurements_viewer.ui_measurement_assign_dialog import Ui_MeasurementAssignDialog\nfrom arthropod_describer.plugin_manager import RegionCompsListModel\nfrom arthropod_describer.color_tolerance_dialog import ColorToleranceDialog\n\nABS_KEY_ROLE = Qt.UserRole\nPARENT_KEY_ROLE = Qt.UserRole + 1\nPROP_KEY_ROLE = Qt.UserRole + 2\nLABEL_ROLE = Qt.UserRole + 3\nLEAF_ITEM_ROLE = Qt.UserRole + 4\n\n\nclass MeasurementAssignDialog(QDialog):\n compute_measurements = Signal(dict)\n\n def __init__(self, state: State, comp_model: RegionCompsListModel, parent: typing.Optional[PySide2.QtWidgets.QWidget] = None,\n f: Qt.WindowFlags = Qt.WindowFlags()):\n super().__init__(parent, f)\n self.ui = Ui_MeasurementAssignDialog()\n self.ui.setupUi(self)\n self.ui.labelTree.setSelectionMode(QAbstractItemView.MultiSelection)\n self.ui.btnAssign.clicked.connect(self.assign_measurements)\n\n self.ui.assignmentTree.itemSelectionChanged.connect(self.assignment_selection_changed)\n self.ui.assignmentTree.clicked.connect(tree_item_double_click_handler(self.ui.assignmentTree))\n self.ui.assignmentTree.selectionModel().selectionChanged.connect(self.deselect_ancestors_of_leaves)\n\n self.ui.measurementTree.itemSelectionChanged.connect(self.measurement_selection_changed)\n\n self.ui.btnLabelSelectAll.clicked.connect(self.ui.labelTree.selectAll)\n self.ui.btnLabelDeselectAll.clicked.connect(self.ui.labelTree.clearSelection)\n\n self.ui.btnMeasSelectAll.clicked.connect(self.ui.measurementTree.selectAll)\n self.ui.btnMeasDeselectAll.clicked.connect(self.ui.measurementTree.clearSelection)\n\n self.ui.btnAssignmentSelectAll.clicked.connect(self.ui.assignmentTree.selectAll)\n self.ui.btnAssignmentDeselectAll.clicked.connect(self.ui.assignmentTree.clearSelection)\n\n self.ui.btnAssignmentRemove.clicked.connect(self.remove_assignments)\n\n self.ui.btnDemoSelectColorAndTolerance.clicked.connect(self.demo_select_color_and_tolerance)\n\n self.ui.buttonBox.button(QDialogButtonBox.Apply).setEnabled(False)\n\n self.ui.buttonBox.button(QDialogButtonBox.Apply).clicked.connect(self.accept)\n\n self.state = state\n self._label_tree_model = LabelTreeModel(self.state, LabelTreeMode.Choosing)\n self.ui.labelTree.setModel(self._label_tree_model)\n self.ui.labelTree.selectionModel().selectionChanged.connect(self.label_selection_changed)\n self.ui.labelTree.setStyleSheet('QTreeView::item:disabled {color: #c0c0c0;}')\n\n self.comps_model = comp_model\n\n self.measurement_items: typing.List[QTreeWidgetItem] = []\n\n self.label_items: typing.Dict[int, QTreeWidgetItem] = {}\n\n # self.assignments: typing.Dict[str, typing.Set[int]] = {}\n self.assignments: typing.Dict[str, typing.Set[int]] = {}\n self.assignment_items: typing.List[QTreeWidgetItem] = []\n self.assignment_prop_items: typing.Dict[str, QTreeWidgetItem] = {}\n self.assignment_label_items: typing.Dict[int, QTreeWidgetItem] = {}\n\n self._setup_demo_color_tolerance_dialog()\n\n self.settings_layout = QVBoxLayout()\n self.ui.scrollAreaWidgetContents.setLayout(self.settings_layout)\n self.param_settings_for_props: typing.Dict[str, typing.List[UserParam]] = {}\n self.param_bindings_for_props: typing.Dict[str, UserParamWidgetBinding] = {}\n self.param_widgets_for_props: typing.Dict[str, QWidget] = {}\n\n def _populate_label_tree(self):\n self.ui.labelTree.clear()\n hierarchy = self.state.storage.get_label_hierarchy2('Labels')\n colormap = hierarchy.colormap\n codes = [hierarchy.code(label) for label in hierarchy.labels]\n codes.sort()\n parent = self.ui.labelTree\n sibling = None\n stack = []\n depth = -1\n used_labels = self.state.storage.used_regions('Labels')\n print(used_labels)\n for code in codes[1:]:\n label = hierarchy.label(code)\n code_depth = hierarchy.get_level(hierarchy.label(code))\n if code_depth > depth:\n if depth >= 0:\n stack.append(parent)\n parent = parent if sibling is None else sibling\n depth = code_depth\n sibling = None\n elif code_depth < depth:\n pop_count = depth - code_depth\n if pop_count > 1:\n for _ in range(pop_count - 1):\n sibling = stack.pop()\n else:\n sibling = parent\n parent = stack.pop()\n depth = code_depth\n twidget = QTreeWidgetItem(parent, after=sibling)\n twidget.setExpanded(True)\n label = hierarchy.label(code)\n label_node = hierarchy.nodes[label]\n # TODO REMOVE\n #twidget.setText(0, self.state.colormap.label_names[label])\n twidget.setText(0, label_node.name)\n twidget.setData(0, Qt.UserRole, label)\n pixmap = QPixmap(24, 24)\n pixmap.fill(QColor.fromRgb(*colormap[label]))\n icon = QIcon(pixmap)\n twidget.setIcon(0, icon)\n self.label_items[label] = twidget\n if label in used_labels or any(map(partial(hierarchy.is_ancestor_of, label), used_labels)):\n twidget.setFlags(Qt.ItemIsEnabled | Qt.ItemIsSelectable)\n else:\n twidget.setFlags(Qt.ItemIsEnabled)\n twidget.setForeground(0, QBrush(QColor.fromRgb(120, 120, 120)))\n sibling = twidget\n self.ui.labelTree.addTopLevelItem(stack[2])\n self.ui.labelTree.doubleClicked.connect(tree_item_double_click_handler(self.ui.labelTree))\n\n def _populate_measurement_comps_tree(self):\n prop_comps: typing.List[PropertyComputation] = self.comps_model.region_comps\n self.ui.measurementTree.clear()\n group_items: typing.Dict[str, QTreeWidgetItem] = {}\n\n for prop_comp in prop_comps:\n if prop_comp.group not in group_items:\n _item = QTreeWidgetItem(self.ui.measurementTree)\n _item.setText(0, prop_comp.group)\n _item.setFlags(Qt.ItemIsEnabled)\n group_items[prop_comp.group] = _item\n parent = group_items[prop_comp.group]\n # parent = QTreeWidgetItem(self.ui.measurementTree)\n # parent.setText(0, prop_comp.info.name)\n # parent.setData(0, Qt.UserRole, prop_comp.info.key)\n # parent.setFlags(Qt.ItemIsEnabled)\n for prop in prop_comp.computes.values():\n kid = QTreeWidgetItem(parent)\n kid.setText(0, prop.name)\n kid.setToolTip(0, prop.description)\n kid.setData(0, Qt.UserRole, prop_comp.info.key)\n parent.addChild(kid)\n self.ui.measurementTree.expandAll()\n self.ui.measurementTree.doubleClicked.connect(tree_item_double_click_handler(self.ui.measurementTree))\n\n def _populate_assignment_tree(self):\n self.ui.assignmentTree.clear()\n top_level = []\n prop_comps: typing.List[PropertyComputation] = self.comps_model.region_comps\n for prop_comp in prop_comps:\n twidget = QTreeWidgetItem(self.ui.assignmentTree)\n twidget.setText(0, prop_comp.info.name)\n twidget.setData(0, ABS_KEY_ROLE, prop_comp.info.key)\n twidget.setData(0, LEAF_ITEM_ROLE, False)\n twidget.setFlags(Qt.ItemIsEnabled)\n top_level.append(twidget)\n twidget.setHidden(True)\n twidget.setExpanded(True)\n props: typing.List[Info] = list(prop_comp.computes.values())\n for prop in props:\n absolute_key = f'{prop_comp.info.key}.{prop.key}'\n self.assignments[absolute_key] = set()\n kid = QTreeWidgetItem()\n kid.setText(0, prop.name)\n kid.setToolTip(0, prop.description)\n kid.setData(0, ABS_KEY_ROLE, absolute_key)\n kid.setData(0, LEAF_ITEM_ROLE, False)\n kid.setExpanded(True)\n kid.setHidden(True)\n kid.setFlags(Qt.ItemIsEnabled)\n twidget.addChild(kid)\n self.assignment_prop_items[absolute_key] = kid\n self.ui.assignmentTree.addTopLevelItems(top_level)\n self.assignment_items = top_level\n\n def show_dialog(self) -> typing.Dict[str, typing.Set[int]]:\n # self._populate_label_tree()\n self._populate_measurement_comps_tree()\n # self._populate_assignment_tree()\n self._label_tree_model = LabelTreeModel(self.state, LabelTreeMode.Choosing)\n self.ui.labelTree.setModel(self._label_tree_model)\n self.ui.labelTree.expandAll()\n self.ui.labelTree.selectionModel().selectionChanged.connect(self.label_selection_changed)\n if self.exec_() == QDialog.Accepted:\n return self.assignments\n #self.compute_measurements.emit(self.assignments)\n #computations = {}\n #for prop_path, labels in self.assignments.items():\n # dot_splits = prop_path.split('.')\n # prop_name = dot_splits.pop()\n # comp_key = '.'.join(dot_splits)\n # prop_labels = computations.setdefault(comp_key, {})\n # prop_labels[prop_name] = labels\n #for computation_key, prop_labels in computations.items():\n # print(f'{computation_key} will compute {prop_labels}')\n #for i in range(self.state.storage.image_count):\n # photo = self.state.storage.get_photo_by_idx(i)\n # for computation_key, prop_labels in computations.items():\n # computation: PropertyComputation = self.comps_model.computations_dict[computation_key]\n # reg_props = computation(photo, prop_labels)\n # for prop in reg_props:\n # prop.info.key = f'{computation_key}.{prop.info.key}'\n # photo['Labels'].set_region_prop(prop.label, prop)\n else:\n self.assignment_label_items.clear()\n self.assignments.clear()\n self.ui.assignmentTree.clear()\n self.ui.buttonBox.button(QDialogButtonBox.Apply).setEnabled(False)\n return {}\n\n # def assign_measurements_(self):\n # # label_selection: typing.List[QTreeWidgetItem] = self.ui.labelTree.selectedItems()\n # label_selection: typing.List[QModelIndex] = self.ui.labelTree.selectedIndexes()\n # prop_selection: typing.List[QModelIndex] = self.ui.measurementTree.selectedIndexes()\n # # TODO replace hardcoded `Labels`\n # hierarchy = self.state.storage.get_label_hierarchy2('Labels')\n # for prop_idx in prop_selection:\n # if not prop_idx.parent().isValid():\n # continue\n # parent = prop_idx.parent()\n # parent_key = self.ui.measurementTree.model().data(parent, Qt.UserRole)\n # prop_key = self.ui.measurementTree.model().data(prop_idx, Qt.UserRole)\n # absolute_key = f'{parent_key}.{prop_key}'\n # prop_item = self.assignment_prop_items[absolute_key]\n # for lab_index in label_selection:\n # lab_node: Node = lab_index.internalPointer()\n # if (label := lab_node.label) not in self.assignments.setdefault(absolute_key, set()):\n # self.assignments[absolute_key].add(label)\n # twidget = QTreeWidgetItem()\n # #label_node = self.state.label_hierarchy.nodes[label]\n # label_node = hierarchy.nodes[label]\n # # TODO REMOVE\n # #twidget.setText(0, self.state.colormap.label_names[label])\n # twidget.setText(0, label_node.name)\n # twidget.setIcon(0, self.label_items[label].icon(0))\n # twidget.setData(0, ABS_KEY_ROLE, f'{absolute_key}')\n # twidget.setData(0, PARENT_KEY_ROLE, parent_key)\n # twidget.setData(0, PROP_KEY_ROLE, prop_key)\n # twidget.setData(0, LABEL_ROLE, label)\n # twidget.setData(0, LEAF_ITEM_ROLE, True)\n # prop_item.addChild(twidget)\n # prop_item.setExpanded(True)\n # parent_item = self.assignment_items[parent.row()]\n # parent_item.setHidden(False)\n # prop_item.setHidden(False)\n # for prop_item in self.assignment_prop_items.values():\n # prop_item.setHidden(prop_item.childCount() == 0)\n #\n # self.ui.buttonBox.button(QDialogButtonBox.Apply).setEnabled(len(self.assignments) > 0)\n\n def assign_measurements(self):\n # to each label in `label_selection` assigns all props in `prop_selection` and stores the assignments\n # in `self.assignments` and also in the QTreeView self.ui.measurementTree\n\n label_selection: typing.List[QModelIndex] = self.ui.labelTree.selectedIndexes()\n prop_selection: typing.List[QModelIndex] = self.ui.measurementTree.selectedIndexes()\n # TODO replace hardcoded `Labels`\n hierarchy = self.state.storage.get_label_hierarchy2('Labels')\n\n for label_index in label_selection:\n if not label_index.isValid():\n continue\n label_node: Node = label_index.internalPointer()\n if label_node.label not in self.assignment_label_items:\n label_item = QTreeWidgetItem()\n label_item.setText(0, label_node.name)\n pixmap = QPixmap(32, 32)\n pixmap.fill(QColor(*label_node.color))\n label_item.setIcon(0, QIcon(pixmap))\n label_item.setData(0, LABEL_ROLE, label_node.label)\n label_item.setData(0, LEAF_ITEM_ROLE, False)\n self.ui.assignmentTree.addTopLevelItem(label_item)\n self.assignment_label_items[label_node.label] = label_item\n label_tree_item: QTreeWidgetItem = self.assignment_label_items[label_node.label]\n for prop_idx in prop_selection:\n prop_parent = prop_idx.parent() # The Plugin index that is the parent of `prop_idx`\n parent_key = self.ui.measurementTree.model().data(prop_parent, Qt.UserRole) # unique key of the parent\n prop_key = self.ui.measurementTree.model().data(prop_idx, Qt.UserRole)\n # absolute_key = f'{parent_key}.{prop_key}'\n absolute_key = prop_key\n # if absolute_key not in self.assignments.setdefault(label_node.label, set()):\n computation = self.comps_model.computations_dict[prop_key]\n if prop_key not in self.param_widgets_for_props and len(computation.user_params) > 0:\n widget = create_params_widget(computation.user_params, self.state)\n binding = UserParamWidgetBinding(self.state)\n binding.bind(computation.user_params, widget)\n group = QGroupBox()\n group.setTitle(computation.info.name)\n layout = QVBoxLayout()\n layout.addWidget(widget)\n group.setLayout(layout)\n self.param_widgets_for_props[prop_key] = group\n self.param_bindings_for_props[prop_key] = binding\n self.settings_layout.addWidget(group)\n if label_node.label not in self.assignments.setdefault(absolute_key, set()):\n # self.assignments[label_node.label].add(absolute_key)\n self.assignments[absolute_key].add(label_node.label)\n twidget = QTreeWidgetItem()\n # twidget.setText(0, self.comps_model.computations_dict[parent_key].computes[prop_key].name)\n twidget.setText(0, self.comps_model.computations_dict[prop_key].info.name)\n twidget.setData(0, ABS_KEY_ROLE, f'{absolute_key}')\n # twidget.setData(0, PARENT_KEY_ROLE, parent_key)\n # twidget.setData(0, PROP_KEY_ROLE, prop_key)\n twidget.setData(0, LABEL_ROLE, label_node.label)\n twidget.setData(0, LEAF_ITEM_ROLE, True)\n label_tree_item.addChild(twidget)\n label_tree_item.setExpanded(True)\n label_tree_item.setHidden(label_tree_item.childCount() == 0)\n\n self.ui.buttonBox.button(QDialogButtonBox.Apply).setEnabled(len(self.assignments) > 0)\n\n def label_selection_changed(self, sel, des):\n self.enable_assign_button()\n\n def measurement_selection_changed(self):\n self.enable_assign_button()\n\n def assignment_selection_changed(self):\n self.ui.btnAssignmentRemove.setEnabled(len(self.ui.assignmentTree.selectedIndexes()) > 0)\n\n def enable_assign_button(self):\n self.ui.btnAssign.setEnabled(len(self.ui.labelTree.selectedIndexes()) > 0 and\n len(self.ui.measurementTree.selectedIndexes()) > 0)\n\n def remove_assignments(self):\n items: typing.List[QTreeWidgetItem] = self.ui.assignmentTree.selectedItems()\n deleted: typing.Set[str] = set()\n\n leaves: typing.List[QTreeWidgetItem] = [item for item in items if item.data(0, LEAF_ITEM_ROLE)]\n nodes: typing.List[QTreeWidgetItem] = [item for item in items if not item.data(0, LEAF_ITEM_ROLE)]\n\n for leaf in leaves:\n key = leaf.data(0, ABS_KEY_ROLE)\n leaf.parent().removeChild(leaf)\n self.assignments[key].remove(leaf.data(0, LABEL_ROLE))\n if len(self.assignments[key]) == 0:\n del self.assignments[key]\n if key in self.param_widgets_for_props:\n widget = self.param_widgets_for_props[key]\n self.settings_layout.removeWidget(widget)\n widget.deleteLater()\n del self.param_widgets_for_props[key]\n del self.param_bindings_for_props[key]\n # del self.param_settings_for_props[key]\n self.ui.assignmentTree.removeItemWidget(leaf, 0)\n\n for node in self.assignment_label_items.values():\n if node.childCount() == 0:\n node.setHidden(True)\n for node in self.assignment_items:\n node.setHidden(all([node.child(i).isHidden() for i in range(node.childCount())]))\n\n print(self.assignments)\n self.ui.buttonBox.button(QDialogButtonBox.Apply).setEnabled(len(self.assignments) > 0)\n self.ui.assignmentTree.update()\n\n def deselect_ancestors_of_leaves(self, selected: QItemSelection, deselected: QItemSelection):\n for index in deselected.indexes():\n if index.child(0, 0).isValid():\n continue\n parent = index.parent()\n while parent.isValid():\n self.ui.assignmentTree.selectionModel().select(parent, QItemSelectionModel.Deselect)\n parent = parent.parent()\n\n def _setup_demo_color_tolerance_dialog(self):\n self._color_tolerance_dialog = ColorToleranceDialog(self.state)\n self._color_tolerance_dialog.hide()\n\n def demo_select_color_and_tolerance(self):\n self._color_tolerance_dialog.get_color_and_tolerances()\n\n\ndef tree_item_double_click_handler(tree_widget: QTreeWidget):\n def select_subtree(index: QModelIndex):\n print(id(tree_widget))\n if not index.child(0, 0).isValid():\n return\n stack = [index]\n while len(stack) > 0:\n idx = stack.pop()\n if idx.isValid():\n curr_idx = idx.child(0, 0)\n child_idx = 0\n indexes_to_select = []\n while curr_idx.isValid():\n indexes_to_select.append(curr_idx)\n stack.append(curr_idx)\n child_idx += 1\n curr_idx = idx.child(child_idx, 0)\n if len(indexes_to_select) > 0:\n tree_widget.selectionModel().select(QItemSelection(indexes_to_select[0], indexes_to_select[-1]),\n QItemSelectionModel.Select)\n return select_subtree\n\n\ndef visit_subtree(root: QTreeWidgetItem):\n stack = [root]\n while len(stack) > 0:\n item = stack.pop()\n for i in range(len(item.childCount())):\n stack.append(item.child(i))\n yield item\n yield None\n","repo_name":"mrazr/maphis","sub_path":"arthropod_describer/measurements_viewer/measurement_assign_dialog.py","file_name":"measurement_assign_dialog.py","file_ext":"py","file_size_in_byte":21359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"}
+{"seq_id":"74354085581","text":"import os\nimport uuid\nfrom flask import Flask\n\n\napp = Flask(__name__)\nmy_uuid = str(uuid.uuid1())\nBLUE = \"#0099FF\"\nGREEN = \"#33CC33\"\n\nCOLOR = BLUE\ncounter = 0 \n\n\n@app.route('/')\ndef hello():\n global counter\n counter += 1\n if counter %2 == 0:\n COLOR = GREEN\n else:\n COLOR = BLUE\n \n return \"\"\"\n \n \n\n Hi, I'm GUID:
\n {}\n \n
\n \n Page Hit Count {}\n\n
\n\n \n \n \"\"\".format(COLOR,my_uuid,counter)\n\nif __name__ == \"__main__\":\n\tapp.run(debug=True,host='0.0.0.0', port=int(os.getenv('PORT', '5000')))\n","repo_name":"davlloyd/devops-bootcamp-anz-python","sub_path":"hello-python/hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"35873265426","text":"import csv\n\nemployees = [\n ['employee', 'salary'],\n ['John Smith', '2500'],\n ['Jenny Scoot', '7500'],\n ['Kate Noris', '10000'],\n]\n\nwith open('employees.csv', 'w', newline='') as csv_file:\n csv_writer = csv.writer(csv_file)\n csv_writer.writerow(employees[0])\n for emp in employees[1:]:\n # if int(emp[1]) > 3000:\n csv_writer.writerow(emp)\n # csv_writer.writerows(employees)\n","repo_name":"Dggz/pybook","sub_path":"sda/intermediate/csving.py","file_name":"csving.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"18378823596","text":"\nclass User:\n # class attributes get defined in the class \n bank_name = \"First National Dojo\"\n # now our method has 2 parameters!\n def __init__(self, name, email_address):\n # we assign them accordingly\n self.name = name\n self.email = email_address\n # the account balance is set to $0\n self.account_balance = 0\n\n def make_deposit(self, amount):\t# takes an argument that is the amount of the deposit\n self.account_balance += amount\t# the specific user's account increases by the amount of the value received\n\n def make_withdrawal(self, amount):\n self.account_balance -= amount\n \n def make_transfer(self, amount, name):\n self.account_balance -= amount\n name.account_balance += amount\n\n def display_user_balance(self):\n print(self.name, self.account_balance)\n\nDevin = User(\"Devin Dale\", \"Devin@DDale.com\")\nJane = User(\"Jane Hylton\", \"Jane@python.com\")\nBrian = User(\"Brian Dale\", \"Brian@python.com\")\n\n\n\nDevin.make_deposit(100)\nDevin.make_deposit(200)\nDevin.make_deposit(50)\nDevin.make_withdrawal(100)\nDevin.make_transfer(20, Jane) # Makes transfer if amount to selected user.\nDevin.display_user_balance()\n\nJane.make_deposit(500)\nJane.make_deposit(200)\nJane.make_withdrawal(100)\nJane.make_withdrawal(25)\nJane.display_user_balance()\n\nBrian.make_deposit(600)\nBrian.make_withdrawal(200)\nBrian.make_withdrawal(50)\nBrian.make_withdrawal(100)\nBrian.display_user_balance()\n\nclass BankAccount:\n def __init__(self, balance = 0, int_rate = .01): \n # default_int_rate = .01\n # default_balance = 0\n # self.int_rate = .01\n # self.balance = balance if balance is None else default_balance\n self.int_rate = int_rate\n self.balance = balance\n def deposit(self, amount):\n self.balance += amount\n return self\n def withdraw(self, amount):\n if self.balance > amount:\n self.balance -= amount\n return self\n else:\n print(\"Insufficient funds: Charging a $5 fee\")\n self.balance -= 5\n return self\n def display_account_info(self):\n print(\"Balance: $\" + str(self.balance) + \" Interest Rate: %\" + str(self.int_rate*100))\n return self\n def yield_interest(self):\n self.balance = (self.balance * self.int_rate) + self.balance\n return self\n def print_all_account_info(self, balance, int_rate):\n print(self.name, self.account_balance, self.balance, self.int_rate)\n\naccount1 = BankAccount(100)\naccount2 = BankAccount()\n\naccount1.display_account_info()\naccount2.display_account_info()\n\naccount1.deposit(100).deposit(200).deposit(600).withdraw(400).yield_interest().display_account_info()\naccount2.deposit(200).deposit(400).withdraw(200).withdraw(300).withdraw(100).withdraw(100).yield_interest().display_account_info()\naccount1.print_all_account_info()","repo_name":"brianpdale/Bootcamp---Public","sub_path":"Python/User.py","file_name":"User.py","file_ext":"py","file_size_in_byte":2881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"3847188129","text":"import random\nimport time\nimport os\n\nrandom.seed()\n\nheight = int(input(\"Height = \"))\nwidth = int(input(\"Width = \"))\nstatus = []\n\nfor i in range(0, height):\n status.append(-1)\nwhile(1):\n status[0] = random.randrange(width)\n for k in status:\n if k != -1:\n print(\" \" * k + \"x\")\n time.sleep(0.3)\n os.system('cls')\n for k in range(height - 1, 0, -1):\n status[k] = status[k - 1]","repo_name":"Varil426/AGH-Introduction-to-CS","sub_path":"RainV2.py","file_name":"RainV2.py","file_ext":"py","file_size_in_byte":415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"10638074294","text":"# Задайте последовательность чисел. Напишите программу,\n# которая выведет список неповторяющихся элементов исходной последовательности.\n\nimport random\n\ndef fill_number_list(n=10, min=1, max=10) -> list:\n number_list = [random.randint(min, max)]\n for i in range (1, n):\n number_list.append(random.randint(min, max)) \n return number_list\n\ndef main():\n source_list = fill_number_list()\n print(source_list)\n unique_numbers = []\n for i in source_list:\n if source_list.count(i) == 1:\n unique_numbers.append(i)\n print(unique_numbers)\n\nif __name__ == '__main__':\n main()\n#s=input()\n#print(s.isdigit.count())\n\n# Второй вариант\n# def row_without_repeatitions(row: list) -> list:\n# sieve = {}\n# clean_row = []\n# for i in row:\n# if i in sieve:\n# sieve[i] += 1\n# else:\n# sieve.setdefault(i, 0)\n# for key, value in sieve.items():\n# if not value:\n# clean_row.append(key)\n\n# return clean_row\n\n\n# if __name__ == '__main__':\n# row = [random.randint(1, 8) for _ in range(10)]\n# print(row)\n# print(row_without_repeatitions(row))\n","repo_name":"FShurik73/Homework-python","sub_path":"Homework4/Task3.py","file_name":"Task3.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"71946763664","text":"from django.db import models\nfrom league.models import Stadium, League\n#reusable apps\nfrom reusable.constants import *\n\n\nclass Category(models.Model):\n name = models.CharField(max_length=50, **NULL)\n\n class Meta:\n verbose_name = 'Categoria'\n verbose_name_plural = 'Categorias'\n \n def __str__(self):\n return self.name.title()\n\n\nclass Team(models.Model):\n name = models.CharField(max_length=100, **REQUIRED)\n fb_id = models.CharField(max_length=50, **NULL)\n picture = models.ImageField(\n upload_to='teams/picture/',\n max_length=1000,\n blank=True, \n null=True\n )\n stadium_team = models.ForeignKey(\n Stadium,\n related_name='equipos_stadium',\n **NULL,\n on_delete=models.CASCADE\n )\n league_team = models.ForeignKey(\n League,\n related_name='equipos_league',\n **NULL,\n on_delete=models.CASCADE\n )\n category_team = models.ForeignKey(\n Category,\n related_name='equipos_categories',\n **NULL,\n on_delete=models.CASCADE\n )\n\n class Meta:\n verbose_name = 'Equipo'\n verbose_name_plural = 'Equipos'\n \n def __str__(self):\n return self.name.title()\n\n","repo_name":"IvanMSP/soccer-league","sub_path":"apps/league/models/teams.py","file_name":"teams.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"1656981973","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nimport sys\n\n# OpenMM Imports\nimport simtk.openmm as mm\nimport simtk.unit as su\nimport simtk.openmm.app as app\n\nimport simtk.openmm.app.charmmpsffile as om_psf\nimport simtk.openmm.app.charmmparameterset as om_paramset\n\n# ParmEd Imports\nfrom parmed.charmm import CharmmPsfFile, CharmmCrdFile, CharmmParameterSet\nfrom parmed.openmm.reporters import StateDataReporter\nfrom parmed import unit as u\n\n\n# Load CHARMM parameter, psf and pdb files from the Solution Builder\ndrive_path = ''\nom_params = om_paramset.CharmmParameterSet(drive_path + 'top_all36_prot.rtf', drive_path + 'par_all36m_prot.prm',\n drive_path + 'top_all36_cgenff.rtf', drive_path + 'par_all36_cgenff.prm',\n drive_path + 'ben.rtf', drive_path + 'ben.prm',\n drive_path + 'toppar_water_ions.str')\nom_solv = om_psf.CharmmPsfFile(drive_path + 'step3_pbcsetup.psf')\nom_crds = app.PDBFile(drive_path + 'step3_pbcsetup.pdb')\n\n\n# Fetching the corners of our solvant box for the PBCs.\ncoords = om_crds.positions\nmin_crds = [coords[0][0], coords[0][1], coords[0][2]]\nmax_crds = [coords[0][0], coords[0][1], coords[0][2]]\n\nfor coord in coords:\n min_crds[0] = min(min_crds[0], coord[0])\n min_crds[1] = min(min_crds[1], coord[1])\n min_crds[2] = min(min_crds[2], coord[2])\n max_crds[0] = max(max_crds[0], coord[0])\n max_crds[1] = max(max_crds[1], coord[1])\n max_crds[2] = max(max_crds[2], coord[2])\n\nom_solv.setBox(max_crds[0]-min_crds[0], max_crds[1]-min_crds[1], max_crds[2]-min_crds[2])\nprint(\"Sidelengths of solvant box: \", om_solv.boxLengths)\n\n\n# Simulation parameters\nstep_size = 2.0* su.femtosecond\nsim_len = 50.0 * su.nanosecond\nsteps = round(sim_len / step_size)\n\nreport_time = 25.0*su.picosecond\nreport_steps = round(report_time / step_size)\nprint(f\"Initialized| step_size = {step_size}, sim_len = {sim_len}, Num steps = {steps}, retport_time = {report_time}\")\n\n\n# Create our OpenMM system with the CHARMM parameters\nsystem = om_solv.createSystem(om_params, nonbondedMethod=app.PME,\n nonbondedCutoff=1 * su.nanometer,\n constraints=app.HBonds)\n\n\n# Create the integrator for the NVT ensemble\nheat_bath = 303.15*su.kelvin\nintegrator = mm.LangevinIntegrator(heat_bath, # Temperature of heat bath\n 1.0/su.picosecond, # Friction coefficient\n step_size) # Time step\n\n\n# Change to CUDA platform\nplatform = mm.openmm.Platform.getPlatformByName('CUDA')\nproperties = {'CudaDeviceIndex': '1'} # you can add other things like the precision here\n\n# Create our Simulation object with our system and chosen integrator\nsim = app.Simulation(om_solv.topology, system, integrator, platform, properties)\n\n\n# Set the positions from our PDB file\nsim.context.setPositions(om_crds.positions)\n\n\n# Minimize Energy\nprint(\"Minimizing Energy\")\nsim.minimizeEnergy()\n\n\n# Reporters\nprint(\"Appending Reporters\")\nsim.reporters.append(StateDataReporter(sys.stdout, round(steps/10), step=True,\n potentialEnergy=False, kineticEnergy=False,\n temperature=True, volume=True, density=False))\n\n# Write out to DCD file\nsim.reporters.append(app.DCDReporter('tryps_ben_solv.dcd', report_steps))\n\n\n# Run (100ns)\nsim.step(steps)\n","repo_name":"SimonParschat/final_project","sub_path":"finalP_test.py","file_name":"finalP_test.py","file_ext":"py","file_size_in_byte":3432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"31719337982","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n'''A bot for my personal use.'''\nfrom __future__ import annotations\n\nimport contextlib\nfrom datetime import datetime\nimport functools\nfrom typing import Any, Callable, Coroutine\n\nimport aiohttp\nimport asqlite\nimport discord\nfrom discord.ext import commands\n\nimport config\n\nclass Ctx(commands.Context):\n '''A custom command context.'''\n bot: Bot\n\n def __init__(self, **kwargs) -> None:\n super().__init__(**kwargs)\n\n async def react(self, emoji):\n '''Adds a reaction to this message.'''\n with contextlib.suppress(discord.HTTPException):\n await self.message.add_reaction(emoji)\n\n async def rocket(self) -> None:\n '''Reacts with a rocket emoji.'''\n await self.react(\"\\N{ROCKET}\")\n\n async def boom(self, message = None, **kwargs) -> None:\n '''Reacts with a boom emoji, and sends an optional error message.'''\n await self.react(\"\\N{COLLISION SYMBOL}\")\n await self.send(message, **kwargs)\n \n async def log(self, **kwargs):\n await self.bot.log(self, **kwargs)\n\n @property\n def session(self):\n return self.bot.session\n \n @property\n def conn(self):\n return self.bot.db\n\n def cursor(self):\n return self.bot.cursor()\n\n @property\n def color(self):\n return self.bot.color\n\nclass Bot(commands.Bot):\n '''Custom bot class with convenience methods and attributes.'''\n def __init__(self, prefixes: list[str], *, color: discord.Color=discord.Color.default(), db: str, webhook_id: int, secret_password: str, **kwargs):\n self.exit_code = 0\n self.color = color\n self.webhook_id = webhook_id\n self.secret_password = secret_password\n self.cog_names = config.cogs\n # late initialization, forced type-ignore\n self.start_time: datetime = None # type: ignore\n self.log: Callable[..., Coroutine[Any]] = None # type: ignore\n self.log_raw: Callable[..., Coroutine[Any]] = None # type: ignore\n self.db: asqlite.Connection = None # type: ignore\n self.session: aiohttp.ClientSession = None # type: ignore\n\n super().__init__(command_prefix=commands.when_mentioned_or(*prefixes), **kwargs)\n for cog in config.cogs:\n try:\n self.load_extension(cog)\n except Exception as exc:\n print(f\"Could not load extension {cog} due to {exc.__class__.__name__}: {exc}\")\n\n # Connection acquisition must be asynchronous\n self.loop.create_task(self.connect_sessions(db=db))\n \n async def close(self):\n print(\"Shutting down...\")\n await self.session.close()\n await self.db.close()\n await super().close()\n\n async def connect_sessions(self, *, db: str):\n self.db = await asqlite.connect(db) # type: ignore\n self.session = aiohttp.ClientSession()\n \n await self.wait_until_ready()\n self.start_time = datetime.utcnow()\n \n self.dispatch(\"initialized\")\n print(f\"Logged in as {self.user} (ID: {self.user.id})\")\n print(\"Invite:\", discord.utils.oauth_url(str(self.user.id)))\n\n def cursor(self):\n '''Obtains a cursor once awaited.'''\n return self.db.cursor()\n\n # Hook for custom context, as well as reply invokes\n async def get_context(self, message: discord.Message, *, cls=commands.Context):\n if message.reference is not None:\n resolved = message.reference.resolved\n if isinstance(resolved, discord.Message):\n resolved.content = f\"{message.content} {resolved.content}\"\n return await super().get_context(resolved, cls=Ctx)\n return await super().get_context(message, cls=Ctx)\n\nbot = Bot(\n [\"rocket \", \"Rocket \"], # auto-capitalization aware\n color=discord.Color(0xe0e0f0),\n db=config.db,\n webhook_id=config.webhook_id,\n secret_password = config.secret_password,\n allowed_mentions=discord.AllowedMentions(everyone=False),\n intents=discord.Intents(\n guilds=True,\n messages=True,\n reactions=True,\n )\n)\n\nbot.run(config.token)\n","repo_name":"RocketRace/rocketbot","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":4146,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"18469914998","text":"################################################################################\n# Vehicle Assignments\n################################################################################\n# This is still a pretty terrible solution, but I haven't been able to figure\n# out a better system, so here we are...\n\ndef assign(emp_num, eno, zone, w_date, config):\n\n # Supervisors\n S1 = config['Supervisors']['S1']\n S2 = config['Supervisors']['S2']\n\n # Police Vehicles\n V1 = config['PO_Vehicles']['V1']\n V2 = config['PO_Vehicles']['V2']\n V3 = config['PO_Vehicles']['V3']\n V4 = config['PO_Vehicles']['V4']\n V5 = config['PO_Vehicles']['V5']\n V6 = config['PO_Vehicles']['V6']\n V7 = config['PO_Vehicles']['V7']\n V8 = config['PO_Vehicles']['V8']\n\n # Bicycles\n B1 = config['Bicycles']['B1']\n\n # CSO Vehicles\n\n C1 = config['CSO_Vehicles']['C1']\n C2 = config['CSO_Vehicles']['C2']\n C3 = config['CSO_Vehicles']['C3']\n C4 = config['CSO_Vehicles']['C4']\n C5 = config['CSO_Vehicles']['C5']\n C6 = config['CSO_Vehicles']['C6']\n C7 = config['CSO_Vehicles']['C7']\n C8 = config['CSO_Vehicles']['C8']\n\n XX = C7\n\n # This 'array' assigns a vehicle (above) to an officer (emp_num), which is\n # according to the order in which an employee appears in the po_num and\n # cso_num lists. i.e. if '507' is the fisrt ENO in the po_num list, then\n # then emp_num[0] is '507'.\n\n patrol = {emp_num[0]: ['', '', '', B1, B1, B1, B1], # Greeks\n emp_num[1]: ['', '', '', B1, B1, B1, B1],\n\n emp_num[2]: ['', '', '', S1, S1, S1, S1], # Lieutenant\n\n emp_num[3]: ['', '', S1, S2, S2, S2, ''], # Sergeants\n emp_num[4]: [S1, S1, S2, '', '', '', S2],\n\n emp_num[5]: ['', '', '', V4, V1, V1, V1], # Officers\n emp_num[6]: [V2, '', '', '', V4, V2, V2],\n emp_num[7]: [V3, V3, '', '', '', V4, V3],\n emp_num[8]: [V5, V5, '', '', '', V5, V5],\n emp_num[9]: [V4, V4, V4, '', '', '', V4],\n emp_num[10]: [V1, V1, V1, V1, '', '', ''],\n emp_num[11]: ['', V2, V2, V2, V2, '', ''],\n emp_num[12]: ['', '', V3, V3, V3, V3, ''],\n\n emp_num[13]: ['', '', '', C1, C1, C1, C1], # CSOs\n emp_num[14]: [C2, '', '', '', C2, C2, C2],\n emp_num[15]: [C3, C3, '', '', '', C3, C3],\n emp_num[16]: [C4, C4, C4, '', '', '', C4],\n emp_num[17]: [C5, C5, C5, '', '', '', C5], #Stewart\n emp_num[18]: [C1, C1, C1, XX, '', '', ''], #Dixon\n emp_num[19]: ['', C2, C2, C2, XX, '', ''],\n emp_num[20]: ['', '', C3, C3, C3, XX, ''],\n emp_num[21]: ['', '', XX, C4, C4, C4, ''],\n emp_num[22]: ['', '', '', C5, C5, C5, XX]\n }\n\n v = patrol[eno_lookup(emp_num, eno)][weekday_num(w_date)]\n\n if zone == 20:\n v = S1\n elif zone == 66:\n v = C6\n\n# The try:except block catches entries on the schedule that are not 'zones'\n# e.g. \"X\" is not a number so it will throw an error.\n try:\n if v == '' and int(str(zone)[:2]) < 30:\n v = V6\n elif v == '' and int(str(zone)[:2]) > 30:\n v = C7\n except ValueError:\n v = ''\n\n return v\n\n# Fix for isoweekday, which returns Sunday as '7'\n# This will now return Sunday = 0, Saturday = 6\ndef weekday_num(day_num):\n if day_num.isoweekday() == 7:\n return 0\n else:\n return day_num.isoweekday()\n\n# Finds the order for employee numbers in the emp_num list\n# In other words if '507' is the first employee number on the list\n# '507' is emp_num[0]\ndef eno_lookup(emp_num, eno):\n return emp_num[emp_num.index(str(eno))]\n","repo_name":"pconwell/excel-sharepoint-schedule","sub_path":"vehicles.py","file_name":"vehicles.py","file_ext":"py","file_size_in_byte":3699,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"17244482810","text":"# %%\nimport re\nfrom collections import defaultdict, Counter\nfrom operator import itemgetter\n\n# %%\n\n\ndef read_input(FileName):\n result = []\n\n with open('6/' + FileName) as fp:\n for _, line in enumerate(fp):\n t = re.findall(r'(\\d+), (\\d+)', line)[0]\n result.append((int(t[0]), int(t[1])))\n\n return result\n\n\ndef Manhattan_Distance(a, b):\n return abs(a[0] - b[0]) + abs(a[1] - b[1])\n\n\n# %%\ninput = read_input('input.txt')\n\nmin_x = min([i[0] for i in input])\nmax_x = max([i[0] for i in input])\nmin_y = min([i[1] for i in input])\nmax_y = max([i[1] for i in input])\n\nmax_dist = 10000\n\ngrid = defaultdict(int)\n\nfor x in range(min_x, max_x + 1):\n for y in range(min_y, max_y + 1):\n key = (x, y)\n total_dist = 0\n\n for coord in input:\n total_dist += Manhattan_Distance(coord, (x, y))\n\n if total_dist < max_dist:\n grid[key] = total_dist\n\n\n# print(*grid.items(), sep='\\n')\n\nsolution = len(grid)\nprint('solution: ', solution)\n","repo_name":"chhenning/python_tutorial","sub_path":"Advent Of Code/2018/6/solution_2.py","file_name":"solution_2.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"39160141607","text":"import matplotlib.pyplot as plt\nimport tensorflow as tf\nfrom src.utils.preprocessing import get_imagenet_label\nfrom src.models.pre_trained import Model\nfrom skimage.transform import resize\n\nclass ImageHandler():\n def __init__(self):\n self.image_list=[]\n self.description_list=[]\n self.label_list=[]\n self.confidence_list = []\n self.pertubation_list = []\n\n def display_images(self,model_name, adv_x, image, eps, perturbations, description, decode_predictions,quantisation=False):\n\n \"\"\"\n Helper function to display the images along with their corresponding adversarial attack pertubations\n\n :param get_imagenet_label: get the imagenet label of the prediction made\n :param interpreter: loading the quantised model\n\n return: plot showing the pertubation as well as the image\n \"\"\"\n initialize_model=Model(model_name)\n pre_trained_model = initialize_model.model\n if quantisation == '32 bit':\n _, label, confidence = get_imagenet_label(pre_trained_model.predict(adv_x), decode_predictions)\n elif quantisation == '16 bit':\n _, label, confidence = initialize_model.predict(adv_x, True,quantisation)\n elif quantisation == '8 bit':\n _, label, confidence = initialize_model.predict(adv_x, True, quantisation)\n self.image_list.append(adv_x[0])\n self.description_list.append(description)\n self.label_list.append(label)\n self.confidence_list.append(confidence)\n self.pertubation_list.append(perturbations)\n return self.image_list, self.description_list, self.label_list,self.confidence_list,self.pertubation_list\n\n def visualize(self, image, image_class, class_confidence):\n\n for img, lbl, conf in zip(image, image_class, class_confidence):\n img = tf.image.flip_up_down(img)\n img=tf.reshape(img, (224,224,3))\n fig = plt.figure(figsize=(15, 16))\n ax2 = fig.add_subplot(4, 4, 2)\n ax2.imshow(img * 0.5 + 0.5, origin='lower',\n extent=[-4, 4, -1, 1], aspect=4)\n plt.title('32 bit -> {} : {:.2f}% Confidence'.format(lbl, conf*100,))\n\n \"\"\"#image,image_class,class_confidence= self.model.predict(r_image,False)\n #image1,image_class1,class_confidence1= self.model.predict(r_image,True)\n \n \n f = plt.figure(figsize=(20,21))\n f.add_subplot(1,2, 1)\n plt.imshow(image[0] * 0.5 + 0.5)\n plt.title('32 Bit-> {} : {:.2f}% Confidence'.format(image_class, class_confidence*100))\n #f.add_subplot(1,2, 2)\n #plt.title('{} -> {} : {:.2f}% Confidence'.format(self.quant_type,image_class1, class_confidence1*100))\n #plt.imshow(image1[0] * 0.5 + 0.5)\n\n # set the spacing between subplots\n #plt.subplots_adjust(left=0.1,\n #bottom=0.1, \n #right=0.9, \n #top=0.9, \n #wspace=0.4, \n #hspace=0.4)\"\"\"\n\n #plt.show(astype('uint8'))\n\n\n def image_drawer(self,image_32,description_32,label,confidence_32,pertubations_32,\n image_16, description_16, label_16, confidence_16,\n image_8, description_8, label_8, confidence_8): \n\n zipped_results = zip(image_32, description_32, label, confidence_32, pertubations_32,\n image_16, description_16, label_16, confidence_16,\n image_8, description_8, label_8, confidence_8)\n for im_32, des_32, lbl_32, conf_32, pertbs_32, im_16, des_16, lbl_16, conf_16, im_8, des_8, lbl_8, conf_8 in zipped_results:\n im_32 = tf.image.flip_up_down(im_32)\n im_16 = tf.image.flip_up_down(im_16)\n im_8 = tf.image.flip_up_down(im_8)\n \n fig =plt.figure(figsize=(15,16))\n ax1 = fig.add_subplot(4,4,1)\n ax1.imshow(tf.reshape(pertbs_32, (224,224,3), name=None), aspect='auto')\n ax2 = fig.add_subplot(4,4,2)\n ax2.imshow(im_32* 0.5 + 0.5,origin='lower', extent=[-4, 4, -1, 1], aspect=4)\n plt.title('32 bit -> {} \\n {} : {:.2f}% Confidence'.format(des_32,\n lbl_32, conf_32*100,))\n \n ax3 = fig.add_subplot(4,4,3)\n ax3.imshow(im_16* 0.5 + 0.5,origin='lower', extent=[-4, 4, -1, 1], aspect=4)\n plt.title('16 bit ->->{} \\n {} : {:.2f}% Confidence'.format(des_16,\n lbl_16, conf_16*100,))\n \n ax4 = fig.add_subplot(4, 4, 4)\n ax4.imshow(im_8 * 0.5 + 0.5, origin='lower',\n extent=[-4, 4, -1, 1], aspect=4)\n plt.title('8 bit ->->{} \\n {} : {:.2f}% Confidence'.format(des_8,\n lbl_8, conf_8*100,))\n \n plt.subplots_adjust(left=0.1,\n bottom=0.1, \n right=0.9, \n top=0.9, \n wspace=0.4, )\n \n\n\n\n \n","repo_name":"YMuskrat/Adversarial-Attacks","sub_path":"src/utils/Plotter.py","file_name":"Plotter.py","file_ext":"py","file_size_in_byte":5181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"18522475058","text":"import requests\nfrom bs4 import BeautifulSoup\n\n\ndependents_list = []\n\n\ndef get_dependents(repo):\n page_num = 3000\n url = 'https://github.com/{}/network/dependents'.format(repo)\n\n for i in range(page_num):\n print(\"GET \" + url)\n r = requests.get(url)\n print(r)\n soup = BeautifulSoup(r.content, \"html.parser\")\n\n dependents_exist = soup.find('h3', {\"data-view-component\": \"true\"})\n if(dependents_exist and dependents_exist.text == \"We haven’t found any dependents for this repository yet.\"):\n return {}\n\n data = [\n \"{}/{}\".format(\n t.find('a', {\"data-repository-hovercards-enabled\": \"\"}).text,\n t.find('a', {\"data-hovercard-type\": \"repository\"}).text\n )\n for t in soup.findAll(\"div\", {\"class\": \"Box-row\"})\n ]\n dependents_list.extend(data)\n\n next_url = soup.find(\n \"div\", {\"class\": \"paginate-container\"})\n next_disabled = soup.find(\n \"button\", {\"disabled\": \"disabled\"})\n if(not next_url or next_disabled):\n return dependents_list\n\n url = next_url.find('a')[\"href\"]\n return dependents_list\n\n\ndef main():\n dependents = get_dependents(\"memgraph/pymgclient\")\n print(len(dependents))\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"memgraph/data-streams","sub_path":"datasets/github/scraper/dependency_graph.py","file_name":"dependency_graph.py","file_ext":"py","file_size_in_byte":1332,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"47"} +{"seq_id":"17289274066","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Nov 7 16:45:06 2019\n\n@author: matthew-bailey\n\"\"\"\n\nfrom typing import Dict, Sequence, NewType, Tuple, Any\n\nimport networkx as nx\nimport numpy as np\nfrom scipy.spatial import Delaunay\nfrom matplotlib.patches import Polygon\nfrom matplotlib.collections import PatchCollection\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as anm\n\nNode = NewType('Node', Any)\nGraph = NewType('Graph', Any)\nCoord = NewType('Coord', np.array)\n\n\ndef calculate_polygon_area(node_list: Sequence[Node],\n coords_dict: Dict[Node, Coord]) -> float:\n \"\"\"\n Calculates the signed area of this polygon,\n using the Shoelace algorithm.\n :param node_list: an ordered list of connected nodes,\n can be clockwise or anticlockwise.\n :param coords_dict: a dictionary, keyed by nodes,\n that has [x, y] coordinates as values.\n :return signed_area: The area of the polygon,\n which is negative if the points are ordered clockwise\n and positive if the points are ordered anticlockwise.\n \"\"\"\n signed_area = 0.0\n for i, node in enumerate(node_list):\n this_coord = coords_dict[node]\n next_node = node_list[(i + 1) % len(node_list)]\n next_coord = coords_dict[next_node]\n signed_area += (this_coord[0] * next_coord[1]\n - this_coord[1] * next_coord[0])\n return 0.5 * signed_area\n\ndef node_list_to_edges(node_list: Sequence[Node],\n is_ring: bool = True):\n \"\"\"\n Takes a list of connected nodes, such that node[i] is connected\n to node[i - 1] and node[i + 1] and turn it into a set of edges.\n This is the opposite function to Shape.to_node_list\n :param node_list: a list of nodes which\n must be a hashable type.\n :param is_ring: is this a linked ring, i.e.\n is node[-1] connected to node[0]\n :return edges: a set of frozensets, each frozenset\n containing two edges.\n \"\"\"\n list_size = len(node_list)\n edges = set()\n\n # If this is a ring, iterate one over the size\n # of the list. If not, make sure to stop\n # before the end.\n if is_ring:\n offset = 0\n else:\n offset = -1\n for i in range(list_size + offset):\n next_index = (i + 1) % list_size\n edges.add(frozenset([node_list[i],\n node_list[next_index]]))\n return frozenset(edges)\n\nclass Shape:\n def __init__(self,\n edges: Sequence[Tuple[Node, Node]],\n coords_dict: Dict[Node, Coord] = None):\n self.edges = edges\n self.coords_dict = coords_dict\n\n def merge(self, other) -> None:\n \"\"\"\n Merges two shapes together, removing their common edges.\n Sets both self and other to the new shape.\n :param other: the shape to merge in to this one.\n \"\"\"\n if self.coords_dict != other.coords_dict:\n raise ValueError(\"Two shapes are not using the same coordinates.\")\n unique_edges = self.edges.symmetric_difference(other.edges)\n new_shape = Shape(unique_edges, coords_dict=self.coords_dict)\n other = new_shape\n self = new_shape\n return new_shape\n\n @property\n def nodes(self):\n nodes = set([node for edge in self.edges\n for node in edge])\n return nodes\n\n def to_node_list(self):\n node_list = [min(self.nodes)]\n seen_nodes = set(node_list)\n while len(node_list) < len(self.edges):\n last_node = node_list[-1]\n # Find the two nodes this is connected to.\n connected_nodes = set()\n for edge in self.edges:\n if last_node in edge:\n connected_nodes = connected_nodes.union(edge)\n connected_nodes = connected_nodes.difference(seen_nodes)\n # Pick the smallest node to move to next, arbitrarily.\n # We'll sort out winding later.\n next_node = min(connected_nodes)\n node_list.append(next_node)\n seen_nodes = set(node_list)\n\n if self.coords_dict is not None:\n signed_area = calculate_polygon_area(node_list, self.coords_dict)\n if signed_area < 0:\n # If the signed area is negative, then the ordering\n # is wrong. That's easily fixed by reversing the list,\n # and then putting the smallest element at the front.\n node_list = list(reversed(node_list))\n node_list = node_list[-1:] + node_list[:-1]\n return node_list\n\n def to_polygon(self):\n \"\"\"\n Turns this shape into a matplotlib polygon object.\n \"\"\"\n node_list = self.to_node_list()\n coord_array = np.empty([len(node_list), 2], dtype=float)\n for i, node in enumerate(node_list):\n coord_array[i, :] = self.coords_dict[node]\n return Polygon(coord_array, closed=True)\n\n def __contains__(self, obj) -> bool:\n \"\"\"\n Override the in / not in magic method, because this shape is\n solely defined by its edges. If an edge is in this shape,\n return True.\n \"\"\"\n return obj in self.edges\n\n def __hash__(self) -> int:\n \"\"\"\n Override the hash magic method, because this shape is\n solely defined by its edges. This means that shapes\n in any rotation or order of edges hash the same.\n \"\"\"\n return hash(self.edges)\n\n def __eq__(self, other) -> bool:\n \"\"\"\n Override the equals magic method, because this shape is\n solely defined by its edges. This means that shapes\n in any rotation or order of edges hash the same.\n \"\"\"\n return self.edges == other.edges\n\n def __str__(self) -> str:\n \"\"\"\n Override the string magic method to make a pretty\n output.\n \"\"\"\n return str(self.to_node_list())\n\n def __len__(self) -> int:\n \"\"\"\n Override the length magic method to return the\n size of the shape\n \"\"\"\n return len(self.edges)\n\n\nclass RingFinder:\n \"\"\"\n A group of subroutines to find rings in a combination\n of a networkx graph and a set of coordinates. The rings\n it identifies correspond to the faces on the polyhedron\n that this graph represents, according to Euler's formula.\n Proceeds by using a Delaunay triangulation which has\n rings well-defined by simplicies and then removes\n edges one-by-one.\n \"\"\"\n def __init__(self,\n graph: Graph,\n coords_dict: Dict[Node, Coord],\n cutoffs=None):\n self.graph: Graph = graph\n self.coords_dict: Dict[Node, Coord] = coords_dict\n \n # Tidying up stage -- remove the long edges,\n # and remove the single coordinate sites.\n if cutoffs is not None:\n self.remove_long_edges(cutoffs)\n self.remove_single_coordinate_sites()\n self.removable_edges = None\n # Now triangulate the graph and do the real heavy lifting.\n self.tri_graph, self.simplices = self.triangulate_graph()\n self.current_shapes = set([Shape(node_list_to_edges(simplex), \n self.coords_dict)\n for simplex in self.simplices]) \n self.identify_rings(max_to_remove=0) \n\n def remove_long_edges(self,\n cutoffs: Sequence[float]):\n \"\"\"\n Remove any edges that are longer than\n a set of cutoffs, useful to make a periodic cell\n aperiodic.\n :param graph: the networkx graph to detect single-coordinate\n nodes in\n :param coords_dict: a dictionary, keyed by nodes,\n with values being the [x, y] coordinates of the nodes, which\n we use to remove long bonds.\n :param cutoffs: an [max_x, max_y] sequence, removing any edges\n with a component longer than max_x or max_y. For the minimum\n image convention, we want these to be half the both length.\n :return graph: a graph minus the edges that are too long. Note\n that this mutates the original graph, so the return value can\n be ignored.\n \"\"\"\n to_remove = set()\n for edge in self.graph.edges():\n pos_a = self.coords_dict[edge[0]]\n pos_b = self.coords_dict[edge[1]]\n distance = np.abs(pos_b - pos_a)\n if distance[0] > cutoffs[0]:\n to_remove.add(edge)\n elif distance[1] > cutoffs[1]:\n to_remove.add(edge)\n self.graph.remove_edges_from(to_remove)\n\n def triangulate_graph(self):\n \"\"\"\n Constructs a Delauney triangulation\n of a set of coordinates, and returns\n it as a networkx graph.\n :param coordinates_dict: a dictionary, with key\n being a node and the value being an [x, y]\n numpy array.\n :return tri_graph: a Delaunay triangulation\n of the original graph.\n :return mapped_simplices: a list of all the\n edges making up triangular simplicies\n \"\"\"\n\n # Turn the coordinate dictionary into\n # an array. The index of a given key\n # corresponds to its position in the\n # sorted list of keys, which is stored\n # in the index_to_key dict.\n coords_array = np.empty([len(self.coords_dict), 2])\n index_to_key = {}\n for i, key in enumerate(sorted(self.coords_dict.keys())):\n if self.coords_dict[key].shape[0] != 2:\n raise RuntimeError(\"Coordinates in the dictionary must be 2D.\")\n index_to_key[i] = key\n coords_array[i, :] = self.coords_dict[key]\n\n tri_graph = nx.Graph()\n delaunay_res = Delaunay(coords_array)\n mapped_simplices = []\n for simplex in delaunay_res.simplices:\n # Convert these indicies to the same ones\n # the master graph uses, to avoid horrors.\n mapped_simplex = [index_to_key[node] for node in simplex]\n mapped_simplices.append(mapped_simplex)\n # Iterate over all the simplex edges and add them to\n # a graph.\n edges = node_list_to_edges(mapped_simplex)\n tri_graph.add_edges_from(edges)\n return tri_graph, mapped_simplices\n\n\n def remove_single_coordinate_sites(self) -> Graph:\n \"\"\"\n Recursively finds all the single coordinate sites,\n and all the sites that would be single coordinate\n if that one were removed, and so on.\n Mutates the input data by deleting entries.\n :param main_graph: the networkx graph to detect single-coordinate\n nodes in\n :param coords_dict: the coordinates of the nodes, which we\n remove to make sure they don't get misused in the Delauney\n triangulation.\n :return graph: a graph minus the single coordinate notes. Note\n that this mutates the original graph, so the return value can\n be ignored.\n \"\"\"\n while True:\n # Find the 0 or 1 coordinate nodes and make a list of them,\n # then remove both their entry in the graph and their\n # coordinate.\n nodes_to_remove = [item[0] for item in self.graph.degree()\n if item[1] < 2]\n if not nodes_to_remove:\n break\n self.graph.remove_nodes_from(nodes_to_remove)\n for node in nodes_to_remove:\n del self.coords_dict[node]\n\n def identify_rings(self,\n max_to_remove:int = None):\n \"\"\"\n Removes the edges from a triangulated graph that do not exist\n in the original graph, identifying rings in the process.\n Start off with a set of simplices as the building blocks\n of rings.\n :param main_graph: the networkx graph to detect cycles in\n :param tri_graph: the Delauney triangulation of main_graph,\n as the same graph type.\n :param simplices: a list of tuples, each of which is three\n node ids representing a triangle.\n :param max_to_remove: the maximum number of edges to remove.\n Useful for making animations, but is None by default.\n \"\"\"\n\n # First we need to check if there are any edges\n # that exist in the main graph that do not exist\n # in the triangulated graph, usually an indication\n # of unphysicality. However, networkx doesn't have\n # consistent ordering of edges, so we need to make it\n # insensitive to (a, b) <-> (b, a) swaps.\n main_edge_set = set([frozenset(edge) for edge in self.graph.edges()])\n tri_edge_set = set([frozenset(edge) for edge in self.tri_graph.edges()])\n\n if not main_edge_set.issubset(tri_edge_set):\n missing_edges = main_edge_set.difference(tri_edge_set)\n raise RuntimeError(\"There are edges in the main graph that do not \" +\n \"exist in the Delauney triangulation: \" +\n f\"{missing_edges}.\")\n\n self.removable_edges = tri_edge_set.difference(main_edge_set)\n if max_to_remove is None:\n max_to_remove = len(self.removable_edges)\n # Each edge that we wish to remove belongs to one or two\n # shapes. Find the shapes it is in, and merge them.\n edges_removed = 1\n edge = self.removable_edges.pop()\n while self.removable_edges:\n edges_removed += 1\n self.remove_one_edge(edge)\n print(edges_removed)\n if edges_removed >= max_to_remove:\n break\n edge = self.removable_edges.pop()\n \n def remove_one_edge(self, edge):\n shapes_with_edge = []\n for shape in self.current_shapes:\n if edge in shape:\n shapes_with_edge.append(shape)\n if len(shapes_with_edge) == 2:\n break\n\n if len(shapes_with_edge) == 1:\n # It's only part of one shape.\n # Scrap it.\n # TODO: this might have to change for periodic.\n self.current_shapes.remove(shapes_with_edge[0])\n return\n \n elif len(shapes_with_edge) == 0:\n # This is a stranded edge. This means\n # something has gone horribly wrong\n # and we should bail out.\n print([str(shape) for shape in self.current_shapes], edge)\n raise ValueError(\"Found an edge associated with no shapes. \" +\n \"Did you remove all single coordinate nodes?\")\n\n new_shape = shapes_with_edge[0].merge(shapes_with_edge[1])\n\n for shape in shapes_with_edge:\n self.current_shapes.remove(shape)\n self.current_shapes.add(new_shape)\n \n @property\n def ring_sizes(self):\n return [len(shape) for shape in self.current_shapes]\n \n def as_polygons(self):\n return [shape.to_polygon() for shape in self.current_shapes]\n\nif __name__ == \"__main__\":\n G: Graph = nx.Graph()\n with open(\"./edges.dat\", \"r\") as fi:\n fi.readline() # Skip header\n for line in fi.readlines():\n x, y = [int(item) for item in line.split(\",\")]\n G.add_edge(x, y)\n\n COORDS_DICT: Dict[Node, Coord] = {}\n with open(\"./coords.dat\", \"r\") as fi:\n fi.readline() # Skip header\n for line in fi.readlines():\n line = line.split(\",\")\n node_id, x, y = int(line[0]), float(line[1]), float(line[2])\n COORDS_DICT[node_id] = np.array([x, y])\n \n ring_finder = RingFinder(G, COORDS_DICT, np.array([20.0, 20.0]))\n\n FIG, AX = plt.subplots()\n FIG.patch.set_visible(False)\n AX.axis('off')\n POLYS = ring_finder.as_polygons()\n SIZES = ring_finder.ring_sizes\n # SIZE_RANGE = max(SIZES) + 1 - min(SIZES)\n SIZE_RANGE = 30 - 4\n THIS_CMAP = plt.cm.get_cmap(\"viridis\")(np.linspace(0, 1, SIZE_RANGE))\n COLOURS = [THIS_CMAP[SIZE - 4] for SIZE in SIZES]\n \n p = PatchCollection(POLYS, alpha=1, linewidth=5,\n linestyle=\"dotted\")\n p.set_color(COLOURS)\n p.set_edgecolor(\"black\")\n AX.add_collection(p)\n AX.set_xlim(0, 155)\n AX.set_ylim(0, 155)\n nx.draw_networkx_edges(ring_finder.graph, ax=AX, pos=COORDS_DICT,\n edge_color=\"black\", zorder=1000, width=5)\n LASTFRAME = 0\n def animate(frame):\n global LASTFRAME\n for i in range(frame - LASTFRAME):\n try:\n edge = ring_finder.removable_edges.pop()\n print(edge, len(ring_finder.removable_edges), i, frame - LASTFRAME)\n ring_finder.remove_one_edge(edge)\n except KeyError:\n break\n LASTFRAME = frame\n patches = ring_finder.as_polygons()\n SIZES = ring_finder.ring_sizes\n COLOURS = [THIS_CMAP[SIZE - 4] for SIZE in SIZES]\n p.set_color(COLOURS)\n p.set_paths(patches)\n p.set_edgecolor(\"red\")\n\n anim = anm.FuncAnimation(FIG, animate,\n frames=range(430),\n interval=1,\n repeat=False)\n Writer = anm.writers['ffmpeg']\n FIG.set_size_inches(8, 8, True)\n writer = Writer(fps=30, metadata=dict(artist='Matt Bailey'), bitrate=-1)\n anim.save(\"animation.mp4\", writer=writer, dpi=100)\n plt.show()\n # nx.draw(G, pos=COORDS_DICT, ax=AX, edge_color=\"red\", width=5)\n\n\n\n","repo_name":"dormrod/rings","sub_path":"matt_ring_finder.py","file_name":"matt_ring_finder.py","file_ext":"py","file_size_in_byte":17462,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"72833025424","text":"import click\nfrom click import echo\n\nfrom npc.util import ParseError\nfrom npc_cli.presenters import tabularize, type_list, wrapped_paragraphs, tag_table_data\nfrom npc_cli.helpers import cwd_campaign\nfrom npc_cli.errors import BadCharacterTypeException, CampaignRequiredException\n\nfrom .main_group import cli, arg_settings, pass_settings\n\n##########################\n# Config description group\n##########################\n\n@cli.group()\ndef describe():\n \"\"\"Show info about systems, types, or tags\"\"\"\n\n###################\n# Describe systems\n###################\n\n@describe.command()\n@pass_settings\ndef systems(settings):\n \"\"\"Show all configured game systems\"\"\"\n systems = [settings.get_system(key) for key in settings.get_system_keys()]\n system_headers = [\"Name\", \"Key\", \"Description\"]\n system_data = [[system.name, system.key, system.desc] for system in systems]\n echo(tabularize(system_data, headers = system_headers, title = \"Configured Systems\"))\n\n campaign = cwd_campaign(settings)\n if campaign:\n echo(f\"\\nCurrently using {campaign.system.name}\")\n\n##########################\n# Describe system details\n##########################\n\n@describe.command()\n@click.option(\"-s\", \"--system\",\n type=click.Choice(arg_settings.get_system_keys(), case_sensitive=False),\n help=\"ID of the game system to show\")\n@pass_settings\ndef system(settings, system):\n \"\"\"Show details about a single system\"\"\"\n campaign = cwd_campaign(settings)\n try:\n if system:\n game_system = settings.get_system(system)\n elif campaign:\n game_system = campaign.system\n else:\n raise CampaignRequiredException\n except ParseError as err:\n raise click.FileError(err.path, hint=err.strerror)\n\n echo(f\"=== {game_system.name} ===\")\n echo(game_system.desc)\n for line in wrapped_paragraphs(game_system.doc):\n echo(\"\")\n echo(line)\n if game_system.links:\n echo(f\"\\nRelevant Links:\")\n for link in game_system.links:\n echo(f'* {link[\"label\"]}: {link[\"url\"]}')\n\n###################\n# Describe types\n###################\n\n@describe.command()\n@click.option(\"-s\", \"--system\",\n type=click.Choice(arg_settings.get_system_keys(), case_sensitive=False),\n help=\"ID of the game system to use\")\n@pass_settings\ndef types(settings, system):\n \"\"\"Show all configured character types\"\"\"\n campaign = cwd_campaign(settings)\n try:\n if system:\n game_system = settings.get_system(system)\n chartypes = game_system.types\n title = f\"Character Types for {game_system.name}\"\n elif campaign:\n chartypes = campaign.types\n title = f\"Character Types in {campaign.name}\"\n else:\n raise CampaignRequiredException(['-s', '--system'])\n except ParseError as err:\n raise click.FileError(err.path, hint=err.strerror)\n\n chartype_headers = [\"Name\", \"Key\", \"Description\"]\n chartype_data = [[chartype.name, chartype.key, chartype.desc] for chartype in chartypes.values()]\n echo(tabularize(chartype_data, headers = chartype_headers, title = title))\n\n########################\n# Describe type details\n########################\n\n@describe.command()\n@click.option(\"-s\", \"--system\",\n type=click.Choice(arg_settings.get_system_keys(), case_sensitive=False),\n help=\"ID of the game system to use\")\n@click.option(\"-t\", \"--type\", \"type_\",\n required=True,\n help=\"Character type to show\")\n@pass_settings\ndef type(settings, system, type_):\n \"\"\"Show details about a single character type\"\"\"\n campaign = cwd_campaign(settings)\n try:\n if system:\n target = settings.get_system(system)\n elif campaign:\n target = campaign\n else:\n raise CampaignRequiredException\n except ParseError as err:\n raise click.FileError(err.path, hint=err.strerror)\n\n if type_ not in target.types:\n raise BadCharacterTypeException(type_, type_list(target.types), ['-t', '--type'])\n chartype = target.get_type(type_)\n\n echo(f\"Character Type: {chartype.name}\")\n echo(f\"ID: {chartype.key}\")\n echo(f\"File suffix: {chartype.default_sheet_suffix}\")\n echo(f\"Sheet template: {chartype.sheet_path}\")\n echo(\"\")\n echo(chartype.desc)\n\n###################\n# Describe tags\n###################\n\n@describe.command()\n@click.option(\"-s\", \"--system\", \"system_key\",\n type=click.Choice(arg_settings.get_system_keys(), case_sensitive=False),\n help=\"ID of the game system to use\")\n@click.option(\"-t\", \"--type\", \"type_\", help=\"Only show valid tags for this character type\")\n@pass_settings\ndef tags(settings, system_key, type_):\n \"\"\"Show all configured tags for this campaign\n\n Can show the tags available to all character types, or just the ones for a specific type.\n \"\"\"\n campaign = cwd_campaign(settings)\n try:\n if system_key:\n target = settings.get_system(system_key)\n system = target\n elif campaign:\n target = campaign\n system = campaign.system\n else:\n raise CampaignRequiredException\n except ParseError as err:\n raise click.FileError(err.path, hint=err.strerror)\n\n headers = [\"Name\", \"Description\"]\n if type_:\n if type_ not in target.types:\n raise BadCharacterTypeException(type_, type_list(target.types), ['-t', '--type'])\n\n title = f\"Tags for {target.get_type(type_).name} in {target.name}\"\n tags = target.type_tags(type_)\n else:\n title = f\"Tags in {target.name}\"\n tags = target.tags\n\n data = tag_table_data(tags)\n echo(tabularize(data, headers = headers, title = title))\n\n#######################\n# Describe tag details\n#######################\n\n@describe.command()\n@click.option(\"-s\", \"--system\", \"system_key\",\n type=click.Choice(arg_settings.get_system_keys(), case_sensitive=False),\n help=\"ID of the game system to use\")\n@click.option(\"-t\", \"--type\", \"type_\",\n help=\"ID of the character type to use for finding the tag\")\n@click.option(\"-a\", \"--tag\", \"tag_name\",\n required=True,\n help=\"Name of the tag to show\")\n@click.option(\"-c\", \"--context\",\n help=\"Name of the tag's parent, if looking for a subtag\")\n@pass_settings\ndef tag(settings, system_key, type_, tag_name, context):\n \"\"\"Show details for the named tag\"\"\"\n campaign = cwd_campaign(settings)\n try:\n if system_key:\n target = settings.get_system(system_key)\n system = target\n elif campaign:\n target = campaign\n system = campaign.system\n else:\n raise CampaignRequiredException\n except ParseError as err:\n raise click.FileError(err.path, hint=err.strerror)\n\n headers = [\"Name\", \"Description\"]\n if type_:\n if type_ not in target.types:\n raise BadCharacterTypeException(type_, type_list(target.types), ['-t', '--type'])\n spec = target.get_type_tag(tag_name, type_)\n else:\n spec = target.get_tag(tag_name)\n\n if spec.needs_context:\n if not context:\n raise click.UsageError(f\"Tag '{tag_name}' is a subtag, so the --context option must be provided\")\n spec = spec.in_context(context)\n title = f\"Tag: @{spec.name} (subtag of @{context})\"\n else:\n title = f\"Tag: @{spec.name}\"\n\n echo(title)\n echo(spec.desc)\n for line in wrapped_paragraphs(spec.doc):\n echo(\"\")\n echo(line)\n","repo_name":"aurule/npc","sub_path":"src/npc_cli/commands/describe_group.py","file_name":"describe_group.py","file_ext":"py","file_size_in_byte":7441,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"47"} +{"seq_id":"3912288738","text":"''' Selection utilities\n\nThis module provides a set of functions to handle selections.\n\n.. Created on Nov 4, 2013\n.. codeauthor:: Robert Langlois
![](\"/img/(\\d+).gif\")
', response):\n score = re.findall(r'平均評価: ', response)[0]\n score = str(float(score) / 10.0)\n else:\n score = str(re.findall(r'平均評価: ([^<]+)', response)).strip(\" [',']\").replace('\\'', '')\n if outline == '':\n dmm_htmlcode = get_html(\n \"https://www.dmm.co.jp/search/=/searchstr=\" + number.replace('-', '') + \"/sort=ranking/\")\n if 'に一致する商品は見つかりませんでした' not in dmm_htmlcode:\n dmm_page = etree.fromstring(dmm_htmlcode, etree.HTMLParser())\n url_detail = str(dmm_page.xpath('//*[@id=\"list\"]/li[1]/div/p[2]/a/@href')).split(',', 1)[0].strip(\n \" ['']\")\n if url_detail != '':\n dmm_detail = get_html(url_detail)\n html = etree.fromstring(dmm_detail, etree.HTMLParser())\n outline = str(html.xpath('//*[@class=\"mg-t0 mg-b20\"]/text()')).strip(\" ['']\").replace('\\\\n',\n '').replace(\n '\\n', '')\n except Exception as error_info:\n print('Error in javbus.getOutlineScore : ' + str(error_info))\n return outline, score\n\n\ndef getSeries(htmlcode):\n html = etree.fromstring(htmlcode, etree.HTMLParser())\n result = str(html.xpath('//span[contains(text(),\"系列\")]/following-sibling::a/text()')).strip(\" ['']\")\n return result\n\n\ndef getCover_small(number): # 从avsox获取封面图\n try:\n htmlcode = get_html('https://avsox.website/cn/search/' + number)\n html = etree.fromstring(htmlcode, etree.HTMLParser())\n counts = len(html.xpath(\"//div[@id='waterfall']/div/a/div\"))\n if counts == 0:\n return ''\n for count in range(1, counts + 1): # 遍历搜索结果，找到需要的番号\n number_get = html.xpath(\n \"//div[@id='waterfall']/div[\" + str(count) + \"]/a/div[@class='photo-info']/span/date[1]/text()\")\n if len(number_get) > 0 and number_get[0].upper() == number.upper():\n cover_small = \\\n html.xpath(\"//div[@id='waterfall']/div[\" + str(count) + \"]/a/div[@class='photo-frame']/img/@src\")[0]\n return cover_small\n except Exception as error_info:\n print('Error in javbus.getCover_small : ' + str(error_info))\n return ''\n\n\ndef getTag(htmlcode): # 获取标签\n tag = []\n soup = BeautifulSoup(htmlcode, 'lxml')\n a = soup.find_all(attrs={'class': 'genre'})\n for i in a:\n if 'onmouseout' in str(i):\n continue\n tag.append(i.get_text())\n return tag\n\n\ndef find_number(number):\n # =======================================================================有码搜索\n if not (re.match('^\\d{4,}', number) or re.match('n\\d{4}', number) or 'HEYZO' in number.upper()):\n htmlcode = get_html('https://www.javbus.com/search/' + number + '&type=1')\n html = etree.fromstring(htmlcode, etree.HTMLParser()) # //table/tr[1]/td[1]/text()\n counts = len(html.xpath(\"//div[@id='waterfall']/div[@id='waterfall']/div\"))\n if counts != 0:\n for count in range(1, counts + 1): # 遍历搜索结果，找到需要的番号\n number_get = html.xpath(\"//div[@id='waterfall']/div[@id='waterfall']/div[\" + str(\n count) + \"]/a[@class='movie-box']/div[@class='photo-info']/span/date[1]/text()\")[0]\n number_get = number_get.upper()\n number = number.upper()\n if number_get == number or number_get == number.replace('-', '') or number_get == number.replace('_',\n ''):\n result_url = html.xpath(\n \"//div[@id='waterfall']/div[@id='waterfall']/div[\" + str(\n count) + \"]/a[@class='movie-box']/@href\")[0]\n return result_url\n # =======================================================================无码搜索\n htmlcode = get_html('https://www.javbus.com/uncensored/search/' + number + '&type=1')\n html = etree.fromstring(htmlcode, etree.HTMLParser()) # //table/tr[1]/td[1]/text()\n counts = len(html.xpath(\"//div[@id='waterfall']/div[@id='waterfall']/div\"))\n if counts == 0:\n return 'not found'\n for count in range(1, counts + 1): # 遍历搜索结果，找到需要的番号\n number_get = html.xpath(\"//div[@id='waterfall']/div[@id='waterfall']/div[\" + str(\n count) + \"]/a[@class='movie-box']/div[@class='photo-info']/span/date[1]/text()\")[0]\n number_get = number_get.upper()\n number = number.upper()\n if number_get == number or number_get == number.replace('-', '') or number_get == number.replace('_', ''):\n result_url = html.xpath(\n \"//div[@id='waterfall']/div[@id='waterfall']/div[\" + str(count) + \"]/a[@class='movie-box']/@href\")[0]\n return result_url\n elif number_get == number.replace('-', '_') or number_get == number.replace('_', '-'):\n result_url = html.xpath(\n \"//div[@id='waterfall']/div[@id='waterfall']/div[\" + str(count) + \"]/a[@class='movie-box']/@href\")[0]\n return result_url\n return 'not found'\n\n\ndef main(number, appoint_url):\n try:\n if appoint_url:\n result_url = appoint_url\n else:\n result_url = find_number(number)\n if result_url == 'not found':\n raise Exception('Movie Data not found in javbus.main!')\n htmlcode = get_html(result_url)\n if str(htmlcode) == 'ProxyError':\n raise TimeoutError\n outline, score = getOutlineScore(number)\n number = getNum(htmlcode)\n dic = {\n 'title': str(getTitle(htmlcode)).replace(number, '').strip().replace(' ', '-'),\n 'studio': getStudio(htmlcode),\n 'publisher': getPublisher(htmlcode),\n 'year': getYear(getRelease(htmlcode)),\n 'outline': outline,\n 'score': score,\n 'runtime': getRuntime(htmlcode).replace('分鐘', '').strip(),\n 'director': getDirector(htmlcode),\n 'actor': getActor(htmlcode),\n 'release': getRelease(htmlcode),\n 'number': number,\n 'cover': getCover(htmlcode),\n 'extrafanart': getExtraFanart(htmlcode),\n 'imagecut': 1,\n 'tag': getTag(htmlcode),\n 'series': getSeries(htmlcode),\n 'actor_photo': getActorPhoto(htmlcode),\n 'website': result_url,\n 'source': 'javbus.py',\n }\n except TimeoutError:\n dic = {\n 'title': '',\n 'website': 'timeout',\n }\n except Exception as error_info:\n print('Error in javbus.main : ' + str(error_info))\n dic = {\n 'title': '',\n 'website': '',\n }\n js = json.dumps(dic, ensure_ascii=False, sort_keys=True, indent=4, separators=(',', ':'), ) # .encode('UTF-8')\n return js\n\n\ndef main_uncensored(number, appoint_url):\n try:\n result_url = ''\n if appoint_url == '':\n result_url = find_number(number)\n else:\n result_url = appoint_url\n if result_url == 'not found':\n raise Exception('Movie Data not found in javbus.main_uncensored!')\n htmlcode = get_html(result_url)\n if str(htmlcode) == 'ProxyError':\n raise TimeoutError\n number = getNum(htmlcode)\n outline = ''\n score = ''\n if 'HEYZO' in number.upper():\n outline, score = getOutlineScore(number)\n dic = {\n 'title': getTitle(htmlcode).replace(number, '').strip().replace(' ', '-'),\n 'studio': getStudio(htmlcode),\n 'publisher': '',\n 'year': getYear(getRelease(htmlcode)),\n 'outline': outline,\n 'score': score,\n 'runtime': getRuntime(htmlcode).replace('分鐘', '').strip(),\n 'director': getDirector(htmlcode),\n 'actor': getActor(htmlcode),\n 'release': getRelease(htmlcode),\n 'number': getNum(htmlcode),\n 'cover': getCover(htmlcode),\n 'extrafanart': getExtraFanart(htmlcode),\n 'tag': getTag(htmlcode),\n 'series': getSeries(htmlcode),\n 'imagecut': 3,\n 'cover_small': getCover_small(number), # 从avsox获取封面图\n 'actor_photo': getActorPhoto(htmlcode),\n 'website': result_url,\n 'source': 'javbus.py',\n }\n if dic['cover_small'] == '':\n dic['imagecut'] = 0\n except TimeoutError:\n dic = {\n 'title': '',\n 'website': 'timeout',\n }\n except Exception as error_info:\n print('Error in javbus.main_uncensored : ' + str(error_info))\n dic = {\n 'title': '',\n 'website': '',\n }\n js = json.dumps(dic, ensure_ascii=False, sort_keys=True, indent=4, separators=(',', ':'), ) # .encode('UTF-8')\n return js\n\n\ndef main_us(number, appoint_url=''):\n try:\n if appoint_url:\n result_url = appoint_url\n else:\n htmlcode = get_html('https://www.javbus.one/search/' + number)\n if str(htmlcode) == 'ProxyError':\n raise TimeoutError\n html = etree.fromstring(htmlcode, etree.HTMLParser()) # //table/tr[1]/td[1]/text()\n counts = len(html.xpath(\"//div[@class='row']/div[@id='waterfall']/div\"))\n if counts == 0:\n raise Exception('Movie Data not found in javbus.main_us!')\n result_url = ''\n cover_small = ''\n for count in range(1, counts + 1): # 遍历搜索结果，找到需要的番号\n number_get = html.xpath(\"//div[@id='waterfall']/div[\" + str(\n count) + \"]/a[@class='movie-box']/div[@class='photo-info']/span/date[1]/text()\")[0]\n if number_get.upper() == number.upper() or number_get.replace('-', '').upper() == number.upper():\n result_url = html.xpath(\n \"//div[@id='waterfall']/div[\" + str(count) + \"]/a[@class='movie-box']/@href\")[0]\n cover_small = html.xpath(\n \"//div[@id='waterfall']/div[\" + str(\n count) + \"]/a[@class='movie-box']/div[@class='photo-frame']/img[@class='img']/@src\")[0]\n break\n if result_url == '':\n raise Exception('Movie Data not found in javbus.main_us!')\n htmlcode = get_html(result_url)\n if str(htmlcode) == 'ProxyError':\n raise TimeoutError\n number = getNum(htmlcode)\n dic = {\n 'title': getTitle(htmlcode).replace(number, '').strip(),\n 'studio': getStudio(htmlcode),\n 'year': getYear(getRelease(htmlcode)),\n 'runtime': getRuntime(htmlcode).replace('分鐘', '').strip(),\n 'director': getDirector(htmlcode),\n 'actor': getActor(htmlcode),\n 'release': getRelease(htmlcode),\n 'number': getNum(htmlcode),\n 'tag': getTag(htmlcode),\n 'series': getSeries(htmlcode),\n 'cover': getCover(htmlcode),\n 'extrafanart': getExtraFanart(htmlcode),\n 'cover_small': '',\n 'imagecut': 0,\n 'actor_photo': getActorPhoto(htmlcode),\n 'publisher': '',\n 'outline': '',\n 'score': '',\n 'website': result_url,\n 'source': 'javbus.py',\n }\n except TimeoutError:\n dic = {\n 'title': '',\n 'website': 'timeout',\n }\n except Exception as error_info:\n print('Error in javbus.main_us : ' + str(error_info))\n dic = {\n 'title': '',\n 'website': '',\n }\n js = json.dumps(dic, ensure_ascii=False, sort_keys=True, indent=4, separators=(',', ':'), ) # .encode('UTF-8')\n return js\n\n\n'''\nprint(find_number('KA-001'))\nprint(main_uncensored('010115-001'))\nprint(main('ssni-644'))\nprint(main_uncensored('012715-793'))\nprint(main_us('sexart.15.06.10'))\nprint(main_uncensored('heyzo-1031'))\n'''\n\n# print(main('ssni-644', \"https://www.javbus.com/SSNI-644\"))\n# print(main('ssni-802', \"\"))\n# print(main_us('DirtyMasseur.20.07.26', \"https://www.javbus.one/DirtyMasseur-20-07-26\"))\n","repo_name":"DevilMayCry4/AVDC","sub_path":"Getter/javbus.py","file_name":"javbus.py","file_ext":"py","file_size_in_byte":15992,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"47"} +{"seq_id":"31248921545","text":"# Import module\nimport xlrd\n\n\ndef main1():\n # Open excel\n wb = xlrd.open_workbook('BD1.xls')\n\n # Select table\n sh = wb.sheet_by_name('Sheet1')\n\n # Min and max row/column\n coluns = sh.ncols\n rows = sh.nrows\n\n # Search word\n word = input('Search: ')\n\n # Load all table\n for i in range(0, rows):\n for j in range(0, coluns):\n c = sh.cell(i, j)\n # Find the search\n if str(c.value).lower().find(word.lower()) != -1:\n for x in range(0, coluns):\n bd2 = sh.cell(i, x).value\n\n return bd2\n","repo_name":"marvinbraescher/Excelfile","sub_path":"Excel2.py","file_name":"Excel2.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"43747697113","text":"from datetime import datetime\nimport os \nfrom dotenv import load_dotenv\n \nclass Scraper:\n \"\"\" Scraper class for pulling data from API endpoint.\"\"\"\n \n def __init__(self, *args):\n \n self.AAPL = args[0]\n self.MSFT = args[1]\n self.IBM = args[2]\n load_dotenv()\n self._api_key = os.getenv('api_key')\n \n def get_api_key(self) -> str:\n \"\"\" u87786returns api key which is stored as a private variable from this class for accessor classes\"\"\"\n \n api_key = self._api_key\n \n return api_key\n \n def pull_stock_data(self, tick, multiplier, timespan, date_to, date_from):\n \"\"\" 5 minute data from 01-30 jan 2022.\n Two extra variables passed are firstly tick which refers to stock name, and duration which refers to the allocated duration of data aggregation.\"\"\"\n \n import requests as r \n \n # user and secret initialisation\n user = 'Bearer'\n api_key = self.get_api_key()\n \n # parsing request url\n url = \"https://api.polygon.io/v2/aggs/ticker/{0}/range/{1}/{2}/{3}/{4}?adjusted=true&sort=asc&limit=5000\".format(tick, multiplier, timespan, date_from, date_to)\n \n # setting session object python3 main.py\n session = r.session()\n\n agg_stock_bars = session.get(url, headers={'Authorization': '{0} {1}'.format(user, api_key), 'Accept': 'text/csv'})\n \n return agg_stock_bars\n \n \n ","repo_name":"nbdevs/WS","sub_path":"collect_data.py","file_name":"collect_data.py","file_ext":"py","file_size_in_byte":1478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"32394619368","text":"import tensorflow as tf \nfrom tensorflow.examples.tutorials.mnist import input_data\n\n# описание обычного слоя {грубо говоря записываем функция активации в каждом нейроне, что бы потом оптимизировать}\ndef fullyconnected_layer(tensor, input_size, out_size):\n W = tf.Variable(tf.truncated_normal([input_size, out_size], stddev = 0.1))\n b = tf.Variable(tf.truncated_normal([out_size], stddev = 0.1))\n return tf.nn.tanh(tf.matmul(tensor,W) + b)\n\n#слой нормализации по минибатчам\ndef batch_norm(tensor, size):\n batch_mean, batch_var = tf.nn.moments(tensor, [0])\n beta = tf.Variable(tf.zeros(size))\n scale = tf.Variable(tf.ones(size))\n return tf.nn.batch_normalization(\n tensor, batch_mean, batch_var, beta,scale, 0.001 )\n\ndef _main():\n mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot = True)\n x = tf.placeholder(tf.float32, [None, 784])\n y = tf.placeholder(tf.float32, [None, 10])\n # layers\n h1 = fullyconnected_layer(x, 784, 100)\n h1_batchnorm = batch_norm(h1, 100)\n h2 = fullyconnected_layer(h1_batchnorm, 100, 100)\n y_logit = fullyconnected_layer(h2, 100, 10)\n # loss function\n loss = tf.nn.sigmoid_cross_entropy_with_logits(logits = y_logit, labels = y)\n train_optimizer = tf.train.GradientDescentOptimizer(0.01).minimize(loss)\n\n correct_prediction = tf.equal(tf.argmax(y_logit,1), tf.argmax(y,1))\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n\n init = tf.initialize_all_variables()\n sess = tf.Session()\n sess.run(init)\n for i in range(1000):\n batch_x, batch_y = mnist.train.next_batch(1000)\n sess.run(train_optimizer, feed_dict={x:batch_x, y:batch_y})\n\n print(\"Accuracy score - %s\" %sess.run(accuracy,\n feed_dict = {x: mnist.test.images, y: mnist.test.labels}))\nif __name__ == \"__main__\":\n _main()","repo_name":"sergey-byk0v/Examples_from_book","sub_path":"minibatch_normalization.py","file_name":"minibatch_normalization.py","file_ext":"py","file_size_in_byte":1954,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"1474306845","text":"# encoding: utf-8\n\nfrom django.test import TestCase\nfrom eventex.subscriptions.forms import SubscriptionForm\n\nclass SubscriptionFormTest(TestCase):\n def test_has_fields(self):\n 'o form tem que ter os campos'\n form = SubscriptionForm()\n self.assertItemsEqual(['name', 'email', 'cpf', 'phone'], form.fields)\n\n def test_cpf_is_digit(self):\n 'CPF somente digitos'\n form = self.make_validated_form(cpf='abc00000000')\n self.assertItemsEqual(['cpf'], form.errors)\n \n def test_cpf_has_11_digits(self):\n 'CPF deve ter 11 digitos'\n form = self.make_validated_form(cpf='123')\n self.assertItemsEqual(['cpf'], form.errors)\n \n def test_email_is_optional(self):\n 'email opcional'\n form = self.make_validated_form(email='')\n self.assertFalse(form.errors)\n \n def make_validated_form(self, **kwargs):\n data = dict(name='Diego Tolentino', email='diegotolentino@gmail.com',\n cpf='12345678901', phone='62-39201997')\n data.update(kwargs)\n form = SubscriptionForm(data)\n form.is_valid()\n return form\n \n","repo_name":"diegotolentino/eventex","sub_path":"eventex/subscriptions/tests/test_forms.py","file_name":"test_forms.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"35946403984","text":"import unittest\r\n\r\nfrom project_exam.student import Student\r\n\r\nclass StudentTest(unittest.TestCase):\r\n STUDENT_NAME = 'PESHO'\r\n\r\n def setUp(self) -> None:\r\n self.student = Student(self.STUDENT_NAME)\r\n\r\n def test_student_init_without_courses(self):\r\n self.assertEqual(self.STUDENT_NAME, self.student.name)\r\n self.assertEqual({}, self.student.courses)\r\n\r\n def test_student_init_with_courses(self):\r\n courses = {\"Python Advanced\": ['note 1', 'note 2']}\r\n\r\n student = Student(self.STUDENT_NAME, courses)\r\n\r\n self.assertEqual(self.STUDENT_NAME, student.name)\r\n self.assertEqual(courses, student.courses)\r\n\r\n def test_enroll_student_updates_cours_notes_when_course_is_already_enrolled(self):\r\n course_name = \"Python Advanced\"\r\n courses = {course_name: ['note 1', 'note 2']}\r\n\r\n student = Student(self.STUDENT_NAME, courses)\r\n\r\n result = student.enroll(course_name, ['note 3', 'note 4'])\r\n\r\n self.assertEqual('Course already added. Notes have been updated.', result)\r\n self.assertEqual(['note 1', 'note 2', 'note 3', 'note 4'], student.courses[course_name])\r\n\r\n def test_enroll_student_extends_courses_with_course_when_add_course_notes_is_not_passed(self):\r\n course_name = \"Python Advanced\"\r\n course_notes = ['note 1', 'note 2']\r\n\r\n result = self.student.enroll(course_name, course_notes)\r\n\r\n self.assertEqual('Course and course notes have been added.', result)\r\n self.assertTrue(course_name in self.student.courses)\r\n self.assertEqual(course_notes, self.student.courses[course_name])\r\n\r\n def test_enroll_student_extends_courses_with_course_when_add_course_notes_is_y(self):\r\n course_name = \"Python Advanced\"\r\n course_notes = ['note 1', 'note 2']\r\n\r\n result = self.student.enroll(course_name, course_notes, 'Y')\r\n\r\n self.assertEqual('Course and course notes have been added.', result)\r\n self.assertTrue(course_name in self.student.courses)\r\n self.assertEqual(course_notes, self.student.courses[course_name])\r\n\r\n def test_enroll_student_extends_courses_with_course_without_notes_when_invalid_add_course_notes_arg_is_passed(self):\r\n course_name = \"Python Advanced\"\r\n course_notes = ['note 1', 'note 2']\r\n\r\n result = self.student.enroll(course_name, course_notes, 'N')\r\n\r\n self.assertEqual('Course has been added.', result)\r\n self.assertTrue(course_name in self.student.courses)\r\n self.assertEqual([], self.student.courses[course_name])\r\n\r\n def test_add_notes_raises_error_when_no_such_course_name(self):\r\n with self.assertRaises(Exception) as error:\r\n self.student.add_notes('Python Advanced', 'Note 3')\r\n self.assertEqual(\"Cannot add notes. Course not found.\", str(error.exception))\r\n\r\n def test_add_notes_raises_error_when_course_exists(self):\r\n course_name = \"Python Advanced\"\r\n courses = {course_name: ['A', 'B']}\r\n student = Student(self.STUDENT_NAME, courses)\r\n\r\n result = student.add_notes(course_name, 'C')\r\n\r\n self.assertEqual(\"Notes have been updated\", result)\r\n self.assertEqual(['A', 'B', 'C'], student.courses[course_name])\r\n\r\n def test_leave_course_raises_error_when_course_not_existing(self):\r\n self.student.enroll('Python Basics', [])\r\n\r\n with self.assertRaises(Exception) as error:\r\n self.student.leave_course('Python Advanced')\r\n self.assertEqual(\"Cannot remove course. Course not found.\", str(error.exception))\r\n\r\n def test_leave_course_when_course_exists(self):\r\n course_name = \"Python Advanced\"\r\n courses = {course_name: []}\r\n student = Student(self.STUDENT_NAME, courses)\r\n\r\n result = student.leave_course(course_name)\r\n\r\n self.assertEqual(\"Course has been removed\", result)\r\n self.assertTrue(course_name not in student.courses)\r\n\r\n","repo_name":"PhilipKolarov/student-test-project","sub_path":"tests/test_student.py","file_name":"test_student.py","file_ext":"py","file_size_in_byte":3939,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25524835432","text":"from pathlib import Path\nfrom typing import Optional\nfrom tqdm import tqdm\n\nNUM_PROCS=4\nINKSCAPE=\"inkscape\"\nICON_CLASSES=[ \"icons\", \"icons-dark\" ]\nICON_SIZES=[ 16, 22, 32, 48, 64, 96]\n\nPATH_SRC=Path(\"./breeze-icons\")\nPATH_PNG=Path(\"./png\")\n\n\ndef get_png_name(p: Path) -> Path:\n *_, cls, cat, size, name = p.with_suffix(\"\").parts\n return PATH_PNG / cls / f\"{cat}-{name}_{size}.png\"\n\ndef parallel_execute(commands, np: Optional[int] = None):\n if np == 1:\n for command in tqdm(commands):\n run_command(command)\n else:\n from multiprocessing.pool import ThreadPool\n import subprocess\n\n def run_command(cmd):\n try:\n res = subprocess.run(cmd)\n return res.returncode\n except Exception as e:\n print(e)\n return 0\n\n tp = ThreadPool(np)\n gen = tp.imap_unordered(run_command, commands, chunksize=1)\n for r in tqdm(gen, total=len(commands), smoothing=0.1):\n pass\n tp.close()\n tp.join()\n\n\ndef update_conversions():\n svg_files = []\n for cls in ICON_CLASSES:\n pclass = PATH_PNG / cls\n pclass.mkdir(parents=True, exist_ok=True)\n sclass = PATH_SRC / cls\n for size in ICON_SIZES:\n svg_files += sclass.glob(f\"*/{size}/*.svg\")\n png_files = [get_png_name(f) for f in svg_files]\n # detect removed files\n print(\"Checking removed files...\")\n existing_png_files = PATH_PNG.glob(\"**/*.png\")\n extras = list(set(existing_png_files) - set(png_files))\n print(len(extras), \" removed files\")\n for p in extras:\n p.unlink()\n # detect new/modified files\n print(\"Checking new/modified files...\")\n commands = []\n for svg, png in zip(svg_files, png_files):\n sstat = svg.stat()\n if sstat.st_size <= 50:\n continue\n with svg.open(\"rt\") as t:\n if \"
Github', 'https://github.com/mlindgren'),\n(' last.fm', 'http://last.fm/user/lindgrenM'),\n(' Stack Overflow', 'http://stackoverflow.com/users/108340/mitch-lindgren'),\n(' Yelp', 'http://mlindgren.yelp.com'),\n(' YouTube', 'http://www.youtube.com/user/lindgrenMitch'),\n)\n\nGITHUB_USER = 'mlindgren'\nGITHUB_REPO_COUNT = 5\nGITHUB_SKIP_FORK = False\nGITHUB_SHOW_USER_LINK = False\n\nX_MIN_READ = True\n\nDEFAULT_PAGINATION = 10\n\nARTICLE_URL = 'entry/{date:%Y}/{date:%m}/{date:%d}/{slug}/'\nARTICLE_SAVE_AS = 'entry/{date:%Y}/{date:%m}/{date:%d}/{slug}/index.html'\nARTICLE_LANG_URL = 'entry/{date:%Y}/{date:%m}/{date:%d}/{slug}-{lang}/'\nARTICLE_LANG_SAVE_AS = 'entry/{date:%Y}/{date:%m}/{date:%d}/{slug}-{lang}/index.html'\nPAGE_URL = 'blog/{slug}/'\nPAGE_SAVE_AS = 'blog/{slug}/index.html'\nARCHIVES_SAVE_AS = 'blog/archives/index.html'\nTAG_URL = 'tag/{slug}/'\nTAG_SAVE_AS = 'tag/{slug}/index.html'\n\nTHEME = '../mlindgren-pelican-theme'\n\nSUMMARY_MAX_LENGTH = None\n\nPLUGIN_PATHS = [\"../pelican-plugins\"]\nPLUGINS = [\"liquid_tags.img\", \"liquid_tags.video\", \"liquid_tags.youtube\",\n \"liquid_tags.include_code\", \"summary\", \"post_stats\", \"render_math\"]\n\nSTATIC_PATHS = ['extra/favicon.ico', 'extra/favicon.png']\nEXTRA_PATH_METADATA = {\n 'extra/favicon.ico': {'path': 'favicon.ico'},\n 'extra/favicon.png': {'path': 'favicon.png'}\n}\n\n# Uncomment following line if you want document-relative URLs when developing\n#RELATIVE_URLS = True\n","repo_name":"mlindgren/blog","sub_path":"pelicanconf.py","file_name":"pelicanconf.py","file_ext":"py","file_size_in_byte":2292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"42729406050","text":"# Title : Caecar Cipher\n# Author : Lance R. Bernal\n# Date: 10-07-2022\n# Description: A simple program to encrypt and decrypt text using caecar cipher. This program can cipher ascii values from 32 upto 122\n\nimport os\n\ndef clrscr(): # function for clearing the termninal\n if os.name == 'posix': \n _= os.system('clear')\n else:\n _= os.system('cls')\n\ndef encrypt(): # function for encrypting the texts\n clrscr()\n divider()\n print(\" Encryption \")\n text = input(\"Enter word/text to be encrypted: \") # stores the string entered by user\n pin = int(input(\"Enter PIN to encrypt [ex. 1234]: \")) # stores the pin entered by user\n\n pin = pin % ord('z') # modulo the pin entered to the maximum ascii value which is ord('z') or 122\n textEncrypt = \"\" # stores the converted string\n #check = [] # stores ascii values of each character for checking\n\n for index in text: # loop for assigning ascii with shifted value for each character of string \n asciiVal = ord(index) \n asciiVal = asciiVal + pin \n if asciiVal <= ord('z'):\n asciiVal = asciiVal\n else: # if the ascii value of a character exceeds ord('z'), it will be modulo-ed, 31 is added to start the shift at the minimum value of ord(' ') or 32\n asciiVal = (asciiVal % ord('z')) + 31\n\n #check.append(asciiVal) # write each encrypted ascii value for checking\n textEncrypt += chr(asciiVal)\n\n clrscr()\n divider()\n\n #print(check) # display encrypted ascii values for checking\n print(\" The encrypted text/word is:\", textEncrypt) # display the encrypted string\n again()\n\ndef decrypt(): # function for decrypting the encrypted string\n clrscr()\n divider()\n print(\" Decryption \")\n text = input(\"Enter word/text to be decrypted: \") # stores the encrypted string\n pin = int(input(\"Enter PIN to decrypt [ex. 1234]: \")) # stores the pin entered by the user\n\n pin = pin % ord('z') # modulo the pin entered to the maximum ascii value which is ord('z') or 122\n textDecrypt = \"\" # stores the converted string\n #check = [] # stores ascii values of each character for checking\n\n for index in text: # loop for decrypting the string using the pin\n asciiVal = ord(index)\n asciiVal = asciiVal - pin\n if asciiVal >= ord(' '):\n asciiVal = asciiVal\n else: # if the ascii value of a character is less than ord(' ') or 32, 31 will be deducted and the ascii value will be modulo-ed to start the shift at ord(' ') or 32\n asciiVal = (asciiVal - 31) % ord('z')\n\n # check.append(asciiVal) # write each decrypted ascii value for checking\n textDecrypt += chr(asciiVal)\n\n clrscr()\n divider()\n # print(check) # display encrypted ascii values for checking\n print(\"The decrypted text/word is:\", textDecrypt) # display the decrypted string\n again()\n\ndef divider(): # function to divide contents in terminal\n print(\" \")\n print(\"*******************************************\")\n print(\" \")\n\ndef again(): # function for going back to main menu/page\n divider()\n choice = input(\" Do you want to go to menu? [y/n]: \")\n if choice == 'y': # if user choose 'y', the program will go to menu, if not it will exit\n menu()\n else:\n exit()\n\ndef menu(): # main menu/page of program \n clrscr()\n divider()\n print(\" Menu \")\n print(\" 1. Encrypt Text\")\n print(\" 2. Decrypt Text\")\n print(\" 3. Exit\")\n print(\" \")\n choice = int(input(\"Enter you choice [1-3]: \"))\n if choice == 1: # the user's choice corresponds a condtion, and the program will proceed accordingly\n encrypt()\n elif choice == 2:\n decrypt()\n else:\n exit()\n\nmenu() # calling of function main","repo_name":"lancewrt/Python","sub_path":"CaecarCipherFInal.py","file_name":"CaecarCipherFInal.py","file_ext":"py","file_size_in_byte":3832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"7964714918","text":"import logging\nimport os\nimport shutil\n\nfrom ops.charm import CharmBase\nfrom ops.framework import StoredState\nfrom ops.main import main\nfrom ops.model import ActiveStatus, WaitingStatus, MaintenanceStatus\nimport subprocess\nimport sys\n\nimport utils\n\nlogger = logging.getLogger(__name__)\n\nEMOJI_CORE_HOOK_EVENT = \"\\U0001F4CC\"\nEMOJI_MESSAGE = \"\\U0001F4AC\"\nEMOJI_GREEN_DOT = \"\\U0001F7E2\"\nEMOJI_RED_DOT = \"\\U0001F534\"\nEMOJI_PACKAGE = \"\\U0001F4E6\"\n\n\nclass IPFSCharm(CharmBase):\n \"\"\"Charm the hello service with all core hooks.\"\"\"\n\n _stored = StoredState()\n\n def __init__(self, *args):\n super().__init__(*args)\n self.framework.observe(self.on.install, self._on_install)\n self.framework.observe(self.on.config_changed, self._on_config_changed)\n self.framework.observe(self.on.start, self._on_start)\n self.framework.observe(self.on.leader_elected, self._on_leader_elected)\n self.framework.observe(self.on.leader_settings_changed, self._on_leader_settings_changed)\n self.framework.observe(self.on.stop, self._on_stop)\n self.framework.observe(self.on.remove, self._on_remove)\n self.framework.observe(self.on.stop, self._on_stop)\n self.framework.observe(self.on.update_status, self._on_update_status)\n self.framework.observe(self.on.collect_metrics, self._on_collect_metrics)\n self.framework.observe(self.on.upgrade_charm, self._on_upgrade_charm)\n\n self._stored.set_default(snap_channel=self.config[\"snap-channel\"],\n restart_on_reconfig=self.config[\"restart-on-reconfig\"])\n\n\n def _on_install(self, event):\n \"\"\"\n Install your software here plus any dependencies, utilities and everything you need to run\n your service over time.\n\n Optionally take care of installing resources attached with the charm deployment.\n\n In this charm, we install a package and a service unit file which will use hello.\n\n This hook is ran after the storage-filesystem-attached hook - only once in the entire lifetime of the unit.\n \"\"\"\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n\n logger.info(f\"Installing from snap ipfs {EMOJI_PACKAGE}\")\n os.system(f\"snap install ipfs --channel={self._stored.snap_channel}\")\n\n # Install unit file for hello (one-shot service)\n shutil.copyfile('templates/etc/systemd/system/ipfs-daemon.service', '/etc/systemd/system/ipfs-daemon.service')\n\n # (re)config hello.\n self._reconfig_ipfs(restart=False)\n\n\n def _on_config_changed(self, event):\n \"\"\"\n Deal with charm configuration changes here.\n\n Detect changes to individual config items, by storing and comparing values in self._stored\n\n This hook is run after the start hook.\n This hook run after the upgrade-charm hook.\n This hook is run after the leader-elected hook.\n \"\"\"\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n\n if self.config[\"snap-channel\"] != self._stored.snap_channel:\n self._stored.snap_channel = self.config[\"snap-channel\"]\n self._reconfig_ipfs(restart=self.config[\"restart-on-reconfig\"])\n\n self._on_update_status(event)\n\n def _on_start(self, event):\n \"\"\"\n Start your service here, possibly defer (wait) until conditions are OK.\n \"\"\"\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n logger.debug(f\"ipfs init\")\n os.system('sudo -u ubuntu ipfs init')\n logger.info(f\"{EMOJI_GREEN_DOT} Starting the ipfs-daemon service...\")\n os.system('systemctl start ipfs-daemon.service')\n\n # Calling update_status gives quick feedback when deploying starts up.\n self._on_update_status(event)\n\n def _on_leader_elected(self, event):\n \"\"\"\n This is only run on the unit which is selected by juju as leader.\n We are not implementing anything here. See the \"leadership\" charm for an example.\n \"\"\"\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n\n def _on_leader_settings_changed(self, event):\n \"\"\"\n This is only run on the unit which is selected by juju as leader.\n We are not implementing anything here. See the \"leadership\" charm for an example.\n \"\"\"\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n\n def _on_update_status(self, event):\n \"\"\"\n This runs every 5 minutes.\n\n Have one place to figure out status for the charm is a good strategy for a beginner charmer.\n \"\"\"\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n\n if not os.system('systemctl is-active ipfs-daemon.service') == 0:\n logger.info(\"ipfs-daemon service is not running.\")\n self.unit.status = MaintenanceStatus(\"Inactive.\")\n else:\n logger.info(f\"ipfs-daemon service is running.\")\n self.unit.status = ActiveStatus(\"Running.\")\n\n if self.model.unit.is_leader():\n self.unit.set_workload_version(utils.getIpfsVersion())\n \n def _on_upgrade_charm(self, event):\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n\n # Re-install systemd unit\n shutil.copyfile('templates/etc/systemd/system/ipfs-daemon.service', '/etc/systemd/system/ipfs-daemon.service')\n\n def _on_stop(self, event):\n \"\"\"\n Bring down your service, possibly defer until all systems are good to go similar to start hook.\n \"\"\"\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n\n logger.info(f\"{EMOJI_RED_DOT} Stopping the ipfs-daemon service...\")\n os.system('systemctl stop ipfs-daemon.service')\n\n\n def _on_remove(self, event):\n \"\"\"\n Remove stuff you might want to clean up.\n\n This hook is run after the stop hook.\n \"\"\"\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n\n logger.info(f\"Removing ipfs {EMOJI_PACKAGE}\")\n os.system('snap remove ipfs')\n\n\n def _on_collect_metrics(self, event):\n \"\"\"\n This runs every 5 minutes - if metrics are defined in metrics.yaml.\n\n We don't implement any metrics this in this charm. See the metrics charm for a working example.\n \n ipfs stats might be useful\n\n \"\"\"\n logger.debug(EMOJI_CORE_HOOK_EVENT + sys._getframe().f_code.co_name)\n\n\n def _reconfig_ipfs(self, restart=False):\n \"\"\"\n Reconfigures the startup parameters of hello.service by modifying the /etc/default/hello file.\n Reloads systemd daemons.\n\n Optionally, restart the service.\n \"\"\"\n logger.info(f\"{EMOJI_MESSAGE} Configuring ipfs-daemon snap-channel: {self._stored.snap_channel}\")\n self._stored.snap_channel = self.config[\"snap-channel\"]\n os.system(f\"snap refresh ipfs --channel={self._stored.snap_channel}\")\n\n with open('/etc/default/ipfs', 'w') as f:\n f.write(f\"CUSTOM_ARGS=\\\\'daemon\\\\'\")\n os.system('systemctl daemon-reload')\n\n if restart:\n logger.info(f\"{EMOJI_GREEN_DOT} Restarting hello.\")\n os.system('systemctl restart ipfs-daemon.service')\n\n def _get_ipfs_peerid(self):\n import json\n f = open('/home/ubuntu/snap/ipfs/common/config')\n data = json.load(f)\n p = data['Identity']['PeerID']\n print(p)\n f.close()\n\n \nif __name__ == \"__main__\":\n main(IPFSCharm)\n","repo_name":"erik78se/ipfs-charms","sub_path":"ipfs-daemon/src/charm.py","file_name":"charm.py","file_ext":"py","file_size_in_byte":7591,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"6"}
+{"seq_id":"38929054676","text":"# Import the required modules\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\r\nfrom selenium.webdriver.chrome.service import Service\r\nimport time\r\nfrom bs4 import BeautifulSoup\r\n\r\n# Main Function\r\nif __name__ == \"__main__\":\r\n\r\n # Enable Performance Logging of Chrome.\r\n desired_capabilities = DesiredCapabilities.CHROME\r\n desired_capabilities[\"goog:loggingPrefs\"] = {\"performance\": \"ALL\"}\r\n\r\n # Create the webdriver object and pass the arguments\r\n options = webdriver.ChromeOptions()\r\n\r\n # Chrome will start in Headless mode\r\n options.add_argument('headless')\r\n\r\n # Ignores any certificate errors if there is any\r\n options.add_argument(\"--ignore-certificate-errors\")\r\n\r\n # Startup the chrome webdriver with executable path and\r\n # pass the chrome options and desired capabilities as\r\n # parameters.\r\n service = Service(executable_path='C:/Users/milov/Downloads/chromedriver_win32/chromedriver.exe',\r\n chrome_options=options,\r\n desired_capabilities=desired_capabilities)\r\n driver = webdriver.Chrome(service=service)\r\n\r\n # Send a request to the website and let it load\r\n driver.get(\"https://soundcloud.com/discover\")\r\n\r\n # Sleeps for 10 seconds\r\n time.sleep(10)\r\n\r\n # Gets content of page\r\n content = driver.page_source\r\n\r\n print(\"Quitting Selenium WebDriver\")\r\n driver.quit()\r\n\r\n # Parse the HTML content using BeautifulSoup\r\n soup = BeautifulSoup(content, 'html.parser')\r\n\r\n # Extract the title and artist of the track\r\n playlists = soup.find_all(\"a\", class_=\"playableTile__artworkLink\")\r\n print(len(playlists))\r\n for playlist in playlists:\r\n print(playlist['href'])\r\n","repo_name":"milovdpas/SoundZam","sub_path":"test-python-scripts/scrape_soundcloud_website.py","file_name":"scrape_soundcloud_website.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"34752272848","text":"import toga\nfrom toga.style.pack import COLUMN, Pack\n\n\ndef action_top_travel_icon(widget):\n print(\"amr\")\n\n\ndef action_top_browser_icon(widget):\n print(\"browser\")\n\n\ndef build(app):\n browser_icon = \"icons/browser.png\"\n plane_icon = \"icons/plane.png\"\n\n\n data = [\n # ('root%s' % i, 'value %s' % i)\n # for i in range(1, 100)\n ]\n\n left_container = toga.Table(headings=['Oracle Open', 'World'], data=data)\n\n right_content = toga.Box(\n style=Pack(direction=COLUMN, padding_top=50)\n )\n\n # for b in range(0, 10):\n # right_content.add(\n # toga.Button(\n # 'Oracle World - %s' % b,\n # on_press=button_handler,\n # style=Pack(width=200, padding=20)\n # )\n # )\n\n right_content.add (\n toga.Button(\n \"Log In / Sign In\",\n style=Pack(width=500,height=100, padding=20)\n )\n )\n\n right_content.add (\n toga.Button(\n \"Oracle World Sessions\",\n style=Pack(width=500,height=100, padding=20)\n )\n )\n\n \n right_container = toga.ScrollContainer(horizontal=False)\n\n right_container.content = right_content\n\n split = toga.SplitContainer()\n\n # The content of the split container can be specified as a simple list:\n # split.content = [left_container, right_container]\n # but you can also specify \"weight\" with each content item, which will\n # set an initial size of the columns to make a \"heavy\" column wider than\n # a narrower one. In this example, the right container will be twice\n # as wide as the left one.\n split.content = [\n (left_container, 1),\n (right_container, 2)\n ]\n\n # Create a \"Things\" menu group to contain some of the commands.\n # No explicit ordering is provided on the group, so it will appear\n # after application-level menus, but *before* the Command group.\n # Items in the Things group are not explicitly ordered either, so they\n # will default to alphabetical ordering within the group.\n things = toga.Group('Things')\n top_travel_icon = toga.Command(\n action_top_travel_icon,\n label='Planner',\n tooltip='Plan Your Trip',\n icon=plane_icon,\n group=things\n )\n\n top_browser_icon = toga.Command(\n action_top_browser_icon,\n label='Browser',\n tooltip='Browser View',\n icon=browser_icon,\n group=things\n )\n \n\n # The order in which commands are added to the app or the toolbar won't\n # alter anything. Ordering is defined by the command definitions.\n app.commands.add(top_travel_icon, top_browser_icon)\n app.main_window.toolbar.add(top_travel_icon, top_browser_icon)\n\n return split\n\n\ndef main():\n return toga.App('OracleWorld2022', 'org.beeware.helloworld', startup=build)\n\n\nif __name__ == '__main__':\n main().main_loop()","repo_name":"RahulMR42/oci-devop-grapql-sample-mobapp-1","sub_path":"src/oracleworld_graphql/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2882,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"7366612293","text":"\ndef fatorial(num, show=False):\n \"\"\"\n -> Calcula o fatorial de um número\n :param num: Número a ser calculado o fatorial\n :param show: (Opcional) Mostra o passo a passo do fatorial\n :return: O valor do fatorial do número num\n \"\"\"\n print(f'_'*40)\n f = 1\n for c in range(num, 0, -1):\n if show:\n print(f'{c}', end=' x ' if c != 1 else ' = ')\n f *= c\n return f\n\n\nprint(fatorial(5, show=True))\n\n","repo_name":"mateuzh/Python","sub_path":"desafio102.py","file_name":"desafio102.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"6"}
+{"seq_id":"39688724244","text":"# 77. Combinations\n# Time: k*nCk (Review: nCk combinations each of length k)\n# Space: k*nCk\nclass Solution:\n def combine(self, n: int, k: int) -> List[List[int]]:\n\n if n==0:\n return []\n\n if k==0:\n return [[]]\n\n if k==1:\n return [[i] for i in range(1,n+1)]\n\n without_n = self.combine(n-1, k)\n with_n = self.combine(n-1, k-1)\n\n for index in range(len(with_n)):\n with_n[index] = [n]+with_n[index]\n\n return without_n + with_n\n","repo_name":"cmattey/leetcode_problems","sub_path":"Python/lc_77_combinations.py","file_name":"lc_77_combinations.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"6"}
+{"seq_id":"21568997174","text":"'''\nAuthor: ‘puzhiyuan’ ‘puzhiyuan185489643@gmail.com’\nDate: 2023-11-19 16:59:44\nLastEditors: ‘puzhiyuan’ ‘puzhiyuan185489643@gmail.com’\nLastEditTime: 2023-11-19 21:59:04\nFilePath: \\AutonomousDriving\\PyTorch\\src\\VGG.py\nDescription: VGG16 and VGG18\n'''\n\nfrom turtle import Turtle\nfrom getDataloader import getFashionMNIST\nfrom getDevice import get_device\nfrom model_test import model_test\nfrom model_train import model_train\nfrom sympy import ode_order\nimport torch\nimport torch.nn as nn\nfrom torchvision import datasets, transforms\n\n\nclass VGG_16(nn.Module):\n def __init__(self, in_channels=1, num_classes=1000, *args, **kwargs) -> None:\n super().__init__(*args, **kwargs)\n self.layers = nn.Sequential(\n nn.Conv2d(in_channels, 64, 3, padding=1), nn.ReLU(),\n nn.Conv2d(64, 64, 3, padding=1), nn.ReLU(),\n nn.MaxPool2d(2, 2),\n nn.Conv2d(64, 128, 3, padding=1), nn.ReLU(),\n nn.Conv2d(128, 128, 3, padding=1), nn.ReLU(),\n nn.MaxPool2d(2, 2),\n nn.Conv2d(128, 256, 3, padding=1), nn.ReLU(),\n nn.Conv2d(256, 256, 3, padding=1), nn.ReLU(),\n nn.Conv2d(256, 256, 3, padding=1), nn.ReLU(),\n nn.MaxPool2d(2, 2),\n nn.Conv2d(256, 512, 3, padding=1), nn.ReLU(),\n nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(),\n nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(),\n nn.MaxPool2d(2, 2),\n nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(),\n nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(),\n nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(),\n nn.MaxPool2d(2, 2),\n nn.Flatten(),\n nn.Linear(7*7*512, 4096), nn.ReLU(),\n nn.Linear(4096, 4096), nn.ReLU(),\n nn.Linear(4096, 4096), nn.ReLU(),\n nn.Linear(4096, num_classes)\n )\n\n def forward(self, X):\n return self.layers(X)\n\n\n\nclass VGG_19(nn.Module):\n def __init__(self, num_classes, *args, **kwargs) -> None:\n super().__init__(*args, **kwargs)\n self.features = self._make_layers([64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M',\n 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'])\n self.classifier = nn.Sequential(\n nn.Linear(7*7*512, 4096),nn.ReLU(), nn.Dropout(),\n nn.Linear(4096, 4096), nn.ReLU(), nn.Dropout(),\n nn.Linear(4096, num_classes)\n )\n def forward(self, X):\n return self.classifier(nn.Flatten(self.features(X)))\n \n\n def _make_layers(self, cfg):\n layers = []\n in_channels = 1\n for i in cfg:\n if i == \"M\":\n layers.append(nn.MaxPool2d(2,2))\n else:\n layers.append(nn.Conv2d(in_channels, i, 3, padding=1))\n layers.append(nn.ReLU())\n in_channels = i\n return nn.Sequential(*layers)\n\n\nif __name__ == \"__main__\":\n batch_size, lr, epochs = 64, 0.01, 30\n device = get_device()\n print(f\"device: {device}\")\n root = \"PyTorch/data\"\n transform = transforms.Compose([transforms.Resize(224), transforms.ToTensor()])\n train_dataloader, test_dataloader = getFashionMNIST(root, transform, batch_size)\n\n model = VGG_16().to(device)\n\n loss = nn.CrossEntropyLoss()\n optimizer = torch.optim.SGD(model.parameters(), lr=lr)\n\n for i in range(epochs):\n train_loss = model_train(model, train_dataloader, loss, optimizer, device)\n test_accuracy = model_test(model, test_dataloader, device)\n print(f\"eopch: {i+1}/{epochs} train_loss: {train_loss} test_accuracy: {test_accuracy}\")\n","repo_name":"puzhiyuan/AutonomousDriving","sub_path":"PyTorch/src/VGG.py","file_name":"VGG.py","file_ext":"py","file_size_in_byte":3656,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"6"}
+{"seq_id":"7346864958","text":"import random\nimport numpy as np\n\ndef data_fmt(file_name):\n \"\"\"\n data format\n\n :arg file_name: string, name of data file\n\n :return: 2Darray including features and label(the last column)\n \"\"\"\n with open(file_name, \"r\") as file:\n content = file.readlines()\n ## dataset.shape(m, n),m=number of samples, n=number of features + 1(label)\n dataset = []\n for line in content:\n dataset.append(list(map(float, line.strip().split(\"\\t\"))))\n return np.array(dataset)\n\ndef selectJrand(i, n):\n \"\"\"\n Function to select the index of another alpha.\n\n :args i: int, index of the selected alpha\n :args n: int, number of all the alpha\n\n :return: int, index of the another alpha\n \"\"\"\n j = i\n while(j == i):\n j = int(random.uniform(0, n))\n return j\n\ndef limit_alpha(alpha, H, L):\n \"\"\"\n Function to limit the range of some alpha.\n\n :args alpha: int\n :args H: int, upper limit\n :args L: int, lower limit\n \n :return: int\n \"\"\"\n if alpha > H:\n alpha = H\n if alpha < L:\n alpha = L\n return alpha\n\ndef SMO_simple(dataset, C, toler, maxIter):\n \"\"\"\n 简易版SMO优化算法\n\n :args dataset: 2Darray including features and label(the last column)\n :args C: int, slack variable(松弛变量)\n :args toler: int, fault tolerance\n :args maxInter: int, maximun number of iterations\n\n :returns b: int, bias\n :returns alphas: vector \n \"\"\"\n features = dataset[:, :-1]\n labels = dataset[:, -1]\n m, n = features.shape\n ## 初始化\n b = 0\n alphas = np.zeros(m)\n iters = 0\n ## 外循环\n while (iters < maxIter):\n ## 改变的alpha对数\n alphaPairsChanged = 0\n ## 遍历样本\n for i in range(m):\n ## 第一个alpha\n fxi = float((alphas*labels).dot(features.dot(features[i, :].T))) + b # 计算预测值\n Ei = fxi - float(labels[i]) # 计算误差\n ## 满足KKT条件，则选取第二个alpha进行计算\n if (((labels[i] * Ei < -toler) and (alphas[i] < C)) or ((labels[i] * Ei > toler) and (alphas[i] > 0))):\n j = selectJrand(i, m) # 随机选取第二个alpha的索引\n fxj = float((alphas*labels).dot((features.dot(features[j, :].T)))) + b # 计算预测值\n Ej = fxj - float(labels[j]) # 计算误差\n ## 记录两个alpha的原始值，便于后续比较\n alphaIold = alphas[i].copy()\n alphaJold = alphas[j].copy()\n ## 如果两个alpha对应的样本标签不同\n if (labels[i] != labels[j]):\n # 求出相应的上下边界\n L = max(0, alphas[j] - alphas[i])\n H = min(C, C+alphas[j] - alphas[i])\n else:\n L = max(0, alphas[j] + alphas[i] - C)\n H = min(C, alphas[j] + alphas[i])\n if L == H: print(\"L == H\");continue\n ## 根据公式计算未经剪辑的alphaj\n #--------------------------\n eta = 2 * features[i, :].dot(features[j, :].T) - \\\n features[i, :].dot(features[i, :].T) - \\\n features[j, :].dot(features[j, :].T)\n # 如果eta>=0，跳出循环\n if eta >= 0:print(\"eta >= 0\");continue\n alphas[j] -= labels[j] * (Ei - Ej) / eta\n alphas[j] = limit_alpha(alphas[j], H, L)\n #--------------------------\n ## 如果改变后的alphaj的值变化不大，跳出本次循环\n if (abs(alphas[j] - alphaJold) < 0.00001):\n print(\"j not moving enough\")\n continue\n ## 否则计算相应的alphai的值\n alphas[i] += labels[j] * labels[i] * (alphaJold - alphas[j])\n ## 分别计算两个alpha情况下对应的b值\n b1 = b - Ei - labels[i] * (alphas[i] - alphaIold) * \\\n features[i, :].dot(features[i, :].T) - labels[j] * \\\n (alphas[j] - alphaJold) * features[i, :].dot(features[j, :].T)\n b2 = b - Ej - labels[i] * (alphas[i] - alphaIold) * \\\n features[i, :].dot(features[j, :].T) - \\\n labels[j] * (alphas[j] - alphaJold) * \\\n features[j, :].dot(features[j, :].T)\n ## 如果0